Classification in Medical Image Analysis Using Adaptive Metric KNN
Chen, Chen; Chernoff, Konstantin; Karemore, Gopal Raghunath; Lo, Pechin Chien Pau; Nielsen, Mads; Lauze, Francois Bernard
2010-01-01
the assumption that images are drawn from Brownian Image Model (BIM), the normalized metric based on variance of the data, the empirical metric is based on the empirical covariance matrix of the unlabeled data, and an optimized metric obtained by minimizing the classification error. The spectral...
Adaptive Metric Kernel Regression
Goutte, Cyril; Larsen, Jan
1998-01-01
Kernel smoothing is a widely used nonparametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this paper, we propose an algorithm that adapts the input metric used in multivariate regression by...... minimising a cross-validation estimate of the generalisation error. This allows one to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard...
Adaptive metric kernel regression
Goutte, Cyril; Larsen, Jan
2000-01-01
Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this contribution, we propose an algorithm that adapts the input metric used in multivariate...... regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the...
Improving GPU-accelerated Adaptive IDW Interpolation Algorithm Using Fast kNN Search
Mei, Gang; Xu, Nengxiong; Xu, Liangliang
2016-01-01
This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast k-Nearest Neighbors (kNN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptively determine the power parameter; and then the desired prediction value of the interpolated point is ...
Multiclass Boosting with Adaptive Group-Based kNN and Its Application in Text Categorization
Lei La
2012-01-01
Full Text Available AdaBoost is an excellent committee-based tool for classification. However, its effectiveness and efficiency in multiclass categorization face the challenges from methods based on support vector machine (SVM, neural networks (NN, naïve Bayes, and k-nearest neighbor (kNN. This paper uses a novel multi-class AdaBoost algorithm to avoid reducing the multi-class classification problem to multiple two-class classification problems. This novel method is more effective. In addition, it keeps the accuracy advantage of existing AdaBoost. An adaptive group-based kNN method is proposed in this paper to build more accurate weak classifiers and in this way control the number of basis classifiers in an acceptable range. To further enhance the performance, weak classifiers are combined into a strong classifier through a double iterative weighted way and construct an adaptive group-based kNN boosting algorithm (AGkNN-AdaBoost. We implement AGkNN-AdaBoost in a Chinese text categorization system. Experimental results showed that the classification algorithm proposed in this paper has better performance both in precision and recall than many other text categorization methods including traditional AdaBoost. In addition, the processing speed is significantly enhanced than original AdaBoost and many other classic categorization algorithms.
Fuzzy Optimized Metric for Adaptive Network Routing
Ahmad Khader Haboush
2012-04-01
Full Text Available Network routing algorithms used today calculate least cost (shortest paths between nodes. The cost of a path is the sum of the cost of all links on that path. The use of a single metric for adaptive routing is insufficient to reflect the actual state of the link. In general, there is a limitation on the accuracy of the link state information obtained by the routing protocol. Hence it becomes useful if two or more metrics can be associated to produce a single metric that can describe the state of the link more accurately. In this paper, a fuzzy inference rule base is implemented to generate the fuzzy cost of each candidate path to be used in routing the incoming calls. This fuzzy cost is based on the crisp values of the different metrics; a fuzzy membership function is defined. The parameters of these membership functions reflect dynamically the requirement of the incoming traffic service as well as the current state of the links in the path. And this paper investigates how three metrics, the mean link bandwidth, queue utilization and the mean link delay, can be related using a simple fuzzy logic algorithm to produce a optimized cost of the link for a certain interval that is more „precise‟ than either of the single metric, to solve routing problem .
Adaptive Metric Learning for Saliency Detection.
Li, Shuang; Lu, Huchuan; Lin, Zhe; Shen, Xiaohui; Price, Brian
2015-11-01
In this paper, we propose a novel adaptive metric learning algorithm (AML) for visual saliency detection. A key observation is that the saliency of a superpixel can be estimated by the distance from the most certain foreground and background seeds. Instead of measuring distance on the Euclidean space, we present a learning method based on two complementary Mahalanobis distance metrics: 1) generic metric learning (GML) and 2) specific metric learning (SML). GML aims at the global distribution of the whole training set, while SML considers the specific structure of a single image. Considering that multiple similarity measures from different views may enhance the relevant information and alleviate the irrelevant one, we try to fuse the GML and SML together and experimentally find the combining result does work well. Different from the most existing methods which are directly based on low-level features, we devise a superpixelwise Fisher vector coding approach to better distinguish salient objects from the background. We also propose an accurate seeds selection mechanism and exploit contextual and multiscale information when constructing the final saliency map. Experimental results on various image sets show that the proposed AML performs favorably against the state-of-the-arts. PMID:26054067
A Unified View of Adaptive Variable-Metric Projection Algorithms
Masahiro Yukawa
2009-01-01
Full Text Available We present a unified analytic tool named variable-metric adaptive projected subgradient method (V-APSM that encompasses the important family of adaptive variable-metric projection algorithms. The family includes the transform-domain adaptive filter, the Newton-method-based adaptive filters such as quasi-Newton, the proportionate adaptive filter, and the Krylov-proportionate adaptive filter. We provide a rigorous analysis of V-APSM regarding several invaluable properties including monotone approximation, which indicates stable tracking capability, and convergence to an asymptotically optimal point. Small metric-fluctuations are the key assumption for the analysis. Numerical examples show (i the robustness of V-APSM against violation of the assumption and (ii the remarkable advantages over its constant-metric counterpart for colored and nonstationary inputs under noisy situations.
Witten's perturbation on strata with general adapted metrics
López, Jesús A. Álvarez; Calaza, Manuel
2015-01-01
Let $M$ be a stratum of a compact stratified space $A$. It is equipped with a general adapted metric $g$, which is slightly more general than the adapted metrics of Nagase and Brasselet-Hector-Saralegi. In particular, $g$ has a general type, which is an extension of the type of an adapted metric. A restriction on this general type is assumed, and then $g$ is called good. We consider the maximum/minimun ideal boundary condition, $d_{\\text{max/min}}$, of the compactly supported de~Rham complex ...
Stability and Performance Metrics for Adaptive Flight Control
Stepanyan, Vahram; Krishnakumar, Kalmanje; Nguyen, Nhan; VanEykeren, Luarens
2009-01-01
This paper addresses the problem of verifying adaptive control techniques for enabling safe flight in the presence of adverse conditions. Since the adaptive systems are non-linear by design, the existing control verification metrics are not applicable to adaptive controllers. Moreover, these systems are in general highly uncertain. Hence, the system's characteristics cannot be evaluated by relying on the available dynamical models. This necessitates the development of control verification metrics based on the system's input-output information. For this point of view, a set of metrics is introduced that compares the uncertain aircraft's input-output behavior under the action of an adaptive controller to that of a closed-loop linear reference model to be followed by the aircraft. This reference model is constructed for each specific maneuver using the exact aerodynamic and mass properties of the aircraft to meet the stability and performance requirements commonly accepted in flight control. The proposed metrics are unified in the sense that they are model independent and not restricted to any specific adaptive control methods. As an example, we present simulation results for a wing damaged generic transport aircraft with several existing adaptive controllers.
Large-Margin kNN Classification Using a Deep Encoder Network
Min, Martin Renqiang; Stanley, David A.; Yuan, Zineng; Bonner, Anthony; Zhang, Zhaolei
2009-01-01
KNN is one of the most popular classification methods, but it often fails to work well with inappropriate choice of distance metric or due to the presence of numerous class-irrelevant features. Linear feature transformation methods have been widely applied to extract class-relevant information to improve kNN classification, which is very limited in many applications. Kernels have been used to learn powerful non-linear feature transformations, but these methods fail to scale to large datasets....
Adapting Binary Information Retrieval Evaluation Metrics for Segment-based Retrieval Tasks
Aly, Robin; Eskevich, Maria; Ordelman, Roeland; Jones, Gareth J.F.
2013-01-01
This report describes metrics for the evaluation of the effectiveness of segment-based retrieval based on existing binary information retrieval metrics. This metrics are described in the context of a task for the hyperlinking of video segments. This evaluation approach re-uses existing evaluation measures from the standard Cranfield evaluation paradigm. Our adaptation approach can in principle be used with any kind of effectiveness measure that uses binary relevance, and for other segment-bae...
A low-cost compact metric adaptive optics system
Mansell, Justin D.; Henderson, Brian; Wiesner, Brennen; Praus, Robert; Coy, Steve
2007-09-01
The application of adaptive optics has been hindered by the cost, size, and complexity of the systems. We describe here progress we have made toward creating low-cost compact turn-key adaptive optics systems. We describe our new low-cost deformable mirror technology developed using polymer membranes, the associated USB interface drive electronics, and different ways that this technology can be configured into a low-cost compact adaptive optics system. We also present results of a parametric study of the stochastic parallel gradient descent (SPGD) control algorithm.
Density estimation using KNN and a potential model
Lu, Yonggang; Qiao, Jiangang; Liao, Li; Yang, Wuyang
2013-10-01
Density-based clustering methods are usually more adaptive than other classical methods in that they can identify clusters of various shapes and can handle noisy data. A novel density estimation method is proposed using both the knearest neighbor (KNN) graph and a hypothetical potential field of the data points to capture the local and global data distribution information respectively. An initial density score computed using KNN is used as the mass of the data point in computing the potential values. Then the computed potential is used as the new density estimation, from which the final clustering result is derived. All the parameters used in the proposed method are determined from the input data automatically. The new clustering method is evaluated by comparing with K-means++, DBSCAN, and CSPV. The experimental results show that the proposed method can determine the number of clusters automatically while producing competitive clustering results compared to the other three methods.
Interesting Metrics Based Adaptive Prediction Technique for Knowledge Discovery
G. Anbukkarasy; N. Sairam
2013-01-01
Prediction is considered as an important factor to predict the future results from the existing information. Decision tree methodology is widely used for predicting the results. But this is not efficient to handle the large, heterogeneous or multi-featured type of data sources. So an adaptive prediction method is proposed by combining the statistical analysis approach of the data mining methods along with the decision tree prediction methodology. So when dealing with large and multi-server ba...
Quality Assessment of Adaptive Bitrate Videos using Image Metrics and Machine Learning
Søgaard, Jacob; Forchhammer, Søren; Brunnström, Kjell
2015-01-01
Adaptive bitrate (ABR) streaming is widely used for distribution of videos over the internet. In this work, we investigate how well we can predict the quality of such videos using well-known image metrics, information about the bitrate levels, and a relatively simple machine learning method...
Large-Margin kNN Classification Using a Deep Encoder Network
Min, Martin Renqiang; Yuan, Zineng; Bonner, Anthony; Zhang, Zhaolei
2009-01-01
KNN is one of the most popular classification methods, but it often fails to work well with inappropriate choice of distance metric or due to the presence of numerous class-irrelevant features. Linear feature transformation methods have been widely applied to extract class-relevant information to improve kNN classification, which is very limited in many applications. Kernels have been used to learn powerful non-linear feature transformations, but these methods fail to scale to large datasets. In this paper, we present a scalable non-linear feature mapping method based on a deep neural network pretrained with restricted boltzmann machines for improving kNN classification in a large-margin framework, which we call DNet-kNN. DNet-kNN can be used for both classification and for supervised dimensionality reduction. The experimental results on two benchmark handwritten digit datasets show that DNet-kNN has much better performance than large-margin kNN using a linear mapping and kNN based on a deep autoencoder pretr...
QRS detection using K-Nearest Neighbor algorithm (KNN and evaluation on standard ECG databases
Indu Saini
2013-07-01
Full Text Available The performance of computer aided ECG analysis depends on the precise and accurate delineation of QRS-complexes. This paper presents an application of K-Nearest Neighbor (KNN algorithm as a classifier for detection of QRS-complex in ECG. The proposed algorithm is evaluated on two manually annotated standard databases such as CSE and MIT-BIH Arrhythmia database. In this work, a digital band-pass filter is used to reduce false detection caused by interference present in ECG signal and further gradient of the signal is used as a feature for QRS-detection. In addition the accuracy of KNN based classifier is largely dependent on the value of K and type of distance metric. The value of K = 3 and Euclidean distance metric has been proposed for the KNN classifier, using fivefold cross-validation. The detection rates of 99.89% and 99.81% are achieved for CSE and MIT-BIH databases respectively. The QRS detector obtained a sensitivity Se = 99.86% and specificity Sp = 99.86% for CSE database, and Se = 99.81% and Sp = 99.86% for MIT-BIH Arrhythmia database. A comparison is also made between proposed algorithm and other published work using CSE and MIT-BIH Arrhythmia databases. These results clearly establishes KNN algorithm for reliable and accurate QRS-detection.
Spatial Queries with Two kNN Predicates
Aly, Ahmed M; Aref, Walid G.; Ouzzani, Mourad
2012-01-01
The widespread use of location-aware devices has led to countless location-based services in which a user query can be arbitrarily complex, i.e., one that embeds multiple spatial selection and join predicates. Amongst these predicates, the k-Nearest-Neighbor (kNN) predicate stands as one of the most important and widely used predicates. Unlike related research, this paper goes beyond the optimization of queries with single kNN predicates, and shows how queries with two kNN predicates can be o...
Hanson, Curt; Schaefer, Jacob; Burken, John J.; Larson, David; Johnson, Marcus
2014-01-01
Flight research has shown the effectiveness of adaptive flight controls for improving aircraft safety and performance in the presence of uncertainties. The National Aeronautics and Space Administration's (NASA)'s Integrated Resilient Aircraft Control (IRAC) project designed and conducted a series of flight experiments to study the impact of variations in adaptive controller design complexity on performance and handling qualities. A novel complexity metric was devised to compare the degrees of simplicity achieved in three variations of a model reference adaptive controller (MRAC) for NASA's F-18 (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) Full-Scale Advanced Systems Testbed (Gen-2A) aircraft. The complexity measures of these controllers are also compared to that of an earlier MRAC design for NASA's Intelligent Flight Control System (IFCS) project and flown on a highly modified F-15 aircraft (McDonnell Douglas, now The Boeing Company, Chicago, Illinois). Pilot comments during the IRAC research flights pointed to the importance of workload on handling qualities ratings for failure and damage scenarios. Modifications to existing pilot aggressiveness and duty cycle metrics are presented and applied to the IRAC controllers. Finally, while adaptive controllers may alleviate the effects of failures or damage on an aircraft's handling qualities, they also have the potential to introduce annoying changes to the flight dynamics or to the operation of aircraft systems. A nuisance rating scale is presented for the categorization of nuisance side-effects of adaptive controllers.
Multiple Closed-Form Local Metric Learning for K-Nearest Neighbor Classifier
Ye, Jianbo
2013-01-01
Many researches have been devoted to learn a Mahalanobis distance metric, which can effectively improve the performance of kNN classification. Most approaches are iterative and computational expensive and linear rigidity still critically limits metric learning algorithm to perform better. We proposed a computational economical framework to learn multiple metrics in closed-form.
Efficient and Flexible KNN Query Processing in Real-Life Road Networks
Lu, Yang; Bui, Bin; Zhao, Jiakui;
2008-01-01
included into the RNG index, which enables the index to support both distance-based and time-based KNN queries and continuous KNN queries. Our work extends previous ones by taking into account more practical scenarios, such as complexities in real-life road networks and time-based KNN queries. Extensive...
metricDTW: local distance metric learning in Dynamic Time Warping
Zhao, Jiaping; Xi, Zerong; Itti, Laurent
2016-01-01
We propose to learn multiple local Mahalanobis distance metrics to perform k-nearest neighbor (kNN) classification of temporal sequences. Temporal sequences are first aligned by dynamic time warping (DTW); given the alignment path, similarity between two sequences is measured by the DTW distance, which is computed as the accumulated distance between matched temporal point pairs along the alignment path. Traditionally, Euclidean metric is used for distance computation between matched pairs, wh...
A Novel Approach for the Diagnosis of Diabetes and Liver Cancer using ANFIS and Improved KNN
C. Kalaiselvi
2014-07-01
Full Text Available The multi-factorial, chronicle, severe diseases are cancer and diabetes. As a result of abnormal level of glucose in body leads to heart attack, kidney disease, renal failure and cancer. Many studies have been proved that several types of cancer are possible in diabetes patients having a high blood sugar. Many approaches are proposed in the past to diagnose both cancer and diabetes. Even though the existing approaches are efficient one, the classification accuracy is poor. An Enhanced approach is proposed to achieve a higher efficiency and lower complexity. Adaptive neuro fuzzy inference system is used to classify the dataset with the help of adaptive group based KNN. The Pima Indian diabetes dataset are used as input dataset and classified based on the attribute information. The experimental result shows the classification accuracy is better than the existing approaches such FLANN, ANN with FUZZYKNN.
A hybrid KNN-MLP algorithm to diagnose bipolar disorder
Mozhgan Mohammad Ghasemi; Mehdi Khalili
2014-01-01
In this paper an attempt has been made to the other corner of the power of neural networks. According to the neural network in the diagnosis of diseases, we use neural network models for diagnosing bipolar disorder; bipolar disorder is the common disorder of depression mood. We have used two neural network models: MLP & KNN. With different percentages of the implementation of neural network models is discussed. And the error was calculated for each model. We can by using the MLP model achieve...
Prediksi Inflasi di Indonesia Menggunakan Algoritma K- Nearest Neighbor (KNN)
Harahap, Nency Lestari
2015-01-01
Developed country is a country that has a strong economy and stability. One of the main indicators used to see the development of the economy of a country is the level of inflation. Therefore, a prediction of inflation important in order to assist the government in making policy to maintain economic stability. In this research, KNearest Neighbor algorithm (KNN) predict inflation based on the exchange rate, the BI Rate, money supply and Gross Domestic Product and inflation rate ...
Cogntive Consistency Analysis in Adaptive Bio-Metric Authentication System Design
Gahangir Hossain
2015-07-01
Full Text Available Cognitive consistency analysis aims to continuously monitor one's perception equilibrium towards successful accomplishment of cognitive task. Opposite to cognitive flexibility analysis – cognitive consistency analysis identifies monotone of perception towards successful interaction process (e.g., biometric authentication and useful in generation of decision support to assist one in need. This study consider fingertip dynamics (e.g., keystroke, tapping, clicking etc. to have insights on instantaneous cognitive states and its effects in monotonic advancement towards successful authentication process. Keystroke dynamics and tapping dynamics are analyzed based on response time data. Finally, cognitive consistency and confusion (inconsistency are computed with Maximal Information Coefficient (MIC and Maximal Asymmetry Score (MAS, respectively. Our preliminary study indicates that a balance between cognitive consistency and flexibility are needed in successful authentication process. Moreover, adaptive and cognitive interaction system requires in depth analysis of user’s cognitive consistency to provide a robust and useful assistance.
Adaptive ant-based routing in wireless sensor networks using Energy Delay metrics
Yao-feng WEN; Yu-quan CHEN; Min PAN
2008-01-01
To find the optimal routing is always an important topic in wireless sensor networks (WSNs). Considering a WSN where the nodes have limited energy, we propose a novel Energy*Delay model based on ant algorithms ("E&D ANTS" for short)to minimize the time delay in transferring a fixed number of data packets in an energy-constrained manner in one round. Our goal is not only to maximize the lifetime of the network but also to provide real-time data transmission services. However, because of the tradeoff of energy and delay in wireless network systems, the reinforcement learning (RL) algorithm is introduced to train the model. In this survey, the paradigm of E&D ANTS is explicated and compared to other ant-based routing algorithms like AntNet and AntChain about the issues of routing information, routing overhead and adaptation. Simulation results show that our method performs about seven times better than AntNet and also outperforms AntChain by more than 150% in terms of energy cost and delay per round.
A hybrid KNN-MLP algorithm to diagnose bipolar disorder
Mozhgan Mohammad Ghasemi
2014-12-01
Full Text Available In this paper an attempt has been made to the other corner of the power of neural networks. According to the neural network in the diagnosis of diseases, we use neural network models for diagnosing bipolar disorder; bipolar disorder is the common disorder of depression mood. We have used two neural network models: MLP & KNN. With different percentages of the implementation of neural network models is discussed. And the error was calculated for each model. We can by using the MLP model achieve an error of 16% for the diagnosis of bipolar disorder.
To assess the biogeophysical impacts of land cover/land use change (LCLUC) on surface temperature, two observation-based metrics and their applicability in climate modeling were explored in this study. Both metrics were developed based on the surface energy balance, and provided insight into the contribution of different aspects of land surface change (such as albedo, surface roughness, net radiation and surface heat fluxes) to changing climate. A revision of the first metric, the intrinsic biophysical mechanism, can be used to distinguish the direct and indirect effects of LCLUC on surface temperature. The other, a decomposed temperature metric, gives a straightforward depiction of separate contributions of all components of the surface energy balance. These two metrics well capture observed and model simulated surface temperature changes in response to LCLUC. Results from paired FLUXNET sites and land surface model sensitivity experiments indicate that surface roughness effects usually dominate the direct biogeophysical feedback of LCLUC, while other effects play a secondary role. However, coupled climate model experiments show that these direct effects can be attenuated by large scale atmospheric changes (indirect feedbacks). When applied to real-time transient LCLUC experiments, the metrics also demonstrate usefulness for assessing the performance of climate models and quantifying land–atmosphere interactions in response to LCLUC. (letter)
AN EFFICIENT TEXT CLASSIFICATION USING KNN AND NAIVE BAYESIAN
J.Sreemathy
2012-03-01
Full Text Available The main objective is to propose a text classification based on the features selection and preprocessing thereby reducing the dimensionality of the Feature vector and increase the classificationaccuracy. Text classification is the process of assigning a document to one or more target categories, based on its contents. In the proposed method, machine learning methods for text classification is used to apply some text preprocessing methods in different dataset, and then to extract feature vectors for each new document by using various feature weighting methods for enhancing the text classification accuracy. Further training the classifier by Naive Bayesian (NB and K-nearest neighbor (KNN algorithms, the predication can be made according to the category distribution among this k nearest neighbors.Experimental results show that the methods are favorable in terms of their effectiveness and efficiencywhen compared with other classifier such as SVM.
kNN method on KASCADE-grande data
KASCADE-grande, located at Forschungszentrum Karlsruhe, is a multi-detector experiment for the measurement of extensive air showers induced by primary cosmic rays in the energy range of 1014-1018 eV. The ''k-Nearest Neighbours'' (KNN) method is a classification procedure applied for a preliminary study of the cosmic ray composition in this energy range. Simulations of different primary particles are used as reference samples. In order to find for each real event the k-Nearest Neighbours in the reference sample, the Mahalanobis distance in the space defined by the muon size, the shower size and age (obtained from the NKG fit of the lateral distribution of the charged particles) is calculated. The probability of the event to be part of one of the simulated primary groups is the percentage of the k neighbours belonging to it. Preliminary results of the application of this technique to KASCADE-grande data with respect to simulated samples are reported
GPU-FS-kNN: a software tool for fast and scalable kNN computation using GPUs.
