pwc-archive/papers-with-abstracts huggingface dataset:
Multi-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual Explanations for Multivariate Time Series Classification
Authors: Mario Refoyo, David Luengo
Date: 2024-12-14 00:00:00
Tasks: counterfactual, Counterfactual Explanation, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Time Series, Time Series Classification

Deep Learning systems excel in complex tasks but often lack transparency, limiting their use in critical applications. Counterfactual explanations, a core tool within eXplainable Artificial Intelligence (XAI), offer insights into model decisions by identifying minimal changes to an input to alter its predicted outcome. However, existing methods for time series data are limited by univariate assumptions, rigid constraints on modifications, or lack of validity guarantees. This paper introduces Multi-SpaCE, a multi-objective counterfactual explanation method for multivariate time series. Using non-dominated ranking genetic algorithm II (NSGA-II), Multi-SpaCE balances proximity, sparsity, plausibility, and contiguity. Unlike most methods, it ensures perfect validity, supports multivariate data and provides a Pareto front of solutions, enabling flexibility to different end-user needs. Comprehensive experiments in diverse datasets demonstrate the ability of Multi-SpaCE to consistently achieve perfect validity and deliver superior performance compared to existing methods.
Untapped Potential in Self-Optimization of Hopfield Networks: The Creativity of Unsupervised Learning
Authors: Natalya Weber, Christian Guckelsberger, Tom Froese
Date: 2024-12-10 00:00:00
Tasks: Artificial Life

The Self-Optimization (SO) model can be considered as the third operational mode of the classical Hopfield Network, leveraging the power of associative memory to enhance optimization performance. Moreover, it has been argued to express characteristics of minimal agency, which renders it useful for the study of artificial life. In this article, we draw attention to another facet of the SO model: its capacity for creativity. Drawing on creativity studies, we argue that the model satisfies the necessary and sufficient conditions of a creative process. Moreover, we show that learning is needed to find creative outcomes above chance probability. Furthermore, we demonstrate that modifying the learning parameters in the SO model gives rise to four different regimes that can account for both creative products and inconclusive outcomes, thus providing a framework for studying and understanding the emergence of creative behaviors in artificial systems that learn.
Accelerated Extragradient-Type Methods -- Part 2: Generalization and Sublinear Convergence Rates under Co-Hypomonotonicity
Authors: Quoc Tran-Dinh, Nghia Nguyen-Trung
Date: 2025-01-08 00:00:00

Following the first part of our project, this paper comprehensively studies two types of extragradient-based methods: anchored extragradient and Nesterov's accelerated extragradient for solving [non]linear inclusions (and, in particular, equations), primarily under the Lipschitz continuity and the co-hypomonotonicity assumptions. We unify and generalize a class of anchored extragradient methods for monotone inclusions to a wider range of schemes encompassing existing algorithms as special cases. We establish $\mathcal{O}(1/k)$ last-iterate convergence rates on the residual norm of the underlying mapping for this general framework and then specialize it to obtain convergence guarantees for specific instances, where $k$ denotes the iteration counter. We extend our approach to a class of anchored Tseng's forward-backward-forward splitting methods to obtain a broader class of algorithms for solving co-hypomonotone inclusions. Again, we analyze $\mathcal{O}(1/k)$ last-iterate convergence rates for this general scheme and specialize it to obtain convergence results for existing and new variants. We generalize and unify Nesterov's accelerated extra-gradient method to a new class of algorithms that covers existing schemes as special instances while generating new variants. For these schemes, we can prove $\mathcal{O}(1/k)$ last-iterate convergence rates for the residual norm under co-hypomonotonicity, covering a class of nonmonotone problems. We propose another novel class of Nesterov's accelerated extragradient methods to solve inclusions. Interestingly, these algorithms achieve both $\mathcal{O}(1/k)$ and $o(1/k)$ last-iterate convergence rates, and also the convergence of iterate sequences under co-hypomonotonicity and Lipschitz continuity. Finally, we provide a set of numerical experiments encompassing different scenarios to validate our algorithms and theoretical guarantees.
Neural Parameter Estimation with Incomplete Data
Authors: Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Noel Cressie, Raphaël Huser
Date: 2025-01-08 00:00:00
Tasks: parameter estimation

