pwc-archive/papers-with-abstracts huggingface dataset:
5G PRS-Based Sensing: A Sensing Reference Signal Approach for Joint Sensing and Communication System
Authors: Zhiqing Wei, YuAn Wang, Liang Ma, Shaoshi Yang, Zhiyong Feng, Chengkang Pan, Qixun Zhang, Yajuan Wang, Huici Wu, Ping Zhang
Date: 2022-11-21 00:00:00
Tasks: Autonomous Driving

The emerging joint sensing and communication (JSC) technology is expected to support new applications and services, such as autonomous driving and extended reality (XR), in the future wireless communication systems. Pilot (or reference) signals in wireless communications usually have good passive detection performance, strong anti-noise capability and good auto-correlation characteristics, hence they bear the potential for applying in radar sensing. In this paper, we investigate how to apply the positioning reference signal (PRS) of the 5th generation (5G) mobile communications in radar sensing. This approach has the unique benefit of compatibility with the most advanced mobile communication system available so far. Thus, the PRS can be regarded as a sensing reference signal to simultaneously realize the functions of radar sensing, communication and positioning in a convenient manner. Firstly, we propose a PRS based radar sensing scheme and analyze its range and velocity estimation performance, based on which we propose a method that improves the accuracy of velocity estimation by using multiple frames. Furthermore, the Cramer-Rao lower bound (CRLB) of the range and velocity estimation for PRS based radar sensing and the CRLB of the range estimation for PRS based positioning are derived. Our analysis and simulation results demonstrate the feasibility and superiority of PRS over other pilot signals in radar sensing. Finally, some suggestions for the future 5G-Advanced and 6th generation (6G) frame structure design containing the sensing reference signal are derived based on our study.
Energy Efficiency Optimization of Intelligent Reflective Surface-assisted Terahertz-RSMA System
Authors: Xiaoyu Chen, Feng Yan, Menghan Hu, Zihuai Lin
Date: 2022-11-21 00:00:00

This paper examines the energy efficiency optimization problem of intelligent reflective surface (IRS)-assisted multi-user rate division multiple access (RSMA) downlink systems under terahertz propagation. The objective function for energy efficiency is optimized using the salp swarm algorithm (SSA) and compared with the successive convex approximation (SCA) technique. SCA technique requires multiple iterations to solve non-convex resource allocation problems, whereas SSA can consume less time to improve energy efficiency effectively. The simulation results show that SSA is better than SCA in improving system energy efficiency, and the time required is significantly reduced, thus optimizing the system's overall performance.
Harmonic-Copuled Riccati Equations and its Applications in Distributed Filtering
Authors: Jiachen Qian, Peihu Duan, Zhisheng Duan, Ling Shi
Date: 2022-11-21 00:00:00

The coupled Riccati equations are cosisted of multiple Riccati-like equations with solutions coupled with each other, which can be applied to depict the properties of more complex systems such as markovian systems or multi-agent systems. This paper manages to formulate and investigate a new kind of coupled Riccati equations, called harmonic-coupled Riccati equations (HCRE), from the matrix iterative law of the consensus on information-based distributed filtering (CIDF) algortihm proposed in [1], where the solutions of the equations are coupled with harmonic means. Firstly, mild conditions of the existence and uniqueness of the solution to HCRE are induced with collective observability and primitiviness of weighting matrix. Then, it is proved that the matrix iterative law of CIDF will converge to the unique solution of the corresponding HCRE, hence can be used to obtain the solution to HCRE. Moreover, through applying the novel theory of HCRE, it is pointed out that the real estimation error covariance of CIDF will also become steady-state and the convergent value is simplified as the solution to a discrete time Lyapunov equation (DLE). Altogether, these new results develop the theory of the coupled Riccati equations, and provide a novel perspective on the performance analysis of CIDF algorithm, which sufficiently reduces the conservativeness of the evaluation techniques in the literature. Finally, the theoretical results are verified with numerical experiments.
Deep Learning-Aided Perturbation Model-Based Fiber Nonlinearity Compensation
Authors: Shenghang Luo, Sunish Kumar Orappanpara Soman, Lutz Lampe, Jeebak Mitra
Date: 2022-11-19 00:00:00
Tasks: Deep Learning, Quantization

