pwc-archive/papers-with-abstracts huggingface dataset:
The Ensemble Epanechnikov Mixture Filter
Authors: Andrey A. Popov, Renato Zanetti
Date: 2024-08-20 00:00:00

In the high-dimensional setting, Gaussian mixture kernel density estimates become increasingly suboptimal. In this work we aim to show that it is practical to instead use the optimal multivariate Epanechnikov kernel. We make use of this optimal Epanechnikov mixture kernel density estimate for the sequential filtering scenario through what we term the ensemble Epanechnikov mixture filter (EnEMF). We provide a practical implementation of the EnEMF that is as cost efficient as the comparable ensemble Gaussian mixture filter. We show on a static example that the EnEMF is robust to growth in dimension, and also that the EnEMF has a significant reduction in error per particle on the 40-variable Lorenz '96 system.
Characteristic Performance Study on Solving Oscillator ODEs via Soft-constrained Physics-informed Neural Network with Small Data
Authors: Kai-Liang Lu, Yu-meng Su, Zhuo Bi, Cheng Qiu, Wen-jun Zhang
Date: 2024-08-19 00:00:00
Tasks: CPU, GPU

This paper compared physics-informed neural network (PINN), conventional neural network (NN) and traditional numerical discretization methods on solving differential equations (DEs) through literature investigation and experimental validation. We focused on the soft-constrained PINN approach and formalized its mathematical framework and computational flow for solving Ordinary DEs and Partial DEs (ODEs/PDEs). The working mechanism and its accuracy and efficiency were experimentally verified by solving typical linear and non-linear oscillator ODEs. We demonstrate that the DeepXDE-based implementation of PINN is not only light code and efficient in training, but also flexible across CPU/GPU platforms. PINN greatly reduces the need for labeled data: when the nonlinearity of the ODE is weak, a very small amount of supervised training data plus a few unsupervised collocation points are sufficient to predict the solution; in the minimalist case, only one or two training points (with initial values) are needed for first- or second-order ODEs, respectively. We also find that, with the aid of collocation points and the use of physical information, PINN has the ability to extrapolate data outside the time domain of the training set, and especially is robust to noisy data, thus with enhanced generalization capabilities. Training is accelerated when the gains obtained along with the reduction in the amount of data outweigh the delay caused by the increase in the loss function terms. The soft-constrained PINN can easily impose a physical law (e.g., conservation of energy) constraint by adding a regularization term to the total loss function, thus improving the solution performance to ODEs that obey this physical law. Furthermore, PINN can also be used for stiff ODEs, PDEs, and other types of DEs, and is becoming a favorable catalyst for the era of Digital Twins.
Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation?
Authors: Daniele Malitesta, Emanuele Rossi, Claudio Pomo, Tommaso Di Noia, Fragkiskos D. Malliaros
Date: 2024-08-21 00:00:00
Tasks: Imputation, Multimodal Recommendation, Recommendation Systems

Generally, items with missing modalities are dropped in multimodal recommendation. However, with this work, we question this procedure, highlighting that it would further damage the pipeline of any multimodal recommender system. First, we show that the lack of (some) modalities is, in fact, a widely-diffused phenomenon in multimodal recommendation. Second, we propose a pipeline that imputes missing multimodal features in recommendation by leveraging traditional imputation strategies in machine learning. Then, given the graph structure of the recommendation data, we also propose three more effective imputation solutions that leverage the item-item co-purchase graph and the multimodal similarities of co-interacted items. Our method can be plugged into any multimodal RSs in the literature working as an untrained pre-processing phase, showing (through extensive experiments) that any data pre-filtering is not only unnecessary but also harmful to the performance.
A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item Graph
Authors: Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Alberto Carlo Maria Mancino, Tommaso Di Noia, Eugenio Di Sciascio
Date: 2024-08-21 00:00:00
Tasks: Recommendation Systems

