pwc-archive/papers-with-abstracts huggingface dataset:
Efficient and Robust Regularized Federated Recommendation
Authors: Langming Liu, Wanyu Wang, Xiangyu Zhao, Zijian Zhang, Chunxu Zhang, Shanru Lin, Yiqi Wang, Lixin Zou, Zitao Liu, Xuetao Wei, Hongzhi Yin, Qing Li
Date: 2024-11-03 00:00:00
Tasks: Federated Learning, Recommendation Systems

Recommender systems play a pivotal role across practical scenarios, showcasing remarkable capabilities in user preference modeling. However, the centralized learning paradigm predominantly used raises serious privacy concerns. The federated recommender system (FedRS) addresses this by updating models on clients, while a central server orchestrates training without accessing private data. Existing FedRS approaches, however, face unresolved challenges, including non-convex optimization, vulnerability, potential privacy leakage risk, and communication inefficiency. This paper addresses these challenges by reformulating the federated recommendation problem as a convex optimization issue, ensuring convergence to the global optimum. Based on this, we devise a novel method, RFRec, to tackle this optimization problem efficiently. In addition, we propose RFRecF, a highly efficient version that incorporates non-uniform stochastic gradient descent to improve communication efficiency. In user preference modeling, both methods learn local and global models, collaboratively learning users' common and personalized interests under the federated learning setting. Moreover, both methods significantly enhance communication efficiency, robustness, and privacy protection, with theoretical support. Comprehensive evaluations on four benchmark datasets demonstrate RFRec and RFRecF's superior performance compared to diverse baselines.
LLMs and the Madness of Crowds
Authors: William F. Bradley
Date: 2024-11-03 00:00:00

We investigate the patterns of incorrect answers produced by large language models (LLMs) during evaluation. These errors exhibit highly non-intuitive behaviors unique to each model. By analyzing these patterns, we measure the similarities between LLMs and construct a taxonomy that categorizes them based on their error correlations. Our findings reveal that the incorrect responses are not randomly distributed but systematically correlated across models, providing new insights into the underlying structures and relationships among LLMs.
G-SPARC: SPectral ARchitectures tackling the Cold-start problem in Graph learning
Authors: Yahel Jacobs, Reut Dayan, Uri Shaham
Date: 2024-11-03 00:00:00
Tasks: Graph Learning, Link Prediction, Node Classification, Node Clustering

Graphs play a central role in modeling complex relationships across various domains. Most graph learning methods rely heavily on neighborhood information, raising the question of how to handle cold-start nodes - nodes with no known connections within the graph. These models often overlook the cold-start nodes, making them ineffective for real-world scenarios. To tackle this, we propose G-SPARC, a novel framework addressing cold-start nodes, that leverages generalizable spectral embedding. This framework enables extension to state-of-the-art methods making them suitable for practical applications. By utilizing a key idea of transitioning from graph representation to spectral representation, our approach is generalizable to cold-start nodes, capturing the global structure of the graph without relying on adjacency data. Experimental results demonstrate that our method outperforms existing models on cold-start nodes across various tasks like node classification, node clustering, and link prediction. G-SPARC provides a breakthrough built-in solution to the cold-start problem in graph learning. Our code will be publicly available upon acceptance.
Performance Evaluation of Deep Learning Models for Water Quality Index Prediction: A Comparative Study of LSTM, TCN, ANN, and MLP
Authors: Muhammad Ismail, Farkhanda Abbas, Shahid Munir Shah, Mahmoud Aljawarneh, Lachhman Das Dhomeja, Fazila Abbas, Muhammad Shoaib, Abdulwahed Fahad Alrefaei, Mohammed Fahad Albeshr
Date: 2024-11-03 00:00:00

Environmental monitoring and predictive modeling of the Water Quality Index (WQI) through the assessment of the water quality.
Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments
Authors: Tomoko Nagai, Takayuki Okuda, Tomoya Nakamura, Yuichiro Sato, Yusuke Sato, Kensaku Kinjo, Kengo Kawamura, Shin Kikuta, Naoto Kumano-go
Date: 2024-11-03 00:00:00
Tasks: Causal Inference

This study examines the educational effect of the Academic Support Center at Kogakuin University. Following the initial assessment, it was suggested that group bias had led to an underestimation of the Center's true impact. To address this issue, the authors applied the theory of causal inference. By using T-learner, the conditional average treatment effect (CATE) of the Center's face-to-face (F2F) personal assistance program was evaluated. Extending T-learner, the authors produced a new CATE function that depends on the number of treatments (F2F sessions) and used the estimated function to predict the CATE performance of F2F assistance.
Anomalous Client Detection in Federated Learning
Authors: Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino
Date: 2024-11-03 00:00:00
Tasks: Anomaly Detection, Edge-computing, Federated Learning

