pwc-archive/papers-with-abstracts huggingface dataset:
Consensus of hierarchical multi-agent systems with a time-varying set of active agents
Authors: Victor Daniel Reyes Dreke, Mircea Lazar
Date: 2022-12-01 00:00:00

Time-varying hierarchical multi-agent systems are common in many applications. A well-known solution to control these systems is to use state feedback controllers that depend on the adjacency matrix to reach consensus. This solution has been applied so far to multi-agent systems with fixed or time-varying communication topologies. In this paper, we consider single-integrator multi-agent systems where only a subset of the agents are active at any given time and the set of active agents is time-varying. This type of multi-agent system is relevant in applications such as modular multilevel converters and water pumping systems. We develop a switching algorithm that periodically selects a set of active agents through a set of graphs that connect the follower agents with a leader agent. We further prove that the developed switching algorithm combined with a classic consensus protocol yields convergence to a consensus state defined by the leader despite a time-varying set of active agents. The effectiveness of the switching algorithm is illustrated in two benchmark examples: a modular multilevel converter and a water pumping system.
Predictive Control with Learning-Based Terminal Costs Using Approximate Value Iteration
Authors: Francisco Moreno-Mora, Lukas Beckenbach, Stefan Streif
Date: 2022-12-01 00:00:00
Tasks: Model Predictive Control

Stability under model predictive control (MPC) schemes is frequently ensured by terminal ingredients. Employing a (control) Lyapunov function as the terminal cost constitutes a common choice. Learning-based methods may be used to construct the terminal cost by relating it to, for instance, an infinite-horizon optimal control problem in which the optimal cost is a Lyapunov function. Value iteration, an approximate dynamic programming (ADP) approach, refers to one particular cost approximation technique. In this work, we merge the results of terminally unconstrained predictive control and approximate value iteration to draw benefits from both fields. A prediction horizon is derived in dependence on different factors such as approximation-related errors to render the closed-loop asymptotically stable further allowing a suboptimality estimate in comparison to an infinite horizon optimal cost. The result extends recent studies on predictive control with ADP-based terminal costs, not requiring a local initial stabilizing controller. We compare this controller in simulation with other terminal cost options to show that the proposed approach leads to a shorter minimal horizon in comparison to previous results.
Towards Wireless Ranging and Synchronization using CubeSat Software-Defined Radio Subsystems
Authors: Markus Gardill, Dominik Pearson, Julian Scharnagl, Klaus Schilling
Date: 2022-12-01 00:00:00

An approach towards wireless ranging and synchronization using commercial of-the-shelf software-defined radio payloads for small satellites, esp. CubeSats is studied. The approach only relies on the programmable logic configuration and processing system software. No hardware modifications or additions to the payloads are necessary. Experimental evaluation of the initial implementation shows a standard deviation of time-of-flight-based ranging measurements in the order of 1 cm, which renders the concept of interest for distributed satellite system missions.
LQG for Constrained Linear Systems: Indirect Feedback Stochastic MPC with Kalman Filtering
Authors: Simon Muntwiler, Kim P. Wabersich, Robert Miklos, Melanie N. Zeilinger
Date: 2022-12-01 00:00:00
Tasks: Model Predictive Control, State Estimation

We present an output feedback stochastic model predictive control (SMPC) approach for linear systems subject to Gaussian disturbances and measurement noise and probabilistic constraints on system states and inputs. The presented approach combines a linear Kalman filter for state estimation with an indirect feedback SMPC, which is initialized with a predicted nominal state, while feedback of the current state estimate enters through the objective of the SMPC problem. For this combination, we establish recursive feasibility of the SMPC problem due to the chosen initialization, and closed-loop chance constraint satisfaction thanks to an appropriate tightening of the constraints in the SMPC problem also considering the state estimation uncertainty. Additionally, we show that for specific design choices in the SMPC problem, the unconstrained linear-quadratic-Gaussian (LQG) solution is recovered if it is feasible for a given initial condition and the considered constraints. We demonstrate this fact for a numerical example, and show that the resulting output feedback controller can provide non-conservative constraint satisfaction.
A Novel Framework for Decentralized Dynamic Resource Allocation Using Voronoi Tessellations
Authors: Bhagyashri Telsang, Seddik Djouadi
Date: 2022-11-30 00:00:00

