pwc-archive/papers-with-abstracts huggingface dataset:
The Power of Word-Frequency Based Alignment-Free Functions: a Comprehensive Large-scale Experimental Analysis -- Version 3
Authors: Giuseppe Cattaneo, Umberto Ferraro Petrillo, Raffaele Giancarlo, Francesco Palini, Chiara Romualdi
Date: 2021-06-27 00:00:00

Motivation: Alignment-free (AF) distance/similarity functions are a key tool for sequence analysis. Experimental studies on real datasets abound and, to some extent, there are also studies regarding their control of false positive rate (Type I error). However, assessment of their power, i.e., their ability to identify true similarity, has been limited to some members of the D2 family by experimental studies on short sequences, not adequate for current applications, where sequence lengths may vary considerably. Such a State of the Art is methodologically problematic, since information regarding a key feature such as power is either missing or limited. Results: By concentrating on a representative set of word-frequency based AF functions, we perform the first coherent and uniform evaluation of the power, involving also Type I error for completeness. Two Alternative models of important genomic features (CIS Regulatory Modules and Horizontal Gene Transfer), a wide range of sequence lengths from a few thousand to millions, and different values of k have been used. As a result, we provide a characterization of those AF functions that is novel and informative. Indeed, we identify weak and strong points of each function considered, which may be used as a guide to choose one for analysis tasks. Remarkably, of the fifteen functions that we have considered, only four stand out, with small differences between small and short sequence length scenarios. Finally, in order to encourage the use of our methodology for validation of future AF functions, the Big Data platform supporting it is public.
Improvement of Bi-directional Communications using Solar Powered Reconfigurable Intelligent Surfaces
Authors: Abdullah Almasoud, Mohamed Y. Selim, Ahamd Alsharoa, Ahmed E. Kamal
Date: 2021-05-09 00:00:00

Recently, there has been a flurry of research on the use of Reconfigurable Intelligent Surfaces (RIS) in wireless networks to create dynamic radio environments. In this paper, we investigate the use of an RIS panel to improve bi-directional communications. Assuming that the RIS will be located on the facade of a building, we propose to connect it to a solar panel that harvests energy to be used to power the RIS panel's smart controller and reflecting elements. Therefore, we present a novel framework to optimally decide the transmit power of each user and the number of elements that will be used to reflect the signal of any two communicating pair in the system (user-user or base station-user). An optimization problem is formulated to jointly minimize a scalarized function of the energy of the communicating pair and the RIS panel and to find the optimal number of reflecting elements used by each user. Although the formulated problem is a mixed-integer non-linear problem, the optimal solution is found by linearizing the non-linear constraints. Besides, a more efficient close to the optimal solution is found using Bender decomposition. Simulation results show that the proposed model is capable of delivering the minimum rate of each user even if line-of-sight communication is not achievable.
Frequency Reflection Modulation for Reconfigurable Intelligent Surface Aided OFDM Systems
Authors: Wenjing Yan, Xiaojun Yuan, Xuanyu Cao
Date: 2021-04-19 00:00:00

Reconfigurable intelligent surface (RIS) based reflection modulation has been considered as a promising information delivery mechanism, and has the potential to realize passive information transfer of a RIS without consuming any additional radio frequency chain and time/frequency/energy resources. The existing on-off reflection modulation (ORM) schemes are based on manipulating the "on/off" states of RIS elements, which may lead to the degradation of RIS reflection efficiency. This paper proposes a frequency reflection modulation (FRM) method for RIS-aided OFDM systems. The FRM-OFDM scheme modulates the frequency of the incident electromagnetic waves, and the RIS information is embedded in the frequency-hoping states of RIS elements. Unlike the ORM-OFDM scheme, the FRM-OFDM scheme can achieve higher reflection efficiency, since the latter does not turn off any reflection element in reflection modulation. We propose a block coordinate descent (BCD) algorithm to maximize the user achievable rate for the FRM-OFDM system by jointly optimizing the phase shift of the RIS and the power allocation at the transmitter. Further, we design a bilinear message passing (BMP) algorithm for the bilinear recovery of both the user symbols and the RIS data. Numerical simulations have verified the efficiency of the designed BCD algorithm for system optimization and the BMP algorithm for signal detection, as well as the superiority of the proposed FRM-OFDM scheme over the ORM-OFDM scheme.
Model-Centric Volumetric Point Cloud Attributes
Authors: Ricardo L. De Queiroz, Camilo Dorea, Davi R. Freitas, Maja Krivokuca, Gustavo P. Sandri
Date: 2021-06-29 00:00:00
Tasks: Attribute, Autonomous Driving, model, Object, Position

