pwc-archive/papers-with-abstracts huggingface dataset:
Coupled Hierarchical Structure Learning using Tree-Wasserstein Distance
Authors: Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne, Ronen Talmon
Date: 2025-01-07 00:00:00
Tasks: Link Prediction, Node Classification

In many applications, both data samples and features have underlying hierarchical structures. However, existing methods for learning these latent structures typically focus on either samples or features, ignoring possible coupling between them. In this paper, we introduce a coupled hierarchical structure learning method using tree-Wasserstein distance (TWD). Our method jointly computes TWDs for samples and features, representing their latent hierarchies as trees. We propose an iterative, unsupervised procedure to build these sample and feature trees based on diffusion geometry, hyperbolic geometry, and wavelet filters. We show that this iterative procedure converges and empirically improves the quality of the constructed trees. The method is also computationally efficient and scales well in high-dimensional settings. Our method can be seamlessly integrated with hyperbolic graph convolutional networks (HGCN). We demonstrate that our method outperforms competing approaches in sparse approximation and unsupervised Wasserstein distance learning on several word-document and single-cell RNA-sequencing datasets. In addition, integrating our method into HGCN enhances performance in link prediction and node classification tasks.
(De)-Indexing and the Right to be Forgotten
Authors: Salvatore Vilella, Giancarlo Ruffo
Date: 2025-01-07 00:00:00
Tasks: Information Retrieval, Management

In the digital age, the challenge of forgetfulness has emerged as a significant concern, particularly regarding the management of personal data and its accessibility online. The right to be forgotten (RTBF) allows individuals to request the removal of outdated or harmful information from public access, yet implementing this right poses substantial technical difficulties for search engines. This paper aims to introduce non-experts to the foundational concepts of information retrieval (IR) and de-indexing, which are critical for understanding how search engines can effectively "forget" certain content. We will explore various IR models, including boolean, probabilistic, vector space, and embedding-based approaches, as well as the role of Large Language Models (LLMs) in enhancing data processing capabilities. By providing this overview, we seek to highlight the complexities involved in balancing individual privacy rights with the operational challenges faced by search engines in managing information visibility.
Towards Reliable Testing for Multiple Information Retrieval System Comparisons
Authors: David Otero, Javier Parapar, Álvaro Barreiro
Date: 2025-01-07 00:00:00
Tasks: Information Retrieval, Retrieval

Null Hypothesis Significance Testing is the \textit{de facto} tool for assessing effectiveness differences between Information Retrieval systems. Researchers use statistical tests to check whether those differences will generalise to online settings or are just due to the samples observed in the laboratory. Much work has been devoted to studying which test is the most reliable when comparing a pair of systems, but most of the IR real-world experiments involve more than two. In the multiple comparisons scenario, testing several systems simultaneously may inflate the errors committed by the tests. In this paper, we use a new approach to assess the reliability of multiple comparison procedures using simulated and real TREC data. Experiments show that Wilcoxon plus the Benjamini-Hochberg correction yields Type I error rates according to the significance level for typical sample sizes while being the best test in terms of statistical power.
Extending ChatGPT with a Browserless System for Web Product Price Extraction
Authors: Jorge Lloret-Gazo
Date: 2025-01-07 00:00:00

With the advenement of ChatGPT, we can find very clean, precise answers to a varied amount of questions. However, for questions such as 'find the price of the lemon cake at zingerman's', the answer looks like 'I can't browse the web right now'. In this paper, we propose a system, called Wextractor, which extends ChatGPT to answer questions as the one mentioned before. Obviously, our system cannot be labeled as `artificial intelligence'. Simply, it offers to cover a kind of transactional search that is not included in the current version of ChatGPT. Moreover, Wextractor includes two improvements with respect to the initial version: social extraction and pointing pattern extraction to improve the answer speed.
Multi-label Cross-lingual automatic music genre classification from lyrics with Sentence BERT
Authors: Tiago Fernandes Tavares, Fabio José Ayres
Date: 2025-01-07 00:00:00
Tasks: Classification, Genre classification, Information Retrieval, Music Genre Classification, Music Information Retrieval, Recommendation Systems, Sentence, Sentence Embeddings

