Memory Based Machine Intelligence Techniques in VLSI hardware

OpenAIRE

James, Alex Pappachen

2012-01-01

We briefly introduce the memory based approaches to emulate machine intelligence in VLSI hardware, describing the challenges and advantages. Implementation of artificial intelligence techniques in VLSI hardware is a practical and difficult problem. Deep architectures, hierarchical temporal memories and memory networks are some of the contemporary approaches in this area of research. The techniques attempt to emulate low level intelligence tasks and aim at providing scalable solutions to high ...

The Effects of Different Electrode Types for Obtaining Surface Machining Shape on Shape Memory Alloy Using Electrochemical Machining

Science.gov (United States)

Choi, S. G.; Kim, S. H.; Choi, W. K.; Moon, G. C.; Lee, E. S.

2017-06-01

Shape memory alloy (SMA) is important material used for the medicine and aerospace industry due to its characteristics called the shape memory effect, which involves the recovery of deformed alloy to its original state through the application of temperature or stress. Consumers in modern society demand stability in parts. Electrochemical machining is one of the methods for obtained these stabilities in parts requirements. These parts of shape memory alloy require fine patterns in some applications. In order to machine a fine pattern, the electrochemical machining method is suitable. For precision electrochemical machining using different shape electrodes, the current density should be controlled precisely. And electrode shape is required for precise electrochemical machining. It is possible to obtain precise square holes on the SMA if the insulation layer controlled the unnecessary current between electrode and workpiece. If it is adjusting the unnecessary current to obtain the desired shape, it will be a great contribution to the medical industry and the aerospace industry. It is possible to process a desired shape to the shape memory alloy by micro controlling the unnecessary current. In case of the square electrode without insulation layer, it derives inexact square holes due to the unnecessary current. The results using the insulated electrode in only side show precise square holes. The removal rate improved in case of insulated electrode than others because insulation layer concentrate the applied current to the machining zone.

A variable-mode stator consequent pole memory machine

Science.gov (United States)

Yang, Hui; Lyu, Shukang; Lin, Heyun; Zhu, Z. Q.

2018-05-01

In this paper, a variable-mode concept is proposed for the speed range extension of a stator-consequent-pole memory machine (SCPMM). An integrated permanent magnet (PM) and electrically excited control scheme is utilized to simplify the flux-weakening control instead of relatively complicated continuous PM magnetization control. Due to the nature of memory machine, the magnetization state of low coercive force (LCF) magnets can be easily changed by applying either a positive or negative current pulse. Therefore, the number of PM poles may be changed to satisfy the specific performance requirement under different speed ranges, i.e. the machine with all PM poles can offer high torque output while that with half PM poles provides wide constant power range. In addition, the SCPMM with non-magnetized PMs can be considered as a dual-three phase electrically excited reluctance machine, which can be fed by an open-winding based dual inverters that provide direct current (DC) bias excitation to further extend the speed range. The effectiveness of the proposed variable-mode operation for extending its operating region and improving the system reliability is verified by both finite element analysis (FEA) and experiments.

Over-Distribution in Source Memory

Science.gov (United States)

Brainerd, C. J.; Reyna, V. F.; Holliday, R. E.; Nakamura, K.

2012-01-01

Semantic false memories are confounded with a second type of error, over-distribution, in which items are attributed to contradictory episodic states. Over-distribution errors have proved to be more common than false memories when the two are disentangled. We investigated whether over-distribution is prevalent in another classic false memory paradigm: source monitoring. It is. Conventional false memory responses (source misattributions) were predominantly over-distribution errors, but unlike semantic false memory, over-distribution also accounted for more than half of true memory responses (correct source attributions). Experimental control of over-distribution was achieved via a series of manipulations that affected either recollection of contextual details or item memory (concreteness, frequency, list-order, number of presentation contexts, and individual differences in verbatim memory). A theoretical model was used to analyze the data (conjoint process dissociation) that predicts that predicts that (a) over-distribution is directly proportional to item memory but inversely proportional to recollection and (b) item memory is not a necessary precondition for recollection of contextual details. The results were consistent with both predictions. PMID:21942494

Sparse distributed memory overview

Science.gov (United States)

Raugh, Mike

1990-01-01

The Sparse Distributed Memory (SDM) project is investigating the theory and applications of massively parallel computing architecture, called sparse distributed memory, that will support the storage and retrieval of sensory and motor patterns characteristic of autonomous systems. The immediate objectives of the project are centered in studies of the memory itself and in the use of the memory to solve problems in speech, vision, and robotics. Investigation of methods for encoding sensory data is an important part of the research. Examples of NASA missions that may benefit from this work are Space Station, planetary rovers, and solar exploration. Sparse distributed memory offers promising technology for systems that must learn through experience and be capable of adapting to new circumstances, and for operating any large complex system requiring automatic monitoring and control. Sparse distributed memory is a massively parallel architecture motivated by efforts to understand how the human brain works. Sparse distributed memory is an associative memory, able to retrieve information from cues that only partially match patterns stored in the memory. It is able to store long temporal sequences derived from the behavior of a complex system, such as progressive records of the system's sensory data and correlated records of the system's motor controls.

Investigating Solution Convergence in a Global Ocean Model Using a 2048-Processor Cluster of Distributed Shared Memory Machines

Directory of Open Access Journals (Sweden)

Chris Hill

2007-01-01

Full Text Available Up to 1920 processors of a cluster of distributed shared memory machines at the NASA Ames Research Center are being used to simulate ocean circulation globally at horizontal resolutions of 1/4, 1/8, and 1/16-degree with the Massachusetts Institute of Technology General Circulation Model, a finite volume code that can scale to large numbers of processors. The study aims to understand physical processes responsible for skill improvements as resolution is increased and to gain insight into what resolution is sufficient for particular purposes. This paper focuses on the computational aspects of reaching the technical objective of efficiently performing these global eddy-resolving ocean simulations. At 1/16-degree resolution the model grid contains 1.2 billion cells. At this resolution it is possible to simulate approximately one month of ocean dynamics in about 17 hours of wallclock time with a model timestep of two minutes on a cluster of four 512-way NUMA Altix systems. The Altix systems' large main memory and I/O subsystems allow computation and disk storage of rich sets of diagnostics during each integration, supporting the scientific objective to develop a better understanding of global ocean circulation model solution convergence as model resolution is increased.

Parallel discrete ordinates algorithms on distributed and common memory systems

International Nuclear Information System (INIS)

Wienke, B.R.; Hiromoto, R.E.; Brickner, R.G.

1987-01-01

The S/sub n/ algorithm employs iterative techniques in solving the linear Boltzmann equation. These methods, both ordered and chaotic, were compared on both the Denelcor HEP and the Intel hypercube. Strategies are linked to the organization and accessibility of memory (common memory versus distributed memory architectures), with common concern for acquisition of global information. Apart from this, the inherent parallelism of the algorithm maps directly onto the two architectures. Results comparing execution times, speedup, and efficiency are based on a representative 16-group (full upscatter and downscatter) sample problem. Calculations were performed on both the Los Alamos National Laboratory (LANL) Denelcor HEP and the LANL Intel hypercube. The Denelcor HEP is a 64-bit multi-instruction, multidate MIMD machine consisting of up to 16 process execution modules (PEMs), each capable of executing 64 processes concurrently. Each PEM can cooperate on a job, or run several unrelated jobs, and share a common global memory through a crossbar switch. The Intel hypercube, on the other hand, is a distributed memory system composed of 128 processing elements, each with its own local memory. Processing elements are connected in a nearest-neighbor hypercube configuration and sharing of data among processors requires execution of explicit message-passing constructs

Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems.

Science.gov (United States)

Shehzad, Danish; Bozkuş, Zeki

2016-01-01

Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks, interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI) is used. MPI_Allgather collective is exercised for spikes exchange after each interval across distributed memory systems. The increase in number of processors though results in achieving concurrency and better performance but it inversely affects MPI_Allgather which increases communication time between processors. This necessitates improving communication methodology to decrease the spikes exchange time over distributed memory systems. This work has improved MPI_Allgather method using Remote Memory Access (RMA) by moving two-sided communication to one-sided communication, and use of recursive doubling mechanism facilitates achieving efficient communication between the processors in precise steps. This approach enhanced communication concurrency and has improved overall runtime making NEURON more efficient for simulation of large neuronal network models.

Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems

Directory of Open Access Journals (Sweden)

Danish Shehzad

2016-01-01

Full Text Available Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks, interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI is used. MPI_Allgather collective is exercised for spikes exchange after each interval across distributed memory systems. The increase in number of processors though results in achieving concurrency and better performance but it inversely affects MPI_Allgather which increases communication time between processors. This necessitates improving communication methodology to decrease the spikes exchange time over distributed memory systems. This work has improved MPI_Allgather method using Remote Memory Access (RMA by moving two-sided communication to one-sided communication, and use of recursive doubling mechanism facilitates achieving efficient communication between the processors in precise steps. This approach enhanced communication concurrency and has improved overall runtime making NEURON more efficient for simulation of large neuronal network models.

A distributed-memory hierarchical solver for general sparse linear systems

Energy Technology Data Exchange (ETDEWEB)

Chen, Chao [Stanford Univ., CA (United States). Inst. for Computational and Mathematical Engineering; Pouransari, Hadi [Stanford Univ., CA (United States). Dept. of Mechanical Engineering; Rajamanickam, Sivasankaran [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Center for Computing Research; Boman, Erik G. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Center for Computing Research; Darve, Eric [Stanford Univ., CA (United States). Inst. for Computational and Mathematical Engineering and Dept. of Mechanical Engineering

2017-12-20

We present a parallel hierarchical solver for general sparse linear systems on distributed-memory machines. For large-scale problems, this fully algebraic algorithm is faster and more memory-efficient than sparse direct solvers because it exploits the low-rank structure of fill-in blocks. Depending on the accuracy of low-rank approximations, the hierarchical solver can be used either as a direct solver or as a preconditioner. The parallel algorithm is based on data decomposition and requires only local communication for updating boundary data on every processor. Moreover, the computation-to-communication ratio of the parallel algorithm is approximately the volume-to-surface-area ratio of the subdomain owned by every processor. We also provide various numerical results to demonstrate the versatility and scalability of the parallel algorithm.

Machine parts recognition using a trinary associative memory

Science.gov (United States)

Awwal, Abdul Ahad S.; Karim, Mohammad A.; Liu, Hua-Kuang

1989-01-01

The convergence mechanism of vectors in Hopfield's neural network in relation to recognition of partially known patterns is studied in terms of both inner products and Hamming distance. It has been shown that Hamming distance should not always be used in determining the convergence of vectors. Instead, inner product weighting coefficients play a more dominant role in certain data representations for determining the convergence mechanism. A trinary neuron representation for associative memory is found to be more effective for associative recall. Applications of the trinary associative memory to reconstruct machine part images that are partially missing are demonstrated by means of computer simulation as examples of the usefulness of this approach.

X-ray evaluation of residual stress distributions within surface machined layer generated by surface machining and sequential welding

International Nuclear Information System (INIS)

Taniguchi, Yuu; Okano, Shigetaka; Mochizuki, Masahito

2017-01-01

The excessive tensile residual stress generated by welding after surface machining may be an important factor to cause stress corrosion cracking (SCC) in nuclear power plants. Therefore we need to understand and control the residual stress distribution appropriately. In this study, residual stress distributions within surface machined layer generated by surface machining and sequential welding were evaluated by X-ray diffraction method. Depth directional distributions were also investigated by electrolytic polishing. In addition, to consider the effect of work hardened layer on the residual stress distributions, we also measured full width at half maximum (FWHM) obtained from X-ray diffraction. Testing material was a low-carbon austenitic stainless steel type SUS316L. Test specimens were prepared by surface machining with different cutting conditions. Then, bead-on-plate welding under the same welding condition was carried out on the test specimens with different surface machined layer. As a result, the tensile residual stress generated by surface machining increased with increasing cutting speed and showed nearly uniform distributions on the surface. Furthermore, the tensile residual stress drastically decreased with increasing measurement depth within surface machined layer. Then, the residual stress approached 0 MPa after the compressive value showed. FWHM also decreased drastically with increasing measurement depth and almost constant value from a certain depth, which was almost equal regardless of the machining condition, within surface machined layer in all specimens. After welding, the transverse distribution of the longitudinal residual stress varied in the area apart from the weld center according to machining conditions and had a maximum value in heat affected zone. The magnitude of the maximum residual stress was almost equal regardless of the machining condition and decreased with increasing measurement depth within surface machined layer. Finally, the

A discrete Fourier transform for virtual memory machines

Science.gov (United States)

Galant, David C.

1992-01-01

An algebraic theory of the Discrete Fourier Transform is developed in great detail. Examination of the details of the theory leads to a computationally efficient fast Fourier transform for the use on computers with virtual memory. Such an algorithm is of great use on modern desktop machines. A FORTRAN coded version of the algorithm is given for the case when the sequence of numbers to be transformed is a power of two.

Tool set for distributed real-time machine control

Science.gov (United States)

Carrott, Andrew J.; Wright, Christopher D.; West, Andrew A.; Harrison, Robert; Weston, Richard H.

1997-01-01

Demands for increased control capabilities require next generation manufacturing machines to comprise intelligent building elements, physically located at the point where the control functionality is required. Networks of modular intelligent controllers are increasingly designed into manufacturing machines and usable standards are slowly emerging. To implement a control system using off-the-shelf intelligent devices from multi-vendor sources requires a number of well defined activities, including (a) the specification and selection of interoperable control system components, (b) device independent application programming and (c) device configuration, management, monitoring and control. This paper briefly discusses the support for the above machine lifecycle activities through the development of an integrated computing environment populated with an extendable software toolset. The toolset supports machine builder activities such as initial control logic specification, logic analysis, machine modeling, mechanical verification, application programming, automatic code generation, simulation/test, version control, distributed run-time support and documentation. The environment itself consists of system management tools and a distributed object-oriented database which provides storage for the outputs from machine lifecycle activities and specific target control solutions.

Migration of vectorized iterative solvers to distributed memory architectures

Energy Technology Data Exchange (ETDEWEB)

Pommerell, C. [AT& T Bell Labs., Murray Hill, NJ (United States); Ruehl, R. [CSCS-ETH, Manno (Switzerland)

1994-12-31

Both necessity and opportunity motivate the use of high-performance computers for iterative linear solvers. Necessity results from the size of the problems being solved-smaller problems are often better handled by direct methods. Opportunity arises from the formulation of the iterative methods in terms of simple linear algebra operations, even if this {open_quote}natural{close_quotes} parallelism is not easy to exploit in irregularly structured sparse matrices and with good preconditioners. As a result, high-performance implementations of iterative solvers have attracted a lot of interest in recent years. Most efforts are geared to vectorize or parallelize the dominating operation-structured or unstructured sparse matrix-vector multiplication, or to increase locality and parallelism by reformulating the algorithm-reducing global synchronization in inner products or local data exchange in preconditioners. Target architectures for iterative solvers currently include mostly vector supercomputers and architectures with one or few optimized (e.g., super-scalar and/or super-pipelined RISC) processors and hierarchical memory systems. More recently, parallel computers with physically distributed memory and a better price/performance ratio have been offered by vendors as a very interesting alternative to vector supercomputers. However, programming comfort on such distributed memory parallel processors (DMPPs) still lags behind. Here the authors are concerned with iterative solvers and their changing computing environment. In particular, they are considering migration from traditional vector supercomputers to DMPPs. Application requirements force one to use flexible and portable libraries. They want to extend the portability of iterative solvers rather than reimplementing everything for each new machine, or even for each new architecture.

A distributed algorithm for machine learning

Science.gov (United States)

Chen, Shihong

2018-04-01

This paper considers a distributed learning problem in which a group of machines in a connected network, each learning its own local dataset, aim to reach a consensus at an optimal model, by exchanging information only with their neighbors but without transmitting data. A distributed algorithm is proposed to solve this problem under appropriate assumptions.

Optimizing Distributed Machine Learning for Large Scale EEG Data Set

Directory of Open Access Journals (Sweden)

M Bilal Shaikh

2017-06-01

Full Text Available Distributed Machine Learning (DML has gained its importance more than ever in this era of Big Data. There are a lot of challenges to scale machine learning techniques on distributed platforms. When it comes to scalability, improving the processor technology for high level computation of data is at its limit, however increasing machine nodes and distributing data along with computation looks as a viable solution. Different frameworks   and platforms are available to solve DML problems. These platforms provide automated random data distribution of datasets which miss the power of user defined intelligent data partitioning based on domain knowledge. We have conducted an empirical study which uses an EEG Data Set collected through P300 Speller component of an ERP (Event Related Potential which is widely used in BCI problems; it helps in translating the intention of subject w h i l e performing any cognitive task. EEG data contains noise due to waves generated by other activities in the brain which contaminates true P300Speller. Use of Machine Learning techniques could help in detecting errors made by P300 Speller. We are solving this classification problem by partitioning data into different chunks and preparing distributed models using Elastic CV Classifier. To present a case of optimizing distributed machine learning, we propose an intelligent user defined data partitioning approach that could impact on the accuracy of distributed machine learners on average. Our results show better average AUC as compared to average AUC obtained after applying random data partitioning which gives no control to user over data partitioning. It improves the average accuracy of distributed learner due to the domain specific intelligent partitioning by the user. Our customized approach achieves 0.66 AUC on individual sessions and 0.75 AUC on mixed sessions, whereas random / uncontrolled data distribution records 0.63 AUC.

A model for Intelligent Random Access Memory architecture (IRAM) cellular automata algorithms on the Associative String Processing machine (ASTRA)

CERN Document Server

Rohrbach, F; Vesztergombi, G

1997-01-01

In the near future, the computer performance will be completely determined by how long it takes to access memory. There are bottle-necks in memory latency and memory-to processor interface bandwidth. The IRAM initiative could be the answer by putting Processor-In-Memory (PIM). Starting from the massively parallel processing concept, one reached a similar conclusion. The MPPC (Massively Parallel Processing Collaboration) project and the 8K processor ASTRA machine (Associative String Test bench for Research \\& Applications) developed at CERN \\cite{kuala} can be regarded as a forerunner of the IRAM concept. The computing power of the ASTRA machine, regarded as an IRAM with 64 one-bit processors on a 64$\\times$64 bit-matrix memory chip machine, has been demonstrated by running statistical physics algorithms: one-dimensional stochastic cellular automata, as a simple model for dynamical phase transitions. As a relevant result for physics, the damage spreading of this model has been investigated.

Teraflop-scale Incremental Machine Learning

OpenAIRE

Özkural, Eray

2011-01-01

We propose a long-term memory design for artificial general intelligence based on Solomonoff's incremental machine learning methods. We use R5RS Scheme and its standard library with a few omissions as the reference machine. We introduce a Levin Search variant based on Stochastic Context Free Grammar together with four synergistic update algorithms that use the same grammar as a guiding probability distribution of programs. The update algorithms include adjusting production probabilities, re-u...

Total recall in distributive associative memories

Science.gov (United States)

Danforth, Douglas G.

1991-01-01

Iterative error correction of asymptotically large associative memories is equivalent to a one-step learning rule. This rule is the inverse of the activation function of the memory. Spectral representations of nonlinear activation functions are used to obtain the inverse in closed form for Sparse Distributed Memory, Selected-Coordinate Design, and Radial Basis Functions.

The performance of disk arrays in shared-memory database machines

Science.gov (United States)

Katz, Randy H.; Hong, Wei

1993-01-01

In this paper, we examine how disk arrays and shared memory multiprocessors lead to an effective method for constructing database machines for general-purpose complex query processing. We show that disk arrays can lead to cost-effective storage systems if they are configured from suitably small formfactor disk drives. We introduce the storage system metric data temperature as a way to evaluate how well a disk configuration can sustain its workload, and we show that disk arrays can sustain the same data temperature as a more expensive mirrored-disk configuration. We use the metric to evaluate the performance of disk arrays in XPRS, an operational shared-memory multiprocessor database system being developed at the University of California, Berkeley.

Object-Oriented Support for Adaptive Methods on Paranel Machines

Directory of Open Access Journals (Sweden)

Sandeep Bhatt

1993-01-01

Full Text Available This article reports on experiments from our ongoing project whose goal is to develop a C++ library which supports adaptive and irregular data structures on distributed memory supercomputers. We demonstrate the use of our abstractions in implementing "tree codes" for large-scale N-body simulations. These algorithms require dynamically evolving treelike data structures, as well as load-balancing, both of which are widely believed to make the application difficult and cumbersome to program for distributed-memory machines. The ease of writing the application code on top of our C++ library abstractions (which themselves are application independent, and the low overhead of the resulting C++ code (over hand-crafted C code supports our belief that object-oriented approaches are eminently suited to programming distributed-memory machines in a manner that (to the applications programmer is architecture-independent. Our contribution in parallel programming methodology is to identify and encapsulate general classes of communication and load-balancing strategies useful across applications and MIMD architectures. This article reports experimental results from simulations of half a million particles using multiple methods.

Adaptive Dynamic Process Scheduling on Distributed Memory Parallel Computers

Directory of Open Access Journals (Sweden)

Wei Shu

1994-01-01

Full Text Available One of the challenges in programming distributed memory parallel machines is deciding how to allocate work to processors. This problem is particularly important for computations with unpredictable dynamic behaviors or irregular structures. We present a scheme for dynamic scheduling of medium-grained processes that is useful in this context. The adaptive contracting within neighborhood (ACWN is a dynamic, distributed, load-dependent, and scalable scheme. It deals with dynamic and unpredictable creation of processes and adapts to different systems. The scheme is described and contrasted with two other schemes that have been proposed in this context, namely the randomized allocation and the gradient model. The performance of the three schemes on an Intel iPSC/2 hypercube is presented and analyzed. The experimental results show that even though the ACWN algorithm incurs somewhat larger overhead than the randomized allocation, it achieves better performance in most cases due to its adaptiveness. Its feature of quickly spreading the work helps it outperform the gradient model in performance and scalability.

Unraveling Network-induced Memory Contention: Deeper Insights with Machine Learning

International Nuclear Information System (INIS)

Groves, Taylor Liles; Grant, Ryan; Gonzales, Aaron; Arnold, Dorian

2017-01-01

Remote Direct Memory Access (RDMA) is expected to be an integral communication mechanism for future exascale systems enabling asynchronous data transfers, so that applications may fully utilize CPU resources while simultaneously sharing data amongst remote nodes. We examine Network-induced Memory Contention (NiMC) on Infiniband networks. We expose the interactions between RDMA, main-memory and cache, when applications and out-of-band services compete for memory resources. We then explore NiMCs resulting impact on application-level performance. For a range of hardware technologies and HPC workloads, we quantify NiMC and show that NiMCs impact grows with scale resulting in up to 3X performance degradation at scales as small as 8K processes even in applications that previously have been shown to be performance resilient in the presence of noise. In addition, this work examines the problem of predicting NiMC's impact on applications by leveraging machine learning and easily accessible performance counters. This approach provides additional insights about the root cause of NiMC and facilitates dynamic selection of potential solutions. Finally, we evaluated three potential techniques to reduce NiMCs impact, namely hardware offloading, core reservation and network throttling.

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

Science.gov (United States)

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-09-21

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

Monte Carlo photon transport on shared memory and distributed memory parallel processors

International Nuclear Information System (INIS)

Martin, W.R.; Wan, T.C.; Abdel-Rahman, T.S.; Mudge, T.N.; Miura, K.

1987-01-01

Parallelized Monte Carlo algorithms for analyzing photon transport in an inertially confined fusion (ICF) plasma are considered. Algorithms were developed for shared memory (vector and scalar) and distributed memory (scalar) parallel processors. The shared memory algorithm was implemented on the IBM 3090/400, and timing results are presented for dedicated runs with two, three, and four processors. Two alternative distributed memory algorithms (replication and dispatching) were implemented on a hypercube parallel processor (1 through 64 nodes). The replication algorithm yields essentially full efficiency for all cube sizes; with the 64-node configuration, the absolute performance is nearly the same as with the CRAY X-MP. The dispatching algorithm also yields efficiencies above 80% in a large simulation for the 64-processor configuration

Document Classification Using Distributed Machine Learning

OpenAIRE

Aydin, Galip; Hallac, Ibrahim Riza

2018-01-01

In this paper, we investigate the performance and success rates of Na\\"ive Bayes Classification Algorithm for automatic classification of Turkish news into predetermined categories like economy, life, health etc. We use Apache Big Data technologies such as Hadoop, HDFS, Spark and Mahout, and apply these distributed technologies to Machine Learning.

Distributed learning enhances relational memory consolidation.

Science.gov (United States)

Litman, Leib; Davachi, Lila

2008-09-01

It has long been known that distributed learning (DL) provides a mnemonic advantage over massed learning (ML). However, the underlying mechanisms that drive this robust mnemonic effect remain largely unknown. In two experiments, we show that DL across a 24 hr interval does not enhance immediate memory performance but instead slows the rate of forgetting relative to ML. Furthermore, we demonstrate that this savings in forgetting is specific to relational, but not item, memory. In the context of extant theories and knowledge of memory consolidation, these results suggest that an important mechanism underlying the mnemonic benefit of DL is enhanced memory consolidation. We speculate that synaptic strengthening mechanisms supporting long-term memory consolidation may be differentially mediated by the spacing of memory reactivation. These findings have broad implications for the scientific study of episodic memory consolidation and, more generally, for educational curriculum development and policy.

Efficient implementations of block sparse matrix operations on shared memory vector machines

International Nuclear Information System (INIS)

Washio, T.; Maruyama, K.; Osoda, T.; Doi, S.; Shimizu, F.

2000-01-01

In this paper, we propose vectorization and shared memory-parallelization techniques for block-type random sparse matrix operations in finite element (FEM) applications. Here, a block corresponds to unknowns on one node in the FEM mesh and we assume that the block size is constant over the mesh. First, we discuss some basic vectorization ideas (the jagged diagonal (JAD) format and the segmented scan algorithm) for the sparse matrix-vector product. Then, we extend these ideas to the shared memory parallelization. After that, we show that the techniques can be applied not only to the sparse matrix-vector product but also to the sparse matrix-matrix product, the incomplete or complete sparse LU factorization and preconditioning. Finally, we report the performance evaluation results obtained on an NEC SX-4 shared memory vector machine for linear systems in some FEM applications. (author)

Distribution Learning in Evolutionary Strategies and Restricted Boltzmann Machines

DEFF Research Database (Denmark)

Krause, Oswin

The thesis is concerned with learning distributions in the two settings of Evolutionary Strategies (ESs) and Restricted Boltzmann Machines (RBMs). In both cases, the distributions are learned from samples, albeit with different goals. Evolutionary Strategies are concerned with finding an optimum ...

Translation techniques for distributed-shared memory programming models

Energy Technology Data Exchange (ETDEWEB)

Fuller, Douglas James [Iowa State Univ., Ames, IA (United States)

2005-01-01

The high performance computing community has experienced an explosive improvement in distributed-shared memory hardware. Driven by increasing real-world problem complexity, this explosion has ushered in vast numbers of new systems. Each new system presents new challenges to programmers and application developers. Part of the challenge is adapting to new architectures with new performance characteristics. Different vendors release systems with widely varying architectures that perform differently in different situations. Furthermore, since vendors need only provide a single performance number (total MFLOPS, typically for a single benchmark), they only have strong incentive initially to optimize the API of their choice. Consequently, only a fraction of the available APIs are well optimized on most systems. This causes issues porting and writing maintainable software, let alone issues for programmers burdened with mastering each new API as it is released. Also, programmers wishing to use a certain machine must choose their API based on the underlying hardware instead of the application. This thesis argues that a flexible, extensible translator for distributed-shared memory APIs can help address some of these issues. For example, a translator might take as input code in one API and output an equivalent program in another. Such a translator could provide instant porting for applications to new systems that do not support the application's library or language natively. While open-source APIs are abundant, they do not perform optimally everywhere. A translator would also allow performance testing using a single base code translated to a number of different APIs. Most significantly, this type of translator frees programmers to select the most appropriate API for a given application based on the application (and developer) itself instead of the underlying hardware.

Balance in machine architecture: Bandwidth on board and offboard, integer/control speed and flops versus memory

International Nuclear Information System (INIS)

Fischler, M.

1992-04-01

The issues to be addressed here are those of ''balance'' in machine architecture. By this, we mean how much emphasis must be placed on various aspects of the system to maximize its usefulness for physics. There are three components that contribute to the utility of a system: How the machine can be used, how big a problem can be attacked, and what the effective capabilities (power) of the hardware are like. The effective power issue is a matter of evaluating the impact of design decisions trading off architectural features such as memory bandwidth and interprocessor communication capabilities. What is studied is the effect these machine parameters have on how quickly the system can solve desired problems. There is a reasonable method for studying this: One selects a few representative algorithms and computes the impact of changing memory bandwidths, and so forth. The only room for controversy here is in the selection of representative problems. The issue of how big a problem can be attacked boils down to a balance of memory size versus power. Although this is a balance issue it is very different than the effective power situation, because no firm answer can be given at this time. The power to memory ratio is highly problem dependent, and optimizing it requires several pieces of physics input, including: how big a lattice is needed for interesting results; what sort of algorithms are best to use; and how many sweeps are needed to get valid results. We seem to be at the threshold of learning things about these issues, but for now, the memory size issue will necessarily be addressed in terms of best guesses, rules of thumb, and researchers' opinions

Prediction of residual stress distributions due to surface machining and welding and crack growth simulation under residual stress distribution

International Nuclear Information System (INIS)

Ihara, Ryohei; Katsuyama, JInya; Onizawa, Kunio; Hashimoto, Tadafumi; Mikami, Yoshiki; Mochizuki, Masahito

2011-01-01

Research highlights: → Residual stress distributions due to welding and machining are evaluated by XRD and FEM. → Residual stress due to machining shows higher tensile stress than welding near the surface. → Crack growth analysis is performed using calculated residual stress. → Crack growth result is affected machining rather than welding. → Machining is an important factor for crack growth. - Abstract: In nuclear power plants, stress corrosion cracking (SCC) has been observed near the weld zone of the core shroud and primary loop recirculation (PLR) pipes made of low-carbon austenitic stainless steel Type 316L. The joining process of pipes usually includes surface machining and welding. Both processes induce residual stresses, and residual stresses are thus important factors in the occurrence and propagation of SCC. In this study, the finite element method (FEM) was used to estimate residual stress distributions generated by butt welding and surface machining. The thermoelastic-plastic analysis was performed for the welding simulation, and the thermo-mechanical coupled analysis based on the Johnson-Cook material model was performed for the surface machining simulation. In addition, a crack growth analysis based on the stress intensity factor (SIF) calculation was performed using the calculated residual stress distributions that are generated by welding and surface machining. The surface machining analysis showed that tensile residual stress due to surface machining only exists approximately 0.2 mm from the machined surface, and the surface residual stress increases with cutting speed. The crack growth analysis showed that the crack depth is affected by both surface machining and welding, and the crack length is more affected by surface machining than by welding.

TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning

OpenAIRE

Tang, Yuan

2016-01-01

TF.Learn is a high-level Python module for distributed machine learning inside TensorFlow. It provides an easy-to-use Scikit-learn style interface to simplify the process of creating, configuring, training, evaluating, and experimenting a machine learning model. TF.Learn integrates a wide range of state-of-art machine learning algorithms built on top of TensorFlow's low level APIs for small to large-scale supervised and unsupervised problems. This module focuses on bringing machine learning t...

Ring interconnection for distributed memory automation and computing system

Energy Technology Data Exchange (ETDEWEB)

Vinogradov, V I [Inst. for Nuclear Research of the Russian Academy of Sciences, Moscow (Russian Federation)

1996-12-31

Problems of development of measurement, acquisition and central systems based on a distributed memory and a ring interface are discussed. It has been found that the RAM LINK-type protocol can be used for ringlet links in non-symmetrical distributed memory architecture multiprocessor system interaction. 5 refs.

Influence of magnet eddy current on magnetization characteristics of variable flux memory machine

Science.gov (United States)

Yang, Hui; Lin, Heyun; Zhu, Z. Q.; Lyu, Shukang

2018-05-01

In this paper, the magnet eddy current characteristics of a newly developed variable flux memory machine (VFMM) is investigated. Firstly, the machine structure, non-linear hysteresis characteristics and eddy current modeling of low coercive force magnet are described, respectively. Besides, the PM eddy current behaviors when applying the demagnetizing current pulses are unveiled and investigated. The mismatch of the required demagnetization currents between the cases with or without considering the magnet eddy current is identified. In addition, the influences of the magnet eddy current on the demagnetization effect of VFMM are analyzed. Finally, a prototype is manufactured and tested to verify the theoretical analyses.

Distributed Shared Memory for the Cell Broadband Engine (DSMCBE)

DEFF Research Database (Denmark)

Larsen, Morten Nørgaard; Skovhede, Kenneth; Vinter, Brian

2009-01-01

in and out of non-coherent local storage blocks for each special processor element. In this paper we present a software library, namely the Distributed Shared Memory for the Cell Broadband Engine (DSMCBE). By using techniques known from distributed shared memory DSMCBE allows programmers to program the CELL...

Machine Learning Analysis Identifies Drosophila Grunge/Atrophin as an Important Learning and Memory Gene Required for Memory Retention and Social Learning.

Science.gov (United States)

Kacsoh, Balint Z; Greene, Casey S; Bosco, Giovanni

2017-11-06

High-throughput experiments are becoming increasingly common, and scientists must balance hypothesis-driven experiments with genome-wide data acquisition. We sought to predict novel genes involved in Drosophila learning and long-term memory from existing public high-throughput data. We performed an analysis using PILGRM, which analyzes public gene expression compendia using machine learning. We evaluated the top prediction alongside genes involved in learning and memory in IMP, an interface for functional relationship networks. We identified Grunge/Atrophin ( Gug/Atro ), a transcriptional repressor, histone deacetylase, as our top candidate. We find, through multiple, distinct assays, that Gug has an active role as a modulator of memory retention in the fly and its function is required in the adult mushroom body. Depletion of Gug specifically in neurons of the adult mushroom body, after cell division and neuronal development is complete, suggests that Gug function is important for memory retention through regulation of neuronal activity, and not by altering neurodevelopment. Our study provides a previously uncharacterized role for Gug as a possible regulator of neuronal plasticity at the interface of memory retention and memory extinction. Copyright © 2017 Kacsoh et al.

Distributed Extreme Learning Machine for Nonlinear Learning over Network

Directory of Open Access Journals (Sweden)

Songyan Huang

2015-02-01

Full Text Available Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN. To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN with radial basis function (RBF hidden neurons has the ability to approximate any continuous functions and, thus, may be used as the nonlinear learning system. However, confined by the communication cost, using the distributed version of the conventional algorithms to train the neural network directly is usually prohibited. Fortunately, based on the theorems provided in the extreme learning machine (ELM literature, we only need to compute the output weights of the SLFN. Computing the output weights itself is a linear learning problem, although the input-output mapping of the overall SLFN is still nonlinear. Using the distributed algorithmto cooperatively compute the output weights of the SLFN, we obtain a distributed extreme learning machine (dELM for nonlinear learning in this paper. This dELM is applied to the regression problem and classification problem to demonstrate its effectiveness and advantages.

Developing a software for tracking the memory states of the machines in the LHCb Filter Farm

CERN Document Server

Jain, Harshit

2017-01-01

The LHCb Event Filter Farm consists of more than 1500 server nodes with a total amount of roughly 65 TB operating memory .The memory is crucial for the success of the LHCb experiment, since the proton-proton collisions are temporarily stored on these memory modules. Unfortunately, the aging nodes of the server farm occasionally suffer losses of their memory modules. The lower the available memory, the lower performance we can get out of it. Inducing the users or administrators to pay attention to this matter is inefficient. One needs to upgrade it to an acceptable way. The aim of this project was to develop a software to monitor a set of test machines. The software stores the data of the memory sticks in advance in a database which will be used for future reference. Then it checks the memory sticks at a future time instant to find any failures. In the case of any such losses the software looks up in the database to find out which memory sticks have lost and displays all information of those sticks in a log fi...

Distributed terascale volume visualization using distributed shared virtual memory

KAUST Repository

Beyer, Johanna; Hadwiger, Markus; Schneider, Jens; Jeong, Wonki; Pfister, Hanspeter

2011-01-01

Table 1 illustrates the impact of different distribution unit sizes, different screen resolutions, and numbers of GPU nodes. We use two and four GPUs (NVIDIA Quadro 5000 with 2.5 GB memory) and a mouse cortex EM dataset (see Figure 2) of resolution

The distribution and the functions of autobiographical memories: Why do older adults remember autobiographical memories from their youth?

Science.gov (United States)

Wolf, Tabea; Zimprich, Daniel

2016-09-01

In the present study, the distribution of autobiographical memories was examined from a functional perspective: we examined whether the extent to which long-term autobiographical memories were rated as having a self-, a directive, or a social function affects the location (mean age) and scale (standard deviation) of the memory distribution. Analyses were based on a total of 5598 autobiographical memories generated by 149 adults aged between 50 and 81 years in response to 51 cue-words. Participants provided their age at the time when the recalled events had happened and rated how frequently they recall these events for self-, directive, and social purposes. While more frequently using autobiographical memories for self-functions was associated with an earlier mean age, memories frequently shared with others showed a narrower distribution around a later mean age. The directive function, by contrast, did not affect the memory distribution. The results strengthen the assumption that experiences from an individual's late adolescence serve to maintain a sense of self-continuity throughout the lifespan. Experiences that are frequently shared with others, in contrast, stem from a narrow age range located in young adulthood.

Implementation of the Lanczos algorithm for the Hubbard model on the Connection Machine system

International Nuclear Information System (INIS)

Leung, P.W.; Oppenheimer, P.E.

1992-01-01

An implementation of the Lanczos algorithm for the exact diagonalization of the two dimensional Hubbard model on a 4x4 square lattice on the Connection Machine CM-2 system is described. The CM-2 is a massively parallel machine with distributed memory. The program is written in C/PARIS. This implementation minimizes memory usage by generating the matrix elements as needed instead of storing them. The Lanczos vectors are stored across the local memory of the processors. Using translational symmetry only, the dimension of the Hilbert space at half filling is more than 10 million. A speed of about 2.4 min per iteration is achieved on a 64K CM-2. This implementation is scalable. Running it on a bigger machine with more processors speeds up the process. The performance analysis of this implementation is shown and discuss its advantages and disadvantages are discussed

Sparse Distributed Memory: understanding the speed and robustness of expert memory

Directory of Open Access Journals (Sweden)

Marcelo Salhab Brogliato

2014-04-01

Full Text Available How can experts, sometimes in exacting detail, almost immediately and very precisely recall memory items from a vast repertoire? The problem in which we will be interested concerns models of theoretical neuroscience that could explain the speed and robustness of an expert's recollection. The approach is based on Sparse Distributed Memory, which has been shown to be plausible, both in a neuroscientific and in a psychological manner, in a number of ways. A crucial characteristic concerns the limits of human recollection, the `tip-of-tongue' memory event--which is found at a non-linearity in the model. We expand the theoretical framework, deriving an optimization formula to solve to this non-linearity. Numerical results demonstrate how the higher frequency of rehearsal, through work or study, immediately increases the robustness and speed associated with expert memory.

Stochastic Distribution of Wear of Carbide Tools during Machining ...

African Journals Online (AJOL)

Journal of the Nigerian Association of Mathematical Physics ... The stochastic point model was used to determine the rate of wear distribution of the carbide tool ... Keywords: cutting speed, feed rate, machining time, tool life, reliability, wear.

SU-E-T-113: Dose Distribution Using Respiratory Signals and Machine Parameters During Treatment

International Nuclear Information System (INIS)

Imae, T; Haga, A; Saotome, N; Kida, S; Nakano, M; Takeuchi, Y; Shiraki, T; Yano, K; Yamashita, H; Nakagawa, K; Ohtomo, K

2014-01-01

Purpose: Volumetric modulated arc therapy (VMAT) is a rotational intensity-modulated radiotherapy (IMRT) technique capable of acquiring projection images during treatment. Treatment plans for lung tumors using stereotactic body radiotherapy (SBRT) are calculated with planning computed tomography (CT) images only exhale phase. Purpose of this study is to evaluate dose distribution by reconstructing from only the data such as respiratory signals and machine parameters acquired during treatment. Methods: Phantom and three patients with lung tumor underwent CT scans for treatment planning. They were treated by VMAT while acquiring projection images to derive their respiratory signals and machine parameters including positions of multi leaf collimators, dose rates and integrated monitor units. The respiratory signals were divided into 4 and 10 phases and machine parameters were correlated with the divided respiratory signals based on the gantry angle. Dose distributions of each respiratory phase were calculated from plans which were reconstructed from the respiratory signals and the machine parameters during treatment. The doses at isocenter, maximum point and the centroid of target were evaluated. Results and Discussion: Dose distributions during treatment were calculated using the machine parameters and the respiratory signals detected from projection images. Maximum dose difference between plan and in treatment distribution was −1.8±0.4% at centroid of target and dose differences of evaluated points between 4 and 10 phases were no significant. Conclusion: The present method successfully evaluated dose distribution using respiratory signals and machine parameters during treatment. This method is feasible to verify the actual dose for moving target

Memory-assisted measurement-device-independent quantum key distribution

Science.gov (United States)

Panayi, Christiana; Razavi, Mohsen; Ma, Xiongfeng; Lütkenhaus, Norbert

2014-04-01

A protocol with the potential of beating the existing distance records for conventional quantum key distribution (QKD) systems is proposed. It borrows ideas from quantum repeaters by using memories in the middle of the link, and that of measurement-device-independent QKD, which only requires optical source equipment at the user's end. For certain memories with short access times, our scheme allows a higher repetition rate than that of quantum repeaters with single-mode memories, thereby requiring lower coherence times. By accounting for various sources of nonideality, such as memory decoherence, dark counts, misalignment errors, and background noise, as well as timing issues with memories, we develop a mathematical framework within which we can compare QKD systems with and without memories. In particular, we show that with the state-of-the-art technology for quantum memories, it is potentially possible to devise memory-assisted QKD systems that, at certain distances of practical interest, outperform current QKD implementations.

Memory-assisted measurement-device-independent quantum key distribution

International Nuclear Information System (INIS)

Panayi, Christiana; Razavi, Mohsen; Ma, Xiongfeng; Lütkenhaus, Norbert

2014-01-01

A protocol with the potential of beating the existing distance records for conventional quantum key distribution (QKD) systems is proposed. It borrows ideas from quantum repeaters by using memories in the middle of the link, and that of measurement-device-independent QKD, which only requires optical source equipment at the user's end. For certain memories with short access times, our scheme allows a higher repetition rate than that of quantum repeaters with single-mode memories, thereby requiring lower coherence times. By accounting for various sources of nonideality, such as memory decoherence, dark counts, misalignment errors, and background noise, as well as timing issues with memories, we develop a mathematical framework within which we can compare QKD systems with and without memories. In particular, we show that with the state-of-the-art technology for quantum memories, it is potentially possible to devise memory-assisted QKD systems that, at certain distances of practical interest, outperform current QKD implementations. (paper)

The Distributed Nature of Working Memory

NARCIS (Netherlands)

Christophel, Thomas B.; Klink, P. Christiaan; Spitzer, Bernhard; Roelfsema, Pieter R.; Haynes, John-Dylan

2017-01-01

Studies in humans and non-human primates have provided evidence for storage of working memory contents in multiple regions ranging from sensory to parietal and prefrontal cortex. We discuss potential explanations for these distributed representations: (i) features in sensory regions versus

A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification.

Science.gov (United States)

Wen, Cuihong; Zhang, Jing; Rebelo, Ana; Cheng, Fanyong

2016-01-01

Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM). The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM), which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs) and Neural Networks (NNs).

Distributed Control System Design for Portable PC Based CNC Machine

Directory of Open Access Journals (Sweden)

Roni Permana Saputra

2014-07-01

Full Text Available The demand on automated machining has been increased and emerges improvement research to achieve many goals such as portability, low cost manufacturability, interoperability, and simplicity in machine usage. These improvements are conducted without ignoring the performance analysis and usability evaluation. This research has designed a distributed control system in purpose to control a portable CNC machine. The design consists of main processing unit, secondary processing unit, motor control, and motor driver. A preliminary simulation has been conducted for performance analysis including linear accuracy and circular accuracy. The results achieved in the simulation provide linear accuracy up to 2 μm with total cost for the whole processing unit is up to 5 million IDR.

Distributed terascale volume visualization using distributed shared virtual memory

KAUST Repository

Beyer, Johanna

2011-10-01

Table 1 illustrates the impact of different distribution unit sizes, different screen resolutions, and numbers of GPU nodes. We use two and four GPUs (NVIDIA Quadro 5000 with 2.5 GB memory) and a mouse cortex EM dataset (see Figure 2) of resolution 21,494 x 25,790 x 1,850 = 955GB. The size of the virtual distribution units significantly influences the data distribution between nodes. Small distribution units result in a high depth complexity for compositing. Large distribution units lead to a low utilization of GPUs, because in the worst case only a single distribution unit will be in view, which is rendered by only a single node. The choice of an optimal distribution unit size depends on three major factors: the output screen resolution, the block cache size on each node, and the number of nodes. Currently, we are working on optimizing the compositing step and network communication between nodes. © 2011 IEEE.

A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification.

Directory of Open Access Journals (Sweden)

Cuihong Wen

Full Text Available Optical Music Recognition (OMR has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM. The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM, which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs and Neural Networks (NNs.

Paging memory from random access memory to backing storage in a parallel computer

Science.gov (United States)

Archer, Charles J; Blocksome, Michael A; Inglett, Todd A; Ratterman, Joseph D; Smith, Brian E

2013-05-21

Paging memory from random access memory (`RAM`) to backing storage in a parallel computer that includes a plurality of compute nodes, including: executing a data processing application on a virtual machine operating system in a virtual machine on a first compute node; providing, by a second compute node, backing storage for the contents of RAM on the first compute node; and swapping, by the virtual machine operating system in the virtual machine on the first compute node, a page of memory from RAM on the first compute node to the backing storage on the second compute node.

34 CFR 395.8 - Distribution and use of income from vending machines on Federal property.

Science.gov (United States)

2010-07-01

... 34 Education 2 2010-07-01 2010-07-01 false Distribution and use of income from vending machines on... use of income from vending machines on Federal property. (a) Vending machine income from vending... the basis of each prior year's operation, except that vending machine income shall not accrue to any...

A Comparison of Two Paradigms for Distributed Shared Memory

NARCIS (Netherlands)

Levelt, W.G.; Kaashoek, M.F.; Bal, H.E.; Tanenbaum, A.S.

1992-01-01

Two paradigms for distributed shared memory on loosely‐coupled computing systems are compared: the shared data‐object model as used in Orca, a programming language specially designed for loosely‐coupled computing systems, and the shared virtual memory model. For both paradigms two systems are

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

OpenAIRE

Abadi, Martín; Agarwal, Ashish; Barham, Paul; Brevdo, Eugene; Chen, Zhifeng; Citro, Craig; Corrado, Greg S.; Davis, Andy; Dean, Jeffrey; Devin, Matthieu; Ghemawat, Sanjay; Goodfellow, Ian; Harp, Andrew; Irving, Geoffrey; Isard, Michael

2016-01-01

TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algo...

