pwc-archive/papers-with-abstracts huggingface dataset:
Kernel Sum of Squares for Data Adapted Kernel Learning of Dynamical Systems from Data: A global optimization approach
Authors: Daniel Lengyel, Panos Parpas, Boumediene Hamzi, Houman Owhadi
Date: 2024-08-12 00:00:00
Tasks: global-optimization, Time Series, Time Series Analysis

This paper examines the application of the Kernel Sum of Squares (KSOS) method for enhancing kernel learning from data, particularly in the context of dynamical systems. Traditional kernel-based methods, despite their theoretical soundness and numerical efficiency, frequently struggle with selecting optimal base kernels and parameter tuning, especially with gradient-based methods prone to local optima. KSOS mitigates these issues by leveraging a global optimization framework with kernel-based surrogate functions, thereby achieving more reliable and precise learning of dynamical systems. Through comprehensive numerical experiments on the Logistic Map, Henon Map, and Lorentz System, KSOS is shown to consistently outperform gradient descent in minimizing the relative-$\rho$ metric and improving kernel accuracy. These results highlight KSOS's effectiveness in predicting the behavior of chaotic dynamical systems, demonstrating its capability to adapt kernels to underlying dynamics and enhance the robustness and predictive power of kernel-based approaches, making it a valuable asset for time series analysis in various scientific fields.
Causal Graph Aided Causal Discovery in an Observational Aneurysmal Subarachnoid Hemorrhage Study
Authors: Carlo Berzuini, Davide Luciani, Hiren C. Patel
Date: 2024-08-12 00:00:00
Tasks: Causal Discovery, Causal Inference

Causal inference methods for observational data are increasingly recognized as a valuable complement to randomized clinical trials (RCTs). They can, under strong assumptions, emulate RCTs or help refine their focus. Our approach to causal inference uses causal directed acyclic graphs (DAGs). We are motivated by a concern that many observational studies in medicine begin without a clear definition of their objectives, without awareness of the scientific potential, and without tools to identify the necessary in itinere adjustments. We present and illustrate methods that provide "midway insights" during study's course, identify meaningful causal questions within the study's reach and point to the necessary data base enhancements for these questions to be meaningfully tackled. The method hinges on concepts of identification and positivity. Concepts are illustrated through an analysis of data generated by patients with aneurysmal Subarachnoid Hemorrhage (aSAH) halfway through a study, focusing in particular on the consequences of external ventricular drain (EVD) in strata of the aSAH population. In addition, we propose a method for multicenter studies, to monitor the impact of changes in practice at an individual center's level, by leveraging principles of instrumental variable (IV) inference.
Bayesian Learning in a Nonlinear Multiscale State-Space Model
Authors: Nayely Vélez-Cruz, Manfred D. Laubichler
Date: 2024-08-12 00:00:00

The ubiquity of multiscale interactions in complex systems is well-recognized, with development and heredity serving as a prime example of how processes at different temporal scales influence one another. This work introduces a novel multiscale state-space model to explore the dynamic interplay between systems interacting across different time scales, with feedback between each scale. We propose a Bayesian learning framework to estimate unknown states by learning the unknown process noise covariances within this multiscale model. We develop a Particle Gibbs with Ancestor Sampling (PGAS) algorithm for inference and demonstrate through simulations the efficacy of our approach.
Langevin dynamics for high-dimensional optimization: the case of multi-spiked tensor PCA
Authors: Gérard Ben Arous, Cédric Gerbelot, Vanessa Piccolo
Date: 2024-08-12 00:00:00

