pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive control of dynamic networks
Authors: Chunyu Pan, Zhao Su, Haoyu Zheng, Changsheng Zhang, Weixiong Zhang, Xizhe Zhang
Date: 2023-02-20 00:00:00

Real-world network systems are inherently dynamic, with network topologies undergoing continuous changes over time. External control signals can be applied to a designated set of nodes within a network, known as the Minimum Driver Set (MDS), to steer the network from any state to a desired one. However, the efficacy of the incumbent MDS may diminish as the network topologies evolve. Previous research has often overlooked this challenge, assuming foreknowledge of future changes in network topologies. In reality, the evolution of network topologies is typically unpredictable, rendering the control of dynamic networks exceptionally challenging. Here, we introduce adaptive control - a novel approach to dynamically construct a series of MDSs to accommodate variations in network topology without prior knowledge. We present an efficient algorithm for adaptive control that minimizes adjustments to MDSs and overall control costs throughout the control period. Extensive experimental evaluation on synthetic and real dynamic networks demonstrated our algorithm's superior performance over several state-of-the-art methods. Adaptive control is general and broadly applicable to various applications in diverse fields.
Screening Signal-Manipulating Agents via Contests
Authors: Yingkai Li, Xiaoyun Qiu
Date: 2023-02-17 00:00:00

We study the design of screening mechanisms subject to competition and manipulation. A social planner has limited resources to allocate to multiple agents using only signals manipulable through unproductive effort. We show that the welfare-maximizing mechanism takes the form of a contest and characterize the optimal contest. We apply our results to two settings: either the planner has one item or a number of items proportional to the number of agents. We show that in both settings, with sufficiently many agents, a winner-takes-all contest is never optimal. In particular, the planner always benefits from randomizing the allocation to some agents.
Generalization-baed similarity
Authors: Christian Antić
Date: 2023-02-13 00:00:00

Detecting and exploiting similarities between seemingly distant objects is without doubt an important human ability. This paper develops \textit{from the ground up} an abstract algebraic and qualitative notion of similarity based on the observation that sets of generalizations encode important properties of elements. We show that similarity defined in this way has appealing mathematical properties. As we construct our notion of similarity from first principles using only elementary concepts of universal algebra, to convince the reader of its plausibility, we show that it can model fundamental relations occurring in mathematics and be naturally embedded into first-order logic via model-theoretic types. Finally, we sketch some potential applications to theoretical computer science and artificial intelligence.
Two Cellular Resource Based Models Linking Growth and Parts Characteristics Aids the Study and Optimization of Synthetic Gene Circuits
Authors: Huijuan Wang, Maurice HT Ling, Tze Kwang Chua, Chueh Loo Poh
Date: 2023-02-19 00:00:00

A major challenge in synthetic genetic circuit development is the inter-dependency between heterologous gene expressions by circuits and host's growth rate. Increasing heterologous gene expression increases burden to the host, resulting in host growth reduction; which reduces overall heterologous protein abundance. Hence, it is difficult to design predictable genetic circuits. Here, we develop two biophysical models; one for promoter, another for RBS; to correlate heterologous gene expression and growth reduction. We model cellular resource allocation in E. coli to describe the burden, as growth reduction, caused by genetic circuits. To facilitate their uses in genetic circuit design, inputs to the model are common characteristics of biological parts [e.g. relative promoter strength (RPU) and relative ribosome binding sites strength (RRU)]. The models suggest that E. coli's growth rate reduces linearly with increasing RPU / RRU of the genetic circuits; thus, providing 2 handy models taking parts characteristics as input to estimate growth rate reduction for fine tuning genetic circuit design in silico prior to construction. Our promoter model correlates well with experiments using various genetic circuits, both single and double expression cassettes, up to a relative promoter unit of 3.7 with a 60% growth rate reduction (average R2 ~ 0.9).
Personalized speech enhancement combining band-split RNN and speaker attentive module
Authors: Xiaohuai Le, Li Chen, Chao He, Yiqing Guo, Cheng Chen, Xianjun Xia, Jing Lu
Date: 2023-02-20 00:00:00
Tasks: Speech Enhancement

