pwc-archive/papers-with-abstracts huggingface dataset:
Probabilistic Streaming Tensor Decomposition
Authors: Yishuai Du, Yimin Zheng, Kuang-Chih Lee, Shandian Zhe
Date: 2018-12-31 00:00:00
Tasks: Bayesian Inference, Tensor Decomposition

Tensor decomposition is a fundamental tool for multiway data analysis. While most decomposition algorithms operate a collection of static data and perform batch processes, many applications produce data in a streaming manner - every time a subset of entries are generated, and previously seen entries cannot be revisited. In such scenarios, traditional decomposition approaches will be inappropriate, because they cannot provide timely updates when new data come in, and they need to access the whole dataset many times for batch optimization. To address this issue, we propose POST, a PrObabilistic Streaming Tensor decomposition algorithm, which enables real-time updates and predictions upon receiving new tensor entries, and supports dynamic growth of all the modes. Compared with the state-of-the-art streaming decomposition approach MAST, POST is more flexible in that it can handle arbitrary orders of streaming entries, and hence is more widely applicable. In addition, as a Bayesian inference algorithm, POST can quantify the uncertainty of the latent embeddings via their posterior distributions, and the confidence levels of the missing entry value predictions. On several real-world datasets, POST exhibits better or comparable predictive performance than MAST and other static decomposition algorithms.
Probabilistic Streaming Tensor Decomposition with Side Information
Authors: Yimin Zheng, Shandian Zhe
Date: 2019-11-27 00:00:00
Tasks: Tensor Decomposition

Tensor decomposition is an essential tool to analyze high-order interactions in multiway data. While most tensor decomposition approaches are developed for static data, many real-world applications generate tensor elements in a streaming fashion. On the
other hand, the side information, such as a variety of the features for the entities and interactions, are produced in the meantime, which can greatly relieve data sparsity and potentially help find factors of better quality. In this thesis, we develop a Bayesian streaming tensor decomposition algorithm that can incrementally update the latent factors with streaming tensor elements in an arbitrary order, and meanwhile integrate the side information to enhance the factor quality. Experiments on four real-world datasets show that our method can improve upon existing streaming decomposition methods that do not exploit side information, and obtain at least comparable prediction accuracy to the state-of-the-art static tensor decomposition approaches.
Graph Attention Networks for Anti-Spoofing
Authors: Hemlata Tak, Jee-weon Jung, Jose Patino, Massimiliano Todisco, Nicholas Evans
Date: 2021-04-08 00:00:00
Tasks: Graph Attention, Speaker Verification

The cues needed to detect spoofing attacks against automatic speaker verification are often located in specific spectral sub-bands or temporal segments. Previous works show the potential to learn these using either spectral or temporal self-attention mechanisms but not the relationships between neighbouring sub-bands or segments. This paper reports our use of graph attention networks (GATs) to model these relationships and to improve spoofing detection performance. GATs leverage a self-attention mechanism over graph structured data to model the data manifold and the relationships between nodes. Our graph is constructed from representations produced by a ResNet. Nodes in the graph represent information either in specific sub-bands or temporal segments. Experiments performed on the ASVspoof 2019 logical access database show that our GAT-based model with temporal attention outperforms all of our baseline single systems. Furthermore, GAT-based systems are complementary to a set of existing systems. The fusion of GAT-based models with more conventional countermeasures delivers a 47% relative improvement in performance compared to the best performing single GAT system.
Trade-offs in Decentralized Multi-Antenna Architectures: The WAX Decomposition
Authors: Juan Vidal Alegría, Fredrik Rusek, Ove Edfors
Date: 2020-12-01 00:00:00
Tasks: CPU

