pwc-archive/papers-with-abstracts huggingface dataset:
Wireless Connectivity and Localization for Advanced Air Mobility Services
Authors: Priyanka Sinha, Md Moin Uddin Chowdhury, Ismail Guvenc, David W. Matolak, Kamesh Namuduri
Date: 2022-02-21 00:00:00

By serving as an analog to traffic signal lights, communication signaling for drone to drone communications holds the key to the success of advanced air mobility (AAM) in both urban and rural settings. Deployment of AAM applications such as air taxis and air ambulances, especially at large-scale, requires a reliable channel for a point-to-point and broadcast communication between two or more aircraft. Achieving such high reliability, in a highly mobile environment, requires communication systems designed for agility and efficiency. This paper presents the foundations for establishing and maintaining a reliable communication channel among multiple aircraft in unique AAM settings. Subsequently, it presents concepts and results on wireless coverage and mobility for AAM services using cellular networks as a ground network infrastructure. Finally, we analyze the wireless localization performance at 3D AAM corridors when cellular networks are utilized, considering different corridor heights and base station densities. We highlight future research directions and open problems to improve wireless coverage and localization throughout the manuscript.
Coordinated Sum-Rate Maximization in Multicell MU-MIMO with Deep Unrolling
Authors: Lukas Schynol, Marius Pesavento
Date: 2022-02-21 00:00:00
Tasks: Rolling Shutter Correction

Coordinated weighted sum-rate maximization in multicell MIMO networks with intra- and intercell interference and local channel state at the base stations is recognized as an important yet difficult problem. A classical, locally optimal solution is obtained by the weighted minimum mean squared error (WMMSE) algorithm which facilitates a distributed implementation in multicell networks. However, it often suffers from slow convergence and therefore large communication overhead. To obtain more practical solutions, the unrolling/unfolding of traditional iterative algorithms gained significant attention. In this work, we demonstrate a complete unfolding of the WMMSE algorithm for transceiver design in multicell MU-MIMO interference channels with local channel state information. The resulting architecture termed GCN-WMMSE applies ideas from graph signal processing and is agnostic to different wireless network topologies, while exhibiting a low number of trainable parameters and high efficiency w.r.t. training data. It significantly reduces the number of required iterations while achieving performance similar to the WMMSE algorithm, alleviating the overhead in a distributed deployment. Additionally, we review previous architectures based on unrolling the WMMSE algorithm and compare them to GCN-WMMSE in their specific applicable domains.
Counterfactual Regret Minimization for Anti-jamming Game of Frequency Agile Radar
Authors: Huayue Li, Zhaowei Han, Wenqiang Pu, Liangqi Liu, Kang Li, Bo Jiu
Date: 2022-02-21 00:00:00
Tasks: counterfactual

The competition between radar and jammer is one emerging issue in modern electronic warfare, which in principle can be viewed as a non-cooperative game with two players. In this work, the competition between a frequency agile (FA) radar and a noise-modulated jammer is considered. As modern FA radar adopts coherent processing with several pulses, the competition is hence in a multiple-round way where each pulse can be modeled as one round interaction between the radar and jammer. To capture such multiple-round property as well as imperfect information inside the game, i.e., radar and jammer are unable to know the upcoming signal, we propose an extensive-form game formulation for such competition. Since the number of game information states grows exponentially with respect to number of pulses, finding Nash Equilibrium (NE) strategies may be a computationally intractable task. To effectively solve the game, a learning-based algorithm called deep Counterfactual Regret Minimization (CFR) is utilized. Numerical simulations demonstrates the effectiveness of deep CFR algorithm for approximately finding NE and obtaining the best response strategy.
DFT-Spread Orthogonal Time Frequency Space System with Superimposed Pilots for Terahertz Integrated Sensing and Communication
Authors: Yongzhi Wu, Chong Han, Zhi Chen
Date: 2022-02-21 00:00:00
Tasks: Integrated sensing and communication, ISAC, parameter estimation

