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10th International Symposium on Quality Electronic design

作者： Das, Debasish Scott, William Nazarian, Shahin Zhou, Hai Northwestern Univ EECS Evanston IL 60208 USA Magma Design Automat San Jose CA 95110 USA

ISBN: (纸本)9781424429523

Logic Cell modeling is an important component in the analysis and design of CMOS integrated circuits, mostly due to nonlinear behavior of CMOS cells with respect to the voltage signal at their input and output pins. A current-based model for CMOS logic cells is presented which can be used for effective crosstalk noise and delta delay analysis in CMOS VLSI circuits. Existing current source models are expensive and need a new set of Spice-based characterization which is not compatible with typical EDA tools. In this paper we present Imodel, a simple nonlinear logic cell model that can be derived from the typical cell libraries such as NLDM, with accuracy much higher than NLDM-based cell delay models. In fact, our experiments show an average error of 3% compared to Spice. This level of accuracy comes with a maximum runtime penalty of 19% compared to NLDM-based cell delay models on medium sized industrial designs.

关键词： Crosstalk analysis Gate modeling Simulation algorithm design Convex optimization

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Do the Ends Justify the Means? Variation in the Distributive and Procedural Fairness of Machine Learning algorithms

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JOURNAL OF BUSINESS ETHICS 2022年第4期181卷 1083-1095页

作者： Morse, Lily Teodorescu, Mike Horia M. Awwad, Yazeed Kane, Gerald C. West Virginia Univ Coll Business & Econ Morgantown WV 26506 USA Boston Coll Carroll Sch Management Chestnut Hill MA 02167 USA MIT D Lab 77 Massachusetts Ave Cambridge MA 02139 USA

Recent advances in machine learning methods have created opportunities to eliminate unfairness from algorithmic decision making. Multiple computational techniques (i.e., algorithmic fairness criteria) have arisen out of this work. Yet, urgent questions remain about the perceived fairness of these criteria and in which situations organizations should use them. In this paper, we seek to gain insight into these questions by exploring fairness perceptions of five algorithmic criteria. We focus on two key dimensions of fairness evaluations: distributive fairness and procedural fairness. We shed light on variation in the potential for different algorithmic criteria to facilitate distributive fairness. Subsequently, we discuss procedural fairness and provide a framework for understanding how algorithmic criteria relate to essential aspects of this construct, which helps to identify when a specific criterion is suitable. From a practical standpoint, we encourage organizations to recognize that managing fairness in machine learning systems is complex, and that adopting a blind or one-size-fits-all mentality toward algorithmic criteria will surely damage people's attitudes and trust in automated technology. Instead, firms should carefully consider the subtle yet significant differences between these technical solutions.

关键词： Fairness Machine learning Distributive fairness Procedural fairness algorithm design

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Resource Allocation for Secure Communication in Systems with Wireless Information and Power Transfer

Resource Allocation for Secure Communication in Systems with...

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IEEE Globecom Workshops (GC Wkshps)

作者： Ng, Derrick Wing Kwan Schober, Robert Univ Erlangen Nurnberg Inst Digital Commun Nurnberg Germany Univ British Columbia Vancouver BC V5Z 1M9 Canada

ISBN: (纸本)9781479928514

This paper considers secure communication in a multiuser multiple-input single-output (MISO) downlink system with simultaneous wireless information and power transfer. We study the design of resource allocation algorithms minimizing the total transmit power for the case when the receivers are able to harvest energy from the radio frequency. In particular, the algorithm design is formulated as a non-convex optimization problem which takes into account artificial noise generation to combat potential eavesdroppers, a minimum required signal-to-interference-plus-noise ratio (SINR) at the desired receiver, maximum tolerable SINRs at the potential eavesdroppers, and a minimum required power delivered to the receivers. We adopt a semidefinite programming (SDP) relaxation approach to obtain an upper bound solution for the considered problem. The tightness of the upper bound is revealed by examining a sufficient condition for the global optimal solution. Inspired by the sufficient condition, we propose two suboptimal resource allocation schemes enhancing secure communication and facilitating efficient energy harvesting. Simulation results demonstrate a close-to-optimal performance achieved by the proposed suboptimal schemes and significant transmit power savings by optimization of the artificial noise generation.