Target speaker information can be utilized in speech enhancement (SE) models to more effectively extract the desired speech. Previous works introduce the speaker embedding into speech enhancement models by means of concatenation or affine transformation. In this paper, we propose a speaker attentive module to calculate the attention scores between the speaker embedding and the intermediate features, which are used to rescale the features. By merging this module in the state-of-the-art SE model, we construct the personalized SE model for ICASSP Signal Processing Grand Challenge: DNS Challenge 5 (2023). Our system achieves a final score of 0.529 on the blind test set of track1 and 0.549 on track2.
RobustDistiller: Compressing Universal Speech Representations for Enhanced Environment Robustness
Authors: Heitor R. Guimarães, Arthur Pimentel, Anderson R. Avila, Mehdi Rezagholizadeh, Boxing Chen, Tiago H. Falk
Date: 2023-02-18 00:00:00
Tasks: Knowledge Distillation, Multi-Task Learning

Self-supervised speech pre-training enables deep neural network models to capture meaningful and disentangled factors from raw waveform signals. The learned universal speech representations can then be used across numerous downstream tasks. These representations, however, are sensitive to distribution shifts caused by environmental factors, such as noise and/or room reverberation. Their large sizes, in turn, make them unfeasible for edge applications. In this work, we propose a knowledge distillation methodology termed RobustDistiller which compresses universal representations while making them more robust against environmental artifacts via a multi-task learning objective. The proposed layer-wise distillation recipe is evaluated on top of three well-established universal representations, as well as with three downstream tasks. Experimental results show the proposed methodology applied on top of the WavLM Base+ teacher model outperforming all other benchmarks across noise types and levels, as well as reverberation times. Oftentimes, the obtained results with the student model (24M parameters) achieved results inline with those of the teacher model (95M).
Multi-dimensional frequency dynamic convolution with confident mean teacher for sound event detection
Authors: Shengchang Xiao, Xueshuai Zhang, Pengyuan Zhang
Date: 2023-02-18 00:00:00
Tasks: Event Detection, Sound Event Detection

Recently, convolutional neural networks (CNNs) have been widely used in sound event detection (SED). However, traditional convolution is deficient in learning time-frequency domain representation of different sound events. To address this issue, we propose multi-dimensional frequency dynamic convolution (MFDConv), a new design that endows convolutional kernels with frequency-adaptive dynamic properties along multiple dimensions. MFDConv utilizes a novel multi-dimensional attention mechanism with a parallel strategy to learn complementary frequency-adaptive attentions, which substantially strengthen the feature extraction ability of convolutional kernels. Moreover, in order to promote the performance of mean teacher, we propose the confident mean teacher to increase the accuracy of pseudo-labels from the teacher and train the student with high confidence labels. Experimental results show that the proposed methods achieve 0.470 and 0.692 of PSDS1 and PSDS2 on the DESED real validation dataset.
Deep AHS: A Deep Learning Approach to Acoustic Howling Suppression
Authors: Hao Zhang, Meng Yu, Dong Yu
Date: 2023-02-18 00:00:00
Tasks: Deep Learning, Speech Separation

In this paper, we formulate acoustic howling suppression (AHS) as a supervised learning problem and propose a deep learning approach, called Deep AHS, to address it. Deep AHS is trained in a teacher forcing way which converts the recurrent howling suppression process into an instantaneous speech separation process to simplify the problem and accelerate the model training. The proposed method utilizes properly designed features and trains an attention based recurrent neural network (RNN) to extract the target signal from the microphone recording, thus attenuating the playback signal that may lead to howling. Different training strategies are investigated and a streaming inference method implemented in a recurrent mode used to evaluate the performance of the proposed method for real-time howling suppression. Deep AHS avoids howling detection and intrinsically prohibits howling from happening, allowing for more flexibility in the design of audio systems. Experimental results show the effectiveness of the proposed method for howling suppression under different scenarios.
Comprehensive Framework for Controlling Nonlinear Multi-Species Water Quality Dynamics
Authors: Salma M. Elsherif, Ahmad F. Taha, Ahmed A. Abokifa, Lina Sela
Date: 2023-02-20 00:00:00