Effect of processing conditions on residual stress distributions by bead-on-plate welding after surface machining

International Nuclear Information System (INIS)

Ihara, Ryohei; Mochizuki, Masahito

2014-01-01

Residual stress is important factor for stress corrosion cracking (SCC) that has been observed near the welded zone in nuclear power plants. Especially, surface residual stress is significant for SCC initiation. In the joining processes of pipes, butt welding is conducted after surface machining. Residual stress is generated by both processes, and residual stress distribution due to surface machining is varied by the subsequent butt welding. In previous paper, authors reported that residual stress distribution generated by bead on plate welding after surface machining has a local maximum residual stress near the weld metal. The local maximum residual stress shows approximately 900 MPa that exceeds the stress threshold for SCC initiation. Therefore, for the safety improvement of nuclear power plants, a study on the local maximum residual stress is important. In this study, the effect of surface machining and welding conditions on residual stress distribution generated by welding after surface machining was investigated. Surface machining using lathe machine and bead on plate welding with tungsten inert gas (TIG) arc under various conditions were conducted for plate specimens made of SUS316L. Then, residual stress distributions were measured by X-ray diffraction method (XRD). As a result, residual stress distributions have the local maximum residual stress near the weld metal in all specimens. The values of the local maximum residual stresses are almost the same. The location of the local maximum residual stress is varied by welding condition. It could be consider that the local maximum residual stress is generated by same generation mechanism as welding residual stress in surface machined layer that has high yield stress. (author)

Strategies and Principles of Distributed Machine Learning on Big Data

Directory of Open Access Journals (Sweden)

Eric P. Xing

2016-06-01

Full Text Available The rise of big data has led to new demands for machine learning (ML systems to learn complex models, with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer powerful predictive analytics (such as high-dimensional latent features, intermediate representations, and decision functions thereupon. In order to run ML algorithms at such scales, on a distributed cluster with tens to thousands of machines, it is often the case that significant engineering efforts are required—and one might fairly ask whether such engineering truly falls within the domain of ML research. Taking the view that “big” ML systems can benefit greatly from ML-rooted statistical and algorithmic insights—and that ML researchers should therefore not shy away from such systems design—we discuss a series of principles and strategies distilled from our recent efforts on industrial-scale ML solutions. These principles and strategies span a continuum from application, to engineering, and to theoretical research and development of big ML systems and architectures, with the goal of understanding how to make them efficient, generally applicable, and supported with convergence and scaling guarantees. They concern four key questions that traditionally receive little attention in ML research: How can an ML program be distributed over a cluster? How can ML computation be bridged with inter-machine communication? How can such communication be performed? What should be communicated between machines? By exposing underlying statistical and algorithmic characteristics unique to ML programs but not typically seen in traditional computer programs, and by dissecting successful cases to reveal how we have harnessed these principles to design and develop both high-performance distributed ML software as well as general-purpose ML frameworks, we present opportunities for ML researchers and practitioners to further shape and enlarge the area

MACHINE LEARNING FOR THE SELF-ORGANIZATION OF DISTRIBUTED SYSTEMS IN ECONOMIC APPLICATIONS

Directory of Open Access Journals (Sweden)

Jerzy Balicki

2017-03-01

Full Text Available In this paper, an application of machine learning to the problem of self-organization of distributed systems has been discussed with regard to economic applications, with particular emphasis on supervised neural network learning to predict stock investments and some ratings of companies. In addition, genetic programming can play an important role in the preparation and testing of several financial information systems. For this reason, machine learning applications have been discussed because some software applications can be automatically constructed by genetic programming. To obtain a competitive advantage, machine learning can be used for the management of self-organizing cloud computing systems performing calculations for business. Also the use of selected economic self-organizing distributed systems has been described, including some testing methods of predicting borrower reliability. Finally, some conclusions and directions for further research have been proposed.

The reminiscence bump without memories: The distribution of imagined word-cued and important autobiographical memories in a hypothetical 70-year-old.

Science.gov (United States)

Koppel, Jonathan; Berntsen, Dorthe

2016-08-01

The reminiscence bump is the disproportionate number of autobiographical memories dating from adolescence and early adulthood. It has often been ascribed to a consolidation of the mature self in the period covered by the bump. Here we stripped away factors relating to the characteristics of autobiographical memories per se, most notably factors that aid in their encoding or retention, by asking students to generate imagined word-cued and imagined 'most important' autobiographical memories of a hypothetical, prototypical 70-year-old of their own culture and gender. We compared the distribution of these fictional memories with the distributions of actual word-cued and most important autobiographical memories in a sample of 61-70-year-olds. We found a striking similarity between the temporal distributions of the imagined memories and the actual memories. These results suggest that the reminiscence bump is largely driven by constructive, schematic factors at retrieval, thereby challenging most existing theoretical accounts. Copyright © 2016 Elsevier Inc. All rights reserved.

Memory-assisted quantum key distribution resilient against multiple-excitation effects

Science.gov (United States)

Lo Piparo, Nicolò; Sinclair, Neil; Razavi, Mohsen

2018-01-01

Memory-assisted measurement-device-independent quantum key distribution (MA-MDI-QKD) has recently been proposed as a technique to improve the rate-versus-distance behavior of QKD systems by using existing, or nearly-achievable, quantum technologies. The promise is that MA-MDI-QKD would require less demanding quantum memories than the ones needed for probabilistic quantum repeaters. Nevertheless, early investigations suggest that, in order to beat the conventional memory-less QKD schemes, the quantum memories used in the MA-MDI-QKD protocols must have high bandwidth-storage products and short interaction times. Among different types of quantum memories, ensemble-based memories offer some of the required specifications, but they typically suffer from multiple excitation effects. To avoid the latter issue, in this paper, we propose two new variants of MA-MDI-QKD both relying on single-photon sources for entangling purposes. One is based on known techniques for entanglement distribution in quantum repeaters. This scheme turns out to offer no advantage even if one uses ideal single-photon sources. By finding the root cause of the problem, we then propose another setup, which can outperform single memory-less setups even if we allow for some imperfections in our single-photon sources. For such a scheme, we compare the key rate for different types of ensemble-based memories and show that certain classes of atomic ensembles can improve the rate-versus-distance behavior.

Virtual Vector Machine for Bayesian Online Classification

OpenAIRE

Minka, Thomas P.; Xiang, Rongjing; Yuan; Qi

2012-01-01

In a typical online learning scenario, a learner is required to process a large data stream using a small memory buffer. Such a requirement is usually in conflict with a learner's primary pursuit of prediction accuracy. To address this dilemma, we introduce a novel Bayesian online classi cation algorithm, called the Virtual Vector Machine. The virtual vector machine allows you to smoothly trade-off prediction accuracy with memory size. The virtual vector machine summarizes the information con...

MACHINE LEARNING FOR THE SELF-ORGANIZATION OF DISTRIBUTED SYSTEMS IN ECONOMIC APPLICATIONS

OpenAIRE

Jerzy Balicki; Waldemar Korłub

2017-01-01

In this paper, an application of machine learning to the problem of self-organization of distributed systems has been discussed with regard to economic applications, with particular emphasis on supervised neural network learning to predict stock investments and some ratings of companies. In addition, genetic programming can play an important role in the preparation and testing of several financial information systems. For this reason, machine learning applications have been discussed because ...

A Hybrid Approach to Processing Big Data Graphs on Memory-Restricted Systems

KAUST Repository

Harshvardhan,

2015-05-01

With the advent of big-data, processing large graphs quickly has become increasingly important. Most existing approaches either utilize in-memory processing techniques that can only process graphs that fit completely in RAM, or disk-based techniques that sacrifice performance. In this work, we propose a novel RAM-Disk hybrid approach to graph processing that can scale well from a single shared-memory node to large distributed-memory systems. It works by partitioning the graph into sub graphs that fit in RAM and uses a paging-like technique to load sub graphs. We show that without modifying the algorithms, this approach can scale from small memory-constrained systems (such as tablets) to large-scale distributed machines with 16, 000+ cores.

Nonvolatile Memory Materials for Neuromorphic Intelligent Machines.

Science.gov (United States)

Jeong, Doo Seok; Hwang, Cheol Seong

2018-04-18

Recent progress in deep learning extends the capability of artificial intelligence to various practical tasks, making the deep neural network (DNN) an extremely versatile hypothesis. While such DNN is virtually built on contemporary data centers of the von Neumann architecture, physical (in part) DNN of non-von Neumann architecture, also known as neuromorphic computing, can remarkably improve learning and inference efficiency. Particularly, resistance-based nonvolatile random access memory (NVRAM) highlights its handy and efficient application to the multiply-accumulate (MAC) operation in an analog manner. Here, an overview is given of the available types of resistance-based NVRAMs and their technological maturity from the material- and device-points of view. Examples within the strategy are subsequently addressed in comparison with their benchmarks (virtual DNN in deep learning). A spiking neural network (SNN) is another type of neural network that is more biologically plausible than the DNN. The successful incorporation of resistance-based NVRAM in SNN-based neuromorphic computing offers an efficient solution to the MAC operation and spike timing-based learning in nature. This strategy is exemplified from a material perspective. Intelligent machines are categorized according to their architecture and learning type. Also, the functionality and usefulness of NVRAM-based neuromorphic computing are addressed. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Secure Virtualization Environment Based on Advanced Memory Introspection

Directory of Open Access Journals (Sweden)

Shuhui Zhang

2018-01-01

Full Text Available Most existing virtual machine introspection (VMI technologies analyze the status of a target virtual machine under the assumption that the operating system (OS version and kernel structure information are known at the hypervisor level. In this paper, we propose a model of virtual machine (VM security monitoring based on memory introspection. Using a hardware-based approach to acquire the physical memory of the host machine in real time, the security of the host machine and VM can be diagnosed. Furthermore, a novel approach for VM memory forensics based on the virtual machine control structure (VMCS is put forward. By analyzing the memory of the host machine, the running VMs can be detected and their high-level semantic information can be reconstructed. Then, malicious activity in the VMs can be identified in a timely manner. Moreover, by mutually analyzing the memory content of the host machine and VMs, VM escape may be detected. Compared with previous memory introspection technologies, our solution can automatically reconstruct the comprehensive running state of a target VM without any prior knowledge and is strongly resistant to attacks with high reliability. We developed a prototype system called the VEDefender. Experimental results indicate that our system can handle the VMs of mainstream Linux and Windows OS versions with high efficiency and does not influence the performance of the host machine and VMs.

Differentiation and Response Bias in Episodic Memory: Evidence from Reaction Time Distributions

Science.gov (United States)

Criss, Amy H.

2010-01-01

In differentiation models, the processes of encoding and retrieval produce an increase in the distribution of memory strength for targets and a decrease in the distribution of memory strength for foils as the amount of encoding increases. This produces an increase in the hit rate and decrease in the false-alarm rate for a strongly encoded compared…

Massively Parallel Polar Decomposition on Distributed-Memory Systems

KAUST Repository

Ltaief, Hatem; Sukkari, Dalal E.; Esposito, Aniello; Nakatsukasa, Yuji; Keyes, David E.

2018-01-01

We present a high-performance implementation of the Polar Decomposition (PD) on distributed-memory systems. Building upon on the QR-based Dynamically Weighted Halley (QDWH) algorithm, the key idea lies in finding the best rational approximation

Improvement of the thickness distribution of a quartz crystal wafer by numerically controlled plasma chemical vaporization machining

International Nuclear Information System (INIS)

Shibahara, Masafumi; Yamamura, Kazuya; Sano, Yasuhisa; Sugiyama, Tsuyoshi; Endo, Katsuyoshi; Mori, Yuzo

2005-01-01

To improve the thickness uniformity of thin quartz crystal wafer, a new machining process that utilizes an atmospheric pressure plasma was developed. In an atmospheric pressure plasma process, since the kinetic energy of ions that impinge to the wafer surface is small and the density of the reactive species is large, high-efficiency machining without damage is realized, and the thickness distribution is corrected by numerically controlled scanning of the quartz wafer to the localized high-density plasma. By using our developed machining process, the thickness distribution of an AT cut wafer was improved from 174 nm [peak to valley (p-v)] to 67 nm (p-v) within 94 s. Since there are no unwanted spurious modes in the machined quartz wafer, it was proved that the developed machining method has a high machining efficiency without any damage

Scaling Techniques for Massive Scale-Free Graphs in Distributed (External) Memory

KAUST Repository

Pearce, Roger; Gokhale, Maya; Amato, Nancy M.

2013-01-01

We present techniques to process large scale-free graphs in distributed memory. Our aim is to scale to trillions of edges, and our research is targeted at leadership class supercomputers and clusters with local non-volatile memory, e.g., NAND Flash

Distributed state machine supervision for long-baseline gravitational-wave detectors

International Nuclear Information System (INIS)

Rollins, Jameson Graef

2016-01-01

The Laser Interferometer Gravitational-wave Observatory (LIGO) consists of two identical yet independent, widely separated, long-baseline gravitational-wave detectors. Each Advanced LIGO detector consists of complex optical-mechanical systems isolated from the ground by multiple layers of active seismic isolation, all controlled by hundreds of fast, digital, feedback control systems. This article describes a novel state machine-based automation platform developed to handle the automation and supervisory control challenges of these detectors. The platform, called Guardian, consists of distributed, independent, state machine automaton nodes organized hierarchically for full detector control. User code is written in standard Python and the platform is designed to facilitate the fast-paced development process associated with commissioning the complicated Advanced LIGO instruments. While developed specifically for the Advanced LIGO detectors, Guardian is a generic state machine automation platform that is useful for experimental control at all levels, from simple table-top setups to large-scale multi-million dollar facilities.

Distributed state machine supervision for long-baseline gravitational-wave detectors

Energy Technology Data Exchange (ETDEWEB)

Rollins, Jameson Graef, E-mail: jameson.rollins@ligo.org [LIGO Laboratory, California Institute of Technology, Pasadena, California 91125 (United States)

2016-09-15

The Laser Interferometer Gravitational-wave Observatory (LIGO) consists of two identical yet independent, widely separated, long-baseline gravitational-wave detectors. Each Advanced LIGO detector consists of complex optical-mechanical systems isolated from the ground by multiple layers of active seismic isolation, all controlled by hundreds of fast, digital, feedback control systems. This article describes a novel state machine-based automation platform developed to handle the automation and supervisory control challenges of these detectors. The platform, called Guardian, consists of distributed, independent, state machine automaton nodes organized hierarchically for full detector control. User code is written in standard Python and the platform is designed to facilitate the fast-paced development process associated with commissioning the complicated Advanced LIGO instruments. While developed specifically for the Advanced LIGO detectors, Guardian is a generic state machine automation platform that is useful for experimental control at all levels, from simple table-top setups to large-scale multi-million dollar facilities.

Childhood amnesia in the making: different distributions of autobiographical memories in children and adults.

Science.gov (United States)

Bauer, Patricia J; Larkina, Marina

2014-04-01

Within the memory literature, a robust finding is of childhood amnesia: a relative paucity among adults for autobiographical or personal memories from the first 3 to 4 years of life, and from the first 7 years, a smaller number of memories than would be expected based on normal forgetting. Childhood amnesia is observed in spite of strong evidence that during the period eventually obscured by the amnesia, children construct and preserve autobiographical memories. Why early memories seemingly are lost to recollection is an unanswered question. In the present research, we examined the issue by using the cue word technique to chart the distributions of autobiographical memories in samples of children ages 7 to 11 years and samples of young and middle-aged adults. Among adults, the distributions were best fit by the power function, whereas among children, the exponential function provided a better fit to the distributions of memories. The findings suggest that a major source of childhood amnesia is a constant rate of forgetting in childhood, seemingly resulting from failed consolidation, the outcome of which is a smaller pool of memories available for later retrieval.

Production of a double-humped ion velocity distribution function in a single-ended Q-machine

DEFF Research Database (Denmark)

Andersen, S.A.; Jensen, Vagn Orla; Michelsen, Poul

1970-01-01

An experimental method of producing a double-humped velocity distribution function for the ions in a Q-machine is described. The method is based on charge exchange processes between neutral ceasium and the ions in a ceasium plasma.......An experimental method of producing a double-humped velocity distribution function for the ions in a Q-machine is described. The method is based on charge exchange processes between neutral ceasium and the ions in a ceasium plasma....

Data Provenance for Agent-Based Models in a Distributed Memory

Directory of Open Access Journals (Sweden)

Delmar B. Davis

2018-04-01

Full Text Available Agent-Based Models (ABMs assist with studying emergent collective behavior of individual entities in social, biological, economic, network, and physical systems. Data provenance can support ABM by explaining individual agent behavior. However, there is no provenance support for ABMs in a distributed setting. The Multi-Agent Spatial Simulation (MASS library provides a framework for simulating ABMs at fine granularity, where agents and spatial data are shared application resources in a distributed memory. We introduce a novel approach to capture ABM provenance in a distributed memory, called ProvMASS. We evaluate our technique with traditional data provenance queries and performance measures. Our results indicate that a configurable approach can capture provenance that explains coordination of distributed shared resources, simulation logic, and agent behavior while limiting performance overhead. We also show the ability to support practical analyses (e.g., agent tracking and storage requirements for different capture configurations.

Neuronal model with distributed delay: analysis and simulation study for gamma distribution memory kernel.

Science.gov (United States)

Karmeshu; Gupta, Varun; Kadambari, K V

2011-06-01

A single neuronal model incorporating distributed delay (memory)is proposed. The stochastic model has been formulated as a Stochastic Integro-Differential Equation (SIDE) which results in the underlying process being non-Markovian. A detailed analysis of the model when the distributed delay kernel has exponential form (weak delay) has been carried out. The selection of exponential kernel has enabled the transformation of the non-Markovian model to a Markovian model in an extended state space. For the study of First Passage Time (FPT) with exponential delay kernel, the model has been transformed to a system of coupled Stochastic Differential Equations (SDEs) in two-dimensional state space. Simulation studies of the SDEs provide insight into the effect of weak delay kernel on the Inter-Spike Interval(ISI) distribution. A measure based on Jensen-Shannon divergence is proposed which can be used to make a choice between two competing models viz. distributed delay model vis-á-vis LIF model. An interesting feature of the model is that the behavior of (CV(t))((ISI)) (Coefficient of Variation) of the ISI distribution with respect to memory kernel time constant parameter η reveals that neuron can switch from a bursting state to non-bursting state as the noise intensity parameter changes. The membrane potential exhibits decaying auto-correlation structure with or without damped oscillatory behavior depending on the choice of parameters. This behavior is in agreement with empirically observed pattern of spike count in a fixed time window. The power spectral density derived from the auto-correlation function is found to exhibit single and double peaks. The model is also examined for the case of strong delay with memory kernel having the form of Gamma distribution. In contrast to fast decay of damped oscillations of the ISI distribution for the model with weak delay kernel, the decay of damped oscillations is found to be slower for the model with strong delay kernel.

Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Support Distribution Machines

Science.gov (United States)

Ntampaka, Michelle; Trac, Hy; Sutherland, Dougal; Fromenteau, Sebastien; Poczos, Barnabas; Schneider, Jeff

2018-01-01

We study dynamical mass measurements of galaxy clusters contaminated by interlopers and show that a modern machine learning (ML) algorithm can predict masses by better than a factor of two compared to a standard scaling relation approach. We create two mock catalogs from Multidark’s publicly available N-body MDPL1 simulation, one with perfect galaxy cluster membership infor- mation and the other where a simple cylindrical cut around the cluster center allows interlopers to contaminate the clusters. In the standard approach, we use a power-law scaling relation to infer cluster mass from galaxy line-of-sight (LOS) velocity dispersion. Assuming perfect membership knowledge, this unrealistic case produces a wide fractional mass error distribution, with a width E=0.87. Interlopers introduce additional scatter, significantly widening the error distribution further (E=2.13). We employ the support distribution machine (SDM) class of algorithms to learn from distributions of data to predict single values. Applied to distributions of galaxy observables such as LOS velocity and projected distance from the cluster center, SDM yields better than a factor-of-two improvement (E=0.67) for the contaminated case. Remarkably, SDM applied to contaminated clusters is better able to recover masses than even the scaling relation approach applied to uncon- taminated clusters. We show that the SDM method more accurately reproduces the cluster mass function, making it a valuable tool for employing cluster observations to evaluate cosmological models.

Operation of a quantum dot in the finite-state machine mode: Single-electron dynamic memory

International Nuclear Information System (INIS)

Klymenko, M. V.; Klein, M.; Levine, R. D.; Remacle, F.

2016-01-01

A single electron dynamic memory is designed based on the non-equilibrium dynamics of charge states in electrostatically defined metallic quantum dots. Using the orthodox theory for computing the transfer rates and a master equation, we model the dynamical response of devices consisting of a charge sensor coupled to either a single and or a double quantum dot subjected to a pulsed gate voltage. We show that transition rates between charge states in metallic quantum dots are characterized by an asymmetry that can be controlled by the gate voltage. This effect is more pronounced when the switching between charge states corresponds to a Markovian process involving electron transport through a chain of several quantum dots. By simulating the dynamics of electron transport we demonstrate that the quantum box operates as a finite-state machine that can be addressed by choosing suitable shapes and switching rates of the gate pulses. We further show that writing times in the ns range and retention memory times six orders of magnitude longer, in the ms range, can be achieved on the double quantum dot system using experimentally feasible parameters, thereby demonstrating that the device can operate as a dynamic single electron memory.

Operation of a quantum dot in the finite-state machine mode: Single-electron dynamic memory

Energy Technology Data Exchange (ETDEWEB)

Klymenko, M. V. [Department of Chemistry, University of Liège, B4000 Liège (Belgium); Klein, M. [The Fritz Haber Center for Molecular Dynamics and the Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 91904 (Israel); Levine, R. D. [The Fritz Haber Center for Molecular Dynamics and the Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 91904 (Israel); Crump Institute for Molecular Imaging and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine and Department of Chemistry and Biochemistry, University of California, Los Angeles, California 90095 (United States); Remacle, F., E-mail: fremacle@ulg.ac.be [Department of Chemistry, University of Liège, B4000 Liège (Belgium); The Fritz Haber Center for Molecular Dynamics and the Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 91904 (Israel)

2016-07-14

A single electron dynamic memory is designed based on the non-equilibrium dynamics of charge states in electrostatically defined metallic quantum dots. Using the orthodox theory for computing the transfer rates and a master equation, we model the dynamical response of devices consisting of a charge sensor coupled to either a single and or a double quantum dot subjected to a pulsed gate voltage. We show that transition rates between charge states in metallic quantum dots are characterized by an asymmetry that can be controlled by the gate voltage. This effect is more pronounced when the switching between charge states corresponds to a Markovian process involving electron transport through a chain of several quantum dots. By simulating the dynamics of electron transport we demonstrate that the quantum box operates as a finite-state machine that can be addressed by choosing suitable shapes and switching rates of the gate pulses. We further show that writing times in the ns range and retention memory times six orders of magnitude longer, in the ms range, can be achieved on the double quantum dot system using experimentally feasible parameters, thereby demonstrating that the device can operate as a dynamic single electron memory.

Human-machine interactions

Science.gov (United States)

Forsythe, J Chris [Sandia Park, NM; Xavier, Patrick G [Albuquerque, NM; Abbott, Robert G [Albuquerque, NM; Brannon, Nathan G [Albuquerque, NM; Bernard, Michael L [Tijeras, NM; Speed, Ann E [Albuquerque, NM

2009-04-28

Digital technology utilizing a cognitive model based on human naturalistic decision-making processes, including pattern recognition and episodic memory, can reduce the dependency of human-machine interactions on the abilities of a human user and can enable a machine to more closely emulate human-like responses. Such a cognitive model can enable digital technology to use cognitive capacities fundamental to human-like communication and cooperation to interact with humans.

Advanced Electrical Machines and Machine-Based Systems for Electric and Hybrid Vehicles

Directory of Open Access Journals (Sweden)

Ming Cheng

2015-09-01

Full Text Available The paper presents a number of advanced solutions on electric machines and machine-based systems for the powertrain of electric vehicles (EVs. Two types of systems are considered, namely the drive systems designated to the EV propulsion and the power split devices utilized in the popular series-parallel hybrid electric vehicle architecture. After reviewing the main requirements for the electric drive systems, the paper illustrates advanced electric machine topologies, including a stator permanent magnet (stator-PM motor, a hybrid-excitation motor, a flux memory motor and a redundant motor structure. Then, it illustrates advanced electric drive systems, such as the magnetic-geared in-wheel drive and the integrated starter generator (ISG. Finally, three machine-based implementations of the power split devices are expounded, built up around the dual-rotor PM machine, the dual-stator PM brushless machine and the magnetic-geared dual-rotor machine. As a conclusion, the development trends in the field of electric machines and machine-based systems for EVs are summarized.

Power profiling of Cholesky and QR factorizations on distributed memory systems

KAUST Repository

Bosilca, George; Ltaief, Hatem; Dongarra, Jack

2012-01-01

with a dynamic distributed scheduler (DAGuE) to leverage distributed memory systems. We present performance results (Gflop/s) as well as the power profile (Watts) of two common dense factorizations needed to solve linear systems of equations, namely

A Data Flow Model to Solve the Data Distribution Changing Problem in Machine Learning

Directory of Open Access Journals (Sweden)

Shang Bo-Wen

2016-01-01

Full Text Available Continuous prediction is widely used in broad communities spreading from social to business and the machine learning method is an important method in this problem.When we use the machine learning method to predict a problem. We use the data in the training set to fit the model and estimate the distribution of data in the test set.But when we use machine learning to do the continuous prediction we get new data as time goes by and use the data to predict the future data, there may be a problem. As the size of the data set increasing over time, the distribution changes and there will be many garbage data in the training set.We should remove the garbage data as it reduces the accuracy of the prediction. The main contribution of this article is using the new data to detect the timeliness of historical data and remove the garbage data.We build a data flow model to describe how the data flow among the test set, training set, validation set and the garbage set and improve the accuracy of prediction. As the change of the data set, the best machine learning model will change.We design a hybrid voting algorithm to fit the data set better that uses seven machine learning models predicting the same problem and uses the validation set putting different weights on the learning models to give better model more weights. Experimental results show that, when the distribution of the data set changes over time, our time flow model can remove most of the garbage data and get a better result than the traditional method that adds all the data to the data set; our hybrid voting algorithm has a better prediction result than the average accuracy of other predict models

Parallel Breadth-First Search on Distributed Memory Systems

Energy Technology Data Exchange (ETDEWEB)

Computational Research Division; Buluc, Aydin; Madduri, Kamesh

2011-04-15

Data-intensive, graph-based computations are pervasive in several scientific applications, and are known to to be quite challenging to implement on distributed memory systems. In this work, we explore the design space of parallel algorithms for Breadth-First Search (BFS), a key subroutine in several graph algorithms. We present two highly-tuned par- allel approaches for BFS on large parallel systems: a level-synchronous strategy that relies on a simple vertex-based partitioning of the graph, and a two-dimensional sparse matrix- partitioning-based approach that mitigates parallel commu- nication overhead. For both approaches, we also present hybrid versions with intra-node multithreading. Our novel hybrid two-dimensional algorithm reduces communication times by up to a factor of 3.5, relative to a common vertex based approach. Our experimental study identifies execu- tion regimes in which these approaches will be competitive, and we demonstrate extremely high performance on lead- ing distributed-memory parallel systems. For instance, for a 40,000-core parallel execution on Hopper, an AMD Magny- Cours based system, we achieve a BFS performance rate of 17.8 billion edge visits per second on an undirected graph of 4.3 billion vertices and 68.7 billion edges with skewed degree distribution.

High speed vision processor with reconfigurable processing element array based on full-custom distributed memory

Science.gov (United States)

Chen, Zhe; Yang, Jie; Shi, Cong; Qin, Qi; Liu, Liyuan; Wu, Nanjian

2016-04-01

In this paper, a hybrid vision processor based on a compact full-custom distributed memory for near-sensor high-speed image processing is proposed. The proposed processor consists of a reconfigurable processing element (PE) array, a row processor (RP) array, and a dual-core microprocessor. The PE array includes two-dimensional processing elements with a compact full-custom distributed memory. It supports real-time reconfiguration between the PE array and the self-organized map (SOM) neural network. The vision processor is fabricated using a 0.18 µm CMOS technology. The circuit area of the distributed memory is reduced markedly into 1/3 of that of the conventional memory so that the circuit area of the vision processor is reduced by 44.2%. Experimental results demonstrate that the proposed design achieves correct functions.

Implementation of Parallel Dynamic Simulation on Shared-Memory vs. Distributed-Memory Environments

Energy Technology Data Exchange (ETDEWEB)

Jin, Shuangshuang; Chen, Yousu; Wu, Di; Diao, Ruisheng; Huang, Zhenyu

2015-12-09

Power system dynamic simulation computes the system response to a sequence of large disturbance, such as sudden changes in generation or load, or a network short circuit followed by protective branch switching operation. It consists of a large set of differential and algebraic equations, which is computational intensive and challenging to solve using single-processor based dynamic simulation solution. High-performance computing (HPC) based parallel computing is a very promising technology to speed up the computation and facilitate the simulation process. This paper presents two different parallel implementations of power grid dynamic simulation using Open Multi-processing (OpenMP) on shared-memory platform, and Message Passing Interface (MPI) on distributed-memory clusters, respectively. The difference of the parallel simulation algorithms and architectures of the two HPC technologies are illustrated, and their performances for running parallel dynamic simulation are compared and demonstrated.

Power profiling of Cholesky and QR factorizations on distributed memory systems

KAUST Repository

Bosilca, George

2012-08-30

This paper presents the power profile of two high performance dense linear algebra libraries on distributed memory systems, ScaLAPACK and DPLASMA. From the algorithmic perspective, their methodologies are opposite. The former is based on block algorithms and relies on multithreaded BLAS and a two-dimensional block cyclic data distribution to achieve high parallel performance. The latter is based on tile algorithms running on top of a tile data layout and uses fine-grained task parallelism combined with a dynamic distributed scheduler (DAGuE) to leverage distributed memory systems. We present performance results (Gflop/s) as well as the power profile (Watts) of two common dense factorizations needed to solve linear systems of equations, namely Cholesky and QR. The reported numbers show that DPLASMA surpasses ScaLAPACK not only in terms of performance (up to 2X speedup) but also in terms of energy efficiency (up to 62 %). © 2012 Springer-Verlag (outside the USA).

Distributed Memory Parallel Computing with SEAWAT

Science.gov (United States)

Verkaik, J.; Huizer, S.; van Engelen, J.; Oude Essink, G.; Ram, R.; Vuik, K.

2017-12-01

Fresh groundwater reserves in coastal aquifers are threatened by sea-level rise, extreme weather conditions, increasing urbanization and associated groundwater extraction rates. To counteract these threats, accurate high-resolution numerical models are required to optimize the management of these precious reserves. The major model drawbacks are long run times and large memory requirements, limiting the predictive power of these models. Distributed memory parallel computing is an efficient technique for reducing run times and memory requirements, where the problem is divided over multiple processor cores. A new Parallel Krylov Solver (PKS) for SEAWAT is presented. PKS has recently been applied to MODFLOW and includes Conjugate Gradient (CG) and Biconjugate Gradient Stabilized (BiCGSTAB) linear accelerators. Both accelerators are preconditioned by an overlapping additive Schwarz preconditioner in a way that: a) subdomains are partitioned using Recursive Coordinate Bisection (RCB) load balancing, b) each subdomain uses local memory only and communicates with other subdomains by Message Passing Interface (MPI) within the linear accelerator, c) it is fully integrated in SEAWAT. Within SEAWAT, the PKS-CG solver replaces the Preconditioned Conjugate Gradient (PCG) solver for solving the variable-density groundwater flow equation and the PKS-BiCGSTAB solver replaces the Generalized Conjugate Gradient (GCG) solver for solving the advection-diffusion equation. PKS supports the third-order Total Variation Diminishing (TVD) scheme for computing advection. Benchmarks were performed on the Dutch national supercomputer (https://userinfo.surfsara.nl/systems/cartesius) using up to 128 cores, for a synthetic 3D Henry model (100 million cells) and the real-life Sand Engine model ( 10 million cells). The Sand Engine model was used to investigate the potential effect of the long-term morphological evolution of a large sand replenishment and climate change on fresh groundwater resources

Integrated Multi-Scale Data Analytics and Machine Learning for the Distribution Grid and Building-to-Grid Interface

Energy Technology Data Exchange (ETDEWEB)

Stewart, Emma M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Hendrix, Val [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Deka, Deepjyoti [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-03-16

This white paper introduces the application of advanced data analytics to the modernized grid. In particular, we consider the field of machine learning and where it is both useful, and not useful, for the particular field of the distribution grid and buildings interface. While analytics, in general, is a growing field of interest, and often seen as the golden goose in the burgeoning distribution grid industry, its application is often limited by communications infrastructure, or lack of a focused technical application. Overall, the linkage of analytics to purposeful application in the grid space has been limited. In this paper we consider the field of machine learning as a subset of analytical techniques, and discuss its ability and limitations to enable the future distribution grid and the building-to-grid interface. To that end, we also consider the potential for mixing distributed and centralized analytics and the pros and cons of these approaches. Machine learning is a subfield of computer science that studies and constructs algorithms that can learn from data and make predictions and improve forecasts. Incorporation of machine learning in grid monitoring and analysis tools may have the potential to solve data and operational challenges that result from increasing penetration of distributed and behind-the-meter energy resources. There is an exponentially expanding volume of measured data being generated on the distribution grid, which, with appropriate application of analytics, may be transformed into intelligible, actionable information that can be provided to the right actors – such as grid and building operators, at the appropriate time to enhance grid or building resilience, efficiency, and operations against various metrics or goals – such as total carbon reduction or other economic benefit to customers. While some basic analysis into these data streams can provide a wealth of information, computational and human boundaries on performing the analysis

The Milan Project: A New Method for High-Assurance and High-Performance Computing on Large-Scale Distributed Platforms

National Research Council Canada - National Science Library

Kedem, Zvi

2000-01-01

...: Calypso, Chime, and Charlotte; which enable applications developed for ideal, shared memory, parallel machines to execute on distributed platforms that are subject to failures, slowdowns, and changing resource availability...

Immigration, language proficiency, and autobiographical memories: Lifespan distribution and second-language access.

Science.gov (United States)

Esposito, Alena G; Baker-Ward, Lynne

2016-08-01

This investigation examined two controversies in the autobiographical literature: how cross-language immigration affects the distribution of autobiographical memories across the lifespan and under what circumstances language-dependent recall is observed. Both Spanish/English bilingual immigrants and English monolingual non-immigrants participated in a cue word study, with the bilingual sample taking part in a within-subject language manipulation. The expected bump in the number of memories from early life was observed for non-immigrants but not immigrants, who reported more memories for events surrounding immigration. Aspects of the methodology addressed possible reasons for past discrepant findings. Language-dependent recall was influenced by second-language proficiency. Results were interpreted as evidence that bilinguals with high second-language proficiency, in contrast to those with lower second-language proficiency, access a single conceptual store through either language. The final multi-level model predicting language-dependent recall, including second-language proficiency, age of immigration, internal language, and cue word language, explained ¾ of the between-person variance and (1)/5 of the within-person variance. We arrive at two conclusions. First, major life transitions influence the distribution of memories. Second, concept representation across multiple languages follows a developmental model. In addition, the results underscore the importance of considering language experience in research involving memory reports.

Design of multiple sequence alignment algorithms on parallel, distributed memory supercomputers.

Science.gov (United States)

Church, Philip C; Goscinski, Andrzej; Holt, Kathryn; Inouye, Michael; Ghoting, Amol; Makarychev, Konstantin; Reumann, Matthias

2011-01-01

The challenge of comparing two or more genomes that have undergone recombination and substantial amounts of segmental loss and gain has recently been addressed for small numbers of genomes. However, datasets of hundreds of genomes are now common and their sizes will only increase in the future. Multiple sequence alignment of hundreds of genomes remains an intractable problem due to quadratic increases in compute time and memory footprint. To date, most alignment algorithms are designed for commodity clusters without parallelism. Hence, we propose the design of a multiple sequence alignment algorithm on massively parallel, distributed memory supercomputers to enable research into comparative genomics on large data sets. Following the methodology of the sequential progressiveMauve algorithm, we design data structures including sequences and sorted k-mer lists on the IBM Blue Gene/P supercomputer (BG/P). Preliminary results show that we can reduce the memory footprint so that we can potentially align over 250 bacterial genomes on a single BG/P compute node. We verify our results on a dataset of E.coli, Shigella and S.pneumoniae genomes. Our implementation returns results matching those of the original algorithm but in 1/2 the time and with 1/4 the memory footprint for scaffold building. In this study, we have laid the basis for multiple sequence alignment of large-scale datasets on a massively parallel, distributed memory supercomputer, thus enabling comparison of hundreds instead of a few genome sequences within reasonable time.

Lifetime-Based Memory Management for Distributed Data Processing Systems

DEFF Research Database (Denmark)

Lu, Lu; Shi, Xuanhua; Zhou, Yongluan

2016-01-01

create a large amount of long-living data objects in the heap, which may quickly saturate the garbage collector, especially when handling a large dataset, and hence would limit the scalability of the system. To eliminate this problem, we propose a lifetime-based memory management framework, which...... the garbage collection time by up to 99.9%, 2) to achieve up to 22.7x speed up in terms of execution time in cases without data spilling and 41.6x speedup in cases with data spilling, and 3) to consume up to 46.6% less memory.......In-memory caching of intermediate data and eager combining of data in shuffle buffers have been shown to be very effective in minimizing the re-computation and I/O cost in distributed data processing systems like Spark and Flink. However, it has also been widely reported that these techniques would...

Mapping robust parallel multigrid algorithms to scalable memory architectures

Science.gov (United States)

Overman, Andrea; Vanrosendale, John

1993-01-01

The convergence rate of standard multigrid algorithms degenerates on problems with stretched grids or anisotropic operators. The usual cure for this is the use of line or plane relaxation. However, multigrid algorithms based on line and plane relaxation have limited and awkward parallelism and are quite difficult to map effectively to highly parallel architectures. Newer multigrid algorithms that overcome anisotropy through the use of multiple coarse grids rather than relaxation are better suited to massively parallel architectures because they require only simple point-relaxation smoothers. In this paper, we look at the parallel implementation of a V-cycle multiple semicoarsened grid (MSG) algorithm on distributed-memory architectures such as the Intel iPSC/860 and Paragon computers. The MSG algorithms provide two levels of parallelism: parallelism within the relaxation or interpolation on each grid and across the grids on each multigrid level. Both levels of parallelism must be exploited to map these algorithms effectively to parallel architectures. This paper describes a mapping of an MSG algorithm to distributed-memory architectures that demonstrates how both levels of parallelism can be exploited. The result is a robust and effective multigrid algorithm for distributed-memory machines.

Mnemonic transmission, social contagion, and emergence of collective memory: Influence of emotional valence, group structure, and information distribution.

Science.gov (United States)

Choi, Hae-Yoon; Kensinger, Elizabeth A; Rajaram, Suparna

2017-09-01

Social transmission of memory and its consequence on collective memory have generated enduring interdisciplinary interest because of their widespread significance in interpersonal, sociocultural, and political arenas. We tested the influence of 3 key factors-emotional salience of information, group structure, and information distribution-on mnemonic transmission, social contagion, and collective memory. Participants individually studied emotionally salient (negative or positive) and nonemotional (neutral) picture-word pairs that were completely shared, partially shared, or unshared within participant triads, and then completed 3 consecutive recalls in 1 of 3 conditions: individual-individual-individual (control), collaborative-collaborative (identical group; insular structure)-individual, and collaborative-collaborative (reconfigured group; diverse structure)-individual. Collaboration enhanced negative memories especially in insular group structure and especially for shared information, and promoted collective forgetting of positive memories. Diverse group structure reduced this negativity effect. Unequally distributed information led to social contagion that creates false memories; diverse structure propagated a greater variety of false memories whereas insular structure promoted confidence in false recognition and false collective memory. A simultaneous assessment of network structure, information distribution, and emotional valence breaks new ground to specify how network structure shapes the spread of negative memories and false memories, and the emergence of collective memory. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Frequent Statement and Dereference Elimination for Imperative and Object-Oriented Distributed Programs

Science.gov (United States)

El-Zawawy, Mohamed A.

2014-01-01

This paper introduces new approaches for the analysis of frequent statement and dereference elimination for imperative and object-oriented distributed programs running on parallel machines equipped with hierarchical memories. The paper uses languages whose address spaces are globally partitioned. Distributed programs allow defining data layout and threads writing to and reading from other thread memories. Three type systems (for imperative distributed programs) are the tools of the proposed techniques. The first type system defines for every program point a set of calculated (ready) statements and memory accesses. The second type system uses an enriched version of types of the first type system and determines which of the ready statements and memory accesses are used later in the program. The third type system uses the information gather so far to eliminate unnecessary statement computations and memory accesses (the analysis of frequent statement and dereference elimination). Extensions to these type systems are also presented to cover object-oriented distributed programs. Two advantages of our work over related work are the following. The hierarchical style of concurrent parallel computers is similar to the memory model used in this paper. In our approach, each analysis result is assigned a type derivation (serves as a correctness proof). PMID:24892098

Magnetic Flux Distribution of Linear Machines with Novel Three-Dimensional Hybrid Magnet Arrays

Directory of Open Access Journals (Sweden)

Nan Yao

2017-11-01

Full Text Available The objective of this paper is to propose a novel tubular linear machine with hybrid permanent magnet arrays and multiple movers, which could be employed for either actuation or sensing technology. The hybrid magnet array produces flux distribution on both sides of windings, and thus helps to increase the signal strength in the windings. The multiple movers are important for airspace technology, because they can improve the system’s redundancy and reliability. The proposed design concept is presented, and the governing equations are obtained based on source free property and Maxwell equations. The magnetic field distribution in the linear machine is thus analytically formulated by using Bessel functions and harmonic expansion of magnetization vector. Numerical simulation is then conducted to validate the analytical solutions of the magnetic flux field. It is proved that the analytical model agrees with the numerical results well. Therefore, it can be utilized for the formulation of signal or force output subsequently, depending on its particular implementation.

Feature-Based Visual Short-Term Memory Is Widely Distributed and Hierarchically Organized.

Science.gov (United States)

Dotson, Nicholas M; Hoffman, Steven J; Goodell, Baldwin; Gray, Charles M

2018-06-15

Feature-based visual short-term memory is known to engage both sensory and association cortices. However, the extent of the participating circuit and the neural mechanisms underlying memory maintenance is still a matter of vigorous debate. To address these questions, we recorded neuronal activity from 42 cortical areas in monkeys performing a feature-based visual short-term memory task and an interleaved fixation task. We find that task-dependent differences in firing rates are widely distributed throughout the cortex, while stimulus-specific changes in firing rates are more restricted and hierarchically organized. We also show that microsaccades during the memory delay encode the stimuli held in memory and that units modulated by microsaccades are more likely to exhibit stimulus specificity, suggesting that eye movements contribute to visual short-term memory processes. These results support a framework in which most cortical areas, within a modality, contribute to mnemonic representations at timescales that increase along the cortical hierarchy. Copyright © 2018 Elsevier Inc. All rights reserved.

The distribution of controlled drugs on banknotes via counting machines.

Science.gov (United States)

Carter, James F; Sleeman, Richard; Parry, Joanna

2003-03-27

Bundles of paper, similar to sterling banknotes, were counted in banks in England and Wales. Subsequent analysis showed that the counting process, both by machine and by hand, transferred nanogram amounts of cocaine to the paper. Crystalline material, similar to cocaine hydrochloride, could be observed on the surface of the paper following counting. The geographical distribution of contamination broadly followed Government statistics for cocaine usage within the UK. Diacetylmorphine, Delta(9)-tetrahydrocannabinol (THC) and 3,4-methylenedioxymethylamphetamine (MDMA) were not detected during this study.

Dynamic overset grid communication on distributed memory parallel processors

Science.gov (United States)

Barszcz, Eric; Weeratunga, Sisira K.; Meakin, Robert L.

1993-01-01

A parallel distributed memory implementation of intergrid communication for dynamic overset grids is presented. Included are discussions of various options considered during development. Results are presented comparing an Intel iPSC/860 to a single processor Cray Y-MP. Results for grids in relative motion show the iPSC/860 implementation to be faster than the Cray implementation.

Virtual Machine Language 2.1

Science.gov (United States)

Riedel, Joseph E.; Grasso, Christopher A.

2012-01-01

VML (Virtual Machine Language) is an advanced computing environment that allows spacecraft to operate using mechanisms ranging from simple, time-oriented sequencing to advanced, multicomponent reactive systems. VML has developed in four evolutionary stages. VML 0 is a core execution capability providing multi-threaded command execution, integer data types, and rudimentary branching. VML 1 added named parameterized procedures, extensive polymorphism, data typing, branching, looping issuance of commands using run-time parameters, and named global variables. VML 2 added for loops, data verification, telemetry reaction, and an open flight adaptation architecture. VML 2.1 contains major advances in control flow capabilities for executable state machines. On the resource requirements front, VML 2.1 features a reduced memory footprint in order to fit more capability into modestly sized flight processors, and endian-neutral data access for compatibility with Intel little-endian processors. Sequence packaging has been improved with object-oriented programming constructs and the use of implicit (rather than explicit) time tags on statements. Sequence event detection has been significantly enhanced with multi-variable waiting, which allows a sequence to detect and react to conditions defined by complex expressions with multiple global variables. This multi-variable waiting serves as the basis for implementing parallel rule checking, which in turn, makes possible executable state machines. The new state machine feature in VML 2.1 allows the creation of sophisticated autonomous reactive systems without the need to develop expensive flight software. Users specify named states and transitions, along with the truth conditions required, before taking transitions. Transitions with the same signal name allow separate state machines to coordinate actions: the conditions distributed across all state machines necessary to arm a particular signal are evaluated, and once found true, that

Understanding Notional Machines through Traditional Teaching with Conceptual Contraposition and Program Memory Tracing

Directory of Open Access Journals (Sweden)

Jeisson Hidalgo-Céspedes

2016-08-01

Full Text Available A correct understanding about how computers run code is mandatory in order to effectively learn to program. Lectures have historically been used in programming courses to teach how computers execute code, and students are assessed through traditional evaluation methods, such as exams. Constructivism learning theory objects to students’ passiveness during lessons, and traditional quantitative methods for evaluating a complex cognitive process such as understanding. Constructivism proposes complimentary techniques, such as conceptual contraposition and colloquies. We enriched lectures of a “Programming II” (CS2 course combining conceptual contraposition with program memory tracing, then we evaluated students’ understanding of programming concepts through colloquies. Results revealed that these techniques applied to the lecture are insufficient to help students develop satisfactory mental models of the C++ notional machine, and colloquies behaved as the most comprehensive traditional evaluations conducted in the course.

A tm Plug-In for Distributed Text Mining in R

Directory of Open Access Journals (Sweden)

Stefan Theussl

2012-11-01

Full Text Available R has gained explicit text mining support with the tm package enabling statisticians to answer many interesting research questions via statistical analysis or modeling of (text corpora. However, we typically face two challenges when analyzing large corpora: (1 the amount of data to be processed in a single machine is usually limited by the available main memory (i.e., RAM, and (2 the more data to be analyzed the higher the need for efficient procedures for calculating valuable results. Fortunately, adequate programming models like MapReduce facilitate parallelization of text mining tasks and allow for processing data sets beyond what would fit into memory by using a distributed file system possibly spanning over several machines, e.g., in a cluster of workstations. In this paper we present a plug-in package to tm called tm.plugin.dc implementing a distributed corpus class which can take advantage of the Hadoop MapReduce library for large scale text mining tasks. We show on the basis of an application in culturomics that we can efficiently handle data sets of significant size.

