pwc-archive/papers-with-abstracts huggingface dataset:
Detecting Shared Data Manipulation in Distributed Optimization Algorithms
Authors: Mohannad Alkhraijah, Rachel Harris, Samuel Litchfield, David Huggins, Daniel K. Molzahn
Date: 2023-10-20 00:00:00
Tasks: Bilevel Optimization, Distributed Optimization

This paper investigates the vulnerability of the Alternating Direction Method of Multipliers (ADMM) algorithm to shared data manipulation, with a focus on solving optimal power flow (OPF) problems. Deliberate data manipulation may cause the ADMM algorithm to converge to suboptimal solutions. We derive two sufficient conditions for detecting data manipulation based on the theoretical convergence trajectory of the ADMM algorithm. We evaluate the detection conditions' performance on three data manipulation strategies we previously proposed: simple, feedback, and bilevel optimization attacks. We then extend these three data manipulation strategies to avoid detection by considering both the detection conditions and a neural network (NN) detection model in the attacks. We also propose an adversarial NN training framework to detect shared data manipulation. We illustrate the performance of our data manipulation strategy and detection framework on OPF problems. The results show that the proposed detection conditions successfully detect most of the data manipulation attacks. However, a bilevel optimization attack strategy that incorporates the detection methods may avoid being detected. Countering this, our proposed adversarial training framework detects all the instances of the bilevel optimization attack.
Deep Reinforcement Learning-Enabled Adaptive Forecasting-Aided State Estimation in Distribution Systems with Multi-Source Multi-Rate Data
Authors: Ying Zhang, Junbo Zhao, Di Shi, Sungjoo Chung
Date: 2023-10-20 00:00:00
Tasks: Deep Reinforcement Learning, Prediction, State Estimation

Distribution system state estimation (DSSE) is paramount for effective state monitoring and control. However, stochastic outputs of renewables and asynchronous streaming of multi-rate measurements in practical systems largely degrade the estimation performance. This paper proposes a deep reinforcement learning (DRL)-enabled adaptive DSSE algorithm in unbalanced distribution systems, which tackles hybrid measurements with different time scales efficiently. We construct a three-step forecasting-aided state estimation framework, including DRL-based parameter identification, prediction, and state estimation, with multi-rate measurements incorporating limited synchrophasor data. Furthermore, a DRL-based adaptive parameter identification mechanism is embedded in the prediction step. As a novel attempt at utilizing DRL to enable DSSE adaptive to varying operating conditions, this method improves the prediction performance and further facilitates accurate state estimation. Case studies in two unbalanced feeders indicate that our method captures state variation with multi-source multi-rate data efficiently, outperforming the traditional methods.
Online energy management system for a fuel cell/battery hybrid system with multiple fuel cell stacks
Authors: Junzhe Shi, Ulf Jakob Flø Aarsnes, Shengyu Tao, Ruiting Wang, Dagfinn Nærheim, Scott Moura
Date: 2023-10-20 00:00:00
Tasks: Computational Efficiency, energy management, Management

Fuel cell (FC)/battery hybrid systems have attracted substantial attention for achieving zero-emissions buses, trucks, ships, and planes. An online energy management system (EMS) is essential for these hybrid systems, it controls energy flow and ensures optimal system performance. Key aspects include fuel efficiency and mitigating FC and battery degradation. This paper proposes a health-aware EMS for FC and battery hybrid systems with multiple FC stacks. The proposed EMS employs mixed integer quadratic programming (MIQP) to control each FC stack in the hybrid system independently, i.e., MIQP-based individual stack control (ISC), with significant fuel cost reductions, FC and battery degradations. The proposed method is compared with classical dynamic programming (DP), with a 2243 times faster computational speed than the DP method while maintaining nearoptimal performance. The case study results show that ISC achieves a 64.68 % total cost reduction compared to CSC in the examined scenario, with substantial reductions across key metrics including battery degradation (4 %), hydrogen fuel consumption (22 %), fuel cell idling loss (99 %), and fuel cell load-change loss (41 %)
Enhancing Building Energy Efficiency through Advanced Sizing and Dispatch Methods for Energy Storage
Authors: Min Gyung Yu, Xu Ma, Bowen Huang, Karthik Devaprasad, Fredericka Brown, Di wu
Date: 2023-10-19 00:00:00
Tasks: Decision Making

