pwc-archive/papers-with-abstracts huggingface dataset:
THz Band Channel Measurements and Statistical Modeling for Urban D2D Environments
Authors: Naveed A. Abbasi, Jorge Gomez-Ponce, Revanth Kondaveti, Shahid M. Shaikbepari, Shreyas Rao, Shadi Abu-Surra, Gary Xu, Charlie Zhang, Andreas F. Molisch
Date: 2021-09-28 00:00:00

THz band is envisioned to be used in 6G systems to meet the ever-increasing demand for data rate. However, before an eventual system design and deployment can proceed, detailed channel sounding measurements are required to understand key channel characteristics. In this paper, we present a first extensive set of channel measurements for urban outdoor environments that are ultra-wideband (1 GHz 3dB bandwidth), and double-directional where both the transmitter and receiver are at the same height. In all, we present measurements at 38 Tx/Rx location pairs, consisting of a total of nearly 50,000 impulse responses, at both line-of-sight (LoS) and non-line-of-sight (NLoS) cases in the 1-100 m range. We provide modeling for path loss, shadowing, delay spread, angular spread and multipath component (MPC) power distribution. We find, among other things, that outdoor communication over tens of meters is feasible in this frequency range even in NLoS scenarios, that omni-directional delay spreads of up to 100 ns, and directional delay spreads of up to 10 ns are observed, while angular spreads are also quite significant, and a surprisingly large number of MPCs are observed for 1 GHz bandwidth and 13 degree beamwidth. These results constitute an important first step towards better understanding the wireless channel in the THz band.
Compositional Abstractions of Interconnected Discrete-Time Switched Systems
Authors: Abdalla Swikir, Majid Zamani
Date: 2021-09-28 00:00:00
Tasks: Relation

In this paper, we introduce a compositional method for the construction of finite abstractions of interconnected discrete-time switched systems. Particularly, we use a notion of so-called alternating simulation function as a relation between each switched subsystem and its finite abstraction. Based on some small-gain type conditions, we use those alternating simulation functions to construct compositionally an overall alternating simulation function as a relation between an interconnection of finite abstractions and that of switched subsystems. This overall alternating simulation function allows one to quantify the mismatch between the output behavior of the interconnection of switched subsystems and that of their finite abstractions. Additionally, we provide an approach to construct finite abstractions together with their corresponding alternating simulation functions for discrete-time switched subsystems under standard assumptions ensuring incremental input-to-state stability of a switched subsystem. Finally, we apply our results to a model of road traffic by constructing compositionally a finite abstraction of the network containing $50$ cells of $1000$ meters each. We use the constructed finite abstractions as substitutes to design controllers compositionally keeping the density of traffic lower than $30$ vehicles per cell.
Safety-Critical Control Synthesis for Unknown Sampled-Data Systems via Control Barrier Functions
Authors: Luyao Niu, Hongchao Zhang, Andrew Clark
Date: 2021-09-28 00:00:00

As the complexity of control systems increases, safety becomes an increasingly important property since safety violations can damage the plant and put the system operator in danger. When the system dynamics are unknown, safety-critical synthesis becomes more challenging. Additionally, modern systems are controlled digitally and hence behave as sampled-data systems, i.e., the system dynamics evolve continuously while the control input is applied at discrete time steps. In this paper, we study the problem of control synthesis for safety-critical sampled-data systems with unknown dynamics. We overcome the challenges introduced by sampled-data implementation and unknown dynamics by constructing a set of control barrier function (CBF)-based constraints. By satisfying the constructed CBF constraint at each sampling time, we guarantee the unknown sampled-data system is safe for all time. We formulate a non-convex program to solve for the control signal at each sampling time. We decompose the non-convex program into two convex sub-problems. We illustrate the proposed approach using a numerical case study.
LSTM-based approach to detect cyber attacks on market-based congestion management methods
Authors: Omniyah Gul M Khan, Amr Youssef, Ehab El-Saadany, Magdy Salama
Date: 2021-09-27 00:00:00
Tasks: Management