Avalanches and generalized memory associativity in a network model for conscious and unconscious mental functioning

Science.gov (United States)

Siddiqui, Maheen; Wedemann, Roseli S.; Jensen, Henrik Jeldtoft

2018-01-01

We explore statistical characteristics of avalanches associated with the dynamics of a complex-network model, where two modules corresponding to sensorial and symbolic memories interact, representing unconscious and conscious mental processes. The model illustrates Freud's ideas regarding the neuroses and that consciousness is related with symbolic and linguistic memory activity in the brain. It incorporates the Stariolo-Tsallis generalization of the Boltzmann Machine in order to model memory retrieval and associativity. In the present work, we define and measure avalanche size distributions during memory retrieval, in order to gain insight regarding basic aspects of the functioning of these complex networks. The avalanche sizes defined for our model should be related to the time consumed and also to the size of the neuronal region which is activated, during memory retrieval. This allows the qualitative comparison of the behaviour of the distribution of cluster sizes, obtained during fMRI measurements of the propagation of signals in the brain, with the distribution of avalanche sizes obtained in our simulation experiments. This comparison corroborates the indication that the Nonextensive Statistical Mechanics formalism may indeed be more well suited to model the complex networks which constitute brain and mental structure.

A view of Kanerva's sparse distributed memory

Science.gov (United States)

Denning, P. J.

1986-01-01

Pentti Kanerva is working on a new class of computers, which are called pattern computers. Pattern computers may close the gap between capabilities of biological organisms to recognize and act on patterns (visual, auditory, tactile, or olfactory) and capabilities of modern computers. Combinations of numeric, symbolic, and pattern computers may one day be capable of sustaining robots. The overview of the requirements for a pattern computer, a summary of Kanerva's Sparse Distributed Memory (SDM), and examples of tasks this computer can be expected to perform well are given.

Trinary Associative Memory Would Recognize Machine Parts

Science.gov (United States)

Liu, Hua-Kuang; Awwal, Abdul Ahad S.; Karim, Mohammad A.

1991-01-01

Trinary associative memory combines merits and overcomes major deficiencies of unipolar and bipolar logics by combining them in three-valued logic that reverts to unipolar or bipolar binary selectively, as needed to perform specific tasks. Advantage of associative memory: one obtains access to all parts of it simultaneously on basis of content, rather than address, of data. Consequently, used to exploit fully parallelism and speed of optical computing.

Distributed-Memory Breadth-First Search on Massive Graphs

Energy Technology Data Exchange (ETDEWEB)

Buluc, Aydin [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Computational Research Division; Beamer, Scott [Univ. of California, Berkeley, CA (United States). Dept. of Electrical Engineering and Computer Sciences; Madduri, Kamesh [Pennsylvania State Univ., University Park, PA (United States). Computer Science & Engineering Dept.; Asanovic, Krste [Univ. of California, Berkeley, CA (United States). Dept. of Electrical Engineering and Computer Sciences; Patterson, David [Univ. of California, Berkeley, CA (United States). Dept. of Electrical Engineering and Computer Sciences

2017-09-26

This chapter studies the problem of traversing large graphs using the breadth-first search order on distributed-memory supercomputers. We consider both the traditional level-synchronous top-down algorithm as well as the recently discovered direction optimizing algorithm. We analyze the performance and scalability trade-offs in using different local data structures such as CSR and DCSC, enabling in-node multithreading, and graph decompositions such as 1D and 2D decomposition.

76 FR 32231 - International Business Machines (IBM), Sales and Distribution Business Unit, Global Sales...

Science.gov (United States)

2011-06-03

... for the workers and former workers of International Business Machines (IBM), Sales and Distribution... reconsideration alleges that IBM outsourced to India and China. During the reconsideration investigation, it was..., Armonk, New York. The subject worker group supply computer software development and maintenance services...

Unconditional polarization qubit quantum memory at room temperature

Science.gov (United States)

Namazi, Mehdi; Kupchak, Connor; Jordaan, Bertus; Shahrokhshahi, Reihaneh; Figueroa, Eden

2016-05-01

The creation of global quantum key distribution and quantum communication networks requires multiple operational quantum memories. Achieving a considerable reduction in experimental and cost overhead in these implementations is thus a major challenge. Here we present a polarization qubit quantum memory fully-operational at 330K, an unheard frontier in the development of useful qubit quantum technology. This result is achieved through extensive study of how optical response of cold atomic medium is transformed by the motion of atoms at room temperature leading to an optimal characterization of room temperature quantum light-matter interfaces. Our quantum memory shows an average fidelity of 86.6 +/- 0.6% for optical pulses containing on average 1 photon per pulse, thereby defeating any classical strategy exploiting the non-unitary character of the memory efficiency. Our system significantly decreases the technological overhead required to achieve quantum memory operation and will serve as a building block for scalable and technologically simpler many-memory quantum machines. The work was supported by the US-Navy Office of Naval Research, Grant Number N00141410801 and the Simons Foundation, Grant Number SBF241180. B. J. acknowledges financial assistance of the National Research Foundation (NRF) of South Africa.

Parallel SN algorithms in shared- and distributed-memory environments

International Nuclear Information System (INIS)

Haghighat, Alireza; Hunter, Melissa A.; Mattis, Ronald E.

1995-01-01

Different 2-D spatial domain partitioning Sn transport theory algorithms have been developed on the basis of the Block-Jacobi iterative scheme. These algorithms have been incorporated into TWOTRAN-II, and tested on a shared-memory CRAY Y-MP C90 and a distributed-memory IBM SP1. For a series of fixed source r-z geometry homogeneous problems, parallel efficiencies in a range of 50-90% are achieved on the C90 with 6 processors, and lower values (20-60%) are obtained on the SP1. It is demonstrated that better performance is attainable if one addresses issues such as convergence rate, load-balancing, and granularity for both architectures, as well as message passing (network bandwidth and latency) for SP1. (author). 17 refs, 4 figs

Propulsion of a Molecular Machine by Asymmetric Distribution of Reaction Products

Science.gov (United States)

Golestanian, Ramin; Liverpool, Tanniemola B.; Ajdari, Armand

2005-06-01

A simple model for the reaction-driven propulsion of a small device is proposed as a model for (part of) a molecular machine in aqueous media. The motion of the device is driven by an asymmetric distribution of reaction products. The propulsive velocity of the device is calculated as well as the scale of the velocity fluctuations. The effects of hydrodynamic flow as well as a number of different scenarios for the kinetics of the reaction are addressed.

Optimizing memory use in Java applications, garbage collectors

Directory of Open Access Journals (Sweden)

Ştefan PREDA

2016-05-01

Full Text Available Java applications are diverse, depending by use case, exist application that use small amount of memory till application that use huge amount, tens or hundreds of gigabits. Java Virtual Machine is designed to automatically manage memory for applications. Even in this case due diversity of hardware, software that coexist on the same system and applications itself, these automatic decision need to be accompanied by developer or system administrator to triage optimal memory use. After developer big role to write optimum code from memory allocation perspective , optimizing memory use at Java Virtual Machine and application level become in last year's one of the most important task. This is explained in special due increased demand in applications scalability.

76 FR 21033 - International Business Machines (IBM), Sales and Distribution Business Unit, Global Sales...

Science.gov (United States)

2011-04-14

... DEPARTMENT OF LABOR Employment and Training Administration [TA-W-74,364] International Business Machines (IBM), Sales and Distribution Business Unit, Global Sales Solution Department, Off-Site Teleworker in Centerport, New York; Notice of Affirmative Determination Regarding Application for Reconsideration By application dated November 29, 2011,...

Design and Implementation of Distributed Crawler System Based on Scrapy

Science.gov (United States)

Fan, Yuhao

2018-01-01

At present, some large-scale search engines at home and abroad only provide users with non-custom search services, and a single-machine web crawler cannot sovle the difficult task. In this paper, Through the study and research of the original Scrapy framework, the original Scrapy framework is improved by combining Scrapy and Redis, a distributed crawler system based on Web information Scrapy framework is designed and implemented, and Bloom Filter algorithm is applied to dupefilter modul to reduce memory consumption. The movie information captured from douban is stored in MongoDB, so that the data can be processed and analyzed. The results show that distributed crawler system based on Scrapy framework is more efficient and stable than the single-machine web crawler system.

An Investigation of the Micro-Electrical Discharge Machining of Nickel-Titanium Shape Memory Alloy Using Grey Relations Coupled with Principal Component Analysis

Directory of Open Access Journals (Sweden)

Mustufa Haider Abidi

2017-11-01

Full Text Available Shape memory alloys (SMAs are advanced engineering materials which possess shape memory effects and super-elastic properties. Their high strength, high wear-resistance, pseudo plasticity, etc., makes the machining of Ni-Ti based SMAs difficult using traditional techniques. Among all non-conventional processes, micro-electric discharge machining (micro-EDM is considered one of the leading processes for micro-machining, owing to its high aspect ratio and capability to machine hard-to-cut materials with good surface finish.The selection of the most appropriate input parameter combination to provide the optimum values for various responses is very important in micro-EDM. This article demonstrates the methodology for optimizing multiple quality characteristics (overcut, taper angle and surface roughness to enhance the quality of micro-holes in Ni-Ti based alloy, using the Grey–Taguchi method. A Taguchi-based grey relational analysis coupled with principal component analysis (Grey-PCA methodology was implemented to investigate the effect of three important micro-EDM process parameters, namely capacitance, voltage and electrode material.The analysis of the individual responses established the importance of multi-response optimization. The main effects plots for the micro-EDM parameters and Analysis of Variance (ANOVA indicate that every parameter does not produce same effect on individual responses, and also that the percent contribution of each parameter to individual response is highly varied. As a result, multi-response optimization was implemented using Grey-PCA. Further, this study revealed that the electrode material had the strongest effect on the multi-response parameter, followed by the voltage and capacitance. The main effects plot for the Grey-PCA shows that the micro-EDM parameters “capacitance” at level-2 (i.e., 475 pF, “discharge voltage” at level-1 (i.e., 80 V and the “electrode material” Cu provided the best multi-response.

Removing the Restrictions Imposed on Finite State Machines ...

African Journals Online (AJOL)

This study determines an effective method of removing the fixed and finite state amount of memory that restricts finite state machines from carrying out compilation jobs that require larger amount of memory. The study is ... The conclusion reviewed the various steps followed and made projections for further reading. Keyword: ...

Operating System For Numerically Controlled Milling Machine

Science.gov (United States)

Ray, R. B.

1992-01-01

OPMILL program is operating system for Kearney and Trecker milling machine providing fast easy way to program manufacture of machine parts with IBM-compatible personal computer. Gives machinist "equation plotter" feature, which plots equations that define movements and converts equations to milling-machine-controlling program moving cutter along defined path. System includes tool-manager software handling up to 25 tools and automatically adjusts to account for each tool. Developed on IBM PS/2 computer running DOS 3.3 with 1 MB of random-access memory.

Determination and shaping of the ion-velocity distribution function in a single-ended Q machine

DEFF Research Database (Denmark)

Andersen, S.A.; Jensen, Vagn Orla; Michelsen, Poul

1971-01-01

An electrostatic energy analyzer with a resolution better than 0.03 eV was constructed. This analyzer was used to determine the ion-velocity distribution function at different densities and plate temperatures in a single-ended Q machine. In all regions good agreement with theoretical predictions...... based on simple, physical pictures is obtained. It is shown that within certain limits the velocity distribution function can be shaped; double-humped distribution functions have been obtained. The technique used here is suggested as an accurate method for determination of plasma densities within 10...

Learning to read aloud: A neural network approach using sparse distributed memory

Science.gov (United States)

Joglekar, Umesh Dwarkanath

1989-01-01

An attempt to solve a problem of text-to-phoneme mapping is described which does not appear amenable to solution by use of standard algorithmic procedures. Experiments based on a model of distributed processing are also described. This model (sparse distributed memory (SDM)) can be used in an iterative supervised learning mode to solve the problem. Additional improvements aimed at obtaining better performance are suggested.

A Linear Algebra Framework for Static High Performance Fortran Code Distribution

Directory of Open Access Journals (Sweden)

Corinne Ancourt

1997-01-01

Full Text Available High Performance Fortran (HPF was developed to support data parallel programming for single-instruction multiple-data (SIMD and multiple-instruction multiple-data (MIMD machines with distributed memory. The programmer is provided a familiar uniform logical address space and specifies the data distribution by directives. The compiler then exploits these directives to allocate arrays in the local memories, to assign computations to elementary processors, and to migrate data between processors when required. We show here that linear algebra is a powerful framework to encode HPF directives and to synthesize distributed code with space-efficient array allocation, tight loop bounds, and vectorized communications for INDEPENDENT loops. The generated code includes traditional optimizations such as guard elimination, message vectorization and aggregation, and overlap analysis. The systematic use of an affine framework makes it possible to prove the compilation scheme correct.

A Machine-to-Machine protocol benchmark for eHealth applications - Use case: Respiratory rehabilitation.

Science.gov (United States)

Talaminos-Barroso, Alejandro; Estudillo-Valderrama, Miguel A; Roa, Laura M; Reina-Tosina, Javier; Ortega-Ruiz, Francisco

2016-06-01

M2M (Machine-to-Machine) communications represent one of the main pillars of the new paradigm of the Internet of Things (IoT), and is making possible new opportunities for the eHealth business. Nevertheless, the large number of M2M protocols currently available hinders the election of a suitable solution that satisfies the requirements that can demand eHealth applications. In the first place, to develop a tool that provides a benchmarking analysis in order to objectively select among the most relevant M2M protocols for eHealth solutions. In the second place, to validate the tool with a particular use case: the respiratory rehabilitation. A software tool, called Distributed Computing Framework (DFC), has been designed and developed to execute the benchmarking tests and facilitate the deployment in environments with a large number of machines, with independence of the protocol and performance metrics selected. DDS, MQTT, CoAP, JMS, AMQP and XMPP protocols were evaluated considering different specific performance metrics, including CPU usage, memory usage, bandwidth consumption, latency and jitter. The results obtained allowed to validate a case of use: respiratory rehabilitation of chronic obstructive pulmonary disease (COPD) patients in two scenarios with different types of requirement: Home-Based and Ambulatory. The results of the benchmark comparison can guide eHealth developers in the choice of M2M technologies. In this regard, the framework presented is a simple and powerful tool for the deployment of benchmark tests under specific environments and conditions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

High Performance Polar Decomposition on Distributed Memory Systems

KAUST Repository

Sukkari, Dalal E.

2016-08-08

The polar decomposition of a dense matrix is an important operation in linear algebra. It can be directly calculated through the singular value decomposition (SVD) or iteratively using the QR dynamically-weighted Halley algorithm (QDWH). The former is difficult to parallelize due to the preponderant number of memory-bound operations during the bidiagonal reduction. We investigate the latter scenario, which performs more floating-point operations but exposes at the same time more parallelism, and therefore, runs closer to the theoretical peak performance of the system, thanks to more compute-bound matrix operations. Profiling results show the performance scalability of QDWH for calculating the polar decomposition using around 9200 MPI processes on well and ill-conditioned matrices of 100K×100K problem size. We study then the performance impact of the QDWH-based polar decomposition as a pre-processing step toward calculating the SVD itself. The new distributed-memory implementation of the QDWH-SVD solver achieves up to five-fold speedup against current state-of-the-art vendor SVD implementations. © Springer International Publishing Switzerland 2016.

Large-scale Ising-machines composed of magnetic neurons

Science.gov (United States)

Mizushima, Koichi; Goto, Hayato; Sato, Rie

2017-10-01

We propose Ising-machines composed of magnetic neurons, that is, magnetic bits in a recording track. In large-scale machines, the sizes of both neurons and synapses need to be reduced, and neat and smart connections among neurons are also required to achieve all-to-all connectivity among them. These requirements can be fulfilled by adopting magnetic recording technologies such as race-track memories and skyrmion tracks because the area of a magnetic bit is almost two orders of magnitude smaller than that of static random access memory, which has normally been used as a semiconductor neuron, and the smart connections among neurons are realized by using the read and write methods of these technologies.

Efficient packing of patterns in sparse distributed memory by selective weighting of input bits

Science.gov (United States)

Kanerva, Pentti

1991-01-01

When a set of patterns is stored in a distributed memory, any given storage location participates in the storage of many patterns. From the perspective of any one stored pattern, the other patterns act as noise, and such noise limits the memory's storage capacity. The more similar the retrieval cues for two patterns are, the more the patterns interfere with each other in memory, and the harder it is to separate them on retrieval. A method is described of weighting the retrieval cues to reduce such interference and thus to improve the separability of patterns that have similar cues.

[Distribution of neural memory, loading factor, its regulation and optimization].

Science.gov (United States)

Radchenko, A N

1999-01-01

Recording and retrieving functions of the neural memory are simulated as a control of local conformational processes in neural synaptic fields. The localization of conformational changes is related to the afferent temporal-spatial pulse pattern flow, the microstructure of connections and a plurality of temporal delays in synaptic fields and afferent pathways. The loci of conformations are described by sets of afferent addresses named address domains. Being superimposed on each other, address domains form a multilayer covering of the address space of the neuron or the ensemble. The superposition factor determines the dissemination of the conformational process, and the fuzzing of memory, and its accuracy and reliability. The engram is formed as detects in the packing of the address space and hence can be retrieved in inverse form. The accuracy of the retrieved information depends on the threshold level of conformational transitions, the distribution of conformational changes in synaptic fields of the neuronal population, and the memory loading factor. The latter is represented in the model by a slow potential. It reflects total conformational changes and displaces the membrane potential to monostable conformational regimes, by governing the exit from the recording regime, the potentiation of the neurone, and the readiness to reproduction. A relative amplitude of the slow potential and the coefficient of postconformational modification of ionic conductivity, which provides maximum reliability, accuracy, and capacity of memory, are calculated.

The use of fractal dimension calculation algorithm to determine the nature of autobiographical memories distribution across the life span

Science.gov (United States)

Mitina, Olga V.; Nourkova, Veronica V.

In the given research we offer the technique for the calculation of the density of events which people retrieve from autobiographical memory. We wanted to prove a non-uniformity nature of memories distribution in the course of time and were interested with the law of distribution of these events during life course.

Analysis towards VMEM File of a Suspended Virtual Machine

Science.gov (United States)

Song, Zheng; Jin, Bo; Sun, Yongqing

With the popularity of virtual machines, forensic investigators are challenged with more complicated situations, among which discovering the evidences in virtualized environment is of significant importance. This paper mainly analyzes the file suffixed with .vmem in VMware Workstation, which stores all pseudo-physical memory into an image. The internal file structure of .vmem file is studied and disclosed. Key information about processes and threads of a suspended virtual machine is revealed. Further investigation into the Windows XP SP3 heap contents is conducted and a proof-of-concept tool is provided. Different methods to obtain forensic memory images are introduced, with both advantages and limits analyzed. We conclude with an outlook.

Experimental study on Response Parameters of Ni-rich NiTi Shape Memory Alloy during Wire Electric Discharge Machining

Science.gov (United States)

Bisaria, Himanshu; Shandilya, Pragya

2018-03-01

Nowadays NiTi SMAs are gaining more prominence due to their unique properties such as superelasticity, shape memory effect, high fatigue strength and many other enriched physical and mechanical properties. The current studies explore the effect of machining parameters namely, peak current (Ip), pulse off time (TOFF), and pulse on time (TON) on wire wear ratio (WWR), and dimensional deviation (DD) in WEDM. It was found that high discharge energy was mainly ascribed to high WWR and DD. The WWR and DD increased with the increase in pulse on time and peak current whereas high pulse off time was favourable for low WWR and DD.

Extending and implementing the Self-adaptive Virtual Processor for distributed memory architectures

NARCIS (Netherlands)

van Tol, M.W.; Koivisto, J.

2011-01-01

Many-core architectures of the future are likely to have distributed memory organizations and need fine grained concurrency management to be used effectively. The Self-adaptive Virtual Processor (SVP) is an abstract concurrent programming model which can provide this, but the model and its current

The DELPHI distributed information system for exchanging LEP machine related information

International Nuclear Information System (INIS)

Doenszelmann, M.; Gaspar, C.

1994-01-01

An information management system was designed and implemented to interchange information between the DELPHI experiment at CERN and the monitoring/control system for the LEP (Large Electron Positron Collider) accelerator. This system is distributed and communicates with many different sources and destinations (LEP) using different types of communication. The system itself communicates internally via a communication system based on a publish-and-subscribe mechanism, DIM (Distributed Information Manager). The information gathered by this system is used for on-line as well as off-line data analysis. Therefore it logs the information to a database and makes it available to operators and users via DUI (DELPHI User Interface). The latter was extended to be capable of displaying ''time-evolution'' plots. It also handles a protocol, implemented using a finite state machine, SMI (State Management Interface), for (semi-)automatic running of the Data Acquisition System and the Slow Controls System. ((orig.))

Global assessment of soil organic carbon stocks and spatial distribution of histosols: the Machine Learning approach

Science.gov (United States)

Hengl, Tomislav

2016-04-01

Preliminary results of predicting distribution of soil organic soils (Histosols) and soil organic carbon stock (in tonnes per ha) using global compilations of soil profiles (about 150,000 points) and covariates at 250 m spatial resolution (about 150 covariates; mainly MODIS seasonal land products, SRTM DEM derivatives, climatic images, lithological and land cover and landform maps) are presented. We focus on using a data-driven approach i.e. Machine Learning techniques that often require no knowledge about the distribution of the target variable or knowledge about the possible relationships. Other advantages of using machine learning are (DOI: 10.1371/journal.pone.0125814): All rules required to produce outputs are formalized. The whole procedure is documented (the statistical model and associated computer script), enabling reproducible research. Predicted surfaces can make use of various information sources and can be optimized relative to all available quantitative point and covariate data. There is more flexibility in terms of the spatial extent, resolution and support of requested maps. Automated mapping is also more cost-effective: once the system is operational, maintenance and production of updates are an order of magnitude faster and cheaper. Consequently, prediction maps can be updated and improved at shorter and shorter time intervals. Some disadvantages of automated soil mapping based on Machine Learning are: Models are data-driven and any serious blunders or artifacts in the input data can propagate to order-of-magnitude larger errors than in the case of expert-based systems. Fitting machine learning models is at the order of magnitude computationally more demanding. Computing effort can be even tens of thousands higher than if e.g. linear geostatistics is used. Many machine learning models are fairly complex often abstract and any interpretation of such models is not trivial and require special multidimensional / multivariable plotting and data mining

Multi-objective problem of the modified distributed parallel machine and assembly scheduling problem (MDPMASP) with eligibility constraints

Science.gov (United States)

Amallynda, I.; Santosa, B.

2017-11-01

This paper proposes a new generalization of the distributed parallel machine and assembly scheduling problem (DPMASP) with eligibility constraints referred to as the modified distributed parallel machine and assembly scheduling problem (MDPMASP) with eligibility constraints. Within this generalization, we assume that there are a set non-identical factories or production lines, each one with a set unrelated parallel machine with different speeds in processing them disposed to a single assembly machine in series. A set of different products that are manufactured through an assembly program of a set of components (jobs) according to the requested demand. Each product requires several kinds of jobs with different sizes. Beside that we also consider to the multi-objective problem (MOP) of minimizing mean flow time and the number of tardy products simultaneously. This is known to be NP-Hard problem, is important to practice, as the former criterions to reflect the customer's demand and manufacturer's perspective. This is a realistic and complex problem with wide range of possible solutions, we propose four simple heuristics and two metaheuristics to solve it. Various parameters of the proposed metaheuristic algorithms are discussed and calibrated by means of Taguchi technique. All proposed algorithms are tested by Matlab software. Our computational experiments indicate that the proposed problem and fourth proposed algorithms are able to be implemented and can be used to solve moderately-sized instances, and giving efficient solutions, which are close to optimum in most cases.

Parallelization and automatic data distribution for nuclear reactor simulations

Energy Technology Data Exchange (ETDEWEB)

Liebrock, L.M. [Liebrock-Hicks Research, Calumet, MI (United States)

1997-07-01

Detailed attempts at realistic nuclear reactor simulations currently take many times real time to execute on high performance workstations. Even the fastest sequential machine can not run these simulations fast enough to ensure that the best corrective measure is used during a nuclear accident to prevent a minor malfunction from becoming a major catastrophe. Since sequential computers have nearly reached the speed of light barrier, these simulations will have to be run in parallel to make significant improvements in speed. In physical reactor plants, parallelism abounds. Fluids flow, controls change, and reactions occur in parallel with only adjacent components directly affecting each other. These do not occur in the sequentialized manner, with global instantaneous effects, that is often used in simulators. Development of parallel algorithms that more closely approximate the real-world operation of a reactor may, in addition to speeding up the simulations, actually improve the accuracy and reliability of the predictions generated. Three types of parallel architecture (shared memory machines, distributed memory multicomputers, and distributed networks) are briefly reviewed as targets for parallelization of nuclear reactor simulation. Various parallelization models (loop-based model, shared memory model, functional model, data parallel model, and a combined functional and data parallel model) are discussed along with their advantages and disadvantages for nuclear reactor simulation. A variety of tools are introduced for each of the models. Emphasis is placed on the data parallel model as the primary focus for two-phase flow simulation. Tools to support data parallel programming for multiple component applications and special parallelization considerations are also discussed.

Parallelization and automatic data distribution for nuclear reactor simulations

International Nuclear Information System (INIS)

Liebrock, L.M.

1997-01-01

Detailed attempts at realistic nuclear reactor simulations currently take many times real time to execute on high performance workstations. Even the fastest sequential machine can not run these simulations fast enough to ensure that the best corrective measure is used during a nuclear accident to prevent a minor malfunction from becoming a major catastrophe. Since sequential computers have nearly reached the speed of light barrier, these simulations will have to be run in parallel to make significant improvements in speed. In physical reactor plants, parallelism abounds. Fluids flow, controls change, and reactions occur in parallel with only adjacent components directly affecting each other. These do not occur in the sequentialized manner, with global instantaneous effects, that is often used in simulators. Development of parallel algorithms that more closely approximate the real-world operation of a reactor may, in addition to speeding up the simulations, actually improve the accuracy and reliability of the predictions generated. Three types of parallel architecture (shared memory machines, distributed memory multicomputers, and distributed networks) are briefly reviewed as targets for parallelization of nuclear reactor simulation. Various parallelization models (loop-based model, shared memory model, functional model, data parallel model, and a combined functional and data parallel model) are discussed along with their advantages and disadvantages for nuclear reactor simulation. A variety of tools are introduced for each of the models. Emphasis is placed on the data parallel model as the primary focus for two-phase flow simulation. Tools to support data parallel programming for multiple component applications and special parallelization considerations are also discussed

A microcomputer network for the control of digitising machines

International Nuclear Information System (INIS)

Seller, P.

1981-01-01

A distributed microcomputing network operates in the Bubble Chamber Research Group Scanning Laboratory at the Rutherford and Appleton Laboratories. A microcomputer at each digitising table buffers information, controls the functioning of the table and enhances the machine/operator interface. The system consists of fourteen microcomputers together with a VAX 11/780 computer used for data analysis. These are inter-connected via a packet switched network. This paper will describe the features of the combined system, including the distributed computing architecture and the packet switched method of communication. This paper will also describe in detail a high speed packet switching controller used as a central node of the network. This controller is a multiprocessor microcomputer system with eighteen central processor units, thirty-four direct memory access channels and thirty-four prioritorised and vectored interrupt channels. This microcomputer is of general interest as a communications controller due to its totally programmable nature. (orig.)

Periodic bidirectional associative memory neural networks with distributed delays

Science.gov (United States)

Chen, Anping; Huang, Lihong; Liu, Zhigang; Cao, Jinde

2006-05-01

Some sufficient conditions are obtained for the existence and global exponential stability of a periodic solution to the general bidirectional associative memory (BAM) neural networks with distributed delays by using the continuation theorem of Mawhin's coincidence degree theory and the Lyapunov functional method and the Young's inequality technique. These results are helpful for designing a globally exponentially stable and periodic oscillatory BAM neural network, and the conditions can be easily verified and be applied in practice. An example is also given to illustrate our results.

Discrete time analysis of a repairable machine

OpenAIRE

Alfa, Attahiru Sule; Castro, I. T.

2002-01-01

We consider, in discrete time, a single machine system that operates for a period of time represented by a general distribution. This machine is subject to failures during operations and the occurrence of these failures depends on how many times the machine has previously failed. Some failures are repairable and the repair times may or may not depend on the number of times the machine was previously repaired. Repair times also have a general distribution. The operating times...

Distribution of return point memory states for systems with stochastic inputs

International Nuclear Information System (INIS)

Amann, A; Brokate, M; Rachinskii, D; Temnov, G

2011-01-01

We consider the long term effect of stochastic inputs on the state of an open loop system which exhibits the so-called return point memory. An example of such a system is the Preisach model; more generally, systems with the Preisach type input-state relationship, such as in spin-interaction models, are considered. We focus on the characterisation of the expected memory configuration after the system has been effected by the input for sufficiently long period of time. In the case where the input is given by a discrete time random walk process, or the Wiener process, simple closed form expressions for the probability density of the vector of the main input extrema recorded by the memory state, and scaling laws for the dimension of this vector, are derived. If the input is given by a general continuous Markov process, we show that the distribution of previous memory elements can be obtained from a Markov chain scheme which is derived from the solution of an associated one-dimensional escape type problem. Formulas for transition probabilities defining this Markov chain scheme are presented. Moreover, explicit formulas for the conditional probability densities of previous main extrema are obtained for the Ornstein-Uhlenbeck input process. The analytical results are confirmed by numerical experiments.

Commonality and Variability Analysis for Xenon Family of Separation Virtual Machine Monitors (CVAX)

Science.gov (United States)

2017-07-18

the sponsor (e.g., military, intelligence community, other government, commercial, medical ) and upon the type of system (e.g., application in the...loads. • Machine memory. Xen’s terminology for hardware memory present on a chip. • Misuse case. Abuse case. Attacker-product interaction that the...on connections between domains. • Physical memory. Xen’s terminology , short for pseudo-physical memory. Physical memory is the Xen term for the

Efficient tuning in supervised machine learning

NARCIS (Netherlands)

Koch, Patrick

2013-01-01

The tuning of learning algorithm parameters has become more and more important during the last years. With the fast growth of computational power and available memory databases have grown dramatically. This is very challenging for the tuning of parameters arising in machine learning, since the

Patterns of particle distribution in multiparticle systems by random walks with memory enhancement and decay

Science.gov (United States)

Tan, Zhi-Jie; Zou, Xian-Wu; Huang, Sheng-You; Zhang, Wei; Jin, Zhun-Zhi

2002-07-01

We investigate the pattern of particle distribution and its evolution with time in multiparticle systems using the model of random walks with memory enhancement and decay. This model describes some biological intelligent walks. With decrease in the memory decay exponent α, the distribution of particles changes from a random dispersive pattern to a locally dense one, and then returns to the random one. Correspondingly, the fractal dimension Df,p characterizing the distribution of particle positions increases from a low value to a maximum and then decreases to the low one again. This is determined by the degree of overlap of regions consisting of sites with remanent information. The second moment of the density ρ(2) was introduced to investigate the inhomogeneity of the particle distribution. The dependence of ρ(2) on α is similar to that of Df,p on α. ρ(2) increases with time as a power law in the process of adjusting the particle distribution, and then ρ(2) tends to a stable equilibrium value.

Two alternate proofs of Wang's lune formula for sparse distributed memory and an integral approximation

Science.gov (United States)

Jaeckel, Louis A.

1988-01-01

In Kanerva's Sparse Distributed Memory, writing to and reading from the memory are done in relation to spheres in an n-dimensional binary vector space. Thus it is important to know how many points are in the intersection of two spheres in this space. Two proofs are given of Wang's formula for spheres of unequal radii, and an integral approximation for the intersection in this case.

A portable implementation of ARPACK for distributed memory parallel architectures

Energy Technology Data Exchange (ETDEWEB)

Maschhoff, K.J.; Sorensen, D.C.

1996-12-31

ARPACK is a package of Fortran 77 subroutines which implement the Implicitly Restarted Arnoldi Method used for solving large sparse eigenvalue problems. A parallel implementation of ARPACK is presented which is portable across a wide range of distributed memory platforms and requires minimal changes to the serial code. The communication layers used for message passing are the Basic Linear Algebra Communication Subprograms (BLACS) developed for the ScaLAPACK project and Message Passing Interface(MPI).

Customizable Memory Schemes for Data Parallel Architectures

NARCIS (Netherlands)

Gou, C.

2011-01-01

Memory system efficiency is crucial for any processor to achieve high performance, especially in the case of data parallel machines. Processing capabilities of parallel lanes will be wasted, when data requests are not accomplished in a sustainable and timely manner. Irregular vector memory accesses

Computerized scoring algorithms for the Autobiographical Memory Test.

Science.gov (United States)

Takano, Keisuke; Gutenbrunner, Charlotte; Martens, Kris; Salmon, Karen; Raes, Filip

2018-02-01

Reduced specificity of autobiographical memories is a hallmark of depressive cognition. Autobiographical memory (AM) specificity is typically measured by the Autobiographical Memory Test (AMT), in which respondents are asked to describe personal memories in response to emotional cue words. Due to this free descriptive responding format, the AMT relies on experts' hand scoring for subsequent statistical analyses. This manual coding potentially impedes research activities in big data analytics such as large epidemiological studies. Here, we propose computerized algorithms to automatically score AM specificity for the Dutch (adult participants) and English (youth participants) versions of the AMT by using natural language processing and machine learning techniques. The algorithms showed reliable performances in discriminating specific and nonspecific (e.g., overgeneralized) autobiographical memories in independent testing data sets (area under the receiver operating characteristic curve > .90). Furthermore, outcome values of the algorithms (i.e., decision values of support vector machines) showed a gradient across similar (e.g., specific and extended memories) and different (e.g., specific memory and semantic associates) categories of AMT responses, suggesting that, for both adults and youth, the algorithms well capture the extent to which a memory has features of specific memories. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Kmerind: A Flexible Parallel Library for K-mer Indexing of Biological Sequences on Distributed Memory Systems.

Science.gov (United States)

Pan, Tony; Flick, Patrick; Jain, Chirag; Liu, Yongchao; Aluru, Srinivas

2017-10-09

Counting and indexing fixed length substrings, or k-mers, in biological sequences is a key step in many bioinformatics tasks including genome alignment and mapping, genome assembly, and error correction. While advances in next generation sequencing technologies have dramatically reduced the cost and improved latency and throughput, few bioinformatics tools can efficiently process the datasets at the current generation rate of 1.8 terabases every 3 days. We present Kmerind, a high performance parallel k-mer indexing library for distributed memory environments. The Kmerind library provides a set of simple and consistent APIs with sequential semantics and parallel implementations that are designed to be flexible and extensible. Kmerind's k-mer counter performs similarly or better than the best existing k-mer counting tools even on shared memory systems. In a distributed memory environment, Kmerind counts k-mers in a 120 GB sequence read dataset in less than 13 seconds on 1024 Xeon CPU cores, and fully indexes their positions in approximately 17 seconds. Querying for 1% of the k-mers in these indices can be completed in 0.23 seconds and 28 seconds, respectively. Kmerind is the first k-mer indexing library for distributed memory environments, and the first extensible library for general k-mer indexing and counting. Kmerind is available at https://github.com/ParBLiSS/kmerind.

Virtual memory support for distributed computing environments using a shared data object model

Science.gov (United States)

Huang, F.; Bacon, J.; Mapp, G.

1995-12-01

Conventional storage management systems provide one interface for accessing memory segments and another for accessing secondary storage objects. This hinders application programming and affects overall system performance due to mandatory data copying and user/kernel boundary crossings, which in the microkernel case may involve context switches. Memory-mapping techniques may be used to provide programmers with a unified view of the storage system. This paper extends such techniques to support a shared data object model for distributed computing environments in which good support for coherence and synchronization is essential. The approach is based on a microkernel, typed memory objects, and integrated coherence control. A microkernel architecture is used to support multiple coherence protocols and the addition of new protocols. Memory objects are typed and applications can choose the most suitable protocols for different types of object to avoid protocol mismatch. Low-level coherence control is integrated with high-level concurrency control so that the number of messages required to maintain memory coherence is reduced and system-wide synchronization is realized without severely impacting the system performance. These features together contribute a novel approach to the support for flexible coherence under application control.

Parallel algorithms for testing finite state machines:Generating UIO sequences

OpenAIRE

Hierons, RM; Turker, UC

2016-01-01

This paper describes an efficient parallel algorithm that uses many-core GPUs for automatically deriving Unique Input Output sequences (UIOs) from Finite State Machines. The proposed algorithm uses the global scope of the GPU's global memory through coalesced memory access and minimises the transfer between CPU and GPU memory. The results of experiments indicate that the proposed method yields considerably better results compared to a single core UIO construction algorithm. Our algorithm is s...

How are rescaled range analyses affected by different memory and distributional properties? A Monte Carlo study

Czech Academy of Sciences Publication Activity Database

Krištoufek, Ladislav

2012-01-01

Roč. 391, č. 17 (2012), s. 4252-4260 ISSN 0378-4371 R&D Projects: GA ČR GA402/09/0965 Grant - others:GA UK(CZ) 118310; SVV(CZ) 261 501 Institutional support: RVO:67985556 Keywords : Rescaled range analysis * Modified rescaled range analysis * Hurst exponent * Long - term memory * Short- term memory Subject RIV: AH - Economics Impact factor: 1.676, year: 2012 http://library.utia.cas.cz/separaty/2012/E/kristoufek-how are rescaled range analyses affected by different memory and distributional properties.pdf

Identification of memory reactivation during sleep by EEG classification.

Science.gov (United States)

Belal, Suliman; Cousins, James; El-Deredy, Wael; Parkes, Laura; Schneider, Jules; Tsujimura, Hikaru; Zoumpoulaki, Alexia; Perapoch, Marta; Santamaria, Lorena; Lewis, Penelope

2018-04-17

Memory reactivation during sleep is critical for consolidation, but also extremely difficult to measure as it is subtle, distributed and temporally unpredictable. This article reports a novel method for detecting such reactivation in standard sleep recordings. During learning, participants produced a complex sequence of finger presses, with each finger cued by a distinct audio-visual stimulus. Auditory cues were then re-played during subsequent sleep to trigger neural reactivation through a method known as targeted memory reactivation (TMR). Next, we used electroencephalography data from the learning session to train a machine learning classifier, and then applied this classifier to sleep data to determine how successfully each tone had elicited memory reactivation. Neural reactivation was classified above chance in all participants when TMR was applied in SWS, and in 5 of the 14 participants to whom TMR was applied in N2. Classification success reduced across numerous repetitions of the tone cue, suggesting either a gradually reducing responsiveness to such cues or a plasticity-related change in the neural signature as a result of cueing. We believe this method will be valuable for future investigations of memory consolidation. Copyright © 2018 Elsevier Inc. All rights reserved.

vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design

OpenAIRE

Rhu, Minsoo; Gimelshein, Natalia; Clemons, Jason; Zulfiqar, Arslan; Keckler, Stephen W.

2016-01-01

The most widely used machine learning frameworks require users to carefully tune their memory usage so that the deep neural network (DNN) fits into the DRAM capacity of a GPU. This restriction hampers a researcher's flexibility to study different machine learning algorithms, forcing them to either use a less desirable network architecture or parallelize the processing across multiple GPUs. We propose a runtime memory manager that virtualizes the memory usage of DNNs such that both GPU and CPU...

A QDWH-Based SVD Software Framework on Distributed-Memory Manycore Systems

KAUST Repository

Sukkari, Dalal

2017-01-01

This paper presents a high performance software framework for computing a dense SVD on distributed- memory manycore systems. Originally introduced by Nakatsukasa et al. (Nakatsukasa et al. 2010; Nakatsukasa and Higham 2013), the SVD solver relies on the polar decomposition using the QR Dynamically-Weighted Halley algorithm (QDWH). Although the QDWH-based SVD algorithm performs a significant amount of extra floating-point operations compared to the traditional SVD with the one-stage bidiagonal reduction, the inherent high level of concurrency associated with Level 3 BLAS compute-bound kernels ultimately compensates for the arithmetic complexity overhead. Using the ScaLAPACK two-dimensional block cyclic data distribution with a rectangular processor topology, the resulting QDWH-SVD further reduces excessive communications during the panel factorization, while increasing the degree of parallelism during the update of the trailing submatrix, as opposed to relying to the default square processor grid. After detailing the algorithmic complexity and the memory footprint of the algorithm, we conduct a thorough performance analysis and study the impact of the grid topology on the performance by looking at the communication and computation profiling trade-offs. We report performance results against state-of-the-art existing QDWH software implementations (e.g., Elemental) and their SVD extensions on large-scale distributed-memory manycore systems based on commodity Intel x86 Haswell processors and Knights Landing (KNL) architecture. The QDWH-SVD framework achieves up to 3/8-fold on the Haswell/KNL-based platforms, respectively, against ScaLAPACK PDGESVD and turns out to be a competitive alternative for well and ill-conditioned matrices. We finally come up herein with a performance model based on these empirical results. Our QDWH-based polar decomposition and its SVD extension are freely available at https://github.com/ecrc/qdwh.git and https

Achieving memory scalability in the GYSELA code to fit Exascale constraints

International Nuclear Information System (INIS)

Rozar, Fabien; Latu, Guillaume; Roman, Jean

2014-01-01

Gyrokinetic simulations lead to huge computational needs. Up to now, the semi-Lagrangian code Gysela performed large simulations using a few thousands cores (65 k cores). But to understand more accurately the nature of the plasma turbulence, finer resolutions are wished which make Gysela a good candidate to exploit the computational power of future Exascale machines. Among the Exascale challenges, the less memory per core issue is one of the must critical. This paper deals with memory management in order to reduce the memory peak, and presents an approach to understand the memory behaviour of an application when dealing with very large meshes. This enables us to extrapolate the behaviour of Gysela for expected capabilities of Exascale machine. (authors)

Administrator of 9/11 victim compensation fund to administer Hokie Spirit Memorial Fund distributions

OpenAIRE

Hincker, Lawrence

2007-01-01

Virginia Tech President Charles Steger has asked Kenneth R. Feinberg, who served as "Special Master of the federal September 11th Victim Compensation Fund of 2001," to administer distributions of the university Hokie Spirit Memorial Fund (HSMF).

Recognition of simple visual images using a sparse distributed memory: Some implementations and experiments

Science.gov (United States)

Jaeckel, Louis A.

1990-01-01

Previously, a method was described of representing a class of simple visual images so that they could be used with a Sparse Distributed Memory (SDM). Herein, two possible implementations are described of a SDM, for which these images, suitably encoded, will serve both as addresses to the memory and as data to be stored in the memory. A key feature of both implementations is that a pattern that is represented as an unordered set with a variable number of members can be used as an address to the memory. In the 1st model, an image is encoded as a 9072 bit string to be used as a read or write address; the bit string may also be used as data to be stored in the memory. Another representation, in which an image is encoded as a 256 bit string, may be used with either model as data to be stored in the memory, but not as an address. In the 2nd model, an image is not represented as a vector of fixed length to be used as an address. Instead, a rule is given for determining which memory locations are to be activated in response to an encoded image. This activation rule treats the pieces of an image as an unordered set. With this model, the memory can be simulated, based on a method of computing the approximate result of a read operation.

Discrete Ziggurat: A time-memory trade-off for sampling from a Gaussian distribution over the integers

NARCIS (Netherlands)

Buchmann, J.; Cabarcas, D.; Göpfert, F.; Hülsing, A.T.; Weiden, P.; Lange, T.; Lauter, K.; Lisonek, P.

2014-01-01

Several lattice-based cryptosystems require to sample from a discrete Gaussian distribution over the integers. Existing methods to sample from such a distribution either need large amounts of memory or they are very slow. In this paper we explore a different method that allows for a flexible

Distributed collaborative probabilistic design for turbine blade-tip radial running clearance using support vector machine of regression

Science.gov (United States)

Fei, Cheng-Wei; Bai, Guang-Chen

2014-12-01

To improve the computational precision and efficiency of probabilistic design for mechanical dynamic assembly like the blade-tip radial running clearance (BTRRC) of gas turbine, a distribution collaborative probabilistic design method-based support vector machine of regression (SR)(called as DCSRM) is proposed by integrating distribution collaborative response surface method and support vector machine regression model. The mathematical model of DCSRM is established and the probabilistic design idea of DCSRM is introduced. The dynamic assembly probabilistic design of aeroengine high-pressure turbine (HPT) BTRRC is accomplished to verify the proposed DCSRM. The analysis results reveal that the optimal static blade-tip clearance of HPT is gained for designing BTRRC, and improving the performance and reliability of aeroengine. The comparison of methods shows that the DCSRM has high computational accuracy and high computational efficiency in BTRRC probabilistic analysis. The present research offers an effective way for the reliability design of mechanical dynamic assembly and enriches mechanical reliability theory and method.

A translator and simulator for the Burroughs D machine

Science.gov (United States)

Roberts, J.

1972-01-01

The D Machine is described as a small user microprogrammable computer designed to be a versatile building block for such diverse functions as: disk file controllers, I/O controllers, and emulators. TRANSLANG is an ALGOL-like language, which allows D Machine users to write microprograms in an English-like format as opposed to creating binary bit pattern maps. The TRANSLANG translator parses TRANSLANG programs into D Machine microinstruction bit patterns which can be executed on the D Machine simulator. In addition to simulation and translation, the two programs also offer several debugging tools, such as: a full set of diagnostic error messages, register dumps, simulated memory dumps, traces on instructions and groups of instructions, and breakpoints.

A nanojet: propulsion of a molecular machine by an asymmetric distribution of reaction--products

Science.gov (United States)

Liverpool, Tanniemola; Golestanian, Ramin; Ajdari, Armand

2006-03-01

A simple model for the reaction-driven propulsion of a small device is proposed as a model for (part of) a molecular machine in aqueous media. Motion of the device is driven by an asymmetric distribution of reaction products. We calculate the propulsive velocity of the device as well as the scale of the velocity fluctuations. We also consider the effects of hydrodynamic flow as well as a number of different scenarios for the kinetics of the reaction.

Effects of pole flux distribution in a homopolar linear synchronous machine

Science.gov (United States)

Balchin, M. J.; Eastham, J. F.; Coles, P. C.

1994-05-01

Linear forms of synchronous electrical machine are at present being considered as the propulsion means in high-speed, magnetically levitated (Maglev) ground transportation systems. A homopolar form of machine is considered in which the primary member, which carries both ac and dc windings, is supported on the vehicle. Test results and theoretical predictions are presented for a design of machine intended for driving a 100 passenger vehicle at a top speed of 400 km/h. The layout of the dc magnetic circuit is examined to locate the best position for the dc winding from the point of view of minimum core weight. Measurements of flux build-up under the machine at different operating speeds are given for two types of secondary pole: solid and laminated. The solid pole results, which are confirmed theoretically, show that this form of construction is impractical for high-speed drives. Measured motoring characteristics are presented for a short length of machine which simulates conditions at the leading and trailing ends of the full-sized machine. Combination of the results with those from a cylindrical version of the machine make it possible to infer the performance of the full-sized traction machine. This gives 0.8 pf and 0.9 efficiency at 300 km/h, which is much better than the reported performance of a comparable linear induction motor (0.52 pf and 0.82 efficiency). It is therefore concluded that in any projected high-speed Maglev systems, a linear synchronous machine should be the first choice as the propulsion means.