Point clouds have recently gained interest, especially for real-time applications and for 3D-scanned material, such as is used in autonomous driving, architecture, and engineering, to model real estate for renovation or display. Point clouds are associated with geometry information and attributes such as color. Be the color unique or direction-dependent (in the case of plenoptic point clouds), it reflects the colors observed by cameras displaced around the object. Hence, not only are the viewing references assumed, but the illumination spectrum and illumination geometry is also implicit. We propose a model-centric description of the 3D object, that is independent of the illumination and of the position of the cameras. We want to be able to describe the objects themselves such that, at a later stage, the rendering of the model may decide where to place illumination, from which it may calculate the image viewed by a given camera. We want to be able to describe transparent or translucid objects, mirrors, fishbowls, fog and smoke. Volumetric clouds may allow us to describe the air, however ``empty'', and introduce air particles, in a manner independent of the viewer position. For that, we rely on some eletromagnetic properties to arrive at seven attributes per voxel that would describe the material and its color or transparency. Three attributes are for the transmissivity of each color, three are for the attenuation of each color, and another attribute is for diffuseness. These attributes give information about the object to the renderer, with whom lies the decision on how to render and depict each object.
Caching and Computation Offloading in High Altitude Platform Station (HAPS) Assisted Intelligent Transportation Systems
Authors: Qiqi Ren, Omid Abbasi, Gunes Karabulut Kurt, Halim Yanikomeroglu, Jian Chen
Date: 2021-06-28 00:00:00
Tasks: Edge-computing

Edge intelligence, a new paradigm to accelerate artificial intelligence (AI) applications by leveraging computing resources on the network edge, can be used to improve intelligent transportation systems (ITS). However, due to physical limitations and energy-supply constraints, the computing powers of edge equipment are usually limited. High altitude platform station (HAPS) computing can be considered as a promising extension of edge computing. HAPS is deployed in the stratosphere to provide wide coverage and strong computational capabilities. It is suitable to coordinate terrestrial resources and store the fundamental data associated with ITS-based applications. In this work, three computing layers,i.e., vehicles, terrestrial network edges, and HAPS, are integrated to build a computation framework for ITS, where the HAPS data library stores the fundamental data needed for the applications. In addition, the caching technique is introduced for network edges to store some of the fundamental data from the HAPS so that large propagation delays can be reduced. We aim to minimize the delay of the system by optimizing computation offloading and caching decisions as well as bandwidth and computing resource allocations. The simulation results highlight the benefits of HAPS computing for mitigating delays and the significance of caching at network edges.
N-Singer: A Non-Autoregressive Korean Singing Voice Synthesis System for Pronunciation Enhancement
Authors: Gyeong-Hoon Lee, Tae-Woo Kim, Hanbin Bae, Min-Ji Lee, Young-Ik Kim, Hoon-Young Cho
Date: 2021-06-29 00:00:00
Tasks: Singing Voice Synthesis