Federated learning (FL), with the growing IoT and edge computing, is seen as a promising solution for applications that are latency- and privacy-aware. However, due to the widespread dispersion of data across many clients, it is challenging to monitor client anomalies caused by malfunctioning devices or unexpected events. The majority of FL solutions now in use concentrate on the classification problem, ignoring situations in which anomaly detection may also necessitate privacy preservation and effectiveness. The system in federated learning is unable to manage the potentially flawed behavior of its clients completely. These behaviors include sharing arbitrary parameter values and causing a delay in convergence since clients are chosen at random without knowing the malfunctioning behavior of the client. Client selection is crucial in terms of the efficiency of the federated learning framework. The challenges such as client drift and handling slow clients with low computational capability are well-studied in FL. However, the detection of anomalous clients either for security or for overall performance in the FL frameworks is hardly studied in the literature. In this paper, we propose an anomaly client detection algorithm to overcome malicious client attacks and client drift in FL frameworks. Instead of random client selection, our proposed method utilizes anomaly client detection to remove clients from the FL framework, thereby enhancing the security and efficiency of the overall system. This proposed method improves the global model convergence in almost 50\% fewer communication rounds compared with widely used random client selection using the MNIST dataset.
DPCL-Diff: The Temporal Knowledge Graph Reasoning Based on Graph Node Diffusion Model with Dual-Domain Periodic Contrastive Learning
Authors: Yukun Cao, Lisheng Wang, Luobin Huang
Date: 2024-11-03 00:00:00
Tasks: Contrastive Learning, Image Generation

Temporal knowledge graph (TKG) reasoning that infers future missing facts is an essential and challenging task. Predicting future events typically relies on closely related historical facts, yielding more accurate results for repetitive or periodic events. However, for future events with sparse historical interactions, the effectiveness of this method, which focuses on leveraging high-frequency historical information, diminishes. Recently, the capabilities of diffusion models in image generation have opened new opportunities for TKG reasoning. Therefore, we propose a graph node diffusion model with dual-domain periodic contrastive learning (DPCL-Diff). Graph node diffusion model (GNDiff) introduces noise into sparsely related events to simulate new events, generating high-quality data that better conforms to the actual distribution. This generative mechanism significantly enhances the model's ability to reason about new events. Additionally, the dual-domain periodic contrastive learning (DPCL) maps periodic and non-periodic event entities to Poincar\'e and Euclidean spaces, leveraging their characteristics to distinguish similar periodic events effectively. Experimental results on four public datasets demonstrate that DPCL-Diff significantly outperforms state-of-the-art TKG models in event prediction, demonstrating our approach's effectiveness. This study also investigates the combined effectiveness of GNDiff and DPCL in TKG tasks.
Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation
Authors: Mingrui Liu, Sixiao Zhang, Cheng Long
Date: 2024-11-03 00:00:00
Tasks: Mixture-of-Experts, Sequential Recommendation

Sequential recommendation (SR) systems excel at capturing users' dynamic preferences by leveraging their interaction histories. Most existing SR systems assign a single embedding vector to each item to represent its features, and various types of models are adopted to combine these item embeddings into a sequence representation vector to capture the user intent. However, we argue that this representation alone is insufficient to capture an item's multi-faceted nature (e.g., movie genres, starring actors). Besides, users often exhibit complex and varied preferences within these facets (e.g., liking both action and musical films in the facet of genre), which are challenging to fully represent. To address the issues above, we propose a novel structure called Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation (FAME). We leverage sub-embeddings from each head in the last multi-head attention layer to predict the next item separately. This approach captures the potential multi-faceted nature of items without increasing model complexity. A gating mechanism integrates recommendations from each head and dynamically determines their importance. Furthermore, we introduce a Mixture-of-Experts (MoE) network in each attention head to disentangle various user preferences within each facet. Each expert within the MoE focuses on a specific preference. A learnable router network is adopted to compute the importance weight for each expert and aggregate them. We conduct extensive experiments on four public sequential recommendation datasets and the results demonstrate the effectiveness of our method over existing baseline models.
Reconstructing MODIS Normalized Difference Snow Index Product on Greenland Ice Sheet Using Spatiotemporal Extreme Gradient Boosting Model
Authors: Fan Ye, Qing Cheng, Weifeng Hao, Dayu Yu
Date: 2024-11-03 00:00:00

