pwc-archive/papers-with-abstracts huggingface dataset:
Evaluating Gender Bias in Large Language Models
Authors: Michael Döll, Markus Döhring, Andreas Müller
Date: 2024-11-14 00:00:00
Tasks: Model Selection, Sentence, Sentence Completion

Gender bias in artificial intelligence has become an important issue, particularly in the context of language models used in communication-oriented applications. This study examines the extent to which Large Language Models (LLMs) exhibit gender bias in pronoun selection in occupational contexts. The analysis evaluates the models GPT-4, GPT-4o, PaLM 2 Text Bison and Gemini 1.0 Pro using a self-generated dataset. The jobs considered include a range of occupations, from those with a significant male presence to those with a notable female concentration, as well as jobs with a relatively equal gender distribution. Three different sentence processing methods were used to assess potential gender bias: masked tokens, unmasked sentences, and sentence completion. In addition, the LLMs suggested names of individuals in specific occupations, which were then examined for gender distribution. The results show a positive correlation between the models' pronoun choices and the gender distribution present in U.S. labor force data. Female pronouns were more often associated with female-dominated occupations, while male pronouns were more often associated with male-dominated occupations. Sentence completion showed the strongest correlation with actual gender distribution, while name generation resulted in a more balanced 'politically correct' gender distribution, albeit with notable variations in predominantly male or female occupations. Overall, the prompting method had a greater impact on gender distribution than the model selection itself, highlighting the complexity of addressing gender bias in LLMs. The findings highlight the importance of prompting in gender mapping.
Evaluating the Predictive Capacity of ChatGPT for Academic Peer Review Outcomes Across Multiple Platforms
Authors: Mike Thelwall, Abdullah Yaghi
Date: 2024-11-14 00:00:00
Tasks: Articles

While previous studies have demonstrated that Large Language Models (LLMs) can predict peer review outcomes to some extent, this paper builds on that by introducing two new contexts and employing a more robust method - averaging multiple ChatGPT scores. The findings that averaging 30 ChatGPT predictions, based on reviewer guidelines and using only the submitted titles and abstracts, failed to predict peer review outcomes for F1000Research (Spearman's rho=0.00). However, it produced mostly weak positive correlations with the quality dimensions of SciPost Physics (rho=0.25 for validity, rho=0.25 for originality, rho=0.20 for significance, and rho = 0.08 for clarity) and a moderate positive correlation for papers from the International Conference on Learning Representations (ICLR) (rho=0.38). Including the full text of articles significantly increased the correlation for ICLR (rho=0.46) and slightly improved it for F1000Research (rho=0.09), while it had variable effects on the four quality dimension correlations for SciPost LaTeX files. The use of chain-of-thought system prompts slightly increased the correlation for F1000Research (rho=0.10), marginally reduced it for ICLR (rho=0.37), and further decreased it for SciPost Physics (rho=0.16 for validity, rho=0.18 for originality, rho=0.18 for significance, and rho=0.05 for clarity). Overall, the results suggest that in some contexts, ChatGPT can produce weak pre-publication quality assessments. However, the effectiveness of these assessments and the optimal strategies for employing them vary considerably across different platforms, journals, and conferences. Additionally, the most suitable inputs for ChatGPT appear to differ depending on the platform.
Nonlinear Neural Dynamics and Classification Accuracy in Reservoir Computing
Authors: Claus Metzner, Achim Schilling, Andreas Maier, Patrick Krauss
Date: 2024-11-15 00:00:00

