pwc-archive/papers-with-abstracts huggingface dataset:
Deep Unrolling for Magnetic Resonance Fingerprinting
Authors: Dongdong Chen, Mike E. Davies, Mohammad Golbabaee
Date: 2022-01-23 00:00:00
Tasks: compressed sensing, Magnetic Resonance Fingerprinting, Rolling Shutter Correction

Magnetic Resonance Fingerprinting (MRF) has emerged as a promising quantitative MR imaging approach. Deep learning methods have been proposed for MRF and demonstrated improved performance over classical compressed sensing algorithms. However many of these end-to-end models are physics-free, while consistency of the predictions with respect to the physical forward model is crucial for reliably solving inverse problems. To address this, recently [1] proposed a proximal gradient descent framework that directly incorporates the forward acquisition and Bloch dynamic models within an unrolled learning mechanism. However, [1] only evaluated the unrolled model on synthetic data using Cartesian sampling trajectories. In this paper, as a complementary to [1], we investigate other choices of encoders to build the proximal neural network, and evaluate the deep unrolling algorithm on real accelerated MRF scans with non-Cartesian k-space sampling trajectories.
Large-Scale Graph Reinforcement Learning in Wireless Control Systems
Authors: Vinicius Lima, Mark Eisen, Konstantinos Gatsis, Alejandro Ribeiro
Date: 2022-01-24 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

Modern control systems routinely employ wireless networks to exchange information between spatially distributed plants, actuators and sensors. With wireless networks defined by random, rapidly changing transmission conditions that challenge assumptions commonly held in the design of control systems, proper allocation of communication resources is essential to achieve reliable operation. Designing resource allocation policies, however, is challenging, motivating recent works to successfully exploit deep learning and deep reinforcement learning techniques to design resource allocation and scheduling policies for wireless control systems (WCSs). As the number of learnable parameters in a neural network grows with the size of the input signal, deep reinforcement learning may fail to scale, limiting the immediate generalization of such scheduling and resource allocation policies to large-scale systems. The interference and fading patterns among plants and controllers in the network, however, induce a time-varying graph that can be used to construct policy representations based on graph neural networks (GNNs), with the number of learnable parameters now independent of the number of plants in the network. We further establish in the context of WCSs that, due to inherent invariance to graph permutations, the GNN is able to model scalable and transferable resource allocation policies, which are subsequently trained with primal-dual reinforcement learning. Numerical experiments show that the proposed graph reinforcement learning approach yields policies that not only outperform baseline solutions and deep reinforcement learning based policies in large-scale systems, but that can also be transferred across networks of varying size.
Pedestrian Dead Reckoning System using Quasi-static Magnetic Field Detection
Authors: Liqiang Zhang, Kai Guo, Yu Liu
Date: 2022-01-24 00:00:00
Tasks: Position, valid

Kalman filter-based Inertial Navigation System (INS) is a reliable and efficient method to estimate the position of a pedestrian indoors. Classical INS-based methodology which is called IEZ (INS-EKF-ZUPT) makes use of an Extended Kalman Filter (EKF), a Zero velocity UPdaTing (ZUPT) to calculate the position and attitude of a person. However, heading error which is a key factor of the whole Pedestrian Dead Reckoning (PDR) system is unobservable for IEZ-based PDR system. To minimize the error, Electronic Com-pass (EC) algorithm becomes a valid method. But magnetic disturbance may have a big negative effect on it. In this paper, the Quasi-static Magnetic field Detection (QMD) method is proposed to detect the pure magnetic field and then selects EC algorithm or Heuristic heading Drift Reduction algorithm (HDR) according to the detection result, which implements the complementation of the two methods. Meanwhile, the QMD, EC, and HDR algorithms are integrated into the IEZ framework to form a new PDR solution which is named Advanced IEZ (AIEZ).
Estimation of Bistatic Radar Detection Performance Under Discrete Clutter Conditions Using Stochastic Geometry
Authors: Shobha Sundar Ram, Gourab Ghatak
Date: 2022-01-24 00:00:00
Tasks: Diversity

