pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning based Positioning with Multi-task Learning and Uncertainty-based Fusion
Authors: Anastasios Foliadis, Mario H. Castañeda, Richard A. Stirling-Gallacher, Reiner S. Thomä
Date: 2024-03-13 00:00:00
Tasks: Multi-Task Learning

Deep learning (DL) methods have been shown to improve the performance of several use cases for the fifth-generation (5G) New radio (NR) air interface. In this paper we investigate user equipment (UE) positioning using the channel state information (CSI) fingerprints between a UE and multiple base stations (BSs). In such a setup, we consider two different fusion techniques: early and late fusion. With early fusion, a single DL model can be trained for UE positioning by combining the CSI fingerprints of the multiple BSs as input. With late fusion, a separate DL model is trained at each BS using the CSI specific to that BS and the outputs of these individual models are then combined to determine the UE's position. In this work we compare these different fusion techniques and show that fusing the outputs of separate models achieves higher positioning accuracy, especially in a dynamic scenario. We also show that the combination of multiple outputs further benefits from considering the uncertainty of the output of the DL model at each BS. For a more efficient training of the DL model across BSs, we additionally propose a multi-task learning (MTL) scheme by sharing some parameters across the models while jointly training all models. This method, not only improves the accuracy of the individual models, but also of the final combined estimate. Lastly, we evaluate the reliability of the uncertainty estimation to determine which of the fusion methods provides the highest quality of uncertainty estimates.
Distributed Deep Learning for Modulation Classification in 6G Cell-Free Wireless Networks
Authors: Dieter Verbruggen, Hazem Sallouha, Sofie Pollin
Date: 2024-03-13 00:00:00
Tasks: Diversity

In the evolution of 6th Generation (6G) technology, the emergence of cell-free networking presents a paradigm shift, revolutionizing user experiences within densely deployed networks where distributed access points collaborate. However, the integration of intelligent mechanisms is crucial for optimizing the efficiency, scalability, and adaptability of these 6G cell-free networks. One application aiming to optimize spectrum usage is Automatic Modulation Classification (AMC), a vital component for classifying and dynamically adjusting modulation schemes. This paper explores different distributed solutions for AMC in cell-free networks, addressing the training, computational complexity, and accuracy of two practical approaches. The first approach addresses scenarios where signal sharing is not feasible due to privacy concerns or fronthaul limitations. Our findings reveal that maintaining comparable accuracy is remarkably achievable, yet it comes with an increase in computational demand. The second approach considers a central model and multiple distributed models collaboratively classifying the modulation. This hybrid model leverages diversity gain through signal combining and requires synchronization and signal sharing. The hybrid model demonstrates superior performance, achieving a 2.5% improvement in accuracy with equivalent total computational load. Notably, the hybrid model distributes the computational load across multiple devices, resulting in a lower individual computational load.
3D Spectrum Mapping and Reconstruction under Multi-Radiation Source Scenarios
Authors: Wang Jie, Lin Zhipeng, Zhu Qiuming, Wu Qihui, Lan Tianxu, Zhao Yi, Bai Yunpeng, Zhong Weizhi
Date: 2024-03-13 00:00:00
Tasks: Management

Spectrum map construction, which is crucial in cognitive radio (CR) system, visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation. Traditional reconstruction methods are generally for two-dimensional (2D) spectrum map and driven by abundant sampling data. In this paper, we propose a data-model-knowledge-driven reconstruction scheme to construct the three-dimensional (3D) spectrum map under multi-radiation source scenarios. We firstly design a maximum and minimum path loss difference (MMPLD) clustering algorithm to detect the number of radiation sources in a 3D space. Then, we develop a joint location-power estimation method based on the heuristic population evolutionary optimization algorithm. Considering the variation of electromagnetic environment, we self-learn the path loss (PL) model based on the sampling data. Finally, the 3D spectrum is reconstructed according to the self-learned PL model and the extracted knowledge of radiation sources. Simulations show that the proposed 3D spectrum map reconstruction scheme not only has splendid adaptability to the environment, but also achieves high spectrum construction accuracy even when the sampling rate is very low.
Sensor Network Localization via Riemannian Conjugate Gradient and Rank Reduction: An Extended Version
Authors: Yicheng Li, Xinghua Sun
Date: 2024-03-13 00:00:00
Tasks: Matrix Completion

