pwc-archive/papers-with-abstracts huggingface dataset:
R3CD: Scene Graph to Image Generation with Relation-aware Compositional Contrastive Control Diffusion
Authors: Jinxiu Liu, Qi Liu
Date: 2024-03-24 00:00:00
Proceeding: Proceedings of the AAAI Conference on Artificial Intelligence 2024 3
Tasks: Denoising, Image Generation, Relation, Scene Graph Generation, Text-to-Image Generation

Image generation tasks have achieved remarkable performance using large-scale diffusion models. However, these models are limited to capturing the abstract relations (viz., interactions excluding positional relations) among multiple entities of complex scene graphs. Two main problems exist: 1) fail to depict more concise and accurate interactions via abstract relations; 2) fail to generate complete entities. To address that, we propose a novel Relation-aware Compositional Contrastive Control Diffusion method, dubbed as R3CD, that leverages large-scale diffusion models to learn abstract interactions from scene graphs. Herein, a scene graph transformer based on node and edge encoding is first designed to perceive both local and global information from input scene graphs, whose embeddings are initialized by a T5 model. Then a joint contrastive loss based on attention maps and denoising steps is developed to control the diffusion model to understand and further generate images, whose spatial structures and interaction features are consistent with a priori relation. Extensive experiments are conducted on two datasets: Visual Genome and COCO-Stuff, and demonstrate that the proposal outperforms existing models both in quantitative and qualitative metrics to generate more realistic and diverse images according to different scene graph specifications.
Optimum Codesign for Image Denoising Between Type-2 Fuzzy Identifier and Matrix Completion Denoiser
Authors: Qi Liu, Xiaopeng Li, Jicheng Yang
Date: 2020-10-13 00:00:00
Proceeding: IEEE Transactions on Fuzzy Systems 2020 10
Tasks: Denoising, Image Denoising, Low-Rank Matrix Completion, Matrix Completion, Matrix Factorization / Decomposition

With the wide deployment of digital image capturing equipment, the need of denoising to produce a crystal clear image from noisy capture environment has become indispensable. In this article, a novel type-2 fuzzy-based filter is proposed for denoising images corrupted by impulse noise, especially for the high density of salt-and-pepper noise. It operates two stages, namely, type-2 fuzzy identifier and matrix completion denoiser. In the proposed method, the type-2 fuzzy identifier is first employed to identify and trim the entries contaminated by impulse noise in the data matrix from fuzzy system. Then, the trimmed data matrix is utilized to retrieve the noiseless data matrix with the matrix completion technology. Herein, a novel matrix completion technique is developed without a priori rank information compared to its counterparts. Simulation results are presented, which vividly show the denoised images obtained by the proposed method can achieve crystal clear image with strong structural integrity, and are showing good performance in terms of peak signal-to-noise ratio.
From Simulated to Visual Data: A Robust Low-Rank Tensor Completion Approach using lp-Regression for Outlier Resistance
Authors: Qi Liu, Xiaopeng Li, Hui Cao, Yuntao Wu
Date: 2021-09-20 00:00:00
Proceeding: IEEE Transactions on Circuits and Systems for Video Technology 2021 9
Tasks: Denoising, Image Denoising, Image Inpainting, regression, Tensor Decomposition

Low-rank tensor completion (LRTC) that aims to restore the latent clean data from an incomplete and/or degraded observation, shows promising results in ubiquitous tensorial data completion applications. Most tensor completion approaches are vulnerable to outliers since their derivations are based on ℓ2 -space to be robust against Gaussian noise. In this work, to tackle this issue, ℓp -regression (0<p<2) is employed to achieve outlier resistance, where a factored form of tensor train (TT)-format representation is regularized by the low-TT-rank prior to exploit the inter-fibers correlation. On the basis of that, an effective iterative ℓp -regression TT completion method (referred to ℓp -TTC) is proposed, with the advantage of not requiring the hard-to-determine user-defined weights in TT rank model. Extensive experiment results are presented to demonstrate the outlier resistance of the proposed ℓp -TTC, and showing the effective and superior performance in both bistatic MIMO radar localization and color image inpainting and denoising, compared with state-of-the-art tensor completion approaches.
Chronic Obstructive Pulmonary Disease Severity Classification using lung sound
Authors: Khanaghavalle G R, Rahul G, Senajith S R, Vishnuvasan T S, Keerthana S
Date: 2024-06-06 00:00:00
Proceeding: 10th International Conference on Communication and Signal Processing (ICCSP) 2024 6
Tasks: Classification, Diagnostic, Medical Image Analysis