Current research on multi-antenna architectures is trending towards increasing the amount of antennas in the base stations (BSs) so as to increase the spectral efficiency. As a result, the interconnection bandwidth and computational complexity required to process the data using centralized architectures is becoming prohibitively high. Decentralized architectures can reduce these requirements by pre-processing the data before it arrives at a central processing unit (CPU). However, performing decentralized processing introduces also cost in complexity/interconnection bandwidth at the antenna end which is in general being ignored. This paper aims at studying the interplay between level of decentralization and the associated complexity/interconnection bandwidth requirement at the antenna end. To do so, we propose a general framework for centralized/decentralized architectures that can explore said interplay by adjusting some system parameters, namely the number of connections to the CPU (level of decentralization), and the number of multiplications/outputs per antenna (complexity/interconnection bandwidth). We define a novel matrix decomposition, the WAX decomposition, that allows information-lossless processing within our proposed framework, and we use it to obtain the operational limits of the interplay under study. We also look into some of the limitations of the WAX decomposition.
Herding an Adversarial Swarm in Three-dimensional Spaces
Authors: Weifan Zhang, Vishnu S. Chipade, Dimitra Panagou
Date: 2020-07-08 00:00:00

This paper presents a defense approach to safeguard a protected area against an attack by a swarm of adversarial agents in three-dimensional (3D) space. We extend our 2D `StringNet Herding' approach, in which a closed formation of string-barriers is established around the adversarial swarm to confine their motion and herd them to a safe area, to 3D spaces by introducing 3D-StringNet. 3D-StringNet is a closed 3D formation of triangular net-like barriers. We provide a systematic approach to generate three types of 3D formations that are used in the 3D herding process and modifications to the finite-time convergent control laws developed in \cite{chipade2020swarmherding} that are required for a 3D environment. Furthermore, for given initial positions of the defenders, we provide conditions on the initial positions of the attackers for which the defenders are guaranteed to gather as a specified formation at a position on the shortest path of the attackers to the protected area before attackers reach there.
Electromagnetic Radiation Reduction in 5G Networks and Beyond using Thermal Radiation Mode
Authors: Haneet Kour, Rakesh Kumar Jha
Date: 2021-04-08 00:00:00

With the massive increase in the popularity of smartphones and mobile data applications demanding bandwidth requiring data rates of the order of Gigabits per second, exploration of untapped frequency spectrum such as millimeter-wave has begun. Along with providing seamless connectivity and catering to achieving high Quality of Service and Quality of Experience, investigations are ongoing to enhance our knowledge about biological safety at high frequencies. There is a need to ensure safety and reliability for the exposed public and updating the government policies regarding safety standards and regulations. This article is consecrated to provide an insight into health effects pertaining to millimeter frequencies, addressing aspects such as thermal heating in the body tissues with temperature rise, specific absorption rate, power density. As a solution, a proposal has been given for Electromagnetic radiation reduction for the mobile communication system in the form of a proposed mode that is, Thermal Radiation mode endorsing its safe use, promoting Green WCN along with increased energy efficiency and reduced complexity for the future generations to come. The proposal also validates reduced power density, Specific Absorption Rate, and temperature elevation produced in the human tissue when compared to other models in the form of simulation results obtained. It can increase the safety and reliability of 5G and beyond i.e. 6G networks in the future.
Leaderless collective motions in affine formation control
Authors: Hector Garcia de Marina, Juan Jimenez Castellanos, Weijia Yao
Date: 2021-04-07 00:00:00

This paper proposes a novel distributed technique to induce collective motions in affine formation control. Instead of the traditional leader-follower strategy, we propose modifying the original weights that build the Laplacian matrix so that a designed steady-state motion of the desired shape emerges from the agents' local interactions. The proposed technique allows a rich collection of collective motions such as rotations around the centroid, translations, scalings, and shearings of a reference shape. These motions can be applied in useful collective behaviors such as \emph{shaped} consensus (the rendezvous with a particular shape), escorting one of the team agents, or area coverage. We prove the global stability and effectiveness of our proposed technique rigorously, and we provide some illustrative numerical simulations.
Phoneme-based Distribution Regularization for Speech Enhancement
Authors: Yajing Liu, Xiulian Peng, Zhiwei Xiong, Yan Lu
Date: 2021-04-08 00:00:00
Tasks: Speech Enhancement