Market-based congestion management methods adopt Demand Side Management (DSM) techniques to alleviate congestion in the day-ahead market. Reliance of these methods on the communication layer makes it prone to cyber attacks affecting the security, reliability, and economic operation of the distribution network. In this paper, we focus on Load Altering Attacks that would compromise the operation of market-based congestion management methods. A detection technique is proposed using Long Short-term Memory (LSTM) Recurrent Neural Networks (RNN). IEEE 33 bus system is used as a case study to demonstrate the effectiveness of the proposed technique. An accuracy of 97% was obtained proving the capability of using LSTM-RNN to detect a load altering cyber attack compromising aggregators in the network.
Nonlinear modeling and feedback control of boom barrier automation
Authors: Daniel Cunico, Angelo Cenedese, Luca Zaccarian, Mauro Borgo
Date: 2021-09-27 00:00:00

We address modeling and control of a gate access automation system. A model of the mechatronic system is derived and identified. Then an approximate explicit feedback linearization scheme is proposed, which ensures almost linear response between the external input and the delivered torque. A nonlinear optimization problem is solved offline to generate a feasible trajectory associated with a feedforward action and a low level feedback controller is designed to track it. The feedback gains can be conveniently tuned by solving a set of convex linear matrix inequalities, performing a multi-objective trade-off between disturbances attenuation and closed-loop performance. Finally, the proposed control strategy is tested on the real system and experimental results show that it can effectively meet the requirements in terms of robustness, load disturbance rejection and tracking performance.
Mechanical Normal Form of First Order State-Space Systems
Authors: Mayet Johannes, Kammermeier Benjamin
Date: 2021-09-23 00:00:00
Tasks: Form

In this work a state transformation is presented that transforms a given state-space system to a normal form related to mechanical systems. The underlying state-space system must meet certain requirements such that a transformation exist. If the requirements are satisfied one obtains second order differential equations which allow the application of customized and specialized algorithms.
IRMAC: Interpretable Refined Motifs in Binary Classification for Smart Grid Applications
Authors: Rui Yuan, S. Ali Pourmousavi, Wen L. Soong, Giang Nguyen, Jon A. R. Liisberg
Date: 2021-09-23 00:00:00
Tasks: Binary Classification, Classification, Dimensionality Reduction, Time Series, Time Series Analysis

Modern power systems are experiencing the challenge of high uncertainty with the increasing penetration of renewable energy resources and the electrification of heating systems. In this paradigm shift, understanding electricity users' demand is of utmost value to retailers, aggregators, and policymakers. However, behind-the-meter (BTM) equipment and appliances at the household level are unknown to the other stakeholders mainly due to privacy concerns and tight regulations. In this paper, we seek to identify residential consumers based on their BTM equipment, mainly rooftop photovoltaic (PV) systems and electric heating, using imported/purchased energy data from utility meters. To solve this problem with an interpretable, fast, secure, and maintainable solution, we propose an integrated method called Interpretable Refined Motifs And binary Classification (IRMAC). The proposed method comprises a novel shape-based pattern extraction technique, called Refined Motif (RM) discovery, and a single-neuron classifier. The first part extracts a sub-pattern from the long time series considering the frequency of occurrences, average dissimilarity, and time dynamics while emphasising specific times with annotated distances. The second part identifies users' types with linear complexity while preserving the transparency of the algorithms. With the real data from Australia and Denmark, the proposed method is tested and verified in identifying PV owners and electrical heating system users.
Diversity of growth rates maximizes phytoplankton productivity in an eddying ocean
Authors: Mara Freilich, Glenn Flierl, Amala Mahadevan
Date: 2021-09-28 00:00:00
Tasks: Diversity