Recently, graph neural networks (GNNs)-based recommender systems have encountered great success in recommendation. As the number of GNNs approaches rises, some works have started questioning the theoretical and empirical reasons behind their superior performance. Nevertheless, this investigation still disregards that GNNs treat the recommendation data as a topological graph structure. Building on this assumption, in this work, we provide a novel evaluation perspective on GNNs-based recommendation, which investigates the impact of the graph topology on the recommendation performance. To this end, we select some (topological) properties of the recommendation data and three GNNs-based recommender systems (i.e., LightGCN, DGCF, and SVD-GCN). Then, starting from three popular recommendation datasets (i.e., Yelp2018, Gowalla, and Amazon-Book) we sample them to obtain 1,800 size-reduced datasets that still resemble the original ones but can encompass a wider range of topological structures. We use this procedure to build a large pool of samples for which data characteristics and recommendation performance of the selected GNNs models are measured. Through an explanatory framework, we find strong correspondences between graph topology and GNNs performance, offering a novel evaluation perspective on these models.
Mathematical Information Retrieval: Search and Question Answering
Authors: Richard Zanibbi, Behrooz Mansouri, Anurag Agarwal
Date: 2024-08-21 00:00:00
Tasks: Information Retrieval, Math, Mathematical Question Answering, Question Answering, Retrieval

Mathematical information is essential for technical work, but its creation, interpretation, and search are challenging. To help address these challenges, researchers have developed multimodal search engines and mathematical question answering systems. This book begins with a simple framework characterizing the information tasks that people and systems perform as we work to answer math-related questions. The framework is used to organize and relate the other core topics of the book, including interactions between people and systems, representing math formulas in sources, and evaluation. We close by addressing some key questions and presenting directions for future work. This book is intended for students, instructors, and researchers interested in systems that help us find and use mathematical information.
Oh, Behave! Country Representation Dynamics Created by Feedback Loops in Music Recommender Systems
Authors: Oleg Lesota, Jonas Geiger, Max Walder, Dominik Kowald, Markus Schedl
Date: 2024-08-21 00:00:00
Tasks: Music Recommendation, Recommendation Systems

Recent work suggests that music recommender systems are prone to disproportionally frequent recommendations of music from countries more prominently represented in the training data, notably the US. However, it remains unclear to what extent feedback loops in music recommendation influence the dynamics of such imbalance. In this work, we investigate the dynamics of representation of local (i.e., country-specific) and US-produced music in user profiles and recommendations. To this end, we conduct a feedback loop simulation study using the standardized LFM-2b dataset. The results suggest that most of the investigated recommendation models decrease the proportion of music from local artists in their recommendations. Furthermore, we find that models preserving average proportions of US and local music do not necessarily provide country-calibrated recommendations. We also look into popularity calibration and, surprisingly, find that the most popularity-calibrated model in our study (ItemKNN) provides the least country-calibrated recommendations. In addition, users from less represented countries (e.g., Finland) are, in the long term, most affected by the under-representation of their local music in recommendations.
A Quick, trustworthy spectral knowledge Q&A system leveraging retrieval-augmented generation on LLM
Authors: Jiheng Liang, Ziru Yu, Zujie Xie, Xiangyang Yu
Date: 2024-08-21 00:00:00
Tasks: Language Modelling, Large Language Model, RAG, Retrieval, Retrieval-augmented Generation