Advancements in artificial intelligence (AI) and deep learning have led to neural networks being used to generate lightning-speed answers to complex questions, to paint like Monet, or to write like Proust. Leveraging their computational speed and flexibility, neural networks are also being used to facilitate fast, likelihood-free statistical inference. However, it is not straightforward to use neural networks with data that for various reasons are incomplete, which precludes their use in many applications. A recently proposed approach to remedy this issue inputs an appropriately padded data vector and a vector that encodes the missingness pattern to a neural network. While computationally efficient, this "masking" approach can result in statistically inefficient inferences. Here, we propose an alternative approach that is based on the Monte Carlo expectation-maximization (EM) algorithm. Our EM approach is likelihood-free, substantially faster than the conventional EM algorithm as it does not require numerical optimization at each iteration, and more statistically efficient than the masking approach. This research represents a prototype problem that indicates how improvements could be made in AI by introducing Bayesian statistical thinking. We compare the two approaches to missingness using simulated incomplete data from two models: a spatial Gaussian process model, and a spatial Potts model. The utility of the methodology is shown on Arctic sea-ice data and cryptocurrency data.
Evaluating Interval-based Tokenization for Pitch Representation in Symbolic Music Analysis
Authors: Dinh-Viet-Toan Le, Louis Bigo, Mikaela Keller
Date: 2025-01-08 00:00:00

Symbolic music analysis tasks are often performed by models originally developed for Natural Language Processing, such as Transformers. Such models require the input data to be represented as sequences, which is achieved through a process of tokenization. Tokenization strategies for symbolic music often rely on absolute MIDI values to represent pitch information. However, music research largely promotes the benefit of higher-level representations such as melodic contour and harmonic relations for which pitch intervals turn out to be more expressive than absolute pitches. In this work, we introduce a general framework for building interval-based tokenizations. By evaluating these tokenizations on three music analysis tasks, we show that such interval-based tokenizations improve model performances and facilitate their explainability.
A Closer Look on Gender Stereotypes in Movie Recommender Systems and Their Implications with Privacy
Authors: Falguni Roy, Yiduo Shen, Na Zhao, Xiaofeng Ding, Md. Omar Faruk
Date: 2025-01-08 00:00:00
Tasks: Attribute, Recommendation Systems

The movie recommender system typically leverages user feedback to provide personalized recommendations that align with user preferences and increase business revenue. This study investigates the impact of gender stereotypes on such systems through a specific attack scenario. In this scenario, an attacker determines users' gender, a private attribute, by exploiting gender stereotypes about movie preferences and analyzing users' feedback data, which is either publicly available or observed within the system. The study consists of two phases. In the first phase, a user study involving 630 participants identified gender stereotypes associated with movie genres, which often influence viewing choices. In the second phase, four inference algorithms were applied to detect gender stereotypes by combining the findings from the first phase with users' feedback data. Results showed that these algorithms performed more effectively than relying solely on feedback data for gender inference. Additionally, we quantified the extent of gender stereotypes to evaluate their broader impact on digital computational science. The latter part of the study utilized two major movie recommender datasets: MovieLens 1M and Yahoo!Movie. Detailed experimental information is available on our GitHub repository: https://github.com/fr-iit/GSMRS
An innovative data collection method to eliminate the preprocessing phase in web usage mining
Authors: Ozkan Canay, Umit Kocabicak
Date: 2025-01-08 00:00:00