In this work, we approach the problem of resource allocation in a team of agents through the framework of Centroidal Voronoi Tessellations. CVTs provide a natural way to embed a desired global trend in the team through probability distributions, and in one-dimensional spaces, CVTs offer an inherent line structure allowing for a simple communication graph and scalability. We first consider the amount of resource to be allocated to be a constant and provide an analytical solution to such static resource allocation problem by embedding the allocation constraint within the distribution through a system of nonlinear equations. Using the solution of such a constrained CVT minimization problem as an initialization step, we propose a decentralized dynamic resource allocation solution that employs a one-step update when the desired distribution is Gaussian. We introduce a "civility model" for negotiations between the agents to allow for flexibility in local preferences and maintaining robustness against local disturbances. We demonstrate the effectiveness of the proposed method by considering the application of demand-response in smart grids through the problem of power allocation in a group of building thermal loads.
The kinetic theory of mutation rates
Authors: Lorenzo Pareschi, Giuseppe Toscani
Date: 2022-11-30 00:00:00

The Luria--Delbr\"uck mutation model is a cornerstone of evolution theory and has been mathematically formulated in a number of ways. In this paper we illustrate how this model of mutation rates can be derived by means of classical statistical mechanics tools, in particular by modeling the phenomenon resorting to methodologies borrowed from classical kinetic theory of rarefied gases. The aim is to construct a linear kinetic model that can reproduce the Luria--Delbr\"uck distribution starting from the elementary interactions that qualitatively and quantitatively describe the variation of mutated cells. The kinetic description is easily adaptable to different situations and makes it possible to clearly identify the differences between the elementary variations leading to the formulations of Luria--Delbr\"uck, Lea--Coulson, and Kendall, respectively. The kinetic approach additionally emphasizes basic principles which not only help to unify existing results but also allow for useful extensions.
Emerging Diversity in a Population of Evolving Intransitive Dice
Authors: Julius B. Kirkegaard, Kim Sneppen
Date: 2022-12-01 00:00:00
Tasks: Diversity

Exploiting the mathematical curiosity of intransitive dice, we present a simple theoretical model for co-evolution that captures scales ranging from the genome of the individual to the system-wide emergence of species diversity. We study a set of evolving agents that interact competitively in a closed system, in which both the dynamics of mutations and competitive advantage emerge directly from interpreting a genome as the sides of a die. The model demonstrates sympatric speciation where new species evolve from existing ones while in contact with the entire ecosystem. Allowing free mutations both in the genomes and the mutation rates, we find, in contrast to hierarchical models of fitness, the emergence of a metastable state of finite mutation rate and diversity.
ML framework for global river flood predictions based on the Caravan dataset
Authors: Ioanna Bouri, Manu Lahariya, Omer Nivron, Enrique Portales Julia, Dietmar Backes, Piotr Bilinski, Guy Schumann
Date: 2022-11-14 00:00:00
Tasks: Prediction

Reliable prediction of river floods in the first 72 hours can reduce harm because emergency agencies have sufficient time to prepare and deploy for help at the scene. Such river flood prediction models already exist and perform relatively well in most high-income countries. But, due to the limited availability of data, these models are lacking in low-income countries. Here, we offer the first global river flood prediction framework based on the newly published Caravan dataset. Our framework aims to serve as a benchmark for future global river flood prediction research. To support generalizability claims we include custom data evaluation splits. Further, we propose and evaluate a novel two-path LSTM architecture (2P-LSTM) against three baseline models. Finally, we evaluate the generated models on different locations in Africa and Asia that were not part of the Caravan dataset.
A POMDP Model for Safe Geological Carbon Sequestration
Authors: Anthony Corso, Yizheng Wang, Markus Zechner, Jef Caers, Mykel J. Kochenderfer
Date: 2022-10-25 00:00:00
Tasks: Decision Making, model