We propose a metric called the bistatic radar detection coverage probability to evaluate the detection performance of a bistatic radar under discrete clutter conditions. Such conditions are commonly encountered in indoor and outdoor environments where passive radars receivers are deployed with opportunistic illuminators. Backscatter and multipath from the radar environment give rise to ghost targets and point clutter responses in the radar signatures resulting in deterioration in the detection performance. In our work, we model the clutter points as a Poisson point process to account for the diversity in their number and spatial distribution. Using stochastic geometry formulations we provide an analytical framework to estimate the probability that the signal to clutter and noise ratio from a target at any particular position in the bistatic radar plane is above a predefined threshold. Using the metric, we derive key radar system perspectives regarding the radar performance under noise and clutter limited conditions; the range at which the bistatic radar framework can be approximated to a monostatic framework; and the optimal radar transmitted power and bandwidth. Our theoretical results are experimentally validated with Monte Carlo simulations.
Enhanced motor imagery-based EEG classification using a discriminative graph Fourier subspace
Authors: Maliheh Miri, Vahid Abootalebi, Hamid Behjat
Date: 2022-01-23 00:00:00
Tasks: Brain Computer Interface, Classification, EEG, Electroencephalogram (EEG), Motor Imagery

Dealing with irregular domains, graph signal processing (GSP) has attracted much attention especially in brain imaging analysis. Motor imagery tasks are extensively utilized in brain-computer interface (BCI) systems that perform classification using features extracted from Electroencephalogram signals. In this paper, a GSP-based approach is presented for two-class motor imagery tasks classification. The proposed method exploits simultaneous diagonalization of two matrices that quantify the covariance structure of graph spectral representation of data from each class, providing a discriminative subspace where distinctive features are extracted from the data. The performance of the proposed method was evaluated on Dataset IVa from BCI Competition III. Experimental results show that the proposed method outperforms two state-of-the-art alternative methods.
Inter-Numerology Interference Pre-Equalization for 5G Mixed-Numerology Communications
Authors: Bugra Alp Cevikgibi, Ali Murat Demirtas, Tolga Girici, Huseyin Arslan
Date: 2022-01-23 00:00:00

This article proposes a pre-equalization method to remove inter-numerology interference (INI) that occurs in multi-numerology OFDM frame structures of fifth-generation New Radio (5G-NR) and beyond on the transmitter side. In the literature, guard bands, filters and interference cancellation methods are used to reduce the INI. In this work, we mathematically model how the INI is generated and how it can be removed completely for multi-numerology systems on the transmitter side.
$α$-Fairness User Pairing for Downlink NOMA Systems with Imperfect Successive Interference Cancellation
Authors: Nemalidinne Siva Mouni, Pavan Reddy M., Abhinav Kumar, Prabhat K. Upadhyay
Date: 2022-01-23 00:00:00
Tasks: Fairness

Non-orthogonal multiple access (NOMA) is considered as one of the predominant multiple access technique for the next-generation cellular networks. We consider a 2-user pair downlink NOMA system with imperfect successive interference cancellation (SIC). We consider bounds on the power allocation factors and then formulate the power allocation as an optimization problem to achieve {$\alpha$-Fairness} among the paired users. We show that {$\alpha$-Fairness} based power allocation factor coincides with lower bound on power allocation factor in case of perfect SIC and $\alpha > 2$. Further, as long as the proposed criterion is satisfied, it converges to the upper bound with increasing imperfection in SIC. Similarly, we show that, for $0<\alpha<1$, the optimal power allocation factor coincides with the derived lower bound on power allocation. Based on these observations, we then propose a low complexity sub-optimal algorithm. Through extensive simulations, we analyse the performance of the proposed algorithm and compare the performance against the state-of-the-art algorithms. We show that even though Near-Far based pairing achieves better fairness than the proposed algorithms, it fails to achieve rates equivalent to its orthogonal multiple access counterparts with increasing imperfections in SIC. Further, we show that the proposed optimal and sub-optimal algorithms achieve significant improvements in terms of fairness as compared to the state-of-the-art algorithms.
Copula-Based Modeling of RIS-Assisted Communications: Outage Probability Analysis
Authors: Imène Trigui, Damoon Shahbaztabar, Wessam Ajib, Wei-Ping Zhu
Date: 2022-01-22 00:00:00