Terahertz (THz) integrated sensing and communication (ISAC) is a promising interdisciplinary technology that realizes simultaneously transmitting Terabit-per-second (Tbps) and millimeter-level accurate environment or human activity sensing. However, both communication performance and sensing accuracy are influenced by the Doppler effects, which are especially severe in the THz band. Moreover, peak-to-average power ratio (PAPR) degrades the THz power amplifier (PA) efficiency. In this paper, a discrete Fourier transform spread orthogonal time frequency space (DFT-s-OTFS) system with superimposed pilots is proposed to improve the robustness to Doppler effects and reduce PAPR for THz ISAC. Then, a two-phase sensing parameter estimation algorithm is developed to integrate sensing functionality into the DFT-s-OTFS waveform. Meanwhile, a low-complexity iterative channel estimation and data detection method with a conjugate gradient based equalizer is proposed to recover the data symbols of DFT-s-OTFS. The proposed DFT-s-OTFS waveform can improve the PA efficiency by 10% on average compared to OTFS. Simulation results demonstrate that the proposed two-phase sensing estimation algorithm for THz DFT-s-OTFS systems is able to realize millimeter-level range estimation accuracy and decimeter-per-second-level velocity estimation accuracy. Moreover, the effectiveness of the iterative method for data detection aided by superimposed pilots in DFT-s-OTFS systems is validated by the simulations and the bit error rate performance is not degraded by the Doppler effects.
Design and Detection of Controller Manipulation Attack on RIS Assisted Communication
Authors: Siddharth Sankar Acharjee, Arpan Chattopadhyay
Date: 2022-02-21 00:00:00

In this paper, we introduce a new attack called controller manipulation attack (CMA) on a Reconfigurable Intelligent Surface (RIS) assisted communication system between a transmitter and a receiver. An attacker has the capability to manipulate the RIS controller and modify the phase shift induced by the RIS elements. The goal of the attacker is to minimize the data rate at the receiver, subject to a constraint on the attack detection probability at the receiver. We consider a number of attack detection models: (i) composite hypothesis testing based attack detection in a given fading block for known channel gains, (ii) sequential quickest detection of CMA in a given fading block for known channel gains, (iii) nonparametric hypothesis test to detect CMA for unknown channel gains over a fading block, and (iv) signal-to-noise-ratio (SNR) moment based detection over possibly multiple fading blocks. In the first case, a simple energy detector turns out to be uniformly most powerful (UMP). In the second case, simplification of the standard CUSUM test and its performance bounds are obtained. In the third case, non-parametric Kolmogorov-Smirnov test is further simplified to a simple per-sample double threshold test. The attack against these three detectors are designed via novel optimization formulations and semidefinite relaxation based solutions. In the fourth case, we consider threshold detection using moments of SNR; various SNR moments under no attack are obtained analytically for large RIS and then used to formulate the attack design problem as a linear program. Finally, numerical results illustrate the performance and trade-offs associated with the attack schemes, and also demonstrate their efficacy.
Sensing as a Service in 6G Perceptive Networks: A Unified Framework for ISAC Resource Allocation
Authors: Fuwang Dong, Fan Liu, Yuanhao Cui, Wei Wang, Kaifeng Han, Zhiqin Wang
Date: 2022-02-21 00:00:00
Tasks: Fairness, ISAC, parameter estimation

In the upcoming next-generation (5G-Advanced and 6G) wireless networks, sensing as a service will play a more important role than ever before. Recently, the concept of perceptive network is proposed as a paradigm shift that provides sensing and communication (S&C) services simultaneously. This type of technology is typically referred to as Integrated Sensing and Communications (ISAC). In this paper, we propose the concept of sensing quality of service (QoS) in terms of diverse applications. Specifically, the probability of detection, the Cramer-Rao bound (CRB) for parameter estimation and the posterior CRB for moving target indication are employed to measure the sensing QoS for detection, localization, and tracking, respectively. Then, we establish a unified framework for ISAC resource allocation, where the fairness and the comprehensiveness optimization criteria are considered for the aforementioned sensing services. The proposed schemes can flexibly allocate the limited power and bandwidth resources according to both S&C QoSs. Finally, we study the performance trade-off between S&C services in different resource allocation schemes by numerical simulations.
RFTacho: Non-intrusive RF monitoring of rotating machines
Authors: Mohammad Heggo, Laksh Bhatia, Julie A. McCann
Date: 2022-02-20 00:00:00
Tasks: Avg

