pwc-archive/papers-with-abstracts huggingface dataset:
A swarm algorithm for collaborative traffic in vehicular networks
Authors: Jamal Toutouh, Enrique Alba
Date: 2025-01-17 00:00:00

Vehicular ad hoc networks (VANETs) allow vehicles to exchange warning messages with each other. These specific kinds of networks help reduce hazardous traffic situations and improve safety, which are two of the main objectives in developing Intelligent Transportation Systems (ITS). For this, the performance of VANETs should guarantee the delivery of messages in a required time. An obstacle to this is that the data traffic generated may cause network congestion. Data congestion control is used to enhance network capabilities, increasing the reliability of the VANET by decreasing packet losses and communication delays. In this study, we propose a swarm intelligence based distributed congestion control strategy to maintain the channel usage level under the threshold of network malfunction, while keeping the quality-of-service of the VANET high. An exhaustive experimentation shows that the proposed strategy improves the throughput of the network, the channel usage, and the stability of the communications in comparison with other competing congestion control strategies.
Dendritic Localized Learning: Toward Biologically Plausible Algorithm
Authors: Changze Lv, Jingwen Xu, Yiyang Lu, Xiaohua Wang, Zhenghua Wang, Zhibo Xu, Di Yu, Xin Du, Xiaoqing Zheng, Xuanjing Huang
Date: 2025-01-17 00:00:00

Backpropagation is the foundational algorithm for training neural networks and a key driver of deep learning's success. However, its biological plausibility has been challenged due to three primary limitations: weight symmetry, reliance on global error signals, and the dual-phase nature of training, as highlighted by the existing literature. Although various alternative learning approaches have been proposed to address these issues, most either fail to satisfy all three criteria simultaneously or yield suboptimal results. Inspired by the dynamics and plasticity of pyramidal neurons, we propose Dendritic Localized Learning (DLL), a novel learning algorithm designed to overcome these challenges. Extensive empirical experiments demonstrate that DLL satisfies all three criteria of biological plausibility while achieving state-of-the-art performance among algorithms that meet these requirements. Furthermore, DLL exhibits strong generalization across a range of architectures, including MLPs, CNNs, and RNNs. These results, benchmarked against existing biologically plausible learning algorithms, offer valuable empirical insights for future research. We hope this study can inspire the development of new biologically plausible algorithms for training multilayer networks and advancing progress in both neuroscience and machine learning.
Evaluation and Efficiency Comparison of Evolutionary Algorithms for Service Placement Optimization in Fog Architectures
Authors: Carlos Guerrero, Isaac Lera, Carlos Juiz
Date: 2025-01-17 00:00:00
Tasks: Diversity, Evolutionary Algorithms

This study compares three evolutionary algorithms for the problem of fog service placement: weighted sum genetic algorithm (WSGA), non-dominated sorting genetic algorithm II (NSGA-II), and multiobjective evolutionary algorithm based on decomposition (MOEA/D). A model for the problem domain (fog architecture and fog applications) and for the optimization (objective functions and solutions) is presented. Our main concerns are related to optimize the network latency, the service spread and the use of the resources. The algorithms are evaluated with a random Barabasi-Albert network topology with 100 devices and with two experiment sizes of 100 and 200 application services. The results showed that NSGA-II obtained the highest optimizations of the objectives and the highest diversity of the solution space. On the contrary, MOEA/D was better to reduce the execution times. The WSGA algorithm did not show any benefit with regard to the other two algorithms.
Principled model selection for stochastic dynamics
Authors: Andonis Gerardos, Pierre Ronceray
Date: 2025-01-17 00:00:00
Tasks: model, Model Selection

Complex dynamical systems, from macromolecules to ecosystems, are often modeled by stochastic differential equations. To learn such models from data, a common approach involves sparse selection among a large function library. However, we show that overfitting arises - not just from individual model complexity, but also from the combinatorial growth of possible models. To address this, we introduce Parsimonious Stochastic Inference (PASTIS), a principled method combining likelihood-estimation statistics with extreme value theory to suppress superfluous parameters. PASTIS outperforms existing methods and reliably identifies minimal models, even with low sampling rates or measurement error. It extends to stochastic partial differential equations, and applies to ecological networks and reaction-diffusion dynamics.
A Family of Controllable Momentum Coefficients for Forward-Backward Accelerated Algorithms
Authors: Mingwei Fu, Bin Shi
Date: 2025-01-17 00:00:00