A global health concern, chronic obstructive pulmonary disease (COPD) demands early detection and intervention for effective treatment. Leveraging the extensive 12-channel lung sound dataset "RespiratoryDatabase@TR", our study establishes a robust multiclass COPD severity diagnostic system. We employ a rigorous feature extraction procedure, including spectrogram, Mel spectrogram, and chromogram analysis, alongside specific data preprocessing and augmentation methods. The RESNET50 model is chosen for training, ensuring precise classification across COPD severity levels. Our findings underscore the significance of sound-based prognoses, particularly in early COPD diagnosis. With an estimated 251 million people globally affected by COPD, innovative solutions like ours are crucial. The amalgamation of extensive datasets and advanced machine learning techniques holds the promise of transforming COPD diagnosis and treatment on a global scale, improving the lives of millions affected by this illness.
Efficient Low-Rank Matrix Factorization based on l1,ε-norm for Online Background Subtraction
Authors: Qi Liu, Xiaopeng Li
Date: 2021-11-19 00:00:00
Proceeding: IEEE Transactions on Circuits and Systems for Video Technology 2021 11
Tasks: Low-Rank Matrix Completion, Matrix Completion, Matrix Factorization / Decomposition, Video Background Subtraction

Background subtraction refers to extracting the foreground from an observed video, and is the fundamental problem of various applications. There are two kinds of popular methods to deal with background separation, namely, robust principal component analysis (RPCA) and low-rank matrix factorization (LRMF). Nevertheless, the drawback of RPCA requires tuning penalty parameter to attain an ideal result. Compared with RPCA, the ℓ1 -norm based LRMF does not involve extra parameters tuning, but it is challenging to optimize the ℓ1 -norm based minimization because of the nonsmooth ℓ1 -norm. In addition, it becomes time-consuming to find the optimal solution. In this work, we propose to employ smooth ℓ1,ϵ -norm, an approximation of ℓ1 -norm, to tackle background subtraction. Thus, the proposed model inherits the superiority of LRMF and even becomes tractable. Then the resultant optimization problem is solved by alternating minimization and gradient descent where the step-size of the gradient descent is adaptively updated via backtracking line searching approach. The proposed method is proved to be locally convergent. Experimental results on synthetic and real-world data demonstrate that our method outperforms the state-of-the-art algorithms in terms of reconstruction loss, computational speed and hardware performance.
A Parameter Adaptive Trajectory Tracking and Motion Control Framework for Autonomous Vehicle
Authors: Jiarui Song, Yingbo Sun, Qing Dong, Xuewu Ji
Date: 2024-11-25 00:00:00
Tasks: Autonomous Vehicles, Bayesian Optimization, GPR

This paper studies the trajectory tracking and motion control problems for autonomous vehicles (AVs). A parameter adaptive control framework for AVs is proposed to enhance tracking accuracy and yaw stability. While establishing linear quadratic regulator (LQR) and three robust controllers, the control framework addresses trajectory tracking and motion control in a modular fashion, without introducing complexity into each controller. The robust performance has been guaranteed in three robust controllers by considering the parameter uncertainties, mismatch of unmodeled subsystem as well as external disturbance, comprehensively. Also, the dynamic characteristics of uncertain parameters are identified by Recursive Least Squares (RLS) algorithm, while the boundaries of three robust factors are determined through combining Gaussian Process Regression (GPR) and Bayesian optimization machine learning methods, reducing the conservatism of the controller. Sufficient conditions for closed-loop stability under the diverse robust factors are provided by the Lyapunov method analytically. The simulation results on MATLAB/Simulink and Carsim joint platform demonstrate that the proposed methodology considerably improves tracking accuracy, driving stability, and robust performance, guaranteeing the feasibility and capability of driving in extreme scenarios.
SafeLight: Enhancing Security in Optical Convolutional Neural Network Accelerators
Authors: Salma Afifi, Ishan Thakkar, Sudeep Pasricha
Date: 2024-11-22 00:00:00