Reservoir computing - information processing based on untrained recurrent neural networks with random connections - is expected to depend on the nonlinear properties of the neurons and the resulting oscillatory, chaotic, or fixpoint dynamics of the network. However, the required degree of nonlinearity and the range of suitable dynamical regimes for a given task are not fully understood. To clarify these questions, we study the accuracy of a reservoir computer in artificial classification tasks of varying complexity, while tuning the neuron's degree of nonlinearity and the reservoir's dynamical regime. We find that, even for activation functions with extremely reduced nonlinearity, weak recurrent interactions and small input signals, the reservoir is able to compute useful representations, detectable only in higher order principal components, that render complex classificiation tasks linearly separable for the readout layer. When increasing the recurrent coupling, the reservoir develops spontaneous dynamical behavior. Nevertheless, the input-related computations can 'ride on top' of oscillatory or fixpoint attractors without much loss of accuracy, whereas chaotic dynamics reduces task performance more drastically. By tuning the system through the full range of dynamical phases, we find that the accuracy peaks both at the oscillatory/chaotic and at the chaotic/fixpoint phase boundaries, thus supporting the 'edge of chaos' hypothesis. Our results, in particular the robust weakly nonlinear operating regime, may offer new perspectives both for technical and biological neural networks with random connectivity.
Difficulties of the NSGA-II with the Many-Objective LeadingOnes Problem
Authors: Benjamin Doerr, Dimitri Korkotashvili, Martin S. Krejca
Date: 2024-11-15 00:00:00

The NSGA-II is the most prominent multi-objective evolutionary algorithm (cited more than 50,000 times). Very recently, a mathematical runtime analysis has proven that this algorithm can have enormous difficulties when the number of objectives is larger than two (Zheng, Doerr. IEEE Transactions on Evolutionary Computation (2024)). However, this result was shown only for the OneMinMax benchmark problem, which has the particularity that all solutions are on the Pareto front, a fact heavily exploited in the proof of this result. In this work, we show a comparable result for the LeadingOnesTrailingZeroes benchmark. This popular benchmark problem appears more natural in that most of its solutions are not on the Pareto front. With a careful analysis of the population dynamics of the NGSA-II optimizing this benchmark, we manage to show that when the population grows on the Pareto front, then it does so much faster by creating known Pareto optima than by spreading out on the Pareto front. Consequently, already when still a constant fraction of the Pareto front is unexplored, the crowding distance becomes the crucial selection mechanism, and thus the same problems arise as in the optimization of OneMinMax. With these and some further arguments, we show that the NSGA-II, with a population size by at most a constant factor larger than the Pareto front, cannot compute the Pareto front in less than exponential time.
Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad
Authors: Jonathan Warrell, Francesco Alesiani, Cameron Smith, Anja Mösch, Martin Renqiang Min
Date: 2024-11-14 00:00:00

Levels of selection and multilevel evolutionary processes are essential concepts in evolutionary theory, and yet there is a lack of common mathematical models for these core ideas. Here, we propose a unified mathematical framework for formulating and optimizing multilevel evolutionary processes and genetic algorithms over arbitrarily many levels based on concepts from category theory and population genetics. We formulate a multilevel version of the Wright-Fisher process using this approach, and we show that this model can be analyzed to clarify key features of multilevel selection. Particularly, we derive an extended multilevel probabilistic version of Price's Equation via the Kantorovich Monad, and we use this to characterize regimes of parameter space within which selection acts antagonistically or cooperatively across levels. Finally, we show how our framework can provide a unified setting for learning genetic algorithms (GAs), and we show how we can use a Variational Optimization and a multi-level analogue of coalescent analysis to fit multilevel GAs to simulated data.
G-computation for increasing performances of clinical trials with individual randomization and binary response
Authors: Joe de Keizer, Rémi Lenain, Raphaël Porcher, Sarah Zoha, Arthur Chatton, Yohann Foucher
Date: 2024-11-15 00:00:00

