pwc-archive/papers-with-abstracts huggingface dataset:
Reconfigurable Intelligent Surfaces for N-LOS Radar Surveillance
Authors: Augusto Aubry, Antonio De Maio, Massimo Rosamilia
Date: 2021-04-01 00:00:00

This paper deals with the use of Reconfigurable Intelligent Surfaces (RISs) for radar surveillance in Non-Line Of Sight (N-LOS) scenarios. First of all, the geometry of the scene and the new system concept is described with emphasis on the required operative modes and the role played by the RIS. Then, the specific radar equation (including the RIS effect) is developed to manage the coverage requirements in the challenging region where the LOS is not present. Both noise and clutter interference cases (pulse length-limited and beamwidth-limited surface clutter as well as volume clutter) are considered. Hence, a digression on the use of the radar timeline for the new operative mode is presented together with the data acquisition procedure and the resolution issues for the range, azimuth, and Doppler domains. Finally, the interplay among the system parameters and, in particular, those involving the RIS is discussed and analyzed via numerical simulations.
Radar Human Motion Classification Using Multi-Antenna System
Authors: Patrick A. Schooley, Syed A. Hamza
Date: 2021-04-01 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

This paper considers human activity classification for an indoor radar system. Human motions generate nonstationary radar returns which represent Doppler and micro-Doppler signals. The time-frequency (TF) analysis of micro-Doppler signals can discern subtle variations on the motion by precisely revealing velocity components of various moving body parts. We consider radar for activity monitoring using TF-based machine learning approach exploiting both temporal and spatial degrees of freedom. The proposed approach captures different human motion representations more vividly in joint-variable data domains achieved through beamforming at the receiver. The radar data is collected using real time measurements at 77 GHz using four receive antennas, and subsequently micro-Doppler signatures are analyzed through machine learning algorithm for classifications of human walking motions. We present the performance of the proposed multi antenna approach in separating and classifying two closely walking persons moving in opposite directions.
A Novel Algorithm to Report CSI in MIMO-Based Wireless Networks
Authors: Muhammad Karam Shehzad, Luca Rose, Mohamad Assaad
Date: 2021-04-01 00:00:00

In wireless communication, accurate channel state information (CSI) is of pivotal importance. In practice, due to processing and feedback delays, estimated CSI can be outdated, which can severely deteriorate the performance of the communication system. Besides, to feedback estimated CSI, a strong compression of the CSI, evaluated at the user equipment (UE), is performed to reduce the over-the-air (OTA) overhead. Such compression strongly reduces the precision of the estimated CSI, which ultimately impacts the performance of multiple-input multiple-output (MIMO) precoding. Motivated by such issues, we present a novel scalable idea of reporting CSI in wireless networks, which is applicable to both time-division duplex (TDD) and frequency-division duplex (FDD) systems. In particular, the novel approach introduces the use of a channel predictor function, e.g., Kalman filter (KF), at both ends of the communication system to predict CSI. Simulation-based results demonstrate that the novel approach reduces not only the channel mean-squared-error (MSE) but also the OTA overhead to feedback the estimated CSI when there is immense variation in the mobile radio channel. Besides, in the immobile radio channel, feedback can be eliminated, which brings the benefit of further reducing the OTA overhead. Additionally, the proposed method provides a significant signal-to-noise ratio (SNR) gain in both the channel conditions, i.e., highly mobile and immobile.
Full-Duplex mmWave Massive MIMO Systems: A Joint Hybrid Precoding/Combining and Self-Interference Cancellation Design
Authors: Asil Koc, Tho Le-Ngoc
Date: 2021-04-01 00:00:00

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems have been considered as one of the primary candidates for the fifth generation (5G) and beyond 5G wireless communication networks to satisfy the ever-increasing capacity demands. Full-duplex technology can further enhance the advantages of mmWave massive MIMO systems. However, strong self-interference (SI) is the major limiting factor in full-duplex technology. Hence, this paper proposes a novel angular-based joint hybrid precoding/combining (AB-JHPC) technique for the full-duplex mmWave massive-MIMO systems. Our primary goals are listed as: (i) improving the self-interference cancellation (SIC), (ii) increasing the intended signal power, (iii) decreasing the channel estimation overhead, (iv) designing the massive MIMO systems with a low number of RF chains. First, the RF-stage of AB-JHPC is developed via slow time-varying angle-of-departure (AoD) and angle-of-arrival (AoA) information. A joint transmit/receive RF beamformer design is proposed for covering (excluding) the AoD/AoA support of intended (SI) channel. Second, the BB-stage of AB-JHPC is constructed via the reduced-size effective intended channel. After using the well-known singular value decomposition(SVD) approach at the BB-stage, we also propose a new semi-blind minimum mean square error (S-MMSE) technique to further suppress the residual SI power by using AoD/AoA parameters. The numerical results demonstrate that the SI signal is remarkably canceled via the proposed AB-JHPC technique. It is shown that AB-JHPC achieves 85.7 dB SIC and the total amount of SIC almost linearly increases via antenna isolation techniques. We observe that the proposed full-duplex mmWave massive MIMO systems double the achievable rate capacity compared to its half-duplex counterpart as the antenna array size increases and the transmit/receive antenna isolation improves.
Dealing with CSI Compression to Reduce Losses and Overhead: An Artificial Intelligence Approach
Authors: Muhammad Karam Shehzad, Luca Rose, Mohamad Assaad
Date: 2021-04-01 00:00:00