Recently, end-to-end Korean singing voice systems have been designed to generate realistic singing voices. However, these systems still suffer from a lack of robustness in terms of pronunciation accuracy. In this paper, we propose N-Singer, a non-autoregressive Korean singing voice system, to synthesize accurate and pronounced Korean singing voices in parallel. N-Singer consists of a Transformer-based mel-generator, a convolutional network-based postnet, and voicing-aware discriminators. It can contribute in the following ways. First, for accurate pronunciation, N-Singer separately models linguistic and pitch information without other acoustic features. Second, to achieve improved mel-spectrograms, N-Singer uses a combination of Transformer-based modules and convolutional network-based modules. Third, in adversarial training, voicing-aware conditional discriminators are used to capture the harmonic features of voiced segments and noise components of unvoiced segments. The experimental results prove that N-Singer can synthesize a natural singing voice in parallel with a more accurate pronunciation than the baseline model.
HJB Based Optimal Safe Control Using Control Barrier Functions
Authors: Hassan Almubarak, Evangelos A. Theodorou, Nader Sadegh
Date: 2021-06-29 00:00:00

This work proposes an optimal safe controller minimizing an infinite horizon cost functional subject to control barrier functions (CBFs) safety conditions. The constrained optimal control problem is reformulated as a minimization problem of the Hamilton-Jacobi-Bellman (HJB) equation subjected to the safety constraints. By solving the optimization problem, we are able to construct a closed form solution that satisfies optimality and safety conditions. The proposed solution is shown to be continuous and thus it renders the safe set forward invariant while minimizing the given cost. Hence, optimal stabilizability and safety objectives are achieved simultaneously. To synthesize the optimal safe controller, we present a modified Galerkin successive approximation approach which guarantees an optimal safe solution given a stabilizing safe initialization. The proposed algorithm is implemented on a constrained nonlinear system to show its efficacy.
Small-Signal Stability Analysis of a DC Shipboard Microgrid With Droop-Controlled Batteries and Constant Power Resources
Authors: Francesco Conte, Fabio D'Agostino, Samuele Grillo, Stefano Massucco, Federico Silvestro
Date: 2021-02-15 00:00:00

The presence of constant power loads (CPLs) in dc shipboard microgrids may lead to unstable conditions. The present work investigates the stability properties of dc microgrids where CPLs are fed by fuel cells (FCs), and energy storage systems (ESSs) equipped with voltage droop control. With respect to the previous literature, the dynamics of the duty cycles of the dc-dc converters implementing the droop regulation are considered. A mathematical model has been derived, and tuned to best mimic the behavior of the electrical representation implemented in DIgSILENT. Then the model is used to find the sufficient conditions for stability with respect to the droop coefficient, the dc-bus capacitor, and the inductances of the dc-dc converters.
Measurement-Based Parameter Identification of DC-DC Converters with Adaptive Approximate Bayesian Computation
Authors: Seyyed Rashid Khazeiynasab, Issa Batarseh
Date: 2021-06-29 00:00:00

The recent advances in power plants and energy resources have extended the applications of DC-DC converters in the power systems (especially in the context of DC micro-grids). Parameter identification can extract the parameters of the converters and generate accurate discrete simulation models. In this paper, we propose a measurement-based converter parameter calibration method by an adaptive Approximate Bayesian Computation with sequential Monte Carlo sampler (ABC SMC), which estimates the parameters related to passive and parasitic components. At first, we propose to find suitable prior distribution for the parameter which we don't know the prior information about them. With having prior distributions, we can use the ABC SMC to find the exact values of the parameters of the converter. We chose the distance function carefully and based on the simulations we assigned the best method for the threshold sequencing. For improving the computationally of the algorithm, we propose an adaptive weight that helps the algorithm to find the optimal values with fewer simulations. The effectiveness of the proposed method is validated for a DC-DC buck converter. The results show that the proposed approach can accurately and efficiently estimate the posterior distributions of the buck parameters subject to gross errors in the prior distributions of the parameters. The proposed algorithm can also be applied to other parameter identifications and optimization applications such as rectifiers, filters, or power supplies, among others.
Polynomial-Time Algorithms for Structurally Observable Graphs by Controlling Minimal Vertices
Authors: Shiyong Zhu, Jianquan Lu, Daniel W. C. Ho, Jinde Cao
Date: 2021-06-29 00:00:00

