1,922 results
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2. Critique of “Planetary Normal Mode Computation: Parallel Algorithms, Performance, and Reproducibility” by SCC Team From University of Washington.
- Author
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Liu, David, Cinnamon, Matthew, Garvin, Thorne, Karavanov, Andrei, Park, Sungchan, Strobeck, Darius, and Lumsdaine, Andrew
- Subjects
PLANETARY interiors ,SCHOOL contests ,PARALLEL programming ,RUNNING races ,PARALLEL algorithms ,SCALABILITY - Abstract
One of the tasks for the SC19 Student Cluster Competition is to reproduce the results in the reproducibility challenge article “Computing Planetary Interior Normal Modes with a Highly Parallel Polynomial Filtering Eigensolver”, by J. Shi et al., which describes a highly parallel algorithm for computing planetary normal modes. In running experiments from the article, we study the weak and strong scalability of the algorithm, as well as the relationship between model size, degree of polynomial filter, and execution time. We investigate these findings on a two-node, 64-core Intel Skylake-based Xeon cluster. Unfortunately, we are able to confirm some, but not all, of the original findings, with discrepancies possibly due to a low number of experimental runs due to competition time limits as well as nonuniform scaling of compute resources. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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3. Novel Structure-Exploiting Techniques Based Delay-Dependent Stability Analysis of Multi-Area LFC With Improved Numerical Tractability.
- Author
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Jin, Li, He, Yong, Zhang, Chuan-Ke, Shangguan, Xing-Chen, Jiang, Lin, and Wu, Min
- Subjects
LINEAR matrix inequalities ,STABILITY criterion ,MATRIX inequalities ,LYAPUNOV stability ,STABILITY theory ,ELECTRICITY pricing - Abstract
Time-domain indirect methods based on Lyapunov stability theory and linear matrix inequality techniques (LMIs) have been applied for delay-dependent stability analysis of large-scale load frequency control (LFC) schemes. This paper aims to enhance the numerical tractability of large-scale LMIs by exploiting the special characteristics of the LFC loops. First, in the typical LFC model, only a few delayed states that are directly influenced by transmission delays are distinguished from other normal system states. Hence, an improved reconstruction model is formed, based on which the delay-dependent stability condition is established with the decreased order of the LMIs and decision variables. Then, to further improve the numerical tractability of the developed stability criterion, all weighting matrices required in the augmented Lyapunov functional are enforced to have structural restrictions by proposing an extended symmetry-exploiting technique. Case studies show that the method proposed in this paper significantly improves the calculation efficiency of stability criterion established for multi-area power systems at the cost of only a minor reduction in computational accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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4. Pharmacological, Non-Pharmacological Policies and Mutation: An Artificial Intelligence Based Multi-Dimensional Policy Making Algorithm for Controlling the Casualties of the Pandemic Diseases.
- Author
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Tutsoy, Onder
- Subjects
ARTIFICIAL intelligence ,PANDEMICS ,PARAMETRIC modeling ,ALGORITHMS ,VACCINATION policies ,MULTIDIMENSIONAL databases - Abstract
Fighting against the pandemic diseases with unique characters requires new sophisticated approaches like the artificial intelligence. This paper develops an artificial intelligence algorithm to produce multi-dimensional policies for controlling and minimizing the pandemic casualties under the limited pharmacological resources. In this respect, a comprehensive parametric model with a priority and age-specific vaccination policy and a variety of non-pharmacological policies are introduced. This parametric model is utilized for constructing an artificial intelligence algorithm by following the exact analogy of the model-based solution. Also, this parametric model is manipulated by the artificial intelligence algorithm to seek for the best multi-dimensional non-pharmacological policies that minimize the future pandemic casualties as desired. The role of the pharmacological and non-pharmacological policies on the uncertain future casualties are extensively addressed on the real data. It is shown that the developed artificial intelligence algorithm is able to produce efficient policies which satisfy the particular optimization targets such as focusing on minimization of the death casualties more than the infected casualties or considering the curfews on the people age over 65 rather than the other non-pharmacological policies. The paper finally analyses a variety of the mutant virus cases and the corresponding non-pharmacological policies aiming to reduce the morbidity and mortality rates. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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5. Modeling a unit cell: crystallographic refinement procedure using the biomolecular MD simulation platform Amber
- Author
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Mikhailovskii, Oleg, Xue, Yi, and Skrynnikov, Nikolai R.
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computational modeling ,amber ,Science ,phenix ,restrained simulations ,protein crystals ,intracrystalline water ,protein structure determination ,General Chemistry ,Condensed Matter Physics ,Research Papers ,Biochemistry ,molecular replacement ,molecular dynamics ,rfree ,protein structure refinement ,R free ,General Materials Science ,ensemble model ,maximum likelihood ,x-ray crystallography - Abstract
Based on the molecular-dynamics simulation platform Amber, a refinement algorithm has been developed to obtain structural models of protein crystals in the form of a fully hydrated unit cell. These models typically yield better R free values and MolProbity scores compared with the PDB-deposited or Phenix-refined coordinates. The new algorithm has a favorable radius of convergence, is simple to use and is fast., A procedure has been developed for the refinement of crystallographic protein structures based on the biomolecular simulation program Amber. The procedure constructs a model representing a crystal unit cell, which generally contains multiple protein molecules and is fully hydrated with TIP3P water. Periodic boundary conditions are applied to the cell in order to emulate the crystal lattice. The refinement is conducted in the form of a specially designed short molecular-dynamics run controlled by the Amber ff14SB force field and the maximum-likelihood potential that encodes the structure-factor-based restraints. The new Amber-based refinement procedure has been tested on a set of 84 protein structures. In most cases, the new procedure led to appreciably lower R free values compared with those reported in the original PDB depositions or obtained by means of the industry-standard phenix.refine program. In particular, the new method has the edge in refining low-accuracy scrambled models. It has also been successful in refining a number of molecular-replacement models, including one with an r.m.s.d. of 2.15 Å. In addition, Amber-refined structures consistently show superior MolProbity scores. The new approach offers a highly realistic representation of protein–protein interactions in the crystal, as well as of protein–water interactions. It also offers a realistic representation of protein crystal dynamics (akin to ensemble-refinement schemes). Importantly, the method fully utilizes the information from the available diffraction data, while relying on state-of-the-art molecular-dynamics modeling to assist with those elements of the structure that do not diffract well (for example mobile loops or side chains). Finally, it should be noted that the protocol employs no tunable parameters, and the calculations can be conducted in a matter of several hours on desktop computers equipped with graphical processing units or using a designated web service.
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- 2022
6. Efficient Distributed Clustering Algorithms on Star-Schema Heterogeneous Graphs.
- Author
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Chen, Lu, Gao, Yunjun, Huang, Xingrui, Jensen, Christian S., and Zheng, Bolong
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DISTRIBUTED computing ,COMPUTER systems ,DISTRIBUTED algorithms ,GAME theory ,SOCIAL media ,ALGORITHMS - Abstract
Many datasets including social media data and bibliographic data can be modeled as graphs. Clustering such graphs is able to provide useful insights into the structure of the data. To improve the quality of clustering, node attributes can be taken into account, resulting in attributed graphs. Existing attributed graph clustering methods generally consider attribute similarity and structural similarity separately. In this paper, we represent attributed graphs as star-schema heterogeneous graphs, where attributes are modeled as different types of graph nodes. This enables the use of personalized pagerank (PPR) as a unified distance measure that captures both structural and attribute similarities. We employ DBSCAN for clustering, and we update edge weights iteratively to balance the importance of different attributes. The rapidly growing volume of data nowadays challenges traditional clustering algorithms, and thus, a distributed method is required. Hence, we adopt a popular distributed graph computing system Blogel, based on which, we develop four exact and approximate approaches that enable efficient PPR score computation when edge weights are updated. To improve the effectiveness of the clustering, we propose a simple yet effective edge weight update strategy based on entropy. In addition, we present a game theory based method that enables trading efficiency for result quality. Extensive experiments on real-life datasets offer insights into the effectiveness and efficiency of our proposals. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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7. LAEO-Net++: Revisiting People Looking at Each Other in Videos.
