pwc-archive/papers-with-abstracts huggingface dataset:
Causes and consequences of dispersal in biodiverse spatially structured systems: what is old and what is new?
Authors: Emanuel A. Fronhofer, Dries Bonte, Elvire Bestion, Julien Cote, Jhelam N. Deshpande, Alison B. Duncan, Thomas Hovestadt, Oliver Kaltz, Sally Keith, Hanna Kokko, Delphine Legrand, Sarthak P. Malusare, Thomas Parmentier, Camille Saade, Nicolas Schtickzelle, Giacomo Zilio, François Massol
Date: 2023-11-30 00:00:00
Tasks: valid

Dispersal is a well recognized driver of ecological and evolutionary dynamics, and simultaneously an evolving trait. Dispersal evolution has traditionally been studied in single-species metapopulations so that it remains unclear how dispersal evolves in spatially structured communities and food webs. Since most natural systems are biodiverse and spatially structured, and thus affected by dispersal and its evolution, this knowledge gap should be bridged. Here we discuss whether knowledge established in single-species systems holds in spatially structured multispecies systems and highlight generally valid and fundamental principles. Most biotic interactions form the ecological theatre for the evolutionary dispersal play because interactions mediate patterns of fitness expectations in space and time. While this allows for a simple transposition of certain known drivers to a multispecies context, other drivers may require more complex transpositions, or might not be transferred. We discuss an important quantitative modulator of dispersal evolution in the increased trait dimensionality of biodiverse meta-systems and an additional driver in co-dispersal. We speculate that scale and selection pressure mismatches due to co-dispersal, together with increased trait dimensionality may lead to slower and more "diffuse" evolution in biodiverse meta-systems. Open questions and potential consequences in both ecological and evolutionary terms call for more investigation.
Inference using a composite-likelihood approximation for stochastic metapopulation model of disease spread
Authors: Gaël Beaunée, Pauline Ezanno, Alain Joly, Pierre Nicolas, Elisabeta Vergu
Date: 2023-11-29 00:00:00
Tasks: Diagnostic

Spatio-temporal pathogen spread is often partially observed at the metapopulation scale. Available data correspond to proxies and are incomplete, censored and heterogeneous. Moreover, representing such biological systems often leads to complex stochastic models. Such complexity together with data characteristics make the analysis of these systems a challenge. Our objective was to develop a new inference procedure to estimate key parameters of stochastic metapopulation models of animal disease spread from longitudinal and spatial datasets, while accurately accounting for characteristics of census data. We applied our procedure to provide new knowledge on the regional spread of \emph{Mycobacterium avium} subsp. \emph{paratuberculosis} (\emph{Map}), which causes bovine paratuberculosis, a worldwide endemic disease. \emph{Map} spread between herds through trade movements was modeled with a stochastic mechanistic model. Comprehensive data from 2005 to 2013 on cattle movements in 12,857 dairy herds in Brittany (western France) and partial data on animal infection status in 2,278 herds sampled from 2007 to 2013 were used. Inference was performed using a new criterion based on a Monte-Carlo approximation of a composite likelihood, coupled to a numerical optimization algorithm (Nelder-Mead Simplex-like). Our criterion showed a clear superiority to alternative ones in identifying the right parameter values, as assessed by an empirical identifiability on simulated data. Point estimates and profile likelihoods allowed us to establish the initial state of the system, identify the risk of pathogen introduction from outside the metapopulation, and confirm the assumption of the low sensitivity of the diagnostic test. Our inference procedure could easily be applied to other spatio-temporal infection dynamics, especially when ABC-like methods face challenges in defining relevant summary statistics.
Computation of Greeks under rough Volterra stochastic volatility models using the Malliavin calculus approach
Authors: Mishari Al-Foraih, Jan Pospíšil, Josep Vives
Date: 2023-12-01 00:00:00