Built-In Test Engine For Memory Test

OpenAIRE

McEvoy, Paul; Farrell, Ronan

2004-01-01

In this paper we will present an on-chip method for testing high performance memory devices, that occupies minimal area and retains full flexibility. This is achieved through microcode test instructions and the associated on-chip state machine. In addition, the proposed methodology will enable at-speed testing of memory devices. The relevancy of this work is placed in context with an introduction to memory testing and the techniques and algorithms generally used today.

Sensorimotor memory of object weight distribution during multidigit grasp.

Science.gov (United States)

Albert, Frederic; Santello, Marco; Gordon, Andrew M

2009-10-09

We studied the ability to transfer three-digit force sharing patterns learned through consecutive lifts of an object with an asymmetric center of mass (CM). After several object lifts, we asked subjects to rotate and translate the object to the contralateral hand and perform one additional lift. This task was performed under two weight conditions (550 and 950 g) to determine the extent to which subjects would be able to transfer weight and CM information. Learning transfer was quantified by measuring the extent to which force sharing patterns and peak object roll on the first post-translation trial resembled those measured on the pre-translation trial with the same CM. We found that the overall gain of fingertip forces was transferred following object rotation, but that the scaling of individual digit forces was specific to the learned digit-object configuration, and thus was not transferred following rotation. As a result, on the first post-translation trial there was a significantly larger object roll following object lift-off than on the pre-translation trial. This suggests that sensorimotor memories for weight, requiring scaling of fingertip force gain, may differ from memories for mass distribution.

Massively Parallel Polar Decomposition on Distributed-Memory Systems

KAUST Repository

Ltaief, Hatem

2018-01-01

We present a high-performance implementation of the Polar Decomposition (PD) on distributed-memory systems. Building upon on the QR-based Dynamically Weighted Halley (QDWH) algorithm, the key idea lies in finding the best rational approximation for the scalar sign function, which also corresponds to the polar factor for symmetric matrices, to further accelerate the QDWH convergence. Based on the Zolotarev rational functions—introduced by Zolotarev (ZOLO) in 1877— this new PD algorithm ZOLO-PD converges within two iterations even for ill-conditioned matrices, instead of the original six iterations needed for QDWH. ZOLO-PD uses the property of Zolotarev functions that optimality is maintained when two functions are composed in an appropriate manner. The resulting ZOLO-PD has a convergence rate up to seventeen, in contrast to the cubic convergence rate for QDWH. This comes at the price of higher arithmetic costs and memory footprint. These extra floating-point operations can, however, be processed in an embarrassingly parallel fashion. We demonstrate performance using up to 102, 400 cores on two supercomputers. We demonstrate that, in the presence of a large number of processing units, ZOLO-PD is able to outperform QDWH by up to 2.3X speedup, especially in situations where QDWH runs out of work, for instance, in the strong scaling mode of operation.

Sensory Dissonance Using Memory Model

DEFF Research Database (Denmark)

Jensen, Karl Kristoffer

2015-01-01

Music may occur concurrently or in temporal sequences. Current machine-based methods for the estimation of qualities of the music are unable to take into account the influence of temporal context. A method for calculating dissonance from audio, called sensory dissonance is improved by the use of ...... of a memory model. This approach is validated here by the comparison of the sensory dissonance using memory model to data obtained using human subjects....

Immigration, Language Proficiency, and Autobiographical Memories: Lifespan Distribution and Second-Language Access

OpenAIRE

Esposito, Alena G.; Baker-Ward, Lynne

2015-01-01

This investigation examined two controversies in the autobiographical literature: how cross-language immigration affects the distribution of autobiographical memories across the lifespan and under what circumstances language-dependent recall is observed. Both Spanish/English bilingual immigrants and English monolingual non-immigrants participated in a cue word study, with the bilingual sample taking part in a within-subject language manipulation. The expected bump in the num...

Support vector machine for diagnosis cancer disease: A comparative study

Directory of Open Access Journals (Sweden)

Nasser H. Sweilam

2010-12-01

Full Text Available Support vector machine has become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. Training a support vector machine requires the solution of a very large quadratic programming problem. Traditional optimization methods cannot be directly applied due to memory restrictions. Up to now, several approaches exist for circumventing the above shortcomings and work well. Another learning algorithm, particle swarm optimization, Quantum-behave Particle Swarm for training SVM is introduced. Another approach named least square support vector machine (LSSVM and active set strategy are introduced. The obtained results by these methods are tested on a breast cancer dataset and compared with the exact solution model problem.

Research on intelligent machine self-perception method based on LSTM

Science.gov (United States)

Wang, Qiang; Cheng, Tao

2018-05-01

In this paper, we use the advantages of LSTM in feature extraction and processing high-dimensional and complex nonlinear data, and apply it to the autonomous perception of intelligent machines. Compared with the traditional multi-layer neural network, this model has memory, can handle time series information of any length. Since the multi-physical domain signals of processing machines have a certain timing relationship, and there is a contextual relationship between states and states, using this deep learning method to realize the self-perception of intelligent processing machines has strong versatility and adaptability. The experiment results show that the method proposed in this paper can obviously improve the sensing accuracy under various working conditions of the intelligent machine, and also shows that the algorithm can well support the intelligent processing machine to realize self-perception.

Digital Extension of Music Memory Music as a Collective Cultural Memory

Directory of Open Access Journals (Sweden)

Dimitrije Buzarovski

2014-11-01

Full Text Available Artistic works represent a very important part of collective cultural memory. Every artistic work, by definition, can confirm its existence only through the presence in collective cultural memory. The migration from author’s individual memory to common collective cultural memory forms the cultural heritage. This equally applies to tangible and intangible cultural artifacts. Being part of collective cultural memory, music reflects the spatial (geographic and temporal (historic dimensions of this memory. Until the appearance of written signs (scores music was preserved only through collective cultural memory. Scores have facilitated further distribution of music artifacts. The appearance of different means for audio, and later audio/video recordings have greatly improved the distribution of music. The transition from analog to digital recording and carriers has been a revolutionary step which substantially extended the chances for the survival of music artifacts in collective memory.

Scaling Techniques for Massive Scale-Free Graphs in Distributed (External) Memory

KAUST Repository

Pearce, Roger

2013-05-01

We present techniques to process large scale-free graphs in distributed memory. Our aim is to scale to trillions of edges, and our research is targeted at leadership class supercomputers and clusters with local non-volatile memory, e.g., NAND Flash. We apply an edge list partitioning technique, designed to accommodate high-degree vertices (hubs) that create scaling challenges when processing scale-free graphs. In addition to partitioning hubs, we use ghost vertices to represent the hubs to reduce communication hotspots. We present a scaling study with three important graph algorithms: Breadth-First Search (BFS), K-Core decomposition, and Triangle Counting. We also demonstrate scalability on BG/P Intrepid by comparing to best known Graph500 results. We show results on two clusters with local NVRAM storage that are capable of traversing trillion-edge scale-free graphs. By leveraging node-local NAND Flash, our approach can process thirty-two times larger datasets with only a 39% performance degradation in Traversed Edges Per Second (TEPS). © 2013 IEEE.

Memory intensive functional architecture for distributed computer control systems

International Nuclear Information System (INIS)

Dimmler, D.G.

1983-10-01

A memory-intensive functional architectue for distributed data-acquisition, monitoring, and control systems with large numbers of nodes has been conceptually developed and applied in several large-scale and some smaller systems. This discussion concentrates on: (1) the basic architecture; (2) recent expansions of the architecture which now become feasible in view of the rapidly developing component technologies in microprocessors and functional large-scale integration circuits; and (3) implementation of some key hardware and software structures and one system implementation which is a system for performing control and data acquisition of a neutron spectrometer at the Brookhaven High Flux Beam Reactor. The spectrometer is equipped with a large-area position-sensitive neutron detector

Interoperable mesh components for large-scale, distributed-memory simulations

International Nuclear Information System (INIS)

Devine, K; Leung, V; Diachin, L; Miller, M

2009-01-01

SciDAC applications have a demonstrated need for advanced software tools to manage the complexities associated with sophisticated geometry, mesh, and field manipulation tasks, particularly as computer architectures move toward the petascale. In this paper, we describe a software component - an abstract data model and programming interface - designed to provide support for parallel unstructured mesh operations. We describe key issues that must be addressed to successfully provide high-performance, distributed-memory unstructured mesh services and highlight some recent research accomplishments in developing new load balancing and MPI-based communication libraries appropriate for leadership class computing. Finally, we give examples of the use of parallel adaptive mesh modification in two SciDAC applications.

FY1995 distributed control of man-machine cooperative multi agent systems; 1995 nendo ningen kyochogata multi agent kikai system no jiritsu seigyo

Energy Technology Data Exchange (ETDEWEB)

NONE

1997-03-01

In the near future, distributed autonomous systems will be practical in many situations, e.g., interactive production systems, hazardous environments, nursing homes, and individual houses. The agents which consist of the distributed system must not give damages to human being and should be working economically. In this project man-machine cooperative multi agent systems are studied in many kind of respects, and basic design technology, basic control technique are developed by establishing fundamental theories and by constructing experimental systems. In this project theoretical and experimental studies are conducted in the following sub-projects: (1) Distributed cooperative control in multi agent type actuation systems (2) Control of non-holonomic systems (3) Man-machine Cooperative systems (4) Robot systems learning human skills (5) Robust force control of constrained systems In each sub-project cooperative nature between machine agent systems and human being, interference between artificial multi agents and environment and new function emergence in coordination of the multi agents and the environment, robust force control against for the environments, control methods for non-holonomic systems, robot systems which can mimic and learn human skills were studied. In each sub-project, some problems were hi-lighted and solutions for the problems have been given based on construction of experimental systems. (NEDO)

Properties of a memory network in psychology

International Nuclear Information System (INIS)

Wedemann, Roseli S.; Donangelo, Raul; Carvalho, Luis A. V. de

2007-01-01

We have previously described neurotic psychopathology and psychoanalytic working-through by an associative memory mechanism, based on a neural network model, where memory was modelled by a Boltzmann machine (BM). Since brain neural topology is selectively structured, we simulated known microscopic mechanisms that control synaptic properties, showing that the network self-organizes to a hierarchical, clustered structure. Here, we show some statistical mechanical properties of the complex networks which result from this self-organization. They indicate that a generalization of the BM may be necessary to model memory

Properties of a memory network in psychology

Science.gov (United States)

Wedemann, Roseli S.; Donangelo, Raul; de Carvalho, Luís A. V.

2007-12-01

We have previously described neurotic psychopathology and psychoanalytic working-through by an associative memory mechanism, based on a neural network model, where memory was modelled by a Boltzmann machine (BM). Since brain neural topology is selectively structured, we simulated known microscopic mechanisms that control synaptic properties, showing that the network self-organizes to a hierarchical, clustered structure. Here, we show some statistical mechanical properties of the complex networks which result from this self-organization. They indicate that a generalization of the BM may be necessary to model memory.

Superior memory efficiency of quantum devices for the simulation of continuous-time stochastic processes

Science.gov (United States)

Elliott, Thomas J.; Gu, Mile

2018-03-01

Continuous-time stochastic processes pervade everyday experience, and the simulation of models of these processes is of great utility. Classical models of systems operating in continuous-time must typically track an unbounded amount of information about past behaviour, even for relatively simple models, enforcing limits on precision due to the finite memory of the machine. However, quantum machines can require less information about the past than even their optimal classical counterparts to simulate the future of discrete-time processes, and we demonstrate that this advantage extends to the continuous-time regime. Moreover, we show that this reduction in the memory requirement can be unboundedly large, allowing for arbitrary precision even with a finite quantum memory. We provide a systematic method for finding superior quantum constructions, and a protocol for analogue simulation of continuous-time renewal processes with a quantum machine.

A trade-off between local and distributed information processing associated with remote episodic versus semantic memory.

Science.gov (United States)

Heisz, Jennifer J; Vakorin, Vasily; Ross, Bernhard; Levine, Brian; McIntosh, Anthony R

2014-01-01

Episodic memory and semantic memory produce very different subjective experiences yet rely on overlapping networks of brain regions for processing. Traditional approaches for characterizing functional brain networks emphasize static states of function and thus are blind to the dynamic information processing within and across brain regions. This study used information theoretic measures of entropy to quantify changes in the complexity of the brain's response as measured by magnetoencephalography while participants listened to audio recordings describing past personal episodic and general semantic events. Personal episodic recordings evoked richer subjective mnemonic experiences and more complex brain responses than general semantic recordings. Critically, we observed a trade-off between the relative contribution of local versus distributed entropy, such that personal episodic recordings produced relatively more local entropy whereas general semantic recordings produced relatively more distributed entropy. Changes in the relative contributions of local and distributed entropy to the total complexity of the system provides a potential mechanism that allows the same network of brain regions to represent cognitive information as either specific episodes or more general semantic knowledge.

Modeling of long-range memory processes with inverse cubic distributions by the nonlinear stochastic differential equations

Science.gov (United States)

Kaulakys, B.; Alaburda, M.; Ruseckas, J.

2016-05-01

A well-known fact in the financial markets is the so-called ‘inverse cubic law’ of the cumulative distributions of the long-range memory fluctuations of market indicators such as a number of events of trades, trading volume and the logarithmic price change. We propose the nonlinear stochastic differential equation (SDE) giving both the power-law behavior of the power spectral density and the long-range dependent inverse cubic law of the cumulative distribution. This is achieved using the suggestion that when the market evolves from calm to violent behavior there is a decrease of the delay time of multiplicative feedback of the system in comparison to the driving noise correlation time. This results in a transition from the Itô to the Stratonovich sense of the SDE and yields a long-range memory process.

Distributed memory in a heterogeneous network, as used in the CERN-PS complex timing system

CERN Document Server

Kovaltsov, V I

1995-01-01

The Distributed Table Manager (DTM) is a fast and efficient utility for distributing named binary data structures called Tables, of arbitrary size and structure, around a heterogeneous network of computers to a set of registered clients. The Tables are transmitted over a UDP network between DTM servers in network format, where the servers perform the conversions to and from host format for local clients. The servers provide clients with synchronization mechanisms, a choice of network data flows, and table options such as keeping table disc copies, shared memory or heap memory table allocation, table read/write permissions, and table subnet broadcasting. DTM has been designed to be easily maintainable, and to automatically recover from the type of errors typically encountered in a large control system network. The DTM system is based on a three level server daemon hierarchy, in which an inter daemon protocol handles network failures, and incorporates recovery procedures which will guarantee table consistency w...

Dedup Est Machina : Memory Deduplication as an Advanced Exploitation Vector

NARCIS (Netherlands)

Bosman, Erik; Razavi, Kaveh; Bos, Herbert; Giuffrida, Cristiano

2016-01-01

Memory deduplication, a well-known technique to reduce the memory footprint across virtual machines, is now also a default-on feature inside the Windows 8.1 and Windows 10 operating systems. Deduplication maps multiple identical copies of a physical page onto a single shared copy with copy-on-write

The cognitive approach to conscious machines

CERN Document Server

Haikonen, Pentti O

2003-01-01

Could a machine have an immaterial mind? The author argues that true conscious machines can be built, but rejects artificial intelligence and classical neural networks in favour of the emulation of the cognitive processes of the brain-the flow of inner speech, inner imagery and emotions. This results in a non-numeric meaning-processing machine with distributed information representation and system reactions. It is argued that this machine would be conscious; it would be aware of its own existence and its mental content and perceive this as immaterial. Novel views on consciousness and the mind-

Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy.

Science.gov (United States)

Sengupta, Partho P; Huang, Yen-Min; Bansal, Manish; Ashrafi, Ali; Fisher, Matt; Shameer, Khader; Gall, Walt; Dudley, Joel T

2016-06-01

Associating a patient's profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical judgment. We hypothesized that a similar process using a cognitive computing tool would be well suited for learning and recalling multidimensional attributes of speckle tracking echocardiography data sets derived from patients with known constrictive pericarditis and restrictive cardiomyopathy. Clinical and echocardiographic data of 50 patients with constrictive pericarditis and 44 with restrictive cardiomyopathy were used for developing an associative memory classifier-based machine-learning algorithm. The speckle tracking echocardiography data were normalized in reference to 47 controls with no structural heart disease, and the diagnostic area under the receiver operating characteristic curve of the associative memory classifier was evaluated for differentiating constrictive pericarditis from restrictive cardiomyopathy. Using only speckle tracking echocardiography variables, associative memory classifier achieved a diagnostic area under the curve of 89.2%, which improved to 96.2% with addition of 4 echocardiographic variables. In comparison, the area under the curve of early diastolic mitral annular velocity and left ventricular longitudinal strain were 82.1% and 63.7%, respectively. Furthermore, the associative memory classifier demonstrated greater accuracy and shorter learning curves than other machine-learning approaches, with accuracy asymptotically approaching 90% after a training fraction of 0.3 and remaining flat at higher training fractions. This study demonstrates feasibility of a cognitive machine-learning approach for learning and recalling patterns observed during echocardiographic evaluations. Incorporation of machine-learning algorithms in cardiac imaging may aid standardized assessments and support the quality of interpretations, particularly for novice readers with limited experience. © 2016

Operational derivation of Boltzmann distribution with Maxwell's demon model.

Science.gov (United States)

Hosoya, Akio; Maruyama, Koji; Shikano, Yutaka

2015-11-24

The resolution of the Maxwell's demon paradox linked thermodynamics with information theory through information erasure principle. By considering a demon endowed with a Turing-machine consisting of a memory tape and a processor, we attempt to explore the link towards the foundations of statistical mechanics and to derive results therein in an operational manner. Here, we present a derivation of the Boltzmann distribution in equilibrium as an example, without hypothesizing the principle of maximum entropy. Further, since the model can be applied to non-equilibrium processes, in principle, we demonstrate the dissipation-fluctuation relation to show the possibility in this direction.

The Effect of Operational Cutting Parameters on Nitinol-60 in Wire Electrodischarge Machining

Directory of Open Access Journals (Sweden)

Ali Akbar LotfiNeyestanak

2013-01-01

Full Text Available Shape memory alloys are a kind of active materials, which have significant characteristics in comparison with other alloys. Since these materials are applicable in different fields such as aerospace, automobile industry, medicine, and dentistry, the effects of wire electrodischarge machining on the properties of these alloys have been studied. In this paper, changes in the shape recovery ability and microhardness of the machined surface of Nitonol-60 shape memory alloy have been studied considering recasting and formation of resolidificated layer on the shape memory alloy surface. XRD and EDXA analyses of the surface layer of the sample besides a microscopic study of the shape memory alloy layer by SEM and a study of the changes in mechanical properties of the surface layer were done by performing microhardness and tension tests on the work piece surface. Considering the surface layer, reversible strain has been studied according to the shape recovery percentage of Nitinol-60 shape memory alloy. Results show that the surface layer formed on the surface of the samples has caused changes in both physical and mechanical properties of the cut surface because of the penetration of the separated materials in comparison with deeper layers of the piece.

Peak performance: remote memory revisited

NARCIS (Netherlands)

Mühleisen, H.; Gonçalves, R.; Kersten, M.; Johnson, R.; Kemper, A.

2013-01-01

Many database systems share a need for large amounts of fast storage. However, economies of scale limit the utility of extending a single machine with an arbitrary amount of memory. The recent broad availability of the zero-copy data transfer protocol RDMA over low-latency and high-throughput

Distributed patterns of brain activity that lead to forgetting

Directory of Open Access Journals (Sweden)

Ilke eOztekin

2011-08-01

Full Text Available Proactive interference (PI, in which irrelevant information from prior learning disrupts memory performance, is widely viewed as a major cause of forgetting. However, the hypothesized spontaneous recovery (i.e. automatic retrieval of interfering information presumed to be at the base of PI remains to be demonstrated directly. Moreover, it remains unclear at what point during learning and/or retrieval interference impacts memory performance. In order to resolve these open questions, we employed a machine-learning algorithm to identify distributed patterns of brain activity associated with retrieval of interfering information that engenders PI and causes forgetting. Participants were scanned using functional magnetic resonance imaging during an item recognition task. We induced PI by constructing sets of three consecutive study lists from the same semantic category. The classifier quantified the magnitude of category-related activity at encoding and retrieval. Category-specific activity during retrieval increased across lists, consistent with the category information becoming increasingly available and producing interference. Critically, this increase was correlated with individual differences in forgetting and the deployment of frontal lobe mechanisms that resolve interference. Collectively, these findings suggest that distributed patterns of brain activity pertaining to the interfering information during retrieval contribute to forgetting. The prefrontal cortex mediates the relationship between the spontaneous recovery of interfering information at retrieval and individual differences in memory performance.

Global asymptotic stability analysis of bidirectional associative memory neural networks with distributed delays and impulse

International Nuclear Information System (INIS)

Huang Zaitang; Luo Xiaoshu; Yang Qigui

2007-01-01

Many systems existing in physics, chemistry, biology, engineering and information science can be characterized by impulsive dynamics caused by abrupt jumps at certain instants during the process. These complex dynamical behaviors can be model by impulsive differential system or impulsive neural networks. This paper formulates and studies a new model of impulsive bidirectional associative memory (BAM) networks with finite distributed delays. Several fundamental issues, such as global asymptotic stability and existence and uniqueness of such BAM neural networks with impulse and distributed delays, are established

Machine learning of network metrics in ATLAS Distributed Data Management

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00218873; The ATLAS collaboration; Toler, Wesley; Vamosi, Ralf; Bogado Garcia, Joaquin Ignacio

2017-01-01

The increasing volume of physics data poses a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from one of our ongoing automation efforts that focuses on network metrics. First, we describe our machine learning framework built atop the ATLAS Analytics Platform. This framework can automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for network-aware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our m...

Machine learning of network metrics in ATLAS Distributed Data Management

Science.gov (United States)

Lassnig, Mario; Toler, Wesley; Vamosi, Ralf; Bogado, Joaquin; ATLAS Collaboration

2017-10-01

The increasing volume of physics data poses a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from one of our ongoing automation efforts that focuses on network metrics. First, we describe our machine learning framework built atop the ATLAS Analytics Platform. This framework can automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for networkaware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our models.

Evaluation of a Connectionless NoC for a Real-Time Distributed Shared Memory Many-Core System

NARCIS (Netherlands)

Rutgers, J.H.; Bekooij, Marco Jan Gerrit; Smit, Gerardus Johannes Maria

2012-01-01

Real-time embedded systems like smartphones tend to comprise an ever increasing number of processing cores. For scalability and the need for guaranteed performance, the use of a connection-oriented network-on-chip (NoC) is advocated. Furthermore, a distributed shared memory architecture is preferred

MSAProbs-MPI: parallel multiple sequence aligner for distributed-memory systems.

Science.gov (United States)

González-Domínguez, Jorge; Liu, Yongchao; Touriño, Juan; Schmidt, Bertil

2016-12-15

MSAProbs is a state-of-the-art protein multiple sequence alignment tool based on hidden Markov models. It can achieve high alignment accuracy at the expense of relatively long runtimes for large-scale input datasets. In this work we present MSAProbs-MPI, a distributed-memory parallel version of the multithreaded MSAProbs tool that is able to reduce runtimes by exploiting the compute capabilities of common multicore CPU clusters. Our performance evaluation on a cluster with 32 nodes (each containing two Intel Haswell processors) shows reductions in execution time of over one order of magnitude for typical input datasets. Furthermore, MSAProbs-MPI using eight nodes is faster than the GPU-accelerated QuickProbs running on a Tesla K20. Another strong point is that MSAProbs-MPI can deal with large datasets for which MSAProbs and QuickProbs might fail due to time and memory constraints, respectively. Source code in C ++ and MPI running on Linux systems as well as a reference manual are available at http://msaprobs.sourceforge.net CONTACT: jgonzalezd@udc.esSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

OPMILL - MICRO COMPUTER PROGRAMMING ENVIRONMENT FOR CNC MILLING MACHINES THREE AXIS EQUATION PLOTTING CAPABILITIES

Science.gov (United States)

Ray, R. B.

1994-01-01

OPMILL is a computer operating system for a Kearney and Trecker milling machine that provides a fast and easy way to program machine part manufacture with an IBM compatible PC. The program gives the machinist an "equation plotter" feature which plots any set of equations that define axis moves (up to three axes simultaneously) and converts those equations to a machine milling program that will move a cutter along a defined path. Other supported functions include: drill with peck, bolt circle, tap, mill arc, quarter circle, circle, circle 2 pass, frame, frame 2 pass, rotary frame, pocket, loop and repeat, and copy blocks. The system includes a tool manager that can handle up to 25 tools and automatically adjusts tool length for each tool. It will display all tool information and stop the milling machine at the appropriate time. Information for the program is entered via a series of menus and compiled to the Kearney and Trecker format. The program can then be loaded into the milling machine, the tool path graphically displayed, and tool change information or the program in Kearney and Trecker format viewed. The program has a complete file handling utility that allows the user to load the program into memory from the hard disk, save the program to the disk with comments, view directories, merge a program on the disk with one in memory, save a portion of a program in memory, and change directories. OPMILL was developed on an IBM PS/2 running DOS 3.3 with 1 MB of RAM. OPMILL was written for an IBM PC or compatible 8088 or 80286 machine connected via an RS-232 port to a Kearney and Trecker Data Mill 700/C Control milling machine. It requires a "D:" drive (fixed-disk or virtual), a browse or text display utility, and an EGA or better display. Users wishing to modify and recompile the source code will also need Turbo BASIC, Turbo C, and Crescent Software's QuickPak for Turbo BASIC. IBM PC and IBM PS/2 are registered trademarks of International Business Machines. Turbo

Soft-Deep Boltzmann Machines

OpenAIRE

Kiwaki, Taichi

2015-01-01

We present a layered Boltzmann machine (BM) that can better exploit the advantages of a distributed representation. It is widely believed that deep BMs (DBMs) have far greater representational power than its shallow counterpart, restricted Boltzmann machines (RBMs). However, this expectation on the supremacy of DBMs over RBMs has not ever been validated in a theoretical fashion. In this paper, we provide both theoretical and empirical evidences that the representational power of DBMs can be a...

Convergence dynamics of hybrid bidirectional associative memory neural networks with distributed delays

International Nuclear Information System (INIS)

Liao Xiaofeng; Wong, K.-W.; Yang Shizhong

2003-01-01

In this Letter, the characteristics of the convergence dynamics of hybrid bidirectional associative memory neural networks with distributed transmission delays are studied. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, the Lyapunov functionals are constructed and the generalized Halanay-type inequalities are employed to derive the delay-independent sufficient conditions under which the networks converge exponentially to the equilibria associated with temporally uniform external inputs. Some examples are given to illustrate the correctness of our results

Computational Model-Based Prediction of Human Episodic Memory Performance Based on Eye Movements

Science.gov (United States)

Sato, Naoyuki; Yamaguchi, Yoko

Subjects' episodic memory performance is not simply reflected by eye movements. We use a ‘theta phase coding’ model of the hippocampus to predict subjects' memory performance from their eye movements. Results demonstrate the ability of the model to predict subjects' memory performance. These studies provide a novel approach to computational modeling in the human-machine interface.

Simulation of the Mitra 15 micro-machine on IBM 360/91. Microprogramming of the fast Fourier transform

International Nuclear Information System (INIS)

Augustides, Jean

1973-01-01

As computing time did not allow working in real time, and as different methods had been proposed to reduce the computing time (the Cooley-Tuckey method, the use of specific FFT processors), this thesis proposes an intermediate solution, between the entirely programmed solution and the entirely cabled solution. After some generalities on micro-programming (definition, history, benefits and drawbacks, perspectives), the author reports the micro-programming of the Mitra 15 micro-machine: description of the machine, memory, command memory and associated circuits, interruptions and suspensions, descriptions of micro-statements, execution of a micro-statement. Programs and subroutines developed for the modelling of Mitra 15 micro-machine on IBM 360/91 are presented. Then, the author reports the micro-programming of the fast Fourier transform: theory, program description, and test

Accelerated Cyclic Reduction: A Distributed-Memory Fast Solver for Structured Linear Systems

KAUST Repository

Chávez, Gustavo

2017-12-15

We present Accelerated Cyclic Reduction (ACR), a distributed-memory fast solver for rank-compressible block tridiagonal linear systems arising from the discretization of elliptic operators, developed here for three dimensions. Algorithmic synergies between Cyclic Reduction and hierarchical matrix arithmetic operations result in a solver that has O(kNlogN(logN+k2)) arithmetic complexity and O(k Nlog N) memory footprint, where N is the number of degrees of freedom and k is the rank of a block in the hierarchical approximation, and which exhibits substantial concurrency. We provide a baseline for performance and applicability by comparing with the multifrontal method with and without hierarchical semi-separable matrices, with algebraic multigrid and with the classic cyclic reduction method. Over a set of large-scale elliptic systems with features of nonsymmetry and indefiniteness, the robustness of the direct solvers extends beyond that of the multigrid solver, and relative to the multifrontal approach ACR has lower or comparable execution time and size of the factors, with substantially lower numerical ranks. ACR exhibits good strong and weak scaling in a distributed context and, as with any direct solver, is advantageous for problems that require the solution of multiple right-hand sides. Numerical experiments show that the rank k patterns are of O(1) for the Poisson equation and of O(n) for the indefinite Helmholtz equation. The solver is ideal in situations where low-accuracy solutions are sufficient, or otherwise as a preconditioner within an iterative method.

Accelerated Cyclic Reduction: A Distributed-Memory Fast Solver for Structured Linear Systems

KAUST Repository

Chá vez, Gustavo; Turkiyyah, George; Zampini, Stefano; Ltaief, Hatem; Keyes, David E.

2017-01-01

We present Accelerated Cyclic Reduction (ACR), a distributed-memory fast solver for rank-compressible block tridiagonal linear systems arising from the discretization of elliptic operators, developed here for three dimensions. Algorithmic synergies between Cyclic Reduction and hierarchical matrix arithmetic operations result in a solver that has O(kNlogN(logN+k2)) arithmetic complexity and O(k Nlog N) memory footprint, where N is the number of degrees of freedom and k is the rank of a block in the hierarchical approximation, and which exhibits substantial concurrency. We provide a baseline for performance and applicability by comparing with the multifrontal method with and without hierarchical semi-separable matrices, with algebraic multigrid and with the classic cyclic reduction method. Over a set of large-scale elliptic systems with features of nonsymmetry and indefiniteness, the robustness of the direct solvers extends beyond that of the multigrid solver, and relative to the multifrontal approach ACR has lower or comparable execution time and size of the factors, with substantially lower numerical ranks. ACR exhibits good strong and weak scaling in a distributed context and, as with any direct solver, is advantageous for problems that require the solution of multiple right-hand sides. Numerical experiments show that the rank k patterns are of O(1) for the Poisson equation and of O(n) for the indefinite Helmholtz equation. The solver is ideal in situations where low-accuracy solutions are sufficient, or otherwise as a preconditioner within an iterative method.

Performance assessment of commercial relays for anti-islanding protection of rotating machine based distributed generation

Energy Technology Data Exchange (ETDEWEB)

Katiraei, F. [Quanta Technology, Houston, TX (United States); Abbey, C. [Natural Resources Canada, Ottawa, ON (Canada). CANMET Energy Technology Centre; Da Cunha, I. [LeapFrog Energy Technologies Inc., Mississauga, ON (Canada); Brisette, Y. [Hydro-Quebec, Montreal, PQ (Canada). Research Inst

2008-07-01

According to power industry standards, distributed generation must stop energizing the power grid upon loss of the main system. Either passive or active methods may be used to fulfill this requirement. Passive methods rely on locally measured signals to determine whether the main grid is present, while active methods inject a perturbation into the system that will manifest itself in locally measured signals if the main grid is not present. This paper compared simulation and experimental results for various commercially available relays for passive anti-islanding protection of small (below 500 kW) distributed generators using either synchronous or induction generators. A commercial multifunction relay and an application specific relay for rate-of-change-of-frequency and vector shift were modelled in simulation. Simulation results were compared with tests using a 25 kV induction generator. Results obtained for the induction machine based DG were in good agreement with trip times associated with under/overvoltage relays. The poor results with frequency based relays may be attributed to the method used for calculating frequency. Sensitivity analysis on the degree of capacitor compensation revealed a small non-detection zone, suggesting that this risk should be evaluated for induction machine based interconnections. These results showed that accurate relay modeling is challenging, particularly for frequency based techniques. Other methods for relay testing, such as hardware-in-the-loop, may be more appropriate than simulation, and are more practical in terms of cost effectiveness, than extensive field trials. 7 refs., 1 tab., 6 figs.

Testing the ghost with the machine

International Nuclear Information System (INIS)

De Zubicaray, G.

2002-01-01

Since its introduction during the 1990s, functional magnetic resonance imaging (fMRI) has been used to investigate brain activity occurring during a bewildering variety of sensory, motor and cognitive tasks. That is, a machine is being used to test 'the ghost in the machine' - the human mind. The use of imaging techniques to investigate these issues has even led to the emergence of a new scientific field called cognitive neuroscience. Currently, there are only a few groups in Australia actively publishing fMRI studies in the international literature, and the majority of these laboratories are clustered on the east coast. My own research with fMRI has focused on areas such as language and memory, with a special interest in how we solve competitive processes in our thinking

Distributed trace using central performance counter memory

Science.gov (United States)

Satterfield, David L.; Sexton, James C.

2013-01-22

A plurality of processing cores, are central storage unit having at least memory connected in a daisy chain manner, forming a daisy chain ring layout on an integrated chip. At least one of the plurality of processing cores places trace data on the daisy chain connection for transmitting the trace data to the central storage unit, and the central storage unit detects the trace data and stores the trace data in the memory co-located in with the central storage unit.

Particle simulation on a distributed memory highly parallel processor

International Nuclear Information System (INIS)

Sato, Hiroyuki; Ikesaka, Morio

1990-01-01

This paper describes parallel molecular dynamics simulation of atoms governed by local force interaction. The space in the model is divided into cubic subspaces and mapped to the processor array of the CAP-256, a distributed memory, highly parallel processor developed at Fujitsu Labs. We developed a new technique to avoid redundant calculation of forces between atoms in different processors. Experiments showed the communication overhead was less than 5%, and the idle time due to load imbalance was less than 11% for two model problems which contain 11,532 and 46,128 argon atoms. From the software simulation, the CAP-II which is under development is estimated to be about 45 times faster than CAP-256 and will be able to run the same problem about 40 times faster than Fujitsu's M-380 mainframe when 256 processors are used. (author)

On the Parallel Elliptic Single/Multigrid Solutions about Aligned and Nonaligned Bodies Using the Virtual Machine for Multiprocessors

Directory of Open Access Journals (Sweden)

A. Averbuch

1994-01-01

Full Text Available Parallel elliptic single/multigrid solutions around an aligned and nonaligned body are presented and implemented on two multi-user and single-user shared memory multiprocessors (Sequent Symmetry and MOS and on a distributed memory multiprocessor (a Transputer network. Our parallel implementation uses the Virtual Machine for Muli-Processors (VMMP, a software package that provides a coherent set of services for explicitly parallel application programs running on diverse multiple instruction multiple data (MIMD multiprocessors, both shared memory and message passing. VMMP is intended to simplify parallel program writing and to promote portable and efficient programming. Furthermore, it ensures high portability of application programs by implementing the same services on all target multiprocessors. The performance of our algorithm is investigated in detail. It is seen to fit well the above architectures when the number of processors is less than the maximal number of grid points along the axes. In general, the efficiency in the nonaligned case is higher than in the aligned case. Alignment overhead is observed to be up to 200% in the shared-memory case and up to 65% in the message-passing case. We have demonstrated that when using VMMP, the portability of the algorithms is straightforward and efficient.

Experiences and results multitasking a hydrodynamics code on global and local memory machines

International Nuclear Information System (INIS)

Mandell, D.

1987-01-01

A one-dimensional, time-dependent Lagrangian hydrodynamics code using a Godunov solution method has been multimasked for the Cray X-MP/48, the Intel iPSC hypercube, the Alliant FX series and the IBM RP3 computers. Actual multitasking results have been obtained for the Cray, Intel and Alliant computers and simulated results were obtained for the Cray and RP3 machines. The differences in the methods required to multitask on each of the machines is discussed. Results are presented for a sample problem involving a shock wave moving down a channel. Comparisons are made between theoretical speedups, predicted by Amdahl's law, and the actual speedups obtained. The problems of debugging on the different machines are also described

JACOS: AI-based simulation system for man-machine system behavior in NPP

International Nuclear Information System (INIS)

Yoshida, Kazuo; Yokobayashi, Masao; Tanabe, Fumiya; Komiya, Akitoshi

2001-08-01

A prototype of a computer simulation system named JACOS (JAERI COgnitive Simulation system) has been developed at JAERI (Japan Atomic Energy Research Institute) to simulate the man-machine system behavior in which both the cognitive behavior of a human operator and the plant behavior affect each other. The objectives of this system development is to provide man-machine system analysts with detailed information on the cognitive process of an operator and the plant behavior affected by operator's actions in accidental situations of a nuclear power plant. The simulation system consists of an operator model and a plant model which are coupled dynamically. The operator model simulates an operator's cognitive behavior in accidental situations based on the decision ladder model of Rasmussen, and is implemented using the AI-techniques of the distributed cooperative inference method with the so-called blackboard architecture. Rule-based behavior is simulated using knowledge representation with If-Then type of rules. Knowledge-based behavior is simulated using knowledge representation with MFM (Multilevel Flow Modeling) and qualitative reasoning method. Cognitive characteristics of attentional narrowing, limitation of short-term memory, and knowledge recalling from long-term memory are also taken into account. The plant model of a 3-loop PWR is also developed using a best estimate thermal-hydraulic analysis code RELAP5/MOD2. This report is prepared as User's Manual for JACOS. The first chapter of this report describes both operator and plant models in detail. The second chapter includes instructive descriptions for program installation, building of a knowledge base for operator model, execution of simulation and analysis of simulation results. The examples of simulation with JACOS are shown in the third chapter. (author)

More than a filter: Feature-based attention regulates the distribution of visual working memory resources.

Science.gov (United States)

Dube, Blaire; Emrich, Stephen M; Al-Aidroos, Naseem

2017-10-01

Across 2 experiments we revisited the filter account of how feature-based attention regulates visual working memory (VWM). Originally drawing from discrete-capacity ("slot") models, the filter account proposes that attention operates like the "bouncer in the brain," preventing distracting information from being encoded so that VWM resources are reserved for relevant information. Given recent challenges to the assumptions of discrete-capacity models, we investigated whether feature-based attention plays a broader role in regulating memory. Both experiments used partial report tasks in which participants memorized the colors of circle and square stimuli, and we provided a feature-based goal by manipulating the likelihood that 1 shape would be probed over the other across a range of probabilities. By decomposing participants' responses using mixture and variable-precision models, we estimated the contributions of guesses, nontarget responses, and imprecise memory representations to their errors. Consistent with the filter account, participants were less likely to guess when the probed memory item matched the feature-based goal. Interestingly, this effect varied with goal strength, even across high probabilities where goal-matching information should always be prioritized, demonstrating strategic control over filter strength. Beyond this effect of attention on which stimuli were encoded, we also observed effects on how they were encoded: Estimates of both memory precision and nontarget errors varied continuously with feature-based attention. The results offer support for an extension to the filter account, where feature-based attention dynamically regulates the distribution of resources within working memory so that the most relevant items are encoded with the greatest precision. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Optimal Rules for Single Machine Scheduling with Stochastic Breakdowns

Directory of Open Access Journals (Sweden)

Jinwei Gu

2014-01-01

Full Text Available This paper studies the problem of scheduling a set of jobs on a single machine subject to stochastic breakdowns, where jobs have to be restarted if preemptions occur because of breakdowns. The breakdown process of the machine is independent of the jobs processed on the machine. The processing times required to complete the jobs are constants if no breakdown occurs. The machine uptimes are independently and identically distributed (i.i.d. and are subject to a uniform distribution. It is proved that the Longest Processing Time first (LPT rule minimizes the expected makespan. For the large-scale problem, it is also showed that the Shortest Processing Time first (SPT rule is optimal to minimize the expected total completion times of all jobs.

5th IFToMM Symposium on the History of Machines and Mechanisms

CERN Document Server

Ceccarelli, Marco

2016-01-01

This volume includes contributions presented at the Fifth IFToMM Symposium on the History of Machines and Mechanisms, held at Universidad Autonoma de Queretaro, Santiago de Queretaro, QRO, Mexico, in June 2016. It contains work on theories and facts concerning mechanisms and machines from antiquity to current times as viewed in the present day. Topics include modern reviews of past works; people, history, and their works; direct memories of the recent past; historic development theories; the history of the design of machines and mechanisms; developments of mechanical design and automation; the historic development of teaching; the history of schools of engineering and the education of engineers. .

The Generalized Quantum Episodic Memory Model.

Science.gov (United States)

Trueblood, Jennifer S; Hemmer, Pernille

2017-11-01

Recent evidence suggests that experienced events are often mapped to too many episodic states, including those that are logically or experimentally incompatible with one another. For example, episodic over-distribution patterns show that the probability of accepting an item under different mutually exclusive conditions violates the disjunction rule. A related example, called subadditivity, occurs when the probability of accepting an item under mutually exclusive and exhaustive instruction conditions sums to a number >1. Both the over-distribution effect and subadditivity have been widely observed in item and source-memory paradigms. These phenomena are difficult to explain using standard memory frameworks, such as signal-detection theory. A dual-trace model called the over-distribution (OD) model (Brainerd & Reyna, 2008) can explain the episodic over-distribution effect, but not subadditivity. Our goal is to develop a model that can explain both effects. In this paper, we propose the Generalized Quantum Episodic Memory (GQEM) model, which extends the Quantum Episodic Memory (QEM) model developed by Brainerd, Wang, and Reyna (2013). We test GQEM by comparing it to the OD model using data from a novel item-memory experiment and a previously published source-memory experiment (Kellen, Singmann, & Klauer, 2014) examining the over-distribution effect. Using the best-fit parameters from the over-distribution experiments, we conclude by showing that the GQEM model can also account for subadditivity. Overall these results add to a growing body of evidence suggesting that quantum probability theory is a valuable tool in modeling recognition memory. Copyright © 2016 Cognitive Science Society, Inc.

Generalized memory associativity in a network model for the neuroses

Science.gov (United States)

Wedemann, Roseli S.; Donangelo, Raul; de Carvalho, Luís A. V.

2009-03-01

We review concepts introduced in earlier work, where a neural network mechanism describes some mental processes in neurotic pathology and psychoanalytic working-through, as associative memory functioning, according to the findings of Freud. We developed a complex network model, where modules corresponding to sensorial and symbolic memories interact, representing unconscious and conscious mental processes. The model illustrates Freud's idea that consciousness is related to symbolic and linguistic memory activity in the brain. We have introduced a generalization of the Boltzmann machine to model memory associativity. Model behavior is illustrated with simulations and some of its properties are analyzed with methods from statistical mechanics.

Efficient Machine Learning Approach for Optimizing Scientific Computing Applications on Emerging HPC Architectures

Energy Technology Data Exchange (ETDEWEB)

Arumugam, Kamesh [Old Dominion Univ., Norfolk, VA (United States)

2017-05-01

Efficient parallel implementations of scientific applications on multi-core CPUs with accelerators such as GPUs and Xeon Phis is challenging. This requires - exploiting the data parallel architecture of the accelerator along with the vector pipelines of modern x86 CPU architectures, load balancing, and efficient memory transfer between different devices. It is relatively easy to meet these requirements for highly structured scientific applications. In contrast, a number of scientific and engineering applications are unstructured. Getting performance on accelerators for these applications is extremely challenging because many of these applications employ irregular algorithms which exhibit data-dependent control-ow and irregular memory accesses. Furthermore, these applications are often iterative with dependency between steps, and thus making it hard to parallelize across steps. As a result, parallelism in these applications is often limited to a single step. Numerical simulation of charged particles beam dynamics is one such application where the distribution of work and memory access pattern at each time step is irregular. Applications with these properties tend to present significant branch and memory divergence, load imbalance between different processor cores, and poor compute and memory utilization. Prior research on parallelizing such irregular applications have been focused around optimizing the irregular, data-dependent memory accesses and control-ow during a single step of the application independent of the other steps, with the assumption that these patterns are completely unpredictable. We observed that the structure of computation leading to control-ow divergence and irregular memory accesses in one step is similar to that in the next step. It is possible to predict this structure in the current step by observing the computation structure of previous steps. In this dissertation, we present novel machine learning based optimization techniques to address

Memory and History: Some considerations on antinomies and paradoxes

Directory of Open Access Journals (Sweden)

J Šubrt

2015-12-01

Full Text Available Collective memory does not retain the memories of the past as historical events really happened, but as they are remembered in the present. Memory includes only elements of the past, not the past as a whole. Theoretical thinking about memory has been shaped by opinions often arising from very different starting points. This article outlines ten antinomies characterised by the following terms: individual and collective memory, spirit and matter, saving and deleting, irrevocable and revocable history, spontaneous and purposeful memory, myth and science, rationality and irrationality. The text explains that memory works in a selective way and the contents which are stored in it have no permanent form, but change over time according to the needs of the specific present. Human memory does not work as a rational machine, but rather is prone to distortions and errors. An important role in shaping collective memory is played by ideological influence and deep-rooted historical myths.

Smile (System/Machine-Independent Local Environment)

Energy Technology Data Exchange (ETDEWEB)

Fletcher, J.G.

1988-04-01

This document defines the characteristics of Smile, a System/machine-independent local environment. This environment consists primarily of a number of primitives (types, macros, procedure calls, and variables) that a program may use; these primitives provide facilities, such as memory allocation, timing, tasking and synchronization beyond those typically provided by a programming language. The intent is that a program will be portable from system to system and from machine to machine if it relies only on the portable aspects of its programming language and on the Smile primitives. For this to be so, Smile itself must be implemented on each system and machine, most likely using non-portable constructions; that is, while the environment provided by Smile is intended to be portable, the implementation of Smile is not necessarily so. In order to make the implementation of Smile as easy as possible and thereby expedite the porting of programs to a new system or a new machine, Smile has been defined to provide a minimal portable environment; that is, simple primitives are defined, out of which more complex facilities may be constructed using portable procedures. The implementation of Smile can be as any of the following: the underlying software environment for the operating system of an otherwise {open_quotes}bare{close_quotes} machine, a {open_quotes}guest{close_quotes} system environment built upon a preexisting operating system, an environment within a {open_quotes}user{close_quotes} process run by an operating system, or a single environment for an entire machine, encompassing both system and {open_quotes}user{close_quotes} processes. In the first three of these cases the tasks provided by Smile are {open_quotes}lightweight processes{close_quotes} multiplexed within preexisting processes or the system, while in the last case they also include the system processes themselves.

Nonlinear machine learning and design of reconfigurable digital colloids.