关键词： Communication system security Resource utilization power transfer Transmitter power Receivers Upper bound management of resources COMMUNICATION SYSTEMS artificial noise Energy sources multiple-input single-output algorithm design noise generators wireless

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Forecasting the demand of mobile clinic services at vulnerable communities based on integrated multi-source data

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IISE TRANSACTIONS ON HEALTHCARE SYSTEMS ENGINEERING 2021年第2期11卷 113-127页

作者： Majeed, Bilal Peng, Jiming Li, Ang Lin, Ying Delgado, Rigoberto, I Univ Houston Dept Ind Engn Houston TX 77204 USA Univ Texas El Paso Coll Business Adm El Paso TX 79968 USA

Demand forecasting plays an important role in the deployment of mobile clinic services to vulnerable communities such as school zones and census tracts as it can help the service provider to maximize its coverage under limited resources. In this paper, we consider the issue of how to predict the vaccination delinquency in schools and census tracts. Such an issue is rather challenging as the delinquency is only observed in schools for which very limited information is available;while rich demographic and economic information is available for census tracts, no observations of delinquency have been made at the census tract level. To address the above challenge, we first develop a hierarchical approach to forecast the demand for vaccinations in schools and census tracts. In the first stage of the hierarchical approach, we solve a linear optimization model to compute an association matrix that can align some common features in both census tracts and school zones. Then we use the estimated association to develop a forecasting model to predict the vaccination delinquency in both schools and census tracts. A non-convex quadratic optimization (QO) model is also proposed to find the association matrix and the forecasting model simultaneously. We also introduce an alternative update scheme for the non-convex QO and establish the convergence of the algorithm. Moreover, the two association matrices generated from the proposed approaches can be used to impute the information in the school zone data, which further allows us to apply existing forecasting models to predict the demand in school zones based on the imputed data. A case study from the Houston Independent School District (HISD) and its associated communities is reported to demonstrate the efficacy of the new models and techniques.

关键词： Demand forecasting convex and non-convex optimization algorithm design data imputation integration of multi-source data[]

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From Group Recommendations to Group Formation 15

From Group Recommendations to Group Formation

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ACM SIGMOD International Conference on Management of Data

作者： Roy, Senjuti Basu Lakshmanan, Laks V. S. Liu, Rui Univ Washington Tacoma Tacoma WA 98402 USA Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9781450327589

There has been significant recent interest in the area of group recommendations, where, given groups of users of a recommender system, one wants to recommend top-k items to a group that maximize the satisfaction of the group members, according to a chosen semantics of group satisfaction. Examples semantics of satisfaction of a recommended item-set to a group include the so-called least misery (LM) and aggregate voting (AV). We consider the complementary problem of how to form groups such that the users in the formed groups are most satisfied with the suggested top-k recommendations. We assume that the recommendations will be generated according to one of the two group recommendation semantics - LM or AV. Rather than assuming groups are given, or rely on ad hoc group formation dynamics, our framework allows a strategic approach for forming groups of users in order to maximize satisfaction. We show that the problem is NP-hard to solve optimally under both semantics. Furthermore, we develop two efficient algorithms for group formation under LM and show that they achieve bounded absolute error. We develop efficient heuristic algorithms for group formation under AV. We validate our results and demonstrate the scalability and effectiveness of our group formation algorithms on two large real data sets.