Using Malliavin calculus techniques we obtain formulas for computing Greeks under different rough Volterra stochastic volatility models. In particular we obtain formulas for rough versions of Stein-Stein, SABR and Bergomi models and numerically demonstrate the convergence.
Investigate The ESG Score Methodology
Authors: Zhi Chen
Date: 2023-11-30 00:00:00

Whether the Refinitiv provide a reliable and trusted methodology in the process of aggregating 10 category scores to overall score?
An algorithm for forensic toolmark comparisons
Authors: Maria Cuellar, Sheng Gao, Heike Hofmann
Date: 2023-11-19 00:00:00
Tasks: Clustering, Specificity

Forensic toolmark analysis traditionally relies on subjective human judgment, leading to inconsistencies and lack of transparency. The multitude of variables, including angles and directions of mark generation, further complicates comparisons. To address this, we first generate a dataset of 3D toolmarks from various angles and directions using consecutively manufactured slotted screwdrivers. By using PAM clustering, we find that there is clustering by tool rather than angle or direction. Using Known Match and Known Non-Match densities, we establish thresholds for classification. Fitting Beta distributions to the densities, we allow for the derivation of likelihood ratios for new toolmark pairs. With a cross-validated sensitivity of 98% and specificity of 96%, our approach enhances the reliability of toolmark analysis. This approach is applicable to slotted screwdrivers, and for screwdrivers that are made with a similar production method. With data collection of other tools and factors, it could be applied to compare toolmarks of other types. This empirically trained, open-source solution offers forensic examiners a standardized means to objectively compare toolmarks, potentially decreasing the number of miscarriages of justice in the legal system.
Transport Equation based Physics Informed Neural Network to predict the Yield Strength of Architected Materials
Authors: Akshansh Mishra
Date: 2023-07-29 00:00:00
Tasks: Computational Efficiency

In this research, the application of the Physics-Informed Neural Network (PINN) model is explored to solve transport equation-based Partial Differential Equations (PDEs). The primary objective is to analyze the impact of different activation functions incorporated within the PINN model on its predictive performance, specifically assessing the Mean Squared Error (MSE) and Mean Absolute Error (MAE). The dataset used in the study consists of a varied set of input parameters related to strut diameter, unit cell size, and the corresponding yield stress values. Through this investigation the aim is to understand the effectiveness of the PINN model and the significance of choosing appropriate activation functions for solving complex PDEs in real-world applications. The outcomes suggest that the choice of activation function may have minimal influence on the model's predictive accuracy for this particular problem. The PINN model showcases exceptional generalization capabilities, indicating its capacity to avoid overfitting with the provided dataset. The research underscores the importance of striking a balance between performance and computational efficiency while selecting an activation function for specific real-world applications. These valuable findings contribute to advancing the understanding and potential adoption of PINN as an effective tool for solving challenging PDEs in diverse scientific and engineering domains.
Enhancing ML-Based DoS Attack Detection Through Combinatorial Fusion Analysis
Authors: Evans Owusu, Mohamed Rahouti, D. Frank Hsu, Kaiqi Xiong, Yufeng Xin
Date: 2023-10-02 00:00:00
Tasks: Diversity

Mitigating Denial-of-Service (DoS) attacks is vital for online service security and availability. While machine learning (ML) models are used for DoS attack detection, new strategies are needed to enhance their performance. We suggest an innovative method, combinatorial fusion, which combines multiple ML models using advanced algorithms. This includes score and rank combinations, weighted techniques, and diversity strength of scoring systems. Through rigorous evaluations, we demonstrate the effectiveness of this fusion approach, considering metrics like precision, recall, and F1-score. We address the challenge of low-profiled attack classification by fusing models to create a comprehensive solution. Our findings emphasize the potential of this approach to improve DoS attack detection and contribute to stronger defense mechanisms.
The theoretical limits of biometry
Authors: Gaëlle Candel
Date: 2023-11-06 00:00:00
Tasks: Face Recognition

