pwc-archive/papers-with-abstracts huggingface dataset:
High-Resolution Remote Sensing Bitemporal Image Change Detection Based on Feature Interaction and Multitask Learning
Authors: Chunhui Zhao, Yingjie Tang, Shou Feng, Yuanze Fan, Wei Li, Ran Tao, and Lifu Zhang
Date: 2023-05-11 00:00:00
Proceeding: 2023 2023 5
Tasks: Change Detection, Domain Adaptation

With the development of remote sensing technology, high-resolution (HR) remote sensing optical images have gradually become the main source of change detection data. Albeit, the change detection for HR remote sensing images still faces challenges: 1) in complex scenes, a region contains a large amount of semantic information, which makes it difficult to accurately locate the boundaries between different semantics in the feature maps and 2) due to the inability to maintain consistent conditions such as light, weather, and other factors when acquiring bitemporal images, confounding factors such as the style of bitemporal data that are not related to change detection can cause detection difficulties. Therefore, a change detection method based on feature interaction and multitask learning (FMCD) is proposed in this article. To improve the ability to detect changes in complex scenes, FMCD models the context information of features through a multilevel feature interaction module, so as to obtain representative features, and to improve the sensitivity of the model to changes, the interaction between two temporal features is realized through the mix attention block (MAB). In addition, to eliminate the influence of weather and other factors, FMCD adopts a multitask learning strategy, takes domain adaptation as an auxiliary task, and maps the features of bitemporal images to the same space through the feature relationship adaptation module (FRAM) and feature distribution adaptation module (FDAM). Experiments on three datasets show that the proposed method is superior to other state-of-the-art methods.
An Object Fine-Grained Change Detection Method Based on Frequency Decoupling Interaction for High-Resolution Remote Sensing Images
Authors: Yingjie Tang, Shou Feng, Chunhui Zhao, Yuanze Fan, Qian Shi, Wei Li, and Ran Tao
Date: 2023-11-30 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2023 11
Tasks: Change Detection

Change detection is a prominent research direction in the field of remote sensing image processing. However, most current change detection methods focus solely on detecting changes without being able to differentiate the types of changes, such as “appear” or “disappear” of objects. Accurate detection of change types is of great significance in guiding decisionmaking processes. To address this issue, this article introduces the object fine-grained change detection (OFCD) task and proposes a method based on frequency decoupling interaction (FDINet). Specifically, in order to enhance the model’s ability to detect change types and improve its robustness to temporal information, a temporal exchange framework is designed. Additionally, to better capture spatial–temporal correlation in bi-temporal features, a wavelet interaction module (WIM) is proposed. This module utilizes wavelet transform for frequency decoupling, separating features into different components based on their frequency magnitudes. Then the module applies different interaction methods according to the characteristics of these frequency components. Finally, to aggregate complementary information from different-scale feature maps and enhance the representational capabilities of the extracted features, a feature aggregation and upsampling module (FAUM) is adopted. A series of experiments show the superiority of FDINet over most stateof-the-art methods, achieving good results on three different datasets.
AI Model for Predicting Binding Affinity of Antidiabetic Compounds Targeting PPAR
Authors: La Ode Aman, Aiyi Asnawi
Date: 2024-12-28 00:00:00
Tasks: Drug Discovery, Molecular Docking

This study aims to develop a deep learning model for predicting the binding affinity of ligands targeting the Peroxisome Proliferator-Activated Receptor (PPAR) family, using 2D molecular descriptors. A dataset of 3,764 small molecules with known binding affinities, sourced from the ChEMBL database, was preprocessed by eliminating duplicates and incomplete data. Molecular docking simulations using AutoDock Vina were performed to predict binding affinities for the PPAR receptor family. 2D molecular descriptors were computed from the SMILES notation of each ligand, capturing essential structural and physicochemical features. These descriptors, along with the predicted binding affinities, were used to train a deep learning model to predict binding affinity as a regression task. The model was evaluated using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared. Results indicated strong performance with an R squared value of 0.861 for the training set and 0.655 for the test set, suggesting good model generalization. The model shows promise for predicting ligand-receptor interactions and can be applied in drug discovery efforts targeting PPAR-related diseases.
A Novel Supervisory Control Algorithm to Avoid Deadlock in a Manufacturing System Based on Petri Net in Presence of Resource Failure
Authors: Ahmad Bagheri, Mohammadhossein Aghaazizi, Ali Doustmohammadi
Date: 2024-12-29 00:00:00
Tasks: Management