Motivated by the issue of inaccurate channel state information (CSI) at the base station (BS), which is commonly due to feedback/processing delays and compression problems, in this paper, we introduce a scalable idea of adopting artificial intelligence (AI) aided CSI acquisition. The proposed scheme enhances the CSI compression, which is done at the mobile terminal (MT), along with accurate recovery of estimated CSI at the BS. Simulation-based results corroborate the validity of the proposed scheme. Numerically, nearly 100\% recovery of the estimated CSI is observed with relatively lower overhead than the benchmark scheme. The proposed idea can bring potential benefits in the wireless communication environment, e.g., ultra-reliable and low latency communication (URLLC), where imperfect CSI and overhead is intolerable.
Online Non-linear Topology Identification from Graph-connected Time Series
Authors: Rohan Money, Joshin Krishnan, Baltasar Beferull-Lozano
Date: 2021-03-31 00:00:00
Tasks: Time Series, Time Series Analysis

Estimating the unknown causal dependencies among graph-connected time series plays an important role in many applications, such as sensor network analysis, signal processing over cyber-physical systems, and finance engineering. Inference of such causal dependencies, often know as topology identification, is not well studied for non-linear non-stationary systems, and most of the existing methods are batch-based which are not capable of handling streaming sensor signals. In this paper, we propose an online kernel-based algorithm for topology estimation of non-linear vector autoregressive time series by solving a sparse online optimization framework using the composite objective mirror descent method. Experiments conducted on real and synthetic data sets show that the proposed algorithm outperforms the state-of-the-art methods for topology estimation.
Long-Time Behavior of a PDE Replicator Equation for Multilevel Selection in Group-Structured Populations
Authors: Daniel B. Cooney, Yoichiro Mori
Date: 2021-04-01 00:00:00

In many biological systems, natural selection acts simultaneously on multiple levels of organization. This scenario typically presents an evolutionary conflict between the incentive of individuals to cheat and the collective incentive to establish cooperation within a group. Generalizing previous work on multilevel selection in evolutionary game theory, we consider a hyperbolic PDE model of a group-structured population, in which members within a single group compete with each other for individual-level replication; while the group also competes against other groups for group-level replication. We derive a threshold level of the relative strength of between-group competition such that defectors take over the population below the threshold while cooperation persists in the long-time population above the threshold. Under stronger assumptions on the initial distribution of group compositions, we further prove that the population converges to a steady state density supporting cooperation for between-group selection strength above the threshold. We further establish long-time bounds on the time-average of the collective payoff of the population, showing that the long-run population cannot outperform the payoff of a full-cooperator group even in the limit of infinitely-strong between-group competition. When the group replication rate is maximized by an intermediate level of within-group cooperation, individual-level selection casts a long shadow on the dynamics of multilevel selection: no level of between-group competition can erase the effects of the individual incentive to defect. We further extend our model to study the case of multiple types of groups, showing how the games that groups play can coevolve with the level of cooperation.
Clustering Schemes on the Torus with Application to RNA Clashes
Authors: Henrik Wiechers, Benjamin Eltzner, Stephan F. Huckemann, Kanti V. Mardia
Date: 2021-02-28 00:00:00
Tasks: Clustering

Molecular structures of RNA molecules reconstructed from X-ray crystallography frequently contain errors. Motivated by this problem we examine clustering on a torus since RNA shapes can be described by dihedral angles. A previously developed clustering method for torus data involves two tuning parameters and we assess clustering results for different parameter values in relation to the problem of so-called RNA clashes. This clustering problem is part of the dynamically evolving field of statistics on manifolds. Statistical problems on the torus highlight general challenges for statistics on manifolds. Therefore, the torus PCA and clustering methods we propose make an important contribution to directional statistics and statistics on manifolds in general.
A network-based analysis of disease modules from a taxonomic perspective
Authors: Giorgio Grani, Lorenzo Madeddu, Paola Velardi
Date: 2021-04-01 00:00:00
Tasks: Diagnostic

