pwc-archive/papers-with-abstracts huggingface dataset:
Benchmarking of DL Libraries and Models on Mobile Devices
Authors: Qiyang Zhang, Xiang Li, Xiangying Che, Xiao Ma, Ao Zhou, Mengwei Xu, Shangguang Wang, Yun Ma, Xuanzhe Liu
Date: 2022-02-14 00:00:00
Tasks: Benchmarking, GPU, Quantization

Deploying deep learning (DL) on mobile devices has been a notable trend in recent years. To support fast inference of on-device DL, DL libraries play a critical role as algorithms and hardware do. Unfortunately, no prior work ever dives deep into the ecosystem of modern DL libs and provides quantitative results on their performance. In this paper, we first build a comprehensive benchmark that includes 6 representative DL libs and 15 diversified DL models. We then perform extensive experiments on 10 mobile devices, which help reveal a complete landscape of the current mobile DL libs ecosystem. For example, we find that the best-performing DL lib is severely fragmented across different models and hardware, and the gap between those DL libs can be rather huge. In fact, the impacts of DL libs can overwhelm the optimizations from algorithms or hardware, e.g., model quantization and GPU/DSP-based heterogeneous computing. Finally, atop the observations, we summarize practical implications to different roles in the DL lib ecosystem.
Deep Learning based Coverage and Rate Manifold Estimation in Cellular Networks
Authors: Washim Uddin Mondal, Praful D. Mankar, Goutam Das, Vaneet Aggarwal, Satish V. Ukkusuri
Date: 2022-02-13 00:00:00

This article proposes Convolutional Neural Network-based Auto Encoder (CNN-AE) to predict location-dependent rate and coverage probability of a network from its topology. We train the CNN utilising BS location data of India, Brazil, Germany, and the USA and compare its performance with stochastic geometry (SG) based analytical models. In comparison to the best-fitted SG-based model, CNN-AE improves the coverage and rate prediction errors by a margin of as large as $40\%$ and $25\%$ respectively. As an application, we propose a low complexity, provably convergent algorithm that, using trained CNN-AE, can compute locations of new BSs that need to be deployed in a network in order to satisfy pre-defined spatially heterogeneous performance goals.
Learning Branch Probabilities in Compiler from Datacenter Workloads
Authors: Easwaran Raman, Xinliang David Li
Date: 2022-02-10 00:00:00

Estimating the probability with which a conditional branch instruction is taken is an important analysis that enables many optimizations in modern compilers. When using Profile Guided Optimizations (PGO), compilers are able to make a good estimation of the branch probabilities. In the absence of profile information, compilers resort to using heuristics for this purpose. In this work, we propose learning branch probabilities from a large corpus of data obtained from datacenter workloads. Using metrics including Root Mean Squared Error, Mean Absolute Error and cross-entropy, we show that the machine learning model improves branch probability estimation by 18-50% in comparison to compiler heuristics. This translates to performance improvement of up to 8.1% on 24 out of a suite of 40 benchmarks with a 1% geomean improvement on the suite. This also results in greater than 1.2% performance improvement in an important search application.
Low-latency Monaural Speech Enhancement with Deep Filter-bank Equalizer
Authors: Chengshi Zheng, Wenzhe Liu, Andong Li, Yuxuan Ke, XiaoDong Li
Date: 2022-02-14 00:00:00
Tasks: Deep Learning, Speech Enhancement

It is highly desirable that speech enhancement algorithms can achieve good performance while keeping low latency for many applications, such as digital hearing aids, acoustically transparent hearing devices, and public address systems. To improve the performance of traditional low-latency speech enhancement algorithms, a deep filter-bank equalizer (FBE) framework was proposed, which integrated a deep learning-based subband noise reduction network with a deep learning-based shortened digital filter mapping network. In the first network, a deep learning model was trained with a controllable small frame shift to satisfy the low-latency demand, i.e., $\le$ 4 ms, so as to obtain (complex) subband gains, which could be regarded as an adaptive digital filter in each frame. In the second network, to reduce the latency, this adaptive digital filter was implicitly shortened by a deep learning-based framework, and was then applied to noisy speech to reconstruct the enhanced speech without the overlap-add method. Experimental results on the WSJ0-SI84 corpus indicated that the proposed deep FBE with only 4-ms latency achieved much better performance than traditional low-latency speech enhancement algorithms in terms of the indices such as PESQ, STOI, and the amount of noise reduction.
Speech Analysis for Automatic Mania Assessment in Bipolar Disorder
Authors: Pınar Baki, Heysem Kaya, Elvan Çiftçi, Hüseyin Güleç, Albert Ali Salah
Date: 2022-02-05 00:00:00