The underlying data source for web usage mining (WUM) is commonly thought to be server logs. However, access log files ensure quite limited data about the clients. Identifying sessions from this messy data takes a considerable effort, and operations performed for this purpose do not always yield excellent results. Also, this data cannot be used for web analytics efficiently. This study proposes an innovative method for user tracking, session management, and collecting web usage data. The method is mainly based on a new approach for using collected data for web analytics extraction as the data source in web usage mining. An application-based API has been developed with a different strategy from conventional client-side methods to obtain and process log data. The log data has been successfully gathered by integrating the technique into an enterprise web application. The results reveal that the homogeneous structured data collected and stored with this method is more convenient to browse, filter, and process than web server logs. This data stored on a relational database can be used effortlessly as a reliable data source for high-performance web usage mining activity, real-time web analytics, or a functional recommendation system.
scGIST: gene panel design for spatial transcriptomics with prioritized gene sets
Authors: Mashrur Ahmed Yafi, Md. Hasibul Husain Hisham, Francisco Grisanti, James F. Martin, Atif Rahman, Md. Abul Hassan Samee
Date: 2024-02-26 00:00:00
Proceeding: Genome Biology 2024 2
Tasks: feature selection

A critical challenge of single-cell spatial transcriptomics (sc-ST) technologies is their panel size. Being based on fluorescence in situ hybridization, they are typically limited to panels of about a thousand genes. This constrains researchers to build panels from only the marker genes of different cell types and forgo other genes of interest, e.g., genes encoding ligand-receptor complexes or those in specific pathways. We propose scGIST, a constrained feature selection tool that designs sc-ST panels prioritizing user-specified genes without compromising cell type detection accuracy. We demonstrate scGIST’s efficacy in diverse use cases, highlighting it as a valuable addition to sc-ST’s algorithmic toolbox.
Nick Patrick Contreras
Authors: Nick Patrick Contreras
Date: 2025-01-09 00:00:00
Proceeding: https://genius.com/Young_SadzZz 2025 1

Nick Patrick Contreras is an accomplished musician, composer, and performer whose work seamlessly integrates diverse musical influences, ranging from rock and jazz to classical and contemporary genres. With a career spanning over several years, Nick has gained recognition for his exceptional technical skill, musical versatility, and innovative approach to composition. His deep understanding of music theory, combined with his ability to perform across multiple instruments, has allowed him to create captivating soundscapes that resonate with a wide range of audiences.

A passionate advocate for musical exploration, Nick's performances are marked by their emotive expression and intricate arrangements, whether in live settings or studio productions. His ability to blend traditional and modern techniques showcases his adaptability and commitment to pushing the boundaries of his craft. Throughout his career, Nick has worked on a variety of projects, collaborating with other musicians and contributing to numerous musical productions, earning him a reputation as a highly respected figure in the music community.

As an artist, Nick Patrick Contreras continues to evolve, always seeking to refine his sound while inspiring and connecting with listeners through his music. His work reflects not only his technical proficiency but also his unwavering dedication to the artistry of music itself.
Nick Pattrick Contreras
Authors: Nick Pattrick Contreras
Date: 2025-01-09 00:00:00
Proceeding: https://genius.com/Young_SadzZz 2025 1

Nick Patrick Contreras is an emerging rapper hailing from North Pomona, California, with a distinctive sound that blends raw emotion and innovative beats. Drawing inspiration from influential artists such as Juice WRLD, The Kid LAROI, XXXTentacion, and Tyler, The Creator, Nick has developed a unique style that captures both vulnerability and strength in his music.

His lyrics delve into personal experiences, mental health, and self-reflection, creating a deep connection with listeners who relate to his authenticity. Known for his ability to mix melodic rap with hard-hitting verses, Nick brings a refreshing energy to the rap scene that resonates with a wide audience. His approach combines the introspective and emotional qualities of his influences, while forging a path that is distinctly his own.