In the subtropical gyres, phytoplankton rely on eddies for transporting nutrients from depth to the euphotic zone. But, what controls the rate of nutrient supply for new production? We show that vertical nutrient flux depends both on the vertical motion within the eddying flow and varies nonlinearly with the growth rate of the phytoplankton itself. Flux is maximized when the growth rate matches the inverse of the decorrelation timescale for vertical motion. Using a three-dimensional ocean model and a linear nutrient uptake model, we find that phytoplankton productivity is maximized for a growth rate of 1/3 day$^{-1}$, which corresponds to the timescale of submesoscale dynamics. Variability in the frequency of vertical motion across different physical features of the flow favors phytoplankton production with different growth rates. Such a growth-transport feedback can generate diversity in the phytoplankton community structure at submesoscales and higher net productivity in the presence of community diversity.
No-Regret Forecasting with Egalitarian Committees
Authors: Jiun-Hua Su
Date: 2021-09-28 00:00:00
Tasks: regression

The forecast combination puzzle is often found in literature: The equal-weight scheme tends to outperform sophisticated methods of combining individual forecasts. Exploiting this finding, we propose a hedge egalitarian committees algorithm (HECA), which can be implemented via mixed integer quadratic programming. Specifically, egalitarian committees are formed by the ridge regression with shrinkage toward equal weights; subsequently, the forecasts provided by these committees are averaged by the hedge algorithm. We establish the no-regret property of HECA. Using data collected from the ECB Survey of Professional Forecasters, we find the superiority of HECA relative to the equal-weight scheme during the COVID-19 recession.
Gaussian and Student's $t$ mixture vector autoregressive model with application to the effects of the Euro area monetary policy shock
Authors: Savi Virolainen
Date: 2021-09-28 00:00:00

A new mixture vector autoregressive model based on Gaussian and Student's $t$ distributions is introduced. As its mixture components, our model incorporates conditionally homoskedastic linear Gaussian vector autoregressions and conditionally heteroskedastic linear Student's $t$ vector autoregressions. For a $p$th order model, the mixing weights depend on the full distribution of the preceding $p$ observations, which leads to attractive practical and theoretical properties such as ergodicity and full knowledge of the stationary distribution of $p+1$ consecutive observations. A structural version of the model with statistically identified shocks is also proposed. The empirical application studies the effects of the Euro area monetary policy shock. We fit a two-regime model to the data and find the effects, particularly on inflation, stronger in the regime that mainly prevails before the Financial crisis than in the regime that mainly dominates after it. The introduced methods are implemented in the accompanying R package gmvarkit.
bqror: An R package for Bayesian Quantile Regression in Ordinal Models
Authors: Prajual Maheshwari, Mohammad Arshad Rahman
Date: 2021-09-28 00:00:00
Tasks: quantile regression, regression

This article describes an R package bqror that estimates Bayesian quantile regression for ordinal models introduced in Rahman (2016). The paper classifies ordinal models into two types and offers computationally efficient, yet simple, Markov chain Monte Carlo (MCMC) algorithms for estimating ordinal quantile regression. The generic ordinal model with 3 or more outcomes (labeled ORI model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings algorithm. Whereas an ordinal model with exactly 3 outcomes (labeled ORII model) is estimated using Gibbs sampling only. In line with the Bayesian literature, we suggest using marginal likelihood for comparing alternative quantile regression models and explain how to compute the same. The models and their estimation procedures are illustrated via multiple simulation studies and implemented in two applications. The article also describes several other functions contained within the bqror package, which are necessary for estimation, inference, and assessing model fit.
MSR-NV: Neural Vocoder Using Multiple Sampling Rates
Authors: Kentaro Mitsui, Kei Sawada
Date: 2021-09-28 00:00:00