- Author
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Marin-Jimenez, Manuel J., Kalogeiton, Vicky, Medina-Suarez, Pablo, and Zisserman, Andrew
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VIDEOS ,SOCIAL interaction ,SOCIAL networks ,BASE pairs ,TASK analysis ,MAGNETIC recording heads - Abstract
Capturing the ‘mutual gaze’ of people is essential for understanding and interpreting the social interactions between them. To this end, this paper addresses the problem of detecting people Looking At Each Other (LAEO) in video sequences. For this purpose, we propose LAEO-Net++, a new deep CNN for determining LAEO in videos. In contrast to previous works, LAEO-Net++ takes spatio-temporal tracks as input and reasons about the whole track. It consists of three branches, one for each character's tracked head and one for their relative position. Moreover, we introduce two new LAEO datasets: UCO-LAEO and AVA-LAEO. A thorough experimental evaluation demonstrates the ability of LAEO-Net++ to successfully determine if two people are LAEO and the temporal window where it happens. Our model achieves state-of-the-art results on the existing TVHID-LAEO video dataset, significantly outperforming previous approaches. Finally, we apply LAEO-Net++ to a social network, where we automatically infer the social relationship between pairs of people based on the frequency and duration that they LAEO, and show that LAEO can be a useful tool for guided search of human interactions in videos. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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8. Modeling and Signal Integrity Analysis of RRAM-Based Neuromorphic Chip Crossbar Array Using Partial Equivalent Element Circuit (PEEC) Method.
- Author
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Li, Yan, Fang, Lidan, Tao, Tuomin, Li, Da, Liu, En-Xiao, Jin, Ning, Ahmed, Manareldeen, and Li, Er-Ping
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SIGNAL integrity (Electronics) ,CIRCUIT elements ,HIGH performance computing - Abstract
This paper provides a comprehensive study of signal integrity issues in RRAM-based neuromorphic chip crossbar arrays due to interconnect parasitic. First, the parasitic parameters of the crossbar array are calculated by the partial equivalent element circuit (PEEC) method with an efficient unit-cell approach. Numerical experiments show that for a $50\times 50$ array scale, this method consumes only 1.5% of the calculation time of the commercial software based 3D model, which translates to a calculation speed up of 72 times. Moreover, the PEEC circuit simulation results match well with those of the 3D model. Then, we investigate the effects of parasitic parameters such as capacitance and inductance, as well as the feature size of the crossbar array on signal integrity. All of them will lead to corresponding changes in parasitic effects, which in turn result in the most common signal integrity issues such as crosstalk, time delay and mutual capacitive coupling induced sneak path problem. Different from other studies, the excitation used in this paper is the neural spike signal generated by the Izhikevich neuron model, which is both rich in dynamic characteristics and high in computational efficiency. Finally, based on the study we propose a simple but effective design scheme for reduction of signal distortion, which can provide valuable design guidance for neuromorphic systems to achieve high performance and high computational accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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9. Binary Neural Networks in FPGAs: Architectures, Tool Flows and Hardware Comparisons.
- Author
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Su, Yuanxin, Seng, Kah Phooi, Ang, Li Minn, and Smith, Jeremy
- Subjects
DEEP learning ,MATRIX multiplications ,SCIENTIFIC community - Abstract
Binary neural networks (BNNs) are variations of artificial/deep neural network (ANN/DNN) architectures that constrain the real values of weights to the binary set of numbers {−1,1}. By using binary values, BNNs can convert matrix multiplications into bitwise operations, which accelerates both training and inference and reduces hardware complexity and model sizes for implementation. Compared to traditional deep learning architectures, BNNs are a good choice for implementation in resource-constrained devices like FPGAs and ASICs. However, BNNs have the disadvantage of reduced performance and accuracy because of the tradeoff due to binarization. Over the years, this has attracted the attention of the research community to overcome the performance gap of BNNs, and several architectures have been proposed. In this paper, we provide a comprehensive review of BNNs for implementation in FPGA hardware. The survey covers different aspects, such as BNN architectures and variants, design and tool flows for FPGAs, and various applications for BNNs. The final part of the paper gives some benchmark works and design tools for implementing BNNs in FPGAs based on established datasets used by the research community. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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10. A Survey on Multi-Task Learning.
- Author
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Zhang, Yu and Yang, Qiang
- Subjects
REINFORCEMENT learning ,ACTIVE learning ,MACHINE learning ,SUPERVISED learning ,ARTIFICIAL intelligence ,TASK performance - Abstract
Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL from the perspective of algorithmic modeling, applications and theoretical analyses. For algorithmic modeling, we give a definition of MTL and then classify different MTL algorithms into five categories, including feature learning approach, low-rank approach, task clustering approach, task relation learning approach and decomposition approach as well as discussing the characteristics of each approach. In order to improve the performance of learning tasks further, MTL can be combined with other learning paradigms including semi-supervised learning, active learning, unsupervised learning, reinforcement learning, multi-view learning and graphical models. When the number of tasks is large or the data dimensionality is high, we review online, parallel and distributed MTL models as well as dimensionality reduction and feature hashing to reveal their computational and storage advantages. Many real-world applications use MTL to boost their performance and we review representative works in this paper. Finally, we present theoretical analyses and discuss several future directions for MTL. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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11. Quality Driven Systematic Approximation for Binary-Weight Neural Network Deployment.
- Author
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Gong, Yu, Cai, Hao, Wu, Haige, Ge, Wei, Yan, Hao, Wang, Zhen, Shi, Longxing, and Liu, Bo
- Subjects
ARTIFICIAL neural networks - Abstract
Neural networks (NNs) with large scales of artificial neurons are increasingly used in recognition and classification tasks. In power-constrained scenarios, the tradeoff between performance and hardware consumptions must be carefully evaluated before silicon tape-out. In this paper, we proposed a systematic approach to design ultra-low power NN system. This work is motivated by the facts that NNs are resilient to approximation in many of the computations and NNs are outputting statistical tensors which are acceptable to less-than-perfect results. We resort to the front-back end approach with a twofold aim: (1) a fast and accurate design approach is proposed by estimating the computing quality of low-power approximate adder arrays, and it is adopted to evaluate the neural network system; (2) a quality configurable engine with different approximation degrees while processing NNs is implemented. The proposed work is demonstrated with a comprehensive keyword spotting (KWS) system as an ultra-low power NN engine. The experimental environment is setup with ten keywords from the google speech command dataset (GSCD) using an industrial 22-nm ultra-low-leakage (ULL) process. Comparing to the state-of-the-art KWS processors, the proposed approximate NN engine can demonstrate over 60% improvement in power efficiency and $1.1\times $ area efficiency while achieving similar recognition accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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12. Computer-Aided Systematic Topology Derivation of Single-Inductor Multi-Input Multi-Output Converters From Working Principle.
- Author
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Mo, Liping, Huang, Jiangming, Chen, Guipeng, Qing, Xinlin, and Hu, Yihua
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TOPOLOGY ,GRAPH theory ,MATRIX converters - Abstract
In this paper, aiming to systematically derive single-inductor multi-input multi-output (SI-MIMO) converters having favorable merits of high-density and low-cost, a basic model is firstly extracted from the working principle. Theoretically, by enumerating all possible connections of the basic model and one-by-one manually judging their effectiveness according to working criterions, multiple viable SI-MIMO topologies can be obtained, but with heavy workload. To simplify the topology derivation process, this paper also proposes a computer-aided method to replace the manual effort. By modelling the basic model and working criterions with graph theory, MATLAB codes can be developed to automatically screen out the effective topologies. With the proposed method, 8 three-port, 28 four-port and 115 five-port converters with single inductor are conveniently obtained, including both the existing and new topologies. Moreover, by configuring ports and switches in each derived topology, multiple specific converters can be further expanded. These converters can provide more options for practical engineering applications, and a new topology is also introduced and experimentally verified in the paper. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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13. VERTICOX: Vertically Distributed Cox Proportional Hazards Model Using the Alternating Direction Method of Multipliers.
- Author
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Dai, Wenrui, Jiang, Xiaoqian, Bonomi, Luca, Li, Yong, Xiong, Hongkai, and Ohno-Machado, Lucila
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PROPORTIONAL hazards models ,SURVIVAL analysis (Biometry) ,PARAMETER estimation ,PARAMETERS (Statistics) - Abstract
The Cox proportional hazards model is a popular semi-parametric model for survival analysis. In this paper, we aim at developing a federated algorithm for the Cox proportional hazards model over vertically partitioned data (i.e., data from the same patient are stored at different institutions). We propose a novel algorithm, namely VERTICOX, to obtain the global model parameters in a distributed fashion based on the Alternating Direction Method of Multipliers (ADMM) framework. The proposed model computes intermediary statistics and exchanges them to calculate the global model without collecting individual patient-level data. We demonstrate that our algorithm achieves equivalent accuracy for the estimation of model parameters and statistics to that of its centralized realization. The proposed algorithm converges linearly under the ADMM framework. Its computational complexity and communication costs are polynomially and linearly associated with the number of subjects, respectively. Experimental results show that VERTICOX can achieve accurate model parameter estimation to support federated survival analysis over vertically distributed data by saving bandwidth and avoiding exchange of information about individual patients. The source code for VERTICOX is available at: https://github.com/daiwenrui/VERTICOX. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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14. A Review of Natural Fiber-Reinforced Composites for Lower-Limb Prosthetic Designs.