Science.gov (United States)

Long, Andrew W; Phillips, Carolyn L; Jankowksi, Eric; Ferguson, Andrew L

2016-09-14

Digital colloids, a cluster of freely rotating "halo" particles tethered to the surface of a central particle, were recently proposed as ultra-high density memory elements for information storage. Rational design of these digital colloids for memory storage applications requires a quantitative understanding of the thermodynamic and kinetic stability of the configurational states within which information is stored. We apply nonlinear machine learning to Brownian dynamics simulations of these digital colloids to extract the low-dimensional intrinsic manifold governing digital colloid morphology, thermodynamics, and kinetics. By modulating the relative size ratio between halo particles and central particles, we investigate the size-dependent configurational stability and transition kinetics for the 2-state tetrahedral (N = 4) and 30-state octahedral (N = 6) digital colloids. We demonstrate the use of this framework to guide the rational design of a memory storage element to hold a block of text that trades off the competing design criteria of memory addressability and volatility.

Save Now [Y/N]? Machine Memory at War in Iain Banks' "Look to Windward"

Science.gov (United States)

Blackmore, Tim

2010-01-01

Creating memory during and after wartime trauma is vexed by state attempts to control public and private discourse. Science fiction author Iain Banks' novel "Look to Windward" proposes different ways of preserving memory and culture, from posthuman memory devices, to artwork, to architecture, to personal, local ways of remembering.…

Computation of emotions in man and machines.

Science.gov (United States)

Robinson, Peter; el Kaliouby, Rana

2009-12-12

The importance of emotional expression as part of human communication has been understood since Aristotle, and the subject has been explored scientifically since Charles Darwin and others in the nineteenth century. Advances in computer technology now allow machines to recognize and express emotions, paving the way for improved human-computer and human-human communications. Recent advances in psychology have greatly improved our understanding of the role of affect in communication, perception, decision-making, attention and memory. At the same time, advances in technology mean that it is becoming possible for machines to sense, analyse and express emotions. We can now consider how these advances relate to each other and how they can be brought together to influence future research in perception, attention, learning, memory, communication, decision-making and other applications. The computation of emotions includes both recognition and synthesis, using channels such as facial expressions, non-verbal aspects of speech, posture, gestures, physiology, brain imaging and general behaviour. The combination of new results in psychology with new techniques of computation is leading to new technologies with applications in commerce, education, entertainment, security, therapy and everyday life. However, there are important issues of privacy and personal expression that must also be considered.

A homotopy method for solving Riccati equations on a shared memory parallel computer

International Nuclear Information System (INIS)

Zigic, D.; Watson, L.T.; Collins, E.G. Jr.; Davis, L.D.

1993-01-01

Although there are numerous algorithms for solving Riccati equations, there still remains a need for algorithms which can operate efficiently on large problems and on parallel machines. This paper gives a new homotopy-based algorithm for solving Riccati equations on a shared memory parallel computer. The central part of the algorithm is the computation of the kernel of the Jacobian matrix, which is essential for the corrector iterations along the homotopy zero curve. Using a Schur decomposition the tensor product structure of various matrices can be efficiently exploited. The algorithm allows for efficient parallelization on shared memory machines

Power Electronics and Electric Machines | Transportation Research | NREL

Science.gov (United States)

Power Electronics and Electric Machines NREL's power electronics and electric machines research helping boost the performance of power electronics components and systems, while driving down size, weight technical barriers to EDV commercialization. EDVs rely heavily on power electronics to distribute the proper

Machine Learning in Medicine.

Science.gov (United States)

Deo, Rahul C

2015-11-17

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome. © 2015 American Heart Association, Inc.

Machine Learning in Medicine

Science.gov (United States)

Deo, Rahul C.

2015-01-01

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games – tasks which would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in healthcare. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades – and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome. PMID:26572668

Three-Dimensional Cellular Structures Enhanced By Shape Memory Alloys

Science.gov (United States)

Nathal, Michael V.; Krause, David L.; Wilmoth, Nathan G.; Bednarcyk, Brett A.; Baker, Eric H.

2014-01-01

This research effort explored lightweight structural concepts married with advanced smart materials to achieve a wide variety of benefits in airframe and engine components. Lattice block structures were cast from an aerospace structural titanium alloy Ti-6Al-4V and a NiTi shape memory alloy (SMA), and preliminary properties have been measured. A finite element-based modeling approach that can rapidly and accurately capture the deformation response of lattice architectures was developed. The Ti-6-4 and SMA material behavior was calibrated via experimental tests of ligaments machined from the lattice. Benchmark testing of complete lattice structures verified the main aspects of the model as well as demonstrated the advantages of the lattice structure. Shape memory behavior of a sample machined from a lattice block was also demonstrated.

Designing machines for lattice physics and algorithm investigation

International Nuclear Information System (INIS)

Fischler, M.; Atac, R.; Cook, A.

1989-10-01

Special-purpose computers are appropriate tools for the study of lattice gauge theory. While these machines deliver considerable processing power, it is also important to be able to program complex physics ideas and investigate algorithms on them. We examine features that facilitate coding of physics problems, and flexibility in algorithms. Appropriate balances among power, memory, communications and I/O capabilities are presented. 10 refs

Quantum cloning machines and the applications

Energy Technology Data Exchange (ETDEWEB)

Fan, Heng, E-mail: hfan@iphy.ac.cn [Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190 (China); Collaborative Innovation Center of Quantum Matter, Beijing 100190 (China); Wang, Yi-Nan; Jing, Li [School of Physics, Peking University, Beijing 100871 (China); Yue, Jie-Dong [Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190 (China); Shi, Han-Duo; Zhang, Yong-Liang; Mu, Liang-Zhu [School of Physics, Peking University, Beijing 100871 (China)

2014-11-20

No-cloning theorem is fundamental for quantum mechanics and for quantum information science that states an unknown quantum state cannot be cloned perfectly. However, we can try to clone a quantum state approximately with the optimal fidelity, or instead, we can try to clone it perfectly with the largest probability. Thus various quantum cloning machines have been designed for different quantum information protocols. Specifically, quantum cloning machines can be designed to analyze the security of quantum key distribution protocols such as BB84 protocol, six-state protocol, B92 protocol and their generalizations. Some well-known quantum cloning machines include universal quantum cloning machine, phase-covariant cloning machine, the asymmetric quantum cloning machine and the probabilistic quantum cloning machine. In the past years, much progress has been made in studying quantum cloning machines and their applications and implementations, both theoretically and experimentally. In this review, we will give a complete description of those important developments about quantum cloning and some related topics. On the other hand, this review is self-consistent, and in particular, we try to present some detailed formulations so that further study can be taken based on those results.

Quantum cloning machines and the applications

International Nuclear Information System (INIS)

Fan, Heng; Wang, Yi-Nan; Jing, Li; Yue, Jie-Dong; Shi, Han-Duo; Zhang, Yong-Liang; Mu, Liang-Zhu

2014-01-01

No-cloning theorem is fundamental for quantum mechanics and for quantum information science that states an unknown quantum state cannot be cloned perfectly. However, we can try to clone a quantum state approximately with the optimal fidelity, or instead, we can try to clone it perfectly with the largest probability. Thus various quantum cloning machines have been designed for different quantum information protocols. Specifically, quantum cloning machines can be designed to analyze the security of quantum key distribution protocols such as BB84 protocol, six-state protocol, B92 protocol and their generalizations. Some well-known quantum cloning machines include universal quantum cloning machine, phase-covariant cloning machine, the asymmetric quantum cloning machine and the probabilistic quantum cloning machine. In the past years, much progress has been made in studying quantum cloning machines and their applications and implementations, both theoretically and experimentally. In this review, we will give a complete description of those important developments about quantum cloning and some related topics. On the other hand, this review is self-consistent, and in particular, we try to present some detailed formulations so that further study can be taken based on those results

Fault Diagnosis for Distribution Networks Using Enhanced Support Vector Machine Classifier with Classical Multidimensional Scaling

Directory of Open Access Journals (Sweden)

Ming-Yuan Cho

2017-09-01

Full Text Available In this paper, a new fault diagnosis techniques based on time domain reflectometry (TDR method with pseudo-random binary sequence (PRBS stimulus and support vector machine (SVM classifier has been investigated to recognize the different types of fault in the radial distribution feeders. This novel technique has considered the amplitude of reflected signals and the peaks of cross-correlation (CCR between the reflected and incident wave for generating fault current dataset for SVM. Furthermore, this multi-layer enhanced SVM classifier is combined with classical multidimensional scaling (CMDS feature extraction algorithm and kernel parameter optimization to increase training speed and improve overall classification accuracy. The proposed technique has been tested on a radial distribution feeder to identify ten different types of fault considering 12 input features generated by using Simulink software and MATLAB Toolbox. The success rate of SVM classifier is over 95% which demonstrates the effectiveness and the high accuracy of proposed method.

Suppressing my memories by listening to yours: The effect of socially triggered context-based prediction error on memory.

Science.gov (United States)

Vlasceanu, Madalina; Drach, Rae; Coman, Alin

2018-05-03

The mind is a prediction machine. In most situations, it has expectations as to what might happen. But when predictions are invalidated by experience (i.e., prediction errors), the memories that generate these predictions are suppressed. Here, we explore the effect of prediction error on listeners' memories following social interaction. We find that listening to a speaker recounting experiences similar to one's own triggers prediction errors on the part of the listener that lead to the suppression of her memories. This effect, we show, is sensitive to a perspective-taking manipulation, such that individuals who are instructed to take the perspective of the speaker experience memory suppression, whereas individuals who undergo a low-perspective-taking manipulation fail to show a mnemonic suppression effect. We discuss the relevance of these findings for our understanding of the bidirectional influences between cognition and social contexts, as well as for the real-world situations that involve memory-based predictions.

Event parallelism: Distributed memory parallel computing for high energy physics experiments

International Nuclear Information System (INIS)

Nash, T.

1989-05-01

This paper describes the present and expected future development of distributed memory parallel computers for high energy physics experiments. It covers the use of event parallel microprocessor farms, particularly at Fermilab, including both ACP multiprocessors and farms of MicroVAXES. These systems have proven very cost effective in the past. A case is made for moving to the more open environment of UNIX and RISC processors. The 2nd Generation ACP Multiprocessor System, which is based on powerful RISC systems, is described. Given the promise of still more extraordinary increases in processor performance, a new emphasis on point to point, rather than bussed, communication will be required. Developments in this direction are described. 6 figs

Event parallelism: Distributed memory parallel computing for high energy physics experiments

International Nuclear Information System (INIS)

Nash, T.

1989-01-01

This paper describes the present and expected future development of distributed memory parallel computers for high energy physics experiments. It covers the use of event parallel microprocessor farms, particularly at Fermilab, including both ACP multiprocessors and farms of MicroVAXES. These systems have proven very cost effective in the past. A case is made for moving to the more open environment of UNIX and RISC processors. The 2nd Generation ACP Multiprocessor System, which is based on powerful RISC systems, is described. Given the promise of still more extraordinary increases in processor performance, a new emphasis on point to point, rather than bussed, communication will be required. Developments in this direction are described. (orig.)

Event parallelism: Distributed memory parallel computing for high energy physics experiments

Science.gov (United States)

Nash, Thomas

1989-12-01

This paper describes the present and expected future development of distributed memory parallel computers for high energy physics experiments. It covers the use of event parallel microprocessor farms, particularly at Fermilab, including both ACP multiprocessors and farms of MicroVAXES. These systems have proven very cost effective in the past. A case is made for moving to the more open environment of UNIX and RISC processors. The 2nd Generation ACP Multiprocessor System, which is based on powerful RISC system, is described. Given the promise of still more extraordinary increases in processor performance, a new emphasis on point to point, rather than bussed, communication will be required. Developments in this direction are described.

Capacity for patterns and sequences in Kanerva's SDM as compared to other associative memory models. [Sparse, Distributed Memory

Science.gov (United States)

Keeler, James D.

1988-01-01

The information capacity of Kanerva's Sparse Distributed Memory (SDM) and Hopfield-type neural networks is investigated. Under the approximations used here, it is shown that the total information stored in these systems is proportional to the number connections in the network. The proportionality constant is the same for the SDM and Hopfield-type models independent of the particular model, or the order of the model. The approximations are checked numerically. This same analysis can be used to show that the SDM can store sequences of spatiotemporal patterns, and the addition of time-delayed connections allows the retrieval of context dependent temporal patterns. A minor modification of the SDM can be used to store correlated patterns.

Visualization and characterization of individual type III protein secretion machines in live bacteria.

Science.gov (United States)

Zhang, Yongdeng; Lara-Tejero, María; Bewersdorf, Jörg; Galán, Jorge E

2017-06-06

Type III protein secretion machines have evolved to deliver bacterially encoded effector proteins into eukaryotic cells. Although electron microscopy has provided a detailed view of these machines in isolation or fixed samples, little is known about their organization in live bacteria. Here we report the visualization and characterization of the Salmonella type III secretion machine in live bacteria by 2D and 3D single-molecule switching superresolution microscopy. This approach provided access to transient components of this machine, which previously could not be analyzed. We determined the subcellular distribution of individual machines, the stoichiometry of the different components of this machine in situ, and the spatial distribution of the substrates of this machine before secretion. Furthermore, by visualizing this machine in Salmonella mutants we obtained major insights into the machine's assembly. This study bridges a major resolution gap in the visualization of this nanomachine and may serve as a paradigm for the examination of other bacterially encoded molecular machines.

Compound induction electric rotating machine

Energy Technology Data Exchange (ETDEWEB)

Decesare, D

1987-07-28

The present invention generally relates to dynamo-electric machines cabable of operating in a generator mode or in a motor mode and more specifically, to increased efficiency compound interaction AC and/or DC dynamo-electric machines. This patent describes such a machine having a distributed armature winding in a cylindrical rotor wound to form axial and substantially radial winding portions and including permanent and/or electromagnets to couple magnetic flux into the peripheral or circumferential surface of the rotor, and to provide interaction between a magnetic field formed beyond the rotor axial surfaces and the rotor to thereby enhance the total induction of flux into the rotor for improved, more efficient operation. 28 figs.,

Efficient implementation of multidimensional fast fourier transform on a distributed-memory parallel multi-node computer

Science.gov (United States)

Bhanot, Gyan V [Princeton, NJ; Chen, Dong [Croton-On-Hudson, NY; Gara, Alan G [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Steinmacher-Burow, Burkhard D [Mount Kisco, NY; Vranas, Pavlos M [Bedford Hills, NY

2012-01-10

The present in invention is directed to a method, system and program storage device for efficiently implementing a multidimensional Fast Fourier Transform (FFT) of a multidimensional array comprising a plurality of elements initially distributed in a multi-node computer system comprising a plurality of nodes in communication over a network, comprising: distributing the plurality of elements of the array in a first dimension across the plurality of nodes of the computer system over the network to facilitate a first one-dimensional FFT; performing the first one-dimensional FFT on the elements of the array distributed at each node in the first dimension; re-distributing the one-dimensional FFT-transformed elements at each node in a second dimension via "all-to-all" distribution in random order across other nodes of the computer system over the network; and performing a second one-dimensional FFT on elements of the array re-distributed at each node in the second dimension, wherein the random order facilitates efficient utilization of the network thereby efficiently implementing the multidimensional FFT. The "all-to-all" re-distribution of array elements is further efficiently implemented in applications other than the multidimensional FFT on the distributed-memory parallel supercomputer.

CloudLM: a Cloud-based Language Model for Machine Translation

Directory of Open Access Journals (Sweden)

Ferrández-Tordera Jorge

2016-04-01

Full Text Available Language models (LMs are an essential element in statistical approaches to natural language processing for tasks such as speech recognition and machine translation (MT. The advent of big data leads to the availability of massive amounts of data to build LMs, and in fact, for the most prominent languages, using current techniques and hardware, it is not feasible to train LMs with all the data available nowadays. At the same time, it has been shown that the more data is used for a LM the better the performance, e.g. for MT, without any indication yet of reaching a plateau. This paper presents CloudLM, an open-source cloud-based LM intended for MT, which allows to query distributed LMs. CloudLM relies on Apache Solr and provides the functionality of state-of-the-art language modelling (it builds upon KenLM, while allowing to query massive LMs (as the use of local memory is drastically reduced, at the expense of slower decoding speed.

Bearingless AC Homopolar Machine Design and Control for Distributed Flywheel Energy Storage

Science.gov (United States)

Severson, Eric Loren

The increasing ownership of electric vehicles, in-home solar and wind generation, and wider penetration of renewable energies onto the power grid has created a need for grid-based energy storage to provide energy-neutral services. These services include frequency regulation, which requires short response-times, high power ramping capabilities, and several charge cycles over the course of one day; and diurnal load-/generation-following services to offset the inherent mismatch between renewable generation and the power grid's load profile, which requires low self-discharge so that a reasonable efficiency is obtained over a 24 hour storage interval. To realize the maximum benefits of energy storage, the technology should be modular and have minimum geographic constraints, so that it is easily scalable according to local demands. Furthermore, the technology must be economically viable to participate in the energy markets. There is currently no storage technology that is able to simultaneously meet all of these needs. This dissertation focuses on developing a new energy storage device based on flywheel technology to meet these needs. It is shown that the bearingless ac homopolar machine can be used to overcome key obstacles in flywheel technology, namely: unacceptable self-discharge and overall system cost and complexity. Bearingless machines combine the functionality of a magnetic bearing and a motor/generator into a single electromechanical device. Design of these machines is particularly challenging due to cross-coupling effects and trade-offs between motor and magnetic bearing capabilities. The bearingless ac homopolar machine adds to these design challenges due to its 3D flux paths requiring computationally expensive 3D finite element analysis. At the time this dissertation was started, bearingless ac homopolar machines were a highly immature technology. This dissertation advances the state-of-the-art of these machines through research contributions in the areas of

Diffusion with space memory modelled with distributed order space fractional differential equations

Directory of Open Access Journals (Sweden)

M. Caputo

2003-06-01

Full Text Available Distributed order fractional differential equations (Caputo, 1995, 2001; Bagley and Torvik, 2000a,b were fi rst used in the time domain; they are here considered in the space domain and introduced in the constitutive equation of diffusion. The solution of the classic problems are obtained, with closed form formulae. In general, the Green functions act as low pass fi lters in the frequency domain. The major difference with the case when a single space fractional derivative is present in the constitutive equations of diffusion (Caputo and Plastino, 2002 is that the solutions found here are potentially more fl exible to represent more complex media (Caputo, 2001a. The difference between the space memory medium and that with the time memory is that the former is more fl exible to represent local phenomena while the latter is more fl exible to represent variations in space. Concerning the boundary value problem, the difference with the solution of the classic diffusion medium, in the case when a constant boundary pressure is assigned and in the medium the pressure is initially nil, is that one also needs to assign the fi rst order space derivative at the boundary.

Parallel definition of tear film maps on distributed-memory clusters for the support of dry eye diagnosis.

Science.gov (United States)

González-Domínguez, Jorge; Remeseiro, Beatriz; Martín, María J

2017-02-01

The analysis of the interference patterns on the tear film lipid layer is a useful clinical test to diagnose dry eye syndrome. This task can be automated with a high degree of accuracy by means of the use of tear film maps. However, the time required by the existing applications to generate them prevents a wider acceptance of this method by medical experts. Multithreading has been previously successfully employed by the authors to accelerate the tear film map definition on multicore single-node machines. In this work, we propose a hybrid message-passing and multithreading parallel approach that further accelerates the generation of tear film maps by exploiting the computational capabilities of distributed-memory systems such as multicore clusters and supercomputers. The algorithm for drawing tear film maps is parallelized using Message Passing Interface (MPI) for inter-node communications and the multithreading support available in the C++11 standard for intra-node parallelization. The original algorithm is modified to reduce the communications and increase the scalability. The hybrid method has been tested on 32 nodes of an Intel cluster (with two 12-core Haswell 2680v3 processors per node) using 50 representative images. Results show that maximum runtime is reduced from almost two minutes using the previous only-multithreaded approach to less than ten seconds using the hybrid method. The hybrid MPI/multithreaded implementation can be used by medical experts to obtain tear film maps in only a few seconds, which will significantly accelerate and facilitate the diagnosis of the dry eye syndrome. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning

International Nuclear Information System (INIS)

Veronesi, F; Grassi, S

2016-01-01

Wind resource assessment is a key aspect of wind farm planning since it allows to estimate the long term electricity production. Moreover, wind speed time-series at high resolution are helpful to estimate the temporal changes of the electricity generation and indispensable to design stand-alone systems, which are affected by the mismatch of supply and demand. In this work, we present a new generalized statistical methodology to generate the spatial distribution of wind speed time-series, using Switzerland as a case study. This research is based upon a machine learning model and demonstrates that statistical wind resource assessment can successfully be used for estimating wind speed time-series. In fact, this method is able to obtain reliable wind speed estimates and propagate all the sources of uncertainty (from the measurements to the mapping process) in an efficient way, i.e. minimizing computational time and load. This allows not only an accurate estimation, but the creation of precise confidence intervals to map the stochasticity of the wind resource for a particular site. The validation shows that machine learning can minimize the bias of the wind speed hourly estimates. Moreover, for each mapped location this method delivers not only the mean wind speed, but also its confidence interval, which are crucial data for planners. (paper)

Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning

Science.gov (United States)

Veronesi, F.; Grassi, S.

2016-09-01

Wind resource assessment is a key aspect of wind farm planning since it allows to estimate the long term electricity production. Moreover, wind speed time-series at high resolution are helpful to estimate the temporal changes of the electricity generation and indispensable to design stand-alone systems, which are affected by the mismatch of supply and demand. In this work, we present a new generalized statistical methodology to generate the spatial distribution of wind speed time-series, using Switzerland as a case study. This research is based upon a machine learning model and demonstrates that statistical wind resource assessment can successfully be used for estimating wind speed time-series. In fact, this method is able to obtain reliable wind speed estimates and propagate all the sources of uncertainty (from the measurements to the mapping process) in an efficient way, i.e. minimizing computational time and load. This allows not only an accurate estimation, but the creation of precise confidence intervals to map the stochasticity of the wind resource for a particular site. The validation shows that machine learning can minimize the bias of the wind speed hourly estimates. Moreover, for each mapped location this method delivers not only the mean wind speed, but also its confidence interval, which are crucial data for planners.

JACOS: AI-based simulation system for man-machine system behavior in NPP

Energy Technology Data Exchange (ETDEWEB)

Yoshida, Kazuo; Yokobayashi, Masao; Tanabe, Fumiya [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment; Kawase, Katsumi [CSK Corp., Tokyo (Japan); Komiya, Akitoshi [Computer Associated Laboratory, Inc., Hitachinaka, Ibaraki (Japan)

2001-08-01

A prototype of a computer simulation system named JACOS (JAERI COgnitive Simulation system) has been developed at JAERI (Japan Atomic Energy Research Institute) to simulate the man-machine system behavior in which both the cognitive behavior of a human operator and the plant behavior affect each other. The objectives of this system development is to provide man-machine system analysts with detailed information on the cognitive process of an operator and the plant behavior affected by operator's actions in accidental situations of a nuclear power plant. The simulation system consists of an operator model and a plant model which are coupled dynamically. The operator model simulates an operator's cognitive behavior in accidental situations based on the decision ladder model of Rasmussen, and is implemented using the AI-techniques of the distributed cooperative inference method with the so-called blackboard architecture. Rule-based behavior is simulated using knowledge representation with If-Then type of rules. Knowledge-based behavior is simulated using knowledge representation with MFM (Multilevel Flow Modeling) and qualitative reasoning method. Cognitive characteristics of attentional narrowing, limitation of short-term memory, and knowledge recalling from long-term memory are also taken into account. The plant model of a 3-loop PWR is also developed using a best estimate thermal-hydraulic analysis code RELAP5/MOD2. This report is prepared as User's Manual for JACOS. The first chapter of this report describes both operator and plant models in detail. The second chapter includes instructive descriptions for program installation, building of a knowledge base for operator model, execution of simulation and analysis of simulation results. The examples of simulation with JACOS are shown in the third chapter. (author)

Surface Characteristics of Machined NiTi Shape Memory Alloy: The Effects of Cryogenic Cooling and Preheating Conditions

Science.gov (United States)

Kaynak, Y.; Huang, B.; Karaca, H. E.; Jawahir, I. S.

2017-07-01

This experimental study focuses on the phase state and phase transformation response of the surface and subsurface of machined NiTi alloys. X-ray diffraction (XRD) analysis and differential scanning calorimeter techniques were utilized to measure the phase state and the transformation response of machined specimens, respectively. Specimens were machined under dry machining at ambient temperature, preheated conditions, and cryogenic cooling conditions at various cutting speeds. The findings from this research demonstrate that cryogenic machining substantially alters austenite finish temperature of martensitic NiTi alloy. Austenite finish ( A f) temperature shows more than 25 percent increase resulting from cryogenic machining compared with austenite finish temperature of as-received NiTi. Dry and preheated conditions do not substantially alter austenite finish temperature. XRD analysis shows that distinctive transformation from martensite to austenite occurs during machining process in all three conditions. Complete transformation from martensite to austenite is observed in dry cutting at all selected cutting speeds.

Adaptability of optimization concept in the context of cryogenic distribution for superconducting magnets of fusion machine

Science.gov (United States)

Sarkar, Biswanath; Bhattacharya, Ritendra Nath; Vaghela, Hitensinh; Shah, Nitin Dineshkumar; Choukekar, Ketan; Badgujar, Satish

2012-06-01

Cryogenic distribution system (CDS) plays a vital role for reliable operation of largescale fusion machines in a Tokamak configuration. Managing dynamic heat loads from the superconducting magnets, namely, toroidal field, poloidal field, central solenoid and supporting structure is the most important function of the CDS along with the static heat loads. Two concepts are foreseen for the configuration of the CDS: singular distribution and collective distribution. In the first concept, each magnet is assigned with one distribution box having its own sub-cooler bath. In the collective concept, it is possible to share one common bath for more than one magnet system. The case study has been performed with an identical dynamic heat load profile applied to both concepts in the same time domain. The choices of a combined system from the magnets are also part of the study without compromising the system functionality. Process modeling and detailed simulations have been performed for both the options using Aspen HYSYS®. Multiple plasma pulses per day have been considered to verify the residual energy deposited in the superconducting magnets at the end of the plasma pulse. Preliminary 3D modeling using CATIA® has been performed along with the first level of component sizing.

Battling memory requirements of array programming through streaming

DEFF Research Database (Denmark)

Kristensen, Mads Ruben Burgdorff; Avery, James Emil; Blum, Troels

2016-01-01

A barrier to efficient array programming, for example in Python/NumPy, is that algorithms written as pure array operations completely without loops, while most efficient on small input, can lead to explosions in memory use. The present paper presents a solution to this problem using array streaming......, implemented in the automatic parallelization high-performance framework Bohrium. This makes it possible to use array programming in Python/NumPy code directly, even when the apparent memory requirement exceeds the machine capacity, since the automatic streaming eliminates the temporary memory overhead...... by performing calculations in per-thread registers. Using Bohrium, we automatically fuse, JIT-compile, and execute NumPy array operations on GPGPUs without modification to the user programs. We present performance evaluations of three benchmarks, all of which show dramatic reductions in memory use from...

Applications and modelling of bulk HTSs in brushless ac machines

International Nuclear Information System (INIS)

Barnes, G.J.

2000-01-01

The use of high temperature superconducting material in its bulk form for engineering applications is attractive due to the large power densities that can be achieved. In brushless electrical machines, there are essentially four properties that can be exploited; their hysteretic nature, their flux shielding properties, their ability to trap large flux densities and their ability to produce levitation. These properties translate to hysteresis machines, reluctance machines, trapped-field synchronous machines and linear motors respectively. Each one of these machines is addressed separately and computer simulations that reveal the current and field distributions within the machines are used to explain their operation. (author)

Working Memory and Distributed Vocabulary Learning.

Science.gov (United States)

Atkins, Paul W. B.; Baddeley, Alan D.

1998-01-01

Tested the hypothesis that individual differences in immediate-verbal-memory span predict success in second-language vocabulary acquisition. In the two-session study, adult subjects learned 56 English-Finnish translations. Tested one week later, subjects were less likely to remember those words they had difficulty learning, even though they had…

Meeting the memory challenges of brain-scale network simulation

Directory of Open Access Journals (Sweden)

Susanne eKunkel

2012-01-01

Full Text Available The development of high-performance simulation software is crucial for studying the brain connectome. Using connectome data to generate neurocomputational models requires software capable of coping with models on a variety of scales: from the microscale, investigating plasticity and dynamics of circuits in local networks, to the macroscale, investigating the interactions between distinct brain regions. Prior to any serious dynamical investigation, the first task of network simulations is to check the consistency of data integrated in the connectome and constrain ranges for yet unknown parameters. Thanks to distributed computing techniques, it is possible today to routinely simulate local cortical networks of around 10^5 neurons with up to 10^9 synapses on clusters and multi-processor shared-memory machines. However, brain-scale networks are one or two orders of magnitude larger than such local networks, in terms of numbers of neurons and synapses as well as in terms of computational load. Such networks have been studied in individual studies, but the underlying simulation technologies have neither been described in sufficient detail to be reproducible nor made publicly available. Here, we discover that as the network model sizes approach the regime of meso- and macroscale simulations, memory consumption on individual compute nodes becomes a critical bottleneck. This is especially relevant on modern supercomputers such as the Bluegene/P architecture where the available working memory per CPU core is rather limited. We develop a simple linear model to analyze the memory consumption of the constituent components of a neuronal simulator as a function of network size and the number of cores used. This approach has multiple benefits. The model enables identification of key contributing components to memory saturation and prediction of the effects of potential improvements to code before any implementation takes place.

A cross-cultural study of the lifespan distributions of life script events and autobiographical memories of life story events

DEFF Research Database (Denmark)

Zaragoza Scherman, Alejandra; Salgado, Sinué; Shao, Zhifang

Cultural Life Script Theory provides a cultural explanation of the reminiscence bump: adults older than 40 years remember more life events happening between 15 - 30 years of age. The cultural life script represents semantic knowledge about commonly shared expectations regarding the order and timing...... of major transitional life events in an idealized life course. By comparing the lifespan distribution of life scripts events and memories of life story events, we can determine the degree to which the cultural life script serves as a recall template for autobiographical memories, especially of positive...

Intelligent machines in the twenty-first century: foundations of inference and inquiry.

Science.gov (United States)

Knuth, Kevin H

2003-12-15

The last century saw the application of Boolean algebra to the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines, in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. Recent advances in our understanding of the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we recently identified the algebra of questions as the free distributive algebra, which will now allow us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper, we examine the foundations of inference and inquiry. We begin with a history of inferential reasoning, highlighting key concepts that have led to the automation of inference in modern machine-learning systems. We then discuss the foundations of inference in more detail using a modern viewpoint that relies on the mathematics of partially ordered sets and the scaffolding of lattice theory. This new viewpoint allows us to develop the logic of inquiry and introduce a measure describing the relevance of a proposed question to an unresolved issue. Last, we will demonstrate the automation of inference, and discuss how this new logic of inquiry will enable intelligent machines to ask questions. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them not only to make inferences from data, but also to decide which question to ask, which experiment to perform, or which measurement to take given what they have

AC Losses and Their Thermal Effect in High Temperature Superconducting Machines

DEFF Research Database (Denmark)

Song, Xiaowei (Andy); Mijatovic, Nenad; Zou, Shengnan

2015-01-01

In transient operations or fault conditions, high temperature superconducting (HTS) machines suffer AC losses which have an influence on the thermal stability of superconducting windings. In this paper, a method to calculate AC losses and their thermal effect in HTS machines is presented....... The method consists of three sub-models that are coupled only in one direction. The magnetic field distribution is first solved in a machine model, assuming a uniform current distribution in HTS windings. The magnetic fields on the boundaries are then used as inputs for an AC loss model which has...

AC Losses and Their Thermal Effect in High-Temperature Superconducting Machines

DEFF Research Database (Denmark)

Song, Xiaowei (Andy); Mijatovic, Nenad; Zou, Shengnan

2016-01-01

In transient operations or fault conditions, hightemperature superconducting (HTS) machines suffer ac losses, which have an influence on the thermal stability of superconducting windings. In this paper, a method to calculate ac losses and their thermal effect in HTS machines is presented....... The method consists of three submodels that are coupled only in one direction. The magnetic field distribution is first solved in a machine model, assuming a uniform current distribution in HTS windings. The magnetic fields on the boundaries are then used as inputs for an ac loss model that has a homogeneous...

Memory Reconsolidation and Computational Learning

Science.gov (United States)

2010-03-01

Siegelmann-Danieli and H.T. Siegelmann, "Robust Artificial Life Via Artificial Programmed Death," Artificial Inteligence 172(6-7), April 2008: 884-898. F...STATEMENT Unrestricted 13. SUPPLEMENTARY NOTES 20100402019 14. ABSTRACT Memory models are central to Artificial Intelligence and Machine...beyond [1]. The advances cited are a significant step toward creating Artificial Intelligence via neural networks at the human level. Our network

Scikit-learn: Machine Learning in Python

OpenAIRE

Pedregosa, Fabian; Varoquaux, Gaël; Gramfort, Alexandre; Michel, Vincent; Thirion, Bertrand; Grisel, Olivier; Blondel, Mathieu; Prettenhofer, Peter; Weiss, Ron; Dubourg, Vincent; Vanderplas, Jake; Passos, Alexandre; Cournapeau, David; Brucher, Matthieu; Perrot, Matthieu

2011-01-01

International audience; Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic ...

Scikit-learn: Machine Learning in Python

OpenAIRE

Pedregosa, Fabian; Varoquaux, Gaël; Gramfort, Alexandre; Michel, Vincent; Thirion, Bertrand; Grisel, Olivier; Blondel, Mathieu; Louppe, Gilles; Prettenhofer, Peter; Weiss, Ron; Dubourg, Vincent; Vanderplas, Jake; Passos, Alexandre; Cournapeau, David; Brucher, Matthieu

2012-01-01

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings....

Monte Carlo simulation of radiation treatment machine heads

International Nuclear Information System (INIS)

Mohan, R.

1988-01-01

Monte Carlo simulations of radiation treatment machine heads provide practical means for obtaining energy spectra and angular distributions of photons and electrons. So far, most of the work published in the literature has been limited to photons and the contaminant electrons knocked out by photons. This chapter will be confined to megavoltage photon beams produced by medical linear accelerators and 60 Co teletherapy units. The knowledge of energy spectra and angular distributions of photons and contaminant electrons emerging from such machines is important for a variety of applications in radiation dosimetry

Context-aware distributed cloud computing using CloudScheduler

Science.gov (United States)

Seuster, R.; Leavett-Brown, CR; Casteels, K.; Driemel, C.; Paterson, M.; Ring, D.; Sobie, RJ; Taylor, RP; Weldon, J.

2017-10-01

The distributed cloud using the CloudScheduler VM provisioning service is one of the longest running systems for HEP workloads. It has run millions of jobs for ATLAS and Belle II over the past few years using private and commercial clouds around the world. Our goal is to scale the distributed cloud to the 10,000-core level, with the ability to run any type of application (low I/O, high I/O and high memory) on any cloud. To achieve this goal, we have been implementing changes that utilize context-aware computing designs that are currently employed in the mobile communication industry. Context-awareness makes use of real-time and archived data to respond to user or system requirements. In our distributed cloud, we have many opportunistic clouds with no local HEP services, software or storage repositories. A context-aware design significantly improves the reliability and performance of our system by locating the nearest location of the required services. We describe how we are collecting and managing contextual information from our workload management systems, the clouds, the virtual machines and our services. This information is used not only to monitor the system but also to carry out automated corrective actions. We are incrementally adding new alerting and response services to our distributed cloud. This will enable us to scale the number of clouds and virtual machines. Further, a context-aware design will enable us to run analysis or high I/O application on opportunistic clouds. We envisage an open-source HTTP data federation (for example, the DynaFed system at CERN) as a service that would provide us access to existing storage elements used by the HEP experiments.

A reliability-based preventive maintenance methodology for the projection spot welding machine

Directory of Open Access Journals (Sweden)

Fayzimatov Ulugbek

2018-06-01

Full Text Available An effective operations of a projection spot welding (PSW machine is closely related to the effec-tiveness of the maintenance. Timely maintenance can prevent failures and improve reliability and maintainability of the machine. Therefore, establishing the maintenance frequency for the welding machine is one of the most important tasks for plant engineers. In this regard, reliability analysis of the welding machine can be used to establish preventive maintenance intervals (PMI and to identify the critical parts of the system. In this reliability and maintainability study, analysis of the PSW machine was carried out. The failure and repair data for analysis were obtained from automobile manufacturing company located in Uzbekistan. The machine was divided into three main sub-systems: electrical, pneumatic and hydraulic. Different distributions functions for all sub-systems was tested and their parameters tabulated. Based on estimated parameters of the analyzed distribu-tions, PMI for the PSW machines sub-systems at different reliability levels was calculated. Finally, preventive measures for enhancing the reliability of the PSW machine sub-systems are suggested.

LiF thermoluminescence dosimetry for mapping absorbed dose distributions in the gamma ray disinfection of machine-baled sheep wool

International Nuclear Information System (INIS)

Dexi Jiang

1985-01-01

The measurement of absorbed dose distributions of 60 Co γ-rays in machine-baled sheep wool, which is disinfected of certain parasitic bacteria (e.g. Brucella bacilli) by γ-ray treatment, is summarized. The preparation and main physical properties of the LiF-TLD are described, as well as the shape, structure and the activity of the 60 Co source and typical dose distributions measured around the source in free air. The results of dose distributions measured by the LiF-TLD agreed within +-5% with those given by a calibrated ionization chamber. The exposure rates (units R/min) at three typical measurement points inside a bale of sheep's wool were found to be quite uniform: centre 3.8x10 3 (+-2.1%); upper region 3.9x10 3 (+-2.4%); lower region 3.9x10 3 (+-1.9%). (author)

Improvement of automatic fish feeder machine design

Science.gov (United States)

Chui Wei, How; Salleh, S. M.; Ezree, Abdullah Mohd; Zaman, I.; Hatta, M. H.; Zain, B. A. Md; Mahzan, S.; Rahman, M. N. A.; Mahmud, W. A. W.

2017-10-01

Nation Plan of action for management of fishing is target to achieve an efficient, equitable and transparent management of fishing capacity in marine capture fisheries by 2018. However, several factors influence the fishery production and efficiency of marine system such as automatic fish feeder machine could be taken in consideration. Two latest fish feeder machines have been chosen as the reference for this study. Based on the observation, it has found that the both machine was made with heavy structure, low water and temperature resistance materials. This research’s objective is to develop the automatic feeder machine to increase the efficiency of fish feeding. The experiment has conducted to testing the new design of machine. The new machine with maximum storage of 5 kg and functioning with two DC motors. This machine able to distribute 500 grams of pellets within 90 seconds and longest distance of 4.7 meter. The higher speed could reduce time needed and increase the distance as well. The minimum speed range for both motor is 110 and 120 with same full speed range of 255.

Thermodynamic Model of Spatial Memory

Science.gov (United States)

Kaufman, Miron; Allen, P.

1998-03-01

We develop and test a thermodynamic model of spatial memory. Our model is an application of statistical thermodynamics to cognitive science. It is related to applications of the statistical mechanics framework in parallel distributed processes research. Our macroscopic model allows us to evaluate an entropy associated with spatial memory tasks. We find that older adults exhibit higher levels of entropy than younger adults. Thurstone's Law of Categorical Judgment, according to which the discriminal processes along the psychological continuum produced by presentations of a single stimulus are normally distributed, is explained by using a Hooke spring model of spatial memory. We have also analyzed a nonlinear modification of the ideal spring model of spatial memory. This work is supported by NIH/NIA grant AG09282-06.

Research on machine learning framework based on random forest algorithm

Science.gov (United States)

Ren, Qiong; Cheng, Hui; Han, Hai

2017-03-01

With the continuous development of machine learning, industry and academia have released a lot of machine learning frameworks based on distributed computing platform, and have been widely used. However, the existing framework of machine learning is limited by the limitations of machine learning algorithm itself, such as the choice of parameters and the interference of noises, the high using threshold and so on. This paper introduces the research background of machine learning framework, and combined with the commonly used random forest algorithm in machine learning classification algorithm, puts forward the research objectives and content, proposes an improved adaptive random forest algorithm (referred to as ARF), and on the basis of ARF, designs and implements the machine learning framework.

Modelling tick abundance using machine learning techniques and satellite imagery

DEFF Research Database (Denmark)

Kjær, Lene Jung; Korslund, L.; Kjelland, V.

satellite images to run Boosted Regression Tree machine learning algorithms to predict overall distribution (presence/absence of ticks) and relative tick abundance of nymphs and larvae in southern Scandinavia. For nymphs, the predicted abundance had a positive correlation with observed abundance...... the predicted distribution of larvae was mostly even throughout Denmark, it was primarily around the coastlines in Norway and Sweden. Abundance was fairly low overall except in some fragmented patches corresponding to forested habitats in the region. Machine learning techniques allow us to predict for larger...... the collected ticks for pathogens and using the same machine learning techniques to develop prevalence maps of the ScandTick region....

Hardware support for collecting performance counters directly to memory

Science.gov (United States)

Gara, Alan; Salapura, Valentina; Wisniewski, Robert W.

2012-09-25

Hardware support for collecting performance counters directly to memory, in one aspect, may include a plurality of performance counters operable to collect one or more counts of one or more selected activities. A first storage element may be operable to store an address of a memory location. A second storage element may be operable to store a value indicating whether the hardware should begin copying. A state machine may be operable to detect the value in the second storage element and trigger hardware copying of data in selected one or more of the plurality of performance counters to the memory location whose address is stored in the first storage element.

Short-Term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine

Energy Technology Data Exchange (ETDEWEB)

Jiang, Huaiguang; Zhang, Yingchen

2016-11-14

This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vector regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.

Exploring cluster Monte Carlo updates with Boltzmann machines.

Science.gov (United States)

Wang, Lei

2017-11-01

Boltzmann machines are physics informed generative models with broad applications in machine learning. They model the probability distribution of an input data set with latent variables and generate new samples accordingly. Applying the Boltzmann machines back to physics, they are ideal recommender systems to accelerate the Monte Carlo simulation of physical systems due to their flexibility and effectiveness. More intriguingly, we show that the generative sampling of the Boltzmann machines can even give different cluster Monte Carlo algorithms. The latent representation of the Boltzmann machines can be designed to mediate complex interactions and identify clusters of the physical system. We demonstrate these findings with concrete examples of the classical Ising model with and without four-spin plaquette interactions. In the future, automatic searches in the algorithm space parametrized by Boltzmann machines may discover more innovative Monte Carlo updates.

Exploring cluster Monte Carlo updates with Boltzmann machines

Science.gov (United States)

Wang, Lei

2017-11-01

Boltzmann machines are physics informed generative models with broad applications in machine learning. They model the probability distribution of an input data set with latent variables and generate new samples accordingly. Applying the Boltzmann machines back to physics, they are ideal recommender systems to accelerate the Monte Carlo simulation of physical systems due to their flexibility and effectiveness. More intriguingly, we show that the generative sampling of the Boltzmann machines can even give different cluster Monte Carlo algorithms. The latent representation of the Boltzmann machines can be designed to mediate complex interactions and identify clusters of the physical system. We demonstrate these findings with concrete examples of the classical Ising model with and without four-spin plaquette interactions. In the future, automatic searches in the algorithm space parametrized by Boltzmann machines may discover more innovative Monte Carlo updates.

Effect of starting microstructure upon the nucleation sites and distribution of graphite particles during a graphitising anneal of an experimental medium-carbon machining steel

Energy Technology Data Exchange (ETDEWEB)

Inam, A., E-mail: aqil.ceet@pu.edu.pk; Brydson, R., E-mail: mtlrmdb@leeds.ac.uk; Edmonds, D.V., E-mail: d.v.edmonds@leeds.ac.uk

2015-08-15

The potential for using graphite particles as an internal lubricant during machining is considered. Graphite particles were found to form during graphitisation of experimental medium-carbon steel alloyed with Si and Al. The graphite nucleation sites were strongly influenced by the starting microstructure, whether ferrite–pearlite, bainite or martensite, as revealed by light and electron microscopy. Favourable nucleation sites in the ferrite–pearlite starting microstructure were, not unexpectedly, found to be located within pearlite colonies, no doubt due to the presence of abundant cementite as a source of carbon. In consequence, the final distribution of graphite nodules in ferrite–pearlite microstructures was less uniform than for the bainite microstructure studied. In the case of martensite, this study found a predominance of nucleation at grain boundaries, again leading to less uniform graphite dispersions. - Highlights: • Metallography of formation of graphite particles in experimental carbon steel. • Potential for using graphite in steel as an internal lubricant during machining. • Microstructure features expected to influence improved machinability studied. • Influence of pre-anneal starting microstructure on graphite nucleation sites. • Influence of pre-anneal starting microstructure on graphite distribution. • Potential benefit is new free-cutting steel compositions without e.g. Pb alloying.

Axial flux permanent magnet brushless machines

CERN Document Server

Gieras, Jacek F; Kamper, Maarten J

2008-01-01

Axial Flux Permanent Magnet (AFPM) brushless machines are modern electrical machines with a lot of advantages over their conventional counterparts. They are being increasingly used in consumer electronics, public life, instrumentation and automation system, clinical engineering, industrial electromechanical drives, automobile manufacturing industry, electric and hybrid electric vehicles, marine vessels and toys. They are also used in more electric aircrafts and many other applications on larger scale. New applications have also emerged in distributed generation systems (wind turbine generators

Mechanical design of walking machines.

Science.gov (United States)

Arikawa, Keisuke; Hirose, Shigeo

2007-01-15

The performance of existing actuators, such as electric motors, is very limited, be it power-weight ratio or energy efficiency. In this paper, we discuss the method to design a practical walking machine under this severe constraint with focus on two concepts, the gravitationally decoupled actuation (GDA) and the coupled drive. The GDA decouples the driving system against the gravitational field to suppress generation of negative power and improve energy efficiency. On the other hand, the coupled drive couples the driving system to distribute the output power equally among actuators and maximize the utilization of installed actuator power. First, we depict the GDA and coupled drive in detail. Then, we present actual machines, TITAN-III and VIII, quadruped walking machines designed on the basis of the GDA, and NINJA-I and II, quadruped wall walking machines designed on the basis of the coupled drive. Finally, we discuss walking machines that travel on three-dimensional terrain (3D terrain), which includes the ground, walls and ceiling. Then, we demonstrate with computer simulation that we can selectively leverage GDA and coupled drive by walking posture control.

Efficient implementation of a multidimensional fast fourier transform on a distributed-memory parallel multi-node computer

Science.gov (United States)

Bhanot, Gyan V [Princeton, NJ; Chen, Dong [Croton-On-Hudson, NY; Gara, Alan G [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Steinmacher-Burow, Burkhard D [Mount Kisco, NY; Vranas, Pavlos M [Bedford Hills, NY

2008-01-01

The present in invention is directed to a method, system and program storage device for efficiently implementing a multidimensional Fast Fourier Transform (FFT) of a multidimensional array comprising a plurality of elements initially distributed in a multi-node computer system comprising a plurality of nodes in communication over a network, comprising: distributing the plurality of elements of the array in a first dimension across the plurality of nodes of the computer system over the network to facilitate a first one-dimensional FFT; performing the first one-dimensional FFT on the elements of the array distributed at each node in the first dimension; re-distributing the one-dimensional FFT-transformed elements at each node in a second dimension via "all-to-all" distribution in random order across other nodes of the computer system over the network; and performing a second one-dimensional FFT on elements of the array re-distributed at each node in the second dimension, wherein the random order facilitates efficient utilization of the network thereby efficiently implementing the multidimensional FFT. The "all-to-all" re-distribution of array elements is further efficiently implemented in applications other than the multidimensional FFT on the distributed-memory parallel supercomputer.