Bipolar disorder is a mental disorder that causes periods of manic and depressive episodes. In this work, we classify recordings from Bipolar Disorder corpus that contain 7 different tasks, into hypomania, mania, and remission classes using only speech features. We perform our experiments on splitted tasks from the interviews. Best results achieved on the model trained with 6th and 7th tasks together gives 0.53 UAR (unweighted average recall) result which is higher than the baseline results of the corpus.
Active and Passive Hybrid Detection Method for Power CPS False Data Injection Attacks with Improved AKF and GRU-CNN
Authors: Zhaoyang Qu, Xiaoyong Bo, Tong Yu, Yaowei Liu, Yunchang Dong, Zhongfeng Kan, Lei Wang, Yang Li
Date: 2022-02-14 00:00:00
Tasks: State Estimation

Influenced by deep penetration of the new generation of information technology, power systems have gradually evolved into highly coupled cyber-physical systems (CPS). Among many possible power CPS network attacks, a false data injection attacks (FDIAs) is the most serious. Taking account of the fact that the existing knowledge-driven detection process for FDIAs has been in a passive detection state for a long time and ignores the advantages of data-driven active capture of features, an active and passive hybrid detection method for power CPS FDIAs with improved adaptive Kalman filter (AKF) and convolutional neural networks (CNN) is proposed in this paper. First, we analyze the shortcomings of the traditional AKF algorithm in terms of filtering divergence and calculation speed. The state estimation algorithm based on non-negative positive-definite adaptive Kalman filter (NDAKF) is improved, and a passive detection method of FDIAs is constructed, with similarity Euclidean distance detection and residual detection at its core. Then, combined with the advantages of gate recurrent unit (GRU) and CNN in terms of temporal memory and feature-expression ability, an active detection method of FDIAs based on a GRU-CNN hybrid neural network is proposed. Finally, the results of joint knowledge-driven and data-driven parallel detection are used to define a mixed fixed-calculation formula, and an active and passive hybrid detection method of FDIAs is established, considering the characteristic constraints of the parallel mode. A simulation system example of power CPS FDIAs verifies the effectiveness and accuracy of the method proposed in this paper.
Adaptive Control with Guaranteed Transient Behavior and Zero Steady-State Error for Systems with Time-Varying Parameters
Authors: Hefu Ye, Yongduan Song
Date: 2022-02-13 00:00:00

It is nontrivial to achieve global zero-error regulation for uncertain nonlinear systems. The underlying problem becomes even more challenging if mismatched uncertainties and unknown time-varying control gain are involved, yet certain performance specifications are also pursued. In this work, we present an adaptive control method, which, without the persistent excitation (PE) condition, is able to ensure global zero-error regulation with guaranteed output performance for parametric strict-feedback systems involving fast time-varying parameters in the feedback path and input path. The development of our control scheme benefits from generalized t-dependent and x-dependent functions, a novel coordinate transformation and "congelation of variables" method. Both theoretical analysis and numerical simulation verify the effectiveness and benefits of the proposed method.
Distributed Periodic Event-triggered Control of Nonlinear Multi-Agent Systems
Authors: Koen J. A. Scheres, Victor S. Dolk, Michelle S. Chong, Romain Postoyan, W. P. Maurice H. Heemels
Date: 2022-02-13 00:00:00

