2002-01-01
NYYD Ensemble'i duost Traksmann - Lukk E.-S. Tüüri teosega "Symbiosis", mis on salvestatud ka hiljuti ilmunud NYYD Ensemble'i CDle. 2. märtsil Rakvere Teatri väikeses saalis ja 3. märtsil Rotermanni Soolalaos, kavas Tüür, Kaumann, Berio, Reich, Yun, Hauta-aho, Buckinx
Nestor, Siim, 1974-
2002-01-01
Jaapani megaorkestri AMT esinemisest Linnakultuurifestivali üritusel "Operation B" Von Krahlis 8. nov. Estonia kontserdisaalis toimuvast ansambli NYYD kontserdist 11. nov. Klubis Wimbledon 8. nov. toimuvast peost "Garage, this is paradise". Saku Suurhallis toimuvast peost "East West"
NYYD Ensemble ja Riho Sibul / Anneli Remme
Remme, Anneli, 1968-
2001-01-01
Gavin Bryarsi teos "Jesus' Blood Never Failed Me Yet" NYYD Ensemble'i ja Riho Sibula esituses 27. detsembril Pauluse kirikus Tartus ja 28. detsembril Rootsi- Mihkli kirikus Tallinnas. Kaastegevad Tartu Ülikooli Kammerkoor (Tartus) ja kammerkoor Voces Musicales (Tallinnas). Kunstiline juht Olari Elts
Uudised : ERSO menu Rootsis. Õhtu Metropolitan Operas. NYYD Ensemble'i kontserdid / Madis Kolk
Kolk, Madis, 1953-
2001-01-01
ERSO kontserdireisist Rootsis. "Klassikaraadio" teeb 17. veebr. otseülekande New Yorgi Metropolitan Operast, kus tuleb ettekandele Rossini ooepr "Itaallanna Alzhiiris". NYYD Ensemble'i kontsertidest Pärnus ja Tallinnas kontserdisarjas "I Got Rhythm"
Teater. Muusika. Nyyd / Anneli Remme
Remme, Anneli, 1968-
2005-01-01
ERSO ja NYYD Ensemble'i ühiskontserdil esitatud Brett Deani "Kontserdist vioolale ja orkestrile" ja teosest "Game Over", Küberstuudio etendusest "Lend-ajad", hispaania laulja Fatima Miranda kontsertetendusest "Diapassion", Michel van der Aa kammerooperist "One" rahvusvahelise uue muusika festivali "NYYD 2005" raames
Kontserdipeegel : ERSO, Nordic Voices ja NYYD Ensemble / Igor Garshnek
Garšnek, Igor, 1958-
1999-01-01
22. sept. Estonia kontserdisaalis toimunud ERSO kontserdist. Kavas Debussy "Fauni pärastlõuna", R.Straussi Oboekontsert ja F.Mendelssohni 4. sümfoonia. 24. sept. Estonia kontserdisaalis toimunud ans. Nordic Voices kontserdist. ETV suures stuudios toimunud multimeediaetendusest. Kavas M.Nymani "Letters, Riddles & Writs" ja L.Andriesseni "M is for Man, Music, Mozart"
Nyyd!! : [luuletused] / Karl Martin Sinijärv
Sinijärv, Karl Martin, 1971-
1998-01-01
Sisu: Nyyd!! ; Sõnad ; Forever ; Dekadentiline ; No name ; Axioomid ; Salatanze ruumityhi ; "soome-ugri rahvatants..." ; On ; Nirvaana ; Avastus ; Hyvastijätt ; "Valget veini andke mulle, tyybid..." ; "Raev rebib rindasid kyynistel kurjadel..." ; "Miks mitte mõrvata, kui nälgib näitlejanna..." ; "jah tapke tapke tapke kõiki teisi..." ; Cornelia ; docheri rodiny ; "Surm tuleb mõne võõra mehe kujul..." ; "Nõid naerda nutab, syytus syytab sysi..." ; "Veel vähe vargseid väreleb vinjette..." ; "Las leegitseb su pilgu all mu nimi..." ; "Kui templitrepilt otsin juudaseekleid..." ; "harv juhtum on et mõtlen jälle sinust..." ; "Sa näed - päev vaikib ega vasta sulle..." ; "hallaööde hallituse halin..." ; "ma kirjutan teemast mööda..." ; "Pimedusest ja surmast..." ; "Kus on mu päevade ratas..." ; "Öö mask..." ; "kohiseb maailma vaim kõiksuse teedel..." ; "kalkun..." ; "olla kalkun..." ; "kalkun kui parim..." ; "perekond ja kodu..." ; "rebaste reede..." ; "olla või mitte olla..." ; "ja mata kalkun verevasse merre..." ; "paremates peredes..." ; "kahekordne sildnik..." "läheb portselanipoodi..." ; "loobun..." ; "tokolopopulos..." ; "siiski..." ; "elu on igal pool elu..." ; "kes eelistab sotsialismile..." ; "karu tuli koopast..." ; "mo äär..." ; "meri on paksem kui mesi..." ; "veriorel kõriora..." ; "kopsudest kõik koorukesed..." ; abiks algajale isale ; "ma tahan nendest punastest tassidest juua..." ; "Väikese Matthiase Sõbra Laul Nõrkevale Yhiskonnale ; "ma naeran. vabadust ja iseoma armu..." ; "kui diktaator kui asine asjamees alfreedo..." ; "nyyd armu..." ; "Päikeseid..." ; (üdiviidingut üüdes---)
NYYD 09 : Terav skalpell ja soojad peegeldused / Evi Arujärv
Arujärv, Evi, 1953-
2009-01-01
20.-25. oktoobrini toimunud rahvusvahelisest uue muusika festivalist NYYD, keskmeks olid muusikateater ja multimeediaprojektid. Saksa helilooja Heiner Goebbelsi lavastusest Eraritjaritjaka, prantsuse helilooja Georges Aperghis'e lavastusest Machinations, Märt-Matis Lille kammerooperist "Indiate uurimine", Mart Kangro ja Ansambel U: lavatükist "Harmoonia"
Lock, Gerhard, 1978-
2009-01-01
Festivalil NYYD 2009 (20.-25. oktoober), mis oli pühendatud teatri- ja multimeediaprojektidele, etendunud multimeedialavastustest: Saksa helilooja Heiner Goebbelsi lavastusest Eraritjaritjaka, prantsuse helilooja Georges Aperghis'e lavastusest Machinations
Multiplex bead-based immunoassay for the free soluble forms of the HLA-G receptors, ILT2 and ILT4
DEFF Research Database (Denmark)
Wu, Ching-Lien; Svendsen, Signe Goul; Riviere, Adrien
2016-01-01
also be found as soluble molecules. In this work, we present a multiplex luminex-based assay to measure soluble ILT2 (sILT2) and soluble ILT4 (sILT4) molecules together. It is based on two antibody pairs, GHI/75 and HP-F1-PE for ILT2 and 27D6 and 42D1-PE for ILT4. The characterization of our method...... in the plasma of healthy controls, but that elevated levels of plasmatic sILT2 were present in non-muscle-infiltrating bladder cancer patients. This demonstrated that the titration test is indeed working, and that soluble ILT2 molecules do exist in pathological contexts, which relevance may now be sought...
Bug Diagnosis by String Matching: Application to ILTS for Translation.
Chen, Liang; Tokuda, Naoyuki
2003-01-01
Discusses a new template-automaton-based knowledge database system for an interactive intelligent language tutoring system (ILTS) for Japanese-English translation, whereby model translations as well as a taxonomy of bugs extracted from ill-formed translations typical of nonnative learners are collected. (Author/VWL)
Dalum, Alf Seljenes; Griffiths, David James; Valen, Elin Christine; Amthor, Karoline Skaar; Austbø, Lars; Koppang, Erling Olaf; Press, Charles McLean; Kvellestad, Agnar
2016-11-01
The interbranchial lymphoid tissue (ILT) of Atlantic salmon originates from an embryological location that in higher vertebrates gives rise to both primary and secondary lymphoid tissues. Still much is unknown about the morphological and functional development of the ILT. In the present work a standardized method of organ volume determination was established to study its development in relation to its containing gill and the thymus. Based on morphological findings and gene transcription data, the ILT shows no signs of primary lymphoid function. In contrast to the thymus, an ILT-complex first became discernible after the yolk-sac period. After its appearance, the ILT-complex constitutes 3-7% of the total volume of the gill (excluding the gill arch) with the newly described distal ILT constituting a major part, and in adult fish it is approximately 13 times larger than the thymus. Confined regions of T-cell proliferation are present within the ILT. Communication with systemic circulation through the distal ILT is also highly plausible thus offering both internal and external recruitment of immune cells in the growing ILT. Copyright © 2016 Elsevier Ltd. All rights reserved.
Brenk, Manuela; Scheler, Marina; Koch, Susanne; Neumann, Jürgen; Takikawa, Osamu; Häcker, Georg; Bieber, Thomas; von Bubnoff, Dagmar
2009-07-01
Tryptophan catabolism through IDO activity can cause nonresponsiveness and tolerance acting on T cells. Given the crucial importance of dendritic cells (DCs) in the initiation of a T cell response, surprisingly little is known about the impact of IDO activity and tryptophan deprivation on DCs themselves. In the present study, we show that human DCs differentiated under low-tryptophan conditions acquire strong tolerogenic capacity. This effect is associated with a markedly decreased Ag uptake as well as the down-regulation of costimulatory molecules (CD40, CD80). In contrast, the inhibitory receptors ILT3 and ILT4 are significantly increased. Functionally, tryptophan-deprived DCs show a reduced capacity to stimulate T cells, which can be restored by blockade of ILT3. Moreover, ILT3(high)ILT4(high) DCs lead to the induction of CD4(+)CD25(+) Foxp3(+) T regulatory cells with suppressive activity from CD4(+)CD25(-) T cells. The generation of ILT3(high)ILT4(high) DCs with tolerogenic properties by tryptophan deprivation is linked to a stress response pathway mediated by the GCN2 kinase. These results demonstrate that tryptophan degradation establishes a regulatory microenvironment for DCs, enabling these cells to induce T regulatory cells. The impact of IDO thus extends beyond local immune suppression to a systemic control of the immune response.
Use of ILT-based mask optimization for local printability enhancement
Tritchkov, Alexander; Kobelkov, Sergey; Rodin, Sergei; Sakajiri, Kyohei; Egorov, Evgueni; Woo, Soung-Su
2014-07-01
In this paper we study the trade-offs and benefits of using ILT-based SRAF placement/OPC over conventional SRAF placement/OPC for various front-end and back-end design configurations on a full chip. We explore the use models and benefits of using ILT-based Local Printability Enhancement (LPE) in an automated flow to eliminate hot spots that can be present on the full chip after conventional SRAF placement/OPC. We study the impact on process-window, performance, and mask manufacturability.
Zhang, Yanwen; Zhao, Jianqiang; Qiu, Lijun; Zhang, Pei; Li, Juan; Yang, Dong; Wei, Xiaojuan; Han, Yali; Nie, Siyue; Sun, Yuping
2016-08-01
Non-small cell lung cancer (NSCLC) is the most common malignant tumor in the world, of which prognosis is generally poor due to insufficient mechanistic understanding. To explore the molecular pathogenesis of NSCLC, the co-expression of immunoglobulin-like transcript 4 (ILT4) and its ligand human leukocyte antigen G (HLA-G) in NSCLC tissues and cells were investigated. Here, we detected the expression of ILT4 and HLA-G in 81 tumor specimens from primary NSCLC patients, and we found that co-expression of ILT4/HLA-G was significantly associated with regional lymph node involvement, advanced stages, and the overall survival of patients. In NSCLC cell lines, HLA-G expression increased/decreased accordingly when ILT4 was up-/down-regulated, and ILT4 expression increased in a concentration-dependent manner via the stimulation of HLA-G fusion protein. Interestingly, HLA-G fusion protein could also up-regulate the phospho-ERK1/2 expression, which means the activation of extracellular signal-regulated kinase (ERK) signaling. All in all, our results indicate that the ILT4-HLA-G interaction might play an important role in NSCLC progression. Identification of ILT4 and HLA-G expression may provide an indicator to predict prognosis and guide prevention and treatment of NSCLC.
Brown, Damien P; Jones, Des C; Anderson, Katie J; Lapaque, Nicolas; Buerki, Robin A; Trowsdale, John; Allen, Rachel L
2009-10-27
Leukocyte Ig-like receptors (LILR) are a family of innate immune receptors with immunomodulatory functions. High-level expression of the receptors LILRB2 (ILT4) and LILRB4 (ILT3) is a feature of tolerogenic antigen presenting cells and has been observed in cancer and transplant situations. There are relatively few studies regarding these receptors in the context of infection and it is not yet clear how LILRB4 exerts its inhibitory effects. We studied the effects of LILRB4 ligation on antigen presenting cell phenotype, and the expression of LILRB2 and LILRB4 on Salmonella-infected antigen presenting cells. Ligation of LILRB4 throughout in vitro culture of dendritic cells led to an upregulation of the co-stimulatory protein CD86. Alterations in the production of IL-8 and IL-10 by LILRB4-ligated macrophages were also observed. Infection with Salmonella typhimurium or TLR stimulation with Salmonella components led to an upregulation of LILRB2 and LILRB4. Our results indicate that the inhibitory effects of LILRB4 do not result from a failure to upregulate co-stimulatory proteins. In addition to the high level expression that can render antigen presenting cells tolerogenic, there may be a role for lower level expression and activity of LILRB2 and LILRB4 in response to TLR signalling during an immune response to bacterial infection.
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Buerki Robin A
2009-10-01
Full Text Available Abstract Background Leukocyte Ig-like receptors (LILR are a family of innate immune receptors with immunomodulatory functions. High-level expression of the receptors LILRB2 (ILT4 and LILRB4 (ILT3 is a feature of tolerogenic antigen presenting cells and has been observed in cancer and transplant situations. There are relatively few studies regarding these receptors in the context of infection and it is not yet clear how LILRB4 exerts its inhibitory effects. Results We studied the effects of LILRB4 ligation on antigen presenting cell phenotype, and the expression of LILRB2 and LILRB4 on Salmonella-infected antigen presenting cells. Ligation of LILRB4 throughout in vitro culture of dendritic cells led to an upregulation of the co-stimulatory protein CD86. Alterations in the production of IL-8 and IL-10 by LILRB4-ligated macrophages were also observed. Infection with Salmonella typhimurium or TLR stimulation with Salmonella components led to an upregulation of LILRB2 and LILRB4. Conclusion Our results indicate that the inhibitory effects of LILRB4 do not result from a failure to upregulate co-stimulatory proteins. In addition to the high level expression that can render antigen presenting cells tolerogenic, there may be a role for lower level expression and activity of LILRB2 and LILRB4 in response to TLR signalling during an immune response to bacterial infection.
Re, Matteo; Valentini, Giorgio
2012-03-01
Ensemble methods are statistical and computational learning procedures reminiscent of the human social learning behavior of seeking several opinions before making any crucial decision. The idea of combining the opinions of different "experts" to obtain an overall “ensemble” decision is rooted in our culture at least from the classical age of ancient Greece, and it has been formalized during the Enlightenment with the Condorcet Jury Theorem[45]), which proved that the judgment of a committee is superior to those of individuals, provided the individuals have reasonable competence. Ensembles are sets of learning machines that combine in some way their decisions, or their learning algorithms, or different views of data, or other specific characteristics to obtain more reliable and more accurate predictions in supervised and unsupervised learning problems [48,116]. A simple example is represented by the majority vote ensemble, by which the decisions of different learning machines are combined, and the class that receives the majority of “votes” (i.e., the class predicted by the majority of the learning machines) is the class predicted by the overall ensemble [158]. In the literature, a plethora of terms other than ensembles has been used, such as fusion, combination, aggregation, and committee, to indicate sets of learning machines that work together to solve a machine learning problem [19,40,56,66,99,108,123], but in this chapter we maintain the term ensemble in its widest meaning, in order to include the whole range of combination methods. Nowadays, ensemble methods represent one of the main current research lines in machine learning [48,116], and the interest of the research community on ensemble methods is witnessed by conferences and workshops specifically devoted to ensembles, first of all the multiple classifier systems (MCS) conference organized by Roli, Kittler, Windeatt, and other researchers of this area [14,62,85,149,173]. Several theories have been
Gregori, Silvia; Tomasoni, Daniela; Pacciani, Valentina; Scirpoli, Miriam; Battaglia, Manuela; Magnani, Chiara Francesca; Hauben, Ehud; Roncarolo, Maria-Grazia
2010-08-12
Type 1 T regulatory (Tr1) cells suppress immune responses in vivo and in vitro and play a key role in maintaining tolerance to self- and non-self-antigens. Interleukin-10 (IL-10) is the crucial driving factor for Tr1 cell differentiation, but the molecular mechanisms underlying this induction remain unknown. We identified and characterized a subset of IL-10-producing human dendritic cells (DCs), termed DC-10, which are present in vivo and can be induced in vitro in the presence of IL-10. DC-10 are CD14(+), CD16(+), CD11c(+), CD11b(+), HLA-DR(+), CD83(+), CD1a(-), CD1c(-), express the Ig-like transcripts (ILTs) ILT2, ILT3, ILT4, and HLA-G antigen, display high levels of CD40 and CD86, and up-regulate CD80 after differentiation in vitro. DC-10 isolated from peripheral blood or generated in vitro are potent inducers of antigen-specific IL-10-producing Tr1 cells. Induction of Tr1 cells by DC-10 is IL-10-dependent and requires the ILT4/HLA-G signaling pathway. Our data indicate that DC-10 represents a novel subset of tolerogenic DCs, which secrete high levels of IL-10, express ILT4 and HLA-G, and have the specific function to induce Tr1 cells.
Deep, Prakash; Paninjath, Sankaranarayanan; Pereira, Mark; Buck, Peter
2016-05-01
At advanced technology nodes mask complexity has been increased because of large-scale use of resolution enhancement technologies (RET) which includes Optical Proximity Correction (OPC), Inverse Lithography Technology (ILT) and Source Mask Optimization (SMO). The number of defects detected during inspection of such mask increased drastically and differentiation of critical and non-critical defects are more challenging, complex and time consuming. Because of significant defectivity of EUVL masks and non-availability of actinic inspection, it is important and also challenging to predict the criticality of defects for printability on wafer. This is one of the significant barriers for the adoption of EUVL for semiconductor manufacturing. Techniques to decide criticality of defects from images captured using non actinic inspection images is desired till actinic inspection is not available. High resolution inspection of photomask images detects many defects which are used for process and mask qualification. Repairing all defects is not practical and probably not required, however it's imperative to know which defects are severe enough to impact wafer before repair. Additionally, wafer printability check is always desired after repairing a defect. AIMSTM review is the industry standard for this, however doing AIMSTM review for all defects is expensive and very time consuming. Fast, accurate and an economical mechanism is desired which can predict defect printability on wafer accurately and quickly from images captured using high resolution inspection machine. Predicting defect printability from such images is challenging due to the fact that the high resolution images do not correlate with actual mask contours. The challenge is increased due to use of different optical condition during inspection other than actual scanner condition, and defects found in such images do not have correlation with actual impact on wafer. Our automated defect simulation tool predicts
Yoo, In-gyu; Jung, Min-ye; Yoo, Eun-young; Park, Ji-hyuk; Kang, Dae-hyuk; Lee, Jin
2014-01-01
Stroke patients have major problems with impaired upper-extremity function. Unfortunately, many patients do not experience a full recovery from movement deficits in the upper extremities. The purpose of this study was to compare the effectiveness of inter-limb learning transfer (ILT) to the contralateral upper limb after both hemisphere-specific and -unspecific ipsilateral upper limb training for stroke patients with hemiparesis. Twenty-four stroke patients with hemiparesis participated. The hemisphere-specific training group performed reaching movements in a customized training setting in which non-dominant limb training participants began from a single starting location and proceeded to one of three target locations (1S3T condition); the dominant limb training participants started from one of three starting locations and proceeded to a single target location (3S1T condition). The hemisphere-unspecific training group performed these movements starting under reverse-start and target conditions. The non-dominant to dominant limb transfer, the hemisphere-specific training group performance time decreased significantly as compared with the pre-training session (p training session in the biceps brachii muscles and increased significantly in the upper trapezius muscles (p transfer in the hemisphere-specific training group significantly increased RMS amplitudes from the pre-training session in the biceps brachii and triceps muscles (p training session in the biceps brachii muscles and decreased significantly in the upper trapezius muscles (p training group showed no significant differences in inter-limb learning transfer (ILT). The transfer of hemisphere-specific training from one arm to the other had a more positive influence on functional recovery than did hemisphere-unspecific training for patients with stroke and hemiparesis.
Saurabh, Abhinav; Thakral, Deepshi; Mourya, Manish K; Singh, Amar; Mohan, Anant; Bhatnagar, Anuj K; Mitra, Dipendra K; Kanga, Uma
2016-09-01
Human leukocyte antigen-G (HLA-G) is an anti-inflammatory and immunosuppressive molecule that can modulate immune cell activation. The role of HLA-G in tuberculosis, an immune-mediated and chronic bacterial disease remains to be elucidated. We investigated the expression profile of soluble and membrane bound HLA-G in pulmonary TB (PTB), TB pleural effusion (TB-PE, localized disease) and Miliary TB (disseminated form). The expression of HLA-G receptor, ILT-2 was also determined on the immune cells. We observed that the plasma sHLA-G levels were significantly increased in Miliary TB than in TB-PE patients. In contrast, immunophenotyping revealed that the percent frequency of CD3(+) T cells expressing HLA-G was significantly reduced in Miliary TB as compared to TB-PE, whereas frequency of CD14(+) monocytes expressing HLA-G was significantly higher in TB-PE patients. Strikingly in the TB-PE cases, comparison of disease site, i.e. pleural effusion with peripheral blood showed increased expression of both soluble and surface HLA-G, whereas ILT-2 expressing cells were reduced at the local disease site. Furthermore, we demonstrated that in TB-PE cases, HLA-G expression on CD3(+) T cells was influenced by broad spectrum MMP inhibitor. Thus, differential expression of HLA-G could potentially be a useful biomarker to distinguish different states of TB disease. Copyright © 2016 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
X. Yang
2009-07-01
Full Text Available A new class of ensemble filters, called the Diffuse Ensemble Filter (DEnF, is proposed in this paper. The DEnF assumes that the forecast errors orthogonal to the first guess ensemble are uncorrelated with the latter ensemble and have infinite variance. The assumption of infinite variance corresponds to the limit of "complete lack of knowledge" and differs dramatically from the implicit assumption made in most other ensemble filters, which is that the forecast errors orthogonal to the first guess ensemble have vanishing errors. The DEnF is independent of the detailed covariances assumed in the space orthogonal to the ensemble space, and reduces to conventional ensemble square root filters when the number of ensembles exceeds the model dimension. The DEnF is well defined only in data rich regimes and involves the inversion of relatively large matrices, although this barrier might be circumvented by variational methods. Two algorithms for solving the DEnF, namely the Diffuse Ensemble Kalman Filter (DEnKF and the Diffuse Ensemble Transform Kalman Filter (DETKF, are proposed and found to give comparable results. These filters generally converge to the traditional EnKF and ETKF, respectively, when the ensemble size exceeds the model dimension. Numerical experiments demonstrate that the DEnF eliminates filter collapse, which occurs in ensemble Kalman filters for small ensemble sizes. Also, the use of the DEnF to initialize a conventional square root filter dramatically accelerates the spin-up time for convergence. However, in a perfect model scenario, the DEnF produces larger errors than ensemble square root filters that have covariance localization and inflation. For imperfect forecast models, the DEnF produces smaller errors than the ensemble square root filter with inflation. These experiments suggest that the DEnF has some advantages relative to the ensemble square root filters in the regime of small ensemble size, imperfect model, and copious
World Music Ensemble: Kulintang
Beegle, Amy C.
2012-01-01
As instrumental world music ensembles such as steel pan, mariachi, gamelan and West African drums are becoming more the norm than the exception in North American school music programs, there are other world music ensembles just starting to gain popularity in particular parts of the United States. The kulintang ensemble, a drum and gong ensemble…
Ensemble clustering in deterministic ensemble Kalman filters
Directory of Open Access Journals (Sweden)
Javier Amezcua
2012-07-01
Full Text Available Ensemble clustering (EC can arise in data assimilation with ensemble square root filters (EnSRFs using non-linear models: an M-member ensemble splits into a single outlier and a cluster of M–1 members. The stochastic Ensemble Kalman Filter does not present this problem. Modifications to the EnSRFs by a periodic resampling of the ensemble through random rotations have been proposed to address it. We introduce a metric to quantify the presence of EC and present evidence to dispel the notion that EC leads to filter failure. Starting from a univariate model, we show that EC is not a permanent but transient phenomenon; it occurs intermittently in non-linear models. We perform a series of data assimilation experiments using a standard EnSRF and a modified EnSRF by a resampling though random rotations. The modified EnSRF thus alleviates issues associated with EC at the cost of traceability of individual ensemble trajectories and cannot use some of algorithms that enhance performance of standard EnSRF. In the non-linear regimes of low-dimensional models, the analysis root mean square error of the standard EnSRF slowly grows with ensemble size if the size is larger than the dimension of the model state. However, we do not observe this problem in a more complex model that uses an ensemble size much smaller than the dimension of the model state, along with inflation and localisation. Overall, we find that transient EC does not handicap the performance of the standard EnSRF.
Dimensionality Reduction Ensembles
Farrelly, Colleen M.
2017-01-01
Ensemble learning has had many successes in supervised learning, but it has been rare in unsupervised learning and dimensionality reduction. This study explores dimensionality reduction ensembles, using principal component analysis and manifold learning techniques to capture linear, nonlinear, local, and global features in the original dataset. Dimensionality reduction ensembles are tested first on simulation data and then on two real medical datasets using random forest classifiers; results ...
Multilevel ensemble Kalman filter
Chernov, Alexey
2016-01-06
This work embeds a multilevel Monte Carlo (MLMC) sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF). In terms of computational cost vs. approximation error the asymptotic performance of the multilevel ensemble Kalman filter (MLEnKF) is superior to the EnKF s.
The Ensembl REST API: Ensembl Data for Any Language.
Yates, Andrew; Beal, Kathryn; Keenan, Stephen; McLaren, William; Pignatelli, Miguel; Ritchie, Graham R S; Ruffier, Magali; Taylor, Kieron; Vullo, Alessandro; Flicek, Paul
2015-01-01
We present a Web service to access Ensembl data using Representational State Transfer (REST). The Ensembl REST server enables the easy retrieval of a wide range of Ensembl data by most programming languages, using standard formats such as JSON and FASTA while minimizing client work. We also introduce bindings to the popular Ensembl Variant Effect Predictor tool permitting large-scale programmatic variant analysis independent of any specific programming language. The Ensembl REST API can be accessed at http://rest.ensembl.org and source code is freely available under an Apache 2.0 license from http://github.com/Ensembl/ensembl-rest. © The Author 2014. Published by Oxford University Press.
Oza, Nikunj C.
2004-01-01
Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, Le., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.
National Aeronautics and Space Administration — Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve...
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Marin-Garcia Pablo
2010-05-01
Full Text Available Abstract Background The maturing field of genomics is rapidly increasing the number of sequenced genomes and producing more information from those previously sequenced. Much of this additional information is variation data derived from sampling multiple individuals of a given species with the goal of discovering new variants and characterising the population frequencies of the variants that are already known. These data have immense value for many studies, including those designed to understand evolution and connect genotype to phenotype. Maximising the utility of the data requires that it be stored in an accessible manner that facilitates the integration of variation data with other genome resources such as gene annotation and comparative genomics. Description The Ensembl project provides comprehensive and integrated variation resources for a wide variety of chordate genomes. This paper provides a detailed description of the sources of data and the methods for creating the Ensembl variation databases. It also explores the utility of the information by explaining the range of query options available, from using interactive web displays, to online data mining tools and connecting directly to the data servers programmatically. It gives a good overview of the variation resources and future plans for expanding the variation data within Ensembl. Conclusions Variation data is an important key to understanding the functional and phenotypic differences between individuals. The development of new sequencing and genotyping technologies is greatly increasing the amount of variation data known for almost all genomes. The Ensembl variation resources are integrated into the Ensembl genome browser and provide a comprehensive way to access this data in the context of a widely used genome bioinformatics system. All Ensembl data is freely available at http://www.ensembl.org and from the public MySQL database server at ensembldb.ensembl.org.
Chen, Ling; Xu, Zheng; Chang, Chris; Ho, Sophey; Liu, Zhuoru; Vlad, George; Cortesini, Raffaello; Clynes, Raphael A; Luo, Yun; Suciu-Foca, Nicole
2014-02-01
Alloantigen specific CD8 T suppressor cells can be generated in vitro either by multiple stimulations of CD3 T cells with allogeneic APC or by single stimulation in primary MLC containing recombinant ILT3.Fc protein. The aim of the present study was to determine whether multiple MLC stimulation induced in CD8(+) CD28(-) T suppressor cells molecular changes that are similar to those observed in CD8 T suppressor cells from primary MLC containing ILT3.Fc protein. Our study demonstrates that the characteristic signatures of CD8 T suppressor cells, generated by either of these methods are the same consisting of up-regulation of the BCL6 transcriptional repressor and down-regulation of inflammatory microRNAs, miR-21, miR-30b, miR-146a, and miR-155 expression. In conclusion microRNAs which are increased under inflammatory conditions in activated CD4 and CD8 T cells with helper or cytotoxic function show low levels of expression in CD8 T cells which have acquired antigen-specific suppressor activity. Copyright © 2014. Published by Elsevier Inc.
The semantic similarity ensemble
Directory of Open Access Journals (Sweden)
Andrea Ballatore
2013-12-01
Full Text Available Computational measures of semantic similarity between geographic terms provide valuable support across geographic information retrieval, data mining, and information integration. To date, a wide variety of approaches to geo-semantic similarity have been devised. A judgment of similarity is not intrinsically right or wrong, but obtains a certain degree of cognitive plausibility, depending on how closely it mimics human behavior. Thus selecting the most appropriate measure for a specific task is a significant challenge. To address this issue, we make an analogy between computational similarity measures and soliciting domain expert opinions, which incorporate a subjective set of beliefs, perceptions, hypotheses, and epistemic biases. Following this analogy, we define the semantic similarity ensemble (SSE as a composition of different similarity measures, acting as a panel of experts having to reach a decision on the semantic similarity of a set of geographic terms. The approach is evaluated in comparison to human judgments, and results indicate that an SSE performs better than the average of its parts. Although the best member tends to outperform the ensemble, all ensembles outperform the average performance of each ensemble's member. Hence, in contexts where the best measure is unknown, the ensemble provides a more cognitively plausible approach.
Yin, Dong-shan; Gao, Yu-ping; Zhao, Shu-hong
2017-07-01
Millisecond pulsars can generate another type of time scale that is totally independent of the atomic time scale, because the physical mechanisms of the pulsar time scale and the atomic time scale are quite different from each other. Usually the pulsar timing observations are not evenly sampled, and the internals between two data points range from several hours to more than half a month. Further more, these data sets are sparse. All this makes it difficult to generate an ensemble pulsar time scale. Hence, a new algorithm to calculate the ensemble pulsar time scale is proposed. Firstly, a cubic spline interpolation is used to densify the data set, and make the intervals between data points uniform. Then, the Vondrak filter is employed to smooth the data set, and get rid of the high-frequency noises, and finally the weighted average method is adopted to generate the ensemble pulsar time scale. The newly released NANOGRAV (North American Nanohertz Observatory for Gravitational Waves) 9-year data set is used to generate the ensemble pulsar time scale. This data set includes the 9-year observational data of 37 millisecond pulsars observed by the 100-meter Green Bank telescope and the 305-meter Arecibo telescope. It is found that the algorithm used in this paper can reduce effectively the influence caused by the noises in pulsar timing residuals, and improve the long-term stability of the ensemble pulsar time scale. Results indicate that the long-term (> 1 yr) stability of the ensemble pulsar time scale is better than 3.4 × 10-15.
Black, Rhonda
2016-01-01
This research study addressed measuring the level of instructional leadership effectiveness of the high school assistant principal and the high school instructional leadership teams (ILT) at over forty (40) Shelby County Schools. More specifically, this research study examined their impact on teacher effectiveness and student achievement in their…
DEFF Research Database (Denmark)
Hansen, Lars Kai; Salamon, Peter
1990-01-01
We propose several means for improving the performance an training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining generalization error can be reduced by invoking ensembles of similar...... networks....
Multilevel ensemble Kalman filtering
Hoel, Haakon
2016-01-08
The ensemble Kalman filter (EnKF) is a sequential filtering method that uses an ensemble of particle paths to estimate the means and covariances required by the Kalman filter by the use of sample moments, i.e., the Monte Carlo method. EnKF is often both robust and efficient, but its performance may suffer in settings where the computational cost of accurate simulations of particles is high. The multilevel Monte Carlo method (MLMC) is an extension of classical Monte Carlo methods which by sampling stochastic realizations on a hierarchy of resolutions may reduce the computational cost of moment approximations by orders of magnitude. In this work we have combined the ideas of MLMC and EnKF to construct the multilevel ensemble Kalman filter (MLEnKF) for the setting of finite dimensional state and observation spaces. The main ideas of this method is to compute particle paths on a hierarchy of resolutions and to apply multilevel estimators on the ensemble hierarchy of particles to compute Kalman filter means and covariances. Theoretical results and a numerical study of the performance gains of MLEnKF over EnKF will be presented. Some ideas on the extension of MLEnKF to settings with infinite dimensional state spaces will also be presented.
Lalloo, Chitra; Stinson, Jennifer N; Brown, Stephen C; Campbell, Fiona; Isaac, Lisa; Henry, James L
2014-11-01
To evaluate clinical feasibility of the Pain-QuILT (previously known as the Iconic Pain Assessment Tool) from the perspective of adolescents with chronic pain and members of their interdisciplinary health team. The Pain-QuILT (PQ), a web-based tool that records the visual self-report of sensory pain in the form of time-stamped records, was directly compared with standard interview questions that were transformed to a paper-based tool. Qualitative, semi-structured interviews were used to refine the PQ. Adolescents with chronic pain aged 12 to 18 years used the PQ and comparator tool (randomized order) to self-report pain before a scheduled clinic appointment, and then took part in a semi-structured interview. The health team used these pain reports (PQ and comparator) during patient appointments, and later participated in focus group interviews. Interview audio recordings were transcribed verbatim and underwent a simple line-by-line content analysis to identify key concepts. A total of 17 adolescents and 9 health team members completed the study. All adolescents felt that the PQ was easy to use and understand. The median time required for completion of the PQ and comparator tool was 3.3 and 3.6 minutes, respectively. Overall, 15/17 (88%) of adolescents preferred the PQ to self-report their pain versus the comparator. The health team indicated that the PQ was a clinically useful tool and identified minor barriers to implementation. Consultations with adolescents and their health team indicate that the PQ is a clinically feasible tool for eliciting detailed self-report records of the sensory experience of chronic pain.
Tailored Random Graph Ensembles
International Nuclear Information System (INIS)
Roberts, E S; Annibale, A; Coolen, A C C
2013-01-01
Tailored graph ensembles are a developing bridge between biological networks and statistical mechanics. The aim is to use this concept to generate a suite of rigorous tools that can be used to quantify and compare the topology of cellular signalling networks, such as protein-protein interaction networks and gene regulation networks. We calculate exact and explicit formulae for the leading orders in the system size of the Shannon entropies of random graph ensembles constrained with degree distribution and degree-degree correlation. We also construct an ergodic detailed balance Markov chain with non-trivial acceptance probabilities which converges to a strictly uniform measure and is based on edge swaps that conserve all degrees. The acceptance probabilities can be generalized to define Markov chains that target any alternative desired measure on the space of directed or undirected graphs, in order to generate graphs with more sophisticated topological features.
Perkins, A. L.; Zambo, S. J.; Elmore, P. A.
2016-02-01
In regions with sparse bathymetry, data learning algorithms have shown skill in recognizing dominant features such as seamounts and ridges. The structure of these features provides a means to impute data values to increase the resolution. When two different types of classifiers identify the same acreage - we have two possible interpretations of the sparse data. In this paper we construct an ensemble data fitting method, designed for sparse Bathymetric acreage that arbitrates between two competing nominal data categories. Each categorical data type leads to different data imputation interpretations. From these two interpretations, we construct an ensemble regression to minimize a weighted average of the two categorical interpretations. We demonstrate the method using an idealized Bathymetric data set from which two interpretations are possible.
Multilevel ensemble Kalman filtering
Hoel, Hakon
2016-06-14
This work embeds a multilevel Monte Carlo sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF) in the setting of finite dimensional signal evolution and noisy discrete-time observations. The signal dynamics is assumed to be governed by a stochastic differential equation (SDE), and a hierarchy of time grids is introduced for multilevel numerical integration of that SDE. The resulting multilevel EnKF is proved to asymptotically outperform EnKF in terms of computational cost versus approximation accuracy. The theoretical results are illustrated numerically.
Multinomial logistic regression ensembles.
Lee, Kyewon; Ahn, Hongshik; Moon, Hojin; Kodell, Ralph L; Chen, James J
2013-05-01
This article proposes a method for multiclass classification problems using ensembles of multinomial logistic regression models. A multinomial logit model is used as a base classifier in ensembles from random partitions of predictors. The multinomial logit model can be applied to each mutually exclusive subset of the feature space without variable selection. By combining multiple models the proposed method can handle a huge database without a constraint needed for analyzing high-dimensional data, and the random partition can improve the prediction accuracy by reducing the correlation among base classifiers. The proposed method is implemented using R, and the performance including overall prediction accuracy, sensitivity, and specificity for each category is evaluated on two real data sets and simulation data sets. To investigate the quality of prediction in terms of sensitivity and specificity, the area under the receiver operating characteristic (ROC) curve (AUC) is also examined. The performance of the proposed model is compared to a single multinomial logit model and it shows a substantial improvement in overall prediction accuracy. The proposed method is also compared with other classification methods such as the random forest, support vector machines, and random multinomial logit model.
On Ensemble Nonlinear Kalman Filtering with Symmetric Analysis Ensembles
Luo, Xiaodong
2010-09-19
The ensemble square root filter (EnSRF) [1, 2, 3, 4] is a popular method for data assimilation in high dimensional systems (e.g., geophysics models). Essentially the EnSRF is a Monte Carlo implementation of the conventional Kalman filter (KF) [5, 6]. It is mainly different from the KF at the prediction steps, where it is some ensembles, rather then the means and covariance matrices, of the system state that are propagated forward. In doing this, the EnSRF is computationally more efficient than the KF, since propagating a covariance matrix forward in high dimensional systems is prohibitively expensive. In addition, the EnSRF is also very convenient in implementation. By propagating the ensembles of the system state, the EnSRF can be directly applied to nonlinear systems without any change in comparison to the assimilation procedures in linear systems. However, by adopting the Monte Carlo method, the EnSRF also incurs certain sampling errors. One way to alleviate this problem is to introduce certain symmetry to the ensembles, which can reduce the sampling errors and spurious modes in evaluation of the means and covariances of the ensembles [7]. In this contribution, we present two methods to produce symmetric ensembles. One is based on the unscented transform [8, 9], which leads to the unscented Kalman filter (UKF) [8, 9] and its variant, the ensemble unscented Kalman filter (EnUKF) [7]. The other is based on Stirling’s interpolation formula (SIF), which results in the divided difference filter (DDF) [10]. Here we propose a simplified divided difference filter (sDDF) in the context of ensemble filtering. The similarity and difference between the sDDF and the EnUKF will be discussed. Numerical experiments will also be conducted to investigate the performance of the sDDF and the EnUKF, and compare them to a well‐established EnSRF, the ensemble transform Kalman filter (ETKF) [2].
Ensemble manifold regularization.
Geng, Bo; Tao, Dacheng; Xu, Chao; Yang, Linjun; Hua, Xian-Sheng
2012-06-01
We propose an automatic approximation of the intrinsic manifold for general semi-supervised learning (SSL) problems. Unfortunately, it is not trivial to define an optimization function to obtain optimal hyperparameters. Usually, cross validation is applied, but it does not necessarily scale up. Other problems derive from the suboptimality incurred by discrete grid search and the overfitting. Therefore, we develop an ensemble manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. Algorithmically, we designed EMR carefully so it 1) learns both the composite manifold and the semi-supervised learner jointly, 2) is fully automatic for learning the intrinsic manifold hyperparameters implicitly, 3) is conditionally optimal for intrinsic manifold approximation under a mild and reasonable assumption, and 4) is scalable for a large number of candidate manifold hyperparameters, from both time and space perspectives. Furthermore, we prove the convergence property of EMR to the deterministic matrix at rate root-n. Extensive experiments over both synthetic and real data sets demonstrate the effectiveness of the proposed framework.
Diurnal Ensemble Surface Meteorology Statistics
U.S. Environmental Protection Agency — Excel file containing diurnal ensemble statistics of 2-m temperature, 2-m mixing ratio and 10-m wind speed. This Excel file contains figures for Figure 2 in the...
DEFF Research Database (Denmark)
2004-01-01
Within the framework of the PSO-Ensemble project (FU2101) a demo application has been created. The application use ECMWF ensemble forecasts. Two instances of the application are running; one for Nysted Offshore and one for the total production (except Horns Rev) in the Eltra area. The output is a...... is available via two password-protected web-pages hosted at IMM and is used daily by Elsam and E2....
Transition from Poisson to circular unitary ensemble
Indian Academy of Sciences (India)
ensemble (SE). These are defined by invariance of the ensemble measure under the orthogonal, unitary and symplectic transformations respectively and are related to the time reversal and rotational symmetries of the system. Gaussian ensembles. (GE) of Hermitian matrices and circular ensembles (CE) of unitary matrices ...
Adaptive correction of ensemble forecasts
Pelosi, Anna; Battista Chirico, Giovanni; Van den Bergh, Joris; Vannitsem, Stephane
2017-04-01
Forecasts from numerical weather prediction (NWP) models often suffer from both systematic and non-systematic errors. These are present in both deterministic and ensemble forecasts, and originate from various sources such as model error and subgrid variability. Statistical post-processing techniques can partly remove such errors, which is particularly important when NWP outputs concerning surface weather variables are employed for site specific applications. Many different post-processing techniques have been developed. For deterministic forecasts, adaptive methods such as the Kalman filter are often used, which sequentially post-process the forecasts by continuously updating the correction parameters as new ground observations become available. These methods are especially valuable when long training data sets do not exist. For ensemble forecasts, well-known techniques are ensemble model output statistics (EMOS), and so-called "member-by-member" approaches (MBM). Here, we introduce a new adaptive post-processing technique for ensemble predictions. The proposed method is a sequential Kalman filtering technique that fully exploits the information content of the ensemble. One correction equation is retrieved and applied to all members, however the parameters of the regression equations are retrieved by exploiting the second order statistics of the forecast ensemble. We compare our new method with two other techniques: a simple method that makes use of a running bias correction of the ensemble mean, and an MBM post-processing approach that rescales the ensemble mean and spread, based on minimization of the Continuous Ranked Probability Score (CRPS). We perform a verification study for the region of Campania in southern Italy. We use two years (2014-2015) of daily meteorological observations of 2-meter temperature and 10-meter wind speed from 18 ground-based automatic weather stations distributed across the region, comparing them with the corresponding COSMO
Comparative Visualization of Ensembles Using Ensemble Surface Slicing.
Alabi, Oluwafemi S; Wu, Xunlei; Harter, Jonathan M; Phadke, Madhura; Pinto, Lifford; Petersen, Hannah; Bass, Steffen; Keifer, Michael; Zhong, Sharon; Healey, Chris; Taylor, Russell M
2012-01-22
By definition, an ensemble is a set of surfaces or volumes derived from a series of simulations or experiments. Sometimes the series is run with different initial conditions for one parameter to determine parameter sensitivity. The understanding and identification of visual similarities and differences among the shapes of members of an ensemble is an acute and growing challenge for researchers across the physical sciences. More specifically, the task of gaining spatial understanding and identifying similarities and differences between multiple complex geometric data sets simultaneously has proved challenging. This paper proposes a comparison and visualization technique to support the visual study of parameter sensitivity. We present a novel single-image view and sampling technique which we call Ensemble Surface Slicing (ESS). ESS produces a single image that is useful for determining differences and similarities between surfaces simultaneously from several data sets. We demonstrate the usefulness of ESS on two real-world data sets from our collaborators.
Algorithms on ensemble quantum computers.
Boykin, P Oscar; Mor, Tal; Roychowdhury, Vwani; Vatan, Farrokh
2010-06-01
In ensemble (or bulk) quantum computation, all computations are performed on an ensemble of computers rather than on a single computer. Measurements of qubits in an individual computer cannot be performed; instead, only expectation values (over the complete ensemble of computers) can be measured. As a result of this limitation on the model of computation, many algorithms cannot be processed directly on such computers, and must be modified, as the common strategy of delaying the measurements usually does not resolve this ensemble-measurement problem. Here we present several new strategies for resolving this problem. Based on these strategies we provide new versions of some of the most important quantum algorithms, versions that are suitable for implementing on ensemble quantum computers, e.g., on liquid NMR quantum computers. These algorithms are Shor's factorization algorithm, Grover's search algorithm (with several marked items), and an algorithm for quantum fault-tolerant computation. The first two algorithms are simply modified using a randomizing and a sorting strategies. For the last algorithm, we develop a classical-quantum hybrid strategy for removing measurements. We use it to present a novel quantum fault-tolerant scheme. More explicitly, we present schemes for fault-tolerant measurement-free implementation of Toffoli and σ(z)(¼) as these operations cannot be implemented "bitwise", and their standard fault-tolerant implementations require measurement.
Quantum ensembles of quantum classifiers.
Schuld, Maria; Petruccione, Francesco
2018-02-09
Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer. Following the success of collective decision making with ensembles in classical machine learning, this paper introduces the concept of quantum ensembles of quantum classifiers. Creating the ensemble corresponds to a state preparation routine, after which the quantum classifiers are evaluated in parallel and their combined decision is accessed by a single-qubit measurement. This framework naturally allows for exponentially large ensembles in which - similar to Bayesian learning - the individual classifiers do not have to be trained. As an example, we analyse an exponentially large quantum ensemble in which each classifier is weighed according to its performance in classifying the training data, leading to new results for quantum as well as classical machine learning.
On Constructing Ensembles for Combinatorial Optimisation.
Hart, Emma; Sim, Kevin
2018-01-01
Although the use of ensemble methods in machine-learning is ubiquitous due to their proven ability to outperform their constituent algorithms, ensembles of optimisation algorithms have received relatively little attention. Existing approaches lag behind machine-learning in both theory and practice, with no principled design guidelines available. In this article, we address fundamental questions regarding ensemble composition in optimisation using the domain of bin-packing as an example. In particular, we investigate the trade-off between accuracy and diversity, and whether diversity metrics can be used as a proxy for constructing an ensemble, proposing a number of novel metrics for comparing algorithm diversity. We find that randomly composed ensembles can outperform ensembles of high-performing algorithms under certain conditions and that judicious choice of diversity metric is required to construct good ensembles. The method and findings can be generalised to any metaheuristic ensemble, and lead to better understanding of how to undertake principled ensemble design.
Ensemble Equivalence for Distinguishable Particles
Directory of Open Access Journals (Sweden)
Antonio Fernández-Peralta
2016-07-01
Full Text Available Statistics of distinguishable particles has become relevant in systems of colloidal particles and in the context of applications of statistical mechanics to complex networks. In this paper, we present evidence that a commonly used expression for the partition function of a system of distinguishable particles leads to huge fluctuations of the number of particles in the grand canonical ensemble and, consequently, to nonequivalence of statistical ensembles. We will show that the alternative definition of the partition function including, naturally, Boltzmann’s correct counting factor for distinguishable particles solves the problem and restores ensemble equivalence. Finally, we also show that this choice for the partition function does not produce any inconsistency for a system of distinguishable localized particles, where the monoparticular partition function is not extensive.
Advanced Atmospheric Ensemble Modeling Techniques
Energy Technology Data Exchange (ETDEWEB)
Buckley, R. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Chiswell, S. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Kurzeja, R. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Maze, G. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Viner, B. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Werth, D. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)
2017-09-29
Ensemble modeling (EM), the creation of multiple atmospheric simulations for a given time period, has become an essential tool for characterizing uncertainties in model predictions. We explore two novel ensemble modeling techniques: (1) perturbation of model parameters (Adaptive Programming, AP), and (2) data assimilation (Ensemble Kalman Filter, EnKF). The current research is an extension to work from last year and examines transport on a small spatial scale (<100 km) in complex terrain, for more rigorous testing of the ensemble technique. Two different release cases were studied, a coastal release (SF6) and an inland release (Freon) which consisted of two release times. Observations of tracer concentration and meteorology are used to judge the ensemble results. In addition, adaptive grid techniques have been developed to reduce required computing resources for transport calculations. Using a 20- member ensemble, the standard approach generated downwind transport that was quantitatively good for both releases; however, the EnKF method produced additional improvement for the coastal release where the spatial and temporal differences due to interior valley heating lead to the inland movement of the plume. The AP technique showed improvements for both release cases, with more improvement shown in the inland release. This research demonstrated that transport accuracy can be improved when models are adapted to a particular location/time or when important local data is assimilated into the simulation and enhances SRNL’s capability in atmospheric transport modeling in support of its current customer base and local site missions, as well as our ability to attract new customers within the intelligence community.
Ensemble method for dengue prediction.
Buczak, Anna L; Baugher, Benjamin; Moniz, Linda J; Bagley, Thomas; Babin, Steven M; Guven, Erhan
2018-01-01
In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico) during four dengue seasons: 1) peak height (i.e., maximum weekly number of cases during a transmission season; 2) peak week (i.e., week in which the maximum weekly number of cases occurred); and 3) total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date. Our approach used ensemble models created by combining three disparate types of component models: 1) two-dimensional Method of Analogues models incorporating both dengue and climate data; 2) additive seasonal Holt-Winters models with and without wavelet smoothing; and 3) simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations. Our ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week. The Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru.
Spectral Diagonal Ensemble Kalman Filters
Czech Academy of Sciences Publication Activity Database
Kasanický, Ivan; Mandel, Jan; Vejmelka, Martin
2015-01-01
Roč. 22, č. 4 (2015), s. 485-497 ISSN 1023-5809 R&D Projects: GA ČR GA13-34856S Grant - others:NSF(US) DMS -1216481 Institutional support: RVO:67985807 Keywords : data assimilation * ensemble Kalman filter * spectral representation Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.321, year: 2015
Multimodel ensembles of wheat growth
DEFF Research Database (Denmark)
Martre, Pierre; Wallach, Daniel; Asseng, Senthold
2015-01-01
, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24...
Genetic Algorithm Optimized Neural Networks Ensemble as ...
African Journals Online (AJOL)
Marquardt algorithm by varying conditions such as inputs, hidden neurons, initialization, training sets and random Gaussian noise injection to ... Several such ensembles formed the population which was evolved to generate the fittest ensemble.
Global Ensemble Forecast System (GEFS) [1 Deg.
National Oceanic and Atmospheric Administration, Department of Commerce — The Global Ensemble Forecast System (GEFS) is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental...
Localization of atomic ensembles via superfluorescence
International Nuclear Information System (INIS)
Macovei, Mihai; Evers, Joerg; Keitel, Christoph H.; Zubairy, M. Suhail
2007-01-01
The subwavelength localization of an ensemble of atoms concentrated to a small volume in space is investigated. The localization relies on the interaction of the ensemble with a standing wave laser field. The light scattered in the interaction of the standing wave field and the atom ensemble depends on the position of the ensemble relative to the standing wave nodes. This relation can be described by a fluorescence intensity profile, which depends on the standing wave field parameters and the ensemble properties and which is modified due to collective effects in the ensemble of nearby particles. We demonstrate that the intensity profile can be tailored to suit different localization setups. Finally, we apply these results to two localization schemes. First, we show how to localize an ensemble fixed at a certain position in the standing wave field. Second, we discuss localization of an ensemble passing through the standing wave field
DEFF Research Database (Denmark)
Ben Bouallègue, Zied; Heppelmann, Tobias; Theis, Susanne E.
2015-01-01
. The new approach which preserves the dynamical development of the ensemble members is called dynamic ensemble copula coupling (d-ECC). The ensemble based empirical copulas, ECC and d-ECC, are applied to wind forecasts from the high resolution ensemble system COSMO-DEEPS run operationally at the German...
Ensemble methods for handwritten digit recognition
DEFF Research Database (Denmark)
Hansen, Lars Kai; Liisberg, Christian; Salamon, P.
1992-01-01
Neural network ensembles are applied to handwritten digit recognition. The individual networks of the ensemble are combinations of sparse look-up tables (LUTs) with random receptive fields. It is shown that the consensus of a group of networks outperforms the best individual of the ensemble...
Ensemble methods for seasonal limited area forecasts
DEFF Research Database (Denmark)
Arritt, Raymond W.; Anderson, Christopher J.; Takle, Eugene S.
2004-01-01
The ensemble prediction methods used for seasonal limited area forecasts were examined by comparing methods for generating ensemble simulations of seasonal precipitation. The summer 1993 model over the north-central US was used as a test case. The four methods examined included the lagged....... The mixed-physics ensemble performed well in terms of equitable threat score, especially for higher precipitation amounts....
Hydrological Ensemble Prediction System (HEPS)
Thielen-Del Pozo, J.; Schaake, J.; Martin, E.; Pailleux, J.; Pappenberger, F.
2010-09-01
Flood forecasting systems form a key part of ‘preparedness' strategies for disastrous floods and provide hydrological services, civil protection authorities and the public with information of upcoming events. Provided the warning leadtime is sufficiently long, adequate preparatory actions can be taken to efficiently reduce the impacts of the flooding. Following on the success of the use of ensembles for weather forecasting, the hydrological community now moves increasingly towards Hydrological Ensemble Prediction Systems (HEPS) for improved flood forecasting using operationally available NWP products as inputs. However, these products are often generated on relatively coarse scales compared to hydrologically relevant basin units and suffer systematic biases that may have considerable impact when passed through the non-linear hydrological filters. Therefore, a better understanding on how best to produce, communicate and use hydrologic ensemble forecasts in hydrological short-, medium- und long term prediction of hydrological processes is necessary. The "Hydrologic Ensemble Prediction Experiment" (HEPEX), is an international initiative consisting of hydrologists, meteorologist and end-users to advance probabilistic hydrologic forecast techniques for flood, drought and water management applications. Different aspects of the hydrological ensemble processor are being addressed including • Production of useful meteorological products relevant for hydrological applications, ranging from nowcasting products to seasonal forecasts. The importance of hindcasts that are consistent with the operational weather forecasts will be discussed to support bias correction and downscaling, statistically meaningful verification of HEPS, and the development and testing of operating rules; • Need for downscaling and post-processing of weather ensembles to reduce bias before entering hydrological applications; • Hydrological model and parameter uncertainty and how to correct and
Triticeae resources in Ensembl Plants.
