pwc-archive/papers-with-abstracts huggingface dataset:
Method to Annotate Arrhythmias by Deep Network
Authors: Weijia Lu, Jie Shuai, Shuyan Gu, Joel Xue
Date: 2018-06-09 00:00:00
Tasks: Rhythm

This study targets to automatically annotate on arrhythmia by deep network. The investigated types include sinus rhythm, asystole (Asys), supraventricular tachycardia (Tachy), ventricular flutter or fibrillation (VF/VFL), ventricular tachycardia (VT). Methods: 13s limb lead ECG chunks from MIT malignant ventricular arrhythmia database (VFDB) and MIT normal sinus rhythm database were partitioned into subsets for 5-fold cross validation. These signals were resampled to 200Hz, filtered to remove baseline wandering, projected to 2D gray spectrum and then fed into a deep network with brand-new structure. In this network, a feature vector for a single time point was retrieved by residual layers, from which latent representation was extracted by variational autoencoder (VAE). These front portions were trained to meet a certain threshold in loss function, then fixed while training procedure switched to remaining bidirectional recurrent neural network (RNN), the very portions to predict an arrhythmia category. Attention windows were polynomial lumped on RNN outputs for learning from details to outlines. And over sampling was employed for imbalanced data. The trained model was wrapped into docker image for deployment in edge or cloud. Conclusion: Promising sensitivities were achieved in four arrhythmias and good precision rates in two ventricular arrhythmias were also observed. Moreover, it was proven that latent representation by VAE, can significantly boost the speed of convergence and accuracy.
Stochastic Maximum Likelihood Direction Finding in the Presence of Nonuniform Noise Fields
Authors: Ming-yan Gong, Bin Lyu
Date: 2023-02-09 00:00:00

In this letter, we employ and design the expectation--conditional maximization either (ECME) algorithm, a generalisation of the EM algorithm, for solving the maximum likelihood direction finding problem of stochastic sources, which may be correlated, in unknown nonuniform noise. Unlike alternating maximization, the ECME algorithm updates both the source and noise covariance matrix estimates by explicit formulas and can guarantee that both estimates are positive semi-definite and definite, respectively. Thus, the ECME algorithm is computationally efficient and operationally stable. Simulation results confirm the effectiveness of the algorithm.
On the Design of a Heuristic based on Artificial Neural Networks for the Near Optimal Solving of the (N2-1)-puzzle
Authors: Vojtech Cahlik, Pavel Surynek
Date: 2019-09-01 00:00:00
Proceeding: International Joint Conference on Computational Intelligence (IJCCI) 2019 9

This paper addresses optimal and near-optimal solving of the (N2–1)-puzzle using the A* search algorithm. We develop a novel heuristic based on artificial neural networks (ANNs) called ANN-distance that attempts to estimate the minimum number of moves necessary to reach the goal configuration of the puzzle. With a well trained ANN-distance heuristic, whose inputs are just the positions of the pebbles, we are able to achieve better accuracy of predictions than with conventional heuristics such as those derived from the Manhattan distance or pattern database heuristics. Though we cannot guarantee admissibility of ANN-distance, an experimental evaluation on random 15-puzzles shows that in most cases ANN-distance calculates the true minimum distance from the goal, and furthermore, A* search with the ANN-distance heuristic usually finds an optimal solution or a solution that is very close to the optimum. Moreover, the underlying neural network in ANN-distance consumes much less memory than a comparable pattern database.
Near Optimal Solving of the (N2-1)-puzzle Using Heuristics Based on Artificial Neural Networks
Authors: Vojtech Cahlik, Pavel Surynek
Date: 2021-07-01 00:00:00
Proceeding: International Joint Conference on Computational Intelligence (IJCCI) 2021 7