We present a general emulation-based framework to address the distributed control of multi-agent systems over packet-based networks. We consider the setup where information is only transmitted at (non-uniform) sampling times and where packets are received with unknown delays. We design local dynamic periodic event-triggering mechanisms to generate the transmissions. The triggering mechanisms can run on non-synchronized digital platforms, i.e., we ensure that the conditions must only be verified at asynchronous sampling times, which may differ for each platform. Different stability and performance characteristics can be considered as we follow a general dissipativity-based approach. Moreover, Zeno-free properties are guaranteed by design. The results are illustrated on a consensus problem.
Robust Consensus of Higher-Order Multi-Agent Systems With Attrition and Inclusion of Agents and Switching Topologies
Authors: Jinraj V Pushpangathan, Harikumar Kandath, Rajdeep Dutta, Rajarshi Bardhan, J. Senthilnath
Date: 2022-02-13 00:00:00

Some of the issues associated with the practical applications of consensus of multi-agent systems (MAS) include switching topologies, attrition and inclusion of agents from an existing network, and model uncertainties of agents. In this paper, a single distributed dynamic state-feedback protocol referred to as the Robust Attrition-Inclusion Distributed Dynamic (RAIDD) consensus protocol, is synthesized for achieving the consensus of MAS with attrition and inclusion of linear time-invariant higher-order uncertain homogeneous agents and switching topologies. A state consensus problem termed as the Robust Attrition-Inclusion (RAI) consensus problem is formulated to find this RAIDD consensus protocol. To solve this RAI consensus problem, first, the sufficient condition for the existence of the RAIDD protocol is obtained using the $\nu$-gap metric-based simultaneous stabilization approach. Next, the RAIDD consensus protocol is attained using the Glover-McFarlane robust stabilization method if the sufficient condition is satisfied. The performance of this RAIDD protocol is validated by numerical simulations.
Multicasting in NOMA-Based UAV Networks: Path Design and Throughput Maximization
Authors: Shima Salarhosseini, Mohammad Reza Javan, Ali Nazari
Date: 2022-02-12 00:00:00

In this paper, we propose a new resource allocation framework for unmanned aerial vehicle (UAV) assisted multicast wireless networks in which the network users according to their request are divided into several multicast groups. We adopt power domain non-orthogonal multiple access (PD-NOMA) as the transmission technology using which the dedicated signals of multicast groups are superimposed and transmitted simultaneously as the UAV passes over the communication area for fixed and mobile users. We discuss the proposed scenarios from two perspectives, offline and online mode. In offline mode, we implement the problem for fixed and mobile users whose locations are predictable (the location of users, over the communication time, is known at the beginning of the communication time) and in online mode for mobile users whose locations are unpredictable (the location of users, over the communication time, is unknown at the beginning of the communication time). Also, we proposed a scenario in which the online model the number of mobile users can grow in each time slot. We formulate the problem of joint power allocation and UAV trajectory design as an optimization problem that is non-linear and nonconvex for two proposed scenarios. To solve the problem, we adopt an alternate search method (ASM), successive convex approximation (SCA), and geometric programming (GP). Using simulation results, the performance of the proposed scheme is evaluated for different values of the network parameters.
An efficient implementation of graph-based invariant set algorithm for constrained nonlinear dynamical systems
Authors: Benjamin Decardi-Nelsona, Jinfeng Liu
Date: 2022-02-12 00:00:00
Tasks: graph construction

The graph-based invariant set (GIS) algorithm is a promising set-based technique for computing the largest (with respect to inclusion) control invariant set of general discrete-time nonlinear dynamical systems. However, like other invariant set algorithms for nonlinear systems, the GIS algorithm may require a lot of resources when computing the control invariant set. This limits its applicability to higher dimensional systems. In this work, we present an improved and efficient implementation of the GIS algorithm for general discrete-time controlled nonlinear systems. We first identify the bottlenecks through extensive analysis, and then provide remedial procedures to improve the implementation of the GIS algorithm. Specifically, we developed an adaptive subdivision scheme using a supervised machine learning-based algorithm to reduce the cell growth rate and parallelize the graph construction step. We extensively demonstrate the performance of the improved GIS algorithm using a numerical example and compare the result to that of the standard GIS algorithm. The results show that the adaptive subdivision and the parallelization improved the speed of the algorithm by about 8x and 3x respectively, that of the standard GIS algorithm.
A Fully Decentralized Tuning-free Inexact Projection Method for P2P Energy Trading
Authors: Meiyi Li, Javad Mohammadi, Soummya Kar
Date: 2022-02-12 00:00:00
Tasks: Decision Making, energy trading