Music genres are shaped by both the stylistic features of songs and the cultural preferences of artists' audiences. Automatic classification of music genres using lyrics can be useful in several applications such as recommendation systems, playlist creation, and library organization. We present a multi-label, cross-lingual genre classification system based on multilingual sentence embeddings generated by sBERT. Using a bilingual Portuguese-English dataset with eight overlapping genres, we demonstrate the system's ability to train on lyrics in one language and predict genres in another. Our approach outperforms the baseline approach of translating lyrics and using a bag-of-words representation, improving the genrewise average F1-Score from 0.35 to 0.69. The classifier uses a one-vs-all architecture, enabling it to assign multiple genre labels to a single lyric. Experimental results reveal that dataset centralization notably improves cross-lingual performance. This approach offers a scalable solution for genre classification across underrepresented languages and cultural domains, advancing the capabilities of music information retrieval systems.
MRF-Net: An Infrared Remote Sensing Image Thin Cloud Removal Method With the Intra-Inter Coherent Constraint
Authors: Qizhi Xu, Jiuchen Chen, Xinyu Yan, Wei Li
Date: 2024-10-07 00:00:00
Proceeding: TGRS 2024 10
Tasks: Cloud Removal

The usability of infrared remote sensing data is often compromised by thin cloud cover. To address this problem, we proposed the multiscale residual fusion network (MRF-Net) to remove thin cloud from infrared remote sensing imagery. Initially, we developed a thin cloud simulation method utilizing Perlin noise and affine transformation to generate high-fidelity thin cloud representations. Subsequently, to accurately discern and eliminate thin cloud from infrared images, we proposed MRF-Net. This model incorporates a multiscale feature fusion module (MSFFM) for extracting shallow features, a residual dense network module (RDNM) for in-depth feature extraction, a residual Swin transformer module (RSTM) for capturing global features, and attention mechanisms to selectively enhance target information. The Swin transformer, a hierarchical Transformer whose representation is computed with shifted windows, is employed to improve the efficiency of global feature extraction. Finally, we devised a combined loss function that accounts for both intrablock and interblock constraints to ensure de-clouding consistency across different image blocks. The intrablock constraint focuses on removing thin cloud within each image block, while the interblock constraint is designed to enhance the consistency of cloud removal between blocks. We have assembled a dataset comprising both simulated and real data to validate the efficiency of our proposed method. Experimental results have shown that our method effectively eliminates thin cloud and surpasses existing state-of-the-art methods. The source codes are available at https://github.com/CastleChen339/MRF-Net .
mFabric: An Efficient and Scalable Fabric for Mixture-of-Experts Training
Authors: Xudong Liao, Yijun Sun, Han Tian, Xinchen Wan, Yilun Jin, Zilong Wang, Zhenghang Ren, Xinyang Huang, Wenxue Li, Kin Fai Tse, Zhizhen Zhong, Guyue Liu, Ying Zhang, Xiaofeng Ye, Yiming Zhang, Kai Chen
Date: 2025-01-07 00:00:00
Tasks: Blocking, GPU, Mixture-of-Experts

Mixture-of-Expert (MoE) models outperform conventional models by selectively activating different subnets, named \emph{experts}, on a per-token basis. This gated computation generates dynamic communications that cannot be determined beforehand, challenging the existing GPU interconnects that remain \emph{static} during the distributed training process. In this paper, we advocate for a first-of-its-kind system, called mFabric, that unlocks topology reconfiguration \emph{during} distributed MoE training. Towards this vision, we first perform a production measurement study and show that the MoE dynamic communication pattern has \emph{strong locality}, alleviating the requirement of global reconfiguration. Based on this, we design and implement a \emph{regionally reconfigurable high-bandwidth domain} on top of existing electrical interconnects using optical circuit switching (OCS), achieving scalability while maintaining rapid adaptability. We have built a fully functional mFabric prototype with commodity hardware and a customized collective communication runtime that trains state-of-the-art MoE models with \emph{in-training} topology reconfiguration across 32 A100 GPUs. Large-scale packet-level simulations show that mFabric delivers comparable performance as the non-blocking fat-tree fabric while boosting the training cost efficiency (e.g., performance per dollar) of four representative MoE models by 1.2$\times$--1.5$\times$ and 1.9$\times$--2.3$\times$ at 100 Gbps and 400 Gbps link bandwidths, respectively.
Stochastically Constrained Best Arm Identification with Thompson Sampling
Authors: Le Yang, Siyang Gao, Cheng Li, Yi Wang
Date: 2025-01-07 00:00:00
Tasks: Thompson Sampling