It is well established that resource failure, including robots and machines, in a manufacturing system can result in deadlocks. This issue not only hampers the system's performance but can also inflict significant damage on the manufacturing process. In this paper, we present a new algorithm developed through modeling of a manufacturing system using Petri net that ensures the liveness of the net in the event of such a failure. To detect possible failures, we first design a recovery subnet that is integrated into the resource. Next, we analyze the effects of failures on each state of the network to identify forbidden states. Finally, we propose an algorithm that optimally adds control places and establishes new constant vectors within the network, enabling effective management of remaining resources across different parts of the net. The proposed algorithm has been implemented in a system featuring three manufacturing lines, demonstrating its error-free operation while ensuring key properties such as boundedness, liveness, and performance continuity within the net.
Addressing Vulnerabilities in AI-Image Detection: Challenges and Proposed Solutions
Authors: Justin Jiang
Date: 2024-11-26 00:00:00
Tasks: Misinformation

The rise of advanced AI models like Generative Adversarial Networks (GANs) and diffusion models such as Stable Diffusion has made the creation of highly realistic images accessible, posing risks of misuse in misinformation and manipulation. This study evaluates the effectiveness of convolutional neural networks (CNNs), as well as DenseNet architectures, for detecting AI-generated images. Using variations of the CIFAKE dataset, including images generated by different versions of Stable Diffusion, we analyze the impact of updates and modifications such as Gaussian blurring, prompt text changes, and Low-Rank Adaptation (LoRA) on detection accuracy. The findings highlight vulnerabilities in current detection methods and propose strategies to enhance the robustness and reliability of AI-image detection systems.
ProtScan: Modeling and Prediction of RNA-Protein Interactions
Authors: Gianluca Corrado, Michael Uhl, Rolf Backofen, Andrea Passerini, Fabrizio Costa
Date: 2024-12-30 00:00:00
Tasks: Prediction

CLIP-seq methods are valuable techniques to experimentally determine transcriptome-wide binding sites of RNA-binding proteins. Despite the constant improvement of such techniques (e.g. eCLIP), the results are affected by various types of noise and depend on experimental conditions such as cell line, tissue, gene expression levels, stress conditions etc., paving the way for the in silico modeling of RNA-protein interactions. Here we present ProtScan, a predictive tool based on consensus kernelized SGD regression. ProtScan denoises and generalizes the information contained in CLIP-seq experiments. It outperforms competitor state-of the-art methods and can be used to model RNA-protein interactions on a transcriptome-wide scale.
Mol2Lang-VLM: Vision- and Text-Guided Generative Pre-trained Language Models for Advancing Molecule Captioning through Multimodal Fusion
Authors: Duong Tran, Nhat Truong Pham, Nguyen Nguyen, and Balachandran Manavalan
Date: 2024-08-15 00:00:00
Proceeding: Association for Computational Linguistics 2024 8
Tasks: Caption Generation, Decoder, Molecule Captioning

This paper introduces Mol2Lang-VLM, an enhanced method for refining generative pre-trained language models for molecule captioning using multimodal features to achieve more accurate caption generation. Our approach leverages the encoder and decoder blocks of the Transformer-based architecture by introducing third sub-layers into both. Specifically, we insert sub-layers in the encoder to fuse features from SELFIES strings and molecular images, while the decoder fuses features from SMILES strings and their corresponding descriptions. Moreover, cross multi-head attention is employed instead of common multi-head attention to enable the decoder to attend to the encoder’s output, thereby integrating the encoded contextual information for better and more accurate caption generation. Performance evaluation on the CheBI-20 and L+M-24 benchmark datasets demonstrates Mol2Lang-VLM’s superiority, achieving higher accuracy and quality in caption generation compared to existing methods. Our code and pre-processed data are available at https://github.com/nhattruongpham/mol-lang-bridge/tree/mol2lang/.
Causal Hangover Effects
Authors: Andreas Santucci, Eric Lax
Date: 2024-12-30 00:00:00

