pwc-archive/papers-with-abstracts huggingface dataset:
Augmenting Ad-Hoc IR Dataset for Interactive Conversational Search
Authors: Pierre Erbacher, Jian-Yun Nie, Philippe Preux, Laure Soulier
Date: 2023-11-10 00:00:00
Tasks: Conversational Search

A peculiarity of conversational search systems is that they involve mixed-initiatives such as system-generated query clarifying questions. Evaluating those systems at a large scale on the end task of IR is very challenging, requiring adequate datasets containing such interactions. However, current datasets only focus on either traditional ad-hoc IR tasks or query clarification tasks, the latter being usually seen as a reformulation task from the initial query. The only two datasets known to us that contain both document relevance judgments and the associated clarification interactions are Qulac and ClariQ. Both are based on the TREC Web Track 2009-12 collection, but cover a very limited number of topics (237 topics), far from being enough for training and testing conversational IR models. To fill the gap, we propose a methodology to automatically build large-scale conversational IR datasets from ad-hoc IR datasets in order to facilitate explorations on conversational IR. Our methodology is based on two processes: 1) generating query clarification interactions through query clarification and answer generators, and 2) augmenting ad-hoc IR datasets with simulated interactions. In this paper, we focus on MsMarco and augment it with query clarification and answer simulations. We perform a thorough evaluation showing the quality and the relevance of the generated interactions for each initial query. This paper shows the feasibility and utility of augmenting ad-hoc IR datasets for conversational IR.
Exploring Fine-tuning ChatGPT for News Recommendation
Authors: Xinyi Li, Yongfeng Zhang, Edward C Malthouse
Date: 2023-11-10 00:00:00
Tasks: News Recommendation, Recommendation Systems

News recommendation systems (RS) play a pivotal role in the current digital age, shaping how individuals access and engage with information. The fusion of natural language processing (NLP) and RS, spurred by the rise of large language models such as the GPT and T5 series, blurs the boundaries between these domains, making a tendency to treat RS as a language task. ChatGPT, renowned for its user-friendly interface and increasing popularity, has become a prominent choice for a wide range of NLP tasks. While previous studies have explored ChatGPT on recommendation tasks, this study breaks new ground by investigating its fine-tuning capability, particularly within the news domain. In this study, we design two distinct prompts: one designed to treat news RS as the ranking task and another tailored for the rating task. We evaluate ChatGPT's performance in news recommendation by eliciting direct responses through the formulation of these two tasks. More importantly, we unravel the pivotal role of fine-tuning data quality in enhancing ChatGPT's personalized recommendation capabilities, and illustrates its potential in addressing the longstanding challenge of the "cold item" problem in RS. Our experiments, conducted using the Microsoft News dataset (MIND), reveal significant improvements achieved by ChatGPT after fine-tuning, especially in scenarios where a user's topic interests remain consistent, treating news RS as a ranking task. This study illuminates the transformative potential of fine-tuning ChatGPT as a means to advance news RS, offering more effective news consumption experiences.
Collective Sampling: An Ex Ante Perspective
Authors: Yangfan Zhou
Date: 2023-11-09 00:00:00

I study collective dynamic information acquisition. Players decide when to stop sequential sampling via a collective stopping rule, which specifies decisive coalitions that can terminate information acquisition upon agreement. I develop a methodology to characterize equilibria using an ex ante perspective. Instead of stopping strategies, players choose distributions over posterior beliefs subject to majorization constraints. Equilibrium sampling regions are characterized via a fixed-point argument based on concavification. Collective sampling generates learning inefficiencies and having more decisive coalitions typically reduces learning. I apply the model to committee search and competition in persuasion.
Predictive AI for SME and Large Enterprise Financial Performance Management
Authors: Ricardo Cuervo
Date: 2023-09-22 00:00:00
Tasks: Management