Bolser, Dan M; Kerhornou, Arnaud; Walts, Brandon; Kersey, Paul
2015-01-01
Recent developments in DNA sequencing have enabled the large and complex genomes of many crop species to be determined for the first time, even those previously intractable due to their polyploid nature. Indeed, over the course of the last 2 years, the genome sequences of several commercially important cereals, notably barley and bread wheat, have become available, as well as those of related wild species. While still incomplete, comparison with other, more completely assembled species suggests that coverage of genic regions is likely to be high. Ensembl Plants (http://plants.ensembl.org) is an integrative resource organizing, analyzing and visualizing genome-scale information for important crop and model plants. Available data include reference genome sequence, variant loci, gene models and functional annotation. For variant loci, individual and population genotypes, linkage information and, where available, phenotypic information are shown. Comparative analyses are performed on DNA and protein sequence alignments. The resulting genome alignments and gene trees, representing the implied evolutionary history of the gene family, are made available for visualization and analysis. Driven by the case of bread wheat, specific extensions to the analysis pipelines and web interface have recently been developed to support polyploid genomes. Data in Ensembl Plants is accessible through a genome browser incorporating various specialist interfaces for different data types, and through a variety of additional methods for programmatic access and data mining. These interfaces are consistent with those offered through the Ensembl interface for the genomes of non-plant species, including those of plant pathogens, pests and pollinators, facilitating the study of the plant in its environment. © The Author 2014. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists.
Benchmarking Commercial Conformer Ensemble Generators.
Friedrich, Nils-Ole; de Bruyn Kops, Christina; Flachsenberg, Florian; Sommer, Kai; Rarey, Matthias; Kirchmair, Johannes
2017-11-27
We assess and compare the performance of eight commercial conformer ensemble generators (ConfGen, ConfGenX, cxcalc, iCon, MOE LowModeMD, MOE Stochastic, MOE Conformation Import, and OMEGA) and one leading free algorithm, the distance geometry algorithm implemented in RDKit. The comparative study is based on a new version of the Platinum Diverse Dataset, a high-quality benchmarking dataset of 2859 protein-bound ligand conformations extracted from the PDB. Differences in the performance of commercial algorithms are much smaller than those observed for free algorithms in our previous study (J. Chem. Inf. 2017, 57, 529-539). For commercial algorithms, the median minimum root-mean-square deviations measured between protein-bound ligand conformations and ensembles of a maximum of 250 conformers are between 0.46 and 0.61 Å. Commercial conformer ensemble generators are characterized by their high robustness, with at least 99% of all input molecules successfully processed and few or even no substantial geometrical errors detectable in their output conformations. The RDKit distance geometry algorithm (with minimization enabled) appears to be a good free alternative since its performance is comparable to that of the midranked commercial algorithms. Based on a statistical analysis, we elaborate on which algorithms to use and how to parametrize them for best performance in different application scenarios.
Dimensionality Reduction Through Classifier Ensembles
Oza, Nikunj C.; Tumer, Kagan; Norwig, Peter (Technical Monitor)
1999-01-01
In data mining, one often needs to analyze datasets with a very large number of attributes. Performing machine learning directly on such data sets is often impractical because of extensive run times, excessive complexity of the fitted model (often leading to overfitting), and the well-known "curse of dimensionality." In practice, to avoid such problems, feature selection and/or extraction are often used to reduce data dimensionality prior to the learning step. However, existing feature selection/extraction algorithms either evaluate features by their effectiveness across the entire data set or simply disregard class information altogether (e.g., principal component analysis). Furthermore, feature extraction algorithms such as principal components analysis create new features that are often meaningless to human users. In this article, we present input decimation, a method that provides "feature subsets" that are selected for their ability to discriminate among the classes. These features are subsequently used in ensembles of classifiers, yielding results superior to single classifiers, ensembles that use the full set of features, and ensembles based on principal component analysis on both real and synthetic datasets.
Heat fluctuations and initial ensembles
Kim, Kwangmoo; Kwon, Chulan; Park, Hyunggyu
2014-09-01
Time-integrated quantities such as work and heat increase incessantly in time during nonequilibrium processes near steady states. In the long-time limit, the average values of work and heat become asymptotically equivalent to each other, since they only differ by a finite energy change in average. However, the fluctuation theorem (FT) for the heat is found not to hold with the equilibrium initial ensemble, while the FT for the work holds. This reveals an intriguing effect of everlasting initial memory stored in rare events. We revisit the problem of a Brownian particle in a harmonic potential dragged with a constant velocity, which is in contact with a thermal reservoir. The heat and work fluctuations are investigated with initial Boltzmann ensembles at temperatures generally different from the reservoir temperature. We find that, in the infinite-time limit, the FT for the work is fully recovered for arbitrary initial temperatures, while the heat fluctuations significantly deviate from the FT characteristics except for the infinite initial-temperature limit (a uniform initial ensemble). Furthermore, we succeed in calculating finite-time corrections to the heat and work distributions analytically, using the modified saddle point integral method recently developed by us. Interestingly, we find noncommutativity between the infinite-time limit and the infinite-initial-temperature limit for the probability distribution function (PDF) of the heat.
Wind Power Prediction using Ensembles
DEFF Research Database (Denmark)
Giebel, Gregor; Badger, Jake; Landberg, Lars
2005-01-01
offshore wind farm and the whole Jutland/Funen area. The utilities used these forecasts for maintenance planning, fuel consumption estimates and over-the-weekend trading on the Leipzig power exchange. Othernotable scientific results include the better accuracy of forecasts made up from a simple...... superposition of two NWP provider (in our case, DMI and DWD), an investigation of the merits of a parameterisation of the turbulent kinetic energy within thedelivered wind speed forecasts, and the finding that a “naïve” downscaling of each of the coarse ECMWF ensemble members with higher resolution HIRLAM did...
Multiscale Clock Ensembling Using Wavelets
2010-11-01
allows an energy decomposition of the signal as well, referred to as the wavelet variance. This variance is defined by ) var ()( 2 llX Wv (11...and it can be shown that for a very wide class of signals and for an appropriately chosen wavelet that ) var ()( 1 2 Xv l lX . One such...42 nd Annual Precise Time and Time Interval (PTTI) Meeting 527 MULTISCALE CLOCK ENSEMBLING USING WAVELETS Ken Senior Naval Center
Statistical ensembles in quantum mechanics
International Nuclear Information System (INIS)
Blokhintsev, D.
1976-01-01
The interpretation of quantum mechanics presented in this paper is based on the concept of quantum ensembles. This concept differs essentially from the canonical one by that the interference of the observer into the state of a microscopic system is of no greater importance than in any other field of physics. Owing to this fact, the laws established by quantum mechanics are not of less objective character than the laws governing classical statistical mechanics. The paradoxical nature of some statements of quantum mechanics which result from the interpretation of the wave functions as the observer's notebook greatly stimulated the development of the idea presented. (Auth.)
A Statistical Description of Neural Ensemble Dynamics
Directory of Open Access Journals (Sweden)
John D Long
2011-11-01
Full Text Available The growing use of multi-channel neural recording techniques in behaving animals has produced rich datasets, providing new insights into how the brain mediates behavior. One limitation of these techniques is they do not provide information about the underlying anatomical connections among the recorded neurons within an ensemble. Moreover, the set of possible interactions grows exponentially with ensemble size. This limitation is at the heart of the challenge one confronts when interpreting these data. Several groups have attempted the challenging inverse problem of inferring the connectivity among the recorded neurons from ensemble data. Unfortunately, the combination of expert knowledge and ensemble data is often insufficient for selecting a unique model of these interactions. Our approach shifts away from modeling the network diagram of the ensemble toward analyzing the dynamics of the ensemble as they relate to behavior. Our contribution consists of adapting techniques from signal processing and Bayesian statistics to track changes in the dynamics of ensemble data on time-scales comparable with behavior. We employ a Bayesian estimator to weigh prior information against the available ensemble data, and use an adaptive quantization technique to aggregate poorly estimated regions of the ensemble data space. Importantly, our method is capable of detecting changes in both the magnitude and structure of correlations among neurons missed by firing rate metrics. We show that this method is scalable across a wide range of time-scales and ensemble sizes. Lastly, the performance of this method on both simulated and real ensemble data is used to demonstrate its utility for describing the dynamics of ensemble data as they relate to behavior.
Remme, Anneli, 1968-
2008-01-01
Helena Tulve autoriplaadist "Lijnen". Esitavad NYYD Ensemble, dirigent Olari Elts, Stockholm Saxophone Quartet, Silesian String Quartet, solistid Arianna Savall, Emmanuelle Ophele-Gaubert ja Mihkel Peäske
Our Man in Tallinn / Martin Anderson
Anderson, Martin
1997-01-01
Muljeid kontserdist "Warner Classics Gala" "Estonia" kontserdisaalis, kus E. Mägi, E.-S. Tüüri, V. Tormise, U. Sisaski jt. loomingut esitasid "Camerata Tallinn", NYYD Ensemble, RAM, pianist Lauri Väinmaa
Multi-Model Ensemble Wake Vortex Prediction
Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.
2015-01-01
Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.
Urban runoff forecasting with ensemble weather predictions
DEFF Research Database (Denmark)
Pedersen, Jonas Wied; Courdent, Vianney Augustin Thomas; Vezzaro, Luca
This research shows how ensemble weather forecasts can be used to generate urban runoff forecasts up to 53 hours into the future. The results highlight systematic differences between ensemble members that needs to be accounted for when these forecasts are used in practice.......This research shows how ensemble weather forecasts can be used to generate urban runoff forecasts up to 53 hours into the future. The results highlight systematic differences between ensemble members that needs to be accounted for when these forecasts are used in practice....
Layered Ensemble Architecture for Time Series Forecasting.
Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin
2016-01-01
Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered ensemble architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an ensemble of multilayer perceptron (MLP) networks. While the first ensemble layer tries to find an appropriate lag, the second ensemble layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an ensemble. LEA trains different networks in the ensemble by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the ensemble. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other ensemble and nonensemble methods.
Popular Music and the Instrumental Ensemble.
Boespflug, George
1999-01-01
Discusses popular music, the role of the musical performer as a creator, and the styles of jazz and popular music. Describes the pop ensemble at the college level, focusing on improvisation, rehearsals, recording, and performance. Argues that pop ensembles be used in junior and senior high school. (CMK)
Characterizing Ensembles of Superconducting Qubits
Sears, Adam; Birenbaum, Jeff; Hover, David; Rosenberg, Danna; Weber, Steven; Yoder, Jonilyn L.; Kerman, Jamie; Gustavsson, Simon; Kamal, Archana; Yan, Fei; Oliver, William
We investigate ensembles of up to 48 superconducting qubits embedded within a superconducting cavity. Such arrays of qubits have been proposed for the experimental study of Ising Hamiltonians, and efficient methods to characterize and calibrate these types of systems are still under development. Here we leverage high qubit coherence (> 70 μs) to characterize individual devices as well as qubit-qubit interactions, utilizing the common resonator mode for a joint readout. This research was funded by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA) under Air Force Contract No. FA8721-05-C-0002. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the US Government.
Percolation in the canonical ensemble
Hu, Hao; Blöte, Henk W. J.; Deng, Youjin
2012-12-01
We study the bond percolation problem under the constraint that the total number of occupied bonds is fixed, so that the canonical ensemble applies. We show via an analytical approach that at criticality, the constraint can induce new finite-size corrections with exponent ycan = 2yt - d both in energy-like and magnetic quantities, where yt = 1/ν is the thermal renormalization exponent and d is the spatial dimension. Furthermore, we find that while most of the universal parameters remain unchanged, some universal amplitudes, like the excess cluster number, can be modified and become non-universal. We confirm these predictions by extensive Monte Carlo simulations of the two-dimensional percolation problem which has ycan = -1/2. This article is part of ‘Lattice models and integrability’, a special issue of Journal of Physics A: Mathematical and Theoretical in honour of F Y Wu's 80th birthday.
Derivation of Mayer Series from Canonical Ensemble
International Nuclear Information System (INIS)
Wang Xian-Zhi
2016-01-01
Mayer derived the Mayer series from both the canonical ensemble and the grand canonical ensemble by use of the cluster expansion method. In 2002, we conjectured a recursion formula of the canonical partition function of a fluid (X.Z. Wang, Phys. Rev. E 66 (2002) 056102). In this paper we give a proof for this formula by developing an appropriate expansion of the integrand of the canonical partition function. We further derive the Mayer series solely from the canonical ensemble by use of this recursion formula. (paper)
Derivation of Mayer Series from Canonical Ensemble
Wang, Xian-Zhi
2016-02-01
Mayer derived the Mayer series from both the canonical ensemble and the grand canonical ensemble by use of the cluster expansion method. In 2002, we conjectured a recursion formula of the canonical partition function of a fluid (X.Z. Wang, Phys. Rev. E 66 (2002) 056102). In this paper we give a proof for this formula by developing an appropriate expansion of the integrand of the canonical partition function. We further derive the Mayer series solely from the canonical ensemble by use of this recursion formula.
Calibration of decadal ensemble predictions
Pasternack, Alexander; Rust, Henning W.; Bhend, Jonas; Liniger, Mark; Grieger, Jens; Müller, Wolfgang; Ulbrich, Uwe
2017-04-01
Decadal climate predictions are of great socio-economic interest due to the corresponding planning horizons of several political and economic decisions. Due to uncertainties of weather and climate, forecasts (e.g. due to initial condition uncertainty), they are issued in a probabilistic way. One issue frequently observed for probabilistic forecasts is that they tend to be not reliable, i.e. the forecasted probabilities are not consistent with the relative frequency of the associated observed events. Thus, these kind of forecasts need to be re-calibrated. While re-calibration methods for seasonal time scales are available and frequently applied, these methods still have to be adapted for decadal time scales and its characteristic problems like climate trend and lead time dependent bias. Regarding this, we propose a method to re-calibrate decadal ensemble predictions that takes the above mentioned characteristics into account. Finally, this method will be applied and validated to decadal forecasts from the MiKlip system (Germany's initiative for decadal prediction).
Hydrological ensemble predictions for reservoir inflow management
Zalachori, Ioanna; Ramos, Maria-Helena; Garçon, Rémy; Gailhard, Joel
2013-04-01
Hydrologic forecasting is a topic of special importance for a variety of users with different purposes. It concerns operational hydrologists interested in forecasting hazardous events (eg., floods and droughts) for early warning and prevention, as well as planners and managers searching to optimize the management of water resources systems at different space-time scales. The general aim of this study is to investigate the benefits of using hydrological ensemble predictions for reservoir inflow management. Ensemble weather forecasts are used as input to a hydrologic forecasting model and daily ensemble streamflow forecasts are generated up to a lead time of 7 days. Forecasts are then integrated into a heuristic decision model for reservoir management procedures. Performance is evaluated in terms of potential gain in energy production. The sensitivity of the results to various reservoir characteristics and future streamflow scenarios is assessed. A set of 11 catchments in France is used to illustrate the added value of ensemble streamflow forecasts for reservoir management.
Ensemble Machine Learning Methods and Applications
Ma, Yunqian
2012-01-01
It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face detection and are now being applied in areas as diverse as object trackingand bioinformatics. Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including various contributions from researchers in leading industrial research labs. At once a solid theoretical study and a practical guide, the volume is a windfall for r...
Ozone ensemble forecast with machine learning algorithms
Mallet , Vivien; Stoltz , Gilles; Mauricette , Boris
2009-01-01
International audience; We apply machine learning algorithms to perform sequential aggregation of ozone forecasts. The latter rely on a multimodel ensemble built for ozone forecasting with the modeling system Polyphemus. The ensemble simulations are obtained by changes in the physical parameterizations, the numerical schemes, and the input data to the models. The simulations are carried out for summer 2001 over western Europe in order to forecast ozone daily peaks and ozone hourly concentrati...
Orbital magnetism in ensembles of ballistic billiards
International Nuclear Information System (INIS)
Ullmo, D.; Richter, K.; Jalabert, R.A.
1993-01-01
The magnetic response of ensembles of small two-dimensional structures at finite temperatures is calculated. Using semiclassical methods and numerical calculation it is demonstrated that only short classical trajectories are relevant. The magnetic susceptibility is enhanced in regular systems, where these trajectories appear in families. For ensembles of squares large paramagnetic susceptibility is obtained, in good agreement with recent measurements in the ballistic regime. (authors). 20 refs., 2 figs
Ensembles of Classifiers based on Dimensionality Reduction
Schclar, Alon; Rokach, Lior; Amit, Amir
2013-01-01
We present a novel approach for the construction of ensemble classifiers based on dimensionality reduction. Dimensionality reduction methods represent datasets using a small number of attributes while preserving the information conveyed by the original dataset. The ensemble members are trained based on dimension-reduced versions of the training set. These versions are obtained by applying dimensionality reduction to the original training set using different values of the input parameters. Thi...
Liu, Li; Xu, Yue-Ping
2017-04-01
Ensemble flood forecasting driven by numerical weather prediction products is becoming more commonly used in operational flood forecasting applications.In this study, a hydrological ensemble flood forecasting system based on Variable Infiltration Capacity (VIC) model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated.The hydrological model is optimized by parallel programmed ɛ-NSGAII multi-objective algorithm and two respectively parameterized models are determined to simulate daily flows and peak flows coupled with a modular approach.The results indicatethat the ɛ-NSGAII algorithm permits more efficient optimization and rational determination on parameter setting.It is demonstrated that the multimodel ensemble streamflow mean have better skills than the best singlemodel ensemble mean (ECMWF) and the multimodel ensembles weighted on members and skill scores outperform other multimodel ensembles. For typical flood event, it is proved that the flood can be predicted 3-4 days in advance, but the flows in rising limb can be captured with only 1-2 days ahead due to the flash feature. With respect to peak flows selected by Peaks Over Threshold approach, the ensemble means from either singlemodel or multimodels are generally underestimated as the extreme values are smoothed out by ensemble process.
Towards a GME ensemble forecasting system: Ensemble initialization using the breeding technique
Directory of Open Access Journals (Sweden)
Jan D. Keller
2008-12-01
Full Text Available The quantitative forecast of precipitation requires a probabilistic background particularly with regard to forecast lead times of more than 3 days. As only ensemble simulations can provide useful information of the underlying probability density function, we built a new ensemble forecasting system (GME-EFS based on the GME model of the German Meteorological Service (DWD. For the generation of appropriate initial ensemble perturbations we chose the breeding technique developed by Toth and Kalnay (1993, 1997, which develops perturbations by estimating the regions of largest model error induced uncertainty. This method is applied and tested in the framework of quasi-operational forecasts for a three month period in 2007. The performance of the resulting ensemble forecasts are compared to the operational ensemble prediction systems ECMWF EPS and NCEP GFS by means of ensemble spread of free atmosphere parameters (geopotential and temperature and ensemble skill of precipitation forecasting. This comparison indicates that the GME ensemble forecasting system (GME-EFS provides reasonable forecasts with spread skill score comparable to that of the NCEP GFS. An analysis with the continuous ranked probability score exhibits a lack of resolution for the GME forecasts compared to the operational ensembles. However, with significant enhancements during the 3 month test period, the first results of our work with the GME-EFS indicate possibilities for further development as well as the potential for later operational usage.
Ensemble forecasts of road surface temperatures
Sokol, Zbyněk; Bližňák, Vojtěch; Sedlák, Pavel; Zacharov, Petr; Pešice, Petr; Škuthan, Miroslav
2017-05-01
This paper describes a new ensemble technique for road surface temperature (RST) forecasting using an energy balance and heat conduction model. Compared to currently used deterministic forecasts, the proposed technique allows the estimation of forecast uncertainty and probabilistic forecasts. The ensemble technique is applied to the METRo-CZ model and stems from error covariance analyses of the forecasted air temperature and humidity 2 m above the ground, wind speed at 10 m and total cloud cover N in octas by the numerical weather prediction (NWP) model. N is used to estimate the shortwave and longwave radiation fluxes. These variables are used to calculate the boundary conditions in the METRo-CZ model. We found that the variable N is crucial for generating the ensembles. Nevertheless, the ensemble spread is too small and underestimates the uncertainty in the RST forecast. One of the reasons is not considering errors in the rain and snow forecast by the NWP model when generating ensembles. Technical issues, such as incorrect sky view factors and the current state of road surface conditions also contribute to errors. Although the ensemble technique underestimates the uncertainty in the RST forecasts, it provides additional information to road authorities who provide winter road maintenance.
Rotationally invariant family of Levy-like random matrix ensembles
International Nuclear Information System (INIS)
Choi, Jinmyung; Muttalib, K A
2009-01-01
We introduce a family of rotationally invariant random matrix ensembles characterized by a parameter λ. While λ = 1 corresponds to well-known critical ensembles, we show that λ ≠ 1 describes 'Levy-like' ensembles, characterized by power-law eigenvalue densities. For λ > 1 the density is bounded, as in Gaussian ensembles, but λ < 1 describes ensembles characterized by densities with long tails. In particular, the model allows us to evaluate, in terms of a novel family of orthogonal polynomials, the eigenvalue correlations for Levy-like ensembles. These correlations differ qualitatively from those in either the Gaussian or the critical ensembles. (fast track communication)
Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing
Toye, Habib
2017-05-26
We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e., an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every 3 days. Real-time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.
Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing
Toye, Habib; Zhan, Peng; Gopalakrishnan, Ganesh; Kartadikaria, Aditya R.; Huang, Huang; Knio, Omar; Hoteit, Ibrahim
2017-07-01
We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e., an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every 3 days. Real-time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.
DEFF Research Database (Denmark)
Ben Bouallègue, Zied; Heppelmann, Tobias; Theis, Susanne E.
2016-01-01
approach, called d-ECC, is applied to wind forecasts from the high resolution ensemble system COSMO-DE-EPS run operationally at the German weather service. Scenarios generated by ECC and d-ECC are compared and assessed in the form of time series by means of multivariate verification tools and in a product......Probabilistic forecasts in the form of ensemble of scenarios are required for complex decision making processes. Ensemble forecasting systems provide such products but the spatio-temporal structures of the forecast uncertainty is lost when statistical calibration of the ensemble forecasts...... is applied for each lead time and location independently. Non-parametric approaches allow the reconstruction of spatio-temporal joint probability distributions at a low computational cost. For example, the ensemble copula coupling (ECC) method rebuilds the multivariate aspect of the forecast from...
Simulations in generalized ensembles through noninstantaneous switches
Giovannelli, Edoardo; Cardini, Gianni; Chelli, Riccardo
2015-10-01
Generalized-ensemble simulations, such as replica exchange and serial generalized-ensemble methods, are powerful simulation tools to enhance sampling of free energy landscapes in systems with high energy barriers. In these methods, sampling is enhanced through instantaneous transitions of replicas, i.e., copies of the system, between different ensembles characterized by some control parameter associated with thermodynamical variables (e.g., temperature or pressure) or collective mechanical variables (e.g., interatomic distances or torsional angles). An interesting evolution of these methodologies has been proposed by replacing the conventional instantaneous (trial) switches of replicas with noninstantaneous switches, realized by varying the control parameter in a finite time and accepting the final replica configuration with a Metropolis-like criterion based on the Crooks nonequilibrium work (CNW) theorem. Here we revise these techniques focusing on their correlation with the CNW theorem in the framework of Markovian processes. An outcome of this report is the derivation of the acceptance probability for noninstantaneous switches in serial generalized-ensemble simulations, where we show that explicit knowledge of the time dependence of the weight factors entering such simulations is not necessary. A generalized relationship of the CNW theorem is also provided in terms of the underlying equilibrium probability distribution at a fixed control parameter. Illustrative calculations on a toy model are performed with serial generalized-ensemble simulations, especially focusing on the different behavior of instantaneous and noninstantaneous replica transition schemes.
Ensemble habitat mapping of invasive plant species
Stohlgren, T.J.; Ma, P.; Kumar, S.; Rocca, M.; Morisette, J.T.; Jarnevich, C.S.; Benson, N.
2010-01-01
Ensemble species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. Ensemble models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and ensemble modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, ensemble models were the only models that ranked in the top three models for both field validation and test data. Ensemble models may be more robust than individual species-environment matching models for risk analysis. ?? 2010 Society for Risk Analysis.
Embedded random matrix ensembles in quantum physics
Kota, V K B
2014-01-01
Although used with increasing frequency in many branches of physics, random matrix ensembles are not always sufficiently specific to account for important features of the physical system at hand. One refinement which retains the basic stochastic approach but allows for such features consists in the use of embedded ensembles. The present text is an exhaustive introduction to and survey of this important field. Starting with an easy-to-read introduction to general random matrix theory, the text then develops the necessary concepts from the beginning, accompanying the reader to the frontiers of present-day research. With some notable exceptions, to date these ensembles have primarily been applied in nuclear spectroscopy. A characteristic example is the use of a random two-body interaction in the framework of the nuclear shell model. Yet, topics in atomic physics, mesoscopic physics, quantum information science and statistical mechanics of isolated finite quantum systems can also be addressed using these ensemb...
Lattice gauge theory in the microcanonical ensemble
International Nuclear Information System (INIS)
Callaway, D.J.E.; Rahman, A.
1983-01-01
The microcanonical-ensemble formulation of lattice gauge theory proposed recently is examined in detail. Expectation values in this new ensemble are determined by solving a large set of coupled ordinary differential equations, after the fashion of a molecular dynamics simulation. Following a brief review of the microcanonical ensemble, calculations are performed for the gauge groups U(1), SU(2), and SU(3). The results are compared and contrasted with standard methods of computation. Several advantages of the new formalism are noted. For example, no random numbers are required to update the system. Also, this update is performed in a simultaneous fashion. Thus the microcanonical method presumably adapts well to parallel processing techniques, especially when the p action is highly nonlocal (such as when fermions are included)
Cosmological ensemble and directional averages of observables
Bonvin, Camille; Durrer, Ruth; Maartens, Roy; Umeh, Obinna
2015-01-01
We show that at second order ensemble averages of observables and directional averages do not commute due to gravitational lensing. In principle this non-commutativity is significant for a variety of quantities we often use as observables. We derive the relation between the ensemble average and the directional average of an observable, at second-order in perturbation theory. We discuss the relevance of these two types of averages for making predictions of cosmological observables, focussing on observables related to distances and magnitudes. In particular, we show that the ensemble average of the distance is increased by gravitational lensing, whereas the directional average of the distance is decreased. We show that for a generic observable, there exists a particular function of the observable that is invariant under second-order lensing perturbations.
Precipitation Ensembles from Single-Value Forecasts for Hydrological Ensemble Forecasting
Demargne, J.; Schaake, J.; Wu, L.; Welles, E.; Herr, H.; Seo, D.
2005-05-01
An ensemble pre-processor was developed to produce short-term precipitation ensembles using operational single-value forecasts. The methodology attempts to quantify the uncertainty in the single-value forecast and to capture the skill therein. These precipitation ensemble forecasts could be then ingested in the NOAA/National Weather Service (NWS) Ensemble Streamflow Prediction (ESP) system to produce probabilistic hydrological forecasts that reflect the uncertainty in forecast precipitation. The procedure constructs the joint distribution of forecast and observed precipitation from historical pairs of forecast and observed values. The probability distribution function of the future events that may occur given a particular single-value forecast is then the conditional distribution of observed precipitation given the forecast. To generate individual ensemble members for each lead time and each location, the historical observed values are replaced with values sampled from the conditional distribution given the single-value forecast. The replacement procedure matches the ranks of historical and rescaled values to preserve the space-time properties of observed precipitation in the ensemble traces. Currently, the ensemble pre-processor is being tested and evaluated at four NOAA/NWS River Forecast Centers (RFCs) in the U.S.A. In this contribution, we present the results thus far from the field and retrospective evaluations, and key science issues that must be addressed toward national operational implementation.
two-body random matrix ensembles
Indian Academy of Sciences (India)
2015-02-03
Feb 3, 2015 ... Random matrix theory (RMT) introduced to describe statistical properties of the energy levels of complex nuclei has seen tremendous growth recently [1]. It is now well rec- ognized that, the quantum system whose classical counterpart is chaotic, will follow one of the three classical random matrix ensembles ...
The Phantasmagoria of Competition in School Ensembles
Abramo, Joseph Michael
2017-01-01
Participation in competition festivals--where students and ensembles compete against each other for high scores and accolades--is a widespread practice in North American formal music education. In this article, I use Marx's theories of labor, value, and phantasmagoria to suggest a capitalist logic that structures these competitions. Marx's…
A method for ensemble wildland fire simulation
Mark A. Finney; Isaac C. Grenfell; Charles W. McHugh; Robert C. Seli; Diane Trethewey; Richard D. Stratton; Stuart Brittain
2011-01-01
An ensemble simulation system that accounts for uncertainty in long-range weather conditions and two-dimensional wildland fire spread is described. Fuel moisture is expressed based on the energy release component, a US fire danger rating index, and its variation throughout the fire season is modeled using time series analysis of historical weather data. This analysis...
Designing boosting ensemble of relational fuzzy systems.
Scherer, Rafał
2010-10-01
A method frequently used in classification systems for improving classification accuracy is to combine outputs of several classifiers. Among various types of classifiers, fuzzy ones are tempting because of using intelligible fuzzy if-then rules. In the paper we build an AdaBoost ensemble of relational neuro-fuzzy classifiers. Relational fuzzy systems bond input and output fuzzy linguistic values by a binary relation; thus, fuzzy rules have additional, comparing to traditional fuzzy systems, weights - elements of a fuzzy relation matrix. Thanks to this the system is better adjustable to data during learning. In the paper an ensemble of relational fuzzy systems is proposed. The problem is that such an ensemble contains separate rule bases which cannot be directly merged. As systems are separate, we cannot treat fuzzy rules coming from different systems as rules from the same (single) system. In the paper, the problem is addressed by a novel design of fuzzy systems constituting the ensemble, resulting in normalization of individual rule bases during learning. The method described in the paper is tested on several known benchmarks and compared with other machine learning solutions from the literature.
Agonism/antagonism switching in allosteric ensembles.
Motlagh, Hesam N; Hilser, Vincent J
2012-03-13
Ligands for several transcription factors can act as agonists under some conditions and antagonists under others. The structural and molecular bases of such effects are unknown. Previously, we demonstrated how the folding of intrinsically disordered (ID) protein sequences, in particular, and population shifts, in general, could be used to mediate allosteric coupling between different functional domains, a model that has subsequently been validated in several systems. Here it is shown that population redistribution within allosteric systems can be used as a mechanism to tune protein ensembles such that a given ligand can act as both an agonist and an antagonist. Importantly, this mechanism can be robustly encoded in the ensemble, and does not require that the interactions between the ligand and the protein differ when it is acting either as an agonist or an antagonist. Instead, the effect is due to the relative probabilities of states prior to the addition of the ligand. The ensemble view of allostery that is illuminated by these studies suggests that rather than being seen as switches with fixed responses to allosteric activation, ensembles can evolve to be "functionally pluripotent," with the capacity to up or down regulate activity in response to a stimulus. This result not only helps to explain the prevalence of intrinsic disorder in transcription factors and other cell signaling proteins, it provides important insights about the energetic ground rules governing site-to-site communication in all allosteric systems.
Marking up lattice QCD configurations and ensembles
Energy Technology Data Exchange (ETDEWEB)
P.Coddington; B.Joo; C.M.Maynard; D.Pleiter; T.Yoshie
2007-10-01
QCDml is an XML-based markup language designed for sharing QCD configurations and ensembles world-wide via the International Lattice Data Grid (ILDG). Based on the latest release, we present key ingredients of the QCDml in order to provide some starting points for colleagues in this community to markup valuable configurations and submit them to the ILDG.
Embedded feature ranking for ensemble MLP classifiers.
Windeatt, Terry; Duangsoithong, Rakkrit; Smith, Raymond
2011-06-01
A feature ranking scheme for multilayer perceptron (MLP) ensembles is proposed, along with a stopping criterion based upon the out-of-bootstrap estimate. To solve multi-class problems feature ranking is combined with modified error-correcting output coding. Experimental results on benchmark data demonstrate the versatility of the MLP base classifier in removing irrelevant features.
Understanding the Ensemble Pianist: A Theoretical Framework
Kokotsaki, Dimitra
2007-01-01
The aim of this study was to develop a theoretical model of the attainment of high quality in musical ensemble performance as perceived by the pianist and to identify the factors affecting this process. The research has followed an inductive interpretative approach, applying qualitative methods. The analytic material was collected through the…
Ensemble of classifiers based network intrusion detection system performance bound
CSIR Research Space (South Africa)
Mkuzangwe, Nenekazi NP
2017-11-01
Full Text Available This paper provides a performance bound of a network intrusion detection system (NIDS) that uses an ensemble of classifiers. Currently researchers rely on implementing the ensemble of classifiers based NIDS before they can determine the performance...
Global Ensemble Forecast System (GEFS) [2.5 Deg.
National Oceanic and Atmospheric Administration, Department of Commerce — The Global Ensemble Forecast System (GEFS) is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental...
Impact of hybrid GSI analysis using ETR ensembles
Indian Academy of Sciences (India)
NCMRWF Global Forecast. System) with ETR (Ensemble Transform with Rescaling) based Global Ensemble Forecast (GEFS) of resolution T-190L28 is investigated. The experiment is conducted for a period of one week in June 2013 and forecast ...
Optimal initial perturbations for El Nino ensemble prediction with ensemble Kalman filter
Energy Technology Data Exchange (ETDEWEB)
Ham, Yoo-Geun; Kang, In-Sik [Seoul National University, School of Earth and Environment Sciences, Seoul (Korea); Kug, Jong-Seong [Korea Ocean Research and Development Institute, Ansan (Korea)
2009-12-15
A method for selecting optimal initial perturbations is developed within the framework of an ensemble Kalman filter (EnKF). Among the initial conditions generated by EnKF, ensemble members with fast growing perturbations are selected to optimize the ENSO seasonal forecast skills. Seasonal forecast experiments show that the forecast skills with the selected ensemble members are significantly improved compared with other ensemble members for up to 1-year lead forecasts. In addition, it is found that there is a strong relationship between the forecast skill improvements and flow-dependent instability. That is, correlation skills are significantly improved over the region where the predictable signal is relatively small (i.e. an inverse relationship). It is also shown that forecast skills are significantly improved during ENSO onset and decay phases, which are the most unpredictable periods among the ENSO events. (orig.)
Quantum canonical ensemble: A projection operator approach
Magnus, Wim; Lemmens, Lucien; Brosens, Fons
2017-09-01
Knowing the exact number of particles N, and taking this knowledge into account, the quantum canonical ensemble imposes a constraint on the occupation number operators. The constraint particularly hampers the systematic calculation of the partition function and any relevant thermodynamic expectation value for arbitrary but fixed N. On the other hand, fixing only the average number of particles, one may remove the above constraint and simply factorize the traces in Fock space into traces over single-particle states. As is well known, that would be the strategy of the grand-canonical ensemble which, however, comes with an additional Lagrange multiplier to impose the average number of particles. The appearance of this multiplier can be avoided by invoking a projection operator that enables a constraint-free computation of the partition function and its derived quantities in the canonical ensemble, at the price of an angular or contour integration. Introduced in the recent past to handle various issues related to particle-number projected statistics, the projection operator approach proves beneficial to a wide variety of problems in condensed matter physics for which the canonical ensemble offers a natural and appropriate environment. In this light, we present a systematic treatment of the canonical ensemble that embeds the projection operator into the formalism of second quantization while explicitly fixing N, the very number of particles rather than the average. Being applicable to both bosonic and fermionic systems in arbitrary dimensions, transparent integral representations are provided for the partition function ZN and the Helmholtz free energy FN as well as for two- and four-point correlation functions. The chemical potential is not a Lagrange multiplier regulating the average particle number but can be extracted from FN+1 -FN, as illustrated for a two-dimensional fermion gas.
The classicality and quantumness of a quantum ensemble
Energy Technology Data Exchange (ETDEWEB)
Zhu Xuanmin [School for Theoretical Physics and Department of Applied Physics, Hunan University, Changsha 410082 (China); Hefei National Laboratory for Physical Sciences at Microscale, University of Science and Technology of China, Hefei, Anhui 230026 (China); Department of Modern Physics, University of Science and Technology of China, Hefei, Anhui 230026 (China); Pang Shengshi [Hefei National Laboratory for Physical Sciences at Microscale, University of Science and Technology of China, Hefei, Anhui 230026 (China); Department of Modern Physics, University of Science and Technology of China, Hefei, Anhui 230026 (China); Wu Shengjun, E-mail: shengjun@ustc.edu.c [Hefei National Laboratory for Physical Sciences at Microscale, University of Science and Technology of China, Hefei, Anhui 230026 (China); Department of Modern Physics, University of Science and Technology of China, Hefei, Anhui 230026 (China); Liu Quanhui, E-mail: qhliu@hnu.c [School for Theoretical Physics and Department of Applied Physics, Hunan University, Changsha 410082 (China)
2011-05-02
In this Letter, we investigate the classicality and quantumness of a quantum ensemble. We define a quantity called ensemble classicality based on classical cloning strategy (ECCC) to characterize how classical a quantum ensemble is. An ensemble of commuting states has a unit ECCC, while a general ensemble can have a ECCC less than 1. We also study how quantum an ensemble is by defining a related quantity called quantumness. We find that the classicality of an ensemble is closely related to how perfectly the ensemble can be cloned, and that the quantumness of the ensemble used in a quantum key distribution (QKD) protocol is exactly the attainable lower bound of the error rate in the sifted key. - Highlights: A quantity is defined to characterize how classical a quantum ensemble is. The classicality of an ensemble is closely related to the cloning performance. Another quantity is also defined to investigate how quantum an ensemble is. This quantity gives the lower bound of the error rate in a QKD protocol.
Exploring and Listening to Chinese Classical Ensembles in General Music
Zhang, Wenzhuo
2017-01-01
Music diversity is valued in theory, but the extent to which it is efficiently presented in music class remains limited. Within this article, I aim to bridge this gap by introducing four genres of Chinese classical ensembles--Qin and Xiao duets, Jiang Nan bamboo and silk ensembles, Cantonese ensembles, and contemporary Chinese orchestras--into the…
Critical Listening in the Ensemble Rehearsal: A Community of Learners
Bell, Cindy L.
2018-01-01
This article explores a strategy for engaging ensemble members in critical listening analysis of performances and presents opportunities for improving ensemble sound through rigorous dialogue, reflection, and attentive rehearsing. Critical listening asks ensemble members to draw on individual playing experience and knowledge to describe what they…
Data assimilation in integrated hydrological modeling using ensemble Kalman filtering
DEFF Research Database (Denmark)
Rasmussen, Jørn; Madsen, H.; Jensen, Karsten Høgh
2015-01-01
Groundwater head and stream discharge is assimilated using the ensemble transform Kalman filter in an integrated hydrological model with the aim of studying the relationship between the filter performance and the ensemble size. In an attempt to reduce the required number of ensemble members...
Improving Climate Projections Using "Intelligent" Ensembles
Baker, Noel C.; Taylor, Patrick C.
2015-01-01
Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and
Demonstrating the value of larger ensembles in forecasting physical systems
Directory of Open Access Journals (Sweden)
Reason L. Machete
2016-12-01
Full Text Available Ensemble simulation propagates a collection of initial states forward in time in a Monte Carlo fashion. Depending on the fidelity of the model and the properties of the initial ensemble, the goal of ensemble simulation can range from merely quantifying variations in the sensitivity of the model all the way to providing actionable probability forecasts of the future. Whatever the goal is, success depends on the properties of the ensemble, and there is a longstanding discussion in meteorology as to the size of initial condition ensemble most appropriate for Numerical Weather Prediction. In terms of resource allocation: how is one to divide finite computing resources between model complexity, ensemble size, data assimilation and other components of the forecast system. One wishes to avoid undersampling information available from the model's dynamics, yet one also wishes to use the highest fidelity model available. Arguably, a higher fidelity model can better exploit a larger ensemble; nevertheless it is often suggested that a relatively small ensemble, say ~16 members, is sufficient and that larger ensembles are not an effective investment of resources. This claim is shown to be dubious when the goal is probabilistic forecasting, even in settings where the forecast model is informative but imperfect. Probability forecasts for a ‘simple’ physical system are evaluated at different lead times; ensembles of up to 256 members are considered. The pure density estimation context (where ensemble members are drawn from the same underlying distribution as the target differs from the forecasting context, where one is given a high fidelity (but imperfect model. In the forecasting context, the information provided by additional members depends also on the fidelity of the model, the ensemble formation scheme (data assimilation, the ensemble interpretation and the nature of the observational noise. The effect of increasing the ensemble size is quantified by
Statistical ensembles for money and debt
Viaggiu, Stefano; Lionetto, Andrea; Bargigli, Leonardo; Longo, Michele
2012-10-01
We build a statistical ensemble representation of two economic models describing respectively, in simplified terms, a payment system and a credit market. To this purpose we adopt the Boltzmann-Gibbs distribution where the role of the Hamiltonian is taken by the total money supply (i.e. including money created from debt) of a set of interacting economic agents. As a result, we can read the main thermodynamic quantities in terms of monetary ones. In particular, we define for the credit market model a work term which is related to the impact of monetary policy on credit creation. Furthermore, with our formalism we recover and extend some results concerning the temperature of an economic system, previously presented in the literature by considering only the monetary base as a conserved quantity. Finally, we study the statistical ensemble for the Pareto distribution.
ABCD of Beta Ensembles and Topological Strings
Krefl, Daniel
2012-01-01
We study beta-ensembles with Bn, Cn, and Dn eigenvalue measure and their relation with refined topological strings. Our results generalize the familiar connections between local topological strings and matrix models leading to An measure, and illustrate that all those classical eigenvalue ensembles, and their topological string counterparts, are related one to another via various deformations and specializations, quantum shifts and discrete quotients. We review the solution of the Gaussian models via Macdonald identities, and interpret them as conifold theories. The interpolation between the various models is plainly apparent in this case. For general polynomial potential, we calculate the partition function in the multi-cut phase in a perturbative fashion, beyond tree-level in the large-N limit. The relation to refined topological string orientifolds on the corresponding local geometry is discussed along the way.
Ensemble training to improve recognition using 2D ear
Middendorff, Christopher; Bowyer, Kevin W.
2009-05-01
The ear has gained popularity as a biometric feature due to the robustness of the shape over time and across emotional expression. Popular methods of ear biometrics analyze the ear as a whole, leaving these methods vulnerable to error due to occlusion. Many researchers explore ear recognition using an ensemble, but none present a method for designing the individual parts that comprise the ensemble. In this work, we introduce a method of modifying the ensemble shapes to improve performance. We determine how different properties of an ensemble training system can affect overall performance. We show that ensembles built from small parts will outperform ensembles built with larger parts, and that incorporating a large number of parts improves the performance of the ensemble.
Various multistage ensembles for prediction of heating energy consumption
Directory of Open Access Journals (Sweden)
Radisa Jovanovic
2015-04-01
Full Text Available Feedforward neural network models are created for prediction of daily heating energy consumption of a NTNU university campus Gloshaugen using actual measured data for training and testing. Improvement of prediction accuracy is proposed by using neural network ensemble. Previously trained feed-forward neural networks are first separated into clusters, using k-means algorithm, and then the best network of each cluster is chosen as member of an ensemble. Two conventional averaging methods for obtaining ensemble output are applied; simple and weighted. In order to achieve better prediction results, multistage ensemble is investigated. As second level, adaptive neuro-fuzzy inference system with various clustering and membership functions are used to aggregate the selected ensemble members. Feedforward neural network in second stage is also analyzed. It is shown that using ensemble of neural networks can predict heating energy consumption with better accuracy than the best trained single neural network, while the best results are achieved with multistage ensemble.
Ensemble methods for Etna volcano warning system
Scandura, Danila; Cannavò, Flavio; Aliotta, Marco; Cassisi, Carmelo; Montalto, Placido
2017-04-01
The large amount of signals used for volcanic monitoring allow detecting volcano criticalities with unprecedented reliability. At the same time, the use of different monitoring networks makes essential the development of a system that synthesizes into a single information the overall state of the volcano. In this context, the ensemble learning techniques can play a useful role accepting different nature inputs and synthesizing the information in a single output. In broad terms, these techniques use many weak learning algorithms to achieve the best predictive performance compared to any obtained from classical learning algorithms. By averaging the results of each weak learner, the ensemble algorithms reduce the risk of using a single non-discriminative weak learning algorithm and allows for a more accurate classification. Here we used the ensemble techniques to classify three different states of Etna volcano: 1) Quiet; 2) Strombolian activity; 3) Lava fountain. We carried out several simulations using a large data set spanning the 2011-2015 time interval, including the records of most part of monitored geophysical parameters and the corresponding volcanic state. Simulations were performed subdividing the available data into training and test sets. We checked the ability of the proposed method to recognize automatically the lava fountain episodes. To this purpose, we tested different ensemble techniques changing associated parameters and weak learners. The found system was able to identify the lava fountain episode with a reliability over 70% and to detect the beginning of lava fountain episode in the totality of the test cases. Results suggest that the proposed system can be seen as a promising tool for civil protection purposes.
Ensemble forecasts of road surface temperatures
Czech Academy of Sciences Publication Activity Database
Sokol, Zbyněk; Bližňák, Vojtěch; Sedlák, Pavel; Zacharov, Petr, jr.; Pešice, Petr; Škuthan, M.
2017-01-01
Roč. 187, 1 May (2017), s. 33-41 ISSN 0169-8095 R&D Projects: GA ČR GA13-34856S; GA TA ČR(CZ) TA01031509 Institutional support: RVO:68378289 Keywords : ensemble prediction * road surface temperature * road weather forecast Subject RIV: DG - Athmosphere Sciences, Meteorology OBOR OECD: Meteorology and atmospheric sciences Impact factor: 3.778, year: 2016 http://www.sciencedirect.com/science/article/pii/S0169809516307311
Bioprocess optimization under uncertainty using ensemble modeling.
Liu, Yang; Gunawan, Rudiyanto
2017-02-20
The performance of model-based bioprocess optimizations depends on the accuracy of the mathematical model. However, models of bioprocesses often have large uncertainty due to the lack of model identifiability. In the presence of such uncertainty, process optimizations that rely on the predictions of a single "best fit" model, e.g. the model resulting from a maximum likelihood parameter estimation using the available process data, may perform poorly in real life. In this study, we employed ensemble modeling to account for model uncertainty in bioprocess optimization. More specifically, we adopted a Bayesian approach to define the posterior distribution of the model parameters, based on which we generated an ensemble of model parameters using a uniformly distributed sampling of the parameter confidence region. The ensemble-based process optimization involved maximizing the lower confidence bound of the desired bioprocess objective (e.g. yield or product titer), using a mean-standard deviation utility function. We demonstrated the performance and robustness of the proposed strategy in an application to a monoclonal antibody batch production by mammalian hybridoma cell culture. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.
Multivariate localization methods for ensemble Kalman filtering
Roh, S.
2015-05-08
In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (entry-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.
Multivariate localization methods for ensemble Kalman filtering
Roh, S.
2015-12-03
In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.
Microcanonical ensemble extensive thermodynamics of Tsallis statistics
International Nuclear Information System (INIS)
Parvan, A.S.
2005-01-01
The microscopic foundation of the generalized equilibrium statistical mechanics based on the Tsallis entropy is given by using the Gibbs idea of statistical ensembles of the classical and quantum mechanics.The equilibrium distribution functions are derived by the thermodynamic method based upon the use of the fundamental equation of thermodynamics and the statistical definition of the functions of the state of the system. It is shown that if the entropic index ξ = 1/q - 1 in the microcanonical ensemble is an extensive variable of the state of the system, then in the thermodynamic limit z bar = 1/(q - 1)N = const the principle of additivity and the zero law of thermodynamics are satisfied. In particular, the Tsallis entropy of the system is extensive and the temperature is intensive. Thus, the Tsallis statistics completely satisfies all the postulates of the equilibrium thermodynamics. Moreover, evaluation of the thermodynamic identities in the microcanonical ensemble is provided by the Euler theorem. The principle of additivity and the Euler theorem are explicitly proved by using the illustration of the classical microcanonical ideal gas in the thermodynamic limit
Microcanonical ensemble extensive thermodynamics of Tsallis statistics
International Nuclear Information System (INIS)
Parvan, A.S.
2006-01-01
The microscopic foundation of the generalized equilibrium statistical mechanics based on the Tsallis entropy is given by using the Gibbs idea of statistical ensembles of the classical and quantum mechanics. The equilibrium distribution functions are derived by the thermodynamic method based upon the use of the fundamental equation of thermodynamics and the statistical definition of the functions of the state of the system. It is shown that if the entropic index ξ=1/(q-1) in the microcanonical ensemble is an extensive variable of the state of the system, then in the thermodynamic limit z-bar =1/(q-1)N=const the principle of additivity and the zero law of thermodynamics are satisfied. In particular, the Tsallis entropy of the system is extensive and the temperature is intensive. Thus, the Tsallis statistics completely satisfies all the postulates of the equilibrium thermodynamics. Moreover, evaluation of the thermodynamic identities in the microcanonical ensemble is provided by the Euler theorem. The principle of additivity and the Euler theorem are explicitly proved by using the illustration of the classical microcanonical ideal gas in the thermodynamic limit
Gradient Flow Analysis on MILC HISQ Ensembles
Energy Technology Data Exchange (ETDEWEB)
Brown, Nathan [Washington U., St. Louis; Bazavov, Alexei [Brookhaven; Bernard, Claude [Washington U., St. Louis; DeTar, Carleton [Utah U.; Foley, Justin [Utah U.; Gottlieb, Steven [Indiana U.; Heller, Urs M. [APS, New York; Hetrick, J. E. [U. Pacific, Stockton; Komijani, Javad [Washington U., St. Louis; Laiho, Jack [Syracuse U.; Levkova, Ludmila [Utah U.; Oktay, M. B. [Utah U.; Sugar, Robert [UC, Santa Barbara; Toussaint, Doug [Arizona U.; Van de Water, Ruth S. [Fermilab; Zhou, Ran [Fermilab
2014-11-14
We report on a preliminary scale determination with gradient-flow techniques on the $N_f = 2 + 1 + 1$ HISQ ensembles generated by the MILC collaboration. The ensembles include four lattice spacings, ranging from 0.15 to 0.06 fm, and both physical and unphysical values of the quark masses. The scales $\\sqrt{t_0}/a$ and $w_0/a$ are computed using Symanzik flow and the cloverleaf definition of $\\langle E \\rangle$ on each ensemble. Then both scales and the meson masses $aM_\\pi$ and $aM_K$ are adjusted for mistunings in the charm mass. Using a combination of continuum chiral perturbation theory and a Taylor series ansatz in the lattice spacing, the results are simultaneously extrapolated to the continuum and interpolated to physical quark masses. Our preliminary results are $\\sqrt{t_0} = 0.1422(7)$fm and $w_0 = 0.1732(10)$fm. We also find the continuum mass-dependence of $w_0$.
Modeling polydispersive ensembles of diamond nanoparticles
International Nuclear Information System (INIS)
Barnard, Amanda S
2013-01-01
While significant progress has been made toward production of monodispersed samples of a variety of nanoparticles, in cases such as diamond nanoparticles (nanodiamonds) a significant degree of polydispersivity persists, so scaling-up of laboratory applications to industrial levels has its challenges. In many cases, however, monodispersivity is not essential for reliable application, provided that the inevitable uncertainties are just as predictable as the functional properties. As computational methods of materials design are becoming more widespread, there is a growing need for robust methods for modeling ensembles of nanoparticles, that capture the structural complexity characteristic of real specimens. In this paper we present a simple statistical approach to modeling of ensembles of nanoparticles, and apply it to nanodiamond, based on sets of individual simulations that have been carefully selected to describe specific structural sources that are responsible for scattering of fundamental properties, and that are typically difficult to eliminate experimentally. For the purposes of demonstration we show how scattering in the Fermi energy and the electronic band gap are related to different structural variations (sources), and how these results can be combined strategically to yield statistically significant predictions of the properties of an entire ensemble of nanodiamonds, rather than merely one individual ‘model’ particle or a non-representative sub-set. (paper)
EnsembleGraph: Interactive Visual Analysis of Spatial-Temporal Behavior for Ensemble Simulation Data
Energy Technology Data Exchange (ETDEWEB)
Shu, Qingya; Guo, Hanqi; Che, Limei; Yuan, Xiaoru; Liu, Junfeng; Liang, Jie
2016-04-19
We present a novel visualization framework—EnsembleGraph— for analyzing ensemble simulation data, in order to help scientists understand behavior similarities between ensemble members over space and time. A graph-based representation is used to visualize individual spatiotemporal regions with similar behaviors, which are extracted by hierarchical clustering algorithms. A user interface with multiple-linked views is provided, which enables users to explore, locate, and compare regions that have similar behaviors between and then users can investigate and analyze the selected regions in detail. The driving application of this paper is the studies on regional emission influences over tropospheric ozone, which is based on ensemble simulations conducted with different anthropogenic emission absences using the MOZART-4 (model of ozone and related tracers, version 4) model. We demonstrate the effectiveness of our method by visualizing the MOZART-4 ensemble simulation data and evaluating the relative regional emission influences on tropospheric ozone concentrations. Positive feedbacks from domain experts and two case studies prove efficiency of our method.
Argumentation based joint learning: a novel ensemble learning approach.
Directory of Open Access Journals (Sweden)
Junyi Xu
Full Text Available Recently, ensemble learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel ensemble learning method: argumentation based multi-agent joint learning (AMAJL, which integrates ideas from multi-agent argumentation, ensemble learning, and association rule mining. In AMAJL, argumentation technology is introduced as an ensemble strategy to integrate multiple base classifiers and generate a high performance ensemble classifier. We design an argumentation framework named Arena as a communication platform for knowledge integration. Through argumentation based joint learning, high quality individual knowledge can be extracted, and thus a refined global knowledge base can be generated and used independently for classification. We perform numerous experiments on multiple public datasets using AMAJL and other benchmark methods. The results demonstrate that our method can effectively extract high quality knowledge for ensemble classifier and improve the performance of classification.
Ensemble-Based Data Assimilation in Reservoir Characterization: A Review
Directory of Open Access Journals (Sweden)
Seungpil Jung
2018-02-01
Full Text Available This paper presents a review of ensemble-based data assimilation for strongly nonlinear problems on the characterization of heterogeneous reservoirs with different production histories. It concentrates on ensemble Kalman filter (EnKF and ensemble smoother (ES as representative frameworks, discusses their pros and cons, and investigates recent progress to overcome their drawbacks. The typical weaknesses of ensemble-based methods are non-Gaussian parameters, improper prior ensembles and finite population size. Three categorized approaches, to mitigate these limitations, are reviewed with recent accomplishments; improvement of Kalman gains, add-on of transformation functions, and independent evaluation of observed data. The data assimilation in heterogeneous reservoirs, applying the improved ensemble methods, is discussed on predicting unknown dynamic data in reservoir characterization.