Nesterov's accelerated gradient method (NAG) marks a pivotal advancement in gradient-based optimization, achieving faster convergence compared to the vanilla gradient descent method for convex functions. However, its algorithmic complexity when applied to strongly convex functions remains unknown, as noted in the comprehensive review by Chambolle and Pock [2016]. This issue, aside from the critical step size, was addressed by Li et al. [2024b], with the monotonic case further explored by Fu and Shi [2024]. In this paper, we introduce a family of controllable momentum coefficients for forward-backward accelerated methods, focusing on the critical step size $s=1/L$. Unlike traditional linear forms, the proposed momentum coefficients follow an $\alpha$-th power structure, where the parameter $r$ is adaptively tuned to $\alpha$. Using a Lyapunov function specifically designed for $\alpha$, we establish a controllable $O\left(1/k^{2\alpha} \right)$ convergence rate for the NAG-$\alpha$ method, provided that $r > 2\alpha$. At the critical step size, NAG-$\alpha$ achieves an inverse polynomial convergence rate of arbitrary degree by adjusting $r$ according to $\alpha > 0$. We further simplify the Lyapunov function by expressing it in terms of the iterative sequences $x_k$ and $y_k$, eliminating the need for phase-space representations. This simplification enables us to extend the controllable $O \left(1/k^{2\alpha} \right)$ rate to the monotonic variant, M-NAG-$\alpha$, thereby enhancing optimization efficiency. Finally, by leveraging the fundamental inequality for composite functions, we extended the controllable $O\left(1/k^{2\alpha} \right)$ rate to proximal algorithms, including the fast iterative shrinkage-thresholding algorithm (FISTA-$\alpha$) and its monotonic counterpart (M-FISTA-$\alpha$).
Robust density estimation over star-shaped density classes
Authors: Xiaolong Liu, Matey Neykov
Date: 2025-01-17 00:00:00
Tasks: Density Estimation

We establish a novel criterion for comparing the performance of two densities, $g_1$ and $g_2$, within the context of corrupted data. Utilizing this criterion, we propose an algorithm to construct a density estimator within a star-shaped density class, $\mathcal{F}$, under conditions of data corruption. We proceed to derive the minimax upper and lower bounds for density estimation across this star-shaped density class, characterized by densities that are uniformly bounded above and below (in the sup norm), in the presence of adversarially corrupted data. Specifically, we assume that a fraction $\epsilon \leq \frac{1}{3}$ of the $N$ observations are arbitrarily corrupted. We obtain the minimax upper bound $\max\{ \tau_{\overline{J}}^2, \epsilon \} \wedge d^2$. Under certain conditions, we obtain the minimax risk, up to proportionality constants, under the squared $L_2$ loss as $$ \max\left\{ \tau^{*2} \wedge d^2, \epsilon \wedge d^2 \right\}, $$ where $\tau^* := \sup\left\{ \tau : N\tau^2 \leq \log \mathcal{M}_{\mathcal{F}}^{\text{loc}}(\tau, c) \right\}$ for a sufficiently large constant $c$. Here, $\mathcal{M}_{\mathcal{F}}^{\text{loc}}(\tau, c)$ denotes the local entropy of the set $\mathcal{F}$, and $d$ is the $L_2$ diameter of $\mathcal{F}$.
MechIR: A Mechanistic Interpretability Framework for Information Retrieval
Authors: Andrew Parry, Catherine Chen, Carsten Eickhoff, Sean MacAvaney
Date: 2025-01-17 00:00:00
Tasks: Diagnostic, Information Retrieval, Retrieval