The rapid proliferation of deep learning has revolutionized computing hardware, driving innovations to improve computationally expensive multiply-and-accumulate operations in deep neural networks. Among these innovations are integrated silicon-photonic systems that have emerged as energy-efficient platforms capable of achieving light speed computation and communication, positioning optical neural network (ONN) platforms as a transformative technology for accelerating deep learning models such as convolutional neural networks (CNNs). However, the increasing complexity of optical hardware introduces new vulnerabilities, notably the risk of hardware trojan (HT) attacks. Despite the growing interest in ONN platforms, little attention has been given to how HT-induced threats can compromise performance and security. This paper presents an in-depth analysis of the impact of such attacks on the performance of CNN models accelerated by ONN accelerators. Specifically, we show how HTs can compromise microring resonators (MRs) in a state-of-the-art non-coherent ONN accelerator and reduce classification accuracy across CNN models by up to 7.49% to 80.46% by just targeting 10% of MRs. We then propose techniques to enhance ONN accelerator robustness against these attacks and show how the best techniques can effectively recover the accuracy drops.
When IoT Meet LLMs: Applications and Challenges
Authors: Ibrahim Kok, Orhan Demirci, Suat Ozdemir
Date: 2024-11-20 00:00:00
Tasks: Cloud Computing, Decision Making

Recent advances in Large Language Models (LLMs) have positively and efficiently transformed workflows in many domains. One such domain with significant potential for LLM integration is the Internet of Things (IoT), where this integration brings new opportunities for improved decision making and system interaction. In this paper, we explore the various roles of LLMs in IoT, with a focus on their reasoning capabilities. We show how LLM-IoT integration can facilitate advanced decision making and contextual understanding in a variety of IoT scenarios. Furthermore, we explore the integration of LLMs with edge, fog, and cloud computing paradigms, and show how this synergy can optimize resource utilization, enhance real-time processing, and provide scalable solutions for complex IoT applications. To the best of our knowledge, this is the first comprehensive study covering IoT-LLM integration between edge, fog, and cloud systems. Additionally, we propose a novel system model for industrial IoT applications that leverages LLM-based collective intelligence to enable predictive maintenance and condition monitoring. Finally, we highlight key challenges and open issues that provide insights for future research in the field of LLM-IoT integration.
MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices
Authors: Mohammadali Shakerdargah, Shan Lu, Chao GAO, Di Niu
Date: 2024-11-20 00:00:00
Tasks: Edge-computing, Scheduling

The advent of foundation models have revolutionized various fields, enabling unprecedented task accuracy and flexibility in computational linguistics, computer vision and other domains. Attention mechanism has become an essential component of foundation models, due to their superb capability of capturing correlations in a sequence. However, attention results in quadratic complexity in memory and compute as the context length grows. Although many fusion-based exact attention acceleration algorithms have been developed for datacenter-grade GPUs and accelerators leveraging multi-core parallelism and data locality, yet it remains a significant challenge to accelerate attention on resource-constrained edge neural accelerators with limited compute units and stringent on-chip caches. In this paper, we propose a scheme for exact attention inference acceleration on memory-constrained edge accelerators, by parallelizing the utilization of heterogeneous compute units, i.e., vector processing units and matrix processing units. Our method involves scheduling workloads onto these different compute units in a multi-tiered tiling scheme to process tiled vector workloads and matrix workloads in attention as two streams, respecting the workload dependencies. We search for tiling factors to maximize the parallelization of both compute units while considering I/O overhead, and propose a proactive cache overwrite strategy to avoid undesirable cache spills in reality. Extensive results based on open-sourced simulation frameworks show up to 2.75x speedup and 54% reduction in energy consumption as compared to the state-of-the-art attention fusion method (FLAT) in the edge computing scenario. Further experiments on a real-world edge neural processing unit demonstrate speedup of up to 1.76x for attention as compared to FLAT, without affecting model output accuracy.
Llama Guard 3-1B-INT4: Compact and Efficient Safeguard for Human-AI Conversations
Authors: Igor Fedorov, Kate Plawiak, Lemeng Wu, Tarek Elgamal, Naveen Suda, Eric Smith, Hongyuan Zhan, Jianfeng Chi, Yuriy Hulovatyy, Kimish Patel, Zechun Liu, Changsheng Zhao, Yangyang Shi, Tijmen Blankevoort, Mahesh Pasupuleti, Bilge Soran, Zacharie Delpierre Coudert, Rachad Alao, Raghuraman Krishnamoorthi, Vikas Chandra
Date: 2024-11-18 00:00:00
Tasks: CPU