We address the design of heuristics for near-optimal solving of the (N2–1)-puzzle using the A* search algorithm in this paper. The A* search algorithm explores configurations of the puzzle in the order determined by a heuristic that tries to estimate the minimum number of moves needed to reach the goal from the given configuration. To guarantee finding an optimal solution, the A* algorithm requires heuristics that estimate the number of moves from below. Common heuristics for the (N2–1)-puzzle often underestimate the true number of moves greatly in order to meet the admissibility requirement. The worse the estimation is the more configurations the search algorithm needs to explore. We therefore relax from the admissibility requirement and design a novel heuristic that tries estimating the minimum number of moves remaining as precisely as possible while overestimation of the true distance is permitted. Our heuristic called ANN-distance is based on a deep artificial neural network (ANN). We experimentally show that with a well trained ANN-distance heuristic, whose inputs are just the positions of the tiles, we are able to achieve better accuracy of estimation than with conventional heuristics such as those derived from the Manhattan distance or pattern database heuristics. Though we cannot guarantee admissibility of ANN-distance due to possible overestimation of the true number of moves, an experimental evaluation on random 15-puzzles shows that in most cases the ANN-distance calculates the true minimum distance from the goal or an estimation that is very close to the true distance. Consequently, A* search with the ANN-distance heuristic usually finds an optimal solution or a solution that is very close to the optimum. Moreover, the underlying neural network in ANN-distance consumes much less memory than a comparable pattern database. We also show that a deep artificial neural network can be more powerful than a shallow artificial neural network, and also trained our heuristic to prefer underestimating the optimal solution cost, which pushed the solutions towards better optimality.
Adapting the Size of Artificial Neural Networks Using Dynamic Auto-Sizing
Authors: Vojtech Cahlik, Pavel Kordik, Miroslav Cepek
Date: 2022-11-01 00:00:00
Proceeding: IEEE International Conference on Computer Sciences and Information Technologies (CSIT) 2022 11

We introduce dynamic auto-sizing, a novel approach to training artificial neural networks which allows the models to automatically adapt their size to the problem domain. The size of the models can be further controlled during the learning process by modifying the applied strength of regularization. The ability of dynamic auto-sizing models to expand or shrink their hidden layers is achieved by periodically growing and pruning entire units such as neurons or filters. For this purpose, we introduce weighted L1 regularization, a novel regularization method for inducing structured sparsity. Besides analyzing the behavior of dynamic auto-sizing, we evaluate predictive performance of models trained using the method and show that such models can provide a predictive advantage over traditional approaches.
Pricing cyber-insurance for systems via maturity models
Authors: Henry Skeoch, David Pym
Date: 2023-02-09 00:00:00
Tasks: Management

Pricing insurance for risks associated with information technology systems presents a complex modelling challenge, combining the disciplines of operations management, security, and economics. This work proposes a socioeconomic modelling framework for cyber-insurance decisions compromised of entity relationship diagrams, security maturity models, and economic models, addressing a long-standing research challenge of capturing organizational structure in the design and pricing of cyber-insurance policies. Insurance pricing is usually informed by the long experience insurance companies have of the magnitude and frequency of losses that arise in organizations based on their size, industry sector, and location. Consequently, their calculations of premia will start from a baseline determined by these considerations. A unique challenge of cyber-insurance is that data history is limited and not necessarily informative of future loss risk meaning that established actuarial methodology for other lines of insurance may not be the optimal pricing strategy. The modelling framework proposed in this paper provides a vehicle for agreement between practitioners in the cyber-insurance ecosystem on cyber-security risks and allows for the users to choose their desired level of abstraction in the description of a system.
The Gradient of Generative AI Release: Methods and Considerations
Authors: Irene Solaiman
Date: 2023-02-05 00:00:00

