pwc-archive/papers-with-abstracts huggingface dataset:
Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review
Authors: Leon Witt, Mathis Heyer, Kentaroh Toyoda, Wojciech Samek, Dan Li
Date: 2022-05-07 00:00:00
Tasks: Articles, Federated Learning, Systematic Literature Review

The advent of Federated Learning (FL) has ignited a new paradigm for parallel and confidential decentralized Machine Learning (ML) with the potential of utilizing the computational power of a vast number of IoT, mobile and edge devices without data leaving the respective device, ensuring privacy by design. Yet, in order to scale this new paradigm beyond small groups of already entrusted entities towards mass adoption, the Federated Learning Framework (FLF) has to become (i) truly decentralized and (ii) participants have to be incentivized. This is the first systematic literature review analyzing holistic FLFs in the domain of both, decentralized and incentivized federated learning. 422 publications were retrieved, by querying 12 major scientific databases. Finally, 40 articles remained after a systematic review and filtering process for in-depth examination. Although having massive potential to direct the future of a more distributed and secure AI, none of the analyzed FLF is production-ready. The approaches vary heavily in terms of use-cases, system design, solved issues and thoroughness. We are the first to provide a systematic approach to classify and quantify differences between FLF, exposing limitations of current works and derive future directions for research in this novel domain.
RISCLESS: A Reinforcement Learning Strategy to Exploit Unused Cloud Resources
Authors: Sidahmed Yalles, Mohamed Handaoui, Jean-Emile Dartois, Olivier Barais, Laurent d'Orazio, Jalil Boukhobza
Date: 2022-04-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

One of the main objectives of Cloud Providers (CP) is to guarantee the Service-Level Agreement (SLA) of customers while reducing operating costs. To achieve this goal, CPs have built large-scale datacenters. This leads, however, to underutilized resources and an increase in costs. A way to improve the utilization of resources is to reclaim the unused parts and resell them at a lower price. Providing SLA guarantees to customers on reclaimed resources is a challenge due to their high volatility. Some state-of-the-art solutions consider keeping a proportion of resources free to absorb sudden variation in workloads. Others consider stable resources on top of the volatile ones to fill in for the lost resources. However, these strategies either reduce the amount of reclaimable resources or operate on less volatile ones such as Amazon Spot instance. In this paper, we proposed RISCLESS, a Reinforcement Learning strategy to exploit unused Cloud resources. Our approach consists of using a small proportion of stable on-demand resources alongside the ephemeral ones in order to guarantee customers SLA and reduce the overall costs. The approach decides when and how much stable resources to allocate in order to fulfill customers' demands. RISCLESS improved the CPs' profits by an average of 15.9% compared to state-of-the-art strategies. It also reduced the SLA violation time by an average of 36.7% while increasing the amount of used ephemeral resources by 19.5% on average
A General Framework for a Class of Quarrels: The Quarrelling Paradox Revisited
Authors: Arash Abizadeh, Adrian Vetta
Date: 2022-05-17 00:00:00

If a measure of voting power assigns greater voting power to a player because it no longer effectively cooperates with another, then the measure displays the quarrelling paradox and violates the quarrel postulate. We provide formal criteria by which to judge whether a given conception of quarrelling is (a) reasonable and (b) fit to serve as the basis for a reasonable quarrel postulate. To achieve this, we formalize a general framework distinguishing between three degrees of quarrelling (weak, strong, cataclysmic), symmetric vs. asymmetrical quarrels, and reciprocal vs. non-reciprocal quarrels, and which thereby yields twelve conceptions of quarrelling, which encompasses the two conceptions proposed by Felsenthal and Machover and by Laruelle and Valenciano, respectively. We argue that the two existing formulations of the quarrel postulate based on these conceptions are unreasonable. In contrast, we prove that the symmetric, weak conception of quarrelling identified by our framework -- whether reciprocal or not -- is fit to serve as the basis for a reasonable quarrel postulate. Furthermore, the classic Shapley-Shubik index and Penrose-Banzhaf measure both satisfy the quarrel postulate based on a symmetric weak quarrel.
Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review
Authors: Ziqing Zhu, Ze Hu, Ka Wing Chan, Siqi Bu, Bin Zhou, Shiwei Xia
Date: 2022-05-07 00:00:00
Tasks: Computational Efficiency, Decision Making, reinforcement-learning, Reinforcement Learning (RL)