Measuring rotation speed is essential to many engineering applications; it elicits faults undetectable by vibration monitoring alone and enhances the vibration signal analysis of rotating machines. Optical, magnetic or mechanical Tachometers are currently state-of-art. Their limitations are they require line-of-sight, direct access to the rotating object. This paper proposes RFTacho, a rotation speed measurement \emph{system} that leverages novel hardware and signal processing algorithms to produce highly accurate readings conveniently. RFTacho uses RF Orbital Angular Momentum (OAM) waves to measure rotation speed of multiple machines simultaneously with no requirements from the machine's properties. OAM antennas allow it to operate in high-scattering environments, commonly found in industries, as they are resilient to de-polarization compared to linearly polarized antennas. RFTacho achieves this by using two novel signal processing algorithms to extract the rotation speed of several rotating objects simultaneously amidst noise arising from high-scattering environments, non-line-of-sight scenarios and dynamic environmental conditions with a resolution of $1 rpm$. We test RFTacho on several real-world machines like fans, motors, air conditioners. Results show that RFTacho has avg. error of $<0.5\%$ compared to ground truth. We demonstrate RFTacho's simultaneous multiple-object measurement capability that other tachometers do not have. Initial experiments show that RFTacho can measure speeds as high as 7000 rpm (theoretically 60000 rpm) with high resiliency at different coverage distances and orientation angles, requiring only 150 mW transmit power while operating in the 5 GHz license-exempt band. RFTacho is the first RF-based sensing system that combines OAM waves and novel processing approaches to measure the rotation speed of multiple machines simultaneously in a non-intrusive way.
Innovative semantic communication system
Authors: Chen Dong, Haotai Liang, Xiaodong Xu, Shujun Han, Bizhu Wang, Ping Zhang
Date: 2022-02-19 00:00:00
Tasks: Decoder, Semantic Communication

Traditional communication systems focus on the transmission process, and the context-dependent meaning has been ignored. The fact that 5G system has approached Shannon limit and the increasing amount of data will cause communication bottleneck, such as the increased delay problems. Inspired by the ability of artificial intelligence to understand semantics, we propose a new communication paradigm, which integrates artificial intelligence and communication, the semantic communication system. Semantic communication is at the second level of communication based on Shannon and Weaver\cite{6197583}, which retains the semantic features of the transmitted information and recovers the signal at the receiver, thus compressing the communication traffic without losing important information. Different from other semantic communication systems, the proposed system not only transmits semantic information but also transmits semantic decoder. In addition, a general semantic metrics is proposed to measure the quality of semantic communication system. In particular, the semantic communication system for image, namely AESC-I, is designed to verify the feasibility of the new paradigm. Simulations are conducted on our system with the additive white Gaussian noise (AWGN) and the multipath fading channel using MNIST and Cifar10 datasets. The experimental results show that DeepSC-I can effectively extract semantic information and reconstruct images at a relatively low SNR.
Integrated Human Activity Sensing and Communications
Authors: Xinyu Li, Yuanhao Cui, J. Andrew Zhang, Fan Liu, Daqing Zhang, Lajos Hanzo
Date: 2022-02-19 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Integrated sensing and communication, ISAC

Advances in wireless communication and signal processing facilitate integrated sensing and communication a compelling technology that intrinsically combines sensing and communication functionalities for the dual purpose exploitation of wireless hardware resources and pursues mutual benefits. Consequently, the next generation communications network will be perceptive. In this article, we provide a review of human related sensing in the context of ISAC. We first present a general ISAC receiver signal processing framework, with a focus on human activity recognition. Based on its specific spatial deployments, we then categorize ISAC HAR into monostatic, bistatic, and distributed deployments, and discuss their properties, critical research problems and solutions. To facilitate the system's realization and improve its recognition performance, we then explore the inherent connections between the physical layer system parameters and HAR performance metrics. Experimental results are presented for characterizing the sensing potentials of different ISAC systems. Finally, we review the technical challenges and identify the open research problems.
Probability equivalent level of Value at Risk and higher-order Expected Shortfalls
Authors: Matyas Barczy, Fanni K. Nedényi, László Sütő
Date: 2022-02-20 00:00:00

