pwc-archive/papers-with-abstracts huggingface dataset:
Imaging Using Millimeter Wave Communication Networks: A Bonus SAR
Authors: Husheng Li
Date: 2020-11-17 00:00:00

In the next generations of cellular communication networks, higher density of base stations and higher frequency bands will be adopted. If being reflected by targets, the communication signal also brings information of the targets, in addition to the communication messages, to the receivers. In this paper, it is proposed to leverage the reflected communication signals to reconstruct an image of the environment. Due to the analogy to traditional synthetic aperture radar (SAR) and inverse SAR (ISAR), the principles of SAR and ISAR, namely the tomography via Fourier transformation, are adopted with necessary improvements. The algorithms are further refined to estimate the 3-dimensional silhouette of the environment. Numerical simulations are carried out to demonstrate the proposed algorithms.
Taming and Leveraging Interference in Mobile Radar Networks
Authors: Husheng Li
Date: 2020-11-17 00:00:00
Tasks: Autonomous Driving, Diversity, Management

Mobile radar networks, such as autonomous driving systems, are subject to the severe challenge of mutual interference. Despite the inborn interference-proof capability in frequency modulation continuous waveform (FMCW) radar, interference management is necessary for dense radar networks. The approaches combatting the radar interference include the timing diversity of waveform parameters, data-driven detection interference, and signal processing based decoupling of interference. Moreover, the leverage of radar interference for inferring the information of interfere is studied. Simulations are carried out in the scenario of radar interferences in vehicular networks, which demonstrates the performance of the proposed algorithms.
Whole-Genome Sequence of the Trypoxylus dichotomus Japanese rhinoceros beetle
Authors: Norichika Ogata
Date: 2020-11-17 00:00:00

The draft whole-genome sequence of the Japanese rhinoceros beetle, Trypoxylus dichotomus was obtained using long-read PacBio sequence technology. The final assembled genome consisted of 739 Mbp in 2,347 contigs, with 24.5x mean coverage and a G+C content of 35.99%.
Comparative transcriptome analysis reveals key epigenetic targets in SARS-CoV-2 infection
Authors: Marisol Salgado-Albarran, Erick I. Navarro-Delgado, Aylin Del Moral-Morales, Nicolas Alcaraz, Jan Baumbach, Rodrigo Gonzalez-Barrios, Ernesto Soto-Reyes
Date: 2020-11-17 00:00:00

COVID-19 is an infection caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome coronavirus 2), which has caused a global outbreak. Current research efforts are focused on the understanding of the molecular mechanisms involved in SARS-CoV-2 infection in order to propose drug-based therapeutic options. Transcriptional changes due to epigenetic regulation are key host cell responses to viral infection and have been studied in SARS-CoV and MERS-CoV; however, such changes are not fully described for SARS-CoV-2. In this study, we analyzed multiple transcriptomes obtained from cell lines infected with MERS-CoV, SARS-CoV and SARS-CoV-2, and from COVID-19 patient-derived samples. Using integrative analyses of gene co-expression networks and de-novo pathway enrichment, we characterize different gene modules and protein pathways enriched with Transcription Factors or Epifactors relevant for SARS-CoV-2 infection. We identified EP300, MOV10, RELA and TRIM25 as top candidates, and more than 60 additional proteins involved in the epigenetic response during viral infection that have therapeutic potential. Our results show that targeting the epigenetic machinery could be a feasible alternative to treat COVID-19.
The stochastic dynamics of early epidemics: probability of establishment, initial growth rate, and infection cluster size at first detection
Authors: Peter Czuppon, Emmanuel Schertzer, François Blanquart, Florence Débarre
Date: 2020-11-17 00:00:00