- Author
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Castro-Franco, Angel D., Siqueiros-Hernández, Miriam, García-Angel, Virginia, Mendoza-Muñoz, Ismael, Vargas-Osuna, Lidia E., and Magaña-Almaguer, Hernán D.
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FIBROUS composites ,PROSTHETICS ,FATIGUE limit ,RESIDUAL limbs - Abstract
This paper presents a comprehensive review of natural fiber-reinforced composites (NFRCs) for lower-limb prosthetic designs. It covers the characteristics, types, and properties of natural fiber-reinforced composites as well as their advantages and drawbacks in prosthetic designs. This review also discusses successful prosthetic designs that incorporate NFRCs and the factors that make them effective. Additionally, this study explores the use of computational biomechanical models to evaluate the effectiveness of prosthetic devices and the key factors that are considered. Overall, this document provides a valuable resource for anyone interested in using NFRCs for lower-limb prosthetic designs. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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15. A Lightweight Chip-Scale Chemical Mechanical Polishing Model Based on Polynomial Network.
- Author
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Ji, Ruian, Chen, Rong, and Chen, Lan
- Subjects
MECHANICAL models ,GRINDING & polishing ,POLYNOMIALS ,COMPUTATIONAL complexity ,CHEMICAL reactions ,SEMICONDUCTOR devices - Abstract
Chemical mechanical polishing/planarization (CMP) combines physical grinding and chemical reactions to planarize the wafer surface. The complex mechanism of CMP brings great challenges to the mechanism-based modeling process. The data-driven CMP modeling process is limited by insufficient datasets. At the same time, these two types of models generally have high computational complexity. In this paper, we introduce the group method of data handling (GMDH)-type polynomial network to build the CMP model to address the above challenges. We designed and manufactured the test chip using a 28nm process. The measurement data from the test chip shows that compared with the mechanism-based CMP model, the trained CMP model based on GMDH-type polynomial network has higher accuracy and lower computational complexity, with the average simulation speed being 115x faster. Experiments based on silicon data show that this modeling method has a small demand for data, and 20 randomly selected sets of data can meet the needs for modeling the current CMP process. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
16. Critique of “Planetary Normal Mode Computation: Parallel Algorithms, Performance, and Reproducibility” by SCC Team From Tsinghua University.
- Author
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Zhang, Chen, Zhao, Chenggang, He, Jiaao, Chen, Shengqi, Zheng, Liyan, Huang, Kezhao, Han, Wentao, and Zhai, Jidong
- Subjects
LANCZOS method ,PLANETARY interiors ,SCHOOL contests ,PARALLEL algorithms ,POLYNOMIALS ,SCALABILITY - Abstract
In this article we present our results from the SC19 Student Cluster Competition Reproducibility Challenge. The challenge entails reproducing the article entitled “Computing Planetary Interior Normal Modes with A Highly Parallel Polynomial Filtering Eigensolver” presented at SC’18, which proposes a parallel polynomial filtered Lanczos algorithm to directly calculate the planetary normal modes of heterogeneous planets. The proposed algorithm showed excellent performance with relatively low memory consumption and high parallel efficiency. In this work, we reproduce the scaling tests in that article on a cluster using Intel Cascade Lake architecture and use the proposed algorithm to illustrate specific normal modes of Mars. We compare the results obtained on our cluster with those in the original article. We also design a new metric to better analyze the results. In addition, we use the profiling tool Intel VTune Amplifier to explain our discoveries. Our results demonstrate that the given models show great scalability, which is similar to the original article. The required normal modes of Mars are also successfully calculated and visualized. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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17. Modular Multilevel Converter Impedance Computation Based on Periodic Small-Signal Analysis and Vector Fitting.
- Author
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Giudice, Davide del, Brambilla, Angelo, Linaro, Daniele, and Bizzarri, Federico
- Subjects
VECTOR analysis ,PICTURE archiving & communication systems ,TRANSFER functions ,ALGEBRAIC functions ,ELECTRIC circuits - Abstract
Instability and oscillation issues originating in high-voltage direct current (HVDC) systems comprising modular multilevel converters (MMCs) are gaining increasing interest in the research community. To detect such phenomena in advance, considerable effort has been devoted recently to developing MMC models for small-signal analysis. The derivation of such models is a challenging task due to the topology and complex control structure of MMCs, which results in them having a multi-frequency response. To address this issue, scholars developed methods based on dynamic phasors and harmonic state-space modelling, which, however, require extensive pen-and-paper computations. In this paper, the periodic small-signal analysis (PAC) is adopted to determine numerically several MMCs transfer functions. Such functions can be computed between any electrical circuit node or input/output port, without the need to recast the three-phase average MMC model to derive a linear equivalent one, possibly in the DQ-frame. We show how vector fitting allows converting these functions to equivalent algebraic representations, which can be profitably used to design MMCs and study their stability following some parameter changes. To showcase this feature, we exploit one of these transfer functions to detect DC-side instability in a point-to-point HVDC system. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
18. Data-Driven Production Planning Models for Wafer Fabs: An Exploratory Study.
- Author
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Volker, Tobias and Monch, Lars
- Subjects
PRODUCTION planning ,SEMICONDUCTOR wafers ,WORK in process ,SEMICONDUCTOR devices - Abstract
Cycle time, which is of order of ten weeks for most products in semiconductor wafer fabrication facilities (wafer fabs), must be explicitly considered in production planning models. Cycle times depend nonlinearly on the resource workload in a wafer fab. Different data-driven (DD) production planning formulations are studied in this paper. Such formulations are based on a set of system states representing the congestion behavior of a wafer fab with work in process (WIP) and resulting output levels. Different WIP-output relations in the DD formulations are investigated. In addition, different methods are proposed to gather representative sets of system states. The DD formulations are compared with the Allocated Clearing Function (ACF) formulation with respect to profit and cost values using simulation models of a scaled-down and a large-sized multi-product wafer fab, respectively. The simulation results show that some DD variants are able to outperform the ACF formulation under several experimental conditions. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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19. On Non-Detectability of Non-Computability and the Degree of Non-Computability of Solutions of Circuit and Wave Equations on Digital Computers.
- Author
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Boche, Holger and Pohl, Volker
- Subjects
TURING machines ,DIFFERENTIABLE functions ,QUANTUM computing ,DIFFERENTIABLE dynamical systems ,WAVE equation ,COMPUTERS - Abstract
It is known that there exist mathematical problems of practical relevance which cannot be computed on a Turing machine. An important example is the calculation of the first derivative of continuously differentiable functions. This paper precisely classifies the non-computability of the first derivative, and of the maximum-norm of the first derivative in the Zheng-Weihrauch hierarchy. Based on this classification, the paper investigates whether it is possible that a Turing machine detects this non-computability of the first derivative by observing the data of the problem, and whether it is possible to detect upper bounds for the peak value of the first derivative of continuously differentiable functions. So from a practical point of view, the question is whether it is possible to implement an exit-flag functionality for observing non-computability of the first derivative. This paper even studies two different types of exit-flag functionality. A strong one, where the Turing machine always has to stop, and a weak one, where the Turing machine stops if and only if the input lies within the corresponding set of interest. It will be shown that non-computability of the first derivative is not detectable by a Turing machine for two concrete examples, namely for the problem of computing the input–output behavior of simple analog circuits and for solutions of the three-dimensional wave equation. In addition, it is shown that it is even impossible to detect an upper bound for the maximum norm of the first derivative. In particular, it is shown that all three problems are not even semidecidable. Finally, we briefly discuss implications of these results for analog and quantum computing. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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- View/download PDF
20. VR-PRUNE: Decidable Variable-Rate Dataflow for Signal Processing Systems.
- Author
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Boutellier, Jani, Ma, Yujunrong, Wu, Jiahao, Khan, Mir, and Bhattacharyya, Shuvra S.