It's not unreasonable to think that in-game sporting performance can be affected partly by what takes place off the court. We can't observe what happens between games directly. Instead, we proxy for the possibility of athletes partying by looking at play following games in party cities. We are interested to see if teams exhibit a decline in performance the day following a game in a city with active nightlife; we call this a "hangover effect". Part of the question is determining a reasonable way to measure levels of nightlife, and correspondingly which cities are notorious for it; we colloquially refer to such cities as "party cities". To carry out this study, we exploit data on bookmaker spreads: the expected score differential between two teams after conditioning on observable performance in past games and expectations about the upcoming game. We expect a team to meet the spread half the time, since this is one of the easiest ways for bookmakers to guarantee a profit. We construct a model which attempts to estimate the causal effect of visiting a "party city" on subsequent day performance as measured by the odds of beating the spread. In particular, we only consider the hangover effect on games played back-to-back within 24 hours of each other. To the extent that odds of beating the spread against next day opponent is uncorrelated with playing in a party city the day before, which should be the case under an efficient betting market, we have identification in our variable of interest. We find that visiting a city with active nightlife the day prior to a game does have a statistically significant negative effect on a team's likelihood of meeting bookmakers' expectations for both NBA and MLB.
Quantum Machine Learning: The Superhero That Classical Machine Learning Never Knew It Needed
Authors: Mahule Roy
Date: 2024-06-29 00:00:00
Proceeding: International Journal For Multidisciplinary Research 2024 6
Tasks: Quantum Machine Learning

Quantum Machine Learning (QML) is an emerging interdisciplinary field that combines the principles of quantum mechanics and machine learning to develop algorithms that can potentially outperform classical algorithms in certain tasks. QML leverages the unique properties of quantum systems, such as superposition and entanglement, to process information in ways that are not possible with classical computers. This paper provides a comprehensive overview of QML, including its principles, algorithms, and applications. We focus particularly on supervised learning methods, which involve training a quantum model on labeled data to make predictions on new, unseen data. We discuss the potential of QML to revolutionize various domains, such as finance, chemistry, and materials science, and highlight the challenges associated with the development and implementation of QML algorithms, including the need for more advanced quantum hardware and software. This paper aims to provide a clear understanding of the current state of QML research and its potential impact on future computational capabilities.
ALZHEIMER ANALYSIS USING MACHINE LEARNING AND DEEP LEARNING TECHNIQUES
Authors: Mahule Roy
Date: 2024-05-10 00:00:00
Proceeding: International Journal of Novel Trends and Innovation 2024 5
Tasks: Deep Learning, Diagnostic

This study explores the application of Machine Learning (ML) and Deep Learning (DL) architectures for the early detection of Alzheimer's disease. A comprehensive analysis is conducted using traditional ML models, including Support Vector Machine (SVM), Random Forest, and Logistic Regression, alongside advanced DL models employing Convolutional Neural Networks (CNN). Additionally, the performance of the widely used Inception method, a specialized CNN architecture, is investigated for comparative purposes. The study involves preprocessing diverse datasets comprising demographic information, genetic data, and neuroimaging scans. Performance evaluation metrics such as accuracy, precision, recall, and F1-score are employed, utilizing cross validation techniques to ensure robust results. The findings offer insights into the strengths and limitations of each model, shedding light on their applicability in Alzheimer's analysis. This research contributes to the growing body of knowledge on ML and DL applications in neurodegenerative disease detection, paving the way for enhanced diagnostic methodologies and intervention strategies.
Training of Neural Network on a Fashion MNIST Dataset Using OpenMP for Image Recognition
Authors: Mahule Roy
Date: 2024-05-16 00:00:00
Proceeding: International Journal of Current Science 2024 5