As increasingly powerful generative AI systems are developed, the release method greatly varies. We propose a framework to assess six levels of access to generative AI systems: fully closed; gradual or staged access; hosted access; cloud-based or API access; downloadable access; and fully open. Each level, from fully closed to fully open, can be viewed as an option along a gradient. We outline key considerations across this gradient: release methods come with tradeoffs, especially around the tension between concentrating power and mitigating risks. Diverse and multidisciplinary perspectives are needed to examine and mitigate risk in generative AI systems from conception to deployment. We show trends in generative system release over time, noting closedness among large companies for powerful systems and openness among organizations founded on principles of openness. We also enumerate safety controls and guardrails for generative systems and necessary investments to improve future releases.
Exploring the Cognitive Dynamics of Artificial Intelligence in the Post-COVID-19 and Learning 3.0 Era: A Case Study of ChatGPT
Authors: Lingfei Luan, Xi Lin, Wenbiao Li
Date: 2023-02-03 00:00:00
Tasks: Ethics

The emergence of artificial intelligence has incited a paradigm shift across the spectrum of human endeavors, with ChatGPT serving as a catalyst for the transformation of various established domains, including but not limited to education, journalism, security, and ethics. In the post-pandemic era, the widespread adoption of remote work has prompted the educational sector to reassess conventional pedagogical methods. This paper is to scrutinize the underlying psychological principles of ChatGPT, delve into the factors that captivate user attention, and implicate its ramifications on the future of learning. The ultimate objective of this study is to instigate a scholarly discourse on the interplay between technological advancements in education and the evolution of human learning patterns, raising the question of whether technology is driving human evolution or vice versa.
Near-Field Terahertz Communications: Model-Based and Model-Free Channel Estimation
Authors: Ahmet M. Elbir, Wei Shi, Anastasios K. Papazafeiropoulos, Pandelis Kourtessis, Symeon Chatzinotas
Date: 2023-02-09 00:00:00
Tasks: Federated Learning, model

Terahertz (THz) band is expected to be one of the key enabling technologies of the sixth generation (6G) wireless networks because of its abundant available bandwidth and very narrow beam width. Due to high frequency operations, electrically small array apertures are employed, and the signal wavefront becomes spherical in the near-field. Therefore, near-field signal model should be considered for channel acquisition in THz systems. Unlike prior works which mostly ignore the impact of near-field beam-split (NB) and consider either narrowband scenario or far-field models, this paper introduces both a model-based and a model-free techniques for wideband THz channel estimation in the presence of NB. The model-based approach is based on orthogonal matching pursuit (OMP) algorithm, for which we design an NB-aware dictionary. The key idea is to exploit the angular and range deviations due to the NB. We then employ the OMP algorithm, which accounts for the deviations thereby ipso facto mitigating the effect of NB. We further introduce a federated learning (FL)-based approach as a model-free solution for channel estimation in a multi-user scenario to achieve reduced complexity and training overhead. Through numerical simulations, we demonstrate the effectiveness of the proposed channel estimation techniques for wideband THz systems in comparison with the existing state-of-the-art techniques.
Algebraic characterizations of least model and uniform equivalence of propositional Krom logic programs
Authors: Christian Antić
Date: 2023-01-20 00:00:00

This research note provides algebraic characterizations of the least model, subsumption, and uniform equivalence of propositional Krom logic programs.
Designing Fairness in Autonomous Peer-to-peer Energy Trading
Authors: Varsha Behrunani, Andrew Irvine, Giuseppe Belgioioso, Philipp Heer, John Lygeros, Florian Dörfler
Date: 2023-02-09 00:00:00
Tasks: energy management, energy trading, Fairness, Management, Privacy Preserving

Several autonomous energy management and peer-to-peer trading mechanisms for future energy markets have been recently proposed based on optimization and game theory. In this paper, we study the impact of trading prices on the outcome of these market designs for energy-hub networks. We prove that, for a generic choice of trading prices, autonomous peer-to-peer trading is always network-wide beneficial but not necessarily individually beneficial for each hub. Therefore, we leverage hierarchical game theory to formalize the problem of designing locally-beneficial and network-wide fair peer-to-peer trading prices. Then, we propose a scalable and privacy-preserving price-mediation algorithm that provably converges to a profile of such prices. Numerical simulations on a 3-hub network show that the proposed algorithm can indeed incentivize active participation of energy hubs in autonomous peer-to-peer trading schemes.
Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models
Authors: Tung Phung, José Cambronero, Sumit Gulwani, Tobias Kohn, Rupak Majumdar, Adish Singla, Gustavo Soares
Date: 2023-01-24 00:00:00