Geological carbon capture and sequestration (CCS), where CO$_2$ is stored in subsurface formations, is a promising and scalable approach for reducing global emissions. However, if done incorrectly, it may lead to earthquakes and leakage of CO$_2$ back to the surface, harming both humans and the environment. These risks are exacerbated by the large amount of uncertainty in the structure of the storage formation. For these reasons, we propose that CCS operations be modeled as a partially observable Markov decision process (POMDP) and decisions be informed using automated planning algorithms. To this end, we develop a simplified model of CCS operations based on a 2D spillpoint analysis that retains many of the challenges and safety considerations of the real-world problem. We show how off-the-shelf POMDP solvers outperform expert baselines for safe CCS planning. This POMDP model can be used as a test bed to drive the development of novel decision-making algorithms for CCS operations.
An Edge Alignment-based Orientation Selection Method for Neutron Tomography
Authors: Diyu Yang, Shimin Tang, Singanallur V. Venkatakrishnan, Mohammad S. N. Chowdhury, Yuxuan Zhang, Hassina Z. Bilheux, Gregery T. Buzzard, Charles A. Bouman
Date: 2022-12-01 00:00:00
Tasks: Diversity

Neutron computed tomography (nCT) is a 3D characterization technique used to image the internal morphology or chemical composition of samples in biology and materials sciences. A typical workflow involves placing the sample in the path of a neutron beam, acquiring projection data at a predefined set of orientations, and processing the resulting data using an analytic reconstruction algorithm. Typical nCT scans require hours to days to complete and are then processed using conventional filtered back-projection (FBP), which performs poorly with sparse views or noisy data. Hence, the main methods in order to reduce overall acquisition time are the use of an improved sampling strategy combined with the use of advanced reconstruction methods such as model-based iterative reconstruction (MBIR). In this paper, we propose an adaptive orientation selection method in which an MBIR reconstruction on previously-acquired measurements is used to define an objective function on orientations that balances a data-fitting term promoting edge alignment and a regularization term promoting orientation diversity. Using simulated and experimental data, we demonstrate that our method produces high-quality reconstructions using significantly fewer total measurements than the conventional approach.
Imaging across multiple spatial scales with the multi-camera array microscope
Authors: Mark Harfouche, Kanghyun Kim, Kevin C. Zhou, Pavan Chandra Konda, Sunanda Sharma, Eric E. Thomson, Colin Cooke, Shiqi Xu, Lucas Kreiss, Amey Chaware, Xi Yang, Xing Yao, Vinayak Pathak, Martin Bohlen, Ron Appel, Aurélien Bègue, Clare Cook, Jed Doman, John Efromson, Gregor Horstmeyer, Jaehee Park, Paul Reamey, Veton Saliu, Eva Naumann, Roarke Horstmeyer
Date: 2022-11-30 00:00:00

This article experimentally examines different configurations of a novel multi-camera array microscope (MCAM) imaging technology. The MCAM is based upon a densely packed array of "micro-cameras" to jointly image across a large field-of-view at high resolution. Each micro-camera within the array images a unique area of a sample of interest, and then all acquired data with 54 micro-cameras are digitally combined into composite frames, whose total pixel counts significantly exceed the pixel counts of standard microscope systems. We present results from three unique MCAM configurations for different use cases. First, we demonstrate a configuration that simultaneously images and estimates the 3D object depth across a 100 x 135 mm^2 field-of-view (FOV) at approximately 20 um resolution, which results in 0.15 gigapixels (GP) per snapshot. Second, we demonstrate an MCAM configuration that records video across a continuous 83 x 123 mm^2 FOV with two-fold increased resolution (0.48 GP per frame). Finally, we report a third high-resolution configuration (2 um resolution) that can rapidly produce 9.8 GP composites of large histopathology specimens.
Quantity restrictions and price discounts on Russian oil
Authors: Henrik Wachtmeister, Johan Gars, Daniel Spiro
Date: 2022-12-01 00:00:00