Agent-based solutions lend themselves well to address privacy concerns and the computational scalability needs of future distributed electric grids and end-use energy exchanges. Decentralized decision-making methods are the key to enabling peer-to-peer energy trading between electricity prosumers. However, the performance of existing decentralized decision-making algorithms highly depends on the algorithmic design and hyperparameter tunings, limiting applicability. This paper aims to address this gap by proposing a decentralized inexact projection method that does not rely on parameter tuning or central coordination to achieve the optimal solution for Peer-to-Peer (P2P) energy trading problems. The proposed algorithm does not require parameter readjustments, and once tuned, it converges for a wide range of P2P setups. Moreover, each prosumer only needs to share limited information (i.e., updated coupled variable) with neighboring prosumers. The IEEE 13 bus test system is used to showcase our proposed method's robustness and privacy advantages.
Application of Modular Vehicle Technology to Mitigate Bus Bunching
Authors: Zaid Saeed Khan, Weili He, Monica Menendez
Date: 2022-02-12 00:00:00

The stochastic nature of public transport systems leads to headway variability and bus bunching, causing both operator and passenger cost to increase significantly. Traditional strategies to counter bus bunching, including bus-holding, stop-skipping, and bus substitution/insertion, suffer from trade-offs and shortcomings. Autonomous modular vehicle (AMV) technology provides an additional level of flexibility in bus dispatching and operations, which can offer significant benefits in mitigating bus bunching compared to strategies available with conventional buses. This paper introduces a novel alternative to stop-skipping by leveraging the new capabilities offered by AMVs (in particular, en-route coupling and decoupling of modular units). We develop a simple bus-splitting strategy that directs a modular bus to decouple into individual units when it experiences a headway longer than a given threshold. We then use a macroscopic simulation to present a proof-of-concept evaluation of the proposed modular strategy compared to a benchmark traditional stop-skipping strategy and the base (no control) case. We find that the proposed strategy outperforms the benchmark in decreasing each of the three travel time components: waiting time, in-vehicle time, and walking time (which it eliminates completely). It therefore reduces the overhead of bus bunching and thus the travel cost by more than twice as much as the benchmark for busy bus lines. Simultaneously, it also reduces headway variability to a comparable degree. Furthermore, we analyze different control thresholds for applying the proposed strategy, and show that it is most effective when applied proactively, i.e. with the control action being triggered even by small headway deviations.
An Invariant Set Construction Method, Applied to Safe Coordination of Thermostatic Loads
Authors: Sunho Jang, Necmiye Ozay, Johanna L. Mathieu
Date: 2022-02-11 00:00:00

We consider the problem of coordinating a collection of switched subsystems under both local and global constraints for safe operation of the system. Although an invariant set can be leveraged to construct a safety-guaranteed controller for this kind of problem, computing an invariant set is not scalable to high-dimensional systems. In this paper, we introduce a strategy to obtain an implicit representation of a controlled invariant set for a collection of switched subsystems, and construct a safety-guaranteed controller to coordinate the subsystems using the representation. Specifically, we incorporate the invariant set into a model predictive controller to guarantee safety and recursive feasibility. Since the amount of computations is independent of the number of subsystems, this approach scales to large collections of switched subsystems. We use our approach to safely control a collection of thermostatically controlled loads to provide grid balancing services. The problem includes constraints on each load's temperature and duration it must remain in a mode after a switch, and also on aggregate power consumption to ensure network safety. Numerical simulations show that the proposed approach outperforms benchmark strategies in terms of safety and recursive feasibility.
STG-GAN: A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systems
Authors: Jinlei Zhang, Hua Li, Lixing Yang, Guangyin Jin, Jianguo Qi, Ziyou Gao
Date: 2022-02-10 00:00:00
Tasks: Generative Adversarial Network, Prediction

