pwc-archive/papers-with-abstracts huggingface dataset:
AnalogXpert: Automating Analog Topology Synthesis by Incorporating Circuit Design Expertise into Large Language Models
Authors: Haoyi Zhang, Shizhao Sun, Yibo Lin, Runsheng Wang, Jiang Bian
Date: 2024-12-17 00:00:00
Tasks: 2k, Code Generation, Graph Generation

Analog circuits are crucial in modern electronic systems, and automating their design has attracted significant research interest. One of major challenges is topology synthesis, which determines circuit components and their connections. Recent studies explore large language models (LLM) for topology synthesis. However, the scenarios addressed by these studies do not align well with practical applications. Specifically, existing work uses vague design requirements as input and outputs an ideal model, but detailed structural requirements and device-level models are more practical. Moreover, current approaches either formulate topology synthesis as graph generation or Python code generation, whereas practical topology design is a complex process that demands extensive design knowledge. In this work, we propose AnalogXpert, a LLM-based agent aiming at solving practical topology synthesis problem by incorporating circuit design expertise into LLMs. First, we represent analog topology as SPICE code and introduce a subcircuit library to reduce the design space, in the same manner as experienced designers. Second, we decompose the problem into two sub-task (i.e., block selection and block connection) through the use of CoT and incontext learning techniques, to mimic the practical design process. Third, we introduce a proofreading strategy that allows LLMs to incrementally correct the errors in the initial design, akin to human designers who iteratively check and adjust the initial topology design to ensure accuracy. Finally, we construct a high-quality benchmark containing both real data (30) and synthetic data (2k). AnalogXpert achieves 40% and 23% success rates on the synthetic dataset and real dataset respectively, which is markedly better than those of GPT-4o (3% on both the synthetic dataset and the real dataset).
ChipAlign: Instruction Alignment in Large Language Models for Chip Design via Geodesic Interpolation
Authors: Chenhui Deng, Yunsheng Bai, Haoxing Ren
Date: 2024-12-15 00:00:00
Tasks: Instruction Following

Recent advancements in large language models (LLMs) have expanded their application across various domains, including chip design, where domain-adapted chip models like ChipNeMo have emerged. However, these models often struggle with instruction alignment, a crucial capability for LLMs that involves following explicit human directives. This limitation impedes the practical application of chip LLMs, including serving as assistant chatbots for hardware design engineers. In this work, we introduce ChipAlign, a novel approach that utilizes a training-free model merging strategy, combining the strengths of a general instruction-aligned LLM with a chip-specific LLM. By considering the underlying manifold in the weight space, ChipAlign employs geodesic interpolation to effectively fuse the weights of input LLMs, producing a merged model that inherits strong instruction alignment and chip expertise from the respective instruction and chip LLMs. Our results demonstrate that ChipAlign significantly enhances instruction-following capabilities of existing chip LLMs, achieving up to a 26.6% improvement on the IFEval benchmark, while maintaining comparable expertise in the chip domain. This improvement in instruction alignment also translates to notable gains in instruction-involved QA tasks, delivering performance enhancements of 3.9% on the OpenROAD QA benchmark and 8.25% on production-level chip QA benchmarks, surpassing state-of-the-art baselines.
RoboSignature: Robust Signature and Watermarking on Network Attacks
Authors: Aryaman Shaan, Garvit Banga, Raghav Mantri
Date: 2024-12-22 00:00:00
Tasks: Decoder

Generative models have enabled easy creation and generation of images of all kinds given a single prompt. However, this has also raised ethical concerns about what is an actual piece of content created by humans or cameras compared to model-generated content like images or videos. Watermarking data generated by modern generative models is a popular method to provide information on the source of the content. The goal is for all generated images to conceal an invisible watermark, allowing for future detection or identification. The Stable Signature finetunes the decoder of Latent Diffusion Models such that a unique watermark is rooted in any image produced by the decoder. In this paper, we present a novel adversarial fine-tuning attack that disrupts the model's ability to embed the intended watermark, exposing a significant vulnerability in existing watermarking methods. To address this, we further propose a tamper-resistant fine-tuning algorithm inspired by methods developed for large language models, tailored to the specific requirements of watermarking in LDMs. Our findings emphasize the importance of anticipating and defending against potential vulnerabilities in generative systems.
The Patterns of Digital Deception
Authors: Gregory M. Dickinson
Date: 2024-12-25 00:00:00