Financial performance management is at the core of business management and has historically relied on financial ratio analysis using Balance Sheet and Income Statement data to assess company performance as compared with competitors. Little progress has been made in predicting how a company will perform or in assessing the risks (probabilities) of financial underperformance. In this study I introduce a new set of financial and macroeconomic ratios that supplement standard ratios of Balance Sheet and Income Statement. I also provide a set of supervised learning models (ML Regressors and Neural Networks) and Bayesian models to predict company performance. I conclude that the new proposed variables improve model accuracy when used in tandem with standard industry ratios. I also conclude that Feedforward Neural Networks (FNN) are simpler to implement and perform best across 6 predictive tasks (ROA, ROE, Net Margin, Op Margin, Cash Ratio and Op Cash Generation); although Bayesian Networks (BN) can outperform FNN under very specific conditions. BNs have the additional benefit of providing a probability density function in addition to the predicted (expected) value. The study findings have significant potential helping CFOs and CEOs assess risks of financial underperformance to steer companies in more profitable directions; supporting lenders in better assessing the condition of a company and providing investors with tools to dissect financial statements of public companies more accurately.
Risk Management of Unmanned Aerial Vehicles
Authors: Hamid Reza Naji, Aref Ayari
Date: 2023-11-07 00:00:00
Tasks: Management

This paper presents an efficient risk management model for unmanned aerial vehicles or UAVs. Our proposed risk management establishes a cyclic model with a continuous and iterative structure that is very adaptable to agile methods and all IT-related resources. This model can be used in many applications, but as a case study, we have discussed it for UAVs. The increasing use of UAVs or drones in many fields and the existence of different threats is the main reason to have an efficient risk management method for them. In this paper, we cover risks based on IT-driven assets to decrease the chance of losing any data, failing the equipment or the system, and missing the reputation or credit based on cyclic and iterative flow. Our current risk management model for UAVs or drones is based on qualitative measures and can cover most of IT-based risks.
The Biological Data Sustainability Paradox
Authors: Terence R. Johnson, Philip E. Bourne
Date: 2023-11-09 00:00:00

Biological data in digital form has become a, if not the, driving force behind innovations in biology, medicine, and the environment. No study and no model would be complete without access to digital data (including text) collected by others and available in public repositories. With this ascent in the fundamental importance of data for reproducible scientific progress has come a troubling paradox.
Fully-Passive versus Semi-Passive IRS-Enabled Sensing: SNR and CRB Comparison
Authors: Xianxin Song, Xinmin Li, Xiaoqi Qin, Jie Xu, Tony Xiao Han, Derrick Wing Kwan Ng
Date: 2023-11-10 00:00:00

This paper investigates the sensing performance of two intelligent reflecting surface (IRS)-enabled non-line-of-sight (NLoS) sensing systems with fully-passive and semi-passive IRSs, respectively. In particular, we consider a fundamental setup with one base station (BS), one uniform linear array (ULA) IRS, and one point target in the NLoS region of the BS. Accordingly, we analyze the sensing signal-to-noise ratio (SNR) performance for a target detection scenario and the estimation Cram\'er-Rao bound (CRB) performance for a target's direction-of-arrival (DoA) estimation scenario, in cases where the transmit beamforming at the BS and the reflective beamforming at the IRS are jointly optimized. First, for the target detection scenario, we characterize the maximum sensing SNR when the BS-IRS channels are line-of-sight (LoS) and Rayleigh fading, respectively. It is revealed that when the number of reflecting elements $N$ equipped at the IRS becomes sufficiently large, the maximum sensing SNR increases proportionally to $N^2$ for the semi-passive-IRS sensing system, but proportionally to $N^4$ for the fully-passive-IRS counterpart. Then, for the target's DoA estimation scenario, we analyze the minimum CRB performance when the BS-IRS channel follows Rayleigh fading. Specifically, when $N$ grows, the minimum CRB decreases inversely proportionally to $N^4$ and $N^6$ for the semi-passive and fully-passive-IRS sensing systems, respectively. Finally, numerical results are presented to corroborate our analysis across various transmit and reflective beamforming design schemes under general channel setups. It is shown that the fully-passive-IRS sensing system outperforms the semi-passive counterpart when $N$ exceeds a certain threshold. This advantage is attributed to the additional reflective beamforming gain in the IRS-BS path, which efficiently compensates for the path loss for a large $N$.
MaDEVIoT: Cyberattacks on EV Charging Can Disrupt Power Grid Operation
Authors: Samrat Acharya, Hafiz Anwar Ullah Khan, Ramesh Karri, Yury Dvorkin
Date: 2023-11-10 00:00:00

