pwc-archive/papers-with-abstracts huggingface dataset:
Resilient Average Consensus: A Detection and Compensation Approach
Authors: Wenzhe Zheng, Zhiyu He, Jianping He, Chengcheng Zhao, Chongrong Fang
Date: 2022-02-22 00:00:00

We study the problem of resilient average consensus for multi-agent systems with misbehaving nodes. To protect consensus valuefrom being influenced by misbehaving nodes, we address this problem by detecting misbehaviors, mitigating the corresponding adverse impact and achieving the resilient average consensus. In this paper, general types of misbehaviors are considered,including deception attacks, accidental faults and link failures. We characterize the adverse impact of misbehaving nodes in a distributed manner via two-hop communication information and develop a deterministic detection-compensation-based consensus (D-DCC) algorithm with a decaying fault-tolerant error bound. Considering scenarios where information sets are intermittently available due to link failures, a stochastic extension named stochastic detection-compensation-based consensus(S-DCC) algorithm is proposed. We prove that D-DCC and S-DCC allow nodes to asymptotically achieve resilient averageconsensus exactly and in expectation, respectively. Then, the Wasserstein distance is introduced to analyze the accuracy ofS-DCC. Finally, extensive simulations are conducted to verify the effectiveness of the proposed algorithm
Station-keeping of $L_2$ halo orbits under sampled-data model predictive control
Authors: Mohamed Elobaid, Mattia Mattioni, Salvatore Monaco, Dorothée Normand-Cyrot
Date: 2022-02-22 00:00:00
Tasks: Model Predictive Control

The paper deals with the design of an improved model predictive control scheme for achieving station-keeping in a quasi Halo orbit around the $L_2$ point in the Earth-Moon system. The improvement is obtained thanks to a multi-rate sampled-data trajectory planner that allows for simplifying the optimization problem of the model predictive controller while guaranteeing feasibility and convergence to the desired orbit. The multi-rate planner is designed based on a simplified model of the dynamics under a preliminary nonlinear regulation feedback. The proposed control scheme is shown to outperform recent station-keeping nonlinear model predictive control designs both in terms of tracking error and energy expenditures in different situations. Finally, a brief study of aspects pertaining to computational time are carried out so highlighting the possibility for real time implementation on modern hardware.
On the stability of nonlinear sampled-data systems and their continuous-time limits
Authors: Alexis J. Vallarella, Hernan Haimovich
Date: 2022-02-21 00:00:00

This work deals with the stability analysis of nonlinear sampled-data systems under nonuniform sampling. It establishes novel relationships between the stability property of the exact discrete-time model for a given sequence of (aperiodic) sampling instants and the stability property of the continuous-time system when the maximum admissible sampling period converges to zero. These results can be used to infer stability properties for the sampled-data system by direct inspection of the stability of the mentioned continuous-time system, a task which is typically easier than the analysis of the closed-loop sampled-data system. Compared to the literature, our results allow to prove stronger (asymptotic) sampled-data stability properties for nonlinear systems in cases for which existing results only guarantee practical stability.
Demand and Price Fluctuations Effect on Risk and Profit of Single and Clustered Microgrids during COVID-19 Pandemic
Authors: Tohid Khalili, Ali Bidram, Janie M. Chermak
Date: 2022-02-21 00:00:00
Tasks: Clustering

COVID19s widespread distribution is wreaking havoc on peoples lives all over the world. This pandemic has also had a significant impact on energy consumption. Its influence can be seen in the power systems operation and the market as well. The power consumers habits and demand curves have been changed at a breakneck pace. In this work, a one year mixed integer programming (MIP) problem has been developed to compare the power consumption between 2019 and 2020 in the United States as an example regarding the COVID19 pandemic effect in order to better prepare for possible similar future events. 100 percent renewable single microgrids (SMGs) are studied using wind turbines and photovoltaics. Batteries are also employed since it is inevitable when the system uses renewables. Additionally, it is possible for the SMGs to trade power with the main grid as needed. The effect of the SMGs clustering to form the multi microgrids (MMGs) is also considered. In order to investigate the risk of the system during the COVID19 and formation of MMG, downside risk constraints are applied to the proposed model. Furthermore, a stylized short run consumers demand model is proposed, using elasticity and assessed responses regarding the average household consumption for households during on peak and off peak periods. The simulation results show that COVID19 generally reduces the demand, increases the profit of the system, and decreases the economic risk of the power systems operation. Moreover, SMGs clustering to organize MMG dramatically enhances the profit of the system as well as improves the risk level of the system.
General theory of specific binding: insights from a genetic-mechano-chemical protein model
Authors: John M McBride, Jean-Pierre Eckmann, Tsvi Tlusty
Date: 2022-02-22 00:00:00
Tasks: Specificity