Energy storage and electrification of buildings hold great potential for future decarbonized energy systems. However, there are several technical and economic barriers that prevent large-scale adoption and integration of energy storage in buildings. These barriers include integration with building control systems, high capital costs, and the necessity to identify and quantify value streams for different stakeholders. To overcome these obstacles, it is crucial to develop advanced sizing and dispatch methods to assist planning and operational decision-making for integrating energy storage in buildings. This work develops a simple and flexible optimal sizing and dispatch framework for thermal energy storage (TES) and battery energy storage (BES) systems in large-scale office buildings. The optimal sizes of TES, BES, as well as other building assets are determined in a joint manner instead of sequentially to avoid sub-optimal solutions. The solution is determined considering both capital costs in optimal sizing and operational benefits in optimal dispatch. With the optimally sized systems, we implemented real-time operation using the model-based control (MPC), facilitating the effective and efficient management of energy resources. Comprehensive assessments are performed using simulation studies to quantify potential energy and economic benefits by different utility tariffs and climate locations, to improve our understanding of the techno-economic performance of different TES and BES systems, and to identify barriers to adopting energy storage for buildings. Finally, the proposed framework will provide guidance to a broad range of stakeholders to properly design energy storage in buildings and maximize potential benefits, thereby advancing affordable building energy storage deployment and helping accelerate the transition towards a cleaner and more equitable energy economy.
Efficient online cross-covariance monitoring with incremental SVD: An approach for the detection of emerging dependency patterns in IoT systems
Authors: Xinmiao Luan, Qing Zou, Jian Li, Andi Wang
Date: 2023-10-19 00:00:00

The development of the manufacturing systems has made it increasingly necessary to monitor the data generated by multiple interconnected subsystems with rapid incoming of samples. Based on incremental Singular Value Decomposition (ISVD), we develop a general online monitoring approach for the relationship of data generated from two interconnected subsystems, where each subsystem produces big data streams with several variation patterns in normal working condition. When special situation happens and new associations occur, a very small amount of computation is sufficient to update the system status and compute the control statistics by using this approach. The proposed method reduces computational overhead and retains only a small number of pairs of possible dependent patterns at each step. The validation of the method through simulation studies and a case study on semiconductor manufacturing processes further supports its effectiveness.
Closed-Loop Motion Planning for Differentially Flat Systems: A Time-Varying Optimization Framework
Authors: Tianqi Zheng, John W. Simpson-Porco, Enrique Mallada
Date: 2023-10-19 00:00:00
Tasks: Motion Planning

Motion planning and control are two core components of the robotic systems autonomy stack. The standard approach to combine these methodologies comprises an offline/open-loop stage, planning, that designs a feasible and safe trajectory to follow, and an online/closed-loop stage, tracking, that corrects for unmodeled dynamics and disturbances. Such an approach generally introduces conservativeness into the planning stage, which becomes difficult to overcome as the model complexity increases and real-time decisions need to be made in a changing environment. This work addresses these challenges for the class of differentially flat nonlinear systems by integrating planning and control into a cohesive closed-loop task. Precisely, we develop an optimization-based framework that aims to steer a differentially flat system to a trajectory implicitly defined via a constrained time-varying optimization problem. To that end, we generalize the notion of feedback linearization, which makes non-linear systems behave as linear systems, and develop controllers that effectively transform a differentially flat system into an optimization algorithm that seeks to find the optimal solution of a (possibly time-varying) optimization problem. Under sufficient regularity assumptions, we prove global asymptotic convergence for the optimization dynamics to the minimizer of the time-varying optimization problem. We illustrate the effectiveness of our method with two numerical examples: a multi-robot tracking problem and a robot obstacle avoidance problem.
Free space optics communication system design using iterative optimization
Authors: Gebrehiwet Gebrekrstos Lema
Date: 2023-10-12 00:00:00

