pwc-archive/papers-with-abstracts huggingface dataset:
WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks
Authors: Jingwen Tong, Jiawei Shao, Qiong Wu, Wei Guo, Zijian Li, Zehong Lin, Jun Zhang
Date: 2024-09-12 00:00:00
Tasks: Decision Making, Language Modeling, Language Modelling, Large Language Model, Management

Wireless networks are increasingly facing challenges due to their expanding scale and complexity. These challenges underscore the need for advanced AI-driven strategies, particularly in the upcoming 6G networks. In this article, we introduce WirelessAgent, a novel approach leveraging large language models (LLMs) to develop AI agents capable of managing complex tasks in wireless networks. It can effectively improve network performance through advanced reasoning, multimodal data processing, and autonomous decision making. Thereafter, we demonstrate the practical applicability and benefits of WirelessAgent for network slicing management. The experimental results show that WirelessAgent is capable of accurately understanding user intent, effectively allocating slice resources, and consistently maintaining optimal performance.
Rapid Parameter Estimation for Extreme Mass Ratio Inspirals Using Machine Learning
Authors: Bo Liang, Hong Guo, Tianyu Zhao, He Wang, Herik Evangelinelis, Yuxiang Xu, Chang Liu, Manjia Liang, Xiaotong Wei, Yong Yuan, Peng Xu, Minghui Du, Wei-Liang Qian, Ziren Luo
Date: 2024-09-12 00:00:00
Tasks: Astronomy, Computational Efficiency, parameter estimation

Extreme-mass-ratio inspiral (EMRI) signals pose significant challenges in gravitational wave (GW) astronomy owing to their low-frequency nature and highly complex waveforms, which occupy a high-dimensional parameter space with numerous variables. Given their extended inspiral timescales and low signal-to-noise ratios, EMRI signals warrant prolonged observation periods. Parameter estimation becomes particularly challenging due to non-local parameter degeneracies, arising from multiple local maxima, as well as flat regions and ridges inherent in the likelihood function. These factors lead to exceptionally high time complexity for parameter analysis while employing traditional matched filtering and random sampling methods. To address these challenges, the present study applies machine learning to Bayesian posterior estimation of EMRI signals, leveraging the recently developed flow matching technique based on ODE neural networks. Our approach demonstrates computational efficiency several orders of magnitude faster than the traditional Markov Chain Monte Carlo (MCMC) methods, while preserving the unbiasedness of parameter estimation. We show that machine learning technology has the potential to efficiently handle the vast parameter space, involving up to seventeen parameters, associated with EMRI signals. Furthermore, to our knowledge, this is the first instance of applying machine learning, specifically the Continuous Normalizing Flows (CNFs), to EMRI signal analysis. Our findings highlight the promising potential of machine learning in EMRI waveform analysis, offering new perspectives for the advancement of space-based GW detection and GW astronomy.
Reinforcement Learning Discovers Efficient Decentralized Graph Path Search Strategies
Authors: Alexei Pisacane, Victor-Alexandru Darvariu, Mirco Musolesi
Date: 2024-09-12 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sociology

Graph path search is a classic computer science problem that has been recently approached with Reinforcement Learning (RL) due to its potential to outperform prior methods. Existing RL techniques typically assume a global view of the network, which is not suitable for large-scale, dynamic, and privacy-sensitive settings. An area of particular interest is search in social networks due to its numerous applications. Inspired by seminal work in experimental sociology, which showed that decentralized yet efficient search is possible in social networks, we frame the problem as a collaborative task between multiple agents equipped with a limited local view of the network. We propose a multi-agent approach for graph path search that successfully leverages both homophily and structural heterogeneity. Our experiments, carried out over synthetic and real-world social networks, demonstrate that our model significantly outperforms learned and heuristic baselines. Furthermore, our results show that meaningful embeddings for graph navigation can be constructed using reward-driven learning.
A convolutional neural network approach to deblending seismic data
Authors: Jing Sun, Sigmund Slang, Thomas Elboth, Thomas Larsen Greiner, Steven McDonald, Leiv-J Gelius
Date: 2024-09-12 00:00:00