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SIGNAL processing ,ADAPTIVE signal processing ,ELECTRIC power filters ,DATA flow computing ,HETEROGENEOUS computing - Abstract
The dataflow concept has been successfully used for modeling and synthesizing signal processing applications since decades, and recently, dataflow has also been discovered to match the computation model of machine learning applications, leading to extremely successful dataflow based application design frameworks. One of the most attractive features of dataflow, especially for signal processing, is related to its formal nature: when properly defined, a dataflow-based application model can be analytically verified for correctness at the stage of application design. This paper proposes VR-PRUNE, a novel dataflow model of computation that is aimed for design of high-performance signal processing software, together with runtime support that allows efficient application deployment to heterogeneous GPU-equipped platforms. Compared to prior work, VR-PRUNE features variable token rate processing, which enables designing adaptive signal processing applications, and implementing solutions that, e.g., allow trading-off between power consumption and filtering bandwidth at runtime. The paper presents the formal concepts of VR-PRUNE, as well as four application examples from domains related to signal processing, accompanied with quantitative results, which show that using VR-PRUNE enables, for example, application power-performance scaling, and on the other hand describing adaptive application behavior with 59% fewer dataflow graph components compared to previous work. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
21. T-PAIR: Temporal Node-Pair Embedding for Automatic Biomedical Hypothesis Generation.
- Author
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Akujuobi, Uchenna, Spranger, Michael, Palaniappan, Sucheendra K., and Zhang, Xiangliang
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VIRTUAL networks ,SCIENCE education ,HYPOTHESIS ,PREDICTION models ,TEXT mining ,TASK analysis - Abstract
In this paper, we study an automatic hypothesis generation (HG) problem, which refers to the discovery of meaningful implicit connections between scientific terms, including but not limited to diseases, chemicals, drugs, and genes extracted from databases of biomedical publications. Most prior studies of this problem focused on the use of static information of terms and largely ignored the temporal dynamics of scientific term relations. Even when the dynamics were considered in a few recent studies, they learned the representations for the scientific terms, rather than focusing on the term-pair relations. Since the HG problem is to predict term-pair connections, it is not enough to know with whom the terms are connected, it is more important to know how the connections have been formed (in a dynamic process). We formulate this HG problem as a future connectivity prediction in a dynamic attributed graph. The key is to capture the temporal evolution of node-pair (term-pair) relations. We propose an inductive edge (node-pair) embedding method named T-PAIR, utilizing both the graphical structure and node attribute to encode the temporal node-pair relationship. We demonstrate the efficiency of the proposed model on three real-world datasets, which are three graphs constructed from Pubmed papers published until 2019 in Neurology, Immunotherapy, and Virology, respectively. Evaluations were conducted on predicting future term-pair relations between millions of seen terms (in the transductive setting), as well as on the relations involving unseen terms (in the inductive setting). Experiment results and case study analyses show the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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- View/download PDF
22. A 5.28-mm² 4.5-pJ/SOP Energy-Efficient Spiking Neural Network Hardware With Reconfigurable High Processing Speed Neuron Core and Congestion-Aware Router.
- Author
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Pu, Junran, Goh, Wang Ling, Nambiar, Vishnu P., Wong, Ming Ming, and Do, Anh Tuan
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NEURONS ,SPEED ,NEUROMORPHICS ,HARDWARE ,ENERGY consumption - Abstract
In recent years, fast computation, low power, and small footprint are the key motivations for building SNN hardware. The unique features of SNN hardware have not been fully exploited, where the computation speed and energy efficiency of the SNN hardware can be improved according to the sparse spiking and non-uniform traffic of SNN. In this paper, we propose a 5.28-mm $^{2}~4096$ -neuron 1M-synapse energy-efficient digital SNN hardware that can achieve ultra-low energy per synaptic operation of 4.5 pJ. The proposed neuron computing unit is implemented in pipeline architecture to achieve high synaptic processing speed. The proposed spike processing unit can significantly increase the processing speed of the neuron core by $1.9\times $ and $9.4\times $ when the spike injection rate is 50% and 10%, respectively. Besides, the increase in the processing speed of the neuron core leads to a reduction in energy consumption of up to 81.5%. An event-driven clock gating circuit that can reduce the power consumption of the proposed neuron block by more than 70% is proposed in this paper. This paper proposes a supervised STDP+ algorithm for SNN training, and the classification accuracy of the MNIST digits is 89.6% with 73.6% weight sparsity of the output layer. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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- View/download PDF
23. An Initial Case Study of the Computational Modeling of Cord–Rubber Segments.
- Author
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Láštic, Daniel, Řehák, Kamil, Prokop, Aleš, Krpalek, David, Šplíchal, Jan, and Žlábek, Pavel
- Subjects
FINITE element method ,STRAINS & stresses (Mechanics) ,RAILROAD trains ,MANUFACTURING processes ,TEXTILE fibers ,RUBBER - Abstract
This paper presents an initial study of the computational modeling for determining the stress–strain behavior of the cord–rubber segment, tailored to be used as a flexible member in the construction of torque transfer coupling in railway vehicles. The presented computational models use the multibody simulation approach for the assessment of segment deformation under diverse loading characteristics. The results are then used in the finite element computational models of the cord–rubber segment. The computational model is validated through tension tests of cord–rubber segments. The homogenized and microstructural models show their ability to simulate the overall stiffness of the segment relatively accurately in tension, but they are limited in not being able to include the influence of the segment's manufacturing technology. The micro-scale computational model then incorporates realistic representations of cord bundles, considering various cross-sectional shapes, whereas a very important factor significantly influencing the correlation of the results of the technical experiment and simulation is the consideration of the manufacturing technology in the process of building the computational model. The results show that the computational modeling approach in this work can be used for the determination of the optimal manufacturing technology conditions with regard to the stress–strain behavior of the given segment. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
24. AngoraPy: A Python toolkit for modeling anthropomorphic goal-driven sensorimotor systems.
- Author
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Weidler, Tonio, Goebel, Rainer, and Senden, Mario
- Subjects
ARTIFICIAL neural networks ,LARGE-scale brain networks ,PYTHON programming language ,GOAL (Psychology) ,CONVOLUTIONAL neural networks ,COMPUTATIONAL neuroscience ,NEUROSCIENCES - Abstract
Goal-driven deep learning increasingly supplements classical modeling approaches in computational neuroscience. The strength of deep neural networks as models of the brain lies in their ability to autonomously learn the connectivity required to solve complex and ecologically valid tasks, obviating the need for hand-engineered or hypothesis-driven connectivity patterns. Consequently, goal-driven models can generate hypotheses about the neurocomputations underlying cortical processing that are grounded in macro- and mesoscopic anatomical properties of the network's biological counterpart. Whereas, goal-driven modeling is already becoming prevalent in the neuroscience of perception, its application to the sensorimotor domain is currently hampered by the complexity of the methods required to train models comprising the closed sensation-action loop. This paper describes AngoraPy, a Python library that mitigates this obstacle by providing researchers with the tools necessary to train complex recurrent convolutional neural networks that model the human sensorimotor system. To make the technical details of this toolkit more approachable, an illustrative example that trains a recurrent toy model on in-hand object manipulation accompanies the theoretical remarks. An extensive benchmark on various classical, 3D robotic, and anthropomorphic control tasks demonstrates AngoraPy's general applicability to a wide range of tasks. Together with its ability to adaptively handle custom architectures, the flexibility of this toolkit demonstrates its power for goal-driven sensorimotor modeling. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
25. Dual-Stream Complex-Valued Convolutional Network for Authentic Dehazed Image Quality Assessment.
- Author
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Guan, Tuxin, Li, Chaofeng, Zheng, Yuhui, Wu, Xiaojun, and Bovik, Alan C.