The development of neural vocoders (NVs) has resulted in the high-quality and fast generation of waveforms. However, conventional NVs target a single sampling rate and require re-training when applied to different sampling rates. A suitable sampling rate varies from application to application due to the trade-off between speech quality and generation speed. In this study, we propose a method to handle multiple sampling rates in a single NV, called the MSR-NV. By generating waveforms step-by-step starting from a low sampling rate, MSR-NV can efficiently learn the characteristics of each frequency band and synthesize high-quality speech at multiple sampling rates. It can be regarded as an extension of the previously proposed NVs, and in this study, we extend the structure of Parallel WaveGAN (PWG). Experimental evaluation results demonstrate that the proposed method achieves remarkably higher subjective quality than the original PWG trained separately at 16, 24, and 48 kHz, without increasing the inference time. We also show that MSR-NV can leverage speech with lower sampling rates to further improve the quality of the synthetic speech.
North America Bixby Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2021
Authors: Myungjong Kim, Taeyeon Ki, Aviral Anshu, Vijendra Raj Apsingekar
Date: 2021-09-28 00:00:00
Tasks: Clustering, speaker-diarization, Speaker Diarization, Speaker Recognition, Speech Separation

This paper describes the submission to the speaker diarization track of VoxCeleb Speaker Recognition Challenge 2021 done by North America Bixby Lab of Samsung Research America. Our speaker diarization system consists of four main components such as overlap speech detection and speech separation, robust speaker embedding extraction, spectral clustering with fused affinity matrix, and leakage filtering-based postprocessing. We evaluated our system on the VoxConverse dataset and the challenge evaluation set, which contain natural conversations of multiple talkers collected from YouTube. Our system obtained 4.46%, 6.39%, and 6.16% of the diarization error rate on the VoxConverse development, test, and the challenge evaluation set, respectively.
Hyperparameter selection for Discrete Mumford-Shah
Authors: Charles-Gérard Lucas, Barbara Pascal, Nelly Pustelnik, Patrice Abry
Date: 2021-09-28 00:00:00
Tasks: Contour Detection, Denoising, Image Denoising, Image Reconstruction

This work focuses on a parameter-free joint piecewise smooth image denoising and contour detection. Formulated as the minimization of a discrete Mumford-Shah functional and estimated via a theoretically grounded alternating minimization scheme, the bottleneck of such a variational approach lies in the need to fine-tune their hyperparameters, while not having access to ground truth data. To that aim, a Stein-like strategy providing optimal hyperparameters is designed, based on the minimization of an unbiased estimate of the quadratic risk. Efficient and automated minimization of the estimate of the risk crucially relies on an unbiased estimate of the gradient of the risk with respect to hyperparameters. Its practical implementation is performed using a forward differentiation of the alternating scheme minimizing the Mumford-Shah functional, requiring exact differentiation of the proximity operators involved. Intensive numerical experiments are performed on synthetic images with different geometry and noise levels, assessing the accuracy and the robustness of the proposed procedure. The resulting parameter-free piecewise-smooth estimation and contour detection procedure, not requiring prior image processing expertise nor annotated data, can then be applied to real-world images.
3N-GAN: Semi-Supervised Classification of X-Ray Images with a 3-Player Adversarial Framework
Authors: Shafin Haque, Ayaan Haque
Date: 2021-09-22 00:00:00
Tasks: Classification

The success of deep learning for medical imaging tasks, such as classification, is heavily reliant on the availability of large-scale datasets. However, acquiring datasets with large quantities of labeled data is challenging, as labeling is expensive and time-consuming. Semi-supervised learning (SSL) is a growing alternative to fully-supervised learning, but requires unlabeled samples for training. In medical imaging, many datasets lack unlabeled data entirely, so SSL can't be conventionally utilized. We propose 3N-GAN, or 3 Network Generative Adversarial Networks, to perform semi-supervised classification of medical images in fully-supervised settings. We incorporate a classifier into the adversarial relationship such that the generator trains adversarially against both the classifier and discriminator. Our preliminary results show improved classification performance and GAN generations over various algorithms. Our work can seamlessly integrate with numerous other medical imaging model architectures and SSL methods for greater performance.
The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images
Authors: Devran Ugurlu, Esther Puyol-Anton, Bram Ruijsink, Alistair Young, Ines Machado, Kerstin Hammernik, Andrew P. King, Julia A. Schnabel
Date: 2021-09-22 00:00:00
Tasks: Left Ventricle Segmentation, Right Ventricle Segmentation, Segmentation