Fiber nonlinearity effects cap achievable rates and ranges in long-haul optical fiber communication links. Conventional nonlinearity compensation methods, such as perturbation theory-based nonlinearity compensation (PB-NLC), attempt to compensate for the nonlinearity by approximating analytical solutions to the signal propagation over optical fibers. However, their practical usability is limited by model mismatch and the immense computational complexity associated with the analytical computation of perturbation triplets and the nonlinearity distortion field. Recently, machine learning techniques have been used to optimise parameters of PB-based approaches, which traditionally have been determined analytically from physical models. It has been claimed in the literature that the learned PB-NLC approaches have improved performance and/or reduced computational complexity over their non-learned counterparts. In this paper, we first revisit the acclaimed benefits of the learned PB-NLC approaches by carefully carrying out a comprehensive performance-complexity analysis utilizing state-of-the-art complexity reduction methods. Interestingly, our results show that least squares-based PB-NLC with clustering quantization has the best performance-complexity trade-off among the learned PB-NLC approaches. Second, we advance the state-of-the-art of learned PB-NLC by proposing and designing a fully learned structure. We apply a bi-directional recurrent neural network for learning perturbation triplets that are alike those obtained from the analytical computation and are used as input features for the neural network to estimate the nonlinearity distortion field. Finally, we demonstrate through numerical simulations that our proposed fully learned approach achieves an improved performance-complexity trade-off compared to the existing learned and non-learned PB-NLC techniques.
Multi-timescale Event Detection in Nonintrusive Load Monitoring based on MDL Principle
Authors: Bo Liu, Jianfeng Zhang, Wenpeng Luan, Zishuai Liu, Yixin Yu
Date: 2022-11-19 00:00:00
Tasks: Action Detection, Activity Detection, Event Detection, Non-Intrusive Load Monitoring

Load event detection is the fundamental step for the event-based non-intrusive load monitoring (NILM). However, existing event detection methods with fixed parameters may fail in coping with the inherent multi-timescale characteristics of events and their event detection accuracy is easily affected by the load fluctuation. In this regard, this paper extends our previously designed two-stage event detection framework, and proposes a novel multi-timescale event detection method based on the principle of minimum description length (MDL). Following the completion of step-like event detection in the first stage, a long-transient event detection scheme with variable-length sliding window is designed for the second stage, which is intended to provide the observation and characterization of the same event at different time scales. In that, the context information in the aggregated load data is mined by motif discovery, and then based on the MDL principle, the proper observation scales are selected for different events and the corresponding detection results are determined. In the post-processing step, a load fluctuation location method based on voice activity detection (VAD) is proposed to identify and remove the unreasonable events caused by fluctuations. Based on newly proposed evaluation metrics, the comparison tests on public and private datasets demonstrate that our method achieves higher detection accuracy and integrity for events of various appliances across different scenarios.
The Perfect Match: RIS-enabled MIMO Channel Estimation Using Tensor Decomposition
Authors: Bilal Ahmad, Kevin Weinberger, Aydin Sezgin, Bilal Zafar, Martin Haardt
Date: 2022-11-18 00:00:00
Tasks: Tensor Decomposition

The deployment of reconfigurable intelligent surfaces (RISs) in a communication system provides control over the propagation environment, which facilitates the augmentation of a multitude of communication objectives. As these performance gains are highly dependent on the applied phase shifts at the RIS, accurate channel state information at the transceivers is imperative. However, not only do RISs traditionally lack signal processing capabilities, but their end-to-end channels also consist of multiple components. Hence, conventional channel estimation (CE) algorithms become incompatible with RIS-aided communication systems as they fail to provide the necessary information about the channel components, which are essential for a beneficial RIS configuration. To enable the full potential of RISs, we propose to use tensor-decomposition-based CE, which facilitates smart configuration of the RIS by providing the required channel components. We use canonical polyadic (CP) decomposition, that exploits a structured time domain pilot sequence. Compared to other state-of-the-art decomposition methods, the proposed Semi-Algebraic CP decomposition via Simultaneous Matrix Diagonalization (SECSI) algorithm is more time efficient as it does not require an iterative process. The benefits of SECSI for RIS-aided networks are validated with numerical results, which show the improved individual and end-to-end CE accuracy of SECSI.
Methods for Cryo-EM Single Particle Reconstruction of Macromolecules having Continuous Heterogeneity
Authors: Bogdan Toader, Fred J. Sigworth, Roy R. Lederman
Date: 2022-11-19 00:00:00