Tracing disinfectant (e.g., chlorine) and contaminants evolution in water networks requires the solution of 1- D advection-reaction (AR) partial differential equations (PDEs). With the absence of analytical solutions in many scenarios, numerical solutions require high-resolution time- and spacediscretizations resulting in large model dimensions. This adds complexity to the water quality control problem. In addition, considering multi-species water quality dynamics rather than the single-species dynamics produces a more accurate description of the reaction dynamics under abnormal hazardous conditions (e.g., contamination events). Yet, these dynamics introduces nonlinear reaction formulation to the model. To that end, solving nonlinear 1-D AR PDEs in real time is critical in achieving monitoring and control goals for various scaled networks with a high computational burden. In this work, we propose a novel comprehensive framework to overcome the large-dimensionality issue by introducing different approaches for applying model order reduction (MOR) algorithms to the nonlinear system followed by applying real-time water quality regulation algorithm that is based on an advanced model to maintain desirable disinfectant levels in water networks under multi-species dynamics. The performance of this framework is validated using rigorous numerical case studies under a wide range of scenarios demonstrating the challenges associated with regulating water quality under such conditions.
Specifying Autonomous System Behaviour
Authors: Andrew Sogokon, Burak Yuksek, Gokhan Inalhan, Neeraj Suri
Date: 2023-02-20 00:00:00

Specifying the intended behaviour of autonomous systems is becoming increasingly important but is fraught with many challenges. This technical report provides an overview of existing work on specifications of autonomous systems and places a particular emphasis on formal specification, i.e. mathematically rigorous approaches to specification that require an appropriate formalism. Given the breadth of this domain, our coverage is necessarily incomplete but serves to provide a brief introduction to some of the difficulties that specifying autonomous systems entails, as well as existing approaches to addressing these difficulties currently pursued in academia and industry.
A Study on V2G impact on the Reliability of Modern Distribution Networks
Authors: Stine Fleischer Myhre, Olav Bjarte Fosso, Oddbjørn Gjerde, Poul Einar Heegaard
Date: 2023-02-20 00:00:00

The increased penetration of electrical vehicles (EVs) in the distribution system creates a power system with many small moving batteries. With better charging technologies and market structures, the opportunity of using EVs for reliability purposes increases. By utilizing EVs for Vehicle-to-Grid (V2G), they can support the distribution network during outages or other issues. In this paper, we aim to investigate the impact V2G has on the reliability of modern distribution networks. We propose three new EV-oriented reliability indices to provide an overview of the impact experienced by the EVs in the system. For the reliability study, we use our developed reliability assessment tool for distribution networks. The tool is an open available software where the example network and datasets are embedded. We have extended this reliability tool to include a method for evaluating and calculating the reliability of distribution networks containing EVs with V2G possibilities. The paper also includes a thorough sensitivity analysis to further investigate the V2G impact of different parameters.
An algorithm for DC segmentation of AC power systems to mitigate electromechanical oscillations
Authors: Mathieu Robin, Javier Renedo, Juan Carlos Gonzalez-Torres, Aurelio Garcia-Cerrada, Abdelkrim Benchaib, Pablo Garcia-Gonzalez
Date: 2023-02-20 00:00:00
Tasks: Segmentation