Statistical characterization of the signal-to-noise ratio (SNR) of reconfigurable intelligent surface (RIS)-assistedcommunications in the presence of phase noise is an important open issue. In this letter, we exploit the concept of copula modeling to capture the non-standard dependence features that appear due to the presence of discrete phase noise. In particular,we consider the outage probability of RIS systems in Rayleighfading channels and provide joint distributions to characterize the dependencies due to the use of finite resolution phase shifters at the RIS. Numerical assessments confirm the validity of closed-form expressions of the outage probability and motivate the use of bivariate copula for further RIS studies.
Target Sensing with Intelligent Reflecting Surface: Architecture and Performance
Authors: Xiaodan Shao, Changsheng You, Wenyan Ma, Xiaoming Chen, Rui Zhang
Date: 2022-01-22 00:00:00

Intelligent reflecting surface (IRS) has emerged as a promising technology to reconfigure the radio propagation environment by dynamically controlling wireless signal's amplitude and/or phase via a large number of reflecting elements. In contrast to the vast literature on studying IRS's performance gains in wireless communications, we study in this paper a new application of IRS for sensing/localizing targets in wireless networks. Specifically, we propose a new self-sensing IRS architecture where the IRS controller is capable of transmitting probing signals that are not only directly reflected by the target (referred to as the direct echo link), but also consecutively reflected by the IRS and then the target (referred to as the IRS-reflected echo link). Moreover, dedicated sensors are installed at the IRS for receiving both the direct and IRS-reflected echo signals from the target, such that the IRS can sense the direction of its nearby target by applying a customized multiple signal classification (MUSIC) algorithm. However, since the angle estimation mean square error (MSE) by the MUSIC algorithm is intractable, we propose to optimize the IRS passive reflection for maximizing the average echo signals' total power at the IRS sensors and derive the resultant Cramer-Rao bound (CRB) of the angle estimation MSE. Last, numerical results are presented to show the effectiveness of the proposed new IRS sensing architecture and algorithm, as compared to other benchmark sensing systems/algorithms.
Topological Signal Processing over Generalized Cell Complexes
Authors: Stefania Sardellitti, Sergio Barbarossa
Date: 2022-01-22 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

Topological Signal Processing (TSP) over simplicial complexes is a framework that has been recently proposed, as a generalization of graph signal processing (GSP), to extend GSP to analyzing signals defined over sets of any order (i.e., not only vertices of a graph) and to capture multiway relations of any order among the data. However, simplicial complexes are required to satisfy the so-called inclusion property, according to which, if a set belongs to the complex, then all its subsets must also belong to the complex. In some applications, this is a severe limitation. To overcome this limit, in this paper we extend TSP to deal with signals defined over cell complexes and we also generalize the concept of cell complexes to include hollow cells. We show that, even if the algebraic formulation does not change significantly, the extension to the generalized cell complexes considerably broadens the number of applications. Most important, the new representation provides a much better trade-off between the complexity of the representation and its accuracy. In addition, we propose a method to infer the structure of the cell complex from data and we propose distributed filtering strategies, including a method to retrieve the sparsest representation of the harmonic component. We quantify the advantages of using cell complexes instead of simplicial complexes, in terms of the complexity/accuracy trade-off, for different applications such image segmentation and recovering of real flows measured on data traffic and transportation networks.
Joint CFO and Channel Estimation for RIS-aided Multi-user Massive MIMO Systems
Authors: Sumin Jeong, Arman Farhang, Nemanja Stefan Perović, Mark F. Flanagan
Date: 2022-01-21 00:00:00