关键词： group recommendations algorithm design optimization methods

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Triangle-aware Spectral Sparsifiers and Community Detection 21

Triangle-aware Spectral Sparsifiers and Community Detection

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27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Sotiropoulos, Konstantinos Tsourakakis, Charalampos E. Boston Univ Boston MA 02215 USA ISI Fdn Turin Italy

ISBN: (纸本)9781450383325

Triangle-aware graph partitioning has proven to be a successful approach to finding communities in real-world data [8, 40, 51, 54]. But how can we explain its empirical success? Triangle-aware graph partitioning methods rely on the count of triangles an edge is contained in, in contrast to the well-established measure of effective resistance [12] that requires global information about the graph. In this work we advance the understanding of triangle-based graph partitioning in two ways. First, we introduce a novel triangle-aware sparsification scheme. Our scheme provably produces a spectral sparsifier with high probability [46, 47] on graphs that exhibit strong triadic closure, a hallmark property of real-world networks. Importantly, our sampling scheme is amenable to distributed computing, since it relies simply on computing node degrees, and edge triangle counts. Finally, we compare our methods to the Spielman-Srivastava sparsification algorithm [46] on a wide variety of realworld graphs, and we verify the applicability of our proposed sparsification scheme on real-world networks. Secondly, we develop a data-driven approach towards understanding properties of real-world communities with respect to effective resistances, and triangle counts. Our empirical approach is mainly based on the notion of ground-truth communities in datasets made available originally by Yang and Leskovec [53]. We perform a study of triangle-aware measures, and effective resistances on edges within, and across communities, and we discover certain interesting empirical findings. For example, we observe that the Jaccard similarity of an edge used by Satuluri [40], and the closely related Tectonic similarity measure introduced by Tsourakakis et al. [51] provide consistently good signals of whether an edge is contained within a community or not.

关键词： community detection graph mining spectral sparsifier algorithm design

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DCLEVERNET: Deep Combinatorial Learning for Efficient EV Charging Scheduling in Large-scale Networked Facilities 23

DCLEVERNET: Deep Combinatorial Learning for Efficient EV Cha...

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14th ACM International Conference on Future Energy Systems (e-Energy)

作者： Alshehhi, Bushra Karapetyan, Areg Elbassioni, Khaled Chau, Sid Chi-Kin Khonji, Majid Khalifa Univ POB 127788 Abu Dhabi U Arab Emirates New York Univ Abu Dhabi POB 129188 Abu Dhabi U Arab Emirates Australian Natl Univ Canberra ACT 2601 Australia

ISBN: (纸本)9798400700323

With the electrification of transportation, the rising uptake of electric vehicles (EVs) might stress distribution networks significantly, leaving their performance degraded and stability jeopardized. To accommodate these new loads cost-effectively, modern power grids require coordinated or "smart" charging strategies capable of optimizing EV charging scheduling in a scalable and efficient fashion. With this in view, the present work focuses on reservation management programs for large-scale, networked EV charging stations. We formulate a time-coupled binary optimization problem that maximizes EV users' total welfare gain while accounting for the network's available power capacity and stations' occupancy limits. To tackle the problem at scale while retaining high solution quality, a data-driven optimization framework combining techniques from the fields of Deep Learning and Approximation algorithms is introduced. The framework's key ingredient is a novel input-output processing scheme for neural networks that allows direct extrapolation to problem sizes substantially larger than those included in the training set. Extensive numerical simulations based on synthetic and real-world data traces verify the effectiveness and superiority of the presented approach over two representative scheduling algorithms. Lastly, we round up the contributions by listing several immediate extensions to the proposed framework and outlining the prospects for further exploration.

关键词： Electric Vehicles Charging Scheduling Deep Neural Networks algorithm design Combinatorial Optimization Smart Grid

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Quantum Particle Swarm Optimization Based on Bloch Coordinates of Qubits

Quantum Particle Swarm Optimization Based on Bloch Coordinat...

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9th International Conference on Natural Computation (ICNC)

作者： Liu, Xiande Liu, Xiaoming Northeast Petr Univ Sch Comp & Informat Technol Daqing Heilongjiang Peoples R China

ISBN: (纸本)9781467347143

To improve the efficiency of particle swarm optimization, a quantum particle swarm optimization algorithm is proposed on the basis of analyzing the search process of particle swarm optimization algorithm. In the proposed algorithm, particles are endoded by qubits described on the Bloch sphere, each particle occupy three locations of the search space, and each location represents a optimization solution. By employing the search method of general PSO to adjust the two parameters of qubit, the qubits rotation are performed on the Bloch sphere, which can simultaneously update three locations occupied by a qubit and quickly approach the global optimal solution. The experimental results of standard test function extreme optimization and fuzzy controller parameters optimization show that the proposed algorithm is superior to other similar algorithm in optimization ability and optimization efficiency.