This paper explores the application of OpenMP for accelerating the training of neural networks in Fashion MNIST data recognition. OpenMP's parallelization capabilities will be harnessed to distribute computation across multiple threads, enhancing efficiency. Leveraging the MNIST dataset, we aim to assess the impact of OpenMP on recognition accuracy, training time, and resource utilization. The study will encompass variations in thread count, processor architectures, and dataset sizes. The anticipated outcomes include improved efficiency and reduced training times, providing valuable insights for optimizing OpenMP configurations in fashion recognition systems.
Effect of Screen Time on Glaucoma
Authors: Mahule Roy
Date: 2024-12-15 00:00:00
Proceeding: International Conference on Advances and Challenges in Medical Technology Translation 2024 12

Glaucoma, characterized by elevated intraocular pressure (IOP) and optic nerve damage, encompasses various types with distinct pathogenic mechanisms. Research has identified key factors influencing glaucoma, such as environmental influences, stress, and age-related factors. Our study focuses on the impact of stress on IOP levels in glaucoma patients and evaluates different machine learning (ML) models for enhanced glaucoma detection using OCT and Color Fundus images. Additionally, we explore the environmental implications of elevated IOP, emphasizing lifestyle interventions like yoga to potentially reduce IOP levels. As a practical application, we propose the development of a dedicated mobile app as a digital wellness program for glaucoma patients.
Comparative Study of Machine Unlearning Techniques for Computer Vision and NLP Models
Authors: Mahule Roy
Date: 2025-02-14 00:00:00
Proceeding: Mena ML Conference 2025 2
Tasks: Machine Unlearning

Machine unlearning is an emerging field in machine learning that focuses on efficiently removing the influence of specific data from a trained model. This capability is critical in scenarios requiring compliance with data privacy regulations or when erroneous data needs to be removed without retraining from scratch. In this study, we explore the importance of machine unlearning as a way to enhance privacy simultaneously not affecting the efficiency of machine learning models. Using the CIFAR- 10 and CIFAR-100 dataset, we implement various unlearning methods like retraining on the retained set, instruction fine tuning a LLM model to forget biased sentences and distillation techniques. These methods allowed our models to forget specific contexts while not comprising on the model accuracy. My implementations yielded promising results in terms of unlearning effectiveness and we have used various unlearning metrics to compare with our implementation and the baseline performance.The outcomes demonstrate the potential these methods have to balance between privacy and model accuracy effectively.
XAI-ResUNet: Analysing the Impact of Pre-training in ResUNet Architectures for Multiple Sclerosis Lesion Segmentation using EigenGradCAM
Authors: Vayangi Ganepola, Prateek Mathur, Oluwabukola Adegboro, Julia Dietlmeier, Aonghus Lawlor, Noel E. O’Connor, Claudia Mazo
Date: 2024-09-25 00:00:00
Proceeding: IMVIP 2024 9
Tasks: Explainable Artificial Intelligence (XAI), Lesion Detection, Lesion Segmentation, Medical Image Segmentation

Multiple Sclerosis (MS) is a chronic disease that causes lesions in the central nervous system. Diagnosing MS is a challenging process that strongly relies on Magnetic Resonance Imaging (MRI) and exhibits high and largely unexplained variability across patients. In this paper, we propose a novel XAI-ResUNet architecture by integrating ResNet-50 as an encoder in a U-Net-like architecture and demystifying the detection and segmentation process by incorporating EigenGradCAM into the encoder. We used the training and testing sets of the MSSEG-2016 dataset for our experiments. We compared three architectures with ResNet-50 pre-trained on ImageNet, RadImageNet, and without pre-training. Our preliminary results show that ResNet-50 pre-trained on ImageNet outperforms the others, achieving a DSC of 0.638. We also demonstrate how pre-training on large image datasets can affect MS lesion detection and segmentation. Our code is publicly available at https://github.com/VishmiVishara/XAI-ResUNet.
GASLITEing the Retrieval: Exploring Vulnerabilities in Dense Embedding-based Search
Authors: Matan Ben-Tov, Mahmood Sharif
Date: 2024-12-30 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation, Text Retrieval

