pwc-archive/papers-with-abstracts huggingface dataset:
Learning non-Gaussian spatial distributions via Bayesian transport maps with parametric shrinkage
Authors: Anirban Chakraborty, Matthias Katzfuss
Date: 2024-09-28 00:00:00

Many applications, including climate-model analysis and stochastic weather generators, require learning or emulating the distribution of a high-dimensional and non-Gaussian spatial field based on relatively few training samples. To address this challenge, a recently proposed Bayesian transport map (BTM) approach consists of a triangular transport map with nonparametric Gaussian-process (GP) components, which is trained to transform the distribution of interest distribution to a Gaussian reference distribution. To improve the performance of this existing BTM, we propose to shrink the map components toward a ``base'' parametric Gaussian family combined with a Vecchia approximation for scalability. The resulting ShrinkTM approach is more accurate than the existing BTM, especially for small numbers of training samples. It can even outperform the ``base'' family when trained on a single sample of the spatial field. We demonstrate the advantage of ShrinkTM though numerical experiments on simulated data and on climate-model output.
Solving FDR-Controlled Sparse Regression Problems with Five Million Variables on a Laptop
Authors: Fabian Scheidt, Jasin Machkour, Michael Muma
Date: 2024-09-27 00:00:00
Tasks: Variable Selection

Currently, there is an urgent demand for scalable multivariate and high-dimensional false discovery rate (FDR)-controlling variable selection methods to ensure the repro-ducibility of discoveries. However, among existing methods, only the recently proposed Terminating-Random Experiments (T-Rex) selector scales to problems with millions of variables, as encountered in, e.g., genomics research. The T-Rex selector is a new learning framework based on early terminated random experiments with computer-generated dummy variables. In this work, we propose the Big T-Rex, a new implementation of T-Rex that drastically reduces its Random Access Memory (RAM) consumption to enable solving FDR-controlled sparse regression problems with millions of variables on a laptop. We incorporate advanced memory-mapping techniques to work with matrices that reside on solid-state drive and two new dummy generation strategies based on permutations of a reference matrix. Our nu-merical experiments demonstrate a drastic reduction in memory demand and computation time. We showcase that the Big T-Rex can efficiently solve FDR-controlled Lasso-type problems with five million variables on a laptop in thirty minutes. Our work empowers researchers without access to high-performance clusters to make reproducible discoveries in large-scale high-dimensional data.
Mixed-Precision Embeddings for Large-Scale Recommendation Models
Authors: Shiwei Li, Zhuoqi Hu, Xing Tang, Haozhao Wang, Shijie Xu, Weihong Luo, Yuhua Li, Xiuqiang He, Ruixuan Li
Date: 2024-09-30 00:00:00
Tasks: Quantization, Recommendation Systems

Embedding techniques have become essential components of large databases in the deep learning era. By encoding discrete entities, such as words, items, or graph nodes, into continuous vector spaces, embeddings facilitate more efficient storage, retrieval, and processing in large databases. Especially in the domain of recommender systems, millions of categorical features are encoded as unique embedding vectors, which facilitates the modeling of similarities and interactions among features. However, numerous embedding vectors can result in significant storage overhead. In this paper, we aim to compress the embedding table through quantization techniques. Given that features vary in importance levels, we seek to identify an appropriate precision for each feature to balance model accuracy and memory usage. To this end, we propose a novel embedding compression method, termed Mixed-Precision Embeddings (MPE). Specifically, to reduce the size of the search space, we first group features by frequency and then search precision for each feature group. MPE further learns the probability distribution over precision levels for each feature group, which can be used to identify the most suitable precision with a specially designed sampling strategy. Extensive experiments on three public datasets demonstrate that MPE significantly outperforms existing embedding compression methods. Remarkably, MPE achieves about 200x compression on the Criteo dataset without comprising the prediction accuracy.
The Devil is in the Sources! Knowledge Enhanced Cross-Domain Recommendation in an Information Bottleneck Perspective
Authors: Binbin Hu, Weifan Wang, Hanshu Wang, Ziqi Liu, Bin Shen, Yong He, Jiawei Chen
Date: 2024-09-29 00:00:00
Tasks: Recommendation Systems