Large Language Model (LLM) has demonstrated significant success in a range of natural language processing (NLP) tasks within general domain. The emergence of LLM has introduced innovative methodologies across diverse fields, including the natural sciences. Researchers aim to implement automated, concurrent process driven by LLM to supplant conventional manual, repetitive and labor-intensive work. In the domain of spectral analysis and detection, it is imperative for researchers to autonomously acquire pertinent knowledge across various research objects, which encompasses the spectroscopic techniques and the chemometric methods that are employed in experiments and analysis. Paradoxically, despite the recognition of spectroscopic detection as an effective analytical method, the fundamental process of knowledge retrieval remains both time-intensive and repetitive. In response to this challenge, we first introduced the Spectral Detection and Analysis Based Paper(SDAAP) dataset, which is the first open-source textual knowledge dataset for spectral analysis and detection and contains annotated literature data as well as corresponding knowledge instruction data. Subsequently, we also designed an automated Q\&A framework based on the SDAAP dataset, which can retrieve relevant knowledge and generate high-quality responses by extracting entities in the input as retrieval parameters. It is worth noting that: within this framework, LLM is only used as a tool to provide generalizability, while RAG technique is used to accurately capture the source of the knowledge.This approach not only improves the quality of the generated responses, but also ensures the traceability of the knowledge. Experimental results show that our framework generates responses with more reliable expertise compared to the baseline.
Learning Deep Tree-based Retriever for Efficient Recommendation: Theory and Method
Authors: Ze Liu, Jin Zhang, Chao Feng, Defu Lian, Jie Wang, Enhong Chen
Date: 2024-08-21 00:00:00
Tasks: Binary Classification, Multi-class Classification, Retrieval

Although advancements in deep learning have significantly enhanced the recommendation accuracy of deep recommendation models, these methods still suffer from low recommendation efficiency. Recently proposed tree-based deep recommendation models alleviate the problem by directly learning tree structure and representations under the guidance of recommendation objectives. To guarantee the effectiveness of beam search for recommendation accuracy, these models strive to ensure that the tree adheres to the max-heap assumption, where a parent node's preference should be the maximum among its children's preferences. However, they employ a one-versus-all strategy, framing the training task as a series of independent binary classification objectives for each node, which limits their ability to fully satisfy the max-heap assumption. To this end, we propose a Deep Tree-based Retriever (DTR for short) for efficient recommendation. DTR frames the training task as a softmax-based multi-class classification over tree nodes at the same level, enabling explicit horizontal competition and more discriminative top-k selection among them, which mimics the beam search behavior during training. To mitigate the suboptimality induced by the labeling of non-leaf nodes, we propose a rectification method for the loss function, which further aligns with the max-heap assumption in expectation. As the number of tree nodes grows exponentially with the levels, we employ sampled softmax to approximate optimization and thereby enhance efficiency. Furthermore, we propose a tree-based sampling method to reduce the bias inherent in sampled softmax. Theoretical results reveal DTR's generalization capability, and both the rectification method and tree-based sampling contribute to improved generalization. The experiments are conducted on four real-world datasets, validating the effectiveness of the proposed method.
Parallel Algorithms for Median Consensus Clustering in Complex Networks
Authors: Md Taufique Hussain, Mahantesh Halappanavar, Samrat Chatterjee, Filippo Radicchi, Santo Fortunato, Ariful Azad
Date: 2024-08-21 00:00:00
Tasks: Clustering

We develop an algorithm that finds the consensus of many different clustering solutions of a graph. We formulate the problem as a median set partitioning problem and propose a greedy optimization technique. Unlike other approaches that find median set partitions, our algorithm takes graph structure into account and finds a comparable quality solution much faster than the other approaches. For graphs with known communities, our consensus partition captures the actual community structure more accurately than alternative approaches. To make it applicable to large graphs, we remove sequential dependencies from our algorithm and design a parallel algorithm. Our parallel algorithm achieves 35x speedup when utilizing 64 processing cores for large real-world graphs from single-cell experiments.
biorecap: an R package for summarizing bioRxiv preprints with a local LLM
Authors: Stephen D. Turner
Date: 2024-08-21 00:00:00
Tasks: Language Modelling, Large Language Model