Biometry has proved its capability in terms of recognition accuracy. Now, it is widely used for automated border control with the biometric passport, to unlock a smartphone or a computer with a fingerprint or a face recognition algorithm. While identity verification is widely democratized, pure identification with no additional clues is still a work in progress. The identification difficulty depends on the population size, as the larger the group is, the larger the confusion risk. For collision prevention, biometric traits must be sufficiently distinguishable to scale to considerable groups, and algorithms should be able to capture their differences accurately. Most biometric works are purely experimental, and it is impossible to extrapolate the results to a smaller or a larger group. In this work, we propose a theoretical analysis of the distinguishability problem, which governs the error rates of biometric systems. We demonstrate simple relationships between the population size and the number of independent bits necessary to prevent collision in the presence of noise. This work provides the lowest lower bound for memory requirements. The results are very encouraging, as the biometry of the whole Earth population can fit in a regular disk, leaving some space for noise and redundancy.
Medication abortion via digital health in the United States: a systematic scoping review
Authors: Fekede Asefa Kumsa, Rameshwari Prasad, Arash Shaban-Nejad
Date: 2023-07-18 00:00:00
Tasks: Articles

Digital health, including telemedicine, has increased access to abortion care. The convenience, flexibility of appointment times, and ensured privacy to abortion users may make abortion services via telemedicine preferable. This scoping review systematically mapped studies conducted on abortion services via telemedicine, including their effectiveness and acceptability for abortion users and providers. All published papers included abortion services via telemedicine in the United States were considered. Articles were searched in PubMed, CINAHL, and Google Scholar databases in September 2022. The findings were synthesized narratively, and the PRISMA-ScR guidelines were used to report this study. Out of 757 retrieved articles, 33 articles were selected based on the inclusion criteria. These studies were published between 2011 and 2022, with 24 published in the last 3 years. The study found that telemedicine increased access to abortion care in the United States, especially for people in remote areas or those worried about stigma from in-person visits. The effectiveness of abortion services via telemedicine was comparable to in-clinic visits, with 6% or fewer abortions requiring surgical intervention. Both care providers and abortion seekers expressed positive perceptions of telemedicine-based abortion services. However, abortion users reported mixed emotions, with some preferring in-person visits. The most common reasons for choosing telemedicine included the distance to the abortion clinic, convenience, privacy, cost, flexibility of appointment times, and state laws imposing waiting periods or restrictive policies. Telemedicine offered a preferable option for abortion seekers and providers. The feasibility of accessing abortion services via telemedicine in low-resource settings needs further investigation.
Integration of Swin UNETR and statistical shape modeling for a semi-automated segmentation of the knee and biomechanical modeling of articular cartilage
Authors: Reza Kakavand, Mehrdad Palizi, Peyman Tahghighi, Reza Ahmadi, Neha Gianchandani, Samer Adeeb, Roberto Souza, W. Brent Edwards, Amin Komeili
Date: 2023-09-18 00:00:00
Tasks: Segmentation