Bioactive focus in conformational ensembles: a pluralistic approach
Habgood, Matthew
2017-12-01
Computational generation of conformational ensembles is key to contemporary drug design. Selecting the members of the ensemble that will approximate the conformation most likely to bind to a desired target (the bioactive conformation) is difficult, given that the potential energy usually used to generate and rank the ensemble is a notoriously poor discriminator between bioactive and non-bioactive conformations. In this study an approach to generating a focused ensemble is proposed in which each conformation is assigned multiple rankings based not just on potential energy but also on solvation energy, hydrophobic or hydrophilic interaction energy, radius of gyration, and on a statistical potential derived from Cambridge Structural Database data. The best ranked structures derived from each system are then assembled into a new ensemble that is shown to be better focused on bioactive conformations. This pluralistic approach is tested on ensembles generated by the Molecular Operating Environment's Low Mode Molecular Dynamics module, and by the Cambridge Crystallographic Data Centre's conformation generator software.
Ensemble Risk Model of Emergency Admissions (ERMER).
Mesgarpour, Mohsen; Chaussalet, Thierry; Chahed, Salma
2017-07-01
About half of hospital readmissions can be avoided with preventive interventions. Developing decision support tools for identification of patients' emergency readmission risk is an important area of research. Because, it remains unclear how to design features and develop predictive models that can adjust continuously to a fast-changing healthcare system and population characteristics. The objective of this study was to develop a generic ensemble Bayesian risk model of emergency readmission. We produced a decision support tool that predicts risk of emergency readmission using England's Hospital Episode Statistics inpatient database. Firstly, we used a framework to develop an optimal set of features. Then, a combination of Bayes Point Machine (BPM) models for different cohorts was considered to create an optimised ensemble model, which is stronger than the individual generative and non-linear classifications. The developed Ensemble Risk Model of Emergency Admissions (ERMER) was trained and tested using three time-frames: 1999-2004, 2000-05 and 2004-09, each of which includes about 20% of patients in England during the trigger year. Comparisons are made for different time-frames, sub-populations, risk cut-offs, risk bands and top risk segments. The precision was 71.6-73.9%, the specificity was 88.3-91.7% and the sensitivity was 42.1-49.2% across different time-frames. Moreover, the Area Under the Curve was 75.9-77.1%. The decision support tool performed considerably better than the previous modelling approaches, and it was robust and stable with high precision. Moreover, the framework and the Bayesian model allow the model to continuously adjust it to new significant features, different population characteristics and changes in the system. Copyright © 2017 Elsevier B.V. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Man, Jun [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Zhang, Jiangjiang [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Li, Weixuan [Pacific Northwest National Laboratory, Richland Washington USA; Zeng, Lingzao [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Wu, Laosheng [Department of Environmental Sciences, University of California, Riverside California USA
2016-10-01
The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees of freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.
Convergence of the Square Root Ensemble Kalman Filter in the Large Ensemble Limit
Czech Academy of Sciences Publication Activity Database
Kwiatkowski, E.; Mandel, Jan
2015-01-01
Roč. 3, č. 1 (2015), s. 1-17 ISSN 2166-2525 R&D Projects: GA ČR GA13-34856S Institutional support: RVO:67985807 Keywords : data assimilation * Lp laws of large numbers * Hilbert space * ensemble Kalman filter Subject RIV: IN - Informatics, Computer Science
Ensemble atmospheric dispersion calculations for decision support systems
International Nuclear Information System (INIS)
Borysiewicz, M.; Potempski, S.; Galkowski, A.; Zelazny, R.
2003-01-01
This document describes two approaches to long-range atmospheric dispersion of pollutants based on the ensemble concept. In the first part of the report some experiences related to the exercises undertaken under the ENSEMBLE project of the European Union are presented. The second part is devoted to the implementation of mesoscale numerical prediction models RAMS and atmospheric dispersion model HYPACT on Beowulf cluster and theirs usage for ensemble forecasting and long range atmospheric ensemble dispersion calculations based on available meteorological data from NCEO, NOAA (USA). (author)
ANALYSE THE PERFORMANCE OF ENSEMBLE CLASSIFIERS USING SAMPLING TECHNIQUES
Directory of Open Access Journals (Sweden)
M. Balamurugan
2016-07-01
Full Text Available In Ensemble classifiers, the Combination of multiple prediction models of classifiers is important for making progress in a variety of difficult prediction problems. Ensemble of classifiers proved potential in getting higher accuracy compared to single classifier. Even though by the usage ensemble classifiers, still there is in-need to improve its performance. There are many possible ways available to increase the performance of ensemble classifiers. One of the ways is sampling, which plays a major role for improving the quality of ensemble classifier. Since, it helps in reducing the bias in input data set of ensemble. Sampling is the process of extracting the subset of samples from the original dataset. In this research work, analysis is done on sampling techniques for ensemble classifiers. In ensemble classifier, specifically one of the probability based sampling techniques is being always used. Samples are gathered in a process which gives all the individuals in the population of equal chances, such that, sampling bias is removed. In this paper, analyse the performance of ensemble classifiers by using various sampling techniques and list out their drawbacks.
Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts
National Research Council Canada - National Science Library
Berrocal, Veronica J; Raftery, Adrian E; Gneiting, Tilmann
2006-01-01
.... Bayesian model averaging (BMA) is a statistical postprocessing method for forecast ensembles that generates calibrated probabilistic forecast products for weather quantities at individual sites...
Design ensemble machine learning model for breast cancer diagnosis.
Hsieh, Sheau-Ling; Hsieh, Sung-Huai; Cheng, Po-Hsun; Chen, Chi-Huang; Hsu, Kai-Ping; Lee, I-Shun; Wang, Zhenyu; Lai, Feipei
2012-10-01
In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, ensemble ones have been developed for classifications. In addition, a combined ensemble model with these three schemes has been constructed for further validations. The experimental results indicate that the ensemble learning performs better than individual single ones. Moreover, the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models.
Ensemble Kalman filtering with residual nudging
Luo, X.
2012-10-03
Covariance inflation and localisation are two important techniques that are used to improve the performance of the ensemble Kalman filter (EnKF) by (in effect) adjusting the sample covariances of the estimates in the state space. In this work, an additional auxiliary technique, called residual nudging, is proposed to monitor and, if necessary, adjust the residual norms of state estimates in the observation space. In an EnKF with residual nudging, if the residual norm of an analysis is larger than a pre-specified value, then the analysis is replaced by a new one whose residual norm is no larger than a pre-specified value. Otherwise, the analysis is considered as a reasonable estimate and no change is made. A rule for choosing the pre-specified value is suggested. Based on this rule, the corresponding new state estimates are explicitly derived in case of linear observations. Numerical experiments in the 40-dimensional Lorenz 96 model show that introducing residual nudging to an EnKF may improve its accuracy and/or enhance its stability against filter divergence, especially in the small ensemble scenario.
Face recognition using ensemble string matching.
Chen, Weiping; Gao, Yongsheng
2013-12-01
In this paper, we present a syntactic string matching approach to solve the frontal face recognition problem. String matching is a powerful partial matching technique, but is not suitable for frontal face recognition due to its requirement of globally sequential representation and the complex nature of human faces, containing discontinuous and non-sequential features. Here, we build a compact syntactic Stringface representation, which is an ensemble of strings. A novel ensemble string matching approach that can perform non-sequential string matching between two Stringfaces is proposed. It is invariant to the sequential order of strings and the direction of each string. The embedded partial matching mechanism enables our method to automatically use every piece of non-occluded region, regardless of shape, in the recognition process. The encouraging results demonstrate the feasibility and effectiveness of using syntactic methods for face recognition from a single exemplar image per person, breaking the barrier that prevents string matching techniques from being used for addressing complex image recognition problems. The proposed method not only achieved significantly better performance in recognizing partially occluded faces, but also showed its ability to perform direct matching between sketch faces and photo faces.
Deterministic Mean-Field Ensemble Kalman Filtering
Law, Kody
2016-05-03
The proof of convergence of the standard ensemble Kalman filter (EnKF) from Le Gland, Monbet, and Tran [Large sample asymptotics for the ensemble Kalman filter, in The Oxford Handbook of Nonlinear Filtering, Oxford University Press, Oxford, UK, 2011, pp. 598--631] is extended to non-Gaussian state-space models. A density-based deterministic approximation of the mean-field limit EnKF (DMFEnKF) is proposed, consisting of a PDE solver and a quadrature rule. Given a certain minimal order of convergence k between the two, this extends to the deterministic filter approximation, which is therefore asymptotically superior to standard EnKF for dimension d<2k. The fidelity of approximation of the true distribution is also established using an extension of the total variation metric to random measures. This is limited by a Gaussian bias term arising from nonlinearity/non-Gaussianity of the model, which arises in both deterministic and standard EnKF. Numerical results support and extend the theory.
Performance Analysis of Local Ensemble Kalman Filter
Tong, Xin T.
2018-03-01
Ensemble Kalman filter (EnKF) is an important data assimilation method for high-dimensional geophysical systems. Efficient implementation of EnKF in practice often involves the localization technique, which updates each component using only information within a local radius. This paper rigorously analyzes the local EnKF (LEnKF) for linear systems and shows that the filter error can be dominated by the ensemble covariance, as long as (1) the sample size exceeds the logarithmic of state dimension and a constant that depends only on the local radius; (2) the forecast covariance matrix admits a stable localized structure. In particular, this indicates that with small system and observation noises, the filter error will be accurate in long time even if the initialization is not. The analysis also reveals an intrinsic inconsistency caused by the localization technique, and a stable localized structure is necessary to control this inconsistency. While this structure is usually taken for granted for the operation of LEnKF, it can also be rigorously proved for linear systems with sparse local observations and weak local interactions. These theoretical results are also validated by numerical implementation of LEnKF on a simple stochastic turbulence in two dynamical regimes.
Ensemble Kalman filtering with residual nudging
Directory of Open Access Journals (Sweden)
Xiaodong Luo
2012-10-01
Full Text Available Covariance inflation and localisation are two important techniques that are used to improve the performance of the ensemble Kalman filter (EnKF by (in effect adjusting the sample covariances of the estimates in the state space. In this work, an additional auxiliary technique, called residual nudging, is proposed to monitor and, if necessary, adjust the residual norms of state estimates in the observation space. In an EnKF with residual nudging, if the residual norm of an analysis is larger than a pre-specified value, then the analysis is replaced by a new one whose residual norm is no larger than a pre-specified value. Otherwise, the analysis is considered as a reasonable estimate and no change is made. A rule for choosing the pre-specified value is suggested. Based on this rule, the corresponding new state estimates are explicitly derived in case of linear observations. Numerical experiments in the 40-dimensional Lorenz 96 model show that introducing residual nudging to an EnKF may improve its accuracy and/or enhance its stability against filter divergence, especially in the small ensemble scenario.
Nanobiosensing with Arrays and Ensembles of Nanoelectrodes
Directory of Open Access Journals (Sweden)
Najmeh Karimian
2016-12-01
Full Text Available Since the first reports dating back to the mid-1990s, ensembles and arrays of nanoelectrodes (NEEs and NEAs, respectively have gained an important role as advanced electroanalytical tools thank to their unique characteristics which include, among others, dramatically improved signal/noise ratios, enhanced mass transport and suitability for extreme miniaturization. From the year 2000 onward, these properties have been exploited to develop electrochemical biosensors in which the surfaces of NEEs/NEAs have been functionalized with biorecognition layers using immobilization modes able to take the maximum advantage from the special morphology and composite nature of their surface. This paper presents an updated overview of this field. It consists of two parts. In the first, we discuss nanofabrication methods and the principles of functioning of NEEs/NEAs, focusing, in particular, on those features which are important for the development of highly sensitive and miniaturized biosensors. In the second part, we review literature references dealing the bioanalytical and biosensing applications of sensors based on biofunctionalized arrays/ensembles of nanoelectrodes, focusing our attention on the most recent advances, published in the last five years. The goal of this review is both to furnish fundamental knowledge to researchers starting their activity in this field and provide critical information on recent achievements which can stimulate new ideas for future developments to experienced scientists.
Cluster Ensemble-Based Image Segmentation
Directory of Open Access Journals (Sweden)
Xiaoru Wang
2013-07-01
Full Text Available Image segmentation is the foundation of computer vision applications. In this paper, we propose a new cluster ensemble-based image segmentation algorithm, which overcomes several problems of traditional methods. We make two main contributions in this paper. First, we introduce the cluster ensemble concept to fuse the segmentation results from different types of visual features effectively, which can deliver a better final result and achieve a much more stable performance for broad categories of images. Second, we exploit the PageRank idea from Internet applications and apply it to the image segmentation task. This can improve the final segmentation results by combining the spatial information of the image and the semantic similarity of regions. Our experiments on four public image databases validate the superiority of our algorithm over conventional single type of feature or multiple types of features-based algorithms, since our algorithm can fuse multiple types of features effectively for better segmentation results. Moreover, our method is also proved to be very competitive in comparison with other state-of-the-art segmentation algorithms.
Online cross-validation-based ensemble learning.
Benkeser, David; Ju, Cheng; Lendle, Sam; van der Laan, Mark
2018-01-30
Online estimators update a current estimate with a new incoming batch of data without having to revisit past data thereby providing streaming estimates that are scalable to big data. We develop flexible, ensemble-based online estimators of an infinite-dimensional target parameter, such as a regression function, in the setting where data are generated sequentially by a common conditional data distribution given summary measures of the past. This setting encompasses a wide range of time-series models and, as special case, models for independent and identically distributed data. Our estimator considers a large library of candidate online estimators and uses online cross-validation to identify the algorithm with the best performance. We show that by basing estimates on the cross-validation-selected algorithm, we are asymptotically guaranteed to perform as well as the true, unknown best-performing algorithm. We provide extensions of this approach including online estimation of the optimal ensemble of candidate online estimators. We illustrate excellent performance of our methods using simulations and a real data example where we make streaming predictions of infectious disease incidence using data from a large database. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
A review of issues in ensemble-based Kalman filtering
Energy Technology Data Exchange (ETDEWEB)
Ehrendorfer, M. [Dept. of Meteorology and Geophysics, The Univ. of Reading (United Kingdom)
2007-12-15
Ensemble-based data assimilation methods related to the fundamental theory of Kalman filtering have been explored in a variety of mostly non-operational data assimilation contexts over the past decade with increasing intensity. While promising properties have been reported, a number of issues that arise in the development and application of ensemble-based data assimilation techniques, such as in the basic form of the ensemble Kalman filter (EnKF), still deserve particular attention. The necessity of employing an ensemble of small size represents a fundamental issue which in turn leads to several related points that must be carefully considered. In particular, the need to correct for sampling noise in the covariance structure estimated from the finite ensemble must be mentioned. Covariance inflation, localization through a Schur/Hadamard product, preventing the occurrence of filter divergence and inbreeding, as well as the loss of dynamical balances, are all issues directly related to the use of small ensemble sizes. Attempts to reduce effectively the sampling error due to small ensembles and at the same time maintaining an ensemble spread that realistically describes error structures have given rise to the development of variants of the basic form of the EnKF. These include, for example, the Ensemble Adjustment Kalman Filter (EAKF), the Ensemble Transform Kalman Filter (ETKF), the Ensemble Square-Root Filter (EnSRF), and the Local Ensemble Kalman Filter (LEKF). Further important considerations within ensemble-based Kalman filtering concern issues such as the treatment of model error, stochastic versus deterministic updating algorithms, the case of implementation and computational cost, serial processing of observations, avoiding the appearance of undesired dynamic imbalances, and the treatment of non-Gaussianity and nonlinearity. The discussion of the above issues within ensemble-based Kalman filtering forms the central topic of this article, that starts out with a
Influence of horizontal resolution and ensemble size on model performance
CSIR Research Space (South Africa)
Dalton, A
2014-10-01
Full Text Available Computing costs increase with an increase in global model resolution and ensemble size. This paper strives to determine the extent to which resolution and ensemble size affect seasonal forecast skill when simulating mid-summer rainfall totals over...
Collaborative Composing in High School String Chamber Music Ensembles
Hopkins, Michael T.
2015-01-01
The purpose of this study was to examine collaborative composing in high school string chamber music ensembles. Research questions included the following: (a) How do high school string instrumentalists in chamber music ensembles use verbal and musical forms of communication to collaboratively compose a piece of music? (b) How do selected variables…
Competitive Learning Neural Network Ensemble Weighted by Predicted Performance
Ye, Qiang
2010-01-01
Ensemble approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among base classifiers promotes ensemble performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different…
Practice Makes Perfect?: Effective Practice Instruction in Large Ensembles
Prichard, Stephanie
2012-01-01
Helping young musicians learn how to practice effectively is a challenge faced by all music educators. This article presents a system of individual music practice instruction that can be seamlessly integrated within large-ensemble rehearsals. Using a step-by-step approach, large-ensemble conductors can teach students to identify and isolate…
Ensemble Learning for Multi-Source Neural Machine Translation
Garmash, E.; Monz, C.
2016-01-01
In this paper we describe and evaluate methods to perform ensemble prediction in neural machine translation (NMT). We compare two methods of ensemble set induction: sampling parameter initializations for an NMT system, which is a relatively established method in NMT (Sutskever et al., 2014), and NMT
Robust ensemble-based multi-objective optimization
Fonseca, R.M.; Stordahl, A.; Leeuwenburgh, O.; Van den Hof, P.M.J.; Jansen, J.D.
2014-01-01
We consider robust ensemble-based multi-objective optimization using a hierarchical switching algorithm for combined long-term and short term water flooding optimization. We apply a modified formulation of the ensemble gradient which results in improved performance compared to earlier formulations.
A Comparison of Ensemble Kalman Filters for Storm Surge Assimilation
Altaf, Muhammad
2014-08-01
This study evaluates and compares the performances of several variants of the popular ensembleKalman filter for the assimilation of storm surge data with the advanced circulation (ADCIRC) model. Using meteorological data from Hurricane Ike to force the ADCIRC model on a domain including the Gulf ofMexico coastline, the authors implement and compare the standard stochastic ensembleKalman filter (EnKF) and three deterministic square root EnKFs: the singular evolutive interpolated Kalman (SEIK) filter, the ensemble transform Kalman filter (ETKF), and the ensemble adjustment Kalman filter (EAKF). Covariance inflation and localization are implemented in all of these filters. The results from twin experiments suggest that the square root ensemble filters could lead to very comparable performances with appropriate tuning of inflation and localization, suggesting that practical implementation details are at least as important as the choice of the square root ensemble filter itself. These filters also perform reasonably well with a relatively small ensemble size, whereas the stochastic EnKF requires larger ensemble sizes to provide similar accuracy for forecasts of storm surge.
Ensemble dispersion forecasting - Part 2. Application and evaluation
DEFF Research Database (Denmark)
Galmarini, S.; Bianconi, R.; Addis, R.
2004-01-01
of the dispersion of ETEX release 1 and the model ensemble is compared with the monitoring data. The scope of the comparison is to estimate to what extent the ensemble analysis is an improvement with respect to the single model results and represents a superior analysis of the process evolution. (C) 2004 Elsevier...
An iterative ensemble Kalman filter for reservoir engineering applications
Krymskaya, M.V.; Hanea, R.G.; Verlaan, M.
2009-01-01
The study has been focused on examining the usage and the applicability of ensemble Kalman filtering techniques to the history matching procedures. The ensemble Kalman filter (EnKF) is often applied nowadays to solving such a problem. Meanwhile, traditional EnKF requires assumption of the
Deterministic entanglement of Rydberg ensembles by engineered dissipation
DEFF Research Database (Denmark)
Dasari, Durga; Mølmer, Klaus
2014-01-01
We propose a scheme that employs dissipation to deterministically generate entanglement in an ensemble of strongly interacting Rydberg atoms. With a combination of microwave driving between different Rydberg levels and a resonant laser coupling to a short lived atomic state, the ensemble can be d...
Programming in the Zone: Repertoire Selection for the Large Ensemble
Hopkins, Michael
2013-01-01
One of the great challenges ensemble directors face is selecting high-quality repertoire that matches the musical and technical levels of their ensembles. Thoughtful repertoire selection can lead to increased student motivation as well as greater enthusiasm for the music program from parents, administrators, teachers, and community members. Common…
Adaptive calibration of (u,v)‐wind ensemble forecasts
DEFF Research Database (Denmark)
Pinson, Pierre
2012-01-01
Ensemble forecasts of (u,v)‐wind are of crucial importance for a number of decision‐making problems related to e.g. air traffic control, ship routeing and energy management. The skill of these ensemble forecasts as generated by NWP‐based models can be maximised by correcting for their lack of suf...
Modality-Driven Classification and Visualization of Ensemble Variance
Energy Technology Data Exchange (ETDEWEB)
Bensema, Kevin; Gosink, Luke; Obermaier, Harald; Joy, Kenneth I.
2016-10-01
Advances in computational power now enable domain scientists to address conceptual and parametric uncertainty by running simulations multiple times in order to sufficiently sample the uncertain input space. While this approach helps address conceptual and parametric uncertainties, the ensemble datasets produced by this technique present a special challenge to visualization researchers as the ensemble dataset records a distribution of possible values for each location in the domain. Contemporary visualization approaches that rely solely on summary statistics (e.g., mean and variance) cannot convey the detailed information encoded in ensemble distributions that are paramount to ensemble analysis; summary statistics provide no information about modality classification and modality persistence. To address this problem, we propose a novel technique that classifies high-variance locations based on the modality of the distribution of ensemble predictions. Additionally, we develop a set of confidence metrics to inform the end-user of the quality of fit between the distribution at a given location and its assigned class. We apply a similar method to time-varying ensembles to illustrate the relationship between peak variance and bimodal or multimodal behavior. These classification schemes enable a deeper understanding of the behavior of the ensemble members by distinguishing between distributions that can be described by a single tendency and distributions which reflect divergent trends in the ensemble.
Directory of Open Access Journals (Sweden)
Kazuo Saito
2012-01-01
Full Text Available The effect of lateral boundary perturbations (LBPs on the mesoscale breeding (MBD method and the local ensemble transform Kalman filter (LETKF as the initial perturbations generators for mesoscale ensemble prediction systems (EPSs was examined. A LBPs method using the Japan Meteorological Agency's (JMA's operational one-week global ensemble prediction was developed and applied to the mesoscale EPS of the Meteorological Research Institute for the World Weather Research Programme, Beijing 2008 Olympics Research and Development Project. The amplitude of the LBPs was adjusted based on the ensemble spread statistics considering the difference of the forecast times of the JMA's one-week EPS and the associated breeding/ensemble Kalman filter (EnKF cycles. LBPs in the ensemble forecast increase the ensemble spread and improve the accuracy of the ensemble mean forecast. In the MBD method, if LBPs were introduced in its breeding cycles, the growth rate of the generated bred vectors is increased, and the ensemble spread and the root mean square errors (RMSEs of the ensemble mean are further improved in the ensemble forecast. With LBPs in the breeding cycles, positional correspondences to the meteorological disturbances and the orthogonality of the bred vectors are improved. Brier Skill Scores (BSSs also showed a remarkable effect of LBPs in the breeding cycles. LBPs showed a similar effect with the LETKF. If LBPs were introduced in the EnKF data assimilation cycles, the ensemble spread, ensemble mean accuracy, and BSSs for precipitation were improved, although the relative advantage of LETKF as the initial perturbations generator against MDB was not necessarily clear. LBPs in the EnKF cycles contribute not to the orthogonalisation but to prevent the underestimation of the forecast error near the lateral boundary.The accuracy of the LETKF analyses was compared with that of the mesoscale 4D-VAR analyses. With LBPs in the LETKF cycles, the RMSEs of the
The Road to DLCZ Protocol in Rubidium Ensemble
Li, Chang; Pu, Yunfei; Jiang, Nan; Chang, Wei; Zhang, Sheng; Center for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua Univ Team
2017-04-01
Quantum communication is the powerful approach achieving a fully secure information transferal. The DLCZ protocol ensures that photon linearly decays with transferring distance increasing, which improves the success potential and shortens the time to build up an entangled channel. Apart from that, it provides an advanced idea that building up a quantum internet based on different nodes connected to different sites and themselves. In our laboratory, three sets of laser-cooled Rubidium 87 ensemble have been built. Two of them serve as the single photon emitter, which generate the entanglement between ensemble and photon. What's more, crossed AODs are equipped to multiplex and demultiplex optical circuit so that ensemble is divided into 2 hundred of 2D sub-memory cells. And the third ensemble is used as quantum telecommunication, which converts 780nm photon into telecom-wavelength one. And we have been building double-MOT system, which provides more atoms in ensemble and larger optical density.
An educational model for ensemble streamflow simulation and uncertainty analysis
Directory of Open Access Journals (Sweden)
A. AghaKouchak
2013-02-01
Full Text Available This paper presents the hands-on modeling toolbox, HBV-Ensemble, designed as a complement to theoretical hydrology lectures, to teach hydrological processes and their uncertainties. The HBV-Ensemble can be used for in-class lab practices and homework assignments, and assessment of students' understanding of hydrological processes. Using this modeling toolbox, students can gain more insights into how hydrological processes (e.g., precipitation, snowmelt and snow accumulation, soil moisture, evapotranspiration and runoff generation are interconnected. The educational toolbox includes a MATLAB Graphical User Interface (GUI and an ensemble simulation scheme that can be used for teaching uncertainty analysis, parameter estimation, ensemble simulation and model sensitivity. HBV-Ensemble was administered in a class for both in-class instruction and a final project, and students submitted their feedback about the toolbox. The results indicate that this educational software had a positive impact on students understanding and knowledge of uncertainty in hydrological modeling.
A Statistical Ensemble for Soft Granular Matter
Henkes, Silke; O'Hern, Corey; Chakraborty, Bulbul
2007-03-01
Work on packings of soft spheres (PRE 68, 011306 (2003)) has shown the existence of a Jamming transition and has highlighted the need for a general statistical framework to describe granular packings. This work presents an extension of the formalism proposed by Edwards (Physica A 157, 1080 (1989)) to packings of soft particles. We base our analysis on a height formalism developed in two dimensions (PRL 88, 115505 (2002)) to extract a topological invariant γ, the trace of the global stress tensor, which is conserved under internal rearrangements of the system. Upon assuming a flat measure in γ-space, we can derive a canonical distribution of the local γ-values in a grain packing. We then check the predictions of this ensemble against distributions of mechanically stable packings of frictionless disks obtained from computer simulations. Work supported by NSF-DMR 0549762.
ARM Cloud Retrieval Ensemble Data Set (ACRED)
Energy Technology Data Exchange (ETDEWEB)
Zhao, C; Xie, S; Klein, SA; McCoy, R; Comstock, JM; Delanoë, J; Deng, M; Dunn, M; Hogan, RJ; Jensen, MP; Mace, GG; McFarlane, SA; O’Connor, EJ; Protat, A; Shupe, MD; Turner, D; Wang, Z
2011-09-12
This document describes a new Atmospheric Radiation Measurement (ARM) data set, the ARM Cloud Retrieval Ensemble Data Set (ACRED), which is created by assembling nine existing ground-based cloud retrievals of ARM measurements from different cloud retrieval algorithms. The current version of ACRED includes an hourly average of nine ground-based retrievals with vertical resolution of 45 m for 512 layers. The techniques used for the nine cloud retrievals are briefly described in this document. This document also outlines the ACRED data availability, variables, and the nine retrieval products. Technical details about the generation of ACRED, such as the methods used for time average and vertical re-grid, are also provided.
Global Optimization Ensemble Model for Classification Methods
Directory of Open Access Journals (Sweden)
Hina Anwar
2014-01-01
Full Text Available Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity.
Modeling Coordination Problems in a Music Ensemble
DEFF Research Database (Denmark)
Frimodt-Møller, Søren R.
2008-01-01
This paper considers in general terms, how musicians are able to coordinate through rational choices in a situation of (temporary) doubt in an ensemble performance. A fictitious example involving a 5-bar development in an unknown piece of music is analyzed in terms of epistemic logic, more...... specifically a multi-agent system, where it is shown that perfect coordination can only be certain to take place if the musicians have common knowledge of certain rules of the composition. We subsequently argue, however, that the musicians need not agree on the central features of the piece of music in order...... to coordinate. Such coordination can be described in terms of Michael Bacharach's theory of variable frames as an aid to solve game theoretic coordination problems....
Data assimilation the ensemble Kalman filter
Evensen, Geir
2007-01-01
Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. It is demonstrated how the different methods can be derived from a common theoretical basis, as well as how they differ and/or are related to each other, and which properties characterize them, using several examples. Rather than emphasize a particular discipline such as oceanography or meteorology, it presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements. The mathematics level is modest, although it requires knowledge of basic spatial statistics, Bayesian statistics, and calculus of variations. Readers will also appreciate the introduction to the mathematical methods used and detailed derivations, which should b...
Dynamical Engineering of Interactions in Qudit Ensembles
Choi, Soonwon; Yao, Norman Y.; Lukin, Mikhail D.
2017-11-01
We propose and analyze a method to engineer effective interactions in an ensemble of d -level systems (qudits) driven by global control fields. In particular, we present (i) a necessary and sufficient condition under which a given interaction can be decoupled, (ii) the existence of a universal sequence that decouples any (cancelable) interaction, and (iii) an efficient algorithm to engineer a target Hamiltonian from an initial Hamiltonian (if possible). We illustrate the potential of this method with two examples. Specifically, we present a 6-pulse sequence that decouples effective spin-1 dipolar interactions and demonstrate that a spin-1 Ising chain can be engineered to study transitions among three distinct symmetry protected topological phases. Our work enables new approaches for the realization of both many-body quantum memories and programmable analog quantum simulators using existing experimental platforms.
Engineering defined motor ensembles with DNA origami.
Goodman, Brian S; Reck-Peterson, Samara L
2014-01-01
Many cytoskeletal motors function in groups to coordinate the spatial and temporal positioning of cellular cargo. While methods to study the biophysical properties of single motors are well established, methods to understand how multiple motors work synergistically or antagonistically are less well developed. Here, we describe a three-dimensional synthetic cargo structure made using DNA origami, which can be used to template defined numbers and types of cytoskeletal motors with programmable geometries and spacing. We describe methods for building the DNA origami structure, covalently attaching motors to DNA, forming the motor-DNA origami structure complex, and single-molecule assays to examine the motile properties of motor ensembles. © 2014 Elsevier Inc. All rights reserved.
Ensemble methods with outliers for phonocardiogram classification.
Nabhan Homsi, Masun; Warrick, Philip
2017-07-31
Heart sound classification and analysis play an important role in the early diagnosis and prevention of cardiovascular disease. To this end, this paper introduces a novel method for automatic classification of normal and abnormal heart sound recordings. Signals are first preprocessed to extract a total of 131 features in the time, frequency, wavelet and statistical domains from the entire signal and from the timings of the states. Outlier signals are then detected and separated from those with a standard range using an interquartile range algorithm. After that, feature extreme values are given special consideration, and finally features are reduced to the most significant ones using a feature reduction technique. In the classification stage, the selected features either for standard or outlier signals are fed separately into an ensemble of 20 two-step classifiers for the classification task. The first step of the classifier is represented by a nested set of ensemble algorithms which was cross-validated on the training dataset provided by PhysioNet Challenge 2016, while the second one uses a voting rule of the class label. The results show that this method is able to recognize heart sound recordings efficiently, achieving an overall score of 96.30% for standard signals and 90.18% for outlier signals on a cross-validated experiment using the available training data. The approach of our proposed method helped reduce overfitting and improved classification performance, achieving an overall score on the hidden test set of 80.1% (79.6% sensitivity and 80.6% specificity).
Multicomponent ensemble models to forecast induced seismicity
Király-Proag, E.; Gischig, V.; Zechar, J. D.; Wiemer, S.
2018-01-01
In recent years, human-induced seismicity has become a more and more relevant topic due to its economic and social implications. Several models and approaches have been developed to explain underlying physical processes or forecast induced seismicity. They range from simple statistical models to coupled numerical models incorporating complex physics. We advocate the need for forecast testing as currently the best method for ascertaining if models are capable to reasonably accounting for key physical governing processes—or not. Moreover, operational forecast models are of great interest to help on-site decision-making in projects entailing induced earthquakes. We previously introduced a standardized framework following the guidelines of the Collaboratory for the Study of Earthquake Predictability, the Induced Seismicity Test Bench, to test, validate, and rank induced seismicity models. In this study, we describe how to construct multicomponent ensemble models based on Bayesian weightings that deliver more accurate forecasts than individual models in the case of Basel 2006 and Soultz-sous-Forêts 2004 enhanced geothermal stimulation projects. For this, we examine five calibrated variants of two significantly different model groups: (1) Shapiro and Smoothed Seismicity based on the seismogenic index, simple modified Omori-law-type seismicity decay, and temporally weighted smoothed seismicity; (2) Hydraulics and Seismicity based on numerically modelled pore pressure evolution that triggers seismicity using the Mohr-Coulomb failure criterion. We also demonstrate how the individual and ensemble models would perform as part of an operational Adaptive Traffic Light System. Investigating seismicity forecasts based on a range of potential injection scenarios, we use forecast periods of different durations to compute the occurrence probabilities of seismic events M ≥ 3. We show that in the case of the Basel 2006 geothermal stimulation the models forecast hazardous levels
Robust Ensemble Filtering and Its Relation to Covariance Inflation in the Ensemble Kalman Filter
Luo, Xiaodong
2011-12-01
A robust ensemble filtering scheme based on the H∞ filtering theory is proposed. The optimal H∞ filter is derived by minimizing the supremum (or maximum) of a predefined cost function, a criterion different from the minimum variance used in the Kalman filter. By design, the H∞ filter is more robust than the Kalman filter, in the sense that the estimation error in the H∞ filter in general has a finite growth rate with respect to the uncertainties in assimilation, except for a special case that corresponds to the Kalman filter. The original form of the H∞ filter contains global constraints in time, which may be inconvenient for sequential data assimilation problems. Therefore a variant is introduced that solves some time-local constraints instead, and hence it is called the time-local H∞ filter (TLHF). By analogy to the ensemble Kalman filter (EnKF), the concept of ensemble time-local H∞ filter (EnTLHF) is also proposed. The general form of the EnTLHF is outlined, and some of its special cases are discussed. In particular, it is shown that an EnKF with certain covariance inflation is essentially an EnTLHF. In this sense, the EnTLHF provides a general framework for conducting covariance inflation in the EnKF-based methods. Some numerical examples are used to assess the relative robustness of the TLHF–EnTLHF in comparison with the corresponding KF–EnKF method.
Viney, N.R.; Bormann, H.; Breuer, L.; Bronstert, A.; Croke, B.F.W.; Frede, H.; Graff, T.; Hubrechts, L.; Huisman, J.A.; Jakeman, A.J.; Kite, G.W.; Lanini, J.; Leavesley, G.; Lettenmaier, D.P.; Lindstrom, G.; Seibert, J.; Sivapalan, M.; Willems, P.
2009-01-01
This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to assess various ensemble predictions of catchment streamflow. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. The models are calibrated twice using different sets of input data. The two predictions from each model are then combined by simple averaging to produce a single-model ensemble. The 10 resulting single-model ensembles are combined in various ways to produce multi-model ensemble predictions. Both the single-model ensembles and the multi-model ensembles are shown to give predictions that are generally superior to those of their respective constituent models, both during a 7-year calibration period and a 9-year validation period. This occurs despite a considerable disparity in performance of the individual models. Even the weakest of models is shown to contribute useful information to the ensembles they are part of. The best model combination methods are a trimmed mean (constructed using the central four or six predictions each day) and a weighted mean ensemble (with weights calculated from calibration performance) that places relatively large weights on the better performing models. Conditional ensembles, in which separate model weights are used in different system states (e.g. summer and winter, high and low flows) generally yield little improvement over the weighted mean ensemble. However a conditional ensemble that discriminates between rising and receding flows shows moderate improvement. An analysis of ensemble predictions shows that the best ensembles are not necessarily those containing the best individual models. Conversely, it appears that some models that predict well individually do not necessarily combine well with other models in
Decadal climate predictions improved by ocean ensemble dispersion filtering
Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.
2017-06-01
Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.Plain Language SummaryDecadal predictions aim to predict the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. The ocean memory due to its heat capacity holds big potential skill. In recent years, more precise initialization techniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions. Ensembles are another important aspect. Applying slightly perturbed predictions to trigger the famous butterfly effect results in an ensemble. Instead of evaluating one prediction, but the whole ensemble with its
Ensemble Kalman filtering without the intrinsic need for inflation
Directory of Open Access Journals (Sweden)
M. Bocquet
2011-10-01
Full Text Available The main intrinsic source of error in the ensemble Kalman filter (EnKF is sampling error. External sources of error, such as model error or deviations from Gaussianity, depend on the dynamical properties of the model. Sampling errors can lead to instability of the filter which, as a consequence, often requires inflation and localization. The goal of this article is to derive an ensemble Kalman filter which is less sensitive to sampling errors. A prior probability density function conditional on the forecast ensemble is derived using Bayesian principles. Even though this prior is built upon the assumption that the ensemble is Gaussian-distributed, it is different from the Gaussian probability density function defined by the empirical mean and the empirical error covariance matrix of the ensemble, which is implicitly used in traditional EnKFs. This new prior generates a new class of ensemble Kalman filters, called finite-size ensemble Kalman filter (EnKF-N. One deterministic variant, the finite-size ensemble transform Kalman filter (ETKF-N, is derived. It is tested on the Lorenz '63 and Lorenz '95 models. In this context, ETKF-N is shown to be stable without inflation for ensemble size greater than the model unstable subspace dimension, at the same numerical cost as the ensemble transform Kalman filter (ETKF. One variant of ETKF-N seems to systematically outperform the ETKF with optimally tuned inflation. However it is shown that ETKF-N does not account for all sampling errors, and necessitates localization like any EnKF, whenever the ensemble size is too small. In order to explore the need for inflation in this small ensemble size regime, a local version of the new class of filters is defined (LETKF-N and tested on the Lorenz '95 toy model. Whatever the size of the ensemble, the filter is stable. Its performance without inflation is slightly inferior to that of LETKF with optimally tuned inflation for small interval between updates, and
Ligand docking simulations by generalized-ensemble algorithms.
Okamoto, Yuko; Kokubo, Hironori; Tanaka, Toshimasa
2013-01-01
In protein chemistry and structural biology, conventional simulations in physical statistical mechanical ensembles, such as the canonical ensemble with fixed temperature and isobaric-isothermal ensemble with fixed temperature and pressure, face a great difficulty. This is because there exist a huge number of local-minimum-energy states in the system and the conventional simulations tend to get trapped in these states, giving wrong results. Generalized-ensemble algorithms are based on artificial unphysical ensembles and overcome the above difficulty by performing random walks in potential energy, volume, and other physical quantities or their corresponding conjugate parameters such as temperature and pressure. The advantage of generalized-ensemble simulations lies in the fact that they not only avoid getting trapped in states of energy local minima but also allow the calculations of physical quantities as functions of temperature or other parameters from a single simulation run. In this chapter, we review the generalized-ensemble algorithms. Some of their specific examples such as replica-exchange molecular dynamics and replica-exchange umbrella sampling are described in detail. Examples of their applications to drug design are presented. © 2013 Elsevier Inc. All rights reserved.
A multiphysical ensemble system of numerical snow modelling
Lafaysse, Matthieu; Cluzet, Bertrand; Dumont, Marie; Lejeune, Yves; Vionnet, Vincent; Morin, Samuel
2017-05-01
Physically based multilayer snowpack models suffer from various modelling errors. To represent these errors, we built the new multiphysical ensemble system ESCROC (Ensemble System Crocus) by implementing new representations of different physical processes in the deterministic coupled multilayer ground/snowpack model SURFEX/ISBA/Crocus. This ensemble was driven and evaluated at Col de Porte (1325 m a.s.l., French alps) over 18 years with a high-quality meteorological and snow data set. A total number of 7776 simulations were evaluated separately, accounting for the uncertainties of evaluation data. The ability of the ensemble to capture the uncertainty associated to modelling errors is assessed for snow depth, snow water equivalent, bulk density, albedo and surface temperature. Different sub-ensembles of the ESCROC system were studied with probabilistic tools to compare their performance. Results show that optimal members of the ESCROC system are able to explain more than half of the total simulation errors. Integrating members with biases exceeding the range corresponding to observational uncertainty is necessary to obtain an optimal dispersion, but this issue can also be a consequence of the fact that meteorological forcing uncertainties were not accounted for. The ESCROC system promises the integration of numerical snow-modelling errors in ensemble forecasting and ensemble assimilation systems in support of avalanche hazard forecasting and other snowpack-modelling applications.
Progressive freezing of interacting spins in isolated finite magnetic ensembles
Bhattacharya, Kakoli; Dupuis, Veronique; Le-Roy, Damien; Deb, Pritam
2017-02-01
Self-organization of magnetic nanoparticles into secondary nanostructures provides an innovative way for designing functional nanomaterials with novel properties, different from the constituent primary nanoparticles as well as their bulk counterparts. Collective magnetic properties of such complex closed packing of magnetic nanoparticles makes them more appealing than the individual magnetic nanoparticles in many technological applications. This work reports the collective magnetic behaviour of magnetic ensembles comprising of single domain Fe3O4 nanoparticles. The present work reveals that the ensemble formation is based on the re-orientation and attachment of the nanoparticles in an iso-oriented fashion at the mesoscale regime. Comprehensive dc magnetic measurements show the prevalence of strong interparticle interactions in the ensembles. Due to the close range organization of primary Fe3O4 nanoparticles in the ensemble, the spins of the individual nanoparticles interact through dipolar interactions as realized from remnant magnetization measurements. Signature of super spin glass like behaviour in the ensembles is observed in the memory studies carried out in field cooled conditions. Progressive freezing of spins in the ensembles is corroborated from the Vogel-Fulcher fit of the susceptibility data. Dynamic scaling of relaxation reasserted slow spin dynamics substantiating cluster spin glass like behaviour in the ensembles.
Reliable probabilities through statistical post-processing of ensemble predictions
Van Schaeybroeck, Bert; Vannitsem, Stéphane
2013-04-01
We develop post-processing or calibration approaches based on linear regression that make ensemble forecasts more reliable. We enforce climatological reliability in the sense that the total variability of the prediction is equal to the variability of the observations. Second, we impose ensemble reliability such that the spread around the ensemble mean of the observation coincides with the one of the ensemble members. In general the attractors of the model and reality are inhomogeneous. Therefore ensemble spread displays a variability not taken into account in standard post-processing methods. We overcome this by weighting the ensemble by a variable error. The approaches are tested in the context of the Lorenz 96 model (Lorenz 1996). The forecasts become more reliable at short lead times as reflected by a flatter rank histogram. Our best method turns out to be superior to well-established methods like EVMOS (Van Schaeybroeck and Vannitsem, 2011) and Nonhomogeneous Gaussian Regression (Gneiting et al., 2005). References [1] Gneiting, T., Raftery, A. E., Westveld, A., Goldman, T., 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Weather Rev. 133, 1098-1118. [2] Lorenz, E. N., 1996: Predictability - a problem partly solved. Proceedings, Seminar on Predictability ECMWF. 1, 1-18. [3] Van Schaeybroeck, B., and S. Vannitsem, 2011: Post-processing through linear regression, Nonlin. Processes Geophys., 18, 147.
An Efficient Ensemble Learning Method for Gene Microarray Classification
Directory of Open Access Journals (Sweden)
Alireza Osareh
2013-01-01
Full Text Available The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.
Selecting a climate model subset to optimise key ensemble properties
Directory of Open Access Journals (Sweden)
N. Herger
2018-02-01
Full Text Available End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history. Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used.
Selecting a climate model subset to optimise key ensemble properties
Herger, Nadja; Abramowitz, Gab; Knutti, Reto; Angélil, Oliver; Lehmann, Karsten; Sanderson, Benjamin M.
2018-02-01
End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history. Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used.
Soil texture reclassification by an ensemble model
Cisty, Milan; Hlavcova, Kamila
2015-04-01
a prerequisite for solving some subsequent task, this bias is propagated to the subsequent modelling or other work. Therefore, for the sake of achieving more general and precise outputs while solving such tasks, the authors of the present paper are proposing a hybrid approach, which has the potential for obtaining improved results. Although the authors continue recommending the use of the mentioned parametric PSD models in the proposed methodology, the final prediction is made by an ensemble machine learning algorithm based on regression trees, the so-called Random Forest algorithm, which is built on top of the outputs of such models, which serves as an ensemble members. An improvement in precision was proved, and it is documented in the paper that the ensemble model worked better than any of its constituents. References Nemes, A., Wosten, J.H.M., Lilly, A., Voshaar, J.H.O.: Evaluation of different procedures to interpolate particle-size distributions to achieve compatibility within soil databases. Geoderma 90, 187- 202 (1999) Hwang, S.: Effect of texture on the performance of soil particle-size distribution models. Geoderma 123, 363-371 (2004) Botula, Y.D., Cornelis, W.M., Baert, G., Mafuka, P., Van Ranst, E.: Particle size distribution models for soils of the humid tropics. J Soils Sediments. 13, 686-698 (2013)
Ensemble Deep Learning for Biomedical Time Series Classification.
Jin, Lin-Peng; Dong, Jun
2016-01-01
Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimental results show that the proposed method has certain advantages compared to some well-known ensemble methods, such as Bagging and AdaBoost .
Ensemble Deep Learning for Biomedical Time Series Classification
Directory of Open Access Journals (Sweden)
Lin-peng Jin
2016-01-01
Full Text Available Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimental results show that the proposed method has certain advantages compared to some well-known ensemble methods, such as Bagging and AdaBoost.
Device and Method for Gathering Ensemble Data Sets
Racette, Paul E. (Inventor)
2014-01-01
An ensemble detector uses calibrated noise references to produce ensemble sets of data from which properties of non-stationary processes may be extracted. The ensemble detector comprising: a receiver; a switching device coupled to the receiver, the switching device configured to selectively connect each of a plurality of reference noise signals to the receiver; and a gain modulation circuit coupled to the receiver and configured to vary a gain of the receiver based on a forcing signal; whereby the switching device selectively connects each of the plurality of reference noise signals to the receiver to produce an output signal derived from the plurality of reference noise signals and the forcing signal.
Parallel quantum computing in a single ensemble quantum computer
International Nuclear Information System (INIS)
Long Guilu; Xiao, L.
2004-01-01
We propose a parallel quantum computing mode for ensemble quantum computer. In this mode, some qubits are in pure states while other qubits are in mixed states. It enables a single ensemble quantum computer to perform 'single-instruction-multidata' type of parallel computation. Parallel quantum computing can provide additional speedup in Grover's algorithm and Shor's algorithm. In addition, it also makes a fuller use of qubit resources in an ensemble quantum computer. As a result, some qubits discarded in the preparation of an effective pure state in the Schulman-Varizani and the Cleve-DiVincenzo algorithms can be reutilized
Directory of Open Access Journals (Sweden)
G. Thirel
2010-08-01
Full Text Available The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Ensemble streamflow forecast systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF 10-day Ensemble Prediction System (EPS. Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm
Liu, Li; Gao, Chao; Xuan, Weidong; Xu, Yue-Ping
2017-11-01
Ensemble flood forecasts by hydrological models using numerical weather prediction products as forcing data are becoming more commonly used in operational flood forecasting applications. In this study, a hydrological ensemble flood forecasting system comprised of an automatically calibrated Variable Infiltration Capacity model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated. The hydrological model is optimized by the parallel programmed ε-NSGA II multi-objective algorithm. According to the solutions by ε-NSGA II, two differently parameterized models are determined to simulate daily flows and peak flows at each of the three hydrological stations. Then a simple yet effective modular approach is proposed to combine these daily and peak flows at the same station into one composite series. Five ensemble methods and various evaluation metrics are adopted. The results show that ε-NSGA II can provide an objective determination on parameter estimation, and the parallel program permits a more efficient simulation. It is also demonstrated that the forecasts from ECMWF have more favorable skill scores than other Ensemble Prediction Systems. The multimodel ensembles have advantages over all the single model ensembles and the multimodel methods weighted on members and skill scores outperform other methods. Furthermore, the overall performance at three stations can be satisfactory up to ten days, however the hydrological errors can degrade the skill score by approximately 2 days, and the influence persists until a lead time of 10 days with a weakening trend. With respect to peak flows selected by the Peaks Over Threshold approach, the ensemble means from single models or multimodels are generally underestimated, indicating that the ensemble mean can bring overall improvement in forecasting of flows. For
Cluster ensembles, quantization and the dilogarithm
DEFF Research Database (Denmark)
Fock, Vladimir; Goncharov, Alexander B.
2009-01-01
A cluster ensemble is a pair of positive spaces (i.e. varieties equipped with positive atlases), coming with an action of a symmetry group . The space is closely related to the spectrum of a cluster algebra [ 12 ]. The two spaces are related by a morphism . The space is equipped with a closed -form...... the algebra of functions on the -deformed -space has a large center, which includes the algebra of functions on the original -space. The main example is provided by the pair of moduli spaces assigned in [ 7 ] to a topological surface with a finite set of points at the boundary and a split semisimple algebraic...... group . It is an algebraic-geometric avatar of higher Teichmüller theory on related to . We suggest that there exists a duality between the and spaces. In particular, we conjecture that the tropical points of one of the spaces parametrise a basis in the space of functions on the Langlands dual space. We...
Asymptotic expansions for the Gaussian unitary ensemble
DEFF Research Database (Denmark)
Haagerup, Uffe; Thorbjørnsen, Steen
2012-01-01
Let g : R ¿ C be a C8-function with all derivatives bounded and let trn denote the normalized trace on the n × n matrices. In Ref. 3 Ercolani and McLaughlin established asymptotic expansions of the mean value ¿{trn(g(Xn))} for a rather general class of random matrices Xn, including the Gaussian U...... and covariance considered above correspond to, respectively, the one- and two-dimensional Cauchy (or Stieltjes) transform of the ....... Unitary Ensemble (GUE). Using an analytical approach, we provide in the present paper an alternative proof of this asymptotic expansion in the GUE case. Specifically we derive for a random matrix Xn that where k is an arbitrary positive integer. Considered as mappings of g, we determine the coefficients...... aj(g), j ¿ N, as distributions (in the sense of L. Schwarts). We derive a similar asymptotic expansion for the covariance Cov{Trn[f(Xn)], Trn[g(Xn)]}, where f is a function of the same kind as g, and Trn = n trn. Special focus is drawn to the case where and for ¿, µ in C\\R. In this case the mean...
Orchestrating Distributed Resource Ensembles for Petascale Science
Energy Technology Data Exchange (ETDEWEB)
Baldin, Ilya; Mandal, Anirban; Ruth, Paul; Yufeng, Xin
2014-04-24
Distributed, data-intensive computational science applications of interest to DOE scientific com- munities move large amounts of data for experiment data management, distributed analysis steps, remote visualization, and accessing scientific instruments. These applications need to orchestrate ensembles of resources from multiple resource pools and interconnect them with high-capacity multi- layered networks across multiple domains. It is highly desirable that mechanisms are designed that provide this type of resource provisioning capability to a broad class of applications. It is also important to have coherent monitoring capabilities for such complex distributed environments. In this project, we addressed these problems by designing an abstract API, enabled by novel semantic resource descriptions, for provisioning complex and heterogeneous resources from multiple providers using their native provisioning mechanisms and control planes: computational, storage, and multi-layered high-speed network domains. We used an extensible resource representation based on semantic web technologies to afford maximum flexibility to applications in specifying their needs. We evaluated the effectiveness of provisioning using representative data-intensive ap- plications. We also developed mechanisms for providing feedback about resource performance to the application, to enable closed-loop feedback control and dynamic adjustments to resource allo- cations (elasticity). This was enabled through development of a novel persistent query framework that consumes disparate sources of monitoring data, including perfSONAR, and provides scalable distribution of asynchronous notifications.
Ensemble-based forecasting at Horns Rev: Ensemble conversion and kernel dressing
DEFF Research Database (Denmark)
Pinson, Pierre; Madsen, Henrik
For management and trading purposes, information on short-term wind generation (from few hours to few days ahead) is even more crucial at large offshore wind farms, since they concentrate a large capacity at a single location. The most complete information that can be provided today consists....... The obtained ensemble forecasts of wind power are then converted into predictive distributions with an original adaptive kernel dressing method. The shape of the kernels is driven by a mean-variance model, the parameters of which are recursively estimated in order to maximize the overall skill of obtained...
Directory of Open Access Journals (Sweden)
Shulin Lyu
2018-01-01
The σ function, namely, the derivative of the log of the smallest eigenvalue distributions of the finite-n LUE or the JUE, satisfies the Jimbo–Miwa–Okamoto σ form of PV and PVI, although in the shift Jacobi case, with the weight xα(1−xβ, the β parameter does not show up in the equation. We also obtain the asymptotic expansions for the smallest eigenvalue distributions of the Laguerre unitary and Jacobi unitary ensembles after appropriate double scalings, and obtained the constants in the asymptotic expansion of the gap probabilities, expressed in term of the Barnes G-function valuated at special point.
Ensembles on configuration space classical, quantum, and beyond
Hall, Michael J W
2016-01-01
This book describes a promising approach to problems in the foundations of quantum mechanics, including the measurement problem. The dynamics of ensembles on configuration space is shown here to be a valuable tool for unifying the formalisms of classical and quantum mechanics, for deriving and extending the latter in various ways, and for addressing the quantum measurement problem. A description of physical systems by means of ensembles on configuration space can be introduced at a very fundamental level: the basic building blocks are a configuration space, probabilities, and Hamiltonian equations of motion for the probabilities. The formalism can describe both classical and quantum systems, and their thermodynamics, with the main difference being the choice of ensemble Hamiltonian. Furthermore, there is a natural way of introducing ensemble Hamiltonians that describe the evolution of hybrid systems; i.e., interacting systems that have distinct classical and quantum sectors, allowing for consistent descriptio...
HIGH-RESOLUTION ATMOSPHERIC ENSEMBLE MODELING AT SRNL
Energy Technology Data Exchange (ETDEWEB)
Buckley, R.; Werth, D.; Chiswell, S.; Etherton, B.
2011-05-10
The High-Resolution Mid-Atlantic Forecasting Ensemble (HME) is a federated effort to improve operational forecasts related to precipitation, convection and boundary layer evolution, and fire weather utilizing data and computing resources from a diverse group of cooperating institutions in order to create a mesoscale ensemble from independent members. Collaborating organizations involved in the project include universities, National Weather Service offices, and national laboratories, including the Savannah River National Laboratory (SRNL). The ensemble system is produced from an overlapping numerical weather prediction model domain and parameter subsets provided by each contributing member. The coordination, synthesis, and dissemination of the ensemble information are performed by the Renaissance Computing Institute (RENCI) at the University of North Carolina-Chapel Hill. This paper discusses background related to the HME effort, SRNL participation, and example results available from the RENCI website.
Probing RNA native conformational ensembles with structural constraints
DEFF Research Database (Denmark)
Fonseca, Rasmus; van den Bedem, Henry; Bernauer, Julie
2016-01-01
substates, which are difficult to characterize experimentally and computationally. Here, we present an innovative, entirely kinematic computational procedure to efficiently explore the native ensemble of RNA molecules. Our procedure projects degrees of freedom onto a subspace of conformation space defined...
Can an ensemble give anything more than Gaussian probabilities?