Proteins need to selectively interact with specific targets among a multitude of similar molecules in the cell. But despite a firm physical understanding of binding interactions, we lack a general theory of how proteins evolve high specificity. Here, we present such a model that combines chemistry, mechanics and genetics, and explains how their interplay governs the evolution of specific protein-ligand interactions. The model shows that there are many routes to achieving molecular discrimination - by varying degrees of flexibility and shape/chemistry complementarity - but the key ingredient is precision. Harder discrimination tasks require more collective and precise coaction of structure, forces and movements. Proteins can achieve this through correlated mutations extending far from a binding site, which fine-tune the localized interaction with the ligand. Thus, the solution of more complicated tasks is enabled by increasing the protein size, and proteins become more evolvable and robust when they are larger than the bare minimum required for discrimination. The model makes testable, specific predictions about the role of flexibility and shape mismatch in discrimination, and how evolution can independently tune affinity and specificity. Thus, the proposed theory of specific binding addresses the natural question of "why are proteins so big?". A possible answer is that molecular discrimination is often a hard task best performed by adding more layers to the protein.
Ghost projection. II. Beam shaping using realistic spatially-random masks
Authors: David Ceddia, Andrew M. Kingston, Daniele Pelliccia, Alexander Rack, David M. Paganin
Date: 2022-02-18 00:00:00
Tasks: Experimental Design

Spatial light modulation is important for many scientific and industrial applications. The spatial light modulator and optical data projector both rely on precisely configurable optical elements to shape a light beam. Here we explore an image-projection approach which does not require a configurable beam-shaping element. We term this approach ghost projection on account of its conceptual relation to computational ghost imaging. Instead of a configurable beam shaping element, the method transversely displaces a single illuminated mask, such as a spatially-random screen, to create specified distributions of radiant exposure. The method has potential applicability to image projection employing a variety of radiation and matter wave fields, such as hard x rays, neutrons, muons, atomic beams and molecular beams. Building on our previous theoretical and computational studies, we here seek to understand the effects, sensitivity, and tolerance of some key experimental limitations of the method. Focusing on the case of hard x rays, we employ experimentally acquired masks to numerically study the deleterious effects of photon shot noise, inaccuracies in random-mask exposure time, and inaccuracies in mask positioning, as well as adapting to spatially non-uniform illumination. Understanding the influence of these factors will assist in optimizing experimental design and work towards achieving ghost projection in practice.
Macroeconomic Effect of Uncertainty and Financial Shocks: a non-Gaussian VAR approach
Authors: Olli Palmén
Date: 2022-02-22 00:00:00
Tasks: Time Series, Time Series Analysis

The Great Recession highlighted the role of financial and uncertainty shocks as drivers of business cycle fluctuations. However, the fact that uncertainty shocks may affect economic activity by tightening financial conditions makes empirically distinguishing these shocks difficult. This paper examines the macroeconomic effects of the financial and uncertainty shocks in the United States in an SVAR model that exploits the non-normalities of the time series to identify the uncertainty and the financial shock. The results show that macroeconomic uncertainty and financial shocks seem to affect business cycles independently as well as through dynamic interaction. Uncertainty shocks appear to tighten financial conditions, whereas there appears to be no causal relationship between financial conditions and uncertainty. Moreover, the results suggest that uncertainty shocks may have persistent effects on output and investment that last beyond the business cycle.
Discrimination Against Immigrants in the Criminal Justice System: Evidence from Pretrial Detentions
Authors: Patricio Domínguez, Nicolás Grau, Damián Vergara
Date: 2022-02-22 00:00:00

This paper tests for discrimination against immigrant defendants in the criminal justice system in Chile using a decade of nationwide administrative records on pretrial detentions. Observational benchmark regressions show that immigrant defendants are 8.6 percentage points less likely to be released pretrial relative to Chilean defendants with similar proxies for pretrial misconduct potential. Diagnostics for omitted variable bias -- including a novel test to assess the quality of the proxy vector based on comparisons of pretrial misconduct rates among released defendants -- suggest that the discrimination estimates are not driven by omitted variable bias and that, if anything, failing to fully account for differences in misconduct potential leads to an underestimation of discrimination. Our estimates suggest that discrimination stems from an informational problem because judges do not observe criminal records in origin countries, with stereotypes and taste-based discrimination playing a role in the problem's resolution. We find that discrimination is especially large for drug offenses and that discrimination increased after a recent immigration wave.
Information Design in Smooth Games
Authors: Alex Smolin, Takuro Yamashita
Date: 2022-02-22 00:00:00