Large language models (LLMs), such as Codex, hold great promise in enhancing programming education by automatically generating feedback for students. We investigate using LLMs to generate feedback for fixing syntax errors in Python programs, a key scenario in introductory programming. More concretely, given a student's buggy program, our goal is to generate feedback comprising a fixed program along with a natural language explanation describing the errors/fixes, inspired by how a human tutor would give feedback. While using LLMs is promising, the critical challenge is to ensure high precision in the generated feedback, which is imperative before deploying such technology in classrooms. The main research question we study is: Can we develop LLMs-based feedback generation techniques with a tunable precision parameter, giving educators quality control over the feedback that students receive? To this end, we introduce PyFiXV, our technique to generate high-precision feedback powered by Codex. The key idea behind PyFiXV is to use a novel run-time validation mechanism to decide whether the generated feedback is suitable for sharing with the student; notably, this validation mechanism also provides a precision knob to educators. We perform an extensive evaluation using two real-world datasets of Python programs with syntax errors and show the efficacy of PyFiXV in generating high-precision feedback.
ODD-Centric Contextual Sensitivity Analysis Applied To A Non-Linear Vehicle Dynamics Model
Authors: Richard Schubert, Marcus Nolte, Arnaud de La Fortelle, Markus Maurer
Date: 2023-02-09 00:00:00
Tasks: Sensitivity, Trajectory Planning

Advanced driving functions, for assistance or full automation, require strong guarantees to be deployed. This means that such functions may not be available all the time, like now commercially available SAE Level 3 modes that are made available only on some roads and at law speeds. The specification of such restriction is described technically in the Operational Design Domain (ODD) which is a fundamental concept for the design of automated driving systems (ADS). In this work, we focus on the example of trajectory planning and control which are crucial functions for SAE level 4+ vehicles and often rely on model-based methods. Hence, the quality of the underlying models has to be evaluated with respect to the ODD. Mathematical analyses such as uncertainty and sensitivity analysis support the quantitative assessment of model quality in general. In this paper, we present a new approach to assess the quality of vehicle dynamics models using an ODD-centric sensitivity analysis. The sensitivity analysis framework is implemented for a 10-DoF nonlinear double-track vehicle dynamics model used inside a model-predictive trajectory controller. The model sensitivity is evaluated with respect to given ODD and maneuver parameters. Based on the results, ODD-compliant behavior generation strategies with the goal of minimizing model sensitivity are outlined.
A Hopfield-like model with complementary encodings of memories
Authors: Louis Kang, Taro Toyoizumi
Date: 2023-02-09 00:00:00
Tasks: Retrieval

We present a Hopfield-like autoassociative network for memories representing examples of concepts. Each memory is encoded by two activity patterns with complementary properties. The first is dense and correlated across examples within concepts, and the second is sparse and exhibits no correlation among examples. The network stores each memory as a linear combination of its encodings. During retrieval, the network recovers sparse or dense patterns with a high or low activity threshold, respectively. As more memories are stored, the dense representation at low threshold shifts from examples to concepts, which are learned from accumulating common example features. Meanwhile, the sparse representation at high threshold maintains distinctions between examples due to the high capacity of sparse, decorrelated patterns. Thus, a single network can retrieve memories at both example and concept scales and perform heteroassociation between them. We obtain our results by deriving macroscopic mean-field equations that yield capacity formulas for sparse examples, dense examples, and dense concepts. We also perform network simulations that verify our theoretical results and explicitly demonstrate the capabilities of the network.
Stochastic switching of delayed feedback suppresses oscillations in genetic regulatory systems
Authors: Bhargav R. Karamched, Christopher E. Miles
Date: 2023-02-09 00:00:00

