pwc-archive/papers-with-abstracts huggingface dataset:
Semantic Mobile Base Station Placement
Authors: Kritik Soman, K. S. Venkatesh
Date: 2021-08-11 00:00:00
Tasks: Autonomous Vehicles, Semantic Segmentation

Location of Base Stations (BS) in mobile networks plays an important role in coverage and received signal strength. As Internet ofThings (IoT), autonomous vehicles and smart cities evolve, wireless net-work coverage will have an important role in ensuring seamless connectivity. Due to use of higher carrier frequencies, blockages cause communication to primarily be Line of Sight (LoS), increasing the importance of base station placement. In this paper, we propose a novel placement pipeline in which we perform semantic segmentation of aerial drone imagery using DeepLabv3+ and create its 2.5D model with the help ofDigital Surface Model (DSM). This is used along with Vienna simulator for finding the best location for deploying base stations by formulating the problem as a multi-objective function and solving it using Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The case with and without prior deployed base station is considered. We evaluate the basestation deployment based on Signal to Interference Noise Ratio (SINR)coverage probability and user down-link throughput. This is followed by comparison with other base station placement methods and the bene-fits offered by our approach. Our work is novel as it considers scenarios where there is high ground elevation and building density variation, and shows that irregular BS placement improves coverage.
Log-Spectral Matching GAN: PPG-based Atrial Fibrillation Detection can be Enhanced by GAN-based Data Augmentation with Integration of Spectral Loss
Authors: Cheng Ding, Ran Xiao, Duc Do, David Scott Lee, Shadi Kalantarian, Randall J Lee, Xiao Hu
Date: 2021-08-11 00:00:00
Tasks: Atrial Fibrillation Detection, Data Augmentation, Photoplethysmography (PPG)

Photoplethysmography (PPG) is a ubiquitous physiological measurement that detects beat-to-beat pulsatile blood volume changes and hence has a potential for monitoring cardiovascular conditions, particularly in ambulatory settings. A PPG dataset that is created for a particular use case is often imbalanced, due to a low prevalence of the pathological condition it targets to predict and the paroxysmal nature of the condition as well. To tackle this problem, we propose log-spectral matching GAN (LSM-GAN), a generative model that can be used as a data augmentation technique to alleviate the class imbalance in a PPG dataset to train a classifier. LSM-GAN utilizes a novel generator that generates a synthetic signal without a up-sampling process of input white noises, as well as adds the mismatch between real and synthetic signals in frequency domain to the conventional adversarial loss. In this study, experiments are designed focusing on examining how the influence of LSM-GAN as a data augmentation technique on one specific classification task - atrial fibrillation (AF) detection using PPG. We show that by taking spectral information into consideration, LSM-GAN as a data augmentation solution can generate more realistic PPG signals. The code of LSM-GAN is available at https://github.com/chengding0713/Log-Spectral-matching-GAN.
Stability and Convergence of Stochastic Particle Flow Filters
Authors: Liyi Dai, Fred Daum
Date: 2021-08-11 00:00:00
Tasks: Bayesian Inference

In this paper, we examine dynamic properties of particle flows for a recently derived parameterized family of stochastic particle flow filters for nonlinear filtering and Bayesian inference. In particular, we establish that particles maintain desired posterior distribution without the Gaussian assumption on measurement likelihood. Adopting the concept of Lyapunov stability, we further show that particles stay close but do not converge to the maximum likelihood estimate of the posterior distribution. The results demonstrate that stability of particle flows is maintained for this family of stochastic particle flow filters.
Ensuring Reliable Connectivity to Cellular-Connected UAVs with Up-tilted Antennas and Interference Coordination
Authors: Md Moin Uddin Chowdhury, Ismail Guvenc, Walid Saad, Arupjyoti Bhuyan
Date: 2021-08-11 00:00:00