Ahmed Shamsul Arefin
Full Text Available BACKGROUND: The analysis of biological networks has become a major challenge due to the recent development of high-throughput techniques that are rapidly producing very large data sets. The exploding volumes of biological data are craving for extreme computational power and special computing facilities (i.e. super-computers. An inexpensive solution, such as General Purpose computation based on Graphics Processing Units (GPGPU, can be adapted to tackle this challenge, but the limitation of the device internal memory can pose a new problem of scalability. An efficient data and computational parallelism with partitioning is required to provide a fast and scalable solution to this problem. RESULTS: We propose an efficient parallel formulation of the k-Nearest Neighbour (kNN search problem, which is a popular method for classifying objects in several fields of research, such as pattern recognition, machine learning and bioinformatics. Being very simple and straightforward, the performance of the kNN search degrades dramatically for large data sets, since the task is computationally intensive. The proposed approach is not only fast but also scalable to large-scale instances. Based on our approach, we implemented a software tool GPU-FS-kNN (GPU-based Fast and Scalable k-Nearest Neighbour for CUDA enabled GPUs. The basic approach is simple and adaptable to other available GPU architectures. We observed speed-ups of 50-60 times compared with CPU implementation on a well-known breast microarray study and its associated data sets. CONCLUSION: Our GPU-based Fast and Scalable k-Nearest Neighbour search technique (GPU-FS-kNN provides a significant performance improvement for nearest neighbour computation in large-scale networks. Source code and the software tool is available under GNU Public License (GPL at https://sourceforge.net/p/gpufsknn/.
Accelerating k-NN Algorithm with Hybrid MPI and OpenSHMEM
Lin, Jian; Hamidouche, Khaled; Zheng, Jie; Lu, Xiaoyi; Vishnu, Abhinav; Panda, Dhabaleswar
2015-08-05
Machine Learning algorithms are benefiting from the continuous improvement of programming models, including MPI, MapReduce and PGAS. k-Nearest Neighbors (k-NN) algorithm is a widely used machine learning algorithm, applied to supervised learning tasks such as classification. Several parallel implementations of k-NN have been proposed in the literature and practice. However, on high-performance computing systems with high-speed interconnects, it is important to further accelerate existing designs of the k-NN algorithm through taking advantage of scalable programming models. To improve the performance of k-NN on large-scale environment with InfiniBand network, this paper proposes several alternative hybrid MPI+OpenSHMEM designs and performs a systemic evaluation and analysis on typical workloads. The hybrid designs leverage the one-sided memory access to better overlap communication with computation than the existing pure MPI design, and propose better schemes for efficient buffer management. The implementation based on k-NN program from MaTEx with MVAPICH2-X (Unified MPI+PGAS Communication Runtime over InfiniBand) shows up to 9.0% time reduction for training KDD Cup 2010 workload over 512 cores, and 27.6% time reduction for small workload with balanced communication and computation. Experiments of running with varied number of cores show that our design can maintain good scalability.
Scalable Large-Margin Mahalanobis Distance Metric Learning
Shen, Chunhua; Wang, Lei
2010-01-01
For many machine learning algorithms such as $k$-Nearest Neighbor ($k$-NN) classifiers and $ k $-means clustering, often their success heavily depends on the metric used to calculate distances between different data points. An effective solution for defining such a metric is to learn it from a set of labeled training samples. In this work, we propose a fast and scalable algorithm to learn a Mahalanobis distance metric. By employing the principle of margin maximization to achieve better generalization performances, this algorithm formulates the metric learning as a convex optimization problem and a positive semidefinite (psd) matrix is the unknown variable. a specialized gradient descent method is proposed. our algorithm is much more efficient and has a better performance in scalability compared with existing methods. Experiments on benchmark data sets suggest that, compared with state-of-the-art metric learning algorithms, our algorithm can achieve a comparable classification accuracy with reduced computation...
A Novel Approach to Face Recognition using Image Segmentation based on SPCA-KNN Method
P. Kamencay
2013-04-01
Full Text Available In this paper we propose a novel method for face recognition using hybrid SPCA-KNN (SIFT-PCA-KNN approach. The proposed method consists of three parts. The first part is based on preprocessing face images using Graph Based algorithm and SIFT (Scale Invariant Feature Transform descriptor. Graph Based topology is used for matching two face images. In the second part eigen values and eigen vectors are extracted from each input face images. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. In the final part a nearest neighbor classifier is designed for classifying the face images based on the SPCA-KNN algorithm. The algorithm has been tested on 100 different subjects (15 images for each class. The experimental result shows that the proposed method has a positive effect on overall face recognition performance and outperforms other examined methods.
Indexing the bit-code and distance for fast KNN search in high-dimensional spaces
LIANG Jun-jie; FENG Yu-cai
2007-01-01
Various index structures have recently been proposed to facilitate high-dimensional KNN queries, among which the techniques of approximate vector presentation and one-dimensional (iD) transformation can break the curse of dimensionality.Based on the two techniques above, a novel high-dimensional index is proposed, called Bit-code and Distance based index (BD).BD is based on a special partitioning strategy which is optimized for high-dimensional data. By the definitions of bit code and transformation function, a high-dimensional vector can be first approximately represented and then transformed into a 1D vector,the key managed by a B+-tree. A new KNN search algorithm is also proposed that exploits the bit code and distance to prune the search space more effectively. Results of extensive experiments using both synthetic and real data demonstrated that BD outperforms the existing index structures for KNN search in high-dimensional spaces.
López-Hoffman, Laura; Breshears, David D.; Allen, Craig D.; Miller, Marc L.
2013-01-01
Under a changing climate, devising strategies to help stakeholders adapt to alterations to ecosystems and their services is of utmost importance. In western North America, diminished snowpack and river flows are causing relatively gradual, homogeneous (system-wide) changes in ecosystems and services. In addition, increased climate variability is also accelerating the incidence of abrupt and patchy disturbances such as fires, floods and droughts. This paper posits that two key variables often considered in landscape ecology—the rate of change and the degree of patchiness of change—can aid in developing climate change adaptation strategies. We use two examples from the “borderland” region of the southwestern United States and northwestern Mexico. In piñon-juniper woodland die-offs that occurred in the southwestern United States during the 2000s, ecosystem services suddenly crashed in some parts of the system while remaining unaffected in other locations. The precise timing and location of die-offs was uncertain. On the other hand, slower, homogeneous change, such as the expected declines in water supply to the Colorado River delta, will likely impact the entire ecosystem, with ecosystem services everywhere in the delta subject to alteration, and all users likely exposed. The rapidity and spatial heterogeneity of faster, patchy climate change exemplified by tree die-off suggests that decision-makers and local stakeholders would be wise to operate under a Rawlsian “veil of ignorance,” and implement adaptation strategies that allow ecosystem service users to equitably share the risk of sudden loss of ecosystem services before actual ecosystem changes occur. On the other hand, in the case of slower, homogeneous, system-wide impacts to ecosystem services as exemplified by the Colorado River delta, adaptation strategies can be implemented after the changes begin, but will require a fundamental rethinking of how ecosystems and services are used and valued. In
A Weighted Discrete KNN Method for Mandarin Speech and Emotion Recognition
Pao, Tsang-Long; Liao, Wen-Yuan; Chen, Yu-Te
2008-01-01
In this chapter, we present a speech emotion recognition system to compare several classifiers on the clean speech and noisy speech. Our proposed WD-KNN classifier outperforms the other three KNN-based classifiers at every SNR level and achieves highest accuracy from clean speech to 20dB noisy speech when compared with all other classifiers. Similar to (Neiberg et al, 2006), GMM is a feasible technique for emotion classification on the frame level and the results of GMM are better than perfor...
Klauder, J R
1998-01-01
Canonical quantization may be approached from several different starting points. The usual approaches involve promotion of c-numbers to q-numbers, or path integral constructs, each of which generally succeeds only in Cartesian coordinates. All quantization schemes that lead to Hilbert space vectors and Weyl operators---even those that eschew Cartesian coordinates---implicitly contain a metric on a flat phase space. This feature is demonstrated by studying the classical and quantum ``aggregations'', namely, the set of all facts and properties resident in all classical and quantum theories, respectively. Metrical quantization is an approach that elevates the flat phase space metric inherent in any canonical quantization to the level of a postulate. Far from being an unwanted structure, the flat phase space metric carries essential physical information. It is shown how the metric, when employed within a continuous-time regularization scheme, gives rise to an unambiguous quantization procedure that automatically ...
Photometric redshift estimation for quasars by integration of KNN and SVM
Han, Bo; Ding, Hong-Peng; Zhang, Yan-Xia; Zhao, Yong-Heng
2016-05-01
The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is an unsolved problem with a long history and it still exists in the current photometric redshift estimation approaches (such as the k-nearest neighbor (KNN) algorithm). In this paper, we propose a novel two-stage approach by integration of KNN and support vector machine (SVM) methods together. In the first stage, we apply the KNN algorithm to photometric data and estimate their corresponding z phot. Our analysis has found two dense regions with catastrophic failure, one in the range of z phot ɛ [0.3, 1.2] and the other in the range of zphot ɛ [1.2, 2.1]. In the second stage, we map the photometric input pattern of points falling into the two ranges from their original attribute space into a high dimensional feature space by using a Gaussian kernel function from an SVM. In the high dimensional feature space, many outliers resulting from catastrophic failure by simple Euclidean distance computation in KNN can be identified by a classification hyperplane of SVM and can be further corrected. Experimental results based on the Sloan Digital Sky Survey (SDSS) quasar data show that the two-stage fusion approach can significantly mitigate catastrophic failure and improve the estimation accuracy of photometric redshifts of quasars. The percents in different |δz| ranges and root mean square (rms) error by the integrated method are 83.47%, 89.83%, 90.90% and 0.192, respectively, compared to the results by KNN (71.96%, 83.78%, 89.73% and 0.204).
Schiaffino, L.; Rosado Muñoz, A.; Guerrero Martínez, J.; Francés Villora, J.; Gutiérrez, A.; Martínez Torres, I.; Kohan, y. D. R.
2016-04-01
Deep Brain Stimulation (DBS) applies electric pulses into the subthalamic nucleus (STN) improving tremor and other symptoms associated to Parkinson’s disease. Accurate STN detection for proper location and implant of the stimulating electrodes is a complex task and surgeons are not always certain about final location. Signals from the STN acquired during DBS surgery are obtained with microelectrodes, having specific characteristics differing from other brain areas. Using supervised learning, a trained model based on previous microelectrode recordings (MER) can be obtained, being able to successfully classify the STN area for new MER signals. The K Nearest Neighbours (K-NN) algorithm has been successfully applied to STN detection. However, the use of the fuzzy form of the K-NN algorithm (KNN-F) has not been reported. This work compares the STN detection algorithm of K-NN and KNN-F. Real MER recordings from eight patients where previously classified by neurophysiologists, defining 15 features. Sensitivity and specificity for the classifiers are obtained, Wilcoxon signed rank non-parametric test is used as statistical hypothesis validation. We conclude that the performance of KNN-F classifier is higher than K-NN with p<0.01 in STN specificity.
KNN/BNT composite lead-free films for high-frequency ultrasonic transducer applications.
Lau, Sien Ting; Ji, Hong Fen; Li, Xiang; Ren, Wei; Zhou, Qifa; Shung, K Kirk
2011-01-01
Lead-free K(0.5)Na(0.5)NbO(3)/Bi(0.5)Na(0.5)TiO(3) (KNN/ BNT) films have been fabricated by a composite sol-gel technique. Crystalline KNN fine powder was dispersed in the BNT precursor solution to form a composite slurry which was then spin-coated onto a platinum-buffered Si substrate. Repeated layering and vacuum infiltration were applied to produce 5-μm-thick dense composite film. By optimizing the sintering temperature, the films exhibited good dielectric and ferroelectric properties comparable to PZT films. A 193-MHz high-frequency ultrasonic transducer fabricated from this composite film showed a -6-dB bandwidth of approximately 34%. A tungsten wire phantom was imaged to demonstrate the capability of the transducer. PMID:21244994
A Statistical Approach for Iris Recognition Using K-NN Classifier
Dolly Choudhary
2013-04-01
Full Text Available Irish recognition has always been an attractive goal for researchers. The identification of the person based on iris recognition is very popular due to the uniqueness of the pattern of iris. Although a number of methods for iris recognition have been proposed by many researchers in the last few years. This paper proposes statistical texture feature based iris matching method for recognition using K-NN classifier. Statistical texture measures such as mean, standard deviation, entropy, skewness etc., and six features are computed of normalized iris image. K-NN classifier matches the input iris with the trained iris images by calculating the Euclidean distance between two irises. The performance of the system is evaluated on 500 iris images, which gives good classification accuracy with reduced FAR/FRR.
Processing and characterizations of BNT-KNN ceramics for actuator applications
Mallam Chandrasekhar
2016-06-01
Full Text Available BNT-KNN powder (with composition 0.93Bi0.5Na0.5TiO3–0.07K0.5Na0.5NbO3 was synthesized as a single perovskite phase by conventional solid state reaction route and dense ceramics were obtained by sintering of powder compacts at 1100 °C for 4 h. Dielectric study confirmed relaxor behaviour, whereas the microstructure study showed sharp cornered cubic like grains with an average grain size ∼1.15 µm. The saturated polarization vs. electric field (P-E hysteresis loops confirmed the ferroelectric (FE nature while the butterfly shaped strain vs. electric field (S-E loops suggested the piezoelectric nature of the BNT-KNN ceramic samples. Maximum electric field induced strain of ∼0.62% suggested the usefulness of this system for actuator applications.
Akmal Mat Harttat Maziati
2015-06-01
Full Text Available Alkaline niobate mainly potassium sodium niobate, (KxNa1-x NbO3 (abreviated as KNN has long attracted attention as piezoelectric materials as its high Curie temperature (Tc and piezoelectric properties. The volatility of alkaline element (K, Na is, however detrimental to the stoichiometry of KNN, contributing to the failure to achieve high-density structure and lead to the formation of intrinsic defects. By partially doping of several rare-earth elements, the inherent defects could be improved significantly. Therefore, considerable attempts have been made to develop doped-KNN based ceramic materials with high electrical properties. In this paper, these research activities are reviewed, including dopants type and doping role in KNN perovskite structure.
Performance Analysis of kNN on large datasets using CUDA & Pthreads : Comparing between CPU & GPU
Kankatala, Sriram
2015-01-01
Several organizations have large databases which are growing at a rapid rate day by day, which need to be regularly maintained. Content based searches are similar searched based on certain features that are obtained from various multi media data. For various applications like multimedia content retrieval, data mining, pattern recognition, etc., performing the nearest neighbor search is a challenging task in multidimensional data. The important factors in nearest neighbor search kNN are search...
A Statistical Approach for Iris Recognition Using K-NN Classifier
Dolly Choudhary; Ajay Kumar Singh; Shamik Tiwari
2013-01-01
Irish recognition has always been an attractive goal for researchers. The identification of the person based on iris recognition is very popular due to the uniqueness of the pattern of iris. Although a number of methods for iris recognition have been proposed by many researchers in the last few years. This paper proposes statistical texture feature based iris matching method for recognition using K-NN classifier. Statistical texture measures such as mean, standard deviation, entropy, skewness...
Quevedo, Hernando
2016-01-01
We review the problem of describing the gravitational field of compact stars in general relativity. We focus on the deviations from spherical symmetry which are expected to be due to rotation and to the natural deformations of mass distributions. We assume that the relativistic quadrupole moment takes into account these deviations, and consider the class of axisymmetric static and stationary quadrupolar metrics which satisfy Einstein's equations in empty space and in the presence of matter represented by a perfect fluid. We formulate the physical conditions that must be satisfied for a particular spacetime metric to describe the gravitational field of compact stars. We present a brief review of the main static and axisymmetric exact solutions of Einstein's vacuum equations, satisfying all the physical conditions. We discuss how to derive particular stationary and axisymmetric solutions with quadrupolar properties by using the solution generating techniques which correspond either to Lie symmetries and B\\"acku...
In April 1978 a meeting of senior metrication officers convened by the Commonwealth Science Council of the Commonwealth Secretariat, was held in London. The participants were drawn from Australia, Bangladesh, Britain, Canada, Ghana, Guyana, India, Jamaica, Papua New Guinea, Solomon Islands and Trinidad and Tobago. Among other things, the meeting resolved to develop a set of guidelines to assist countries to change to SI and to compile such guidelines in the form of a working manual
Pijpers, F P
2006-01-01
Scientific output varies between research fields and between disciplines within a field such as astrophysics. Even in fields where publication is the primary output, there is considerable variation in publication and hence in citation rates. Data from the Smithsonian/NASA Astrophysics Data System is used to illustrate this problem and argue against a "one size fits all" approach to performance metrics, especially over the short time-span covered by the Research Assessment Exercise (soon underway in the UK).
Klauder, John R.
1998-01-01
Canonical quantization may be approached from several different starting points. The usual approaches involve promotion of c-numbers to q-numbers, or path integral constructs, each of which generally succeeds only in Cartesian coordinates. All quantization schemes that lead to Hilbert space vectors and Weyl operators---even those that eschew Cartesian coordinates---implicitly contain a metric on a flat phase space. This feature is demonstrated by studying the classical and quantum ``aggregati...
Siparov, S V
2015-01-01
The suggested approach makes it possible to produce a consistent description of motions of a physical system. It is shown that the concept of force fields defining the systems dynamics is equivalent to the choice of the corresponding metric of an anisotropic space, which is used for the modeling of physical reality and the processes that take place. The examples from hydrodynamics, electrodynamics, quantum mechanics and theory of gravitation are discussed. This approach makes it possible to get rid of some known paradoxes. It can be also used for the further development of the theory.
QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases
Indu Saini; Dilbag Singh; Arun Khosla
2013-01-01
The performance of computer aided ECG analysis depends on the precise and accurate delineation of QRS-complexes. This paper presents an application of K-Nearest Neighbor (KNN) algorithm as a classifier for detection of QRS-complex in ECG. The proposed algorithm is evaluated on two manually annotated standard databases such as CSE and MIT-BIH Arrhythmia database. In this work, a digital band-pass filter is used to reduce false detection caused by interference present in ECG signal and further ...
Sharp metric obstructions for quasi-Einstein metrics
Case, Jeffrey S
2011-01-01
Using the tractor calculus to study conformally warped manifolds, we adapt results of Gover and Nurowski to give sharp metric obstructions to the existence of quasi-Einstein metrics on suitably generic manifolds. We do this by introducing an analogue of the curvature tractor, itself the tractor analogue of the curvature of the Fefferman-Graham ambient metric. We then use these obstructions to produce a tensorial invariant which is polynomial in the Riemann curvature and its divergence, and which gives the desired obstruction. In particular, this leads to a generalization to arbitrary dimensions of an algorithm due to Bartnik and Tod for finding static metrics. We also explore the consequences of this work for gradient Ricci solitons, finding an obstruction to their existence on suitably generic manifolds, and observing an interesting similarity between the nonnegativity of the curvature tractor and Hamilton's matrix Harnack inequality.
Lead-free KNN-modified piezoceramics of the system (Li,Na,K)(Nb,Ta,Sb)O3 were prepared by conventional solid-state sintering. The X-ray diffraction patterns revealed a perovskite phase, together with some minor secondary phase, which was assigned to K3LiNb6O17, tetragonal tungsten-bronze (TTB). A structural evolution toward a pure tetragonal structure with the increasing sintering time was observed, associated with the decrease of TTB phase. A correlation between higher tetragonality and higher piezoelectric response was clearly evidenced. Contrary to the case of the LiTaO3 modified KNN, very large abnormal grains with TTB structure were not detected. As a consequence, the simultaneous modification by tantalum and antimony seems to induce during sintering a different behaviour from the one of LiTaO3 modified KNN.
Photometric Redshift Estimation for Quasars by Integration of KNN and SVM
Han, Bo; Zhang, Yanxia; Zhao, Yongheng
2016-01-01
The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is still an unsolved problem for a long time and exists in the current photometric redshift estimation approaches (such as $k$-nearest-neighbor). In this paper, we propose a novel two-stage approach by integration of $k$-nearest-neighbor (KNN) and support vector machine (SVM) methods together. In the first stage, we apply KNN algorithm on photometric data and estimate their corresponding z$_{\\rm phot}$. By analysis, we find two dense regions with catastrophic failure, one in the range of z$_{\\rm phot}\\in[0.3,1.2]$, the other in the range of z$_{\\rm phot}\\in [1.2,2.1]$. In the second stage, we map the photometric input pattern of points falling into the two ranges from original attribute space into a high dimensional feature space by Gaussian kernel function in SVM. In the high dimensional feature space,...