Accurate channel estimation is essential to achieve the performance gains promised by the use of reconfigurable intelligent surfaces (RISs) in wireless communications. In the uplink of multi-user orthogonal frequency division multiple access (OFDMA) systems, synchronization errors such as carrier frequency offsets (CFOs) can significantly degrade the channel estimation performance. This becomes more critical in RIS-aided communications, as even a small channel estimation error leads to a significant performance loss. Motivated by this, we propose a joint CFO and channel estimation method for RIS-aided multi-user massive multiple-input multiple-output (MIMO) systems. Our proposed pilot structure allows accurate estimation of the CFOs without multi-user interference (MUI), using the same pilot resources for both CFO estimation and channel estimation. For joint estimation of multiple users' CFOs, a correlation-based approach is devised using the received signals at all BS antennas. Using least-squares (LS) estimation with the obtained CFO values, the channels of all users are jointly estimated. For optimization of the RIS phase shifts at the data transmission stage, we propose a projected gradient method (PGM). Simulation results demonstrate that the proposed method provides an improvement in the normalized mean-square error (NMSE) of channel estimation as well as in the bit error rate (BER) performance.
Power Forward Performance in Semimartingale Markets with Stochastic Integrated Factors
Authors: Lijun Bo, Agostino Capponi, Chao Zhou
Date: 2022-01-24 00:00:00
Tasks: Triplet

We study the forward investment performance process (FIPP) in an incomplete semimartingale market model with closed and convex portfolio constraints, when the investor's risk preferences are of the power form. We provide necessary and sufficient conditions for the existence of such FIPP. In a semimartingale factor model, we show that the FIPP can be recovered as a triplet of processes which admit an integral representation with respect to semimartingales. Using an integrated stochastic factor model, we relate the factor representation of the triplet of processes to the smooth solution of an ill-posed partial integro-differential Hamilton-Jacobi-Bellman (HJB) equation. We develop explicit constructions for the class of time-monotone FIPPs, generalizing existing results from Brownian to semimartingale market models.
Modelling preventive measures and their effect on generation times in emerging epidemics
Authors: Martina Favero, Gianpaolo Scalia Tomba, Tom Britton
Date: 2022-01-24 00:00:00

We present a stochastic epidemic model to study the effect of various preventive measures, such as uniform reduction of contacts and transmission, vaccination, isolation, screening and contact tracing, on a disease outbreak in a homogeneously mixing community. The model is based on an infectivity process, which we define through stochastic contact and infectiousness processes, so that each individual has an independent infectivity profile. In particular, we monitor variations of the reproduction number and of the distribution of generation times. We show that some interventions, i.e. uniform reduction and vaccination, affect the former while leaving the latter unchanged, whereas other interventions, i.e. isolation, screening and contact tracing, affect both quantities. We provide a theoretical analysis of the variation of these quantities, and we show that, in practice, the variation of the generation time distribution can be significant and that it can cause biases in the estimation of reproduction numbers. The framework, because of its general nature, captures the properties of many infectious diseases, but particular emphasis is on COVID-19, for which numerical results are provided.
Information-Based Trading
Authors: George Bouzianis, Lane P. Hughston, Leandro Sánchez-Betancourt
Date: 2022-01-21 00:00:00

We consider a pair of traders in a market where the information available to the second trader is a strict subset of the information available to the first trader. The traders make prices based on the information available concerning a security that pays a random cash flow at a fixed time $T$ in the future. Market information is modelled in line with the scheme of Brody, Hughston & Macrina (2007, 2008) and Brody, Davis, Friedman & Hughston (2009). The risk-neutral distribution of the cash flow is known to the traders, who make prices with a fixed multiplicative bid-offer spread and report their prices to a game master who declares that a trade has been made when the bid price of one of the traders crosses the offer price of the other. We prove that the value of the first trader's position is strictly greater than that of the second. The results are analyzed by use of simulation studies and generalized to situations where (a) there is a hierarchy of traders, (b) there are multiple successive trades, and (c) there is inventory aversion.
Dynamic optimization of volatile fatty acids to enrich biohydrogen production using a deep learning neural network
Authors: Mahmood Mahmoodi-Eshkaftaki, Gustavo Mockaitis, Mohammad Rafie Rafiee
Date: 2022-01-24 00:00:00
Tasks: regression