We study information design in games in which each player has a continuum of actions. We show that an information structure is designer-optimal whenever the equilibrium play it induces can also be implemented in a principal-agent contracting problem. We use this observation to solve three novel applications. In an investment game, the optimal structure fully informs a single investor while providing no information to others. In an expectation polarization game, the optimal structure fully informs half of the players while providing no information to the other half. In a price competition game, the optimal structure is noise-free Gaussian and recommends prices linearly in the states. Our analysis further informs on the robustness and uniqueness of the optimal information structures.
Location-based Initial Access for Wireless Power Transfer with Physically Large Arrays
Authors: Benjamin J. B. Deutschmann, Thomas Wilding, Erik G. Larsson, Klaus Witrisal
Date: 2022-02-22 00:00:00
Tasks: Diversity

Radio frequency (RF) wireless power transfer (WPT) is a promising technology for 6G use cases. It enables a massive, yet sustainable deployment of batteryless energy neutral (EN) devices at unprecedented scale. Recent research on 6G is exploring high operating frequencies up to the THz spectrum, where antenna arrays with large apertures are capable of forming narrow, "laser-like" beams. At sub-10 GHz frequencies, physically large antenna arrays are considered that are operating in the array near field. Transmitting spherical wavefronts, power can be focused in a focal point rather than a beam, which allows for efficient and radiation-safe WPT. We formulate a multipath channel model comprising specular components and diffuse scattering to find the WPT power budget in a realistic indoor scenario. Specular components can be predicted by means of a geometric model. This is used to transmit power via multiple beams simultaneously, increasing the available power budget and expanding the initial access distance. We show that exploiting this "beam diversity" reduces the required fading margin for the initial access to EN devices.
An Energy-concentrated Wavelet Transform for Time Frequency Analysis of Transient Signals
Authors: Haoran Dong, Gang Yu
Date: 2022-02-22 00:00:00

Transient signals are often composed of a series of modes that have multivalued time-dependent instantaneous frequency (IF), which brings challenges to the development of signal processing technology. Fortunately, the group delay (GD) of such signal can be well expressed as a single valued function of frequency. By considering the frequency-domain signal model, we present a postprocessing method called wavelet transform (WT)-based time-reassigned synchrosqueezing transform (WTSST). Our proposed method embeds a two-dimensional GD operator into a synchrosqueezing framework to generate a time-frequency representation (TFR) of transient signal with high energy concentration and allows to retrieve the whole or part of the signal. The theoretical analyses of the WTSST are provided, including the analysis of GD candidate accuracy and signal reconstruction accuracy. Moreover, based on WTSST, the WT-based time-reassigned multisynchrosqueezing transform (WTMSST) is proposed by introducing a stepwise refinement scheme, which further improves the drawback that the WTSST method is unable to deal with strong frequency-varying signal. Simulation and real signal analysis illustrate that the proposed methods have the capacity to appropriately describe the features of transient signals.
Online Learning of Trellis Diagram Using Neural Network for Robust Detection and Decoding
Authors: Jie Yang, Qinghe Du, Yi Jiang
Date: 2022-02-22 00:00:00

This paper studies machine learning-assisted maximum likelihood (ML) and maximum a posteriori (MAP) receivers for a communication system with memory, which can be modelled by a trellis diagram. The prerequisite of the ML/MAP receiver is to obtain the likelihood of the received samples under different state transitions of the trellis diagram, which relies on the channel state information (CSI) and the distribution of the channel noise. We propose to learn the trellis diagram real-time using an artificial neural network (ANN) trained by a pilot sequence. This approach, termed as the online learning of trellis diagram (OLTD), requires neither the CSI nor statistics of the noise, and can be incorporated into the classic Viterbi and the BCJR algorithm. %Compared with the state-of-the-art ViterbiNet and BCJRNet algorithms in the literature, it It is shown to significantly outperform the model-based methods in non-Gaussian channels. It requires much less training overhead than the state-of-the-art methods, and hence is more feasible for real implementations. As an illustrative example, the OLTD-based BCJR is applied to a Bluetooth low energy (BLE) receiver trained only by a 256-sample pilot sequence. Moreover, the OLTD-based BCJR can accommodate for turbo equalization, while the state-of-the-art BCJRNet/ViterbiNet cannot. As an interesting by-product, we propose an enhancement to the BLE standard by introducing a bit interleaver to its physical layer; the resultant improvement of the receiver sensitivity can make it a better fit for some Internet of Things (IoT) communications.
Qualitative Behavior of a Metabolic Pathway with Hybrid Feedback
Authors: Claudia Lopez-Zazueta, Vincent Fromion
Date: 2022-02-14 00:00:00

We study the qualitative behavior of a model to represent local regulation in a metabolic network. The model is based on the end-product control structure introduced in [A. Goelzer, F. Bekkal Brikci, I. Martin-Verstraete et al., BMC Syst Biol 2 (2008), pp. 1--18]. In this class of regulation, the metabolite effector is the end-product of a metabolic pathway. We suppose the input to the pathway to switch between zero and a positive value according to the concentration of the metabolite effector. Considering the switched system as a differential inclusion, we prove that it converges to a globally uniformly asymptotically stable equilibrium point, reaches the sliding mode or oscillates around the sliding mode depending on the positive value of the input. Finally, we show that in any case the solution of the switched system is the limit of solutions of equation sequences with smooth or piecewise linear inputs.
Universal approximation of credit portfolio losses using Restricted Boltzmann Machines
Authors: Giuseppe Genovese, Ashkan Nikeghbali, Nicola Serra, Gabriele Visentin
Date: 2022-02-22 00:00:00
Tasks: Management

