pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Satellite Beam Hopping and Power Allocation Using Deep Reinforcement Learning
Authors: Xia Xie, Kexin Fan, Wenfeng Deng, Nikolaos Pappas, Qinyu Zhang
Date: 2025-01-04 00:00:00
Tasks: Deep Reinforcement Learning, Scheduling

In non-geostationary orbit (NGSO) satellite communication systems, effectively utilizing beam hopping (BH) technology is crucial for addressing uneven traffic demands. However, optimizing beam scheduling and resource allocation in multi-NGSO BH scenarios remains a significant challenge. This paper proposes a multi-NGSO BH algorithm based on deep reinforcement learning (DRL) to optimize beam illumination patterns and power allocation. By leveraging three degrees of freedom (i.e., time, space, and power), the algorithm aims to optimize the long-term throughput and the long-term cumulative average delay (LTCAD). The solution is based on proximal policy optimization (PPO) with a hybrid action space combining discrete and continuous actions. Using two policy networks with a shared base layer, the proposed algorithm jointly optimizes beam scheduling and power allocation. One network selects beam illumination patterns in the discrete action space, while the other manages power allocation in the continuous space. Simulation results show that the proposed algorithm significantly reduces LTCAD while maintaining high throughput in time-varying traffic scenarios. Compared to the four benchmark methods, it improves network throughput by up to $8.9\%$ and reduces LTCAD by up to $69.2\%$
A Dual Augmented Two-tower Model for Online Large-scale Recommendation
Authors: Yantao Yu, Weipeng Wang, Zhoutian Feng, Daiyue Xue
Date: 2021-08-15 00:00:00
Proceeding: DLP-KDD 2021 8
Tasks: Recommendation Systems

Many modern recommender systems have a very large corpus, and a common industrial recipe for handling large-scale retrieval is to learn query and item representations from their content features with the two-tower model. However, the model suffers from lack of information interaction between the two towers. Besides, imbalanced category data also hinders the model performance. In this paper, we propose a novel model named Dual Augmented Two-tower Model (DAT), which integrates a novel Adaptive-Mimic Mechanism (AMM) and a Category Alignment Loss (CAL). Our AMM customizes an augmented vector for each query and item to mitigate the lack of information interaction. Moreover, our CAL can further improve performance by aligning item representation of uneven categories. Offline experiments on large-scale datasets are conducted to show the superior performance of DAT. More-over, online A/B testings confirm that DAT can lead to improved recommendation quality for industrial applications.
Predicting Novel Functional Roles of Designed Small Biomolecules: An ML Approach Utilizing PubChem Compound and Substance Identifiers (CID-SID ML model)
Authors: Mariya L. Ivanova, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic
Date: 2025-01-04 00:00:00
Tasks: Drug Discovery

Significance and Object: The proposed methodology aims to provide time- and cost-effective approach for the early stage in drug discovery. The machine learning models developed in this study used only the identification numbers provided by PubChem. Thus, a drug development researcher who has obtained a PubChem CID and SID can easily identify new functionality of their compound. The approach was demonstrated, using four bioassay which were on (i) the antagonists of human D3 dopamine receptors; (ii) the promoter Rab9 activators; (iii) small molecule inhibitors of CHOP to regulate the unfolded protein response to ER stress; (iv) antagonists of the human M1 muscarinic receptor. Solution: The four bioassays used for demonstration of the approach were provided by PubChem. For each bioassay, the generated by PubChem CIDs, SIDs were extracted together with the corresponding activity. The resulting dataset was sifted with the dataset on a water solubility bioassay, remaining only the compounds common for both bioassays. In this way, the inactive compounds were reduced. Then, all active compounds were added, and the resulted dataset was later used for machine learning based on scikit learn algorithms. Results: The average values of the ML models` metrics for the four bioassays were: 83.82% Accuracy with 5.35 standard deviation; 87.9% Precision with 5.04 standard deviation; 77.1% Recall with 7.65 standard deviation; 82.1% F1 with 6.44 standard deviation; 83.4% ROC with 5.09 standard deviation. Since the methodology was publicly available as a preprint, four more machine ML models have been developed. Their results are discussed in the "Results and Discussion" section.
STEAM-EEG: Spatiotemporal EEG Analysis with Markov Transfer Fields and Attentive CNNs
Authors: Jiahao Qin, Feng Liu
Date: 2024-12-07 00:00:00
Tasks: Decision Making, EEG, Electroencephalogram (EEG), Time Series

