pwc-archive/papers-with-abstracts huggingface dataset:
Near-Field Wireless Power Transfer with Dynamic Metasurface Antennas
Authors: Haiyang Zhang, Nir Shlezinger, Francesco Guidi, Davide Dardari, Mohammadreza F Imani, Yonina C Eldar
Date: 2021-10-10 00:00:00

Radio frequency wireless power transfer (WPT) enables charging low-power mobile devices without relying on wired infrastructure. Current existing WPT systems are typically designed assuming far-field propagation, where the radiated energy is steered in given angles, resulting in limited efficiency and possible radiation in undesired locations. When large arrays at high frequencies, such as DMA, are employed, WPT might take place in the radiating near-field (Fresnel) region where spherical wave propagation holds, rather than plane wave propagation as in the far-field. In this paper, we study WPT systems charging multiple devices in the Fresnel region, where the energy transmitter is equipped with an emerging DMA, exploring how the antenna configuration can exploit the spherical wavefront to generate focused energy beams. In particular, after presenting a mathematical model for DMA-based radiating near-field WPT systems, we characterize the weighted sum-harvested energy maximization problem of the considered system, and we propose an efficient solution to jointly design the DMA weights and digital precoding vector. Simulation results show that our design generates focused energy beams that are capable of improving energy transfer efficiency in the radiating near-field with minimal energy pollution.
Deep Reinforcement Learning for Optimizing RIS-Assisted HD-FD Wireless Systems
Authors: Alice Faisal, Ibrahim Al-Nahhal, Octavia A. Dobre, Telex M. N. Ngatched
Date: 2021-10-10 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

This letter investigates the reconfigurable intelligent surface (RIS)-assisted multiple-input single-output (MISO) wireless system, where both half-duplex (HD) and full-duplex (FD) operating modes are considered together, for the first time in the literature. The goal is to maximize the rate by optimizing the RIS phase shifts. A novel deep reinforcement learning (DRL) algorithm is proposed to solve the formulated non-convex optimization problem. The complexity analysis and Monte Carlo simulations illustrate that the proposed DRL algorithm significantly improves the rate compared to the non-optimized scenario in both HD and FD operating modes using a single parameter setting. Besides, it significantly reduces the computational complexity of the downlink HD MISO system and improves the achievable rate with a reduced number of steps per episode compared to the conventional DRL algorithm.
A Variational Bayes Moving Horizon Estimation Adaptive Filter with Guaranteed Stability
Authors: Xiangxiang Dong, Giorgio Battistelli, Luigi Chisci, Yunze Cai
Date: 2021-10-10 00:00:00
Tasks: State Estimation

This paper addresses state estimation of linear systems with special attention on unknown process and measurement noise covariances, aiming to enhance estimation accuracy while preserving the stability guarantee of the Kalman filter. To this end, the full information estimation problem over a finite interval is firstly addressed. Then, a novel adaptive variational Bayesian (VB) moving horizon estimation (MHE) method is proposed, exploiting VB inference, MHE and Monte Carlo integration with importance sampling for joint estimation of the unknown process and measurement noise covariances, along with the state trajectory over a moving window of fixed length. Further, it is proved that the proposed adaptive VB MHE filter ensures mean-square boundedness of the estimation error with any number of importance samples and VB iterations, as well as for any window length. Finally, simulation results on a target tracking example demonstrate the effectiveness of the VB MHE filter with enhanced estimation accuracy and convergence properties compared to the conventional non-adaptive Kalman filter and other existing adaptive filters.
Uncertainty in Data-Driven Kalman Filtering for Partially Known State-Space Models
Authors: Itzik Klein, Guy Revach, Nir Shlezinger, Jonas E. Mehr, Ruud J. G. van Sloun, Yonina. C. Eldar
Date: 2021-10-10 00:00:00
Tasks: State Space Models

Providing a metric of uncertainty alongside a state estimate is often crucial when tracking a dynamical system. Classic state estimators, such as the Kalman filter (KF), provide a time-dependent uncertainty measure from knowledge of the underlying statistics, however, deep learning based tracking systems struggle to reliably characterize uncertainty. In this paper, we investigate the ability of KalmanNet, a recently proposed hybrid model-based deep state tracking algorithm, to estimate an uncertainty measure. By exploiting the interpretable nature of KalmanNet, we show that the error covariance matrix can be computed based on its internal features, as an uncertainty measure. We demonstrate that when the system dynamics are known, KalmanNet-which learns its mapping from data without access to the statistics-provides uncertainty similar to that provided by the KF; and while in the presence of evolution model-mismatch, KalmanNet pro-vides a more accurate error estimation.
RTSNet: Learning to Smooth in Partially Known State-Space Models (Preprint)
Authors: Guy Revach, Xiaoyong Ni, Nir Shlezinger, Ruud J. G. van Sloun, Yonina C. Eldar
Date: 2021-10-10 00:00:00
Tasks: State Space Models