Evaluation of normalization methods for cDNA microarray data by k-NN classification
Myers Connie
2005-07-01
Full Text Available Abstract Background Non-biological factors give rise to unwanted variations in cDNA microarray data. There are many normalization methods designed to remove such variations. However, to date there have been few published systematic evaluations of these techniques for removing variations arising from dye biases in the context of downstream, higher-order analytical tasks such as classification. Results Ten location normalization methods that adjust spatial- and/or intensity-dependent dye biases, and three scale methods that adjust scale differences were applied, individually and in combination, to five distinct, published, cancer biology-related cDNA microarray data sets. Leave-one-out cross-validation (LOOCV classification error was employed as the quantitative end-point for assessing the effectiveness of a normalization method. In particular, a known classifier, k-nearest neighbor (k-NN, was estimated from data normalized using a given technique, and the LOOCV error rate of the ensuing model was computed. We found that k-NN classifiers are sensitive to dye biases in the data. Using NONRM and GMEDIAN as baseline methods, our results show that single-bias-removal techniques which remove either spatial-dependent dye bias (referred later as spatial effect or intensity-dependent dye bias (referred later as intensity effect moderately reduce LOOCV classification errors; whereas double-bias-removal techniques which remove both spatial- and intensity effect reduce LOOCV classification errors even further. Of the 41 different strategies examined, three two-step processes, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, all of which removed intensity effect globally and spatial effect locally, appear to reduce LOOCV classification errors most consistently and effectively across all data sets. We also found that the investigated scale normalization methods do not reduce LOOCV classification error. Conclusion Using LOOCV error of k-NNs as the
Evaluation of normalization methods for cDNA microarray data by k-NN classification
Wu, Wei; Xing, Eric P; Myers, Connie; Mian, Saira; Bissell, Mina J
2004-12-17
Non-biological factors give rise to unwanted variations in cDNA microarray data. There are many normalization methods designed to remove such variations. However, to date there have been few published systematic evaluations of these techniques for removing variations arising from dye biases in the context of downstream, higher-order analytical tasks such as classification. Ten location normalization methods that adjust spatial- and/or intensity-dependent dye biases, and three scale methods that adjust scale differences were applied, individually and in combination, to five distinct, published, cancer biology-related cDNA microarray data sets. Leave-one-out cross-validation (LOOCV) classification error was employed as the quantitative end-point for assessing the effectiveness of a normalization method. In particular, a known classifier, k-nearest neighbor (k-NN), was estimated from data normalized using a given technique, and the LOOCV error rate of the ensuing model was computed. We found that k-NN classifiers are sensitive to dye biases in the data. Using NONRM and GMEDIAN as baseline methods, our results show that single-bias-removal techniques which remove either spatial-dependent dye bias (referred later as spatial effect) or intensity-dependent dye bias (referred later as intensity effect) moderately reduce LOOCV classification errors; whereas double-bias-removal techniques which remove both spatial- and intensity effect reduce LOOCV classification errors even further. Of the 41 different strategies examined, three two-step processes, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, all of which removed intensity effect globally and spatial effect locally, appear to reduce LOOCV classification errors most consistently and effectively across all data sets. We also found that the investigated scale normalization methods do not reduce LOOCV classification error. Using LOOCV error of k-NNs as the evaluation criterion, three double
A ROBUST GA/KNN BASED HYPOTHESIS VERIFICATION SYSTEM FOR VEHICLE DETECTION
Nima Khairdoost
2015-03-01
Full Text Available Vehicle detection is an important issue in driver assistance systems and self-guided vehicles that includes two stages of hypothesis generation and verification. In the first stage, potential vehicles are hypothesized and in the second stage, all hypothesis are verified. The focus of this work is on the second stage. We extract Pyramid Histograms of Oriented Gradients (PHOG features from a traffic image as candidates of feature vectors to detect vehicles. Principle Component Analysis (PCA and Linear Discriminant Analysis (LDA are applied to these PHOG feature vectors as dimension reduction and feature selection tools parallelly. After feature fusion, we use Genetic Algorithm (GA and cosine similarity-based K Nearest Neighbor (KNN classification to improve the performance and generalization of the features. Our tests show good classification accuracy of more than 97% correct classification on realistic on-road vehicle images.
Zhu, Benpeng; Zhang, Zhiqiang; Ma, Teng; Yang, Xiaofei; Li, Yongxiang; Shung, K. Kirk; Zhou, Qifa
2015-04-01
Using tape-casting technology, 35 μm free-standing (100)-textured Li doped KNN (KNLN) thick film was prepared by employing NaNbO3 (NN) as template. It exhibited similar piezoelectric behavior to lead containing materials: a longitudinal piezoelectric coefficient (d33) of ˜150 pm/V and an electromechanical coupling coefficient (kt) of 0.44. Based on this thick film, a 52 MHz side-looking miniature transducer with a bandwidth of 61.5% at -6 dB was built for Intravascular ultrasound (IVUS) imaging. In comparison with 40 MHz PMN-PT single crystal transducer, the rabbit aorta image had better resolution and higher noise-to-signal ratio, indicating that lead-free (100)-textured KNLN thick film may be suitable for IVUS (>50 MHz) imaging.
Composition studies by kNN procedure with KASCADE-Grande data
KASCADE-Grande, located at Forschungszentrum Karlsruhe, is a multi-detector experiment for the measurement of extensive air showers induced by primary cosmic rays in the energy range of 1014-1018 eV. The ''k-Nearest Neighbours'' (kNN) method is a classification procedure applied for a preliminary study of the cosmic ray composition in this energy range. Simulations of different primary particles are used as reference samples. In order to find for each real event the k Nearest Neighbours in the reference sample, the Mahalanobis distance in the space defined by the choosen observables is calculated. The probability of the event to be part of one of the simulated primary groups is the percentage of the k neighbours belonging to it. An attempt to separate light from heavy primaries in the energy range of KASCADE-Grande is here reported.
Automatic Classification of Protein Structure Using the Maximum Contact Map Overlap Metric
Rumen Andonov
2015-10-01
Full Text Available In this work, we propose a new distance measure for comparing two protein structures based on their contact map representations. We show that our novel measure, which we refer to as the maximum contact map overlap (max-CMO metric, satisfies all properties of a metric on the space of protein representations. Having a metric in that space allows one to avoid pairwise comparisons on the entire database and, thus, to significantly accelerate exploring the protein space compared to no-metric spaces. We show on a gold standard superfamily classification benchmark set of 6759 proteins that our exact k-nearest neighbor (k-NN scheme classifies up to 224 out of 236 queries correctly and on a larger, extended version of the benchmark with 60; 850 additional structures, up to 1361 out of 1369 queries. Our k-NN classification thus provides a promising approach for the automatic classification of protein structures based on flexible contact map overlap alignments.
Bargatze, L. F.
2015-12-01
Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted
Random Forests for Metric Learning with Implicit Pairwise Position Dependence
Xiong, Caiming; Xu, Ran; Corso, Jason J
2012-01-01
Metric learning makes it plausible to learn distances for complex distributions of data from labeled data. However, to date, most metric learning methods are based on a single Mahalanobis metric, which cannot handle heterogeneous data well. Those that learn multiple metrics throughout the space have demonstrated superior accuracy, but at the cost of computational efficiency. Here, we take a new angle to the metric learning problem and learn a single metric that is able to implicitly adapt its distance function throughout the feature space. This metric adaptation is accomplished by using a random forest-based classifier to underpin the distance function and incorporate both absolute pairwise position and standard relative position into the representation. We have implemented and tested our method against state of the art global and multi-metric methods on a variety of data sets. Overall, the proposed method outperforms both types of methods in terms of accuracy (consistently ranked first) and is an order of ma...
李华兵; 杨昆
2016-01-01
针对现有差异甲基化区域 DMRs 识别方法中过度删除显著性弱的甲基化位点、DMRs 长度受限以及不能直接处理多类的问题，提出了一种利用滑动窗口和 KNN 算法识别不同类别间DMRs 的算法.算法先通过滑动窗口结合 KNN 分类器筛选候选区域，再根据误差率合并候选区域得到 DMRs.真实数据上的实验表明，算法的分类性能、聚类指数明显优于对照算法，扩展了对照的 Ong 算法识别的 DMRs 长度，并能发现 Ong 算法未发现的 DMRs.%In view of the shortcomings of the existing methods for identifying differentially methylated regions(DMRs),such as over deletion of sites that significance are weaker,region length limitation and can’t be directly processed by the multi-class.An algorithm of identifying DMRs based on sliding window and k-nearest neighbor(KNN)is proposed.In this method,candidate regions are obtained using sliding windows and KNN,and it merges candidate regions to get DMRs.Through real data simulation results demonstrate the method is superior to control method, such as classification performance,cluster index,the DMRs length of the control methods of Ong is extended and find some DMRs that can’t be found in control algorithm of Ong.
V. Myroniuk
2015-01-01
This paper deals with modern experience of statistical inventory of forests using ground-based inventory and remote sensing data (RSD). A detailed analysis of the k-NN method of classification of satellite images is given and features of its applying for thematic mapping of forest fund under the statistical forest inventory defined. The algorithm for calculating the stock of plantings for the statistical software with R open source is shown on the example of local research material.
Mattioli W; Quatrini V; Di Paolo S; Di Santo D; Giuliarelli D; Angelini A.; Portoghesi L; Corona P
2012-01-01
Estimation and mapping of forest attributes are a fundamental support for forest management planning. This study describes a practical experimentation concerning the use of design-based k-Nearest Neighbors (k-NN) approach to estimate and map selected attributes in the framework of inventories at forest management level. The study area was the Chiarino forest within the Gran Sasso and Monti della Laga National Park (central Italy). Aboveground biomass and current annual increment of tree volum...
Feature Selection and Predictors of Falls with Foot Force Sensors Using KNN-Based Algorithms
Shengyun Liang
2015-11-01
Full Text Available The aging process may lead to the degradation of lower extremity function in the elderly population, which can restrict their daily quality of life and gradually increase the fall risk. We aimed to determine whether objective measures of physical function could predict subsequent falls. Ground reaction force (GRF data, which was quantified by sample entropy, was collected by foot force sensors. Thirty eight subjects (23 fallers and 15 non-fallers participated in functional movement tests, including walking and sit-to-stand (STS. A feature selection algorithm was used to select relevant features to classify the elderly into two groups: at risk and not at risk of falling down, for three KNN-based classifiers: local mean-based k-nearest neighbor (LMKNN, pseudo nearest neighbor (PNN, local mean pseudo nearest neighbor (LMPNN classification. We compared classification performances, and achieved the best results with LMPNN, with sensitivity, specificity and accuracy all 100%. Moreover, a subset of GRFs was significantly different between the two groups via Wilcoxon rank sum test, which is compatible with the classification results. This method could potentially be used by non-experts to monitor balance and the risk of falling down in the elderly population.
Performance Analysis of Random Forests with SVM and KNN in Classification of Ancient Kannada Scripts
Soumya A
2014-07-01
Full Text Available Ancient inscriptions which reveal the details of yester years are difficult to interpret by modern readers and efforts are being made in automating such tasks of deciphering historical records. The Kannada script which is used to write in Kannada language has gradually evolved from the ancient script known as Brahmi. Kannada script has traveled a long way from the earlier Brahmi model and has undergone a number of changes during the regimes of Ashoka, Shatavahana, Kadamba, Ganga, Rashtrakuta, Chalukya, Hoysala , Vijayanagara and Wodeyar dynasties. In this paper we discuss on Classification of ancient Kannada Scripts during three different periods Ashoka, Kadamba and Satavahana. A reconstructed grayscale ancient Kannada epigraph image is input, which is binarized using Otsu’s method. Normalized Central and Zernike Moment features are extracted for classification. The RF Classifier designed is tested on handwritten base characters belonging to Ashoka, Satavahana and Kadamba dynasties. For each dynasty, 105 handwritten samples with 35 base characters are considered. The classification rates for the training and testing base characters from Satavahana period, for varying number of trees and thresholds of RF are determined. Finally a Comparative analysis of the Classification rates is made for the designed RF with SVM and k-NN classifiers, for the ancient Kannada base characters from 3 different eras Ashoka, Kadamba and Satavahana period.
Improved Apriori and KNN approach for Virtual machine based intrusion detection
Suneetha Valluru#1 , Mrs N. Rajeswari#2
2012-10-01
Full Text Available Nowadays, as information systems are usually more accessible to the world wide web, the advantage of secure networks is tremendously increased. New intelligent Intrusion Detection Systems (IDSs that based on sophisticated algorithms as an alternative to current signature-base detections are really in demand. Intrusion detection is one of network security area of technology main research directions. Data mining technology was applied to network intrusion detection system (NIDS, may discover the new pattern due to massive network data, to scale back the workload of the manual compilation intrusion behavior patterns and normal behavior patterns.Virtualization is now a more popular service hosting platform. Recently, intrusion detection systems (IDSs which utilize virtualization are now introduced. A particular challenge inside current virtualization-based IDS systems is considered in this project. In this particular proposed system a new chi-square based feature selection that evaluates the relative importance of individual features. After feature selection proposed techniques like KNN and Modified apriori are applied on the data with less false positive rates.
Mattioli W
2012-02-01
Full Text Available Estimation and mapping of forest attributes are a fundamental support for forest management planning. This study describes a practical experimentation concerning the use of design-based k-Nearest Neighbors (k-NN approach to estimate and map selected attributes in the framework of inventories at forest management level. The study area was the Chiarino forest within the Gran Sasso and Monti della Laga National Park (central Italy. Aboveground biomass and current annual increment of tree volume were selected as the attributes of interest for the test. Field data were acquired within 28 sample plots selected by stratified random sampling. Satellite data were acquired by a Landsat 5 TM multispectral image. Attributes from field surveys and Landsat image processing were coupled by k-NN to predict the attributes of interest for each pixel of the Landsat image. Achieved results demonstrate the effectiveness of the k-NN approach for statistical estimation, that is compatible with the produced forest attribute raster maps and also proves to be characterized, in the considered study case, by a precision double than that obtained by conventional inventory based on field sample plots only.
Using quality metrics with laser range scanners
MacKinnon, David K.; Aitken, Victor; Blais, Francois
2008-02-01
We have developed a series of new quality metrics that are generalizable to a variety of laser range scanning systems, including those acquiring measurements in the mid-field. Moreover, these metrics can be integrated into either an automated scanning system, or a system that guides a minimally trained operator through the scanning process. In particular, we represent the quality of measurements with regard to aliasing and sampling density for mid-field measurements, two issues that have not been well addressed in contemporary literature. We also present a quality metric that addresses the issue of laser spot motion during sample acquisition. Finally, we take into account the interaction between measurement resolution and measurement uncertainty where necessary. These metrics are presented within the context of an adaptive scanning system in which quality metrics are used to minimize the number of measurements obtained during the acquisition of a single range image.
Zhang, Xiaoling; Shen, Yi-Bing
2012-01-01
In this paper, a characteristic condition of Einstein Kropina metrics is given. By the characteristic condition, we prove that a non-Riemannian Kropina metric $F=\\frac{\\alpha^2}{\\beta}$ with constant Killing form $\\beta$ on an n-dimensional manifold $M$, $n\\geq 2$, is an Einstein metric if and only if $\\alpha$ is also an Einstein metric. By using the navigation data $(h,W)$, it is proved that an n-dimensional ($n\\geq2$) Kropina metric $F=\\frac{\\alpha^2}{\\beta}$ is Einstein if and only if the ...
Fernández, V. V.; Moya, A. M.; Rodrigues Jr., W. A.
2002-01-01
In this paper we introduce the concept of metric Clifford algebra $\\mathcal{C\\ell}(V,g)$ for a $n$-dimensional real vector space $V$ endowed with a metric extensor $g$ whose signature is $(p,q)$, with $p+q=n$. The metric Clifford product on $\\mathcal{C\\ell}(V,g)$ appears as a well-defined \\emph{deformation}(induced by $g$) of an euclidean Clifford product on $\\mathcal{C\\ell}(V)$. Associated with the metric extensor $g,$ there is a gauge metric extensor $h$ which codifies all the geometric inf...
Prevention of Spammers and Promoters in Video Social Networks using SVM-KNN
Indira K
2014-10-01
Full Text Available As online social networks acquire larger user bases, they also become more interesting targets for spammers and promoters. Spam can take very different forms on social websites, especially in the form of videos and cannot always be detected by analyzing textual content. There are online video sharing systems that allow the users to post videos s response to any type of discussion topic. This feature encourages some of the users to post polluted content illegally as responses and there may be content promoters who try to promote them in the top listed search. Content pollution like spread advertise to generate sales, disseminate pornography, and compromise system reputation may threaten the trust of users on the system, thus weaken its success in promoting social interactions. As a solution for this problem, we classify the users as spammers, content promoters and legitimate users by building a test collection of real YouTube users using which we can provide a classification we use of content, individual and social attributes that help in characterizing each user class. For effective classification we use SVMKNN which is an active learning approach. Our proposed approach poses a promising alternative to simply considering all users as legitimate or to randomly selecting users for manual inspection. In simple SVM training is very slow on whole dataset and not works very well on multiple classes. To overcome this problem and to provide efficient classification in fast manner we proposed new approach is SVM-KNN. Train a Support Vector Machine on K no of collections of nearest neighbours.
Knowledge discovery in medical systems using differential diagnosis, LAMSTAR & k-NN.
Isola, Rahul; Carvalho, Rebeck; Tripathy, Amiya Kumar
2012-11-01
Medical data is an ever-growing source of information generated from the hospitals in the form of patient records. When mined properly the information hidden in these records is a huge resource bank for medical research. As of now, this data is mostly used only for clinical work. This data often contains hidden patterns and relationships, that can lead to better diagnosis, better medicines, better treatment and overall, a platform to better understand the mechanisms governing almost all aspects of the medical domain. Unfortunately, discovery of these hidden patterns and relationships often goes unexploited. However there is on-going research in medical diagnosis which can predict the diseases of the heart, lungs and various tumours based on the past data collected from the patients.They are mostly limited to domain specific systems that predict diseases restricted to their area of operation like heart, brain and various other domains. These are not applicable to the whole medical dataset. The system proposed in this paper uses this vast storage of information so that diagnosis based on this historical data can be made. It focuses on computing the probability of occurrence of a particular ailment from the medical data by mining it using a unique algorithm which increases accuracy of such diagnosis by combining the key points of Neural Networks, Large Memory Storage and Retrieval (LAMSTAR), k-NN and Differential Diagnosis all integrated into one single algorithm. The system uses a Service-Oriented Architecture wherein the system components of diagnosis, information portal and other miscellaneous services are provided.This algorithm can be used in solving a few common problems that are encountered in automated diagnosis these days, which include: diagnosis of multiple diseases showing similar symptoms, diagnosis of a person suffering from multiple diseases, receiving faster and more accurate second opinion and faster identification of trends present in the medical
Algebraic mesh quality metrics
KNUPP,PATRICK
2000-04-24
Quality metrics for structured and unstructured mesh generation are placed within an algebraic framework to form a mathematical theory of mesh quality metrics. The theory, based on the Jacobian and related matrices, provides a means of constructing, classifying, and evaluating mesh quality metrics. The Jacobian matrix is factored into geometrically meaningful parts. A nodally-invariant Jacobian matrix can be defined for simplicial elements using a weight matrix derived from the Jacobian matrix of an ideal reference element. Scale and orientation-invariant algebraic mesh quality metrics are defined. the singular value decomposition is used to study relationships between metrics. Equivalence of the element condition number and mean ratio metrics is proved. Condition number is shown to measure the distance of an element to the set of degenerate elements. Algebraic measures for skew, length ratio, shape, volume, and orientation are defined abstractly, with specific examples given. Combined metrics for shape and volume, shape-volume-orientation are algebraically defined and examples of such metrics are given. Algebraic mesh quality metrics are extended to non-simplical elements. A series of numerical tests verify the theoretical properties of the metrics defined.
Prentice, Julia C; Frakt, Austin B; Pizer, Steven D
2016-04-01
Increasingly, performance metrics are seen as key components for accurately measuring and improving health care value. Disappointment in the ability of chosen metrics to meet these goals is exemplified in a recent Institute of Medicine report that argues for a consensus-building process to determine a simplified set of reliable metrics. Overall health care goals should be defined and then metrics to measure these goals should be considered. If appropriate data for the identified goals are not available, they should be developed. We use examples from our work in the Veterans Health Administration (VHA) on validating waiting time and mental health metrics to highlight other key issues for metric selection and implementation. First, we focus on the need for specification and predictive validation of metrics. Second, we discuss strategies to maintain the fidelity of the data used in performance metrics over time. These strategies include using appropriate incentives and data sources, using composite metrics, and ongoing monitoring. Finally, we discuss the VA's leadership in developing performance metrics through a planned upgrade in its electronic medical record system to collect more comprehensive VHA and non-VHA data, increasing the ability to comprehensively measure outcomes. PMID:26951272
Regina Lopes Schimitt
2011-01-01
Full Text Available INTRODUÇÃO: O ritmo social é um conceito que integra a relação entre Zeitgebers (sincronizadores sociais e os marcadores de tempo endógenos, e pode ser avaliado com a Escala de Ritmo Social (Social Rhythm Metric-17, SRM-17. O objetivo deste estudo foi realizar a adaptação da versão brasileira da SRM-17 para o português angolano, comparando as duas escalas em populações que utilizam o mesmo idioma mas apresentam diferenças culturais. MÉTODOS: A versão brasileira da SRM-17 foi submetida à avaliação de 10 estudantes universitários angolanos, que analisaram o grau de clareza de cada um dos 15 itens do instrumento usando uma escala visual analógica de 10 cm e propuseram modificações ao texto. Foi realizada revisão dos resultados para a elaboração da versão final, bem como prova de leitura e relatório final. RESULTADOS: A versão final angolana manteve uma equivalência de itens com relação à versão em português brasileiro. A versão avaliada demonstrou um grau satisfatório de clareza e equivalência semântica na maioria dos itens. Porém, alguns itens apresentaram um escore na clareza inferior à média aritmética de compreensão global do instrumento (8,38±1,0. CONCLUSÃO: Apesar de o português ser o idioma oficial nos dois países, há diferenças culturais significativas nas duas populações. Este trabalho apresenta uma versão adaptada à realidade angolana de um instrumento específico para aferir ritmo social. O processo de adaptação transcultural deve efetivar-se com estudos de validação do instrumento final em uma amostra maior da população, onde também poderão ser avaliadas as equivalências operacional, de medida e funcional.INTRODUCTION: Social rhythm is a concept that correlates social Zeitgebers (synchronizers with endogenous markers of time, and can be assessed with the Social Rhythm Metric-17 (SRM-17. The aim of this study was to adapt the Brazilian version of the SRM-17 to Angolan
Chistyakov, Vyacheslav
2015-01-01
Aimed toward researchers and graduate students familiar with elements of functional analysis, linear algebra, and general topology; this book contains a general study of modulars, modular spaces, and metric modular spaces. Modulars may be thought of as generalized velocity fields and serve two important purposes: generate metric spaces in a unified manner and provide a weaker convergence, the modular convergence, whose topology is non-metrizable in general. Metric modular spaces are extensions of metric spaces, metric linear spaces, and classical modular linear spaces. The topics covered include the classification of modulars, metrizability of modular spaces, modular transforms and duality between modular spaces, metric and modular topologies. Applications illustrated in this book include: the description of superposition operators acting in modular spaces, the existence of regular selections of set-valued mappings, new interpretations of spaces of Lipschitzian and absolutely continuous mappings, the existe...
Cuturi, Marco
2011-01-01
Transportation distances have been used for more than a decade now in machine learning to compare histograms of features. They have one parameter: the ground metric, which can be any metric between the features themselves. As is the case for all parameterized distances, transportation distances can only prove useful in practice when this parameter is carefully chosen. To date, the only option available to practitioners to set the ground metric parameter was to rely on a priori knowledge of the features, which limited considerably the scope of application of transportation distances. We propose to lift this limitation and consider instead algorithms that can learn the ground metric using only a training set of labeled histograms. We call this approach ground metric learning. We formulate the problem of learning the ground metric as the minimization of the difference of two polyhedral convex functions over a convex set of distance matrices. We follow the presentation of our algorithms with promising experimenta...