Creation of smart composites using an embroidery machine

Science.gov (United States)

Torii, Nobuhiro; Oka, Kosuke; Ikeda, Tadashige

2016-04-01

A smart composite with functional fibers and reinforcement fibers optimally placed with an embroidery machine was created. Fiber orientation affects mechanical properties of composite laminates significantly. Accordingly, if the fibers can be placed along a desired curved path, fiber reinforced plastic (FRP) structures can be designed more lightly and more sophisticatedly. To this end a tailored fiber placement method using the embroidery machine have been studied. To add functions to the FRP structures, shape memory alloy (SMA) wires were placed as functional fibers. First, for a certain purpose the paths of the reinforcement fibers and the SMA wires were simultaneously optimized in analysis. Next, the reinforcement fibers and tubes with the SMA wires were placed on fabrics by using the embroidery machine and this fabric was impregnated with resin by using the vacuum assisted resin transfer molding method. This smart composite was activated by applying voltage to the SMA wires. Fundamental properties of the smart composite were examined and the feasibility of the proposed creation method was shown.

Studies of electron collisions with polyatomic molecules using distributed-memory parallel computers

International Nuclear Information System (INIS)

Winstead, C.; Hipes, P.G.; Lima, M.A.P.; McKoy, V.

1991-01-01

Elastic electron scattering cross sections from 5--30 eV are reported for the molecules C 2 H 4 , C 2 H 6 , C 3 H 8 , Si 2 H 6 , and GeH 4 , obtained using an implementation of the Schwinger multichannel method for distributed-memory parallel computer architectures. These results, obtained within the static-exchange approximation, are in generally good agreement with the available experimental data. These calculations demonstrate the potential of highly parallel computation in the study of collisions between low-energy electrons and polyatomic gases. The computational methodology discussed is also directly applicable to the calculation of elastic cross sections at higher levels of approximation (target polarization) and of electronic excitation cross sections

Global exponential stability of bidirectional associative memory neural networks with distributed delays

Science.gov (United States)

Song, Qiankun; Cao, Jinde

2007-05-01

A bidirectional associative memory neural network model with distributed delays is considered. By constructing a new Lyapunov functional, employing the homeomorphism theory, M-matrix theory and the inequality (a[greater-or-equal, slanted]0,bk[greater-or-equal, slanted]0,qk>0 with , and r>1), a sufficient condition is obtained to ensure the existence, uniqueness and global exponential stability of the equilibrium point for the model. Moreover, the exponential converging velocity index is estimated, which depends on the delay kernel functions and the system parameters. The results generalize and improve the earlier publications, and remove the usual assumption that the activation functions are bounded . Two numerical examples are given to show the effectiveness of the obtained results.

Analysis of machining and machine tools

CERN Document Server

Liang, Steven Y

2016-01-01

This book delivers the fundamental science and mechanics of machining and machine tools by presenting systematic and quantitative knowledge in the form of process mechanics and physics. It gives readers a solid command of machining science and engineering, and familiarizes them with the geometry and functionality requirements of creating parts and components in today’s markets. The authors address traditional machining topics, such as: single and multiple point cutting processes grinding components accuracy and metrology shear stress in cutting cutting temperature and analysis chatter They also address non-traditional machining, such as: electrical discharge machining electrochemical machining laser and electron beam machining A chapter on biomedical machining is also included. This book is appropriate for advanced undergraduate and graduate mechani cal engineering students, manufacturing engineers, and researchers. Each chapter contains examples, exercises and their solutions, and homework problems that re...

Electromagnetic interference of implantable cardiac devices from a shoulder massage machine.

Science.gov (United States)

Yoshida, Saeko; Fujiwara, Kousaku; Kohira, Satoshi; Hirose, Minoru

2014-09-01

Shoulder massage machines have two pads that are driven by solenoid coils to perform a per cussive massage on the shoulders. There have been concerns that such machines might create electromagnetic interference (EMI) in implantable cardiac devices because of the time-varying magnetic fields produced by the alternating current in the solenoid coils. The objective of this study was to investigate the potential EMI from one such shoulder massage machine on implantable cardiac devices. We measured the distribution profile of the magnetic field intensity around the massage machine. Furthermore, we performed an inhibition test and an asynchronous test on an implantable cardiac pacemaker using the standardized Irnich human body model. We examined the events on an implantable cardioverter-defibrillator (ICD) using a pacemaker programmer while the massage machine was in operation. The magnetic field distribution profile exhibited a peak intensity of 212 (A/m) in one of the solenoid coils. The maximal interference distance between the massage machine and the implantable cardiac pacemaker was 28 cm. Ventricular fibrillation was induced when the massage machine was brought near the electrode of the ICD and touched the Irnich human body model. It is necessary to provide a "don't use" warning on the box or the exterior of the massage machines or in the user manuals and to caution patients with implanted pacemakers about the dangers and appropriate usage of massage machines.

Neural bases of orthographic long-term memory and working memory in dysgraphia.

Science.gov (United States)

Rapp, Brenda; Purcell, Jeremy; Hillis, Argye E; Capasso, Rita; Miceli, Gabriele

2016-02-01

Spelling a word involves the retrieval of information about the word's letters and their order from long-term memory as well as the maintenance and processing of this information by working memory in preparation for serial production by the motor system. While it is known that brain lesions may selectively affect orthographic long-term memory and working memory processes, relatively little is known about the neurotopographic distribution of the substrates that support these cognitive processes, or the lesions that give rise to the distinct forms of dysgraphia that affect these cognitive processes. To examine these issues, this study uses a voxel-based mapping approach to analyse the lesion distribution of 27 individuals with dysgraphia subsequent to stroke, who were identified on the basis of their behavioural profiles alone, as suffering from deficits only affecting either orthographic long-term or working memory, as well as six other individuals with deficits affecting both sets of processes. The findings provide, for the first time, clear evidence of substrates that selectively support orthographic long-term and working memory processes, with orthographic long-term memory deficits centred in either the left posterior inferior frontal region or left ventral temporal cortex, and orthographic working memory deficits primarily arising from lesions of the left parietal cortex centred on the intraparietal sulcus. These findings also contribute to our understanding of the relationship between the neural instantiation of written language processes and spoken language, working memory and other cognitive skills. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Shape memory of polyurethanes with silver nanoparticles

International Nuclear Information System (INIS)

Monteiro, Fernanda M.A.; Souza, Patterson P. de; Pereira, Iaci M.; Silva, Livio B.J. da; Orefice, Rodrigo L.

2011-01-01

Biodegradable polyurethane nano composites were synthesized in an aqueous environment and have their shape memory properties investigated. The matrix based in isopharane diisocyanate and poly(caprolactone diol) (Mn=1250, 2000 g.mol -1 ) was prepared by the prepolymer mixing process. The silver nanoparticles were produced by mixing AgNO 3 and tannic acid. The shape memory properties were measured using universal testing machine (DL3000, EMIC). The shape memory cycle consisted of the following steps: samples were deformed at room temperature; the mechanical constraints on the polymers were removed; samples were cooled down to 0 deg C and to retain the deformed shape; three processes were tested to recover the shape: (a) samples were heated up to 80 deg C in an oven, (b) immersed in pH 4.0 and (c) immersed in pH 7.0. To study the shape memory effect on the nanostructure, small angle X-ray scattering, wide angle X-ray scattering, infrared spectroscopy experiments were carried on. (author)

High-bandwidth memory interface

CERN Document Server

Kim, Chulwoo; Song, Junyoung

2014-01-01

This book provides an overview of recent advances in memory interface design at both the architecture and circuit levels. Coverage includes signal integrity and testing, TSV interface, high-speed serial interface including equalization, ODT, pre-emphasis, wide I/O interface including crosstalk, skew cancellation, and clock generation and distribution. Trends for further bandwidth enhancement are also covered.   • Enables readers with minimal background in memory design to understand the basics of high-bandwidth memory interface design; • Presents state-of-the-art techniques for memory interface design; • Covers memory interface design at both the circuit level and system architecture level.

Is the dose distribution distorted in IMRT and RapidArc treatment when patient plans are swapped across beam‐matched machines?

Science.gov (United States)

Radha, Chandrasekaran Anu; Subramani, Vendhan; Gunasekaran, Madhan Kumar

2016-01-01

The purpose of this study is to evaluate the degree of dose distribution distortion in advanced treatments like IMRT and RapidArc when patient plans are swapped across dosimetrically equivalent so‐called “beam‐matched” machines. For this purpose the entire work is divided into two stages. At forefront stage all basic beam properties of 6 MV X‐rays like PDD, profiles, output factors, TPR20/10 and MLC transmission of two beam‐matched machines — Varian Clinac iX and Varian 600 C/D Unique — are compared and evaluated for differences. At second stage 40 IMRT and RapidArc patient plans from the pool of head and neck (H&N) and pelvis sites are selected for the study. The plans are swapped across the machines for dose recalculation and the DVHs of target and critical organs are evaluated for dose differences. Following this, the accuracy of the beam‐matching at the TPS level for treatments like IMRT and RapidArc are compared. On PDD, profile (central 80%) and output factor comparison between the two machines, a maximum percentage disagreement value of −2.39%,−2.0% and −2.78%, respectively, has been observed. The maximum dose difference observed at volumes in IMRT and RapidArc treatments for H&N dose prescription of 69.3 Gy/33 fractions is 0.88 Gy and 0.82 Gy, respectively. Similarly, for pelvis, with a dose prescription of 50 Gy/25 fractions, a maximum dose difference of 0.55 Gy and 0.53 Gy is observed at volumes in IMRT and RapidArc treatments, respectively. Overall results of the swapped plans between two machines' 6 MV X‐rays are well within the limits of accepted clinical tolerance. PACS number(s): 87.56.bd PMID:27685106

Efficient calculation of open quantum system dynamics and time-resolved spectroscopy with distributed memory HEOM (DM-HEOM).

Science.gov (United States)

Kramer, Tobias; Noack, Matthias; Reinefeld, Alexander; Rodríguez, Mirta; Zelinskyy, Yaroslav

2018-06-11

Time- and frequency-resolved optical signals provide insights into the properties of light-harvesting molecular complexes, including excitation energies, dipole strengths and orientations, as well as in the exciton energy flow through the complex. The hierarchical equations of motion (HEOM) provide a unifying theory, which allows one to study the combined effects of system-environment dissipation and non-Markovian memory without making restrictive assumptions about weak or strong couplings or separability of vibrational and electronic degrees of freedom. With increasing system size the exact solution of the open quantum system dynamics requires memory and compute resources beyond a single compute node. To overcome this barrier, we developed a scalable variant of HEOM. Our distributed memory HEOM, DM-HEOM, is a universal tool for open quantum system dynamics. It is used to accurately compute all experimentally accessible time- and frequency-resolved processes in light-harvesting molecular complexes with arbitrary system-environment couplings for a wide range of temperatures and complex sizes. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

Neural networks for perception human and machine perception

CERN Document Server

Wechsler, Harry

1991-01-01

Neural Networks for Perception, Volume 1: Human and Machine Perception focuses on models for understanding human perception in terms of distributed computation and examples of PDP models for machine perception. This book addresses both theoretical and practical issues related to the feasibility of both explaining human perception and implementing machine perception in terms of neural network models. The book is organized into two parts. The first part focuses on human perception. Topics on network model ofobject recognition in human vision, the self-organization of functional architecture in t

Multi-functional dielectric elastomer artificial muscles for soft and smart machines

Science.gov (United States)

Anderson, Iain A.; Gisby, Todd A.; McKay, Thomas G.; O'Brien, Benjamin M.; Calius, Emilio P.

2012-08-01

Dielectric elastomer (DE) actuators are popularly referred to as artificial muscles because their impressive actuation strain and speed, low density, compliant nature, and silent operation capture many of the desirable physical properties of muscle. Unlike conventional robots and machines, whose mechanisms and drive systems rapidly become very complex as the number of degrees of freedom increases, groups of DE artificial muscles have the potential to generate rich motions combining many translational and rotational degrees of freedom. These artificial muscle systems can mimic the agonist-antagonist approach found in nature, so that active expansion of one artificial muscle is taken up by passive contraction in the other. They can also vary their stiffness. In addition, they have the ability to produce electricity from movement. But departing from the high stiffness paradigm of electromagnetic motors and gearboxes leads to new control challenges, and for soft machines to be truly dexterous like their biological analogues, they need precise control. Humans control their limbs using sensory feedback from strain sensitive cells embedded in muscle. In DE actuators, deformation is inextricably linked to changes in electrical parameters that include capacitance and resistance, so the state of strain can be inferred by sensing these changes, enabling the closed loop control that is critical for a soft machine. But the increased information processing required for a soft machine can impose a substantial burden on a central controller. The natural solution is to distribute control within the mechanism itself. The octopus arm is an example of a soft actuator with a virtually infinite number of degrees of freedom (DOF). The arm utilizes neural ganglia to process sensory data at the local "arm" level and perform complex tasks. Recent advances in soft electronics such as the piezoresistive dielectric elastomer switch (DES) have the potential to be fully integrated with actuators

ClimateSpark: An In-memory Distributed Computing Framework for Big Climate Data Analytics

Science.gov (United States)

Hu, F.; Yang, C. P.; Duffy, D.; Schnase, J. L.; Li, Z.

2016-12-01

Massive array-based climate data is being generated from global surveillance systems and model simulations. They are widely used to analyze the environment problems, such as climate changes, natural hazards, and public health. However, knowing the underlying information from these big climate datasets is challenging due to both data- and computing- intensive issues in data processing and analyzing. To tackle the challenges, this paper proposes ClimateSpark, an in-memory distributed computing framework to support big climate data processing. In ClimateSpark, the spatiotemporal index is developed to enable Apache Spark to treat the array-based climate data (e.g. netCDF4, HDF4) as native formats, which are stored in Hadoop Distributed File System (HDFS) without any preprocessing. Based on the index, the spatiotemporal query services are provided to retrieve dataset according to a defined geospatial and temporal bounding box. The data subsets will be read out, and a data partition strategy will be applied to equally split the queried data to each computing node, and store them in memory as climateRDDs for processing. By leveraging Spark SQL and User Defined Function (UDFs), the climate data analysis operations can be conducted by the intuitive SQL language. ClimateSpark is evaluated by two use cases using the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) climate reanalysis dataset. One use case is to conduct the spatiotemporal query and visualize the subset results in animation; the other one is to compare different climate model outputs using Taylor-diagram service. Experimental results show that ClimateSpark can significantly accelerate data query and processing, and enable the complex analysis services served in the SQL-style fashion.

3D hierarchical spatial representation and memory of multimodal sensory data

Science.gov (United States)

Khosla, Deepak; Dow, Paul A.; Huber, David J.

2009-04-01

This paper describes an efficient method and system for representing, processing and understanding multi-modal sensory data. More specifically, it describes a computational method and system for how to process and remember multiple locations in multimodal sensory space (e.g., visual, auditory, somatosensory, etc.). The multimodal representation and memory is based on a biologically-inspired hierarchy of spatial representations implemented with novel analogues of real representations used in the human brain. The novelty of the work is in the computationally efficient and robust spatial representation of 3D locations in multimodal sensory space as well as an associated working memory for storage and recall of these representations at the desired level for goal-oriented action. We describe (1) A simple and efficient method for human-like hierarchical spatial representations of sensory data and how to associate, integrate and convert between these representations (head-centered coordinate system, body-centered coordinate, etc.); (2) a robust method for training and learning a mapping of points in multimodal sensory space (e.g., camera-visible object positions, location of auditory sources, etc.) to the above hierarchical spatial representations; and (3) a specification and implementation of a hierarchical spatial working memory based on the above for storage and recall at the desired level for goal-oriented action(s). This work is most useful for any machine or human-machine application that requires processing of multimodal sensory inputs, making sense of it from a spatial perspective (e.g., where is the sensory information coming from with respect to the machine and its parts) and then taking some goal-oriented action based on this spatial understanding. A multi-level spatial representation hierarchy means that heterogeneous sensory inputs (e.g., visual, auditory, somatosensory, etc.) can map onto the hierarchy at different levels. When controlling various machine

Superconducting Coil Winding Machine Control System

Energy Technology Data Exchange (ETDEWEB)

Nogiec, J. M. [Fermilab; Kotelnikov, S. [Fermilab; Makulski, A. [Fermilab; Walbridge, D. [Fermilab; Trombly-Freytag, K. [Fermilab

2016-10-05

The Spirex coil winding machine is used at Fermilab to build coils for superconducting magnets. Recently this ma-chine was equipped with a new control system, which al-lows operation from both a computer and a portable remote control unit. This control system is distributed between three layers, implemented on a PC, real-time target, and FPGA, providing respectively HMI, operational logic and direct controls. The system controls motion of all mechan-ical components and regulates the cable tension. Safety is ensured by a failsafe, redundant system.

Running and machine studies in 1990

International Nuclear Information System (INIS)

1991-03-01

This annual report described the GANIL performance and machine studies. During the year 1990, the machine has been operated for 36 weeks divided into periods of 5, 6 or 7 weeks; consequently the number of beam setting up has been reduced. From 5682 hours of scheduled beam 3239 hours have been delivered on target. Very heavy ions (Pb, U) are now accelerated owing to the OAE modification. Many experiments have been completed with the new medium energy beam facility. The machine studies were devoted to the development ot the following items: production of 157 Gd 19+ ions, acceleration of 238 U 59+ at 24 MeV/u, SSC1 orbit precession, charge state distribution and energy spread after stripping [fr

LHCb experience with running jobs in virtual machines

CERN Document Server

McNab, A; Luzzi, C

2015-01-01

The LHCb experiment has been running production jobs in virtual machines since 2013 as part of its DIRAC-based infrastructure. We describe the architecture of these virtual machines and the steps taken to replicate the WLCG worker node environment expected by user and production jobs. This relies on the uCernVM system for providing root images for virtual machines. We use the CernVM-FS distributed filesystem to supply the root partition files, the LHCb software stack, and the bootstrapping scripts necessary to configure the virtual machines for us. Using this approach, we have been able to minimise the amount of contextualisation which must be provided by the virtual machine managers. We explain the process by which the virtual machine is able to receive payload jobs submitted to DIRAC by users and production managers, and how this differs from payloads executed within conventional DIRAC pilot jobs on batch queue based sites. We describe our operational experiences in running production on VM based sites mana...

Comparison between sparsely distributed memory and Hopfield-type neural network models

Science.gov (United States)

Keeler, James D.

1986-01-01

The Sparsely Distributed Memory (SDM) model (Kanerva, 1984) is compared to Hopfield-type neural-network models. A mathematical framework for comparing the two is developed, and the capacity of each model is investigated. The capacity of the SDM can be increased independently of the dimension of the stored vectors, whereas the Hopfield capacity is limited to a fraction of this dimension. However, the total number of stored bits per matrix element is the same in the two models, as well as for extended models with higher order interactions. The models are also compared in their ability to store sequences of patterns. The SDM is extended to include time delays so that contextual information can be used to cover sequences. Finally, it is shown how a generalization of the SDM allows storage of correlated input pattern vectors.

Three Types of Memory in Emergency Medical Services Communication

Science.gov (United States)

Angeli, Elizabeth L.

2015-01-01

This article examines memory and distributed cognition involved in the writing practices of emergency medical services (EMS) professionals. Results from a 16-month study indicate that EMS professionals rely on distributed cognition and three kinds of memory: individual, collaborative, and professional. Distributed cognition and the three types of…

A distributed parallel genetic algorithm of placement strategy for virtual machines deployment on cloud platform.

Science.gov (United States)

Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong

2014-01-01

The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.

A Distributed Parallel Genetic Algorithm of Placement Strategy for Virtual Machines Deployment on Cloud Platform

Directory of Open Access Journals (Sweden)

Yu-Shuang Dong

2014-01-01

Full Text Available The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.

Unstructured Adaptive Meshes: Bad for Your Memory?

Science.gov (United States)

Biswas, Rupak; Feng, Hui-Yu; VanderWijngaart, Rob

2003-01-01

This viewgraph presentation explores the need for a NASA Advanced Supercomputing (NAS) parallel benchmark for problems with irregular dynamical memory access. This benchmark is important and necessary because: 1) Problems with localized error source benefit from adaptive nonuniform meshes; 2) Certain machines perform poorly on such problems; 3) Parallel implementation may provide further performance improvement but is difficult. Some examples of problems which use irregular dynamical memory access include: 1) Heat transfer problem; 2) Heat source term; 3) Spectral element method; 4) Base functions; 5) Elemental discrete equations; 6) Global discrete equations. Nonconforming Mesh and Mortar Element Method are covered in greater detail in this presentation.

Episodic memory in aspects of large-scale brain networks

Science.gov (United States)

Jeong, Woorim; Chung, Chun Kee; Kim, June Sic

2015-01-01

Understanding human episodic memory in aspects of large-scale brain networks has become one of the central themes in neuroscience over the last decade. Traditionally, episodic memory was regarded as mostly relying on medial temporal lobe (MTL) structures. However, recent studies have suggested involvement of more widely distributed cortical network and the importance of its interactive roles in the memory process. Both direct and indirect neuro-modulations of the memory network have been tried in experimental treatments of memory disorders. In this review, we focus on the functional organization of the MTL and other neocortical areas in episodic memory. Task-related neuroimaging studies together with lesion studies suggested that specific sub-regions of the MTL are responsible for specific components of memory. However, recent studies have emphasized that connectivity within MTL structures and even their network dynamics with other cortical areas are essential in the memory process. Resting-state functional network studies also have revealed that memory function is subserved by not only the MTL system but also a distributed network, particularly the default-mode network (DMN). Furthermore, researchers have begun to investigate memory networks throughout the entire brain not restricted to the specific resting-state network (RSN). Altered patterns of functional connectivity (FC) among distributed brain regions were observed in patients with memory impairments. Recently, studies have shown that brain stimulation may impact memory through modulating functional networks, carrying future implications of a novel interventional therapy for memory impairment. PMID:26321939

Episodic memory in aspects of large-scale brain networks

Directory of Open Access Journals (Sweden)

Woorim eJeong

2015-08-01

Full Text Available Understanding human episodic memory in aspects of large-scale brain networks has become one of the central themes in neuroscience over the last decade. Traditionally, episodic memory was regarded as mostly relying on medial temporal lobe (MTL structures. However, recent studies have suggested involvement of more widely distributed cortical network and the importance of its interactive roles in the memory process. Both direct and indirect neuro-modulations of the memory network have been tried in experimental treatments of memory disorders. In this review, we focus on the functional organization of the MTL and other neocortical areas in episodic memory. Task-related neuroimaging studies together with lesion studies suggested that specific sub-regions of the MTL are responsible for specific components of memory. However, recent studies have emphasized that connectivity within MTL structures and even their network dynamics with other cortical areas are essential in the memory process. Resting-state functional network studies also have revealed that memory function is subserved by not only the MTL system but also a distributed network, particularly the default-mode network. Furthermore, researchers have begun to investigate memory networks throughout the entire brain not restricted to the specific resting-state network. Altered patterns of functional connectivity among distributed brain regions were observed in patients with memory impairments. Recently, studies have shown that brain stimulation may impact memory through modulating functional networks, carrying future implications of a novel interventional therapy for memory impairment.

Effect of Machine Smoking Intensity and Filter Ventilation Level on Gas-Phase Temperature Distribution Inside a Burning Cigarette

Directory of Open Access Journals (Sweden)

Li Bin

2015-01-01

Full Text Available Accurate measurements of cigarette coal temperature are essential to understand the thermophysical and thermo-chemical processes in a burning cigarette. The last system-atic studies of cigarette burning temperature measurements were conducted in the mid-1970s. Contemporary cigarettes have evolved in design features and multiple standard machine-smoking regimes have also become available, hence there is a need to re-examine cigarette combustion. In this work, we performed systematic measurements on gas-phase temperature of burning cigarettes using an improved fine thermocouple technique. The effects of machine-smoking parameters (puff volume and puff duration and filter ventilation levels were studied with high spatial and time resolutions during single puffs. The experimental results were presented in a number of differ-ent ways to highlight the dynamic and complex thermal processes inside a burning coal. A mathematical distribution equation was used to fit the experimental temperature data. Extracting and plotting the distribution parameters against puffing time revealed complex temperature profiles under different coal volume as a function of puffing intensities or filter ventilation levels. By dividing the coal volume prior to puffing into three temperature ranges (low-temperature from 200 to 400 °C, medium-temperature from 400 to 600 °C, and high-temperature volume above 600 °C by following their development at different smoking regimes, useful mechanistic details were obtained. Finally, direct visualisation of the gas-phase temperature through detailed temperature and temperature gradient contour maps provided further insights into the complex thermo-physics of the burning coal. [Beitr. Tabakforsch. Int. 26 (2014 191-203

Transductive and matched-pair machine learning for difficult target detection problems

Science.gov (United States)

Theiler, James

2014-06-01

This paper will describe the application of two non-traditional kinds of machine learning (transductive machine learning and the more recently proposed matched-pair machine learning) to the target detection problem. The approach combines explicit domain knowledge to model the target signal with a more agnostic machine-learning approach to characterize the background. The concept is illustrated with simulated data from an elliptically-contoured background distribution, on which a subpixel target of known spectral signature but unknown spatial extent has been implanted.

DISTRIBUTED SYSTEM FOR HUMAN MACHINE INTERACTION IN VIRTUAL ENVIRONMENTS

Directory of Open Access Journals (Sweden)

Abraham Obed Chan-Canche

2017-07-01

Full Text Available The communication networks built by multiple devices and sensors are becoming more frequent. These device networks allow human-machine interaction development which aims to improve the human performance generating an adaptive environment in response to the information provided by it. The problem of this work is the quick integration of a device network that allows the development of a flexible immersive environment for different uses.

Human processor modelling language (HPML): Estimate working memory load through interaction

OpenAIRE

Geisler, J.; Scheben, C.

2007-01-01

To operate machines over their user interface may cause high load on human's working memory. This load can decrease performance in the working task significantly if this task is a cognitive challenging one, e. g. diagnosis. With the »Human Processor Modelling Language« (HPML) the interaction activity can be modelled with a directed graph. From such models a condensed indicator value for working memory load can be estimated. Thus different user interface solutions can get compared with respect...

Prioritizing Test Cases for Memory Leaks in Android Applications

Institute of Scientific and Technical Information of China (English)

Ju Qian; Di Zhou

2016-01-01

Mobile applications usually can only access limited amount of memory. Improper use of the memory can cause memory leaks, which may lead to performance slowdowns or even cause applications to be unexpectedly killed. Although a large body of research has been devoted into the memory leak diagnosing techniques after leaks have been discovered, it is still challenging to find out the memory leak phenomena at first. Testing is the most widely used technique for failure discovery. However, traditional testing techniques are not directed for the discovery of memory leaks. They may spend lots of time on testing unlikely leaking executions and therefore can be inefficient. To address the problem, we propose a novel approach to prioritize test cases according to their likelihood to cause memory leaks in a given test suite. It firstly builds a prediction model to determine whether each test can potentially lead to memory leaks based on machine learning on selected code features. Then, for each input test case, we partly run it to get its code features and predict its likelihood to cause leaks. The most suspicious test cases will be suggested to run at first in order to reveal memory leak faults as soon as possible. Experimental evaluation on several Android applications shows that our approach is effective.

Direct observation of interaction between plasma ions and grid-excited pulses in a Q-machine

DEFF Research Database (Denmark)

Andersen, S.A.; Jensen, Vagn Orla; Michelsen, Poul

1970-01-01

The change in velocity-distribution function caused by interaction between ions and density pulses in a Q-machine is observed experimentally.......The change in velocity-distribution function caused by interaction between ions and density pulses in a Q-machine is observed experimentally....

Designing anticancer peptides by constructive machine learning.

Science.gov (United States)

Grisoni, Francesca; Neuhaus, Claudia; Gabernet, Gisela; Müller, Alex; Hiss, Jan; Schneider, Gisbert

2018-04-21

Constructive machine learning enables the automated generation of novel chemical structures without the need for explicit molecular design rules. This study presents the experimental application of such a generative model to design membranolytic anticancer peptides (ACPs) de novo. A recurrent neural network with long short-term memory cells was trained on alpha-helical cationic amphipathic peptide sequences and then fine-tuned with 26 known ACPs. This optimized model was used to generate unique and novel amino acid sequences. Twelve of the peptides were synthesized and tested for their activity on MCF7 human breast adenocarcinoma cells and selectivity against human erythrocytes. Ten of these peptides were active against cancer cells. Six of the active peptides killed MCF7 cancer cells without affecting human erythrocytes with at least threefold selectivity. These results advocate constructive machine learning for the automated design of peptides with desired biological activities. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

From shoebox to performative agent: the computer as personal memory machine

NARCIS (Netherlands)

van Dijck, J.

2005-01-01

Digital technologies offer new opportunities in the everyday lives of people: with still expanding memory capacities, the computer is rapidly becoming a giant storage and processing facility for recording and retrieving ‘bits of life’. Software engineers and companies promise not only to expand the

Machine rates for selected forest harvesting machines

Science.gov (United States)

R.W. Brinker; J. Kinard; Robert Rummer; B. Lanford

2002-01-01

Very little new literature has been published on the subject of machine rates and machine cost analysis since 1989 when the Alabama Agricultural Experiment Station Circular 296, Machine Rates for Selected Forest Harvesting Machines, was originally published. Many machines discussed in the original publication have undergone substantial changes in various aspects, not...

One-way shared memory

DEFF Research Database (Denmark)

Schoeberl, Martin

2018-01-01

Standard multicore processors use the shared main memory via the on-chip caches for communication between cores. However, this form of communication has two limitations: (1) it is hardly time-predictable and therefore not a good solution for real-time systems and (2) this single shared memory...... is a bottleneck in the system. This paper presents a communication architecture for time-predictable multicore systems where core-local memories are distributed on the chip. A network-on-chip constantly copies data from a sender core-local memory to a receiver core-local memory. As this copying is performed...... in one direction we call this architecture a one-way shared memory. With the use of time-division multiplexing for the memory accesses and the network-on-chip routers we achieve a time-predictable solution where the communication latency and bandwidth can be bounded. An example architecture for a 3...

Changing concepts of working memory

Science.gov (United States)

Ma, Wei Ji; Husain, Masud; Bays, Paul M

2014-01-01

Working memory is widely considered to be limited in capacity, holding a fixed, small number of items, such as Miller's ‘magical number’ seven or Cowan's four. It has recently been proposed that working memory might better be conceptualized as a limited resource that is distributed flexibly among all items to be maintained in memory. According to this view, the quality rather than the quantity of working memory representations determines performance. Here we consider behavioral and emerging neural evidence for this proposal. PMID:24569831

Extending OpenMP for NUMA Machines

Directory of Open Access Journals (Sweden)

John Bircsak

2000-01-01

Full Text Available This paper describes extensions to OpenMP that implement data placement features needed for NUMA architectures. OpenMP is a collection of compiler directives and library routines used to write portable parallel programs for shared-memory architectures. Writing efficient parallel programs for NUMA architectures, which have characteristics of both shared-memory and distributed-memory architectures, requires that a programmer control the placement of data in memory and the placement of computations that operate on that data. Optimal performance is obtained when computations occur on processors that have fast access to the data needed by those computations. OpenMP -- designed for shared-memory architectures -- does not by itself address these issues. The extensions to OpenMP Fortran presented here have been mainly taken from High Performance Fortran. The paper describes some of the techniques that the Compaq Fortran compiler uses to generate efficient code based on these extensions. It also describes some additional compiler optimizations, and concludes with some preliminary results.

Smell your way back to childhood: autobiographical odor memory.

Science.gov (United States)

Willander, Johan; Larsson, Maria

2006-04-01

This study addressed age distributions and experiential qualities of autobiographical memories evoked by different sensory cues. Ninety-three older adults were presented with one of three cue types (word, picture, or odor) and were asked to relate any autobiographical event for the given cue. The main aims were to explore whether (1) the age distribution of olfactory-evoked memories differs from memories cued by words and pictures and (2) the experiential qualities of the evoked memories vary over the different cues. The results showed that autobiographical memories triggered by olfactory information were older than memories associated with verbal and visual information. Specifically, most odor-cued memories were located to the first decade of life (memories associated with verbal and visual cues peaked in early adulthood (11-20 years). Also, odor-evoked memories were associated with stronger feelings of being brought back in time and had been thought of less often than memories evoked by verbal and visual information. This pattern of findings suggests that odor-evoked memories may be different from other memory experiences.

Performance analysis of a composite dual-winding reluctance machine

International Nuclear Information System (INIS)

Anih, Linus U.; Obe, Emeka S.

2009-01-01

The electromagnetic energy conversion process of a composite dual-winding asynchronous reluctance machine is presented. The mechanism of torque production is explained using the magnetic fields distributions. The dynamic model developed in dq-rotor reference frame from first principles depicts the machine operation and response to sudden load change. The device is self-starting in the absence of rotor conductors and its starting current is lower than that of a conventional induction machine. Although the machine possesses salient pole rotors, it is clearly shown that its performance is that of an induction motor operating at half the synchronous speed. Hence the device produces synchronous torque while operating asynchronously. Simple tests were conducted on a prototype demonstration machine and the results obtained are seen to be in tune with the theory and the steady-state calculations.

Compiler design handbook optimizations and machine code generation

CERN Document Server

Srikant, YN

2003-01-01

The widespread use of object-oriented languages and Internet security concerns are just the beginning. Add embedded systems, multiple memory banks, highly pipelined units operating in parallel, and a host of other advances and it becomes clear that current and future computer architectures pose immense challenges to compiler designers-challenges that already exceed the capabilities of traditional compilation techniques. The Compiler Design Handbook: Optimizations and Machine Code Generation is designed to help you meet those challenges. Written by top researchers and designers from around the

Examining procedural working memory processing in obsessive-compulsive disorder.

Science.gov (United States)

Shahar, Nitzan; Teodorescu, Andrei R; Anholt, Gideon E; Karmon-Presser, Anat; Meiran, Nachshon

2017-07-01

Previous research has suggested that a deficit in working memory might underlie the difficulty of obsessive-compulsive disorder (OCD) patients to control their thoughts and actions. However, a recent meta-analyses found only small effect sizes for working memory deficits in OCD. Recently, a distinction has been made between declarative and procedural working memory. Working memory in OCD was tested mostly using declarative measurements. However, OCD symptoms typically concerns actions, making procedural working-memory more relevant. Here, we tested the operation of procedural working memory in OCD. Participants with OCD and healthy controls performed a battery of choice reaction tasks under high and low procedural working memory demands. Reaction-times (RT) were estimated using ex-Gaussian distribution fitting, revealing no group differences in the size of the RT distribution tail (i.e., τ parameter), known to be sensitive to procedural working memory manipulations. Group differences, unrelated to working memory manipulations, were found in the leading-edge of the RT distribution and analyzed using a two-stage evidence accumulation model. Modeling results suggested that perceptual difficulties might underlie the current group differences. In conclusion, our results suggest that procedural working-memory processing is most likely intact in OCD, and raise a novel, yet untested assumption regarding perceptual deficits in OCD. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Oracle database 12c release 2 in-memory tips and techniques for maximum performance

CERN Document Server

Banerjee, Joyjeet

2017-01-01

This Oracle Press guide shows, step-by-step, how to optimize database performance and cut transaction processing time using Oracle Database 12c Release 2 In-Memory. Oracle Database 12c Release 2 In-Memory: Tips and Techniques for Maximum Performance features hands-on instructions, best practices, and expert tips from an Oracle enterprise architect. You will learn how to deploy the software, use In-Memory Advisor, build queries, and interoperate with Oracle RAC and Multitenant. A complete chapter of case studies illustrates real-world applications. • Configure Oracle Database 12c and construct In-Memory enabled databases • Edit and control In-Memory options from the graphical interface • Implement In-Memory with Oracle Real Application Clusters • Use the In-Memory Advisor to determine what objects to keep In-Memory • Optimize In-Memory queries using groups, expressions, and aggregations • Maximize performance using Oracle Exadata Database Machine and In-Memory option • Use Swingbench to create d...

Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features

Directory of Open Access Journals (Sweden)

Ramesh Kumar Lama

2017-01-01

Full Text Available Alzheimer’s disease (AD is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR images to discriminate AD, mild cognitive impairment (MCI, and healthy control (HC subjects using a support vector machine (SVM, an import vector machine (IVM, and a regularized extreme learning machine (RELM. The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer’s disease neuroimaging initiative (ADNI datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.

Limits of the memory coefficient in measuring correlated bursts

Science.gov (United States)

Jo, Hang-Hyun; Hiraoka, Takayuki

2018-03-01

Temporal inhomogeneities in event sequences of natural and social phenomena have been characterized in terms of interevent times and correlations between interevent times. The inhomogeneities of interevent times have been extensively studied, while the correlations between interevent times, often called correlated bursts, are far from being fully understood. For measuring the correlated bursts, two relevant approaches were suggested, i.e., memory coefficient and burst size distribution. Here a burst size denotes the number of events in a bursty train detected for a given time window. Empirical analyses have revealed that the larger memory coefficient tends to be associated with the heavier tail of the burst size distribution. In particular, empirical findings in human activities appear inconsistent, such that the memory coefficient is close to 0, while burst size distributions follow a power law. In order to comprehend these observations, by assuming the conditional independence between consecutive interevent times, we derive the analytical form of the memory coefficient as a function of parameters describing interevent time and burst size distributions. Our analytical result can explain the general tendency of the larger memory coefficient being associated with the heavier tail of burst size distribution. We also find that the apparently inconsistent observations in human activities are compatible with each other, indicating that the memory coefficient has limits to measure the correlated bursts.

A Distributed Algorithm for the Cluster-Based Outlier Detection Using Unsupervised Extreme Learning Machines

Directory of Open Access Journals (Sweden)

Xite Wang

2017-01-01

Full Text Available Outlier detection is an important data mining task, whose target is to find the abnormal or atypical objects from a given dataset. The techniques for detecting outliers have a lot of applications, such as credit card fraud detection and environment monitoring. Our previous work proposed the Cluster-Based (CB outlier and gave a centralized method using unsupervised extreme learning machines to compute CB outliers. In this paper, we propose a new distributed algorithm for the CB outlier detection (DACB. On the master node, we collect a small number of points from the slave nodes to obtain a threshold. On each slave node, we design a new filtering method that can use the threshold to efficiently speed up the computation. Furthermore, we also propose a ranking method to optimize the order of cluster scanning. At last, the effectiveness and efficiency of the proposed approaches are verified through a plenty of simulation experiments.

Static Memory Deduplication for Performance Optimization in Cloud Computing

Directory of Open Access Journals (Sweden)

Gangyong Jia

2017-04-01

Full Text Available In a cloud computing environment, the number of virtual machines (VMs on a single physical server and the number of applications running on each VM are continuously growing. This has led to an enormous increase in the demand of memory capacity and subsequent increase in the energy consumption in the cloud. Lack of enough memory has become a major bottleneck for scalability and performance of virtualization interfaces in cloud computing. To address this problem, memory deduplication techniques which reduce memory demand through page sharing are being adopted. However, such techniques suffer from overheads in terms of number of online comparisons required for the memory deduplication. In this paper, we propose a static memory deduplication (SMD technique which can reduce memory capacity requirement and provide performance optimization in cloud computing. The main innovation of SMD is that the process of page detection is performed offline, thus potentially reducing the performance cost, especially in terms of response time. In SMD, page comparisons are restricted to the code segment, which has the highest shared content. Our experimental results show that SMD efficiently reduces memory capacity requirement and improves performance. We demonstrate that, compared to other approaches, the cost in terms of the response time is negligible.

Static Memory Deduplication for Performance Optimization in Cloud Computing.

Science.gov (United States)

Jia, Gangyong; Han, Guangjie; Wang, Hao; Yang, Xuan

2017-04-27

In a cloud computing environment, the number of virtual machines (VMs) on a single physical server and the number of applications running on each VM are continuously growing. This has led to an enormous increase in the demand of memory capacity and subsequent increase in the energy consumption in the cloud. Lack of enough memory has become a major bottleneck for scalability and performance of virtualization interfaces in cloud computing. To address this problem, memory deduplication techniques which reduce memory demand through page sharing are being adopted. However, such techniques suffer from overheads in terms of number of online comparisons required for the memory deduplication. In this paper, we propose a static memory deduplication (SMD) technique which can reduce memory capacity requirement and provide performance optimization in cloud computing. The main innovation of SMD is that the process of page detection is performed offline, thus potentially reducing the performance cost, especially in terms of response time. In SMD, page comparisons are restricted to the code segment, which has the highest shared content. Our experimental results show that SMD efficiently reduces memory capacity requirement and improves performance. We demonstrate that, compared to other approaches, the cost in terms of the response time is negligible.

PCI bus content-addressable-memory (CAM) implementation on FPGA for pattern recognition/image retrieval in a distributed environment

Science.gov (United States)

Megherbi, Dalila B.; Yan, Yin; Tanmay, Parikh; Khoury, Jed; Woods, C. L.

2004-11-01

Recently surveillance and Automatic Target Recognition (ATR) applications are increasing as the cost of computing power needed to process the massive amount of information continues to fall. This computing power has been made possible partly by the latest advances in FPGAs and SOPCs. In particular, to design and implement state-of-the-Art electro-optical imaging systems to provide advanced surveillance capabilities, there is a need to integrate several technologies (e.g. telescope, precise optics, cameras, image/compute vision algorithms, which can be geographically distributed or sharing distributed resources) into a programmable system and DSP systems. Additionally, pattern recognition techniques and fast information retrieval, are often important components of intelligent systems. The aim of this work is using embedded FPGA as a fast, configurable and synthesizable search engine in fast image pattern recognition/retrieval in a distributed hardware/software co-design environment. In particular, we propose and show a low cost Content Addressable Memory (CAM)-based distributed embedded FPGA hardware architecture solution with real time recognition capabilities and computing for pattern look-up, pattern recognition, and image retrieval. We show how the distributed CAM-based architecture offers a performance advantage of an order-of-magnitude over RAM-based architecture (Random Access Memory) search for implementing high speed pattern recognition for image retrieval. The methods of designing, implementing, and analyzing the proposed CAM based embedded architecture are described here. Other SOPC solutions/design issues are covered. Finally, experimental results, hardware verification, and performance evaluations using both the Xilinx Virtex-II and the Altera Apex20k are provided to show the potential and power of the proposed method for low cost reconfigurable fast image pattern recognition/retrieval at the hardware/software co-design level.

In silico machine learning methods in drug development.

Science.gov (United States)

Dobchev, Dimitar A; Pillai, Girinath G; Karelson, Mati

2014-01-01

Machine learning (ML) computational methods for predicting compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties are being increasingly applied in drug discovery and evaluation. Recently, machine learning techniques such as artificial neural networks, support vector machines and genetic programming have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic targets. These methods are particularly useful for screening compound libraries of diverse chemical structures, "noisy" and high-dimensional data to complement QSAR methods, and in cases of unavailable receptor 3D structure to complement structure-based methods. A variety of studies have demonstrated the potential of machine-learning methods for predicting compounds as potential drug candidates. The present review is intended to give an overview of the strategies and current progress in using machine learning methods for drug design and the potential of the respective model development tools. We also regard a number of applications of the machine learning algorithms based on common classes of diseases.

Alchemical and structural distribution based representation for universal quantum machine learning

Science.gov (United States)

Faber, Felix A.; Christensen, Anders S.; Huang, Bing; von Lilienfeld, O. Anatole

2018-06-01

We introduce a representation of any atom in any chemical environment for the automatized generation of universal kernel ridge regression-based quantum machine learning (QML) models of electronic properties, trained throughout chemical compound space. The representation is based on Gaussian distribution functions, scaled by power laws and explicitly accounting for structural as well as elemental degrees of freedom. The elemental components help us to lower the QML model's learning curve, and, through interpolation across the periodic table, even enable "alchemical extrapolation" to covalent bonding between elements not part of training. This point is demonstrated for the prediction of covalent binding in single, double, and triple bonds among main-group elements as well as for atomization energies in organic molecules. We present numerical evidence that resulting QML energy models, after training on a few thousand random training instances, reach chemical accuracy for out-of-sample compounds. Compound datasets studied include thousands of structurally and compositionally diverse organic molecules, non-covalently bonded protein side-chains, (H2O)40-clusters, and crystalline solids. Learning curves for QML models also indicate competitive predictive power for various other electronic ground state properties of organic molecules, calculated with hybrid density functional theory, including polarizability, heat-capacity, HOMO-LUMO eigenvalues and gap, zero point vibrational energy, dipole moment, and highest vibrational fundamental frequency.

Machine learning and dyslexia: Classification of individual structural neuro-imaging scans of students with and without dyslexia

Science.gov (United States)

Tamboer, P.; Vorst, H.C.M.; Ghebreab, S.; Scholte, H.S.

2016-01-01

Meta-analytic studies suggest that dyslexia is characterized by subtle and spatially distributed variations in brain anatomy, although many variations failed to be significant after corrections of multiple comparisons. To circumvent issues of significance which are characteristic for conventional analysis techniques, and to provide predictive value, we applied a machine learning technique – support vector machine – to differentiate between subjects with and without dyslexia. In a sample of 22 students with dyslexia (20 women) and 27 students without dyslexia (25 women) (18–21 years), a classification performance of 80% (p dyslexia (r = 0.47). Furthermore, various significant correlations were found between the three anatomical regions and behavioural measures of spelling, phonology and whole-word-reading. No correlations were found with behavioural measures of short-term memory and visual/attentional confusion. These data indicate that the LOFG, ROFG and the LIPL are neuro-endophenotype and potentially biomarkers for types of dyslexia related to reading, spelling and phonology. In a second and independent sample of 876 young adults of a general population, the trained classifier of the first sample was tested, resulting in a classification performance of 59% (p = 0.07; d-prime = 0.65). This decline in classification performance resulted from a large percentage of false alarms. This study provided support for the use of machine learning in anatomical brain imaging. PMID:27114899

A Screen Space GPGPU Surface LIC Algorithm for Distributed Memory Data Parallel Sort Last Rendering Infrastructures

Energy Technology Data Exchange (ETDEWEB)

Loring, Burlen; Karimabadi, Homa; Rortershteyn, Vadim

2014-07-01

The surface line integral convolution(LIC) visualization technique produces dense visualization of vector fields on arbitrary surfaces. We present a screen space surface LIC algorithm for use in distributed memory data parallel sort last rendering infrastructures. The motivations for our work are to support analysis of datasets that are too large to fit in the main memory of a single computer and compatibility with prevalent parallel scientific visualization tools such as ParaView and VisIt. By working in screen space using OpenGL we can leverage the computational power of GPUs when they are available and run without them when they are not. We address efficiency and performance issues that arise from the transformation of data from physical to screen space by selecting an alternate screen space domain decomposition. We analyze the algorithm's scaling behavior with and without GPUs on two high performance computing systems using data from turbulent plasma simulations.