To integrate unmanned aerial vehicles (UAVs) in future large-scale deployments, a new wireless communication paradigm, namely, the cellular-connected UAV has recently attracted interest. However, the line-of-sight dominant air-to-ground channels along with the antenna pattern of the cellular ground base stations (GBSs) introduce critical interference issues in cellular-connected UAV communications. In particular, the complex antenna pattern and the ground reflection (GR) from the down-tilted antennas create both coverage holes and patchy coverage for the UAVs in the sky, which leads to unreliable connectivity from the underlying cellular network. To overcome these challenges, in this paper, we propose a new cellular architecture that employs an extra set of co-channel antennas oriented towards the sky to support UAVs on top of the existing down-tilted antennas for ground user equipment (GUE). To model the GR stemming from the down-tilted antennas, we propose a path-loss model, which takes both antenna radiation pattern and configuration into account. Next, we formulate an optimization problem to maximize the minimum signal-to-interference ratio (SIR) of the UAVs by tuning the up-tilt (UT) angles of the up-tilted antennas. Since this is an NP-hard problem, we propose a genetic algorithm (GA) based heuristic method to optimize the UT angles of these antennas. After obtaining the optimal UT angles, we integrate the 3GPP Release-10 specified enhanced inter-cell interference coordination (eICIC) to reduce the interference stemming from the down-tilted antennas. Our simulation results based on the hexagonal cell layout show that the proposed interference mitigation method can ensure higher minimum SIRs for the UAVs over baseline methods while creating minimal impact on the SIR of GUEs.
Millimeter-Wave NR-U and WiGig Coexistence: Joint User Grouping, Beam Coordination and Power Control
Authors: Xiaoxia Xu, Qimei Chen, Hao Jiang, Jun Huang
Date: 2021-08-11 00:00:00

Millimeter wave (mmWave) communication is a promising New Radio in Unlicensed (NR-U) technology to meet with the ever-increasing data rate and connectivity requirements in future wireless networks. However, the development of NR-U networks should consider the coexistence with the incumbent Wireless Gigabit (WiGig) networks. In this paper, we introduce a novel multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) based mmWave NR-U and WiGig coexistence network for uplink transmission. Our aim for the proposed coexistence network is to maximize the spectral efficiency while ensuring the strict NR-U delay requirement and the WiGig transmission performance in real time environments. A joint user grouping, hybrid beam coordination and power control strategy is proposed, which is formulated as a Lyapunov optimization based mixed-integer nonlinear programming (MINLP) with unit-modulus and nonconvex coupling constraints. Hence, we introduce a penalty dual decomposition (PDD) framework, which first transfers the formulated MINLP into a tractable augmented Lagrangian (AL) problem. Thereafter, we integrate both convex-concave procedure (CCCP) and inexact block coordinate update (BCU) methods to approximately decompose the AL problem into multiple nested convex subproblems, which can be iteratively solved under the PDD framework. Numerical results illustrate the performance improvement ability of the proposed strategy, as well as demonstrate the effectiveness to guarantee the NR-U traffic delay and WiGig network performance.
Dual-Functional Radar-Communication Waveform Design: A Symbol-Level Precoding Approach
Authors: Rang Liu, Ming Li, Qian Liu, A. Lee Swindlehurst
Date: 2021-08-11 00:00:00

Dual-functional radar-communication (DFRC) systems can simultaneously perform both radar and communication functionalities using the same hardware platform and spectrum resource. In this paper, we consider multi-input multi-output (MIMO) DFRC systems and focus on transmit beamforming designs to provide both radar sensing and multi-user communications. Unlike conventional block-level precoding techniques, we propose to use the recently emerged symbol-level precoding approach in DFRC systems, which provides additional degrees of freedom (DoFs) that guarantee preferable instantaneous transmit beampatterns for radar sensing and achieve better communication performance. In particular, the squared error between the designed and desired beampatterns is minimized subject to the quality-of-service (QoS) requirements of the communication users and the constant-modulus power constraint. Two efficient algorithms are developed to solve this non-convex problem on both the Euclidean and Riemannian spaces. The first algorithm employs penalty dual decomposition (PDD), majorization-minimization (MM), and block coordinate descent (BCD) methods to convert the original optimization problem into two solvable sub-problems, and iteratively solves them using efficient algorithms. The second algorithm provides a much faster solution at the price of a slight performance loss, first transforming the original problem into Riemannian space, and then utilizing the augmented Lagrangian method (ALM) to obtain an unconstrained problem that is subsequently solved via a Riemannian Broyden-Fletcher-Goldfarb-Shanno (RBFGS) algorithm. Extensive simulations verify the distinct advantages of the proposed symbol-level precoding designs in both radar sensing and multi-user communications.
6G Internet of Things: A Comprehensive Survey
Authors: Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, Dusit Niyato, Octavia Dobre, H. Vincent Poor
Date: 2021-08-11 00:00:00
Tasks: Autonomous Driving, Survey