This paper addresses the Sensor Network Localization (SNL) problem using received signal strength. The SNL is formulated as an Euclidean Distance Matrix Completion (EDMC) problem under the unit ball sample model. Using the Burer-Monteiro factorization type cost function, the EDMC is solved by Riemannian conjugate gradient with Hager-Zhang line search method on a quotient manifold. A "rank reduction" preprocess is proposed for proper initialization and to achieve global convergence with high probability. Simulations on a synthetic scene show that our approach attains better localization accuracy and is computationally efficient compared to several baseline methods. Characterization of a small local basin of attraction around the global optima of the s-stress function under Bernoulli sampling rule and incoherence matrix completion framework is conducted for the first time. Theoretical result conjectures that the Euclidean distance problem with a structure-less sample mask can be effectively handled using spectral initialization followed by vanilla first-order methods. This preliminary analysis, along with the aforementioned numerical accomplishments, provides insights into revealing the landscape of the s-stress function and may stimulate the design of simpler algorithms to tackle the non-convex formulation of general EDMC problems.
User-Centric Beam Selection and Precoding Design for Coordinated Multiple-Satellite Systems
Authors: Vu Nguyen Ha, Duy H. N. Nguyen, Juan C. -M. Duncan, Jorge L. Gonzalez-Rios, Juan A. Vasquez, Geoffrey Eappen, Luis M. Garces-Socarras, Rakesh Palisetty, Symeon Chatzinotas, Bjorn Ottersten
Date: 2024-03-13 00:00:00

This paper introduces a joint optimization framework for user-centric beam selection and linear precoding (LP) design in a coordinated multiple-satellite (CoMSat) system, employing a Digital-Fourier-Transform-based (DFT) beamforming (BF) technique. Regarding serving users at their target SINRs and minimizing the total transmit power, the scheme aims to efficiently determine satellites for users to associate with and activate the best cluster of beams together with optimizing LP for every satellite-to-user transmission. These technical objectives are first framed as a complex mixed-integer programming (MIP) challenge. To tackle this, we reformulate it into a joint cluster association and LP design problem. Then, by theoretically analyzing the duality relationship between downlink and uplink transmissions, we develop an efficient iterative method to identify the optimal solution. Additionally, a simpler duality approach for rapid beam selection and LP design is presented for comparison purposes. Simulation results underscore the effectiveness of our proposed schemes across various settings.
Sparse Bayesian Learning-Based Hierarchical Construction for 3D Radio Environment Maps Incorporating Channel Shadowing
Authors: Wang Jie, Zhu Qiuming, Lin Zhipeng, Chen Junting, Ding Guoru, Wu Qihui, Gu Guochen, Gao Qianhao
Date: 2024-03-13 00:00:00
Tasks: Computational Efficiency

The radio environment map (REM) visually displays the spectrum information over the geographical map and plays a significant role in monitoring, management, and security of spectrum resources.In this paper, we present an efficient 3D REM construction scheme based on the sparse Bayesian learning (SBL), which aims to recovery the accurate REM with limited and optimized sampling data.In order to reduce the number of sampling sensors, an efficient sparse sampling method for unknown scenarios is proposed. For the given construction accuracy and the priority of each location, the quantity and sampling locations can be jointly optimized.With the sparse sampled data, by mining the spectrum situation sparsity and channel propagation characteristics, an SBL-based spectrum data hierarchical recovery algorithm is developed to estimate the missing data of unsampled locations.Finally, the simulated 3D REM data in the campus scenario are used to verify the proposed methods as well as to compare with the state-of-the-art. We also analyze the recovery performance and the impact of different parameters on the constructed REMs. Numerical results demonstrate that the proposed scheme can ensure the construction accuracy and improve the computational efficiency under the low sampling rate.
Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion
Authors: Arian Eamaz, Farhang Yeganegi, Yunqiao Hu, Mojtaba Soltanalian, Shunqiao Sun
Date: 2024-03-13 00:00:00
Tasks: Matrix Completion, Quantization