Emerging epidemics and local infection clusters are initially prone to stochastic effects that can substantially impact the epidemic trajectory. While numerous studies are devoted to the deterministic regime of an established epidemic, mathematical descriptions of the initial phase of epidemic growth are comparatively rarer. Here, we review existing mathematical results on the epidemic size over time, and derive new results to elucidate the early dynamics of an infection cluster started by a single infected individual. We show that the initial growth of epidemics that eventually take off is accelerated by stochasticity. These results are critical to improve early cluster detection and control. As an application, we compute the distribution of the first detection time of an infected individual in an infection cluster depending on the testing effort, and estimate that the SARS-CoV-2 variant of concern Alpha detected in September 2020 first appeared in the United Kingdom early August 2020. We also compute a minimal testing frequency to detect clusters before they exceed a given threshold size. These results improve our theoretical understanding of early epidemics and will be useful for the study and control of local infectious disease clusters.
In-band Perturbation based OSNR Estimation
Authors: F. J. Vaquero-Caballero, D. Charlton, M. E. Mousa-Pasandi, D. J. Ives, C. Laperle, M. Hubbard, M. Reimer, M. OSullivan, S. J. Savory
Date: 2020-10-17 00:00:00

Perturbed spectra are modelled to estimate OSNR for a single channel amplified link. Perturbation-dependent nonlinear noise is separated from constant ASE noise using a set of propagated perturbed spectra. A least mean square fitting is used to estimate OSNR with standard deviation of 0.16 dB.
Reducing the Mutual Outage Probability of Cooperative Non-Orthogonal Multiple Access
Authors: Sana Riaz, Fahd Ahmed Khan, Sajid Saleem, Qasim Zeeshan Ahmed
Date: 2020-10-28 00:00:00
Tasks: Diversity

In this letter, a new power allocation scheme is proposed to improve the reliability of cooperative non-orthogonal multiple access (CO-NOMA). The strong user is allocated the maximum power, whereas the weak user is allocated the minimum power. This power allocation alters the decoding sequence along with the signal-to-interference plus noise ratio (SINR), at the users. The weak user benefits from receiving multiple copies of the signal whereas the strong user benefits from the higher power allocation. Numerical simulation results show that the proposed scheme has a lower mutual outage probability (MOP) and offers better reliability as compared to the conventional power allocation scheme for CONOMA. An exact closed-form expression of MOP is derived for the two-user CO-NOMA system and it is shown that each user achieves full diversity. The proposed allocation is able to achieve approximately 30% higher transmission rate at 15 dB as compared to conventional CO-NOMA in a practical non-power balanced scenario.
Reconfigurable Intelligent Surface Assisted Cooperative Non-orthogonal Multiple Access Systems
Authors: Jiakuo Zuo, Yuanwei Liu, Naofal Al-Dhahir
Date: 2020-11-18 00:00:00

This paper considers downlink of reconfigurable intelligent surface (RIS) assisted cooperative non-orthogonal multiple access (CNOMA) systems. Our objective is to minimize the total transmit power by jointly optimizing the active beamforming vectors, transmit-relaying power, and RIS phase shifts. The formulated problem is a mixed-integer nonlinear programming (MINLP) problem. To tackle this problem, the alternating optimization approach is utilized to decouple the variables. In each alternative procedure, the optimal solutions for the active beamforming vectors, transmit-relaying power and phase shifts are obtained. However, the proposed algorithm has high complexity since the optimal phase shifts are solved by integer linear programming (ILP) whose computational complexity is exponential in the number of variables. To strike a good computational complexity-optimality trade-off, a low-complexity suboptimal algorithm is proposed by invoking the iterative penalty function based semidefinite programming (SDP) and the successive refinement approaches. Numerical results illustrate that: i) the proposed RIS-CNOMA system, aided by our proposed algorithms, outperforms the conventional CNOMA system. ii) the proposed low-complexity suboptimal algorithm can achieve the near-optimal performance. iii) whether the RIS-CNOMA system outperforms the RIS assisted non-orthogonal multiple access (RIS-NOMA) system depends not only on the users' locations but also on the RIS' location.
Linear Phase Retrieval for Near-Field Measurements with Locally Known Phase Relations
Authors: Alexander Paulus, Jonas Kornprobst, Josef Knapp, Thomas F. Eibert
Date: 2020-11-12 00:00:00
Tasks: Retrieval