The smoothing task is core to many signal processing applications. A widely popular smoother is the Rauch-Tung-Striebel (RTS) algorithm, which achieves minimal mean-squared error recovery with low complexity for linear Gaussian state space (SS) models, yet is limited in systems that are only partially known, as well as non-linear and non-Gaussian. In this work we propose RTSNet, a highly efficient model-based and data-driven smoothing algorithm suitable for partially known SS models. RTSNet integrates dedicated trainable models into the flow of the classical RTS smoother, while iteratively refining its sequence estimate via deep unfolding methodology. As a result, RTSNet learns from data to reliably smooth when operating under model mismatch and non-linearities while retaining the efficiency and interpretability of the traditional RTS smoothing algorithm. Our empirical study demonstrates that RTSNet overcomes non-linearities and model mismatch, outperforming classic smoothers operating with both mismatched and accurate domain knowledge. Moreover, while RTSNet is based on compact neural networks, which leads to faster training and inference times, it demonstrates improved performance over previously proposed deep smoothers in non-linear settings.
Stable and Transferable Wireless Resource Allocation Policies via Manifold Neural Networks
Authors: Zhiyang Wang, Luana Ruiz, Mark Eisen, Alejandro Ribeiro
Date: 2021-10-10 00:00:00

We consider the problem of resource allocation in large scale wireless networks. When contextualizing wireless network structures as graphs, we can model the limits of very large wireless systems as manifolds. To solve the problem in the machine learning framework, we propose the use of Manifold Neural Networks (MNNs) as a policy parametrization. In this work, we prove the stability of MNN resource allocation policies under the absolute perturbations to the Laplace-Beltrami operator of the manifold, representing system noise and dynamics present in wireless systems. These results establish the use of MNNs in achieving stable and transferable allocation policies for large scale wireless networks. We verify our results in numerical simulations that show superior performance relative to baseline methods.
Stability of Neural Networks on Manifolds to Relative Perturbations
Authors: Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
Date: 2021-10-10 00:00:00

Graph Neural Networks (GNNs) show impressive performance in many practical scenarios, which can be largely attributed to their stability properties. Empirically, GNNs can scale well on large size graphs, but this is contradicted by the fact that existing stability bounds grow with the number of nodes. Graphs with well-defined limits can be seen as samples from manifolds. Hence, in this paper, we analyze the stability properties of convolutional neural networks on manifolds to understand the stability of GNNs on large graphs. Specifically, we focus on stability to relative perturbations of the Laplace-Beltrami operator. To start, we construct frequency ratio threshold filters which separate the infinite-dimensional spectrum of the Laplace-Beltrami operator. We then prove that manifold neural networks composed of these filters are stable to relative operator perturbations. As a product of this analysis, we observe that manifold neural networks exhibit a trade-off between stability and discriminability. Finally, we illustrate our results empirically in a wireless resource allocation scenario where the transmitter-receiver pairs are assumed to be sampled from a manifold.
Mixture Model Auto-Encoders: Deep Clustering through Dictionary Learning
Authors: Alexander Lin, Andrew H. Song, Demba Ba
Date: 2021-10-10 00:00:00
Tasks: Clustering, Deep Clustering, Dictionary Learning

State-of-the-art approaches for clustering high-dimensional data utilize deep auto-encoder architectures. Many of these networks require a large number of parameters and suffer from a lack of interpretability, due to the black-box nature of the auto-encoders. We introduce Mixture Model Auto-Encoders (MixMate), a novel architecture that clusters data by performing inference on a generative model. Derived from the perspective of sparse dictionary learning and mixture models, MixMate comprises several auto-encoders, each tasked with reconstructing data in a distinct cluster, while enforcing sparsity in the latent space. Through experiments on various image datasets, we show that MixMate achieves competitive performance compared to state-of-the-art deep clustering algorithms, while using orders of magnitude fewer parameters.
Analysis of Ground Plane Size, Topography and Location on a Monopole Antenna's Performance Utilizing 3-D Printing
Authors: Vito Ciraco
Date: 2021-10-10 00:00:00