This paper presents Llama Guard 3-1B-INT4, a compact and efficient Llama Guard model, which has been open-sourced to the community during Meta Connect 2024. We demonstrate that Llama Guard 3-1B-INT4 can be deployed on resource-constrained devices, achieving a throughput of at least 30 tokens per second and a time-to-first-token of 2.5 seconds or less on a commodity Android mobile CPU. Notably, our experiments show that Llama Guard 3-1B-INT4 attains comparable or superior safety moderation scores to its larger counterpart, Llama Guard 3-1B, despite being approximately 7 times smaller in size (440MB).
Generative AI on the Edge: Architecture and Performance Evaluation
Authors: Zeinab Nezami, Maryam Hafeez, Karim Djemame, Syed Ali Raza Zaidi
Date: 2024-11-18 00:00:00
Tasks: CPU, Raspberry Pi 5

6G's AI native vision of embedding advance intelligence in the network while bringing it closer to the user requires a systematic evaluation of Generative AI (GenAI) models on edge devices. Rapidly emerging solutions based on Open RAN (ORAN) and Network-in-a-Box strongly advocate the use of low-cost, off-the-shelf components for simpler and efficient deployment, e.g., in provisioning rural connectivity. In this context, conceptual architecture, hardware testbeds and precise performance quantification of Large Language Models (LLMs) on off-the-shelf edge devices remains largely unexplored. This research investigates computationally demanding LLM inference on a single commodity Raspberry Pi serving as an edge testbed for ORAN. We investigate various LLMs, including small, medium and large models, on a Raspberry Pi 5 Cluster using a lightweight Kubernetes distribution (K3s) with modular prompting implementation. We study its feasibility and limitations by analyzing throughput, latency, accuracy and efficiency. Our findings indicate that CPU-only deployment of lightweight models, such as Yi, Phi, and Llama3, can effectively support edge applications, achieving a generation throughput of 5 to 12 tokens per second with less than 50\% CPU and RAM usage. We conclude that GenAI on the edge offers localized inference in remote or bandwidth-constrained environments in 6G networks without reliance on cloud infrastructure.
Towards Efficient Neurally-Guided Program Induction for ARC-AGI
Authors: Simon Ouellette
Date: 2024-11-13 00:00:00
Tasks: ARC, Program induction

ARC-AGI is an open-world problem domain in which the ability to generalize out-of-distribution is a crucial quality. Under the program induction paradigm, we present a series of experiments that reveal the efficiency and generalization characteristics of various neurally-guided program induction approaches. The three paradigms we consider are Learning the grid space, Learning the program space, and Learning the transform space. We implement and experiment thoroughly on the first two, and retain the second one for ARC-AGI submission. After identifying the strengths and weaknesses of both of these approaches, we suggest the third as a potential solution, and run preliminary experiments.
EEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification Method
Authors: Wei Peng, Kang Liu, Jiaxi Shi, Jianchen Hu
Date: 2024-11-12 00:00:00
Proceeding: journal 2024 11
Tasks: Brain Computer Interface, EEG, Motor Imagery

The electroencephalography (EEG)-based motor imagery (MI) classification is a critical and challenging task in brain-computer interface (BCI) technology, which plays a significant role in assisting patients with functional impairments to regain mobility. We present a novel multi-scale atrous convolutional neural network (CNN) model called EEG-dilated convolution network (DCNet) to enhance the accuracy and efficiency of the EEG-based MI classification tasks. We incorporate the $1\times1$ convolutional layer and utilize the multi-branch parallel atrous convolutional architecture in EEG-DCNet to capture the highly nonlinear characteristics and multi-scale features of the EEG signals. Moreover, we utilize the sliding window to enhance the temporal consistency and utilize the attension mechanism to improve the accuracy of recognizing user intentions. The experimental results (via the BCI-IV-2a ,BCI-IV-2b and the High-Gamma datasets) show that EEG-DCNet outperforms existing state-of-the-art (SOTA) approaches in terms of classification accuracy and Kappa scores. Furthermore, since EEG-DCNet requires less number of parameters, the training efficiency and memory consumption are also improved. The experiment code is open-sourced at \href{https://github.com/Kanyooo/EEG-DCNet}{here}.
A Composite Fault Diagnosis Model for NPPs Based on Bayesian-EfficientNet Module
Authors: Siwei Li, Jiangwen Chen, Hua Lin, Wei Wang
Date: 2024-11-13 00:00:00
Tasks: Deep Learning, Fault Diagnosis, Transfer Learning