Cross-domain Recommendation (CDR) aims to alleviate the data sparsity and the cold-start problems in traditional recommender systems by leveraging knowledge from an informative source domain. However, previously proposed CDR models pursue an imprudent assumption that the entire information from the source domain is equally contributed to the target domain, neglecting the evil part that is completely irrelevant to users' intrinsic interest. To address this concern, in this paper, we propose a novel knowledge enhanced cross-domain recommendation framework named CoTrans, which remolds the core procedures of CDR models with: Compression on the knowledge from the source domain and Transfer of the purity to the target domain. Specifically, following the theory of Graph Information Bottleneck, CoTrans first compresses the source behaviors with the perception of information from the target domain. Then to preserve all the important information for the CDR task, the feedback signals from both domains are utilized to promote the effectiveness of the transfer procedure. Additionally, a knowledge-enhanced encoder is employed to narrow gaps caused by the non-overlapped items across separate domains. Comprehensive experiments on three widely used cross-domain datasets demonstrate that CoTrans significantly outperforms both single-domain and state-of-the-art cross-domain recommendation approaches.
Meta Learning to Rank for Sparsely Supervised Queries
Authors: Xuyang Wu, Ajit Puthenputhussery, Hongwei Shang, Changsung Kang, Yi Fang
Date: 2024-09-29 00:00:00
Tasks: Learning-To-Rank, Meta-Learning

Supervisory signals are a critical resource for training learning to rank models. In many real-world search and retrieval scenarios, these signals may not be readily available or could be costly to obtain for some queries. The examples include domains where labeling requires professional expertise, applications with strong privacy constraints, and user engagement information that are too scarce. We refer to these scenarios as sparsely supervised queries which pose significant challenges to traditional learning to rank models. In this work, we address sparsely supervised queries by proposing a novel meta learning to rank framework which leverages fast learning and adaption capability of meta-learning. The proposed approach accounts for the fact that different queries have different optimal parameters for their rankers, in contrast to traditional learning to rank models which only learn a global ranking model applied to all the queries. In consequence, the proposed method would yield significant advantages especially when new queries are of different characteristics with the training queries. Moreover, the proposed meta learning to rank framework is generic and flexible. We conduct a set of comprehensive experiments on both public datasets and a real-world e-commerce dataset. The results demonstrate that the proposed meta-learning approach can significantly enhance the performance of learning to rank models with sparsely labeled queries.
HTML-LSTM: Information Extraction from HTML Tables in Web Pages using Tree-Structured LSTM
Authors: Kazuki Kawamura, Akihiro Yamamoto
Date: 2024-09-28 00:00:00
Tasks: Retrieval