We investigate the probability equivalent level of Value at Risk and $n^{\mathrm{th}}$-order Expected Shortfall (called PELVE_n), which can be considered as a variant of the notion of the probability equivalent level of Value at Risk and Expected Shortfall (called PELVE) due to Li and Wang (2022). We study the finiteness, uniqueness and several properties of PELVE_n, we calculate PELVE_n of some notable distributions, PELVE_2 of a random variable having generalized Pareto excess distribution, and we describe the asymptotic behaviour of PELVE_2 of regularly varying distributions as the level tends to $0$. Some properties of $n^{\mathrm{th}}$-order Expected Shortfall are also investigated. Among others, it turns out that the Gini Shortfall at some level $p\in[0,1)$ corresponding to a (loading) parameter $\lambda\geq 0$ is the linear combination of the Expected Shortfall at level $p$ and the $2^{\mathrm{nd}}$-order Expected Shortfall at level $p$ with coefficients $1-2\lambda$ and $2\lambda$, respectively.
Identifying OCRs in cfDNA WGS Data by Correlation Clustering
Authors: Farshad Noravesh, Fahimeh Palizban
Date: 2022-02-19 00:00:00
Tasks: Clustering, graph construction, Optical Character Recognition (OCR), Specificity

In the recent decade, the emergence of liquid biopsy has significantly improved cancer monitoring and detection. Dying cells, including those originating from tumors, shed their DNA into the bloodstream and contribute to a pool of circulating fragments called cell-free DNA (cfDNA). Identifying the tissue origin of these DNA fragments from their epigenetic features has implications in various clinical contexts. Open chromatin regions (OCRs) are important epigenetic features of DNA that reflect cell types of origin. Profiling these features by DNase-seq, ATAC-seq, and histone ChIP-seq provides insights into tissue-specific and disease-specific regulatory mechanisms. Integration of genomic and epigenomic features for cancer detection by liquid biopsy has previously been reported. However, many multimodal analyses require large amounts of cfDNA input and/or multiple types of experiments to cover the genomic and epigenomic aspects of a single sample which is cost and time prohibitive. Thus, methods that capture genomic and epigenomic profiles in a single experiment type with low input requirements are of importance. Predicting OCRs from whole genome sequencing (WGS) data is one such approach. Here, we applied a correlation clustering algorithm to predict OCRs. We used local sequencing depth as input to our algorithm. Multiple processing steps were then applied as follows: count normalization, discrete Fourier transform conversion, graph construction, graph cut optimization by linear programming, and clustering. To validate the proposed method, we compared the output of our predictions (OCR vs. non-OCR) with previously validated open chromatin regions related to human blood samples of the ATAC-db. The percentage of overlap between them is greater than 67%.
The functional architecture of the early vision and neurogeometric models
Authors: Dmitri V. Alekseevsky, Andrea Spiro
Date: 2022-02-21 00:00:00

The initial sections of the paper give a concise presentation, specially designed for a mathematically oriented audience, of some of the most basic facts on the functional architecture of early vision. Such information is usually scattered in a variety of papers and books, which are not easily accessible by non-specialists. Our goal is thus to offer a handy and short introduction to this topics, which might be helpful for researchers willing to enter the area of the applications of modern Differential Geometry in studies on the visual systems, baptized neurogeometry by J. Petitot. We then offer a survey of three of the most important neurogeometric models: Petitot's contact model of the primary visual cortex, its extension to A. Sarti, G. Citti and J. Petitot's symplectic model, and P. C. Bressloff and J. D. Cowan's spherical model of hypercolumns. We finally discuss the main points of the so-called ``conformal model'' for hypercolumns (a model that was briefly presented in [D. V. Alekseevsky, Conformal model of hypercolumns in V1 cortex and the Moebius group, in ``Geometric science of information'', pp. 65--72, Springer, 2021] and given in detail in [D. V. Alekseevsky and A. Spiro, Conformal models for hypercolumns in the primary visual cortex V1, arXiv 2024]), which can be considered as a synthesis of the symplectic and the spherical models.
Frontal effective connectivity increases with task demands and time on task: a Dynamic Causal Model of electrocorticogram in macaque monkeys
Authors: Katharina Wegner, Charles R. E. Wilson, Emmanuel Procyk, Karl J. Friston, Frederik Van de Steen, Dimitris A. Pinotsis, Daniele Marinazzo
Date: 2022-02-21 00:00:00