The sixth generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) towards a future of fully intelligent and autonomous systems. In this article, we explore the emerging opportunities brought by 6G technologies in IoT networks and applications, by conducting a holistic survey on the convergence of 6G and IoT. We first shed light on some of the most fundamental 6G technologies that are expected to empower future IoT networks, including edge intelligence, reconfigurable intelligent surfaces, space-air-ground-underwater communications, Terahertz communications, massive ultra-reliable and low-latency communications, and blockchain. Particularly, compared to the other related survey papers, we provide an in-depth discussion of the roles of 6G in a wide range of prospective IoT applications via five key domains, namely Healthcare Internet of Things, Vehicular Internet of Things and Autonomous Driving, Unmanned Aerial Vehicles, Satellite Internet of Things, and Industrial Internet of Things. Finally, we highlight interesting research challenges and point out potential directions to spur further research in this promising area.
Circulant Shift-based Beamforming for Secure Communication with Low-resolution Phased Arrays
Authors: Kartik Patel, Nitin Jonathan Myers, Robert W. Heath Jr
Date: 2021-08-10 00:00:00

Millimeter wave (mmWave) technology can achieve high-speed communication due to the large available spectrum. Furthermore, the use of directional beams in mmWave system provides a natural defense against physical layer security attacks. In practice, however, the beams are imperfect due to mmWave hardware limitations such as the low-resolution of the phase shifters. These imperfections in the beam pattern introduce an energy leakage that can be exploited by an eavesdropper. To defend against such eavesdropping attacks, we propose a directional modulation-based defense technique where the transmitter applies random circulant shifts of a beamformer. We show that the use of random circulant shifts together with appropriate phase adjustment induces artificial phase noise (APN) in the directions different from that of the target receiver. Our method corrupts the phase at the eavesdropper without affecting the communication link of the target receiver. We also experimentally verify the APN induced due to circulant shifts, using channel measurements from a 2-bit mmWave phased array testbed. Using simulations, we study the performance of the proposed defense technique against a greedy eavesdropping strategy in a vehicle-to-infrastructure scenario. The proposed technique achieves better defense than the antenna subset modulation, without compromising on the communication link with the target receiver.
Enumeration of binary trees compatible with a perfect phylogeny
Authors: Julia A. Palacios, Anand Bhaskar, Filippo Disanto, Noah A. Rosenberg
Date: 2021-08-10 00:00:00

Evolutionary models used for describing molecular sequence variation suppose that at a non-recombining genomic segment, sequences share ancestry that can be represented as a genealogy--a rooted, binary, timed tree, with tips corresponding to individual sequences. Under the in?fitely-many-sites mutation model, mutations are randomly superimposed along the branches of the genealogy, so that every mutation occurs at a chromosomal site that has not previously mutated; if a mutation occurs at an interior branch, then all individuals descending from that branch carry the mutation. The implication is that observed patterns of molecular variation from this model impose combinatorial constraints on the hidden state space of genealogies. In particular, observed molecular variation can be represented in the form of a perfect phylogeny, a tree structure that fully encodes the mutational differences among sequences. For a sample of n sequences, a perfect phylogeny might not possess n distinct leaves, and hence might be compatible with many possible binary tree structures that could describe the evolutionary relationships among the n sequences. Here, we investigate enumerative properties of the set of binary ranked and unranked tree shapes that are compatible with a perfect phylogeny, and hence, the binary ranked and unranked tree shapes conditioned on an observed pattern of mutations under the in?nitely-many-sites mutation model. We provide a recursive enumeration of these shapes. We consider both perfect phylogenies that can be represented as binary and those that are multifurcating. The results have implications for computational aspects of the statistical inference of evolutionary parameters that underlie sets of molecular sequences.
Markovian approximations of stochastic Volterra equations with the fractional kernel
Authors: Christian Bayer, Simon Breneis
Date: 2021-08-11 00:00:00