Short-term passenger flow prediction is an important but challenging task for better managing urban rail transit (URT) systems. Some emerging deep learning models provide good insights to improve short-term prediction accuracy. However, there exist many complex spatiotemporal dependencies in URT systems. Most previous methods only consider the absolute error between ground truth and predictions as the optimization objective, which fails to account for spatial and temporal constraints on the predictions. Furthermore, a large number of existing prediction models introduce complex neural network layers to improve accuracy while ignoring their training efficiency and memory occupancy, decreasing the chances to be applied to the real world. To overcome these limitations, we propose a novel deep learning-based spatiotemporal graph generative adversarial network (STG-GAN) model with higher prediction accuracy, higher efficiency, and lower memory occupancy to predict short-term passenger flows of the URT network. Our model consists of two major parts, which are optimized in an adversarial learning manner: (1) a generator network including gated temporal conventional networks (TCN) and weight sharing graph convolution networks (GCN) to capture structural spatiotemporal dependencies and generate predictions with a relatively small computational burden; (2) a discriminator network including a spatial discriminator and a temporal discriminator to enhance the spatial and temporal constraints of the predictions. The STG-GAN is evaluated on two large-scale real-world datasets from Beijing Subway. A comparison with those of several state-of-the-art models illustrates its superiority and robustness. This study can provide critical experience in conducting short-term passenger flow predictions, especially from the perspective of real-world applications.
On the influence of input triggering on the dynamics of the Jansen-Rit oscillators network
Authors: Sheida Kazemi, Yousef Jamali
Date: 2022-02-14 00:00:00

We investigate the dynamical properties of a network of coupled neural oscillators with identical Jansen-Rit masses. The connections between nodes follow the regular Watts-Strogatz topology. Each node receives deterministic external input plus internal input based on the output signal from the neighbors. This paper aims to change these two inputs and analyze the generated results. First, we attempt to analyze the model using the mean-field approximation, i.e., identical inputs for all nodes. Then, this assumption is relaxed. A more detailed analysis of these two states is discussed. We use the Pearson correlation coefficient to measure the amount of synchronization. As a result of the mean-field approach, we find that despite observed changes in behavior, there is no phase transition. Surprisingly, both the first (discontinuous) and second (continuous) phase transition occurs by relaxing the mean-field assumption. We also demonstrate how changes in input can result in pathological oscillations similar to those observed in epilepsy. Results show that coupled Jansen-Rit masses emerge a variety of behaviors influenced by various external and internal inputs. Moreover, our findings indicate that delta waves can be generated by altering these inputs in a network of Jansen-Rit neural mass models, which has not been previously observed in a single Jansen-Rit neural mass model analysis. \\ Overall, a wide range of behavioral patterns, including epilepsy, healthy behavior, and the transition between synchrony, and asynchrony are examined comprehensively in this paper. Moreover, this work highlights the putative contribution of external and internal inputs in studying the phase transition and synchronization of neural mass models.
Probabilistic fair behaviors spark its boost in the Ultimatum Game: the strength of good Samaritans
Authors: Guozhong Zheng, Jiqiang Zhang, Rizhou Liang, Lin Ma, Li Chen
Date: 2022-02-12 00:00:00
Tasks: Fairness

Behavioral experiments on the Ultimatum Game have shown that we human beings have remarkable preference in fair play, contradicting the predictions by the game theory. Most of the existing models seeking for explanations, however, strictly follow the assumption of \emph{Homo economicus} in orthodox Economics that people are self-interested and fully rational to maximize their earnings. Here we relax this assumption by allowing that people probabilistically choose to be "good Samaritans", acting as fair players from time to time. For well-mixed and homogeneously structured populations, we numerically show that as this probability increases the level of fairness undergoes from the low scenario abruptly to the full fairness state, where occasional fair behaviors ($\sim5\%$) are sufficient to drive the whole population to behave in the half-half split manner. We also develop a mean-field theory, which correctly reproduces the first-order phase transition and points out that the bistability is an intrinsic property of this game and small fair acts lead to dramatical change due to its bifurcation structure. Heterogeneously structured populations, however, display continuous fairness transition; surprisingly, very few hub nodes acting as fair players are able to entrain the whole population to the full fairness state. Our results thus reveal the unexpected strength of "good Samaritans", which may constitute a new explanation for the emergence of fairness in our society.