This paper investigates the effects of coarse quantization with mixed precision on measurements obtained from sparse linear arrays, synthesized by a collaborative automotive radar sensing strategy. The mixed quantization precision significantly reduces the data amount that needs to be shared from radar nodes to the fusion center for coherent processing. We utilize the low-rank properties inherent in the constructed Hankel matrix of the mixed-precision array, to recover azimuth angles from quantized measurements. Our proposed approach addresses the challenge of mixed-quantized Hankel matrix completion, allowing for accurate estimation of the azimuth angles of interest. To evaluate the recovery performance of the proposed scheme, we establish a quasi-isometric embedding with a high probability for mixed-precision quantization. The effectiveness of our proposed scheme is demonstrated through numerical results, highlighting successful reconstruction.
ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training
Authors: Le Zhuo, Zewen Chi, Minghao Xu, Heyan Huang, Heqi Zheng, Conghui He, Xian-Ling Mao, Wentao Zhang
Date: 2024-02-28 00:00:00
Tasks: In-Context Learning, Language Modeling, Language Modelling, Large Language Model

We propose ProtLLM, a versatile cross-modal large language model (LLM) for both protein-centric and protein-language tasks. ProtLLM features a unique dynamic protein mounting mechanism, enabling it to handle complex inputs where the natural language text is interspersed with an arbitrary number of proteins. Besides, we propose the protein-as-word language modeling approach to train ProtLLM. By developing a specialized protein vocabulary, we equip the model with the capability to predict not just natural language but also proteins from a vast pool of candidates. Additionally, we construct a large-scale interleaved protein-text dataset, named InterPT, for pre-training. This dataset comprehensively encompasses both (1) structured data sources like protein annotations and (2) unstructured data sources like biological research papers, thereby endowing ProtLLM with crucial knowledge for understanding proteins. We evaluate ProtLLM on classic supervised protein-centric tasks and explore its novel protein-language applications. Experimental results demonstrate that ProtLLM not only achieves superior performance against protein-specialized baselines on protein-centric tasks but also induces zero-shot and in-context learning capabilities on protein-language tasks.
DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design
Authors: Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, Quanquan Gu
Date: 2024-02-26 00:00:00
Tasks: Avg, Drug Design, Drug Discovery

Designing 3D ligands within a target binding site is a fundamental task in drug discovery. Existing structured-based drug design methods treat all ligand atoms equally, which ignores different roles of atoms in the ligand for drug design and can be less efficient for exploring the large drug-like molecule space. In this paper, inspired by the convention in pharmaceutical practice, we decompose the ligand molecule into two parts, namely arms and scaffold, and propose a new diffusion model, DecompDiff, with decomposed priors over arms and scaffold. In order to facilitate the decomposed generation and improve the properties of the generated molecules, we incorporate both bond diffusion in the model and additional validity guidance in the sampling phase. Extensive experiments on CrossDocked2020 show that our approach achieves state-of-the-art performance in generating high-affinity molecules while maintaining proper molecular properties and conformational stability, with up to -8.39 Avg. Vina Dock score and 24.5 Success Rate. The code is provided at https://github.com/bytedance/DecompDiff
Elastic shape analysis for unsupervised clustering of left atrial appendage morphology
Authors: Zan Ahmad, Minglang Yin, Yashil Sukurdeep, Noam Rotenberg, Eugene Kholmovski, Natalia A. Trayanova
Date: 2024-03-13 00:00:00
Tasks: Clustering

Morphological variations in the left atrial appendage (LAA) are associated with different levels of ischemic stroke risk for patients with atrial fibrillation (AF). Studying LAA morphology can elucidate mechanisms behind this association and lead to the development of advanced stroke risk stratification tools. However, current categorical descriptions of LAA morphologies are qualitative and inconsistent across studies, which impedes advancements in our understanding of stroke pathogenesis in AF. To mitigate these issues, we introduce a quantitative pipeline that combines elastic shape analysis with unsupervised learning for the categorization of LAA morphology in AF patients. As part of our pipeline, we compute pairwise elastic distances between LAA meshes from a cohort of 20 AF patients, and leverage these distances to cluster our shape data. We demonstrate that our method clusters LAA morphologies based on distinctive shape features, overcoming the innate inconsistencies of current LAA categorization systems, and paving the way for improved stroke risk metrics using objective LAA shape groups.
Characterisation of Anti-Arrhythmic Drug Effects on Cardiac Electrophysiology using Physics-Informed Neural Networks
Authors: Ching-En Chiu, Arieh Levy Pinto, Rasheda A Chowdhury, Kim Christensen, Marta Varela
Date: 2024-03-13 00:00:00
Tasks: parameter estimation