Macromolecules change their shape (conformation) in the process of carrying out their functions. The imaging by cryo-electron microscopy of rapidly-frozen, individual copies of macromolecules (single particles) is a powerful and general approach to understanding the motions and energy landscapes of macromolecules. Widely-used computational methods already allow the recovery of a few distinct conformations from heterogeneous single-particle samples, but the treatment of complex forms of heterogeneity such as the continuum of possible transitory states and flexible regions remains largely an open problem. In recent years there has been a surge of new approaches for treating the more general problem of continuous heterogeneity. This paper surveys the current state of the art in this area.
Integrating molecular models into CryoEM heterogeneity analysis using scalable high-resolution deep Gaussian mixture models
Authors: Muyuan Chen, Bogdan Toader, Roy Lederman
Date: 2022-11-18 00:00:00
Tasks: Single Particle Analysis

Resolving the structural variability of proteins is often key to understanding the structure-function relationship of those macromolecular machines. Single particle analysis using Cryogenic electron microscopy (CryoEM), combined with machine learning algorithms, provides a way to reveal the dynamics within the protein system from noisy micrographs. Here, we introduce an improved computational method that uses Gaussian mixture models for protein structure representation and deep neural networks for conformation space embedding. By integrating information from molecular models into the heterogeneity analysis, we can resolve complex protein conformational changes at near atomic resolution and present the results in a more interpretable form.
Hierarchically Modular Dynamical Neural Network Relaxing in a Warped Space: Basic Model and its Characteristics
Authors: Kazuyoshi Tsutsumi, Ernst Niebur
Date: 2022-11-21 00:00:00

We propose a hierarchically modular, dynamical neural network model whose architecture minimizes a specifically designed energy function and defines its temporal characteristics. The model has an internal and an external space that are connected with a layered internetwork that consists of a pair of forward and backward subnets composed of static neurons (with an instantaneous time-course). Dynamical neurons with large time constants in the internal space determine the overall time-course. The model offers a framework in which state variables in the network relax in a warped space, due to the cooperation between dynamic and static neurons. We assume that the system operates in either a learning or an association mode, depending on the presence or absence of feedback paths and input ports. In the learning mode, synaptic weights in the internetwork are modified by strong inputs corresponding to repetitive neuronal bursting, which represents sinusoidal or quasi-sinusoidal waves in the short-term average density of nerve impulses or in the membrane potential. A two-dimensional mapping relationship can be formed by employing signals with different frequencies based on the same mechanism as Lissajous curves. In the association mode, the speed of convergence to a goal point greatly varies with the mapping relationship of the previously trained internetwork, and owing to this property, the convergence trajectory in the two-dimensional model with the non-linear mapping internetwork cannot go straight but instead must curve. We further introduce a constrained association mode with a given target trajectory and elucidate that in the internal space, an output trajectory is generated, which is mapped from the external space according to the inverse of the mapping relationship of the forward subnet.
Hodge-Decomposition of Brain Networks
Authors: D. Vijay Anand, Anass B El-Yaagoubi, Hernando Ombao, Moo K. Chung
Date: 2022-11-19 00:00:00