Directory of Open Access Journals (Sweden)
J. C. W. Denholm-Price
2003-01-01
Full Text Available Can a relatively small numerical weather prediction ensemble produce any more forecast information than can be reproduced by a Gaussian probability density function (PDF? This question is examined using site-specific probability forecasts from the UK Met Office. These forecasts are based on the 51-member Ensemble Prediction System of the European Centre for Medium-range Weather Forecasts. Verification using Brier skill scores suggests that there can be statistically-significant skill in the ensemble forecast PDF compared with a Gaussian fit to the ensemble. The most significant increases in skill were achieved from bias-corrected, calibrated forecasts and for probability forecasts of thresholds that are located well inside the climatological limits at the examined sites. Forecast probabilities for more climatologically-extreme thresholds, where the verification more often lies within the tails or outside of the PDF, showed little difference in skill between the forecast PDF and the Gaussian forecast.
Dissipation induced asymmetric steering of distant atomic ensembles
Cheng, Guangling; Tan, Huatang; Chen, Aixi
2018-04-01
The asymmetric steering effects of separated atomic ensembles denoted by the effective bosonic modes have been explored by the means of quantum reservoir engineering in the setting of the cascaded cavities, in each of which an atomic ensemble is involved. It is shown that the steady-state asymmetric steering of the mesoscopic objects is unconditionally achieved via the dissipation of the cavities, by which the nonlocal interaction occurs between two atomic ensembles, and the direction of steering could be easily controlled through variation of certain tunable system parameters. One advantage of the present scheme is that it could be rather robust against parameter fluctuations, and does not require the accurate control of evolution time and the original state of the system. Furthermore, the double-channel Raman transitions between the long-lived atomic ground states are used and the atomic ensembles act as the quantum network nodes, which makes our scheme insensitive to the collective spontaneous emission of atoms.
Relation between native ensembles and experimental structures of proteins
DEFF Research Database (Denmark)
Best, R. B.; Lindorff-Larsen, Kresten; DePristo, M. A.
2006-01-01
Different experimental structures of the same protein or of proteins with high sequence similarity contain many small variations. Here we construct ensembles of "high-sequence similarity Protein Data Bank" (HSP) structures and consider the extent to which such ensembles represent the structural...... Data Bank ensembles; moreover, we show that the effects of uncertainties in structure determination are insufficient to explain the results. These results highlight the importance of accounting for native-state protein dynamics in making comparisons with ensemble-averaged experimental data and suggest...... heterogeneity of the native state in solution. We find that different NMR measurements probing structure and dynamics of given proteins in solution, including order parameters, scalar couplings, and residual dipolar couplings, are remarkably well reproduced by their respective high-sequence similarity Protein...
Prediction of Weather Impacted Airport Capacity using Ensemble Learning
Wang, Yao Xun
2011-01-01
Ensemble learning with the Bagging Decision Tree (BDT) model was used to assess the impact of weather on airport capacities at selected high-demand airports in the United States. The ensemble bagging decision tree models were developed and validated using the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) data and weather forecast at these airports. The study examines the performance of BDT, along with traditional single Support Vector Machines (SVM), for airport runway configuration selection and airport arrival rates (AAR) prediction during weather impacts. Testing of these models was accomplished using observed weather, weather forecast, and airport operation information at the chosen airports. The experimental results show that ensemble methods are more accurate than a single SVM classifier. The airport capacity ensemble method presented here can be used as a decision support model that supports air traffic flow management to meet the weather impacted airport capacity in order to reduce costs and increase safety.
On the distribution of eigenvalues of certain matrix ensembles
International Nuclear Information System (INIS)
Bogomolny, E.; Bohigas, O.; Pato, M.P.
1995-01-01
Invariant random matrix ensembles with weak confinement potentials of the eigenvalues, corresponding to indeterminate moment problems, are investigated. These ensembles are characterized by the fact that the mean density of eigenvalues tends to a continuous function with increasing matrix dimension contrary to the usual cases where it grows indefinitely. It is demonstrated that the standard asymptotic formulae are not applicable in these cases and that the asymptotic distribution of eigenvalues can deviate from the classical ones. (author)
Nitrogen-vacancy ensemble magnetometry based on pump absorption
DEFF Research Database (Denmark)
Ahmadi, Sepehr; El-Ella, Haitham A.R.; Hansen, Jørn B.
2017-01-01
We demonstrate magnetic field sensing by recording the variation in the pump light absorption with nitrogen-vacancy center ensemble. At a frequency of 10 mHz we obtain a noise floor of ~30 nT/√Hz.......We demonstrate magnetic field sensing by recording the variation in the pump light absorption with nitrogen-vacancy center ensemble. At a frequency of 10 mHz we obtain a noise floor of ~30 nT/√Hz....
Ocean Ensemble Forecasting in the Navy Earth System Prediction Capability
Rowley, C. D.; Hogan, P. J.; Frolov, S.; Wei, M.; Thoppil, P. G.; Smedstad, O. M.; Barton, N. P.; Bishop, C. H.
2016-12-01
An extended range ensemble forecast system is being developed in the US Navy Earth System Prediction Capability (ESPC). A global ocean ensemble generation capability to support the coupled ESPC ensemble forecast has been developed, and initial assessments are underway. The ocean ensemble generation is based on a perturbed-observation analysis developed for the Navy Coupled Ocean Data Assimilation system (NCODA). The resulting analysis perturbations are used to represent uncertainty in the initial conditions of a global ocean forecast ensemble using the Hybrid Coordinate Ocean Model (HYCOM). For cycling with HYCOM, the NCODA system performs a 3D variational analysis of temperature, salinity, geopotential, and vector velocity using remotely-sensed SST, SSH, and sea ice concentration, plus in situ observations of temperature, salinity, and currents from ships, buoys, XBTs, CTDs, profiling floats, and autonomous gliders. Sea surface height is assimilated through synthetic temperature and salinity profiles generated using the Modular Ocean Data Assimilation System (MODAS) historical regression database with surface height and surface temperature as inputs. Perturbations to the surface and profile observations use random samples from a normal distribution scaled by the observation error standard deviation, which combines estimates of instrument and representation error. Perturbations to the synthetic profiles are generated by supplying the perturbed surface inputs to the MODAS system, resulting in correlated profile changes with vertical correlations associated with historical uncertainty about thermocline depth and gradients. Initial results from a cycling global analysis show the analysis perturbations have scales and amplitudes consistent with short term forecast error covariances, and improve measures of ensemble forecast skill regionally and globally. Assessments of the global ocean ensemble forecast skill using the perturbed observation analysis will be presented
Spectral statistics in semiclassical random-matrix ensembles
International Nuclear Information System (INIS)
Feingold, M.; Leitner, D.M.; Wilkinson, M.
1991-01-01
A novel random-matrix ensemble is introduced which mimics the global structure inherent in the Hamiltonian matrices of autonomous, ergodic systems. Changes in its parameters induce a transition between a Poisson and a Wigner distribution for the level spacings, P(s). The intermediate distributions are uniquely determined by a single scaling variable. Semiclassical constraints force the ensemble to be in a regime with Wigner P(s) for systems with more than two freedoms
Berlin, Konstantin; Castañeda, Carlos A.; Schneidman-Duhovny, Dina; Sali, Andrej; Nava-Tudela, Alfredo; Fushman, David
2013-01-01
Structural analysis of proteins and nucleic acids is complicated by their inherent flexibility, conferred, for example, by linkers between their contiguous domains. Therefore, the macromolecule needs to be represented by an ensemble of conformations instead of a single conformation. Determining this ensemble is challenging because the experimental data are a convoluted average of contributions from multiple conformations. As the number of the ensemble degrees of freedom generally greatly exceeds the number of independent observables, directly deconvolving experimental data into a representative ensemble is an ill-posed problem. Recent developments in sparse approximations and compressive sensing have demonstrated that useful information can be recovered from underdetermined (ill-posed) systems of linear equations by using sparsity regularization. Inspired by these advances, we designed Sparse Ensemble Selection (SES) method for recovering multiple conformations from a limited number of observations. SES is more general and accurate than previously published minimum-ensemble methods, and we use it to obtain representative conformational ensembles of Lys48-linked di-ubiquitin, characterized by the residual dipolar coupling data measured at several pH conditions. These representative ensembles are validated against NMR chemical shift perturbation data and compared to maximum-entropy results. The SES method reproduced and quantified the previously observed pH dependence of the major conformation of Lys48-linked di-ubiquitin, and revealed lesser-populated conformations that are pre-organized for binding known di-ubiquitin receptors, thus providing insights into possible mechanisms of receptor recognition by polyubiquitin. SES is applicable to any experimental observables that can be expressed as a weighted linear combination of data for individual states. PMID:24093873
Development of modern folk and instrumental ensembles in China
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Jiang Y.
2016-09-01
Full Text Available in the XX century the influence of traditional musical forms is becoming more diverse, in particular, the use of multimedia art forms. The Chinese as well as Western traditions and Ensemble Performance has undergone many changes, mutated forms of a combination of instruments, a creative approach to music, gradually breaking geographical boundaries, began to develop in the direction of further integration. In modern Chinese folk ensemble there is a combination of various Chinese folk instruments.
An automated approach to network features of protein structure ensembles
Bhattacharyya, Moitrayee; Bhat, Chanda R; Vishveshwara, Saraswathi
2013-01-01
Network theory applied to protein structures provides insights into numerous problems of biological relevance. The explosion in structural data available from PDB and simulations establishes a need to introduce a standalone-efficient program that assembles network concepts/parameters under one hood in an automated manner. Herein, we discuss the development/application of an exhaustive, user-friendly, standalone program package named PSN-Ensemble, which can handle structural ensembles generated through molecular dynamics (MD) simulation/NMR studies or from multiple X-ray structures. The novelty in network construction lies in the explicit consideration of side-chain interactions among amino acids. The program evaluates network parameters dealing with topological organization and long-range allosteric communication. The introduction of a flexible weighing scheme in terms of residue pairwise cross-correlation/interaction energy in PSN-Ensemble brings in dynamical/chemical knowledge into the network representation. Also, the results are mapped on a graphical display of the structure, allowing an easy access of network analysis to a general biological community. The potential of PSN-Ensemble toward examining structural ensemble is exemplified using MD trajectories of an ubiquitin-conjugating enzyme (UbcH5b). Furthermore, insights derived from network parameters evaluated using PSN-Ensemble for single-static structures of active/inactive states of β2-adrenergic receptor and the ternary tRNA complexes of tyrosyl tRNA synthetases (from organisms across kingdoms) are discussed. PSN-Ensemble is freely available from http://vishgraph.mbu.iisc.ernet.in/PSN-Ensemble/psn_index.html. PMID:23934896
GA-Based Membrane Evolutionary Algorithm for Ensemble Clustering
Wang, Yanhua; Liu, Xiyu; Xiang, Laisheng
2017-01-01
Ensemble clustering can improve the generalization ability of a single clustering algorithm and generate a more robust clustering result by integrating multiple base clusterings, so it becomes the focus of current clustering research. Ensemble clustering aims at finding a consensus partition which agrees as much as possible with base clusterings. Genetic algorithm is a highly parallel, stochastic, and adaptive search algorithm developed from the natural selection and evolutionary mechanism of...
Pycobra: A Python Toolbox for Ensemble Learning and Visualisation
Guedj, Benjamin; Srinivasa Desikan, Bhargav
2017-01-01
We introduce \\texttt{pycobra}, a Python library devoted to ensemble learning (regression and classification) and visualisation. Its main assets are the implementation of several ensemble learning algorithms, a flexible and generic interface to compare and blend any existing machine learning algorithm available in Python libraries (as long as a \\texttt{predict} method is given), and visualisation tools such as Voronoi tessellations. \\texttt{pycobra} is fully \\texttt{scikit-learn} compatible an...
Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast
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Jinyin Ye
2016-01-01
Full Text Available TIGGE (THORPEX International Grand Global Ensemble was a major part of the THORPEX (Observing System Research and Predictability Experiment. It integrates ensemble precipitation products from all the major forecast centers in the world and provides systematic evaluation on the multimodel ensemble prediction system. Development of meteorologic-hydrologic coupled flood forecasting model and early warning model based on the TIGGE precipitation ensemble forecast can provide flood probability forecast, extend the lead time of the flood forecast, and gain more time for decision-makers to make the right decision. In this study, precipitation ensemble forecast products from ECMWF, NCEP, and CMA are used to drive distributed hydrologic model TOPX. We focus on Yi River catchment and aim to build a flood forecast and early warning system. The results show that the meteorologic-hydrologic coupled model can satisfactorily predict the flow-process of four flood events. The predicted occurrence time of peak discharges is close to the observations. However, the magnitude of the peak discharges is significantly different due to various performances of the ensemble prediction systems. The coupled forecasting model can accurately predict occurrence of the peak time and the corresponding risk probability of peak discharge based on the probability distribution of peak time and flood warning, which can provide users a strong theoretical foundation and valuable information as a promising new approach.
On the forecast skill of a convection-permitting ensemble
Schellander-Gorgas, Theresa; Wang, Yong; Meier, Florian; Weidle, Florian; Wittmann, Christoph; Kann, Alexander
2017-01-01
The 2.5 km convection-permitting (CP) ensemble AROME-EPS (Applications of Research to Operations at Mesoscale - Ensemble Prediction System) is evaluated by comparison with the regional 11 km ensemble ALADIN-LAEF (Aire Limitée Adaption dynamique Développement InterNational - Limited Area Ensemble Forecasting) to show whether a benefit is provided by a CP EPS. The evaluation focuses on the abilities of the ensembles to quantitatively predict precipitation during a 3-month convective summer period over areas consisting of mountains and lowlands. The statistical verification uses surface observations and 1 km × 1 km precipitation analyses, and the verification scores involve state-of-the-art statistical measures for deterministic and probabilistic forecasts as well as novel spatial verification methods. The results show that the convection-permitting ensemble with higher-resolution AROME-EPS outperforms its mesoscale counterpart ALADIN-LAEF for precipitation forecasts. The positive impact is larger for the mountainous areas than for the lowlands. In particular, the diurnal precipitation cycle is improved in AROME-EPS, which leads to a significant improvement of scores at the concerned times of day (up to approximately one-third of the scored verification measure). Moreover, there are advantages for higher precipitation thresholds at small spatial scales, which are due to the improved simulation of the spatial structure of precipitation.
Evolutionary Ensemble for In Silico Prediction of Ames Test Mutagenicity
Chen, Huanhuan; Yao, Xin
Driven by new regulations and animal welfare, the need to develop in silico models has increased recently as alternative approaches to safety assessment of chemicals without animal testing. This paper describes a novel machine learning ensemble approach to building an in silico model for the prediction of the Ames test mutagenicity, one of a battery of the most commonly used experimental in vitro and in vivo genotoxicity tests for safety evaluation of chemicals. Evolutionary random neural ensemble with negative correlation learning (ERNE) [1] was developed based on neural networks and evolutionary algorithms. ERNE combines the method of bootstrap sampling on training data with the method of random subspace feature selection to ensure diversity in creating individuals within an initial ensemble. Furthermore, while evolving individuals within the ensemble, it makes use of the negative correlation learning, enabling individual NNs to be trained as accurate as possible while still manage to maintain them as diverse as possible. Therefore, the resulting individuals in the final ensemble are capable of cooperating collectively to achieve better generalization of prediction. The empirical experiment suggest that ERNE is an effective ensemble approach for predicting the Ames test mutagenicity of chemicals.
Concrete ensemble Kalman filters with rigorous catastrophic filter divergence.
Kelly, David; Majda, Andrew J; Tong, Xin T
2015-08-25
The ensemble Kalman filter and ensemble square root filters are data assimilation methods used to combine high-dimensional, nonlinear dynamical models with observed data. Ensemble methods are indispensable tools in science and engineering and have enjoyed great success in geophysical sciences, because they allow for computationally cheap low-ensemble-state approximation for extremely high-dimensional turbulent forecast models. From a theoretical perspective, the dynamical properties of these methods are poorly understood. One of the central mysteries is the numerical phenomenon known as catastrophic filter divergence, whereby ensemble-state estimates explode to machine infinity, despite the true state remaining in a bounded region. In this article we provide a breakthrough insight into the phenomenon, by introducing a simple and natural forecast model that transparently exhibits catastrophic filter divergence under all ensemble methods and a large set of initializations. For this model, catastrophic filter divergence is not an artifact of numerical instability, but rather a true dynamical property of the filter. The divergence is not only validated numerically but also proven rigorously. The model cleanly illustrates mechanisms that give rise to catastrophic divergence and confirms intuitive accounts of the phenomena given in past literature.
Three-model ensemble wind prediction in southern Italy
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R. C. Torcasio
2016-03-01
Full Text Available Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013 three-model ensemble (TME experiment for wind prediction is considered. The models employed, run operationally at National Research Council – Institute of Atmospheric Sciences and Climate (CNR-ISAC, are RAMS (Regional Atmospheric Modelling System, BOLAM (BOlogna Limited Area Model, and MOLOCH (MOdello LOCale in H coordinates. The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System. Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System of the ECMWF (European Centre for Medium-Range Weather Forecast for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.
Protein folding simulations by generalized-ensemble algorithms.
Yoda, Takao; Sugita, Yuji; Okamoto, Yuko
2014-01-01
In the protein folding problem, conventional simulations in physical statistical mechanical ensembles, such as the canonical ensemble with fixed temperature, face a great difficulty. This is because there exist a huge number of local-minimum-energy states in the system and the conventional simulations tend to get trapped in these states, giving wrong results. Generalized-ensemble algorithms are based on artificial unphysical ensembles and overcome the above difficulty by performing random walks in potential energy, volume, and other physical quantities or their corresponding conjugate parameters such as temperature, pressure, etc. The advantage of generalized-ensemble simulations lies in the fact that they not only avoid getting trapped in states of energy local minima but also allows the calculations of physical quantities as functions of temperature or other parameters from a single simulation run. In this article we review the generalized-ensemble algorithms. Four examples, multicanonical algorithm, replica-exchange method, replica-exchange multicanonical algorithm, and multicanonical replica-exchange method, are described in detail. Examples of their applications to the protein folding problem are presented.
SVM and SVM Ensembles in Breast Cancer Prediction.
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Min-Wei Huang
Full Text Available Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.
Three-model ensemble wind prediction in southern Italy
Torcasio, Rosa Claudia; Federico, Stefano; Calidonna, Claudia Roberta; Avolio, Elenio; Drofa, Oxana; Landi, Tony Christian; Malguzzi, Piero; Buzzi, Andrea; Bonasoni, Paolo
2016-03-01
Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013) three-model ensemble (TME) experiment for wind prediction is considered. The models employed, run operationally at National Research Council - Institute of Atmospheric Sciences and Climate (CNR-ISAC), are RAMS (Regional Atmospheric Modelling System), BOLAM (BOlogna Limited Area Model), and MOLOCH (MOdello LOCale in H coordinates). The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System). Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System) of the ECMWF (European Centre for Medium-Range Weather Forecast) for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.
Selecting, weeding, and weighting biased climate model ensembles
Jackson, C. S.; Picton, J.; Huerta, G.; Nosedal Sanchez, A.
2012-12-01
In the Bayesian formulation, the "log-likelihood" is a test statistic for selecting, weeding, or weighting climate model ensembles with observational data. This statistic has the potential to synthesize the physical and data constraints on quantities of interest. One of the thorny issues for formulating the log-likelihood is how one should account for biases. While in the past we have included a generic discrepancy term, not all biases affect predictions of quantities of interest. We make use of a 165-member ensemble CAM3.1/slab ocean climate models with different parameter settings to think through the issues that are involved with predicting each model's sensitivity to greenhouse gas forcing given what can be observed from the base state. In particular we use multivariate empirical orthogonal functions to decompose the differences that exist among this ensemble to discover what fields and regions matter to the model's sensitivity. We find that the differences that matter are a small fraction of the total discrepancy. Moreover, weighting members of the ensemble using this knowledge does a relatively poor job of adjusting the ensemble mean toward the known answer. This points out the shortcomings of using weights to correct for biases in climate model ensembles created by a selection process that does not emphasize the priorities of your log-likelihood.
Genetic Programming Based Ensemble System for Microarray Data Classification
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Kun-Hong Liu
2015-01-01
Full Text Available Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP based new ensemble system (named GPES, which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average. Each individual of the GP is an ensemble system, and they become more and more accurate in the evolutionary process. The feature selection technique and balanced subsampling technique are applied to increase the diversity in each ensemble system. The final ensemble committee is selected by a forward search algorithm, which is shown to be capable of fitting data automatically. The performance of GPES is evaluated using five binary class and six multiclass microarray datasets, and results show that the algorithm can achieve better results in most cases compared with some other ensemble systems. By using elaborate base classifiers or applying other sampling techniques, the performance of GPES may be further improved.
Schweiner, Frank; Laturner, Jeanine; Main, Jörg; Wunner, Günter
2017-11-01
Until now only for specific crossovers between Poissonian statistics (P), the statistics of a Gaussian orthogonal ensemble (GOE), or the statistics of a Gaussian unitary ensemble (GUE) have analytical formulas for the level spacing distribution function been derived within random matrix theory. We investigate arbitrary crossovers in the triangle between all three statistics. To this aim we propose an according formula for the level spacing distribution function depending on two parameters. Comparing the behavior of our formula for the special cases of P→GUE, P→GOE, and GOE→GUE with the results from random matrix theory, we prove that these crossovers are described reasonably. Recent investigations by F. Schweiner et al. [Phys. Rev. E 95, 062205 (2017)2470-004510.1103/PhysRevE.95.062205] have shown that the Hamiltonian of magnetoexcitons in cubic semiconductors can exhibit all three statistics in dependence on the system parameters. Evaluating the numerical results for magnetoexcitons in dependence on the excitation energy and on a parameter connected with the cubic valence band structure and comparing the results with the formula proposed allows us to distinguish between regular and chaotic behavior as well as between existent or broken antiunitary symmetries. Increasing one of the two parameters, transitions between different crossovers, e.g., from the P→GOE to the P→GUE crossover, are observed and discussed.
EnsembleGASVR: A novel ensemble method for classifying missense single nucleotide polymorphisms
Rapakoulia, Trisevgeni
2014-04-26
Motivation: Single nucleotide polymorphisms (SNPs) are considered the most frequently occurring DNA sequence variations. Several computational methods have been proposed for the classification of missense SNPs to neutral and disease associated. However, existing computational approaches fail to select relevant features by choosing them arbitrarily without sufficient documentation. Moreover, they are limited to the problem ofmissing values, imbalance between the learning datasets and most of them do not support their predictions with confidence scores. Results: To overcome these limitations, a novel ensemble computational methodology is proposed. EnsembleGASVR facilitates a twostep algorithm, which in its first step applies a novel evolutionary embedded algorithm to locate close to optimal Support Vector Regression models. In its second step, these models are combined to extract a universal predictor, which is less prone to overfitting issues, systematizes the rebalancing of the learning sets and uses an internal approach for solving the missing values problem without loss of information. Confidence scores support all the predictions and the model becomes tunable by modifying the classification thresholds. An extensive study was performed for collecting the most relevant features for the problem of classifying SNPs, and a superset of 88 features was constructed. Experimental results show that the proposed framework outperforms well-known algorithms in terms of classification performance in the examined datasets. Finally, the proposed algorithmic framework was able to uncover the significant role of certain features such as the solvent accessibility feature, and the top-scored predictions were further validated by linking them with disease phenotypes. © The Author 2014.
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E. Crestani
2013-04-01
Full Text Available Estimating the spatial variability of hydraulic conductivity K in natural aquifers is important for predicting the transport of dissolved compounds. Especially in the nonreactive case, the plume evolution is mainly controlled by the heterogeneity of K. At the local scale, the spatial distribution of K can be inferred by combining the Lagrangian formulation of the transport with a Kalman-filter-based technique and assimilating a sequence of time-lapse concentration C measurements, which, for example, can be evaluated on site through the application of a geophysical method. The objective of this work is to compare the ensemble Kalman filter (EnKF and the ensemble smoother (ES capabilities to retrieve the hydraulic conductivity spatial distribution in a groundwater flow and transport modeling framework. The application refers to a two-dimensional synthetic aquifer in which a tracer test is simulated. Moreover, since Kalman-filter-based methods are optimal only if each of the involved variables fit to a Gaussian probability density function (pdf and since this condition may not be met by some of the flow and transport state variables, issues related to the non-Gaussianity of the variables are analyzed and different transformation of the pdfs are considered in order to evaluate their influence on the performance of the methods. The results show that the EnKF reproduces with good accuracy the hydraulic conductivity field, outperforming the ES regardless of the pdf of the concentrations.
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Tong-chao Nan
2017-01-01
Full Text Available Though the ensemble Kalman filter (EnKF has been successfully applied in many areas, it requires explicit and accurate model and measurement error information, leading to difficulties in practice when only limited information on error mechanisms of observational instruments for subsurface systems is accessible. To handle the uncertain errors, we applied a robust data assimilation algorithm, the ensemble H-infinity filter (EnHF, to estimation of aquifer hydraulic heads and conductivities in a flow model with uncertain/correlated observational errors. The impacts of spatial and temporal correlations in measurements were analyzed, and the performance of EnHF was compared with that of the EnKF. The results show that both EnHF and EnKF are able to estimate hydraulic conductivities properly when observations are free of error; EnHF can provide robust estimates of hydraulic conductivities even when no observational error information is provided. In contrast, the estimates of EnKF seem noticeably undermined because of correlated errors and inaccurate error statistics, and filter divergence was observed. It is concluded that EnHF is an efficient assimilation algorithm when observational errors are unknown or error statistics are inaccurate.
Developing an Ensemble Prediction System based on COSMO-DE
Theis, S.; Gebhardt, C.; Buchhold, M.; Ben Bouallègue, Z.; Ohl, R.; Paulat, M.; Peralta, C.
2010-09-01
The numerical weather prediction model COSMO-DE is a configuration of the COSMO model with a horizontal grid size of 2.8 km. It has been running operationally at DWD since 2007, it covers the area of Germany and produces forecasts with a lead time of 0-21 hours. The model COSMO-DE is convection-permitting, which means that it does without a parametrisation of deep convection and simulates deep convection explicitly. One aim is an improved forecast of convective heavy rain events. Convection-permitting models are in operational use at several weather services, but currently not in ensemble mode. It is expected that an ensemble system could reveal the advantages of a convection-permitting model even better. The probabilistic approach is necessary, because the explicit simulation of convective processes for more than a few hours cannot be viewed as a deterministic forecast anymore. This is due to the chaotic behaviour and short life cycle of the processes which are simulated explicitly now. In the framework of the project COSMO-DE-EPS, DWD is developing and implementing an ensemble prediction system (EPS) for the model COSMO-DE. The project COSMO-DE-EPS comprises the generation of ensemble members, as well as the verification and visualization of the ensemble forecasts and also statistical postprocessing. A pre-operational mode of the EPS with 20 ensemble members is foreseen to start in 2010. Operational use is envisaged to start in 2012, after an upgrade to 40 members and inclusion of statistical postprocessing. The presentation introduces the project COSMO-DE-EPS and describes the design of the ensemble as it is planned for the pre-operational mode. In particular, the currently implemented method for the generation of ensemble members will be explained and discussed. The method includes variations of initial conditions, lateral boundary conditions, and model physics. At present, pragmatic methods are applied which resemble the basic ideas of a multi-model approach
Combining 2-m temperature nowcasting and short range ensemble forecasting
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A. Kann
2011-12-01
Full Text Available During recent years, numerical ensemble prediction systems have become an important tool for estimating the uncertainties of dynamical and physical processes as represented in numerical weather models. The latest generation of limited area ensemble prediction systems (LAM-EPSs allows for probabilistic forecasts at high resolution in both space and time. However, these systems still suffer from systematic deficiencies. Especially for nowcasting (0–6 h applications the ensemble spread is smaller than the actual forecast error. This paper tries to generate probabilistic short range 2-m temperature forecasts by combining a state-of-the-art nowcasting method and a limited area ensemble system, and compares the results with statistical methods. The Integrated Nowcasting Through Comprehensive Analysis (INCA system, which has been in operation at the Central Institute for Meteorology and Geodynamics (ZAMG since 2006 (Haiden et al., 2011, provides short range deterministic forecasts at high temporal (15 min–60 min and spatial (1 km resolution. An INCA Ensemble (INCA-EPS of 2-m temperature forecasts is constructed by applying a dynamical approach, a statistical approach, and a combined dynamic-statistical method. The dynamical method takes uncertainty information (i.e. ensemble variance from the operational limited area ensemble system ALADIN-LAEF (Aire Limitée Adaptation Dynamique Développement InterNational Limited Area Ensemble Forecasting which is running operationally at ZAMG (Wang et al., 2011. The purely statistical method assumes a well-calibrated spread-skill relation and applies ensemble spread according to the skill of the INCA forecast of the most recent past. The combined dynamic-statistical approach adapts the ensemble variance gained from ALADIN-LAEF with non-homogeneous Gaussian regression (NGR which yields a statistical mbox{correction} of the first and second moment (mean bias and dispersion for Gaussian distributed continuous
On the proper use of Ensembles for Predictive Uncertainty assessment
Todini, Ezio; Coccia, Gabriele; Ortiz, Enrique
2015-04-01
Probabilistic forecasting has become popular in the last decades. Hydrological probabilistic forecasts have been based either on uncertainty processors (Krzysztofowic, 1999; Todini, 2004; Todini, 2008) or on ensembles, following meteorological traditional approaches and the establishment of the HEPEX program (http://hepex.irstea.fr. Unfortunately, the direct use of ensembles as a measure of the predictive density is an incorrect practice, because the ensemble measures the spread of the forecast instead of, following the definition of predictive uncertainty, the conditional probability of the future outcome conditional on the forecast. Only few correct approaches are reported in the literature, which correctly use the ensemble to estimate an expected conditional predictive density (Reggiani et al., 2009), similarly to what is done when several predictive models are available as in the BMA (Raftery et al., 2005) or MCP(Todini, 2008; Coccia and Todini, 2011) approaches. A major problem, limiting the correct use of ensembles, is in fact the difficulty of defining the time dependence of the ensemble members, due to the lack of a consistent ranking: in other words, when dealing with multiple models, the ith model remains the ith model regardless to the time of forecast, while this does not happen when dealing with ensemble members, since there is no definition for the ith member of an ensemble. Nonetheless, the MCP approach (Todini, 2008; Coccia and Todini, 2011), essentially based on a multiple regression in the Normal space, can be easily extended to use ensembles to represent the local (in time) smaller or larger conditional predictive uncertainty, as a function of the ensemble spread. This is done by modifying the classical linear regression equations, impliying perfectly observed predictors, to alternative regression equations similar to the Kalman filter ones, allowing for uncertain predictors. In this way, each prediction in time accounts for both the predictive
Probing protein ensemble rigidity and hydrogen-deuterium exchange.
Sljoka, Adnan; Wilson, Derek
2013-10-01
Protein rigidity and flexibility can be analyzed accurately and efficiently using the program floppy inclusion and rigid substructure topography (FIRST). Previous studies using FIRST were designed to analyze the rigidity and flexibility of proteins using a single static (snapshot) structure. It is however well known that proteins can undergo spontaneous sub-molecular unfolding and refolding, or conformational dynamics, even under conditions that strongly favor a well-defined native structure. These (local) unfolding events result in a large number of conformers that differ from each other very slightly. In this context, proteins are better represented as a thermodynamic ensemble of 'native-like' structures, and not just as a single static low-energy structure. Working with this notion, we introduce a novel FIRST-based approach for predicting rigidity/flexibility of the protein ensemble by (i) averaging the hydrogen bonding strengths from the entire ensemble and (ii) by refining the mathematical model of hydrogen bonds. Furthermore, we combine our FIRST-ensemble rigidity predictions with the ensemble solvent accessibility data of the backbone amides and propose a novel computational method which uses both rigidity and solvent accessibility for predicting hydrogen-deuterium exchange (HDX). To validate our predictions, we report a novel site specific HDX experiment which characterizes the native structural ensemble of Acylphosphatase from hyperthermophile Sulfolobus solfataricus (Sso AcP). The sub-structural conformational dynamics that is observed by HDX data, is closely matched with the FIRST-ensemble rigidity predictions, which could not be attained using the traditional single 'snapshot' rigidity analysis. Moreover, the computational predictions of regions that are protected from HDX and those that undergo exchange are in very good agreement with the experimental HDX profile of Sso AcP.
On the clustering of climate models in ensemble seasonal forecasting
Yuan, Xing; Wood, Eric F.
2012-09-01
Multi-model ensemble seasonal forecasting system has expanded in recent years, with a dozen coupled climate models around the world being used to produce hindcasts or real-time forecasts. However, many models are sharing similar atmospheric or oceanic components which may result in similar forecasts. This raises questions of whether the ensemble is over-confident if we treat each model equally, or whether we can obtain an effective subset of models that can retain predictability and skill as well. In this study, we use a hierarchical clustering method based on inverse trigonometric cosine function of the anomaly correlation of pairwise model hindcasts to measure the similarities among twelve American and European seasonal forecast models. Though similarities are found between models sharing the same atmospheric component, different versions of models from the same center sometimes produce quite different temperature forecasts, which indicate that detailed physics packages such as radiation and land surface schemes need to be analyzed in interpreting the clustering result. Uncertainties in clustering for different forecast lead times also make reducing redundant models more complicated. Predictability analysis shows that multi-model ensemble is not necessarily better than a single model, while the cluster ensemble shows consistent improvement against individual models. The eight model-based cluster ensemble forecast shows comparable performance to the total twelve model ensemble in terms of probabilistic forecast skill for accuracy and discrimination. This study also manifests that models developed in U.S. and Europe are more independent from each other, suggesting the necessity of international collaboration in enhancing multi-model ensemble seasonal forecasting.
A multi-model ensemble approach to seabed mapping
Diesing, Markus; Stephens, David
2015-06-01
Seabed habitat mapping based on swath acoustic data and ground-truth samples is an emergent and active marine science discipline. Significant progress could be achieved by transferring techniques and approaches that have been successfully developed and employed in such fields as terrestrial land cover mapping. One such promising approach is the multiple classifier system, which aims at improving classification performance by combining the outputs of several classifiers. Here we present results of a multi-model ensemble applied to multibeam acoustic data covering more than 5000 km2 of seabed in the North Sea with the aim to derive accurate spatial predictions of seabed substrate. A suite of six machine learning classifiers (k-Nearest Neighbour, Support Vector Machine, Classification Tree, Random Forest, Neural Network and Naïve Bayes) was trained with ground-truth sample data classified into seabed substrate classes and their prediction accuracy was assessed with an independent set of samples. The three and five best performing models were combined to classifier ensembles. Both ensembles led to increased prediction accuracy as compared to the best performing single classifier. The improvements were however not statistically significant at the 5% level. Although the three-model ensemble did not perform significantly better than its individual component models, we noticed that the five-model ensemble did perform significantly better than three of the five component models. A classifier ensemble might therefore be an effective strategy to improve classification performance. Another advantage is the fact that the agreement in predicted substrate class between the individual models of the ensemble could be used as a measure of confidence. We propose a simple and spatially explicit measure of confidence that is based on model agreement and prediction accuracy.
Quantifying Monte Carlo uncertainty in ensemble Kalman filter
Energy Technology Data Exchange (ETDEWEB)
Thulin, Kristian; Naevdal, Geir; Skaug, Hans Julius; Aanonsen, Sigurd Ivar
2009-01-15
This report is presenting results obtained during Kristian Thulin PhD study, and is a slightly modified form of a paper submitted to SPE Journal. Kristian Thulin did most of his portion of the work while being a PhD student at CIPR, University of Bergen. The ensemble Kalman filter (EnKF) is currently considered one of the most promising methods for conditioning reservoir simulation models to production data. The EnKF is a sequential Monte Carlo method based on a low rank approximation of the system covariance matrix. The posterior probability distribution of model variables may be estimated fram the updated ensemble, but because of the low rank covariance approximation, the updated ensemble members become correlated samples from the posterior distribution. We suggest using multiple EnKF runs, each with smaller ensemble size to obtain truly independent samples from the posterior distribution. This allows a point-wise confidence interval for the posterior cumulative distribution function (CDF) to be constructed. We present a methodology for finding an optimal combination of ensemble batch size (n) and number of EnKF runs (m) while keeping the total number of ensemble members ( m x n) constant. The optimal combination of n and m is found through minimizing the integrated mean square error (MSE) for the CDFs and we choose to define an EnKF run with 10.000 ensemble members as having zero Monte Carlo error. The methodology is tested on a simplistic, synthetic 2D model, but should be applicable also to larger, more realistic models. (author). 12 refs., figs.,tabs
Probing protein ensemble rigidity and hydrogen-deuterium exchange
Sljoka, Adnan; Wilson, Derek
2013-10-01
Protein rigidity and flexibility can be analyzed accurately and efficiently using the program floppy inclusion and rigid substructure topography (FIRST). Previous studies using FIRST were designed to analyze the rigidity and flexibility of proteins using a single static (snapshot) structure. It is however well known that proteins can undergo spontaneous sub-molecular unfolding and refolding, or conformational dynamics, even under conditions that strongly favor a well-defined native structure. These (local) unfolding events result in a large number of conformers that differ from each other very slightly. In this context, proteins are better represented as a thermodynamic ensemble of ‘native-like’ structures, and not just as a single static low-energy structure. Working with this notion, we introduce a novel FIRST-based approach for predicting rigidity/flexibility of the protein ensemble by (i) averaging the hydrogen bonding strengths from the entire ensemble and (ii) by refining the mathematical model of hydrogen bonds. Furthermore, we combine our FIRST-ensemble rigidity predictions with the ensemble solvent accessibility data of the backbone amides and propose a novel computational method which uses both rigidity and solvent accessibility for predicting hydrogen-deuterium exchange (HDX). To validate our predictions, we report a novel site specific HDX experiment which characterizes the native structural ensemble of Acylphosphatase from hyperthermophile Sulfolobus solfataricus (Sso AcP). The sub-structural conformational dynamics that is observed by HDX data, is closely matched with the FIRST-ensemble rigidity predictions, which could not be attained using the traditional single ‘snapshot’ rigidity analysis. Moreover, the computational predictions of regions that are protected from HDX and those that undergo exchange are in very good agreement with the experimental HDX profile of Sso AcP.
Implementation of single qubit in QD ensembles
International Nuclear Information System (INIS)
Alegre, T.P. Mayer
2004-01-01
Full text: During the last decades the semiconductor industry has achieved the production of exponentially shrinking components. This fact points to fundamental limits of integration, making computation with single atoms or particles like an electron an ultimate goal. To get to this limit, quantum systems in solid state have to be manipulated in a controllable fashion. The assessment of quantum degrees of freedom for information processing may allow exponentially faster performance for certain classes of problems. The essential aspect to be explored in quantum information processing resides in the superposition of states that allows resources such as entangled states to be envisaged. The quest for the optimal system to host a quantum variable that is sufficiently isolated from the environment encompasses implementations spanning optical, atomic, molecular and solid state systems. In the solid state, a variety of proposals have come forth, each one having its own advantages and disadvantages. The main conclusion from these e efforts is that there is no decisive technology upon which quantum information devices will be built. Self-assembled quantum dots (SAQDs or QDs), can be grown with size uniformity that enables the observation of single electron loading events. They can in turn be used to controllably trap single electrons into discrete levels, atom-like, with their corresponding shells. Hund's rules and Pauli exclusion principle are observed in these nanostructures and are key in allowing and preserving a particular quantum state. Provided that one can trap one electron in a QD ensemble, the corresponding spin can be manipulated by an external magnetic field by either conventional Electron Spin Resonance (ESR) techniques or g-tensor modulation resonance (g-TMR). By analogy with Nuclear Magnetic Resonance, single qubit operations are proposed, which at some point in time should be scaled, provided that spin-spin interactions can be controlled. Read out can be
Ensemble Kalman filtering with one-step-ahead smoothing
Raboudi, Naila F.
2018-01-11
The ensemble Kalman filter (EnKF) is widely used for sequential data assimilation. It operates as a succession of forecast and analysis steps. In realistic large-scale applications, EnKFs are implemented with small ensembles and poorly known model error statistics. This limits their representativeness of the background error covariances and, thus, their performance. This work explores the efficiency of the one-step-ahead (OSA) smoothing formulation of the Bayesian filtering problem to enhance the data assimilation performance of EnKFs. Filtering with OSA smoothing introduces an updated step with future observations, conditioning the ensemble sampling with more information. This should provide an improved background ensemble in the analysis step, which may help to mitigate the suboptimal character of EnKF-based methods. Here, the authors demonstrate the efficiency of a stochastic EnKF with OSA smoothing for state estimation. They then introduce a deterministic-like EnKF-OSA based on the singular evolutive interpolated ensemble Kalman (SEIK) filter. The authors show that the proposed SEIK-OSA outperforms both SEIK, as it efficiently exploits the data twice, and the stochastic EnKF-OSA, as it avoids observational error undersampling. They present extensive assimilation results from numerical experiments conducted with the Lorenz-96 model to demonstrate SEIK-OSA’s capabilities.
Skill forecasting from different wind power ensemble prediction methods
International Nuclear Information System (INIS)
Pinson, Pierre; Nielsen, Henrik A; Madsen, Henrik; Kariniotakis, George
2007-01-01
This paper presents an investigation on alternative approaches to the providing of uncertainty estimates associated to point predictions of wind generation. Focus is given to skill forecasts in the form of prediction risk indices, aiming at giving a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the dispersion of ensemble members for a single prediction horizon, or over a set of successive look-ahead times. It is shown on the test case of a Danish offshore wind farm how prediction risk indices may be related to several levels of forecast uncertainty (and energy imbalances). Wind power ensemble predictions are derived from the transformation of ECMWF and NCEP ensembles of meteorological variables to power, as well as by a lagged average approach alternative. The ability of risk indices calculated from the various types of ensembles forecasts to resolve among situations with different levels of uncertainty is discussed
Adiabatically deformed ensemble: Engineering nonthermal states of matter
Kennes, D. M.
2017-07-01
We propose a route towards engineering nonthermal states of matter, which show largely unexplored physics. The main idea relies on the adiabatic passage of a thermal ensemble under slow variations of the system Hamiltonian. If the temperature of the initial thermal ensemble is either zero or infinite, the ensemble after the passage is a simple thermal one with the same vanishing or infinite temperature. However, for any finite nonzero temperature, intriguing nonthermal ensembles can be achieved. We exemplify this in (a) a single oscillator, (b) a dimerized interacting one-dimensional chain of spinless fermions, (c) a BCS-type superconductor, and (d) the topological Kitaev chain. We solve these models with a combination of methods: either exactly, numerically using the density matrix renormalization group, or within an approximate functional renormalization group scheme. The designed states show strongly nonthermal behavior in each of the considered models. For example, for the chain of spinless fermions we exemplify how long-ranged nonthermal power-law correlations can be stabilized, and for the Kitaev chain we elucidate how the nonthermal ensemble can largely alter the transition temperature separating topological and trivial phases.
Universal critical wrapping probabilities in the canonical ensemble
Directory of Open Access Journals (Sweden)
Hao Hu
2015-09-01
Full Text Available Universal dimensionless quantities, such as Binder ratios and wrapping probabilities, play an important role in the study of critical phenomena. We study the finite-size scaling behavior of the wrapping probability for the Potts model in the random-cluster representation, under the constraint that the total number of occupied bonds is fixed, so that the canonical ensemble applies. We derive that, in the limit L→∞, the critical values of the wrapping probability are different from those of the unconstrained model, i.e. the model in the grand-canonical ensemble, but still universal, for systems with 2yt−d>0 where yt=1/ν is the thermal renormalization exponent and d is the spatial dimension. Similar modifications apply to other dimensionless quantities, such as Binder ratios. For systems with 2yt−d≤0, these quantities share same critical universal values in the two ensembles. It is also derived that new finite-size corrections are induced. These findings apply more generally to systems in the canonical ensemble, e.g. the dilute Potts model with a fixed total number of vacancies. Finally, we formulate an efficient cluster-type algorithm for the canonical ensemble, and confirm these predictions by extensive simulations.
Visualizing Confidence in Cluster-Based Ensemble Weather Forecast Analyses.
Kumpf, Alexander; Tost, Bianca; Baumgart, Marlene; Riemer, Michael; Westermann, Rudiger; Rautenhaus, Marc
2018-01-01
In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we - a team of visualization scientists and meteorologists-deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows meteorologists how representative a clustering result is, and with respect to which changes in the selected region it becomes unstable. Furthermore, our solution helps to identify those ensemble members which stably belong to a given cluster and can thus be considered similar. In a real-world application case we show how our approach is used to analyze the clustering behavior of different regions in a forecast of "Tropical Cyclone Karl", guiding the user towards the cluster robustness information required for subsequent ensemble analysis.
Application of evolutionary computation on ensemble forecast of quantitative precipitation
Dufek, Amanda S.; Augusto, Douglas A.; Dias, Pedro L. S.; Barbosa, Helio J. C.
2017-09-01
An evolutionary computation algorithm known as genetic programming (GP) has been explored as an alternative tool for improving the ensemble forecast of 24-h accumulated precipitation. Three GP versions and six ensembles' languages were applied to several real-world datasets over southern, southeastern and central Brazil during the rainy period from October to February of 2008-2013. According to the results, the GP algorithms performed better than two traditional statistical techniques, with errors 27-57% lower than simple ensemble mean and the MASTER super model ensemble system. In addition, the results revealed that GP algorithms outperformed the best individual forecasts, reaching an improvement of 34-42%. On the other hand, the GP algorithms had a similar performance with respect to each other and to the Bayesian model averaging, but the former are far more versatile techniques. Although the results for the six ensembles' languages are almost indistinguishable, our most complex linear language turned out to be the best overall proposal. Moreover, some meteorological attributes, including the weather patterns over Brazil, seem to play an important role in the prediction of daily rainfall amount.
Are paleoclimate model ensembles consistent with the MARGO data synthesis?
Directory of Open Access Journals (Sweden)
J. C. Hargreaves
2011-08-01
Full Text Available We investigate the consistency of various ensembles of climate model simulations with the Multiproxy Approach for the Reconstruction of the Glacial Ocean Surface (MARGO sea surface temperature data synthesis. We discover that while two multi-model ensembles, created through the Paleoclimate Model Intercomparison Projects (PMIP and PMIP2, pass our simple tests of reliability, an ensemble based on parameter variation in a single model does not perform so well. We show that accounting for observational uncertainty in the MARGO database is of prime importance for correctly evaluating the ensembles. Perhaps surprisingly, the inclusion of a coupled dynamical ocean (compared to the use of a slab ocean does not appear to cause a wider spread in the sea surface temperature anomalies, but rather causes systematic changes with more heat transported north in the Atlantic. There is weak evidence that the sea surface temperature data may be more consistent with meridional overturning in the North Atlantic being similar for the LGM and the present day. However, the small size of the PMIP2 ensemble prevents any statistically significant results from being obtained.
Fluctuation, stationarity, and ergodic properties of random-matrix ensembles
International Nuclear Information System (INIS)
Pandey, A.
1979-01-01
The properties of random-matrix ensembles and the application of such ensembles to energy-level fluctuations and strength fluctuations are discussed. The two-point correlation function for complex spectra described by the three standard Gaussian ensembles is calculated, and its essential simplicity, displayed by an elementary procedure that derives from the dominance of binary correlations. The resultant function is exact for the unitary case and a very good approximation to the orthogonal and symplectic cases. The same procedure yields the spectrum for a Gaussian orthogonal ensemble (GOE) deformed by a pairing interaction. Several extensions are given and relationships to other problems of current interest are discussed. The standard fluctuation measures are rederived for the GOE, and their extensions to the unitary and symplectic cases are given. The measures are shown to derive, for the most part, from the two-point function, and new relationships between them are established, answering some long-standing questions. Some comparisons with experimental values are also made. All the cluster functions, and therefore the fluctuation measures, are shown to be stationary and strongly ergodic, thus justifying the use of random matrices for individual spectra. Strength fluctuations in the orthogonal ensemble are also considered. The Porter-Thomas distribution in its various forms is rederived and its ergodicity is established
Ensemble streamflow predictions: from climate scenarios to probabilistic weather predictions
Fortin, V.; Evora, N.; Perreault, L.; Trinh, N.; Favre, A.; Benoit, H.
2004-05-01
Ensemble streamflow predictions (ESP) are obtained by processing an ensemble of meteorological scenarios through a rainfall-runoff hydrological model to obtain hydrological scenarios. Until recently, these scenarios were typically taken from the climatology. Now that more accurate medium- and long-term numerical weather predictions (NWP) are available, it is tempting to replace climatology by numerical weather forecasts. At least two approaches are possible to take into account the uncerta1000 inty on the meteorological forecast: (1) let a meteorologist propose a subjective probabilistic forecast based on one or more deterministic NWPs, or (2) take advantage of ensemble meteorological forecasts, which are built precisely to assess the level of uncertainty on the deterministic forecast. Practical solutions to problems encountered with both types of meteorological forecasts are discussed, and the methodology used by Hydro-Québec to score the resulting streamflow forecasts is presented.
Optical properties of indium phosphide nanowire ensembles at various temperatures
Energy Technology Data Exchange (ETDEWEB)
Lohn, Andrew J; Onishi, Takehiro; Kobayashi, Nobuhiko P [Baskin School of Engineering, University of California Santa Cruz, Santa Cruz, CA 95064 (United States); Nanostructured Energy Conversion Technology and Research (NECTAR), Advanced Studies Laboratories, University of California Santa Cruz-NASA Ames Research Center, Moffett Field, CA 94035 (United States)
2010-09-03
Ensembles that contain two types (zincblende and wurtzite) of indium phosphide nanowires grown on non-single crystalline surfaces were studied by micro-photoluminescence and micro-Raman spectroscopy at various low temperatures. The obtained spectra are discussed with the emphasis on the effects of differing lattice types, geometries, and crystallographic orientations present within an ensemble of nanowires grown on non-single crystalline surfaces. In the photoluminescence spectra, a typical Varshni dependence of band gap energy on temperature was observed for emissions from zincblende nanowires and in the high temperature regime energy transfer from excitonic transitions and band-edge transitions was identified. In contrast, the photoluminescence emissions associated with wurtzite nanowires were rather insensitive to temperature. Raman spectra were collected simultaneously from zincblende and wurtzite nanowires coexisting in an ensemble. Raman peaks of the wurtzite nanowires are interpreted as those related to the zincblende nanowires by a folding of the phonon dispersion.
Cavity quantum electrodynamics with a Rydberg-blocked atomic ensemble
DEFF Research Database (Denmark)
Guerlin, Christine; Brion, Etienne; Esslinger, Tilman
2010-01-01
The realization of a Jaynes-Cummings model in the optical domain is proposed for an atomic ensemble. The scheme exploits the collective coupling of the atoms to a quantized cavity mode and the nonlinearity introduced by coupling to high-lying Rydberg states. A two-photon transition resonantly...... effective Jaynes-Cummings model. We use numerical simulations to show that the cavity transmission can be used to reveal detailed properties of the Jaynes-Cummings ladder of excited states and that the atomic nonlinearity gives rise to highly nontrivial photon emission from the cavity. Finally, we suggest...... couples the single-atom ground state |g> to a Rydberg state |e>via a nonresonant intermediate state |i>, but due to the interaction between Rydberg atoms only a single atom can be resonantly excited in the ensemble. This restricts the state space of the ensemble to the collective ground state |G...
ENRICHMENT OF ENSEMBLE LEARNING USING K-MODES RANDOM SAMPLING
Directory of Open Access Journals (Sweden)
Balamurugan Mahalingam
2017-10-01
Full Text Available Ensemble of classifiers combines the more than one prediction models of classifiers into single model for classifying the new instances. Unbiased samples could help the ensemble classifiers to build the efficient prediction model. Existing sampling techniques fails to give the unbiased samples. To overcome this problem, the paper introduces a k-modes random sample technique which combines the k-modes cluster algorithm and simple random sampling technique to take the sample from the dataset. In this paper, the impact of random sampling technique in the Ensemble learning algorithm is shown. Random selection was done properly by using k-modes random sampling technique. Hence, sample will reflect the characteristics of entire dataset.
Deviations from Wick's theorem in the canonical ensemble
Schönhammer, K.
2017-07-01
Wick's theorem for the expectation values of products of field operators for a system of noninteracting fermions or bosons plays an important role in the perturbative approach to the quantum many-body problem. A finite-temperature version holds in the framework of the grand canonical ensemble, but not for the canonical ensemble appropriate for systems with fixed particle number such as ultracold quantum gases in optical lattices. Here we present formulas for expectation values of products of field operators in the canonical ensemble using a method in the spirit of Gaudin's proof of Wick's theorem for the grand canonical case. The deviations from Wick's theorem are examined quantitatively for two simple models of noninteracting fermions.
New vigour involving statisticians to overcome ensemble fatigue
Benestad, Rasmus; Sillmann, Jana; Thorarinsdottir, Thordis Linda; Guttorp, Peter; Mesquita, Michel D. S.; Tye, Mari R.; Uotila, Petteri; Maule, Cathrine Fox; Thejll, Peter; Drews, Martin; Parding, Kajsa M.
2017-10-01
Climate simulation data comprise a range of different phenomena with complex and interacting processes. Yet our understanding of the climate is incomplete despite the huge volumes of data, of which only a small fraction has been explored, and many questions remain, particularly those on the character and origin of uncertainties associated with model simulations and how further modelling efforts can improve understanding. Here, we question whether climate model information could be used more effectively and how so-called 'ensembles of opportunity' should be interpreted. Statisticians can contribute substantially to designing 'smarter' ensemble experiments, improving the distillation of information from ensembles, and helping interpret the relative merits of additional simulations. Future progress may be enhanced by increasing collaborations with statisticians.
A Flexible Approach for the Statistical Visualization of Ensemble Data
Energy Technology Data Exchange (ETDEWEB)
Potter, K. [Univ. of Utah, Salt Lake City, UT (United States). SCI Institute; Wilson, A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bremer, P. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Williams, Dean N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Pascucci, V. [Univ. of Utah, Salt Lake City, UT (United States). SCI Institute; Johnson, C. [Univ. of Utah, Salt Lake City, UT (United States). SCI Institute
2009-09-29
Scientists are increasingly moving towards ensemble data sets to explore relationships present in dynamic systems. Ensemble data sets combine spatio-temporal simulation results generated using multiple numerical models, sampled input conditions and perturbed parameters. While ensemble data sets are a powerful tool for mitigating uncertainty, they pose significant visualization and analysis challenges due to their complexity. We present a collection of overview and statistical displays linked through a high level of interactivity to provide a framework for gaining key scientific insight into the distribution of the simulation results as well as the uncertainty associated with the data. In contrast to methods that present large amounts of diverse information in a single display, we argue that combining multiple linked statistical displays yields a clearer presentation of the data and facilitates a greater level of visual data analysis. We demonstrate this approach using driving problems from climate modeling and meteorology and discuss generalizations to other fields.