The spatiotemporally continuous data of normalized difference snow index (NDSI) are key to understanding the mechanisms of snow occurrence and development as well as the patterns of snow distribution changes. However, the presence of clouds, particularly prevalent in polar regions such as the Greenland Ice Sheet (GrIS), introduces a significant number of missing pixels in the MODIS NDSI daily data. To address this issue, this study proposes the utilization of a spatiotemporal extreme gradient boosting (STXGBoost) model generate a comprehensive NDSI dataset. In the proposed model, various input variables are carefully selected, encompassing terrain features, geometry-related parameters, and surface property variables. Moreover, the model incorporates spatiotemporal variation information, enhancing its capacity for reconstructing the NDSI dataset. Verification results demonstrate the efficacy of the STXGBoost model, with a coefficient of determination of 0.962, root mean square error of 0.030, mean absolute error of 0.011, and negligible bias (0.0001). Furthermore, simulation comparisons involving missing data and cross-validation with Landsat NDSI data illustrate the model's capability to accurately reconstruct the spatial distribution of NDSI data. Notably, the proposed model surpasses the performance of traditional machine learning models, showcasing superior NDSI predictive capabilities. This study highlights the potential of leveraging auxiliary data to reconstruct NDSI in GrIS, with implications for broader applications in other regions. The findings offer valuable insights for the reconstruction of NDSI remote sensing data, contributing to the further understanding of spatiotemporal dynamics in snow-covered regions.
Privacy-Preserving Customer Churn Prediction Model in the Context of Telecommunication Industry
Authors: Joydeb Kumar Sana, M Sohel Rahman, M Saifur Rahman
Date: 2024-11-03 00:00:00
Tasks: Cloud Computing, Prediction, Privacy Preserving

Data is the main fuel of a successful machine learning model. A dataset may contain sensitive individual records e.g. personal health records, financial data, industrial information, etc. Training a model using this sensitive data has become a new privacy concern when someone uses third-party cloud computing. Trained models also suffer privacy attacks which leads to the leaking of sensitive information of the training data. This study is conducted to preserve the privacy of training data in the context of customer churn prediction modeling for the telecommunications industry (TCI). In this work, we propose a framework for privacy-preserving customer churn prediction (PPCCP) model in the cloud environment. We have proposed a novel approach which is a combination of Generative Adversarial Networks (GANs) and adaptive Weight-of-Evidence (aWOE). Synthetic data is generated from GANs, and aWOE is applied on the synthetic training dataset before feeding the data to the classification algorithms. Our experiments were carried out using eight different machine learning (ML) classifiers on three openly accessible datasets from the telecommunication sector. We then evaluated the performance using six commonly employed evaluation metrics. In addition to presenting a data privacy analysis, we also performed a statistical significance test. The training and prediction processes achieve data privacy and the prediction classifiers achieve high prediction performance (87.1\% in terms of F-Measure for GANs-aWOE based Na\"{\i}ve Bayes model). In contrast to earlier studies, our suggested approach demonstrates a prediction enhancement of up to 28.9\% and 27.9\% in terms of accuracy and F-measure, respectively.
HOBBIT: A Mixed Precision Expert Offloading System for Fast MoE Inference
Authors: Peng Tang, Jiacheng Liu, Xiaofeng Hou, YiFei PU, Jing Wang, Pheng-Ann Heng, Chao Li, Minyi Guo
Date: 2024-11-03 00:00:00
Tasks: Mixture-of-Experts

The Mixture-of-Experts (MoE) architecture has demonstrated significant advantages in the era of Large Language Models (LLMs), offering enhanced capabilities with reduced inference costs. However, deploying MoE-based LLMs on memoryconstrained edge devices remains challenging due to their substantial memory requirements. While existing expertoffloading methods alleviate the memory requirements, they often incur significant expert-loading costs or compromise model accuracy. We present HOBBIT, a mixed precision expert offloading system to enable flexible and efficient MoE inference. Our key insight is that dynamically replacing less critical cache-miss experts with low precision versions can substantially reduce expert-loading latency while preserving model accuracy. HOBBIT introduces three innovative techniques that map the natural hierarchy of MoE computation: (1) a token-level dynamic expert loading mechanism, (2) a layer-level adaptive expert prefetching technique, and (3) a sequence-level multidimensional expert caching policy. These innovations fully leverage the benefits of mixedprecision expert inference. By implementing HOBBIT on top of the renowned LLM inference framework Llama.cpp, we evaluate its performance across different edge devices with representative MoE models. The results demonstrate that HOBBIT achieves up to a 9.93x speedup in decoding compared to state-of-the-art MoE offloading systems.
GramSeq-DTA: A grammar-based drug-target affinity prediction approach fusing gene expression information
Authors: Kusal Debnath, Pratip Rana, Preetam Ghosh
Date: 2024-11-03 00:00:00
Tasks: Drug Discovery, Prediction