Mechanistic interpretability is an emerging diagnostic approach for neural models that has gained traction in broader natural language processing domains. This paradigm aims to provide attribution to components of neural systems where causal relationships between hidden layers and output were previously uninterpretable. As the use of neural models in IR for retrieval and evaluation becomes ubiquitous, we need to ensure that we can interpret why a model produces a given output for both transparency and the betterment of systems. This work comprises a flexible framework for diagnostic analysis and intervention within these highly parametric neural systems specifically tailored for IR tasks and architectures. In providing such a framework, we look to facilitate further research in interpretable IR with a broader scope for practical interventions derived from mechanistic interpretability. We provide preliminary analysis and look to demonstrate our framework through an axiomatic lens to show its applications and ease of use for those IR practitioners inexperienced in this emerging paradigm.
A Worrying Reproducibility Study of Intent-Aware Recommendation Models
Authors: Faisal Shehzad, Maurizio Ferrari Dacrema, Dietmar Jannach
Date: 2025-01-17 00:00:00
Tasks: Recommendation Systems

Lately, we have observed a growing interest in intent-aware recommender systems (IARS). The promise of such systems is that they are capable of generating better recommendations by predicting and considering the underlying motivations and short-term goals of consumers. From a technical perspective, various sophisticated neural models were recently proposed in this emerging and promising area. In the broader context of complex neural recommendation models, a growing number of research works unfortunately indicates that (i) reproducing such works is often difficult and (ii) that the true benefits of such models may be limited in reality, e.g., because the reported improvements were obtained through comparisons with untuned or weak baselines. In this work, we investigate if recent research in IARS is similarly affected by such problems. Specifically, we tried to reproduce five contemporary IARS models that were published in top-level outlets, and we benchmarked them against a number of traditional non-neural recommendation models. In two of the cases, running the provided code with the optimal hyperparameters reported in the paper did not yield the results reported in the paper. Worryingly, we find that all examined IARS approaches are consistently outperformed by at least one traditional model. These findings point to sustained methodological issues and to a pressing need for more rigorous scholarly practices.
Empirical Evaluation of Embedding Models in the Context of Text Classification in Document Review in Construction Delay Disputes
Authors: Fusheng Wei, Robert Neary, Han Qin, Qiang Mao, Jianping Zhang
Date: 2025-01-16 00:00:00
Tasks: Binary Classification, text-classification, Text Classification

Text embeddings are numerical representations of text data, where words, phrases, or entire documents are converted into vectors of real numbers. These embeddings capture semantic meanings and relationships between text elements in a continuous vector space. The primary goal of text embeddings is to enable the processing of text data by machine learning models, which require numerical input. Numerous embedding models have been developed for various applications. This paper presents our work in evaluating different embeddings through a comprehensive comparative analysis of four distinct models, focusing on their text classification efficacy. We employ both K-Nearest Neighbors (KNN) and Logistic Regression (LR) to perform binary classification tasks, specifically determining whether a text snippet is associated with 'delay' or 'not delay' within a labeled dataset. Our research explores the use of text snippet embeddings for training supervised text classification models to identify delay-related statements during the document review process of construction delay disputes. The results of this study highlight the potential of embedding models to enhance the efficiency and accuracy of document analysis in legal contexts, paving the way for more informed decision-making in complex investigative scenarios.
Enhancing AI Safety Through the Fusion of Low Rank Adapters
Authors: Satya Swaroop Gudipudi, Sreeram Vipparla, Harpreet Singh, Shashwat Goel, Ponnurangam Kumaraguru
Date: 2024-12-30 00:00:00

Instruction fine-tuning of large language models (LLMs) is a powerful method for improving task-specific performance, but it can inadvertently lead to a phenomenon where models generate harmful responses when faced with malicious prompts. In this paper, we explore Low-Rank Adapter Fusion (LoRA) as a means to mitigate these risks while preserving the model's ability to handle diverse instructions effectively. Through an extensive comparative analysis against established baselines using recognized benchmark datasets, we demonstrate a 42\% reduction in the harmfulness rate by leveraging LoRA fusion between a task adapter and a safety adapter, the latter of which is specifically trained on our safety dataset. However, we also observe exaggerated safety behaviour, where the model rejects safe prompts that closely resemble unsafe ones
A Systematic Review of Machine Learning Methods for Multimodal EEG Data in Clinical Application
Authors: Siqi Zhao, Wangyang Li, Xiru Wang, Stevie Foglia, Hongzhao Tan, Bohan Zhang, Ameer Hamoodi, Aimee Nelson, Zhen Gao
Date: 2024-12-31 00:00:00
Tasks: Decision Making, EEG, Seizure Detection, Systematic Literature Review