- Subjects
CONVOLUTIONAL neural networks ,PERCEPTUAL illusions ,PERCEPTUAL learning - Abstract
Effectively evaluating the perceptual quality of dehazed images remains an under-explored research issue. In this paper, we propose a no-reference complex-valued convolutional neural network (CV-CNN) model to conduct automatic dehazed image quality evaluation. Specifically, a novel CV-CNN is employed that exploits the advantages of complex-valued representations, achieving better generalization capability on perceptual feature learning than real-valued ones. To learn more discriminative features to analyze the perceptual quality of dehazed images, we design a dual-stream CV-CNN architecture. The dual-stream model comprises a distortion-sensitive stream that operates on the dehazed RGB image, and a haze-aware stream on a novel dark channel difference image. The distortion-sensitive stream accounts for perceptual distortion artifacts, while the haze-aware stream addresses the possible presence of residual haze. Experimental results on three publicly available dehazed image quality assessment (DQA) databases demonstrate the effectiveness and generalization of our proposed CV-CNN DQA model as compared to state-of-the-art no-reference image quality assessment algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
26. Efficient Crowd Counting via Dual Knowledge Distillation.
- Author
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Wang, Rui, Hao, Yixue, Hu, Long, Li, Xianzhi, Chen, Min, Miao, Yiming, and Humar, Iztok
- Subjects
DISTILLATION ,CROWDS ,RESEARCH personnel ,COUNTING ,KNOWLEDGE transfer - Abstract
Most researchers focus on designing accurate crowd counting models with heavy parameters and computations but ignore the resource burden during the model deployment. A real-world scenario demands an efficient counting model with low-latency and high-performance. Knowledge distillation provides an elegant way to transfer knowledge from a complicated teacher model to a compact student model while maintaining accuracy. However, the student model receives the wrong guidance with the supervision of the teacher model due to the inaccurate information understood by the teacher in some cases. In this paper, we propose a dual-knowledge distillation (DKD) framework, which aims to reduce the side effects of the teacher model and transfer hierarchical knowledge to obtain a more efficient counting model. First, the student model is initialized with global information transferred by the teacher model via adaptive perspectives. Then, the self-knowledge distillation forces the student model to learn the knowledge by itself, based on intermediate feature maps and target map. Specifically, the optimal transport distance is utilized to measure the difference of feature maps between the teacher and the student to perform the distribution alignment of the counting area. Extensive experiments are conducted on four challenging datasets, demonstrating the superiority of DKD. When there are only approximately 6% of the parameters and computations from the original models, the student model achieves a faster and more accurate counting performance as the teacher model even surpasses it. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
27. A New Power Flow Model With a Single Nonconvex Quadratic Constraint: The LMI Approach.
- Author
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Abolpour, Roozbeh, Hesamzadeh, Mohammad Reza, and Dehghani, Maryam
- Subjects
ELECTRICAL load ,LINEAR matrix inequalities ,TRANSMISSION line matrix methods ,MATRIX inequalities - Abstract
In this paper, we propose a new mathematical model for power flow problem based on the linear and nonlinear matrix inequality theory. We start with rectangular model of power flow (PF) problem and then reformulate it as a Bilinear Matrix Inequality (BMI) model. A Theorem is proved which is able to convert this BMI model to a Linear Matrix Inequality (LMI) model along with One Nonconvex Quadratic Constraint (ONQC). Our proposed LMI-ONQC model for PF problem has only one single nonconvex quadratic constraint irrespective of the network size, while in the rectangular and BMI models the number of nonconvex constraints grows as the network size grows. This interesting property leads to reduced complexity level in our LMI-ONQC model which in turn makes it easier to solve for finding a PF solution. The non-conservativeness, iterative LMI solvability, well-defined and easy-to-understand geometry and pathwise connectivity of feasibility region are other important properties of proposed LMI-ONQC model which are discussed in this paper. An illustrative two-bus example is carefully studied to show different properties of our LMI-ONQC model. We have also tested our LMI-ONQC model on 30 different power-system cases including four ill-conditioned systems and compared it with a group of existing approaches. The numerical results show the promising performance of our LMI-ONQC model and its solution algorithm to find a PF solution. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
28. Analysis of Electrodynamic Transients in the ITER PF Joints.
- Author
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Breschi, Marco, Cavallucci, Lorenzo, Ribani, Pier Luigi, and Gauthier, Florent
- Subjects
TRANSIENT analysis ,PANCAKES, waffles, etc. ,SUPERCONDUCTING wire ,WIRE ,SUPERCONDUCTING cables ,MAGNETS - Abstract
The paper presents a numerical study on electrodynamic behavior of the inter-pancake twin-box joints of the Poloidal Field (PF) coils of the ITER Magnet System. This work was carried out with the THELMA code, developed at the University of Bologna. The THELMA model was adapted to describe both the superconducting wires and the resistive saddle of the joint in a set of reference configurations representative of a set of experimental tests performed on a joint sample at the SULTAN facility in Villigen, Switzerland. The model was applied to analyze both the DC joint resistance measurements and the AC loss vs frequency curve, in order to obtain a full representation of the joint electromagnetic behavior. The paper reports a comparison between the numerical and experimental results and parametric studies on the impact of the main model parameters on the loss vs frequency curve. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
29. Interpretable Memristive LSTM Network Design for Probabilistic Residential Load Forecasting.
- Author
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Li, Chaojie, Dong, Zhaoyang, Ding, Lan, Petersen, Henry, Qiu, Zihang, Chen, Guo, and Prasad, Deo
- Subjects
LOAD forecasting (Electric power systems) ,FORECASTING ,BUILT environment ,TIME series analysis - Abstract
Memristive LSTM networks have been proven as a powerful Neuromorphic Computing Architecture (NCA) for various time series forecasting tasks and are recognized as the next generation of AI. However, a lack of model explainability makes it hard to properly interpret forecasting results for existing memristive LSTM networks, which makes this NCA unreliable, unaccountable and untrustworthy. In this paper, an interpretable memristive (IM) LSTM network design is proposed for time series forecasting, where the mixture attention technique is embedded into IM-LSTM cells for characterizing the variable-wise feature and the temporal importance. The updating rules and training approach are also presented for this interpretable memristive LSTM network. We evaluate this approach on a probabilistic residential load forecasting task incorporating PV. By improving model interpretability, the most influential predictive factors can be verified by Built Environment domain experts, demonstrating the effectiveness of our design. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
30. FPGA-NHAP: A General FPGA-Based Neuromorphic Hardware Acceleration Platform With High Speed and Low Power.
- Author
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Liu, Yijun, Chen, Yuehai, Ye, Wujian, and Gui, Yu
- Subjects
FIELD programmable gate arrays - Abstract
Spiking neural network (SNN) can process discrete spikes and offers a high degree of real-time performance and excellent energy efficiency ratio. However, most current neuromorphic hardware platforms lack efficient driven algorithms and only support a single type of neuron model, which has slow speed and poor scalability. This paper proposes a general FPGA-based neuromorphic hardware acceleration platform (FPGA-NHAP), supporting the effective inference and acceleration of SNN network with low power, high speed and good scalability. First, a neuron computing unit is designed to simulate the both LIF and Izhikevich (IZH) neurons with the parallel spike caching and scheduling technique. Second, a novel integrated driven update algorithm is proposed to complete the spike encoding of external data, reducing the waiting time of neuron state update effectively. Third, the proposed platform is implemented using a RISC-V processor and a Xilinx FPGA, simulating 16,384 neurons and 16.8 million synapses with a power consumption of 0.535 W. Finally, two different three-layer SNN networks are deployed on the proposed platform for recognition tasks on the MNIST and Fashion-MNIST datasets, achieving the accuracy of 97.70%, 85.14% (LIF) and 97.81%, 83.16% (IZH), frame rates of 208 frame/s, 128 frame/s (LIF) and 206 frame/s, 141 frame/s (IZH), respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
31. A tutorial on fitting joint models of M/EEG and behavior to understand cognition
- Author
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Nunez, Michael D, Fernandez, Kianté, Srinivasan, Ramesh, and Vandekerckhove, Joachim
- Subjects
Biological Psychology ,Cognitive and Computational Psychology ,Mathematical Sciences ,Statistics ,Psychology ,Behavioral and Social Science ,Neurosciences ,Bioengineering ,Clinical Research ,Basic Behavioral and Social Science ,Computational modeling ,Cognitive modeling ,Electroencephalography ,Magnetoencephalography ,Neuroscience ,Artificial Intelligence and Image Processing ,Cognitive Sciences ,Experimental Psychology ,Biological psychology ,Cognitive and computational psychology - Abstract
We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document.