In this paper, we propose a novel method for extracting information from HTML tables with similar contents but with a different structure. We aim to integrate multiple HTML tables into a single table for retrieval of information containing in various Web pages. The method is designed by extending tree-structured LSTM, the neural network for tree-structured data, in order to extract information that is both linguistic and structural information of HTML data. We evaluate the proposed method through experiments using real data published on the WWW.
Utilizing Collaborative Filtering in a Personalized Research-Paper Recommendation System
Authors: Mahamudul Hasan, Anika Tasnim Islam, Nabila Islam
Date: 2024-09-28 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Recommendation system is such a platform that helps people to easily find out the things they need within a few seconds. It is implemented based on the preferences of similar users or items. In this digital era, the internet has provided us with huge opportunities to use a lot of open resources for our own needs. But there are too many resources on the internet from which finding the precise one is a difficult job. Recommendation system has made this easier for people. Research-paper recommendation system is a system that is developed for people with common research interests using a collaborative filtering recommender system. In this paper, coauthor, keyword, reference, and common citation similarities are calculated using Jaccard Similarity to find the final similarity and to find the top-n similar users. Based on the test of top-n similar users of the target user research paper recommendations have been made. Finally, the accuracy of our recommendation system has been calculated. An impressive result has been found using our proposed system.
An Efficient Multi-threaded Collaborative Filtering Approach in Recommendation System
Authors: Mahamudul Hasan
Date: 2024-09-28 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Recommender systems are a subset of information filtering systems designed to predict and suggest items that users may find interesting or relevant based on their preferences, behaviors, or interactions. By analyzing user data such as past activities, ratings, and preferences, these systems generate personalized recommendations for products, services, or content, with common applications including online retail, media streaming platforms, and social media. Recommender systems are typically categorized into three types: content-based filtering, which recommends items similar to those the user has shown interest in; collaborative filtering, which analyzes the preferences of similar users; and hybrid methods, which combine both approaches to improve accuracy. These systems enhance user experience by reducing information overload and providing personalized suggestions, thus increasing engagement and satisfaction. However, building a scalable recommendation system capable of handling numerous users efficiently is a significant challenge, particularly when considering both performance consistency and user data security, which are emerging research topics. The primary objective of this research is to address these challenges by reducing the processing time in recommendation systems. A multithreaded similarity approach is employed to achieve this, where users are divided into independent threads that run in parallel. This parallelization significantly reduces computation time compared to traditional methods, resulting in a faster, more efficient, and scalable recommendation system that ensures improved performance without compromising user data security.
TwinCL: A Twin Graph Contrastive Learning Model for Collaborative Filtering
Authors: Chengkai Liu, Jianling Wang, James Caverlee
Date: 2024-09-27 00:00:00
Tasks: Collaborative Filtering, Contrastive Learning, Data Augmentation

In the domain of recommendation and collaborative filtering, Graph Contrastive Learning (GCL) has become an influential approach. Nevertheless, the reasons for the effectiveness of contrastive learning are still not well understood. In this paper, we challenge the conventional use of random augmentations on graph structure or embedding space in GCL, which may disrupt the structural and semantic information inherent in Graph Neural Networks. Moreover, fixed-rate data augmentation proves to be less effective compared to augmentation with an adaptive rate. In the initial training phases, significant perturbations are more suitable, while as the training approaches convergence, milder perturbations yield better results. We introduce a twin encoder in place of random augmentations, demonstrating the redundancy of traditional augmentation techniques. The twin encoder updating mechanism ensures the generation of more diverse contrastive views in the early stages, transitioning to views with greater similarity as training progresses. In addition, we investigate the learned representations from the perspective of alignment and uniformity on a hypersphere to optimize more efficiently. Our proposed Twin Graph Contrastive Learning model -- TwinCL -- aligns positive pairs of user and item embeddings and the representations from the twin encoder while maintaining the uniformity of the embeddings on the hypersphere. Our theoretical analysis and experimental results show that the proposed model optimizing alignment and uniformity with the twin encoder contributes to better recommendation accuracy and training efficiency performance. In comprehensive experiments on three public datasets, our proposed TwinCL achieves an average improvement of 5.6% (NDCG@10) in recommendation accuracy with faster training speed, while effectively mitigating popularity bias.
Lab-AI: Using Retrieval Augmentation to Enhance Language Models for Personalized Lab Test Interpretation in Clinical Medicine
Authors: Xiaoyu Wang, Haoyong Ouyang, Balu Bhasuran, Xiao Luo, Karim Hanna, Mia Liza A. Lustria, Carl Yang, Zhe He
Date: 2024-09-16 00:00:00
Tasks: Language Modeling, Language Modelling, RAG, Retrieval, Retrieval-augmented Generation