A linear and thus convex phase retrieval algorithm for the application in phaseless near-field far-field transformations is presented. The formulation exploits locally known phase relations among sets of measurement samples, which can in practice be acquired with multi-channel receivers. Due to the linearity of the formulation, a reliable phaseless transformation is achieved, which completely avoids the problem of local minima - the Achilles heel of most existing phase retrieval techniques. Furthermore, the necessary number of measurements are kept close to that of fully-coherent antenna measurements. Comparisons with an already existing approach exploiting local phase relations demonstrate the accuracy and reliability for synthetic data.
Consensus of Multi-Agent Systems Using Back-Tracking and History Following Algorithms
Date: 2020-11-17 00:00:00

This paper proposes two algorithms, namely "back-tracking" and "history
following", to reach consensus in case of communication loss for a network of
distributed agents with switching topologies. To reach consensus in distributed
control, considered communication topology forms a strongly connected graph.
The graph is no more strongly connected whenever an agent loses
communication.Whenever an agent loses communication, the topology is no more
strongly connected. The proposed back-tracking algorithm makes sure that the
agent backtracks its position unless the communication is reestablished, and
path is changed to reach consensus. In history following, the agents use their
memory and move towards previous consensus point until the communication is
regained. Upon regaining communication, a new consensus point is calculated
depending on the current positions of the agents and they change their
trajectories accordingly. Simulation results, for a network of six agents, show
that when the agents follow the previous history, the average consensus time is
less than that of back-tracking. However, situation may arise in history
following where a false notion of reaching consensus makes one of the agents
stop at a point near to the actual consensus point. An obstacle avoidance
algorithm is integrated with the proposed algorithms to avoid collisions.
Hardware implementation for a three robots system shows the effectiveness of
the algorithms.
Emotions in Online Content Diffusion
Authors: Yifan Yu, Shan Huang, Yuchen Liu, Yong Tan
Date: 2020-11-17 00:00:00
Tasks: Articles, Marketing

Social media-transmitted online information, which is associated with emotional expressions, shapes our thoughts and actions. In this study, we incorporate social network theories and analyses and use a computational approach to investigate how emotional expressions, particularly \textit{negative discrete emotional expressions} (i.e., anxiety, sadness, anger, and disgust), lead to differential diffusion of online content in social media networks. We rigorously quantify diffusion cascades' structural properties (i.e., size, depth, maximum breadth, and structural virality) and analyze the individual characteristics (i.e., age, gender, and network degree) and social ties (i.e., strong and weak) involved in the cascading process. In our sample, more than six million unique individuals transmitted 387,486 randomly selected articles in a massive-scale online social network, WeChat. We detect the expression of discrete emotions embedded in these articles, using a newly generated domain-specific and up-to-date emotion lexicon. We apply a partial-linear instrumental variable approach with a double machine learning framework to causally identify the impact of the negative discrete emotions on online content diffusion. We find that articles with more expressions of anxiety spread to a larger number of individuals and diffuse more deeply, broadly, and virally. Expressions of anger and sadness, however, reduce cascades' size and maximum breadth. We further show that the articles with different degrees of negative emotional expressions tend to spread differently based on individual characteristics and social ties. Our results shed light on content marketing and regulation, utilizing negative emotional expressions.
Design and Stability Analysis of a Shared Mobility Market
Date: 2020-11-18 00:00:00
Tasks: Decision Making

In recent years, we have witnessed a remarkable surge of usage in shared
vehicles in our cities. Shared mobility offers a future of no congestion in
busy city roads with increasing populations of travelers, passengers, and
drivers. Given the behavioral decision-making of travelers and the shared
vehicles' operators, however, the question is "how can we ensure a
socially-acceptable assignment between travelers and vehicles?" In other words,
how can we design a shared mobility system that assigns each traveler to the
"right" vehicle? In this paper, we design a shared mobility market consisted of
travelers and vehicles in a transportation network. We formulate a linear
programming problem and derive the optimal assignment between travelers and
vehicles. In addition, we provide the necessary and sufficient conditions for
the stable traveler-vehicle profit allocation. Our goal is twofold: maximize
the social welfare of all travelers with the optimal assignment while ensuring
the feasibility and stability of the traveler-vehicle profit allocation while
respecting the decision-making of both the travelers and the vehicles'
operators.
A Vision of XR-aided Teleoperation System Towards 5G/B5G
Authors: Fenghe Hu, Yansha Deng, Hui Zhou, Tae Hun Jung, Chan-Byoung Chae, A. Hamid Aghvami
Date: 2020-11-18 00:00:00