For economic and efficiency reasons, blended acquisition of seismic data is becoming more and more commonplace. Seismic deblending methods are always computationally demanding and normally consist of multiple processing steps. Besides, the parameter setting is not always trivial. Machine learning-based processing has the potential to significantly reduce processing time and to change the way seismic deblending is carried out. We present a data-driven deep learning-based method for fast and efficient seismic deblending. The blended data are sorted from the common source to the common channel domain to transform the character of the blending noise from coherent events to incoherent distributions. A convolutional neural network (CNN) is designed according to the special character of seismic data, and performs deblending with comparable results to those obtained with conventional industry deblending algorithms. To ensure authenticity, the blending was done numerically and only field seismic data were employed, including more than 20000 training examples. After training and validation of the network, seismic deblending can be performed in near real time. Experiments also show that the initial signal to noise ratio (SNR) is the major factor controlling the quality of the final deblended result. The network is also demonstrated to be robust and adaptive by using the trained model to firstly deblend a new data set from a different geological area with a slightly different delay time setting, and secondly deblend shots with blending noise in the top part of the data.
A framework for measuring the training efficiency of a neural architecture
Authors: Eduardo Cueto-Mendoza, John D. Kelleher
Date: 2024-09-12 00:00:00

Measuring Efficiency in neural network system development is an open research problem. This paper presents an experimental framework to measure the training efficiency of a neural architecture. To demonstrate our approach, we analyze the training efficiency of Convolutional Neural Networks and Bayesian equivalents on the MNIST and CIFAR-10 tasks. Our results show that training efficiency decays as training progresses and varies across different stopping criteria for a given neural model and learning task. We also find a non-linear relationship between training stopping criteria, training Efficiency, model size, and training Efficiency. Furthermore, we illustrate the potential confounding effects of overtraining on measuring the training efficiency of a neural architecture. Regarding relative training efficiency across different architectures, our results indicate that CNNs are more efficient than BCNNs on both datasets. More generally, as a learning task becomes more complex, the relative difference in training efficiency between different architectures becomes more pronounced.
Tidal MerzA: Combining affective modelling and autonomous code generation through Reinforcement Learning
Authors: Elizabeth Wilson, György Fazekas, Geraint Wiggins
Date: 2024-09-12 00:00:00
Tasks: Code Generation, Music Generation, reinforcement-learning, Reinforcement Learning

This paper presents Tidal-MerzA, a novel system designed for collaborative performances between humans and a machine agent in the context of live coding, specifically focusing on the generation of musical patterns. Tidal-MerzA fuses two foundational models: ALCAA (Affective Live Coding Autonomous Agent) and Tidal Fuzz, a computational framework. By integrating affective modelling with computational generation, this system leverages reinforcement learning techniques to dynamically adapt music composition parameters within the TidalCycles framework, ensuring both affective qualities to the patterns and syntactical correctness. The development of Tidal-MerzA introduces two distinct agents: one focusing on the generation of mini-notation strings for musical expression, and another on the alignment of music with targeted affective states through reinforcement learning. This approach enhances the adaptability and creative potential of live coding practices and allows exploration of human-machine creative interactions. Tidal-MerzA advances the field of computational music generation, presenting a novel methodology for incorporating artificial intelligence into artistic practices.
InterACT: Inter-dependency Aware Action Chunking with Hierarchical Attention Transformers for Bimanual Manipulation
Authors: Andrew Lee, Ian Chuang, Ling-Yuan Chen, Iman Soltani
Date: 2024-09-12 00:00:00
Tasks: Chunking, Decoder, Imitation Learning