We study nonconvex optimization in high dimensions through Langevin dynamics, focusing on the multi-spiked tensor PCA problem. This tensor estimation problem involves recovering $r$ hidden signal vectors (spikes) from noisy Gaussian tensor observations using maximum likelihood estimation. We study the number of samples required for Langevin dynamics to efficiently recover the spikes and determine the necessary separation condition on the signal-to-noise ratios (SNRs) for exact recovery, distinguishing the cases $p \ge 3$ and $p=2$, where $p$ denotes the order of the tensor. In particular, we show that the sample complexity required for recovering the spike associated with the largest SNR matches the well-known algorithmic threshold for the single-spike case, while this threshold degrades when recovering all $r$ spikes. As a key step, we provide a detailed characterization of the trajectory and interactions of low-dimensional projections that capture the high-dimensional dynamics.
OpenResearcher: Unleashing AI for Accelerated Scientific Research
Authors: Yuxiang Zheng, Shichao Sun, Lin Qiu, Dongyu Ru, Cheng Jiayang, Xuefeng Li, Jifan Lin, Binjie Wang, Yun Luo, Renjie Pan, Yang Xu, Qingkai Min, Zizhao Zhang, Yiwen Wang, Wenjie Li, PengFei Liu
Date: 2024-08-13 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation

The rapid growth of scientific literature imposes significant challenges for researchers endeavoring to stay updated with the latest advancements in their fields and delve into new areas. We introduce OpenResearcher, an innovative platform that leverages Artificial Intelligence (AI) techniques to accelerate the research process by answering diverse questions from researchers. OpenResearcher is built based on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge. Moreover, we develop various tools for OpenResearcher to understand researchers' queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine these answers. OpenResearcher can flexibly use these tools to balance efficiency and effectiveness. As a result, OpenResearcher enables researchers to save time and increase their potential to discover new insights and drive scientific breakthroughs. Demo, video, and code are available at: https://github.com/GAIR-NLP/OpenResearcher.
Reformulating Conversational Recommender Systems as Tri-Phase Offline Policy Learning
Authors: Gangyi Zhang, Chongming Gao, Hang Pan, Runzhe Teng, Ruizhe Li
Date: 2024-08-13 00:00:00
Tasks: Recommendation Systems, User Simulation

Existing Conversational Recommender Systems (CRS) predominantly utilize user simulators for training and evaluating recommendation policies. These simulators often oversimplify the complexity of user interactions by focusing solely on static item attributes, neglecting the rich, evolving preferences that characterize real-world user behavior. This limitation frequently leads to models that perform well in simulated environments but falter in actual deployment. Addressing these challenges, this paper introduces the Tri-Phase Offline Policy Learning-based Conversational Recommender System (TCRS), which significantly reduces dependency on real-time interactions and mitigates overfitting issues prevalent in traditional approaches. TCRS integrates a model-based offline learning strategy with a controllable user simulation that dynamically aligns with both personalized and evolving user preferences. Through comprehensive experiments, TCRS demonstrates enhanced robustness, adaptability, and accuracy in recommendations, outperforming traditional CRS models in diverse user scenarios. This approach not only provides a more realistic evaluation environment but also facilitates a deeper understanding of user behavior dynamics, thereby refining the recommendation process.
BMX: Entropy-weighted Similarity and Semantic-enhanced Lexical Search
Authors: Xianming Li, Julius Lipp, Aamir Shakir, Rui Huang, Jing Li
Date: 2024-08-13 00:00:00
Tasks: Information Retrieval, Retrieval

BM25, a widely-used lexical search algorithm, remains crucial in information retrieval despite the rise of pre-trained and large language models (PLMs/LLMs). However, it neglects query-document similarity and lacks semantic understanding, limiting its performance. We revisit BM25 and introduce BMX, a novel extension of BM25 incorporating entropy-weighted similarity and semantic enhancement techniques. Extensive experiments demonstrate that BMX consistently outperforms traditional BM25 and surpasses PLM/LLM-based dense retrieval in long-context and real-world retrieval benchmarks. This study bridges the gap between classical lexical search and modern semantic approaches, offering a promising direction for future information retrieval research. The reference implementation of BMX can be found in Baguetter, which was created in the context of this work. The code can be found here: https://github.com/mixedbread-ai/baguetter.
Prompt Tuning as User Inherent Profile Inference Machine
Authors: Yusheng Lu, Zhaocheng Du, Xiangyang Li, Xiangyu Zhao, Weiwen Liu, Yichao Wang, Huifeng Guo, Ruiming Tang, Zhenhua Dong, Yongrui Duan
Date: 2024-08-13 00:00:00
Tasks: Quantization, Recommendation Systems, World Knowledge