We propose to analyze dynamically changing brain networks by decomposing them into three orthogonal components through the Hodge decomposition. We propose to quantify the magnitude and relative strength of each component. We performed extensive simulation studies with known ground truth. The Hodge decomposition is then applied to the dynamically changing human brain networks obtained from a resting-state functional magnetic resonance imaging study. Our results indicate that the components of the Hodge decomposition contain biologically interpretable topological features that provide statistically significant findings not easily captured by traditional methods.
Hypergraph Analysis Toolbox for Chromosome Conformation
Authors: Joshua Pickard, Can Chen, Rahmy Salman, Cooper Stansbury, Sion Kim, Amit Surana, Anthony Bloch, Indika Rajapakse
Date: 2022-11-21 00:00:00

Recent advances in biological technologies, such as multi-way chromosome conformation capture (3C), require development of methods for analysis of multi-way interactions. Hypergraphs are mathematically tractable objects that can be utilized to precisely represent and analyze multi-way interactions. Here we present the Hypergraph Analysis Toolbox (HAT), a software package for visualization and analysis of multi-way interactions in complex systems.
RL Boltzmann Generators for Conformer Generation in Data-Sparse Environments
Authors: Yash Patel, Ambuj Tewari
Date: 2022-11-19 00:00:00

The generation of conformers has been a long-standing interest to structural chemists and biologists alike. A subset of proteins known as intrinsically disordered proteins (IDPs) fail to exhibit a fixed structure and, therefore, must also be studied in this light of conformer generation. Unlike in the small molecule setting, ground truth data are sparse in the IDP setting, undermining many existing conformer generation methods that rely on such data for training. Boltzmann generators, trained solely on the energy function, serve as an alternative but display a mode collapse that similarly preclude their direct application to IDPs. We investigate the potential of training an RL Boltzmann generator against a closely related "Gibbs score," and demonstrate that conformer coverage does not track well with such training. This suggests that the inadequacy of solely training against the energy is independent of the modeling modality
Optimal performance of a tontine overlay subject to withdrawal constraints
Authors: Peter A. Forsyth, Kenneth R. Vetzal, G. Westmacott
Date: 2022-11-18 00:00:00

We consider the holder of an individual tontine retirement account, with maximum and minimum withdrawal amounts (per year) specified. The tontine account holder initiates the account at age 65, and earns mortality credits while alive, but forfeits all wealth in the account upon death. The holder desires to maximize total withdrawals, and minimize the expected shortfall, assuming the holder survives to age 95. The investor controls the amount withdrawn each year and the fraction of the investments in stocks and bonds. The optimal controls are determined based on a parametric model fitted to almost a century of market data. The optimal control algorithm is based on dynamic programming and solution of a partial integro differential equation (PIDE) using Fourier methods. The optimal strategy (based on the parametric model) is tested out of sample using stationary block bootstrap resampling of the historical data. In terms of an expected total withdrawal, expected shortfall (EW-ES) efficient frontier, the tontine overlay greatly outperforms an optimal strategy (without the tontine overlay), which in turn outperforms a constant weight strategy with withdrawals based on the ubiquitous four per cent rule.
Learning Heterogeneous Agent Cooperation via Multiagent League Training
Authors: Qingxu Fu, Xiaolin Ai, Jianqiang Yi, Tenghai Qiu, Wanmai Yuan, Zhiqiang Pu
Date: 2022-11-13 00:00:00
Tasks: Diversity, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Many multiagent systems in the real world include multiple types of agents with different abilities and functionality. Such heterogeneous multiagent systems have significant practical advantages. However, they also come with challenges compared with homogeneous systems for multiagent reinforcement learning, such as the non-stationary problem and the policy version iteration issue. This work proposes a general-purpose reinforcement learning algorithm named Heterogeneous League Training (HLT) to address heterogeneous multiagent problems. HLT keeps track of a pool of policies that agents have explored during training, gathering a league of heterogeneous policies to facilitate future policy optimization. Moreover, a hyper-network is introduced to increase the diversity of agent behaviors when collaborating with teammates having different levels of cooperation skills. We use heterogeneous benchmark tasks to demonstrate that (1) HLT promotes the success rate in cooperative heterogeneous tasks; (2) HLT is an effective approach to solving the policy version iteration problem; (3) HLT provides a practical way to assess the difficulty of learning each role in a heterogeneous team.
Leveraging Orbital Information and Atomic Feature in Deep Learning Model
Authors: Xiangrui Yang
Date: 2022-10-29 00:00:00
Tasks: Deep Learning, Graph Representation Learning, Property Prediction, Representation Learning