Electroencephalogram (EEG) signals play a pivotal role in biomedical research and clinical applications, including epilepsy diagnosis, sleep disorder analysis, and brain-computer interfaces. However, the effective analysis and interpretation of these complex signals often present significant challenges. This paper presents a novel approach that integrates computer graphics techniques with biological signal pattern recognition, specifically using Markov Transfer Fields (MTFs) for EEG time series imaging. The proposed framework (STEAM-EEG) employs the capabilities of MTFs to capture the spatiotemporal dynamics of EEG signals, transforming them into visually informative images. These images are then rendered, visualised, and modelled using state-of-the-art computer graphics techniques, thereby facilitating enhanced data exploration, pattern recognition, and decision-making. The code could be accessed from GitHub.
Dynamic Feature Fusion: Combining Global Graph Structures and Local Semantics for Blockchain Fraud Detection
Authors: Zhang Sheng, Liangliang Song, Yanbin Wang
Date: 2025-01-03 00:00:00
Tasks: Fraud Detection, graph construction, Representation Learning

The advent of blockchain technology has facilitated the widespread adoption of smart contracts in the financial sector. However, current fraud detection methodologies exhibit limitations in capturing both global structural patterns within transaction networks and local semantic relationships embedded in transaction data. Most existing models focus on either structural information or semantic features individually, leading to suboptimal performance in detecting complex fraud patterns.In this paper, we propose a dynamic feature fusion model that combines graph-based representation learning and semantic feature extraction for blockchain fraud detection. Specifically, we construct global graph representations to model account relationships and extract local contextual features from transaction data. A dynamic multimodal fusion mechanism is introduced to adaptively integrate these features, enabling the model to capture both structural and semantic fraud patterns effectively. We further develop a comprehensive data processing pipeline, including graph construction, temporal feature enhancement, and text preprocessing. Experimental results on large-scale real-world blockchain datasets demonstrate that our method outperforms existing benchmarks across accuracy, F1 score, and recall metrics. This work highlights the importance of integrating structural relationships and semantic similarities for robust fraud detection and offers a scalable solution for securing blockchain systems.
Gender Bias in Text-to-Video Generation Models: A case study of Sora
Authors: Mohammad Nadeem, Shahab Saquib Sohail, Erik Cambria, Björn W. Schuller, Amir Hussain
Date: 2024-12-30 00:00:00
Tasks: Text-to-Video Generation, Video Generation

The advent of text-to-video generation models has revolutionized content creation as it produces high-quality videos from textual prompts. However, concerns regarding inherent biases in such models have prompted scrutiny, particularly regarding gender representation. Our study investigates the presence of gender bias in OpenAI's Sora, a state-of-the-art text-to-video generation model. We uncover significant evidence of bias by analyzing the generated videos from a diverse set of gender-neutral and stereotypical prompts. The results indicate that Sora disproportionately associates specific genders with stereotypical behaviors and professions, which reflects societal prejudices embedded in its training data.
INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models
Authors: Di Jin, Xing Liu, Yu Liu, Jia Qing Yap, Andrea Wong, Adriana Crespo, Qi Lin, Zhiyuan Yin, Qiang Yan, Ryan Ye
Date: 2024-12-28 00:00:00
Tasks: Fairness, Image Generation