We consider rough stochastic volatility models where the variance process satisfies a stochastic Volterra equation with the fractional kernel, as in the rough Bergomi and the rough Heston model. In particular, the variance process is therefore not a Markov process or semimartingale, and has quite low H\"older-regularity. In practice, simulating such rough processes thus often results in high computational cost. To remedy this, we study approximations of stochastic Volterra equations using an $N$-dimensional diffusion process defined as solution to a system of ordinary stochastic differential equation. If the coefficients of the stochastic Volterra equation are Lipschitz continuous, we show that these approximations converge strongly with superpolynomial rate in $N$. Finally, we apply this approximation to compute the implied volatility smile of a European call option under the rough Bergomi and the rough Heston model.
Investigating the spatial resolution of EMG and MMG based on a systemic multi-scale model
Authors: Thomas Klotz, Leonardo Gizzi, Oliver Röhrle
Date: 2021-08-11 00:00:00
Tasks: Electromyography (EMG)

While electromyography (EMG) and magnetomyography (MMG) are both methods to measure the electrical activity of skeletal muscles, no systematic comparison between both signals exists. Within this work, we propose a systemic in silico model for EMG and MMG and test the hypothesis that MMG surpasses EMG in terms of spatial selectivity. The results show that MMG provides a slightly better spatial selectivity than EMG when recorded directly on the muscle surface. However, there is a remarkable difference in spatial selectivity for non-invasive surface measurements. The spatial selectivity of the MMG components aligned with the muscle fibres and normal to the body surface outperforms the spatial selectivity of surface EMG. Particularly, for the MMG's normal-to-the-surface component the influence of subcutaneous fat is minimal. Further, for the first time, we analyse the contribution of different structural components, i.e., muscle fibres from different motor units and the extracellular space, to the measurable biomagnetic field. Notably, the simulations show that the normal-to-the-surface MMG component, the contribution from volume currents in the extracellular space and in surrounding inactive tissues is negligible. Further, our model predicts a surprisingly high contribution of the passive muscle fibres to the observable magnetic field.
Periodontitis and preeclampsia in pregnancy: A systematic review and meta-analysis
Authors: Quynh-Anh Le, Rahena Akhter, Kimberly M. Coulton, Ngoc T. N Vo, Le T. Y Duong, Hoang V. Nong, Albert Yaacoub, George Condous, Joerg Eberhard, Ralph Nanan
Date: 2021-08-09 00:00:00

Objectives: A conflicting body of evidence suggests localized periodontal inflammation to spread systemically during pregnancy inducing adverse pregnancy outcomes. This systematic review and meta-analysis aimed to specifically evaluate the relationship between periodontitis and preeclampsia. Methods: Electronic searches were carried out in Medline, Pubmed, Cochrane Controlled Clinical Trial Register to identify and select observational case-control and cohort studies that analyzed the association between periodontal disease and preeclampsia. Prisma guidelines and Moose checklist were followed. Results: Thirty studies including six cohorts and twenty-four case-control studies were selected. Periodontitis was significantly associated with increased risk for preeclampsia, especially in a subgroup analysis including cohort studies and subgroup analysis with lower-middle-income countries. Conclusion: Periodontitis appears as a significant risk factor for preeclampsia, which might be even more pronounced in lower-middle-income countries.
Deep2Lead: A distributed deep learning application for small molecule lead optimization
Authors: Tarun Kumar Chawdhury, David J. Grant, Hyun Yong Jin
Date: 2021-08-09 00:00:00
Tasks: Drug Discovery

Lead optimization is a key step in drug discovery to produce potent and selective compounds. Historically, in silico screening and structure-based small molecule designing facilitated the processes. Although the recent application of deep learning to drug discovery piloted the possibility of their in silico application lead optimization steps, the real-world application is lacking due to the tool availability. Here, we developed a single user interface application, called Deep2Lead. Our web-based application integrates VAE and DeepPurpose DTI and allows a user to quickly perform a lead optimization task with no prior programming experience.
MuCoMiD: A Multitask Convolutional Learning Framework for miRNA-Disease Association Prediction
Authors: Thi Ngan Dong, Megha Khosla
Date: 2021-08-08 00:00:00