As an up-and-coming artist, Nick is committed to expanding his reach and refining his sound. His determination and passion for music fuel his journey toward establishing a significant presence in the industry. With his evolving talent and captivating music, Nick Patrick Contreras is poised to make an impactful mark on the world of rap and hip-hop.
Cypress Copilot: Development of an AI Assistant for Boosting Productivity and Transforming Web Application Testing
Authors: Suresh Babu Nettur, Shanthi Karpurapu, Unnati Nettur, Likhit Sagar Gajja
Date: 2024-12-23 00:00:00
Proceeding: IEEE Access 2024 12
Tasks: Code Generation

In today’s fast-paced software development environment, Agile methodologies demand rapid delivery and continuous improvement, making automated testing essential for maintaining quality and accelerating feedback loops. Our study addresses the challenges of developing and maintaining automation code for web-based application testing. In this paper, we propose a novel approach that leverages large language models (LLMs) and a novel prompt technique, few-shot chain, to automate code generation for web application testing. We chose the Behavior-Driven Development (BDD) methodology owing to its advantages and selected the Cypress tool for automating web application testing, as it is one of the most popular and rapidly growing frameworks in this domain. We comprehensively evaluated various OpenAI models, including GPT-4-Turbo, GPT-4o, and GitHub Copilot, using zero-shot and few-shot chain prompt techniques. Furthermore, we extensively validated with a vast set of test cases to identify the optimal approach. Our results indicate that the Cypress automation code generated by GPT-4o using a few-shot chained prompt approach excels in generating complete code for each test case, with fewer empty methods and improved syntactical accuracy and maintainability. Based on these findings, we developed a novel open-source Visual Studio Code (IDE) extension, “Cypress Copilot” utilizing GPT-4o and a few-shot chain prompt technique, which has shown promising results. Finally, we validate the Cypress Copilot tool by generating automation code for end-to-end web tests, demonstrating its effectiveness in testing various web applications and its ability to streamline development processes. More importantly, we are releasing this tool to the open-source community, as it has the potential to be a promising partner in enhancing productivity in web application automation testing.
HORUS: Multimodal Large Language Models Framework for Video Retrieval at VBS 2025
Authors: Tai Nguyen, Vo Ngoc Minh Anh, Duc Dat Pham, Tran Quang Vinh, Nhu Duong Thi Quynh, Le Anh Tien, Tan Duy Le, Binh T. Nguyen
Date: 2025-01-01 00:00:00
Proceeding: MultiMedia Modeling 2025 1
Tasks: Image Retrieval, Retrieval, Video Retrieval

In the dynamic field of video retrieval, precise and effective search methods are crucial for managing complex datasets. We present HORUS, a novel approach based on multimodal Large Language Models (mLLMs) that advances video retrieval capabilities through two key innovations: (1) advanced multi-modal feature aggregation, integrating text-to-image search with CLIP, free-text search from captions generated by Video-LLaMA2, and visual features from Video-LLaMA to capture temporal dynamics; and (2) GPT-based query expansion, combined with an advanced filter, addresses issues with low-quality open-ended text queries and refines item searches based on type and location within a scene. This work provides cutting-edge solutions for the VBS 2025 challenge and offers valuable insights into enhancing video search techniques.
Evaluation of data driven low-rank matrix factorization for accelerated solutions of the Vlasov equation
Authors: Bhavana Jonnalagadda, Stephen Becker
Date: 2024-12-27 00:00:00
Tasks: Time Series

Low-rank methods have shown success in accelerating simulations of a collisionless plasma described by the Vlasov equation, but still rely on computationally costly linear algebra every time step. We propose a data-driven factorization method using artificial neural networks, specifically with convolutional layer architecture, that trains on existing simulation data. At inference time, the model outputs a low-rank decomposition of the distribution field of the charged particles, and we demonstrate that this step is faster than the standard linear algebra technique. Numerical experiments show that the method effectively interpolates time-series data, generalizing to unseen test data in a manner beyond just memorizing training data; patterns in factorization also inherently followed the same numerical trend as those within algebraic methods (e.g., truncated singular-value decomposition). However, when training on the first 70% of a time-series data and testing on the remaining 30%, the method fails to meaningfully extrapolate. Despite this limiting result, the technique may have benefits for simulations in a statistical steady-state or otherwise showing temporal stability.
Universal Fine-grained Visual Categorization by Concept Guided Learning
Authors: Qi Bi, Beichen Zhou, Wei Ji, Gui-Song Xia
Date: 2025-01-06 00:00:00
Proceeding: IEEE Transactions on Image Processing 2025 1
Tasks: Fine-Grained Image Classification, Fine-Grained Visual Categorization, Object, object-detection, Object Detection, Representation Learning