Metrics for Sustainable Manufacturing
Reich-Weiser, Corinne; Vijayaraghavan, Athulan; Dornfeld, David
2008-01-01
A sustainable manufacturing strategy requires metrics for decision making at all levels of the enterprise. In this paper, a methodology is developed for designing sustainable manufacturing metrics given the speciﬁc concerns to be addressed. A top-down approach is suggested that follows the framework of goal and scope deﬁnition: (1) goal- what are the concerns addressed and what is the appropriate metric type to achieve the goal (2) scope what is the appropriate geographic and manufacturing ex...
结合SVM和KNN的Web日志挖掘技术研究方法%Research method of Web log mining technology with combination of SVM and KNN
曾俊
2012-01-01
将SVM和KNN算法结合在一起,组成一种新的Web文本分类算法-SVM-KNN算法.当Web文本和SVM最优超平面的距离大于预选设定的阈值,则采用SVM进行分类,反之采用SVM作为代表点的KNN算法对样本分类.实证结果表明,SVM-KNN分类算法的分类精度比单纯SVM或KNN分类算法有不同程度的提高,为Web数据挖掘提供了一种有效的分类方法.%This paper used SVM and KNN algorithm together to form a new classification algorithm for Web text-SVM-KNN algorithm. When optimal super plane distance of Web text and SVM was greater than the preselected threshold, used SVM to classify, otherwise it adopted KNN algorithm to classify the samples of SVM as the representative point. The experimental results show that the accuracy of SVM-KNN classification algorithm are better than pure SVM or KNN classification algorithm, and the Web text classification provides an effective classification method.
Metrics, Media and Advertisers: Discussing Relationship
Marco Aurelio de Souza Rodrigues
2014-11-01
Full Text Available This study investigates how Brazilian advertisers are adapting to new media and its attention metrics. In-depth interviews were conducted with advertisers in 2009 and 2011. In 2009, new media and its metrics were celebrated as innovations that would increase advertising campaigns overall efficiency. In 2011, this perception has changed: New media’s profusion of metrics, once seen as an advantage, started to compromise its ease of use and adoption. Among its findings, this study argues that there is an opportunity for media groups willing to shift from a product-focused strategy towards a customer-centric one, through the creation of new, simple and integrative metrics.
ZHAOZhen-gang
2005-01-01
We have constructed the positive definite metric matrixes for the bounded domains of Rn and proved an inequality which is about the Jacobi matrix of a harmonic mapping on a bounded domain of Rn and the metric matrix of the same bounded domain.
Surveillance Metrics Sensitivity Study
Bierbaum, R; Hamada, M; Robertson, A
2011-11-01
In September of 2009, a Tri-Lab team was formed to develop a set of metrics relating to the NNSA nuclear weapon surveillance program. The purpose of the metrics was to develop a more quantitative and/or qualitative metric(s) describing the results of realized or non-realized surveillance activities on our confidence in reporting reliability and assessing the stockpile. As a part of this effort, a statistical sub-team investigated various techniques and developed a complementary set of statistical metrics that could serve as a foundation for characterizing aspects of meeting the surveillance program objectives. The metrics are a combination of tolerance limit calculations and power calculations, intending to answer level-of-confidence type questions with respect to the ability to detect certain undesirable behaviors (catastrophic defects, margin insufficiency defects, and deviations from a model). Note that the metrics are not intended to gauge product performance but instead the adequacy of surveillance. This report gives a short description of four metrics types that were explored and the results of a sensitivity study conducted to investigate their behavior for various inputs. The results of the sensitivity study can be used to set the risk parameters that specify the level of stockpile problem that the surveillance program should be addressing.
Surveillance metrics sensitivity study.
Hamada, Michael S. (Los Alamos National Laboratory); Bierbaum, Rene Lynn; Robertson, Alix A. (Lawrence Livermore Laboratory)
2011-09-01
In September of 2009, a Tri-Lab team was formed to develop a set of metrics relating to the NNSA nuclear weapon surveillance program. The purpose of the metrics was to develop a more quantitative and/or qualitative metric(s) describing the results of realized or non-realized surveillance activities on our confidence in reporting reliability and assessing the stockpile. As a part of this effort, a statistical sub-team investigated various techniques and developed a complementary set of statistical metrics that could serve as a foundation for characterizing aspects of meeting the surveillance program objectives. The metrics are a combination of tolerance limit calculations and power calculations, intending to answer level-of-confidence type questions with respect to the ability to detect certain undesirable behaviors (catastrophic defects, margin insufficiency defects, and deviations from a model). Note that the metrics are not intended to gauge product performance but instead the adequacy of surveillance. This report gives a short description of four metrics types that were explored and the results of a sensitivity study conducted to investigate their behavior for various inputs. The results of the sensitivity study can be used to set the risk parameters that specify the level of stockpile problem that the surveillance program should be addressing.
Metrics for Probabilistic Geometries
Tosi, Alessandra; Hauberg, Søren; Vellido, Alfredo; Lawrence, Neil D.
We investigate the geometrical structure of probabilistic generative dimensionality reduction models using the tools of Riemannian geometry. We explicitly define a distribution over the natural metric given by the models. We provide the necessary algorithms to compute expected metric tensors where...
Cooper, Gloria S., Ed.; Magisos, Joel H., Ed.
Designed to meet the job-related metric measurement needs of students interested in transportation, this instructional package is one of five for the marketing and distribution cluster, part of a set of 55 packages for metric instruction in different occupations. The package is intended for students who already know the occupational terminology,…
Metrics for Food Distribution.
Cooper, Gloria S., Ed.; Magisos, Joel H., Ed.
Designed to meet the job-related metric measurement needs of students interested in food distribution, this instructional package is one of five for the marketing and distribution cluster, part of a set of 55 packages for metric instruction in different occupations. The package is intended for students who already know the occupational…
Cooper, Gloria S., Ed.; Magisos, Joel H., Ed.
Designed to meet the job-related metric measurement needs of cosmetology students, this instructional package on cosmetology is part of a set of 55 packages for metric instruction in different occupations. The package is intended for students who already know the occupational terminology, measurement terms, and tools currently in use. Each of the…
Metrics for Secretarial, Stenography.
Cooper, Gloria S., Ed.; Magisos, Joel H., Ed.
Designed to meet the job-related metric measurement needs of secretarial, stenography students, this instructional package is one of three for the business and office occupations cluster, part of a set of 55 packages for metric instruction in different occupations. The package is intended for students who already know the occupational terminology,…
S Wiegand; S Flege; O Baake; W Ensinger
2012-10-01
Thin films of (Na0.5K0.5)NbO3 (KNN) were synthesized on Pt/Ti/SiO2/Si substrates with repeated spin-coating after fabrication of the precursor solution by a sol–gel process. The KNN precursor solution was prepared from K- and Na-acetate, Nb-pentaethoxide and 1,3-propanediol. Based on three characteristic temperatures derived from thermal analysis (TG–DTA) experiments, five heat treatment programs were developed. All programs lead to single phase perovskite KNN films with random crystal orientation, but only the programs that included a treatment after each single spin-coating step provided pore free surfaces with grains of about 100 nm size. The lowest leakage current at 150 kV cm-1 was obtained for the temperature program that included pyrolysis and calcination steps after each deposited layer.
Huang, Jian; Liu, Gui-xiong
2016-04-01
The identification of targets varies in different surge tests. A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm (k-NN) for equipment under test status identification was proposed after using feature matching to identify equipment status had to train new patterns every time before testing. First, color space (L*a*b*, hue saturation lightness (HSL), hue saturation value (HSV)) to segment was selected according to the high luminance points ratio and white luminance points ratio of the image. Second, the unknown class sample S r was classified by the k-NN algorithm with training set T z according to the feature vector, which was formed from number of pixels, eccentricity ratio, compactness ratio, and Euler's numbers. Last, while the classification confidence coefficient equaled k, made S r as one sample of pre-training set T z '. The training set T z increased to T z+1 by T z if T z was saturated. In nine series of illuminant, indicator light, screen, and disturbances samples (a total of 21600 frames), the algorithm had a 98.65%identification accuracy, also selected five groups of samples to enlarge the training set from T 0 to T 5 by itself.
结合FSVM和KNN的人脸表情识别%Facial Expression Recognition Based on FSVM and KNN
王小虎; 黄银珍; 张石清
2013-01-01
为了提高人脸表情的正确识别率，提出了一种组合模糊支持向量机（FSVM ）和K-近邻（KNN）的人脸表情识别的新方法。该方法通过主成分分析（PCA ）提取人脸表情特征，对于待分类的不同区域，根据区分程度自适应划分为不同区域类型；并结合FSVM和KNN算法的特点，对不同区域类型切换分类算法。实验表明，此方法既能保证分类的精确度，又能简化计算复杂度。%To improve the recognition accuracy ,a new approach for facial expression recognition based on Fuzzy Support Vector Machine (FSVM ) and K-Nearest Neighbor (KNN) is presented in this paper .At first ,the feature of the static facial expression image is extracted by the Principle Component Analysis (PCA ) ,then ,the algorithm divide the region into different types ,and combine with the characteristic of the FSVM and KNN ,switch the classification methods to the different types .The result of the experiment show that proposed algorithm can achieve good recognition accuracy ,and can simplify the computation complexity .
面向轨迹数据流的KNN近似查询%KNN Approximate Query for Trajectory Data Stream
王考杰; 郑雪峰; 宋一丁; 曲阜平
2011-01-01
This paper proposes a novel approach for continuous approximate query over trajectory stream based on sliding window. Through local clustering, the sliding window is divide into various sized basic windows and sampling the data elements of a basic window using biased sampling rate, forms trajectory stream synopses. Toward such synopses, it can implement distributed K-Nearest Neighbor(KNN) queries utilizing the plane sweep algorithm of computational geometry. The extensive experiments verify the effectiveness of proposed algorithm and it has better expansibility.%提出一种基于滑动窗口的K-最近邻(KNN)近似查询算法.将滑动窗口内数据通过聚类划分成若干大小不一的基本窗口,针对每个基本窗口给定一个采样率,对窗口内数据进行偏倚采样,形成数据流摘要,并基于该摘要,采用计算几何平面扫描算法执行分布式最近邻查询.仿真实验结果表明该算法有效,且具有较好的可扩展性.
M. Punithavalli
2012-01-01
Full Text Available The high usage of software system poses high quality demand from users, which results in increased software complexity. To address these complexities, software quality engineering methods should be updated accordingly and enhance their quality assuring methods. Fault prediction, a sub-task of SQE, is designed to solve this issue and provide a strategy to identify faulty parts of a program, so that the testing process can concentrate only on those regions. This will improve the testing process and indirectly help to reduce development life cycle, project risks, resource and infrastructure costs. Measuring quality using software metrics for fault identification is gaining wide interest in software industry as they help to reduce time and cost. Existing system use either traditional simple metrics or object oriented metrics during fault detection combined with single classifier prediction system. This study combines the use of simple and object oriented metrics and uses a multiple classifier prediction system to identify module faults. In this study, a total of 20 metrics combining both traditional and OO metrics are used for fault detection. To analyze the performance of these metrics on fault module detection, the study proposes the use of ensemble classifiers that uses three frequently used classifiers, Back Propagation Neural Network (BPNN, Support Vector Machine (SVM and K-Nearest Neighbour (KNN. A novel classifier aggregation method is proposed to combine the classification results. Four methods, Sequential Selection, Random Selection with No Replacement, Selection with Bagging and Selection with Boosting, are used to generate different variants of input dataset. The three classifiers were grouped together as 2-classifier and 3-classifier prediction ensemble models. A total of 16 ensemble models were proposed for fault prediction. The performance of the proposed prediciton models was analyzed using accuracy, precision, recall and F
Carver, Gary P.
1994-05-01
The federal agencies are working with industry to ease adoption of the metric system. The goal is to help U.S. industry compete more successfully in the global marketplace, increase exports, and create new jobs. The strategy is to use federal procurement, financial assistance, and other business-related activities to encourage voluntary conversion. Based upon the positive experiences of firms and industries that have converted, federal agencies have concluded that metric use will yield long-term benefits that are beyond any one-time costs or inconveniences. It may be time for additional steps to move the Nation out of its dual-system comfort zone and continue to progress toward metrication. This report includes 'Metric Highlights in U.S. History'.
Dominici, Diego
2011-01-01
This work introduces a distance between natural numbers not based on their position on the real line but on their arithmetic properties. We prove some metric properties of this distance and consider a possible extension.
Paul POCATILU
2007-01-01
Many software and IT projects fail in completing theirs objectives because different causes of which the management of the projects has a high weight. In order to have successfully projects, lessons learned have to be used, historical data to be collected and metrics and indicators have to be computed and used to compare them with past projects and avoid failure to happen. This paper presents some metrics that can be used for the IT project management.
2007-01-01
Full Text Available Many software and IT projects fail in completing theirs objectives because different causes of which the management of the projects has a high weight. In order to have successfully projects, lessons learned have to be used, historical data to be collected and metrics and indicators have to be computed and used to compare them with past projects and avoid failure to happen. This paper presents some metrics that can be used for the IT project management.
Mass Customization Measurements Metrics
Nielsen, Kjeld; Brunø, Thomas Ditlev; Jørgensen, Kaj Asbjørn;
2014-01-01
A recent survey has indicated that 17 % of companies have ceased mass customizing less than 1 year after initiating the effort. This paper presents measurement for a company’s mass customization performance, utilizing metrics within the three fundamental capabilities: robust process design, choice...... navigation, and solution space development. A mass customizer when assessing performance with these metrics can identify within which areas improvement would increase competitiveness the most and enable more efficient transition to mass customization....
Ion IVAN
2007-09-01
Full Text Available The objectives of IT projects are presented. The quality requirements that these projects must fulfill are established. Quality and evaluation indicators for running IT projects are built and verified. Project quality characteristics are presented and discussed. Model refinement for IT project metrics is treated and a software structure is proposed. For an IT project which is designed for software development, quality evaluation and project implementation mode metrics are used.
Sýkorová, Veronika
2008-01-01
The aim of the thesis is to prove measurability of the Data Quality which is a relatively subjective measure and thus is difficult to measure. In doing this various aspects of measuring the quality of data are analyzed and a Complex Data Quality Monitoring System is introduced with the aim to provide a concept for measuring/monitoring the overall Data Quality in an organization. The system is built on a metrics hierarchy decomposed into particular detailed metrics, dimensions enabling multidi...
Optimal Detection Range of RFID Tag for RFID-based Positioning System Using the k-NN Algorithm
Han, Soohee; Kim, Junghwan; Park, Choung-Hwan; Yoon, Hee-Cheon; Heo, Joon
2009-01-01
Positioning technology to track a moving object is an important and essential component of ubiquitous computing environments and applications. An RFID-based positioning system using the k-nearest neighbor (k-NN) algorithm can determine the position of a moving reader from observed reference data. In this study, the optimal detection range of an RFID-based positioning system was determined on the principle that tag spacing can be derived from the detection range. It was assumed that reference tags without signal strength information are regularly distributed in 1-, 2- and 3-dimensional spaces. The optimal detection range was determined, through analytical and numerical approaches, to be 125% of the tag-spacing distance in 1-dimensional space. Through numerical approaches, the range was 134% in 2-dimensional space, 143% in 3-dimensional space. PMID:22408540
Optimal Detection Range of RFID Tag for RFID-based Positioning System Using the k-NN Algorithm
Joon Heo
2009-06-01
Full Text Available Positioning technology to track a moving object is an important and essential component of ubiquitous computing environments and applications. An RFID-based positioning system using the k-nearest neighbor (k-NN algorithm can determine the position of a moving reader from observed reference data. In this study, the optimal detection range of an RFID-based positioning system was determined on the principle that tag spacing can be derived from the detection range. It was assumed that reference tags without signal strength information are regularly distributed in 1-, 2- and 3-dimensional spaces. The optimal detection range was determined, through analytical and numerical approaches, to be 125% of the tag-spacing distance in 1-dimensional space. Through numerical approaches, the range was 134% in 2-dimensional space, 143% in 3-dimensional space.
Metric derived numbers and continuous metric differentiability via homeomorphisms
Duda, Jakub; Maleva, Olga
2006-01-01
We define the notions of unilateral metric derivatives and ``metric derived numbers'' in analogy with Dini derivatives (also referred to as ``derived numbers'') and establish their basic properties. We also prove that the set of points where a path with values in a metric space with continuous metric derivative is not ``metrically differentiable'' (in a certain strong sense) is $\\sigma$-symmetrically porous and provide an example of a path for which this set is uncountable. In the second part...
Dissertation: Geodesics of Random Riemannian Metrics
LaGatta, Tom
2011-01-01
We introduce Riemannian First-Passage Percolation (Riemannian FPP) as a new model of random differential geometry, by considering a random, smooth Riemannian metric on $\\mathbb R^d$. We are motivated in our study by the random geometry of first-passage percolation (FPP), a lattice model which was developed to model fluid flow through porous media. By adapting techniques from standard FPP, we prove a shape theorem for our model, which says that large balls under this metric converge to a deterministic shape under rescaling. As a consequence, we show that smooth random Riemannian metrics are geodesically complete with probability one. In differential geometry, geodesics are curves which locally minimize length. They need not do so globally: consider great circles on a sphere. For lattice models of FPP, there are many open questions related to minimizing geodesics; similarly, it is interesting from a geometric perspective when geodesics are globally minimizing. In the present study, we show that for any fixed st...
On Ptolemaic metric simplicial complexes
Buckley, S M; Wraith, D J
2009-01-01
We show that under certain mild conditions, a metric simplicial complex which satisfies the Ptolemy inequality is a CAT(0) space. Ptolemy's inequality is closely related to inversions of metric spaces. For a large class of metric simplicial complexes, we characterize those which are isometric to Euclidean space in terms of metric inversions.
Frye, Jason Neal; Veitch, Cynthia K.; Mateski, Mark Elliot; Michalski, John T.; Harris, James Mark; Trevino, Cassandra M.; Maruoka, Scott
2012-03-01
Threats are generally much easier to list than to describe, and much easier to describe than to measure. As a result, many organizations list threats. Fewer describe them in useful terms, and still fewer measure them in meaningful ways. This is particularly true in the dynamic and nebulous domain of cyber threats - a domain that tends to resist easy measurement and, in some cases, appears to defy any measurement. We believe the problem is tractable. In this report we describe threat metrics and models for characterizing threats consistently and unambiguously. The purpose of this report is to support the Operational Threat Assessment (OTA) phase of risk and vulnerability assessment. To this end, we focus on the task of characterizing cyber threats using consistent threat metrics and models. In particular, we address threat metrics and models for describing malicious cyber threats to US FCEB agencies and systems.
Kyril Tintarev
2007-05-01
Full Text Available The paper studies energy functionals on quasimetric spaces, defined by quadratic measure-valued Lagrangeans. This general model of medium, known as metric fractals, includes nested fractals and sub-Riemannian manifolds. In particular, the quadratic form of the Lagrangean satisfies Sobolev inequalities with the critical exponent determined by the (quasimetric homogeneous dimension, which is also involved in the asymptotic distribution of the form's eigenvalues. This paper verifies that the axioms of the metric fractal are preserved by space products, leading thus to examples of non-differentiable media of arbitrary intrinsic dimension.
Moya, A. M.; Fernadez, V. V.; Rodrigues Jr., W. A.
2005-01-01
In this paper, the second in a series of eight we continue our development of the basic tools of the multivector and extensor calculus which are used in our formulation of the differential geometry of smooth manifolds of arbitrary topology . We introduce metric and gauge extensors, pseudo-orthogonal metric extensors, gauge bases, tetrad bases and prove the remarkable golden formula, which permit us to view any Clifford algebra Cl(V,G) as a deformation of the euclidean Clifford algebra Cl(V,G_...
Evaluation metrics for biostatistical and epidemiological collaborations.
Rubio, Doris McGartland; Del Junco, Deborah J; Bhore, Rafia; Lindsell, Christopher J; Oster, Robert A; Wittkowski, Knut M; Welty, Leah J; Li, Yi-Ju; Demets, Dave
2011-10-15
Increasing demands for evidence-based medicine and for the translation of biomedical research into individual and public health benefit have been accompanied by the proliferation of special units that offer expertise in biostatistics, epidemiology, and research design (BERD) within academic health centers. Objective metrics that can be used to evaluate, track, and improve the performance of these BERD units are critical to their successful establishment and sustainable future. To develop a set of reliable but versatile metrics that can be adapted easily to different environments and evolving needs, we consulted with members of BERD units from the consortium of academic health centers funded by the Clinical and Translational Science Award Program of the National Institutes of Health. Through a systematic process of consensus building and document drafting, we formulated metrics that covered the three identified domains of BERD practices: the development and maintenance of collaborations with clinical and translational science investigators, the application of BERD-related methods to clinical and translational research, and the discovery of novel BERD-related methodologies. In this article, we describe the set of metrics and advocate their use for evaluating BERD practices. The routine application, comparison of findings across diverse BERD units, and ongoing refinement of the metrics will identify trends, facilitate meaningful changes, and ultimately enhance the contribution of BERD activities to biomedical research. PMID:21284015
MA Zhi-Hao
2008-01-01
Metric of quantum states plays an important role in quantum information theory. In this letter, we find the deep connection between quantum logic theory and quantum information theory. Using the method of quantum logic, we can get a famous inequality in quantum information theory, and we answer a question raised by S. Gudder.
Kerr metric, static observers and Fermi coordinates
The coordinate transformation which maps the Kerr metric written in standard Boyer-Lindquist coordinates to its corresponding form adapted to the natural local coordinates of an observer at rest at a fixed position in the equatorial plane, i.e., Fermi coordinates for the neighbourhood of a static observer world line, is derived and discussed in a way which extends to any uniformly circularly orbiting observer there
Energy lies at the backbone of any advanced society and constitutes an essential prerequisite for economic growth, social order and national defense. However there is an Achilles heel to today's energy and technology relationship; namely a precarious intimacy between energy and the fiscal, social, and technical systems it supports. Recently, widespread and persistent disruptions in energy systems have highlighted the extent of this dependence and the vulnerability of increasingly optimized systems to changing conditions. Resilience is an emerging concept that offers to reconcile considerations of performance under dynamic environments and across multiple time frames by supplementing traditionally static system performance measures to consider behaviors under changing conditions and complex interactions among physical, information and human domains. This paper identifies metrics useful to implement guidance for energy-related planning, design, investment, and operation. Recommendations are presented using a matrix format to provide a structured and comprehensive framework of metrics relevant to a system's energy resilience. The study synthesizes previously proposed metrics and emergent resilience literature to provide a multi-dimensional model intended for use by leaders and practitioners as they transform our energy posture from one of stasis and reaction to one that is proactive and which fosters sustainable growth. - Highlights: • Resilience is the ability of a system to recover from adversity. • There is a need for methods to quantify and measure system resilience. • We developed a matrix-based approach to generate energy resilience metrics. • These metrics can be used in energy planning, system design, and operations
3D Face Recognition based on Radon Transform, PCA, LDA using KNN and SVM
P. S. Hiremath
2014-06-01
Full Text Available Biometrics (or biometric authentication refers to the identification of humans by their characteristics or traits. Bio-metrics is used in computer science as a form of identification and access control. It is also used to identify individuals in groups that are under surveillance. Biometric identifiers are the distinctive, measurable characteristics used to label and describe individuals. Three dimensional (3D human face recognition is emerging as a significant biometric technology. Research interest into 3D face recognition has increased during recent years due to the availability of improved 3D acquisition devices and processing algorithms. Three dimensional face recognition also helps to resolve some of the issues associated with two dimensional (2D face recognition. In the previous research works, there are several methods for face recognition using range images that are limited to the data acquisition and pre-processing stage only. In the present paper, we have proposed a 3D face recognition algorithm which is based on Radon transform, Principal Component Analysis (PCA and Linear Discriminant Analysis (LDA. The Radon transform (RT is a fundamental tool to normalize 3D range data. The PCA is used to reduce the dimensionality of feature space, and the LDA is used to optimize the features, which are finally used to recognize the faces. The experimentation has been done using three publicly available databases, namely, Bhosphorus, Texas and CASIA 3D face databases. The experimental results are shown that the proposed algorithm is efficient in terms of accuracy and detection time, in comparison with other methods based on PCA only and RT+PCA. It is observed that 40 Eigen faces of PCA and 5 LDA components lead to an average recognition rate of 99.20% using SVM classifier.
