pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Wideband DoA Estimation with a Robust Iterative Method for Uniform Circular Arrays
Authors: Xiaorui Ding, Wenbo Xu, Yue Wang
Date: 2022-10-30 00:00:00

Direction-of-arrival (DoA) is a critical parameter in wireless channel estimation. With the ever-increasing requirement of high data rate and ubiquitous devices in wireless communication systems, effective wideband DoA estimation is desirable. In this paper, an iterative coherent signal-subspace method including three main steps in each iteration is proposed for wideband two-dimensional (2D) DoA estimation with a uniform circular array. The first step selects partial frequency points for the subsequent focusing process. The second step performs the focusing process, where the angle intervals are designed to generate focusing matrices with robustness, and the signal-subspaces at the selected frequency points are focused into a reference frequency. The third step estimates DoAs with the multiple signal classification (MUSIC) algorithm, where the range of the MUSIC spatial spectrum is constrained by the aforementioned angle intervals. The key parameters of the proposed method in the current iteration are adjusted based on the estimation results in the previous iterations. Besides, the Cram\'er-Rao bound of the investigated scenario of DoA estimation is derived as a performance benchmark, based on which the guidelines for practical application are provided. The simulation results indicate the proposed method enjoys better estimation performance and preferable efficiency when compared with the benchmark methods.
Distributed Swarm Learning for Internet of Things at the Edge: Where Artificial Intelligence Meets Biological Intelligence
Authors: Yue Wang, Zhi Tian, Xin Fan, Yan Huo, Cameron Nowzari, Kai Zeng
Date: 2022-10-29 00:00:00

With the proliferation of versatile Internet of Things (IoT) services, smart IoT devices are increasingly deployed at the edge of wireless networks to perform collaborative machine learning tasks using locally collected data, giving rise to the edge learning paradigm. Due to device restrictions and resource constraints, edge learning among massive IoT devices faces major technical challenges caused by the communication bottleneck, data and device heterogeneity, non-convex optimization, privacy and security concerns, and dynamic environments. To overcome these challenges, this article studies a new framework of distributed swarm learning (DSL) through a holistic integration of artificial intelligence and biological swarm intelligence. Leveraging efficient and robust signal processing and communication techniques, DSL contributes to novel tools for learning and optimization tailored for real-time operations of large-scale IoT in edge wireless environments, which will benefit a wide range of edge IoT applications.
Joint Localization and Beamforming for Reconfigurable Intelligent Surface Aided 5G mmWave Communication Systems
Authors: Yunis Xanthos, Wanting Lyu, Songjie Yang, Chadi Assi, Xianbing Zou, Ning Wei
Date: 2022-10-27 00:00:00

Reconfigurable intelligent surface (RIS) is an attractive technology to improve the transmission rate of millimetre-wave (mmWave) communication systems. The previous {research} on RIS technology mainly focused on improving the transmission rate and security rate of the mmWave communication systems. Since the emergence of RIS technology creates the conditions for generating an intelligent radio environment, it also has potential advantages on improving the localization accuracy of the mmWave communication systems. Deployed on walls and objects, RISs are capable of significantly improving communications and positioning coverage by controlling the multi-path reflection. This paper considers the RIS-aided mmWave localization system and proposes a joint beamforming and localization problem. However, since the objective function depends on the unknown UE's position and instantaneous channel state information (CSI), this beamforming and localization technology based on RIS assistance is challenging. To solve this problem, we propose a new joint localization and beamforming optimization (JLBO) algorithm, and give the proof of its convergence. The simulation results show that the RIS can improve the user localization accuracy of the system and the proposed scheme has a significant performance improvement compared with the traditional schemes.
Evolution and Function of SMC Proteins
Authors: J. C. Phillips
Date: 2022-10-29 00:00:00