Bimanual manipulation presents unique challenges compared to unimanual tasks due to the complexity of coordinating two robotic arms. In this paper, we introduce InterACT: Inter-dependency aware Action Chunking with Hierarchical Attention Transformers, a novel imitation learning framework designed specifically for bimanual manipulation. InterACT leverages hierarchical attention mechanisms to effectively capture inter-dependencies between dual-arm joint states and visual inputs. The framework comprises a Hierarchical Attention Encoder, which processes multi-modal inputs through segment-wise and cross-segment attention mechanisms, and a Multi-arm Decoder that generates each arm's action predictions in parallel, while sharing information between the arms through synchronization blocks by providing the other arm's intermediate output as context. Our experiments, conducted on various simulated and real-world bimanual manipulation tasks, demonstrate that InterACT outperforms existing methods. Detailed ablation studies further validate the significance of key components, including the impact of CLS tokens, cross-segment encoders, and synchronization blocks on task performance. We provide supplementary materials and videos on our project page.
UGAD: Universal Generative AI Detector utilizing Frequency Fingerprints
Authors: Inzamamul Alam, Muhammad Shahid Muneer, Simon S. Woo
Date: 2024-09-12 00:00:00
Tasks: Key Detection

In the wake of a fabricated explosion image at the Pentagon, an ability to discern real images from fake counterparts has never been more critical. Our study introduces a novel multi-modal approach to detect AI-generated images amidst the proliferation of new generation methods such as Diffusion models. Our method, UGAD, encompasses three key detection steps: First, we transform the RGB images into YCbCr channels and apply an Integral Radial Operation to emphasize salient radial features. Secondly, the Spatial Fourier Extraction operation is used for a spatial shift, utilizing a pre-trained deep learning network for optimal feature extraction. Finally, the deep neural network classification stage processes the data through dense layers using softmax for classification. Our approach significantly enhances the accuracy of differentiating between real and AI-generated images, as evidenced by a 12.64% increase in accuracy and 28.43% increase in AUC compared to existing state-of-the-art methods.
Enhancing Cross-Market Recommendation System with Graph Isomorphism Networks: A Novel Approach to Personalized User Experience
Authors: Sümeyye Öztürk, Ahmed Burak Ercan, resul tugay, Şule Gündüz Öğüdücü
Date: 2024-09-12 00:00:00
Tasks: Recommendation Systems, Specificity

In today's world of globalized commerce, cross-market recommendation systems (CMRs) are crucial for providing personalized user experiences across diverse market segments. However, traditional recommendation algorithms have difficulties dealing with market specificity and data sparsity, especially in new or emerging markets. In this paper, we propose the CrossGR model, which utilizes Graph Isomorphism Networks (GINs) to improve CMR systems. It outperforms existing benchmarks in NDCG@10 and HR@10 metrics, demonstrating its adaptability and accuracy in handling diverse market segments. The CrossGR model is adaptable and accurate, making it well-suited for handling the complexities of cross-market recommendation tasks. Its robustness is demonstrated by consistent performance across different evaluation timeframes, indicating its potential to cater to evolving market trends and user preferences. Our findings suggest that GINs represent a promising direction for CMRs, paving the way for more sophisticated, personalized, and context-aware recommendation systems in the dynamic landscape of global e-commerce.
Deep Multimodal Learning with Missing Modality: A Survey
Authors: Renjie Wu, Hu Wang, Hsiang-Ting Chen, Gustavo Carneiro
Date: 2024-09-12 00:00:00
Tasks: Survey

During multimodal model training and testing, certain data modalities may be absent due to sensor limitations, cost constraints, privacy concerns, or data loss, negatively affecting performance. Multimodal learning techniques designed to handle missing modalities can mitigate this by ensuring model robustness even when some modalities are unavailable. This survey reviews recent progress in Multimodal Learning with Missing Modality (MLMM), focusing on deep learning methods. It provides the first comprehensive survey that covers the motivation and distinctions between MLMM and standard multimodal learning setups, followed by a detailed analysis of current methods, applications, and datasets, concluding with challenges and future directions.
Over-the-Air Federated Learning via Weighted Aggregation
Authors: Seyed Mohammad Azimi-Abarghouyi, Leandros Tassiulas
Date: 2024-09-12 00:00:00
Tasks: Federated Learning