The monopole antenna is widely used in communication applications and is typically mounted on various surfaces that act as ground planes; a prime example being the roof of a car. The shape of the ground plane can drastically change the patterns of the electromagnetic radiation of a monopole antenna as well as its RF performance. Extensive work has been done on the numerical modeling of arbitrarily shaped ground planes. However, due to their geometric complexity, there is very little work reported on the practical testing component of physical antennas with these obscure ground plane structures. This thesis illustrates how the additive manufacturing process presented can be used to physically realize arbitrarily shaped ground planes and provides a low-cost process to verify the numerical model. Ground Planes were modified while maintaining the same antenna length to evaluate the impact on antenna performance. The antenna was not optimized or changed to a standard antenna design. Varying radius spherical ground planes are modelled, as well as modified ground plane structures to evaluate the impact of the ground plane on a 1.3GHz monopole antenna's performance and in some cases to modify the antenna's performance in terms of gain, bandwidth, and radiation pattern. Designs such as the planar ground with horn was found to enhance monopole bandwidth by more than 5 times that of a standard planar ground but significantly deteriorate the antenna's radiation pattern. Moreover, complex geometry such as the fin sphere ground plane offered a 25% increase in gain relative to the standard sphere ground. Designs like the edge-mounted sphere can offer directive gain and radiation characteristics simply by altering the antennas' location mount location with respect to its ground plane. The techniques presented in this thesis offer new ways of producing 3D printed ground planes for RF applications.
Optimization of A Mobile Optical SWIPT System With Asymmetric Spatially Separated Laser Resonator
Authors: Mingliang Xiong, Qingwen Liu, Shengli Zhou
Date: 2021-10-09 00:00:00

High-power and high-rate simultaneous wireless information and power transfer (SWIPT) becomes more and more important with the development of Internet of Things technologies. Optical SWIPT, also known as simultaneous light information and power transfer (SLIPT), has unique advantages such as abundant spectrum resources and low propagation divergence, compared with radio-frequency (RF) SWIPT. However, optical SWIPT faces many challenges in beam steering and receiver positioning/tracking. Resonant beams generated by spatially separated laser resonators (SSLR) have many advantages, including high power, self-aligned mobility, and intrinsic safety. It has been proposed as the carrier of wireless charging and communication. Using resonant beams, mobile electronic devices can be remotely charged and supported with high-rate data transfer. In this paper, we propose a mobile optical SWIPT system based on asymmetric SSLR and present the system optimization procedure. We also determine the boundary of the charging power and communication rate, and discuss the trade-off between power transfer and information transfer. Numerical results show that both the charging power and the communication rate of the optimized asymmetric system are much higher than those of the symmetric system in the previous work.
Sensoring and Application of Multimodal Data for the Detection of Freezing of Gait in Parkinson's Disease
Authors: Wei zhang, Debin Huang, Hantao Li, Lipeng Wang, Yanzhao Wei, Kang Pan, Lin Ma, Huanhuan Feng, Jing Pan, Yuzhu Guo
Date: 2021-10-09 00:00:00
Tasks: EEG, Electroencephalogram (EEG), valid

The accurate and reliable detection or prediction of freezing of gaits (FOG) is important for fall prevention in Parkinson's Disease (PD) and studying the physiological transitions during the occurrence of FOG. Integrating both commercial and self-designed sensors, a protocal has been designed to acquire multimodal physical and physiological information during FOG, including gait acceleration (ACC), electroencephalogram (EEG), electromyogram (EMG), and skin conductance (SC). Two tasks were designed to trigger FOG, including gait initiation failure and FOG during walking. A total number of 12 PD patients completed the experiments and produced a total length of 3 hours and 42 minutes of valid data. The FOG episodes were labeled by two qualified physicians. Each unimodal data and combinations have been used to detect FOG. Results showed that multimodal data benefit the detection of FOG. Among unimodal data, EEG had better discriminative ability than ACC and EMG. However, the acquisition of EEG are more complicated. Multimodal motional and electrophysiological data can also be used to study the physiological transition process during the occurrence of FOG and provide personalised interventions.
Atomic Norm Based Localization and Orientation Estimation for Millimeter-Wave MIMO OFDM Systems
Authors: Jianxiu Li, Maxime Ferreira Da Costa, Urbashi Mitra
Date: 2021-10-08 00:00:00