In a clinical trial, the random allocation aims to balance prognostic factors between arms, preventing true confounders. However, residual differences due to chance may introduce near-confounders. Adjusting on prognostic factors is therefore recommended, especially because the related increase of the power. In this paper, we hypothesized that G-computation associated with machine learning could be a suitable method for randomized clinical trials even with small sample sizes. It allows for flexible estimation of the outcome model, even when the covariates' relationships with outcomes are complex. Through simulations, penalized regressions (Lasso, Elasticnet) and algorithm-based methods (neural network, support vector machine, super learner) were compared. Penalized regressions reduced variance but may introduce a slight increase in bias. The associated reductions in sample size ranged from 17\% to 54\%. In contrast, algorithm-based methods, while effective for larger and more complex data structures, underestimated the standard deviation, especially with small sample sizes. In conclusion, G-computation with penalized models, particularly Elasticnet with splines when appropriate, represents a relevant approach for increasing the power of RCTs and accounting for potential near-confounders.
Semiparametric inference for impulse response functions using double/debiased machine learning
Authors: Daniele Ballinari, Alexander Wehrli
Date: 2024-11-15 00:00:00
Tasks: Time Series

We introduce a double/debiased machine learning (DML) estimator for the impulse response function (IRF) in settings where a time series of interest is subjected to multiple discrete treatments, assigned over time, which can have a causal effect on future outcomes. The proposed estimator can rely on fully nonparametric relations between treatment and outcome variables, opening up the possibility to use flexible machine learning approaches to estimate IRFs. To this end, we extend the theory of DML from an i.i.d. to a time series setting and show that the proposed DML estimator for the IRF is consistent and asymptotically normally distributed at the parametric rate, allowing for semiparametric inference for dynamic effects in a time series setting. The properties of the estimator are validated numerically in finite samples by applying it to learn the IRF in the presence of serial dependence in both the confounder and observation innovation processes. We also illustrate the methodology empirically by applying it to the estimation of the effects of macroeconomic shocks.
InFact: A Strong Baseline for Automated Fact-Checking
Authors: Mark Rothermel, Tobias Braun, Marcus Rohrbach, Anna Rohrbach
Date: 2024-11-01 00:00:00
Proceeding: - 2024 11
Tasks: Claim Verification, Fact Checking, Retrieval

The spread of disinformation poses a global threat to democratic societies, necessitating robust and scalable Automated Fact-Checking (AFC) systems. The AVeriTeC Shared Task Challenge 2024 offers a realistic benchmark for text-based fact-checking methods. This paper presents Information-Retrieving Fact-Checker (InFact), an LLM-based approach that breaks down the task of claim verification into a 6-stage process, including evidence retrieval. When using GPT-4o as the backbone, InFact achieves an AVeriTeC score of 63% on the test set, outperforming all other 20 teams competing in the challenge, and establishing a new strong baseline for future text-only AFC systems. Qualitative analysis of mislabeled instances reveals that InFact often yields a more accurate conclusion than AVeriTeC’s human-annotated ground truth.
An Analysis of the Side Slither On-Orbit Calibration Technique Using the DIRSIG Model
Authors: Aaron Gerace, John Schott, Michael Gartley, Matthew Montanaro
Date: 2014-10-31 00:00:00
Proceeding: journal 2014 10

Pushbroom-style imaging systems exhibit several advantages over line scanners 
when used on space-borne platforms as they typically achieve higher signal-to-noise and 
reduce the need for moving parts. Pushbroom sensors contain thousands of detectors, each 
having a unique radiometric response, which will inevitably lead to streaking and banding 
in the raw data. To take full advantage of the potential exhibited by pushbroom sensors, a 
relative radiometric correction must be performed to eliminate pixel-to-pixel 
non-uniformities in the raw data. Side slither is an on-orbit calibration technique where a 
90-degree yaw maneuver is performed over an invariant site to flatten the data. While this 
technique has been utilized with moderate success for the QuickBird satellite [1] and the 
RapidEye constellation [2], further analysis is required to enable its implementation for the 
Landsat 8 sensors, which have a 15-degree field-of-view and a 0.5% pixel-to-pixel 
uniformity requirement. This work uses the DIRSIG model to analyze the side slither 
maneuver as applicable to the Landsat sensor. A description of favorable sites, how to 
adjust the maneuver to compensate for the curvature of “linear” arrays, how to efficiently 
process the data, and an analysis to assess the quality of the side slither data, are presented.
Dynamic Causal Effects in a Nonlinear World: the Good, the Bad, and the Ugly
Authors: Michal Kolesár, Mikkel Plagborg-Møller
Date: 2024-11-15 00:00:00