Existing speech enhancement methods mainly separate speech from noises at the signal level or in the time-frequency domain. They seldom pay attention to the semantic information of a corrupted signal. In this paper, we aim to bridge this gap by extracting phoneme identities to help speech enhancement. Specifically, we propose a phoneme-based distribution regularization (PbDr) for speech enhancement, which incorporates frame-wise phoneme information into speech enhancement network in a conditional manner. As different phonemes always lead to different feature distributions in frequency, we propose to learn a parameter pair, i.e. scale and bias, through a phoneme classification vector to modulate the speech enhancement network. The modulation parameter pair includes not only frame-wise but also frequency-wise conditions, which effectively map features to phoneme-related distributions. In this way, we explicitly regularize speech enhancement features by recognition vectors. Experiments on public datasets demonstrate that the proposed PbDr module can not only boost the perceptual quality for speech enhancement but also the recognition accuracy of an ASR system on the enhanced speech. This PbDr module could be readily incorporated into other speech enhancement networks as well.
Active Power Control of Waked Wind Farms: Compensation of Turbine Saturation and Thrust Force Balance
Authors: Jean Gonzalez Silva, Bart Doekemeijer, Riccardo Ferrari, Jan-Willem van Wingerden
Date: 2021-04-08 00:00:00

Active power control regulates the total power generated by wind farms with the power consumed on the electricity grid. Due to wake effects, the available power is reduced and turbulence is increased at downstream wind turbines. Such effects lead to a design challenge for wind farm control, where the delicate balance between supply and demand should be maintained, while considering the load balancing in the wind turbine structures. We propose a control architecture based on simple feedback controllers that adjusts the demanded power set points of individual wind turbines to compensate for turbine saturations and to balance thrust forces. For compensation purposes, the dynamics of power tracking in the wind turbines is approximated as a pure time-delay process, and the thrust force balance design is based on an identified linear model of the turbines. In this paper, we show that the proposed control architecture allows the generated power to track its reference even when turbines saturate, while the thrust forces are balanced. In addition, the result shows that the proposed power dispatch strategy, which considers thrust force balance, also avoids turbine saturation, being thus beneficial for energy production. The effectiveness of the proposed feedback controller is demonstrated using high-fidelity computational fluid dynamics simulations of a small wind farm.
A Differential-Cascaded Approach for Adaptive Control of Robot Manipulators
Authors: Hanlei Wang
Date: 2021-04-08 00:00:00

This paper investigates adaptive control of nonlinear robot manipulators with parametric uncertainty. Motivated by generating closed-loop robot dynamics with enhanced transmission capability of a reference torque and with connection to linear dynamics, we develop a new adaptive approach by exploiting forwardstepping design and inertia invariance, yielding differential-cascaded closed-loop dynamics. With this approach, we propose a new class of adaptive controllers for nonlinear robot manipulators. Our particular study concerning adaptive control of robots exhibits a design methodology towards establishing the connection between adaptive control of highly nonlinear uncertain systems (e.g., with a variable inertia matrix) and linear dynamics (typically with the same or increased order), which is a long-standing intractable issue in the literature.
Detection of Cyber-Attacks in Collaborative Intersection Control
Authors: Twan Keijzer, Fabian Jarmolowitz, Riccardo M. G. Ferrari
Date: 2021-04-08 00:00:00
Tasks: Autonomous Vehicles

Road intersections are widely recognized as a lead cause for accidents and traffic delays. In a future scenario with a significant adoption of Cooperative Autonomous Vehicles, solutions based on fully automatic, signage-less Intersection Control would become viable. Such a solution, however, requires communication between vehicles and, possibly, the infrastructure over wireless networks. This increases the attack surface available to a malicious actor, which could lead to dangerous situations. In this paper, we address the safety of Intersection Control algorithms, and design a Sliding-Mode-Observer based solution capable of detecting and estimating false data injection attacks affecting vehicles' communication. With respect to previous literature, a novel detection logic with improved detection performances is presented. Simulation results are provided to show the effectiveness of the proposed approach.
Detection of Network and Sensor Cyber-Attacks in Platoons of Cooperative Autonomous Vehicles: a Sliding-Mode Observer Approach
Authors: Twan Keijzer, Riccardo M. G. Ferrari
Date: 2021-04-08 00:00:00
Tasks: Autonomous Vehicles, Cyber Attack Detection