Evaluation of LDA Ensembles Classifiers for Brain Computer Interface
International Nuclear Information System (INIS)
Arjona, Cristian; Pentácolo, José; Gareis, Iván; Atum, Yanina; Gentiletti, Gerardo; Acevedo, Rubén; Rufiner, Leonardo
2011-01-01
The Brain Computer Interface (BCI) translates brain activity into computer commands. To increase the performance of the BCI, to decode the user intentions it is necessary to get better the feature extraction and classification techniques. In this article the performance of a three linear discriminant analysis (LDA) classifiers ensemble is studied. The system based on ensemble can theoretically achieved better classification results than the individual counterpart, regarding individual classifier generation algorithm and the procedures for combine their outputs. Classic algorithms based on ensembles such as bagging and boosting are discussed here. For the application on BCI, it was concluded that the generated results using ER and AUC as performance index do not give enough information to establish which configuration is better.
Statistical ensembles and molecular dynamics studies of anisotropic solids. II
International Nuclear Information System (INIS)
Ray, J.R.; Rahman, A.
1985-01-01
We have recently discussed how the Parrinello--Rahman theory can be brought into accord with the theory of the elastic and thermodynamic behavior of anisotropic media. This involves the isoenthalpic--isotension ensemble of statistical mechanics. Nose has developed a canonical ensemble form of molecular dynamics. We combine Nose's ideas with the Parrinello--Rahman theory to obtain a canonical form of molecular dynamics appropriate to the study of anisotropic media subjected to arbitrary external stress. We employ this isothermal--isotension ensemble in a study of a fcc→ close-packed structural phase transformation in a Lennard-Jones solid subjected to uniaxial compression. Our interpretation of the Nose theory does not involve a scaling of the time variable. This latter fact leads to simplifications when studying the time dependence of quantities
Towards a spin-ensemble quantum memory for superconducting qubits
Grezes, Cécile; Kubo, Yuimaru; Julsgaard, Brian; Umeda, Takahide; Isoya, Junichi; Sumiya, Hitoshi; Abe, Hiroshi; Onoda, Shinobu; Ohshima, Takeshi; Nakamura, Kazuo; Diniz, Igor; Auffeves, Alexia; Jacques, Vincent; Roch, Jean-François; Vion, Denis; Esteve, Daniel; Moelmer, Klaus; Bertet, Patrice
2016-08-01
This article reviews efforts to build a new type of quantum device, which combines an ensemble of electronic spins with long coherence times, and a small-scale superconducting quantum processor. The goal is to store over long times arbitrary qubit states in orthogonal collective modes of the spin-ensemble, and to retrieve them on-demand. We first present the protocol devised for such a multi-mode quantum memory. We then describe a series of experimental results using NV (as in nitrogen vacancy) center spins in diamond, which demonstrate its main building blocks: the transfer of arbitrary quantum states from a qubit into the spin ensemble, and the multi-mode retrieval of classical microwave pulses down to the single-photon level with a Hahn-echo like sequence. A reset of the spin memory is implemented in-between two successive sequences using optical repumping of the spins. xml:lang="fr"
A Canonical Ensemble Correlation Prediction Model for Seasonal Precipitation Anomaly
Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Guilong
2001-01-01
This report describes an optimal ensemble forecasting model for seasonal precipitation and its error estimation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. This new CCA model includes the following features: (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States precipitation field. The predictor is the sea surface temperature.
Efficient Kernel-Based Ensemble Gaussian Mixture Filtering
Liu, Bo
2015-11-11
We consider the Bayesian filtering problem for data assimilation following the kernel-based ensemble Gaussian-mixture filtering (EnGMF) approach introduced by Anderson and Anderson (1999). In this approach, the posterior distribution of the system state is propagated with the model using the ensemble Monte Carlo method, providing a forecast ensemble that is then used to construct a prior Gaussian-mixture (GM) based on the kernel density estimator. This results in two update steps: a Kalman filter (KF)-like update of the ensemble members and a particle filter (PF)-like update of the weights, followed by a resampling step to start a new forecast cycle. After formulating EnGMF for any observational operator, we analyze the influence of the bandwidth parameter of the kernel function on the covariance of the posterior distribution. We then focus on two aspects: i) the efficient implementation of EnGMF with (relatively) small ensembles, where we propose a new deterministic resampling strategy preserving the first two moments of the posterior GM to limit the sampling error; and ii) the analysis of the effect of the bandwidth parameter on contributions of KF and PF updates and on the weights variance. Numerical results using the Lorenz-96 model are presented to assess the behavior of EnGMF with deterministic resampling, study its sensitivity to different parameters and settings, and evaluate its performance against ensemble KFs. The proposed EnGMF approach with deterministic resampling suggests improved estimates in all tested scenarios, and is shown to require less localization and to be less sensitive to the choice of filtering parameters.
Ensemble-based Probabilistic Forecasting at Horns Rev
DEFF Research Database (Denmark)
Pinson, Pierre; Madsen, Henrik
2009-01-01
forecasting methodology. In a first stage, ensemble forecasts of meteorological variables are converted to power through a suitable power curve model. This modelemploys local polynomial regression, and is adoptively estimated with an orthogonal fitting method. The obtained ensemble forecasts of wind power...... are then converted into predictive distributions with an original adaptive kernel dressing method. The shape of the kernels is driven by a mean-variance model, the parameters of which ore recursively estimated in order to maximize the overall skill of obtained predictive distributions. Such a methodology has...
Ensemble system for Part-of-Speech tagging
Dell'Orletta, Felice
2009-01-01
The paper contains a description of the Felice-POS-Tagger and of its performance in Evalita 2009. Felice-POS-Tagger is an ensemble system that combines six different POS taggers. When evaluated on the official test set, the ensemble system outperforms each of the single tagger components and achieves the highest accuracy score in Evalita 2009 POS Closed Task. It is shown rst that the errors made from the dierent taggers are complementary, and then how to use this complementary behavior to the...
ENSEMBLE methods to reconcile disparate national long range dispersion forecasts
DEFF Research Database (Denmark)
Mikkelsen, Torben; Galmarini, S.; Bianconi, R.
2003-01-01
ENSEMBLE is a web-based decision support system for real-time exchange and evaluation of national long-range dispersion forecasts of nuclear releases with cross-boundary consequences. The system is developed with the purpose to reconcile among disparatenational forecasts for long-range dispersion....... ENSEMBLE addresses the problem of achieving a common coherent strategy across European national emergency management when national long-range dispersion forecasts differ from one another during an accidentalatmospheric release of radioactive material. A series of new decision-making “ENSEMBLE” procedures...
On the ensemble dependence in black hole geometrothermodynamics
Quevedo, Hernando; Quevedo, María N.; Sánchez, Alberto; Taj, Safia
2014-08-01
We investigate the dependence of the thermodynamic properties of black holes on the choice of statistical ensemble for a particular class of Einstein-Maxwell-Gauss-Bonnet black holes with the cosmological constant. We use partial Legendre transformations in the thermodynamic limit in order to compare the results in different ensembles, and show that the phase transition structure depends on the choice of thermodynamic potential. This result implies that thermodynamic metrics that are partially Legendre invariant cannot be employed to describe black hole thermodynamics, and partly explains why a particular thermodynamic metric has been used so far in the framework of black hole geometrothermodynamics.
Good and Bad Neighborhood Approximations for Outlier Detection Ensembles
DEFF Research Database (Denmark)
Kirner, Evelyn; Schubert, Erich; Zimek, Arthur
2017-01-01
Outlier detection methods have used approximate neighborhoods in filter-refinement approaches. Outlier detection ensembles have used artificially obfuscated neighborhoods to achieve diverse ensemble members. Here we argue that outlier detection models could be based on approximate neighborhoods...... in the first place, thus gaining in both efficiency and effectiveness. It depends, however, on the type of approximation, as only some seem beneficial for the task of outlier detection, while no (large) benefit can be seen for others. In particular, we argue that space-filling curves are beneficial...
Reservoir History Matching Using Ensemble Kalman Filters with Anamorphosis Transforms
Aman, Beshir M.
2012-12-01
This work aims to enhance the Ensemble Kalman Filter performance by transforming the non-Gaussian state variables into Gaussian variables to be a step closer to optimality. This is done by using univariate and multivariate Box-Cox transformation. Some History matching methods such as Kalman filter, particle filter and the ensemble Kalman filter are reviewed and applied to a test case in the reservoir application. The key idea is to apply the transformation before the update step and then transform back after applying the Kalman correction. In general, the results of the multivariate method was promising, despite the fact it over-estimated some variables.
Kontserdipeegel / Igor Garshnek
Garšnek, Igor, 1958-
2000-01-01
Kannatusnädala kontsertidest : A. Dvoraki "Reekviemi" ettekanne 20. apr. Kaarli kirikus; J. S. Bachi "Johannese passiooni" ettekanne 21. apr. Estonia kontserdisaalis; NYYD Ensemble'i kontsert sarjas "Hüvastijätud. Kulg ja ootus" 18. apr. Niguliste kirikus; 22. apr. raemuusikaõhtu "New York 1980" sarjas "Ajastu muusikas ja tantsus"
2005-01-01
Koreograafideks on G. Welzman ja R. Haver ning selles kasutatakse nelja eesti helilooja A. Pärdi, M.-M. Lille, L. Sumera ja E.-S. Tüüri muusikat. Eesti gastrollidel esitab muusikat NYYD Ensemble. Lisaks eesti muusikutele on laval ka P. Pärenson
Vesi, ulme ja alleaa / Anneli Remme
Remme, Anneli, 1968-
2000-01-01
Aastalõpufestival "Sünnisõnad". Ka Gavin Bryarsi 'Titanicu hukust' NYYD Ensemble'i esituses ning kujunduseks olnud Jaan Toomiku videost. "Estonia" kontserdisaalis linastunud Fritz Langi imposantsest tummfilmist "Metropolis" (Saksamaa 1927) ja seda saatnud 3 muusiku ansamblist Metropolis Projekt
Kes lavastas Suure Paugu? / Maris Johannes
Johannes, Maris, 1959-
2007-01-01
Eest Muusika päevade raames Tallinna tollilaos etendunud Stephen Hawkingi raamatu "A Brief History of Time" ("Aja lühilugu") ainelisest multimeedialavastusest "Õnne valem", Von Krahli teatrile ja NYYD Ensemble'ile originaalmuusika autor Gavin Bryars, autor ja lavastaja Peeter Jalakas
2007-01-01
9. aprillil esietendub Uus-Sadama tänava tollilao angaaris multimeedialavastus "Õnne valem". Von Krahli teatrile ja NYYD Ensemble'ile originaalmuusika autor on Gavin Bryars, lavastaja Peeter Jalakas. "Õnne valem" põhineb Stephen Hawkingi raamatul "A Brief History of Time"
Natuke surnud, poolsurnud, täitsa surnud / Evi Arujärv
Arujärv, Evi, 1953-
2007-01-01
Eest Muusika päevade raames Tallinna tollilaos etendunud Stephen Hawkingi raamatu "A Brief History of Time" ainelisest multimeedialavastusest "Õnne valem", Von Krahli teatrile ja NYYD Ensemble'ile originaalmuusika autor Gavin Bryars, autor ja lavastaja Peeter Jalakas
Festival 'Sünnisõnad' ئ igaühele midagi / Evi Arujärv
Arujärv, Evi, 1953-
2000-01-01
29. dets. 1999 - 1. jaan. 2000 toimunud festivalist "Sünnisõnad". Lühidalt ka nonstopfilmist 'Sajand arhitektuuri. Elavad pildid' (autor Karin Hallas), arhitektuurimuuseumi näitusest 'Eesti katedraalid'. 30. XII Nyyd Ensemble esitatud Gavin Bryarsi teosest 'Titanicu hukk' (Jaan Toomiku videod) jm.
Esmaspäeval esietendub Peeter Jalaka "Õnne valem" / Andres Laasik
Laasik, Andres, 1960-2016
2007-01-01
Peeter Jalakas lavastab briti helilooja Gavin Bryarsi uudisteost "Õnne valem". Etenduses on kaastegev ka NYYD Ensemble Olari Eltsi juhtimisel. Lavastuse koreograafia on Jalakas loonud koos Narvast pärit tantsija-koreograafi Stanislav Varkkiga, etenduse kunstnik on Kirke Kangro, kostüümikunstnik Reet Aus
Music Ensemble Participation: Personality Traits and Music Experience
Torrance, Tracy A.; Bugos, Jennifer A.
2017-01-01
The purpose of this study was two-fold: (1) to examine the relationship between personality type and ensemble choice and (2) to examine the differences in personality across age and music experience in young adults. Participants (N = 137; 68 instrumentalists, 69 vocalists) completed a demographic survey and the Big Five Personality Inventory.…
Tweet-based Target Market Classification Using Ensemble Method
Directory of Open Access Journals (Sweden)
Muhammad Adi Khairul Anshary
2016-09-01
Full Text Available Target market classification is aimed at focusing marketing activities on the right targets. Classification of target markets can be done through data mining and by utilizing data from social media, e.g. Twitter. The end result of data mining are learning models that can classify new data. Ensemble methods can improve the accuracy of the models and therefore provide better results. In this study, classification of target markets was conducted on a dataset of 3000 tweets in order to extract features. Classification models were constructed to manipulate the training data using two ensemble methods (bagging and boosting. To investigate the effectiveness of the ensemble methods, this study used the CART (classification and regression tree algorithm for comparison. Three categories of consumer goods (computers, mobile phones and cameras and three categories of sentiments (positive, negative and neutral were classified towards three target-market categories. Machine learning was performed using Weka 3.6.9. The results of the test data showed that the bagging method improved the accuracy of CART with 1.9% (to 85.20%. On the other hand, for sentiment classification, the ensemble methods were not successful in increasing the accuracy of CART. The results of this study may be taken into consideration by companies who approach their customers through social media, especially Twitter.
Engaging and Educating Students with Culturally Responsive Performing Ensembles
Mixon, Kevin
2009-01-01
To provide meaningful and motivating connections between students and ensembles, teachers must realize that students' prior experience profoundly affects learning. Many of these experiences are inextricably linked to cultural affiliation. Cultural affiliation is a powerful context for prior experience, and the fundamental principle framing this…
Acharya Nachiketa Multi-model ensemble schemes for predicting ...
Indian Academy of Sciences (India)
AUTHOR INDEX. Acharya Nachiketa. Multi-model ensemble schemes for predicting northeast monsoon rainfall over peninsular India. 795. Agarwal Neeraj see Shahi Naveen R. 337. Aggarwal Neha see Jha Neerja. 663. Ahmed Shakeel see Sarah S. 399. Alavi Amir Hossein see Mousavi Seyyed Mohammad. 1001.
Malignancy and Abnormality Detection of Mammograms using Classifier Ensembling
Directory of Open Access Journals (Sweden)
Nawazish Naveed
2011-07-01
Full Text Available The breast cancer detection and diagnosis is a critical and complex procedure that demands high degree of accuracy. In computer aided diagnostic systems, the breast cancer detection is a two stage procedure. First, to classify the malignant and benign mammograms, while in second stage, the type of abnormality is detected. In this paper, we have developed a novel architecture to enhance the classification of malignant and benign mammograms using multi-classification of malignant mammograms into six abnormality classes. DWT (Discrete Wavelet Transformation features are extracted from preprocessed images and passed through different classifiers. To improve accuracy, results generated by various classifiers are ensembled. The genetic algorithm is used to find optimal weights rather than assigning weights to the results of classifiers on the basis of heuristics. The mammograms declared as malignant by ensemble classifiers are divided into six classes. The ensemble classifiers are further used for multiclassification using one-against-all technique for classification. The output of all ensemble classifiers is combined by product, median and mean rule. It has been observed that the accuracy of classification of abnormalities is more than 97% in case of mean rule. The Mammographic Image Analysis Society dataset is used for experimentation.
Enhancing COSMO-DE ensemble forecasts by inexpensive techniques
Directory of Open Access Journals (Sweden)
Zied Ben Bouallègue
2013-02-01
Full Text Available COSMO-DE-EPS, a convection-permitting ensemble prediction system based on the high-resolution numerical weather prediction model COSMO-DE, is pre-operational since December 2010, providing probabilistic forecasts which cover Germany. This ensemble system comprises 20 members based on variations of the lateral boundary conditions, the physics parameterizations and the initial conditions. In order to increase the sample size in a computationally inexpensive way, COSMO-DE-EPS is combined with alternative ensemble techniques: the neighborhood method and the time-lagged approach. Their impact on the quality of the resulting probabilistic forecasts is assessed. Objective verification is performed over a six months period, scores based on the Brier score and its decomposition are shown for June 2011. The combination of the ensemble system with the alternative approaches improves probabilistic forecasts of precipitation in particular for high precipitation thresholds. Moreover, combining COSMO-DE-EPS with only the time-lagged approach improves the skill of area probabilities for precipitation and does not deteriorate the skill of 2 m-temperature and wind gusts forecasts.
Path planning in uncertain flow fields using ensemble method
Wang, Tong
2016-08-20
An ensemble-based approach is developed to conduct optimal path planning in unsteady ocean currents under uncertainty. We focus our attention on two-dimensional steady and unsteady uncertain flows, and adopt a sampling methodology that is well suited to operational forecasts, where an ensemble of deterministic predictions is used to model and quantify uncertainty. In an operational setting, much about dynamics, topography, and forcing of the ocean environment is uncertain. To address this uncertainty, the flow field is parametrized using a finite number of independent canonical random variables with known densities, and the ensemble is generated by sampling these variables. For each of the resulting realizations of the uncertain current field, we predict the path that minimizes the travel time by solving a boundary value problem (BVP), based on the Pontryagin maximum principle. A family of backward-in-time trajectories starting at the end position is used to generate suitable initial values for the BVP solver. This allows us to examine and analyze the performance of the sampling strategy and to develop insight into extensions dealing with general circulation ocean models. In particular, the ensemble method enables us to perform a statistical analysis of travel times and consequently develop a path planning approach that accounts for these statistics. The proposed methodology is tested for a number of scenarios. We first validate our algorithms by reproducing simple canonical solutions, and then demonstrate our approach in more complex flow fields, including idealized, steady and unsteady double-gyre flows.
ENSEMBLE methods to reconcile disparate national long range dispersion forecasting
Energy Technology Data Exchange (ETDEWEB)
Mikkelsen, T.; Galmarini, S.; Bianconi, R.; French, S. (eds.)
2003-11-01
ENSEMBLE is a web-based decision support system for real-time exchange and evaluation of national long-range dispersion forecasts of nuclear releases with cross-boundary consequences. The system is developed with the purpose to reconcile among disparate national forecasts for long-range dispersion. ENSEMBLE addresses the problem of achieving a common coherent strategy across European national emergency management when national long-range dispersion forecasts differ from one another during an accidental atmospheric release of radioactive material. A series of new decision-making 'ENSEMBLE' procedures and Web-based software evaluation and exchange tools have been created for real-time reconciliation and harmonisation of real-time dispersion forecasts from meteorological and emergency centres across Europe during an accident. The new ENSEMBLE software tools is available to participating national emergency and meteorological forecasting centres, which may choose to integrate them directly into operational emergency information systems, or possibly use them as a basis for future system development. (au)
Canonical Ensemble Model for Black Hole Horizon of Schwarzschild ...
Indian Academy of Sciences (India)
Abstract. In this paper, we use the canonical ensemble model to discuss the radiation of a Schwarzschild–de Sitter black hole on the black hole horizon. Using this model, we calculate the probability distribution from function of the emission shell. And the statistical meaning which compare with the distribution function is ...
Canonical Ensemble Model for Black Hole Radiation Jingyi Zhang
Indian Academy of Sciences (India)
Abstract. In this paper, a canonical ensemble model for the black hole quantum tunnelling radiation is introduced. In this model the probability distribution function corresponding to the emission shell is calculated to second order. The formula of pressure and internal energy of the thermal system is modified, and the ...
Prediction of cardiac arrest recurrence using ensemble classifiers
Indian Academy of Sciences (India)
Home; Journals; Sadhana; Volume 42; Issue 7. Prediction of cardiac arrest recurrence using ensemble classifiers. NACHIKET TAPAS ... Poor survival rate of patients with SCA is one of themost ubiquitous health care problems today. Recent studies show that heart-rate-derived features can act as early predictors of SCA.
Peer-Teaching in the Secondary Music Ensemble
Johnson, Erik
2015-01-01
Peer-teaching is an instructional technique that has been used by teachers world-wide to successfully engage, exercise and deepen student learning. Yet, in some instances, teachers find the application of peer-teaching in large music ensembles at the secondary level to be daunting. This article is meant to be a practical resource for secondary…
A grand-canonical ensemble of randomly triangulated surfaces
International Nuclear Information System (INIS)
Jurkiewicz, J.; Krzywicki, A.; Petersson, B.
1986-01-01
An algorithm is presented generating the grand-canonical ensemble of discrete, randomly triangulated Polyakov surfaces. The algorithm is used to calculate the susceptibility exponent, which controls the existence of the continuum limit of the considered model, for the dimensionality of the embedding space ranging from 0 to 20. (orig.)
Social behaviour shapes hypothalamic neural ensemble representations of conspecific sex
Remedios, Ryan; Kennedy, Ann; Zelikowsky, Moriel; Grewe, Benjamin F.; Schnitzer, Mark J.; Anderson, David J.
2017-10-01
All animals possess a repertoire of innate (or instinctive) behaviours, which can be performed without training. Whether such behaviours are mediated by anatomically distinct and/or genetically specified neural pathways remains unknown. Here we report that neural representations within the mouse hypothalamus, that underlie innate social behaviours, are shaped by social experience. Oestrogen receptor 1-expressing (Esr1+) neurons in the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) control mating and fighting in rodents. We used microendoscopy to image Esr1+ neuronal activity in the VMHvl of male mice engaged in these social behaviours. In sexually and socially experienced adult males, divergent and characteristic neural ensembles represented male versus female conspecifics. However, in inexperienced adult males, male and female intruders activated overlapping neuronal populations. Sex-specific neuronal ensembles gradually separated as the mice acquired social and sexual experience. In mice permitted to investigate but not to mount or attack conspecifics, ensemble divergence did not occur. However, 30 minutes of sexual experience with a female was sufficient to promote the separation of male and female ensembles and to induce an attack response 24 h later. These observations uncover an unexpected social experience-dependent component to the formation of hypothalamic neural assemblies controlling innate social behaviours. More generally, they reveal plasticity and dynamic coding in an evolutionarily ancient deep subcortical structure that is traditionally viewed as a ‘hard-wired’ system.
Bayesian model ensembling using meta-trained recurrent neural networks
Ambrogioni, L.; Berezutskaya, Y.; Gü ç lü , U.; Borne, E.W.P. van den; Gü ç lü tü rk, Y.; Gerven, M.A.J. van; Maris, E.G.G.
2017-01-01
In this paper we demonstrate that a recurrent neural network meta-trained on an ensemble of arbitrary classification tasks can be used as an approximation of the Bayes optimal classifier. This result is obtained by relying on the framework of e-free approximate Bayesian inference, where the Bayesian
Generation of Exotic Quantum States of a Cold Atomic Ensemble
DEFF Research Database (Denmark)
Christensen, Stefan Lund
Over the last decades quantum effects have become more and more controllable, leading to the implementations of various quantum information protocols. These protocols are all based on utilizing quantum correlation. In this thesis we consider how states of an atomic ensemble with such correlations...... — a nanofiber based light-atom interface. Using a dual-frequency probing method we measure and prepare an ensemble with a sub-Poissonian atom number distribution. This is a first step towards the implementation of more exotic quantum states.......Over the last decades quantum effects have become more and more controllable, leading to the implementations of various quantum information protocols. These protocols are all based on utilizing quantum correlation. In this thesis we consider how states of an atomic ensemble with such correlations...... can be created and characterized. First we consider a spin-squeezed state. This state is generated by performing quantum non-demolition measurements of the atomic population difference. We show a spectroscopically relevant noise reduction of -1.7dB, the ensemble is in a many-body entangled state...
Canonical Ensemble Model for Black Hole Radiation Jingyi Zhang
Indian Academy of Sciences (India)
system is modified, and the fundamental equation of thermodynamics is also discussed. Key words. Canonical ensemble model—black hole—quantum tunnelling—fundamental equation of thermodynamics. The quantum tunnelling method, proposed by Parikh & Wilczek, is an effective method of investigating the black hole ...
Improving wave forecasting by integrating ensemble modelling and machine learning
O'Donncha, F.; Zhang, Y.; James, S. C.
2017-12-01
Modern smart-grid networks use technologies to instantly relay information on supply and demand to support effective decision making. Integration of renewable-energy resources with these systems demands accurate forecasting of energy production (and demand) capacities. For wave-energy converters, this requires wave-condition forecasting to enable estimates of energy production. Current operational wave forecasting systems exhibit substantial errors with wave-height RMSEs of 40 to 60 cm being typical, which limits the reliability of energy-generation predictions thereby impeding integration with the distribution grid. In this study, we integrate physics-based models with statistical learning aggregation techniques that combine forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind- and currents-augmented waves in the Monterey Bay area. Ensembles are developed based on multiple simulations perturbing input data (wave characteristics supplied at the model boundaries and winds) to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. The aggregated forecasts are compared to observation data to quantify the performance of the model ensemble and aggregation techniques. The appropriately weighted ensemble model outperforms an individual ensemble member with regard to forecasting wave conditions.
Random matrix ensembles with random interactions: Results for ...
Indian Academy of Sciences (India)
Abstract. We introduce in this paper embedded Gaussian unitary ensemble of random matrices, for m fermions in Ω number of single particle orbits, generated by random two- body interactions that are SU(4) scalar, called EGUE(2)-SU(4). Here the SU(4) algebra corresponds to Wigner's supermultiplet SU(4) symmetry in ...
Ensemble dispersion forecasting - Part 1. Concept, approach and indicators
DEFF Research Database (Denmark)
Galmarini, S.; Bianconi, R.; Klug, W.
2004-01-01
The paper presents an approach to the treatment and analysis of long-range transport and dispersion model forecasts. Long-range is intended here as the space scale of the order of few thousands of kilometers known also as continental scale. The method is called multi-model ensemble dispersion and...
Ensemble modeling for aromatic production in Escherichia coli.
Directory of Open Access Journals (Sweden)
Matthew L Rizk
2009-09-01
Full Text Available Ensemble Modeling (EM is a recently developed method for metabolic modeling, particularly for utilizing the effect of enzyme tuning data on the production of a specific compound to refine the model. This approach is used here to investigate the production of aromatic products in Escherichia coli. Instead of using dynamic metabolite data to fit a model, the EM approach uses phenotypic data (effects of enzyme overexpression or knockouts on the steady state production rate to screen possible models. These data are routinely generated during strain design. An ensemble of models is constructed that all reach the same steady state and are based on the same mechanistic framework at the elementary reaction level. The behavior of the models spans the kinetics allowable by thermodynamics. Then by using existing data from the literature for the overexpression of genes coding for transketolase (Tkt, transaldolase (Tal, and phosphoenolpyruvate synthase (Pps to screen the ensemble, we arrive at a set of models that properly describes the known enzyme overexpression phenotypes. This subset of models becomes more predictive as additional data are used to refine the models. The final ensemble of models demonstrates the characteristic of the cell that Tkt is the first rate controlling step, and correctly predicts that only after Tkt is overexpressed does an increase in Pps increase the production rate of aromatics. This work demonstrates that EM is able to capture the result of enzyme overexpression on aromatic producing bacteria by successfully utilizing routinely generated enzyme tuning data to guide model learning.
ENSEMBLE methods to reconcile disparate national long range dispersion forecasting
International Nuclear Information System (INIS)
Mikkelsen, T.; Galmarini, S.; Bianconi, R.; French, S.
2003-11-01
ENSEMBLE is a web-based decision support system for real-time exchange and evaluation of national long-range dispersion forecasts of nuclear releases with cross-boundary consequences. The system is developed with the purpose to reconcile among disparate national forecasts for long-range dispersion. ENSEMBLE addresses the problem of achieving a common coherent strategy across European national emergency management when national long-range dispersion forecasts differ from one another during an accidental atmospheric release of radioactive material. A series of new decision-making 'ENSEMBLE' procedures and Web-based software evaluation and exchange tools have been created for real-time reconciliation and harmonisation of real-time dispersion forecasts from meteorological and emergency centres across Europe during an accident. The new ENSEMBLE software tools is available to participating national emergency and meteorological forecasting centres, which may choose to integrate them directly into operational emergency information systems, or possibly use them as a basis for future system development. (au)
Canonical Ensemble Model for Black Hole Horizon of Schwarzschild ...
Indian Academy of Sciences (India)
Observed the spherical shell in thermal equilibrium. The temperature of the shell is same as the black hole and the shell is a thermodynamical system. If there are N isolated systems that together compose of a mixture ensemble, then the statistical operator will be defined as. ˆρ =|ψi〉Pi〈ψi|. (3). Pi represents the probability of ...
An ensemble approach to the evolution of complex systems
Indian Academy of Sciences (India)
2014-03-15
Mar 15, 2014 ... (The definition of different ensembles is discussed further in section 4.) In this case, methods such as estimating the algorithmic complexity (Turing 1936; Kolmogorov ..... useful to define in- and out-degrees, respectively, the number of ...... Feschotte C and Gilbert C 2012 Endogenous viruses: insights into.
Light localization in cold and dense atomic ensemble
International Nuclear Information System (INIS)
Sokolov, Igor
2017-01-01
We report on results of theoretical analysis of possibilities of light strong (Anderson) localization in a cold atomic ensemble. We predict appearance of localization in dense atomic systems in strong magnetic field. We prove that in absence of the field the light localization is impossible. (paper)
The National Solo and Ensemble Contest 1929-1937
Meyers, Brian D.
2012-01-01
This study is the first investigation of the nine-year history of the National Solo and Ensemble Contests, held in the United States in conjunction with the National School Band and Orchestra Contests of the late 1920s and early to mid-1930s. Primary sources used include letters from those involved with the planning of the contests, meeting…
Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting
Directory of Open Access Journals (Sweden)
Federico Divina
2018-04-01
Full Text Available The ability to predict short-term electric energy demand would provide several benefits, both at the economic and environmental level. For example, it would allow for an efficient use of resources in order to face the actual demand, reducing the costs associated to the production as well as the emission of CO 2 . To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem. In particular, our approach is based on a stacking ensemble learning scheme, where the predictions produced by three base learning methods are used by a top level method in order to produce final predictions. We tested the proposed scheme on a dataset reporting the energy consumption in Spain over more than nine years. The obtained experimental results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that using an ensemble scheme can achieve very accurate predictions, and thus that it is a suitable approach for addressing the short-term load forecasting problem.
Modelling of drug release from ensembles of aspirin microcapsules ...
African Journals Online (AJOL)
Purpose: In order to determine the drug release profile of an ensemble of aspirin crystals or microcapsules from its particle distribution a mathematical model that considered the individual release characteristics of the component single particles was developed. The model assumed that under sink conditions the release ...
Canonical Ensemble Model for Black Hole Horizon of Schwarzschild ...
Indian Academy of Sciences (India)
In this paper, we use the canonical ensemble model to discuss the radiation of a Schwarzschild–de Sitter black hole on the black hole horizon. Using this model, we calculate the probability distribution from function of the emission shell. And the statistical meaning which compare with the distribution function is used to ...
Exploiting ensemble learning for automatic cataract detection and grading.
Yang, Ji-Jiang; Li, Jianqiang; Shen, Ruifang; Zeng, Yang; He, Jian; Bi, Jing; Li, Yong; Zhang, Qinyan; Peng, Lihui; Wang, Qing
2016-02-01
Cataract is defined as a lenticular opacity presenting usually with poor visual acuity. It is one of the most common causes of visual impairment worldwide. Early diagnosis demands the expertise of trained healthcare professionals, which may present a barrier to early intervention due to underlying costs. To date, studies reported in the literature utilize a single learning model for retinal image classification in grading cataract severity. We present an ensemble learning based approach as a means to improving diagnostic accuracy. Three independent feature sets, i.e., wavelet-, sketch-, and texture-based features, are extracted from each fundus image. For each feature set, two base learning models, i.e., Support Vector Machine and Back Propagation Neural Network, are built. Then, the ensemble methods, majority voting and stacking, are investigated to combine the multiple base learning models for final fundus image classification. Empirical experiments are conducted for cataract detection (two-class task, i.e., cataract or non-cataractous) and cataract grading (four-class task, i.e., non-cataractous, mild, moderate or severe) tasks. The best performance of the ensemble classifier is 93.2% and 84.5% in terms of the correct classification rates for cataract detection and grading tasks, respectively. The results demonstrate that the ensemble classifier outperforms the single learning model significantly, which also illustrates the effectiveness of the proposed approach. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Multi-model ensemble schemes for predicting northeast monsoon ...
Indian Academy of Sciences (India)
Northeast monsoon; multi-model ensemble; rainfall; prediction; principal component regression; single value decomposition. J. Earth Syst. Sci. 120, No. 5, October 2011, pp. 795–805 c Indian Academy of Sciences. 795 ... Rakecha 1983; Krishnan 1984; Raj and Jamadar. 1990; Sridharan and Muthusamy 1990; Singh and.
Korean Percussion Ensemble ("Samulnori") in the General Music Classroom
Kang, Sangmi; Yoo, Hyesoo
2016-01-01
This article introduces "samulnori" (Korean percussion ensemble), its cultural background, and instructional methods as parts of a classroom approach to teaching upper-level general music. We introduce five of eight sections from "youngnam nong-ak" (a style of samulnori) as a repertoire for teaching Korean percussion music to…
Multi-model ensemble schemes for predicting northeast monsoon ...
Indian Academy of Sciences (India)
An attempt has been made to improve the accuracy of predicted rainfall using three different multi-model ensemble (MME) schemes, viz., simple arithmetic mean of models (EM), principal component regression (PCR) and singular value decomposition based multiple linear regressions (SVD). It is found out that among ...
Skill prediction of local weather forecasts based on the ECMWF ensemble
Directory of Open Access Journals (Sweden)
C. Ziehmann
2001-01-01
Full Text Available Ensemble Prediction has become an essential part of numerical weather forecasting. In this paper we investigate the ability of ensemble forecasts to provide an a priori estimate of the expected forecast skill. Several quantities derived from the local ensemble distribution are investigated for a two year data set of European Centre for Medium-Range Weather Forecasts (ECMWF temperature and wind speed ensemble forecasts at 30 German stations. The results indicate that the population of the ensemble mode provides useful information for the uncertainty in temperature forecasts. The ensemble entropy is a similar good measure. This is not true for the spread if it is simply calculated as the variance of the ensemble members with respect to the ensemble mean. The number of clusters in the C regions is almost unrelated to the local skill. For wind forecasts, the results are less promising.
Non-Boltzmann Ensembles and Monte Carlo Simulations
International Nuclear Information System (INIS)
Murthy, K. P. N.
2016-01-01
Boltzmann sampling based on Metropolis algorithm has been extensively used for simulating a canonical ensemble and for calculating macroscopic properties of a closed system at desired temperatures. An estimate of a mechanical property, like energy, of an equilibrium system, is made by averaging over a large number microstates generated by Boltzmann Monte Carlo methods. This is possible because we can assign a numerical value for energy to each microstate. However, a thermal property like entropy, is not easily accessible to these methods. The reason is simple. We can not assign a numerical value for entropy, to a microstate. Entropy is not a property associated with any single microstate. It is a collective property of all the microstates. Toward calculating entropy and other thermal properties, a non-Boltzmann Monte Carlo technique called Umbrella sampling was proposed some forty years ago. Umbrella sampling has since undergone several metamorphoses and we have now, multi-canonical Monte Carlo, entropic sampling, flat histogram methods, Wang-Landau algorithm etc . This class of methods generates non-Boltzmann ensembles which are un-physical. However, physical quantities can be calculated as follows. First un-weight a microstates of the entropic ensemble; then re-weight it to the desired physical ensemble. Carry out weighted average over the entropic ensemble to estimate physical quantities. In this talk I shall tell you of the most recent non- Boltzmann Monte Carlo method and show how to calculate free energy for a few systems. We first consider estimation of free energy as a function of energy at different temperatures to characterize phase transition in an hairpin DNA in the presence of an unzipping force. Next we consider free energy as a function of order parameter and to this end we estimate density of states g ( E , M ), as a function of both energy E , and order parameter M . This is carried out in two stages. We estimate g ( E ) in the first stage
Assimilation of 3D radar reflectivities with an ensemble Kalman filter on the convective scale
Bick, T.; Simmer, C.; Trömel, S.; Wapler, K.; Hendricks Franssen, H.-J.; Stephan, K.; Blahak, U.; Schraff, C.; Reich, H.; Zeng, Y.; Potthast, Roland
2016-01-01
An ensemble data assimilation system for 3D radar reflectivity data is introduced for the convection-permitting numerical weather prediction model of the COnsortium for Small-scale MOdelling (COSMO) based on the Kilometre-scale ENsemble Data Assimilation system (KENDA), developed by Deutscher Wetterdienst and its partners. KENDA provides a state-of-the-art ensemble data assimilation system on the convective scale for operational data assimilation and forecasting based on the Local Ensemble Tr...
Using Ensemble Streamflows for Power Marketing at Bonneville Power Administration
Barton, S. B.; Koski, P.
2014-12-01
Bonneville Power Administration (BPA) is a federal non-profit agency within the Pacific Northwest responsible for marketing the power generated from 31 federal hydro projects throughout the Columbia River Basin. The basin encompasses parts of five states and portions of British Columbia, Canada. BPA works with provincial entities, federal and state agencies, and tribal members to manage the water resources for a variety of purposes including flood risk management, power generation, fisheries, irrigation, recreation, and navigation. This basin is subject to significant hydrologic variability in terms of seasonal volume and runoff shape from year to year which presents new water management challenges each year. The power generation planning group at BPA includes a team of meteorologists and hydrologists responsible for preparing both short-term (up to three weeks) and mid-term (up to 18 months) weather and streamflow forecasts including ensemble streamflow data. Analysts within the mid-term planning group are responsible for running several different hydrologic models used for planning studies. These models rely on these streamflow ensembles as a primary input. The planning studies are run bi-weekly to help determine the amount of energy available, or energy inventory, for forward marketing (selling or purchasing energy up to a year in advance). These studies are run with the objective of meeting the numerous multi-purpose objectives of the basin under the various streamflow conditions within the ensemble set. In addition to ensemble streamflows, an ensemble of seasonal volume forecasts is also provided for the various water conditions in order to set numerous constraints on the system. After meeting all the various requirements of the system, a probabilistic energy inventory is calculated and used for marketing purposes.
A genetic ensemble approach for gene-gene interaction identification
Directory of Open Access Journals (Sweden)
Ho Joshua WK
2010-10-01
Full Text Available Abstract Background It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging. Methods In this paper, we propose a new approach for identifying such gene-gene and gene-environment interactions underlying complex diseases. This is a hybrid algorithm and it combines genetic algorithm (GA and an ensemble of classifiers (called genetic ensemble. Using this approach, the original problem of SNP interaction identification is converted into a data mining problem of combinatorial feature selection. By collecting various single nucleotide polymorphisms (SNP subsets as well as environmental factors generated in multiple GA runs, patterns of gene-gene and gene-environment interactions can be extracted using a simple combinatorial ranking method. Also considered in this study is the idea of combining identification results obtained from multiple algorithms. A novel formula based on pairwise double fault is designed to quantify the degree of complementarity. Conclusions Our simulation study demonstrates that the proposed genetic ensemble algorithm has comparable identification power to Multifactor Dimensionality Reduction (MDR and is slightly better than Polymorphism Interaction Analysis (PIA, which are the two most popular methods for gene-gene interaction identification. More importantly, the identification results generated by using our genetic ensemble algorithm are highly complementary to those obtained by PIA and MDR. Experimental results from our simulation studies and real world data application also confirm the effectiveness of the proposed genetic ensemble algorithm, as well as the potential benefits of
Improving the ensemble optimization method through covariance matrix adaptation (CMA-EnOpt)
Fonseca, R.M.; Leeuwenburgh, O.; Hof, P.M.J. van den; Jansen, J.D.
2013-01-01
Ensemble Optimization (EnOpt) is a rapidly emerging method for reservoir model based production optimization. EnOpt uses an ensemble of controls to approximate the gradient of the objective function with respect to the controls. Current implementations of EnOpt use a Gaussian ensemble with a
The Oral Tradition in the Sankofa Drum and Dance Ensemble: Student Perceptions
Hess, Juliet
2009-01-01
The Sankofa Drum and Dance Ensemble is a Ghanaian drum and dance ensemble that focusses on music in the Ewe tradition. It is based in an elementary school in the Greater Toronto Area and consists of students in Grade 4 through Grade 8. Students in the ensemble study Ghanaian traditional Ewe drumming and dancing in the oral tradition. Nine students…
Making Music or Gaining Grades? Assessment Practices in Tertiary Music Ensembles
Harrison, Scott D.; Lebler, Don; Carey, Gemma; Hitchcock, Matt; O'Bryan, Jessica
2013-01-01
Participation in an ensemble is a significant aspect of tertiary music experience. Learning and assessment practices within ensembles have rarely been investigated in Australia and the perceptions of staff and students as to how they learn and are assessed within ensembles remain largely unexplored. This paper reports on part of a larger project…
Reggiani, P.; Renner, M.; Weerts, A.H.; Van Gelder, P.A.H.J.M.
2009-01-01
Ensemble streamflow forecasts obtained by using hydrological models with ensemble weather products are becoming more frequent in operational flow forecasting. The uncertainty of the ensemble forecast needs to be assessed for these products to become useful in forecasting operations. A comprehensive
Jones, Sara K.
2018-01-01
The purpose of this comparative case study was to examine the motivation for participation in traditional and non-traditional vocal ensembles by students who are not pursuing a career in music and the perceived benefits of this participation. Participants were selected from a traditional mixed choral ensemble and a student-run a cappella ensemble.…
Multsch, S.; Exbrayat, J.-F.; Kirby, M.; Viney, N. R.; Frede, H.-G.; Breuer, L.
2015-04-01
Irrigation agriculture plays an increasingly important role in food supply. Many evapotranspiration models are used today to estimate the water demand for irrigation. They consider different stages of crop growth by empirical crop coefficients to adapt evapotranspiration throughout the vegetation period. We investigate the importance of the model structural versus model parametric uncertainty for irrigation simulations by considering six evapotranspiration models and five crop coefficient sets to estimate irrigation water requirements for growing wheat in the Murray-Darling Basin, Australia. The study is carried out using the spatial decision support system SPARE:WATER. We find that structural model uncertainty among reference ET is far more important than model parametric uncertainty introduced by crop coefficients. These crop coefficients are used to estimate irrigation water requirement following the single crop coefficient approach. Using the reliability ensemble averaging (REA) technique, we are able to reduce the overall predictive model uncertainty by more than 10%. The exceedance probability curve of irrigation water requirements shows that a certain threshold, e.g. an irrigation water limit due to water right of 400 mm, would be less frequently exceeded in case of the REA ensemble average (45%) in comparison to the equally weighted ensemble average (66%). We conclude that multi-model ensemble predictions and sophisticated model averaging techniques are helpful in predicting irrigation demand and provide relevant information for decision making.
Generating precipitation ensembles for flood alert and risk management
Caseri, Angelica; Javelle, Pierre; Ramos, Maria-Helena; Leblois, Etienne
2015-04-01
Floods represent one of the major natural disasters that are often responsible for fatalities and economic losses. Flood warning systems are needed to anticipate the arrival of severe events and mitigate their impacts. Flood alerts are particularly important for risk management and response in the nowcasting of flash floods. In this case, precipitation fields observed in real time play a crucial role and observational uncertainties must be taken into account. In this study, we investigate the potential of a framework which combines a geostatistical conditional simulation method that considers information from precipitation radar and rain gauges, and a distributed rainfall-runoff model to generate an ensemble of precipitation fields and produce probabilistic flood alert maps. We adapted the simulation method proposed by Leblois and Creutin (2013), based on the Turning Band Method (TBM) and a conditional simulation approach, to consider the temporal and spatial characteristics of radar data and rain gauge measurements altogether and generate precipitation ensembles. The AIGA system developed by Irstea and Météo-France for predicting flash floods in the French Mediterranean region (Javelle et al., 2014) was used to transform the generated precipitation ensembles into ensembles of discharge at the outlet of the studied catchments. Finally, discharge ensembles were translated into maps providing information on the probability of exceeding a given flood threshold. A total of 19 events that occurred between 2009 and 2013 in the Var region (southeastern France), a region prone to flash floods, was used to illustrate the approach. Results show that the proposed method is able to simulate an ensemble of realistic precipitation fields and capture peak flows of flash floods. This was shown to be particularly useful at ungauged catchments, where uncertainties on the evaluation of flood peaks are high. The results obtained also show that the approach developed can be used to
The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review
Directory of Open Access Journals (Sweden)
Gulshan Kumar
2012-01-01
Full Text Available In supervised learning-based classification, ensembles have been successfully employed to different application domains. In the literature, many researchers have proposed different ensembles by considering different combination methods, training datasets, base classifiers, and many other factors. Artificial-intelligence-(AI- based techniques play prominent role in development of ensemble for intrusion detection (ID and have many benefits over other techniques. However, there is no comprehensive review of ensembles in general and AI-based ensembles for ID to examine and understand their current research status to solve the ID problem. Here, an updated review of ensembles and their taxonomies has been presented in general. The paper also presents the updated review of various AI-based ensembles for ID (in particular during last decade. The related studies of AI-based ensembles are compared by set of evaluation metrics driven from (1 architecture & approach followed; (2 different methods utilized in different phases of ensemble learning; (3 other measures used to evaluate classification performance of the ensembles. The paper also provides the future directions of the research in this area. The paper will help the better understanding of different directions in which research of ensembles has been done in general and specifically: field of intrusion detection systems (IDSs.
Collective Rabi dynamics of electromagnetically coupled quantum-dot ensembles
Glosser, Connor; Shanker, B.; Piermarocchi, Carlo
2017-09-01
Rabi oscillations typify the inherent nonlinearity of optical excitations in quantum dots. Using an integral kernel formulation to solve the three-dimensional Maxwell-Bloch equations in ensembles of up to 104 quantum dots, we observe features in Rabi oscillations due to the interplay of nonlinearity, nonequilibrium excitation, and electromagnetic coupling between the dots. This approach allows us to observe the dynamics of each dot in the ensemble without resorting to spatial averages. Our simulations predict synchronized multiplets of dots that exchange energy, dots that dynamically couple to screen the effect of incident external radiation, localization of the polarization due to randomness and interactions, as well as wavelength-scale regions of enhanced and suppressed polarization.
Inner structure of vehicular ensembles and random matrix theory
International Nuclear Information System (INIS)
Krbálek, Milan; Hobza, Tomáš
2016-01-01
Highlights: • New class of random matrices (DUE) is proposed and analyzed in detail. • Approximation formula for level spacing distribution in DUE ensembles is analytically derived. • Connection between DUE and vehicular systems (analogical to a well-known link between GUE and Mexico buses) is presented. • It is shown that LS distribution of DUE matrices is the same as clearance distribution measured on expressways. - Abstract: We introduce a special class of random matrices (DUE) whose spectral statistics corresponds to statistics of microscopical quantities detected in vehicular flows. Comparing the level spacing distribution (for ordered eigenvalues in unfolded spectra of DUE matrices) with the time-clearance distribution extracted from various areas of the flux-density diagram (evaluated from original traffic data measured on Czech expressways with high occupancies) we demonstrate that the set of classical systems showing an universality associated with Random Matrix Ensembles can be extended by traffic systems.
Security Enrichment in Intrusion Detection System Using Classifier Ensemble
Directory of Open Access Journals (Sweden)
Uma R. Salunkhe
2017-01-01
Full Text Available In the era of Internet and with increasing number of people as its end users, a large number of attack categories are introduced daily. Hence, effective detection of various attacks with the help of Intrusion Detection Systems is an emerging trend in research these days. Existing studies show effectiveness of machine learning approaches in handling Intrusion Detection Systems. In this work, we aim to enhance detection rate of Intrusion Detection System by using machine learning technique. We propose a novel classifier ensemble based IDS that is constructed using hybrid approach which combines data level and feature level approach. Classifier ensembles combine the opinions of different experts and improve the intrusion detection rate. Experimental results show the improved detection rates of our system compared to reference technique.
Ensemble of texture descriptors and classifiers for face recognition
Directory of Open Access Journals (Sweden)
Alessandra Lumini
2017-01-01
Full Text Available Presented in this paper is a novel system for face recognition that works well in the wild and that is based on ensembles of descriptors that utilize different preprocessing techniques. The power of our proposed approach is demonstrated on two datasets: the FERET dataset and the Labeled Faces in the Wild (LFW dataset. In the FERET datasets, where the aim is identification, we use the angle distance. In the LFW dataset, where the aim is to verify a given match, we use the Support Vector Machine and Similarity Metric Learning. Our proposed system performs well on both datasets, obtaining, to the best of our knowledge, one of the highest performance rates published in the literature on the FERET datasets. Particularly noteworthy is the fact that these good results on both datasets are obtained without using additional training patterns. The MATLAB source of our best ensemble approach will be freely available at https://www.dei.unipd.it/node/2357.
Current path in light emitting diodes based on nanowire ensembles
International Nuclear Information System (INIS)
Limbach, F; Hauswald, C; Lähnemann, J; Wölz, M; Brandt, O; Trampert, A; Hanke, M; Jahn, U; Calarco, R; Geelhaar, L; Riechert, H
2012-01-01
Light emitting diodes (LEDs) have been fabricated using ensembles of free-standing (In, Ga)N/GaN nanowires (NWs) grown on Si substrates in the self-induced growth mode by molecular beam epitaxy. Electron-beam-induced current analysis, cathodoluminescence as well as biased μ-photoluminescence spectroscopy, transmission electron microscopy, and electrical measurements indicate that the electroluminescence of such LEDs is governed by the differences in the individual current densities of the single-NW LEDs operated in parallel, i.e. by the inhomogeneity of the current path in the ensemble LED. In addition, the optoelectronic characterization leads to the conclusion that these NWs exhibit N-polarity and that the (In, Ga)N quantum well states in the NWs are subject to a non-vanishing quantum confined Stark effect. (paper)
Skill forecasting from ensemble predictions of wind power
DEFF Research Database (Denmark)
Pinson, Pierre; Nielsen, Henrik Aalborg; Madsen, Henrik
2009-01-01
Optimal management and trading of wind generation calls for the providing of uncertainty estimates along with the commonly provided short-term wind power point predictions. Alternative approaches for the use of probabilistic forecasting are introduced. More precisely, focus is given to prediction...... risk indices aiming to give a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the spread of ensemble forecasts (i.e. a set...... of alternative scenarios for the coming period) for a single prediction horizon or over a took-ahead period. It is shown on the test case of a Danish offshore wind farm how these prediction risk indices may be related to several levels of forecast uncertainty (and potential energy imbalances). Wind power...
Ensemble structure description of Lys63-linked diubiquitin
Directory of Open Access Journals (Sweden)
Zhu Liu
2016-06-01
Full Text Available The data described herein are related to the article entitled “Lys63-linked ubiquitin chain adopts multiple conformational states for specific target recognition” [1], and to the coordinates for the ensemble structure of Lys63-linked diubiquitin (PDB code 2N2K. A Lys63-linked diubiquitin exists in three conformational states with different orientations for the two subunits, each responsible for binding to a target protein and encoding a specific cell signal. An atomic entry in the ensemble structure file consists multiple lines, representing alternative locations of the atom and recapitulating the dynamics of the protein. Experimental details about obtaining strictly intramolecular paramagnetic restraints and determining the relative occupancies of the conformational states are presented. The experimental design and procedures in this Data article can be useful for characterizing the structure and dynamics of other multi-domain proteins.
Generation of macroscopic singlet states in atomic ensembles
Tóth, Géza; Mitchell, Morgan W.
2010-05-01
We study squeezing of the spin uncertainties by quantum non-demolition (QND) measurement in non-polarized spin ensembles. Unlike the case of polarized ensembles, the QND measurements can be performed with negligible back-action, which allows, in principle, perfect spin squeezing as quantified by Tóth et al (2007 Phys. Rev. Lett. 99 250405). The generated spin states approach many-body singlet states and contain a macroscopic number of entangled particles even when individual spin is large. We introduce the Gaussian treatment of unpolarized spin states and use it to estimate the achievable spin squeezing for realistic experimental parameters. Our proposal might have applications for magnetometry with a high spatial resolution or quantum memories storing information in decoherence free subspaces.
New vigour involving statisticians to overcome ensemble fatigue
DEFF Research Database (Denmark)
Benestad, Rasmus; Sillmann, Jana; Thorarinsdottir, Thordis Linda
2017-01-01
Climate simulation data comprise a range of different phenomena with complex and interacting processes. Yet our understanding of the climate is incomplete despite the huge volumes of data, of which only a small fraction has been explored, and many questions remain, particularly those on the chara......Climate simulation data comprise a range of different phenomena with complex and interacting processes. Yet our understanding of the climate is incomplete despite the huge volumes of data, of which only a small fraction has been explored, and many questions remain, particularly those...... contribute substantially to designing 'smarter' ensemble experiments, improving the distillation of information from ensembles, and helping interpret the relative merits of additional simulations. Future progress may be enhanced by increasing collaborations with statisticians....
Harnessing Disordered-Ensemble Quantum Dynamics for Machine Learning
Fujii, Keisuke; Nakajima, Kohei
2017-08-01
The quantum computer has an amazing potential of fast information processing. However, the realization of a digital quantum computer is still a challenging problem requiring highly accurate controls and key application strategies. Here we propose a platform, quantum reservoir computing, to solve these issues successfully by exploiting the natural quantum dynamics of ensemble systems, which are ubiquitous in laboratories nowadays, for machine learning. This framework enables ensemble quantum systems to universally emulate nonlinear dynamical systems including classical chaos. A number of numerical experiments show that quantum systems consisting of 5-7 qubits possess computational capabilities comparable to conventional recurrent neural networks of 100-500 nodes. This discovery opens up a paradigm for information processing with artificial intelligence powered by quantum physics.
Observation Quality Control with a Robust Ensemble Kalman Filter
Roh, Soojin
2013-12-01
Current ensemble-based Kalman filter (EnKF) algorithms are not robust to gross observation errors caused by technical or human errors during the data collection process. In this paper, the authors consider two types of gross observational errors, additive statistical outliers and innovation outliers, and introduce a method to make EnKF robust to gross observation errors. Using both a one-dimensional linear system of dynamics and a 40-variable Lorenz model, the performance of the proposed robust ensemble Kalman filter (REnKF) was tested and it was found that the new approach greatly improves the performance of the filter in the presence of gross observation errors and leads to only a modest loss of accuracy with clean, outlier-free, observations.
Estimation of Human Heart Activity Using Ensemble Kalman Filter
Directory of Open Access Journals (Sweden)
Pradhnya Arun Priyadarshi
2017-02-01
Full Text Available Heart beat measurement techniques come across various challenges. Electrocardiogram (ECG obtained sometimes does not reveal complete information about electrochemical activity of human heart, because of which functioning of heart cannot be studied properly. In this paper Ensemble Kalman Filter (EnKF is used to generate ECG signal efficiently with better accuracy such that the drawbacks of current techniques are eliminated. Here EnKF is applied to second order mathematical model of human heart, input applied to this mathematical model is a pacemaker signal. The initial values of heart muscle movements and electrochemical activity as a discrete data set are used and prediction steps are commenced. EnKF uses ensemble integration technique to model error statistics which helps obtaining more precise output. The results are obtained with negligible sum squared error, therefore the ECG obtained using EnKF can diagnose the disease related to heart with better accuracy.