Large Language Models (LLMs) have exhibited significant promise in recommender systems by empowering user profiles with their extensive world knowledge and superior reasoning capabilities. However, LLMs face challenges like unstable instruction compliance, modality gaps, and high inference latency, leading to textual noise and limiting their effectiveness in recommender systems. To address these challenges, we propose UserIP-Tuning, which uses prompt-tuning to infer user profiles. It integrates the causal relationship between user profiles and behavior sequences into LLMs' prompts. And employs expectation maximization to infer the embedded latent profile, minimizing textual noise by fixing the prompt template. Furthermore, A profile quantization codebook bridges the modality gap by categorizing profile embeddings into collaborative IDs, which are pre-stored for online deployment. This improves time efficiency and reduces memory usage. Experiments on four public datasets show that UserIP-Tuning outperforms state-of-the-art recommendation algorithms. Additional tests and case studies confirm its effectiveness, robustness, and transferability.
Investigating Characteristics of Media Recommendation Solicitation in r/ifyoulikeblank
Authors: Md Momen Bhuiyan, Donghan Hu, Andrew Jelson, Tanushree Mitra, Sang Won Lee
Date: 2024-08-12 00:00:00
Tasks: Recommendation Systems

Despite the existence of search-based recommender systems like Google, Netflix, and Spotify, online users sometimes may turn to crowdsourced recommendations in places like the r/ifyoulikeblank subreddit. In this exploratory study, we probe why users go to r/ifyoulikeblank, how they look for recommendation, and how the subreddit users respond to recommendation requests. To answer, we collected sample posts from r/ifyoulikeblank and analyzed them using a qualitative approach. Our analysis reveals that users come to this subreddit for various reasons, such as exhausting popular search systems, not knowing what or how to search for an item, and thinking crowd have better knowledge than search systems. Examining users query and their description, we found novel information users provide during recommendation seeking using r/ifyoulikeblank. For example, sometimes they ask for artifacts recommendation based on the tools used to create them. Or, sometimes indicating a recommendation seeker's time constraints can help better suit recommendations to their needs. Finally, recommendation responses and interactions revealed patterns of how requesters and responders refine queries and recommendations. Our work informs future intelligent recommender systems design.
Scalable recommender system based on factor analysis
Authors: Disha Ghandwani, Trevor Hastie
Date: 2024-08-12 00:00:00
Tasks: parameter estimation, Recommendation Systems

Recommender systems have become crucial in the modern digital landscape, where personalized content, products, and services are essential for enhancing user experience. This paper explores statistical models for recommender systems, focusing on crossed random effects models and factor analysis. We extend the crossed random effects model to include random slopes, enabling the capture of varying covariate effects among users and items. Additionally, we investigate the use of factor analysis in recommender systems, particularly for settings with incomplete data. The paper also discusses scalable solutions using the Expectation Maximization (EM) and variational EM algorithms for parameter estimation, highlighting the application of these models to predict user-item interactions effectively.
Competition of Green Supply Chains and R&D Cost-sharing within a Chain--Based on Noncooperative-Cooperative Biform Game Approach
Authors: LI Meng-qi, LI Deng-feng, NAN Jiang-xia
Date: 2023-03-17 00:00:00
Proceeding: Chinese Journal of Management Science 2023 3