Structural Maintenance of Chromosomes, SMCs, proteins have long rod like structures immersed in water. Here we use our hydroanalytic methods based on amino acid sequences to discuss their dynamics at multiple length scales identified by evolution. The length scales are 10 to 100 times longer than used in normal studies of sequence evolution. Their hydropathic profiles exhibit many features unique to their structure and function.
Computational model advance deep brain stimulation for Parkinson's disease
Authors: Yongtong Wu, Kejia Hu, Shenquan Liu
Date: 2022-10-30 00:00:00
Tasks: Anatomy

Deep brain stimulation(DBS)has become an effective intervention for advanced Parkinson's disease, but the exact mechanism of DBS is still unclear. In this review, we discuss the history of DBS, the anatomy and internal architecture of the basal ganglia(BG), the abnormal pathological changes of the BG in Parkinson's disease, and how computational models can help understand and advance DBS. We also describe two types of models:mathematical theoretical models and clinical predictive models. Mathematical theoretical models simulate neurons or neural networks of BG to shed light on the mechanistic principle underlying DBS, while clinical predictive models focus more on patients' outcomes, helping to adapt treatment plans for each patient and advance novel electrode designs. Finally, we provide insights and an outlook on future technologies.
Lost Manuscripts and Extinct Texts: A Dynamic Model of Cultural Transmission
Authors: Jean-Baptiste Camps, Julien Randon-Furling
Date: 2022-10-29 00:00:00

How did written works evolve, disappear or survive down through the ages? In this paper, we propose a unified, formal framework for two fundamental questions in the study of the transmission of texts: how much was lost or preserved from all works of the past, and why do their genealogies (their ``phylogenetic trees'') present the very peculiar shapes that we observe or, more precisely, reconstruct? We argue here that these questions share similarities to those encountered in evolutionary biology, and can be described in terms of ``genetic'' drift and ``natural'' selection. Through agent-based models, we show that such properties as have been observed by philologists since the 1800s can be simulated, and confronted to data gathered for ancient and medieval texts across Europe, in order to obtain plausible estimations of the number of works and manuscripts that existed and were lost.
Mechanistic forecasts of species responses to climate change: the promise of biophysical ecology
Authors: Natalie J. Briscoe, Shane D. Morris, Paul D. Mathewson, Lauren B. Buckley, Marko Jusup, Ofir Levy, Ilya M. D. Maclean, Sylvain Pincebourde, Eric A. Riddell, Jessica A. Roberts, Rafael Schouten, Michael W. Sears, Michael R. Kearney
Date: 2022-10-29 00:00:00

A challenge in global change biology is to predict how species will respond to future environmental change and to manage these responses. To make such predictions and management actions robust to novel futures, we need to accurately characterize how organisms experience their environments and the biological mechanisms by which they respond. All organisms are thermodynamically connected to their environments through the exchange of heat and water at fine spatial and temporal scales and this exchange can be captured with biophysical models. Although mechanistic models based on biophysical ecology have a long history of development and application, their use in global change biology remains limited despite their enormous promise and increasingly accessible software. We contend that greater understanding and training in the theory and methods of biophysical models is vital to expand their application. Our review shows how biophysical models can be implemented to understand and predict climate change impacts on species' behavior, phenology, survival, distribution, and abundance. We illustrate the types of outputs that can be generated, and the data inputs required for different implementations. Examples range from simple calculations of body temperature to more complex analyses of species' distribution limits based on projected energy and water balances, accounting for behavior and phenology. We outline challenges that currently limit the widespread application of biophysical models. We discuss progress and future developments that could allow these models to be applied to many species across large spatial extents and timeframes. We highlight how biophysical models are uniquely suited to solve global change biology problems that involve predicting and interpreting responses to environmental variability and extremes, multiple or shifting constraints, and novel abiotic or biotic environments.
On the number of nucleoproteins in the assembly of coronaviruses: Consequences for COVID-19
Authors: Vladimir R. Chechetkin, Vasily V. Lobzin
Date: 2022-10-31 00:00:00