Free Space Optics (FSO) communication provides attractive bandwidth enhancement with unlicensed bands worldwide spectrum. However, the link capacity and availability are the major concern in the different atmospheric conditions. The reliability of the link is highly dependent on weather conditions that attenuate the signal strength. Hence, this study focuses to mitigate the weather and geographic effects using iterative optimization on FSO communication. The optimization maximizes the visibility distance while guaranteeing the reliability by minimizing the Bit Error Rate (BER). The wireless optical communication system is designed for the data rate of 10 Gbps. The performance of the proposed wireless optical communication is compared against the literature in terms of visibility distance, quality factor, BER, and Eye diagram at different atmospheric conditions. The simulation results have shown that the proposed work has achieved better performance.
EpiGeoPop: A Tool for Developing Spatially Accurate Country-level Epidemiological Models
Authors: Lara Herriott, Henriette L. Capel, Isaac Ellmen, Nathan Schofield, Jiayuan Zhu, Ben Lambert, David Gavaghan, Ioana Bouros, Richard Creswell, Kit Gallagher
Date: 2023-10-20 00:00:00

Mathematical models play a crucial role in understanding the spread of infectious disease outbreaks and influencing policy decisions. These models aid pandemic preparedness by predicting outcomes under hypothetical scenarios and identifying weaknesses in existing frameworks. However, their accuracy, utility, and comparability are being scrutinized. Agent-based models (ABMs) have emerged as a valuable tool, capturing population heterogeneity and spatial effects, particularly when assessing intervention strategies. Here we present EpiGeoPop, a user-friendly tool for rapidly preparing spatially accurate population configurations of entire countries. EpiGeoPop helps to address the problem of complex and time-consuming model set up in ABMs, specifically improving the integration of spatial detail. We subsequently demonstrate the importance of accurate spatial detail in ABM simulations of disease outbreaks using Epiabm, an ABM based on Imperial College London's CovidSim with improved modularity, documentation and testing. Our investigation involves the interplay between population density, the implementation of spatial transmission, and realistic interventions implemented in Epiabm.
Impact of Loss-Framing and Risk Attitudes on Insurance Purchase: Insights from a Game-like Interface Study
Authors: Kunal Rajesh Lahoti, Shivani Hanji, Pratik Kamble, Kavita Vemuri
Date: 2023-10-20 00:00:00

This study investigates the impact of loss-framing and individual risk attitude on willingness- to purchase insurance products utilizing a game-like interface as choice architecture. The application presents events as experienced in real life. Both financial and emotional loss-framing events are followed by choices to purchase insurance. The participant cohorts considered were undergraduate students and older participants; the latter group was further subdivided by income and education. The within-subject analysis reveals that the loss framing effect on insurance consumption is higher in the younger population, though contingent on the insurance product type. Health and accident insurance shows a negative correlation with risk attitudes for younger participants and a positive correlation with accident insurance for older participants. Risk attitude and life insurance products showed no dependency. The findings elucidate the role of age, income, family responsibilities, and risk attitude in purchasing insurance products. Importantly, it confirms the heuristics of framing/nudging.
Metastable Financial Markets
Authors: Diego Marcondes, Adilson Simonis
Date: 2023-10-19 00:00:00
Tasks: Causal Inference, Time Series

Metastability is a phenomenon observed in stochastic systems which stay in a false-equilibrium within a region of its state space until the occurrence of a sequence of rare events that leads to an abrupt transition to a different region. This paper presents financial markets as metastable systems and shows that, under this assumption, financial time series evolve as hidden Markov models. In special, we propose a theory that outlines an explicit causal relation between a financial market and the evolution of a financial time series. In the context of financial economics and causal factor investment, this theory introduces a paradigm shift, suggesting that investment performance fluctuations are primarily driven by the market state rather than direct causation by other variables. While not incompatible with traditional causal inference, our approach addresses the non-stationary evolution of time series through changes in market states, enhancing risk assessment and enabling mitigation strategies.
Non-linear approximations of DSGE models with neural-networks and hard-constraints
Authors: Emmet Hall-Hoffarth
Date: 2023-10-20 00:00:00