The ability to accurately infer cardiac electrophysiological (EP) properties is key to improving arrhythmia diagnosis and treatment. In this work, we developed a physics-informed neural networks (PINNs) framework to predict how different myocardial EP parameters are modulated by anti-arrhythmic drugs. Using $\textit{in vitro}$ optical mapping images and the 3-channel Fenton-Karma model, we estimated the changes in ionic channel conductance caused by these drugs. Our framework successfully characterised the action of drugs HMR1556, nifedipine and lidocaine - respectively, blockade of $I_{K}$, $I_{Ca}$, and $I_{Na}$ currents - by estimating that they decreased the respective channel conductance by $31.8\pm2.7\%$ $(p=8.2 \times 10^{-5})$, $80.9\pm21.6\%$ $(p=0.02)$, and $8.6\pm0.5\%$ $ (p=0.03)$, leaving the conductance of other channels unchanged. For carbenoxolone, whose main action is the blockade of intercellular gap junctions, PINNs also successfully predicted no significant changes $(p>0.09)$ in all ionic conductances. Our results are an important step towards the deployment of PINNs for model parameter estimation from experimental data, bringing this framework closer to clinical or laboratory images analysis and for the personalisation of mathematical models.
Neural, Muscular, and Perceptual responses with shoulder exoskeleton use over Days
Authors: Tiash Rana Mukherjee, Oshin Tyagi, Jingkun Wang, John Kang, Ranjana Mehta
Date: 2024-03-12 00:00:00
Tasks: Electromyography (EMG), Functional Connectivity

Passive shoulder exoskeletons have been widely introduced in the industry to aid upper extremity movements during repetitive overhead work. As an ergonomic intervention, it is important to understand how users adapt to these devices over time and if these induce external stress while working. The study evaluated the use of an exoskeleton over a period of 3 days by assessing the neural, physiological, and perceptual responses of twenty-four participants by comparing a physical task against the same task with an additional cognitive workload. Over days adaptation to task irrespective of task and group were identified. Electromyography (EMG) analysis of shoulder and back muscles reveals lower muscle activity in the exoskeleton group irrespective of task. Functional connectivity analysis using functional near infrared spectroscopy (fNIRS) reveals that exoskeletons benefit users by reducing task demands in the motor planning and execution regions. Sex-based differences were also identified in these neuromuscular assessments.
Nonwoven Reinforced Photocurable Poly(glycerol sebacate)-Based Hydrogels
Authors: Michael Phillips, Giuseppe Tronci, Christopher M. Pask, Stephen J. Russell
Date: 2024-03-13 00:00:00

Implantable hydrogels should ideally possess mechanical properties matched to the surrounding tissues to enable adequate mechanical function while regeneration occurs. This can be challenging, especially when degradable systems with high water content and hydrolysable chemical bonds are required in anatomical sites under constant mechanical stimulation, e.g. a foot ulcer cavity. In these circumstances, the design of hydrogel composites is a promising strategy to provide controlled structural features and macroscopic properties over time. To explore this strategy, the synthesis of a new photocurable elastomeric polymer, poly(glycerol-co-sebacic acid-co-lactic acid-co-polyethylene glycol) acrylate (PGSLPA), is investigated, along with its processing into UV-cured hydrogels, electrospun nonwovens and fibre-reinforced variants, without the need for a high temperature curing step or use of hazardous solvents. The mechanical properties of bioresorbable PGSLPA hydrogels were studied with and without electrospun nonwoven reinforcement and with varied layered configurations, aiming to determine the effects of microstructure on bulk compressive strength and elasticity. The nonwoven reinforced PGSLPA hydrogels exhibited a 60 % increase in compressive strength and an 80 % increase in elastic moduli compared to fibre-free PGSLPA samples. Mechanical properties of the fibre-reinforced hydrogels could also be modulated by altering the layering arrangement of the nonwoven and hydrogel phase. The nanofibre reinforced PGSLPA hydrogels also exhibited good elastic recovery, as evidenced by hysteresis in compression fatigue stress-strain evaluations showing a return to original dimensions.
Trading Large Orders in the Presence of Multiple High-Frequency Anticipatory Traders
Authors: Ziyi Xu, Xue Cheng
Date: 2024-03-13 00:00:00