Current consumer-protection debates focus on the powerful new data-analysis techniques that have disrupted the balance of power between companies and their customers. Online tracking enables sellers to amass troves of historical data, apply machine-learning tools to construct detailed customer profiles, and target those customers with tailored offers that best suit their interests. It is often a win-win. Sellers avoid pumping dud products and consumers see ads for things they actually want to buy. But the same tools are also used for ill -- to target vulnerable members of the population with scams specially tailored to prey on their weaknesses. The result has been a dramatic rise in online fraud that disproportionately impacts those least able to bear the loss. The law's response has been technology centric. Lawmakers race to identify those technologies that drive consumer deception and target them for regulatory restrictions. But that approach comes at a major cost. General-purpose data-analysis and communications tools have both desirable and undesirable uses, and uniform restrictions on their use impede the good along with the bad. A superior approach would focus not on the technological tools of deception but on what this Article identifies as the legal patterns of digital deception -- those aspects of digital technology that have outflanked the law's existing mechanisms for redressing consumer harm. This Article reorients the discussion from the power of new technologies to the shortcomings in existing regulatory structures that have allowed for their abuse. Focus on these patterns of deception will allow regulators to reallocate resources to offset those shortcomings and thereby enhance efforts to combat online fraud without impeding technological innovation.
Retrieval-Augmented Generation for Mobile Edge Computing via Large Language Model
Authors: Runtao Ren, Yinyu Wu, Xuhui Zhang, Jinke Ren, Yanyan Shen, Shuqiang Wang, Kim-Fung Tsang
Date: 2024-12-30 00:00:00
Tasks: Edge-computing, Information Retrieval, Language Modeling, Language Modelling, Large Language Model, RAG, Retrieval, Retrieval-augmented Generation

The rapid evolution of mobile edge computing (MEC) has introduced significant challenges in optimizing resource allocation in highly dynamic wireless communication systems, in which task offloading decisions should be made in real-time. However, existing resource allocation strategies cannot well adapt to the dynamic and heterogeneous characteristics of MEC systems, since they are short of scalability, context-awareness, and interpretability. To address these issues, this paper proposes a novel retrieval-augmented generation (RAG) method to improve the performance of MEC systems. Specifically, a latency minimization problem is first proposed to jointly optimize the data offloading ratio, transmit power allocation, and computing resource allocation. Then, an LLM-enabled information-retrieval mechanism is proposed to solve the problem efficiently. Extensive experiments across multi-user, multi-task, and highly dynamic offloading scenarios show that the proposed method consistently reduces latency compared to several DL-based approaches, achieving 57% improvement under varying user computing ability, 86% with different servers, 30% under distinct transmit powers, and 42% for varying data volumes. These results show the effectiveness of LLM-driven solutions to solve the resource allocation problems in MEC systems.
Design of an improved microstrip antenna operating at a frequency band of 28GHz
Authors: S. O. Zakariyya, B. O. Sadiq, R. A. Alao, J. A. Adesina, E. Obi
Date: 2024-12-29 00:00:00