This paper examines the feasibility of demand-side cyberattacks on power grids launched via internet-connected high-power EV Charging Stations (EVCSs). By distorting power grid frequency and voltage, these attacks can trigger system-wide outages. Our case study focuses on Manhattan, New York, and reveals that such attacks will become feasible by 2030 with increased EV adoption. With a single EVCS company dominating Manhattan, compromising a single EVCS server raises serious power grid security concerns. These attacks can overload power lines and trip over-frequency (OF) protection relays, resulting in a power grid blackout. This study serves as a crucial resource for planning authorities and power grid operators involved in the EV charging infrastructure roll-out, highlighting potential cyberthreats to power grids stemming from high-power EVCSs.
A Time-invariant Network Flow Model for Ride-pooling in Mobility-on-Demand Systems
Authors: Fabio Paparella, Leonardo Pedroso, Theo Hofman, Mauro Salazar
Date: 2023-11-10 00:00:00

This paper presents a framework to incorporate ride-pooling from a mesoscopic point of view, within time-invariant network flow models of Mobility-on-Demand systems. The resulting problem structure remains identical to a standard network flow model, a linear problem, which can be solved in polynomial time for a given ride-pooling request assignment. In order to compute such a ride-pooling assignment, we devise a polynomial-time knapsack-like algorithm that is optimal w.r.t. the minimum user travel time instance of the original problem. Finally, we conduct two case studies of Sioux Falls and Manhattan, where we validate our models against state-of-the-art time-varying results, and we quantitatively highlight the effects that maximum waiting time and maximum delay thresholds have on the vehicle hours traveled, overall pooled rides and actual delay experienced. We show that for a sufficient number of requests, with a maximum waiting time and delay of 5 minutes, it is possible to ride-pool more than 80% of the requests for both case studies. Last, allowing for four people ride-pooling can significantly boost the performance of the system.
Resilient and constrained consensus against adversarial attacks: A distributed MPC framework
Authors: Henglai Wei, Kunwu Zhang, HUI ZHANG, Yang Shi
Date: 2023-11-10 00:00:00
Tasks: Adversarial Attack, Adversarial Attack Detection, Model Predictive Control

There has been a growing interest in realizing the resilient consensus of the multi-agent system (MAS) under cyber-attacks, which aims to achieve the consensus of normal agents (i.e., agents without attacks) in a network, depending on the neighboring information. The literature has developed mean-subsequence-reduced (MSR) algorithms for the MAS with F adversarial attacks and has shown that the consensus is achieved for the normal agents when the communication network is at least (2F+1)-robust. However, such a stringent requirement on the communication network needs to be relaxed to enable more practical applications. Our objective is, for the first time, to achieve less stringent conditions on the network, while ensuring the resilient consensus for the general linear MAS subject to control input constraints. In this work, we propose a distributed resilient consensus framework, consisting of a pre-designed consensus protocol and distributed model predictive control (DMPC) optimization, which can help significantly reduce the requirement on the network robustness and effectively handle the general linear constrained MAS under adversarial attacks. By employing a novel distributed adversarial attack detection mechanism based on the history information broadcast by neighbors and a convex set (i.e., resilience set), we can evaluate the reliability of communication links. Moreover, we show that the recursive feasibility of the associated DMPC optimization problem can be guaranteed. The proposed consensus protocol features the following properties: 1) by minimizing a group of control variables, the consensus performance is optimized; 2) the resilient consensus of the general linear constrained MAS subject to F-locally adversarial attacks is achieved when the communication network is (F+1)-robust. Finally, numerical simulation results are presented to verify the theoretical results.
A TBLMI Framework for Harmonic Robust Control
Authors: Flora Vernerey, Pierre Riedinger, Jamal Daafouz
Date: 2023-11-10 00:00:00