Domain shift refers to the difference in the data distribution of two datasets, normally between the training set and the test set for machine learning algorithms. Domain shift is a serious problem for generalization of machine learning models and it is well-established that a domain shift between the training and test sets may cause a drastic drop in the model's performance. In medical imaging, there can be many sources of domain shift such as different scanners or scan protocols, different pathologies in the patient population, anatomical differences in the patient population (e.g. men vs women) etc. Therefore, in order to train models that have good generalization performance, it is important to be aware of the domain shift problem, its potential causes and to devise ways to address it. In this paper, we study the effect of domain shift on left and right ventricle blood pool segmentation in short axis cardiac MR images. Our dataset contains short axis images from 4 different MR scanners and 3 different pathology groups. The training is performed with nnUNet. The results show that scanner differences cause a greater drop in performance compared to changing the pathology group, and that the impact of domain shift is greater on right ventricle segmentation compared to left ventricle segmentation. Increasing the number of training subjects increased cross-scanner performance more than in-scanner performance at small training set sizes, but this difference in improvement decreased with larger training set sizes. Training models using data from multiple scanners improved cross-domain performance.
Deep Neural Network-aided Soft-Demapping in Optical Coherent Systems: Regression versus Classification
Authors: Pedro J. Freire, Jaroslaw E. Prilepsky, Yevhenii Osadchuk, Sergei K. Turitsyn, Vahid Aref
Date: 2021-09-28 00:00:00
Tasks: regression

We examine here what type of predictive modelling, classification, or regression, using neural networks (NN), fits better the task of soft-demapping based post-processing in coherent optical communications, where the transmission channel is nonlinear and dispersive. For the first time, we present possible drawbacks in using each type of predictive task in a machine learning context, considering the nonlinear coherent optical channel equalization/soft-demapping problem. We study two types of equalizers based on the feed-forward and recurrent NNs, for several transmission scenarios, in linear and nonlinear regimes of the optical channel. We point out that even though from the information theory perspective the cross-entropy loss (classification) is the most suitable option for our problem, the NN models based on the cross-entropy loss function can severely suffer from learning problems. The latter translates into the fact that regression-based learning is typically superior in terms of delivering higher Q-factor and achievable information rates. In short, we show by empirical evidence that loss functions based on cross-entropy may not be necessarily the most suitable option for training communication systems in practical scenarios when overfitting- and vanishing gradients-related problems come into play.
On the Topological Aspects of UAV-Assisted Post-Disaster Wireless Communication Networks
Authors: Maurilio Matracia, Mustafa A. Kishk, Mohamed-Slim Alouini
Date: 2021-09-28 00:00:00
Tasks: Management

In the context of sixth-generation (6G) networks, emergency management systems (EMSs) based on wireless communications have recently gained increasing interest. Hereby, fundamentals and open problems of post-disaster communications are discussed, especially focusing on their topological aspects. The motivation behind this choice is due to the fact that, whenever a natural or a man-made disaster occurs, there is a high chance that the terrestrial communication infrastructure is compromised, and therefore alternative networks need to be deployed efficiently in order to enable the majority of the civilians and the first responders (FRs) to communicate. In this paper, we first provide a brief review of existing aerial ad-hoc networks for post-disaster communications. Next, we shed light on some new aspects of this problem, which are related to the topology of the network supporting the impacted area. Finally, with the aid of selected simulation results, we show how the cellular infrastructure requirements for a disaster-struck region significantly depend on its location and its extension.