The rapid development of large language models (LLMs) and large vision models (LVMs) have propelled the evolution of multi-modal AI systems, which have demonstrated the remarkable potential for industrial applications by emulating human-like cognition. However, they also pose significant ethical challenges, including amplifying harmful content and reinforcing societal biases. For instance, biases in some industrial image generation models highlighted the urgent need for robust fairness assessments. Most existing evaluation frameworks focus on the comprehensiveness of various aspects of the models, but they exhibit critical limitations, including insufficient attention to content generation alignment and social bias-sensitive domains. More importantly, their reliance on pixel-detection techniques is prone to inaccuracies. To address these issues, this paper presents INFELM, an in-depth fairness evaluation on widely-used text-to-image models. Our key contributions are: (1) an advanced skintone classifier incorporating facial topology and refined skin pixel representation to enhance classification precision by at least 16.04%, (2) a bias-sensitive content alignment measurement for understanding societal impacts, (3) a generalizable representation bias evaluation for diverse demographic groups, and (4) extensive experiments analyzing large-scale text-to-image model outputs across six social-bias-sensitive domains. We find that existing models in the study generally do not meet the empirical fairness criteria, and representation bias is generally more pronounced than alignment errors. INFELM establishes a robust benchmark for fairness assessment, supporting the development of multi-modal AI systems that align with ethical and human-centric principles.
Towards Sustainable Large Language Model Serving
Authors: Sophia Nguyen, Beihao Zhou, Yi Ding, Sihang Liu
Date: 2024-12-31 00:00:00
Tasks: GPU, Language Modeling, Language Modelling, Large Language Model, model

In this work, we study LLMs from a carbon emission perspective, addressing both operational and embodied emissions, and paving the way for sustainable LLM serving. We characterize the performance and energy of LLaMA with 1B, 3B, and 7B parameters using two Nvidia GPU types, a latest-generation RTX6000 Ada and an older-generation T4. We analytically model operational carbon emissions based on energy consumption and carbon intensities from three grid regions -- each representing a different energy source mix, and embodied carbon emissions based on chip area and memory size. Our characterization and modeling provide us with an in-depth understanding of the performance, energy, and carbon emissions of LLM serving. Our findings highlight the potential for optimizing sustainable LLM serving systems by considering both operational and embodied carbon emissions simultaneously.
Polarimetric BSSRDF Acquisition of Dynamic Faces
Authors: Hyunho Ha, Inseung Hwang, Nestor Monzon, Jaemin Cho, Donggun Kim, Seung-Hwan Baek, Adolfo Muñoz, Diego Gutierrez, Min H. Kim
Date: 2024-12-29 00:00:00

Acquisition and modeling of polarized light reflection and scattering help reveal the shape, structure, and physical characteristics of an object, which is increasingly important in computer graphics. However, current polarimetric acquisition systems are limited to static and opaque objects. Human faces, on the other hand, present a particularly difficult challenge, given their complex structure and reflectance properties, the strong presence of spatially-varying subsurface scattering, and their dynamic nature. We present a new polarimetric acquisition method for dynamic human faces, which focuses on capturing spatially varying appearance and precise geometry, across a wide spectrum of skin tones and facial expressions. It includes both single and heterogeneous subsurface scattering, index of refraction, and specular roughness and intensity, among other parameters, while revealing biophysically-based components such as inner- and outer-layer hemoglobin, eumelanin and pheomelanin. Our method leverages such components' unique multispectral absorption profiles to quantify their concentrations, which in turn inform our model about the complex interactions occurring within the skin layers. To our knowledge, our work is the first to simultaneously acquire polarimetric and spectral reflectance information alongside biophysically-based skin parameters and geometry of dynamic human faces. Moreover, our polarimetric skin model integrates seamlessly into various rendering pipelines.
GAF-FusionNet: Multimodal ECG Analysis via Gramian Angular Fields and Split Attention
Authors: Jiahao Qin, Feng Liu
Date: 2024-12-07 00:00:00
Tasks: ECG Classification, Time Series, Time Series Analysis