Recently a number of papers have suggested using neural-networks in order to approximate policy functions in DSGE models, while avoiding the curse of dimensionality, which for example arises when solving many HANK models, and while preserving non-linearity. One important step of this method is to represent the constraints of the economic model in question in the outputs of the neural-network. I propose, and demonstrate the advantages of, a novel approach to handling these constraints which involves directly constraining the neural-network outputs, such that the economic constraints are satisfied by construction. This is achieved by a combination of re-scaling operations that are differentiable and therefore compatible with the standard gradient descent approach used when fitting neural-networks. This has a number of attractive properties, and is shown to out-perform the penalty-based approach suggested by the existing literature, which while theoretically sound, can be poorly behaved practice for a number of reasons that I identify.
Persuasion in Veto Bargaining
Authors: Jenny S Kim, KyungMin Kim, Richard Van Weelden
Date: 2023-10-19 00:00:00

We consider the classic veto bargaining model but allow the agenda setter to engage in persuasion to convince the veto player to approve her proposal. We fully characterize the optimal proposal and experiment when Vetoer has quadratic loss, and show that the proposer-optimal can be achieved either by providing no information or with a simple binary experiment. Proposer chooses to reveal partial information when there is sufficient expected misalignment with Vetoer. In this case the opportunity to engage in persuasion strictly benefits Proposer and increases the scope to exercise agenda power.
Monotonicity Failure in Ranked Choice Voting -- Necessary and Sufficient Conditions for 3-Candidate Elections
Authors: Rylie Weaver
Date: 2023-09-18 00:00:00

Ranked choice voting is vulnerable to monotonicity failure - a voting failure where a candidate is cost an election due to losing voter preference or granted an election due to gaining voter preference. Despite increasing use of ranked choice voting at the time of writing of this paper, the frequency of monotonicity failure is still a very open question. This paper builds on previous work to develop conditions which can be used to test if it's possible that monotonicity failure has happened in a 3-candidate ranked choice voting election.
Majority rule as a unique voting method in elections with multiple candidates
Authors: Mateusz Krukowski
Date: 2023-08-10 00:00:00

May's classical theorem states that in a single-winner choose-one voting system with just two candidates, majority rule is the only social choice function satisfying anonimity, neutrality and positive responsiveness axiom. Anonimity and neutrality are usually regarded as very natural constraints on the social choice function. Positive responsiveness, on the other hand, is sometimes deemed too strong of an axiom, which stimulates further search for less stringent conditions. One viable substitute is Gerhard J. Woeginger's "reducibility to subsocieties". We demonstrate that the condition generalizes to more than two candidates and, consequently, characterizes majority rule for elections with multiple candidates.
GenDistiller: Distilling Pre-trained Language Models based on Generative Models
Authors: Yingying Gao, Shilei Zhang, Zihao Cui, Yanhan Xu, Chao Deng, Junlan Feng
Date: 2023-10-20 00:00:00
Tasks: Knowledge Distillation, Language Modeling, Language Modelling, Representation Learning

Self-supervised pre-trained models such as HuBERT and WavLM leverage unlabeled speech data for representation learning and offer significantly improve for numerous downstream tasks. Despite the success of these methods, their large memory and strong computational requirements hinder their application on resource restricted devices. Therefore, this paper introduces GenDistiller, a novel knowledge distillation framework to distill hidden representations from teacher network based on generative language model. The generative structure enables the proposed model to generate the target teacher hidden layers autoregressively, considering the interactions between hidden layers without instroducing additional inputs. A two-dimensional attention mechanism is implemented to ensure the causality of hidden layers, while preserving bidirectional attention in the time dimension. Experiments reveal the advantage of the generative distiller over the baseline system that predicts the hidden layers of teacher network directly without a generatvie model.
Detecting Speech Abnormalities with a Perceiver-based Sequence Classifier that Leverages a Universal Speech Model
Authors: Hagen Soltau, Izhak Shafran, Alex Ottenwess, Joseph R. JR Duffy, Rene L. Utianski, Leland R. Barnard, John L. Stricker, Daniela Wiepert, David T. Jones, Hugo Botha
Date: 2023-10-16 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