Delays and stochasticity have both served as crucially valuable ingredients in mathematical descriptions of control, physical, and biological systems. In this work, we investigate how explicitly dynamical stochasticity in delays modulates the effect of delayed feedback. To do so, we consider a hybrid model where stochastic delays evolve by a continuous-time Markov chain, and between switching events, the system of interest evolves via a deterministic delay equation. Our main contribution is the calculation of an effective delay equation in the fast switching limit. This effective equation maintains the influence of all subsystem delays and cannot be replaced with a single effective delay. To illustrate the relevance of this calculation, we investigate a simple model of stochastically switching delayed feedback motivated by gene regulation. We show that sufficiently fast switching between two oscillatory subsystems can yield stable dynamics.
Covariate Adjustment in Experiments with Matched Pairs
Authors: Yuehao Bai, Liang Jiang, Joseph P. Romano, Azeem M. Shaikh, Yichong Zhang
Date: 2023-02-09 00:00:00

This paper studies inference on the average treatment effect in experiments in which treatment status is determined according to "matched pairs" and it is additionally desired to adjust for observed, baseline covariates to gain further precision. By a "matched pairs" design, we mean that units are sampled i.i.d. from the population of interest, paired according to observed, baseline covariates and finally, within each pair, one unit is selected at random for treatment. Importantly, we presume that not all observed, baseline covariates are used in determining treatment assignment. We study a broad class of estimators based on a "doubly robust" moment condition that permits us to study estimators with both finite-dimensional and high-dimensional forms of covariate adjustment. We find that estimators with finite-dimensional, linear adjustments need not lead to improvements in precision relative to the unadjusted difference-in-means estimator. This phenomenon persists even if the adjustments are interacted with treatment; in fact, doing so leads to no changes in precision. However, gains in precision can be ensured by including fixed effects for each of the pairs. Indeed, we show that this adjustment is the "optimal" finite-dimensional, linear adjustment. We additionally study two estimators with high-dimensional forms of covariate adjustment based on the LASSO. For each such estimator, we show that it leads to improvements in precision relative to the unadjusted difference-in-means estimator and also provide conditions under which it leads to the "optimal" nonparametric, covariate adjustment. A simulation study confirms the practical relevance of our theoretical analysis, and the methods are employed to reanalyze data from an experiment using a "matched pairs" design to study the effect of macroinsurance on microenterprise.
Consider or Choose? The Role and Power of Consideration Sets
Authors: Yi-Chun Akchen, Dmitry Mitrofanov
Date: 2023-02-08 00:00:00
Tasks: Assortment Optimization, Decision Making, Marketing

Consideration sets play a crucial role in discrete choice modeling, where customers often form consideration sets in the first stage and then use a second-stage choice mechanism to select the product with the highest utility. While many recent studies aim to improve choice models by incorporating more sophisticated second-stage choice mechanisms, this paper takes a step back and goes into the opposite extreme. We simplify the second-stage choice mechanism to its most basic form and instead focus on modeling customer choice by emphasizing the role and power of the first-stage consideration set formation. To this end, we study a model that is parameterized solely by a distribution over consideration sets with a bounded rationality interpretation. Intriguingly, we show that this model is characterized by the axiom of symmetric demand cannibalization, enabling complete statistical identification. The latter finding highlights the critical role of consideration sets in the identifiability of two-stage choice models. We also examine the model's implications for assortment planning, proving that the optimal assortment is revenue-ordered within each partition block created by consideration sets. Despite this compelling structure, we establish that the assortment problem under this model is NP-hard even to approximate, highlighting how consideration sets contribute to nontractability, even under the simplest uniform second-stage choice mechanism. Finally, using real-world data, we show that the model achieves prediction performance comparable to other advanced choice models. Given the simplicity of the model's second-stage phase, this result showcases the enormous power of first-stage consideration set formation in capturing customers' decision-making processes.