The establishment of bioRxiv facilitated the rapid adoption of preprints in the life sciences, accelerating the dissemination of new research findings. However, the sheer volume of preprints published daily can be overwhelming, making it challenging for researchers to stay updated on the latest developments. Here, I introduce biorecap, an R package that retrieves and summarizes bioRxiv preprints using a large language model (LLM) running locally on nearly any commodity laptop. biorecap leverages the ollamar package to interface with the Ollama server and API endpoints, allowing users to prompt any local LLM available through Ollama. The package follows tidyverse conventions, enabling users to pipe the output of one function as input to another. Additionally, biorecap provides a single wrapper function that generates a timestamped CSV file and HTML report containing short summaries of recent preprints published in user-configurable subject areas. By combining the strengths of LLMs with the flexibility and security of local execution, biorecap represents an advancement in the tools available for managing the information overload in modern scientific research. The biorecap R package is available on GitHub at https://github.com/stephenturner/biorecap under an open-source (MIT) license.
Towards an Inclusive Approach to Corporate Social Responsibility (CSR) in Morocco: CGEM's Commitment
Authors: Gnaoui Imane, Moutahaddib Aziz
Date: 2024-08-21 00:00:00

Corporate social responsibility encourages companies to integrate social and environmental concerns into their activities and their relations with stakeholders. It encompasses all actions aimed at the social good, above and beyond corporate interests and legal requirements. Various international organizations, authors and researchers have explored the notion of CSR and proposed a range of definitions reflecting their perspectives on the concept. In Morocco, although Moroccan companies are not overwhelmingly embracing CSR, several factors are encouraging them to integrate the CSR approach not only into their discourse, but also into their strategies. The CGEM is actively involved in promoting CSR within Moroccan companies, awarding the "CGEM Label for CSR" to companies that meet the criteria set out in the CSR Charter. The process of labeling Moroccan companies is in full expansion. The graphs presented in this article are broken down according to several criteria, such as company size, sector of activity and listing on the Casablanca Stock Exchange, in order to provide an overview of CSR-labeled companies in Morocco. The approach adopted for this article is a qualitative one aimed at presenting, firstly, the different definitions of the CSR concept and its evolution over time. In this way, the study focuses on the Moroccan context to dissect and analyze the state of progress of CSR integration in Morocco and the various efforts made by the CGEM to implement it. According to the data, 124 Moroccan companies have been awarded the CSR label. For a label in existence since 2006, this figure reflects a certain reluctance on the part of Moroccan companies to fully implement the CSR approach in their strategies. Nevertheless, Morocco is in a transitional phase, marked by the gradual adoption of various socially responsible practices.
Classification of Mitral Regurgitation from Cardiac Cine MRI using Clinically-Interpretable Morphological Features
Authors: Y. On, K. Vimalesvaran, S. Zaman, M. Shun-Shin, J. Howard, N. Linton, G. Cole, A. A. Bharath, M. Varela
Date: 2024-08-21 00:00:00
Tasks: feature selection

The assessment of mitral regurgitation (MR) using cardiac MRI, particularly Cine MRI, is a promising technique due to its wide availability. However, some of the temporal information available in clinical Cine MRI may not be fully utilised, as it requires detailed temporal analysis across different cardiac views. We propose a new approach to identify MR which automatically extracts 4-dimensional (3D + Time) morphological features from the reconstructed mitral annulus (MA) using Cine long-axis (LAX) views MRI. Our feature extraction involves locating the MA insertion points to derive the reconstructed MA geometry and displacements, resulting in a total of 187 candidate features. We identify the 25 most relevant mitral valve features using minimum-redundancy maximum-relevance (MRMR) feature selection technique. We then apply linear discriminant analysis (LDA) and random forest (RF) model to determine the presence of MR. Both LDA and RF demonstrate good performance, with accuracies of 0.72+/-0.05 and 0.73+/-0.09, respectively, in a 5-fold cross-validation analysis. This approach will be incorporated in an automatic tool to identify valvular diseases from Cine MRI by integrating both handcrafted and deep features. Our tool will facilitate the diagnosis of valvular disease from conventional cardiac MRI scans with no additional scanning or image analysis penalty. All code is made available on an open-source basis at: https://github.com/HenryOn2021/MA_Morphological_Features.
Improving Query-by-Vocal Imitation with Contrastive Learning and Audio Pretraining
Authors: Jonathan Greif, Florian Schmid, Paul Primus, Gerhard Widmer
Date: 2024-08-21 00:00:00
Tasks: Contrastive Learning