Following Russia's invasion of Ukraine, Western countries have looked for ways to limit Russia's oil income. This paper considers, theoretically and quantitatively, two such options: 1) an export-quantity restriction and 2) a forced discount on Russian oil. We build a quantifiable model of the global oil market and analyze how each of these policies affect: which Russian oil fields fall out of production; the global oil supply; and the global oil price. By these statics we derive the effects of the policies on Russian oil profits and oil-importers' economic surplus. The effects on Russian oil profits are substantial. In the short run (within the first year), a quantity restriction of 20% yields Russian losses of 62 million USD per day, equivalent to 1.2% of GDP and 32% of military spending. In the long run (beyond a year) new investments become unprofitable. Losses rise to 100 million USD per day, 2% of GDP and 56% of military spending. A price discount of 20% is even more harmful to Russia, yielding losses of 152 million USD per day, equivalent to 3.1% of GDP and 85% of military spending in the short run and long run. A price discount puts generally more burden on Russia and less on importers compared to a quantity restriction. In fact, a price discount implies net gains for oil importers as it essentially redistributes oil rents from Russia to importers. If the restrictions are expected to last for long, the burden on oil importers decreases. Overall, both policies at all levels imply larger relative losses for Russia than for oil importers (in shares of their GDP). The case for a price discount on Russian oil is thus strong. However, Russia may choose not to export at the discounted price, in which case the price-discount sanction becomes a de facto supply restriction.
3 Lessons from Hyperinflationary Periods
Authors: Mark Bergen, Thomas Bergen, Daniel Levy, Rose Semenov
Date: 2022-12-01 00:00:00

Inflation is painful, for firms, customers, employees, and society. But careful study of periods of hyperinflation point to ways that firms can adapt. In particular, companies need to think about how to change prices regularly and cheaply, because constant price changes can ultimately be very, very expensive. And they should consider how to communicate those price changes to customers. Providing clarity and predictability can increase consumer trust and help firms in the long run.
ESG In Corporate Filings: An AI Perspective
Authors: Irene Aldridge, Payton Martin
Date: 2022-11-30 00:00:00
Tasks: Diversity, Management

Our main contribution is that we are using AI to discern the key drivers of variation of ESG mentions in the corporate filings. With AI, we are able to separate "dimensions" along which the corporate management presents their ESG policies to the world. These dimensions are 1) diversity, 2) hazardous materials, and 3) greenhouse gasses. We are also able to identify separate "background" dimensions of unofficial ESG activity in the firms, which provide more color into the firms and their shareholders' thinking about their ESG processes. We then measure investors' response to the ESG activity "factors". The AI techniques presented can assist in building better, more reliable and useful ESG ratings systems.
Impact of Business Analytics and Decision Support Systems on e-commerce in SMEs
Authors: Shah J Miah
Date: 2022-11-30 00:00:00
Tasks: Descriptive, Marketing

With the advancement in the marketing channel, the use of e-commerce has increased tremendously therefore the basic objective of this study is to analyze the impact of business analytics and decision support systems on e-commerce in small and medium enterprises. Small and medium enterprises are becoming a priority for economies as by implementing some policies and regulations these businesses could encourage gain development on an international level. The objective of this study is to analyze the impact of business analytics and decision support systems on e-commerce in small and medium enterprises that investigate the relationship between business analytics and decision support systems in e-commerce businesses. To evaluate the impact of both on e-commerce the, descriptive analysis approach is adopted that reviews the research of different scholars who adopted different plans and strategies to predict the relationship between e-commerce and business analytics. The study contributes to the literature by examining the impact of business analytics in SMEs and provides a comprehensive understanding of its relationship with the decision support system. After analyzing the impact of business analytics and decision support system in SMEs, the research also highlights some limitations and provide future recommendations that are helpful to overcome these limitations.
Average Profits of Prejudiced Algorithms
Authors: David J. Jin
Date: 2022-12-01 00:00:00