We consider the problem of the best arm identification in the presence of stochastic constraints, where there is a finite number of arms associated with multiple performance measures. The goal is to identify the arm that optimizes the objective measure subject to constraints on the remaining measures. We will explore the popular idea of Thompson sampling (TS) as a means to solve it. To the best of our knowledge, it is the first attempt to extend TS to this problem. We will design a TS-based sampling algorithm, establish its asymptotic optimality in the rate of posterior convergence, and demonstrate its superior performance using numerical examples.
Truthful mechanisms for linear bandit games with private contexts
Authors: Yiting Hu, Lingjie Duan
Date: 2025-01-07 00:00:00
Tasks: Thompson Sampling

The contextual bandit problem, where agents arrive sequentially with personal contexts and the system adapts its arm allocation decisions accordingly, has recently garnered increasing attention for enabling more personalized outcomes. However, in many healthcare and recommendation applications, agents have private profiles and may misreport their contexts to gain from the system. For example, in adaptive clinical trials, where hospitals sequentially recruit volunteers to test multiple new treatments and adjust plans based on volunteers' reported profiles such as symptoms and interim data, participants may misreport severe side effects like allergy and nausea to avoid perceived suboptimal treatments. We are the first to study this issue of private context misreporting in a stochastic contextual bandit game between the system and non-repeated agents. We show that traditional low-regret algorithms, such as UCB family algorithms and Thompson sampling, fail to ensure truthful reporting and can result in linear regret in the worst case, while traditional truthful algorithms like explore-then-commit (ETC) and $\epsilon$-greedy algorithm incur sublinear but high regret. We propose a mechanism that uses a linear program to ensure truthfulness while minimizing deviation from Thompson sampling, yielding an $O(\ln T)$ frequentist regret. Our numerical experiments further demonstrate strong performance in multiple contexts and across other distribution families.
Leveraging time and parameters for nonlinear model reduction methods
Authors: Silke Glas, Benjamin Unger
Date: 2025-01-07 00:00:00
Tasks: Decoder

In this paper, we consider model order reduction (MOR) methods for problems with slowly decaying Kolmogorov $n$-widths as, e.g., certain wave-like or transport-dominated problems. To overcome this Kolmogorov barrier within MOR, nonlinear projections are used, which are often realized numerically using autoencoders. These autoencoders generally consist of a nonlinear encoder and a nonlinear decoder and involve costly training of the hyperparameters to obtain a good approximation quality of the reduced system. To facilitate the training process, we show that extending the to-be-reduced system and its corresponding training data makes it possible to replace the nonlinear encoder with a linear encoder without sacrificing accuracy, thus roughly halving the number of hyperparameters to be trained.
Machine learning applications in archaeological practices: a review
Authors: Mathias Bellat, Jordy D. Orellana Figueroa, Jonathan S. Reeves, Ruhollah Taghizadeh-Mehrjardi, Claudio Tennie, Thomas Scholten
Date: 2025-01-07 00:00:00
Tasks: Articles, Ensemble Learning