Accurate interpretation of lab results is crucial in clinical medicine, yet most patient portals use universal normal ranges, ignoring conditional factors like age and gender. This study introduces Lab-AI, an interactive system that offers personalized normal ranges using retrieval-augmented generation (RAG) from credible health sources. Lab-AI has two modules: factor retrieval and normal range retrieval. We tested these on 122 lab tests: 40 with conditional factors and 82 without. For tests with factors, normal ranges depend on patient-specific information. Our results show GPT-4-turbo with RAG achieved a 0.948 F1 score for factor retrieval and 0.995 accuracy for normal range retrieval. GPT-4-turbo with RAG outperformed the best non-RAG system by 33.5% in factor retrieval and showed 132% and 100% improvements in question-level and lab-level performance, respectively, for normal range retrieval. These findings highlight Lab-AI's potential to enhance patient understanding of lab results.
Integrating SPARQL and LLMs for Question Answering over Scholarly Data Sources
Authors: Fomubad Borista Fondi, Azanzi Jiomekong Fidel, Gaoussou Camara
Date: 2024-09-11 00:00:00
Tasks: Extractive Question-Answering, Question Answering

The Scholarly Hybrid Question Answering over Linked Data (QALD) Challenge at the International Semantic Web Conference (ISWC) 2024 focuses on Question Answering (QA) over diverse scholarly sources: DBLP, SemOpenAlex, and Wikipedia-based texts. This paper describes a methodology that combines SPARQL queries, divide and conquer algorithms, and a pre-trained extractive question answering model. It starts with SPARQL queries to gather data, then applies divide and conquer to manage various question types and sources, and uses the model to handle personal author questions. The approach, evaluated with Exact Match and F-score metrics, shows promise for improving QA accuracy and efficiency in scholarly contexts.
Machine Learning Approach to Detect Cardiac Arrhythmias in ECG Signals:
Authors: S. Sahoo a, M. Dash b, S. Behera c, S. Sabut
Date: 2020-01-03 00:00:00
Proceeding: journal 2020 1
Tasks: Articles, Decision Making

ardiac arrhythmia is a condition when the heart rate is irregular either the beat is too slow or too fast.
It occurs due to improper electrical impulses that coordinates the heart beats. Sudden cardiac death may
occurs due to some dangerous arrhythmias conditions. Hence the main objective of the electrocardiogram
(ECG) analysis is to detect the life-threatening arrhythmias accurately for appropriate treatment in order to
save life. Since the last decades, several methods were reported for automatic ECG beat classifications. In
this work, we present a systematic review of the current state-of-the-art methods used to detect cardiac
arrhythmia using on ECG signals. It includes the signal decomposition, feature extraction and machine
learning approaches used for automatic detection and decision making process. The articles covers the preprocessing, detection of QRS complex, feature extraction and classification of ECG beats. Based on the past
studies, it is understood that the automated approach using computer-aided decision making process is
highly required for real-time detection of cardiac arrhythmias. The advantages and limitations of different
methods are discussed and also the future scopes is highlighted in the process of effective detection
of cardiac arrhythmias. This study could be beneficial for researchers to analyze the existing state-of-art
techniques used in detection of arrhythmia conditions
Machine Learning based Cardiac Arrhythmia detection from ECG signal
Authors: Kavya Subramanian; N Krishna Prakash
Date: 2020-10-06 00:00:00
Proceeding: Conference 2020 10
Tasks: Arrhythmia Detection

This paper presents the analysis of heart diseases that are categorized as arrhythmia based on Electrocardiogram (ECG). ECG database of different disease conditions was analyzed. The ECG signals are filtered to remove noise which is caused due to powerline interface or Electromyogram. This filtered signal is segmented to smaller pieces of ECG so that feature extraction is accurate. The features extracted are Peak to peak Interval (R-R Interval), BPM (Beats per minute), P wave to QRS peak. The data set is classified using an SVM classifier algorithm. This algorithm classifies the input ECG signal with varying feature parameters to two different types of arrhythmia. This approach has achieved an accuracy of 91% and the performance regarding other criteria such as precision, recall and F1 score were significantly better indicating the success of the proposed method.
Probabilistic Classification of Near-Surface Shallow-Water Sediments using A Portable Free-Fall Penetrometer
Authors: Md Rejwanur Rahman, Adrian Rodriguez-Marek, Nina Stark, Grace Massey, Carl Friedrichs, Kelly M. Dorgan
Date: 2024-09-30 00:00:00