The design of an improved microstrip antenna operating in the 28 GHz frequency spectrum is the main goal of this work. The design used a Roger RT 5880 LZ substrate with a thickness and permittivity of 0.762mm and 1.96, respectively. The antenna was simulated in CST Microwave Studio. As the antenna feed, a quarter-wave transformer was used to provide an impedance match of 50 ohms. To improve the antenna's performance, a Ushaped element was added to the ground plane. The antenna resonated at 28 GHz frequency, according to simulation data, with a return loss of -21.4 dB, VSWR of 1.18, bandwidth of 2.026 GHz, and gain of 8.19 dB. The proposed antenna exhibits a performance improvement in terms of gain and bandwidth due to the addition of U-shaped element when benchmarked with existing designs in the literature work
Conditional Balance: Improving Multi-Conditioning Trade-Offs in Image Generation
Authors: Nadav Z. Cohen, Oron Nir, Ariel Shamir
Date: 2024-12-25 00:00:00
Proceeding: CVPR 2025 1
Tasks: Denoising, Image Generation, Style Transfer

Balancing content fidelity and artistic style is a pivotal challenge in image generation. While traditional style transfer methods and modern Denoising Diffusion Probabilistic Models (DDPMs) strive to achieve this balance, they often struggle to do so without sacrificing either style, content, or sometimes both. This work addresses this challenge by analyzing the ability of DDPMs to maintain content and style equilibrium. We introduce a novel method to identify sensitivities within the DDPM attention layers, identifying specific layers that correspond to different stylistic aspects. By directing conditional inputs only to these sensitive layers, our approach enables fine-grained control over style and content, significantly reducing issues arising from over-constrained inputs. Our findings demonstrate that this method enhances recent stylization techniques by better aligning style and content, ultimately improving the quality of generated visual content.
Multi-Agent Q-Learning for Real-Time Load Balancing User Association and Handover in Mobile Networks
Authors: Alireza Alizadeh, Byungju Lim, Mai Vu
Date: 2024-12-22 00:00:00
Tasks: Q-Learning

As next generation cellular networks become denser, associating users with the optimal base stations at each time while ensuring no base station is overloaded becomes critical for achieving stable and high network performance. We propose multi-agent online Q-learning (QL) algorithms for performing real-time load balancing user association and handover in dense cellular networks. The load balancing constraints at all base stations couple the actions of user agents, and we propose two multi-agent action selection policies, one centralized and one distributed, to satisfy load balancing at every learning step. In the centralized policy, the actions of UEs are determined by a central load balancer (CLB) running an algorithm based on swapping the worst connection to maximize the total learning reward. In the distributed policy, each UE takes an action based on its local information by participating in a distributed matching game with the BSs to maximize the local reward. We then integrate these action selection policies into an online QL algorithm that adapts in real-time to network dynamics including channel variations and user mobility, using a reward function that considers a handover cost to reduce handover frequency. The proposed multi-agent QL algorithm features low-complexity and fast convergence, outperforming 3GPP max-SINR association. Both policies adapt well to network dynamics at various UE speed profiles from walking, running, to biking and suburban driving, illustrating their robustness and real-time adaptability.
MarsSQE: Stereo Quality Enhancement for Martian Images Using Bi-level Cross-view Attention
Authors: Mai Xu, Yinglin Zhu, Qunliang Xing, Jing Yang, Xin Zou
Date: 2024-12-30 00:00:00

Stereo images captured by Mars rovers are transmitted after lossy compression due to the limited bandwidth between Mars and Earth. Unfortunately, this process results in undesirable compression artifacts. In this paper, we present a novel stereo quality enhancement approach for Martian images, named MarsSQE. First, we establish the first dataset of stereo Martian images. Through extensive analysis of this dataset, we observe that cross-view correlations in Martian images are notably high. Leveraging this insight, we design a bi-level cross-view attention-based quality enhancement network that fully exploits these inherent cross-view correlations. Specifically, our network integrates pixel-level attention for precise matching and patch-level attention for broader contextual information. Experimental results demonstrate the effectiveness of our MarsSQE approach.
On the Estimation of the Time-Dependent Transmission Rate in Epidemiological Models
Authors: Jorge P. Zubelli, Jennifer Loria, Vinicius V. L. Albani
Date: 2024-12-27 00:00:00