The multifunctional nucleoproteins play important role in the life cycle of coronaviruses. The assessment of their quantities is of general interest for the assembly of virions and medical applications. The proliferating nucleoproteins induce the related (auto)immune response and via binding to host RNA affect various regulation mechanisms. In this report we briefly summarize and comment the available experimental data on the subject concerned.
Interactive cohort exploration for spinocerebellar ataxias using synthetic cohort data for visualization
Authors: Philipp Wegner, Sebastian Schaaf, Mischa Uebachs, Marcus Grobe-Einsler, Thomas Klockgether, Juliane Fluck, Jennifer Faber
Date: 2022-10-29 00:00:00
Tasks: Data Visualization

Motivation: Visualization of data is a crucial step to understanding and deriving hypotheses from clinical data. However, for clinicians, visualization often comes with great effort due to the lack of technical knowledge about data handling and visualization. The application offers an easy-to-use solution with an intuitive design that enables various kinds of plotting functions. The aim was to provide an intuitive solution with a low entrance barrier for clinical users. Little to no onboarding is required before creating plots, while the complexity of questions can grow up to specific corner cases. To allow for an easy start and testing with SCAview, we incorporated a synthetic cohort dataset based on real data of rare neurological movement disorders: the most common autosomal-dominantly inherited spinocerebellar ataxias (SCAs) type 1, 2, 3, and 6 (SCA1, 2, 3 and 6). Methods: We created a Django-based backend application that serves the data to a React-based frontend that uses Plotly for plotting. A synthetic cohort was created to deploy a version of SCAview without violating any data protection guidelines. Here, we added normal distributed noise to the data and therefore prevent re-identification while keeping distributions and general correlations. Results: This work presents SCAview, an user-friendly, interactive web-based service that enables data visualization in a clickable interface allowing intuitive graphical handling that aims to enable data visualization in a clickable interface. The service is deployed and can be tested with a synthetic cohort created based on a large, longitudinal dataset from observational studies in the most common SCAs.
Understanding the Maker Protocol
Authors: Jason Chen, Kathy Fogel, Kose John
Date: 2022-10-30 00:00:00

This paper discusses a decentralized finance (DeFi) application called MakerDAO. The Maker Protocol, built on the Ethereum blockchain, enables users to create and hold currency. Current elements of the Maker Protocol are the Dai stable coin, Maker Vaults, and Voting. MakerDAO governs the Maker Protocol by deciding on key parameters (e.g., stability fees, collateral types and rates, etc.) through the voting power of Maker (MKR) holders. The Maker Protocol is one of the largest decentralized applications (DApps) on the Ethereum blockchain and is the first decentralized finance (DeFi) application to earn significant adoption. The objective of this paper is to analyze and discuss the significance, uses, and functions of this DeFi application.
The importance of being scrambled: supercharged Quasi Monte Carlo
Authors: J. Hok, S. Kucherenko
Date: 2022-10-29 00:00:00

In many financial applications Quasi Monte Carlo (QMC) based on Sobol low-discrepancy sequences (LDS) outperforms Monte Carlo showing faster and more stable convergence. However, unlike MC QMC lacks a practical error estimate. Randomized QMC (RQMC) method combines the best of two methods. Application of scrambled LDS allow to compute confidence intervals around the estimated value, providing a practical error bound. Randomization of Sobol' LDS by two methods: Owen's scrambling and digital shift are compared considering computation of Asian options and Greeks using hyperbolic local volatility model. RQMC demonstrated the superior performance over standard QMC showing increased convergence rates and providing practical error bounds.
Stock price reaction to power outages following extreme weather events: Evidence from Texas power outage
Authors: Sherry Hu, Kose John, Balbinder Singh Gill
Date: 2022-10-30 00:00:00

In this study, we evaluate the effects of natural disasters on the stock (market) values of firms located in the affected counties. We are able to measure the change in stock prices of the firms affected by the 2021 Texas winter storm. To measure the abnormal return due to the storm, we use four different benchmark models: (1) the market-adjusted model, (2) the market model, (3) the Fama-French three-factor model, and (4) the Fama French plus momentum model. These statistical models in finance characterize the normal risk-return trade-off.
Assessing the difference between integrated quantiles and integrated cumulative distribution functions
Authors: Yunran Wei, Ricardas Zitikis
Date: 2022-10-30 00:00:00