Platoons of autonomous vehicles are being investigated as a way to increase road capacity and fuel efficiency. Cooperative Adaptive Cruise Control (CACC) is an approach to achieve such platoons, in which vehicles collaborate using wireless communication. While this collaboration improves performance, it also makes the vehicles vulnerable to cyber-attacks. In this paper the performance of a sliding mode observer (SMO) based approach to cyber-attack detection is analysed, considering simultaneous attacks on the communication and local sensors. To this end, the considered cyber-attacks are divided into three classes for which relevant theoretical properties are proven. Furthermore, the harm that attacks within each of these classes can do to the system while avoiding detection is analysed based on simulation examples.
Design and assessment of an eco--driving PMP algorithm for optimal deceleration and gear shifting in trucks
Authors: B. Wingelaar, G. R. Gonçalves da Silva, M. Lazar, Y. Chen, J. T. B. A. Kessels
Date: 2021-04-08 00:00:00

In this paper, an eco--driving Pontryagin maximum principle (PMP) algorithm is designed for optimal deceleration and gear shifting in trucks based on switching among a finite set of driving modes. The PMP algorithm is implemented and assessed in the IPG TruckMaker traffic simulator as an eco--driving assistance system (EDAS). The developed EDAS strategy reduces fuel consumption with an optimized velocity profile and, in practice, allows contextual feedback incorporation from the driver for safety. Furthermore, the optimization over driving modes is computationally inexpensive, allowing the methodology to be used online, in real--time. Simulation results show that significant fuel savings can be achieved proportional to the number of velocity events and the difference between current velocity and final desired velocity for each event.
A Topology-Switching Coalitional Control and Observation Scheme with Stability Guarantees
Authors: Paula Chanfreut, Twan Keijzer, Riccardo M. G. Ferrari, Jose Maria Maestre
Date: 2021-04-08 00:00:00

In this paper a coalitional control and observation scheme is presented in which the coalitions are changed online by enabling and disabling communication links. Transitions between coalitions are made to best balance overall system performance and communication costs. Linear Matrix Inequalities are used to design the controller and observer, guaranteeing stability of the switching system. Simulation results for vehicle platoon control are presented to illustrate the proposed method.
(Integral-)ISS of switched and time-varying impulsive systems based on global state weak linearization
Authors: José L. Mancilla-Aguilar, Hernan Haimovich
Date: 2021-04-08 00:00:00
Tasks: Form

It is shown that impulsive systems of nonlinear, time-varying and/or switched form that allow a stable global state weak linearization are jointly input-to-state stable (ISS) under small inputs and integral ISS (iISS). The system is said to allow a global state weak linearization if its flow and jump equations can be written as a (time-varying, switched) linear part plus a (nonlinear) pertubation satisfying a bound of affine form on the state. This bound reduces to a linear form under zero input but does not force the system to be linear under zero input. The given results generalize and extend previously existing ones in many directions: (a) no (dwell-time or other) constraints are placed on the impulse-time sequence, (b) the system need not be linear under zero input, (c) existence of a (common) Lyapunov function is not required, (d) the perturbation bound need not be linear on the input.
A Centralized Optimization Approach for Bidirectional PEV Impacts Analysis in a Commercial Building-Integrated Microgrid
Authors: Jubair Yusuf, A S M Jahid Hasan, Luis Fernando Enriquez-Contreras, Sadrul Ula
Date: 2021-04-08 00:00:00