Generalized ensemble theory with non-extensive statistics
Shen, Ke-Ming; Zhang, Ben-Wei; Wang, En-Ke
2017-12-01
The non-extensive canonical ensemble theory is reconsidered with the method of Lagrange multipliers by maximizing Tsallis entropy, with the constraint that the normalized term of Tsallis' q -average of physical quantities, the sum ∑ pjq, is independent of the probability pi for Tsallis parameter q. The self-referential problem in the deduced probability and thermal quantities in non-extensive statistics is thus avoided, and thermodynamical relationships are obtained in a consistent and natural way. We also extend the study to the non-extensive grand canonical ensemble theory and obtain the q-deformed Bose-Einstein distribution as well as the q-deformed Fermi-Dirac distribution. The theory is further applied to the generalized Planck law to demonstrate the distinct behaviors of the various generalized q-distribution functions discussed in literature.
Human resource recommendation algorithm based on ensemble learning and Spark
Cong, Zihan; Zhang, Xingming; Wang, Haoxiang; Xu, Hongjie
2017-08-01
Aiming at the problem of “information overload” in the human resources industry, this paper proposes a human resource recommendation algorithm based on Ensemble Learning. The algorithm considers the characteristics and behaviours of both job seeker and job features in the real business circumstance. Firstly, the algorithm uses two ensemble learning methods-Bagging and Boosting. The outputs from both learning methods are then merged to form user interest model. Based on user interest model, job recommendation can be extracted for users. The algorithm is implemented as a parallelized recommendation system on Spark. A set of experiments have been done and analysed. The proposed algorithm achieves significant improvement in accuracy, recall rate and coverage, compared with recommendation algorithms such as UserCF and ItemCF.
The limit shape problem for ensembles of Young diagrams
Hora, Akihito
2016-01-01
This book treats ensembles of Young diagrams originating from group-theoretical contexts and investigates what statistical properties are observed there in a large-scale limit. The focus is mainly on analyzing the interesting phenomenon that specific curves appear in the appropriate scaling limit for the profiles of Young diagrams. This problem is regarded as an important origin of recent vital studies on harmonic analysis of huge symmetry structures. As mathematics, an asymptotic theory of representations is developed of the symmetric groups of degree n as n goes to infinity. The framework of rigorous limit theorems (especially the law of large numbers) in probability theory is employed as well as combinatorial analysis of group characters of symmetric groups and applications of Voiculescu's free probability. The central destination here is a clear description of the asymptotic behavior of rescaled profiles of Young diagrams in the Plancherel ensemble from both static and dynamic points of view.
Complementarity of structure ensembles in protein-protein binding.
Grünberg, Raik; Leckner, Johan; Nilges, Michael
2004-12-01
Protein-protein association is often accompanied by changes in receptor and ligand structure. This interplay between protein flexibility and protein-protein recognition is currently the largest obstacle both to our understanding of and to the reliable prediction of protein complexes. We performed two sets of molecular dynamics simulations for the unbound receptor and ligand structures of 17 protein complexes and applied shape-driven rigid body docking to all combinations of representative snapshots. The crossdocking of structure ensembles increased the likelihood of finding near-native solutions. The free ensembles appeared to contain multiple complementary conformations. These were in general not related to the bound structure. We suggest that protein-protein binding follows a three-step mechanism of diffusion, free conformer selection, and refolding. This model combines previously conflicting ideas and is in better agreement with the current data on interaction forces, time scales, and kinetics.
On-line Learning of Unlearnable True Teacher through Mobile Ensemble Teachers
Hirama, Takeshi; Hukushima, Koji
2008-09-01
The on-line learning of a hierarchical learning model is studied by a method based on statistical mechanics. In our model, a student of a simple perceptron learns from not a true teacher directly, but ensemble teachers who learn from a true teacher with a perceptron learning rule. Since the true teacher and ensemble teachers are expressed as nonmonotonic and simple perceptrons, respectively, the ensemble teachers go around the unlearnable true teacher with the distance between them fixed in an asymptotic steady state. The generalization performance of the student is shown to exceed that of the ensemble teachers in a transient state, as was shown in similar ensemble-teachers models. Furthermore, it is found that moving the ensemble teachers even in the steady state, in contrast to the fixed ensemble teachers, is efficient for the performance of the student.
A short-range ensemble prediction system for southern Africa
CSIR Research Space (South Africa)
Park, R
2012-10-01
Full Text Available numerical weather prediction system over southern Africa using the Conformal- Cubic Atmospheric Model (CCAM). An ensemble prediction system (EPS) combines several individual weather model setups into an average forecast system where each member... that are categorical and exact, with no statistical component. This indicates the ability of the model to predict exact values of precipitation events compared to observed data. From the results it can be seen that for a four-day lead time, the model performs well...
Fluctuations in a quasi-stationary shallow cumulus cloud ensemble
Directory of Open Access Journals (Sweden)
M. Sakradzija
2015-01-01
Full Text Available We propose an approach to stochastic parameterisation of shallow cumulus clouds to represent the convective variability and its dependence on the model resolution. To collect information about the individual cloud lifecycles and the cloud ensemble as a whole, we employ a large eddy simulation (LES model and a cloud tracking algorithm, followed by conditional sampling of clouds at the cloud-base level. In the case of a shallow cumulus ensemble, the cloud-base mass flux distribution is bimodal, due to the different shallow cloud subtypes, active and passive clouds. Each distribution mode can be approximated using a Weibull distribution, which is a generalisation of exponential distribution by accounting for the change in distribution shape due to the diversity of cloud lifecycles. The exponential distribution of cloud mass flux previously suggested for deep convection parameterisation is a special case of the Weibull distribution, which opens a way towards unification of the statistical convective ensemble formalism of shallow and deep cumulus clouds. Based on the empirical and theoretical findings, a stochastic model has been developed to simulate a shallow convective cloud ensemble. It is formulated as a compound random process, with the number of convective elements drawn from a Poisson distribution, and the cloud mass flux sampled from a mixed Weibull distribution. Convective memory is accounted for through the explicit cloud lifecycles, making the model formulation consistent with the choice of the Weibull cloud mass flux distribution function. The memory of individual shallow clouds is required to capture the correct convective variability. The resulting distribution of the subgrid convective states in the considered shallow cumulus case is scale-adaptive – the smaller the grid size, the broader the distribution.
Early Development of a Hazardous Chemical Protective Ensemble.
1986-10-01
this material in the Coast Guard’s HCPE System. The Coast Guard decided to make a closer examination of the chemicals for which the materials were...limited employment. Nevertheless the three material HCPE system allows response personnel to be protected against many more CHRIS chemicals than if...objective of Task II was to develop a Hazardous Chemical Protective Ensemble ( HCPE ) which integrated a self-contained breathing apparatus, liquid cooling
Disease-associated mutations that alter the RNA structural ensemble.
Directory of Open Access Journals (Sweden)
Matthew Halvorsen
2010-08-01
Full Text Available Genome-wide association studies (GWAS often identify disease-associated mutations in intergenic and non-coding regions of the genome. Given the high percentage of the human genome that is transcribed, we postulate that for some observed associations the disease phenotype is caused by a structural rearrangement in a regulatory region of the RNA transcript. To identify such mutations, we have performed a genome-wide analysis of all known disease-associated Single Nucleotide Polymorphisms (SNPs from the Human Gene Mutation Database (HGMD that map to the untranslated regions (UTRs of a gene. Rather than using minimum free energy approaches (e.g. mFold, we use a partition function calculation that takes into consideration the ensemble of possible RNA conformations for a given sequence. We identified in the human genome disease-associated SNPs that significantly alter the global conformation of the UTR to which they map. For six disease-states (Hyperferritinemia Cataract Syndrome, beta-Thalassemia, Cartilage-Hair Hypoplasia, Retinoblastoma, Chronic Obstructive Pulmonary Disease (COPD, and Hypertension, we identified multiple SNPs in UTRs that alter the mRNA structural ensemble of the associated genes. Using a Boltzmann sampling procedure for sub-optimal RNA structures, we are able to characterize and visualize the nature of the conformational changes induced by the disease-associated mutations in the structural ensemble. We observe in several cases (specifically the 5' UTRs of FTL and RB1 SNP-induced conformational changes analogous to those observed in bacterial regulatory Riboswitches when specific ligands bind. We propose that the UTR and SNP combinations we identify constitute a "RiboSNitch," that is a regulatory RNA in which a specific SNP has a structural consequence that results in a disease phenotype. Our SNPfold algorithm can help identify RiboSNitches by leveraging GWAS data and an analysis of the mRNA structural ensemble.
Kinetic theory of non-hamiltonian statistical ensembles
Directory of Open Access Journals (Sweden)
A.V.Zhukov
2006-01-01
Full Text Available A nonequilibrium statistical operator method is developed for ensembles of particles obeying non-Hamiltonian equations of motion in classical phase space. The main consequences of non-zero compressibility of phase space are examined in terms of time-dependent dynamic quantities. The generalized transport equations involve the phase-space compressibility in a non-trivial way. Our results are useful in molecular dynamics simulation studies as well as nonequilibrium or quasiclassical approximations of quantum-classical dynamics.
Probabilistic Determination of Native State Ensembles of Proteins
DEFF Research Database (Denmark)
Olsson, Simon; Vögeli, Beat Rolf; Cavalli, Andrea
2014-01-01
The motions of biological macromolecules are tightly coupled to their functions. However, while the study of fast motions has become increasingly feasible in recent years, the study of slower, biologically important motions remains difficult. Here, we present a method to construct native state en...... of biomolecules very efficiently. The approach may allow for a dramatic reduction in the computational as well as experimental resources needed to obtain accurate conformational ensembles of biological macromolecules in a statistically sound manner....
Robust Manipulation and Computation for Inhomogeneous Quantum Ensembles
2013-07-01
stimulus [33]. Phase models are widely employed in physics, chemistry, and biology [34] to study rhythmic systems where the oscillatory phase, but not the...due to their long duration. This has prompted a surge of theoretical and experimental activities to find shortcuts to adiabaticity in quantum systems... Academy of Sciences, Vol. 108, No. 5, pp. 1879-1884, 2011. 12. J.-S. Li, “Ensemble Control of Finite-Dimensional Time-Varying Linear Systems”, IEEE
Aspects of dynamical dimensional reduction in multigraph ensembles of CDT
Giasemidis, Georgios; Wheater, John F.; Zohren, Stefan
2013-02-01
We study the continuum limit of a "radially reduced" approximation of Causal Dynamical Triangulations (CDT), so-called multigraph ensembles, and explain why they serve as realistic toy models to study the dimensional reduction observed in numerical simulations of four-dimensional CDT. We present properties of this approximation in two, three and four dimensions comparing them with the numerical simulations and pointing out some common features with 2+1 dimensional Hořava-Lifshitz gravity.
Dynamic Metabolic Model Building Based on the Ensemble Modeling Approach
Energy Technology Data Exchange (ETDEWEB)
Liao, James C. [Univ. of California, Los Angeles, CA (United States)
2016-10-01
Ensemble modeling of kinetic systems addresses the challenges of kinetic model construction, with respect to parameter value selection, and still allows for the rich insights possible from kinetic models. This project aimed to show that constructing, implementing, and analyzing such models is a useful tool for the metabolic engineering toolkit, and that they can result in actionable insights from models. Key concepts are developed and deliverable publications and results are presented.
Polynomial Chaos Based Acoustic Uncertainty Predictions from Ocean Forecast Ensembles
Dennis, S.
2016-02-01
Most significant ocean acoustic propagation occurs at tens of kilometers, at scales small compared basin and to most fine scale ocean modeling. To address the increased emphasis on uncertainty quantification, for example transmission loss (TL) probability density functions (PDF) within some radius, a polynomial chaos (PC) based method is utilized. In order to capture uncertainty in ocean modeling, Navy Coastal Ocean Model (NCOM) now includes ensembles distributed to reflect the ocean analysis statistics. Since the ensembles are included in the data assimilation for the new forecast ensembles, the acoustic modeling uses the ensemble predictions in a similar fashion for creating sound speed distribution over an acoustically relevant domain. Within an acoustic domain, singular value decomposition over the combined time-space structure of the sound speeds can be used to create Karhunen-Loève expansions of sound speed, subject to multivariate normality testing. These sound speed expansions serve as a basis for Hermite polynomial chaos expansions of derived quantities, in particular TL. The PC expansion coefficients result from so-called non-intrusive methods, involving evaluation of TL at multi-dimensional Gauss-Hermite quadrature collocation points. Traditional TL calculation from standard acoustic propagation modeling could be prohibitively time consuming at all multi-dimensional collocation points. This method employs Smolyak order and gridding methods to allow adaptive sub-sampling of the collocation points to determine only the most significant PC expansion coefficients to within a preset tolerance. Practically, the Smolyak order and grid sizes grow only polynomially in the number of Karhunen-Loève terms, alleviating the curse of dimensionality. The resulting TL PC coefficients allow the determination of TL PDF normality and its mean and standard deviation. In the non-normal case, PC Monte Carlo methods are used to rapidly establish the PDF. This work was
Marginalized Particle Filtering Framework for Tuning of Ensemble Filters
Czech Academy of Sciences Publication Activity Database
Šmídl, Václav; Hofman, Radek
2011-01-01
Roč. 139, č. 11 (2011), s. 3589-3599 ISSN 0027-0644 R&D Projects: GA MV VG20102013018; GA ČR GP102/08/P250 Institutional research plan: CEZ:AV0Z10750506 Keywords : ensemble finter * marginalized particle filter * data assimilation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 2.688, year: 2011 http://library.utia.cas.cz/separaty/2011/AS/smidl-0367533.pdf
Solutions of matrix models in the DIII generator ensemble
Roussel, Harold
1994-01-01
In this paper we solve two matrix models, using standard and new techniques. The two models are represented by special form of antisymmetric matrices and are classified in the DIII generator ensemble. It is shown that, in the double scaling limit, their free energy has the same behavior as previous models describing oriented and unoriented surfaces. We also found an additional solution for the first model.
Ensemble learned vaccination uptake prediction using web search queries
Hansen, Niels Dalum; Lioma, Christina; Mølbak, Kåre
2016-01-01
We present a method that uses ensemble learning to combine clinical and web-mined time-series data in order to predict future vaccination uptake. The clinical data is official vaccination registries, and the web data is query frequencies collected from Google Trends. Experiments with official vaccine records show that our method predicts vaccination uptake eff?ectively (4.7 Root Mean Squared Error). Whereas performance is best when combining clinical and web data, using solely web data yields...
Error Estimation of An Ensemble Statistical Seasonal Precipitation Prediction Model
Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Gui-Long
2001-01-01
This NASA Technical Memorandum describes an optimal ensemble canonical correlation forecasting model for seasonal precipitation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. Since new CCA scheme is derived for continuous fields of predictor and predictand, an area-factor is automatically included. Thus our model is an improvement of the spectral CCA scheme of Barnett and Preisendorfer. The improvements include (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States (US) precipitation field. The predictor is the sea surface temperature (SST). The US Climate Prediction Center's reconstructed SST is used as the predictor's historical data. The US National Center for Environmental Prediction's optimally interpolated precipitation (1951-2000) is used as the predictand's historical data. Our forecast experiments show that the new ensemble canonical correlation scheme renders a reasonable forecasting skill. For example, when using September-October-November SST to predict the next season December-January-February precipitation, the spatial pattern correlation between the observed and predicted are positive in 46 years among the 50 years of experiments. The positive correlations are close to or greater than 0.4 in 29 years, which indicates excellent performance of the forecasting model. The forecasting skill can be further enhanced when several predictors are used.
Ensemble bayesian model averaging using markov chain Monte Carlo sampling
Energy Technology Data Exchange (ETDEWEB)
Vrugt, Jasper A [Los Alamos National Laboratory; Diks, Cees G H [NON LANL; Clark, Martyn P [NON LANL
2008-01-01
Bayesian model averaging (BMA) has recently been proposed as a statistical method to calibrate forecast ensembles from numerical weather models. Successful implementation of BMA however, requires accurate estimates of the weights and variances of the individual competing models in the ensemble. In their seminal paper (Raftery etal. Mon Weather Rev 133: 1155-1174, 2(05)) has recommended the Expectation-Maximization (EM) algorithm for BMA model training, even though global convergence of this algorithm cannot be guaranteed. In this paper, we compare the performance of the EM algorithm and the recently developed Differential Evolution Adaptive Metropolis (DREAM) Markov Chain Monte Carlo (MCMC) algorithm for estimating the BMA weights and variances. Simulation experiments using 48-hour ensemble data of surface temperature and multi-model stream-flow forecasts show that both methods produce similar results, and that their performance is unaffected by the length of the training data set. However, MCMC simulation with DREAM is capable of efficiently handling a wide variety of BMA predictive distributions, and provides useful information about the uncertainty associated with the estimated BMA weights and variances.
Effective theory of the D = 3 center vortex ensemble
Oxman, L. E.; Reinhardt, H.
2018-03-01
By means of lattice calculations, center vortices have been established as the infrared dominant gauge field configurations of Yang-Mills theory. In this work, we investigate an ensemble of center vortices in D = 3 Euclidean space-time dimension where they form closed flux loops. To account for the properties of center vortices detected on the lattice, they are equipped with tension, stiffness and a repulsive contact interaction. The ensemble of oriented center vortices is then mapped onto an effective theory of a complex scalar field with a U(1) symmetry. For a positive tension, small vortex loops are favoured and the Wilson loop displays a perimeter law while for a negative tension, large loops dominate the ensemble. In this case the U(1) symmetry of the effective scalar field theory is spontaneously broken and the Wilson loop shows an area law. To account for the large quantum fluctuations of the corresponding Goldstone modes, we use a lattice representation, which results in an XY model with frustration, for which we also study the Villain approximation.
Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint
Energy Technology Data Exchange (ETDEWEB)
Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad
2015-12-08
Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.
Ensemble Kinetic Modeling of Metabolic Networks from Dynamic Metabolic Profiles
Jia, Gengjie; Stephanopoulos, Gregory; Gunawan, Rudiyanto
2012-01-01
Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but significant challenges still remain. The difficulties faced vary from finding best-fit parameters in a highly multidimensional search space to incomplete parameter identifiability. To meet some of these challenges, an ensemble modeling method is developed for characterizing a subset of kinetic parameters that give statistically equivalent goodness-of-fit to time series concentration data. The method is based on the incremental identification approach, where the parameter estimation is done in a step-wise manner. Numerical efficacy is achieved by reducing the dimensionality of parameter space and using efficient random parameter exploration algorithms. The shift toward using model ensembles, instead of the traditional “best-fit” models, is necessary to directly account for model uncertainty during the application of such models. The performance of the ensemble modeling approach has been demonstrated in the modeling of a generic branched pathway and the trehalose pathway in Saccharomyces cerevisiae using generalized mass action (GMA) kinetics. PMID:24957767
Adaptive Ensemble with Human Memorizing Characteristics for Data Stream Mining
Directory of Open Access Journals (Sweden)
Yanhuang Jiang
2015-01-01
Full Text Available Combining several classifiers on sequential chunks of training instances is a popular strategy for data stream mining with concept drifts. This paper introduces human recalling and forgetting mechanisms into a data stream mining system and proposes a Memorizing Based Data Stream Mining (MDSM model. In this model, each component classifier is regarded as a piece of knowledge that a human obtains through learning some materials and has a memory retention value reflecting its usefulness in the history. The classifiers with high memory retention values are reserved in a “knowledge repository.” When a new data chunk comes, most useful classifiers will be selected (recalled from the repository and compose the current target ensemble. Based on MDSM, we put forward a new algorithm, MAE (Memorizing Based Adaptive Ensemble, which uses Ebbinghaus forgetting curve as the forgetting mechanism and adopts ensemble pruning as the recalling mechanism. Compared with four popular data stream mining approaches on the datasets with different concept drifts, the experimental results show that MAE achieves high and stable predicting accuracy, especially for the applications with recurring or complex concept drifts. The results also prove the effectiveness of MDSM model.
Tridiagonal realization of the antisymmetric Gaussian β-ensemble
International Nuclear Information System (INIS)
Dumitriu, Ioana; Forrester, Peter J.
2010-01-01
The Householder reduction of a member of the antisymmetric Gaussian unitary ensemble gives an antisymmetric tridiagonal matrix with all independent elements. The random variables permit the introduction of a positive parameter β, and the eigenvalue probability density function of the corresponding random matrices can be computed explicitly, as can the distribution of (q i ), the first components of the eigenvectors. Three proofs are given. One involves an inductive construction based on bordering of a family of random matrices which are shown to have the same distributions as the antisymmetric tridiagonal matrices. This proof uses the Dixon-Anderson integral from Selberg integral theory. A second proof involves the explicit computation of the Jacobian for the change of variables between real antisymmetric tridiagonal matrices, its eigenvalues, and (q i ). The third proof maps matrices from the antisymmetric Gaussian β-ensemble to those realizing particular examples of the Laguerre β-ensemble. In addition to these proofs, we note some simple properties of the shooting eigenvector and associated Pruefer phases of the random matrices.
Ensemble-free configurational temperature for spin systems
Palma, G.; Gutiérrez, G.; Davis, S.
2016-12-01
An estimator for the dynamical temperature in an arbitrary ensemble is derived in the framework of the conjugate variables theorem. We prove directly that its average indeed gives the inverse temperature and that it is independent of the ensemble. We test this estimator numerically by a simulation of the two-dimensional X Y model in the canonical ensemble. As this model is critical in the whole region of temperatures below the Berezinski-Kosterlitz-Thouless critical temperature TBKT, we use a generalization of Wolff's unicluster algorithm. The numerical results allow us to confirm the robustness of the analytical expression for the microscopic estimator of the temperature. This microscopic estimator has also the advantage that it gives a direct measure of the thermalization process and can be used to compute absolute errors associated with statistical fluctuations. In consequence, this estimator allows for a direct, absolute, and stringent test of the ergodicity of the underlying Markov process, which encodes the algorithm used in a numerical simulation.
Girsanov reweighting for path ensembles and Markov state models
Donati, L.; Hartmann, C.; Keller, B. G.
2017-06-01
The sensitivity of molecular dynamics on changes in the potential energy function plays an important role in understanding the dynamics and function of complex molecules. We present a method to obtain path ensemble averages of a perturbed dynamics from a set of paths generated by a reference dynamics. It is based on the concept of path probability measure and the Girsanov theorem, a result from stochastic analysis to estimate a change of measure of a path ensemble. Since Markov state models (MSMs) of the molecular dynamics can be formulated as a combined phase-space and path ensemble average, the method can be extended to reweight MSMs by combining it with a reweighting of the Boltzmann distribution. We demonstrate how to efficiently implement the Girsanov reweighting in a molecular dynamics simulation program by calculating parts of the reweighting factor "on the fly" during the simulation, and we benchmark the method on test systems ranging from a two-dimensional diffusion process and an artificial many-body system to alanine dipeptide and valine dipeptide in implicit and explicit water. The method can be used to study the sensitivity of molecular dynamics on external perturbations as well as to reweight trajectories generated by enhanced sampling schemes to the original dynamics.
Ensemble Kinetic Modeling of Metabolic Networks from Dynamic Metabolic Profiles
Directory of Open Access Journals (Sweden)
Gengjie Jia
2012-11-01
Full Text Available Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but significant challenges still remain. The difficulties faced vary from finding best-fit parameters in a highly multidimensional search space to incomplete parameter identifiability. To meet some of these challenges, an ensemble modeling method is developed for characterizing a subset of kinetic parameters that give statistically equivalent goodness-of-fit to time series concentration data. The method is based on the incremental identification approach, where the parameter estimation is done in a step-wise manner. Numerical efficacy is achieved by reducing the dimensionality of parameter space and using efficient random parameter exploration algorithms. The shift toward using model ensembles, instead of the traditional “best-fit” models, is necessary to directly account for model uncertainty during the application of such models. The performance of the ensemble modeling approach has been demonstrated in the modeling of a generic branched pathway and the trehalose pathway in Saccharomyces cerevisiae using generalized mass action (GMA kinetics.
Non-Hermitian Extensions of Wishart Random Matrix Ensembles
International Nuclear Information System (INIS)
Akemann, G.
2011-01-01
We briefly review the solution of three ensembles of non-Hermitian random matrices generalizing the Wishart-Laguerre (also called chiral) ensembles. These generalizations are realized as Gaussian two-matrix models, where the complex eigenvalues of the product of the two independent rectangular matrices are sought, with the matrix elements of both matrices being either real, complex or quaternion real. We also present the more general case depending on a non-Hermiticity parameter, that allows us to interpolate between the corresponding three Hermitian Wishart ensembles with real eigenvalues and the maximally non-Hermitian case. All three symmetry classes are explicitly solved for finite matrix size N x M for all complex eigenvalue correlations functions (and real or mixed correlations for real matrix elements). These are given in terms of the corresponding kernels built from orthogonal or skew-orthogonal Laguerre polynomials in the complex plane. We then present the corresponding three Bessel kernels in the complex plane in the microscopic large-N scaling limit at the origin, both at weak and strong non-Hermiticity with M - N ≥ 0 fixed. (author)
Ensemble of ground subsidence hazard maps using fuzzy logic
Park, Inhye; Lee, Jiyeong; Saro, Lee
2014-06-01
Hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok, Korea, were constructed using fuzzy ensemble techniques and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, groundwater, and ground subsidence maps. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 70/30 for training and validation of the models. The relationships between the detected ground-subsidence area and the factors were identified and quantified by frequency ratio (FR), logistic regression (LR) and artificial neural network (ANN) models. The relationships were used as factor ratings in the overlay analysis to create ground-subsidence hazard indexes and maps. The three GSH maps were then used as new input factors and integrated using fuzzy-ensemble methods to make better hazard maps. All of the hazard maps were validated by comparison with known subsidence areas that were not used directly in the analysis. As the result, the ensemble model was found to be more effective in terms of prediction accuracy than the individual model.
Mesoscale Predictability and Error Growth in Short Range Ensemble Forecasts
Gingrich, Mark
Although it was originally suggested that small-scale, unresolved errors corrupt forecasts at all scales through an inverse error cascade, some authors have proposed that those mesoscale circulations resulting from stationary forcing on the larger scale may inherit the predictability of the large-scale motions. Further, the relative contributions of large- and small-scale uncertainties in producing error growth in the mesoscales remain largely unknown. Here, 100 member ensemble forecasts are initialized from an ensemble Kalman filter (EnKF) to simulate two winter storms impacting the East Coast of the United States in 2010. Four verification metrics are considered: the local snow water equivalence, total liquid water, and 850 hPa temperatures representing mesoscale features; and the sea level pressure field representing a synoptic feature. It is found that while the predictability of the mesoscale features can be tied to the synoptic forecast, significant uncertainty existed on the synoptic scale at lead times as short as 18 hours. Therefore, mesoscale details remained uncertain in both storms due to uncertainties at the large scale. Additionally, the ensemble perturbation kinetic energy did not show an appreciable upscale propagation of error for either case. Instead, the initial condition perturbations from the cycling EnKF were maximized at large scales and immediately amplified at all scales without requiring initial upscale propagation. This suggests that relatively small errors in the synoptic-scale initialization may have more importance in limiting predictability than errors in the unresolved, small-scale initial conditions.
Optimization of multi-model ensemble forecasting of typhoon waves
Directory of Open Access Journals (Sweden)
Shun-qi Pan
2016-01-01
Full Text Available Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communities. However, due to the complex hydrological and meteorological interaction and uncertainties arising from different modeling systems, quantifying the uncertainties and improving the forecasting accuracy of modeled typhoon-induced waves remain challenging. This paper presents a practical approach to optimizing model-ensemble wave heights in an attempt to improve the accuracy of real-time typhoon wave forecasting. A locally weighted learning algorithm is used to obtain the weights for the wave heights computed by the WAVEWATCH III wave model driven by winds from four different weather models (model-ensembles. The optimized weights are subsequently used to calculate the resulting wave heights from the model-ensembles. The results show that the Optimization is capable of capturing the different behavioral effects of the different weather models on wave generation. Comparison with the measurements at the selected wave buoy locations shows that the optimized weights, obtained through a training process, can significantly improve the accuracy of the forecasted wave heights over the standard mean values, particularly for typhoon-induced peak waves. The results also indicate that the algorithm is easy to implement and practical for real-time wave forecasting.
Thermodynamics and kinetics of a molecular motor ensemble.
Baker, J E; Thomas, D D
2000-10-01
If, contrary to conventional models of muscle, it is assumed that molecular forces equilibrate among rather than within molecular motors, an equation of state and an expression for energy output can be obtained for a near-equilibrium, coworking ensemble of molecular motors. These equations predict clear, testable relationships between motor structure, motor biochemistry, and ensemble motor function, and we discuss these relationships in the context of various experimental studies. In this model, net work by molecular motors is performed with the relaxation of a near-equilibrium intermediate step in a motor-catalyzed reaction. The free energy available for work is localized to this step, and the rate at which this free energy is transferred to work is accelerated by the free energy of a motor-catalyzed reaction. This thermodynamic model implicitly deals with a motile cell system as a dynamic network (not a rigid lattice) of molecular motors within which the mechanochemistry of one motor influences and is influenced by the mechanochemistry of other motors in the ensemble.
GA-Based Membrane Evolutionary Algorithm for Ensemble Clustering
Directory of Open Access Journals (Sweden)
Yanhua Wang
2017-01-01
Full Text Available Ensemble clustering can improve the generalization ability of a single clustering algorithm and generate a more robust clustering result by integrating multiple base clusterings, so it becomes the focus of current clustering research. Ensemble clustering aims at finding a consensus partition which agrees as much as possible with base clusterings. Genetic algorithm is a highly parallel, stochastic, and adaptive search algorithm developed from the natural selection and evolutionary mechanism of biology. In this paper, an improved genetic algorithm is designed by improving the coding of chromosome. A new membrane evolutionary algorithm is constructed by using genetic mechanisms as evolution rules and combines with the communication mechanism of cell-like P system. The proposed algorithm is used to optimize the base clusterings and find the optimal chromosome as the final ensemble clustering result. The global optimization ability of the genetic algorithm and the rapid convergence of the membrane system make membrane evolutionary algorithm perform better than several state-of-the-art techniques on six real-world UCI data sets.
GA-Based Membrane Evolutionary Algorithm for Ensemble Clustering.
Wang, Yanhua; Liu, Xiyu; Xiang, Laisheng
2017-01-01
Ensemble clustering can improve the generalization ability of a single clustering algorithm and generate a more robust clustering result by integrating multiple base clusterings, so it becomes the focus of current clustering research. Ensemble clustering aims at finding a consensus partition which agrees as much as possible with base clusterings. Genetic algorithm is a highly parallel, stochastic, and adaptive search algorithm developed from the natural selection and evolutionary mechanism of biology. In this paper, an improved genetic algorithm is designed by improving the coding of chromosome. A new membrane evolutionary algorithm is constructed by using genetic mechanisms as evolution rules and combines with the communication mechanism of cell-like P system. The proposed algorithm is used to optimize the base clusterings and find the optimal chromosome as the final ensemble clustering result. The global optimization ability of the genetic algorithm and the rapid convergence of the membrane system make membrane evolutionary algorithm perform better than several state-of-the-art techniques on six real-world UCI data sets.
Geometric integrator for simulations in the canonical ensemble
International Nuclear Information System (INIS)
Tapias, Diego; Sanders, David P.; Bravetti, Alessandro
2016-01-01
We introduce a geometric integrator for molecular dynamics simulations of physical systems in the canonical ensemble that preserves the invariant distribution in equations arising from the density dynamics algorithm, with any possible type of thermostat. Our integrator thus constitutes a unified framework that allows the study and comparison of different thermostats and of their influence on the equilibrium and non-equilibrium (thermo-)dynamic properties of a system. To show the validity and the generality of the integrator, we implement it with a second-order, time-reversible method and apply it to the simulation of a Lennard-Jones system with three different thermostats, obtaining good conservation of the geometrical properties and recovering the expected thermodynamic results. Moreover, to show the advantage of our geometric integrator over a non-geometric one, we compare the results with those obtained by using the non-geometric Gear integrator, which is frequently used to perform simulations in the canonical ensemble. The non-geometric integrator induces a drift in the invariant quantity, while our integrator has no such drift, thus ensuring that the system is effectively sampling the correct ensemble.
HIPPI: highly accurate protein family classification with ensembles of HMMs
Directory of Open Access Journals (Sweden)
Nam-phuong Nguyen
2016-11-01
Full Text Available Abstract Background Given a new biological sequence, detecting membership in a known family is a basic step in many bioinformatics analyses, with applications to protein structure and function prediction and metagenomic taxon identification and abundance profiling, among others. Yet family identification of sequences that are distantly related to sequences in public databases or that are fragmentary remains one of the more difficult analytical problems in bioinformatics. Results We present a new technique for family identification called HIPPI (Hierarchical Profile Hidden Markov Models for Protein family Identification. HIPPI uses a novel technique to represent a multiple sequence alignment for a given protein family or superfamily by an ensemble of profile hidden Markov models computed using HMMER. An evaluation of HIPPI on the Pfam database shows that HIPPI has better overall precision and recall than blastp, HMMER, and pipelines based on HHsearch, and maintains good accuracy even for fragmentary query sequences and for protein families with low average pairwise sequence identity, both conditions where other methods degrade in accuracy. Conclusion HIPPI provides accurate protein family identification and is robust to difficult model conditions. Our results, combined with observations from previous studies, show that ensembles of profile Hidden Markov models can better represent multiple sequence alignments than a single profile Hidden Markov model, and thus can improve downstream analyses for various bioinformatic tasks. Further research is needed to determine the best practices for building the ensemble of profile Hidden Markov models. HIPPI is available on GitHub at https://github.com/smirarab/sepp .
Accuracy/diversity and ensemble MLP classifier design.
Windeatt, Terry
2006-09-01
The difficulties of tuning parameters of multilayer perceptrons (MLP) classifiers are well known. In this paper, a measure is described that is capable of predicting the number of classifier training epochs for achieving optimal performance in an ensemble of MLP classifiers. The measure is computed between pairs of patterns on the training data and is based on a spectral representation of a Boolean function. This representation characterizes the mapping from classifier decisions to target label and allows accuracy and diversity to be incorporated within a single measure. Results on many benchmark problems, including the Olivetti Research Laboratory (ORL) face database demonstrate that the measure is well correlated with base-classifier test error, and may be used to predict the optimal number of training epochs. While correlation with ensemble test error is not quite as strong, it is shown in this paper that the measure may be used to predict number of epochs for optimal ensemble performance. Although the technique is only applicable to two-class problems, it is extended here to multiclass through output coding. For the output-coding technique, a random code matrix is shown to give better performance than one-per-class code, even when the base classifier is well-tuned.
SAChES: Scalable Adaptive Chain-Ensemble Sampling.
Energy Technology Data Exchange (ETDEWEB)
Swiler, Laura Painton [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Ray, Jaideep [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Ebeida, Mohamed Salah [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Huang, Maoyi [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Hou, Zhangshuan [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Bao, Jie [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Ren, Huiying [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
2017-08-01
We present the development of a parallel Markov Chain Monte Carlo (MCMC) method called SAChES, Scalable Adaptive Chain-Ensemble Sampling. This capability is targed to Bayesian calibration of com- putationally expensive simulation models. SAChES involves a hybrid of two methods: Differential Evo- lution Monte Carlo followed by Adaptive Metropolis. Both methods involve parallel chains. Differential evolution allows one to explore high-dimensional parameter spaces using loosely coupled (i.e., largely asynchronous) chains. Loose coupling allows the use of large chain ensembles, with far more chains than the number of parameters to explore. This reduces per-chain sampling burden, enables high-dimensional inversions and the use of computationally expensive forward models. The large number of chains can also ameliorate the impact of silent-errors, which may affect only a few chains. The chain ensemble can also be sampled to provide an initial condition when an aberrant chain is re-spawned. Adaptive Metropolis takes the best points from the differential evolution and efficiently hones in on the poste- rior density. The multitude of chains in SAChES is leveraged to (1) enable efficient exploration of the parameter space; and (2) ensure robustness to silent errors which may be unavoidable in extreme-scale computational platforms of the future. This report outlines SAChES, describes four papers that are the result of the project, and discusses some additional results.
Probability Maps for the Visualization of Assimilation Ensemble Flow Data
Hollt, Thomas
2015-05-25
Ocean forecasts nowadays are created by running ensemble simulations in combination with data assimilation techniques. Most of these techniques resample the ensemble members after each assimilation cycle. This means that in a time series, after resampling, every member can follow up on any of the members before resampling. Tracking behavior over time, such as all possible paths of a particle in an ensemble vector field, becomes very difficult, as the number of combinations rises exponentially with the number of assimilation cycles. In general a single possible path is not of interest but only the probabilities that any point in space might be reached by a particle at some point in time. In this work we present an approach using probability-weighted piecewise particle trajectories to allow such a mapping interactively, instead of tracing quadrillions of individual particles. We achieve interactive rates by binning the domain and splitting up the tracing process into the individual assimilation cycles, so that particles that fall into the same bin after a cycle can be treated as a single particle with a larger probability as input for the next time step. As a result we loose the possibility to track individual particles, but can create probability maps for any desired seed at interactive rates.
A New Ensemble Canonical Correlation Prediction Scheme for Seasonal Precipitation
Kim, Kyu-Myong; Lau, William K. M.; Li, Guilong; Shen, Samuel S. P.; Lau, William K. M. (Technical Monitor)
2001-01-01
Department of Mathematical Sciences, University of Alberta, Edmonton, Canada This paper describes the fundamental theory of the ensemble canonical correlation (ECC) algorithm for the seasonal climate forecasting. The algorithm is a statistical regression sch eme based on maximal correlation between the predictor and predictand. The prediction error is estimated by a spectral method using the basis of empirical orthogonal functions. The ECC algorithm treats the predictors and predictands as continuous fields and is an improvement from the traditional canonical correlation prediction. The improvements include the use of area-factor, estimation of prediction error, and the optimal ensemble of multiple forecasts. The ECC is applied to the seasonal forecasting over various parts of the world. The example presented here is for the North America precipitation. The predictor is the sea surface temperature (SST) from different ocean basins. The Climate Prediction Center's reconstructed SST (1951-1999) is used as the predictor's historical data. The optimally interpolated global monthly precipitation is used as the predictand?s historical data. Our forecast experiments show that the ECC algorithm renders very high skill and the optimal ensemble is very important to the high value.
Subseasonal Predictability of Boreal Summer Monsoon Rainfall from Ensemble Forecasts
Directory of Open Access Journals (Sweden)
Nicolas Vigaud
2017-10-01
Full Text Available Subseasonal forecast skill over the broadly defined North American (NAM, West African (WAM and Asian (AM summer monsoon regions is investigated using three Ensemble Prediction Systems (EPS at sub-monthly lead times. Extended Logistic Regression (ELR is used to produce probabilistic forecasts of weekly and week 3–4 averages of precipitation with starts in May–Aug, over the 1999–2010 period. The ELR tercile category probabilities for each model gridpoint are then averaged together with equal weight. The resulting Multi-Model Ensemble (MME forecasts exhibit good reliability, but have generally low sharpness for forecasts beyond 1 week; Multi-model ensembling largely removes negative values of the Ranked Probability Skill Score (RPSS seen in individual forecasts, and broadly improves the skill obtained in any of the three individual models except for the AM. The MME week 3–4 forecasts have generally higher RPSS and comparable reliability over all monsoon regions, compared to week 3 or week 4 forecast separately. Skill is higher during La Niña compared to El Niño and ENSO-neutral conditions over the 1999–2010 period, especially for the NAM. Regionally averaged RPSS is significantly correlated with the Maden-Julian Oscillation (MJO for the AM and WAM. Our results indicate potential for skillful predictions at subseasonal time-scales over the three summer monsoon regions of the Northern Hemisphere.
An ensemble model of QSAR tools for regulatory risk assessment.
Pradeep, Prachi; Povinelli, Richard J; White, Shannon; Merrill, Stephen J
2016-01-01
Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa ( κ ): 0
Verification of Ensemble Water Supply Forecasts for Sierra Nevada Watersheds
Directory of Open Access Journals (Sweden)
Minxue He
2016-11-01
Full Text Available This study verifies the skill and reliability of ensemble water supply forecasts issued by an innovative operational Hydrologic Ensemble Forecast Service (HEFS of the U.S. National Weather Service (NWS at eight Sierra Nevada watersheds in the State of California. The factors potentially influencing the forecast skill and reliability are also explored. Retrospective ensemble forecasts of April–July runoff with 60 traces for these watersheds from 1985 to 2010 are generated with the HEFS driven by raw precipitation and temperature reforecasts from operational Global Ensemble Forecast System (GEFS for the first 15 days and climatology from day 16 up to day 365. Results indicate that the forecast skill is limited when the lead time is long (over three months or before January but increases through the forecast period. There is generally a negative bias in the most probable forecast (median forecast for most study watersheds. When the mean forecast is investigated instead, the bias becomes mostly positive and generally smaller in magnitude. The forecasts, particularly the wet forecasts (with less than 10% exceedance probability are reliable on the average. The low April–July flows (with higher than 90% exceedance probability are forecast more frequently than their actual occurrence frequency, while the medium April–July flows (90% to 10% exceedance are forecast to occur less frequently. The forecast skill and reliability tend to be sensitive to extreme conditions. Particularly, the wet extremes show more significant impact than the dry extremes. Using different forcing data, including pure climatology and Climate Forecast System version 2 (CFSv2 shows no consistent improvement in the forecast skill and reliability, neither does using a longer (than the study period 1985–2010 period of record. Overall, this study is meaningful in the context of (1 establishing a benchmark for future enhancements (i.e., newer version of HEFS, GEFS and CFSv2 to
Spatial Ensemble Postprocessing of Precipitation Forecasts Using High Resolution Analyses
Lang, Moritz N.; Schicker, Irene; Kann, Alexander; Wang, Yong
2017-04-01
Ensemble prediction systems are designed to account for errors or uncertainties in the initial and boundary conditions, imperfect parameterizations, etc. However, due to sampling errors and underestimation of the model errors, these ensemble forecasts tend to be underdispersive, and to lack both reliability and sharpness. To overcome such limitations, statistical postprocessing methods are commonly applied to these forecasts. In this study, a full-distributional spatial post-processing method is applied to short-range precipitation forecasts over Austria using Standardized Anomaly Model Output Statistics (SAMOS). Following Stauffer et al. (2016), observation and forecast fields are transformed into standardized anomalies by subtracting a site-specific climatological mean and dividing by the climatological standard deviation. Due to the need of fitting only a single regression model for the whole domain, the SAMOS framework provides a computationally inexpensive method to create operationally calibrated probabilistic forecasts for any arbitrary location or for all grid points in the domain simultaneously. Taking advantage of the INCA system (Integrated Nowcasting through Comprehensive Analysis), high resolution analyses are used for the computation of the observed climatology and for model training. The INCA system operationally combines station measurements and remote sensing data into real-time objective analysis fields at 1 km-horizontal resolution and 1 h-temporal resolution. The precipitation forecast used in this study is obtained from a limited area model ensemble prediction system also operated by ZAMG. The so called ALADIN-LAEF provides, by applying a multi-physics approach, a 17-member forecast at a horizontal resolution of 10.9 km and a temporal resolution of 1 hour. The performed SAMOS approach statistically combines the in-house developed high resolution analysis and ensemble prediction system. The station-based validation of 6 hour precipitation sums
Towards reliable seasonal ensemble streamflow forecasts for ephemeral rivers
Bennett, James; Wang, Qj; Li, Ming; Robertson, David
2016-04-01
Despite their inherently variable nature, ephemeral rivers are an important water resource in many dry regions. Water managers are likely benefit considerably from even mildly skilful ensemble forecasts of streamflow in ephemeral rivers. As with any ensemble forecast, forecast uncertainty - i.e., the spread of the ensemble - must be reliably quantified to allow users of the forecasts to make well-founded decisions. Correctly quantifying uncertainty in ephemeral rivers is particularly challenging because of the high incidence of zero flows, which are difficult to handle with conventional statistical techniques. Here we apply a seasonal streamflow forecasting system, the model for generating Forecast Guided Stochastic Scenarios (FoGSS), to 26 Australian ephemeral rivers. FoGSS uses post-processed ensemble rainfall forecasts from a coupled ocean-atmosphere prediction system to force an initialised monthly rainfall runoff model, and then applies a staged hydrological error model to describe and propagate hydrological uncertainty in the forecast. FoGSS produces 12-month streamflow forecasts; as forecast skill declines with lead time, the forecasts are designed to transit seamlessly to stochastic scenarios. The ensemble rainfall forecasts used in FoGSS are known to be unbiased and reliable, and we concentrate here on the hydrological error model. The FoGSS error model has several features that make it well suited to forecasting ephemeral rivers. First, FoGSS models the error after data is transformed with a log-sinh transformation. The log-sinh transformation is able to normalise even highly skewed data and homogenise its variance, allowing us to assume that errors are Gaussian. Second, FoGSS handles zero values using data censoring. Data censoring allows streamflow in ephemeral rivers to be treated as a continuous variable, rather than having to model the occurrence of non-zero values and the distribution of non-zero values separately. This greatly simplifies parameter
Stochastic Approaches Within a High Resolution Rapid Refresh Ensemble
Jankov, I.
2017-12-01
It is well known that global and regional numerical weather prediction (NWP) ensemble systems are under-dispersive, producing unreliable and overconfident ensemble forecasts. Typical approaches to alleviate this problem include the use of multiple dynamic cores, multiple physics suite configurations, or a combination of the two. While these approaches may produce desirable results, they have practical and theoretical deficiencies and are more difficult and costly to maintain. An active area of research that promotes a more unified and sustainable system is the use of stochastic physics. Stochastic approaches include Stochastic Parameter Perturbations (SPP), Stochastic Kinetic Energy Backscatter (SKEB), and Stochastic Perturbation of Physics Tendencies (SPPT). The focus of this study is to assess model performance within a convection-permitting ensemble at 3-km grid spacing across the Contiguous United States (CONUS) using a variety of stochastic approaches. A single physics suite configuration based on the operational High-Resolution Rapid Refresh (HRRR) model was utilized and ensemble members produced by employing stochastic methods. Parameter perturbations (using SPP) for select fields were employed in the Rapid Update Cycle (RUC) land surface model (LSM) and Mellor-Yamada-Nakanishi-Niino (MYNN) Planetary Boundary Layer (PBL) schemes. Within MYNN, SPP was applied to sub-grid cloud fraction, mixing length, roughness length, mass fluxes and Prandtl number. In the RUC LSM, SPP was applied to hydraulic conductivity and tested perturbing soil moisture at initial time. First iterative testing was conducted to assess the initial performance of several configuration settings (e.g. variety of spatial and temporal de-correlation lengths). Upon selection of the most promising candidate configurations using SPP, a 10-day time period was run and more robust statistics were gathered. SKEB and SPPT were included in additional retrospective tests to assess the impact of using
Park, Sangwook; Kim, Dong-Joon; Lee, Seung-Woo; Lee, Kie-Woung; Kim, Jongkhun; Song, Eun-Ji; Seo, Kyong-Hwan
2017-08-01
This article describes a three way inter-comparison of forecast skill on an extended medium-range time scale using the Korea Meteorological Administration (KMA) operational ensemble numerical weather prediction (NWP) systems (i.e., atmosphere-only global ensemble prediction system (EPSG) and ocean-atmosphere coupledEPSG) and KMA operational seasonal prediction system, the Global Seasonal forecast system version 5 (GloSea5). The main motivation is to investigate whether the ensemble NWP system can provide advantage over the existing seasonal prediction system for the extended medium-range forecast (30 days) even with putting extra resources in extended integration or coupling with ocean with NWP system. Two types of evaluation statistics are examined: the basic verification statistics - the anomaly correlation and RMSE of 500-hPa geopotential height and 1.5-meter surface temperature for the global and East Asia area, and the other is the Real-time Multivariate Madden and Julian Oscillation (MJO) indices (RMM1 and RMM2) - which is used to examine the MJO prediction skill. The MJO is regarded as a main source of forecast skill in the tropics linked to the mid-latitude weather on monthly time scale. Under limited number of experiment cases, the coupled NWP extends the forecast skill of the NWP by a few more days, and thereafter such forecast skill is overtaken by that of the seasonal prediction system. At present stage, it seems there is little gain from the coupled NWP even though more resources are put into it. Considering this, the best combination of numerical product guidance for operational forecasters for an extended medium-range is extension of the forecast lead time of the current ensemble NWP (EPSG) up to 20 days and use of the seasonal prediction system (GloSea5) forecast thereafter, though there exists a matter of consistency between the two systems.
Conservation of Mass and Preservation of Positivity with Ensemble-Type Kalman Filter Algorithms
Janjic, Tijana; Mclaughlin, Dennis; Cohn, Stephen E.; Verlaan, Martin
2014-01-01
This paper considers the incorporation of constraints to enforce physically based conservation laws in the ensemble Kalman filter. In particular, constraints are used to ensure that the ensemble members and the ensemble mean conserve mass and remain nonnegative through measurement updates. In certain situations filtering algorithms such as the ensemble Kalman filter (EnKF) and ensemble transform Kalman filter (ETKF) yield updated ensembles that conserve mass but are negative, even though the actual states must be nonnegative. In such situations if negative values are set to zero, or a log transform is introduced, the total mass will not be conserved. In this study, mass and positivity are both preserved by formulating the filter update as a set of quadratic programming problems that incorporate non-negativity constraints. Simple numerical experiments indicate that this approach can have a significant positive impact on the posterior ensemble distribution, giving results that are more physically plausible both for individual ensemble members and for the ensemble mean. In two examples, an update that includes a non-negativity constraint is able to properly describe the transport of a sharp feature (e.g., a triangle or cone). A number of implementation questions still need to be addressed, particularly the need to develop a computationally efficient quadratic programming update for large ensemble.
Time-consistent calibration of short-term regional wind power ensemble forecasts
Directory of Open Access Journals (Sweden)
Stephan Späth
2015-04-01
Full Text Available With increasing wind power capacity, accurate uncertainty forecasts get more and more important for grid integration. The uncertainty of forecasts can be quantified by ensemble forecasts. We use ensemble forecasts from the COSMO-DE EPS to generate short-term ensemble forecasts of regionally aggregated wind power. The wind power forecasts are generated by an optimised regional power curve model that is based on minimum score estimation and leads to wind power forecasts with small deterministic errors. Remaining bias and dispersion errors in the wind power forecasts are removed by statistical post-processing (also called calibration with ensemble model output statistics and the temporal rank correlation of the raw ensemble is maintained by ensemble copula coupling. The verification of raw and calibrated ensembles shows both strong improvements by calibration and the benefit of ensuring time consistency with ensemble copula coupling. The improvements are indicated by the multivariate energy score as well as in a proposed univariate verification approach that is based on integrated wind power forecast and measurement trajectories. Slight deficits in time consistency of the forecasts remain because the theoretical assumptions of ensemble copula coupling are not always fulfilled as the COSMO-DE EPS is based on distinguishable ensemble members. The more training days are used for calibration against measurements of regionally aggregated wind power, the lower is the improvement by calibration which contradicts former results for different variables like wind speed.
Hopson, T. M.
2014-12-01
One potential benefit of an ensemble prediction system (EPS) is its capacity to forecast its own forecast error through the ensemble spread-error relationship. In practice, an EPS is often quite limited in its ability to represent the variable expectation of forecast error through the variable dispersion of the ensemble, and perhaps more fundamentally, in its ability to provide enough variability in the ensembles dispersion to make the skill-spread relationship even potentially useful (irrespective of whether the EPS is well-calibrated or not). In this paper we examine the ensemble skill-spread relationship of an ensemble constructed from the TIGGE (THORPEX Interactive Grand Global Ensemble) dataset of global forecasts and a combination of multi-model and post-processing approaches. Both of the multi-model and post-processing techniques are based on quantile regression (QR) under a step-wise forward selection framework leading to ensemble forecasts with both good reliability and sharpness. The methodology utilizes the ensemble's ability to self-diagnose forecast instability to produce calibrated forecasts with informative skill-spread relationships. A context for these concepts is provided by assessing the constructed ensemble in forecasting district-level humidity impacting the incidence of meningitis in the meningitis belt of Africa, and in forecasting flooding events in the Brahmaputra and Ganges basins of South Asia.
Hybrid ensemble 4DVar assimilation of stratospheric ozone using a global shallow water model
Directory of Open Access Journals (Sweden)
D. R. Allen
2016-07-01
Full Text Available Wind extraction from stratospheric ozone (O3 assimilation is examined using a hybrid ensemble 4-D variational assimilation (4DVar shallow water model (SWM system coupled to the tracer advection equation. Stratospheric radiance observations are simulated using global observations of the SWM fluid height (Z, while O3 observations represent sampling by a typical polar-orbiting satellite. Four ensemble sizes were examined (25, 50, 100, and 1518 members, with the largest ensemble equal to the number of dynamical state variables. The optimal length scale for ensemble localization was found by tuning an ensemble Kalman filter (EnKF. This scale was then used for localizing the ensemble covariances that were blended with conventional covariances in the hybrid 4DVar experiments. Both optimal length scale and optimal blending coefficient increase with ensemble size, with optimal blending coefficients varying from 0.2–0.5 for small ensembles to 0.5–1.0 for large ensembles. The hybrid system outperforms conventional 4DVar for all ensemble sizes, while for large ensembles the hybrid produces similar results to the offline EnKF. Assimilating O3 in addition to Z benefits the winds in the hybrid system, with the fractional improvement in global vector wind increasing from ∼ 35 % with 25 and 50 members to ∼ 50 % with 1518 members. For the smallest ensembles (25 and 50 members, the hybrid 4DVar assimilation improves the zonal wind analysis over conventional 4DVar in the Northern Hemisphere (winter-like region and also at the Equator, where Z observations alone have difficulty constraining winds due to lack of geostrophy. For larger ensembles (100 and 1518 members, the hybrid system results in both zonal and meridional wind error reductions, relative to 4DVar, across the globe.