[A new machinability test machine and the machinability of composite resins for core built-up].

Science.gov (United States)

Iwasaki, N

2001-06-01

A new machinability test machine especially for dental materials was contrived. The purpose of this study was to evaluate the effects of grinding conditions on machinability of core built-up resins using this machine, and to confirm the relationship between machinability and other properties of composite resins. The experimental machinability test machine consisted of a dental air-turbine handpiece, a control weight unit, a driving unit of the stage fixing the test specimen, and so on. The machinability was evaluated as the change in volume after grinding using a diamond point. Five kinds of core built-up resins and human teeth were used in this study. The machinabilities of these composite resins increased with an increasing load during grinding, and decreased with repeated grinding. There was no obvious correlation between the machinability and Vickers' hardness; however, a negative correlation was observed between machinability and scratch width.

A Parallel Distributed-Memory Particle Method Enables Acquisition-Rate Segmentation of Large Fluorescence Microscopy Images.

Science.gov (United States)

Afshar, Yaser; Sbalzarini, Ivo F

2016-01-01

Modern fluorescence microscopy modalities, such as light-sheet microscopy, are capable of acquiring large three-dimensional images at high data rate. This creates a bottleneck in computational processing and analysis of the acquired images, as the rate of acquisition outpaces the speed of processing. Moreover, images can be so large that they do not fit the main memory of a single computer. We address both issues by developing a distributed parallel algorithm for segmentation of large fluorescence microscopy images. The method is based on the versatile Discrete Region Competition algorithm, which has previously proven useful in microscopy image segmentation. The present distributed implementation decomposes the input image into smaller sub-images that are distributed across multiple computers. Using network communication, the computers orchestrate the collectively solving of the global segmentation problem. This not only enables segmentation of large images (we test images of up to 10(10) pixels), but also accelerates segmentation to match the time scale of image acquisition. Such acquisition-rate image segmentation is a prerequisite for the smart microscopes of the future and enables online data compression and interactive experiments.

Modeling Confidence and Response Time in Recognition Memory

Science.gov (United States)

Ratcliff, Roger; Starns, Jeffrey J.

2009-01-01

A new model for confidence judgments in recognition memory is presented. In the model, the match between a single test item and memory produces a distribution of evidence, with better matches corresponding to distributions with higher means. On this match dimension, confidence criteria are placed, and the areas between the criteria under the…

Emotional organization of autobiographical memory.

Science.gov (United States)

Schulkind, Matthew D; Woldorf, Gillian M

2005-09-01

The emotional organization of autobiographical memory was examined by determining whether emotional cues would influence autobiographical retrieval in younger and older adults. Unfamiliar musical cues that represented orthogonal combinations of positive and negative valence and high and low arousal were used. Whereas cue valence influenced the valence of the retrieved memories, cue arousal did not affect arousal ratings. However, high-arousal cues were associated with reduced response latencies. A significant bias to report positive memories was observed, especially for the older adults, but neither the distribution of memories across the life span nor response latencies varied across memories differing in valence or arousal. These data indicate that emotional information can serve as effective cues for autobiographical memories and that autobiographical memories are organized in terms of emotional valence but not emotional arousal. Thus, current theories of autobiographical memory must be expanded to include emotional valence as a primary dimension of organization.

The Cortex Transform as an image preprocessor for sparse distributed memory: An initial study

Science.gov (United States)

Olshausen, Bruno; Watson, Andrew

1990-01-01

An experiment is described which was designed to evaluate the use of the Cortex Transform as an image processor for Sparse Distributed Memory (SDM). In the experiment, a set of images were injected with Gaussian noise, preprocessed with the Cortex Transform, and then encoded into bit patterns. The various spatial frequency bands of the Cortex Transform were encoded separately so that they could be evaluated based on their ability to properly cluster patterns belonging to the same class. The results of this study indicate that by simply encoding the low pass band of the Cortex Transform, a very suitable input representation for the SDM can be achieved.

Numerical and machine learning simulation of parametric distributions of groundwater residence time in streams and wells

Science.gov (United States)

Starn, J. J.; Belitz, K.; Carlson, C.

2017-12-01

Groundwater residence-time distributions (RTDs) are critical for assessing susceptibility of water resources to contamination. This novel approach for estimating regional RTDs was to first simulate groundwater flow using existing regional digital data sets in 13 intermediate size watersheds (each an average of 7,000 square kilometers) that are representative of a wide range of glacial systems. RTDs were simulated with particle tracking. We refer to these models as "general models" because they are based on regional, as opposed to site-specific, digital data. Parametric RTDs were created from particle RTDs by fitting 1- and 2-component Weibull, gamma, and inverse Gaussian distributions, thus reducing a large number of particle travel times to 3 to 7 parameters (shape, location, and scale for each component plus a mixing fraction) for each modeled area. The scale parameter of these distributions is related to the mean exponential age; the shape parameter controls departure from the ideal exponential distribution and is partly a function of interaction with bedrock and with drainage density. Given the flexible shape and mathematical similarity of these distributions, any of them are potentially a good fit to particle RTDs. The 1-component gamma distribution provided a good fit to basin-wide particle RTDs. RTDs at monitoring wells and streams often have more complicated shapes than basin-wide RTDs, caused in part by heterogeneity in the model, and generally require 2-component distributions. A machine learning model was trained on the RTD parameters using features derived from regionally available watershed characteristics such as recharge rate, material thickness, and stream density. RTDs appeared to vary systematically across the landscape in relation to watershed features. This relation was used to produce maps of useful metrics with respect to risk-based thresholds, such as the time to first exceedance, time to maximum concentration, time above the threshold

Memory operation mechanism of fullerene-containing polymer memory

Energy Technology Data Exchange (ETDEWEB)

Nakajima, Anri, E-mail: anakajima@hiroshima-u.ac.jp; Fujii, Daiki [Research Institute for Nanodevice and Bio Systems, Hiroshima University, 1-4-2 Kagamiyama, Higashihiroshima, Hiroshima 739-8527 (Japan)

2015-03-09

The memory operation mechanism in fullerene-containing nanocomposite gate insulators was investigated while varying the kind of fullerene in a polymer gate insulator. It was cleared what kind of traps and which positions in the nanocomposite the injected electrons or holes are stored in. The reason for the difference in the easiness of programming was clarified taking the role of the charging energy of an injected electron into account. The dependence of the carrier dynamics on the kind of fullerene molecule was investigated. A nonuniform distribution of injected carriers occurred after application of a large magnitude programming voltage due to the width distribution of the polystyrene barrier between adjacent fullerene molecules. Through the investigations, we demonstrated a nanocomposite gate with fullerene molecules having excellent retention characteristics and a programming capability. This will lead to the realization of practical organic memories with fullerene-containing polymer nanocomposites.

Vibration of machine

International Nuclear Information System (INIS)

Kwak, Mun Gyu; Na, Sung Su; Baek, Gwang Hyeon; Song, Chul Gi; Han, Sang Bo

2001-09-01

This book deals with vibration of machine which gives descriptions of free vibration using SDOF system, forced vibration using SDOF system, vibration of multi-degree of freedom system like introduction and normal form, distribution system such as introduction, free vibration of bar and practice problem, approximate solution like lumped approximations and Raleigh's quotient, engineering by intuition and experience, real problem and experimental method such as technology of signal, fourier transform analysis, frequency analysis and sensor and actuator.

A novel rotor design for a hybrid excited synchronous machine

Directory of Open Access Journals (Sweden)

Paplicki Piotr

2017-03-01

Full Text Available The paper presents three novel rotor design concepts for a three-phase electric controlled permanent magnet synchronous machine (ECPMS-machine with hybrid excitation. The influence of magnets and flux-barriers arrangement on the magnetic field distribution and field-weakening characteristics of the machine is examined, based on a three-dimensional finite element analysis (3D-FEA. Moreover, a prototype rotor design based on a new rotor concept with a good field-weakening capability is presented in detail. Finally, the experimental results of no-load back electromotive force (back-EMF waveforms and field-weakening characteristics versus a control coil current of the machine are reported.

All-to-All Communication on the Connection Machine CM-200

Directory of Open Access Journals (Sweden)

Kapil K. Mathur

1995-01-01

Full Text Available Detailed algorithms for all-to-all broadcast and reduction are given for arrays mapped by binary or binary-reflected Gray code encoding to the processing nodes of binary cube networks. Algorithms are also given for the local computation of the array indices for the communicated data, thereby reducing the demand for the communications bandwidth. For the Connection Machine system CM-200, Hamiltonian cycle-based all-to-all communication algorithms yield a performance that is a factor of 2 to 10 higher than the performance offered by algorithms based on trees, butterfly networks, or the Connection Machine router. The peak data rate achieved for all-to-all broadcast on a 2,048-node Connection Machine system CM-200 is 5.4 Gbyte/s. The index order of the data in local memory depends on implementation details of the algorithms, but it is well defined. If a linear ordering is desired, then including the time for local data reordering reduces the effective peak data rate to 2.5 Gbyte/s.

Gesture-controlled interfaces for self-service machines and other applications

Science.gov (United States)

Cohen, Charles J. (Inventor); Beach, Glenn (Inventor); Cavell, Brook (Inventor); Foulk, Gene (Inventor); Jacobus, Charles J. (Inventor); Obermark, Jay (Inventor); Paul, George (Inventor)

2004-01-01

A gesture recognition interface for use in controlling self-service machines and other devices is disclosed. A gesture is defined as motions and kinematic poses generated by humans, animals, or machines. Specific body features are tracked, and static and motion gestures are interpreted. Motion gestures are defined as a family of parametrically delimited oscillatory motions, modeled as a linear-in-parameters dynamic system with added geometric constraints to allow for real-time recognition using a small amount of memory and processing time. A linear least squares method is preferably used to determine the parameters which represent each gesture. Feature position measure is used in conjunction with a bank of predictor bins seeded with the gesture parameters, and the system determines which bin best fits the observed motion. Recognizing static pose gestures is preferably performed by localizing the body/object from the rest of the image, describing that object, and identifying that description. The disclosure details methods for gesture recognition, as well as the overall architecture for using gesture recognition to control of devices, including self-service machines.

The mysteries of remote memory

Science.gov (United States)

2018-01-01

Long-lasting memories form the basis of our identity as individuals and lie central in shaping future behaviours that guide survival. Surprisingly, however, our current knowledge of how such memories are stored in the brain and retrieved, as well as the dynamics of the circuits involved, remains scarce despite seminal technical and experimental breakthroughs in recent years. Traditionally, it has been proposed that, over time, information initially learnt in the hippocampus is stored in distributed cortical networks. This process—the standard theory of memory consolidation—would stabilize the newly encoded information into a lasting memory, become independent of the hippocampus, and remain essentially unmodifiable throughout the lifetime of the individual. In recent years, several pieces of evidence have started to challenge this view and indicate that long-lasting memories might already ab ovo be encoded, and subsequently stored in distributed cortical networks, akin to the multiple trace theory of memory consolidation. In this review, we summarize these recent findings and attempt to identify the biologically plausible mechanisms based on which a contextual memory becomes remote by integrating different levels of analysis: from neural circuits to cell ensembles across synaptic remodelling and epigenetic modifications. From these studies, remote memory formation and maintenance appear to occur through a multi-trace, dynamic and integrative cellular process ranging from the synapse to the nucleus, and represent an exciting field of research primed to change quickly as new experimental evidence emerges. This article is part of a discussion meeting issue ‘Of mice and mental health: facilitating dialogue between basic and clinical neuroscientists’. PMID:29352028

Learning and memory.

Science.gov (United States)

Brem, Anna-Katharine; Ran, Kathy; Pascual-Leone, Alvaro

2013-01-01

Learning and memory functions are crucial in the interaction of an individual with the environment and involve the interplay of large, distributed brain networks. Recent advances in technologies to explore neurobiological correlates of neuropsychological paradigms have increased our knowledge about human learning and memory. In this chapter we first review and define memory and learning processes from a neuropsychological perspective. Then we provide some illustrations of how noninvasive brain stimulation can play a major role in the investigation of memory functions, as it can be used to identify cause-effect relationships and chronometric properties of neural processes underlying cognitive steps. In clinical medicine, transcranial magnetic stimulation may be used as a diagnostic tool to understand memory and learning deficits in various patient populations. Furthermore, noninvasive brain stimulation is also being applied to enhance cognitive functions, offering exciting translational therapeutic opportunities in neurology and psychiatry. © 2013 Elsevier B.V. All rights reserved.

Shared Memory Parallelization of an Implicit ADI-type CFD Code

Science.gov (United States)

Hauser, Th.; Huang, P. G.

1999-01-01

A parallelization study designed for ADI-type algorithms is presented using the OpenMP specification for shared-memory multiprocessor programming. Details of optimizations specifically addressed to cache-based computer architectures are described and performance measurements for the single and multiprocessor implementation are summarized. The paper demonstrates that optimization of memory access on a cache-based computer architecture controls the performance of the computational algorithm. A hybrid MPI/OpenMP approach is proposed for clusters of shared memory machines to further enhance the parallel performance. The method is applied to develop a new LES/DNS code, named LESTool. A preliminary DNS calculation of a fully developed channel flow at a Reynolds number of 180, Re(sub tau) = 180, has shown good agreement with existing data.

Portable memory consistency for software managed distributed memory in many-core SoC

NARCIS (Netherlands)

Rutgers, J.H.; Bekooij, Marco Jan Gerrit; Smit, Gerardus Johannes Maria

2013-01-01

Porting software to different platforms can require modifications of the application. One of the issues is that the targeted hardware supports another memory consistency model. As a consequence, the completion order of reads and writes in a multi-threaded application can change, which may result in

Machine learning techniques for persuasion dectection in conversation

OpenAIRE

Ortiz, Pedro.

2010-01-01

Approved for public release; distribution is unlimited We determined that it is possible to automatically detect persuasion in conversations using three traditional machine learning techniques, naive bayes, maximum entropy, and support vector machine. These results are the first of their kind and serve as a baseline for all future work in this field. The three techniques consistently outperformed the baseline F-score, but not at a level that would be useful for real world applications. The...

Interacting Memory Systems—Does EEG Alpha Activity Respond to Semantic Long-Term Memory Access in a Working Memory Task?

Directory of Open Access Journals (Sweden)

Barbara Berger

2014-12-01

Full Text Available Memory consists of various individual processes which form a dynamic system co-ordinated by central (executive functions. The episodic buffer as direct interface between episodic long-term memory (LTM and working memory (WM is fairly well studied but such direct interaction is less clear in semantic LTM. Here, we designed a verbal delayed-match-to-sample task specifically to differentiate between pure information maintenance and mental manipulation of memory traces with and without involvement of access to semantic LTM. Task-related amplitude differences of electroencephalographic (EEG oscillatory brain activity showed a linear increase in frontal-midline theta and linear suppression of parietal beta amplitudes relative to memory operation complexity. Amplitude suppression at upper alpha frequency, which was previously found to indicate access to semantic LTM, was only sensitive to mental manipulation in general, irrespective of LTM involvement. This suggests that suppression of upper EEG alpha activity might rather reflect unspecific distributed cortical activation during complex mental processes than accessing semantic LTM.

The Machine within the Machine

CERN Multimedia

Katarina Anthony

2014-01-01

Although Virtual Machines are widespread across CERN, you probably won't have heard of them unless you work for an experiment. Virtual machines - known as VMs - allow you to create a separate machine within your own, allowing you to run Linux on your Mac, or Windows on your Linux - whatever combination you need.   Using a CERN Virtual Machine, a Linux analysis software runs on a Macbook. When it comes to LHC data, one of the primary issues collaborations face is the diversity of computing environments among collaborators spread across the world. What if an institute cannot run the analysis software because they use different operating systems? "That's where the CernVM project comes in," says Gerardo Ganis, PH-SFT staff member and leader of the CernVM project. "We were able to respond to experimentalists' concerns by providing a virtual machine package that could be used to run experiment software. This way, no matter what hardware they have ...

Machine Learning Techniques for Stellar Light Curve Classification

Science.gov (United States)

Hinners, Trisha A.; Tat, Kevin; Thorp, Rachel

2018-07-01

We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time-series data. We preprocessed over 94 GB of Kepler light curves from the Mikulski Archive for Space Telescopes (MAST) to classify according to 10 distinct physical properties using both representation learning and feature engineering approaches. Studies using machine learning in the field have been primarily done on simulated data, making our study one of the first to use real light-curve data for machine learning approaches. We tuned our data using previous work with simulated data as a template and achieved mixed results between the two approaches. Representation learning using a long short-term memory recurrent neural network produced no successful predictions, but our work with feature engineering was successful for both classification and regression. In particular, we were able to achieve values for stellar density, stellar radius, and effective temperature with low error (∼2%–4%) and good accuracy (∼75%) for classifying the number of transits for a given star. The results show promise for improvement for both approaches upon using larger data sets with a larger minority class. This work has the potential to provide a foundation for future tools and techniques to aid in the analysis of astrophysical data.

DOLIB: Distributed Object Library

Energy Technology Data Exchange (ETDEWEB)

D' Azevedo, E.F.

1994-01-01

This report describes the use and implementation of DOLIB (Distributed Object Library), a library of routines that emulates global or virtual shared memory on Intel multiprocessor systems. Access to a distributed global array is through explicit calls to gather and scatter. Advantages of using DOLIB include: dynamic allocation and freeing of huge (gigabyte) distributed arrays, both C and FORTRAN callable interfaces, and the ability to mix shared-memory and message-passing programming models for ease of use and optimal performance. DOLIB is independent of language and compiler extensions and requires no special operating system support. DOLIB also supports automatic caching of read-only data for high performance. The virtual shared memory support provided in DOLIB is well suited for implementing Lagrangian particle tracking techniques. We have also used DOLIB to create DONIO (Distributed Object Network I/O Library), which obtains over a 10-fold improvement in disk I/O performance on the Intel Paragon.

DOLIB: Distributed Object Library

Energy Technology Data Exchange (ETDEWEB)

D`Azevedo, E.F.; Romine, C.H.

1994-10-01

This report describes the use and implementation of DOLIB (Distributed Object Library), a library of routines that emulates global or virtual shared memory on Intel multiprocessor systems. Access to a distributed global array is through explicit calls to gather and scatter. Advantages of using DOLIB include: dynamic allocation and freeing of huge (gigabyte) distributed arrays, both C and FORTRAN callable interfaces, and the ability to mix shared-memory and message-passing programming models for ease of use and optimal performance. DOLIB is independent of language and compiler extensions and requires no special operating system support. DOLIB also supports automatic caching of read-only data for high performance. The virtual shared memory support provided in DOLIB is well suited for implementing Lagrangian particle tracking techniques. We have also used DOLIB to create DONIO (Distributed Object Network I/O Library), which obtains over a 10-fold improvement in disk I/O performance on the Intel Paragon.

Implementing Shared Memory Parallelism in MCBEND

Directory of Open Access Journals (Sweden)

Bird Adam

2017-01-01

Full Text Available MCBEND is a general purpose radiation transport Monte Carlo code from AMEC Foster Wheelers’s ANSWERS® Software Service. MCBEND is well established in the UK shielding community for radiation shielding and dosimetry assessments. The existing MCBEND parallel capability effectively involves running the same calculation on many processors. This works very well except when the memory requirements of a model restrict the number of instances of a calculation that will fit on a machine. To more effectively utilise parallel hardware OpenMP has been used to implement shared memory parallelism in MCBEND. This paper describes the reasoning behind the choice of OpenMP, notes some of the challenges of multi-threading an established code such as MCBEND and assesses the performance of the parallel method implemented in MCBEND.

Micro-CernVM: slashing the cost of building and deploying virtual machines

International Nuclear Information System (INIS)

Blomer, J; Berzano, D; Buncic, P; Charalampidis, I; Ganis, G; Lestaris, G; Meusel, R; Nicolaou, V

2014-01-01

The traditional virtual machine (VM) building and and deployment process is centered around the virtual machine hard disk image. The packages comprising the VM operating system are carefully selected, hard disk images are built for a variety of different hypervisors, and images have to be distributed and decompressed in order to instantiate a virtual machine. Within the HEP community, the CernVM File System (CernVM-FS) has been established in order to decouple the distribution from the experiment software from the building and distribution of the VM hard disk images. We show how to get rid of such pre-built hard disk images altogether. Due to the high requirements on POSIX compliance imposed by HEP application software, CernVM-FS can also be used to host and boot a Linux operating system. This allows the use of a tiny bootable CD image that comprises only a Linux kernel while the rest of the operating system is provided on demand by CernVM-FS. This approach speeds up the initial instantiation time and reduces virtual machine image sizes by an order of magnitude. Furthermore, security updates can be distributed instantaneously through CernVM-FS. By leveraging the fact that CernVM-FS is a versioning file system, a historic analysis environment can be easily re-spawned by selecting the corresponding CernVM-FS file system snapshot.

LHCb experience with running jobs in virtual machines

Science.gov (United States)

McNab, A.; Stagni, F.; Luzzi, C.

2015-12-01

The LHCb experiment has been running production jobs in virtual machines since 2013 as part of its DIRAC-based infrastructure. We describe the architecture of these virtual machines and the steps taken to replicate the WLCG worker node environment expected by user and production jobs. This relies on the uCernVM system for providing root images for virtual machines. We use the CernVM-FS distributed filesystem to supply the root partition files, the LHCb software stack, and the bootstrapping scripts necessary to configure the virtual machines for us. Using this approach, we have been able to minimise the amount of contextualisation which must be provided by the virtual machine managers. We explain the process by which the virtual machine is able to receive payload jobs submitted to DIRAC by users and production managers, and how this differs from payloads executed within conventional DIRAC pilot jobs on batch queue based sites. We describe our operational experiences in running production on VM based sites managed using Vcycle/OpenStack, Vac, and HTCondor Vacuum. Finally we show how our use of these resources is monitored using Ganglia and DIRAC.

Global aspects of radiation memory

International Nuclear Information System (INIS)

Winicour, J

2014-01-01

Gravitational radiation has a memory effect represented by a net change in the relative positions of test particles. Both the linear and nonlinear sources proposed for this radiation memory are of the ‘electric’ type, or E mode, as characterized by the even parity of the polarization pattern. Although ‘magnetic’ type, or B mode, radiation memory is mathematically possible, no physically realistic source has been identified. There is an electromagnetic counterpart to radiation memory in which the velocity of charged test particles obtain a net ‘kick’. Again, the physically realistic sources of electromagnetic radiation memory that have been identified are of the electric type. In this paper, a global null cone description of the electromagnetic field is applied to establish the non-existence of B-mode radiation memory and the non-existence of E-mode radiation memory due to a bound charge distribution. (paper)

The memory of volatility

Directory of Open Access Journals (Sweden)

Kai R. Wenger

2018-03-01

Full Text Available The focus of the volatility literature on forecasting and the predominance of theconceptually simpler HAR model over long memory stochastic volatility models has led to the factthat the actual degree of memory estimates has rarely been considered. Estimates in the literaturerange roughly between 0.4 and 0.6 - that is from the higher stationary to the lower non-stationaryregion. This difference, however, has important practical implications - such as the existence or nonexistenceof the fourth moment of the return distribution. Inference on the memory order is complicatedby the presence of measurement error in realized volatility and the potential of spurious long memory.In this paper we provide a comprehensive analysis of the memory in variances of international stockindices and exchange rates. On the one hand, we find that the variance of exchange rates is subject tospurious long memory and the true memory parameter is in the higher stationary range. Stock indexvariances, on the other hand, are free of low frequency contaminations and the memory is in the lowernon-stationary range. These results are obtained using state of the art local Whittle methods that allowconsistent estimation in presence of perturbations or low frequency contaminations.

Effect of Machining Velocity in Nanoscale Machining Operations

International Nuclear Information System (INIS)

Islam, Sumaiya; Khondoker, Noman; Ibrahim, Raafat

2015-01-01

The aim of this study is to investigate the generated forces and deformations of single crystal Cu with (100), (110) and (111) crystallographic orientations at nanoscale machining operation. A nanoindenter equipped with nanoscratching attachment was used for machining operations and in-situ observation of a nano scale groove. As a machining parameter, the machining velocity was varied to measure the normal and cutting forces. At a fixed machining velocity, different levels of normal and cutting forces were generated due to different crystallographic orientations of the specimens. Moreover, after machining operation percentage of elastic recovery was measured and it was found that both the elastic and plastic deformations were responsible for producing a nano scale groove within the range of machining velocities from 250-1000 nm/s. (paper)

Refueling machine with relative positioning capability

International Nuclear Information System (INIS)

Challberg, R.C.; Jones, C.R.

1998-01-01

A refueling machine is disclosed having relative positioning capability for refueling a nuclear reactor. The refueling machine includes a pair of articulated arms mounted on a refueling bridge. Each arm supports a respective telescoping mast. Each telescoping mast is designed to flex laterally in response to application of a lateral thrust on the end of the mast. A pendant mounted on the end of the mast carries an air-actuated grapple, television cameras, ultrasonic transducers and waterjet thrusters. The ultrasonic transducers are used to detect the gross position of the grapple relative to the bail of a nuclear fuel assembly in the fuel core. The television cameras acquire an image of the bail which is compared to a pre-stored image in computer memory. The pendant can be rotated until the television image and the pre-stored image match within a predetermined tolerance. Similarly, the waterjet thrusters can be used to apply lateral thrust to the end of the flexible mast to place the grapple in a fine position relative to the bail as a function of the discrepancy between the television and pre-stored images. 11 figs

Refueling machine with relative positioning capability

Science.gov (United States)

Challberg, R.C.; Jones, C.R.

1998-12-15

A refueling machine is disclosed having relative positioning capability for refueling a nuclear reactor. The refueling machine includes a pair of articulated arms mounted on a refueling bridge. Each arm supports a respective telescoping mast. Each telescoping mast is designed to flex laterally in response to application of a lateral thrust on the end of the mast. A pendant mounted on the end of the mast carries an air-actuated grapple, television cameras, ultrasonic transducers and waterjet thrusters. The ultrasonic transducers are used to detect the gross position of the grapple relative to the bail of a nuclear fuel assembly in the fuel core. The television cameras acquire an image of the bail which is compared to a pre-stored image in computer memory. The pendant can be rotated until the television image and the pre-stored image match within a predetermined tolerance. Similarly, the waterjet thrusters can be used to apply lateral thrust to the end of the flexible mast to place the grapple in a fine position relative to the bail as a function of the discrepancy between the television and pre-stored images. 11 figs.

Distributed Merge Trees

Energy Technology Data Exchange (ETDEWEB)

Morozov, Dmitriy; Weber, Gunther

2013-01-08

Improved simulations and sensors are producing datasets whose increasing complexity exhausts our ability to visualize and comprehend them directly. To cope with this problem, we can detect and extract significant features in the data and use them as the basis for subsequent analysis. Topological methods are valuable in this context because they provide robust and general feature definitions. As the growth of serial computational power has stalled, data analysis is becoming increasingly dependent on massively parallel machines. To satisfy the computational demand created by complex datasets, algorithms need to effectively utilize these computer architectures. The main strength of topological methods, their emphasis on global information, turns into an obstacle during parallelization. We present two approaches to alleviate this problem. We develop a distributed representation of the merge tree that avoids computing the global tree on a single processor and lets us parallelize subsequent queries. To account for the increasing number of cores per processor, we develop a new data structure that lets us take advantage of multiple shared-memory cores to parallelize the work on a single node. Finally, we present experiments that illustrate the strengths of our approach as well as help identify future challenges.

Early failure analysis of machining centers: a case study

International Nuclear Information System (INIS)

Wang Yiqiang; Jia Yazhou; Jiang Weiwei

2001-01-01

To eliminate the early failures and improve the reliability, nine ex-factory machining centers are traced under field conditions in workshops. Their early failure information throughout the ex-factory run-in test is collected. The field early failure database is constructed based on the collection of field early failure data and the codification of data. Early failure mode and effects analysis is performed to indicate the weak subsystem of a machining center or the troublemaker. The distribution of the time between early failures is analyzed and the optimal ex-factory run-in test time for machining center that may expose sufficiently the early failures and cost minimum is discussed. Suggestions how to arrange ex-factory run-in test and how to take actions to reduce early failures for machining center is proposed

Enhancement of Immune Memory Responses to Respiratory Infection

Science.gov (United States)

2017-08-01

Unlimited Distribution 13. SUPPLEMENTARY NOTES 14. ABSTRACT Maintenance of long - term immunological memory against pathogens is crucial for the rapid...highly expressed in memory B cells in mice, and Atg7 is required for maintenance of long - term memory B cells needed to protect against influenza...AWARD NUMBER: W81XWH-16-1-0361 TITLE: Enhancement of Immune Memory Responses to Respiratory Infection PRINCIPAL INVESTIGATORs: Dr Farrah

A strategy for man-machine system development in process industries

International Nuclear Information System (INIS)

Wirstad, J.

1986-12-01

A framework for Man-Machine System design in process industry projects is reported. It is based in the Guidelines for the Design of Man-Machine interfaces which have been generated in cooperation within the European Workshop for Industrial Computer Systems (EWICS). The application of EWICS Guidelines in industrial projects is demonstrated by six User Scenarios, which represent typical projects from different industries, e.g. electrical power generation and distribution, water control, pulp and paper production, oil and gas production. In all these projects Man-Machine System design has been conducted. It is recommended in the report that each Company develops its set of Man-Machine Systems Standard techniques/procedures. At present there are several techniques/procedures available which, for moderate costs, can be adapted to specific Company conditions. A menu of such Man-Machine System techniques/procedures is presented. Means of estimating the costs and benefits of Man-Machine System design are also described. (author)

[Neuroscience and collective memory: memory schemas linking brain, societies and cultures].

Science.gov (United States)

Legrand, Nicolas; Gagnepain, Pierre; Peschanski, Denis; Eustache, Francis

2015-01-01

During the last two decades, the effect of intersubjective relationships on cognition has been an emerging topic in cognitive neurosciences leading through a so-called "social turn" to the formation of new domains integrating society and cultures to this research area. Such inquiry has been recently extended to collective memory studies. Collective memory refers to shared representations that are constitutive of the identity of a group and distributed among all its members connected by a common history. After briefly describing those evolutions in the study of human brain and behaviors, we review recent researches that have brought together cognitive psychology, neuroscience and social sciences into collective memory studies. Using the reemerging concept of memory schema, we propose a theoretical framework allowing to account for collective memories formation with a specific focus on the encoding process of historical events. We suggest that (1) if the concept of schema has been mainly used to describe rather passive framework of knowledge, such structure may also be implied in more active fashions in the understanding of significant collective events. And, (2) if some schema researches have restricted themselves to the individual level of inquiry, we describe a strong coherence between memory and cultural frameworks. Integrating the neural basis and properties of memory schema to collective memory studies may pave the way toward a better understanding of the reciprocal interaction between individual memories and cultural resources such as media or education. © Société de Biologie, 2016.

Quality assurance of a helical tomotherapy machine

International Nuclear Information System (INIS)

Fenwick, J D; Tome, W A; Jaradat, H A; Hui, S K; James, J A; Balog, J P; DeSouza, C N; Lucas, D B; Olivera, G H; Mackie, T R; Paliwal, B R

2004-01-01

Helical tomotherapy has been developed at the University of Wisconsin, and 'Hi-Art II' clinical machines are now commercially manufactured. At the core of each machine lies a ring-gantry-mounted short linear accelerator which generates x-rays that are collimated into a fan beam of intensity-modulated radiation by a binary multileaf, the modulation being variable with gantry angle. Patients are treated lying on a couch which is translated continuously through the bore of the machine as the gantry rotates. Highly conformal dose-distributions can be delivered using this technique, which is the therapy equivalent of spiral computed tomography. The approach requires synchrony of gantry rotation, couch translation, accelerator pulsing and the opening and closing of the leaves of the binary multileaf collimator used to modulate the radiation beam. In the course of clinically implementing helical tomotherapy, we have developed a quality assurance (QA) system for our machine. The system is analogous to that recommended for conventional clinical linear accelerator QA by AAPM Task Group 40 but contains some novel components, reflecting differences between the Hi-Art devices and conventional clinical accelerators. Here the design and dosimetric characteristics of Hi-Art machines are summarized and the QA system is set out along with experimental details of its implementation. Connections between this machine-based QA work, pre-treatment patient-specific delivery QA and fraction-by-fraction dose verification are discussed

A Parallel Distributed-Memory Particle Method Enables Acquisition-Rate Segmentation of Large Fluorescence Microscopy Images

Science.gov (United States)

Afshar, Yaser; Sbalzarini, Ivo F.

2016-01-01

Modern fluorescence microscopy modalities, such as light-sheet microscopy, are capable of acquiring large three-dimensional images at high data rate. This creates a bottleneck in computational processing and analysis of the acquired images, as the rate of acquisition outpaces the speed of processing. Moreover, images can be so large that they do not fit the main memory of a single computer. We address both issues by developing a distributed parallel algorithm for segmentation of large fluorescence microscopy images. The method is based on the versatile Discrete Region Competition algorithm, which has previously proven useful in microscopy image segmentation. The present distributed implementation decomposes the input image into smaller sub-images that are distributed across multiple computers. Using network communication, the computers orchestrate the collectively solving of the global segmentation problem. This not only enables segmentation of large images (we test images of up to 1010 pixels), but also accelerates segmentation to match the time scale of image acquisition. Such acquisition-rate image segmentation is a prerequisite for the smart microscopes of the future and enables online data compression and interactive experiments. PMID:27046144

A Parallel Distributed-Memory Particle Method Enables Acquisition-Rate Segmentation of Large Fluorescence Microscopy Images.

Directory of Open Access Journals (Sweden)

Yaser Afshar

Full Text Available Modern fluorescence microscopy modalities, such as light-sheet microscopy, are capable of acquiring large three-dimensional images at high data rate. This creates a bottleneck in computational processing and analysis of the acquired images, as the rate of acquisition outpaces the speed of processing. Moreover, images can be so large that they do not fit the main memory of a single computer. We address both issues by developing a distributed parallel algorithm for segmentation of large fluorescence microscopy images. The method is based on the versatile Discrete Region Competition algorithm, which has previously proven useful in microscopy image segmentation. The present distributed implementation decomposes the input image into smaller sub-images that are distributed across multiple computers. Using network communication, the computers orchestrate the collectively solving of the global segmentation problem. This not only enables segmentation of large images (we test images of up to 10(10 pixels, but also accelerates segmentation to match the time scale of image acquisition. Such acquisition-rate image segmentation is a prerequisite for the smart microscopes of the future and enables online data compression and interactive experiments.

The aftermath of memory retrieval for recycling visual working memory representations.

Science.gov (United States)

Park, Hyung-Bum; Zhang, Weiwei; Hyun, Joo-Seok

2017-07-01

We examined the aftermath of accessing and retrieving a subset of information stored in visual working memory (VWM)-namely, whether detection of a mismatch between memory and perception can impair the original memory of an item while triggering recognition-induced forgetting for the remaining, untested items. For this purpose, we devised a consecutive-change detection task wherein two successive testing probes were displayed after a single set of memory items. Across two experiments utilizing different memory-testing methods (whole vs. single probe), we observed a reliable pattern of poor performance in change detection for the second test when the first test had exhibited a color change. The impairment after a color change was evident even when the same memory item was repeatedly probed; this suggests that an attention-driven, salient visual change made it difficult to reinstate the previously remembered item. The second change detection, for memory items untested during the first change detection, was also found to be inaccurate, indicating that recognition-induced forgetting had occurred for the unprobed items in VWM. In a third experiment, we conducted a task that involved change detection plus continuous recall, wherein a memory recall task was presented after the change detection task. The analyses of the distributions of recall errors with a probabilistic mixture model revealed that the memory impairments from both visual changes and recognition-induced forgetting are explained better by the stochastic loss of memory items than by their degraded resolution. These results indicate that attention-driven visual change and recognition-induced forgetting jointly influence the "recycling" of VWM representations.

High speed operation of permanent magnet machines

Science.gov (United States)

El-Refaie, Ayman M.

This work proposes methods to extend the high-speed operating capabilities of both the interior PM (IPM) and surface PM (SPM) machines. For interior PM machines, this research has developed and presented the first thorough analysis of how a new bi-state magnetic material can be usefully applied to the design of IPM machines. Key elements of this contribution include identifying how the unique properties of the bi-state magnetic material can be applied most effectively in the rotor design of an IPM machine by "unmagnetizing" the magnet cavity center posts rather than the outer bridges. The importance of elevated rotor speed in making the best use of the bi-state magnetic material while recognizing its limitations has been identified. For surface PM machines, this research has provided, for the first time, a clear explanation of how fractional-slot concentrated windings can be applied to SPM machines in order to achieve the necessary conditions for optimal flux weakening. A closed-form analytical procedure for analyzing SPM machines designed with concentrated windings has been developed. Guidelines for designing SPM machines using concentrated windings in order to achieve optimum flux weakening are provided. Analytical and numerical finite element analysis (FEA) results have provided promising evidence of the scalability of the concentrated winding technique with respect to the number of poles, machine aspect ratio, and output power rating. Useful comparisons between the predicted performance characteristics of SPM machines equipped with concentrated windings and both SPM and IPM machines designed with distributed windings are included. Analytical techniques have been used to evaluate the impact of the high pole number on various converter performance metrics. Both analytical techniques and FEA have been used for evaluating the eddy-current losses in the surface magnets due to the stator winding subharmonics. Techniques for reducing these losses have been

Distributed cerebellar plasticity implements generalized multiple-scale memory components in real-robot sensorimotor tasks

Directory of Open Access Journals (Sweden)

Claudia eCasellato

2015-02-01

Full Text Available The cerebellum plays a crucial role in motor learning and it acts as a predictive controller. Modeling it and embedding it into sensorimotor tasks allows us to create functional links between plasticity mechanisms, neural circuits and behavioral learning. Moreover, if applied to real-time control of a neurorobot, the cerebellar model has to deal with a real noisy and changing environment, thus showing its robustness and effectiveness in learning. A biologically inspired cerebellar model with distributed plasticity, both at cortical and nuclear sites, has been used. Two cerebellum-mediated paradigms have been designed: an associative Pavlovian task and a vestibulo-ocular reflex, with multiple sessions of acquisition and extinction and with different stimuli and perturbation patterns. The cerebellar controller succeeded to generate conditioned responses and finely tuned eye movement compensation, thus reproducing human-like behaviors. Through a productive plasticity transfer from cortical to nuclear sites, the distributed cerebellar controller showed in both tasks the capability to optimize learning on multiple time-scales, to store motor memory and to effectively adapt to dynamic ranges of stimuli.

Prediction of Machine Tool Condition Using Support Vector Machine

International Nuclear Information System (INIS)

Wang Peigong; Meng Qingfeng; Zhao Jian; Li Junjie; Wang Xiufeng

2011-01-01

Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

Machine Learning with Squared-Loss Mutual Information

Directory of Open Access Journals (Sweden)

Masashi Sugiyama

2012-12-01

Full Text Available Mutual information (MI is useful for detecting statistical independence between random variables, and it has been successfully applied to solving various machine learning problems. Recently, an alternative to MI called squared-loss MI (SMI was introduced. While ordinary MI is the Kullback–Leibler divergence from the joint distribution to the product of the marginal distributions, SMI is its Pearson divergence variant. Because both the divergences belong to the ƒ-divergence family, they share similar theoretical properties. However, a notable advantage of SMI is that it can be approximated from data in a computationally more efficient and numerically more stable way than ordinary MI. In this article, we review recent development in SMI approximation based on direct density-ratio estimation and SMI-based machine learning techniques such as independence testing, dimensionality reduction, canonical dependency analysis, independent component analysis, object matching, clustering, and causal inference.

Simulation of Particulate Flows Multi-Processor Machines with Distributed Memory

Energy Technology Data Exchange (ETDEWEB)

Uhlmann, M.

2004-07-01

We presented a method for the parallelization of an immersed boundary algorithm for particulate flows using the MPI standard of communication. The treatment of the fluid phase used the domain decomposition technique over a Cartesian processor grid. The solution of the Helmholtz problem is approximately factorized an relies upon apparel tri-diagonal solver the Poisson problem is solved by means of a parallel multi-grid technique similar to MUDPACK. for the solid phase we employ a master-slaves technique where one processor handles all the particles contained in its Eulerian fluid sub-domain and zero or more neighbor processors collaborate in the computation of particle-related quantities whenever a particle position over laps the boundary of a sub-domain. the parallel efficiency for some preliminary computations is presented. (Author) 9 refs.

What happens when we compare the lifespan distributions of life script events and autobiographical memories of life story events? A cross-cultural study

DEFF Research Database (Denmark)

Zaragoza Scherman, Alejandra; Salgado, Sinué; Shao, Zhifang

Cultural Life Script Theory (Berntsen and Rubin, 2004), provides a cultural explanation of the reminiscence bump: adults older than 40 years remember a significantly greater amount of life events happening between 15 - 30 years of age (Rubin, Rahal, & Poon, 1998), compared to other lifetime periods....... Most of these memories are rated as emotionally positive (Rubin & Berntsen, 2003). The cultural life script represents culturally shared expectations about the order and timing of life events in an typical, idealised life course. By comparing the lifespan distribution of the life scripts events...... and memories of life story events, we can determine the degree to which the cultural life script serves as a recall template for autobiographical memories, especially of positive life events from adolescence and early adulthood, also known as the reminiscence bump period....

Environmentally Friendly Machining

CERN Document Server

Dixit, U S; Davim, J Paulo

2012-01-01

Environment-Friendly Machining provides an in-depth overview of environmentally-friendly machining processes, covering numerous different types of machining in order to identify which practice is the most environmentally sustainable. The book discusses three systems at length: machining with minimal cutting fluid, air-cooled machining and dry machining. Also covered is a way to conserve energy during machining processes, along with useful data and detailed descriptions for developing and utilizing the most efficient modern machining tools. Researchers and engineers looking for sustainable machining solutions will find Environment-Friendly Machining to be a useful volume.

Weighing the Dark and Light in Cosmology with Machine Learning

Science.gov (United States)

Trac, Hy

2017-09-01

Galaxy clusters contain large amounts of cold dark matter, hot ionized gas, and tens to hundreds of visible galaxies. They are the largest gravitationally bound systems in the Universe and make excellent laboratories for studying cosmology and astrophysics. Historically, Fritz Zwicky postulated the existence of dark matter when he inferred the total mass of the nearby Coma Cluster from the motions of its galaxies and found it to be much larger than the visible mass. Nowadays, the abundance of clusters as a function of mass and time can be used to study structure formation and constrain cosmological parameters. Dynamical measurements of the motions of galaxies can be used to probe the entire mass distribution, but standard analyses yield unwanted high mass errors. First, we show that modern machine learning algorithms can improve mass measurements by more than a factor of two compared to using standard scaling relations. Support Distribution Machines are used to train and test on the entire distribution of galaxy velocities to maximally use available information. Second, we discuss how Deep Learning can be used to train on multi-wavelength images of galaxies and clusters and to predict the underlying total matter distribution. By applying machine learning to observations and simulations, we can map out the dark and light in the Universe. DOE DE-SC0011114, NSF RI-1563887.

Tensor Network Quantum Virtual Machine (TNQVM)

Energy Technology Data Exchange (ETDEWEB)

2016-11-18

There is a lack of state-of-the-art quantum computing simulation software that scales on heterogeneous systems like Titan. Tensor Network Quantum Virtual Machine (TNQVM) provides a quantum simulator that leverages a distributed network of GPUs to simulate quantum circuits in a manner that leverages recent results from tensor network theory.

Smoothing type buffer memory device

International Nuclear Information System (INIS)

Podorozhnyj, D.M.; Yashin, I.V.

1990-01-01

The layout of the micropower 4-bit smoothing type buffer memory device allowing one to record without counting the sequence of input randomly distributed pulses in multi-channel devices with serial poll, is given. The power spent by a memory cell for one binary digit recording is not greater than 0.15 mW, the device dead time is 10 mus

Method of Relative Magnitudes for Calculating Magnetic Fluxes in Electrical Machine

Directory of Open Access Journals (Sweden)

Oleg A.

2018-03-01

Full Text Available Introduction: The article presents the study results of the model of an asynchronous electric motor carried out by the author within the framework of the Priorities Research Program “Research and development in the priority areas of development of Russia’s scientific and technical complex for 2014–2020”. Materials and Methods: A model of an idealized asynchronous machine (with sinusoidal distribution of magnetic induction in air gap is used in vector control systems. It is impossible to create windings for this machine. The basis of the new calculation approach was the Conductivity of Teeth Contours Method, developed at the Electrical Machines Chair of the Moscow Power Engineering Institute (MPEI. Unlike this method, the author used not absolute values, but relative magnitudes of magnetic fluxes. This solution fundamentally improved the method’s capabilities. The relative magnitudes of the magnetic fluxes of the teeth contours do not required the additional consideration for exact structure of magnetic field of tooth and adjacent slots. These structures are identical for all the teeth of the machine and differ only in magnitude. The purpose of the calculations was not traditional harmonic analysis of magnetic induction distribution in air gap of machine, but a refinement of the equations of electric machine model. The vector control researchers used only the cos(θ function as a value of mutual magnetic coupling coefficient between the windings. Results: The author has developed a way to take into account the design of the windings of a real machine by using imaginary measuring winding with the same winding design as a real phase winding. The imaginary winding can be placed in the position of any machine windings. The calculation of the relative magnetic fluxes of this winding helped to estimate the real values of the magnetic coupling coefficients between the windings, and find the correction functions for the model of an idealized

Restoration of fMRI Decodability Does Not Imply Latent Working Memory States

Science.gov (United States)

Schneegans, Sebastian; Bays, Paul M.

2018-01-01

Recent imaging studies have challenged the prevailing view that working memory is mediated by sustained neural activity. Using machine learning methods to reconstruct memory content, these studies found that previously diminished representations can be restored by retrospective cueing or other forms of stimulation. These findings have been interpreted as evidence for an activity-silent working memory state that can be reactivated dependent on task demands. Here, we test the validity of this conclusion by formulating a neural process model of working memory based on sustained activity and using this model to emulate a spatial recall task with retrocueing. The simulation reproduces both behavioral and fMRI results previously taken as evidence for latent states, in particular the restoration of spatial reconstruction quality following an informative cue. Our results demonstrate that recovery of the decodability of an imaging signal does not provide compelling evidence for an activity-silent working memory state. PMID:28820674

Characterization of memory states of the Preisach operator with stochastic inputs

International Nuclear Information System (INIS)

Amann, A.; Brokate, M.; McCarthy, S.; Rachinskii, D.; Temnov, G.