Artificial intelligence and machine learning applications in archaeology have increased significantly in recent years, and these now span all subfields, geographical regions, and time periods. The prevalence and success of these applications have remained largely unexamined, as recent reviews on the use of machine learning in archaeology have only focused only on specific subfields of archaeology. Our review examined an exhaustive corpus of 135 articles published between 1997 and 2022. We observed a significant increase in the number of publications from 2019 onwards. Automatic structure detection and artefact classification were the most represented tasks in the articles reviewed, followed by taphonomy, and archaeological predictive modelling. From the review, clustering and unsupervised methods were underrepresented compared to supervised models. Artificial neural networks and ensemble learning account for two thirds of the total number of models used. However, if machine learning models are gaining in popularity they remain subject to misunderstanding. We observed, in some cases, poorly defined requirements and caveats of the machine learning methods used. Furthermore, the goals and the needs of machine learning applications for archaeological purposes are in some cases unclear or poorly expressed. To address this, we proposed a workflow guide for archaeologists to develop coherent and consistent methodologies adapted to their research questions, project scale and data. As in many other areas, machine learning is rapidly becoming an important tool in archaeological research and practice, useful for the analyses of large and multivariate data, although not without limitations. This review highlights the importance of well-defined and well-reported structured methodologies and collaborative practices to maximise the potential of applications of machine learning methods in archaeology.
Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights
Authors: Sy-Tuyen Ho, Tuan Van Vo, Somayeh Ebrahimkhani, Ngai-Man Cheung
Date: 2025-01-07 00:00:00
Tasks: Neural Architecture Search, Out-of-Distribution Generalization

While ViTs have achieved across machine learning tasks, deploying them in real-world scenarios faces a critical challenge: generalizing under OoD shifts. A crucial research gap exists in understanding how to design ViT architectures, both manually and automatically, for better OoD generalization. To this end, we introduce OoD-ViT-NAS, the first systematic benchmark for ViTs NAS focused on OoD generalization. This benchmark includes 3000 ViT architectures of varying computational budgets evaluated on 8 common OoD datasets. Using this benchmark, we analyze factors contributing to OoD generalization. Our findings reveal key insights. First, ViT architecture designs significantly affect OoD generalization. Second, ID accuracy is often a poor indicator of OoD accuracy, highlighting the risk of optimizing ViT architectures solely for ID performance. Third, we perform the first study of NAS for ViTs OoD robustness, analyzing 9 Training-free NAS methods. We find that existing Training-free NAS methods are largely ineffective in predicting OoD accuracy despite excelling at ID accuracy. Simple proxies like Param or Flop surprisingly outperform complex Training-free NAS methods in predicting OoD accuracy. Finally, we study how ViT architectural attributes impact OoD generalization and discover that increasing embedding dimensions generally enhances performance. Our benchmark shows that ViT architectures exhibit a wide range of OoD accuracy, with up to 11.85% improvement for some OoD shifts. This underscores the importance of studying ViT architecture design for OoD. We believe OoD-ViT-NAS can catalyze further research into how ViT designs influence OoD generalization.
A Multimodal Lightweight Approach to Fault Diagnosis of Induction Motors in High-Dimensional Dataset
Authors: Usman Ali
Date: 2025-01-07 00:00:00
Tasks: Diagnostic, Fault Diagnosis, Transfer Learning

An accurate AI-based diagnostic system for induction motors (IMs) holds the potential to enhance proactive maintenance, mitigating unplanned downtime and curbing overall maintenance costs within an industrial environment. Notably, among the prevalent faults in IMs, a Broken Rotor Bar (BRB) fault is frequently encountered. Researchers have proposed various fault diagnosis approaches using signal processing (SP), machine learning (ML), deep learning (DL), and hybrid architectures for BRB faults. One limitation in the existing literature is the training of these architectures on relatively small datasets, risking overfitting when implementing such systems in industrial environments. This paper addresses this limitation by implementing large-scale data of BRB faults by using a transfer-learning-based lightweight DL model named ShuffleNetV2 for diagnosing one, two, three, and four BRB faults using current and vibration signal data. Spectral images for training and testing are generated using a Short-Time Fourier Transform (STFT). The dataset comprises 57,500 images, with 47,500 used for training and 10,000 for testing. Remarkably, the ShuffleNetV2 model exhibited superior performance, in less computational cost as well as accurately classifying 98.856% of spectral images. To further enhance the visualization of harmonic sidebands resulting from broken bars, Fast Fourier Transform (FFT) is applied to current and vibration data. The paper also provides insights into the training and testing times for each model, contributing to a comprehensive understanding of the proposed fault diagnosis methodology. The findings of our research provide valuable insights into the performance and efficiency of different ML and DL models, offering a foundation for the development of robust fault diagnosis systems for induction motors in industrial settings.
Detecting Neurocognitive Disorders through Analyses of Topic Evolution and Cross-modal Consistency in Visual-Stimulated Narratives
Authors: Jinchao Li, Yuejiao Wang, Junan Li, Jiawen Kang, Bo Zheng, Simon Wong, Brian Mak, Helene Fung, Jean Woo, Man-Wai Mak, Timothy Kwok, Vincent Mok, Xianmin Gong, Xixin Wu, Xunying Liu, Patrick Wong, Helen Meng
Date: 2025-01-07 00:00:00
Tasks: Topic Models