Extended Reality (XR)-aided teleoperation has shown its potential in improving operating efficiency in mission-critical, rich-information and complex scenarios. The multi-sensory XR devices introduce several new types of traffic with unique quality-of-service (QoS) requirements, which are usually defined by three measures---human perception, corresponding sensors, and present devices. To fulfil these requirements, cellular-supported wireless connectivity can be a promising solution that can largely benefit the Robot-to-XR and the XR-to-Robot links. In this article, we present industrial and piloting use cases and identify the service bottleneck of each case. We then cover the QoS of Robot-XR and XR-Robot links by summarizing the sensors' parameters and processing procedures. To realise these use cases, we introduce potential solutions for each case with cellular connections. Finally, we build testbeds to investigate the effectiveness of supporting our proposed links using current wireless topologies.
A Two-Way Transformed Factor Model for Matrix-Variate Time Series
Date: 2020-11-18 00:00:00
Tasks: Time Series, Time Series Analysis, Vocal Bursts Valence Prediction

We propose a new framework for modeling high-dimensional matrix-variate time
series by a two-way transformation, where the transformed data consist of a
matrix-variate factor process, which is dynamically dependent, and three other
blocks of white noises. Specifically, for a given $p_1\times p_2$
matrix-variate time series, we seek common nonsingular transformations to
project the rows and columns onto another $p_1$ and $p_2$ directions according
to the strength of the dynamic dependence of the series on the past values.
Consequently, we treat the data as nonsingular linear row and column
transformations of dynamically dependent common factors and white noise
idiosyncratic components. We propose a common orthonormal projection method to
estimate the front and back loading matrices of the matrix-variate factors.
Under the setting that the largest eigenvalues of the covariance of the
vectorized idiosyncratic term diverge for large $p_1$ and $p_2$, we introduce a
two-way projected Principal Component Analysis (PCA) to estimate the associated
loading matrices of the idiosyncratic terms to mitigate such diverging noise
effects. A diagonal-path white noise testing procedure is proposed to estimate
the order of the factor matrix. %under the assumption that the idiosyncratic
term is a matrix-variate white noise process. Asymptotic properties of the
proposed method are established for both fixed and diverging dimensions as the
sample size increases to infinity. We use simulated and real examples to assess
the performance of the proposed method. We also compare our method with some
existing ones in the literature and find that the proposed approach not only
provides interpretable results but also performs well in out-of-sample
forecasting.
Linking the connectome to action: Emergent dynamics in a robotic model of C. elegans
Authors: Carlos E. Valencia Urbina, Sergio A. Cannas, Pablo M. Gleiser
Date: 2020-11-18 00:00:00

We analyse the neural dynamics and its relation with the emergent behaviour of a robotic vehicle that is controlled by a neural network numerical simulation based on the nervous system of the nematode Caenorhabditis elegans. The robot interacts with the environment through a sensor, that transmits the information to sensory neurons, while motor neurons outputs are connected to wheels. This is enough to allow robot movement in complex environments, avoiding collisions with obstacles. Working with a robotic model makes it possible to keep track simultaneously of the detailed microscopic dynamics of all the neurons and also register the actions of the robot in the environment in real time. This allowed us to study the interplay between connectome and complex behaviors. We found that some basic features of the global neural dynamics and their correlation with behaviour observed in the worm appear spontaneously in the robot, suggesting they are just an emergent property of the connectome.
Uplink Sensing in Perceptive Mobile Networks with Asynchronous Transceivers
Authors: Zhitong Ni, J. Andrew Zhang, Xiaojing Huang, Kai Yang, Jinhong Yuan
Date: 2020-11-18 00:00:00