Applied macroeconomists frequently use impulse response estimators motivated by linear models. We study whether the estimands of such procedures have a causal interpretation when the true data generating process is in fact nonlinear. We show that vector autoregressions and linear local projections onto observed shocks or proxies identify weighted averages of causal effects regardless of the extent of nonlinearities. By contrast, identification approaches that exploit heteroskedasticity or non-Gaussianity of latent shocks are highly sensitive to departures from linearity. Our analysis is based on new results on the identification of marginal treatment effects through weighted regressions, which may also be of interest to researchers outside macroeconomics.
Dynamic Envy-Free Permanency in Child Welfare Systems
Authors: Terence Highsmith
Date: 2024-11-14 00:00:00

Caseworkers in foster care systems seek to place waiting children in the most suitable homes. Furthermore, social work guidelines prioritize heterogeneous attributes of children and homes when deliberating placements. We use insights from market design and dynamic matching to characterize a class of dynamically envy-free mechanisms that incentivize expedient placements when children and homes arrive to the market over time and homes may accept or decline placements. The mechanisms have robustness against justified envy and costly patience. We analyze strategic incentives and efficiency properties of dynamic envy-freeness. Finally, we conduct empirical simulations that affirm that our mechanisms drastically increase placements and reduce waiting costs while maintaining robustness to prediction error versus a naive mechanism that always sequentially runs Deferred Acceptance. Practitioners can implement our mechanisms through assigning priority to child-home matches.
Digital Twin-Assisted Space-Air-Ground Integrated Multi-Access Edge Computing for Low-Altitude Economy: An Online Decentralized Optimization Approach
Authors: Long He, Geng Sun, Zemin Sun, Jiacheng Wang, Hongyang Du, Dusit Niyato, Jiangchuan Liu, Victor C. M. Leung
Date: 2024-11-12 00:00:00
Tasks: Cloud Computing, Decision Making, Edge-computing

The emergence of space-air-ground integrated multi-access edge computing (SAGIMEC) networks opens a significant opportunity for the rapidly growing low altitude economy (LAE), facilitating the development of various applications by offering efficient communication and computing services. However, the heterogeneous nature of SAGIMEC networks, coupled with the stringent computational and communication requirements of diverse applications in the LAE, introduces considerable challenges in integrating SAGIMEC into the LAE. In this work, we first present a digital twin-assisted SAGIMEC paradigm for LAE, where digital twin enables reliable network monitoring and management, while SAGIMEC provides efficient computing offloading services for Internet of Things sensor devices (ISDs). Then, a joint satellite selection, computation offloading, communication resource allocation, computation resource allocation and UAV trajectory control optimization problem (JSC4OP) is formulated to maximize the quality of service (QoS) of ISDs. Given the complexity of JSC4OP, we propose an online decentralized optimization approach (ODOA) to address the problem. Specifically, JSC4OP is first transformed into a real-time decision-making optimization problem (RDOP) by leveraging Lyapunov optimization. Then, to solve the RDOP, we introduce an online learning-based latency prediction method to predict the uncertain system environment and a game theoretic decision-making method to make real-time decisions. Finally, theoretical analysis confirms the effectiveness of the ODOA, while the simulation results demonstrate that the proposed ODOA outperforms other alternative approaches in terms of overall system performance.
A Graph-based Strategic Sensor Deployment Approach for k-coverage in WSN
Authors: Lakshmikanta Sau, Priyadarshi Mukherjee, Sasthi C. Ghosh
Date: 2024-11-15 00:00:00