With the prevalence of sustainable development, finding the balance between economic 
development and environment pollution is an issue that every enterprise in the supply chain needs to solve. The contradiction between high product green level and high green R&D cost leads to the decrease the enterprises' positivity of green technology R&D and profit. In order to meet the green product demands of consumers, many retailers, such as Walmart, have long been committed to help upstream manufacturers to ease the financial pressure by sharing the green R&D cost, which can encourage manufacturers to improve the level of green technology and realize the rational optimization of the supply chain resource. Considering the green competition, price competition and cooperation between manufacturers and retailers, it is worthwhile to explore how to determine the optimal price and the optimal level of green technology. 
For the competition of two green supply chains and the sharing cost of manufacturers and retailers, the equilibrium price and the optimal profit of supply chains are studied by using the noncooperative-cooperative biform game in this paper. In the noncooperative game part, choose wholesale price and retail price as strategies, which constitutes the competitive situation. In the cooperative game part, manufacturers and retailers optimize the level of green technology and the sharing proportion of green R&D cost. Two cooperative games are established to describe the competition of supply chains and the cooperation among supply chain members. Shapley value is used to distribute the profit of each supply chain. Based on the profit of the cooperative game, the Nash equilibrium strategy of supply chain members is obtained by constructing the non-cooperative game. The influence of the product green competitive intensity and cross price sensitivity coefficient on equilibrium strategy and optimal profit is analyzed through a numerical example. The results show that the green technology and the profit of supply chain members are positively correlated with the coefficient of green R&D cost. As the product green competitive intensity increases, the profit of supply chain members is reduced. And enterprises will reduce the investment in green technology, which leads to the lower green level of products. However, the product price competition can prompt manufacturers to produce products with high green level and raise product pricing. This paper provides a new method to study the management for green supply chains with coexistence of competition and cooperation.
UGNCL: Uncertainty-Guided Noisy Correspondence Learning for Efficient Cross-Modal Matching
Authors: Quanxing Zha, Xin Liu, Yiu-ming Cheung, Xing Xu, Nannan Wang, Jianjia Cao
Date: 2024-07-11 00:00:00
Proceeding: SIGIR 2024 7
Tasks: Cross-Modal Retrieval, Cross-modal retrieval with noisy correspondence, Image-text matching, Image-text Retrieval

Cross-modal matching has recently gained significant popularity to facilitate retrieval across multi-modal data, and existing works are highly relied on an implicit assumption that the training data pairs are perfectly aligned. However, such an ideal assumption is extremely impossible due to the inevitably mismatched data pairs, a.k.a. noisy correspondence, which can wrongly enforce the mismatched data to be similar and thus induces the performance degradation. Although some recent methods have attempted to address this problem, they still face two challenging issues: 1) un- reliable data division for training inefficiency and 2) unstable pre- diction for matching failure. To address these problems, we pro- pose an efficient Uncertainty-Guided Noisy Correspondence Learning (UGNCL) framework to achieve noise-robust cross-modal matching. Specifically, a novel Uncertainty Guided Division (UGD) algorithm is reliably designed leverage the potential benefits of derived un- certainty to divide the data into clean, noisy and hard partitions, which can effortlessly mitigate the impact of easily-determined noisy pairs. Meanwhile, an efficient Trusted Robust Loss (TRL) is explicitly designed to recast the soft margins, calibrated by confi- dent yet error soft correspondence labels, for the data pairs in the hard partition through the uncertainty, leading to increase/decrease the importance of matched/mismatched pairs and further alleviate the impact of noisy pairs for robustness improvement. Extensive experiments conducted on three public datasets highlight the su- periorities of the proposed framework, and show its competitive performance compared with the state-of-the-arts. The code is avail- able at https://github.com/qxzha/UGNCL.
Few-Shot Bearing Fault Diagnosis Via Ensembling Transformer-Based Model With Mahalanobis Distance Metric Learning From Multiscale Features
Authors: Manh-Hung Vu, Van-Quang Nguyen, Thi-Thao Tran, Van-Truong Pham, Men-Tzung Lo
Date: 2024-03-25 00:00:00
Proceeding: IEEE Transactions on Instrumentation and Measurement 2024 3
Tasks: Classification, Fault Diagnosis, Few-Shot Learning, Metric Learning