Predicting material properties base on micro structure of materials has long been a challenging problem. Recently many deep learning methods have been developed for material property prediction. In this study, we propose a crystal representation learning framework, Orbital CrystalNet, OCrystalNet, which consists of two parts: atomic descriptor generation and graph representation learning. In OCrystalNet, we first incorporate orbital field matrix (OFM) and atomic features to construct OFM-feature atomic descriptor, and then the atomic descriptor is used as atom embedding in the atom-bond message passing module which takes advantage of the topological structure of crystal graphs to learn crystal representation. To demonstrate the capabilities of OCrystalNet we performed a number of prediction tasks on Material Project dataset and JARVIS dataset and compared our model with other baselines and state of art methods. To further present the effectiveness of OCrystalNet, we conducted ablation study and case study of our model. The results show that our model have various advantages over other state of art models.
Survey of Query-based Text Summarization
Authors: Hang Yu, Jiawei Han
Date: 2022-09-17 00:00:00
Tasks: Survey, Text Summarization

Query-based text summarization is an important real world problem that requires to condense the prolix text data into a summary under the guidance of the query information provided by users. The topic has been studied for a long time and there are many existing interesting research related to query-based text summarization. Yet much of the work is not systematically surveyed. This survey aims at summarizing some interesting work in query-based text summarization methods as well as related generic text summarization methods. Not all taxonomies in this paper exist the related work to the best of our knowledge and some analysis will be presented.
An Enhanced Object Detection Model for Scene Graph Generation
Authors: Mohammad Essam, Dina Khattab, Howida A. Shedeed, Mohamed F. Tolba
Date: 2022-11-18 00:00:00
Proceeding: International Conference on Advanced Intelligent Systems and Informatics 2022 11
Tasks: Graph Generation, Image Captioning, Image Retrieval, Object, object-detection, Object Detection, Retrieval, Scene Graph Generation, Scene Understanding

With computer vision improving, a higher level of understanding is needed to solve more complex problems such as semantic image retrieval, image captioning, and scene understanding. Scene understanding has been a long-studied problem due to its complexity and lack of proper data representation. A scene Graph is one of the most powerful data representations that can better understand the scene context. The task of a Scene Graph is to encode the objects presented in the scene, their attributes, as long as the relationships between these objects. With the scene Graph proving its capabilities in complicated tasks, the automation of scene graph generation became a must. Great research has been made to obtain accurate Scene Graphs using different deep learning architectures. The common module among those different architectures is the object detection module, where objects are firstly located in the input image. In this work, we propose using the most recent object detectors from the YOLOv5 family for the scene graph generation task. The proposed YOLOv5x6 achieved a State-Of-The-Art result of 32.7 mean average precision compared to previous works. Furthermore, the paper reviews the different object detectors used in literature for the scene graph generation.
The Quaero French Medical Corpus: A Ressource for Medical Entity Recognition and Normalization
Authors: Cyril Grouin, Jeremy Leixa, Aurélie Névéol, Sophie Rosset, Xavier Tannier, Pierre Zweigenbaum
Date: 2014-01-01 00:00:00
Proceeding: LREC 2014 1

A vast amount of information in the biomedical domain is available as natural language free text. An increasing number of documents in the field are written in languages other than English. Therefore, it is essential to develop resources, methods and tools that address Natural Language Processing in the variety of languages used by the biomedical community. In this paper, we report on the development of an extensive corpus of biomedical documents in French annotated at the entity and concept level. Three text genres are covered, comprising a total of 103,056 words. Ten entity categories corresponding to UMLS Semantic Groups were annotated, using automatic pre-annotations validated by trained human annotators. The pre-annotation method was found helful for entities and achieved above 0.83 precision for all text genres. Overall, a total of 26,409 entity annotations were mapped to 5,797 unique UMLS concepts.