The primary objective of this paper is to demonstrate that problems related to stability and robust control in the harmonic context can be effectively addressed by formulating them as semidefinite optimization problems, invoking the concept of infinite-dimensional Toeplitz Block LMIs (TBLMIs). One of the central challenges tackled in this study pertains to the efficient resolution of these infinite-dimensional TBLMIs. Exploiting the structured nature of such problems, we introduce a consistent truncation method that effectively reduces the problem to a finite-dimensional convex optimization problem. By consistent we mean that the solution to this finite-dimensional problem allows to closely approximate the infinite-dimensional solution with arbitrary precision. Furthermore, we establish a link between the harmonic framework and the time domain setting, emphasizing the advantages over Periodic Differential LMIs (PDLMIs). We illustrate that our proposed framework is not only theoretically sound but also practically applicable to solving H 2 and H$\infty$ harmonic control design problems. To enable this, we extend the definitions of H 2 and H$\infty$ norms into the harmonic space, leveraging the concepts of the harmonic transfer function and the average trace operator for Toeplitz Block operators. Throughout this paper, we support our theoretical contributions with a range of illustrative examples that demonstrate the effectiveness of our approach.
A Wi-Fi Signal-Based Human Activity Recognition Using High-Dimensional Factor Models
Authors: Junshuo Liu, Fuhai Wang, Zhe Li, Rujing Xiong, Tiebin Mi, Robert Caiming Qiu
Date: 2023-11-10 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Passive sensing techniques based on Wi-Fi signals have emerged as a promising technology in advanced wireless communication systems due to their widespread application and cost-effectiveness. However, the proliferation of low-cost Internet of Things (IoT) devices has led to dense network deployments, resulting in increased levels of noise and interference in Wi-Fi environments. This, in turn, leads to noisy and redundant Channel State Information (CSI) data. As a consequence, the accuracy of human activity recognition based on Wi-Fi signals is compromised. To address this issue, we propose a novel CSI data signal extraction method. We established a human activity recognition system based on the Intel 5300 network interface cards (NICs) and collected a dataset containing six categories of human activities. Using our approach, signals extracted from the CSI data serve as inputs to machine learning (ML) classification algorithms to evaluate classification performance. In comparison to ML methods based on Principal Component Analysis (PCA), our proposed High-Dimensional Factor Model (HDFM) method improves recognition accuracy by 6.8%.
Optimal Privacy-Aware Dynamic Estimation
Authors: Chuanghong Weng, Ehsan Nekouei, Karl H. Johansson
Date: 2023-11-10 00:00:00

In this paper, we develop an information-theoretic framework for the optimal privacy-aware estimation of the states of a (linear or nonlinear) system. In our setup, a private process, modeled as a first-order Markov chain, derives the states of the system, and the state estimates are shared with an untrusted party who might attempt to infer the private process based on the state estimates. As the privacy metric, we use the mutual information between the private process and the state estimates. We first show that the privacy-aware estimation is a closed-loop control problem wherein the estimator controls the belief of the adversary about the private process. We also derive the Bellman optimality principle for the optimal privacy-aware estimation problem, which is used to study the structural properties of the optimal estimator. We next develop a policy gradient algorithm, for computing an optimal estimation policy, based on a novel variational formulation of the mutual information. We finally study the performance of the optimal estimator in a building automation application.
Practical one-shot data-driven design of fractional-order PID controller: Fictitious reference signal approach
Authors: Ansei Yonezawa, Heisei Yonezawa, Shuichi Yahagi, Itsuro Kajiwara
Date: 2023-11-10 00:00:00