This paper offers a mathematical invention that shows how to convert integrated quantiles, which often appear in risk measures, into integrated cumulative distribution functions, which are technically more tractable from various perspectives. The invention helps to avoid a number of technical assumptions that have been traditionally imposed when working with quantities containing quantiles. In particular it helps to completely avoid the requirement of the existence of a probability density function. The developed results explain and illustrate the invention, whose byproducts include the assessment of model uncertainty and misspecification, and the derivation of statistical inference results.
Dynamic Inventory Management with Mean-Field Competition
Authors: Ryan Donnelly, Zi Li
Date: 2022-10-31 00:00:00
Tasks: Management

Agents attempt to maximize expected profits earned by selling multiple units of a perishable product where their revenue streams are affected by the prices they quote as well as the distribution of other prices quoted in the market by other agents. We propose a model which captures this competitive effect and directly analyze the model in the mean-field limit as the number of agents is very large. We classify mean-field Nash equilibrium in terms of the solution to a Hamilton-Jacobi-Bellman equation and a consistency condition and use this to motivate an iterative numerical algorithm to compute equilibrium. Properties of the equilibrium pricing strategies and overall market dynamics are then investigated, in particular how they depend on the strength of the competitive interaction and the ability to oversell the product.
Symbolic Distillation for Learned TCP Congestion Control
Authors: S P Sharan, Wenqing Zheng, Kuo-Feng Hsu, Jiarong Xing, Ang Chen, Zhangyang Wang
Date: 2022-10-24 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning (RL)

Recent advances in TCP congestion control (CC) have achieved tremendous success with deep reinforcement learning (RL) approaches, which use feedforward neural networks (NN) to learn complex environment conditions and make better decisions. However, such "black-box" policies lack interpretability and reliability, and often, they need to operate outside the traditional TCP datapath due to the use of complex NNs. This paper proposes a novel two-stage solution to achieve the best of both worlds: first to train a deep RL agent, then distill its (over-)parameterized NN policy into white-box, light-weight rules in the form of symbolic expressions that are much easier to understand and to implement in constrained environments. At the core of our proposal is a novel symbolic branching algorithm that enables the rule to be aware of the context in terms of various network conditions, eventually converting the NN policy into a symbolic tree. The distilled symbolic rules preserve and often improve performance over state-of-the-art NN policies while being faster and simpler than a standard neural network. We validate the performance of our distilled symbolic rules on both simulation and emulation environments. Our code is available at https://github.com/VITA-Group/SymbolicPCC.
Deep Model Reassembly
Authors: Xingyi Yang, Daquan Zhou, Songhua Liu, Jingwen Ye, Xinchao Wang
Date: 2022-10-24 00:00:00
Tasks: model, Transfer Learning

In this paper, we explore a novel knowledge-transfer task, termed as Deep Model Reassembly (DeRy), for general-purpose model reuse. Given a collection of heterogeneous models pre-trained from distinct sources and with diverse architectures, the goal of DeRy, as its name implies, is to first dissect each model into distinctive building blocks, and then selectively reassemble the derived blocks to produce customized networks under both the hardware resource and performance constraints. Such ambitious nature of DeRy inevitably imposes significant challenges, including, in the first place, the feasibility of its solution. We strive to showcase that, through a dedicated paradigm proposed in this paper, DeRy can be made not only possibly but practically efficiently. Specifically, we conduct the partitions of all pre-trained networks jointly via a cover set optimization, and derive a number of equivalence set, within each of which the network blocks are treated as functionally equivalent and hence interchangeable. The equivalence sets learned in this way, in turn, enable picking and assembling blocks to customize networks subject to certain constraints, which is achieved via solving an integer program backed up with a training-free proxy to estimate the task performance. The reassembled models, give rise to gratifying performances with the user-specified constraints satisfied. We demonstrate that on ImageNet, the best reassemble model achieves 78.6% top-1 accuracy without fine-tuning, which could be further elevated to 83.2% with end-to-end training. Our code is available at https://github.com/Adamdad/DeRy
Adversarial Domain Adaptation for Action Recognition Around the Clock
Authors: Anwaar Ulhaq
Date: 2022-10-25 00:00:00
Tasks: Action Classification, Action Recognition, Domain Adaptation