We investigate the level of success a firm achieves depending on which of two common scoring algorithms is used to screen qualified applicants belonging to a disadvantaged group. Both algorithms are trained on data generated by a prejudiced decision-maker independently of the firm. One algorithm favors disadvantaged individuals, while the other algorithm exemplifies prejudice in the training data. We deliver sharp guarantees for when the firm finds more success with one algorithm over the other, depending on the prejudice level of the decision-maker.
Robust Contracts with Exploration
Authors: Chang Liu
Date: 2022-11-30 00:00:00

We study a two-period moral hazard problem; there are two agents, with action sets that are unknown to the principal. The principal contracts with each agent sequentially, and seeks to maximize the worst-case discounted sum of payoffs, where the worst case is over the possible action sets. The principal observes the action chosen by the first agent, and then offers a new contract to the second agent based on this knowledge, thus having the opportunity to explore in the first period. We introduce and compare three different notions of dynamic worst-case considerations. Within each notion, we define a suitable rule of updating and characterize the principal's optimal payoff guarantee. We find that linear contracts are robustly optimal not only in static settings, but also in dynamic environments with exploration.
High Fidelity Speech Enhancement with Band-split RNN
Authors: Jianwei Yu, Yi Luo, Hangting Chen, Rongzhi Gu, Chao Weng
Date: 2022-12-01 00:00:00
Tasks: Speech Enhancement, Vocal Bursts Intensity Prediction

Despite the rapid progress in speech enhancement (SE) research, enhancing the quality of desired speech in environments with strong noise and interfering speakers remains challenging. In this paper, we extend the application of the recently proposed band-split RNN (BSRNN) model to full-band SE and personalized SE (PSE) tasks. To mitigate the effects of unstable high-frequency components in full-band speech, we perform bi-directional and uni-directional band-level modeling to low-frequency and high-frequency subbands, respectively. For PSE task, we incorporate a speaker enrollment module into BSRNN to utilize target speaker information. Moreover, we utilize a MetricGAN discriminator (MGD) and a multi-resolution spectrogram discriminator (MRSD) to improve perceptual quality metrics. Experimental results show that our system outperforms various top-ranking SE systems, achieves state-of-the-art (SOTA) results on the DNS-2020 test set and ranks among the top 3 in the DNS-2023 challenge.
A Novel Speech Feature Fusion Algorithm for Text-Independent Speaker Recognition
Authors: Biao Ma, Chengben Xu, Ye Zhang
Date: 2022-12-01 00:00:00
Tasks: Speaker Recognition, Text-Independent Speaker Recognition

A novel speech feature fusion algorithm with independent vector analysis (IVA) and parallel convolutional neural network (PCNN) is proposed for text-independent speaker recognition. Firstly, some different feature types, such as the time domain (TD) features and the frequency domain (FD) features, can be extracted from a speaker's speech, and the TD and the FD features can be considered as the linear mixtures of independent feature components (IFCs) with an unknown mixing system. To estimate the IFCs, the TD and the FD features of the speaker's speech are concatenated to build the TD and the FD feature matrix, respectively. Then, a feature tensor of the speaker's speech is obtained by paralleling the TD and the FD feature matrix. To enhance the dependence on different feature types and remove the redundancies of the same feature type, the independent vector analysis (IVA) can be used to estimate the IFC matrices of TD and FD features with the feature tensor. The IFC matrices are utilized as the input of the PCNN to extract the deep features of the TD and FD features, respectively. The deep features can be integrated to obtain the fusion feature of the speaker's speech. Finally, the fusion feature of the speaker's speech is employed as the input of a deep convolutional neural network (DCNN) classifier for speaker recognition. The experimental results show the effectiveness and performances of the proposed speaker recognition system.