We propose a Perceiver-based sequence classifier to detect abnormalities in speech reflective of several neurological disorders. We combine this classifier with a Universal Speech Model (USM) that is trained (unsupervised) on 12 million hours of diverse audio recordings. Our model compresses long sequences into a small set of class-specific latent representations and a factorized projection is used to predict different attributes of the disordered input speech. The benefit of our approach is that it allows us to model different regions of the input for different classes and is at the same time data efficient. We evaluated the proposed model extensively on a curated corpus from the Mayo Clinic. Our model outperforms standard transformer (80.9%) and perceiver (81.8%) models and achieves an average accuracy of 83.1%. With limited task-specific data, we find that pretraining is important and surprisingly pretraining with the unrelated automatic speech recognition (ASR) task is also beneficial. Encodings from the middle layers provide a mix of both acoustic and phonetic information and achieve best prediction results compared to just using the final layer encodings (83.1% vs. 79.6%). The results are promising and with further refinements may help clinicians detect speech abnormalities without needing access to highly specialized speech-language pathologists.
Video Quality Assessment and Coding Complexity of the Versatile Video Coding Standard
Authors: Thomas Amestoy, Naty Sidaty, Wassim Hamidouche, Pierrick Philippe, Daniel Menard
Date: 2023-10-19 00:00:00
Tasks: SSIM, Video Compression, Video Quality Assessment

In recent years, the proliferation of multimedia applications and formats, such as IPTV, Virtual Reality (VR, 360-degree), and point cloud videos, has presented new challenges to the video compression research community. Simultaneously, there has been a growing demand from users for higher resolutions and improved visual quality. To further enhance coding efficiency, a new video coding standard, Versatile Video Coding (VVC), was introduced in July 2020. This paper conducts a comprehensive analysis of coding performance and complexity for the latest VVC standard in comparison to its predecessor, High Efficiency Video Coding (HEVC). The study employs a diverse set of test sequences, covering both High Definition (HD) and Ultra High Definition (UHD) resolutions, and spans a wide range of bit-rates. These sequences are encoded using the reference software encoders of HEVC (HM) and VVC (VTM). The results consistently demonstrate that VVC outperforms HEVC, achieving bit-rate savings of up to 40% on the subjective quality scale, particularly at realistic bit-rates and quality levels. Objective quality metrics, including PSNR, SSIM, and VMAF, support these findings, revealing bit-rate savings ranging from 31% to 40%, depending on the video content, spatial resolution, and the selected quality metric. However, these improvements in coding efficiency come at the cost of significantly increased computational complexity. On average, our results indicate that the VVC decoding process is 1.5 times more complex, while the encoding process becomes at least eight times more complex than that of the HEVC reference encoder. Our simultaneous profiling of the two standards sheds light on the primary evolutionary differences between them and highlights the specific stages responsible for the observed increase in complexity.
Time-Modulated Intelligent Reflecting Surface for Waveform Security
Authors: Zhaoyi Xu, Athina Petropulu
Date: 2023-10-20 00:00:00
Tasks: Diversity

We consider an OFDM transmitter aided by an intelligent reflecting surface (IRS) and propose a novel approach to enhance waveform security by employing time modulation (TM) at the IRS side. By controlling the periodic TM pattern of the IRS elements, the system is designed to preserve communication information towards an authorized recipient and scramble the information towards all other directions. We introduce two modes of TM pattern control: the linear mode, in which we design common TM parameters for entire rows or columns of the IRS, and the planar mode, where we design TM parameters for each individual IRS unit. Due to the required fewer switches, the linear mode is easier to implement as compared to the planar mode. However, the linear model results in a beampattern that has sidelobes, over which the transmitted information is not sufficiently scrambled. We show that the sidelobes of the linear mode can be suppressed by exploiting the high diversity available in that mode.