Teukolsky, Saul A.
2015-06-01
This review describes the events leading up to the discovery of the Kerr metric in 1963 and the enormous impact the discovery has had in the subsequent 50 years. The review discusses the Penrose process, the four laws of black hole mechanics, uniqueness of the solution, and the no-hair theorems. It also includes Kerr perturbation theory and its application to black hole stability and quasi-normal modes. The Kerr metric's importance in the astrophysics of quasars and accreting stellar-mass black hole systems is detailed. A theme of the review is the ‘miraculous’ nature of the solution, both in describing in a simple analytic formula the most general rotating black hole, and in having unexpected mathematical properties that make many calculations tractable. Also included is a pedagogical derivation of the solution suitable for a first course in general relativity.
Trunev A. P.
2013-11-01
Full Text Available We investigate the hypothesis of a plurality of parallel and virtual worlds. It is assumed that sentient beings in each virtual world reach a stage of development that can create a virtual world to simulate the history of their own development. In this case, the virtual worlds are nested within each other, which put a severe restriction on the possible geometry of space-time. Discussed the draft geometry virtual worlds consistently displayed from one world to another. It is shown that in this case, the metric should be universal, depending only on the fundamental constants. There are examples of universal metrics obtained in Einstein's theory of gravitation and Yang-Mills theory
NPScape Metric GIS Data - Housing
National Park Service, Department of the Interior — NPScape housing metrics are calculated using outputs from the Spatially Explicit Regional Growth Model. Metric GIS datasets are produced seamlessly for the United...
Weighted metric multidimensional scaling
Greenacre, Michael J.
2004-01-01
This paper establishes a general framework for metric scaling of any distance measure between individuals based on a rectangular individuals-by-variables data matrix. The method allows visualization of both individuals and variables as well as preserving all the good properties of principal axis methods such as principal components and correspondence analysis, based on the singular-value decomposition, including the decomposition of variance into components along principal axes which provide...
Social media evaluation metrics
Ronalds Skulme; Valerijs Praude
2015-01-01
Background. There are many methods how specialists can evaluate return of online marketing activities. Most of the methods out there are designed for versatile use. But each online marketing tool has its own unique specific metrics that should be taken into account when measuring the return of marketing activities. Authors believe that the methods that are designed to evaluate online marketing activities should also be more specific. Hence authors believe that more specific online marketing r...
Applications of Metric Coinduction
Kozen, Dexter; Ruozzi, Nicholas
2009-01-01
Metric coinduction is a form of coinduction that can be used to establish properties of objects constructed as a limit of finite approximations. One can prove a coinduction step showing that some property is preserved by one step of the approximation process, then automatically infer by the coinduction principle that the property holds of the limit object. This can often be used to avoid complicated analytic arguments involving limits and convergence, replacing them with simpler algebraic arg...
Paul E. Roege; Zachary A. Collier; James Mancillas; John A. McDonagh; Igor Linkov
2014-09-01
Energy lies at the backbone of any advanced society and constitutes an essential prerequisite for economic growth, social order and national defense. However there is an Achilles heel to today?s energy and technology relationship; namely a precarious intimacy between energy and the fiscal, social, and technical systems it supports. Recently, widespread and persistent disruptions in energy systems have highlighted the extent of this dependence and the vulnerability of increasingly optimized systems to changing conditions. Resilience is an emerging concept that offers to reconcile considerations of performance under dynamic environments and across multiple time frames by supplementing traditionally static system performance measures to consider behaviors under changing conditions and complex interactions among physical, information and human domains. This paper identifies metrics useful to implement guidance for energy-related planning, design, investment, and operation. Recommendations are presented using a matrix format to provide a structured and comprehensive framework of metrics relevant to a system?s energy resilience. The study synthesizes previously proposed metrics and emergent resilience literature to provide a multi-dimensional model intended for use by leaders and practitioners as they transform our energy posture from one of stasis and reaction to one that is proactive and which fosters sustainable growth.
Some References on Metric Information.
National Bureau of Standards (DOC), Washington, DC.
This resource work lists metric information published by the U.S. Government and the American National Standards Institute. Also organizations marketing metric materials for education are given. A short table of conversions is included as is a listing of basic metric facts for everyday living. (LS)
Umemura, Masaki; Hotta, Kazuhiro; Nonaka, Hideki; Oda, Kazuo
2016-06-01
We propose an image labeling method for LIDAR intensity image obtained by Mobile Mapping System (MMS) using K-Nearest Neighbor (KNN) of feature obtained by Convolutional Neural Network (CNN). Image labeling assigns labels (e.g., road, cross-walk and road shoulder) to semantic regions in an image. Since CNN is effective for various image recognition tasks, we try to use the feature of CNN (Caffenet) pre-trained by ImageNet. We use 4,096-dimensional feature at fc7 layer in the Caffenet as the descriptor of a region because the feature at fc7 layer has effective information for object classification. We extract the feature by the Caffenet from regions cropped from images. Since the similarity between features reflects the similarity of contents of regions, we can select top K similar regions cropped from training samples with a test region. Since regions in training images have manually-annotated ground truth labels, we vote the labels attached to top K similar regions to the test region. The class label with the maximum vote is assigned to each pixel in the test image. In experiments, we use 36 LIDAR intensity images with ground truth labels. We divide 36 images into training (28 images) and test sets (8 images). We use class average accuracy and pixel-wise accuracy as evaluation measures. Our method was able to assign the same label as human beings in 97.8% of the pixels in test LIDAR intensity images.
Muhammad Bilal
2016-07-01
Full Text Available Sentiment mining is a field of text mining to determine the attitude of people about a particular product, topic, politician in newsgroup posts, review sites, comments on facebook posts twitter, etc. There are many issues involved in opinion mining. One important issue is that opinions could be in different languages (English, Urdu, Arabic, etc.. To tackle each language according to its orientation is a challenging task. Most of the research work in sentiment mining has been done in English language. Currently, limited research is being carried out on sentiment classification of other languages like Arabic, Italian, Urdu and Hindi. In this paper, three classification models are used for text classification using Waikato Environment for Knowledge Analysis (WEKA. Opinions written in Roman-Urdu and English are extracted from a blog. These extracted opinions are documented in text files to prepare a training dataset containing 150 positive and 150 negative opinions, as labeled examples. Testing data set is supplied to three different models and the results in each case are analyzed. The results show that Naïve Bayesian outperformed Decision Tree and KNN in terms of more accuracy, precision, recall and F-measure.
Metric-torsional conformal gravity
When in general geometric backgrounds the metric is accompanied by torsion, the metric conformal properties should correspondingly be followed by analogous torsional conformal properties; however a combined metric torsional conformal structure has never been found which provides a curvature that is both containing metric-torsional degree of freedom and conformally invariant: in this Letter we construct such a metric-torsional conformal curvature. We proceed by building the most general action, then deriving the most general system of field equations; we check their consistency by showing that both conservation laws and trace condition are verified. Final considerations and comments are outlined.
Heidorn, Thomas
1999-01-01
Während die Markpreisrisiken (Zinsen, Aktien und Währungen) mit Hilfe des RiskMetrics-Ansatzes gut beschrieben werden können und dafür relativ überzeugende Derivate zur Absicherung unerwünschter Risiken zur Verfügung stehen, hat diese Entwicklung im Kreditbereich erst begonnen. Bei der Bewertung von Ausfällen konkurrieren einerseits optionstheoretische Ansätze mit der Möglichkeit, historische Ausfallwahrscheinlichkeiten für die Bewertung von Kreditrisiken zu benutzen. Dieser Ansatz für eine K...
A generalization of contact metric manifolds
Kim, J H; Park, J.H.; Sekigawa, K.
2013-01-01
We give a characterization of a contact metric manifold as a special almost contact metric manifold and discuss an almost contact metric manifold which is {a} natural generalization of the contact metric manifolds introduced by Y. Tashiro.
Camanho, Xián O.; Dadhich, Naresh; Molina, Alfred
2015-09-01
We study pure Lovelock vacuum and perfect fluid equations for Kasner-type metrics. These equations correspond to a single Nth order Lovelock term in the action in d=2N+1,2N+2 dimensions, and they capture the relevant gravitational dynamics when aproaching the big-bang singularity within the Lovelock family of theories. Pure Lovelock gravity also bears out the general feature that vacuum in the critical odd dimension, d=2N+1, is kinematic, i.e. we may define an analogue Lovelock-Riemann tensor that vanishes in vacuum for d=2N+1, yet the Riemann curvature is non-zero. We completely classify isotropic and vacuum Kasner metrics for this class of theories in several isotropy types. The different families can be characterized by means of certain higher order 4th rank tensors. We also analyze in detail the space of vacuum solutions for five- and six dimensional pure Gauss-Bonnet theory. It possesses an interesting and illuminating geometric structure and symmetries that carry over to the general case. We also comment on a closely related family of exponential solutions and on the possibility of solutions with complex Kasner exponents. We show that the latter imply the existence of closed timelike curves in the geometry.
Camanho, Xián O; Molina, Alfred
2015-01-01
We study pure Lovelock vacuum and perfect fluid equations for Kasner-type metrics. These equations correspond to a single $N$th order Lovelock term in the action in $d=2N+1,\\,2N+2$ dimensions, and they capture the relevant gravitational dynamics when aproaching the big-bang singularity within the Lovelock family of theories. Pure Lovelock gravity also bears out the general feature that vacuum in the critical odd dimension, $d=2N+1$, is kinematic; i.e. we may define an analogue Lovelock-Riemann tensor that vanishes in vacuum for $d=2N+1$, yet the Riemann curvature is non-zero. We completely classify isotropic and vacuum Kasner metrics for this class of theories in several isotropy types. The different families can be characterized by means of certain higher order 4th rank tensors. We also analyze in detail the space of vacuum solutions for five and six dimensional pure Gauss-Bonnet theory. It possesses an interesting and illuminating geometric structure and symmetries that carry over to the general case. We al...
Completion of a Dislocated Metric Space
P. Sumati Kumari
2015-01-01
Full Text Available We provide a construction for the completion of a dislocated metric space (abbreviated d-metric space; we also prove that the completion of the metric associated with a d-metric coincides with the metric associated with the completion of the d-metric.
A New Complete Class Complexity Metric
Singh, Vinay; Bhattacherjee, Vandana
2014-01-01
Software complexity metrics is essential for minimizing the cost of software maintenance. Package level and System level complexity cannot be measured without class level complexity. This research addresses the class complexity metrics. This paper studies the existing class complexity metrics and proposes a new class complexity metric CCC (Complete class complexity metric). The CCC metric is then analytically evaluated by Weyuker's property.
Clarke, Brian
2011-01-01
We consider geometries on the space of Riemannian metrics conformally equivalent to the widely studied Ebin L^2 metric. Among these we characterize a distinguished metric that can be regarded as a generalization of Calabi's metric on the space of K\\"ahler metrics to the space of Riemannian metrics, and we study its geometry in detail. Unlike the Ebin metric, the geodesic equation involves non-local terms, and we solve it explicitly by using a constant of the motion. We then determine its completion, which gives the first example of a metric on the space of Riemannian metrics whose completion is strictly smaller than that of the Ebin metric.
Quantum correlations for the metric
Wetterich, C
2016-01-01
We discuss the correlation function for the metric for homogeneous and isotropic cosmologies. The exact propagator equation determines the correlation function as the inverse of the second functional derivative of the quantum effective action, for which we take the Einstein-Hilbert approximation. This formulation relates the metric correlation function employed in quantum gravity computations to cosmological observables as the graviton power spectrum. While the graviton correlation function can be obtained equivalently as a solution of the linearized Einstein equations, this does not hold for the vector and scalar components of the metric. We project the metric fluctuations on the subspace of "physical fluctuations", which couple to a conserved energy momentum tensor. On the subspace of physical metric fluctuations the relation to physical sources becomes invertible, such that the effective action and its relation to correlation functions does not need gauge fixing. The physical metric fluctuations have a sim...
Feedback-based gameplay metrics
Marczak, Raphael; Schott, Gareth; Hanna, Pierre; Rouas, Jean-Luc
2013-01-01
International audience The application of gameplay metrics to empirically express a player's engagement with the game system has become more appealing to a broader range of researchers beyond the computer sciences. Within game studies, the appropriation and use of gameplay metrics not only further shifts these methods beyond formalized user testing (e.g. with the aim of product improvement) but creates a demand for a more universal approach to game metric extraction that can be applied to ...
Metric adjusted skew information
Hansen, Frank
2008-01-01
We extend the concept of Wigner-Yanase-Dyson skew information to something we call "metric adjusted skew information" (of a state with respect to a conserved observable). This "skew information" is intended to be a non-negative quantity bounded by the variance (of an observable in a state) that...... vanishes for observables commuting with the state. We show that the skew information is a convex function on the manifold of states. It also satisfies other requirements, proposed by Wigner and Yanase, for an effective measure-of-information content of a state relative to a conserved observable. We...... establish a connection between the geometrical formulation of quantum statistics as proposed by Chentsov and Morozova and measures of quantum information as introduced by Wigner and Yanase and extended in this article. We show that the set of normalized Morozova-Chentsov functions describing the possible...
Applications of Metric Coinduction
Kozen, Dexter
2009-01-01
Metric coinduction is a form of coinduction that can be used to establish properties of objects constructed as a limit of finite approximations. One can prove a coinduction step showing that some property is preserved by one step of the approximation process, then automatically infer by the coinduction principle that the property holds of the limit object. This can often be used to avoid complicated analytic arguments involving limits and convergence, replacing them with simpler algebraic arguments. This paper examines the application of this principle in a variety of areas, including infinite streams, Markov chains, Markov decision processes, and non-well-founded sets. These results point to the usefulness of coinduction as a general proof technique.
Metrics for IT service management
Brooks, Peter
2006-01-01
This book considers the design and implementation of metrics in service organizations using industry standard frameworks. It uses the ITIL process structure and many principles from the ITIL and ISO20000 (BS15000) as a basis. It is a general guide to the use of metrics as a mechanism to control and steer IT service organizations.A major reason for covering this topic is that many organizations have found it very difficult to use metrics properly. This book will deal with the causes of the difficulties to implementing metrics and will present workable solutions.It provides a general gui
Metric-adjusted skew information
Liang, Cai; Hansen, Frank
2010-01-01
We give a truly elementary proof of the convexity of metric-adjusted skew information following an idea of Effros. We extend earlier results of weak forms of superadditivity to general metric-adjusted skew information. Recently, Luo and Zhang introduced the notion of semi-quantum states on a...... bipartite system and proved superadditivity of the Wigner-Yanase-Dyson skew informations for such states. We extend this result to the general metric-adjusted skew information. We finally show that a recently introduced extension to parameter values 1 < p = 2 of the WYD-information is a special case of...... (unbounded) metric-adjusted skew information....
Canonical metrics on complex manifold
YAU Shing-Tung
2008-01-01
@@ Complex manifolds are topological spaces that are covered by coordinate charts where the Coordinate changes are given by holomorphic transformations. For example, Riemann surfaces are one dimensional complex manifolds. In order to understand complex manifolds, it is useful to introduce metrics that are compatible with the complex structure. In general, we should have a pair (M, ds2M) where ds2M is the metric. The metric is said to be canonical if any biholomorphisms of the complex manifolds are automatically isometries. Such metrics can naturally be used to describe invariants of the complex structures of the manifold.
Canonical metrics on complex manifold
YAU; Shing-Tung(Yau; S.-T.)
2008-01-01
Complex manifolds are topological spaces that are covered by coordinate charts where the coordinate changes are given by holomorphic transformations.For example,Riemann surfaces are one dimensional complex manifolds.In order to understand complex manifolds,it is useful to introduce metrics that are compatible with the complex structure.In general,we should have a pair(M,ds~2_M)where ds~2_M is the metric.The metric is said to be canonical if any biholomorphisms of the complex manifolds are automatically isometries.Such metrics can naturally be used to describe invariants of the complex structures of the manifold.
Improved Adaptive-Reinforcement Learning Control for morphing unmanned air vehicles.
Valasek, John; Doebbler, James; Tandale, Monish D; Meade, Andrew J
2008-08-01
This paper presents an improved Adaptive-Reinforcement Learning Control methodology for the problem of unmanned air vehicle morphing control. The reinforcement learning morphing control function that learns the optimal shape change policy is integrated with an adaptive dynamic inversion control trajectory tracking function. An episodic unsupervised learning simulation using the Q-learning method is developed to replace an earlier and less accurate Actor-Critic algorithm. Sequential Function Approximation, a Galerkin-based scattered data approximation scheme, replaces a K-Nearest Neighbors (KNN) method and is used to generalize the learning from previously experienced quantized states and actions to the continuous state-action space, all of which may not have been experienced before. The improved method showed smaller errors and improved learning of the optimal shape compared to the KNN. PMID:18632393
Shinya Tanaka; Tomoaki Takahashi; Tomohiro Nishizono; Fumiaki Kitahara; Hideki Saito; Toshiro Iehara; Eiji Kodani; Yoshio Awaya
2014-01-01
The main objective of this study was to evaluate the effectiveness of adding feature variables, such as forest type information and topographic- and climatic-environmental factors to satellite image data, on the accuracy of stand volume estimates made with the k-nearest neighbor (k-NN) technique in southwestern Japan. Data from the Forest Resources Monitoring Survey—a national plot sampling survey in Japan—was used as in situ data in this study. The estimates obtained from three Landsat Enha...
50+ Metrics for Calendar Mining
Kelemen, Zádor Dániel; Miglász, Dániel
2016-01-01
In this report we propose 50+ metrics which can be measured by organizations in order to identify improvements in various areas such as meeting efficiency, capacity planning or leadership skills, just to new a few. The notion of calendar mining is introduced and support is provided for performing the measurement by a reference data model and queries for all metrics defined.
Metrics for Soft Goods Merchandising.
Cooper, Gloria S., Ed.; Magisos, Joel H., Ed.
Designed to meet the job-related metric measurement needs of students interested in soft goods merchandising, this instructional package is one of five for the marketing and distribution cluster, part of a set of 55 packages for metric instruction in different occupations. The package is intended for students who already know the occupational…
Metrics for Hard Goods Merchandising.
Cooper, Gloria S., Ed.; Magisos, Joel H., Ed.
Designed to meet the job-related metric measurement needs of students interested in hard goods merchandising, this instructional package is one of five for the marketing and distribution cluster, part of a set of 55 packages for metric instruction in different occupations. The package is intended for students who already know the occupational…
Extending Cosmology: The Metric Approach
Mendoza, Sergio
2012-01-01
Comment: 2012, Extending Cosmology: The Metric Approach, Open Questions in Cosmology; Review article for an Intech "Open questions in cosmology" book chapter (19 pages, 3 figures). Available from: http://www.intechopen.com/books/open-questions-in-cosmology/extending-cosmology-the-metric-approach
Busarakam, Kanungnid; Bull, Alan T; Trujillo, Martha E; Riesco, Raul; Sangal, Vartul; van Wezel, Gilles P; Goodfellow, Michael
2016-06-01
A polyphasic study was designed to determine the taxonomic provenance of three Modestobacter strains isolated from an extreme hyper-arid Atacama Desert soil. The strains, isolates KNN 45-1a, KNN 45-2b(T) and KNN 45-3b, were shown to have chemotaxonomic and morphological properties in line with their classification in the genus Modestobacter. The isolates had identical 16S rRNA gene sequences and formed a branch in the Modestobacter gene tree that was most closely related to the type strain of Modestobacter marinus (99.6% similarity). All three isolates were distinguished readily from Modestobacter type strains by a broad range of phenotypic properties, by qualitative and quantitative differences in fatty acid profiles and by BOX fingerprint patterns. The whole genome sequence of isolate KNN 45-2b(T) showed 89.3% average nucleotide identity, 90.1% (SD: 10.97%) average amino acid identity and a digital DNA-DNA hybridization value of 42.4±3.1 against the genome sequence of M. marinus DSM 45201(T), values consistent with its assignment to a separate species. On the basis of all of these data, it is proposed that the isolates be assigned to the genus Modestobacter as Modestobacter caceresii sp. nov. with isolate KNN 45-2b(T) (CECT 9023(T)=DSM 101691(T)) as the type strain. Analysis of the whole-genome sequence of M. caceresii KNN 45-2b(T), with 4683 open reading frames and a genome size of ∽4.96Mb, revealed the presence of genes and gene-clusters that encode for properties relevant to its adaptability to harsh environmental conditions prevalent in extreme hyper arid Atacama Desert soils. PMID:27108251
The Conformal Flow of Metrics and the General Penrose Inequality
Han, Qing
2014-01-01
In this note we show how to adapt Bray's conformal flow of metrics, so that it may be applied to the Penrose inequality for general initial data sets of the Einstein equations. This involves coupling the conformal flow with the generalized Jang equation.
Variable metric conjugate gradient methods
Barth, T.; Manteuffel, T.