Simulation studies like finite element (FE) modeling provide insight into knee joint mechanics without patient experimentation. Generic FE models represent biomechanical behavior of the tissue by overlooking variations in geometry, loading, and material properties of a population. On the other hand, subject-specific models include these specifics, resulting in enhanced predictive precision. However, creating such models is laborious and time-intensive. The present study aimed to enhance subject-specific knee joint FE modeling by incorporating a semi-automated segmentation algorithm. This segmentation was a 3D Swin UNETR for an initial segmentation of the femur and tibia, followed by a statistical shape model (SSM) adjustment to improve surface roughness and continuity. Five hundred and seven magnetic resonance images (MRIs) from the Osteoarthritis Initiative (OAI) database were used to build and validate the segmentation model. A semi-automated FE model was developed using this semi-automated segmentation. On the other hand, a manual FE model was developed through manual segmentation (i.e., the gold standard approach). Both FE models were subjected to gait loading. The predicted mechanical response of manual and semi-automated FE models were compared. In the result, our semi-automated segmentation achieved Dice similarity coefficient (DSC) over 98% for both femur and tibia. The mechanical results (max principal stress, max principal strain, fluid pressure, fibril strain, and contact area) showed no significant differences between the manual and semi-automated FE models, indicating the effectiveness of the proposed semi-automated segmentation in creating accurate knee joint FE models. ( https://data.mendeley.com/datasets/k5hdc9cz7w/1 ).
REACT: Recognize Every Action Everywhere All At Once
Authors: Naga VS Raviteja Chappa, Pha Nguyen, Page Daniel Dobbs, Khoa Luu
Date: 2023-11-27 00:00:00
Tasks: Action Recognition, Activity Recognition, All, Decoder, Group Activity Recognition, Scene Understanding, Specificity

Group Activity Recognition (GAR) is a fundamental problem in computer vision, with diverse applications in sports video analysis, video surveillance, and social scene understanding. Unlike conventional action recognition, GAR aims to classify the actions of a group of individuals as a whole, requiring a deep understanding of their interactions and spatiotemporal relationships. To address the challenges in GAR, we present REACT (\textbf{R}ecognize \textbf{E}very \textbf{Act}ion Everywhere All At Once), a novel architecture inspired by the transformer encoder-decoder model explicitly designed to model complex contextual relationships within videos, including multi-modality and spatio-temporal features. Our architecture features a cutting-edge Vision-Language Encoder block for integrated temporal, spatial, and multi-modal interaction modeling. This component efficiently encodes spatiotemporal interactions, even with sparsely sampled frames, and recovers essential local information. Our Action Decoder Block refines the joint understanding of text and video data, allowing us to precisely retrieve bounding boxes, enhancing the link between semantics and visual reality. At the core, our Actor Fusion Block orchestrates a fusion of actor-specific data and textual features, striking a balance between specificity and context. Our method outperforms state-of-the-art GAR approaches in extensive experiments, demonstrating superior accuracy in recognizing and understanding group activities. Our architecture's potential extends to diverse real-world applications, offering empirical evidence of its performance gains. This work significantly advances the field of group activity recognition, providing a robust framework for nuanced scene comprehension.
FBChain: A Blockchain-based Federated Learning Model with Efficiency and Secure Communication
Authors: Yang Li, Chunhe Xia, Tianbo Wang
Date: 2023-11-21 00:00:00
Tasks: Federated Learning

Privacy and security in the parameter transmission process of federated learning are currently among the most prominent concerns. However, there are two thorny problems caused by unprotected communication methods: "parameter-leakage" and "inefficient-communication". This article proposes Blockchain-based Federated Learning (FBChain) model for federated learning parameter communication to overcome the above two problems. First, we utilize the immutability of blockchain to store the global model and hash value of local model parameters in case of tampering during the communication process, protect data privacy by encrypting parameters, and verify data consistency by comparing the hash values of local parameters, thus addressing the "parameter-leakage" problem. Second, the Proof of Weighted Link Speed (PoWLS) consensus algorithm comprehensively selects nodes with the higher weighted link speed to aggregate global model and package blocks, thereby solving the "inefficient-communication" problem. Experimental results demonstrate the effectiveness of our proposed FBChain model and its ability to improve model communication efficiency in federated learning.
Privacy-Preserving Load Forecasting via Personalized Model Obfuscation
Authors: Shourya Bose, Yu Zhang, Kibaek Kim
Date: 2023-11-21 00:00:00
Tasks: Federated Learning, Load Forecasting, model, Privacy Preserving