Due to the numerous potential applications in visual surveillance and nighttime driving, recognizing human action in low-light conditions remains a difficult problem in computer vision. Existing methods separate action recognition and dark enhancement into two distinct steps to accomplish this task. However, isolating the recognition and enhancement impedes end-to-end learning of the space-time representation for video action classification. This paper presents a domain adaptation-based action recognition approach that uses adversarial learning in cross-domain settings to learn cross-domain action recognition. Supervised learning can train it on a large amount of labeled data from the source domain (daytime action sequences). However, it uses deep domain invariant features to perform unsupervised learning on many unlabelled data from the target domain (night-time action sequences). The resulting augmented model, named 3D-DiNet can be trained using standard backpropagation with an additional layer. It achieves SOTA performance on InFAR and XD145 actions datasets.
Offset-Guided Attention Network for Room-Level Aware Floor Plan Segmentation
Authors: Zhangyu Wang, Ningyuan Sun
Date: 2022-10-22 00:00:00

Recognition of floor plans has been a challenging and popular task. Despite that many recent approaches have been proposed for this task, they typically fail to make the room-level unified prediction. Specifically, multiple semantic categories can be assigned in a single room, which seriously limits their visual quality and applicability. In this paper, we propose a novel approach to recognize the floor plan layouts with a newly proposed Offset-Guided Attention mechanism to improve the semantic consistency within a room. In addition, we present a Feature Fusion Attention module that leverages the channel-wise attention to encourage the consistency of the room, wall, and door predictions, further enhancing the room-level semantic consistency. Experimental results manifest our approach is able to improve the room-level semantic consistency and outperforms the existing works both qualitatively and quantitatively.
An Industrial Workplace Alerting and Monitoring Platform to Prevent Workplace Injury and Accidents
Authors: Sanjay Adhikesaven
Date: 2022-10-25 00:00:00
Tasks: Pose Estimation

Workplace accidents are a critical problem that causes many deaths, injuries, and financial losses. Climate change has a severe impact on industrial workers, partially caused by global warming. To reduce such casualties, it is important to proactively find unsafe environments where injuries could occur by detecting the use of personal protective equipment (PPE) and identifying unsafe activities. Thus, we propose an industrial workplace alerting and monitoring platform to detect PPE use and classify unsafe activity in group settings involving multiple humans and objects over a long period of time. Our proposed method is the first to analyze prolonged actions involving multiple people or objects. It benefits from combining pose estimation with PPE detection in one platform. Additionally, we propose the first open source annotated data set with video data from industrial workplaces annotated with the action classifications and detected PPE. The proposed system can be implemented within the surveillance cameras already present in industrial settings, making it a practical and effective solution.
Medical data clustering based on particle swarm optimisation and genetic algorithm
Authors: Indresh Kumar Gupta, Vikash Yadav and Sushil Kumar
Date: 2019-11-04 00:00:00
Proceeding: International Journal of Advanced Intelligence Paradigms 2019 11
Tasks: Clustering

The main objective of this paper is to present a hybrid technique named as a PSO-GA for solving the constrained optimization problems. In this algorithm, particle swarm optimization (PSO) operates in the direction of improving the vector while the genetic algorithm (GA) has been used for modifying the decision vectors using genetic operators. The balance between the exploration and exploitation abilities have been further improved by incorporating the genetic operators, namely, crossover and mutation in PSO algorithm. The constraints defined in the problem are handled with the help of the parameter-free penalty function. The experimental results of constrained optimization problems are reported and compared with the typical approaches exist in the literature. As shown, the solutions obtained by the proposed approach are superior to those of existing best solutions reported in the literature. Furthermore, experimental results indicate that the proposed approach may yield better solutions to engineering problems than those obtained by using current algorithms.