The COVID-19 pandemic highlighted the need to improve the modeling, estimation, and prediction of how infectious diseases spread. SEIR-like models have been particularly successful in providing accurate short-term predictions. This study fills a notable literature gap by exploring the following question: Is it possible to incorporate a nonparametric susceptible-exposed-infected-removed (SEIR) COVID-19 model into the inverse-problem regularization framework when the transmission coefficient varies over time? Our positive response considers varying degrees of disease severity, vaccination, and other time-dependent parameters. In addition, we demonstrate the continuity, differentiability, and injectivity of the operator that link the transmission parameter to the observed infection numbers. By employing Tikhonov-type regularization to the corresponding inverse problem, we establish the existence and stability of regularized solutions. Numerical examples using both synthetic and real data illustrate the model's estimation accuracy and its ability to fit the data effectively.
Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations
Authors: Hermann G. Matthies
Date: 2024-12-22 00:00:00

Systems may depend on parameters which one may control, or which serve to optimise the system, or are imposed externally, or they could be uncertain. This last case is taken as the ``Leitmotiv'' for the following. A reduced order model is produced from the full order model by some kind of projection onto a relatively low-dimensional manifold or subspace. The parameter dependent reduction process produces a function of the parameters into the manifold. One now wants to examine the relation between the full and the reduced state for all possible parameter values of interest. Similarly, in the field of machine learning, also a function of the parameter set into the image space of the machine learning model is learned on a training set of samples, typically minimising the mean-square error. This set may be seen as a sample from some probability distribution, and thus the training is an approximate computation of the expectation, giving an approximation to the conditional expectation, a special case of an Bayesian updating where the Bayesian loss function is the mean-square error. This offers the possibility of having a combined look at these methods, and also of introducing more general loss functions.
A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection
Authors: Daniel Adu Worae, Athar Sheikh, Spyridon Mastorakis
Date: 2024-12-19 00:00:00
Tasks: Anomaly Detection, Management

The rapid expansion of Internet of Things (IoT) ecosystems has introduced growing complexities in device management and network security. To address these challenges, we present a unified framework that combines context-driven large language models (LLMs) for IoT administrative tasks with a fine-tuned anomaly detection module for network traffic analysis. The framework streamlines administrative processes such as device management, troubleshooting, and security enforcement by harnessing contextual knowledge from IoT manuals and operational data. The anomaly detection model achieves state-of-the-art performance in identifying irregularities and threats within IoT traffic, leveraging fine-tuning to deliver exceptional accuracy. Evaluations demonstrate that incorporating relevant contextual information significantly enhances the precision and reliability of LLM-based responses for diverse IoT administrative tasks. Additionally, resource usage metrics such as execution time, memory consumption, and response efficiency demonstrate the framework's scalability and suitability for real-world IoT deployments.
A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions
Authors: Gordon Owusu Boateng, Hani Sami, Ahmed Alagha, Hanae Elmekki, Ahmad Hammoud, Rabeb Mizouni, Azzam Mourad, Hadi Otrok, Jamal Bentahar, Sami Muhaidat, Chamseddine Talhi, Zbigniew Dziong, Mohsen Guizani
Date: 2024-12-16 00:00:00
Tasks: Management, Survey

The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large Language Models (LLMs) have received tremendous attention due to their unparalleled capabilities in various Natural Language Processing (NLP) tasks and generating context-aware insights, offering transformative potential for automating diverse communication NSM tasks. Contrasting existing surveys that consider a single network domain, this survey investigates the integration of LLMs across different communication network domains, including mobile networks and related technologies, vehicular networks, cloud-based networks, and fog/edge-based networks. First, the survey provides foundational knowledge of LLMs, explicitly detailing the generic transformer architecture, general-purpose and domain-specific LLMs, LLM model pre-training and fine-tuning, and their relation to communication NSM. Under a novel taxonomy of network monitoring and reporting, AI-powered network planning, network deployment and distribution, and continuous network support, we extensively categorize LLM applications for NSM tasks in each of the different network domains, exploring existing literature and their contributions thus far. Then, we identify existing challenges and open issues, as well as future research directions for LLM-driven communication NSM, emphasizing the need for scalable, adaptable, and resource-efficient solutions that align with the dynamic landscape of communication networks. We envision that this survey serves as a holistic roadmap, providing critical insights for leveraging LLMs to enhance NSM.
LINKs: Large Language Model Integrated Management for 6G Empowered Digital Twin NetworKs
Authors: Shufan Jiang, Bangyan Lin, Yue Wu, Yuan Gao
Date: 2024-12-09 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Management, Retrieval