Machine learning (ML) and deep learning (DL) techniques have been widely applied to analyze electroencephalography (EEG) signals for disease diagnosis and brain-computer interfaces (BCI). The integration of multimodal data has been shown to enhance the accuracy of ML and DL models. Combining EEG with other modalities can improve clinical decision-making by addressing complex tasks in clinical populations. This systematic literature review explores the use of multimodal EEG data in ML and DL models for clinical applications. A comprehensive search was conducted across PubMed, Web of Science, and Google Scholar, yielding 16 relevant studies after three rounds of filtering. These studies demonstrate the application of multimodal EEG data in addressing clinical challenges, including neuropsychiatric disorders, neurological conditions (e.g., seizure detection), neurodevelopmental disorders (e.g., autism spectrum disorder), and sleep stage classification. Data fusion occurred at three levels: signal, feature, and decision levels. The most commonly used ML models were support vector machines (SVM) and decision trees. Notably, 11 out of the 16 studies reported improvements in model accuracy with multimodal EEG data. This review highlights the potential of multimodal EEG-based ML models in enhancing clinical diagnostics and problem-solving.
VAG: Voxel Attenuation Grid For Sparse-View CBCT Reconstruction
Authors: Jinhao Qiao; Jiang Liu; Heng Yu; Yi Xiao; Hongshan Yu; Yan Zheng; Sihan Li
Date: 2024-10-30 00:00:00
Proceeding: IEEE International Conference on Image Processing 2024 10

Sparse view CBCT reconstruction has become one of the important research fields to reduce the radiation impact of CT scanning. However, the reconstruction of high-quality 3D CT volumes from sparse and noisy CBCT data still faces challenges such as slow convergence, long computation time, and increased noise. In light of these issues, we propose a voxel attenuation grid representation to explicitly model the attenuation field of the 3D CT volume. Since this representation does not involve the implementation of neural networks, our method for reconstruction is extremely fast. Furthermore, trim regularization and total variation regularization terms are introduced on top of the mean square error loss to optimize the voxel attenuation grid and significantly reduce the noise in the reconstructed 3D CT volume. Experiments on the NSCLC dataset demonstrate the superiority of our method and its potential in clinical applications. Our code will be available at: https://github.com/qiaodongxing/VAG.
Modernized Training of U-Net for Aerial Semantic Segmentation
Authors: Jakub Straka, Ivan Gruber
Date: 2024-01-01 00:00:00
Proceeding: WACV 2024 1
Tasks: Land Cover Classification, Semantic Segmentation

In this paper, we propose an improved training protocol of U-Net architecture for the semantic segmentation of aerial images. We test our approach on the challenging FLAIR #2 dataset. We present an extensive ablation study on the influence of different approach components on the overall performance. The ablation study includes a comparison of different model backbones, image augmentations, learning rate schedulers, loss functions, and training procedures. We additionally propose a two-stage training procedure and evaluate different options for the model ensemble. Based on the results we design the final setup of the model training protocol. This final setup decreases the relative error by approximately 18% and achieves mIoU equal to 0.641, which is a new state-of-the-art result. Our code is available at: https://github.com/strakaj/U-Net-for-remote-sensing
Prince of Umueri HRH Prince Ikeobi Daniel Ekwevi - The Igbo Chineke Eze legend
Authors: Sunday Okoye
Date: 2025-01-21 00:00:00
Proceeding: 01/21 2025 1

A new book authored by Sunday Okoye. Talks about Prince of Umueri HRH Prince Ikeobi Daniel Ekwevi II. The book is a wonderful play of words and narratives celebrating the rich heritage, influential leaders, and legendary icons of Umueri. The article sheds light on the town’s thriving journey toward growth through strange tales.