We introduce a new portfolio credit risk model based on Restricted Boltzmann Machines (RBMs), which are stochastic neural networks capable of universal approximation of loss distributions. We test the model on an empirical dataset of default probabilities of 1'012 US companies and we show that it outperforms commonly used parametric factor copula models -- such as the Gaussian or the t factor copula models -- across several credit risk management tasks. In particular, the model leads to better fits for the empirical loss distribution and more accurate risk measure estimations. We introduce an importance sampling procedure which allows risk measures to be estimated at high confidence levels in a computationally efficient way and which is a substantial improvement over the Monte Carlo techniques currently available for copula models. Furthermore, the statistical factors extracted by the model admit an interpretation in terms of the underlying portfolio sector structure and provide practitioners with quantitative tools for the management of concentration risk. Finally, we show how to use the model for stress testing by estimating stressed risk measures (e.g. stressed VaR) under various macroeconomic stress test scenarios, such as those specified by the FRB's Dodd-Frank Act stress test.
Space Layout of Low-entropy Hydration Shells Guides Protein Binding
Authors: Lin Yang, Shuai Guo, Chengyu Hou, Chencheng Liao, Jiacheng Li, Liping Shi, Xiaoliang Ma, Shenda Jiang, Bing Zheng, Yi Fang, Lin Ye, Xiaodong He
Date: 2022-02-22 00:00:00

Protein-protein binding enables orderly and lawful biological self-organization, and is therefore considered a miracle of nature. Protein-protein binding is steered by electrostatic forces, hydrogen bonding, van der Waals force, and hydrophobic interactions. Among these physical forces, only the hydrophobic interactions can be considered as long-range intermolecular attractions between proteins in intracellular and extracellular fluid. Low-entropy regions of hydration shells around proteins drive hydrophobic attraction among them that essentially coordinate protein-protein docking in rotational-conformational space of mutual orientations at the guidance stage of the binding. Here, an innovative method was developed for identifying the low-entropy regions of hydration shells of given proteins, and we discovered that the largest low-entropy regions of hydration shells on proteins typically cover the binding sites. According to an analysis of determined protein complex structures, shape matching between the largest low-entropy hydration shell region of a protein and that of its partner at the binding sites is revealed as a regular pattern. Protein-protein binding is thus found to be mainly guided by hydrophobic collapse between the shape-matched low-entropy hydration shells that is verified by bioinformatics analyses of hundreds of structures of protein complexes. A simple algorithm is developed to precisely predict protein binding sites.
From chaos to clock in reverberating neural net. Case study
Authors: A. Vidybida, O. Shchur
Date: 2022-02-22 00:00:00

What is the reason for complex dynamical patterns registered from real biological neuronal networks? Noise and dynamical reconfiguring of a network (functional/dynamic connectome) were proposed as possible answers. In this case study, we report a complex dynamical pattern observed in a simple deterministic network of 25 neurons with fixed connectome. After a short initial stimulation, the network is engaged into a complex dynamics, which lasts for a long time. Eventually, with no external intervention, the dynamics comes to a periodic one with a short period. The long transient is positively checked for being chaotic. We conclude that the complex dynamics observed is the output of neural computation performed in the process of neuronal firings and spikes propagation.
Brain representation of perceptual stimuli at different levels of awareness
Authors: Birgitta Dresp-Langley
Date: 2022-02-22 00:00:00

This article questions the widespread assumption that there are brain representations that will always remain unconscious in the sense of being inaccessible to individual awareness under any circumstances. This implies that some part of the knowledge generated by the brain is once and for always excluded from consciousness and, therefore, from being communicated to the outside world. This standpoint neglects the possibility that the human brain might have a capacity for generating metarepresentations of nonconscious knowledge contents at a given moment in time through context sensitive adaptive learning, and is somewhat difficult to reconcile with experimental findings showing that initially subliminal targets can be made available to awareness, or break through to supraliminal levels of processing, when they are embedded in an appropriate perceptual object context (relevance condition). Specific properties of neural network architectures, inspired by the functional organization of the primate cortex, are able to explain how a human brain could generate this kind of perceptual learning. Signals or knowledge processed outside awareness could be made available to awareness through adaptive resonance of bottom-up and top-down signal exchanges in massively parallel neural network architectures; in other words, on the basis of statistically significant signal matches in the domain of time and in the domain of memory content.