We investigate a market with a normal-speed informed trader (IT) who may employ mixed strategy and multiple anticipatory high-frequency traders (HFTs) who are under different inventory pressures, in a three-period Kyle's model. The pure- and mixed-strategy equilibria are considered and the results provide recommendations for IT's randomization strategy with different numbers of HFTs. Some surprising results about investors' profits arise: the improvement of anticipatory traders' speed or a more precise prediction may harm themselves but help IT.
Merino: Entropy-driven Design for Generative Language Models on IoT Devices
Authors: Youpeng Zhao, Ming Lin, Huadong Tang, Qiang Wu, Jun Wang
Date: 2024-02-28 00:00:00
Tasks: CPU, Language Modeling, Language Modelling, Zero-Shot Learning

Generative Large Language Models (LLMs) stand as a revolutionary advancement in the modern era of artificial intelligence (AI). However, scaling down LLMs for resource-constrained hardware, such as Internet-of-Things (IoT) devices requires non-trivial efforts and domain knowledge. In this paper, we propose a novel information-entropy framework for designing mobile-friendly generative language models. The whole design procedure involves solving a mathematical programming (MP) problem, which can be done on the CPU within minutes, making it nearly zero-cost. We evaluate our designed models, termed MeRino, across fourteen NLP downstream tasks, showing their competitive performance against the state-of-the-art autoregressive transformer models under the mobile setting. Notably, MeRino achieves similar or better performance on both language modeling and zero-shot learning tasks, compared to the 350M parameter OPT while being 4.9x faster on NVIDIA Jetson Nano with 5.5x reduction in model size.
Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection
Authors: Wei Ye, Chaoya Jiang, Haiyang Xu, Chenhao Ye, Chenliang Li, Ming Yan, Shikun Zhang, Songhang Huang, Fei Huang
Date: 2024-01-11 00:00:00

Vision Transformers (ViTs) have become increasingly popular in large-scale Vision and Language Pre-training (VLP) models. Although previous VLP research has demonstrated the efficacy of ViTs, these efforts still struggle with computational inefficiencies caused by lengthy visual sequences. To address this challenge, we introduce an efficient VLP approach called TRIPS, which stands for Text-Relevant Image Patch Selection. TRIPS progressively reduces the visual sequence using a text-guided patch-selection layer in the visual backbone, thereby accelerating both training and inference processes. This patch-selection layer dynamically computes text-dependent visual attention, enabling it to identify attentive image tokens with text guidance and fuse inattentive ones in an end-to-end fashion. Importantly, TRIPS does not add any extra parameters and generalizes to most ViT-based VLP models. We incorporate TRIPS into three representative VLP models covering single-stream, dual-stream, and generative paradigms, and conduct extensive experiments on five widely-used multi-modal benchmark datasets. Our experimental results reveal that TRIPS delivers a 40% speedup, while maintaining competitive or superior performance on downstream tasks.
Seg-metrics: a Python package to compute segmentation metrics
Authors: Jingnan Jia, Marius Staring, Berend C. Stoel
Date: 2024-01-12 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Semantic Segmentation

In response to a concerning trend of selectively emphasizing metrics in medical image segmentation (MIS) studies, we introduce \texttt{seg-metrics}, an open-source Python package for standardized MIS model evaluation. Unlike existing packages, \texttt{seg-metrics} offers user-friendly interfaces for various overlap-based and distance-based metrics, providing a comprehensive solution. \texttt{seg-metrics} supports multiple file formats and is easily installable through the Python Package Index (PyPI). With a focus on speed and convenience, \texttt{seg-metrics} stands as a valuable tool for efficient MIS model assessment.