This article focuses on the faults of important mechanical components such as pumps, valves, and pipelines in the reactor coolant system, main steam system, condensate system, and main feedwater system of nuclear power plants (NPPs). It proposes a composite multi-fault diagnosis model based on Bayesian algorithm and EfficientNet large model using data-driven deep learning fault diagnosis technology. The aim is to evaluate the effectiveness of automatic deep learning-based large model technology through transfer learning in nuclear power plant scenarios.
AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings
Authors: Yue Wang, Xu Cao, Yaojun Hu, Haochao Ying, Hongxia Xu, Ruijia Wu, James Matthew Rehg, Jimeng Sun, Jian Wu, Jintai Chen
Date: 2024-11-17 00:00:00
Tasks: Anomaly Detection, Arrhythmia Detection, Rhythm

Electrocardiogram (ECG), a non-invasive and affordable tool for cardiac monitoring, is highly sensitive in detecting acute heart attacks. However, due to the lengthy nature of ECG recordings, numerous machine learning methods have been developed for automated heart disease detection to reduce human workload. Despite these efforts, performance remains suboptimal. A key obstacle is the inherent complexity of ECG data, which includes heterogeneity (e.g., varying sampling rates), high levels of noise, demographic-related pattern shifts, and intricate rhythm-event associations. To overcome these challenges, this paper introduces AnyECG, a foundational model designed to extract robust representations from any real-world ECG data. Specifically, a tailored ECG Tokenizer encodes each fixed-duration ECG fragment into a token and, guided by proxy tasks, converts noisy, continuous ECG features into discrete, compact, and clinically meaningful local rhythm codes. These codes encapsulate basic morphological, frequency, and demographic information (e.g., sex), effectively mitigating signal noise. We further pre-train the AnyECG to learn rhythmic pattern associations across ECG tokens, enabling the capture of cardiac event semantics. By being jointly pre-trained on diverse ECG data sources, AnyECG is capable of generalizing across a wide range of downstream tasks where ECG signals are recorded from various devices and scenarios. The experimental results show that AnyECG achieves an average performance improvement of 6% across four critical tasks-anomaly detection, arrhythmia classification, corrupted lead generation, and ultra-long ECG recognition. AnyECG learns common ECG rhythm from data and significantly outperforms state-of-the-art methods in each of these tasks.
Hybrid Quantum Deep Learning Model for Emotion Detection using raw EEG Signal Analysis
Authors: Ali Asgar Chandanwala, Srutakirti Bhowmik, Parna Chaudhury, Sheena Christabel Pravin
Date: 2024-11-19 00:00:00
Tasks: Deep Learning, EEG, Emotion Recognition

Applications in behavioural research, human-computer interaction, and mental health depend on the ability to recognize emotions. In order to improve the accuracy of emotion recognition using electroencephalography (EEG) data, this work presents a hybrid quantum deep learning technique. Conventional EEG-based emotion recognition techniques are limited by noise and high-dimensional data complexity, which make feature extraction difficult. To tackle these issues, our method combines traditional deep learning classification with quantum-enhanced feature extraction. To identify important brain wave patterns, Bandpass filtering and Welch method are used as preprocessing techniques on EEG data. Intricate inter-band interactions that are essential for determining emotional states are captured by mapping frequency band power attributes (delta, theta, alpha, and beta) to quantum representations. Entanglement and rotation gates are used in a hybrid quantum circuit to maximize the model's sensitivity to EEG patterns associated with different emotions. Promising results from evaluation on a test dataset indicate the model's potential for accurate emotion recognition. The model will be extended for real-time applications and multi-class categorization in future study, which could improve EEG-based mental health screening instruments. This method offers a promising tool for applications in adaptive human-computer systems and mental health monitoring by showcasing the possibilities of fusing traditional deep learning with quantum processing for reliable, scalable emotion recognition.