River Flow Prediction Using the Nearest Neighbor Probabilistic Ensemble Method
Directory of Open Access Journals (Sweden)
H. Sanikhani
2016-02-01
Full Text Available Introduction: In the recent years, researchers interested on probabilistic forecasting of hydrologic variables such river flow.A probabilistic approach aims at quantifying the prediction reliability through a probability distribution function or a prediction interval for the unknown future value. The evaluation of the uncertainty associated to the forecast is seen as a fundamental information, not only to correctly assess the prediction, but also to compare forecasts from different methods and to evaluate actions and decisions conditionally on the expected values. Several probabilistic approaches have been proposed in the literature, including (1 methods that use resampling techniques to assess parameter and model uncertainty, such as the Metropolis algorithm or the Generalized Likelihood Uncertainty Estimation (GLUE methodology for an application to runoff prediction, (2 methods based on processing the forecast errors of past data to produce the probability distributions of future values and (3 methods that evaluate how the uncertainty propagates from the rainfall forecast to the river discharge prediction, as the Bayesian forecasting system. Materials and Methods: In this study, two different probabilistic methods are used for river flow prediction.Then the uncertainty related to the forecast is quantified. One approach is based on linear predictors and in the other, nearest neighbor was used. The nonlinear probabilistic ensemble can be used for nonlinear time series analysis using locally linear predictors, while NNPE utilize a method adapted for one step ahead nearest neighbor methods. In this regard, daily river discharge (twelve years of Dizaj and Mashin Stations on Baranduz-Chay basin in west Azerbijan and Zard-River basin in Khouzestan provinces were used, respectively. The first six years of data was applied for fitting the model. The next three years was used to calibration and the remained three yeas utilized for testing the models
Adidas nopib jalgpalli EM-ilt suurima kasu / Erik Aru
Aru, Erik
2007-01-01
Adidas esitles ametlikku EM-i jalgpalli EUROPASS. Adidase jalgpallivarustuse müük langes suurvõistluste puudumise tõttu sel aastal miljardi euroni, 2008. aastaks kavatsetakse taas jõuda 1,2 miljardi kroonini. Nike tahab suurendada oma turuosa jalgpallivarustuse vallas. Lisa: Jalgpallureid varustab neli firmat
New Software for Ensemble Creation in the Spitzer-Space-Telescope Operations Database
Laher, Russ; Rector, John
2004-01-01
Some of the computer pipelines used to process digital astronomical images from NASA's Spitzer Space Telescope require multiple input images, in order to generate high-level science and calibration products. The images are grouped into ensembles according to well documented ensemble-creation rules by making explicit associations in the operations Informix database at the Spitzer Science Center (SSC). The advantage of this approach is that a simple database query can retrieve the required ensemble of pipeline input images. New and improved software for ensemble creation has been developed. The new software is much faster than the existing software because it uses pre-compiled database stored-procedures written in Informix SPL (SQL programming language). The new software is also more flexible because the ensemble creation rules are now stored in and read from newly defined database tables. This table-driven approach was implemented so that ensemble rules can be inserted, updated, or deleted without modifying software.
DEFF Research Database (Denmark)
Sunyer Pinya, Maria Antonia; Madsen, Henrik; Rosbjerg, Dan
2013-01-01
all these methods is that the climate models are independent. This study addresses the validity of this assumption for two ensembles of regional climate models (RCMs) from the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) project based on the land cells covering Denmark....... Daily precipitation indices from an ensemble of RCMs driven by the 40-yrECMWFRe-Analysis (ERA-40) and an ensemble of the same RCMs driven by different general circulation models (GCMs) are analyzed. Two different methods are used to estimate the amount of independent information in the ensembles....... These are based on different statistical properties of a measure of climate model error. Additionally, a hierarchical cluster analysis is carried out. Regardless of the method used, the effective number of RCMs is smaller than the total number of RCMs. The estimated effective number of RCMs varies depending...
Visualization and classification of physiological failure modes in ensemble hemorrhage simulation
Zhang, Song; Pruett, William Andrew; Hester, Robert
2015-01-01
In an emergency situation such as hemorrhage, doctors need to predict which patients need immediate treatment and care. This task is difficult because of the diverse response to hemorrhage in human population. Ensemble physiological simulations provide a means to sample a diverse range of subjects and may have a better chance of containing the correct solution. However, to reveal the patterns and trends from the ensemble simulation is a challenging task. We have developed a visualization framework for ensemble physiological simulations. The visualization helps users identify trends among ensemble members, classify ensemble member into subpopulations for analysis, and provide prediction to future events by matching a new patient's data to existing ensembles. We demonstrated the effectiveness of the visualization on simulated physiological data. The lessons learned here can be applied to clinically-collected physiological data in the future.
Visualization of Time-Varying Weather Ensembles across Multiple Resolutions.
Biswas, Ayan; Lin, Guang; Liu, Xiaotong; Shen, Han-Wei
2017-01-01
Uncertainty quantification in climate ensembles is an important topic for the domain scientists, especially for decision making in the real-world scenarios. With powerful computers, simulations now produce time-varying and multi-resolution ensemble data sets. It is of extreme importance to understand the model sensitivity given the input parameters such that more computation power can be allocated to the parameters with higher influence on the output. Also, when ensemble data is produced at different resolutions, understanding the accuracy of different resolutions helps the total time required to produce a desired quality solution with improved storage and computation cost. In this work, we propose to tackle these non-trivial problems on the Weather Research and Forecasting (WRF) model output. We employ a moment independent sensitivity measure to quantify and analyze parameter sensitivity across spatial regions and time domain. A comparison of clustering structures across three resolutions enables the users to investigate the sensitivity variation over the spatial regions of the five input parameters. The temporal trend in the sensitivity values is explored via an MDS view linked with a line chart for interactive brushing. The spatial and temporal views are connected to provide a full exploration system for complete spatio-temporal sensitivity analysis. To analyze the accuracy across varying resolutions, we formulate a Bayesian approach to identify which regions are better predicted at which resolutions compared to the observed precipitation. This information is aggregated over the time domain and finally encoded in an output image through a custom color map that guides the domain experts towards an adaptive grid implementation given a cost model. Users can select and further analyze the spatial and temporal error patterns for multi-resolution accuracy analysis via brushing and linking on the produced image. In this work, we collaborate with a domain expert whose
First Assessment of Itaipu Dam Ensemble Inflow Forecasting System
Mainardi Fan, Fernando; Machado Vieira Lisboa, Auder; Gomes Villa Trinidad, Giovanni; Rógenes Monteiro Pontes, Paulo; Collischonn, Walter; Tucci, Carlos; Costa Buarque, Diogo
2017-04-01
Inflow forecasting for Hydropower Plants (HPP) Dams is one of the prominent uses for hydrological forecasts. A very important HPP in terms of energy generation for South America is the Itaipu Dam, located in the Paraná River, between Brazil and Paraguay countries, with a drainage area of 820.000km2. In this work, we present the development of an ensemble forecasting system for Itaipu, operational since November 2015. The system is based in the MGB-IPH hydrological model, includes hydrodynamics simulations of the main river, and is run every day morning forced by seven different rainfall forecasts: (i) CPTEC-ETA 15km; (ii) CPTEC-BRAMS 5km; (iii) SIMEPAR WRF Ferrier; (iv) SIMEPAR WRF Lin; (v) SIMEPAR WRF Morrison; (vi) SIMEPAR WRF WDM6; (vii) SIMEPAR MEDIAN. The last one (vii) corresponds to the median value of SIMEPAR WRF model versions (iii to vi) rainfall forecasts. Besides the developed system, the "traditional" method for inflow forecasting generation for the Itaipu Dam is also run every day. This traditional method consists in the approximation of the future inflow based on the discharge tendency of upstream telemetric gauges. Nowadays, after all the forecasts are run, the hydrology team of Itaipu develop a consensus forecast, based on all obtained results, which is the one used for the Itaipu HPP Dam operation. After one year of operation a first evaluation of the Ensemble Forecasting System was conducted. Results show that the system performs satisfactory for rising flows up to five days lead time. However, some false alarms were also issued by most ensemble members in some cases. And not in all cases the system performed better than the traditional method, especially during hydrograph recessions. In terms of meteorological forecasts, some members usage are being discontinued. In terms of the hydrodynamics representation, it seems that a better information of rivers cross section could improve hydrographs recession curves forecasts. Those opportunities for
Automated ensemble assembly and validation of microbial genomes
2014-01-01
Background The continued democratization of DNA sequencing has sparked a new wave of development of genome assembly and assembly validation methods. As individual research labs, rather than centralized centers, begin to sequence the majority of new genomes, it is important to establish best practices for genome assembly. However, recent evaluations such as GAGE and the Assemblathon have concluded that there is no single best approach to genome assembly. Instead, it is preferable to generate multiple assemblies and validate them to determine which is most useful for the desired analysis; this is a labor-intensive process that is often impossible or unfeasible. Results To encourage best practices supported by the community, we present iMetAMOS, an automated ensemble assembly pipeline; iMetAMOS encapsulates the process of running, validating, and selecting a single assembly from multiple assemblies. iMetAMOS packages several leading open-source tools into a single binary that automates parameter selection and execution of multiple assemblers, scores the resulting assemblies based on multiple validation metrics, and annotates the assemblies for genes and contaminants. We demonstrate the utility of the ensemble process on 225 previously unassembled Mycobacterium tuberculosis genomes as well as a Rhodobacter sphaeroides benchmark dataset. On these real data, iMetAMOS reliably produces validated assemblies and identifies potential contamination without user intervention. In addition, intelligent parameter selection produces assemblies of R. sphaeroides comparable to or exceeding the quality of those from the GAGE-B evaluation, affecting the relative ranking of some assemblers. Conclusions Ensemble assembly with iMetAMOS provides users with multiple, validated assemblies for each genome. Although computationally limited to small or mid-sized genomes, this approach is the most effective and reproducible means for generating high-quality assemblies and enables users to
Attributing varying ENSO amplitudes in climate model ensembles
Watanabe, M.; Kug, J.-S.; Jin, F.-F.; Collins, M.; Ohba, M.; Wittenberg, A.
2012-04-01
Realistic simulation of the El Niño-Southern Oscillation (ENSO) phenomenon, which has a great impact on the global weather and climate, is of primary importance in the coupled atmosphere-ocean modeling. Nevertheless, the ENSO amplitude is known to vary considerably in a multi-model ensemble (MME) archived in the coupled model inter-comparison project phase 3 (CMIP3). Given a large uncertainty in the atmospheric processes having a substantial influence to the models' ENSO intensity, we constructed physics parameter ensembles (PPEs) based on four climate models (two of them are included in the CMIP5 archive) in which parameters in the atmospheric parameterization schemes have been perturbed. Analysis to the 33-member PPEs reveals a positive relationship between the ENSO amplitude and the mean precipitation over the eastern equatorial Pacific in each model. This relationship is explained by the mean state difference controling the ENSO activity but not by the ENSO rectification of the mean state. The wetter mean state in the eastern equatorial Pacific favors an eastward shift in the equatorial zonal wind stress response to El Niño/La Niña, which acts to increase the ENSO amplitude due to enhanced coupled instability. Such a relationship, however, cannot be seen in both CMIP3 and CMIP5 MMEs, indicating that the above mechanism does not explain the diversity in ENSO amplitude across the models. Yet, ensemble historical runs available for some of the CMIP5 models show the positive relationship between the ENSO amplitude and the mean precipitation, providing a useful insight into the ENSO changes under the global warming in individual models.
Control of inhomogeneous atomic ensembles of hyperfine qudits
DEFF Research Database (Denmark)
Mischuck, Brian Edward; Merkel, Seth T.; Deutsch, Ivan H.
2012-01-01
We study the ability to control d-dimensional quantum systems (qudits) encoded in the hyperfine spin of alkali-metal atoms through the application of radio- and microwave-frequency magnetic fields in the presence of inhomogeneities in amplitude and detuning. Such a capability is essential...... to the design of robust pulses that mitigate the effects of experimental uncertainty and also for application to tomographic addressing of particular members of an extended ensemble. We study the problem of preparing an arbitrary state in the Hilbert space from an initial fiducial state. We prove...
Ensemble Methods in Data Mining Improving Accuracy Through Combining Predictions
Seni, Giovanni
2010-01-01
This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although e
Utilising Tree-Based Ensemble Learning for Speaker Segmentation
DEFF Research Database (Denmark)
Abou-Zleikha, Mohamed; Tan, Zheng-Hua; Christensen, Mads Græsbøll
2014-01-01
for a certain condition, the model becomes biased to the data used for training limiting the model’s generalisation ability. In this paper, we propose a BIC-based tuning-free approach for speaker segmentation through the use of ensemble-based learning. A forest of segmentation trees is constructed in which each...... points. The proposed approach is tested on artificially created conversations from the TIMIT database. The approach proposed show very accurate results comparable to those achieved by the-state-of-the-art methods with a 9% (absolute) higher F 1 compared with the standard ΔBIC with optimally tuned penalty...
Ensemble Asteroseismology of the Young Open Cluster NGC 2244
Aerts, Conny; Zwintz, Konstanze; Marcos-Arenal, Pablo; Moravveji, Ehsan; Degroote, Pieter; Papics, Peter; Tkachenko, Andrew; De Ridder, Joris; Briquet, Maryline; Thoul, Anne; Saesen, Sophie; Mowlavi, Nami; Barblan, Fabio; Neiner, Coralie; Pavlovski, Kresimir
2013-01-01
Our goal is to perform in-depth ensemble asteroseismology of the young open cluster NGC2244 with the 2-wheel Kepler mission. While the nominal Kepler mission already implied a revolution in stellar physics for solar-type stars and red giants, it was not possible to perform asteroseismic studies of massive OB stars because such targets were carefully avoided in the FoV in order not to disturb the exoplanet hunting. Now is an excellent time to fill this hole in mission capacity and to focus on ...
M-Learning & e-inclusion. Il progetto ENSEMBLE
Directory of Open Access Journals (Sweden)
Maria Ranieri
2013-03-01
Full Text Available Nel corso degli ultimi dieci anni, l’Unione europea ha più volte sottolineato, nelle sue raccomandazioni e documenti, il ruolo che le ICT possono svolgere per favorire le opportunità di partecipazione ed integrazione dei cittadini più svantaggiati. In questo contesto, il progetto ENSEMBLE, qui presentato, si è proposto di mettere a punto una strategia d’impiego delle tecnologie della comunicazione per promuovere l’integrazione socio-culturale dei cittadini immigrati, facendo leva sull’uso di tecnologie come il lettore MP3 e il telefono cellulare, e sperimentando metodologie didattiche e formati comunicativi adatti agli strumenti impiegati.
Benchmarking ensemble streamflow prediction skill in the UK
Harrigan, Shaun; Prudhomme, Christel; Parry, Simon; Smith, Katie; Tanguy, Maliko
2018-03-01
Skilful hydrological forecasts at sub-seasonal to seasonal lead times would be extremely beneficial for decision-making in water resources management, hydropower operations, and agriculture, especially during drought conditions. Ensemble streamflow prediction (ESP) is a well-established method for generating an ensemble of streamflow forecasts in the absence of skilful future meteorological predictions, instead using initial hydrologic conditions (IHCs), such as soil moisture, groundwater, and snow, as the source of skill. We benchmark when and where the ESP method is skilful across a diverse sample of 314 catchments in the UK and explore the relationship between catchment storage and ESP skill. The GR4J hydrological model was forced with historic climate sequences to produce a 51-member ensemble of streamflow hindcasts. We evaluated forecast skill seamlessly from lead times of 1 day to 12 months initialized at the first of each month over a 50-year hindcast period from 1965 to 2015. Results showed ESP was skilful against a climatology benchmark forecast in the majority of catchments across all lead times up to a year ahead, but the degree of skill was strongly conditional on lead time, forecast initialization month, and individual catchment location and storage properties. UK-wide mean ESP skill decayed exponentially as a function of lead time with continuous ranked probability skill scores across the year of 0.75, 0.20, and 0.11 for 1-day, 1-month, and 3-month lead times, respectively. However, skill was not uniform across all initialization months. For lead times up to 1 month, ESP skill was higher than average when initialized in summer and lower in winter months, whereas for longer seasonal and annual lead times skill was higher when initialized in autumn and winter months and lowest in spring. ESP was most skilful in the south and east of the UK, where slower responding catchments with higher soil moisture and groundwater storage are mainly located
S-AMP: Approximate Message Passing for General Matrix Ensembles
DEFF Research Database (Denmark)
Cakmak, Burak; Winther, Ole; Fleury, Bernard H.
2014-01-01
We propose a novel iterative estimation algorithm for linear observation models called S-AMP. The fixed points of S-AMP are the stationary points of the exact Gibbs free energy under a set of (first- and second-) moment consistency constraints in the large system limit. S-AMP extends...... the approximate message-passing (AMP) algorithm to general matrix ensembles with a well-defined large system size limit. The generalization is based on the S-transform (in free probability) of the spectrum of the measurement matrix. Furthermore, we show that the optimality of S-AMP follows directly from its...
Ensemble learned vaccination uptake prediction using web search queries
DEFF Research Database (Denmark)
Hansen, Niels Dalum; Lioma, Christina; Mølbak, Kåre
2016-01-01
We present a method that uses ensemble learning to combine clinical and web-mined time-series data in order to predict future vaccination uptake. The clinical data is official vaccination registries, and the web data is query frequencies collected from Google Trends. Experiments with official...... vaccine records show that our method predicts vaccination uptake eff?ectively (4.7 Root Mean Squared Error). Whereas performance is best when combining clinical and web data, using solely web data yields comparative performance. To our knowledge, this is the ?first study to predict vaccination uptake...
Sub-Ensemble Coastal Flood Forecasting: A Case Study of Hurricane Sandy
Directory of Open Access Journals (Sweden)
Justin A. Schulte
2017-12-01
Full Text Available In this paper, it is proposed that coastal flood ensemble forecasts be partitioned into sub-ensemble forecasts using cluster analysis in order to produce representative statistics and to measure forecast uncertainty arising from the presence of clusters. After clustering the ensemble members, the ability to predict the cluster into which the observation will fall can be measured using a cluster skill score. Additional sub-ensemble and composite skill scores are proposed for assessing the forecast skill of a clustered ensemble forecast. A recently proposed method for statistically increasing the number of ensemble members is used to improve sub-ensemble probabilistic estimates. Through the application of the proposed methodology to Sandy coastal flood reforecasts, it is demonstrated that statistics computed using only ensemble members belonging to a specific cluster are more representative than those computed using all ensemble members simultaneously. A cluster skill-cluster uncertainty index relationship is identified, which is the cluster analog of the documented spread-skill relationship. Two sub-ensemble skill scores are shown to be positively correlated with cluster forecast skill, suggesting that skillfully forecasting the cluster into which the observation will fall is important to overall forecast skill. The identified relationships also suggest that the number of ensemble members within in each cluster can be used as guidance for assessing the potential for forecast error. The inevitable existence of ensemble member clusters in tidally dominated total water level prediction systems suggests that clustering is a necessary post-processing step for producing representative and skillful total water level forecasts.
Structural Fire Fighting Ensembles: Accumulation and Off-gassing of Combustion Products.
Kirk, Katherine M; Logan, Michael B
2015-01-01
Firefighters may be exposed to toxic combustion products not only during fire fighting operations and training, but also afterwards as a result of contact with contaminated structural fire fighting ensembles. This study characterized the deposition of polycyclic aromatic hydrocarbons (PAHs) onto structural fire fighting ensembles and off-gassing of combustion products from ensembles after multiple exposures to hostile structural attack fire environments. A variety of PAHs were deposited onto the outer layer of structural fire fighting ensembles, with no variation in deposition flux between new ensembles and already contaminated ensembles. Contaminants released from ensembles after use included volatile organic compounds, carbonyl compounds, low molecular weight PAHs, and hydrogen cyanide. Air samples collected in a similar manner after laundering of ensembles according to manufacturer specifications indicated that laundering returns off-gassing concentrations of most of the investigated compounds to pre-exposure levels. These findings suggest that contamination of firefighter protective clothing increases with use, and that storage of unlaundered structural fire fighting ensembles in small, unventilated spaces immediately after use may create a source of future exposure to toxic combustion products for fire fighting personnel.
A Comparison of ETKF and Downscaling in a Regional Ensemble Prediction System
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Hanbin Zhang
2015-03-01
Full Text Available Based on the operational regional ensemble prediction system (REPS in China Meteorological Administration (CMA, this paper carried out comparison of two initial condition perturbation methods: an ensemble transform Kalman filter (ETKF and a dynamical downscaling of global ensemble perturbations. One month consecutive tests are implemented to evaluate the performance of both methods in the operational REPS environment. The perturbation characteristics are analyzed and ensemble forecast verifications are conducted; furthermore, a TC case is investigated. The main conclusions are as follows: the ETKF perturbations contain more power at small scales while the ones derived from downscaling contain more power at large scales, and the relative difference of the two types of perturbations on scales become smaller with forecast lead time. The growth of downscaling perturbations is more remarkable, and the downscaling perturbations have larger magnitude than ETKF perturbations at all forecast lead times. However, the ETKF perturbation variance can represent the forecast error variance better than downscaling. Ensemble forecast verification shows slightly higher skill of downscaling ensemble over ETKF ensemble. A TC case study indicates that the overall performance of the two systems are quite similar despite the slightly smaller error of DOWN ensemble than ETKF ensemble at long range forecast lead times.
An Adaptive Approach to Mitigate Background Covariance Limitations in the Ensemble Kalman Filter
Song, Hajoon
2010-07-01
A new approach is proposed to address the background covariance limitations arising from undersampled ensembles and unaccounted model errors in the ensemble Kalman filter (EnKF). The method enhances the representativeness of the EnKF ensemble by augmenting it with new members chosen adaptively to add missing information that prevents the EnKF from fully fitting the data to the ensemble. The vectors to be added are obtained by back projecting the residuals of the observation misfits from the EnKF analysis step onto the state space. The back projection is done using an optimal interpolation (OI) scheme based on an estimated covariance of the subspace missing from the ensemble. In the experiments reported here, the OI uses a preselected stationary background covariance matrix, as in the hybrid EnKF–three-dimensional variational data assimilation (3DVAR) approach, but the resulting correction is included as a new ensemble member instead of being added to all existing ensemble members. The adaptive approach is tested with the Lorenz-96 model. The hybrid EnKF–3DVAR is used as a benchmark to evaluate the performance of the adaptive approach. Assimilation experiments suggest that the new adaptive scheme significantly improves the EnKF behavior when it suffers from small size ensembles and neglected model errors. It was further found to be competitive with the hybrid EnKF–3DVAR approach, depending on ensemble size and data coverage.
A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting
International Nuclear Information System (INIS)
Tang, Ling; Yu, Lean; Wang, Shuai; Li, Jianping; Wang, Shouyang
2012-01-01
Highlights: ► A hybrid ensemble learning paradigm integrating EEMD and LSSVR is proposed. ► The hybrid ensemble method is useful to predict time series with high volatility. ► The ensemble method can be used for both one-step and multi-step ahead forecasting. - Abstract: In this paper, a novel hybrid ensemble learning paradigm integrating ensemble empirical mode decomposition (EEMD) and least squares support vector regression (LSSVR) is proposed for nuclear energy consumption forecasting, based on the principle of “decomposition and ensemble”. This hybrid ensemble learning paradigm is formulated specifically to address difficulties in modeling nuclear energy consumption, which has inherently high volatility, complexity and irregularity. In the proposed hybrid ensemble learning paradigm, EEMD, as a competitive decomposition method, is first applied to decompose original data of nuclear energy consumption (i.e. a difficult task) into a number of independent intrinsic mode functions (IMFs) of original data (i.e. some relatively easy subtasks). Then LSSVR, as a powerful forecasting tool, is implemented to predict all extracted IMFs independently. Finally, these predicted IMFs are aggregated into an ensemble result as final prediction, using another LSSVR. For illustration and verification purposes, the proposed learning paradigm is used to predict nuclear energy consumption in China. Empirical results demonstrate that the novel hybrid ensemble learning paradigm can outperform some other popular forecasting models in both level prediction and directional forecasting, indicating that it is a promising tool to predict complex time series with high volatility and irregularity.
Song, Xinxia
A cyclone relative approach and an ensemble sensitivity analysis (ESA) were applied to explore some of the possible reasons for extratropical cyclone center mean sea level pressure errors. For the cyclone relative approach, data were extracted within a box region and saved every 6 hours. GEFS (Global Ensemble Forecast System) control member and ensemble members forecast data were utilized in this research. Around the cyclone, errors in fields such as mean sea level pressure and precipitation rapidly increase from day 4 to day 5, and the errors of all fields examined are consistent with the overpredicted and underpredicted cyclones. For example, for an overforecast cyclone, it has more intense PV (potential vorticity) at 320K, a stronger temperature gradient on 925hPa, and greater simulated precipitation than observed, while the underpredicted cyclones have the opposite results. The day 3 precipitation errors and 925 hPa temperature gradient errors are relatively large before the cyclone errors develop, thus suggesting that moisture and latent heat and dry dynamics could contribute to cyclogenesis intensity errors. ESA accompanied with cyclone relative approach implies that moisture may contribute to the cyclogenesis error at an initial stage of cyclone development. There are also hints of upstream errors growing and moving in from ESA cases. AA possible explanation for underpredicted cyclones might be that less moisture on the warm side of cyclones leads to a weaker upper tropospheric latent heat release, and hence a less amplified PV field, and a weaker cyclone. In addition, a weaker temperature gradient at 925 hPa could also cause a weaker cyclone.
Scanning Nanospin Ensemble Microscope for Nanoscale Magnetic and Thermal Imaging.
Tetienne, Jean-Philippe; Lombard, Alain; Simpson, David A; Ritchie, Cameron; Lu, Jianing; Mulvaney, Paul; Hollenberg, Lloyd C L
2016-01-13
Quantum sensors based on solid-state spins provide tremendous opportunities in a wide range of fields from basic physics and chemistry to biomedical imaging. However, integrating them into a scanning probe microscope to enable practical, nanoscale quantum imaging is a highly challenging task. Recently, the use of single spins in diamond in conjunction with atomic force microscopy techniques has allowed significant progress toward this goal, but generalization of this approach has so far been impeded by long acquisition times or by the absence of simultaneous topographic information. Here, we report on a scanning quantum probe microscope which solves both issues by employing a nanospin ensemble hosted in a nanodiamond. This approach provides up to an order of magnitude gain in acquisition time while preserving sub-100 nm spatial resolution both for the quantum sensor and topographic images. We demonstrate two applications of this microscope. We first image nanoscale clusters of maghemite particles through both spin resonance spectroscopy and spin relaxometry, under ambient conditions. Our images reveal fast magnetic field fluctuations in addition to a static component, indicating the presence of both superparamagnetic and ferromagnetic particles. We next demonstrate a new imaging modality where the nanospin ensemble is used as a thermometer. We use this technique to map the photoinduced heating generated by laser irradiation of a single gold nanoparticle in a fluid environment. This work paves the way toward new applications of quantum probe microscopy such as thermal/magnetic imaging of operating microelectronic devices and magnetic detection of ion channels in cell membranes.
Extreme winds over Europe in the ENSEMBLES regional climate models
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S. D. Outten
2013-05-01
Full Text Available Extreme winds cause vast amounts of damage every year and represent a major concern for numerous industries including construction, afforestation, wind energy and many others. Under a changing climate, the intensity and frequency of extreme events are expected to change, and accurate projections of these changes will be invaluable to decision makers and society as a whole. This work examines four regional climate model downscalings over Europe following the SRES A1B scenario from the "ENSEMBLE-based Predictions of Climate Changes and their Impacts" project (ENSEMBLES. It investigates the projected changes in the 50 yr return wind speeds and the associated uncertainties. This is accomplished by employing the peaks-over-threshold method with the use of the generalised Pareto distribution. The models show that, for much of Europe, the 50 yr return wind is projected to change by less than 2 m s−1, while the uncertainties associated with the statistical estimates are larger than this. In keeping with previous works in this field, the largest source of uncertainty is found to be the inter-model spread, with some locations showing differences in the 50 yr return wind of over 20 m s−1 between two different downscalings.
Singular vectors, predictability and ensemble forecasting for weather and climate
International Nuclear Information System (INIS)
Palmer, T N; Zanna, Laure
2013-01-01
The local instabilities of a nonlinear dynamical system can be characterized by the leading singular vectors of its linearized operator. The leading singular vectors are perturbations with the greatest linear growth and are therefore key in assessing the system’s predictability. In this paper, the analysis of singular vectors for the predictability of weather and climate and ensemble forecasting is discussed. An overview of the role of singular vectors in informing about the error growth rate in numerical models of the atmosphere is given. This is followed by their use in the initialization of ensemble weather forecasts. Singular vectors for the ocean and coupled ocean–atmosphere system in order to understand the predictability of climate phenomena such as ENSO and meridional overturning circulation are reviewed and their potential use to initialize seasonal and decadal forecasts is considered. As stochastic parameterizations are being implemented, some speculations are made about the future of singular vectors for the predictability of weather and climate for theoretical applications and at the operational level. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Lyapunov analysis: from dynamical systems theory to applications’. (review)
Class-specific Error Bounds for Ensemble Classifiers
Energy Technology Data Exchange (ETDEWEB)
Prenger, R; Lemmond, T; Varshney, K; Chen, B; Hanley, W
2009-10-06
The generalization error, or probability of misclassification, of ensemble classifiers has been shown to be bounded above by a function of the mean correlation between the constituent (i.e., base) classifiers and their average strength. This bound suggests that increasing the strength and/or decreasing the correlation of an ensemble's base classifiers may yield improved performance under the assumption of equal error costs. However, this and other existing bounds do not directly address application spaces in which error costs are inherently unequal. For applications involving binary classification, Receiver Operating Characteristic (ROC) curves, performance curves that explicitly trade off false alarms and missed detections, are often utilized to support decision making. To address performance optimization in this context, we have developed a lower bound for the entire ROC curve that can be expressed in terms of the class-specific strength and correlation of the base classifiers. We present empirical analyses demonstrating the efficacy of these bounds in predicting relative classifier performance. In addition, we specify performance regions of the ROC curve that are naturally delineated by the class-specific strengths of the base classifiers and show that each of these regions can be associated with a unique set of guidelines for performance optimization of binary classifiers within unequal error cost regimes.
Rainfall estimation with TFR model using Ensemble Kalman filter
Asyiqotur Rohmah, Nabila; Apriliani, Erna
2018-03-01
Rainfall fluctuation can affect condition of other environment, correlated with economic activity and public health. The increasing of global average temperature is influenced by the increasing of CO2 in the atmosphere, which caused climate change. Meanwhile, the forests as carbon sinks that help keep the carbon cycle and climate change mitigation. Climate change caused by rainfall intensity deviations can affect the economy of a region, and even countries. It encourages research on rainfall associated with an area of forest. In this study, the mathematics model that used is a model which describes the global temperatures, forest cover, and seasonal rainfall called the TFR (temperature, forest cover, and rainfall) model. The model will be discretized first, and then it will be estimated by the method of Ensemble Kalman Filter (EnKF). The result shows that the more ensembles used in estimation, the better the result is. Also, the accurateness of simulation result is influenced by measurement variable. If a variable is measurement data, the result of simulation is better.
Medium Range Ensembles Flood Forecasts for Community Level Applications
Fakhruddin, S.; Kawasaki, A.; Babel, M. S.; AIT
2013-05-01
Early warning is a key element for disaster risk reduction. In recent decades, there has been a major advancement in medium range and seasonal forecasting. These could provide a great opportunity to improve early warning systems and advisories for early action for strategic and long term planning. This could result in increasing emphasis on proactive rather than reactive management of adverse consequences of flood events. This can be also very helpful for the agricultural sector by providing a diversity of options to farmers (e.g. changing cropping pattern, planting timing, etc.). An experimental medium range (1-10 days) flood forecasting model has been developed for Bangladesh which provides 51 set of discharge ensembles forecasts of one to ten days with significant persistence and high certainty. This could help communities (i.e. farmer) for gain/lost estimation as well as crop savings. This paper describe the application of ensembles probabilistic flood forecast at the community level for differential decision making focused on agriculture. The framework allows users to interactively specify the objectives and criteria that are germane to a particular situation, and obtain the management options that are possible, and the exogenous influences that should be taken into account before planning and decision making. risk and vulnerability assessment was conducted through community consultation. The forecast lead time requirement, users' needs, impact and management options for crops, livestock and fisheries sectors were identified through focus group discussions, informal interviews and questionnaire survey.
Observation impact in short-range ensemble forecasts
Necker, Tobias; Weissmann, Martin; Sommer, Matthias
2017-04-01
Observation impact assessment offers a great potential for convective-scale data assimilation. It provides information on the contribution of various observations to the observing system and is crucial for the refinement of the observing network as well as the data assimilation system. In the framework of the Hans-Ertel Centre for Weather Research (HErZ), a method for an ensemble-based approximation of observation impact using an observation-based verification metric was developed over the past years. Instead of the subsequent analysis, the method uses subsequent observations for verification that are considerably more independent from the forecast. Recently, the method was adapted to use independent observation types for verification. Results of the impact assessment using radar-derived precipitation observations for verification are presented. Furthermore the impact time of different observation types is investigated. The study covers the high impact weather period in summer 2016 using the pre-operational convective-scale ensemble system of Deutscher Wetterdienst (KENDA/COSMO-DE).
Voltammetry of redox analytes at trace concentrations with nanoelectrode ensembles.
Moretto, Ligia Maria; Pepe, Niki; Ugo, Paolo
2004-04-19
Gold nanoelectrodes ensembles (NEEs) have been prepared by electroless plating of Au nanoelectrode elements within the pores of a microporous polycarbonate template membrane. Cyclic voltammograms recorded in (ferrocenylmethyl) trimethylammonium hexafluorophosphate (FA(+) PF(6)(-)) solutions showed that these NEEs operate in the "total-overlap" response regime, giving well resolved peak shaped voltammograms. Experimental results show that the faradaic/background currents ratios at the NEE are independent on the total geometric area of the ensemble, so that NEE can be enlarged or miniaturized at pleasure without influencing the very favorable signal/noise ratio. Differential pulse voltammetry (DPV) at the NEE is optimized for direct determinations at trace levels. DPV at NEE allowed the determination (with no preconcentration) of trace amounts of FA(+), with a detection limit of 0.02muM. The use of NEE and DPV in cytochrome c (cyt c) solutions showed the possibility to observe the direct electrochemistry of submicromolar concentration of the protein, even without the need of adding any promoter or mediator.
OSPREY: protein design with ensembles, flexibility, and provable algorithms.
Gainza, Pablo; Roberts, Kyle E; Georgiev, Ivelin; Lilien, Ryan H; Keedy, Daniel A; Chen, Cheng-Yu; Reza, Faisal; Anderson, Amy C; Richardson, David C; Richardson, Jane S; Donald, Bruce R
2013-01-01
We have developed a suite of protein redesign algorithms that improves realistic in silico modeling of proteins. These algorithms are based on three characteristics that make them unique: (1) improved flexibility of the protein backbone, protein side-chains, and ligand to accurately capture the conformational changes that are induced by mutations to the protein sequence; (2) modeling of proteins and ligands as ensembles of low-energy structures to better approximate binding affinity; and (3) a globally optimal protein design search, guaranteeing that the computational predictions are optimal with respect to the input model. Here, we illustrate the importance of these three characteristics. We then describe OSPREY, a protein redesign suite that implements our protein design algorithms. OSPREY has been used prospectively, with experimental validation, in several biomedically relevant settings. We show in detail how OSPREY has been used to predict resistance mutations and explain why improved flexibility, ensembles, and provability are essential for this application. OSPREY is free and open source under a Lesser GPL license. The latest version is OSPREY 2.0. The program, user manual, and source code are available at www.cs.duke.edu/donaldlab/software.php. osprey@cs.duke.edu. Copyright © 2013 Elsevier Inc. All rights reserved.
Statistical hadronization and hadronic micro-canonical ensemble II
International Nuclear Information System (INIS)
Becattini, F.; Ferroni, L.
2004-01-01
We present a Monte Carlo calculation of the micro-canonical ensemble of the ideal hadron-resonance gas including all known states up to a mass of about 1.8 GeV and full quantum statistics. The micro-canonical average multiplicities of the various hadron species are found to converge to the canonical ones for moderately low values of the total energy, around 8 GeV, thus bearing out previous analyses of hadronic multiplicities in the canonical ensemble. The main numerical computing method is an importance sampling Monte Carlo algorithm using the product of Poisson distributions to generate multi-hadronic channels. It is shown that the use of this multi-Poisson distribution allows for an efficient and fast computation of averages, which can be further improved in the limit of very large clusters. We have also studied the fitness of a previously proposed computing method, based on the Metropolis Monte Carlo algorithm, for event generation in the statistical hadronization model. We find that the use of the multi-Poisson distribution as proposal matrix dramatically improves the computation performance. However, due to the correlation of subsequent samples, this method proves to be generally less robust and effective than the importance sampling method. (orig.)
Towards constraining extreme temperature projections of the CMIP5 ensemble
Vogel, Martha-Marie; Orth, René; Isabelle Seneviratne, Sonia
2016-04-01
The frequency and intensity of heat waves is expected to change in future in response to global warming. Given the severe impacts of heat waves on ecosystems and society it is important to understand how and where they will intensify. Projections of extreme hot temperatures in the IPCC AR5 model ensemble show large uncertainties for projected changes of extreme temperatures in particular in Central Europe. In this region land-atmosphere coupling can contribute substantially to the development of heat waves. This coupling is also subject to change in future, while model projections display considerable spread. In this work we link projections of changes in extreme temperatures and of changes in land-atmosphere interactions with a particular focus on Central Europe. Uncertainties in projected extreme temperatures can be partly explained by different projected changes of the interplay between latent heat and temperature as well as soil moisture. Given the considerable uncertainty in land-atmosphere coupling representation already in the current climate, we furthermore employ observational data sets to constrain the model ensemble, and consequently the extreme temperature projections.
Time-Hierarchical Clustering and Visualization of Weather Forecast Ensembles.
Ferstl, Florian; Kanzler, Mathias; Rautenhaus, Marc; Westermann, Rudiger
2017-01-01
We propose a new approach for analyzing the temporal growth of the uncertainty in ensembles of weather forecasts which are started from perturbed but similar initial conditions. As an alternative to traditional approaches in meteorology, which use juxtaposition and animation of spaghetti plots of iso-contours, we make use of contour clustering and provide means to encode forecast dynamics and spread in one single visualization. Based on a given ensemble clustering in a specified time window, we merge clusters in time-reversed order to indicate when and where forecast trajectories start to diverge. We present and compare different visualizations of the resulting time-hierarchical grouping, including space-time surfaces built by connecting cluster representatives over time, and stacked contour variability plots. We demonstrate the effectiveness of our visual encodings with forecast examples of the European Centre for Medium-Range Weather Forecasts, which convey the evolution of specific features in the data as well as the temporally increasing spatial variability.
Introducing E-tec: Ensemble-based Topological Entropy Calculation
Roberts, Eric; Smith, Spencer; Sindi, Suzanne; Smith, Kevin
2017-11-01
Topological entropy is a measurement of orbit complexity in a dynamical system that can be estimated in 2D by embedding an initial material curve L0 in the fluid and estimating its growth under the evolution of the flow. This growth is given by L (t) = | L0 | eht , where L (t) is the length of the curve as a function of t and h is the topological entropy. In order to develop a method for computing Eq. (1) that will efficiently scale up in both system size and modeling time, one must be clever about extracting the maximum information from the limited trajectories available. The relative motion of trajectories through phase space encodes global information that is not contained in any individual trajectory. That is, extra information is ''hiding'' in an ensemble of classical trajectories, which is not exploited in a trajectory-by-trajectory approach. Using tools from computational geometry, we introduce a new algorithm designed to take advantage of such additional information that requires only potentially sparse sets of particle trajectories as input and no reliance on any detailed knowledge of the velocity field: the Ensemble-Based Topological Entropy Calculation, or E-tec.
Altered Cortical Ensembles in Mouse Models of Schizophrenia.
Hamm, Jordan P; Peterka, Darcy S; Gogos, Joseph A; Yuste, Rafael
2017-04-05
In schizophrenia, brain-wide alterations have been identified at the molecular and cellular levels, yet how these phenomena affect cortical circuit activity remains unclear. We studied two mouse models of schizophrenia-relevant disease processes: chronic ketamine (KET) administration and Df(16)A +/- , modeling 22q11.2 microdeletions, a genetic variant highly penetrant for schizophrenia. Local field potential recordings in visual cortex confirmed gamma-band abnormalities similar to patient studies. Two-photon calcium imaging of local cortical populations revealed in both models a deficit in the reliability of neuronal coactivity patterns (ensembles), which was not a simple consequence of altered single-neuron activity. This effect was present in ongoing and sensory-evoked activity and was not replicated by acute ketamine administration or pharmacogenetic parvalbumin-interneuron suppression. These results are consistent with the hypothesis that schizophrenia is an "attractor" disease and demonstrate that degraded neuronal ensembles are a common consequence of diverse genetic, cellular, and synaptic alterations seen in chronic schizophrenia. Published by Elsevier Inc.
Precipitation variability and extremes in ENSEMBLES RCM simulations over Portugal
Soares, P. M. M.; Cardoso, R. M.; Miranda, P. M. A.; Viterbo, P.; Belo-Pereira, M.
2012-04-01
A new dataset of daily gridded observations of precipitation, computed from over 400 stations in Portugal, is used to assess the performance of 12 regional climate models at 25 km resolution, from the ENSEMBLES set, all forced by ERA-40 boundary conditions, for the 1961-2000 period. Standard point error statistics, calculated from grid point and basin aggregated data, and precipitation related climate indices are used to analyze the performance of the different models in representing the main spatial and temporal features of the regional climate, and its extreme events. As a whole, the ENSEMBLES models are found to achieve a good representation of those features, with good spatial correlations with observations. There is a small but relevant negative bias in precipitation, especially in the driest months, leading to systematic errors in some indices. The underprediction of precipitation occurs in most percentiles, although this deficiency is partially corrected at the basin level. Interestingly, some of the conclusions concerning the performance of the models are different of what has been found for the contiguous territory of Spain. Finally, models behave quite differently in the simulation of some important aspects of local climate, from the mean climatology to high precipitation regimes in localized mountain ranges and in the subsequent drier regions.
ENSEMBLES RCM simulations: precipitation variability and extremes over Portugal
Soares, P. M.; Cardoso, R. M.; Miranda, P. M.; Belo-Pereira, M.
2012-12-01
A new dataset of daily gridded observations of precipitation, computed from over 400 stations in Portugal, is used to assess the performance of 12 regional climate models at 25 km resolution, from the ENSEMBLES set, all forced by ERA-40 boundary conditions, for the 1961-2000 period. Standard point error statistics, calculated from grid point and basin aggregated data, and precipitation related climate indices are used to analyze the performance of the different models in representing the main spatial and temporal features of the regional climate, and its extreme events. As a whole, the ENSEMBLES models are found to achieve a good representation of those features, with good spatial correlations with observations. There is a small but relevant negative bias in precipitation, especially in the driest months, leading to systematic errors in some indices. The underprediction of precipitation occurs in most percentiles, although this deficiency is partially corrected at the basin level. Interestingly, some of the conclusions concerning the performance of the models are different of what has been found for the contiguous territory of Spain. Finally, models behave quite differently in the simulation of some important aspects of local climate, from the mean climatology to high precipitation regimes in localized mountain ranges and in the subsequent drier regions.
Ensemble Artifact Design For Context Sensitive Decision Support
Directory of Open Access Journals (Sweden)
Shah J Miah
2014-06-01
Full Text Available Although an improvement of design knowledge is an essential goal of design research, current design research predominantly focuses on knowledge concerning the IT artifact (tool design process, rather than a more holistic understanding encompassing the dynamic usage contexts of a technological artifact. Conceptualising a design in context as an “ensemble artifact” (Sein et al., 2011 provides the basis for a more rigorous treatment. This paper describes an IS artifact design framework that has been generated from the development of several practitioner-oriented decision support systems (DSS in which contextual aspects relevant to practitioners’ decision making are considered as integral design themes. We describe five key dimensions of an ensemble artifact design and show their value in designing practitioner-oriented DSS. The features are user centredness, knowledge sharing, situation-specific customisation, reduced model orientation, and practice based secondary design abilities. It is argued that this understanding can contribute to design research knowledge more effectively both to develop dynamic DSS, and by its extensibility to other artifact designs.
Characterization of the critical submanifolds in quantum ensemble control landscapes
International Nuclear Information System (INIS)
Wu Rebing; Rabitz, Herschel; Hsieh, Michael
2008-01-01
The quantum control landscape is defined as the functional that maps the control variables to the expectation values of an observable over the ensemble of quantum systems. Analyzing the topology of such landscapes is important for understanding the origins of the increasing number of laboratory successes in the optimal control of quantum processes. This paper proposes a simple scheme to compute the characteristics of the critical topology of the quantum ensemble control landscapes showing that the set of disjoint critical submanifolds one-to-one corresponds to a finite number of contingency tables that solely depend on the degeneracy structure of the eigenvalues of the initial system density matrix and the observable whose expectation value is to be maximized. The landscape characteristics can be calculated as functions of the table entries, including the dimensions and the numbers of positive and negative eigenvalues of the Hessian quadratic form of each of the connected components of the critical submanifolds. Typical examples are given to illustrate the effectiveness of this method
The hippocampal CA2 ensemble is sensitive to contextual change.
Wintzer, Marie E; Boehringer, Roman; Polygalov, Denis; McHugh, Thomas J
2014-02-19
Contextual learning involves associating cues with an environment and relating them to past experience. Previous data indicate functional specialization within the hippocampal circuit: the dentate gyrus (DG) is crucial for discriminating similar contexts, whereas CA3 is required for associative encoding and recall. Here, we used Arc/H1a catFISH imaging to address the contribution of the largely overlooked CA2 region to contextual learning by comparing ensemble codes across CA3, CA2, and CA1 in mice exposed to familiar, altered, and novel contexts. Further, to manipulate the quality of information arriving in CA2 we used two hippocampal mutant mouse lines, CA3-NR1 KOs and DG-NR1 KOs, that result in hippocampal CA3 neuronal activity that is uncoupled from the animal's sensory environment. Our data reveal largely coherent responses across the CA axis in control mice in purely novel or familiar contexts; however, in the mutant mice subject to these protocols the CA2 response becomes uncoupled from CA1 and CA3. Moreover, we show in wild-type mice that the CA2 ensemble is more sensitive than CA1 and CA3 to small changes in overall context. Our data suggest that CA2 may be tuned to remap in response to any conflict between stored and current experience.
eccCL: parallelized GPU implementation of Ensemble Classifier Chains.
Riemenschneider, Mona; Herbst, Alexander; Rasch, Ari; Gorlatch, Sergei; Heider, Dominik
2017-08-17
Multi-label classification has recently gained great attention in diverse fields of research, e.g., in biomedical application such as protein function prediction or drug resistance testing in HIV. In this context, the concept of Classifier Chains has been shown to improve prediction accuracy, especially when applied as Ensemble Classifier Chains. However, these techniques lack computational efficiency when applied on large amounts of data, e.g., derived from next-generation sequencing experiments. By adapting algorithms for the use of graphics processing units, computational efficiency can be greatly improved due to parallelization of computations. Here, we provide a parallelized and optimized graphics processing unit implementation (eccCL) of Classifier Chains and Ensemble Classifier Chains. Additionally to the OpenCL implementation, we provide an R-Package with an easy to use R-interface for parallelized graphics processing unit usage. eccCL is a handy implementation of Classifier Chains on GPUs, which is able to process up to over 25,000 instances per second, and thus can be used efficiently in high-throughput experiments. The software is available at http://www.heiderlab.de .
Ensemble-based observation impact estimates using the NCEP GFS
Directory of Open Access Journals (Sweden)
Yoichiro Ota
2013-09-01
Full Text Available The impacts of the assimilated observations on the 24-hour forecasts are estimated with the ensemble-based method proposed by Kalnay et al. using an ensemble Kalman filter (EnKF. This method estimates the relative impact of observations in data assimilation similar to the adjoint-based method proposed by Langland and Baker but without using the adjoint model. It is implemented on the National Centers for Environmental Prediction Global Forecasting System EnKF that has been used as part of operational global data assimilation system at NCEP since May 2012. The result quantifies the overall positive impacts of the assimilated observations and the relative importance of the satellite radiance observations compared to other types of observations, especially for the moisture fields. A simple moving localisation based on the average wind, although not optimal, seems to work well. The method is also used to identify the cause of local forecast failure cases in the 24-hour forecasts. Data-denial experiments of the observations identified as producing a negative impact are performed, and forecast errors are reduced as estimated, thus validating the impact estimation.
Adaptive ensemble Kalman filtering of non-linear systems
Directory of Open Access Journals (Sweden)
Tyrus Berry
2013-07-01
Full Text Available A necessary ingredient of an ensemble Kalman filter (EnKF is covariance inflation, used to control filter divergence and compensate for model error. There is an on-going search for inflation tunings that can be learned adaptively. Early in the development of Kalman filtering, Mehra (1970, 1972 enabled adaptivity in the context of linear dynamics with white noise model errors by showing how to estimate the model error and observation covariances. We propose an adaptive scheme, based on lifting Mehra's idea to the non-linear case, that recovers the model error and observation noise covariances in simple cases, and in more complicated cases, results in a natural additive inflation that improves state estimation. It can be incorporated into non-linear filters such as the extended Kalman filter (EKF, the EnKF and their localised versions. We test the adaptive EnKF on a 40-dimensional Lorenz96 model and show the significant improvements in state estimation that are possible. We also discuss the extent to which such an adaptive filter can compensate for model error, and demonstrate the use of localisation to reduce ensemble sizes for large problems.
IASI Radiance Data Assimilation in Local Ensemble Transform Kalman Filter
Cho, K.; Hyoung-Wook, C.; Jo, Y.
2016-12-01
Korea institute of Atmospheric Prediction Systems (KIAPS) is developing NWP model with data assimilation systems. Local Ensemble Transform Kalman Filter (LETKF) system, one of the data assimilation systems, has been developed for KIAPS Integrated Model (KIM) based on cubed-sphere grid and has successfully assimilated real data. LETKF data assimilation system has been extended to 4D- LETKF which considers time-evolving error covariance within assimilation window and IASI radiance data assimilation using KPOP (KIAPS package for observation processing) with RTTOV (Radiative Transfer for TOVS). The LETKF system is implementing semi operational prediction including conventional (sonde, aircraft) observation and AMSU-A (Advanced Microwave Sounding Unit-A) radiance data from April. Recently, the semi operational prediction system updated radiance observations including GPS-RO, AMV, IASI (Infrared Atmospheric Sounding Interferometer) data at July. A set of simulation of KIM with ne30np4 and 50 vertical levels (of top 0.3hPa) were carried out for short range forecast (10days) within semi operation prediction LETKF system with ensemble forecast 50 members. In order to only IASI impact, our experiments used only conventional and IAIS radiance data to same semi operational prediction set. We carried out sensitivity test for IAIS thinning method (3D and 4D). IASI observation number was increased by temporal (4D) thinning and the improvement of IASI radiance data impact on the forecast skill of model will expect.
Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting
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Bijay Neupane
2017-01-01
Full Text Available Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards the aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participates in forecasting 1-h ahead the price for each hour of a day. We propose two different strategies, namely, the Fixed Weight Method (FWM and the Varying Weight Method (VWM, for selecting each hour’s expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features extracted from the past electricity price data, weather data and calendar data. The proposed ensemble model offers better results than the Autoregressive Integrated Moving Average (ARIMA method, the Pattern Sequence-based Forecasting (PSF method and our previous work using Artificial Neural Networks (ANN alone on the datasets for New York, Australian and Spanish electricity markets.
An ensemble self-training protein interaction article classifier.
Chen, Yifei; Hou, Ping; Manderick, Bernard
2014-01-01
Protein-protein interaction (PPI) is essential to understand the fundamental processes governing cell biology. The mining and curation of PPI knowledge are critical for analyzing proteomics data. Hence it is desired to classify articles PPI-related or not automatically. In order to build interaction article classification systems, an annotated corpus is needed. However, it is usually the case that only a small number of labeled articles can be obtained manually. Meanwhile, a large number of unlabeled articles are available. By combining ensemble learning and semi-supervised self-training, an ensemble self-training interaction classifier called EST_IACer is designed to classify PPI-related articles based on a small number of labeled articles and a large number of unlabeled articles. A biological background based feature weighting strategy is extended using the category information from both labeled and unlabeled data. Moreover, a heuristic constraint is put forward to select optimal instances from unlabeled data to improve the performance further. Experiment results show that the EST_IACer can classify the PPI related articles effectively and efficiently.
Determining Causation of Hiatuses in a Large Ensemble
Hedemann, C.; Marotzke, J.; Mauritsen, T.; Jungclaus, J. H.
2016-02-01
We present an energy balance over the surface and ocean mixed layer, using a large ensemble of coupled model simulations of the historical period. The large ensemble allows us to cleanly isolate internal variability in the energy budget and to explore its role in surface warming hiatuses. The observed hiatus in surface temperature warming from 1998-2012 deviated from most model projections. Amongst the explanations for the disparity is quasi-random internal variability, leading to an anomalous increase in ocean heat uptake away from the surface. Locating this "missing heat" in a particular ocean basin has been the goal of many observational and modelling studies, but there is no consensus on where that heat is stored. Such studies often infer the cause of the hiatus by finding large increases of heat in a particular ocean basin, or by pointing to a vertical heat transfer to deeper ocean layers. Our findings suggest that ocean heat uptake can only partly account for hiatuses in the model: internal variability in top of the atmosphere radiation fluxes plays an equally important role in determining the surface layer energy budget. We further find that all major ocean basins, except the Indian, have enough variability in heat content to store the "missing heat", and do so far more frequently than hiatuses occur. It is the sum of all basins acting together - with cumulative or opposing effects - that make the ocean's definitive contribution to the surface energy balance.
"Swarm relaxation": Equilibrating a large ensemble of computer simulations⋆.
Malek, Shahrazad M A; Bowles, Richard K; Saika-Voivod, Ivan; Sciortino, Francesco; Poole, Peter H
2017-11-10
It is common practice in molecular dynamics and Monte Carlo computer simulations to run multiple, separately-initialized simulations in order to improve the sampling of independent microstates. Here we examine the utility of an extreme case of this strategy, in which we run a large ensemble of M independent simulations (a "swarm"), each of which is relaxed to equilibrium. We show that if M is of order [Formula: see text], we can monitor the swarm's relaxation to equilibrium, and confirm its attainment, within [Formula: see text], where [Formula: see text] is the equilibrium relaxation time. As soon as a swarm of this size attains equilibrium, the ensemble of M final microstates from each run is sufficient for the evaluation of most equilibrium properties without further sampling. This approach dramatically reduces the wall-clock time required, compared to a single long simulation, by a factor of several hundred, at the cost of an increase in the total computational effort by a small factor. It is also well suited to modern computing systems having thousands of processors, and is a viable strategy for simulation studies that need to produce high-precision results in a minimum of wall-clock time. We present results obtained by applying this approach to several test cases.
Reducing storage of global wind ensembles with stochastic generators
Jeong, Jaehong
2018-03-09
Wind has the potential to make a significant contribution to future energy resources. Locating the sources of this renewable energy on a global scale is however extremely challenging, given the difficulty to store very large data sets generated by modern computer models. We propose a statistical model that aims at reproducing the data-generating mechanism of an ensemble of runs via a Stochastic Generator (SG) of global annual wind data. We introduce an evolutionary spectrum approach with spatially varying parameters based on large-scale geographical descriptors such as altitude to better account for different regimes across the Earth’s orography. We consider a multi-step conditional likelihood approach to estimate the parameters that explicitly accounts for nonstationary features while also balancing memory storage and distributed computation. We apply the proposed model to more than 18 million points of yearly global wind speed. The proposed SG requires orders of magnitude less storage for generating surrogate ensemble members from wind than does creating additional wind fields from the climate model, even if an effective lossy data compression algorithm is applied to the simulation output.