Drug-target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has demonstrated good results in drug-target affinity prediction. However, these approach lacks information on the relative position of the atoms and bonds. To address this limitation, graph-based representations have been used to some extent. However, solely considering the structural aspect of drugs and targets may be insufficient for accurate DTA prediction. Integrating the functional aspect of these drugs at the genetic level can enhance the prediction capability of the models. To fill this gap, we propose GramSeq-DTA, which integrates chemical perturbation information with the structural information of drugs and targets. We applied a Grammar Variational Autoencoder (GVAE) for drug feature extraction and utilized two different approaches for protein feature extraction: Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). The chemical perturbation data is obtained from the L1000 project, which provides information on the upregulation and downregulation of genes caused by selected drugs. This chemical perturbation information is processed, and a compact dataset is prepared, serving as the functional feature set of the drugs. By integrating the drug, gene, and target features in the model, our approach outperforms the current state-of-the-art DTA prediction models when validated on widely used DTA datasets (BindingDB, Davis, and KIBA). This work provides a novel and practical approach to DTA prediction by merging the structural and functional aspects of biological entities, and it encourages further research in multi-modal DTA prediction.
Enhancing Glucose Level Prediction of ICU Patients through Hierarchical Modeling of Irregular Time-Series
Authors: Hadi Mehdizavareh, Arijit Khan, Simon Lebech Cichosz
Date: 2024-11-03 00:00:00
Tasks: Data Integration, Feature Engineering, Irregular Time Series, Mortality Prediction, Time Series, Time Series Analysis

Accurately predicting blood glucose (BG) levels of ICU patients is critical, as both hypoglycemia (BG < 70 mg/dL) and hyperglycemia (BG > 180 mg/dL) are associated with increased morbidity and mortality. This study presents a proof-of-concept machine learning framework, the Multi-source Irregular Time-Series Transformer (MITST), designed to predict BG levels in ICU patients. In contrast to existing methods that rely heavily on manual feature engineering or utilize limited Electronic Health Record (EHR) data sources, MITST integrates diverse clinical data--including laboratory results, medications, and vital signs without predefined aggregation. The model leverages a hierarchical Transformer architecture, designed to capture interactions among features within individual timestamps, temporal dependencies across different timestamps, and semantic relationships across multiple data sources. Evaluated using the extensive eICU database (200,859 ICU stays across 208 hospitals), MITST achieves a statistically significant ( p < 0.001 ) average improvement of 1.7 percentage points (pp) in AUROC and 1.8 pp in AUPRC over a state-of-the-art random forest baseline. Crucially, for hypoglycemia--a rare but life-threatening condition--MITST increases sensitivity by 7.2 pp, potentially enabling hundreds of earlier interventions across ICU populations. The flexible architecture of MITST allows seamless integration of new data sources without retraining the entire model, enhancing its adaptability for clinical decision support. While this study focuses on predicting BG levels, we also demonstrate MITST's ability to generalize to a distinct clinical task (in-hospital mortality prediction), highlighting its potential for broader applicability in ICU settings. MITST thus offers a robust and extensible solution for analyzing complex, multi-source, irregular time-series data.
Hyperbox Mixture Regression for Process Performance Prediction in Antibody Production
Authors: Ali Nik-Khorasani, Thanh Tung Khuat, Bogdan Gabrys
Date: 2024-11-03 00:00:00
Tasks: regression

This paper addresses the challenges of predicting bioprocess performance, particularly in monoclonal antibody (mAb) production, where conventional statistical methods often fall short due to time-series data's complexity and high dimensionality. We propose a novel Hyperbox Mixture Regression (HMR) model which employs hyperbox-based input space partitioning to enhance predictive accuracy while managing uncertainty inherent in bioprocess data. The HMR model is designed to dynamically generate hyperboxes for input samples in a single-pass process, thereby improving learning speed and reducing computational complexity. Our experimental study utilizes a dataset that contains 106 bioreactors. This study evaluates the model's performance in predicting critical quality attributes in monoclonal antibody manufacturing over a 15-day cultivation period. The results demonstrate that the HMR model outperforms comparable approximators in accuracy and learning speed and maintains interpretability and robustness under uncertain conditions. These findings underscore the potential of HMR as a powerful tool for enhancing predictive analytics in bioprocessing applications.
Differentiable Quantum Computing for Large-scale Linear Control
Authors: Connor Clayton, Jiaqi Leng, Gengzhi Yang, Yi-Ling Qiao, Ming C. Lin, Xiaodi Wu
Date: 2024-11-03 00:00:00

As industrial models and designs grow increasingly complex, the demand for optimal control of large-scale dynamical systems has significantly increased. However, traditional methods for optimal control incur significant overhead as problem dimensions grow. In this paper, we introduce an end-to-end quantum algorithm for linear-quadratic control with provable speedups. Our algorithm, based on a policy gradient method, incorporates a novel quantum subroutine for solving the matrix Lyapunov equation. Specifically, we build a quantum-assisted differentiable simulator for efficient gradient estimation that is more accurate and robust than classical methods relying on stochastic approximation. Compared to the classical approaches, our method achieves a super-quadratic speedup. To the best of our knowledge, this is the first end-to-end quantum application to linear control problems with provable quantum advantage.