In the last decades, various events have shown that electromechanical oscillations are a major concern for large interconnected Alternative Current (AC) power systems. DC segmentation - a method that consist in turning large AC grids into a set of asynchronous AC clusters linked by Direct Current (DC) links - is a promising solution to mitigate this and other issues. However, no systematic segmentation procedure for a given AC power system exists so far. This paper aims at filling this gap and proposes an algorithm for DC segmentation for a given AC power system to mitigate electromechanical oscillations. In this proposal, DC segmentation is implemented with High Voltage Direct Current links based on Voltage Source Converters (VSC-HVDC). The algorithm uses small-signal stability techniques and the concept of dominant inter-area oscillation paths to stop the main inter-area mode of the power system. The algorithm will be explained using a six-generator test system and it will then be used on the Nordic 44 test system. The proposed algorithm for DC segmentation has been validated by means of non-linear time-domain simulation and small-signal stability analysis (SSSA).
Reachability Verification for Stochastic Discrete-time Dynamical Systems
Authors: Bai Xue
Date: 2023-02-20 00:00:00

In this paper we study reachability verification problems of stochastic discrete-time dynamical systems over the infinite time horizon. The reachability verification of interest in this paper is to certify specified lower and upper bounds of the reachability probability, with which the system starting from a designated initial set will enter a desired target set eventually. Existing sufficient conditions for reachability verification over the infinite time horizon are established based on the Doob's non-supermartingle inequality, which are often restrictive. Recently, a set of equations was proposed in [18], to which the solution is able to characterize the exact reachability probability. Inspired by this result, we in this paper propose sufficient conditions for reachability verification based on relaxing this equation. These sufficient conditions are shown to be weaker than the state-of-the-art ones, consequently being capable of providing more opportunities for verifying the reachability property successfully.
Dynamic Reserves for Managing Wind Power
Authors: Marija Ilic, Dongwei Zhao
Date: 2023-02-20 00:00:00

In this paper, we suggest that reserves must be computed dynamically to account for wind power volatility. We formalize the notion of dynamic reserves in support of sequential Day-Ahead-Market (DAM) and Real-Time-Market (RTM) clearing and discuss several ways of accounting for wind power uncertainties. Three different wind integration protocols are considered and their impact on stakeholders, and, notably, on the need for dynamic reserves is analyzed. An optimal strategy is proposed which requires the least reserve as long as the bids are truthful and implementable with high confidence. This can be checked by the existing market monitor and/or by a separate risk buro. Concepts are illustrated in part using publicly available information about NYISO.
Remote Identification Trajectory Coverage in Urban Air Mobility Applications
Authors: Hejun Huang, Billy Mazotti, Joseph Kim, Max Z. Li
Date: 2023-02-19 00:00:00

As Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) continue to mature, a safety-critical system that will need to be implemented in tandem is Remote Identification (Remote ID) for uncrewed aircraft systems (UAS). To ensure successful and efficient deployment (e.g., maximal surveillance of UAS trajectories), as well as to better understand secondary impacts (e.g., consumer privacy risks in collecting real-time UAS trajectory information), the coverage of broadcast-receive Remote ID architectures needs to be characterized. Motivated by this need, we examine theoretical and empirical trajectory coverage of several common Remote ID technologies (e.g., Bluetooth, Wi-Fi) deployed for urban package delivery missions, a commonly-cited use case for UAM and AAM. We derive methods to explicitly compute expected coverage proportions under idealized geometries, as well as conduct case studies with realistic city geographies and UAS path planning algorithms. An example of results include approximate magnitudes of Remote ID receivers needed (approximately 500-5000 receivers needed to achieve 50-95\% coverage for Bluetooth Legacy, and approximately 10-40 receivers needed for the same coverage range for Wi-Fi NAN/Beacon, assuming a cruise altitude of 200 feet) to achieve specific trajectory coverage proportions for San Francisco, California. Our analyses, combined with complementary works related to Remote ID bandwidth and deployment topologies, can help guide municipal authorities and AAM stakeholders in future Remote ID system deployments and upkeep.
An Addendum to the Problem of Zero-Sum LQ Stochastic Mean-Field Dynamic Games\\ (Extended version)
Authors: Samir Aberkane, Vasile Dragan
Date: 2023-02-19 00:00:00