The increasing penetration of renewable generations, along with the deregulation and marketization of power industry, promotes the transformation of power market operation paradigms. The optimal bidding strategy and dispatching methodology under these new paradigms are prioritized concerns for both market participants and power system operators, with obstacles of uncertain characteristics, computational efficiency, as well as requirements of hyperopic decision-making. To tackle these problems, the Reinforcement Learning (RL), as an emerging machine learning technique with advantages compared with conventional optimization tools, is playing an increasingly significant role in both academia and industry. This paper presents a comprehensive review of RL applications in deregulated power market operation including bidding and dispatching strategy optimization, based on more than 150 carefully selected literatures. For each application, apart from a paradigmatic summary of generalized methodology, in-depth discussions of applicability and obstacles while deploying RL techniques are also provided. Finally, some RL techniques that have great potentiality to be deployed in bidding and dispatching problems are recommended and discussed.
The Deployment of IRS in UAV-Empowered 6G Networks
Authors: Mobasshir Mahbub, Raed M. Shubair
Date: 2022-05-07 00:00:00

Intelligent reflecting surfaces (IRSs) with the ability to reconfigure inherent electromagnetic reflection and absorption characteristics in real-time provide unparalleled prospects to improve wireless connectivity in adverse circumstances. Unmanned aerial vehicles (UAV)-assisted wireless networks are evolved as a reliable solution to combat non-line of sight (NLoS) scenarios. Thereby, the IRS-empowered UAV-assisted cellular networks will be a significant role-player to improve the coverage and user experiences. The paper aimed to minimize the path loss and maximize the achievable data rate in IRS-UAV-assisted networks. In this context, the work analyzed path loss and achievable rate utilizing millimeter wave (mmWave) carrier considering the conventional UAV model and IRS-empowered UAV communication model. The research obtained that the IRSempowered UAV communications model can significantly minimize path loss and maximize the achievable data rate compared to the conventional UAV-assisted model.
Formal verification of an industrial UML-like model using mCRL2 (extended version)
Authors: Anna Stramaglia, Jeroen J. A. Keiren
Date: 2022-05-17 00:00:00
Tasks: Translation

Low-code development platforms are gaining popularity. Essentially, such platforms allow to shift from coding to graphical modeling, helping to improve quality and reduce development time. The Cordis SUITE is a low-code development platform that adopts the Unified Modeling Language (UML) to design complex machine-control applications. In this paper we introduce Cordis models and their semantics. To enable formal verification, we define an automatic translation of Cordis models to the process algebraic specification language mCRL2. As a proof of concept, we describe requirements of the control software of an industrial cylinder model developed by Cordis, and show how these can be verified using model checking. We show that our verification approach is effective to uncover subtle issues in the industrial model and its implementation.
Communication-Free Shepherding Navigation with Multiple Steering Agents
Authors: Aiyi Li, Masaki Ogura, Naoki Wakamiya
Date: 2022-05-17 00:00:00

Swarm guidance addresses a challenging problem considering the navigation and control of a group of passive agents. To solve this problem, shepherding offers a bio-inspired technique of navigating such group of agents by using external steering agents with appropriately designed movement law. Although most shepherding researches are mainly based on the availability of centralized instructions, these assumptions are not realistic enough to solve some emerging application problems. Therefore, this paper presents a decentralized shepherding method where each steering agent makes movements based on its own observation without any inter-agent communication. Our numerical simulations confirm the effectiveness of the proposed method by showing its high success rate and low costs in various placement patterns. These advantages particularly improve with the increase in the number of steering agents.
Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networks
Authors: Dario Rossi, Liang Zhang
Date: 2022-04-26 00:00:00
Tasks: Autonomous Driving

The tremendous achievements of Artificial Intelligence (AI) in computer vision, natural language processing, games and robotics, has extended the reach of the AI hype to other fields: in telecommunication networks, the long term vision is to let AI fully manage, and autonomously drive, all aspects of network operation. In this industry vision paper, we discuss challenges and opportunities of Autonomous Driving Network (ADN) driven by AI technologies. To understand how AI can be successfully landed in current and future networks, we start by outlining challenges that are specific to the networking domain, putting them in perspective with advances that AI has achieved in other fields. We then present a system view, clarifying how AI can be fitted in the network architecture. We finally discuss current achievements as well as future promises of AI in networks, mentioning a roadmap to avoid bumps in the road that leads to true large-scale deployment of AI technologies in networks.
Composing General Audio Representation by Fusing Multilayer Features of a Pre-trained Model
Authors: Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Noboru Harada, Kunio Kashino
Date: 2022-05-17 00:00:00