This paper introduces a new federated learning scheme that leverages over-the-air computation. A novel feature of this scheme is the proposal to employ adaptive weights during aggregation, a facet treated as predefined in other over-the-air schemes. This can mitigate the impact of wireless channel conditions on learning performance, without needing channel state information at transmitter side (CSIT). We provide a mathematical methodology to derive the convergence bound for the proposed scheme in the context of computational heterogeneity and general loss functions, supplemented with design insights. Accordingly, we propose aggregation cost metrics and efficient algorithms to find optimized weights for the aggregation. Finally, through numerical experiments, we validate the effectiveness of the proposed scheme. Even with the challenges posed by channel conditions and device heterogeneity, the proposed scheme surpasses other over-the-air strategies by an accuracy improvement of 15% over the scheme using CSIT and 30% compared to the one without CSIT.
In-Situ Fine-Tuning of Wildlife Models in IoT-Enabled Camera Traps for Efficient Adaptation
Authors: Mohammad Mehdi Rastikerdar, Jin Huang, Hui Guan, Deepak Ganesan
Date: 2024-09-12 00:00:00
Tasks: Computational Efficiency, Domain Generalization, Robust classification

Resource-constrained IoT devices increasingly rely on deep learning models for inference tasks in remote environments. However, these models experience significant accuracy drops due to domain shifts when encountering variations in lighting, weather, and seasonal conditions. While cloud-based retraining can address this issue, many IoT deployments operate with limited connectivity and energy constraints, making traditional fine-tuning approaches impractical. We explore this challenge through the lens of wildlife ecology, where camera traps must maintain accurate species classification across changing seasons, weather, and habitats without reliable connectivity. We introduce WildFit, an autonomous in-situ adaptation framework that leverages the key insight that background scenes change more frequently than the visual characteristics of monitored species. WildFit combines background-aware synthesis to generate training samples on-device with drift-aware fine-tuning that triggers model updates only when necessary to conserve resources. Through extensive evaluation on multiple camera trap deployments, we demonstrate that WildFit significantly improves accuracy while greatly reducing adaptation overhead compared to traditional approaches.
Lagrange Duality and Compound Multi-Attention Transformer for Semi-Supervised Medical Image Segmentation
Authors: Fuchen Zheng, Quanjun Li, Weixuan Li, Xuhang Chen, Yihang Dong, Guoheng Huang, Chi-Man Pun, Shoujun Zhou
Date: 2024-09-12 00:00:00
Tasks: Image Segmentation, Medical Diagnosis, Medical Image Segmentation, Segmentation, Semantic Segmentation, Semi-supervised Medical Image Segmentation

Medical image segmentation, a critical application of semantic segmentation in healthcare, has seen significant advancements through specialized computer vision techniques. While deep learning-based medical image segmentation is essential for assisting in medical diagnosis, the lack of diverse training data causes the long-tail problem. Moreover, most previous hybrid CNN-ViT architectures have limited ability to combine various attentions in different layers of the Convolutional Neural Network. To address these issues, we propose a Lagrange Duality Consistency (LDC) Loss, integrated with Boundary-Aware Contrastive Loss, as the overall training objective for semi-supervised learning to mitigate the long-tail problem. Additionally, we introduce CMAformer, a novel network that synergizes the strengths of ResUNet and Transformer. The cross-attention block in CMAformer effectively integrates spatial attention and channel attention for multi-scale feature fusion. Overall, our results indicate that CMAformer, combined with the feature fusion framework and the new consistency loss, demonstrates strong complementarity in semi-supervised learning ensembles. We achieve state-of-the-art results on multiple public medical image datasets. Example code are available at: \url{https://github.com/lzeeorno/Lagrange-Duality-and-CMAformer}.
AFFSegNet: Adaptive Feature Fusion Segmentation Network for Microtumors and Multi-Organ Segmentation
Authors: Fuchen Zheng, Xinyi Chen, Xuhang Chen, Haolun Li, Xiaojiao Guo, Weihuang Liu, Chi-Man Pun, Shoujun Zhou
Date: 2024-09-12 00:00:00
Tasks: Decoder, Image Segmentation, Medical Image Segmentation, Organ Segmentation, Segmentation, Semantic Segmentation, Tumor Segmentation