Ensemble candidate classification for the LOTAAS pulsar survey
Tan, C. M.; Lyon, R. J.; Stappers, B. W.; Cooper, S.; Hessels, J. W. T.; Kondratiev, V. I.; Michilli, D.; Sanidas, S.
2018-03-01
One of the biggest challenges arising from modern large-scale pulsar surveys is the number of candidates generated. Here, we implemented several improvements to the machine learning (ML) classifier previously used by the LOFAR Tied-Array All-Sky Survey (LOTAAS) to look for new pulsars via filtering the candidates obtained during periodicity searches. To assist the ML algorithm, we have introduced new features which capture the frequency and time evolution of the signal and improved the signal-to-noise calculation accounting for broad profiles. We enhanced the ML classifier by including a third class characterizing RFI instances, allowing candidates arising from RFI to be isolated, reducing the false positive return rate. We also introduced a new training data set used by the ML algorithm that includes a large sample of pulsars misclassified by the previous classifier. Lastly, we developed an ensemble classifier comprised of five different Decision Trees. Taken together these updates improve the pulsar recall rate by 2.5 per cent, while also improving the ability to identify pulsars with wide pulse profiles, often misclassified by the previous classifier. The new ensemble classifier is also able to reduce the percentage of false positive candidates identified from each LOTAAS pointing from 2.5 per cent (˜500 candidates) to 1.1 per cent (˜220 candidates).
ToPs: Ensemble Learning With Trees of Predictors
Yoon, Jinsung; Zame, William R.; van der Schaar, Mihaela
2018-04-01
We present a new approach to ensemble learning. Our approach constructs a tree of subsets of the feature space and associates a predictor (predictive model) - determined by training one of a given family of base learners on an endogenously determined training set - to each node of the tree; we call the resulting object a tree of predictors. The (locally) optimal tree of predictors is derived recursively; each step involves jointly optimizing the split of the terminal nodes of the previous tree and the choice of learner and training set (hence predictor) for each set in the split. The feature vector of a new instance determines a unique path through the optimal tree of predictors; the final prediction aggregates the predictions of the predictors along this path. We derive loss bounds for the final predictor in terms of the Rademacher complexity of the base learners. We report the results of a number of experiments on a variety of datasets, showing that our approach provides statistically significant improvements over state-of-the-art machine learning algorithms, including various ensemble learning methods. Our approach works because it allows us to endogenously create more complex learners - when needed - and endogenously match both the learner and the training set to the characteristics of the dataset while still avoiding over-fitting.
Ovis: A framework for visual analysis of ocean forecast ensembles
Hollt, Thomas
2014-08-01
We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations of the sea surface height that is used in ocean forecasting. The position of eddies can be derived directly from the sea surface height and our visualization approach enables their interactive exploration and analysis.The behavior of eddies is important in different application settings of which we present two in this paper. First, we show an application for interactive planning of placement as well as operation of off-shore structures using real-world ensemble simulation data of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by eddies, and the oil and gas industry relies on ocean forecasts for efficient operations. We enable analysis of the spatial domain, as well as the temporal evolution, for planning the placement and operation of structures.Eddies are also important for marine life. They transport water over large distances and with it also heat and other physical properties as well as biological organisms. In the second application we present the usefulness of our tool, which could be used for planning the paths of autonomous underwater vehicles, so called gliders, for marine scientists to study simulation data of the largely unexplored Red Sea. © 1995-2012 IEEE.
Flow ensemble prediction for flash flood warnings at ungauged basins
Demargne, Julie; Javelle, Pierre; Organde, Didier; Caseri, Angelica; Ramos, Maria-Helena; de Saint Aubin, Céline; Jurdy, Nicolas
2015-04-01
Flash floods, which are typically triggered by severe rainfall events, are difficult to monitor and predict at the spatial and temporal scales of interest due to large meteorological and hydrologic uncertainties. In particular, uncertainties in quantitative precipitation forecasts (QPF) and quantitative precipitation estimates (QPE) need to be taken into account to provide skillful flash flood warnings with increased warning lead time. In France, the AIGA discharge-threshold flood warning system is currently being enhanced to ingest high-resolution ensemble QPFs from convection-permitting numerical weather prediction (NWP) models, as well as probabilistic QPEs, to improve flash flood warnings for small-to-medium (from 10 to 1000 km²) ungauged basins. The current deterministic AIGA system is operational in the South of France since 2005. It ingests the operational radar-gauge QPE grids from Météo-France to run a simplified hourly distributed hydrologic model at a 1-km² resolution every 15 minutes (Javelle et al. 2014). This produces real-time peak discharge estimates along the river network, which are subsequently compared to regionalized flood frequency estimates of given return periods. Warnings are then provided to the French national hydro-meteorological and flood forecasting centre (SCHAPI) and regional flood forecasting offices, based on the estimated severity of ongoing events. The calibration and regionalization of the hydrologic model has been recently enhanced to implement an operational flash flood warning system for the entire French territory. To quantify the QPF uncertainty, the COSMO-DE-EPS rainfall ensembles from the Deutscher Wetterdienst (20 members at a 2.8-km resolution for a lead time of 21 hours), which are available on the North-eastern part of France, were ingested in the hydrologic model of the AIGA system. Streamflow ensembles were produced and probabilistic flash flood warnings were derived for the Meuse and Moselle river basins and
NYYD-festivali peakülaline on Mark-Anthony Turnage / Kaur Garshnek
Garšnek, Kaur, 1983-
2007-01-01
20. oktoobril Estonia kontserdisaalis toimuvast briti helilooja teose ettekandest. 21. oktoobril toimuvast festivali lõppkontserdist Estonia kontserdisaalis, kus ettekandele tuleb Helena Tulve pala "Extinction des chses vues"
Constraining the ensemble Kalman filter for improved streamflow forecasting
Maxwell, Deborah H.; Jackson, Bethanna M.; McGregor, James
2018-05-01
Data assimilation techniques such as the Ensemble Kalman Filter (EnKF) are often applied to hydrological models with minimal state volume/capacity constraints enforced during ensemble generation. Flux constraints are rarely, if ever, applied. Consequently, model states can be adjusted beyond physically reasonable limits, compromising the integrity of model output. In this paper, we investigate the effect of constraining the EnKF on forecast performance. A "free run" in which no assimilation is applied is compared to a completely unconstrained EnKF implementation, a 'typical' hydrological implementation (in which mass constraints are enforced to ensure non-negativity and capacity thresholds of model states are not exceeded), and then to a more tightly constrained implementation where flux as well as mass constraints are imposed to force the rate of water movement to/from ensemble states to be within physically consistent boundaries. A three year period (2008-2010) was selected from the available data record (1976-2010). This was specifically chosen as it had no significant data gaps and represented well the range of flows observed in the longer dataset. Over this period, the standard implementation of the EnKF (no constraints) contained eight hydrological events where (multiple) physically inconsistent state adjustments were made. All were selected for analysis. Mass constraints alone did little to improve forecast performance; in fact, several were significantly degraded compared to the free run. In contrast, the combined use of mass and flux constraints significantly improved forecast performance in six events relative to all other implementations, while the remaining two events showed no significant difference in performance. Placing flux as well as mass constraints on the data assimilation framework encourages physically consistent state estimation and results in more accurate and reliable forward predictions of streamflow for robust decision-making. We also
Simulating Quantitative Cellular Responses Using Asynchronous Threshold Boolean Network Ensembles
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Shah Imran
2011-07-01
Full Text Available Abstract Background With increasing knowledge about the potential mechanisms underlying cellular functions, it is becoming feasible to predict the response of biological systems to genetic and environmental perturbations. Due to the lack of homogeneity in living tissues it is difficult to estimate the physiological effect of chemicals, including potential toxicity. Here we investigate a biologically motivated model for estimating tissue level responses by aggregating the behavior of a cell population. We assume that the molecular state of individual cells is independently governed by discrete non-deterministic signaling mechanisms. This results in noisy but highly reproducible aggregate level responses that are consistent with experimental data. Results We developed an asynchronous threshold Boolean network simulation algorithm to model signal transduction in a single cell, and then used an ensemble of these models to estimate the aggregate response across a cell population. Using published data, we derived a putative crosstalk network involving growth factors and cytokines - i.e., Epidermal Growth Factor, Insulin, Insulin like Growth Factor Type 1, and Tumor Necrosis Factor α - to describe early signaling events in cell proliferation signal transduction. Reproducibility of the modeling technique across ensembles of Boolean networks representing cell populations is investigated. Furthermore, we compare our simulation results to experimental observations of hepatocytes reported in the literature. Conclusion A systematic analysis of the results following differential stimulation of this model by growth factors and cytokines suggests that: (a using Boolean network ensembles with asynchronous updating provides biologically plausible noisy individual cellular responses with reproducible mean behavior for large cell populations, and (b with sufficient data our model can estimate the response to different concentrations of extracellular ligands. Our
Abuassba, Adnan O M; Zhang, Dezheng; Luo, Xiong; Shaheryar, Ahmad; Ali, Hazrat
2017-01-01
Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN). It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM, L 2 -norm-optimized ELM (ELML2), and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of training data selected through random resampling. The proposed AELM-Ensemble is evolved by employing an objective function of increasing diversity and accuracy among the final ensemble. Finally, the class label of unseen data is predicted using majority vote approach. Splitting the training data into subsets and incorporation of heterogeneous ELM classifiers result in higher prediction accuracy, better generalization, and a lower number of base classifiers, as compared to other models (Adaboost, Bagging, Dynamic ELM ensemble, data splitting ELM ensemble, and ELM ensemble). The validity of AELME is confirmed through classification on several real-world benchmark datasets.
Directory of Open Access Journals (Sweden)
Adnan O. M. Abuassba
2017-01-01
Full Text Available Extreme Learning Machine (ELM is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN. It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME for classification, which includes Regularized-ELM, L2-norm-optimized ELM (ELML2, and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of training data selected through random resampling. The proposed AELM-Ensemble is evolved by employing an objective function of increasing diversity and accuracy among the final ensemble. Finally, the class label of unseen data is predicted using majority vote approach. Splitting the training data into subsets and incorporation of heterogeneous ELM classifiers result in higher prediction accuracy, better generalization, and a lower number of base classifiers, as compared to other models (Adaboost, Bagging, Dynamic ELM ensemble, data splitting ELM ensemble, and ELM ensemble. The validity of AELME is confirmed through classification on several real-world benchmark datasets.
Improving the ensemble-optimization method through covariance-matrix adaptation
Fonseca, R.M.; Leeuwenburgh, O.; Hof, P.M.J. van den; Jansen, J.D.
2015-01-01
Ensemble optimization (referred to throughout the remainder of the paper as EnOpt) is a rapidly emerging method for reservoirmodel-based production optimization. EnOpt uses an ensemble of controls to approximate the gradient of the objective function with respect to the controls. Current
The "Tse Tsa Watle" Speaker Series: An Example of Ensemble Leadership and Generative Adult Learning
McKendry, Virginia
2017-01-01
This chapter examines an Indigenous speaker series formed to foster intercultural partnerships at a Canadian university. Using ensemble leadership and generative learning theories to make sense of the project, the author argues that ensemble leadership is key to designing the generative learning adult learners need in an era of ambiguity.
R-FCN Object Detection Ensemble based on Object Resolution and Image Quality
DEFF Research Database (Denmark)
Rasmussen, Christoffer Bøgelund; Nasrollahi, Kamal; Moeslund, Thomas B.
2017-01-01
detectors. Ensemble strategies explored were firstly data sampling and selection and secondly combination strategies. Data sampling and selection aimed to create different subsets of data with respect to object size and image quality such that expert R-FCN ensemble members could be trained. Two combination...
Network and Ensemble Enabled Entity Extraction in Informal Text (NEEEEIT) final report
Energy Technology Data Exchange (ETDEWEB)
Kegelmeyer, Philip W. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Shead, Timothy M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Dunlavy, Daniel M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2013-09-01
This SAND report summarizes the activities and outcomes of the Network and Ensemble Enabled Entity Extraction in Information Text (NEEEEIT) LDRD project, which addressed improving the accuracy of conditional random fields for named entity recognition through the use of ensemble methods.
Ensembl Genomes 2013: scaling up access to genome-wide data
Ensembl Genomes (http://www.ensemblgenomes.org) is an integrating resource for genome-scale data from non-vertebrate species. The project exploits and extends technologies for genome annotation, analysis and dissemination, developed in the context of the vertebrate-focused Ensembl project, and provi...
An Ensemble of Adaptive Neuro-Fuzzy Kohonen Networks for Online Data Stream Fuzzy Clustering
Hu, Zhengbing; Bodyanskiy, Yevgeniy V.; Tyshchenko, Oleksii K.; Boiko, Olena O.
2016-01-01
A new approach to data stream clustering with the help of an ensemble of adaptive neuro-fuzzy systems is proposed. The proposed ensemble is formed with adaptive neuro-fuzzy self-organizing Kohonen maps in a parallel processing mode. A final result is chosen by the best neuro-fuzzy self-organizing Kohonen map.
Ensemble methods for reservoir life-cycle optimzation and well placement
Leeuwenburgh, O.; Egberts, P.J.P.; Abbink, O.A.
2010-01-01
Several simple examples are presented that demonstrate the application of an ensemble-based method to production optimization. In particular, some practical aspects of the method such as ensemble size, perturbation, regularization and smoothing, and robust gradient estimation are discussed by
MACC regional multi-model ensemble simulations of birch pollen dispersion in Europe
Sofiev, M.; Berger, U.; Prank, M.; Vira, J.; Arteta, J.; Belmonte, J.; Bergmann, K.C.; Chéroux, F.; Elbern, H.; Friese, E.; Galan, C.; Gehrig, R.; Khvorostyanov, D.; Kranenburg, R.; Kumar, U.; Marécal, V.; Meleux, F.; Menut, L.; Pessi, A.M.; Robertson, L.; Ritenberga, O.; Rodinkova, V.; Saarto, A.; Segers, A.; Severova, E.; Sauliene, I.; Siljamo, P.; Steensen, B.M.; Teinemaa, E.; Thibaudon, M.; Peuch, V.H.
2015-01-01
This paper presents the first ensemble modelling experiment in relation to birch pollen in Europe. The seven-model European ensemble of MACC-ENS, tested in trial simulations over the flowering season of 2010, was run through the flowering season of 2013. The simulations have been compared with
Hall, Richard
2015-01-01
Ensemble work is a key part of any performance-based popular music course and involves students replicating existing music or playing "covers". The creative process in popular music is a collaborative one and the ensemble workshop can be utilised to facilitate active learning and develop musical creativity within a group setting. This is…
Reducing Uncertainties of Hydrologic Model Predictions Using a New Ensemble Pre-Processing Approach
Khajehei, S.; Moradkhani, H.
2015-12-01
Ensemble Streamflow Prediction (ESP) was developed to characterize the uncertainty in hydrologic predictions. However, ESP outputs are still prone to bias due to the uncertainty in the forcing data, initial condition, and model structure. Among these, uncertainty in forcing data has a major impact on the reliability of hydrologic simulations/forecasts. Major steps have been taken in generating less uncertain precipitation forecasts such as the Ensemble Pre-Processing (EPP) to achieve this goal. EPP is introduced as a statistical procedure based on the bivariate joint distribution between observation and forecast to generate ensemble climatologic forecast from single-value forecast. The purpose of this study is to evaluate the performance of pre-processed ensemble precipitation forecast in generating ensemble streamflow predictions. Copula functions used in EPP, model the multivariate joint distribution between univariate variables with any level of dependency. Accordingly, ESP is generated by employing both raw ensemble precipitation forecast as well as pre-processed ensemble precipitation. The ensemble precipitation forecast is taken from Climate Forecast System (CFS) generated by National Weather Service's (NWS) National Centers for Environmental Prediction (NCEP) models. Study is conducted using the precipitation Runoff Modeling System (PRMS) over two basins in the Pacific Northwest USA for the period of 1979 to 2013. Results reveal that applying this new EPP will lead to reduction of uncertainty and overall improvement in the ESP.
Skill of Global Raw and Postprocessed Ensemble Predictions of Rainfall over Northern Tropical Africa
Vogel, Peter; Knippertz, Peter; Fink, Andreas H.; Schlueter, Andreas; Gneiting, Tilmann
2018-04-01
Accumulated precipitation forecasts are of high socioeconomic importance for agriculturally dominated societies in northern tropical Africa. In this study, we analyze the performance of nine operational global ensemble prediction systems (EPSs) relative to climatology-based forecasts for 1 to 5-day accumulated precipitation based on the monsoon seasons 2007-2014 for three regions within northern tropical Africa. To assess the full potential of raw ensemble forecasts across spatial scales, we apply state-of-the-art statistical postprocessing methods in form of Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics (EMOS), and verify against station and spatially aggregated, satellite-based gridded observations. Raw ensemble forecasts are uncalibrated, unreliable, and underperform relative to climatology, independently of region, accumulation time, monsoon season, and ensemble. Differences between raw ensemble and climatological forecasts are large, and partly stem from poor prediction for low precipitation amounts. BMA and EMOS postprocessed forecasts are calibrated, reliable, and strongly improve on the raw ensembles, but - somewhat disappointingly - typically do not outperform climatology. Most EPSs exhibit slight improvements over the period 2007-2014, but overall have little added value compared to climatology. We suspect that the parametrization of convection is a potential cause for the sobering lack of ensemble forecast skill in a region dominated by mesoscale convective systems.
Ocean Predictability and Uncertainty Forecasts Using Local Ensemble Transfer Kalman Filter (LETKF)
Wei, M.; Hogan, P. J.; Rowley, C. D.; Smedstad, O. M.; Wallcraft, A. J.; Penny, S. G.
2017-12-01
Ocean predictability and uncertainty are studied with an ensemble system that has been developed based on the US Navy's operational HYCOM using the Local Ensemble Transfer Kalman Filter (LETKF) technology. One of the advantages of this method is that the best possible initial analysis states for the HYCOM forecasts are provided by the LETKF which assimilates operational observations using ensemble method. The background covariance during this assimilation process is implicitly supplied with the ensemble avoiding the difficult task of developing tangent linear and adjoint models out of HYCOM with the complicated hybrid isopycnal vertical coordinate for 4D-VAR. The flow-dependent background covariance from the ensemble will be an indispensable part in the next generation hybrid 4D-Var/ensemble data assimilation system. The predictability and uncertainty for the ocean forecasts are studied initially for the Gulf of Mexico. The results are compared with another ensemble system using Ensemble Transfer (ET) method which has been used in the Navy's operational center. The advantages and disadvantages are discussed.
Distribution of the Largest Eigenvalues of the Levi-Smirnov Ensemble
International Nuclear Information System (INIS)
Wieczorek, W.
2004-01-01
We calculate the distribution of the k-th largest eigenvalue in the random matrix Levi - Smirnov Ensemble (LSE), using the spectral dualism between LSE and chiral Gaussian Unitary Ensemble (GUE). Then we reconstruct universal spectral oscillations and we investigate an asymptotic behavior of the spectral distribution. (author)
Constructing Better Classifier Ensemble Based on Weighted Accuracy and Diversity Measure
Directory of Open Access Journals (Sweden)
Xiaodong Zeng
2014-01-01
Full Text Available A weighted accuracy and diversity (WAD method is presented, a novel measure used to evaluate the quality of the classifier ensemble, assisting in the ensemble selection task. The proposed measure is motivated by a commonly accepted hypothesis; that is, a robust classifier ensemble should not only be accurate but also different from every other member. In fact, accuracy and diversity are mutual restraint factors; that is, an ensemble with high accuracy may have low diversity, and an overly diverse ensemble may negatively affect accuracy. This study proposes a method to find the balance between accuracy and diversity that enhances the predictive ability of an ensemble for unknown data. The quality assessment for an ensemble is performed such that the final score is achieved by computing the harmonic mean of accuracy and diversity, where two weight parameters are used to balance them. The measure is compared to two representative measures, Kappa-Error and GenDiv, and two threshold measures that consider only accuracy or diversity, with two heuristic search algorithms, genetic algorithm, and forward hill-climbing algorithm, in ensemble selection tasks performed on 15 UCI benchmark datasets. The empirical results demonstrate that the WAD measure is superior to others in most cases.
Directory of Open Access Journals (Sweden)
F. Anctil
2009-11-01
Full Text Available Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap.
In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved system is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS, especially for 72 h forecasts. However, the hydrological ensemble forecasts are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.
An Integrated Pruning Criterion for Ensemble Learning Based on Classification Accuracy and Diversity
DEFF Research Database (Denmark)
Fu, Bin; Wang, Zhihai; Pan, Rong
2013-01-01
Ensemble pruning is an important issue in the field of ensemble learning. Diversity is a key criterion to determine how the pruning process has been done and measure what result has been derived. However, there is few formal definitions of diversity yet. Hence, three important factors that should...
Towards quantum optics and entanglement with electron spin ensembles in semiconductors
van der Wal, Caspar H.; Sladkov, Maksym
We discuss a technique and a material system that enable the controlled realization of quantum entanglement between spin-wave modes of electron ensembles in two spatially separated pieces of semiconductor material. The approach uses electron ensembles in GaAs quantum wells that are located inside
SeFo: A Package for Generating Probabilistic Forecasts from NMME Predictive Ensembles
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Nir Y Krakauer
2016-05-01
Full Text Available Long-range weather forecasts based on output from ensembles of computer simulations are attracting increasing interest. A variety of methods have been proposed to convert the ensemble outputs to calibrated probabilistic forecasts. The package presented here (SeFo, for Seasonal Forecasting implements a number of methods for producing forecasts of monthly surface air temperature anomalies up to 9 months in advance using output from the North American Multi-Model Ensemble (NMME. The package contains modules for downloading and reading past observations and ensemble output; producing forecast probability distributions; and verifying and calibrating a user-determined subset of methods using arbitrary past periods. By changing individual modules, the package could be extended to use other model ensembles, forecast other weather variables, or apply other forecast methods. SeFo is written in the numerical computing language Octave and is available on Bitbucket under the GNU General Public License (Version 3 or later.
A study of fuzzy logic ensemble system performance on face recognition problem
Polyakova, A.; Lipinskiy, L.
2017-02-01
Some problems are difficult to solve by using a single intelligent information technology (IIT). The ensemble of the various data mining (DM) techniques is a set of models which are able to solve the problem by itself, but the combination of which allows increasing the efficiency of the system as a whole. Using the IIT ensembles can improve the reliability and efficiency of the final decision, since it emphasizes on the diversity of its components. The new method of the intellectual informational technology ensemble design is considered in this paper. It is based on the fuzzy logic and is designed to solve the classification and regression problems. The ensemble consists of several data mining algorithms: artificial neural network, support vector machine and decision trees. These algorithms and their ensemble have been tested by solving the face recognition problems. Principal components analysis (PCA) is used for feature selection.
Li, Wenjin
2018-02-01
Transition path ensemble consists of reactive trajectories and possesses all the information necessary for the understanding of the mechanism and dynamics of important condensed phase processes. However, quantitative description of the properties of the transition path ensemble is far from being established. Here, with numerical calculations on a model system, the equipartition terms defined in thermal equilibrium were for the first time estimated in the transition path ensemble. It was not surprising to observe that the energy was not equally distributed among all the coordinates. However, the energies distributed on a pair of conjugated coordinates remained equal. Higher energies were observed to be distributed on several coordinates, which are highly coupled to the reaction coordinate, while the rest were almost equally distributed. In addition, the ensemble-averaged energy on each coordinate as a function of time was also quantified. These quantitative analyses on energy distributions provided new insights into the transition path ensemble.
Skill and relative economic value of medium-range hydrological ensemble predictions
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E. Roulin
2007-01-01
Full Text Available A hydrological ensemble prediction system, integrating a water balance model with ensemble precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF Ensemble Prediction System (EPS, is evaluated for two Belgian catchments using verification methods borrowed from meteorology. The skill of the probability forecast that the streamflow exceeds a given level is measured with the Brier Skill Score. Then the value of the system is assessed using a cost-loss decision model. The verification results of the hydrological ensemble predictions are compared with the corresponding results obtained for simpler alternatives as the one obtained by using of the deterministic forecast of ECMWF which is characterized by a higher spatial resolution or by using of the EPS ensemble mean.
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik
2006-01-01
horizon we aim at supplying quantiles of the wind power production conditional on the information available at the time at which the forecast is generated. This involves: (i) transformation of meteorological ensemble forecasts into wind power ensemble forecasts and (ii) calculation of quantiles based......Meteorological ensemble forecasts aim at quantifying the uncertainty of the future development of the weather by supplying several possible scenarios of this development. Here we address the use of such scenarios in probabilistic forecasting of wind power production. Specifically, for each forecast...... on the wind power ensemble forecasts. Given measurements of power production, representing a region or a single wind farm, we have developed methods applicable for these two steps. While (ii) should in principle be a simple task we found that the probabilistic information contained in the wind power ensembles...
Toward quantum state tomography of a single polariton state of an atomic ensemble
DEFF Research Database (Denmark)
Christensen, S.L.; Béguin, J.B.; Sørensen, H.L.
2013-01-01
We present a proposal and a feasibility study for the creation and quantum state tomography of a single polariton state of an atomic ensemble. The collective non-classical and non-Gaussian state of the ensemble is generated by detection of a single forward-scattered photon. The state is subsequen...... the feasibility of the proposed method for the detection of a non-classical and non-Gaussian state of the mesoscopic atomic ensemble. This work represents the first attempt at hybrid discrete-continuous variable quantum state processing with atomic memories.......We present a proposal and a feasibility study for the creation and quantum state tomography of a single polariton state of an atomic ensemble. The collective non-classical and non-Gaussian state of the ensemble is generated by detection of a single forward-scattered photon. The state...
Online Learning through Moving Ensemble Teachers — An Exact Solution of a Linear Model —
Nabetani, Takahiro; Miyoshi, Seiji
2014-05-01
Since a model in which a student learns from two or more teachers who themselves are learning has a certain similarity with actual human society, the analysis of such a model is interesting. In this paper, a model composed of a true teacher, multiple moving ensemble teachers existing around the true teacher, and a student, which are all linear perceptions, is analyzed using the statistical-mechanical method in the framework of on-line learning. The dependences of the generalization performance on the ensemble teachers' learning rate, the student's learning rate, and the number of ensemble teachers are clarified. Furthermore, it is shown that the generalization error can be reduced to the lower bound in the case of moving ensemble teachers, while there are unattainable generalization errors in the case of stationary ensemble teachers. These results show that it is important for teachers to continue learning in order to educate students.
Sippel, Jason A.; Zhang, Fuqing
2009-01-01
This study uses short-range ensemble forecasts initialized with an Ensemble-Kalman filter to study the dynamics and predictability of Hurricane Humberto, which made landfall along the Texas coast in 2007. Statistical correlation is used to determine why some ensemble members strengthen the incipient low into a hurricane and others do not. It is found that deep moisture and high convective available potential energy (CAPE) are two of the most important factors for the genesis of Humberto. Variations in CAPE result in as much difference (ensemble spread) in the final hurricane intensity as do variations in deep moisture. CAPE differences here are related to the interaction between the cyclone and a nearby front, which tends to stabilize the lower troposphere in the vicinity of the circulation center. This subsequently weakens convection and slows genesis. Eventually the wind-induced surface heat exchange mechanism and differences in landfall time result in even larger ensemble spread. 1
Ensembles of NLP Tools for Data Element Extraction from Clinical Notes.
Kuo, Tsung-Ting; Rao, Pallavi; Maehara, Cleo; Doan, Son; Chaparro, Juan D; Day, Michele E; Farcas, Claudiu; Ohno-Machado, Lucila; Hsu, Chun-Nan
2016-01-01
Natural Language Processing (NLP) is essential for concept extraction from narrative text in electronic health records (EHR). To extract numerous and diverse concepts, such as data elements (i.e., important concepts related to a certain medical condition), a plausible solution is to combine various NLP tools into an ensemble to improve extraction performance. However, it is unclear to what extent ensembles of popular NLP tools improve the extraction of numerous and diverse concepts. Therefore, we built an NLP ensemble pipeline to synergize the strength of popular NLP tools using seven ensemble methods, and to quantify the improvement in performance achieved by ensembles in the extraction of data elements for three very different cohorts. Evaluation results show that the pipeline can improve the performance of NLP tools, but there is high variability depending on the cohort.
Spam comments prediction using stacking with ensemble learning
Mehmood, Arif; On, Byung-Won; Lee, Ingyu; Ashraf, Imran; Choi, Gyu Sang
2018-01-01
Illusive comments of product or services are misleading for people in decision making. The current methodologies to predict deceptive comments are concerned for feature designing with single training model. Indigenous features have ability to show some linguistic phenomena but are hard to reveal the latent semantic meaning of the comments. We propose a prediction model on general features of documents using stacking with ensemble learning. Term Frequency/Inverse Document Frequency (TF/IDF) features are inputs to stacking of Random Forest and Gradient Boosted Trees and the outputs of the base learners are encapsulated with decision tree to make final training of the model. The results exhibits that our approach gives the accuracy of 92.19% which outperform the state-of-the-art method.
Laser noise imposed limitations of ensemble quantum metrology
Plankensteiner, D.; Schachenmayer, J.; Ritsch, H.; Genes, C.
2016-12-01
Laser noise is a decisive limiting factor in high precision spectroscopy of narrow lines using atomic ensembles. In an idealized Doppler and differential-light-shift-free magic wavelength lattice configuration, it remains as one distinct principal limitation beyond collective atomic decay. In this work we study the limitations originating from laser phase and amplitude noise in an idealized Ramsey pulse interrogation scheme with uncorrelated atoms. Phase noise leads to a saturation of the frequency sensitivity with increasing atom number while amplitude noise implies a scaling 1/\\sqrt{τ } with τ being the interrogation time. We employ a technique using decoherence-free subspaces first introduced in Dorner (2012 New J. Phys. 14 043011) which can restore the scaling with the square root of the inverse particle number 1/\\sqrt{N}. Similar results and improvements are obtained numerically for a Rabi spectroscopy setup.
Ensemble Learning or Deep Learning? Application to Default Risk Analysis
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Shigeyuki Hamori
2018-03-01
Full Text Available Proper credit-risk management is essential for lending institutions, as substantial losses can be incurred when borrowers default. Consequently, statistical methods that can measure and analyze credit risk objectively are becoming increasingly important. This study analyzes default payment data and compares the prediction accuracy and classification ability of three ensemble-learning methods—specifically, bagging, random forest, and boosting—with those of various neural-network methods, each of which has a different activation function. The results obtained indicate that the classification ability of boosting is superior to other machine-learning methods including neural networks. It is also found that the performance of neural-network models depends on the choice of activation function, the number of middle layers, and the inclusion of dropout.
Ensemble catchment hydrological modelling for climate change impact analysis
Vansteenkiste, Thomas; Ntegeka, Victor; Willems, Patrick
2014-05-01
It is vital to investigate how the hydrological model structure affects the climate change impact given that future changes not in the range for which the models were calibrated or validated are likely. Thus an ensemble modelling approach which involves a diversity of models with different structures such as spatial resolutions and process descriptions is crucial. The ensemble modelling approach was applied to a set of models: from the lumped conceptual models NAM, PDM and VHM, an intermediate detailed and distributed model WetSpa, to the highly detailed and fully distributed model MIKE-SHE. Explicit focus was given to the high and low flow extremes. All models were calibrated for sub flows and quick flows derived from rainfall and potential evapotranspiration (ETo) time series. In general, all models were able to produce reliable estimates of the flow regimes under the current climate for extreme peak and low flows. An intercomparison of the low and high flow changes under changed climatic conditions was made using climate scenarios tailored for extremes. Tailoring was important for two reasons. First, since the use of many scenarios was not feasible it was necessary to construct few scenarios that would reasonably represent the range of extreme impacts. Second, scenarios would be more informative as changes in high and low flows would be easily traced to changes of ETo and rainfall; the tailored scenarios are constructed using seasonal changes that are defined using different levels of magnitude (high, mean and low) for rainfall and ETo. After simulation of these climate scenarios in the five hydrological models, close agreement was found among the models. The different models predicted similar range of peak flow changes. For the low flows, however, the differences in the projected impact range by different hydrological models was larger, particularly for the drier scenarios. This suggests that the hydrological model structure is critical in low flow predictions
Statistics of optimal information flow in ensembles of regulatory motifs
Crisanti, Andrea; De Martino, Andrea; Fiorentino, Jonathan
2018-02-01
Genetic regulatory circuits universally cope with different sources of noise that limit their ability to coordinate input and output signals. In many cases, optimal regulatory performance can be thought to correspond to configurations of variables and parameters that maximize the mutual information between inputs and outputs. Since the mid-2000s, such optima have been well characterized in several biologically relevant cases. Here we use methods of statistical field theory to calculate the statistics of the maximal mutual information (the "capacity") achievable by tuning the input variable only in an ensemble of regulatory motifs, such that a single controller regulates N targets. Assuming (i) sufficiently large N , (ii) quenched random kinetic parameters, and (iii) small noise affecting the input-output channels, we can accurately reproduce numerical simulations both for the mean capacity and for the whole distribution. Our results provide insight into the inherent variability in effectiveness occurring in regulatory systems with heterogeneous kinetic parameters.
A Note on Functional Averages over Gaussian Ensembles
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Gabriel H. Tucci
2013-01-01
Full Text Available We find a new formula for matrix averages over the Gaussian ensemble. Let H be an n×n Gaussian random matrix with complex, independent, and identically distributed entries of zero mean and unit variance. Given an n×n positive definite matrix A and a continuous function f:ℝ+→ℝ such that ∫0∞e-αt|f(t|2dt0, we find a new formula for the expectation [Tr(f(HAH*]. Taking f(x=log(1+x gives another formula for the capacity of the MIMO communication channel, and taking f(x=(1+x-1 gives the MMSE achieved by a linear receiver.
Quantum repeaters based on atomic ensembles and linear optics
Sangouard, Nicolas; Simon, Christoph; de Riedmatten, Hugues; Gisin, Nicolas
2011-01-01
The distribution of quantum states over long distances is limited by photon loss. Straightforward amplification as in classical telecommunications is not an option in quantum communication because of the no-cloning theorem. This problem could be overcome by implementing quantum repeater protocols, which create long-distance entanglement from shorter-distance entanglement via entanglement swapping. Such protocols require the capacity to create entanglement in a heralded fashion, to store it in quantum memories, and to swap it. One attractive general strategy for realizing quantum repeaters is based on the use of atomic ensembles as quantum memories, in combination with linear optical techniques and photon counting to perform all required operations. Here the theoretical and experimental status quo of this very active field are reviewed. The potentials of different approaches are compared quantitatively, with a focus on the most immediate goal of outperforming the direct transmission of photons.
Nitrogen-vacancy ensemble magnetometry based on pump absorption
DEFF Research Database (Denmark)
Ahmadi, Sepehr; El-Ella, Haitham A. R.; Wojciechowski, Adam M.
2018-01-01
We demonstrate magnetic-field sensing using an ensemble of nitrogen-vacancy centers by recording the variation in the pump-light absorption due to the spin-polarization dependence of the total ground-state population. Using a 532 nm pump laser, we measure the absorption of native nitrogen......-vacancy centers in a chemical-vapor-deposited diamond placed in a resonant optical cavity. For a laser pump power of 0.4 W and a cavity finesse of 45, we obtain a noise floor of ∼100 nT/√Hz spanning a bandwidth up to 125 Hz. We project a photon shot-noise-limited sensitivity of ∼1 pT/√Hz by optimizing...
An Ensemble Multilabel Classification for Disease Risk Prediction
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Runzhi Li
2017-01-01
Full Text Available It is important to identify and prevent disease risk as early as possible through regular physical examinations. We formulate the disease risk prediction into a multilabel classification problem. A novel Ensemble Label Power-set Pruned datasets Joint Decomposition (ELPPJD method is proposed in this work. First, we transform the multilabel classification into a multiclass classification. Then, we propose the pruned datasets and joint decomposition methods to deal with the imbalance learning problem. Two strategies size balanced (SB and label similarity (LS are designed to decompose the training dataset. In the experiments, the dataset is from the real physical examination records. We contrast the performance of the ELPPJD method with two different decomposition strategies. Moreover, the comparison between ELPPJD and the classic multilabel classification methods RAkEL and HOMER is carried out. The experimental results show that the ELPPJD method with label similarity strategy has outstanding performance.
Probabilistic Ensemble Forecast of Summertime Temperatures in Pakistan
Directory of Open Access Journals (Sweden)
Muhammad Hanif
2014-01-01
Full Text Available Snowmelt flooding triggered by intense heat is a major temperature related weather hazard in northern Pakistan, and the frequency of such extreme flood events has increased during the recent years. In this study, the probabilistic temperature forecasts at seasonal and subseasonal time scales based on hindcasts simulations from three state-of-the-art models within the DEMETER project are assessed by the relative operating characteristic (ROC verification method. Results based on direct model outputs reveal significant skill for hot summers in February 3–5 (ROC area=0.707 with lower 95% confidence limit of 0.538 and February 4-5 (ROC area=0.771 with lower 95% confidence limit of 0.623 forecasts when validated against observations. Results for ERA-40 reanalysis also show skill for hot summers. Skilful probabilistic ensemble forecasts of summertime temperatures may be valuable in providing the foreknowledge of snowmelt flooding and water management in Pakistan.
Reweighted ensemble dynamics simulations: Theory, improvement, and application
Gong, Lin-Chen; Zhou, Xin; Ouyang, Zhong-Can
2015-06-01
Based on multiple parallel short molecular dynamics simulation trajectories, we designed the reweighted ensemble dynamics (RED) method to more efficiently sample complex (biopolymer) systems, and to explore their hierarchical metastable states. Here we further present an improvement to depress statistical errors of the RED and we discuss a few keys in practical application of the RED, provide schemes on selection of basis functions, and determination of the free parameter in the RED. We illustrate the application of the improvements in two toy models and in the solvated alanine dipeptide. The results show the RED enables us to capture the topology of multiple-state transition networks, to detect the diffusion-like dynamical behavior in an entropy-dominated system, and to identify solvent effects in the solvated peptides. The illustrations serve as general applications of the RED in more complex biopolymer systems. Project supported by the National Natural Science Foundation of China (Grant No. 11175250).
Multi-wheat-model ensemble responses to interannual climatic variability
DEFF Research Database (Denmark)
Ruane, A C; Hudson, N I; Asseng, S
2016-01-01
We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981–2010 grain yield, and ......-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.......We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981–2010 grain yield, and we...... evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal...
Can ensemble condition in a hall be improved and measured?
DEFF Research Database (Denmark)
Gade, Anders Christian
1988-01-01
of the ceiling reflectors; and (c) changing the position of the orchestra on the platform. These variables were then tested in full scale experiments in the hall including subjective evaluation by the orchestra in order to verify their effects under practical conditions. New objective parameters, which showed......In collaboration with the Danish Broadcasting Corporation an extensive series of experiments has been carried out in The Danish Radio Concert Hall with the practical purpose of trying to improve the ensemble conditions on the platform for the resident symphony orchestra. First, a series...... of experiments in a 1:20 scale model indicated that among several suggested means the following would be the most effective and acceptable: (a) changing the shape of the sidewalls in the platform area in order to make them reflect sound back to the musicians more effectively; (b) lowering and redesigning...
Transition to collective oscillations in finite Kuramoto ensembles
Peter, Franziska; Pikovsky, Arkady
2018-03-01
We present an alternative approach to finite-size effects around the synchronization transition in the standard Kuramoto model. Our main focus lies on the conditions under which a collective oscillatory mode is well defined. For this purpose, the minimal value of the amplitude of the complex Kuramoto order parameter appears as a proper indicator. The dependence of this minimum on coupling strength varies due to sampling variations and correlates with the sample kurtosis of the natural frequency distribution. The skewness of the frequency sample determines the frequency of the resulting collective mode. The effects of kurtosis and skewness hold in the thermodynamic limit of infinite ensembles. We prove this by integrating a self-consistency equation for the complex Kuramoto order parameter for two families of distributions with controlled kurtosis and skewness, respectively.
Ensemble spontaneous activity alterations detected by CISA approach.
Boudaoud, Sofiane; Rix, Hervé; Meste, Olivier; Cazals, Yves
2007-01-01
In this paper, we propose a method for detecting alterations in the Ensemble Spontaneous Activity (ESA), a random signal representing the composite spontaneous contribution of the auditory nerve recorded on the round window. The proposed method is based on shape analysis of the ESA amplitude histogram. For this task, we use a recent approach, the Corrected Integral Shape Averaging (CISA). Using this approach, a shape clustering algorithm is proposed to classify healthy and pathological ESA signals generated by a recent ESA model. This model allows a precise simulation of neural mechanisms occurring in the auditory nerve. The obtained results demonstrate that this shape analysis is very sensitive for detecting a small number of fibers with correlated firing, supposed to occur during a particular type of tinnitus. In comparison, the classical spectral index fails in this detection.
Eu, Byung Chan
2016-01-01
This book presents the fundamentals of irreversible thermodynamics for nonlinear transport processes in gases and liquids, as well as for generalized hydrodynamics extending the classical hydrodynamics of Navier, Stokes, Fourier, and Fick. Together with its companion volume on relativistic theories, it provides a comprehensive picture of the kinetic theory formulated from the viewpoint of nonequilibrium ensembles in both nonrelativistic and, in Vol. 2, relativistic contexts. Theories of macroscopic irreversible processes must strictly conform to the thermodynamic laws at every step and in all approximations that enter their derivation from the mechanical principles. Upholding this as the inviolable tenet, the author develops theories of irreversible transport processes in fluids (gases or liquids) on the basis of irreversible kinetic equations satisfying the H theorem. They apply regardless of whether the processes are near to or far removed from equilibrium, or whether they are linear or nonlinear with respe...
Uncertainty visualization in HARDI based on ensembles of ODFs
Jiao, Fangxiang
2012-02-01
In this paper, we propose a new and accurate technique for uncertainty analysis and uncertainty visualization based on fiber orientation distribution function (ODF) glyphs, associated with high angular resolution diffusion imaging (HARDI). Our visualization applies volume rendering techniques to an ensemble of 3D ODF glyphs, which we call SIP functions of diffusion shapes, to capture their variability due to underlying uncertainty. This rendering elucidates the complex heteroscedastic structural variation in these shapes. Furthermore, we quantify the extent of this variation by measuring the fraction of the volume of these shapes, which is consistent across all noise levels, the certain volume ratio. Our uncertainty analysis and visualization framework is then applied to synthetic data, as well as to HARDI human-brain data, to study the impact of various image acquisition parameters and background noise levels on the diffusion shapes. © 2012 IEEE.
Statistics of optimal information flow in ensembles of regulatory motifs.
Crisanti, Andrea; De Martino, Andrea; Fiorentino, Jonathan
2018-02-01
Genetic regulatory circuits universally cope with different sources of noise that limit their ability to coordinate input and output signals. In many cases, optimal regulatory performance can be thought to correspond to configurations of variables and parameters that maximize the mutual information between inputs and outputs. Since the mid-2000s, such optima have been well characterized in several biologically relevant cases. Here we use methods of statistical field theory to calculate the statistics of the maximal mutual information (the "capacity") achievable by tuning the input variable only in an ensemble of regulatory motifs, such that a single controller regulates N targets. Assuming (i) sufficiently large N, (ii) quenched random kinetic parameters, and (iii) small noise affecting the input-output channels, we can accurately reproduce numerical simulations both for the mean capacity and for the whole distribution. Our results provide insight into the inherent variability in effectiveness occurring in regulatory systems with heterogeneous kinetic parameters.
Ensemble control of the Hardhof well field under constraints
Marti, Beatrice; McLaughlin, Dennis; Kinzelbach, Wolfgang; Kaiser, Hans-Peter
2013-04-01
Practical control of flow in aquifers has been based on deterministic models, not including stochastic information in the optimization (Bauser et al., 2010 or Marti et al., 2012). Only recently robust ensemble control of aquatic systems has been analyzed in linear and synthetic problems (Lin, B., 2012). We propose a control under constraints, which takes into account the stochastic information contained in an ensemble of realizations of a groundwater flow model with uncertain parameters, boundary and initial conditions. This control is applied to a real life problem setting (the Hardhof well field in Zurich) and analyzed with regard to efficiency of the control compared to a similar control based on a deterministic model. The Hardhof well field, which lies in the city of Zurich, Switzerland, provides roughly 15% of the town's drinking water demand from the Limmat valley aquifer. Groundwater and river filtrate are withdrawn in four large horizontal wells, each with a capacity of up to 48'000 m3 per day. The well field is threatened by potential pollution from leachate of a nearby land fill, possible accidents on the adjacent rail and road lines, and by diffuse pollution from former industrial sites and sewers located upstream of the well field. A line of recharge wells and basins forms a hydraulic barrier against the potentially contaminated water and increases the capacity of the well field. The amount and distribution of the artificial recharge to 3 infiltration basins and 12 infiltration wells has to be controlled on a daily basis to guarantee the effectiveness of the hydraulic barrier in the highly dynamic flow field. The Hardhof well field is simulated with a 2D-real-time groundwater flow model. The model is coupled to a controller, minimizing the inflow of potentially contaminated groundwater to the drinking water wells under various constraints (i.e. keeping the groundwater level between given thresholds, guaranteeing production of the drinking water demand
Phase Locking a Clock Oscillator to a Coherent Atomic Ensemble
Directory of Open Access Journals (Sweden)
R. Kohlhaas
2015-04-01
Full Text Available The sensitivity of an atomic interferometer increases when the phase evolution of its quantum superposition state is measured over a longer interrogation interval. In practice, a limit is set by the measurement process, which returns not the phase but its projection in terms of population difference on two energetic levels. The phase interval over which the relation can be inverted is thus limited to the interval [-π/2,π/2]; going beyond it introduces an ambiguity in the readout, hence a sensitivity loss. Here, we extend the unambiguous interval to probe the phase evolution of an atomic ensemble using coherence-preserving measurements and phase corrections, and demonstrate the phase lock of the clock oscillator to an atomic superposition state. We propose a protocol based on the phase lock to improve atomic clocks limited by local oscillator noise, and foresee the application to other atomic interferometers such as inertial sensors.
Multi-Wheat-Model Ensemble Responses to Interannual Climate Variability
Ruane, Alex C.; Hudson, Nicholas I.; Asseng, Senthold; Camarrano, Davide; Ewert, Frank; Martre, Pierre; Boote, Kenneth J.; Thorburn, Peter J.; Aggarwal, Pramod K.; Angulo, Carlos
2016-01-01
We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981e2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-termwarming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.
Asymmetric similarity-weighted ensembles for image segmentation
DEFF Research Database (Denmark)
Cheplygina, V.; Van Opbroek, A.; Ikram, M. A.
2016-01-01
Supervised classification is widely used for image segmentation. To work effectively, these techniques need large amounts of labeled training data, that is representative of the test data. Different patient groups, different scanners or different scanning protocols can lead to differences between...... the images, thus representative data might not be available. Transfer learning techniques can be used to account for these differences, thus taking advantage of all the available data acquired with different protocols. We investigate the use of classifier ensembles, where each classifier is weighted...... according to the similarity between the data it is trained on, and the data it needs to segment. We examine 3 asymmetric similarity measures that can be used in scenarios where no labeled data from a newly introduced scanner or scanning protocol is available. We show that the asymmetry is informative...
Hole digging in ensembles of tunneling molecular magnets
Tupitsyn, I. S.; Stamp, P. C.; Prokof'ev, N. V.
2004-04-01
The nuclear spin-mediated quantum relaxation of ensembles of tunneling magnetic molecules causes a “hole” to appear in the distribution of internal fields in the system. The form of this hole and its time evolution, are studied using Monte Carlo simulations. It is shown that the line shape of the tunneling hole in a partially depolarized sample must have a Lorentzian line shape. The short-time half-width ξo in Fe8 crystals should be ˜E0, the half-width of the nuclear spin multiplet, but this result is not generally true. The Lorentzian hole line shape and the short-time √(t) relaxation in weakly polarized samples are both connected to a correlation time τde(ξ) for bias diffusion, whose inverse value also has a Lorentzian dependence on ξ.
Ensemble neural network-based particle filtering for prognostics
Baraldi, P.; Compare, M.; Sauco, S.; Zio, E.
2013-12-01
Particle Filtering (PF) is used in prognostics applications by reason of its capability of robustly predicting the future behavior of an equipment and, on this basis, its Residual Useful Life (RUL). It is a model-driven approach, as it resorts to analytical models of both the degradation process and the measurement acquisition system. This prevents its applicability to the cases, very common in industry, in which reliable models are lacking. In this work, we propose an original method to extend PF to the case in which an analytical measurement model is not available whereas, instead, a dataset containing pairs «state-measurement» is available. The dataset is used to train a bagged ensemble of Artificial Neural Networks (ANNs) which is, then, embedded in the PF as empirical measurement model.
Orthogonally referenced integrated ensemble for navigation and timing
Smith, Stephen Fulton; Moore, James Anthony
2014-04-01
An orthogonally referenced integrated ensemble for navigation and timing includes a dual-polyhedral oscillator array, including an outer sensing array of oscillators and an inner clock array of oscillators situated inside the outer sensing array. The outer sensing array includes a first pair of sensing oscillators situated along a first axis of the outer sensing array, a second pair of sensing oscillators situated along a second axis of the outer sensing array, and a third pair of sensing oscillators situated along a third axis of the outer sensing array. The inner clock array of oscillators includes a first pair of clock oscillators situated along a first axis of the inner clock array, a second pair of clock oscillators situated along a second axis of the inner clock array, and a third pair of clock oscillators situated along a third axis of the inner clock array.
Reservoir structural model updating using the Ensemble Kalman Filter
Energy Technology Data Exchange (ETDEWEB)
Seiler, Alexandra
2010-09-15
In reservoir characterization, a large emphasis is placed on risk management and uncertainty assessment, and the dangers of basing decisions on a single base-case reservoir model are widely recognized. In the last years, statistical methods for assisted history matching have gained popularity for providing integrated models with quantified uncertainty, conditioned on all available data. Structural modeling is the first step in a reservoir modeling work flow and consists in defining the geometrical framework of the reservoir, based on the information from seismic surveys and well data. Large uncertainties are typically associated with the processing and interpretation of seismic data. However, the structural model is often fixed to a single interpretation in history-matching work flows due to the complexity of updating the structural model and related reservoir grid. This thesis present a method that allows to account for the uncertainties in the structural model and continuously update the model and related uncertainties by assimilation of production data using the Ensemble Kalman Filter (EnKF). We consider uncertainties in the depth of the reservoir horizons and in the fault geometry, and assimilate production data, such as oil production rate, gas-oil ratio and water-cut. In the EnKF model-updating work flow, an ensemble of reservoir models, expressing explicitly the model uncertainty, is created. We present a parameterization that allows to generate different realizations of the structural model to account for the uncertainties in faults and horizons and that maintains the consistency throughout the reservoir characterization project, from the structural model to the prediction of production profiles. The uncertainty in the depth of the horizons is parameterized as simulated depth surfaces, the fault position as a displacement vector and the fault throw as a throw-scaling factor. In the EnKF, the model parameters and state variables are updated sequentially in
The PV Corrosion Fault Prognosis Based on Ensemble Kalman Filter
Directory of Open Access Journals (Sweden)
Radouane Ouladsine
2017-01-01
Full Text Available The degradation of photovoltaic (PV modules remains a major concern on the control and the development of the photovoltaic field, particularly, in regions with difficult climatic conditions. The main degradation modes of the PV modules are corrosion, discoloration, glass breaks, and cracks of cells. However, corrosion and discoloration remain the predominant degradation modes that still require further investigations. In this paper, a model-based PV corrosion prognostic approach, based on an ensemble Kalman filter (EnKF, is introduced to identify the PV corrosion parameters and then estimate the remaining useful life (RUL. Simulations have been conducted using measured data set, and results are reported to show the efficiency of the proposed approach.
Assessment of Optimal Flexibility in Ensemble of Frequency Responsive Loads
Energy Technology Data Exchange (ETDEWEB)
Kundu, Soumya; Hansen, Jacob; Lian, Jianming; Kalsi, Karanjit
2018-04-19
Potential of electrical loads in providing grid ancillary services is often limited due to the uncertainties associated with the load behavior. A knowledge of the expected uncertainties with a load control program would invariably yield to better informed control policies, opening up the possibility of extracting the maximal load control potential without affecting grid operations. In the context of frequency responsive load control, a probabilistic uncertainty analysis framework is presented to quantify the expected error between the target and actual load response, under uncertainties in the load dynamics. A closed-form expression of an optimal demand flexibility, minimizing the expected error in actual and committed flexibility, is provided. Analytical results are validated through Monte Carlo simulations of ensembles of electric water heaters.
Mesoscopic Fluctuations for the Thinned Circular Unitary Ensemble
Berggren, Tomas; Duits, Maurice
2017-09-01
In this paper we study the asymptotic behavior of mesoscopic fluctuations for the thinned Circular Unitary Ensemble. The effect of thinning is that the eigenvalues start to decorrelate. The decorrelation is stronger on the larger scales than on the smaller scales. We investigate this behavior by studying mesoscopic linear statistics. There are two regimes depending on the scale parameter and the thinning parameter. In one regime we obtain a CLT of a classical type and in the other regime we retrieve the CLT for CUE. The two regimes are separated by a critical line. On the critical line the limiting fluctuations are no longer Gaussian, but described by infinitely divisible laws. We argue that this transition phenomenon is universal by showing that the same transition and their laws appear for fluctuations of the thinned sine process in a growing box. The proofs are based on a Riemann-Hilbert problem for integrable operators.
Ensemble ecosystem modeling for predicting ecosystem response to predator reintroduction.
Baker, Christopher M; Gordon, Ascelin; Bode, Michael
2017-04-01
Introducing a new or extirpated species to an ecosystem is risky, and managers need quantitative methods that can predict the consequences for the recipient ecosystem. Proponents of keystone predator reintroductions commonly argue that the presence of the predator will restore ecosystem function, but this has not always been the case, and mathematical modeling has an important role to play in predicting how reintroductions will likely play out. We devised an ensemble modeling method that integrates species interaction networks and dynamic community simulations and used it to describe the range of plausible consequences of 2 keystone-predator reintroductions: wolves (Canis lupus) to Yellowstone National Park and dingoes (Canis dingo) to a national park in Australia. Although previous methods for predicting ecosystem responses to such interventions focused on predicting changes around a given equilibrium, we used Lotka-Volterra equations to predict changing abundances through time. We applied our method to interaction networks for wolves in Yellowstone National Park and for dingoes in Australia. Our model replicated the observed dynamics in Yellowstone National Park and produced a larger range of potential outcomes for the dingo network. However, we also found that changes in small vertebrates or invertebrates gave a good indication about the potential future state of the system. Our method allowed us to predict when the systems were far from equilibrium. Our results showed that the method can also be used to predict which species may increase or decrease following a reintroduction and can identify species that are important to monitor (i.e., species whose changes in abundance give extra insight into broad changes in the system). Ensemble ecosystem modeling can also be applied to assess the ecosystem-wide implications of other types of interventions including assisted migration, biocontrol, and invasive species eradication. © 2016 Society for Conservation Biology.