In this paper, we first address a linear quadratic mean-field game problem with a leader-follower structure. By adopting a Riccati-type approach, we show how one can obtain a state-feedback representation of the pairs of strategies which achieve an open-loop Stackelberg equilibrium in terms of the global solutions of a system of coupled matrix differential Riccati-type equations. In the second part of this paper, we obtain necessary and sufficient conditions for the solvability of the involved coupled generalized Riccati equations.
Solving Differential-Algebraic Equations in Power System Dynamic Analysis with Quantum Computing
Authors: Huynh Trung Thanh Tran, Hieu T. Nguyen, Long T. Vu, Samuel T. Ojetola
Date: 2023-02-19 00:00:00

Power system dynamics are generally modeled by high dimensional nonlinear differential-algebraic equations (DAEs) given a large number of components forming the network. These DAEs' complexity can grow exponentially due to the increasing penetration of distributed energy resources, whereas their computation time becomes sensitive due to the increasing interconnection of the power grid with other energy systems. This paper demonstrates the use of quantum computing algorithms to solve DAEs for power system dynamic analysis. We leverage a symbolic programming framework to equivalently convert the power system's DAEs into ordinary differential equations (ODEs) using index reduction methods and then encode their data into qubits using amplitude encoding. The system nonlinearity is captured by Hamiltonian simulation with truncated Taylor expansion so that state variables can be updated by a quantum linear equation solver. Our results show that quantum computing can solve the power system's DAEs accurately with a computational complexity polynomial in the logarithm of the system dimension. We also illustrate the use of recent advanced tools in scientific machine learning for implementing complex computing concepts, i.e. Taylor expansion, DAEs/ODEs transformation, and quantum computing solver with abstract representation for power engineering applications.
Occupant's Behavior and Emotion Based Indoor Environment's Illumination Regulation
Authors: Shreya Das
Date: 2023-02-19 00:00:00
Tasks: Multi-class Classification

This paper presents an efficient approach for building occupancy modeling to reduce energy consumption. In this work, a novel approach to occupancy modeling based on the posture and comfort level of the occupant is developed, and subsequently, we report a new and efficient framework for detecting posture and emotion from skeleton joints and face points data respectively obtained from the Kinect sensor. The proposed approach is tested in terms of accuracy, region of convergence, and confusion matrix using several machine learning techniques. Out of all the techniques, random forest classifier gave the maximum blind test accuracy for multi-class classification of posture detection. Deep learning is used for emotion detection using several optimizers out of which Adadelta gave the maximum blind test accuracy for multi-class classification. Along with the Kinect sensor, several other sensors such as the magnetic door sensor, pyroelectric sensors, and illumination sensors are connected through a wireless network using Raspberry Pi Zero W. Thus creating an unmanned technique for illumination regulation.
UAV-Aided Post-Disaster Cellular Networks: A Novel Stochastic Geometry Approach
Authors: Maurilio Matracia, Mustafa A. Kishk, Mohamed-Slim Alouini
Date: 2023-02-19 00:00:00
Tasks: Management

Motivated by the need for ubiquitous and reliable communications in post-disaster emergency management systems (EMSs), we hereby present a novel and efficient stochastic geometry (SG) framework. This mathematical model is specifically designed to evaluate the quality of service (QoS) experienced by a typical ground user equipment (UE) residing either inside or outside a generic area affected by a calamity. In particular, we model the functioning terrestrial base stations (TBSs) as an inhomogeneous Poisson point process (IPPP), and assume that a given number of uniformly distributed unmanned aerial vehicles (UAVs) equipped with cellular transceivers is deployed in order to compensate for the damage suffered by some of the existing TBSs. The downlink (DL) coverage probability is then derived based on the maximum average received power association policy and the assumption of Nakagami-m fading conditions for all wireless links. The proposed numerical results show insightful trends in terms of coverage probability, depending on: distance of the UE from the disaster epicenter, disaster radius, quality of resilience (QoR) of the terrestrial network, and fleet of deployed ad-hoc aerial base stations (ABSs). The aim of this paper is therefore to prove the effectiveness of vertical heterogeneous networks (VHetNets) in emergency scenarios, which can both stimulate the involved authorities for their implementation and inspire researchers to further investigate related problems.