Electrocardiogram (ECG) analysis plays a crucial role in diagnosing cardiovascular diseases, but accurate interpretation of these complex signals remains challenging. This paper introduces a novel multimodal framework(GAF-FusionNet) for ECG classification that integrates time-series analysis with image-based representation using Gramian Angular Fields (GAF). Our approach employs a dual-layer cross-channel split attention module to adaptively fuse temporal and spatial features, enabling nuanced integration of complementary information. We evaluate GAF-FusionNet on three diverse ECG datasets: ECG200, ECG5000, and the MIT-BIH Arrhythmia Database. Results demonstrate significant improvements over state-of-the-art methods, with our model achieving 94.5\%, 96.9\%, and 99.6\% accuracy on the respective datasets. Our code will soon be available at https://github.com/Cross-Innovation-Lab/GAF-FusionNet.git.
Optimal bounds for dissatisfaction in perpetual voting
Authors: Alexander Kozachinskiy, Alexander Shen, Tomasz Steifer
Date: 2024-12-20 00:00:00

In perpetual voting, multiple decisions are made at different moments in time. Taking the history of previous decisions into account allows us to satisfy properties such as proportionality over periods of time. In this paper, we consider the following question: is there a perpetual approval voting method that guarantees that no voter is dissatisfied too many times? We identify a sufficient condition on voter behavior -- which we call 'bounded conflicts' condition -- under which a sublinear growth of dissatisfaction is possible. We provide a tight upper bound on the growth of dissatisfaction under bounded conflicts, using techniques from Kolmogorov complexity. We also observe that the approval voting with binary choices mimics the machine learning setting of prediction with expert advice. This allows us to present a voting method with sublinear guarantees on dissatisfaction under bounded conflicts, based on the standard techniques from prediction with expert advice.
Rydberg Atomic Quantum Receivers for Multi-Target DOA Estimation
Authors: Tierui Gong, Chau Yuen, Chong Meng Samson See, Mérouane Debbah, Lajos Hanzo
Date: 2025-01-06 00:00:00

Quantum sensing technologies have experienced rapid progresses since entering the `second quantum revolution'. Among various candidates, schemes relying on Rydberg atoms exhibit compelling advantages for detecting radio frequency signals. Based on this, Rydberg atomic quantum receivers (RAQRs) have emerged as a promising solution to classical wireless communication and sensing. To harness the advantages and exploit the potential of RAQRs in wireless sensing, we investigate the realization of the direction of arrival (DOA) estimation by RAQRs. Specifically, we first conceive a Rydberg atomic quantum uniform linear array (RAQ-ULA) aided wireless receiver for multi-target DOA detection and propose the corresponding signal model of this sensing system. Our model reveals that the presence of the radio-frequency local oscillator in the RAQ-ULA creates sensor gain mismatches, which degrade the DOA estimation significantly by employing the classical Estimation of Signal Parameters via Rotational Invariant Techniques (ESPRIT). To solve this sensor gain mismatch problem, we propose the Rydberg atomic quantum ESPRIT (RAQ-ESPRIT) relying on our model. Lastly, we characterize our scheme through numerical simulations, where the results exhibit that it is capable of reducing the estimation error of its classical counterpart on the order of $> 400$-fold and $> 9000$-fold in the PSL and SQL, respectively.
Digital Twin Aided Channel Estimation: Zone-Specific Subspace Prediction and Calibration
Authors: Sadjad Alikhani, Ahmed Alkhateeb
Date: 2025-01-06 00:00:00
Tasks: Reinforcement Learning (RL)

Effective channel estimation in sparse and high-dimensional environments is essential for next-generation wireless systems, particularly in large-scale MIMO deployments. This paper introduces a novel framework that leverages digital twins (DTs) as priors to enable efficient zone-specific subspace-based channel estimation (CE). Subspace-based CE significantly reduces feedback overhead by focusing on the dominant channel components, exploiting sparsity in the angular domain while preserving estimation accuracy. While DT channels may exhibit inaccuracies, their coarse-grained subspaces provide a powerful starting point, reducing the search space and accelerating convergence. The framework employs a two-step clustering process on the Grassmann manifold, combined with reinforcement learning (RL), to iteratively calibrate subspaces and align them with real-world counterparts. Simulations show that digital twins not only enable near-optimal performance but also enhance the accuracy of subspace calibration through RL, highlighting their potential as a step towards learnable digital twins.
Spectrum Sharing in Satellite-Terrestrial Integrated Networks: Frameworks, Approaches, and Opportunities
Authors: Bodong Shang, Zheng Wang, Xiangyu Li, Chungang Yang, Chao Ren, Haijun Zhang
Date: 2025-01-06 00:00:00
Tasks: Management