Building sector is the largest energy user in the United States. Conventional building energy studies mostly involve Heating, Ventilation, and Air Conditioning (HVAC), and lighting energy consumptions. Recent additions of solar Photovoltaics (PV) along with other Distributed Energy Resources (DER), particularly Plug-in Electric Vehicles (PEV) have added a new dimension to this problem and made it more complex. This paper presents an avant-garde framework for selecting the best charging/discharging level of PEV for a commercial building-integrated microgrid. A typical commercial building is used as a microgrid testbed incorporating all the DERs presented in a smart building. A Mixed Integer Linear Programming (MILP) problem is formulated to optimize the energy and demand cost associated with this building operation. The cost function is solved in conjunction with real data and modified to assess the bidirectional PEV impacts on the flexible building loads that are contributing factors in making energy usage decisions. Finally, the impacts of optimized DERs are investigated on a Distribution System (DS) to show the necessity of a holistic approach for selecting the suitable PEV strategies. The results show that bidirectional fast PEV activities can provide higher cost reduction and less voltage deviation in comparison to slow PEV activities.
Fisher Identifiability Analysis of Longitudinal Vehicle Dynamics
Authors: Aaron Kandel, Mohamed Wahba, Hosam K. Fathy
Date: 2020-11-03 00:00:00
Tasks: parameter estimation

This paper investigates the theoretical Cramer-Rao bounds on estimation accuracy of longitudinal vehicle dynamics parameters. This analysis is motivated by the value of parameter estimation in various applications, including chassis model validation and active safety. Relevant literature addresses this demand through algorithms capable of estimating chassis parameters for diverse conditions. While the implementation of such algorithms has been studied, the question of fundamental limits on their accuracy remains largely unexplored. We address this question by presenting two contributions. First, this paper presents theoretical findings which reveal the prevailing effects underpinning vehicle chassis parameter identifiability. We then validate these findings with data from on-road experiments. Our results demonstrate, among a variety of effects, the strong relevance of road grade variability in determining parameter identifiability from a drive cycle. These findings can motivate improved experimental designs in the future.
Value of information in networked control systems subject to delay
Authors: Siyi Wang, Qingchen Liu, Precious Ugo Abara, John S. Baras, Sandra Hirche
Date: 2021-04-07 00:00:00
Tasks: Scheduling

In this paper, we study the trade-off between the transmission cost and the control performance of the multi-loop networked control system subject to network-induced delay. Within the linear-quadratic-Gaussian (LQG) framework, the joint design of control policy and networking strategy is decomposed into separation optimization problems. Based on the trade-off analysis, a scalable, delay-dependent Value-of-Information (VoI) based scheduling policy is constructed to quantify the value of transmitting the data packet, and enables the decision-makers embedded in subsystems to determine the transmission policy. The proposed scalable VoI inherits the task criticality of the previous VoI metric meanwhile is sensitive to the system parameters such as information freshness and network delays. The VoI-based scheduling policy is proved to outperform the periodical triggering policy and existing Age-of-Information (AoI) based policy for network control system under transmission delay. The effectiveness of the constructed VoI with arbitrary network delay is validated through numerical simulations.
Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics
Authors: Tae Min Hong, Benjamin Carlson, Brandon Eubanks, Stephen Racz, Stephen Roche, Joerg Stelzer, Daniel Stumpp
Date: 2021-04-07 00:00:00
Tasks: BIG-bench Machine Learning, Binary Classification, Classification

We present a novel implementation of classification using the machine learning / artificial intelligence method called boosted decision trees (BDT) on field programmable gate arrays (FPGA). The firmware implementation of binary classification requiring 100 training trees with a maximum depth of 4 using four input variables gives a latency value of about 10 ns, independent of the clock speed from 100 to 320 MHz in our setup. The low timing values are achieved by restructuring the BDT layout and reconfiguring its parameters. The FPGA resource utilization is also kept low at a range from 0.01% to 0.2% in our setup. A software package called fwXmachina achieves this implementation. Our intended user is an expert of custom electronics-based trigger systems in high energy physics experiments or anyone that needs decisions at the lowest latency values for real-time event classification. Two problems from high energy physics are considered, in the separation of electrons vs. photons and in the selection of vector boson fusion-produced Higgs bosons vs. the rejection of the multijet processes.