Growing evidence from recent studies implies that microRNA or miRNA could serve as biomarkers in various complex human diseases. Since wet-lab experiments are expensive and time-consuming, computational techniques for miRNA-disease association prediction have attracted a lot of attention in recent years. Data scarcity is one of the major challenges in building reliable machine learning models. Data scarcity combined with the use of precalculated hand-crafted input features has led to problems of overfitting and data leakage. We overcome the limitations of existing works by proposing a novel multi-tasking graph convolution-based approach, which we refer to as MuCoMiD. MuCoMiD allows automatic feature extraction while incorporating knowledge from five heterogeneous biological information sources (interactions between miRNA/diseases and protein-coding genes (PCG), interactions between protein-coding genes, miRNA family information, and disease ontology) in a multi-task setting which is a novel perspective and has not been studied before. To effectively test the generalization capability of our model, we construct large-scale experiments on standard benchmark datasets as well as our proposed larger independent test sets and case studies. MuCoMiD shows an improvement of at least 3% in 5-fold CV evaluation on HMDDv2.0 and HMDDv3.0 datasets and at least 35% on larger independent test sets with unseen miRNA and diseases over state-of-the-art approaches. We share our code for reproducibility and future research at https://git.l3s.uni-hannover.de/dong/cmtt.
Risk Concentration and the Mean-Expected Shortfall Criterion
Authors: Xia Han, Bin Wang, Ruodu Wang, Qinyu Wu
Date: 2021-08-11 00:00:00
Tasks: Management, Translation

Expected Shortfall (ES, also known as CVaR) is the most important coherent risk measure in finance, insurance, risk management, and engineering. Recently, Wang and Zitikis (2021) put forward four economic axioms for portfolio risk assessment and provide the first economic axiomatic foundation for the family of ES. In particular, the axiom of no reward for concentration (NRC) is arguably quite strong, which imposes an additive form of the risk measure on portfolios with a certain dependence structure. We move away from the axiom of NRC by introducing the notion of concentration aversion, which does not impose any specific form of the risk measure. It turns out that risk measures with concentration aversion are functions of ES and the expectation. Together with the other three standard axioms of monotonicity, translation invariance and lower semicontinuity, concentration aversion uniquely characterizes the family of ES. In addition, we establish an axiomatic foundation for the problem of mean-ES portfolio selection and new explicit formulas for convex and consistent risk measures. Finally, we provide an economic justification for concentration aversion via a few axioms on the attitude of a regulator towards dependence structures.
Vision-Language Transformer and Query Generation for Referring Segmentation
Authors: Henghui Ding, Chang Liu, Suchen Wang, Xudong Jiang
Date: 2021-08-12 00:00:00
Proceeding: ICCV 2021 10
Tasks: Decoder, Generalized Referring Expression Comprehension, Generalized Referring Expression Segmentation, Referring Expression Segmentation, Segmentation

In this work, we address the challenging task of referring segmentation. The query expression in referring segmentation typically indicates the target object by describing its relationship with others. Therefore, to find the target one among all instances in the image, the model must have a holistic understanding of the whole image. To achieve this, we reformulate referring segmentation as a direct attention problem: finding the region in the image where the query language expression is most attended to. We introduce transformer and multi-head attention to build a network with an encoder-decoder attention mechanism architecture that "queries" the given image with the language expression. Furthermore, we propose a Query Generation Module, which produces multiple sets of queries with different attention weights that represent the diversified comprehensions of the language expression from different aspects. At the same time, to find the best way from these diversified comprehensions based on visual clues, we further propose a Query Balance Module to adaptively select the output features of these queries for a better mask generation. Without bells and whistles, our approach is light-weight and achieves new state-of-the-art performance consistently on three referring segmentation datasets, RefCOCO, RefCOCO+, and G-Ref. Our code is available at https://github.com/henghuiding/Vision-Language-Transformer.