To accommodate the increasing communication needs in non-terrestrial networks (NTNs), wireless users in remote areas may require access to more spectrum than is currently allocated. Terrestrial networks (TNs), such as cellular networks, are deployed in specific areas, but many underused licensed spectrum bands remain in remote areas. Therefore, bringing NTNs to a shared spectrum with TNs can improve network capacity under reasonable interference management. However, in satellite-terrestrial integrated networks (STINs), the comprehensive coverage of a satellite and the unbalanced communication resources of STINs make it challenging to effectively manage mutual interference between NTN and TN. This article presents the fundamentals and prospects of spectrum sharing (SS) in STINs by introducing four SS frameworks, their potential application scenarios, and technical challenges. Furthermore, advanced SS approaches related to interference management in STINs and performance metrics of SS in STINs are introduced. Moreover, a preliminary performance evaluation showcases the potential for sharing the spectrum between NTN and TN. Finally, future research opportunities for SS in STINs are discussed.
Blind Training for Channel-Adaptive Digital Semantic Communications
Authors: Yongjeong Oh, Joohyuk Park, Jinho Choi, Jihong Park, Yo-Seb Jeon
Date: 2025-01-04 00:00:00
Tasks: Decoder, Semantic Communication

Semantic encoders and decoders for digital semantic communication (SC) often struggle to adapt to variations in unpredictable channel environments and diverse system designs. To address these challenges, this paper proposes a novel framework for training semantic encoders and decoders to enable channel-adaptive digital SC. The core idea is to use binary symmetric channel (BSC) as a universal representation of generic digital communications, eliminating the need to specify channel environments or system designs. Based on this idea, our framework employs parallel BSCs to equivalently model the relationship between the encoder's output and the decoder's input. The bit-flip probabilities of these BSCs are treated as trainable parameters during end-to-end training, with varying levels of regularization applied to address diverse requirements in practical systems. The advantage of our framework is justified by developing a training-aware communication strategy for the inference stage. This strategy makes communication bit errors align with the pre-trained bit-flip probabilities by adaptively selecting power and modulation levels based on practical requirements and channel conditions. Simulation results demonstrate that the proposed framework outperforms existing training approaches in terms of both task performance and power consumption.
Uncovering the Iceberg in the Sea: Fundamentals of Pulse Shaping and Modulation Design for Random ISAC Signals
Authors: Fan Liu, Yifeng Xiong, Shihang Lu, Shuangyang Li, Weijie Yuan, Christos Masouros, Shi Jin, Giuseppe Caire
Date: 2025-01-03 00:00:00
Tasks: ISAC

Integrated Sensing and Communications (ISAC) is expected to play a pivotal role in future 6G networks. To maximize time-frequency resource utilization, 6G ISAC systems must exploit data payload signals, that are inherently random, for both communication and sensing tasks. This paper provides a comprehensive analysis of the sensing performance of such communication-centric ISAC signals, with a focus on modulation and pulse shaping design to reshape the statistical properties of their auto-correlation functions (ACFs), thereby improving the target ranging performance. We derive a closed-form expression for the expectation of the squared ACF of random ISAC signals, considering arbitrary modulation bases and constellation mappings within the Nyquist pulse shaping framework. The structure is metaphorically described as an ``iceberg hidden in the sea", where the ``iceberg'' represents the squared mean of the ACF of random ISAC signals, that is determined by the pulse shaping filter, and the ``sea level'' characterizes the corresponding variance, caused by the randomness of the data payload. Our analysis shows that, for QAM/PSK constellations with Nyquist pulse shaping, Orthogonal Frequency Division Multiplexing (OFDM) achieves the lowest ranging sidelobe level across all lags. Building on these insights, we propose a novel Nyquist pulse shaping design to enhance the sensing performance of random ISAC signals. Numerical results validate our theoretical findings, showing that the proposed pulse shaping significantly reduces ranging sidelobes compared to conventional root-raised cosine (RRC) pulse shaping, thereby improving the ranging performance.