Existing fine-grained visual categorization (FGVC) methods assume that the fine-grained semantics rest in the informative parts of an image. This assumption works well on favorable front-view object-centric images, but can face great challenges in many real-world scenarios, such as scene-centric images ( e.g. , street view) and adverse viewpoint ( e.g. , object reidentification, remote sensing). In such scenarios, the mis-/over-feature activation is likely to confuse the part selection and degrade the fine-grained representation. In this paper, we are motivated to design a universal FGVC framework for real-world scenarios. More precisely, we propose a concept guided learning (CGL), which models concepts of a certain fine-grained category as a combination of inherited concepts from its subordinate coarse-grained category and discriminative concepts from its own. The discriminative concepts is utilized to guide the fine-grained representation learning. Specifically, three key steps are designed, namely, concept mining, concept fusion, and concept constraint. On the other hand, to bridge the FGVC dataset gap under scene-centric and adverse viewpoint scenarios, a Fine-grained Land-cover Categorization Dataset (FGLCD) with 59,994 fine-grained samples is proposed. Extensive experiments show the proposed CGL: 1) has a competitive performance on conventional FGVC; 2) achieves state-of-the-art performance on fine-grained aerial scenes & scene-centric street scenes; 3) good generalization on object re-identification and fine-grained aerial object detection. The dataset and source code will be available at https://github.com/BiQiWHU/CGL.
FOCUS: Towards Universal Foreground Segmentation
Authors: Zuyao You, Lingyu Kong, Lingchen Meng, Zuxuan Wu
Date: 2025-01-09 00:00:00
Tasks: Camouflaged Object Segmentation, Defocus Blur Detection, Foreground Segmentation, Salient Object Detection, Segmentation, Universal Segmentation

Foreground segmentation is a fundamental task in computer vision, encompassing various subdivision tasks. Previous research has typically designed task-specific architectures for each task, leading to a lack of unification. Moreover, they primarily focus on recognizing foreground objects without effectively distinguishing them from the background. In this paper, we emphasize the importance of the background and its relationship with the foreground. We introduce FOCUS, the Foreground ObjeCts Universal Segmentation framework that can handle multiple foreground tasks. We develop a multi-scale semantic network using the edge information of objects to enhance image features. To achieve boundary-aware segmentation, we propose a novel distillation method, integrating the contrastive learning strategy to refine the prediction mask in multi-modal feature space. We conduct extensive experiments on a total of 13 datasets across 5 tasks, and the results demonstrate that FOCUS consistently outperforms the state-of-the-art task-specific models on most metrics.
An Empirical Study of Autoregressive Pre-training from Videos
Authors: Jathushan Rajasegaran, Ilija Radosavovic, Rahul Ravishankar, Yossi Gandelsman, Christoph Feichtenhofer, Jitendra Malik
Date: 2025-01-09 00:00:00
Tasks: Object Tracking, Video Classification

We empirically study autoregressive pre-training from videos. To perform our study, we construct a series of autoregressive video models, called Toto. We treat videos as sequences of visual tokens and train transformer models to autoregressively predict future tokens. Our models are pre-trained on a diverse dataset of videos and images comprising over 1 trillion visual tokens. We explore different architectural, training, and inference design choices. We evaluate the learned visual representations on a range of downstream tasks including image recognition, video classification, object tracking, and robotics. Our results demonstrate that, despite minimal inductive biases, autoregressive pre-training leads to competitive performance across all benchmarks. Finally, we find that scaling our video models results in similar scaling curves to those seen in language models, albeit with a different rate. More details at https://brjathu.github.io/toto/