In the rapidly evolving landscape of digital twins (DT) and 6G networks, the integration of large language models (LLMs) presents a novel approach to network management. This paper explores the application of LLMs in managing 6G-empowered DT networks, with a focus on optimizing data retrieval and communication efficiency in smart city scenarios. The proposed framework leverages LLMs for intelligent DT problem analysis and radio resource management (RRM) in fully autonomous way without any manual intervention. Our proposed framework -- LINKs, builds up a lazy loading strategy which can minimize transmission delay by selectively retrieving the relevant data. Based on the data retrieval plan, LLMs transform the retrieval task into an numerical optimization problem and utilizing solvers to build an optimal RRM, ensuring efficient communication across the network. Simulation results demonstrate the performance improvements in data planning and network management, highlighting the potential of LLMs to enhance the integration of DT and 6G technologies.
Phoneme-Level Contrastive Learning for User-Defined Keyword Spotting with Flexible Enrollment
Authors: Li Kewei, Zhou Hengshun, Shen Kai, Dai Yusheng, Du Jun
Date: 2024-12-30 00:00:00
Tasks: Contrastive Learning, Data Augmentation, Keyword Spotting

User-defined keyword spotting (KWS) enhances the user experience by allowing individuals to customize keywords. However, in open-vocabulary scenarios, most existing methods commonly suffer from high false alarm rates with confusable words and are limited to either audio-only or text-only enrollment. Therefore, in this paper, we first explore the model's robustness against confusable words. Specifically, we propose Phoneme-Level Contrastive Learning (PLCL), which refines and aligns query and source feature representations at the phoneme level. This method enhances the model's disambiguation capability through fine-grained positive and negative comparisons for more accurate alignment, and it is generalizable to jointly optimize both audio-text and audio-audio matching, adapting to various enrollment modes. Furthermore, we maintain a context-agnostic phoneme memory bank to construct confusable negatives for data augmentation. Based on this, a third-category discriminator is specifically designed to distinguish hard negatives. Overall, we develop a robust and flexible KWS system, supporting different modality enrollment methods within a unified framework. Verified on the LibriPhrase dataset, the proposed approach achieves state-of-the-art performance.
Improving Acoustic Scene Classification in Low-Resource Conditions
Authors: Zhi Chen, Yun-Fei Shao, Yong Ma, Mingsheng Wei, Le Zhang, Wei-Qiang Zhang
Date: 2024-12-30 00:00:00
Tasks: Acoustic Scene Classification, Classification, Data Augmentation, Knowledge Distillation, Model Compression, Quantization, Scene Classification

Acoustic Scene Classification (ASC) identifies an environment based on an audio signal. This paper explores ASC in low-resource conditions and proposes a novel model, DS-FlexiNet, which combines depthwise separable convolutions from MobileNetV2 with ResNet-inspired residual connections for a balance of efficiency and accuracy. To address hardware limitations and device heterogeneity, DS-FlexiNet employs Quantization Aware Training (QAT) for model compression and data augmentation methods like Auto Device Impulse Response (ADIR) and Freq-MixStyle (FMS) to improve cross-device generalization. Knowledge Distillation (KD) from twelve teacher models further enhances performance on unseen devices. The architecture includes a custom Residual Normalization layer to handle domain differences across devices, and depthwise separable convolutions reduce computational overhead without sacrificing feature representation. Experimental results show that DS-FlexiNet excels in both adaptability and performance under resource-constrained conditions.