Objective: Human-curated disease ontologies are widely used for diagnostic evaluation, treatment and data comparisons over time, and clinical decision support. The classification principles underlying these ontologies are guided by the analysis of observable pathological similarities between disorders, often based on anatomical or histological principles. Although, thanks to recent advances in molecular biology, disease ontologies are slowly changing to integrate the etiological and genetic origins of diseases, nosology still reflects this "reductionist" perspective. Proximity relationships of disease modules (hereafter DMs) in the human interactome network are now increasingly used in diagnostics, to identify pathobiologically similar diseases and to support drug repurposing and discovery. On the other hand, similarity relations induced from structural proximity of DMs also have several limitations, such as incomplete knowledge of disease-gene relationships and reliability of clinical trials to assess their validity. The purpose of the study described in this paper is to shed more light on disease similarities by analyzing the relationship between categorical proximity of diseases in human-curated ontologies and structural proximity of the related DM in the interactome. Method: We propose a methodology (and related algorithms) to automatically induce a hierarchical structure from proximity relations between DMs, and to compare this structure with a human-curated disease taxonomy. Results: We demonstrate that the proposed methodology allows to systematically analyze commonalities and differences among structural and categorical similarity of human diseases, help refine and extend human disease classification systems, and identify promising network areas where new disease-gene interactions can be discovered.
The population doctrine in cognitive neuroscience
Authors: R. Becket Ebitz, Benjamin Y. Hayden
Date: 2021-03-31 00:00:00
Tasks: Decision Making

A major shift is happening within neurophysiology: a population doctrine is drawing level with the single-neuron doctrine that has long dominated the field. Population-level ideas have so far had their greatest impact in motor neuroscience, but they hold great promise for resolving open questions in cognition as well. Here, we codify the population doctrine and survey recent work that leverages this view to specifically probe cognition. Our discussion is organized around five core concepts that provide a foundation for population-level thinking: (1) state spaces, (2) manifolds, (3) coding dimensions, (4) subspaces, and (5) dynamics. The work we review illustrates the progress and promise that population neurophysiology holds for cognitive neuroscience$-$for delivering new insight into attention, working memory, decision-making, executive function, learning, and reward processing.
Quantifying efficiency gains of innovative designs of two-arm vaccine trials for COVID-19 using an epidemic simulation model
Authors: Rob Johnson, Chris Jackson, Anne Presanis, Sofia S. Villar, Daniela De Angelis
Date: 2021-03-13 00:00:00

Clinical trials of a vaccine during an epidemic face particular challenges, such as the pressure to identify an effective vaccine quickly to control the epidemic, and the effect that time-space-varying infection incidence has on the power of a trial. We illustrate how the operating characteristics of different trial design elements may be evaluated using a network epidemic and trial simulation model, based on COVID-19 and individually randomised two-arm trials with a binary outcome. We show that "ring" recruitment strategies, prioritising participants at high risk of infection, can result in substantial improvement in terms of power, if sufficiently many contacts of observed cases are at high risk. In addition, we introduce a novel method to make more efficient use of the data from the earliest cases of infection observed in the trial, whose infection may have been too early to be vaccine-preventable. Finally, we compare several methods of response-adaptive randomisation, discussing their advantages and disadvantages in this two-arm context and identifying particular adaptation strategies that preserve power and estimation properties, while slightly reducing the number of infections, given an effective vaccine.
Synergistic effect of Si-hydroxyapatite coating and VEGF adsorption on Ti6Al4V-ELI scaffolds for bone regeneration in an osteoporotic bone environment
Authors: I. Izquierdo-Barba, L. Santos-Ruiz, J. Becerra, M. J. Feito, D. Fernandez-Villa, M. C. Serrano, I. Diaz-Guemes, B. Fernandez-Tome, S. Enciso, F. M. Sanchez Margallo, D. Monopoli, H. Afonso, M. T. Portoles, D. Arcos, M. Vallet-Regi
Date: 2021-03-10 00:00:00

The osteogenic and angiogenic responses to metal macroporous scaffolds coated with silicon substituted hydroxyapatite (SiHA) and decorated with vascular endothelial growth factor (VEGF) have been evaluated in vitro and in vivo. Ti6Al4V-ELI scaffolds were prepared by electron beam melting and subsequently coated with Ca10(PO4)5.6(SiO4)0.4(OH)1.6 following a dip coating method. In vitro studies demonstrated that SiHA stimulates the proliferation of MC3T3-E1 pre-osteoblastic cells, whereas the adsorption of VEGF stimulates the proliferation of EC2 mature endothelial cells. In vivo studies were carried out in an osteoporotic sheep model, evidencing that only the simultaneous presence of both components led to a significant increase of new tissue formation in osteoporotic bone.
Optimal Fees for Geometric Mean Market Makers
Authors: Alex Evans, Guillermo Angeris, Tarun Chitra
Date: 2021-04-01 00:00:00