1994-07-01
1.1 Motivation. In this paper we present a framework that includes many well known iterative methods for the solution of nonsymmetric linear systems of equations, Ax = b. Section 2 begins with a brief review of the conjugate gradient method. Next, we describe a broader class of methods, known as projection methods, to which the conjugate gradient (CG) method and most conjugate gradient-like methods belong. The concept of a method having either a fixed or a variable metric is introduced. Methods that have a metric are referred to as either fixed or variable metric methods. Some relationships between projection methods and fixed (variable) metric methods are discussed. The main emphasis of the remainder of this paper is on variable metric methods. In Section 3 we show how the biconjugate gradient (BCG), and the quasi-minimal residual (QMR) methods fit into this framework as variable metric methods. By modifying the underlying Lanczos biorthogonalization process used in the implementation of BCG and QMR, we obtain other variable metric methods. These, we refer to as generalizations of BCG and QMR.
Double Metric, Generalized Metric and $\\alpha'$-Geometry
Hohm, Olaf
2015-01-01
We relate the unconstrained `double metric' of the `$\\alpha'$-geometry' formulation of double field theory to the constrained generalized metric encoding the spacetime metric and b-field. This is achieved by integrating out auxiliary field components of the double metric in an iterative procedure that induces an infinite number of higher-derivative corrections. As an application we prove that, to first order in $\\alpha'$ and to all orders in fields, the deformed gauge transformations are Green-Schwarz-deformed diffeomorphisms. We also prove that to first order in $\\alpha'$ the spacetime action encodes precisely the Green-Schwarz deformation with Chern-Simons forms based on the torsionless gravitational connection. This seems to be in tension with suggestions in the literature that T-duality requires a torsionful connection, but we explain that these assertions are ambiguous since actions that use different connections are related by field redefinitions.
Conformal Patterson-Walker metrics
Hammerl, Matthias; Šilhan, Josef; Taghavi-Chabert, Arman; Žádník, Vojtěch
2016-01-01
The classical Patterson-Walker construction of a split-signature (pseudo-)Riemannian structure from a given torsion-free affine connection is generalized to a construction of a split-signature conformal structure from a given projective class of connections. A characterization of the induced structures is obtained. We achieve a complete description of Einstein metrics in the conformal class formed by the Patterson-Walker metric. Finally, we describe all symmetries of the conformal Patterson-Walker metric. In both cases we obtain descriptions in terms of geometric data on the original structure.
Issues in Benchmark Metric Selection
Crolotte, Alain
It is true that a metric can influence a benchmark but will esoteric metrics create more problems than they will solve? We answer this question affirmatively by examining the case of the TPC-D metric which used the much debated geometric mean for the single-stream test. We will show how a simple choice influenced the benchmark and its conduct and, to some extent, DBMS development. After examining other alternatives our conclusion is that the “real” measure for a decision-support benchmark is the arithmetic mean.
Daylight metrics and energy savings
Mardaljevic, John; Heschong, Lisa; Lee, Eleanor
2009-12-31
The drive towards sustainable, low-energy buildings has increased the need for simple, yet accurate methods to evaluate whether a daylit building meets minimum standards for energy and human comfort performance. Current metrics do not account for the temporal and spatial aspects of daylight, nor of occupants comfort or interventions. This paper reviews the historical basis of current compliance methods for achieving daylit buildings, proposes a technical basis for development of better metrics, and provides two case study examples to stimulate dialogue on how metrics can be applied in a practical, real-world context.
Rigidity of Quasi-Einstein Metrics
Case, Jeffrey; Shu, Yujen; Wei, Guofang
2008-01-01
We call a metric quasi-Einstein if the $m$-Bakry-Emery Ricci tensor is a constant multiple of the metric tensor. This is a generalization of Einstein metrics, which contains gradient Ricci solitons and is also closely related to the construction of the warped product Einstein metrics. We study properties of quasi-Einstein metrics and prove several rigidity results. We also give a splitting theorem for some K\\"ahler quasi-Einstein metrics.
Using principal component analysis for selecting network behavioral anomaly metrics
Gregorio-de Souza, Ian; Berk, Vincent; Barsamian, Alex
2010-04-01
This work addresses new approaches to behavioral analysis of networks and hosts for the purposes of security monitoring and anomaly detection. Most commonly used approaches simply implement anomaly detectors for one, or a few, simple metrics and those metrics can exhibit unacceptable false alarm rates. For instance, the anomaly score of network communication is defined as the reciprocal of the likelihood that a given host uses a particular protocol (or destination);this definition may result in an unrealistically high threshold for alerting to avoid being flooded by false positives. We demonstrate that selecting and adapting the metrics and thresholds, on a host-by-host or protocol-by-protocol basis can be done by established multivariate analyses such as PCA. We will show how to determine one or more metrics, for each network host, that records the highest available amount of information regarding the baseline behavior, and shows relevant deviances reliably. We describe the methodology used to pick from a large selection of available metrics, and illustrate a method for comparing the resulting classifiers. Using our approach we are able to reduce the resources required to properly identify misbehaving hosts, protocols, or networks, by dedicating system resources to only those metrics that actually matter in detecting network deviations.
Mining metrics for buried treasure
Konkowski, D A
2004-01-01
The same but different: That might describe two metrics. On the surface CLASSI may show two metrics are locally equivalent, but buried beneath one may be a wealth of further structure. This was beautifully describeed in a paper by M.A.H. MacCallum in 1998. Here I will illustrate the effect with two flat metrics -- one describing ordinary Minkowski spacetime and the other describing a three-parameter family of Gal'tsov-Letelier-Tod spacetimes. I will dig out the beautiful hidden classical singularity structure of the latter (a structure first noticed by Tod in 1994) and then show how quantum considerations can illuminate the riches. I will then discuss how quantum structure can help us understand classical singularities and metric parameters in a variety of exact solutions mined from the Exact Solutions book.
Zimmerman, Marianna
1975-01-01
Describes a classroom activity which involved sixth grade students in a learning situation including making ice cream, safety procedures in a science laboratory, calibrating a thermometer, using metric units of volume and mass. (EB)
Metrical Diophantine approximation for quaternions
Dodson, Maurice
2011-01-01
The metrical theory of Diophantine approximation for quaternions is developed using recent results in the general theory. In particular, Quaternionic analogues of the classical theorems of Khintchine, Jarnik and Jarnik-Besicovitch are established.
Metrical Diophantine approximation for quaternions
Dodson, Maurice; Everitt, Brent
2014-11-01
Analogues of the classical theorems of Khintchine, Jarnik and Jarnik-Besicovitch in the metrical theory of Diophantine approximation are established for quaternions by applying results on the measure of general `lim sup' sets.
Nonlocal Metric Realizations of MOND
Woodard, R. P.
2014-01-01
I discuss relativistic extensions of MOND in which the metric couples normally to matter. I argue that MOND might be a residual effect from the vacuum polarization of infrared gravitons produced during primordial inflation. If so, MOND corrections to the gravitational field equations would be nonlocal. Nonocality also results when one constructs metric field equations which reproduce the Tully-Fisher relation, along with sufficient weak lensing. I give the full field equations for the simples...
Stochastic Contraction in Riemannian Metrics
Pham, Quang-Cuong; Slotine, Jean-Jacques
2013-01-01
Stochastic contraction analysis is a recently developed tool for studying the global stability properties of nonlinear stochastic systems, based on a differential analysis of convergence in an appropriate metric. To date, stochastic contraction results and sharp associated performance bounds have been established only in the specialized context of state-independent metrics, which restricts their applicability. This paper extends stochastic contraction analysis to the case of general time- and...
Metrics Generation Process for Mechatronics
WARNIEZ, Aude; Penas, Olivia; Choley, Jean-Yves; Hehenberger, Peter
2016-01-01
International audience Due to the complexity of designing mechatronic systems, providers of these systems need to precisely evaluate their products, design processes and projects all along the design phase and beyond. We propose a metrics generation process and then define related specifications to develop an evaluation tool to build customized metrics for the mechatronic industry with respect to its specific process and expectations. The proposed process has been experimented on the modul...
Validity of Ligand Efficiency Metrics
Murray, Christopher W; Erlanson, Daniel A.; Hopkins, Andrew L.; Keserü, György M; Leeson, Paul D.; Rees, David C.; Reynolds, Charles H.; Richmond, Nicola J.
2014-01-01
A recent viewpoint article (Improving the plausibility of success with inefficient metrics. ACS Med. Chem. Lett.2014, 5, 2–5) argued that the standard definition of ligand efficiency (LE) is mathematically invalid. In this viewpoint, we address this criticism and show categorically that the definition of LE is mathematically valid. LE and other metrics such as lipophilic ligand efficiency (LLE) can be useful during the multiparameter optimization challenge faced by med...
Phantom metrics with Killing spinors
W.A. Sabra
2015-11-01
Full Text Available We study metric solutions of Einstein–anti-Maxwell theory admitting Killing spinors. The analogue of the IWP metric which admits a space-like Killing vector is found and is expressed in terms of a complex function satisfying the wave equation in flat (2+1-dimensional space–time. As examples, electric and magnetic Kasner spaces are constructed by allowing the solution to depend only on the time coordinate. Euclidean solutions are also presented.
田鑫鑫; 陈珉; 王会; 裴恩乐; 袁晓; 沈国平; 蔡锋; 徐桂林
2012-01-01
为了掌握獐(Hydropotes inermis)的警戒行为特征并为重引入项目提供管理依据,以人为干扰源观察獐的警戒反应,发现其警戒模式包括听(hear)或扫视(scan)、盯视(stare)、走开(walk away)、跑开(run away)、吼叫(bark)和压脖(stretch).利用逃跑起始距离对上海松江野化圈养(自主采食)獐、上海华夏圈养(人工饲喂)獐和江苏盐城野生獐警戒性进行比较,得出人工饲喂獐警戒性最小,野生獐警戒性最大.野化獐警戒性提高,表明可通过降低人类活动和种群密度、扩大区域面积等途径野化提高獐警戒性.%From September, 2010 to August, 2011 , we tested the vigilance pattern of the semi-captive Chinese Water Deer ( Hydropotes inermis) with human simulated predator in Songjing, Shanghai, and results suggest that Chinese water deer's vigilance pattern includes hearing and scanning, staring and walking away or running away, and sometimes they bark or stretch their necks while staring. Barking in Chinese water deer mainly functions as an anti-predator behavior against predators instead of sending signals to other deer. Stretching may function as a trial to tell the level of threats from a predator or function as a ritualized behavior which indicates the health status of the water deer. We didn't observe aggressive behavior in Chinese water deer. We used flight initiation distance ( FID) as a metric to compare the vigilance level of water deer populations of different captive status, including captive, human supplementary water deer in Huaxia, captive, free grazing water deer in Songjiang, and wild water deer in Yancheng Natural Reserve. The results suggest that the vigilance level differs significantly, which means captive water deer decrease their vigilance level compared to their wild counterparts, however human raised water deer could be trained to increased vigilance level. Experiences with human, size of space, population density and the existence of
Candelas, Philip; McOrist, Jock
2016-01-01
Heterotic vacua of string theory are realised, at large radius, by a compact threefold with vanishing first Chern class together with a choice of stable holomorphic vector bundle. These form a wide class of potentially realistic four-dimensional vacua of string theory. Despite all their phenomenological promise, there is little understanding of the metric on the moduli space of these. What is sought is the analogue of special geometry for these vacua. The metric on the moduli space is important in phenomenology as it normalises D-terms and Yukawa couplings. It is also of interest in mathematics, since it generalises the metric, first found by Kobayashi, on the space of gauge field connections, to a more general context. Here we construct this metric, correct to first order in alpha', in two ways: first by postulating a metric that is invariant under background gauge transformations of the gauge field, and also by dimensionally reducing heterotic supergravity. These methods agree and the resulting metric is Ka...
Method Points: towards a metric for method complexity
Graham McLeod
1998-11-01
Full Text Available A metric for method complexity is proposed as an aid to choosing between competing methods, as well as in validating the effects of method integration or the products of method engineering work. It is based upon a generic method representation model previously developed by the author and adaptation of concepts used in the popular Function Point metric for system size. The proposed technique is illustrated by comparing two popular I.E. deliverables with counterparts in the object oriented Unified Modeling Language (UML. The paper recommends ways to improve the practical adoption of new methods.
A wavelet contrast metric for the targeting task performance metric
Preece, Bradley L.; Flug, Eric A.
2016-05-01
Target acquisition performance depends strongly on the contrast of the target. The Targeting Task Performance (TTP) metric, within the Night Vision Integrated Performance Model (NV-IPM), uses a combination of resolution, signal-to-noise ratio (SNR), and contrast to predict and model system performance. While the dependence on resolution and SNR are well defined and understood, defining a robust and versatile contrast metric for a wide variety of acquisition tasks is more difficult. In this correspondence, a wavelet contrast metric (WCM) is developed under the assumption that the human eye processes spatial differences in a manner similar to a wavelet transform. The amount of perceivable information, or useful wavelet coefficients, is used to predict the total viewable contrast to the human eye. The WCM is intended to better match the measured performance of the human vision system for high-contrast, low-contrast, and low-observable targets. After further validation, the new contrast metric can be incorporated using a modified TTP metric into the latest Army target acquisition software suite, the NV-IPM.
A Cuckoo Search Algorithm Based on Variable Metric Method and Adaptive Step%基于变尺度法和自适应步长的布谷鸟搜索算法
江浩; 阮奇
2015-01-01
Cuckoo Search ( CS) is a novel meta-heuristic algorithm. Aiming at the defects of weak local search ability,slow convergence velocity and low convergence accuracy,a modified CS algorithm based on DFP and adaptive step is proposed in this paper. In the im-proved cuckoo search algorithm,the step of Lévy flight nonlinear dynamic changes improve convergence velocity. After evolved from Lévy flights and elimination mechanism,the cuckoo populations rapidly access to global minima by DFP. Sixth representative benchmark functions are used to test the performance of DACS algorithm and CS algorithm respectively. The conclusions indicate that DACS algo-rithm has faster convergence speed,higher convergence accuracy and robustness,compared with CS algorithm. Meanwhile,DACS algo-rithm keeps strong global search capability,which is particularly suitable for the optimization of multimodal function and high dimension function.%布谷鸟搜索算法( Cuckoo Search,CS)是一种新型的元启发式算法。针对CS算法局部搜索能力较弱、后期收敛速度偏慢和收敛精度不高等缺点,提出一种基于变尺度法(DFP)和自适应步长(Adaptive Step)的布谷鸟搜索算法(DACS),使Lévy飞行的步长非线性自适应变化来提高算法的收敛速度,同时使经过Lévy飞行机制和淘汰机制进化后的布谷鸟种群再运用DFP快速获取全局最优解。用6种具有各种代表性的测试函数分别测试DACS算法和CS算法的性能。实验结果表明,DACS算法在保持强大的全局搜索能力的同时,比CS算法具有更快的收敛速度、更高的收敛精度和更好的鲁棒性,尤其适合多峰及高维函数的优化。
Clarke, Brian; Rubinstein, Yanir A.
2011-01-01
We consider geometries on the space of Riemannian metrics conformally equivalent to the widely studied Ebin L^2 metric. Among these we characterize a distinguished metric that can be regarded as a generalization of Calabi's metric on the space of K\\"ahler metrics to the space of Riemannian metrics, and we study its geometry in detail. Unlike the Ebin metric, the geodesic equation involves non-local terms, and we solve it explicitly by using a constant of the motion. We then determine its comp...
Projectively related metrics, Weyl nullity, and metric projectively invariant equations
Gover, A Rod
2015-01-01
A metric projective structure is a manifold equipped with the unparametrised geodesics of some pseudo-Riemannian metric. We make acomprehensive treatment of such structures in the case that there is a projective Weyl curvature nullity condition. The analysis is simplified by a fundamental and canonical 2-tensor invariant that we discover. It leads to a new canonical tractor connection for these geometries which is defined on a rank $(n+1)$-bundle. We show this connection is linked to the metrisability equations that govern the existence of metrics compatible with the structure. The fundamental 2-tensor also leads to a new class of invariant linear differential operators that are canonically associated to these geometries; included is a third equation studied by Gallot et al. We apply the results to study the metrisability equation, in the nullity setting described. We obtain strong local and global results on the nature of solutions and also on the nature of the geometries admitting such solutions, obtaining ...
Metrics for measuring net-centric data strategy implementation
Kroculick, Joseph B.
2010-04-01
An enterprise data strategy outlines an organization's vision and objectives for improved collection and use of data. We propose generic metrics and quantifiable measures for each of the DoD Net-Centric Data Strategy (NCDS) data goals. Data strategy metrics can be adapted to the business processes of an enterprise and the needs of stakeholders in leveraging the organization's data assets to provide for more effective decision making. Generic metrics are applied to a specific application where logistics supply and transportation data is integrated across multiple functional groups. A dashboard presents a multidimensional view of the current progress to a state where logistics data shared in a timely and seamless manner among users, applications, and systems.
Eye Tracking Metrics for Workload Estimation in Flight Deck Operation
Ellis, Kyle; Schnell, Thomas
2010-01-01
Flight decks of the future are being enhanced through improved avionics that adapt to both aircraft and operator state. Eye tracking allows for non-invasive analysis of pilot eye movements, from which a set of metrics can be derived to effectively and reliably characterize workload. This research identifies eye tracking metrics that correlate to aircraft automation conditions, and identifies the correlation of pilot workload to the same automation conditions. Saccade length was used as an indirect index of pilot workload: Pilots in the fully automated condition were observed to have on average, larger saccadic movements in contrast to the guidance and manual flight conditions. The data set itself also provides a general model of human eye movement behavior and so ostensibly visual attention distribution in the cockpit for approach to land tasks with various levels of automation, by means of the same metrics used for workload algorithm development.
Learning Objects Reusability Effectiveness Metric (LOREM
Torky Ibrahim Sultan
2014-03-01
Full Text Available In this research we aim to propose an advanced metric to evaluate the effectiveness of learning objects in order to be reused in new contexts. By the way learning objects reusability is achieving economic benefits from educational technology as it saving time and improving quality, but in case of choosing unsuitable learning object it may be less benefit than creating the learning object from scratch. Actually learning objects reusability can facilitate systems development and adaptation. By surveying the current evaluation metrics, we found that while they cover essential aspects, they enables all reviewers of learning objects to evaluate all criteria without paying attention to their roles in creating the learning object which affect their capability to evaluate specific criteria. Our proposed Approach (LOREM is evaluating learning objects based on a group of Aspects which measure their level of effectiveness in order to be reused in other contexts. LOREM classifies reviewers into 3 categories; 1. Academic Group: (Subject Expert Matter “SME” and Instructor. 2. Technical Group: (Instructional Designer “ID”, LO Developer and LO Designer. 3. Students group. The authorization of reviewers in these several categories are differentiated according to reviewer's type, e.g., (Instructor, LO Developer and their area of expert (their expertise subjects for academic and students reviewers.
Synthesis Array Topology Metrics in Location Characterization
Shanmugha Sundaram, GA
2015-08-01
Towards addressing some of the fundamental mysteries in physics at the micro- and macro-cosm level, that form the Key Science Projects (KSPs) for the Square Kilometer Array (SKA; such as Probing the Dark Ages and the Epoch of Reionization in the course of an Evolving Universe; Galaxy Evolution, Cosmology, and Dark Energy; and the Origin and evolution of Cosmic Magnetism) a suitable interfacing of these goals has to be achieved with its optimally designed array configuration, by means of a critical evaluation of the radio imagingcapabilities and metrics. Of the two forerunner sites, viz. Australia and South Africa, where pioneering advancements to state-of-the-art in synthesis array radio astronomy instrumentation are being attempted in the form of pathfinders to the SKA, for its eventual deployment, a diversity of site-dependent topology and design metrics exists. Here, the particular discussion involves those KSPs that relate to galactic morphology and evolution, and explores their suitability as a scientific research goal from the prespective of the location-driven instrument design specification. Relative merits and adaptability with regard to either site shall be presented from invoking well-founded and established array-design and optimization principles designed into a customized software tool.
Metrics correlation and analysis service (MCAS)
The complexity of Grid workflow activities and their associated software stacks inevitably involves multiple organizations, ownership, and deployment domains. In this setting, important and common tasks such as the correlation and display of metrics and debugging information (fundamental ingredients of troubleshooting) are challenged by the informational entropy inherent to independently maintained and operated software components. Because such an information 'pond' is disorganized, it a difficult environment for business intelligence analysis i.e. troubleshooting, incident investigation and trend spotting. The mission of the MCAS project is to deliver a software solution to help with adaptation, retrieval, correlation, and display of workflow-driven data and of type-agnostic events, generated by disjoint middleware.
Metrics correlation and analysis service (MCAS)
Baranovski, Andrew; Dykstra, Dave; Garzoglio, Gabriele; Hesselroth, Ted; Mhashilkar, Parag; Levshina, Tanya; /Fermilab
2009-05-01
The complexity of Grid workflow activities and their associated software stacks inevitably involves multiple organizations, ownership, and deployment domains. In this setting, important and common tasks such as the correlation and display of metrics and debugging information (fundamental ingredients of troubleshooting) are challenged by the informational entropy inherent to independently maintained and operated software components. Because such an information 'pond' is disorganized, it a difficult environment for business intelligence analysis i.e. troubleshooting, incident investigation and trend spotting. The mission of the MCAS project is to deliver a software solution to help with adaptation, retrieval, correlation, and display of workflow-driven data and of type-agnostic events, generated by disjoint middleware.
Squared Metric Facility Location Problem
Fernandes, Cristina G; Miyazawa, Flávio K; Pedrosa, Lehilton L C
2011-01-01
Jain et al. proposed two well-known algorithms for the Metric Facility Location Problem (MFLP), that achieve approximation ratios of 1.861 and 1.61. Mahdian et al. combined the latter algorithm with scaling and greedy augmentation techniques, obtaining a 1.52-approximation for the MFLP. We consider a generalization of the Squared Euclidean Facility Location Problem, when the distance function is a squared metric, which we call Squared Metric Facility Location Problem (SMFLP). We show that the algorithms of Jain et al. and of Mahdian et al., when applied to this variant of the facility location, achieve approximation ratios of 2.87, 2.43, and 2.17, respectively. It is shown that, for the SMFLP, there is no 2.04-approximation algorithm, assuming P $\
Rainbow metric from quantum gravity
Assaniousssi, Mehdi; Lewandowski, Jerzy
2014-01-01
In this letter, we describe a general mechanism for emergence of a rainbow metric from a quantum cosmological model. This idea is based on QFT on a quantum space-time. Under general assumptions, we discover that the quantum space-time on which the field propagates can be replaced by a classical space-time, whose metric depends explicitly on the energy of the field: as shown by an analysis of dispersion relations, quanta of different energy propagate on different metrics, similar to photons in a refractive material (hence the name "rainbow" used in the literature). In deriving this result, we do not consider any specific theory of quantum gravity: the qualitative behavior of high-energy particles on quantum space-time relies only on the assumption that the quantum space-time is described by a wave-function $\\Psi_o$ in a Hilbert space $\\mathcal{H}_G$.