The aim of this paper is to characterize an important class of marked digraphs, called structurally observable graphs (SOGs), and to solve two minimum realization problems. To begin with, by exploring structural observability of large-scale Boolean networks (LSBNs), an underlying type of SOGs is provided based on a recent observability criterion of conjunctive BNs. Besides, SOGs are also proved to have important applicability to structural observability of general discrete-time systems. Further, two minimum realization strategies are considered to induce an SOG from an arbitrarily given digraph by marking and controlling the minimal vertices, respectively. It indicates that one can induce an observable system by means of adding the minimal sensors or modifying the adjacency relation of minimal vertices. Finally, the structural observability of finite-field networks, and the minimum pinned node theorem for Boolean networks are displayed as application and simulation. The most salient superiority is that the designed algorithms are polynomial time and avoid exhaustive brute-force searches. It means that our results can be applied to deal with the observability of large-scale systems (particularly, LSBNs), whose observability analysis and the minimum controlled node theorem are known as intractable problems.
Predictive Control based on Equivalent Dynamic Linearization Model
Authors: Feilong Zhang
Date: 2021-06-29 00:00:00
Tasks: Model Predictive Control

Based on equivalent-dynamic-linearization model (EDLM), we propose a kind of model predictive control (MPC) for single-input and single-output (SISO) nonlinear or linear systems. After compensating the EDLM with disturbance for multiple-input and multiple-output nonlinear or linear systems, the MPC compensated with disturbance is proposed to address the disturbance rejection problem. The system performance analysis results are much clear compared with the system stability analyses on MPC in current works. And this may help the engineers understand how to design, analyze and apply the controller in practical.
Analysis and Control of a Planar Quadrotor
Authors: Praveen Venkatesh, Sanket Vadhvana, Varun Jain
Date: 2021-06-29 00:00:00
Tasks: Position, reinforcement-learning, Reinforcement Learning (RL)

In this paper, we model the planar motion of a quadcopter, and develop a linear model of the same. We perform stability analysis of the open loop system and develop a PD controller for its position control. We compare the closed loop response between the linear and non linear systems using the controller developed for the linear system. We also perform stability analysis for the linear and non linear systems, and compare the PD controller with modern deep neural methods based on reinforcement learning actor-critic networks.
Time-Domain Doppler Biomotion Detections Immune to Unavoidable DC Offsets
Authors: Qinyi Lv, Lingtong Min, Congqi Cao, Shigang Zhou, Deyun Zhou, Chengkai Zhu, Yun Li, Zhongbo Zhu, Xiaojun Li, Lixin Ran
Date: 2021-06-29 00:00:00

In the past decades, continuous Doppler radar sensor-based bio-signal detections have attracted many research interests. A typical example is the Doppler heartbeat detection. While significant progresses have been achieved, reliable, time-domain accurate demodulation of bio-signals in the presence of unavoidable DC offsets remains a technical challenge. Aiming to overcome this difficulty, we propose in this paper a novel demodulation algorithm that does not need to trace and eliminate dynamic DC offsets based on approximating segmented arcs in a quadrature constellation of sampling data to directional chords. Assisted by the principal component analysis, such chords and their directions can be deterministically determined. Simulations and experimental validations showed fully recovery of micron-level pendulum movements and strongly noised human heartbeats, verifying the effectiveness and accuracy of the proposed approach.
Regression analysis of distributional data through Multi-Marginal Optimal transport
Authors: AmirHossein Karimi, Tryphon T. Georgiou
Date: 2021-06-28 00:00:00
Tasks: regression

We formulate and solve a regression problem with time-stamped distributional data. Distributions are considered as points in the Wasserstein space of probability measures, metrized by the 2-Wasserstein metric, and may represent images, power spectra, point clouds of particles, and so on. The regression seeks a curve in the Wasserstein space that passes closest to the dataset. Our regression problem allows utilizing general curves in a Euclidean setting (linear, quadratic, sinusoidal, and so on), lifted to corresponding measure-valued curves in the Wasserstein space. It can be cast as a multi-marginal optimal transport problem that allows efficient computation. Illustrative academic examples are presented.
Bi-static Radar Cross Section Test Method by Using Historic Marconi Set-up and Time Gating
Authors: Yousef Azizi, Mohammad Soleimani, Seyed Hasan Sedighi, Ladislau Matekovits
Date: 2021-06-27 00:00:00