This paper studies a graph-based sensor deployment approach in wireless sensor networks (WSNs). Specifically, in today's world, where sensors are everywhere, detecting various attributes like temperature and movement, their deteriorating lifetime is indeed a very concerning issue. In many scenarios, these sensors are placed in extremely remote areas, where maintenance becomes challenging. As a result, it is not very wise to depend on a single sensor to obtain data from a particular terrain or place. Hence, multiple sensors are deployed in these places, such that no problem arises if one or few of them fail. In this work, this problem of intelligent placement of sensors is modelled from the graph theoretic point of view. We propose a new sensor deployment approach here, which results in lesser sensor density per unit area and less number of sensors as compared to the existing benchmark schemes. Finally, the numerical results also support our claims and provide insights regarding the selection of parameters that enhance the system performance.
XLSR-Mamba: A Dual-Column Bidirectional State Space Model for Spoofing Attack Detection
Authors: Yang Xiao, Rohan Kumar Das
Date: 2024-11-15 00:00:00
Tasks: Audio Deepfake Detection, Automatic Speech Recognition, Mamba, Self-Supervised Learning, speech-recognition, Speech Recognition

Transformers and their variants have achieved great success in speech processing. However, their multi-head self-attention mechanism is computationally expensive. Therefore, one novel selective state space model, Mamba, has been proposed as an alternative. Building on its success in automatic speech recognition, we apply Mamba for spoofing attack detection. Mamba is well-suited for this task as it can capture the artifacts in spoofed speech signals by handling long-length sequences. However, Mamba's performance may suffer when it is trained with limited labeled data. To mitigate this, we propose combining a new structure of Mamba based on a dual-column architecture with self-supervised learning, using the pre-trained wav2vec 2.0 model. The experiments show that our proposed approach achieves competitive results and faster inference on the ASVspoof 2021 LA and DF datasets, and on the more challenging In-the-Wild dataset, it emerges as the strongest candidate for spoofing attack detection. The code has been publicly released in https://github.com/swagshaw/XLSR-Mamba.
Balancing Passenger Transport and Power Distribution: A Distributed Dispatch Policy for Shared Autonomous Electric Vehicles
Authors: Jake Robbennolt, Meiyi Li, Javad Mohammadi, Stephen D. Boyles
Date: 2024-11-15 00:00:00
Tasks: Model Predictive Control

Shared autonomous electric vehicles can provide on-demand transportation for passengers while also interacting extensively with the electric distribution system. This interaction is especially beneficial after a disaster when the large battery capacity of the fleet can be used to restore critical electric loads. We develop a dispatch policy that balances the need to continue serving passengers (especially critical workers) and the ability to transfer energy across the network. The model predictive control policy tracks both passenger and energy flows and provides maximum passenger throughput if any policy can. The resulting mixed integer linear programming problem is difficult to solve for large-scale problems, so a distributed solution approach is developed to improve scalability, privacy, and resilience. We demonstrate that the proposed heuristic, based on the alternating direction method of multipliers, is effective in achieving near-optimal solutions quickly. The dispatch policy is examined in simulation to demonstrate the ability of vehicles to balance these competing objectives with benefits to both systems. Finally, we compare several dispatch behaviors, demonstrating the importance of including operational constraints and objectives from both the transportation and electric systems in the model.
Koopman-based control of nonlinear systems with closed-loop guarantees
Authors: Robin Strässer, Julian Berberich, Manuel Schaller, Karl Worthmann, Frank Allgöwer
Date: 2024-11-15 00:00:00

In this paper, we provide a tutorial overview and an extension of a recently developed framework for data-driven control of unknown nonlinear systems with rigorous closed-loop guarantees. The proposed approach relies on the Koopman operator representation of the nonlinear system, for which a bilinear surrogate model is estimated based on data. In contrast to existing Koopman-based estimation procedures, we state guaranteed bounds on the approximation error using the stability- and certificate-oriented extended dynamic mode decomposition (SafEDMD) framework. The resulting surrogate model and the uncertainty bounds allow us to design controllers via robust control theory and sum-of-squares optimization, guaranteeing desirable properties for the closed-loop system. We present results on stabilization both in discrete and continuous time, and we derive a method for controller design with performance objectives. The benefits of the presented framework over established approaches are demonstrated with a numerical example.