- Published
- 2024
32. Predicting the Unexpected - Analysis and Modeling of the Denial of Expectation
- Author
-
Walch, Marie Christin
- Subjects
Computer Science ,Linguistics ,Concepts and categories ,Decision making ,Discourse ,Perception ,Pragmatics ,Semantics ,Computational Modeling ,Discourse Analysis ,Mathematical modeling - Abstract
This paper explores the use of linguistic strategies, specifically discourse markers like 'but', to express contrasts between expectations and reality when faced with unexpected events. The study concentrates on Denial of Expectation (DofE), themost powerful form of contrast, which arises when the expected value based on background assumptions is not met. The main focus of this paper is to model DofE as a weighted homogeneous relationship between object properties. The aim is to predict DofE for numerical properties in specific contexts. I aim to address a gap in previous models by considering the role of context. This is achieved by analyzing contrastive sentences from German car and motorcycle reviews. The research presents the concept of expectation intervals for scalar properties. These intervals align with expectations and exceeding them triggers a potential contrast. The study incorporates causality, expected behavior, and a shift function in selecting contrastive pairs, transforming the conditions into an algorithm.Keywords: contrast; computational and cognitive modeling; discourse analysis
- Published
- 2024
33. Analytical Models of the Performance of IEEE 802.11p Vehicle to Vehicle Communications.
- Author
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Sepulcre, Miguel, Gonzalez-Martin, Manuel, Gozalvez, Javier, Molina-Masegosa, Rafael, and Coll-Perales, Baldomero
- Subjects
TRAFFIC density ,DATA packeting ,POWER transmission ,ACCESS control ,COMMUNICATION models - Abstract
The critical nature of vehicular communications requires their extensive testing and evaluation. Analytical models can represent an attractive and cost-effective approach for such evaluation if they can adequately model all underlying effects that impact the performance of vehicular communications. Several analytical models have been proposed to date to model vehicular communications based on the IEEE 802.11p (or DSRC) standard. However, existing models normally model in detail the MAC (Medium Access Control), and generally simplify the propagation and interference effects. This reduces their value as an alternative to evaluate the performance of vehicular communications. This paper addresses this gap, and presents new analytical models that accurately model the performance of vehicle-to-vehicle communications based on the IEEE 802.11p standard. The models jointly account for a detailed modeling of the propagation and interference effects, as well as the impact of the hidden terminal problem. The model quantifies the PDR (Packet Delivery Ratio) as a function of the distance between transmitter and receiver. The paper also presents new analytical models to quantify the probability of the four different types of packet errors in IEEE 802.11p. In addition, the paper presents the first analytical model capable to accurately estimate the Channel Busy Ratio (CBR) metric even under high channel load levels. All the analytical models are validated by means of simulation for a wide range of parameters, including traffic densities, packet transmission frequencies, transmission power levels, data rates and packet sizes. An implementation of the models is provided openly to facilitate their use by the community. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
34. Lightning Surge Analysis of HV Transmission Line: Bias AC-Voltage Effect on Multiphase Back-Flashover.
- Author
-
Yamanaka, Akifumi, Nagaoka, Naoto, and Baba, Yoshihiro
- Subjects
ELECTRIC lines ,FLASHOVER ,LIGHTNING ,TIME-domain analysis ,FINITE difference method ,NUMERICAL analysis - Abstract
This paper discusses the back-flashover (BFO) phenomena in a high-voltage (HV) vertical double-circuit transmission line by means of numerical simulations. The single- and multiphase BFOs are analyzed considering 24 cases of AC-voltage angles and nonlinear characteristic of flashovers, taking advantage of the circuit analysis method in time domain. The models used in circuit analysis are the TEM-delay model, which can take into account the non-TEM characteristics of tower and line, and the conventional models. Prior to the BFO analysis, the characteristics of circuit models are compared and discussed with the numerical electromagnetic analysis results. In BFO analysis, it is clarified that the occurrence probability of the multiphase BFO is heavily depending on the bias AC-voltages. The relation of measured BFO phases and AC-voltages are well explained by the TEM-delay model with the valid lightning current magnitude. The conventional circuit models could have underestimated the occurrence of BFOs in HV transmission lines. The analysis and discussions shown in this paper can be utilized for advanced evaluation of lightning performance of transmission lines that considers the seriousness of lightning accidents. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
35. Platform Profit Maximization on Service Provisioning in Mobile Edge Computing.
- Author
-
Huang, Xiaoyao, Zhang, Baoxian, and Li, Cheng
- Subjects
MOBILE computing ,EDGE computing ,PROFIT maximization ,LOGIC design ,INTEGER programming ,CLOUD computing - Abstract
Mobile edge computing has been an important supplement for traditional cloud computing architecture to offer low-delay computing services to mobile users. However, it is in general impossible for edge service providers to overdeploy so much edge resources to satisfy the rapidly increasing while diverse user demands. In this paper, we study a mobile edge computing system consisting of a service platform, cloudlets joining the system, and mobile users. In this study, we focus on a profit-driven perspective such that the service platform purchases computation resource from the resource-rich cloudlets and makes profit by processing tasks from user side. The design objective is to maximize the platform profit subject to budget constraint and stringent delay requirements for task processing. We formulate this problem as a mixed integer programming problem. Due to the NP-hardness of the problem, we design a logic based Benders decomposition algorithm as the offline solution. We further study the scenario where the task arrivals from user side and resource availability at the cloudlets are both stochastic and unknown in advance. We accordingly propose a Multi-Armed Bandit learning based resource purchasing and greedy task scheduling algorithm for the online scenario. Simulations results show the high performance of our proposed algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
36. A New Approach to Represent the Corona Effect and Frequency-Dependent Transmission Line Models in EMT-Type Programs.
- Author
-
Pereira, Thassio Matias and Tavares, Maria Cristina
- Subjects
ELECTRIC lines ,ELECTRIC transients ,WAVE equation - Abstract
This paper presents an accurate and efficient model to represent the corona effect and frequency dependence of line parameters in electromagnetic transient simulations. The new method, named the voltage and frequency dependent line model (VFDLM), can be seen as a more general case of the well-known frequency-dependent (FD) or wideband (WB) line models, wherein the characteristic admittance and propagation function are considered voltage- and frequency-dependent parameters. The time domain traveling wave equations are solved using recursive convolutions and rational approximation through vector fitting (VF). Since the model can be represented by Norton equivalents, it is totally compatible with EMT-type programs. The model is validated through comparisons with three field measurement data available in the literature, and good agreement could be observed between them, showing that the VFDLM is a stable, efficient, and accurate approach to represent the corona effect in EMT-type programs. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
37. LDA-Reg: Knowledge Driven Regularization Using External Corpora.
- Author
-
Yang, Kai, Luo, Zhaojing, Gao, Jinyang, Zhao, Junfeng, Ooi, Beng Chin, and Xie, Bing
- Subjects
CORPORA ,NEURAL development ,DATA mining ,ARTIFICIAL neural networks - Abstract
While recent developments of neural network (NN) models have led to a series of record-breaking achievements in many applications, the lack of sufficiently good datasets remains a problem for some applications. For such a problem, we can however exploit a large number of unstructured text corpora as an external knowledge to complement the training data, and most prevailing neural network solutions employ word embedding methods for such purposes. In this paper, we propose LDA-Reg, a novel knowledge driven regularization framework based on Latent Dirichlet Allocation (LDA) as an alternative to the word embedding methods to adaptively utilize abundant external knowledge and to interpret the NN model. For the joint learning of the parameters, we propose EM-SGD, an effective update method which incorporates Expectation Maximization (EM) and Stochastic Gradient Descent (SGD) to update parameters iteratively. Moreover, we also devise a lazy update and sparse update method for the high-dimensional inputs and sparse inputs respectively. We validate the effectiveness of our regularization framework through an extensive experimental study over real world and standard benchmark datasets. The results show that our proposed framework not only achieves significant improvement over state-of-the-art word embedding methods but also learns interpretable and significant topics for various tasks. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
38. Knowledge Graph Embedding by Double Limit Scoring Loss.
- Author
-
Zhou, Xiaofei, Niu, Lingfeng, Zhu, Qiannan, Zhu, Xingquan, Liu, Ping, Tan, Jianlong, and Guo, Li
- Subjects
KNOWLEDGE graphs ,SPARSE matrices - Abstract
Knowledge graph embedding is an effective way to represent knowledge graph, which greatly enhance the performances on knowledge graph completion tasks, e.g., entity or relation prediction. For knowledge graph embedding models, designing a powerful loss framework is crucial to the discrimination between correct and incorrect triplets. Margin-based ranking loss is a commonly used negative sampling framework to make a suitable margin between the scores of positive and negative triples. However, this loss can not ensure ideal low scores for the positive triplets and high scores for the negative triplets, which is not beneficial for knowledge completion tasks. In this paper, we present a double limit scoring loss to separately set upper bound for correct triplets and lower bound for incorrect triplets, which provides more effective and flexible optimization for knowledge graph embedding. Upon the presented loss framework, we present several knowledge graph embedding models including TransE-SS, TransH-SS, TransD-SS, ProjE-SS and ComplEx-SS. The experimental results on link prediction and triplet classification show that our proposed models have the significant improvement compared to state-of-the-art baselines. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