A new strategy was developed to investigate the effect of volatile fatty acids (VFAs) on the efficiency of biogas production with a focus on improving bio-H$_2$. The inoculum used, anaerobic granular sludge obtained from a UASB reactor treating poultry slaughterhouse wastewater, was pretreated with five different pretreatments. The relationship between VFAs and biogas compounds was studied as time-dependent components. In time-dependent processes with small sample size data, regression models may not be good enough at estimating responses. Therefore, a deep learning neural network (DNN) model was developed to estimate the biogas compounds based on the VFAs. The accuracy of this model to predict the biogas compounds was higher than that of multivariate regression models. Further, it could predict the effect of time changes on biogas compounds. Analysis showed that all the pretreatments were able to increase the ratio of butyric acid / acetic acid successfully, decrease propionic acid drastically, and increase the efficiency of bio-H$_2$ production. As discovered, butyric acid had the greatest effect on bio-H$_2$, and propionic acid had the greatest effect on CH$_4$ production. The best amounts of the VFAs were determined using an optimization method, integrated DNN and desirability analysis, dynamically retrained based on digestion time. Accordingly, optimal ranges of acetic, propionic, and butyric acids were 823.2 - 1534.3, 36.3 - 47.4, and 1522 - 1822 mg/L, respectively, determined for digestion time of 25.23 - 123.63 h. These values resulted in the production of bio-H$_2$, N$_2$, CO$_2$, and CH$_4$ in ranges of 6.4 - 26.2, 12.2 - 43.2, 5 - 25.3, and 0 - 1.4 mmol/L, respectively. The optimum ranges of VFAs are relatively wide ranges and practically can be used in biogas plants.
Isolated parkinsonism is an atypical presentation of GRN and C9orf72 gene mutations
Authors: Fábio Carneiro, Dario Saracino, Vincent Huin, Fabienne Clot, Cécile Delorme, Aurélie Méneret, Stéphane Thobois, Florence Cormier, Jean Christophe Corvol, Timothée Lenglet, Marie Vidailhet, Marie-Odile Habert, Audrey Gabelle, Émilie Beaufils, Karl Mondon, Mélissa Tir, Daniela Andriuta, Alexis Brice, Vincent Deramecourt, Isabelle Le Ber
Date: 2022-01-24 00:00:00

Introduction: A phenotype of isolated parkinsonism mimicking Idiopathic Parkinson's Disease (IPD) is a rare clinical presentation of GRN and C9orf72 mutations, the major genetic causes of frontotemporal dementia (FTD). It still remains controversial if this association is fortuitous or not, and which clinical clues could reliably suggest a genetic FTD etiology in IPD patients. This study aims to describe the clinical characteristics of FTD mutation carriers presenting with IPD phenotype, provide neuropathological evidence of the mutation's causality, and specifically address their "red flags" according to current IPD criteria. Methods: Seven GRN and C9orf72 carriers with isolated parkinsonism at onset, and three patients from the literature were included in this study. To allow better delineation of their phenotype, the presence of supportive, exclusion and "red flag" features from MDS criteria were analyzed for each case. Results: Amongst the ten patients (5 GRN, 5 C9orf72), seven fulfilled probable IPD criteria during all the disease course, while behavioral/language or motoneuron dysfunctions occurred later in three. Disease duration was longer and dopa-responsiveness was more sustained in C9orf72 than in GRN carriers. Subtle motor features, cognitive/behavioral changes, family history of dementia/ALS were suggestive clues for a genetic diagnosis. Importantly, neuropathological examination in one patient revealed typical TDP-43-inclusions without alpha-synucleinopathy, thus demonstrating the causal link between FTD mutations, TDP-43-pathology and PD phenotype. Conclusion: We showed that, altogether, family history of early-onset dementia/ALS, the presence of cognitive/behavioral dysfunction and subtle motor characteristics are atypical features frequently present in the parkinsonian presentations of GRN and C9orf72 mutations .