This study proposes a one-shot data-driven tuning method for a fractional-order proportional-integral-derivative (FOPID) controller. The proposed method tunes the FOPID controller in the model-reference control formulation. A loss function is defined to evaluate the match between a given reference model and the closed-loop response while explicitly considering the closed-loop stability. A loss function value is based on the fictitious reference signal computed using the input/output data. Model matching is achieved via loss function minimization. The proposed method is simple and practical: it needs only one-shot input/output data of a plant (no plant model required), considers the bounded-input bounded-output stability of the closed-loop system from a bounded reference input to a bounded output, and automatically determines the appropriate parameter value via optimization. Numerical simulations show that the proposed approach facilitates good control performance, and destabilization can be avoided even if perfect model matching is unachievable.
Automated Lane Change via Adaptive Interactive MPC: Human-in-the-Loop Experiments
Authors: Viranjan Bhattacharyya, Ardalan Vahidi
Date: 2023-11-10 00:00:00
Tasks: Autonomous Vehicles, Model Predictive Control, Motion Planning

This article presents a new optimal control-based interactive motion planning algorithm for an autonomous vehicle interacting with a human-driven vehicle. The ego vehicle solves a joint optimization problem for its motion planning involving costs and coupled constraints of both vehicles and applies its own actions. The non-convex feasible region and lane discipline are handled by introducing integer decision variables and the resulting optimization problem is a mixed-integer quadratic program (MIQP) which is implemented via model predictive control (MPC). Furthermore, the ego vehicle imputes the cost of human-driven neighboring vehicle (NV) using an inverse optimal control method based on Karush-Kuhn-Tucker (KKT) conditions and adapts the joint optimization cost accordingly. We call the algorithm adaptive interactive mixed-integer MPC (aiMPC). Its interaction with human subjects driving the NV in a mandatory lane change scenario is tested in a developed software-and-human-in-the-loop simulator. Results show the effectiveness of the presented algorithm in terms of enhanced mobility of both the vehicles compared to baseline methods.
Generative Modeling of Residuals for Real-Time Risk-Sensitive Safety with Discrete-Time Control Barrier Functions
Authors: Ryan K. Cosner, Igor Sadalski, Jana K. Woo, Preston Culbertson, Aaron D. Ames
Date: 2023-11-10 00:00:00

A key source of brittleness for robotic systems is the presence of model uncertainty and external disturbances. Most existing approaches to robust control either seek to bound the worst-case disturbance (which results in conservative behavior), or to learn a deterministic dynamics model (which is unable to capture uncertain dynamics or disturbances). This work proposes a different approach: training a state-conditioned generative model to represent the distribution of error residuals between the nominal dynamics and the actual system. In particular we introduce the Online Risk-Informed Optimization controller (ORIO), which uses Discrete-Time Control Barrier Functions, combined with a learned, generative disturbance model, to ensure the safety of the system up to some level of risk. We demonstrate our approach in both simulations and hardware, and show our method can learn a disturbance model that is accurate enough to enable risk-sensitive control of a quadrotor flying aggressively with an unmodelled slung load. We use a conditional variational autoencoder (CVAE) to learn a state-conditioned dynamics residual distribution, and find that the resulting probabilistic safety controller, which can be run at 100Hz on an embedded computer, exhibits less conservative behavior while retaining theoretical safety properties.
Flexibility of Integrated Power and Gas Systems: Gas Flow Modeling and Solution Choices Matter
Authors: Enrica Raheli, Yannick Werner, Jalal Kazempour
Date: 2023-11-09 00:00:00

Due to their slow gas flow dynamics, natural gas pipelines function as short-term storage, the so-called linepack. By efficiently utilizing linepack, the natural gas system can provide flexibility to the power system through the flexible operation of gas-fired power plants. This requires accurately representing the gas flow physics governed by partial differential equations. Although several modeling and solution choices have been proposed in the literature, their impact on the flexibility provision of gas networks to power systems has not been thoroughly analyzed and compared. This paper bridges this gap by first developing a unified framework. We harmonize existing approaches and demonstrate their derivation from and application to the partial differential equations. Secondly, based on the proposed framework, we numerically analyze the implications of various modeling and solution choices on the flexibility provision from gas networks to power systems. One key conclusion is that relaxation-based approaches allow charging and discharging the linepack at physically infeasible high rates, ultimately overestimating the flexibility.