Status report on aerospace metrication
Peterson, A.
1983-10-01
Following passage of PL 94-168, the transition to the use of metric units within the United States has been very slow. The lack of a national plan with no clear understanding or agreement concerning the source of the funds required to effect the transition have been major impediments. There are international pressures from the international standards-making organizations, the ICAO and NATO, to proceed with the unification of standards. Within the commercial aviation field, the United States is resisting the pressure, but is participating in international standardization activities because of a general recognition of the need to support future market requirements and to assist in the resolution of a significant NATO logistics problem. The AIA, with the SAE and other standards-making organizations, is attempting to secure a harmonization of United States and AECMA metric standards. The future transition progress is not expected to accelerate significantly until metric standards are available.
Rainbow metric from quantum gravity
Mehdi Assanioussi
2015-12-01
Full Text Available In this Letter, we describe a general mechanism for emergence of a rainbow metric from a quantum cosmological model. This idea is based on QFT on a quantum spacetime. Under general assumptions, we discover that the quantum spacetime on which the field propagates can be replaced by a classical spacetime, whose metric depends explicitly on the energy of the field: as shown by an analysis of dispersion relations, quanta of different energy propagate on different metrics, similar to photons in a refractive material (hence the name “rainbow” used in the literature. In deriving this result, we do not consider any specific theory of quantum gravity: the qualitative behaviour of high-energy particles on quantum spacetime relies only on the assumption that the quantum spacetime is described by a wave-function Ψo in a Hilbert space HG.
Robertson, Stanley L
2016-01-01
Magnetic Eternally Collapsing Objects (MECO) have been proposed as the central engines of galactic black hole candidates (GBHC) and supermassive active galactic nuclei (AGN). Previous work has shown that their luminosities and spectral and timing characteristics are in good agreement with observations. These features and the formation of jets are generated primarily by the interactions of accretion disks with an intrinsically magnetic central MECO. The interaction of accretion disks with the anchored magnetic fields of the central objects permits a unified description of properties for GBHC, AGN, neutron stars in low mass x-ray binaries and dwarf novae systems. The previously published MECO models have been based on a quasistatic Schwarzschild metric of General Relativity; however, the only essential feature of this metric is its ability to produce extreme gravitational redshifts. For reasons discussed in this article, an alternative development based on a quasistatic exponential metric is considered here.
Moduli spaces of riemannian metrics
Tuschmann, Wilderich
2015-01-01
This book studies certain spaces of Riemannian metrics on both compact and non-compact manifolds. These spaces are defined by various sign-based curvature conditions, with special attention paid to positive scalar curvature and non-negative sectional curvature, though we also consider positive Ricci and non-positive sectional curvature. If we form the quotient of such a space of metrics under the action of the diffeomorphism group (or possibly a subgroup) we obtain a moduli space. Understanding the topology of both the original space of metrics and the corresponding moduli space form the central theme of this book. For example, what can be said about the connectedness or the various homotopy groups of such spaces? We explore the major results in the area, but provide sufficient background so that a non-expert with a grounding in Riemannian geometry can access this growing area of research.
基于GA和KNN的SVM决策树分类方法研究%Research of SVM Decision-treen Classification Based on GA and KNN
陈东莉
2012-01-01
In this paper, a SVM decision-tree algorithm was presented based on GA and KNN. First,GA is used to create optimal or near-optimal decision-tree, which defines a novel separability measure. Then in the class phase, standard SVM is used to make binary classification for the divisible nodes, and SVM combined with KNN are used to classify the fallible nodes. Finally, the multi-classification is a-chieved by the SVM decision-tree. Experimental results show that the proposed method could effectively improve the classification precision in comparison to traditional classification methods.%文章提出了一种基于遗传算法和K近邻的SVM决策树方法,并将其应用于解决SVM多分类问题.算法以基于类分布的类间分离性测度为准则,利用遗传算法对传统的SVM决策树进行优化,生成最优(较优)决策树.在分类阶段,对容易分的节点利用SVM进行分类,而对可分离性差的节点采用SVM和K近邻相结合的分类方法,最终实现多类别分类.实验结果表明,与传统的分类方法相比,该算法的实验效果较好,是一种有效的分类方法.
S-curvature of isotropic Berwald metrics
Akbar TAYEBI; Mehdi RAFIE-RAD
2008-01-01
Isotropic Berwald metrics are as a generalization of Berwald metrics. Shen proved that every Berwald metric is of vanishing S-curvature. In this paper, we generalize this fact and prove that every isotropic Berwald metric is of isotropic S-curvature. Let F = α + β be a Randers metric of isotropic Berwald curvature. Then it corresponds to a conformal vector field through navigation representation.
Kerr-Schild--Kundt Metrics are Universal
Gurses, Metin; Tekin, Bayram
2016-01-01
Universal metrics are the metrics that solve generic gravity theories which defined by covariant field equations built on the powers of the contractions and the covariant derivatives of the Riemann tensor. Here, we show that the Kerr-Schild--Kundt class metrics are universal extending the rather scarce family of universal metrics in the literature. Besides being interesting on their own, these metrics can provide consistent background for quantum field theory at extremely high energies.
Positive Semidefinite Metric Learning with Boosting
Shen, Chunhua; Kim, Junae; Wang, Lei; Hengel, Anton van den
2009-01-01
The learning of appropriate distance metrics is a critical problem in image classification and retrieval. In this work, we propose a boosting-based technique, termed \\BoostMetric, for learning a Mahalanobis distance metric. One of the primary difficulties in learning such a metric is to ensure that the Mahalanobis matrix remains positive semidefinite. Semidefinite programming is sometimes used to enforce this constraint, but does not scale well. \\BoostMetric is instead based on a key observat...
Bessem Samet
2013-01-01
Full Text Available In 2005, Mustafa and Sims (2006 introduced and studied a new class of generalized metric spaces, which are called G-metric spaces, as a generalization of metric spaces. We establish some useful propositions to show that many fixed point theorems on (nonsymmetric G-metric spaces given recently by many authors follow directly from well-known theorems on metric spaces. Our technique can be easily extended to other results as shown in application.
Bartholdi, Laurent; Smale, Nat; Smale, Steve; Baker, Anthony W
2009-01-01
Hodge theory is a beautiful synthesis of geometry, topology, and analysis, which has been developed in the setting of Riemannian manifolds. On the other hand, spaces of images, which are important in the mathematical foundations of vision and pattern recognition, do not fit this framework. This motivates us to develop a version of Hodge theory on metric spaces with a probability measure. We believe that this constitutes a step towards understanding the geometry of vision. The appendix by Anthony Baker provides a separable, compact metric space with infinite dimensional \\alpha-scale homology.
Thermodynamic Metrics and Optimal Paths
Sivak, David; Crooks, Gavin
2012-05-08
A fundamental problem in modern thermodynamics is how a molecular-scale machine performs useful work, while operating away from thermal equilibrium without excessive dissipation. To this end, we derive a friction tensor that induces a Riemannian manifold on the space of thermodynamic states. Within the linear-response regime, this metric structure controls the dissipation of finite-time transformations, and bestows optimal protocols with many useful properties. We discuss the connection to the existing thermodynamic length formalism, and demonstrate the utility of this metric by solving for optimal control parameter protocols in a simple nonequilibrium model.
Einstein metrics in projective geometry
Cap, A; Macbeth, H R
2012-01-01
It is well known that pseudo-Riemannian metrics in the projective class of a given torsion free affine connection can be obtained from (and are equivalent to) the solutions of a certain overdetermined projectively invariant differential equation. This equation is a special case of a so-called first BGG equation. The general theory of such equations singles out a subclass of so-called normal solutions. We prove that non-degerate normal solutions are equivalent to pseudo-Riemannian Einstein metrics in the projective class and observe that this connects to natural projective extensions of the Einstein condition.
Performance Metrics for Haptic Interfaces
Samur, Evren
2012-01-01
Haptics technology is being used more and more in different applications, such as in computer games for increased immersion, in surgical simulators to create a realistic environment for training of surgeons, in surgical robotics due to safety issues and in mobile phones to provide feedback from user action. The existence of these applications highlights a clear need to understand performance metrics for haptic interfaces and their implications on device design, use and application. Performance Metrics for Haptic Interfaces aims at meeting this need by establishing standard practices for the ev
Deser, S.
2006-01-01
We raise, and provide an (unsatisfactory) answer to, the title's question: why, unlike all other fields, does the gravitational "metric" variable not have zero vacuum? After formulating, without begging it, we exhibit additions to the conventional action that express existence of the inverse through a field equation.
Danila, Bogdan; Lobo, Francisco S N; Mak, M K
2016-01-01
We consider the internal structure and the physical properties of specific classes of neutron, quark and Bose-Einstein Condensate stars in the hybrid metric-Palatini gravity theory, which is a combination of the metric and Palatini $f(R)$ formalisms. The theory is very successful in accounting for the observed phenomenology, since it unifies local constraints at the Solar System level and the late-time cosmic acceleration, even if the scalar field is very light. We derive the equilibrium equations for a spherically symmetric configuration (mass continuity and Tolman-Oppenheimer-Volkoff) in the framework of hybrid metric-Palatini theory, and we investigate their solutions numerically for different equations of state of neutron and quark matter, by adopting for the scalar field potential a Higgs-type form. Stellar models, described by the stiff fluid, radiation-like, the bag model and the Bose-Einstein Condensate equations of state are explicitly constructed in both General Relativity and hybrid metric-Palatini...
Ontology-based metrics computation for business process analysis
Carlos Pedrinaci; John Domingue
2009-01-01
Business Process Management (BPM) aims to support the whole life-cycle necessary to deploy and maintain business processes in organisations. Crucial within the BPM lifecycle is the analysis of deployed processes. Analysing business processes requires computing metrics that can help determining the health of business activities and thus the whole enterprise. However, the degree of automation currently achieved cannot support the level of reactivity and adaptation demanded by businesses. In thi...
Charged C-metric in conformal gravity
Lim, Yen-Kheng
2016-01-01
Using a C-metric-type ansatz, we obtain an exact solution to conformal gravity coupled to a Maxwell electromagnetic field. The solution resembles a C-metric spacetime carrying an electromagnetic charge. The metric is cast in a factorised form which allows us to study the domain structure of its static coordinate regions. This metric reduces to the well-known Mannheim-Kazanas metric under an appropriate limiting procedure, and also reduces to the (Anti-)de Sitter C-metric of Einstein gravity f...
Charged C-metric in conformal gravity
Lim, Yen-Kheng
2016-01-01
Using a C-metric-type ansatz, we obtain an exact solution to conformal gravity coupled to a Maxwell electromagnetic field. The solution resembles a C-metric spacetime carrying an electromagnetic charge. The metric is cast in a factorised form which allows us to study the domain structure of its static coordinate regions. This metric reduces to the well-known Mannheim-Kazanas metric under an appropriate limiting procedure, and also reduces to the (Anti-)de Sitter C-metric of Einstein gravity for a particular choice of parameters.
Charged C -metric in conformal gravity
Lim, Yen-Kheng
2016-04-01
Using a C -metric-type ansatz, we obtain an exact solution to conformal gravity coupled to a Maxwell electromagnetic field. The solution resembles a C -metric spacetime carrying an electromagnetic charge. The metric is cast in a factorized form which allows us to study the domain structure of its static coordinate regions. This metric reduces to the well-known Mannheim-Kazanas metric under an appropriate limiting procedure, and also reduces to the (anti)de Sitter C -metric of Einstein gravity for a particular choice of parameters.
Projective Compactifications and Einstein metrics
Cap, Andreas
2013-01-01
For complete affine manifolds we introduce a definition of compactification based on the projective differential geometry (i.e.\\ geodesic path data) of the given connection. The definition of projective compactness involves a real parameter $\\alpha$ called the order of projective compactness. For volume preserving connections, this order is captured by a notion of volume asymptotics that we define. These ideas apply to complete pseudo-Riemannian spaces, via the Levi-Civita connection, and thus provide a notion of compactification alternative to conformal compactification. For each order $\\alpha$, we provide an asymptotic form of a metric which is sufficient for projective compactness of the given order, thus also providing many local examples. Distinguished classes of projectively compactified geometries of orders one and two are associated with Ricci-flat connections and non--Ricci--flat Einstein metrics, respectively. Conversely, these geometric conditions are shown to force the indicated order of projectiv...
Hofer's metrics and boundary depth
Usher, Michael
2011-01-01
We show that if (M,\\omega) is a closed symplectic manifold which admits a nontrivial Hamiltonian vector field all of whose contractible closed orbits are constant, then Hofer's metric on the group of Hamiltonian diffeomorphisms of (M,\\omega) has infinite diameter, and indeed admits infinite-dimensional quasi-isometrically embedded normed vector spaces. A similar conclusion applies to Hofer's metric on various spaces of Lagrangian submanifolds, including those Hamiltonian-isotopic to the diagonal in M x M when M satisfies the above dynamical condition. To prove this, we use the properties of a Floer-theoretic quantity called the boundary depth, which measures the nontriviality of the boundary operator on the Floer complex in a way that encodes robust symplectic-topological information.
Hybrid metric-Palatini gravity
Capozziello, Salvatore; Koivisto, Tomi S; Lobo, Francisco S N; Olmo, Gonzalo J
2015-01-01
Recently, the phenomenology of f(R) gravity has been scrutinized motivated by the possibility to account for the self-accelerated cosmic expansion without invoking dark energy sources. Besides, this kind of modified gravity is capable of addressing the dynamics of several self-gravitating systems alternatively to the presence of dark matter. It has been established that both metric and Palatini versions of these theories have interesting features but also manifest severe and different downsides. A hybrid combination of theories, containing elements from both these two formalisms, turns out to be also very successful accounting for the observed phenomenology and is able to avoid some drawbacks of the original approaches. This article reviews the formulation of this hybrid metric-Palatini approach and its main achievements in passing the local tests and in applications to astrophysical and cosmological scenarios, where it provides a unified approach to the problems of dark energy and dark matter.
Security Metrics in Industrial Control Systems
Collier, Zachary A; Ganin, Alexander A; Kott, Alex; Linkov, Igor
2015-01-01
Risk is the best known and perhaps the best studied example within a much broader class of cyber security metrics. However, risk is not the only possible cyber security metric. Other metrics such as resilience can exist and could be potentially very valuable to defenders of ICS systems. Often, metrics are defined as measurable properties of a system that quantify the degree to which objectives of the system are achieved. Metrics can provide cyber defenders of an ICS with critical insights regarding the system. Metrics are generally acquired by analyzing relevant attributes of that system. In terms of cyber security metrics, ICSs tend to have unique features: in many cases, these systems are older technologies that were designed for functionality rather than security. They are also extremely diverse systems that have different requirements and objectives. Therefore, metrics for ICSs must be tailored to a diverse group of systems with many features and perform many different functions. In this chapter, we first...
On metrically complete Bruhat-Tits buildings
Martin, Benjamin; Schillewaert, Jeroen; Steinke, Günter F.; Struyve, Koen
2011-01-01
Metrical completeness for Bruhat-Tits buildings is a natural and useful condition. In this paper we determine which Bruhat-Tits buildings are metrically complete up to certain cases involving infinite-dimensionality and residue characteristic two.
Metric reconstruction from Weyl scalars
The Kerr geometry has remained an elusive world in which to explore physics and delve into the more esoteric implications of general relativity. Following the discovery, by Kerr in 1963, of the metric for a rotating black hole, the most major advance has been an understanding of its Weyl curvature perturbations based on Teukolsky's discovery of separable wave equations some ten years later. In the current research climate, where experiments across the globe are preparing for the first detection of gravitational waves, a more complete understanding than concerns just the Weyl curvature is now called for. To understand precisely how comparatively small masses move in response to the gravitational waves they emit, a formalism has been developed based on a description of the whole spacetime metric perturbation in the neighbourhood of the emission region. Presently, such a description is not available for the Kerr geometry. While there does exist a prescription for obtaining metric perturbations once curvature perturbations are known, it has become apparent that there are gaps in that formalism which are still waiting to be filled. The most serious gaps include gauge inflexibility, the inability to include sources-which are essential when the emitting masses are considered-and the failure to describe the l = 0 and 1 perturbation properties. Among these latter properties of the perturbed spacetime, arising from a point mass in orbit, are the perturbed mass and axial component of angular momentum, as well as the very elusive Carter constant for non-axial angular momentum. A status report is given on recent work which begins to repair these deficiencies in our current incomplete description of Kerr metric perturbations
Horan, Patrick; Frew, Andrew
2007-01-01
The priority of the research is thus to establish which criteria are important for destination websites and to determine a mechanism for their measurement. These criteria are divided into both macro- and micro- level metrics which each combine to provide information that is actionable from a business’ perspective. This work lays the foundation for the anticipated outcome of this research, a robust methodology for measuring the effectiveness of destination websites coupled with a suite of acti...
Developing a quality assurance metric
Love, Steve; Scoble, Rosa
2006-01-01
Abstract There are a variety of techniques that lecturers can use to get feedback on their teaching - for example, module feedback and coursework results. However, a question arises about how reliable and valid are the content that goes into these quality assurance metrics. The aim of this article is to present a new approach for collecting and analysing qualitative feedback from students that could be used...
Marketing metrics for medical practices.
Zahaluk, David; Baum, Neil
2012-01-01
There's a saying by John Wanamaker who pontificated, "Half the money I spend on advertising is wasted; the trouble is, I don't know which half". Today you have opportunities to determine which parts of your marketing efforts are effective and what is wasted. However, you have to measure your marketing results. This article will discuss marketing metrics and how to use them to get the best bang for your marketing buck. PMID:22834190
Metrics for antibody therapeutics development
Reichert, Janice M
2010-01-01
A wide variety of full-size monoclonal antibodies (mAbs) and therapeutics derived from alternative antibody formats can be produced through genetic and biological engineering techniques. These molecules are now filling the preclinical and clinical pipelines of every major pharmaceutical company and many biotechnology firms. Metrics for the development of antibody therapeutics, including averages for the number of candidates entering clinical study and development phase lengths for mAbs approv...
Multi-Metric Sustainability Analysis
Cowlin, S.; Heimiller, D.; Macknick, J.; Mann, M.; Pless, J.; Munoz, D.
2014-12-01
A readily accessible framework that allows for evaluating impacts and comparing tradeoffs among factors in energy policy, expansion planning, and investment decision making is lacking. Recognizing this, the Joint Institute for Strategic Energy Analysis (JISEA) funded an exploration of multi-metric sustainability analysis (MMSA) to provide energy decision makers with a means to make more comprehensive comparisons of energy technologies. The resulting MMSA tool lets decision makers simultaneously compare technologies and potential deployment locations.
Almost contact metric 3-submersions
Bill Watson
1984-01-01
Full Text Available An almost contact metric 3-submersion is a Riemannian submersion, π from an almost contact metric manifold (M4m+3,(φi,ξi,ηii=13,g onto an almost quaternionic manifold (N4n,(Jii=13,h which commutes with the structure tensors of type (1,1;i.e., π*φi=Jiπ*, for i=1,2,3. For various restrictions on ∇φi, (e.g., M is 3-Sasakian, we show corresponding limitations on the second fundamental form of the fibres and on the complete integrability of the horizontal distribution. Concommitantly, relations are derived between the Betti numbers of a compact total space and the base space. For instance, if M is 3-quasi-Saskian (dΦ=0, then b1(N≤b1(M. The respective φi-holomorphic sectional and bisectional curvature tensors are studied and several unexpected results are obtained. As an example, if X and Y are orthogonal horizontal vector fields on the 3-contact (a relatively weak structure total space of such a submersion, then the respective holomorphic bisectional curvatures satisfy: Bφi(X,Y=B′J′i(X*,Y*−2. Applications to the real differential geometry of Yarg-Milis field equations are indicated based on the fact that a principal SU(2-bundle over a compactified realized space-time can be given the structure of an almost contact metric 3-submersion.
GENERAL RELATIVITY AND METRIC OF LOCAL SUPERCLUSTER
Trunev A. P.
2013-12-01
Full Text Available It is shown that the metric of clusters of galaxies should be universal, depending only on the fundamental constants and compatible with the metric of the universe. There are examples of universal metrics obtained in Einstein's theory of gravitation. On the basis of axisymmetric solutions of Einstein’s equation proposed universal metric describing the properties of galaxies, groups and clusters of galaxies
Software Metrics: Calculation and Optimization of Thresholds
Abhishek Kumar Maheswari
2011-01-01
In this article, we present a algorithmic method for the calculation of thresholds (the starting point for a new state) for a software metric set. To this aim, machine learning and data mining techniques are utilized. We define a data-driven methodology that can be used for efficiency optimization of existing metric sets, for the simplification of complex classification models, and for the calculation of thresholds for a metric set in an environment where no metric set yet exists. The methodo...
Formal analysis of security metrics and risk
Krautsevich L.; Martinelli F.; Yautsiukhin A.
2011-01-01
Security metrics are usually defined informally and, therefore, the rigourous analysis of these metrics is a hard task. This analysis is required to identify the existing relations between the security metrics, which try to quantify the same quality: security. Risk, computed as Annualised Loss Expectancy, is often used in order to give the overall assessment of security as a whole. Risk and security metrics are usually defined separately and the relation between these indicators have not been...
A possible molecular metric for biological evolvability
Aditya Mittal; B Jayaram
2012-07-01
Proteins manifest themselves as phenotypic traits, retained or lost in living systems via evolutionary pressures. Simply put, survival is essentially the ability of a living system to synthesize a functional protein that allows for a response to environmental perturbations (adaptation). Loss of functional proteins leads to extinction. Currently there are no universally applicable quantitative metrics at the molecular level for either measuring ‘evolvability’ of life or for assessing the conditions under which a living system would go extinct and why. In this work, we show emergence of the first such metric by utilizing the recently discovered stoichiometric margin of life for all known naturally occurring (and functional) proteins. The constraint of having well-defined stoichiometries of the 20 amino acids in naturally occurring protein sequences requires utilization of the full scope of degeneracy in the genetic code, i.e. usage of all codons coding for an amino acid, by only 11 of the 20 amino acids. This shows that the non-availability of individual codons for these 11 amino acids would disturb the fine stoichiometric balance resulting in non-functional proteins and hence extinction. Remarkably, these amino acids are found in close proximity of any given amino acid in the backbones of thousands of known crystal structures of folded proteins. On the other hand, stoichiometry of the remaining 9 amino acids, found to be farther/distal from any given amino acid in backbones of folded proteins, is maintained independent of the number of codons available to synthesize them, thereby providing some robustness and hence survivability.
Obtención de polvos cerámicos de BNKT-KNN por el método Pechini
Yasnó, J. P.