Many application studies rely on audio DNN models pre-trained on a large-scale dataset as essential feature extractors, and they extract features from the last layers. In this study, we focus on our finding that the middle layer features of existing supervised pre-trained models are more effective than the late layer features for some tasks. We propose a simple approach to compose features effective for general-purpose applications, consisting of two steps: (1) calculating feature vectors along the time frame from middle/late layer outputs, and (2) fusing them. This approach improves the utility of frequency and channel information in downstream processes, and combines the effectiveness of middle and late layer features for different tasks. As a result, the feature vectors become effective for general purposes. In the experiments using VGGish, PANNs' CNN14, and AST on nine downstream tasks, we first show that each layer output of these models serves different tasks. Then, we demonstrate that the proposed approach significantly improves their performance and brings it to a level comparable to that of the state-of-the-art. In particular, the performance of the non-semantic speech (NOSS) tasks greatly improves, especially on Speech commands V2 with VGGish of +77.1 (14.3% to 91.4%).
Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data
Authors: Alëna Aksënova, Zhehuai Chen, Chung-Cheng Chiu, Daan van Esch, Pavel Golik, Wei Han, Levi King, Bhuvana Ramabhadran, Andrew Rosenberg, Suzan Schwartz, Gary Wang
Date: 2022-05-16 00:00:00
Tasks: Accented Speech Recognition, Benchmarking, Diversity, speech-recognition, Speech Recognition

Building inclusive speech recognition systems is a crucial step towards developing technologies that speakers of all language varieties can use. Therefore, ASR systems must work for everybody independently of the way they speak. To accomplish this goal, there should be available data sets representing language varieties, and also an understanding of model configuration that is the most helpful in achieving robust understanding of all types of speech. However, there are not enough data sets for accented speech, and for the ones that are already available, more training approaches need to be explored to improve the quality of accented speech recognition. In this paper, we discuss recent progress towards developing more inclusive ASR systems, namely, the importance of building new data sets representing linguistic diversity, and exploring novel training approaches to improve performance for all users. We address recent directions within benchmarking ASR systems for accented speech, measure the effects of wav2vec 2.0 pre-training on accented speech recognition, and highlight corpora relevant for diverse ASR evaluations.
Rate-Region Characterization and Channel Estimation for Cell-Free Symbiotic Radio Communications
Authors: Zhuoyin Dai, Ruoguang Li, Jingran Xu, Yong Zeng, Shi Jin
Date: 2022-05-17 00:00:00

Cell-free massive MIMO and symbiotic radio communication have been recently proposed as the promising beyond fifth-generation (B5G) networking architecture and transmission technology, respectively. To reap the benefits of both, this paper studies cell-free symbiotic radio communication systems, where a number of cell-free access points (APs) cooperatively send primary information to a receiver, and simultaneously support the passive backscattering communication of the secondary backscatter device (BD). We first derive the achievable communication rates of the active primary user and passive secondary user under the assumption of perfect channel state information (CSI), based on which the transmit beamforming of the cellfree APs is optimized to characterize the achievable rate-region of cell-free symbiotic communication systems. Furthermore, to practically acquire the CSI of the active and passive channels, we propose an efficient channel estimation method based on two-phase uplink-training, and the achievable rate-region taking into account CSI estimation errors are further characterized. Simulation results are provided to show the effectiveness of our proposed beamforming and channel estimation methods.
A Short Introduction to the Koopman Representation of Dynamical Systems
Authors: Bassam Bamieh
Date: 2022-05-17 00:00:00

The Koopman representation is an infinite dimensional linear representation of linear or nonlinear dynamical systems. It represents the dynamics of output maps (aka observables), which are functions on the state space whose evaluation is interpreted as an output. Conceptually simple derivations and commentary on the Koopman representation are given. We emphasize an important duality between initial conditions and output maps of the original system, and those of the Koopman representation. This duality is an important consideration when this representation is used in data-driven applications such as the Dynamic Mode Decomposition (DMD) and its variants. The adjoint relation between the Koopman representation and the transfer operator of mass transport is also shown.
Flexible and curtailable resource activation in three-phase unbalanced distribution networks
Authors: Md Umar Hashmi, Arpan Koirala, Hakan Ergun, Dirk Van Hertem
Date: 2022-05-16 00:00:00

The need for flexibility and curtailable resources is crucial for ensuring the healthy operation of future distribution networks (DN). In this work, we propose a network-state driven framework that distribution system operators (DSOs) can utilize for activating flexible and curtailable resources for alleviating network voltage and thermal issues, while accounting for network voltage and current imbalances. This approach assumes the availability of dynamic network state information and uses nodal sensitivities for calculating a flexibility activation signal (FAS). The signal design is motivated by volt-Var and volt-watt inverter control, and thus bounded. The FAS also considers network voltage and current imbalances and incentivizes activation of active and reactive power flexibilities for reducing imbalance in addition to mitigating voltage and thermal imbalances in a three-phase unbalanced distribution network. The FAS design resembles optimal power flow duals, often used as locational marginal prices. The gains associated with the imbalance component of the objective function of three-phase unbalanced resource activation (TPU-RA) is performed using Pareto optimality. A numerical case study is presented showing the efficacy of the proposed framework in avoiding network issues while reducing voltage unbalance factor by more than 80\%. Further, DN's flexibility needs are quantified for location and time of day.
Machine learning and atomic layer deposition: predicting saturation times from reactor growth profiles using artificial neural networks
Authors: Angel Yanguas-Gil, Jeffrey W. Elam
Date: 2022-05-10 00:00:00