Perceptive mobile network (PMN) is a recently proposed next-generation network that integrates radar sensing into communication. One major challenge for realizing sensing in PMNs is how to deal with spatially-separated asynchronous transceivers. The asynchrony between sensing receiver and transmitter will cause both timing offsets (TOs) and carrier frequency offsets (CFOs) and lead to degraded sensing accuracy in both ranging and velocity measurements. In this paper, we propose an uplink sensing scheme for PMNs with asynchronous transceivers, targeting at resolving the sensing ambiguity and improving the sensing accuracy. We first adopt a cross-antenna cross-correlation (CACC) operation to remove the sensing ambiguity associated with both TOs and CFOs. Without sensing ambiguity, both actual propagation delay and actual Doppler frequency of multiple targets can be obtained using CACC outputs. To exploit the redundancy of the CACC outputs and reduce the complexity, we then propose a novel mirrored-MUSIC algorithm, which halves the number of unknown parameters to be estimated, for obtaining actual values of delays and Doppler frequencies. Finally, we propose a high-resolution angles-of-arrival (AoAs) estimation algorithm, which jointly processes all measurements from spatial, temporal, and frequency domains. The proposed AoAs estimation algorithm can achieve significantly higher estimation accuracy than that of using samples from the spatial domain only. We also derive the theoretical mean-square-error of the proposed algorithms. Numerical results are provided and validate the effectiveness of the proposed scheme.
On Simultaneous Long-Short Stock Trading Controllers with Cross-Coupling
Authors: Atul Deshpande, John A Gubner, B. Ross Barmish
Date: 2020-11-18 00:00:00

The Simultaneous Long-Short(SLS) controller for trading a single stock is known to guarantee positive expected value of the resulting gain-loss function with respect to a large class of stock price dynamics. In the literature, this is known as the Robust Positive Expectation(RPE)property. An obvious way to extend this theory to the trading of two stocks is to trade each one of them using its own independent SLS controller. Motivated by the fact that such a scheme does not exploit any correlation between the two stocks, we study the case when the relative sign between the drifts of the two stocks is known. The main contributions of this paper are three-fold: First, we put forward a novel architecture in which we cross-couple two SLS controllers for the two-stock case. Second, we derive a closed-form expression for the expected value of the gain-loss function. Third, we use this closed-form expression to prove that the RPE property is guaranteed with respect to a large class of stock-price dynamics. When more information over and above the relative sign is assumed, additional benefits of the new architecture are seen. For example, when bounds or precise values for the means and covariances of the stock returns are included in the model, numerical simulations suggest that our new controller can achieve lower trading risk than a pair of decoupled SLS controllers for the same level of expected trading gain.
3D Grid-Attention Networks for Interpretable Age and Alzheimer's Disease Prediction from Structural MRI
Authors: Pradeep Lam, Alyssa H. Zhu, Iyad Ba Gari, Neda Jahanshad, Paul M. Thompson
Date: 2020-11-18 00:00:00
Tasks: Disease Prediction

We propose an interpretable 3D Grid-Attention deep neural network that can accurately predict a person's age and whether they have Alzheimer's disease (AD) from a structural brain MRI scan. Building on a 3D convolutional neural network, we added two attention modules at different layers of abstraction, so that features learned are spatially related to the global features for the task. The attention layers allow the network to focus on brain regions relevant to the task, while masking out irrelevant or noisy regions. In evaluations based on 4,561 3-Tesla T1-weighted MRI scans from 4 phases of the Alzheimer's Disease Neuroimaging Initiative (ADNI), salience maps for age and AD prediction partially overlapped, but lower-level features overlapped more than higher-level features. The brain age prediction network also distinguished AD and healthy control groups better than another state-of-the-art method. The resulting visual analyses can distinguish interpretable feature patterns that are important for predicting clinical diagnosis. Future work is needed to test performance across scanners and populations.
Getting to a feasible income equality
Authors: Ji-Won Park, Chae Un Kim
Date: 2020-11-18 00:00:00

Income inequality is known to have negative impacts on an economic system, thus has been debated for a hundred years past or more. Numerous ideas have been proposed to quantify income inequality, and the Gini coefficient is a prevalent index. However, the concept of perfect equality in the Gini coefficient is rather idealistic and cannot provide realistic guidance on whether government interventions are needed to adjust income inequality. In this paper, we first propose the concept of a more realistic and feasible income equality that maximizes total social welfare. Then we show that an optimal income distribution representing the feasible equality could be modeled using the sigmoid welfare function and the Boltzmann income distribution. Finally, we carry out an empirical analysis of four countries and demonstrate how optimal income distributions could be evaluated. Our results show that the feasible income equality could be used as a practical guideline for government policies and interventions.