In this paper, a low-cost, simple and reliable bi-static Radar Cross Section (RCS) measurement method making use a historic Marconi set-up is presented. It uses a transmitting (Tx) antenna (located at a constant position, at a reference angle of {\theta} = 0o) and a receiver (Rx) antenna (mounted on a moveable arm calibrated in the azimuthal direction with an accuracy of 0.1o). A time gating method is used to extract the information from the reflection in the time domain; applying time filter allows removing the antenna side lobe effects and other ambient noise. In this method, the Rx antenna (on the movable arm) is used to measure the reflected field in the angular range from 1o to 90o of reflection from the structure (printed PCB) and from the reference configuration represented by a ground (GND) plane of the same dimension. The time gating method is then applied to each pair of PCB / GND measurements to extract the bi-static RCS pattern of the structure at a given frequency. Here comparison of measurement results carried out at 18 GHz and 32 GHz with simulation indicates the successful performance of the proposed method. It can be used as a low-cost, reliable and available option in future measurement and scientific research.
Disorder in cellular packing can alter proliferation dynamics to regulate growth
Authors: Chandrashekar Kuyyamudi, Shakti N. Menon, Fernando Casares, Sitabhra Sinha
Date: 2021-06-29 00:00:00

Controlling growth via cell division is crucial in the development of higher organisms, and yet the mechanisms through which this is achieved, e.g., in epithelial tissue, is not yet fully understood. We show that by coupling the cell cycle oscillator governing cell division to signals that encode inter-cellular contacts, this phenomenon can be seen as a collective dynamical transition in a system of coupled oscillators in lattices with changing degree of disorder. As the distribution of cellular morphological characteristics become more homogeneous over the course of development, the contact-induced signals to the cells increase beyond a critical value to trigger coordinated cessation of oscillations, eventually leading to growth arrest. Our results suggest that the global phenomenon of growth rate reduction as a tissue approaches its appropriate size is causally related to the increasingly regular geometry of local cell-cell contact interfaces.
Epidemiological theory of virus variants
Authors: Giacomo Cacciapaglia, Corentin Cot, Adele de Hoffer, Stefan Hohenegger, Francesco Sannino, Shahram Vatani
Date: 2021-06-28 00:00:00

We propose a physical theory underlying the temporal evolution of competing virus variants that relies on the existence of (quasi) fixed points capturing the large time scale invariance of the dynamics. To motivate our result we first modify the time-honoured compartmental models of the SIR type to account for the existence of competing variants and then show how their evolution can be naturally re-phrased in terms of flow equations ending at quasi fixed points. As the natural next step we employ (near) scale invariance to organise the time evolution of the competing variants within the effective description of the epidemic Renormalization Group framework. We test the resulting theory against the time evolution of COVID-19 virus variants that validate the theory empirically.
A Note on the Topology of the First Stage of 2SLS with Many Instruments
Authors: Guy Tchuente
Date: 2021-06-28 00:00:00

The finite sample properties of estimators are usually understood or approximated using asymptotic theories. Two main asymptotic constructions have been used to characterize the presence of many instruments. The first assumes that the number of instruments increases with the sample size. I demonstrate that in this case, one of the key assumptions used in the asymptotic construction may imply that the number of ``effective" instruments should be finite, resulting in an internal contradiction. The second asymptotic representation considers that the number of instrumental variables (IVs) may be finite, infinite, or even a continuum. The number does not change with the sample size. In this scenario, the regularized estimator obtained depends on the topology imposed on the set of instruments as well as on a regularization parameter. These restrictions may induce a bias or restrict the set of admissible instruments. However, the assumptions are internally coherent. The limitations of many IVs asymptotic assumptions provide support for finite sample distributional studies to better understand the behavior of many IV estimators.