The widespread adoption of smart meters provides access to detailed and localized load consumption data, suitable for training building-level load forecasting models. To mitigate privacy concerns stemming from model-induced data leakage, federated learning (FL) has been proposed. This paper addresses the performance challenges of short-term load forecasting models trained with FL on heterogeneous data, emphasizing privacy preservation through model obfuscation. Our proposed algorithm, Privacy Preserving Federated Learning (PPFL), incorporates personalization layers for localized training at each smart meter. Additionally, we employ a differentially private mechanism to safeguard against data leakage from shared layers. Simulations on the NREL ComStock dataset corroborate the effectiveness of our approach.
GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection
Authors: Jinggang Chen, Junjie Li, Xiaoyang Qu, Jianzong Wang, Jiguang Wan, Jing Xiao
Date: 2023-11-16 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Feature Importance, Out-of-Distribution Detection

Detecting out-of-distribution (OOD) examples is crucial to guarantee the reliability and safety of deep neural networks in real-world settings. In this paper, we offer an innovative perspective on quantifying the disparities between in-distribution (ID) and OOD data -- analyzing the uncertainty that arises when models attempt to explain their predictive decisions. This perspective is motivated by our observation that gradient-based attribution methods encounter challenges in assigning feature importance to OOD data, thereby yielding divergent explanation patterns. Consequently, we investigate how attribution gradients lead to uncertain explanation outcomes and introduce two forms of abnormalities for OOD detection: the zero-deflation abnormality and the channel-wise average abnormality. We then propose GAIA, a simple and effective approach that incorporates Gradient Abnormality Inspection and Aggregation. The effectiveness of GAIA is validated on both commonly utilized (CIFAR) and large-scale (ImageNet-1k) benchmarks. Specifically, GAIA reduces the average FPR95 by 23.10% on CIFAR10 and by 45.41% on CIFAR100 compared to advanced post-hoc methods.
Active Learning-Based Species Range Estimation
Authors: Christian Lange, Elijah Cole, Grant van Horn, Oisin Mac Aodha
Date: 2023-11-03 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Active Learning

We propose a new active learning approach for efficiently estimating the geographic range of a species from a limited number of on the ground observations. We model the range of an unmapped species of interest as the weighted combination of estimated ranges obtained from a set of different species. We show that it is possible to generate this candidate set of ranges by using models that have been trained on large weakly supervised community collected observation data. From this, we develop a new active querying approach that sequentially selects geographic locations to visit that best reduce our uncertainty over an unmapped species' range. We conduct a detailed evaluation of our approach and compare it to existing active learning methods using an evaluation dataset containing expert-derived ranges for one thousand species. Our results demonstrate that our method outperforms alternative active learning methods and approaches the performance of end-to-end trained models, even when only using a fraction of the data. This highlights the utility of active learning via transfer learned spatial representations for species range estimation. It also emphasizes the value of leveraging emerging large-scale crowdsourced datasets, not only for modeling a species' range, but also for actively discovering them.
Utilitarian Algorithm Configuration
Authors: Devon R. Graham, Kevin Leyton-Brown, Tim Roughgarden
Date: 2023-10-31 00:00:00
Proceeding: NeurIPS 2023 11

We present the first nontrivial procedure for configuring heuristic algorithms to maximize the utility provided to their end users while also offering theoretical guarantees about performance. Existing procedures seek configurations that minimize expected runtime. However, very recent theoretical work argues that expected runtime minimization fails to capture algorithm designers' preferences. Here we show that the utilitarian objective also confers significant algorithmic benefits. Intuitively, this is because mean runtime is dominated by extremely long runs even when they are incredibly rare; indeed, even when an algorithm never gives rise to such long runs, configuration procedures that provably minimize mean runtime must perform a huge number of experiments to demonstrate this fact. In contrast, utility is bounded and monotonically decreasing in runtime, allowing for meaningful empirical bounds on a configuration's performance. This paper builds on this idea to describe effective and theoretically sound configuration procedures. We prove upper bounds on the runtime of these procedures that are similar to theoretical lower bounds, while also demonstrating their performance empirically.