We apply Dynamic Causal Models to electrocorticogram recordings from two macaque monkeys performing a problem-solving task that engages working memory, and induces time-on-task effects. We thus provide a computational account of changes in effective connectivity within two regions of the fronto-parietal network, the dorsolateral prefrontal cortex and the pre-supplementary motor area. We find that forward connections between the two regions increased in strength when task demands increased, and as the experimental session progressed. Similarities in the effects of task demands and time on task allow us to interpret changes in frontal connectivity in terms of increased attentional effort allocation that compensates cognitive fatigue.
Effects of noise on leaky integrate-and-fire neuron models for neuromorphic computing applications
Authors: Thi Kim Thoa Thieu, Roderick Melnik
Date: 2022-02-19 00:00:00

Artificial neural networks (ANNs) have been extensively used for the description of problems arising from biological systems and for constructing neuromorphic computing models. The third generation of ANNs, namely, spiking neural networks (SNNs), inspired by biological neurons enable a more realistic mimicry of the human brain. A large class of the problems from these domains is characterized by the necessity to deal with the combination of neurons, spikes and synapses via integrate-and-fire neuron models. Motivated by important applications of the integrate-and-fire of neurons in neuromorphic computing for bio-medical studies, the main focus of the present work is on the analysis of the effects of additive and multiplicative types of random input currents together with a random refractory period on a leaky integrate-and-fire (LIF) synaptic conductance neuron model. Our analysis is carried out via Langevin stochastic dynamics in a numerical setting describing a cell membrane potential. We provide the details of the model, as well as representative numerical examples, and discuss the effects of noise on the time evolution of the membrane potential as well as the spiking activities of neurons in the LIF synaptic conductance model scrutinized here. Furthermore, our numerical results demonstrate that the presence of a random refractory period in the LIF synaptic conductance system may substantially influence an increased irregularity of spike trains of the output neuron.
Evolution and Mutations of Beta 2 Microglobulin
Authors: J. C. Phillips
Date: 2022-02-20 00:00:00

Here we examine the evolution of beta-2 microglobulin in terms of its hydropathic shapes, a theoretical construct that has revealed important trends. The dynamics of many proteins are largely driven by interactions between the protein itself and the thin water film that covers it. \b{eta}2m constitutes the basic building unit of the immunoglobulin superfamily; the evolution of its amino acid sequences from chickens to mice to humans provides new information about its multiple functions. Our hydrodynamic method involves concepts of topological shape evolution towards a critical point for optimized functions. The results are in excellent agreement with experiment for the details of the mouse-human evolution, as well as both the dangerous natural amyloid aggregation mutation D76N, and six other DN test mutations.
AutoClassWeb: a simple web interface for Bayesian clustering
Authors: Pierre Poulain, Jean-Michel Camadro
Date: 2022-02-21 00:00:00
Tasks: Clustering

Objective: Data clustering is a common exploration step in the omics era, notably in genomics and proteomics where many genes or proteins can bequantified from one or more experiments. Bayesian clustering is a powerful algorithm that can classify several thousands of genes or proteins. AutoClass C, its original implementation, handles missing data, automatically determines the best number of clusters but is not user-friendly.Results: We developed an online tool called AutoClassWeb, which provides an easy-to-use web interface for Bayesian clustering with AutoClass. Input data are entered as TSV files. Results are provided in formats that ease further analyses with spreadsheet programs or with programming languages, such as Python or R. AutoClassWeb is implemented in Python and is published under the 3-Clauses BSD license. The source code is available athttps://github.com/pierrepo/autoclassweb along with a detailed documentation.