2013-10-01
Full Text Available Pechini method was used in order to obtain fine ceramic and single-phase powders for a lead-free ferroelectric system 0,97[(Bi1/2Na1/21-x(Bi1/2K1/2xTiO3]-0,03[(Na1/2K1/2NbO3]or BNKT-KNN (x = 0.00, 0.18, 0.21, 0.24, 0.27. This method allowed obtaining powders with 100 % perovskite phase, which was confirmed by X-ray diffraction, for this particular system in all the studied stoichiometries using temperature as low as 600 ºC. The effects on the bonds present in the structure due to variation of the stoichiometry, Na-K, were determined using infrared spectroscopy, FT-IR. Irregular nanoparticles were observed by scanning electron microscopy.El método Pechini fue utilizado para obtener polvos cerámicos finos y monofásicos del sistema ferroeléctrico libre de plomo 0,97[(Bi1/2Na1/21-x(Bi1/2K1/2xTiO3]-0,03[(Na1/2K1/2NbO3] ó BNKT-KNN (x = 0.00, 0.18, 0.21, 0.24, 0.27. Este método permitió la obtención de polvos con 100 % de fase perovskita, para el sistema de interés en todas las estequiometrias estudiadas, a una temperatura tan baja como 600 ºC, lo que fue confirmado por difracción de rayos X. Por medio de espectroscopia infrarroja, FT-IR, se pudo determinar cómo afecta la variación de la estequiometria, Na-K, los enlaces presentes en la estructura. Mediante microscopia electrónica de barrido se observaron partículas nanométricas irregulares.
Quasi-Einstein metrics on hypersurface families
Hall, Stuart James
2012-01-01
We construct quasi-Einstein metrics on some hypersurface families. The hypersurfaces are circle bundles over the product of Fano, K\\"ahler-Einstein manifolds. The quasi-Einstein metrics are related to various gradient K\\"ahler-Ricci solitons constructed by Dancer and Wang and some Hermitian, non-K\\"ahler, Einstein metrics constructed by Wang and Wang on the same manifolds.
Toward an efficient objective metric based on perceptual criteria
Quintard, Ludovic; Larabi, Mohamed-Chaker; Fernandez-Maloigne, Christine
2008-01-01
Quality assessment is a very challenging problem and will still as is since it is difficult to define universal tools. So, subjective assessment is one adapted way but it is tedious, time consuming and needs normalized room. Objective metrics can be with reference, with reduced reference and with no-reference. This paper presents a study carried out for the development of a no-reference objective metric dedicated to the quality evaluation of display devices. Initially, a subjective study has been devoted to this problem by asking a representative panel (15 male and 15 female; 10 young adults, 10 adults and 10 seniors) to answer questions regarding their perception of several criteria for quality assessment. These quality factors were hue, saturation, contrast and texture. This aims to define the importance of perceptual criteria in the human judgment of quality. Following the study, the factors that impact the quality evaluation of display devices have been proposed. The development of a no-reference objective metric has been performed by using statistical tools allowing to separate the important axes. This no-reference metric based on perceptual criteria by integrating some specificities of the human visual system (HVS) has a high correlation with the subjective data.
刘海峰; 刘守生; 姚泽清
2013-01-01
In text categorization, the KNN algorithm is used widely. It is an example-based algorithm. The number of training samples and their position influence the algorithm' s classification performance. A reasonable method for reducing the amount of training data and an optimal weighting way can improve the efficiency of classification. This paper proposes an improved KNN model based on the sample distribution. Firstly, by calculating the distance between the samples, we remove some samples from training set. Secondly, take into account the category deflection; we bring up a better weighting method in order to overcome the defect that the bigger class, higher density of training samples dominated in KNN. The result of experiment shows that the improved KNN classification algorithm improves the efficiency of its classification.%KNN算法是文本分类中广泛应用的算法.作为一种基于实例的算法,训练样本的数量和分布位置影响KNN分类器分类性能.合理的样本剪裁以及样本赋权方法可以提高分类器的效率.提出了一种基于样本分布状况的KNN改进模型.首先基于样本位置对训练集进行删减以节约计算开销,然后针对类偏斜现象对分类器的赋权方式进行优化,改善k近邻选择时大类别、高密度训练样本的占优现象.试验结果表明,本文提出的改进KNN文本分类算法提高了KNN的分类效率.
Web metrics for library and information professionals
Stuart, David
2014-01-01
This is a practical guide to using web metrics to measure impact and demonstrate value. The web provides an opportunity to collect a host of different metrics, from those associated with social media accounts and websites to more traditional research outputs. This book is a clear guide for library and information professionals as to what web metrics are available and how to assess and use them to make informed decisions and demonstrate value. As individuals and organizations increasingly use the web in addition to traditional publishing avenues and formats, this book provides the tools to unlock web metrics and evaluate the impact of this content. The key topics covered include: bibliometrics, webometrics and web metrics; data collection tools; evaluating impact on the web; evaluating social media impact; investigating relationships between actors; exploring traditional publications in a new environment; web metrics and the web of data; the future of web metrics and the library and information professional.Th...
Anabalon, Andres
2016-01-01
In four dimensions, the most general metric admitting two Killing vectors and a rank-two Killing tensor can be parameterized by ten arbitrary functions of a single variable. We show that picking a special vierbien, reducing the system to eight functions, implies the existence of two geodesic and share-free, null congruences, generated by two principal null directions of the Weyl tensor. Thus, if the spacetime is an Einstein manifold, the Goldberg-Sachs theorem implies it is Petrov type D, and by explicit construction, is in the Carter class. Hence, our analysis provide an straightforward connection between the most general integrable structure and the Carter family of spacetimes.
Product Operations Status Summary Metrics
Takagi, Atsuya; Toole, Nicholas
2010-01-01
The Product Operations Status Summary Metrics (POSSUM) computer program provides a readable view into the state of the Phoenix Operations Product Generation Subsystem (OPGS) data pipeline. POSSUM provides a user interface that can search the data store, collect product metadata, and display the results in an easily-readable layout. It was designed with flexibility in mind for support in future missions. Flexibility over various data store hierarchies is provided through the disk-searching facilities of Marsviewer. This is a proven program that has been in operational use since the first day of the Phoenix mission.
Einstein metrics in projective geometry
Cap, A.; Gover, A. R.; Macbeth, H. R.
2012-01-01
It is well known that pseudo-Riemannian metrics in the projective class of a given torsion free affine connection can be obtained from (and are equivalent to) the solutions of a certain overdetermined projectively invariant differential equation. This equation is a special case of a so-called first BGG equation. The general theory of such equations singles out a subclass of so-called normal solutions. We prove that non-degerate normal solutions are equivalent to pseudo-Riemannian Einstein met...
Comparing Resource Adequacy Metrics: Preprint
Ibanez, E.; Milligan, M.
2014-09-01
As the penetration of variable generation (wind and solar) increases around the world, there is an accompanying growing interest and importance in accurately assessing the contribution that these resources can make toward planning reserve. This contribution, also known as the capacity credit or capacity value of the resource, is best quantified by using a probabilistic measure of overall resource adequacy. In recognizing the variable nature of these renewable resources, there has been interest in exploring the use of reliability metrics other than loss of load expectation. In this paper, we undertake some comparisons using data from the Western Electricity Coordinating Council in the western United States.
Anabalón, Andrés; Batista, Carlos
2016-03-01
In four dimensions, the most general metric admitting two commuting Killing vectors and a rank-two Killing tensor can be parametrized by ten arbitrary functions of a single variable. We show that picking a special vierbein, reducing the system to eight functions, implies the existence of two geodesic and share-free, null congruences, generated by two principal null directions of the Weyl tensor. Thus, if the spacetime is an Einstein manifold, the Goldberg-Sachs theorem implies it is Petrov type D, and by explicit construction, is in the Carter class. Hence, our analysis provides a straightforward connection between the most general integrable structure and the Carter family of spacetimes.
This is a corrected and essentially extended version of the unpublished manuscript by Y Nutku and M Sheftel which contains new results. It is proposed to be published in honour of Y Nutku’s memory. All corrections and new results in sections 1, 2 and 4 are due to M Sheftel. We present new anti-self-dual exact solutions of the Einstein field equations with Euclidean and neutral (ultra-hyperbolic) signatures that admit only one rotational Killing vector. Such solutions of the Einstein field equations are determined by non-invariant solutions of Boyer–Finley (BF) equation. For the case of Euclidean signature such a solution of the BF equation was first constructed by Calderbank and Tod. Two years later, Martina, Sheftel and Winternitz applied the method of group foliation to the BF equation and reproduced the Calderbank–Tod solution together with new solutions for the neutral signature. In the case of Euclidean signature we obtain new metrics which asymptotically locally look like a flat space and have a non-removable singular point at the origin. In the case of ultra-hyperbolic signature there exist three inequivalent forms of metric. Only one of these can be obtained by analytic continuation from the Calderbank–Tod solution whereas the other two are new. (paper)
Taxonomy for Information Privacy Metrics
Rasika Dayarathna
2011-10-01
Full Text Available A comprehensive privacy framework is essential for the progress of the information privacy field. Some practical implications of a comprehensive framework are laying foundation for building information privacy metrics and having fruitful discussions. Taxonomy is an essential step in building a framework. This research study attempts to build taxonomy for the information privacy domain based on empirical data. The classical grounded theory approach introduced by Glaser was applied and incidents reported by the International Association of Privacy Professionals (IAPP are used for building the taxonomy. These incidents include privacy related current research works, data breaches, personal views, interviews, and technological innovations. TAMZAnalyzer, an open source qualitative data analysis tool, was used in coding, keeping memos, sorting, and creating categories. The taxonomy is presented in seven themes and several categories including legal, technical, and ethical aspects. The findings of this study helps practitioners understand and discuss the subjects and academia work toward building a comprehensive framework and metrics for the information privacy domain
Multifractal Resilience Metrics for Complex Systems?
Schertzer, D. J.; Tchiguirinskaia, I.; Lovejoy, S.
2011-12-01
The term resilience has become extremely fashionable, especially for complex systems, whereas corresponding operational definitions have remained rather elusive (Carpenter et al. 2001). More precisely, the resilience assessment of man-made systems (from nuclear plants to cities) to geophysical extremes require mathematically defined resilience metrics based on some conceptual definition, e.g. the often cited definition of "ecological resilience" (Hollings 1973): "the capacity of a system to absorb disturbance and reorganize while undergoing change so as to still retain essentially the same function, structure, identity, and feedbacks". Surprisingly, whereas it was acknowledged by Folke et al. (2010) that "multiscale resilience is fundamental for understanding the interplay between persistence and change, adaptability and transformability", the relation between resilience and scaling has not been so much questioned, see however Peterson (2000). We argue that is rather indispensable to go well beyond the attractor approach (Pimm and Lawton 1977; Collings and Wollkind 1990;), as well as extensions (Martin et al., 2011) into the framework of the viability theory (Aubin 1991; Aubin et al. 2011). Indeed, both are rather limited to systems that are complex only in time. Scale symmetries are indeed indispensable to reduce the space-time complexity by defining scale independent observables, which are the singularities of the original, scale dependent fields. These singularities enable to define across-scale resilience, instead of resilience at a given scale.
The metric space of geodesic laminations on a surface: I
Zhu, Xiaodong; Bonahon, Francis
2003-01-01
We consider the space of geodesic laminations on a surface, endowed with the Hausdorff metric d_H and with a variation of this metric called the d_log metric. We compute and/or estimate the Hausdorff dimensions of these two metrics. We also relate these two metrics to another metric which is combinatorially defined in terms of train tracks.
Color Ratios and Chromatic Adaptation
Finlayson, Graham D.; Süsstrunk, Sabine
2002-01-01
In this paper, the performance of chromatic adaptation transforms based on stable color ratios is investigated.It was found that for three different sets of reflectance data, their performance was not statistically different from CMCCAT2000,when applying the chromatic adaptation transforms to Lam’s corresponding color data set and using a perceptual error metric of CIE Delta E94.The sensors with the best color ratio stability are much sharper and more de-correlated than the CMCCAT2000 sensors...
Ramified optimal transportation in geodesic metric spaces
Xia, Qinglan
2009-01-01
An optimal transport path may be viewed as a geodesic in the space of probability measures under a suitable family of metrics. This geodesic may exhibit a tree-shaped branching structure in many applications such as trees, blood vessels, draining and irrigation systems. Here, we extend the study of ramified optimal transportation between probability measures from Euclidean spaces to a geodesic metric space. We investigate the existence as well as the behavior of optimal transport paths under various properties of the metric such as completeness, doubling, or curvature upper boundedness. We also introduce the transport dimension of a probability measure on a complete geodesic metric space, and show that the transport dimension of a probability measure is bounded above by the Minkowski dimension and below by the Hausdorff dimension of the measure. Moreover, we introduce a metric, called "the dimensional distance", on the space of probability measures. This metric gives a geometric meaning to the transport dimen...
A Brief Overview Of Software Testing Metrics
Premal B. Nirpal,
2011-01-01
Full Text Available Metrics are gaining importance and acceptance in corporate sectors as organizations grow, mature and strive to improve enterprise qualities. Measurement of a test process is a required competence for an effective software test manager for designing and evaluating a cost effective test strategy. Effective management of any process requires quantification, measurement and modeling. Software Metrics provide quantitative approach to the development and validation of the software process models. Metrics help organization to obtain the information it needs to continue to improve its productivity, reduce errors and improve acceptance of processes, products and services and achieve the desired Goal. This paper, focusing on metrics lifecycle, various software testing metrics, need for having metrics, evaluation process and arriving at ideal conclusion have also been discussed in the present paper.
From Smooth Curves to Universal Metrics
Gurses, Metin; Tekin, Bayram
2016-01-01
A special class of metrics, called universal metrics, solve all gravity theories defined by covariant field equations purely based on the metric tensor. Since we currently lack the knowledge of what the full of quantum corrected field equations of gravity are at a given microscopic length scale, these metrics are particularly important in understanding quantum fields in curved backgrounds in a consistent way. But, finding explicit universal metrics has been a hard problem as there does not seem to be a procedure for it. In this work, we overcome this difficulty and give a construction of universal metrics of d dimensional spacetime from curves constrained to live in a d-1 dimensional Minkowski spacetime or a Euclidean space.
SOFTWARE METRICS VALIDATION METHODOLOGIES IN SOFTWARE ENGINEERING
K.P. Srinivasan; T. Devi
2014-01-01
In the software measurement validations, assessing the validation of software metrics in software engineering is a very difficult task due to lack of theoretical methodology and empirical methodology [41, 44, 45]. During recent years, there have been a number of researchers addressing the issue of validating software metrics. At present, software metrics are validated theoretically using properties of measures. Further, software measurement plays an important role in understanding...
The definitive guide to IT service metrics
McWhirter, Kurt
2012-01-01
Used just as they are, the metrics in this book will bring many benefits to both the IT department and the business as a whole. Details of the attributes of each metric are given, enabling you to make the right choices for your business. You may prefer and are encouraged to design and create your own metrics to bring even more value to your business - this book will show you how to do this, too.
THE QUALITY METRICS OF INFORMATION SYSTEMS
Zora Arsovski; Slavko Arsovski
2008-01-01
Information system is a special kind of products which is depend upon great number variables related to nature, conditions during implementation and organizational clime and culture. Because that quality metrics of information system (QMIS) has to reflect all previous aspects of information systems. In this paper are presented basic elements of QMIS, characteristics of implementation and operation metrics for IS, team - management quality metrics for IS and organizational aspects of quality m...
Evaluating the value of web metrics
Riihimäki, Tommi
2014-01-01
Objectives of the Study: The unique measurability of websites allows the collection of detailed information about the behavior and characteristics of website visitors. This thesis examines the value of different web metrics based on the behavior of website visitors. The objective is to develop and test a method for identifying key metrics that are the most valuable for site developers to follow. The key web metrics are expected to contain the most useful and relevant information about the...
A Kernel Classification Framework for Metric Learning
Wang, Faqiang; Zuo, Wangmeng; Zhang, Lei; Meng, Deyu; Zhang, David
2013-01-01
Learning a distance metric from the given training samples plays a crucial role in many machine learning tasks, and various models and optimization algorithms have been proposed in the past decade. In this paper, we generalize several state-of-the-art metric learning methods, such as large margin nearest neighbor (LMNN) and information theoretic metric learning (ITML), into a kernel classification framework. First, doublets and triplets are constructed from the training samples, and a family ...
Improved characterization of the Kerr metric
The condition of asymptotic flatness is removed from Simon's characterization of the Kerr metric by vanishing of the complexified Bach tensor. The solution of the stationary vacuum equations of relativity is given for a vanishing Simon tensor. One class of metrics consists of three Ehlersrotated Levi-Civita metrics, which have conformally flat three-spaces. The second class contains Hoffmann's planefronted standing wave solutions, and the third includes the three-parameter Kerr-NUT space-time. (author)
Brand Metrics: A Tool to Measure Performance
Rajagopal, MR; Rajagopal, Amritanshu
2007-01-01
An increasing interest in the continuous evaluation of brand performance has been observed in both managers and academics over recent past using metrics approach. This paper discusses the essential components of a brand metrics strategy and application of brand scorecard as an integrated approach to measure the overall performance of brands. The discussion delineates the process as how different constituents of metrics can be linked to business performance. It has also been argued in the pape...
Generalized tolerance sensitivity and DEA metric sensitivity
Luka Neralić; Richard E. Wendell
2015-01-01
This paper considers the relationship between Tolerance sensitivity analysis in optimization and metric sensitivity analysis in Data Envelopment Analysis (DEA). Herein, we extend the results on the generalized Tolerance framework proposed by Wendell and Chen and show how this framework includes DEA metric sensitivity as a special case. Further, we note how recent results in Tolerance sensitivity suggest some possible extensions of the results in DEA metric sensitivity.
Radiation-dominated area metric cosmology
Schuller, F.; Wohlfarth, M.
2007-01-01
We provide further crucial support for a refined, area metric structure of spacetime. On the basis of the solution of conceptual issues, such as the consistent coupling of fermions and the covariant identification of radiation fields on area metric backgrounds, we show that the radiation-dominated epoch of area metric cosmology is equivalent to that epoch in standard Einstein cosmology. This ensures, in particular, successful nucleosynthesis. This surprising result complements the previously ...
Gravesen, Jens
2015-01-01
The space of colours is a fascinating space. It is a real vector space, but no matter what inner product you put on the space the resulting Euclidean distance does not correspond to human perception of difference between colours. In 1942 MacAdam performed the first experiments on colour matching...... and found the MacAdam ellipses which are often interpreted as defining the metric tensor at their centres. An important question is whether it is possible to define colour coordinates such that the Euclidean distance in these coordinates correspond to human perception. Using cubic splines to represent...... the colour coordinates and an optimisation approach we find new colour coordinates that make the MacAdam ellipses closer to uniform circles than the existing standards....
Metrics for antibody therapeutics development.
Reichert, Janice M
2010-01-01
A wide variety of full-size monoclonal antibodies (mAbs) and therapeutics derived from alternative antibody formats can be produced through genetic and biological engineering techniques. These molecules are now filling the preclinical and clinical pipelines of every major pharmaceutical company and many biotechnology firms. Metrics for the development of antibody therapeutics, including averages for the number of candidates entering clinical study and development phase lengths for mAbs approved in the United States, were derived from analysis of a dataset of over 600 therapeutic mAbs that entered clinical study sponsored, at least in part, by commercial firms. The results presented provide an overview of the field and context for the evaluation of on-going and prospective mAb development programs. The expansion of therapeutic antibody use through supplemental marketing approvals and the increase in the study of therapeutics derived from alternative antibody formats are discussed. PMID:20930555
Inertia Manipulation through Metric Patching
Waite, D
2001-01-01
This paper will present the exact solution for the stress-energy tensor of a spherical matter shell of finite thickness that will patch together different metrics at the boundaries of the shell. The choice of vacuum field solutions for the shell exterior and hollow interior that we make will allow us to manipulate the inertial state of an object within the shell. The choice will cause it to be in a state of acceleration with the shell relative to an external observer for an indefinite time. The stress-energy tensor's solution results in zero ship frame energy requirements, and only finite stress requirements, and we show how any WEC violation can be avoided.
Shi, Y.; Gorban, A. N.; Y Yang, T.
2014-03-01
This case study tests the possibility of prediction for 'success' (or 'winner') components of four stock & shares market indices in a time period of three years from 02-Jul-2009 to 29-Jun-2012.We compare their performance ain two time frames: initial frame three months at the beginning (02/06/2009-30/09/2009) and the final three month frame (02/04/2012-29/06/2012).To label the components, average price ratio between two time frames in descending order is computed. The average price ratio is defined as the ratio between the mean prices of the beginning and final time period. The 'winner' components are referred to the top one third of total components in the same order as average price ratio it means the mean price of final time period is relatively higher than the beginning time period. The 'loser' components are referred to the last one third of total components in the same order as they have higher mean prices of beginning time period. We analyse, is there any information about the winner-looser separation in the initial fragments of the daily closing prices log-returns time series.The Leave-One-Out Cross-Validation with k-NN algorithm is applied on the daily log-return of components using a distance and proximity in the experiment. By looking at the error analysis, it shows that for HANGSENG and DAX index, there are clear signs of possibility to evaluate the probability of long-term success. The correlation distance matrix histograms and 2-D/3-D elastic maps generated from ViDaExpert show that the 'winner' components are closer to each other and 'winner'/'loser' components are separable on elastic maps for HANGSENG and DAX index while for the negative possibility indices, there is no sign of separation.
This case study tests the possibility of prediction for 'success' (or 'winner') components of four stock and shares market indices in a time period of three years from 02-Jul-2009 to 29-Jun-2012.We compare their performance ain two time frames: initial frame three months at the beginning (02/06/2009-30/09/2009) and the final three month frame (02/04/2012-29/06/2012).To label the components, average price ratio between two time frames in descending order is computed. The average price ratio is defined as the ratio between the mean prices of the beginning and final time period. The 'winner' components are referred to the top one third of total components in the same order as average price ratio it means the mean price of final time period is relatively higher than the beginning time period. The 'loser' components are referred to the last one third of total components in the same order as they have higher mean prices of beginning time period. We analyse, is there any information about the winner-looser separation in the initial fragments of the daily closing prices log-returns time series.The Leave-One-Out Cross-Validation with k-NN algorithm is applied on the daily log-return of components using a distance and proximity in the experiment. By looking at the error analysis, it shows that for HANGSENG and DAX index, there are clear signs of possibility to evaluate the probability of long-term success. The correlation distance matrix histograms and 2-D/3-D elastic maps generated from ViDaExpert show that the 'winner' components are closer to each other and 'winner'/'loser' components are separable on elastic maps for HANGSENG and DAX index while for the negative possibility indices, there is no sign of separation