Prince Ikeobi Daniel Ekwevi the Eze Chi line, holds a revered position in the history and development of Umueri, is notably featured in the book as one of its central figures. Through compelling storytelling, Legends of Umueri highlights Prince Ikeobi’s contributions to society and the progress of the town, emphasizing his dedication to fostering economic development, community empowerment, and cultural preservation.

Umueri the cradle of the Eri - Igbo culture is located in the heart of Anambra State, has transformed into a hub for growth and opportunity, rooted in its rich cultural legacy. The book portrays the town’s journey, celebrating its vibrant traditions while showcasing its potential for future advancements in education, commerce, and infrastructure.

Sunday Okoye’s Legends of Umueri is not just a personal account; it captures the spirit of a community that continues to strive for excellence. By profiling individuals like Prince Ikeobi Daniel Ekwevi, the book highlights the importance of visionary leadership and collective effort in building a prosperous society.

Legends of Umueri is now available in bookstores and online.
MOST EXPENSIVE SCHOOL IN NIGERIA IS IN BIAFRA
Authors: MOST EXPENSIVE
Date: 2025-01-21 00:00:00
Proceeding: 01/21 2025 1

Speculation is growing about the construction of a new school in Nigeria, rumored to charge ₦80 million per year in tuition fees, making it the most expensive school in the country. Reports suggest the institution is being built in the Biafra region, sparking debates about its purpose and impact.

The school is rumored to offer world-class facilities, top-tier educators, and an advanced curriculum designed to rival global standards. Advocates see the project as a potential driver of development in the Biafra region based of American Standards, while critics question its exclusivity and relevance to local needs.

Details about the developers remain unconfirmed, and further updates are anticipated as more information comes to light.
Reproducing Popularity Bias in Recommendation: The Effect of Evaluation Strategies
Authors: Savvina Daniil, Mirjam Cuper
Date: 2024-03-07 00:00:00
Proceeding: ACM Transactions on Recommender Systems 2024 3
Tasks: Recommendation Systems

The extent to which popularity bias is propagated by media recommender systems is a current topic within the community, as is the uneven propagation among users with varying interests for niche items. Recent work focused on exactly this topic, with movies being the domain of interest. Later on, two different research teams reproduced the methodology in the domains of music and books, respectively. The results across the different domains diverge. In this paper, we reproduce the three studies and identify four aspects that are relevant in investigating the differences in results: data, algorithms, division of users in groups and evaluation strategy. We run a set of experiments in which we measure general popularity bias propagation and unfair treatment of certain users with various combinations of these aspects. We conclude that all aspects account to some degree for the divergence in results, and should be carefully considered in future studies. Further, we find that the divergence in findings can be in large part attributed to the choice of evaluation strategy.
On Ambisonic Source Separation with Spatially Informed Non-negative Tensor Factorization
Authors: Mateusz Guzik, Konrad Kowalczyk
Date: 2025-01-17 00:00:00

This article presents a Non-negative Tensor Factorization based method for sound source separation from Ambisonic microphone signals. The proposed method enables the use of prior knowledge about the Directions-of-Arrival (DOAs) of the sources, incorporated through a constraint on the Spatial Covariance Matrix (SCM) within a Maximum a Posteriori (MAP) framework. Specifically, this article presents a detailed derivation of four algorithms that are based on two types of cost functions, namely the squared Euclidean distance and the Itakura-Saito divergence, which are then combined with two prior probability distributions on the SCM, that is the Wishart and the Inverse Wishart. The experimental evaluation of the baseline Maximum Likelihood (ML) and the proposed MAP methods is primarily based on first-order Ambisonic recordings, using four different source signal datasets, three with musical pieces and one containing speech utterances. We consider under-determined, determined, as well as over-determined scenarios by separating two, four and six sound sources, respectively. Furthermore, we evaluate the proposed algorithms for different spherical harmonic orders and at different reverberation time levels, as well as in non-ideal prior knowledge conditions, for increasingly more corrupted DOAs. Overall, in comparison with beamforming and a state-of-the-art separation technique, as well as the baseline ML methods, the proposed MAP approach offers superior separation performance in a variety of scenarios, as shown by the analysis of the experimental evaluation results, in terms of the standard objective separation measures, such as the SDR, ISR, SIR and SAR.