Query-by-Vocal Imitation (QBV) is about searching audio files within databases using vocal imitations created by the user's voice. Since most humans can effectively communicate sound concepts through voice, QBV offers the more intuitive and convenient approach compared to text-based search. To fully leverage QBV, developing robust audio feature representations for both the vocal imitation and the original sound is crucial. In this paper, we present a new system for QBV that utilizes the feature extraction capabilities of Convolutional Neural Networks pre-trained with large-scale general-purpose audio datasets. We integrate these pre-trained models into a dual encoder architecture and fine-tune them end-to-end using contrastive learning. A distinctive aspect of our proposed method is the fine-tuning strategy of pre-trained models using an adapted NT-Xent loss for contrastive learning, creating a shared embedding space for reference recordings and vocal imitations. The proposed system significantly enhances audio retrieval performance, establishing a new state of the art on both coarse- and fine-grained QBV tasks.
XCompress: LLM assisted Python-based text compression toolkit
Authors: Emir Öztürk
Date: 2024-08-12 00:00:00
Proceeding: SoftwareX 2024 8
Tasks: Benchmarking, Language Modeling, Language Modelling, Large Language Model, Text Compression

This study introduces XCompress, a Python-based tool for effectively utilizing various compression algorithms. XCompress offers manual, brute force, and Large Language Model (LLM) methods to determine the most suitable algorithm based on the type of text data. Its modular structure allows easy addition of new algorithms and includes functions for benchmarking and result comparison. Tests on diverse text types demonstrate the efficacy of the LLM-assisted Compression Selection Model (CSM). With XCompress, users can determine the most suitable method for their files. Additionally, in academic research, they can easily compare different methods without needing any scripting or programming language.
A Comprehensive Survey on Diffusion Models and Their Applications
Authors: Md Manjurul Ahsan, Shivakumar Raman, Yingtao Liu, Zahed Siddique
Date: 2024-07-01 00:00:00
Tasks: Speech Synthesis, Survey

Diffusion Models are probabilistic models that create realistic samples by simulating the diffusion process, gradually adding and removing noise from data. These models have gained popularity in domains such as image processing, speech synthesis, and natural language processing due to their ability to produce high-quality samples. As Diffusion Models are being adopted in various domains, existing literature reviews that often focus on specific areas like computer vision or medical imaging may not serve a broader audience across multiple fields. Therefore, this review presents a comprehensive overview of Diffusion Models, covering their theoretical foundations and algorithmic innovations. We highlight their applications in diverse areas such as media quality, authenticity, synthesis, image transformation, healthcare, and more. By consolidating current knowledge and identifying emerging trends, this review aims to facilitate a deeper understanding and broader adoption of Diffusion Models and provide guidelines for future researchers and practitioners across diverse disciplines.
Ancient Wisdom, Modern Tools: Exploring Retrieval-Augmented LLMs for Ancient Indian Philosophy
Authors: Priyanka Mandikal
Date: 2024-08-21 00:00:00
Tasks: Information Retrieval, Long Form Question Answering, Philosophy, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

LLMs have revolutionized the landscape of information retrieval and knowledge dissemination. However, their application in specialized areas is often hindered by factual inaccuracies and hallucinations, especially in long-tail knowledge distributions. We explore the potential of retrieval-augmented generation (RAG) models for long-form question answering (LFQA) in a specialized knowledge domain. We present VedantaNY-10M, a dataset curated from extensive public discourses on the ancient Indian philosophy of Advaita Vedanta. We develop and benchmark a RAG model against a standard, non-RAG LLM, focusing on transcription, retrieval, and generation performance. Human evaluations by computational linguists and domain experts show that the RAG model significantly outperforms the standard model in producing factual and comprehensive responses having fewer hallucinations. In addition, a keyword-based hybrid retriever that emphasizes unique low-frequency terms further improves results. Our study provides insights into effectively integrating modern large language models with ancient knowledge systems. Project page with dataset and code: https://sites.google.com/view/vedantany-10m