In this work we explore the application of deep neural networks to the optimization of atomic layer deposition processes based on thickness values obtained at different points of an ALD reactor. We introduce a dataset designed to train neural networks to predict saturation times based on the dose time and thickness values measured at different points of the reactor for a single experimental condition. We then explore different artificial neural network configurations, including depth (number of hidden layers) and size (number of neurons in each layers) to better understand the size and complexity that neural networks should have to achieve high predictive accuracy. The results obtained show that trained neural networks can accurately predict saturation times without requiring any prior information on the surface kinetics. This provides a viable approach to minimize the number of experiments required to optimize new ALD processes in a known reactor. However, the datasets and training procedure depend on the reactor geometry.
Deep Learning of Chaotic Systems from Partially-Observed Data
Authors: Victor Churchill, Dongbin Xiu
Date: 2022-05-12 00:00:00
Tasks: Deep Learning

Recently, a general data driven numerical framework has been developed for learning and modeling of unknown dynamical systems using fully- or partially-observed data. The method utilizes deep neural networks (DNNs) to construct a model for the flow map of the unknown system. Once an accurate DNN approximation of the flow map is constructed, it can be recursively executed to serve as an effective predictive model of the unknown system. In this paper, we apply this framework to chaotic systems, in particular the well-known Lorenz 63 and 96 systems, and critically examine the predictive performance of the approach. A distinct feature of chaotic systems is that even the smallest perturbations will lead to large (albeit bounded) deviations in the solution trajectories. This makes long-term predictions of the method, or any data driven methods, questionable, as the local model accuracy will eventually degrade and lead to large pointwise errors. Here we employ several other qualitative and quantitative measures to determine whether the chaotic dynamics have been learned. These include phase plots, histograms, autocorrelation, correlation dimension, approximate entropy, and Lyapunov exponent. Using these measures, we demonstrate that the flow map based DNN learning method is capable of accurately modeling chaotic systems, even when only a subset of the state variables are available to the DNNs. For example, for the Lorenz 96 system with 40 state variables, when data of only 3 variables are available, the method is able to learn an effective DNN model for the 3 variables and produce accurately the chaotic behavior of the system.
REMuS-GNN: A Rotation-Equivariant Model for Simulating Continuum Dynamics
Authors: Mario Lino, Stati Fotiadis, Anil A. Bharath, Chris Cantwell
Date: 2022-05-05 00:00:00
Tasks: Inductive Bias

Numerical simulation is an essential tool in many areas of science and engineering, but its performance often limits application in practice or when used to explore large parameter spaces. On the other hand, surrogate deep learning models, while accelerating simulations, often exhibit poor accuracy and ability to generalise. In order to improve these two factors, we introduce REMuS-GNN, a rotation-equivariant multi-scale model for simulating continuum dynamical systems encompassing a range of length scales. REMuS-GNN is designed to predict an output vector field from an input vector field on a physical domain discretised into an unstructured set of nodes. Equivariance to rotations of the domain is a desirable inductive bias that allows the network to learn the underlying physics more efficiently, leading to improved accuracy and generalisation compared with similar architectures that lack such symmetry. We demonstrate and evaluate this method on the incompressible flow around elliptical cylinders.
Primal-Dual UNet for Sparse View Cone Beam Computed Tomography Volume Reconstruction
Authors: Philipp Ernst, Soumick Chatterjee, Georg Rose, Andreas Nürnberger
Date: 2022-05-11 00:00:00
Tasks: CT Reconstruction

In this paper, the Primal-Dual UNet for sparse view CT reconstruction is modified to be applicable to cone beam projections and perform reconstructions of entire volumes instead of slices. Experiments show that the PSNR of the proposed method is increased by 10dB compared to the direct FDK reconstruction and almost 3dB compared to the modified original Primal-Dual Network when using only 23 projections. The presented network is not optimized wrt. memory consumption or hyperparameters but merely serves as a proof of concept and is limited to low resolution projections and volumes.
The Power of Tests for Detecting $p$-Hacking
Authors: Graham Elliott, Nikolay Kudrin, Kaspar Wüthrich
Date: 2022-05-16 00:00:00

$p$-Hacking undermines the validity of empirical studies. A flourishing empirical literature investigates the prevalence of $p$-hacking based on the distribution of $p$-values across studies. Interpreting results in this literature requires a careful understanding of the power of methods for detecting $p$-hacking. We theoretically study the implications of likely forms of $p$-hacking on the distribution of $p$-values to understand the power of tests for detecting it. Power depends crucially on the $p$-hacking strategy and the distribution of true effects. Publication bias can enhance the power for testing the joint null of no $p$-hacking and no publication bias.