The geotechnical evaluation of seabed sediments is important for engineering projects and naval applications, offering valuable insights into sediment properties, behavior, and strength. Obtaining high-quality seabed samples can be a challenging task, making in-situ testing an essential part of site characterization. Free Fall Penetrometers (FFP) have emerged as robust tools for rapidly profiling seabed surface sediments, even in energetic nearshore or estuarine conditions and shallow as well as deep depths. While methods for interpretation of traditional offshore Cone Penetration Testing (CPT) data are well-established, their adaptation to FFP data is still an area of research. In this study, we introduce an innovative approach that utilizes machine learning algorithms to create a sediment behavior classification system based on portable free fall penetrometer (PFFP) data. The proposed model leverages PFFP measurements obtained from locations such as Sequim Bay (Washington), the Potomac River, and the York River (Virginia). The result shows 91.1\% accuracy in the class prediction, with the classes representing cohesionless sediment with little to no plasticity, cohesionless sediment with some plasticity, cohesive sediment with low plasticity, and cohesive sediment with high plasticity. The model prediction not only provides the predicted class but also yields an estimate of inherent uncertainty associated with the prediction, which can provide valuable insight about different sediment behaviors. These uncertainties typically range from very low to very high, with lower uncertainties being more common, but they can increase significantly dpending on variations in sediment composition, environmental conditions, and operational techniques. By quantifying uncertainty, the model offers a more comprehensive and informed approach to sediment classification.
GSPR: Multimodal Place Recognition Using 3D Gaussian Splatting for Autonomous Driving
Authors: Zhangshuo Qi, Junyi Ma, Jingyi Xu, Zijie Zhou, Luqi Cheng, Guangming Xiong
Date: 2024-10-01 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles

Place recognition is a crucial module to ensure autonomous vehicles obtain usable localization information in GPS-denied environments. In recent years, multimodal place recognition methods have gained increasing attention due to their ability to overcome the weaknesses of unimodal sensor systems by leveraging complementary information from different modalities. However, challenges arise from the necessity of harmonizing data across modalities and exploiting the spatio-temporal correlations between them sufficiently. In this paper, we propose a 3D Gaussian Splatting-based multimodal place recognition neural network dubbed GSPR. It explicitly combines multi-view RGB images and LiDAR point clouds into a spatio-temporally unified scene representation with the proposed Multimodal Gaussian Splatting. A network composed of 3D graph convolution and transformer is designed to extract high-level spatio-temporal features and global descriptors from the Gaussian scenes for place recognition. We evaluate our method on the nuScenes dataset, and the experimental results demonstrate that our method can effectively leverage complementary strengths of both multi-view cameras and LiDAR, achieving SOTA place recognition performance while maintaining solid generalization ability. Our open-source code is available at https://github.com/QiZS-BIT/GSPR.
FLEX: Expert-level False-Less EXecution Metric for Reliable Text-to-SQL Benchmark
Authors: Heegyu Kim, Taeyang Jeon, Seunghwan Choi, Seungtaek Choi, Hyunsouk Cho
Date: 2024-09-24 00:00:00
Tasks: Text to SQL, Text-To-SQL

Text-to-SQL systems have become crucial for translating natural language into SQL queries in various industries, enabling non-technical users to perform complex data operations. The need for accurate evaluation methods has increased as these systems have grown more sophisticated. However, the Execution Accuracy (EX), the most prevalent evaluation metric, still shows many false positives and negatives. Thus, this paper introduces FLEX (False-Less EXecution), a novel approach to evaluating text-to-SQL systems using large language models (LLMs) to emulate human expert-level evaluation of SQL queries. Our metric improves agreement with human experts (from 62 to 87.04 in Cohen's kappa) with comprehensive context and sophisticated criteria. Our extensive experiments yield several key insights: (1) Models' performance increases by over 2.6 points on average, substantially affecting rankings on Spider and BIRD benchmarks; (2) The underestimation of models in EX primarily stems from annotation quality issues; and (3) Model performance on particularly challenging questions tends to be overestimated. This work contributes to a more accurate and nuanced evaluation of text-to-SQL systems, potentially reshaping our understanding of state-of-the-art performance in this field.