39. Faster Domain Adaptation Networks.
- Author
-
Li, Jingjing, Jing, Mengmeng, Su, Hongzu, Lu, Ke, Zhu, Lei, and Shen, Heng Tao
- Subjects
DEEP learning ,OPTIMAL stopping (Mathematical statistics) ,MACHINE learning ,EDGE computing ,KNOWLEDGE transfer ,COMMUNITIES - Abstract
It is widely acknowledged that the success of deep learning is built upon large-scale training data and tremendous computing power. However, the data and computing power are not always available for many real-world applications. In this paper, we address the machine learning problem where it lacks training data and limits computing power. Specifically, we investigate domain adaptation which is able to transfer knowledge from one labeled source domain to an unlabeled target domain, so that we do not need much training data from the target domain. At the same time, we consider the situation that the running environment is confined, e.g., in edge computing the end device has very limited running resources. Technically, we present the Faster Domain Adaptation (FDA) protocol and further report two paradigms of FDA: early stopping and amid skipping. The former accelerates domain adaptation by multiple early exit points. The latter speeds up the adaptation by wisely skip several amid neural network blocks. Extensive experiments on standard benchmarks verify that our method is able to achieve the comparable and even better accuracy but employ much less computing resources. To the best of our knowledge, there are very few works which investigated accelerating knowledge adaptation in the community. This work is expected to inspire the topic for more discussion. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
40. Mixed-Integer Convex Optimization for DC Microgrid Droop Control.
- Subjects
ELECTRICAL load ,MICROGRIDS ,MONTE Carlo method ,LINEAR programming ,CONVEXITY spaces ,VOLTAGE control - Abstract
Droop control is a viable method for the operation of island DC microgrids in a decentralized architecture. This paper presents a mixed-integer conic optimization formulation for the design of generator droop control, comprising the parameters of a piecewise linear droop curve. The mixed-integer formulation originates from a stochastic optimization framework that considers several operating scenarios for finding the optimal design. The convexity of the mixed-integer problem continuous relaxation gives global optimality guarantees for the design problem. The paper presents computational results using a tight polyhedral approximation of the conic program, leading to a mixed-integer linear programming (MILP) problem that is solved using a state-of-the-art commercial solver. The results from the proposed approach are contrasted with both a classic linear droop control design and a recent piecewise linear formulation. The Monte-Carlo simulation results quantify the extent to which the MILP solution is superior in reducing voltage violations and power loss, and the degree to which the loss is close to that from a conic optimal power flow solution. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
41. On the Tradeoff Between Computation and Communication Costs for Distributed Linearly Separable Computation.
- Author
-
Wan, Kai, Sun, Hua, Ji, Mingyue, and Caire, Giuseppe
- Subjects
DISTRIBUTED computing ,LINEAR codes ,GRID computing ,SYMMETRIC matrices ,COST - Abstract
This paper studies the distributed linearly separable computation problem, which is a generalization of many existing distributed computing problems such as distributed gradient coding and distributed linear transform. A master asks ${\mathsf {N}}$ distributed workers to compute a linearly separable function of ${\mathsf {K}}$ datasets, which is a set of ${\mathsf {K}}_{\mathrm{ c}}$ linear combinations of ${\mathsf {K}}$ equal-length messages (each message is a function of one dataset). We assign some datasets to each worker in an uncoded manner, who then computes the corresponding messages and returns some function of these messages, such that from the answers of any ${\mathsf {N}}_{\mathrm{ r}}$ out of ${\mathsf {N}}$ workers the master can recover the task function with high probability. In the literature, the specific case where ${\mathsf {K}}_{\mathrm{ c}}=1$ or where the computation cost is minimum has been considered. In this paper, we focus on the general case (i.e., general ${\mathsf {K}}_{\mathrm{ c}} $ and general computation cost) and aim to find the minimum communication cost. We first propose a novel converse bound on the communication cost under the constraint of the popular cyclic assignment (widely considered in the literature), which assigns the datasets to the workers in a cyclic way. Motivated by the observation that existing strategies for distributed computing fall short of achieving the converse bound, we propose a novel distributed computing scheme for some system parameters. The proposed computing scheme is optimal for any assignment when ${\mathsf {K}}_{\mathrm{ c}}$ is large and is optimal under the cyclic assignment when the numbers of workers and datasets are equal or ${\mathsf {K}}_{\mathrm{ c}}$ is small. In addition, it is order optimal within a factor of 2 under the cyclic assignment for the remaining cases. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
42. Adversarial Attacks on Time Series.
- Author
-
Karim, Fazle, Majumdar, Somshubra, and Darabi, Houshang
- Subjects
TIME series analysis ,NEAREST neighbor analysis (Statistics) ,DEEP learning - Abstract
Time series classification models have been garnering significant importance in the research community. However, not much research has been done on generating adversarial samples for these models. These adversarial samples can become a security concern. In this paper, we propose utilizing an adversarial transformation network (ATN) on a distilled model to attack various time series classification models. The proposed attack on the classification model utilizes a distilled model as a surrogate that mimics the behavior of the attacked classical time series classification models. Our proposed methodology is applied onto 1-nearest neighbor dynamic time warping (1-NN DTW) and a fully convolutional network (FCN), all of which are trained on 42 University of California Riverside (UCR) datasets. In this paper, we show both models were susceptible to attacks on all 42 datasets. When compared to Fast Gradient Sign Method, the proposed attack generates a larger faction of successful adversarial black-box attacks. A simple defense mechanism is successfully devised to reduce the fraction of successful adversarial samples. Finally, we recommend future researchers that develop time series classification models to incorporating adversarial data samples into their training data sets to improve resilience on adversarial samples. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
43. BitS-Net: Bit-Sparse Deep Neural Network for Energy-Efficient RRAM-Based Compute-In-Memory.
- Author
-
Karimzadeh, Foroozan, Yoon, Jong-Hyeok, and Raychowdhury, Arijit
- Subjects
ENERGY consumption ,ZERO (The number) ,DEEP learning ,COMPUTER architecture ,MOBILE learning - Abstract
The rising popularity of intelligent mobile devices and the computational cost of deep learning-based models call for efficient and accurate on-device inference schemes. We propose a novel model compression scheme that allows inference to be carried out using bit-level sparsity, which can be efficiently implemented using in-memory computing macros. In this paper, we introduce a method called BitS-Net to leverage the benefits of bit-sparsity (where the number of zeros are more than number of ones in binary representation of weight/activation values) when applied to compute-in-memory (CIM) with resistive RAM (RRAM) to develop energy efficient DNN accelerators operating in the inference mode. We demonstrate that BitS-Net improves the energy efficiency by up to 5x for ResNet models on the ImageNet dataset. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
44. Airborne Pesticides—Deep Diving into Sampling and Analysis.
- Author
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Udomkun, Patchimaporn, Boonupara, Thirasant, Sumitsawan, Sulak, Khan, Eakalak, Pongpichan, Siwatt, and Kajitvichyanukul, Puangrat
- Subjects
DEEP diving ,ENVIRONMENTAL sampling ,AIR sampling ,PESTICIDES ,ENVIRONMENTAL monitoring ,SAMPLING (Process) ,ENDOTOXINS - Abstract
The escalating utilization of pesticides has led to pronounced environmental contamination, posing a significant threat to agroecosystems. The extensive and persistent global application of these chemicals has been linked to a spectrum of acute and chronic human health concerns. This review paper focuses on the concentrations of airborne pesticides in both indoor and outdoor environments. The collection of diverse pesticide compounds from the atmosphere is examined, with a particular emphasis on active and passive air sampling techniques. Furthermore, a critical evaluation is conducted on the methodologies employed for the extraction and subsequent quantification of airborne pesticides. This analysis takes into consideration the complexities involved in ensuring accurate measurements, highlighting the advancements and limitations of current practices. By synthesizing these aspects, this review aims to foster a more comprehensive and informed comprehension of the intricate dynamics related to the presence and measurement of airborne pesticides. This, in turn, is poised to significantly contribute to the refinement of environmental monitoring strategies and the augmentation of precise risk assessments. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
45. On Fusogenicity of Positively Charged Phased-Separated Lipid Vesicles: Experiments and Computational Simulations.
- Author
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Wang, Yifei, Palzhanov, Yerbol, Dang, Dang T., Quaini, Annalisa, Olshanskii, Maxim, and Majd, Sheereen
- Subjects
CATIONIC lipids ,MEMBRANE lipids ,MEMBRANE separation ,PHASE separation ,LIPOSOMES - Abstract
This paper studies the fusogenicity of cationic liposomes in relation to their surface distribution of cationic lipids and utilizes membrane phase separation to control this surface distribution. It is found that concentrating the cationic lipids into small surface patches on liposomes, through phase-separation, can enhance liposome's fusogenicity. Further concentrating these lipids into smaller patches on the surface of liposomes led to an increased level of fusogenicity. These experimental findings are supported by numerical simulations using a mathematical model for phase-separated charged liposomes. Findings of this study may be used for design and development of highly fusogenic liposomes with minimal level of toxicity. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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46. Hierarchical Bayesian LSTM for Head Trajectory Prediction on Omnidirectional Images.