2012-01-01

The Preisach operator with inputs defined by a Markov process x t is considered. The question we address is: what is the distribution of the random memory state of the Preisach operator at a given time moment t 0 in the limit r→∞ of infinitely long input history x t , t 0 -r≤t≤t 0 ? In order to answer this question, we introduce a Markov chain (called the memory state Markov chain) where the states are pairs (m k ,M k ) of elements from the monotone sequences of the local minimum input values m k and the local maximum input values M k recorded in the memory state and the index k of the elements plays the role of time. We express the transition probabilities of this Markov chain in terms of the transition probabilities of the input stochastic process and show that the memory state Markov chain and the input process generate the same distribution of the memory states. These results are illustrated by several examples of stochastic inputs such as the Wiener and Bernoulli processes and their mixture (we first discuss a discrete version of these processes and then the continuous time and state setting). The memory state Markov chain is then used to find the distribution of the random number of elements in the memory state sequence. We show that this number has the Poisson distribution for the Wiener and Bernoulli processes inputs. In particular, in the discrete setting, the mean value of the number of elements in the memory state scales as lnN, where N is the number of the input states, while the mean time it takes the input to generate this memory state scales as N 2 for the Wiener process and as N for the Bernoulli process. A similar relationship between the dimension of the memory state vector and the number of iterations in the numerical realization of the input is shown for the mixture of the Wiener and Bernoulli processes, thus confirming that the memory state Markov chain is an efficient tool for generating the distribution of the Preisach operator memory

Characterization of memory states of the Preisach operator with stochastic inputs

Energy Technology Data Exchange (ETDEWEB)

Amann, A. [Department of Applied Mathematics, University College Cork (Ireland); Brokate, M. [Zentrum Mathematik, Technische Universitaet Muenchen (Germany); McCarthy, S. [Department of Applied Mathematics, University College Cork (Ireland); Rachinskii, D., E-mail: d.rachinskii@ucc.ie [Department of Applied Mathematics, University College Cork (Ireland); Temnov, G. [Department of Mathematics, University College Cork (Ireland)

2012-05-01

The Preisach operator with inputs defined by a Markov process x{sup t} is considered. The question we address is: what is the distribution of the random memory state of the Preisach operator at a given time moment t{sub 0} in the limit r{yields}{infinity} of infinitely long input history x{sup t}, t{sub 0}-r{<=}t{<=}t{sub 0}? In order to answer this question, we introduce a Markov chain (called the memory state Markov chain) where the states are pairs (m{sub k},M{sub k}) of elements from the monotone sequences of the local minimum input values m{sub k} and the local maximum input values M{sub k} recorded in the memory state and the index k of the elements plays the role of time. We express the transition probabilities of this Markov chain in terms of the transition probabilities of the input stochastic process and show that the memory state Markov chain and the input process generate the same distribution of the memory states. These results are illustrated by several examples of stochastic inputs such as the Wiener and Bernoulli processes and their mixture (we first discuss a discrete version of these processes and then the continuous time and state setting). The memory state Markov chain is then used to find the distribution of the random number of elements in the memory state sequence. We show that this number has the Poisson distribution for the Wiener and Bernoulli processes inputs. In particular, in the discrete setting, the mean value of the number of elements in the memory state scales as lnN, where N is the number of the input states, while the mean time it takes the input to generate this memory state scales as N{sup 2} for the Wiener process and as N for the Bernoulli process. A similar relationship between the dimension of the memory state vector and the number of iterations in the numerical realization of the input is shown for the mixture of the Wiener and Bernoulli processes, thus confirming that the memory state Markov chain is an efficient tool for

Human short-term spatial memory: precision predicts capacity.

Science.gov (United States)

Banta Lavenex, Pamela; Boujon, Valérie; Ndarugendamwo, Angélique; Lavenex, Pierre

2015-03-01

Here, we aimed to determine the capacity of human short-term memory for allocentric spatial information in a real-world setting. Young adults were tested on their ability to learn, on a trial-unique basis, and remember over a 1-min interval the location(s) of 1, 3, 5, or 7 illuminating pads, among 23 pads distributed in a 4m×4m arena surrounded by curtains on three sides. Participants had to walk to and touch the pads with their foot to illuminate the goal locations. In contrast to the predictions from classical slot models of working memory capacity limited to a fixed number of items, i.e., Miller's magical number 7 or Cowan's magical number 4, we found that the number of visited locations to find the goals was consistently about 1.6 times the number of goals, whereas the number of correct choices before erring and the number of errorless trials varied with memory load even when memory load was below the hypothetical memory capacity. In contrast to resource models of visual working memory, we found no evidence that memory resources were evenly distributed among unlimited numbers of items to be remembered. Instead, we found that memory for even one individual location was imprecise, and that memory performance for one location could be used to predict memory performance for multiple locations. Our findings are consistent with a theoretical model suggesting that the precision of the memory for individual locations might determine the capacity of human short-term memory for spatial information. Copyright © 2015 Elsevier Inc. All rights reserved.

Development of AI (Artificial Intelligence)-based simulation system for man-machine system behavior in accidental situations of nuclear power plant

International Nuclear Information System (INIS)

Yoshida, Kazuo; Yokobayashi, Masao; Tanabe, Fumiya; Kawase, Katumi.

1996-01-01

A prototype version of a computer simulation system named JACOS (JAeri COgnitive Simulation system) has been developed at JAERI (Japan Atomic Energy Research Institute) to simulate the man-machine system behavior in which both the cognitive behavior of a human operator and the plant behavior affect each other. The objectives of this system development is to provide man-machine system analysts with detailed information on the cognitive process of an operator and the plant behavior affected by operator's actions in accidental situations of an NPP (nuclear power plant). The simulation system consists of an operator model and a plant model which are coupled dynamically. The operator model simulates an operator's cognitive behavior in accidental situations based on the decision ladder model of Rasmussen, and is implemented using the AI-techniques of the distributed cooperative inference method with the so-called blackboard architecture. Rule-based behavior is simulated using knowledge representation with If-Then type of rules. Knowledge-based behavior is simulated using knowledge representation with MFM (Multilevel Flow Modeling) and qualitative reasoning method. Cognitive characteristics of attentional narrowing, limitation of short-term memory, and knowledge recalling from long-term memory are also modeled. The plant model of a 3-loop PWR is also developed using a best estimate thermal-hydraulic analysis code RELAP5/MOD2. Some simulations of incidents were performed to verify the human model. It was found that AI-techniques used in the human model are suitable to simulate the operator's cognitive behavior in an NPP accident. The models of cognitive characteristics were investigated in the effects on simulated results of cognitive behaviors. (author)

Generalized Load Sharing for Homogeneous Networks of Distributed Environment

Directory of Open Access Journals (Sweden)

A. Satheesh

2008-01-01

Full Text Available We propose a method for job migration policies by considering effective usage of global memory in addition to CPU load sharing in distributed systems. When a node is identified for lacking sufficient memory space to serve jobs, one or more jobs of the node will be migrated to remote nodes with low memory allocations. If the memory space is sufficiently large, the jobs will be scheduled by a CPU-based load sharing policy. Following the principle of sharing both CPU and memory resources, we present several load sharing alternatives. Our objective is to reduce the number of page faults caused by unbalanced memory allocations for jobs among distributed nodes, so that overall performance of a distributed system can be significantly improved. We have conducted trace-driven simulations to compare CPU-based load sharing policies with our policies. We show that our load sharing policies not only improve performance of memory bound jobs, but also maintain the same load sharing quality as the CPU-based policies for CPU-bound jobs. Regarding remote execution and preemptive migration strategies, our experiments indicate that a strategy selection in load sharing is dependent on the amount of memory demand of jobs, remote execution is more effective for memory-bound jobs, and preemptive migration is more effective for CPU-bound jobs. Our CPU-memory-based policy using either high performance or high throughput approach and using the remote execution strategy performs the best for both CPU-bound and memory-bound job in homogeneous networks of distributed environment.

Hybrid machining processes perspectives on machining and finishing

CERN Document Server

Gupta, Kapil; Laubscher, R F

2016-01-01

This book describes various hybrid machining and finishing processes. It gives a critical review of the past work based on them as well as the current trends and research directions. For each hybrid machining process presented, the authors list the method of material removal, machining system, process variables and applications. This book provides a deep understanding of the need, application and mechanism of hybrid machining processes.

A study of wear in refrigerating machines using thin layer activation

International Nuclear Information System (INIS)

Hammer, P.; Eichhorn, K.; Eifrig, C.

1986-01-01

Wear is studied in a ball-and-socket joint of a newly developed refrigerating machine. Using deuteron activation a 15 μm deep Co-57 layer is generated at the ring-shaped friction area in the steel socket of the joint. The measurement of the Co-57 intensity of the wear particles held back on an oil filter provides information about the wear rate of the socket during machine operation. The measurement of the Co-57 contaminations occuring in the individual parts of the machine at the end of the test gives information about the distribution of the wear particles in the machine and about the material transfer in the ball-and-socket joint. (author)

Parallel phase model : a programming model for high-end parallel machines with manycores.

Energy Technology Data Exchange (ETDEWEB)

Wu, Junfeng (Syracuse University, Syracuse, NY); Wen, Zhaofang; Heroux, Michael Allen; Brightwell, Ronald Brian

2009-04-01

This paper presents a parallel programming model, Parallel Phase Model (PPM), for next-generation high-end parallel machines based on a distributed memory architecture consisting of a networked cluster of nodes with a large number of cores on each node. PPM has a unified high-level programming abstraction that facilitates the design and implementation of parallel algorithms to exploit both the parallelism of the many cores and the parallelism at the cluster level. The programming abstraction will be suitable for expressing both fine-grained and coarse-grained parallelism. It includes a few high-level parallel programming language constructs that can be added as an extension to an existing (sequential or parallel) programming language such as C; and the implementation of PPM also includes a light-weight runtime library that runs on top of an existing network communication software layer (e.g. MPI). Design philosophy of PPM and details of the programming abstraction are also presented. Several unstructured applications that inherently require high-volume random fine-grained data accesses have been implemented in PPM with very promising results.

The scheme machine: A case study in progress in design derivation at system levels

Science.gov (United States)

Johnson, Steven D.

1995-01-01

The Scheme Machine is one of several design projects of the Digital Design Derivation group at Indiana University. It differs from the other projects in its focus on issues of system design and its connection to surrounding research in programming language semantics, compiler construction, and programming methodology underway at Indiana and elsewhere. The genesis of the project dates to the early 1980's, when digital design derivation research branched from the surrounding research effort in programming languages. Both branches have continued to develop in parallel, with this particular project serving as a bridge. However, by 1990 there remained little real interaction between the branches and recently we have undertaken to reintegrate them. On the software side, researchers have refined a mathematically rigorous (but not mechanized) treatment starting with the fully abstract semantic definition of Scheme and resulting in an efficient implementation consisting of a compiler and virtual machine model, the latter typically realized with a general purpose microprocessor. The derivation includes a number of sophisticated factorizations and representations and is also deep example of the underlying engineering methodology. The hardware research has created a mechanized algebra supporting the tedious and massive transformations often seen at lower levels of design. This work has progressed to the point that large scale devices, such as processors, can be derived from first-order finite state machine specifications. This is roughly where the language oriented research stops; thus, together, the two efforts establish a thread from the highest levels of abstract specification to detailed digital implementation. The Scheme Machine project challenges hardware derivation research in several ways, although the individual components of the system are of a similar scale to those we have worked with before. The machine has a custom dual-ported memory to support garbage collection

Grinding, Machining Morphological Studies on C/SiC Composites

Science.gov (United States)

Xiao, Chun-fang; Han, Bing

2018-05-01

C/SiC composite is a typical material difficult to machine. It is hard and brittle. In machining, the cutting force is large, the material removal rate is low, the edge is prone to collapse, and the tool wear is serious. In this paper, the grinding of C/Si composites material along the direction of fiber distribution is studied respectively. The surface microstructure and mechanical properties of C/SiC composites processed by ultrasonic machining were evaluated. The change of surface quality with the change of processing parameters has also been studied. By comparing the performances of conventional grinding and ultrasonic grinding, the surface roughness and functional characteristics of the material can be improved by optimizing the processing parameters.

A Novel Support Vector Machine with Globality-Locality Preserving

Directory of Open Access Journals (Sweden)

Cheng-Long Ma

2014-01-01

Full Text Available Support vector machine (SVM is regarded as a powerful method for pattern classification. However, the solution of the primal optimal model of SVM is susceptible for class distribution and may result in a nonrobust solution. In order to overcome this shortcoming, an improved model, support vector machine with globality-locality preserving (GLPSVM, is proposed. It introduces globality-locality preserving into the standard SVM, which can preserve the manifold structure of the data space. We complete rich experiments on the UCI machine learning data sets. The results validate the effectiveness of the proposed model, especially on the Wine and Iris databases; the recognition rate is above 97% and outperforms all the algorithms that were developed from SVM.

76 FR 174 - International Business Machines (IBM), Global Sales Operations Organization, Sales and...

Science.gov (United States)

2011-01-03

... DEPARTMENT OF LABOR Employment and Training Administration [TA-W-74,575; TA-W-74,575D] International Business Machines (IBM), Global Sales Operations Organization, Sales and Distribution Business Manager Roles; One Teleworker Located in Charleston, WV; International Business Machines (IBM), Global Sales Operations Organization, Sales and...

Evaluation of Short Term Memory Span Function In Children

Directory of Open Access Journals (Sweden)

Barış ERGÜL

2016-12-01

Full Text Available Although details of the information encoded in the short-term memory where it is stored temporarily be recorded in the working memory in the next stage. Repeating the information mentally makes it remain in memory for a long time. Studies investigating the relationship between short-term memory and reading skills that are carried out to examine the relationship between short-term memory processes and reading comprehension. In this study information coming to short-term memory and the factors affecting operation of short term memory are investigated with regression model. The aim of the research is to examine the factors (age, IQ and reading skills that are expected the have an effect on short-term memory in children through regression analysis. One of the assumptions of regression analysis is to examine which has constant variance and normal distribution of the error term. In this study, because the error term is not normally distributed, robust regression techniques were applied. Also, for each technique; coefficient of determination is determined. According to the findings, the increase in age, IQ and reading skills caused the increase in short term memory in children. After applying robust regression techniques, the Winsorized Least Squares (WLS technique gives the highest coefficient of determination.

31 CFR 12.3 - Sale of tobacco products in vending machines prohibited.

Science.gov (United States)

2010-07-01

... RESTRICTION OF SALE AND DISTRIBUTION OF TOBACCO PRODUCTS § 12.3 Sale of tobacco products in vending machines prohibited. The sale of tobacco products in vending machines located in or around any Federal building under... 31 Money and Finance: Treasury 1 2010-07-01 2010-07-01 false Sale of tobacco products in vending...

AHaH Computing–From Metastable Switches to Attractors to Machine Learning

Science.gov (United States)

Nugent, Michael Alexander; Molter, Timothy Wesley

2014-01-01

Modern computing architecture based on the separation of memory and processing leads to a well known problem called the von Neumann bottleneck, a restrictive limit on the data bandwidth between CPU and RAM. This paper introduces a new approach to computing we call AHaH computing where memory and processing are combined. The idea is based on the attractor dynamics of volatile dissipative electronics inspired by biological systems, presenting an attractive alternative architecture that is able to adapt, self-repair, and learn from interactions with the environment. We envision that both von Neumann and AHaH computing architectures will operate together on the same machine, but that the AHaH computing processor may reduce the power consumption and processing time for certain adaptive learning tasks by orders of magnitude. The paper begins by drawing a connection between the properties of volatility, thermodynamics, and Anti-Hebbian and Hebbian (AHaH) plasticity. We show how AHaH synaptic plasticity leads to attractor states that extract the independent components of applied data streams and how they form a computationally complete set of logic functions. After introducing a general memristive device model based on collections of metastable switches, we show how adaptive synaptic weights can be formed from differential pairs of incremental memristors. We also disclose how arrays of synaptic weights can be used to build a neural node circuit operating AHaH plasticity. By configuring the attractor states of the AHaH node in different ways, high level machine learning functions are demonstrated. This includes unsupervised clustering, supervised and unsupervised classification, complex signal prediction, unsupervised robotic actuation and combinatorial optimization of procedures–all key capabilities of biological nervous systems and modern machine learning algorithms with real world application. PMID:24520315

AHaH computing-from metastable switches to attractors to machine learning.

Directory of Open Access Journals (Sweden)

Michael Alexander Nugent

Full Text Available Modern computing architecture based on the separation of memory and processing leads to a well known problem called the von Neumann bottleneck, a restrictive limit on the data bandwidth between CPU and RAM. This paper introduces a new approach to computing we call AHaH computing where memory and processing are combined. The idea is based on the attractor dynamics of volatile dissipative electronics inspired by biological systems, presenting an attractive alternative architecture that is able to adapt, self-repair, and learn from interactions with the environment. We envision that both von Neumann and AHaH computing architectures will operate together on the same machine, but that the AHaH computing processor may reduce the power consumption and processing time for certain adaptive learning tasks by orders of magnitude. The paper begins by drawing a connection between the properties of volatility, thermodynamics, and Anti-Hebbian and Hebbian (AHaH plasticity. We show how AHaH synaptic plasticity leads to attractor states that extract the independent components of applied data streams and how they form a computationally complete set of logic functions. After introducing a general memristive device model based on collections of metastable switches, we show how adaptive synaptic weights can be formed from differential pairs of incremental memristors. We also disclose how arrays of synaptic weights can be used to build a neural node circuit operating AHaH plasticity. By configuring the attractor states of the AHaH node in different ways, high level machine learning functions are demonstrated. This includes unsupervised clustering, supervised and unsupervised classification, complex signal prediction, unsupervised robotic actuation and combinatorial optimization of procedures-all key capabilities of biological nervous systems and modern machine learning algorithms with real world application.

Time-Frequency Analysis of Signals Generated by Rotating Machines

Directory of Open Access Journals (Sweden)

R. Zetik

1999-06-01

Full Text Available This contribution is devoted to the higher order time-frequency analyses of signals. Firstly, time-frequency representations of higher order (TFRHO are defined. Then L-Wigner distribution (LWD is given as a special case of TFRHO. Basic properties of LWD are illustrated based on the analysis of mono-component and multi-component synthetic signals and acoustical signals generated by rotating machine. The obtained results confirm usefulness of LWD application for the purpose of rotating machine condition monitoring.

A new workstation based man/machine interface system for the JT-60 Upgrade

International Nuclear Information System (INIS)

Yonekawa, I.; Shimono, M.; Totsuka, T.; Yamagishi, K.

1992-01-01

Development of a new man/machine interface system was stimulated by the requirements of making the JT-60 operator interface more 'friendly' on the basis of the past five-year operational experience. Eleven Sun/3 workstations and their supervisory mini-computer HIDIC V90/45 are connected through the standard network; Ethernet. The network is also connected to the existing 'ZENKEI' mini-computer system through the shared memory on the HIDIC V90/45 mini-computer. Improved software, such as automatic setting of the discharge conditions, consistency check among the related parameters and easy operation for discharge result data display, offered the 'user-friendly' environments. This new man/machine interface system leads to the efficient operation of the JT-60. (author)

Effects on Machining on Surface Residual Stress of SA 508 and Austenitic Stainless Steel

International Nuclear Information System (INIS)

Lee, Kyoung Soo; Lee, Seong Ho; Park, Chi Yong; Yang, Jun Seok; Lee, Jeong Geun; Park, Jai Hak

2011-01-01

Primary water stress corrosion cracking has occurred in dissimilar weld areas in nuclear power plants. Residual stress is a driving force in the crack. Residual stress may be generated by weld or surface machining. Residual stress due to surface machining depends on the machining method, e.g., milling, grinding, or EDM. The stress is usually distributed on or near the surface of the material. We present the measured residual stress for machining on SA 508 and austenitic stainless steels such as TP304 and F316. The residual stress can be tensile or compressive depending on the machining method. The depth and the magnitude of the residual stress depend on the material and the machining method

The relationship between mother narrative style and child memory.

Science.gov (United States)

Kayıran, Sinan Mahir; Cure, Sena

2011-07-26

The question of whether children and infants have memory capabilities similar to adults has long been of interest. Until recently, it was thought that compared to adults, infants have very limited memory processing abilities. Knowledge about factors affecting a child's memory abilities can help families (specifically mothers) behave in a manner that best benefits their children in language and memory skills. The present study examines one factor that may underlie a child's memory capabilities; namely the mother's narrative style. Convenience sampling was used to select participants. Forty healthy children (mean age of 31.55 months, range 25-37 months) and their mothers were entered into the study. All participants were native Turkish speakers, from similar socioeconomic status backgrounds. Memory was assessed by a modified version of the Magic Shrinking Machine. Narrative style was assessed by the mother "reading" a Frog Story; a picture book with no words in it. Children were then grouped according to their mother's level of narrative style. Children's language skills were measured via the Turkish form of the CDI (Communicative Development Inventory) which was translated to Turkish as TIGE. To explore the relationships between mothers' narrative styles and children's memory and language skills and between children's language skills and memory capabilities, linear regressions were run. There were no significant correlations among any comparisons (P > 0.05). Children's language skills do not improve according to their mothers' narrative styles, and children do not show better memory abilities when mothers use more words and longer sentences. In order to have a better understanding of these relationships, future research that includes several more variables is needed. Child; Mother; Memory; Narrative style.

Problems in ensuring normal actuation of pneumatic drilling machines during their manufacture

Energy Technology Data Exchange (ETDEWEB)

Vasilev, V

1982-01-01

The problems of ensuring normal actuation and reliable operation of pneumatic drilling machines during their manufacturing are examined. The modes of operation are cited with which the normal actuation must be tested. Oscillograms are used to examine the characteristic cases of completion of machines with the impossibility of actuation during operation for one cycle or with an interruption in the mode, along with methods for eliminating these deficiencies. The effect on the actuation process of factors such as the type of rock being drilled, the change in the position of the machine, the axial force, the design of the air distribution device and the impact mechanism, the relationship between the surface areas of the distribution device and the travel of the distributor, the choking of the power and so on is studied. Different methods for completing design are analyzed.

Relationship between neuronal network architecture and naming performance in temporal lobe epilepsy: A connectome based approach using machine learning.

Science.gov (United States)

Munsell, B C; Wu, G; Fridriksson, J; Thayer, K; Mofrad, N; Desisto, N; Shen, D; Bonilha, L

2017-09-09

Impaired confrontation naming is a common symptom of temporal lobe epilepsy (TLE). The neurobiological mechanisms underlying this impairment are poorly understood but may indicate a structural disorganization of broadly distributed neuronal networks that support naming ability. Importantly, naming is frequently impaired in other neurological disorders and by contrasting the neuronal structures supporting naming in TLE with other diseases, it will become possible to elucidate the common systems supporting naming. We aimed to evaluate the neuronal networks that support naming in TLE by using a machine learning algorithm intended to predict naming performance in subjects with medication refractory TLE using only the structural brain connectome reconstructed from diffusion tensor imaging. A connectome-based prediction framework was developed using network properties from anatomically defined brain regions across the entire brain, which were used in a multi-task machine learning algorithm followed by support vector regression. Nodal eigenvector centrality, a measure of regional network integration, predicted approximately 60% of the variance in naming. The nodes with the highest regression weight were bilaterally distributed among perilimbic sub-networks involving mainly the medial and lateral temporal lobe regions. In the context of emerging evidence regarding the role of large structural networks that support language processing, our results suggest intact naming relies on the integration of sub-networks, as opposed to being dependent on isolated brain areas. In the case of TLE, these sub-networks may be disproportionately indicative naming processes that are dependent semantic integration from memory and lexical retrieval, as opposed to multi-modal perception or motor speech production. Copyright © 2017. Published by Elsevier Inc.

Bán, Zsófia and Turai, Hedvig, eds.: "Exposed Memories: Family Pictures in Private and Collective Memory"

Directory of Open Access Journals (Sweden)

Katalin Kádár Lynn

2011-01-01

Full Text Available Zsófia Bán and Hedvig Turai, eds. Exposed Memories: Family Pictures in Private and Collective Memory. AICA: International Association of Art Critics: Hungarian Section: Distributed by Central European University Press, 2010, 193 pp. Reviewed by Katalin Kádár Lynn, Senior Researcher, ELTE.

Memory Effect on Adaptive Decision Making with a Chaotic Semiconductor Laser

Directory of Open Access Journals (Sweden)

Takatomo Mihana

2018-01-01

Full Text Available We investigate the effect of a memory parameter on the performance of adaptive decision making using a tug-of-war method with the chaotic oscillatory dynamics of a semiconductor laser. We experimentally generate chaotic temporal waveforms of the semiconductor laser with optical feedback and apply them for adaptive decision making in solving a multiarmed bandit problem that aims at maximizing the total reward from slot machines whose hit probabilities are dynamically switched. We examine the dependence of making correct decisions on different values of the memory parameter. The degree of adaptivity is found to be enhanced with a smaller memory parameter, whereas the degree of convergence to the correct decision is higher for a larger memory parameter. The relations among the adaptivity, environmental changes, and the difficulties of the problem are also discussed considering the requirement of past decisions. This examination of ultrafast adaptive decision making highlights the importance of memorizing past events and paves the way for future photonic intelligence.

Study of the AC machines winding having fractional q

Science.gov (United States)

Bespalov, V. Y.; Sidorov, A. O.

2018-02-01

The winding schemes with a fractional numbers of slots per pole and phase q have been known and used for a long time. However, in the literature on the low-noise machines design there are not recommended to use. Nevertheless, fractional q windings have been realized in many applications of special AC electrical machines, allowing to improve their performance, including vibroacoustic one. This paper deals with harmonic analysis of windings having integer and fractional q in permanent magnet synchronous motors, a comparison of their characteristics is performed, frequencies of subharmonics are revealed. Optimal winding pitch design is found giving reduce the amplitudes of subharmonics. Distribution factors for subharmonics, fractional and high-order harmonics are calculated, results analysis is represented, allowing for giving recommendations how to calculate distribution factors for different harmonics when q is fractional.

Switching behavior of resistive change memory using oxide nanowires

Science.gov (United States)

Aono, Takashige; Sugawa, Kosuke; Shimizu, Tomohiro; Shingubara, Shoso; Takase, Kouichi

2018-06-01

Resistive change random access memory (ReRAM), which is expected to be the next-generation nonvolatile memory, often has wide switching voltage distributions due to many kinds of conductive filaments. In this study, we have tried to suppress the distribution through the structural restriction of the filament-forming area using NiO nanowires. The capacitor with Ni metal nanowires whose surface is oxidized showed good switching behaviors with narrow distributions. The knowledge gained from our study will be very helpful in producing practical ReRAM devices.

Pileup Mitigation with Machine Learning (PUMML)

Science.gov (United States)

Komiske, Patrick T.; Metodiev, Eric M.; Nachman, Benjamin; Schwartz, Matthew D.

2017-12-01

Pileup involves the contamination of the energy distribution arising from the primary collision of interest (leading vertex) by radiation from soft collisions (pileup). We develop a new technique for removing this contamination using machine learning and convolutional neural networks. The network takes as input the energy distribution of charged leading vertex particles, charged pileup particles, and all neutral particles and outputs the energy distribution of particles coming from leading vertex alone. The PUMML algorithm performs remarkably well at eliminating pileup distortion on a wide range of simple and complex jet observables. We test the robustness of the algorithm in a number of ways and discuss how the network can be trained directly on data.

Study on electromagnetism force of CARR control rod drive mechanism experimental machine

International Nuclear Information System (INIS)

Zhu Xuewei; Zhen Jianxiao; Wang Yulin; Jia Yueguang; Yang Kun; Yin Haozhe

2015-01-01

With the aim of acquiring electromagnetic force and electromagnetic field distributions of control rod drive mechanism (CRDM) in China Advanced Research Reactor (CARR), the force analysis on the CRDM was taken. Manufacturing the experimental machine, the electromagnetic force experiment was taken on it. The electromagnetic field and electromagnetic force simulation analyses of experimental machine were taken, working out distribution data of electromagnetic force and magnetic induction intensity distribution curve, and the effects of permanent magnetic field on electromagnetic field and structure parameters on electromagnetic force. The simulation value is accord with experiment value, the research results provide a reference to electromagnetic force study on CRDM in CARR, and also provide a reference to design of the same type CRDM. (authors)

Logic synthesis for FPGA-based finite state machines

CERN Document Server

Barkalov, Alexander; Kolopienczyk, Malgorzata; Mielcarek, Kamil; Bazydlo, Grzegorz

2016-01-01

This book discusses control units represented by the model of a finite state machine (FSM). It contains various original methods and takes into account the peculiarities of field-programmable gate arrays (FPGA) chips and a FSM model. It shows that one of the peculiarities of FPGA chips is the existence of embedded memory blocks (EMB). The book is devoted to the solution of problems of logic synthesis and reduction of hardware amount in control units. The book will be interesting and useful for researchers and PhD students in the area of Electrical Engineering and Computer Science, as well as for designers of modern digital systems.

Improved Extreme Learning Machine and Its Application in Image Quality Assessment

Directory of Open Access Journals (Sweden)

Li Mao

2014-01-01

Full Text Available Extreme learning machine (ELM is a new class of single-hidden layer feedforward neural network (SLFN, which is simple in theory and fast in implementation. Zong et al. propose a weighted extreme learning machine for learning data with imbalanced class distribution, which maintains the advantages from original ELM. However, the current reported ELM and its improved version are only based on the empirical risk minimization principle, which may suffer from overfitting. To solve the overfitting troubles, in this paper, we incorporate the structural risk minimization principle into the (weighted ELM, and propose a modified (weighted extreme learning machine (M-ELM and M-WELM. Experimental results show that our proposed M-WELM outperforms the current reported extreme learning machine algorithm in image quality assessment.

The Effect of Unreliable Machine for Two Echelons Deteriorating Inventory Model

Directory of Open Access Journals (Sweden)

I Nyoman Sutapa

2014-01-01

Full Text Available Many researchers have developed two echelons supply chain, however only few of them consider deteriorating items and unreliable machine in their models In this paper, we develop an inventory deteriorating model for two echelons supply chain with unreliable machine. The unreliable machine time is assumed uniformly distributed. The model is solved using simple heuristic since a closed form model can not be derived. A numerical example is used to show how the model works. A sensitivity analysis is conducted to show effect of different lost sales cost in the model. The result shows that increasing lost sales cost will increase both manufacture and buyer costs however buyer’s total cost increase higher than manufacture’s total cost as manufacture’s machine is more unreliable.

Machine terms dictionary

Energy Technology Data Exchange (ETDEWEB)

NONE

1979-04-15

This book gives descriptions of machine terms which includes machine design, drawing, the method of machine, machine tools, machine materials, automobile, measuring and controlling, electricity, basic of electron, information technology, quality assurance, Auto CAD and FA terms and important formula of mechanical engineering.

Radioisotope method potentialities in machine reliability and durability enhancement

International Nuclear Information System (INIS)

Postnikov, V.I.

1975-01-01

The development of a surface activation method is reviewed with regard to wear of machine parts. Examples demonstrating the highly promising aspects and practical application of the method are cited. The use of high-sensitivity instruments and variation of activation depth from 10 um to 0.5 mm allows to perform the investigations at a sensitivity of 0.05 um and to estimate the linear values of machine wear. Standard diagrams are presented for measuring the wear of different machine parts by means of surface activation. Investigations performed at several Soviet technological institutes afford a set of dependences, which characterize the distribution of radioactive isotopes in depth under different conditions of activation of diverse metals and alloys and permit to study the wear of any metal

Electronic vending machines for dispensing rapid HIV self-testing kits: a case study.

Science.gov (United States)

Young, Sean D; Klausner, Jeffrey; Fynn, Risa; Bolan, Robert

2014-02-01

This short report evaluates the feasibility of using electronic vending machines for dispensing oral, fluid, rapid HIV self-testing kits in Los Angeles County. Feasibility criteria that needed to be addressed were defined as: (1) ability to find a manufacturer who would allow dispensing of HIV testing kits and could fit them to the dimensions of a vending machine, (2) ability to identify and address potential initial obstacles, trade-offs in choosing a machine location, and (3) ability to gain community approval for implementing this approach in a community setting. To address these issues, we contracted a vending machine company who could supply a customized, Internet-enabled machine that could dispense HIV kits and partnered with a local health center available to host the machine onsite and provide counseling to participants, if needed. Vending machines appear to be feasible technologies that can be used to distribute HIV testing kits.

Fast Failure Recovery for Main-Memory DBMSs on Multicores

OpenAIRE

Wu, Yingjun; Guo, Wentian; Chan, Chee-Yong; Tan, Kian-Lee

2016-01-01

Main-memory database management systems (DBMS) can achieve excellent performance when processing massive volume of on-line transactions on modern multi-core machines. But existing durability schemes, namely, tuple-level and transaction-level logging-and-recovery mechanisms, either degrade the performance of transaction processing or slow down the process of failure recovery. In this paper, we show that, by exploiting application semantics, it is possible to achieve speedy failure recovery wit...

Some relations between quantum Turing machines and Turing machines

OpenAIRE

Sicard, Andrés; Vélez, Mario

1999-01-01

For quantum Turing machines we present three elements: Its components, its time evolution operator and its local transition function. The components are related with the components of deterministic Turing machines, the time evolution operator is related with the evolution of reversible Turing machines and the local transition function is related with the transition function of probabilistic and reversible Turing machines.

Simulating Pre-Asymptotic, Non-Fickian Transport Although Doing Simple Random Walks - Supported By Empirical Pore-Scale Velocity Distributions and Memory Effects

Science.gov (United States)

Most, S.; Jia, N.; Bijeljic, B.; Nowak, W.

2016-12-01

Pre-asymptotic characteristics are almost ubiquitous when analyzing solute transport processes in porous media. These pre-asymptotic aspects are caused by spatial coherence in the velocity field and by its heterogeneity. For the Lagrangian perspective of particle displacements, the causes of pre-asymptotic, non-Fickian transport are skewed velocity distribution, statistical dependencies between subsequent increments of particle positions (memory) and dependence between the x, y and z-components of particle increments. Valid simulation frameworks should account for these factors. We propose a particle tracking random walk (PTRW) simulation technique that can use empirical pore-space velocity distributions as input, enforces memory between subsequent random walk steps, and considers cross dependence. Thus, it is able to simulate pre-asymptotic non-Fickian transport phenomena. Our PTRW framework contains an advection/dispersion term plus a diffusion term. The advection/dispersion term produces time-series of particle increments from the velocity CDFs. These time series are equipped with memory by enforcing that the CDF values of subsequent velocities change only slightly. The latter is achieved through a random walk on the axis of CDF values between 0 and 1. The virtual diffusion coefficient for that random walk is our only fitting parameter. Cross-dependence can be enforced by constraining the random walk to certain combinations of CDF values between the three velocity components in x, y and z. We will show that this modelling framework is capable of simulating non-Fickian transport by comparison with a pore-scale transport simulation and we analyze the approach to asymptotic behavior.

Long-term pitch memory for music recordings is related to auditory working memory precision.

Science.gov (United States)

Van Hedger, Stephen C; Heald, Shannon Lm; Nusbaum, Howard C

2018-04-01

Most individuals have reliable long-term memories for the pitch of familiar music recordings. This pitch memory (1) appears to be normally distributed in the population, (2) does not depend on explicit musical training and (3) only seems to be weakly related to differences in listening frequency estimates. The present experiment was designed to assess whether individual differences in auditory working memory could explain variance in long-term pitch memory for music recordings. In Experiment 1, participants first completed a musical note adjustment task that has been previously used to assess working memory of musical pitch. Afterward, participants were asked to judge the pitch of well-known music recordings, which either had or had not been shifted in pitch. We found that performance on the pitch working memory task was significantly related to performance in the pitch memory task using well-known recordings, even when controlling for overall musical experience and familiarity with each recording. In Experiment 2, we replicated these findings in a separate group of participants while additionally controlling for fluid intelligence and non-pitch-based components of auditory working memory. In Experiment 3, we demonstrated that participants could not accurately judge the pitch of unfamiliar recordings, suggesting that our method of pitch shifting did not result in unwanted acoustic cues that could have aided participants in Experiments 1 and 2. These results, taken together, suggest that the ability to maintain pitch information in working memory might lead to more accurate long-term pitch memory.

Support vector machine in machine condition monitoring and fault diagnosis

Science.gov (United States)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

A hybrid analytical model for open-circuit field calculation of multilayer interior permanent magnet machines

Energy Technology Data Exchange (ETDEWEB)

Zhang, Zhen [School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China); Xia, Changliang [School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China); Tianjin Engineering Center of Electric Machine System Design and Control, Tianjin 300387 (China); Yan, Yan, E-mail: yanyan@tju.edu.cn [School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China); Geng, Qiang [Tianjin Engineering Center of Electric Machine System Design and Control, Tianjin 300387 (China); Shi, Tingna [School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China)

2017-08-01

Highlights: • A hybrid analytical model is developed for field calculation of multilayer IPM machines. • The rotor magnetic field is calculated by the magnetic equivalent circuit method. • The field in the stator and air-gap is calculated by subdomain technique. • The magnetic scalar potential on rotor surface is modeled as trapezoidal distribution. - Abstract: Due to the complicated rotor structure and nonlinear saturation of rotor bridges, it is difficult to build a fast and accurate analytical field calculation model for multilayer interior permanent magnet (IPM) machines. In this paper, a hybrid analytical model suitable for the open-circuit field calculation of multilayer IPM machines is proposed by coupling the magnetic equivalent circuit (MEC) method and the subdomain technique. In the proposed analytical model, the rotor magnetic field is calculated by the MEC method based on the Kirchhoff’s law, while the field in the stator slot, slot opening and air-gap is calculated by subdomain technique based on the Maxwell’s equation. To solve the whole field distribution of the multilayer IPM machines, the coupled boundary conditions on the rotor surface are deduced for the coupling of the rotor MEC and the analytical field distribution of the stator slot, slot opening and air-gap. The hybrid analytical model can be used to calculate the open-circuit air-gap field distribution, back electromotive force (EMF) and cogging torque of multilayer IPM machines. Compared with finite element analysis (FEA), it has the advantages of faster modeling, less computation source occupying and shorter time consuming, and meanwhile achieves the approximate accuracy. The analytical model is helpful and applicable for the open-circuit field calculation of multilayer IPM machines with any size and pole/slot number combination.

A Cognitive Machine Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis from Restrictive Cardiomyopathy

Science.gov (United States)

Sengupta, Partho P.; Huang, Yen-Min; Bansal, Manish; Ashrafi, Ali; Fisher, Matt; Shameer, Khader; Gall, Walt; Dudley, Joel T

2016-01-01

Background Associating a patient’s profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical judgment. We hypothesized that a similar process using a cognitive computing tool would be well suited for learning and recalling multidimensional attributes of speckle tracking echocardiography (STE) data sets derived from patients with known constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM). Methods and Results Clinical and echocardiographic data of 50 patients with CP and 44 with RCM were used for developing an associative memory classifier (AMC) based machine learning algorithm. The STE data was normalized in reference to 47 controls with no structural heart disease, and the diagnostic area under the receiver operating characteristic curve (AUC) of the AMC was evaluated for differentiating CP from RCM. Using only STE variables, AMC achieved a diagnostic AUC of 89·2%, which improved to 96·2% with addition of 4 echocardiographic variables. In comparison, the AUC of early diastolic mitral annular velocity and left ventricular longitudinal strain were 82.1% and 63·7%, respectively. Furthermore, AMC demonstrated greater accuracy and shorter learning curves than other machine learning approaches with accuracy asymptotically approaching 90% after a training fraction of 0·3 and remaining flat at higher training fractions. Conclusions This study demonstrates feasibility of a cognitive machine learning approach for learning and recalling patterns observed during echocardiographic evaluations. Incorporation of machine learning algorithms in cardiac imaging may aid standardized assessments and support the quality of interpretations, particularly for novice readers with limited experience. PMID:27266599

Scheduling stochastic two-machine flow shop problems to minimize expected makespan

Directory of Open Access Journals (Sweden)

Mehdi Heydari

2013-07-01

Full Text Available During the past few years, despite tremendous contribution on deterministic flow shop problem, there are only limited number of works dedicated on stochastic cases. This paper examines stochastic scheduling problems in two-machine flow shop environment for expected makespan minimization where processing times of jobs are normally distributed. Since jobs have stochastic processing times, to minimize the expected makespan, the expected sum of the second machine’s free times is minimized. In other words, by minimization waiting times for the second machine, it is possible to reach the minimum of the objective function. A mathematical method is proposed which utilizes the properties of the normal distributions. Furthermore, this method can be used as a heuristic method for other distributions, as long as the means and variances are available. The performance of the proposed method is explored using some numerical examples.

Autobiographical Memory in Semantic Dementia: A Longitudinal fMRI Study

Science.gov (United States)

Maguire, Eleanor A.; Kumaran, Dharshan; Hassabis, Demis; Kopelman, Michael D.

2010-01-01

Whilst patients with semantic dementia (SD) are known to suffer from semantic memory and language impairments, there is less agreement about whether memory for personal everyday experiences, autobiographical memory, is compromised. In healthy individuals, functional MRI (fMRI) has helped to delineate a consistent and distributed brain network…

Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems

OpenAIRE

Shehzad, Danish; Bozkuş, Zeki

2016-01-01

Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks, interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI) is used. MPI_Allgather collecti...

Between Cultural Memory and Communicative Memory – the Dilemmas of Reconstruction of Annihilated Past of Polish Jews

Directory of Open Access Journals (Sweden)

Krzysztof Malicki

2015-08-01

Full Text Available This text presents briefly some of the elements of Polish discourse about collective memory of Holocaust during transformations. I refer to Assmann’s concept of communicative and cultural memories, which seems to help to explain the phenomenon of present memory, which can be observed. This text concentrates not on Polish memory of annihilation of Jews (so different from the memory of victims or perpetrators, not to mention different memory of other nations but on its internal divisions and dilemmas which it generates. Along with war generation passing away, the decisive role of creating Polish memory about Shoah will be taken over by specialised institutions and rituals commemorating the past. In the early post -war period the transfer of memory happened mainly by the witness. The role of specialised institutions was marginal. From the beginning of XX century along with last witnesses passing away, the memory of Shoah became the focus of institutions (scientific institutions gathering documents including witnesses’ reports and anniversaries. It is a very important moment to look again at the memory distributed from the roots and confront it with models and standards of commemorating the past, which will create the memory of the next generations.

Event boundaries and memory improvement.

Science.gov (United States)

Pettijohn, Kyle A; Thompson, Alexis N; Tamplin, Andrea K; Krawietz, Sabine A; Radvansky, Gabriel A

2016-03-01

The structure of events can influence later memory for information that is embedded in them, with evidence indicating that event boundaries can both impair and enhance memory. The current study explored whether the presence of event boundaries during encoding can structure information to improve memory. In Experiment 1, memory for a list of words was tested in which event structure was manipulated by having participants walk through a doorway, or not, halfway through the word list. In Experiment 2, memory for lists of words was tested in which event structure was manipulated using computer windows. Finally, in Experiments 3 and 4, event structure was manipulated by having event shifts described in narrative texts. The consistent finding across all of these methods and materials was that memory was better when the information was distributed across two events rather than combined into a single event. Moreover, Experiment 4 demonstrated that increasing the number of event boundaries from one to two increased the memory benefit. These results are interpreted in the context of the Event Horizon Model of event cognition. Copyright © 2015 Elsevier B.V. All rights reserved.

Electric field prediction for a human body-electric machine system.

Science.gov (United States)

Ioannides, Maria G; Papadopoulos, Peter J; Dimitropoulou, Eugenia

2004-01-01

A system consisting of an electric machine and a human body is studied and the resulting electric field is predicted. A 3-phase induction machine operating at full load is modeled considering its geometry, windings, and materials. A human model is also constructed approximating its geometry and the electric properties of tissues. Using the finite element technique the electric field distribution in the human body is determined for a distance of 1 and 5 m from the machine and its effects are studied. Particularly, electric field potential variations are determined at specific points inside the human body and for these points the electric field intensity is computed and compared to the limit values for exposure according to international standards.

Testing for structural change in the presence of long memory

OpenAIRE

Krämer, Walter; Sibbertsen, Philipp

2000-01-01

We derive the limiting null distributions of the standard and OLS-based CUSUM-tests for structural change of the coefficients of a linear regression model in the context of long memory disturbances. We show that both tests behave fundamentally different in a long memory environment, as compared to short memory, and that long memory is easily mistaken for structural change when standard critical values are employed.

Design of an axial-flux permanent magnet machine for a solar-powered electric vehicle

NARCIS (Netherlands)

Friedrich, L.A.J.; Bastiaens, K.; Gysen, B.L.J.; Krop, D.C.J.; Lomonova, E.A.

2018-01-01

This paper concerns the design optimization of two axial-flux permanent magnet (AFPM) machines, aimed to be used as a direct drive in-wheel motor for the propulsion of a solar-powered electric vehicle. The internal stator twin external rotor AFPM machine topology having either a distributed or

Cutting Zone Temperature Identification During Machining of Nickel Alloy Inconel 718

Science.gov (United States)

Czán, Andrej; Daniš, Igor; Holubják, Jozef; Zaušková, Lucia; Czánová, Tatiana; Mikloš, Matej; Martikáň, Pavol

2017-12-01

Quality of machined surface is affected by quality of cutting process. There are many parameters, which influence on the quality of the cutting process. The cutting temperature is one of most important parameters that influence the tool life and the quality of machined surfaces. Its identification and determination is key objective in specialized machining processes such as dry machining of hard-to-machine materials. It is well known that maximum temperature is obtained in the tool rake face at the vicinity of the cutting edge. A moderate level of cutting edge temperature and a low thermal shock reduce the tool wear phenomena, and a low temperature gradient in the machined sublayer reduces the risk of high tensile residual stresses. The thermocouple method was used to measure the temperature directly in the cutting zone. An original thermocouple was specially developed for measuring of temperature in the cutting zone, surface and subsurface layers of machined surface. This paper deals with identification of temperature and temperature gradient during dry peripheral milling of Inconel 718. The measurements were used to identification the temperature gradients and to reconstruct the thermal distribution in cutting zone with various cutting conditions.

Memory-built-in quantum cloning in a hybrid solid-state spin register

Science.gov (United States)

Wang, W.-B.; Zu, C.; He, L.; Zhang, W.-G.; Duan, L.-M.

2015-07-01

As a way to circumvent the quantum no-cloning theorem, approximate quantum cloning protocols have received wide attention with remarkable applications. Copying of quantum states to memory qubits provides an important strategy for eavesdropping in quantum cryptography. We report an experiment that realizes cloning of quantum states from an electron spin to a nuclear spin in a hybrid solid-state spin register with near-optimal fidelity. The nuclear spin provides an ideal memory qubit at room temperature, which stores the cloned quantum states for a millisecond under ambient conditions, exceeding the lifetime of the original quantum state carried by the electron spin by orders of magnitude. The realization of a cloning machine with built-in quantum memory provides a key step for application of quantum cloning in quantum information science.

Paying attention to working memory: Similarities in the spatial distribution of attention in mental and physical space.

Science.gov (United States)

Sahan, Muhammet Ikbal; Verguts, Tom; Boehler, Carsten Nicolas; Pourtois, Gilles; Fias, Wim

2016-08-01

Selective attention is not limited to information that is physically present in the external world, but can also operate on mental representations in the internal world. However, it is not known whether the mechanisms of attentional selection operate in similar fashions in physical and mental space. We studied the spatial distributions of attention for items in physical and mental space by comparing how successfully distractors were rejected at varying distances from the attended location. The results indicated very similar distribution characteristics of spatial attention in physical and mental space. Specifically, we found that performance monotonically improved with increasing distractor distance relative to the attended location, suggesting that distractor confusability is particularly pronounced for nearby distractors, relative to distractors farther away. The present findings suggest that mental representations preserve their spatial configuration in working memory, and that similar mechanistic principles underlie selective attention in physical and in mental space.

A hybrid analytical model for open-circuit field calculation of multilayer interior permanent magnet machines

Science.gov (United States)