Constant Function Market Makers (CFMMs) are a family of automated market makers that enable censorship-resistant decentralized exchange on public blockchains. Arbitrage trades have been shown to align the prices reported by CFMMs with those of external markets. These trades impose costs on Liquidity Providers (LPs) who supply reserves to CFMMs. Trading fees have been proposed as a mechanism for compensating LPs for arbitrage losses. However, large fees reduce the accuracy of the prices reported by CFMMs and can cause reserves to deviate from desirable asset compositions. CFMM designers are therefore faced with the problem of how to optimally select fees to attract liquidity. We develop a framework for determining the value to LPs of supplying liquidity to a CFMM with fees when the underlying process follows a general diffusion. Focusing on a popular class of CFMMs which we call Geometric Mean Market Makers (G3Ms), our approach also allows one to select optimal fees for maximizing LP value. We illustrate our methodology by showing that an LP with mean-variance utility will prefer a G3M over all alternative trading strategies as fees approach zero.
TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution
Authors: Karush Suri, Xiao Qi Shi, Konstantinos Plataniotis, Yuri Lawryshyn
Date: 2021-02-16 00:00:00
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Advances in Reinforcement Learning (RL) span a wide variety of applications which motivate development in this area. While application tasks serve as suitable benchmarks for real world problems, RL is seldomly used in practical scenarios consisting of abrupt dynamics. This allows one to rethink the problem setup in light of practical challenges. We present Trade Execution using Reinforcement Learning (TradeR) which aims to address two such practical challenges of catastrophy and surprise minimization by formulating trading as a real-world hierarchical RL problem. Through this lens, TradeR makes use of hierarchical RL to execute trade bids on high frequency real market experiences comprising of abrupt price variations during the 2019 fiscal year COVID19 stock market crash. The framework utilizes an energy-based scheme in conjunction with surprise value function for estimating and minimizing surprise. In a large-scale study of 35 stock symbols from the S&P500 index, TradeR demonstrates robustness to abrupt price changes and catastrophic losses while maintaining profitable outcomes. We hope that our work serves as a motivating example for application of RL to practical problems.
Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction
Authors: Roger Girgis, Florian Golemo, Felipe Codevilla, Martin Weiss, Jim Aldon D'Souza, Samira Ebrahimi Kahou, Felix Heide, Christopher Pal
Date: 2021-02-19 00:00:00
Proceeding: ICLR 2022 4
Tasks: Autonomous Driving, Decoder, GPU, motion prediction, Prediction, Trajectory Modeling, Trajectory Prediction

Robust multi-agent trajectory prediction is essential for the safe control of robotic systems. A major challenge is to efficiently learn a representation that approximates the true joint distribution of contextual, social, and temporal information to enable planning. We propose Latent Variable Sequential Set Transformers which are encoder-decoder architectures that generate scene-consistent multi-agent trajectories. We refer to these architectures as "AutoBots". The encoder is a stack of interleaved temporal and social multi-head self-attention (MHSA) modules which alternately perform equivariant processing across the temporal and social dimensions. The decoder employs learnable seed parameters in combination with temporal and social MHSA modules allowing it to perform inference over the entire future scene in a single forward pass efficiently. AutoBots can produce either the trajectory of one ego-agent or a distribution over the future trajectories for all agents in the scene. For the single-agent prediction case, our model achieves top results on the global nuScenes vehicle motion prediction leaderboard, and produces strong results on the Argoverse vehicle prediction challenge. In the multi-agent setting, we evaluate on the synthetic partition of TrajNet++ dataset to showcase the model's socially-consistent predictions. We also demonstrate our model on general sequences of sets and provide illustrative experiments modelling the sequential structure of the multiple strokes that make up symbols in the Omniglot data. A distinguishing feature of AutoBots is that all models are trainable on a single desktop GPU (1080 Ti) in under 48h.
Hyperspectral image classification using spectral-spatial LSTMs
Authors: Feng Zhou, Renlong Hang, Qingshan Liu, Xiaotong Yuan
Date: 2018-08-20 00:00:00
Tasks: Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

In this paper, we propose a hyperspectral image (HSI) classification method using spectral-spatial long short term memory (LSTM) networks. Specifically, for each pixel, we feed its spectral values in different channels into Spectral LSTM one by one to learn the spectral feature. Meanwhile, we firstly use principle component analysis (PCA) to extract the first principle component from a HSI, and then select local image patches centered at each pixel from it. After that, we feed the row vectors of each image patch into Spatial LSTM one by one to learn the spatial feature for the center pixel. In the classification stage, the spectral and spatial features of each pixel are fed into softmax classifiers respectively to derive two different results, and a decision fusion strategy is further used to obtain a joint spectral-spatial results. Experimental results on three widely used HSIs (i.e., Indian Pines, Pavia University, and Kennedy Space Center) show that our method can improve the classification accuracy by at least 2.69%, 1.53% and 1.08% compared to other state-of-the-art methods.