- Author
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Yang, Li, Xu, Mai, Guo, Yichen, Deng, Xin, Gao, Fangyuan, and Guan, Zhenyu
- Subjects
BAYESIAN field theory ,GAUSSIAN distribution ,HEAD ,FORECASTING ,MAGNETIC recording heads - Abstract
When viewing omnidirectional images (ODIs), viewers can access different viewports via head movement (HM), which sequentially forms head trajectories in spatial-temporal domain. Thus, head trajectories play a key role in modeling human attention on ODIs. In this paper, we establish a large-scale dataset collecting 21,600 head trajectories on 1,080 ODIs. By mining our dataset, we find two important factors influencing head trajectories, i.e., temporal dependency and subject-specific variance. Accordingly, we propose a novel approach integrating hierarchical Bayesian inference into long short-term memory (LSTM) network for head trajectory prediction on ODIs, which is called HiBayes-LSTM. In HiBayes-LSTM, we develop a mechanism of Future Intention Estimation (FIE), which captures the temporal correlations from previous, current and estimated future information, for predicting viewport transition. Additionally, a training scheme called Hierarchical Bayesian inference (HBI) is developed for modeling inter-subject uncertainty in HiBayes-LSTM. For HBI, we introduce a joint Gaussian distribution in a hierarchy, to approximate the posterior distribution over network weights. By sampling subject-specific weights from the approximated posterior distribution, our HiBayes-LSTM approach can yield diverse viewport transition among different subjects and obtain multiple head trajectories. Extensive experiments validate that our HiBayes-LSTM approach significantly outperforms 9 state-of-the-art approaches for trajectory prediction on ODIs, and then it is successfully applied to predict saliency on ODIs. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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47. Mining Data Impressions From Deep Models as Substitute for the Unavailable Training Data.
- Author
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Nayak, Gaurav Kumar, Mopuri, Konda Reddy, Jain, Saksham, and Chakraborty, Anirban
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DATA mining ,OCEAN mining ,COMPUTER vision ,DISTILLATION - Abstract
Pretrained deep models hold their learnt knowledge in the form of model parameters. These parameters act as “memory” for the trained models and help them generalize well on unseen data. However, in absence of training data, the utility of a trained model is merely limited to either inference or better initialization towards a target task. In this paper, we go further and extract synthetic data by leveraging the learnt model parameters. We dub them Data Impressions, which act as proxy to the training data and can be used to realize a variety of tasks. These are useful in scenarios where only the pretrained models are available and the training data is not shared (e.g., due to privacy or sensitivity concerns). We show the applicability of data impressions in solving several computer vision tasks such as unsupervised domain adaptation, continual learning as well as knowledge distillation. We also study the adversarial robustness of lightweight models trained via knowledge distillation using these data impressions. Further, we demonstrate the efficacy of data impressions in generating data-free Universal Adversarial Perturbations (UAPs) with better fooling rates. Extensive experiments performed on benchmark datasets demonstrate competitive performance achieved using data impressions in absence of original training data. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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48. A(DP) $^2$ 2 SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent With Differential Privacy.
- Author
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Xu, Jie, Zhang, Wei, and Wang, Fei
- Subjects
PRIVACY ,HETEROGENEOUS computing ,DEEP learning ,LEAKS (Disclosure of information) - Abstract
As deep learning models are usually massive and complex, distributed learning is essential for increasing training efficiency. Moreover, in many real-world application scenarios like healthcare, distributed learning can also keep the data local and protect privacy. Recently, the asynchronous decentralized parallel stochastic gradient descent (ADPSGD) algorithm has been proposed and demonstrated to be an efficient and practical strategy where there is no central server, so that each computing node only communicates with its neighbors. Although no raw data will be transmitted across different local nodes, there is still a risk of information leak during the communication process for malicious participants to make attacks. In this paper, we present a differentially private version of asynchronous decentralized parallel SGD framework, or A(DP) $^2$ 2 SGD for short, which maintains communication efficiency of ADPSGD and prevents the inference from malicious participants. Specifically, Rényi differential privacy is used to provide tighter privacy analysis for our composite Gaussian mechanisms while the convergence rate is consistent with the non-private version. Theoretical analysis shows A(DP) $^2$ 2 SGD also converges at the optimal $\mathcal {O}(1/\sqrt{T})$ O (1 / T) rate as SGD. Empirically, A(DP) $^2$ 2 SGD achieves comparable model accuracy as the differentially private version of Synchronous SGD (SSGD) but runs much faster than SSGD in heterogeneous computing environments. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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49. Recurrent Multi-Frame Deraining: Combining Physics Guidance and Adversarial Learning.
- Author
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Yang, Wenhan, Tan, Robby T., Feng, Jiashi, Wang, Shiqi, Cheng, Bin, and Liu, Jiaying
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IMAGE color analysis ,PHYSICS - Abstract
Existing video rain removal methods mainly focus on rain streak removal and are solely trained based on the synthetic data, which neglect more complex degradation factors, e.g., rain accumulation, and the prior knowledge in real rain data. Thus, in this paper, we build a more comprehensive rain model with several degradation factors and construct a novel two-stage video rain removal method that combines the power of synthetic videos and real data. Specifically, a novel two-stage progressive network is proposed: recovery guided by a physics model, and further restoration by adversarial learning. The first stage performs an inverse recovery process guided by our proposed rain model. An initially estimated background frame is obtained based on the input rain frame. The second stage employs adversarial learning to refine the result, i.e., recovering the overall color and illumination distributions of the frame, the background details that are failed to be recovered in the first stage, and removing the artifacts generated in the first stage. Furthermore, we also introduce a more comprehensive rain model that includes degradation factors, e.g., occlusion and rain accumulation, which appear in real scenes yet ignored by existing methods. This model, which generates more realistic rain images, will train and evaluate our models better. Extensive evaluations on synthetic and real videos show the effectiveness of our method in comparisons to the state-of-the-art methods. Our datasets, results and code are available at: https://github.com/flyywh/Recurrent-Multi-Frame-Deraining. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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50. Point-of-Interest Recommendation With Global and Local Context.
- Author
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Han, Peng, Shang, Shuo, Sun, Aixin, Zhao, Peilin, Zheng, Kai, and Zhang, Xiangliang
- Subjects
MATRIX decomposition ,DATA distribution ,LINEAR programming - Abstract
The task of point of interest (POI) recommendation aims to recommend unvisited places to users based on their check-in history. A major challenge in POI recommendation is data sparsity, because a user typically visits only a very small number of POIs among all available POIs. In this paper, we propose AUC-MF to address the POI recommendation problem by maximizing Area Under the ROC curve (AUC). AUC has been widely used for measuring classification performance with imbalanced data distributions. To optimize AUC, we transform the recommendation task to a classification problem, where the visited locations are positive examples and the unvisited are negative ones. We define a new lambda for AUC to utilize the LambdaMF model, which combines the lambda-based method and matrix factorization model in collaborative filtering. Many studies have shown that geographic information plays an important role in POI recommendation. In this study, we focus on two levels geographic information: local similarity and global similarity. We further show that AUC-MF can be easily extended to incorporate geographical contextual information for POI recommendation. Specifically, we propose two novel methods to incorporate geographical information in AUC-MF. Different from most existing models where the contextual information are incorporated into the objective function, the incorporation of contextual information in AUC-MF is a refinement of the model and a sampling strategy. The sampling strategy could speedup convergence and the refining of recommendations is independent of training of the model. This mechanism also enables AUC-MF to be able produce recommendations refined towards different contextual information, with minimum computational cost. Experiments on two datasets show that the proposed AUC-MF outperforms state-of-the-art methods significantly in terms of recommendation accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
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