关键词： quantum computation Bloch coordinates particle swarm optimization algorithm design

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On Scheduling Ring-All-Reduce Learning Jobs in Multi-Tenant GPU Clusters with Communication Contention 22

On Scheduling Ring-All-Reduce Learning Jobs in Multi-Tenant ...

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23rd International Symposium on Theory, algorithmic Foundations, and Protocol design for Mobile Networks and Mobile Computing (MobiHoc)

作者： Yu, Menglu Ji, Bo Rajan, Hridesh Liu, Jia Iowa State Univ Dept Comp Sci Ames IA 50011 USA Virginia Tech Dept Comp Sci Blacksburg VA USA Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA

ISBN: (纸本)9781450391658

Powered by advances in deep learning (DL) techniques, machine learning and artificial intelligence have achieved astonishing successes. However, the rapidly growing needs for DL also led to communication- and resource-intensive distributed training jobs for large-scale DL training, which are typically deployed over GPU clusters. To sustain the ever-increasing demand for DL training, the so-called "ring-all-reduce" (RAR) technologies have recently emerged as a favorable computing architecture to efficiently process network communication and computation load in GPU clusters. The most salient feature of RAR is that it removes the need for dedicated parameter servers, thus alleviating the potential communication bottleneck. However, when multiple RAR-based DL training jobs are deployed over GPU clusters, communication bottlenecks could still occur due to contentions between DL training jobs. So far, there remains a lack of theoretical understanding on how to design contention-aware resource scheduling algorithms for RAR-based DL training jobs, which motivates us to fill this gap in this work. Our main contributions are three-fold: i) We develop a new analytical model that characterizes both communication overhead related to the worker distribution of the job and communication contention related to the co-location of different jobs;ii) Based on the proposed analytical model, we formulate the problem as a non-convex integer program to minimize the makespan of all RAR-based DL training jobs. To address the unique structure in this problem that is not amenable for optimization algorithm design, we reformulate the problem into an integer linear program that enables provable approximation algorithm design called SJF-BCO (Smallest Job First with Balanced Contention and Overhead);and iii) We conduct extensive experiments to show the superiority of SJF-BCO over existing schedulers. Collectively, our results contribute to the state-of-the-art of distributed GPU system optimizatio

关键词： Distributed machine learning systems communication contention resource scheduling algorithm design

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Predication of Sunspot Number Based on Parallel Process Neural Networks

Predication of Sunspot Number Based on Parallel Process Neur...

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2nd International Conference on Information Technology and Management Innovation (ICITMI 2013)

作者： Gao Yan Yu Tiecheng Li Zhi Li Yunjiang Li Xin Daqing Oilfield Co LTD Oil Gas Working Team 8 Nat Gas Branch Co Daqing Peoples R China Daqing Oilfield Co LTD Nat Gas Branch Co Daqing Peoples R China Daqing Oilfield Co LTD Daqing Peoples R China Daqing Oilfield Co LTD Well Test & Product Test Branch Co Daqing Peoples R China Daqing Hongweijiye Automat Technol Co LTD Daqing Peoples R China

ISBN: (纸本)9783037858646

To solve prediction of sunspot number, a parallel process neural networks model is proposed in this paper, Firstly, by dividing the whole time-varying process into several small time intervals, the process neural networks are constructed in these small time intervals, which may disperse the load of networks. Then, employing the orthogonal basis expansion in functional space, the learning algorithm of the above-mentioned model is designed. The experimental results of time series predication of sunspots show that the proposed method has great potential for complicated nonlinear time series prediction.

关键词： Time series predication Sunspot number Parallel process neural networks algorithm design

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