Dense embedding-based text retrieval$\unicode{x2013}$retrieval of relevant passages from corpora via deep learning encodings$\unicode{x2013}$has emerged as a powerful method attaining state-of-the-art search results and popularizing the use of Retrieval Augmented Generation (RAG). Still, like other search methods, embedding-based retrieval may be susceptible to search-engine optimization (SEO) attacks, where adversaries promote malicious content by introducing adversarial passages to corpora. To faithfully assess and gain insights into the susceptibility of such systems to SEO, this work proposes the GASLITE attack, a mathematically principled gradient-based search method for generating adversarial passages without relying on the corpus content or modifying the model. Notably, GASLITE's passages (1) carry adversary-chosen information while (2) achieving high retrieval ranking for a selected query distribution when inserted to corpora. We use GASLITE to extensively evaluate retrievers' robustness, testing nine advanced models under varied threat models, while focusing on realistic adversaries targeting queries on a specific concept (e.g., a public figure). We found GASLITE consistently outperformed baselines by $\geq$140% success rate, in all settings. Particularly, adversaries using GASLITE require minimal effort to manipulate search results$\unicode{x2013}$by injecting a negligible amount of adversarial passages ($\leq$0.0001% of the corpus), they could make them visible in the top-10 results for 61-100% of unseen concept-specific queries against most evaluated models. Inspecting variance in retrievers' robustness, we identify key factors that may contribute to models' susceptibility to SEO, including specific properties in the embedding space's geometry.
Language-based Audio Retrieval with Co-Attention Networks
Authors: Haoran Sun, Zimu Wang, Qiuyi Chen, Jianjun Chen, Jia Wang, Haiyang Zhang
Date: 2024-12-30 00:00:00
Tasks: AudioCaps, Learning Semantic Representations, Natural Language Queries, Retrieval

In recent years, user-generated audio content has proliferated across various media platforms, creating a growing need for efficient retrieval methods that allow users to search for audio clips using natural language queries. This task, known as language-based audio retrieval, presents significant challenges due to the complexity of learning semantic representations from heterogeneous data across both text and audio modalities. In this work, we introduce a novel framework for the language-based audio retrieval task that leverages co-attention mechanismto jointly learn meaningful representations from both modalities. To enhance the model's ability to capture fine-grained cross-modal interactions, we propose a cascaded co-attention architecture, where co-attention modules are stacked or iterated to progressively refine the semantic alignment between text and audio. Experiments conducted on two public datasets show that the proposed method can achieve better performance than the state-of-the-art method. Specifically, our best performed co-attention model achieves a 16.6% improvement in mean Average Precision on Clotho dataset, and a 15.1% improvement on AudioCaps.
GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors
Authors: Chengming Zhang, Xinheng Ding, Baixi Sun, Xiaodong Yu, Weijian Zheng, Zhen Xie, Dingwen Tao
Date: 2024-12-19 00:00:00
Tasks: MME

Heterogeneous hardware like Gaudi processor has been developed to enhance computations, especially matrix operations for Transformer-based large language models (LLMs) for generative AI tasks. However, our analysis indicates that Transformers are not fully optimized on such emerging hardware, primarily due to inadequate optimizations in non-matrix computational kernels like Softmax and in heterogeneous resource utilization, particularly when processing long sequences. To address these issues, we propose an integrated approach (called GFormer) that merges sparse and linear attention mechanisms. GFormer aims to maximize the computational capabilities of the Gaudi processor's Matrix Multiplication Engine (MME) and Tensor Processing Cores (TPC) without compromising model quality. GFormer includes a windowed self-attention kernel and an efficient outer product kernel for causal linear attention, aiming to optimize LLM inference on Gaudi processors. Evaluation shows that GFormer significantly improves efficiency and model performance across various tasks on the Gaudi processor and outperforms state-of-the-art GPUs.