Early detection of neurocognitive disorders (NCDs) is crucial for timely intervention and disease management. Given that language impairments manifest early in NCD progression, visual-stimulated narrative (VSN)-based analysis offers a promising avenue for NCD detection. Current VSN-based NCD detection methods primarily focus on linguistic microstructures (e.g., pauses, lexical diversity), which are potentially linked to bottom-up (stimulus-driven) cognitive processing. While these features illuminate basic language abilities, the higher-order linguistic macrostructures (e.g., thematic or logical development), which may reflect top-down (concept-driven) cognitive abilities, remain underexplored. These patterns are crucial for NCD detection yet challenging to quantify due to their abstract and complex nature. To bridge this gap, we propose two novel dynamic macrostructural approaches: (1) Dynamic Topic Model (DTM) to track topic evolution over time, and (2) Text-Image Temporal Alignment Network (TITAN) to measure cross-modal consistency between speech and visual stimuli. Experimental results validated the efficiency of proposed approaches in NCD detection, with TITAN achieving superior performance both on the CU-MARVEL-RABBIT corpus (F1 = 0.7238) and the ADReSS corpus (F1 = 0.8889). The feature contribution analysis revealed that macrostructural features (e.g., topic variability, topic change rate, and topic consistency) constituted the most significant contributors in the model's decision pathways, outperforming investigated microstructural features. These findings underscore the critical role of macrostructural patterns in understanding cognitive impairment mechanisms in NCDs.
Run-and-tumble chemotaxis using reinforcement learning
Authors: Ramesh Pramanik, Shradha Mishra, Sakuntala Chatterjee
Date: 2025-01-07 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Bacterial cells use run-and-tumble motion to climb up attractant concentration gradient in their environment. By extending the uphill runs and shortening the downhill runs the cells migrate towards the higher attractant zones. Motivated by this, we formulate a reinforcement learning (RL) algorithm where an agent moves in one dimension in the presence of an attractant gradient. The agent can perform two actions: either persistent motion in the same direction or reversal of direction. We assign costs for these actions based on the recent history of the agent's trajectory. We ask the question: which RL strategy works best in different types of attractant environment. We quantify efficiency of the RL strategy by the ability of the agent (a) to localize in the favorable zones after large times, and (b) to learn about its complete environment. Depending on the attractant profile and the initial condition, we find an optimum balance is needed between exploration and exploitation to ensure the most efficient performance.
Imitation Learning of MPC with Neural Networks: Error Guarantees and Sparsification
Authors: Hendrik Alsmeier, Lukas Theiner, Anton Savchenko, Ali Mesbah, Rolf Findeisen
Date: 2025-01-07 00:00:00
Tasks: Imitation Learning, Model Predictive Control

This paper presents a framework for bounding the approximation error in imitation model predictive controllers utilizing neural networks. Leveraging the Lipschitz properties of these neural networks, we derive a bound that guides dataset design to ensure the approximation error remains at chosen limits. We discuss how this method can be used to design a stable neural network controller with performance guarantees employing existing robust model predictive control approaches for data generation. Additionally, we introduce a training adjustment, which is based on the sensitivities of the optimization problem and reduces dataset density requirements based on the derived bounds. We verify that the proposed augmentation results in improvements to the network's predictive capabilities and a reduction of the Lipschitz constant. Moreover, on a simulated inverted pendulum problem, we show that the approach results in a closer match of the closed-loop behavior between the imitation and the original model predictive controller.