Advanced deep-learning models have shown excellent performance in the task of fault-bearing diagnosis over traditional machine learning and signal-processing techniques. Few-shot learning approach has also been attracting a lot of attention in this task to address the problem of limited training data. Nevertheless, cutting-edge models for fault-bearing diagnosis are often based on convolutional neural networks (CNNs) that emphasize local features of input data. Besides, accurate classification of fault-bearing signals is still nontrivial due to the variations of data, fault types, acquisition conditions, and extremely limited data, leaving space for research on this topic. In this study, we propose a novel end-to-end approach for fault-bearing diagnosis even in the case of limited data with artificial and real faults. In particular, we propose a module for automatic feature extraction from input data namely multiscale large kernel feature extraction. The extracted features are then fed into a two-branch model including a global and a local branch. The global one includes a transformer architecture with cross-attention to handle global context and obtain the correlation between the query and support sets. The local branch is a metric-based model consisting of Mahalanobis distance for separating local features from the support set. The outputs from the two branches are then ensembled for classification purposes. Intensive experiments and ablation studies have been made on the two public datasets including CWRU and PU. Qualitative and quantitative results with different degrees of training samples by the proposed model in comparison with other state-of-the-arts have shown the superior performance of the proposed approach. Our code will be published at https://github.com/HungVu307/Few-shot-via-ensembling-Transformer-with-Mahalanobis-distance
Lyrics Transcription for Humans: A Readability-Aware Benchmark
Authors: Ondřej Cífka, Hendrik Schreiber, Luke Miner, Fabian-Robert Stöter
Date: 2024-07-30 00:00:00
Tasks: Automatic Lyrics Transcription

Writing down lyrics for human consumption involves not only accurately capturing word sequences, but also incorporating punctuation and formatting for clarity and to convey contextual information. This includes song structure, emotional emphasis, and contrast between lead and background vocals. While automatic lyrics transcription (ALT) systems have advanced beyond producing unstructured strings of words and are able to draw on wider context, ALT benchmarks have not kept pace and continue to focus exclusively on words. To address this gap, we introduce Jam-ALT, a comprehensive lyrics transcription benchmark. The benchmark features a complete revision of the JamendoLyrics dataset, in adherence to industry standards for lyrics transcription and formatting, along with evaluation metrics designed to capture and assess the lyric-specific nuances, laying the foundation for improving the readability of lyrics. We apply the benchmark to recent transcription systems and present additional error analysis, as well as an experimental comparison with a classical music dataset.
A Comprehensive System Architecture using Field Programmable Gate Arrays Technology, Dijkstra's Algorithm, and Edge Computing for Emergency Response in Smart Cities
Authors: Mahamat Abdel Aziz Assoul, Abakar Mahamat Tahir, Taibi Mahmoud, Garrik Brel Jagho Mdemaya, Milliam Maxime Zekeng Ndadji
Date: 2024-08-09 00:00:00
Tasks: Edge-computing

Efficient emergency response systems are crucial for smart cities. But their implementation is highly challenging, particularly in regions like Chad where infrastructural constraints are prevalent. The urgency for optimized response times and resource allocation in emergency scenarios is magnified in these contexts, yet existing solutions often assume robust infrastructure and uninterrupted connectivity, which is not always available. Most of the time, they are based on system architectures pre-designed for other purposes. This paper addresses these critical challenges by proposing a comprehensive system architecture that integrates Field Programmable Gate Arrays (FPGAs), Dijkstra's algorithm, and Edge Computing. The objective is to enhance emergency response through accelerated route planning and resource allocation, addressing gaps left by traditional cloud-based systems. Methodologically, key characteristics of the desired system are identified, then its components are described and their integration is explained; the system leverages FPGA-based computations and a distributed implementation of Dijkstra's algorithm to compute the shortest paths rapidly, while Edge Computing ensures decentralized and resilient processing. A theoretical analysis highlights promising improvements in response times and resource management. The proposed system architecture not only enhances emergency response efficiency but is also adaptable to infrastructural constraints of Chadian-like environments.