Medical image segmentation, a crucial task in computer vision, facilitates the automated delineation of anatomical structures and pathologies, supporting clinicians in diagnosis, treatment planning, and disease monitoring. Notably, transformers employing shifted window-based self-attention have demonstrated exceptional performance. However, their reliance on local window attention limits the fusion of local and global contextual information, crucial for segmenting microtumors and miniature organs. To address this limitation, we propose the Adaptive Semantic Segmentation Network (ASSNet), a transformer architecture that effectively integrates local and global features for precise medical image segmentation. ASSNet comprises a transformer-based U-shaped encoder-decoder network. The encoder utilizes shifted window self-attention across five resolutions to extract multi-scale features, which are then propagated to the decoder through skip connections. We introduce an augmented multi-layer perceptron within the encoder to explicitly model long-range dependencies during feature extraction. Recognizing the constraints of conventional symmetrical encoder-decoder designs, we propose an Adaptive Feature Fusion (AFF) decoder to complement our encoder. This decoder incorporates three key components: the Long Range Dependencies (LRD) block, the Multi-Scale Feature Fusion (MFF) block, and the Adaptive Semantic Center (ASC) block. These components synergistically facilitate the effective fusion of multi-scale features extracted by the decoder while capturing long-range dependencies and refining object boundaries. Comprehensive experiments on diverse medical image segmentation tasks, including multi-organ, liver tumor, and bladder tumor segmentation, demonstrate that ASSNet achieves state-of-the-art results. Code and models are available at: \url{https://github.com/lzeeorno/ASSNet}.
A Spatiotemporal Stealthy Backdoor Attack against Cooperative Multi-Agent Deep Reinforcement Learning
Authors: Yinbo Yu, Saihao Yan, Jiajia Liu
Date: 2024-09-12 00:00:00
Tasks: Backdoor Attack, Deep Reinforcement Learning, SMAC, SMAC+

Recent studies have shown that cooperative multi-agent deep reinforcement learning (c-MADRL) is under the threat of backdoor attacks. Once a backdoor trigger is observed, it will perform abnormal actions leading to failures or malicious goals. However, existing proposed backdoors suffer from several issues, e.g., fixed visual trigger patterns lack stealthiness, the backdoor is trained or activated by an additional network, or all agents are backdoored. To this end, in this paper, we propose a novel backdoor attack against c-MADRL, which attacks the entire multi-agent team by embedding the backdoor only in a single agent. Firstly, we introduce adversary spatiotemporal behavior patterns as the backdoor trigger rather than manual-injected fixed visual patterns or instant status and control the attack duration. This method can guarantee the stealthiness and practicality of injected backdoors. Secondly, we hack the original reward function of the backdoored agent via reward reverse and unilateral guidance during training to ensure its adverse influence on the entire team. We evaluate our backdoor attacks on two classic c-MADRL algorithms VDN and QMIX, in a popular c-MADRL environment SMAC. The experimental results demonstrate that our backdoor attacks are able to reach a high attack success rate (91.6\%) while maintaining a low clean performance variance rate (3.7\%).
Universal Pooling Method of Multi-layer Features from Pretrained Models for Speaker Verification
Authors: Jin Sob Kim, Hyun Joon Park, WooSeok Shin, Sung Won Han
Date: 2024-09-12 00:00:00
Tasks: Speaker Verification, Text-Independent Speaker Verification

Recent advancements in automatic speaker verification (ASV) studies have been achieved by leveraging large-scale pretrained networks. In this study, we analyze the approaches toward such a paradigm and underline the significance of interlayer information processing as a result. Accordingly, we present a novel approach for exploiting the multilayered nature of pretrained models for ASV, which comprises a layer/frame-level network and two steps of pooling architectures for each layer and frame axis. Specifically, we let convolutional architecture directly processes a stack of layer outputs.Then, we present a channel attention-based scheme of gauging layer significance and squeeze the layer level with the most representative value. Finally, attentive statistics over frame-level representations yield a single vector speaker embedding. Comparative experiments are designed using versatile data environments and diverse pretraining models to validate the proposed approach. The experimental results demonstrate the stability of the approach using multi-layer outputs in leveraging pretrained architectures. Then, we verify the superiority of the proposed ASV backend structure, which involves layer-wise operations, in terms of performance improvement along with cost efficiency compared to the conventional method. The ablation study shows how the proposed interlayer processing aids in maximizing the advantage of utilizing pretrained models.