Herein, an atomic norm based method for accurately estimating the location and orientation of a target from millimeter-wave multi-input-multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM) signals is presented. A novel virtual channel matrix is introduced and an algorithm to extract localization-relevant channel parameters from its atomic norm decomposition is designed. Then, based on the extended invariance principle, a weighted least squares problem is proposed to accurately recover the location and orientation using both line-of-sight and non-line-of-sight channel information. Numerical results highlight performance improvements over a prior method and the resultant performance nearly achieves the Cramer-Rao lower bound.
Using Subobservers to Synthesize Opacity-Enforcing Supervisors
Authors: Richard Hugh Moulton, Behnam Behinaein Hamgini, Zahra Abedi Khouzani, Rômulo Meira-Góes, Fei Wang, Karen Rudie
Date: 2021-10-08 00:00:00

In discrete-event system control, the worst-case time complexity for computing a system's observer is exponential in the number of that system's states. This results in practical difficulties since some problems require calculating multiple observers for a changing system, e.g., synthesizing an opacity-enforcing supervisor. Although calculating these observers in an iterative manner allows us to synthesize an opacity-enforcing supervisor and although methods have been proposed to reduce the computational demands, room exists for a practical and intuitive solution. Here we extend the subautomaton relationship to the notion of a subobserver and demonstrate its use in reducing the computations required for iterated observer calculations. We then demonstrate the subobserver relationship's power by simplifying state-of-the-art synthesis approaches for opacity-enforcing supervisors under realistic assumptions.
Near Optimal Interpolation based Time-Limited Model Order Reduction
Authors: Kasturi Das, Srinivasan Krishnaswamy, Somanath Majhi
Date: 2021-10-08 00:00:00

This paper presents an interpolatory framework for time-limited $H_2$ optimal model order reduction named Limited Time Iterative Rational Krylov Algorithm (LT-IRKA). The algorithm yields high fidelity reduced order models over limited time intervals of the form, $\begin{bmatrix}0 & \tau \end{bmatrix}$ with $\tau < \infty$ for linear time invariant (LTI) systems. Using the time limited $H_2$ norm, we derive interpolation based $H_{2,\tau}$ optimality conditions. The LT-IRKA yields a near optimal $H_2(\tau)$ reduced order system. The nearness to the exact $H_2(\tau)$ optimal reduced system is quantized in terms of the errors in the interpolation based $H_2(\tau)$ optimality conditions. We demonstrate with numerical examples how the proposed algorithm nearly satisfies the time-limited optimality conditions and also how it performs with respect to the Time-Limited Two sided Iteration Algorithm (TL-TSIA), the Time-Limited Balanced Truncation (TL-BT), the Iterative Rational Krylov Algorithm (IRKA) and the Time-Limited Pseudo Optimal Rational Krylov (TL-PORK) Algorithm over a finite time interval.
A Proximal Algorithm for Sampling from Non-smooth Potentials
Authors: Jiaming Liang, Yongxin Chen
Date: 2021-10-09 00:00:00

In this work, we examine sampling problems with non-smooth potentials. We propose a novel Markov chain Monte Carlo algorithm for sampling from non-smooth potentials. We provide a non-asymptotical analysis of our algorithm and establish a polynomial-time complexity $\tilde {\cal O}(d\varepsilon^{-1})$ to obtain $\varepsilon$ total variation distance to the target density, better than most existing results under the same assumptions. Our method is based on the proximal bundle method and an alternating sampling framework. This framework requires the so-called restricted Gaussian oracle, which can be viewed as a sampling counterpart of the proximal mapping in convex optimization. One key contribution of this work is a fast algorithm that realizes the restricted Gaussian oracle for any convex non-smooth potential with bounded Lipschitz constant.
Propensity matrix method for age dependent stochastic infectious disease models
Authors: Péter Boldog, Norbert Bogya, Zsolt Vizi
Date: 2021-10-10 00:00:00

Mathematical modeling is one of the key factors of the effective control of newly found infectious diseases, such as COVID-19. Our knowledge about the parameters and the course of the infection is highly limited in the beginning of the epidemic, hence computer implementation of the models have to be quick and flexible. The propensity matrix - update graph method we discuss in this paper serves as a convenient approach to efficiently implement age structured stochastic epidemic models. The code base we implemented for our forecasting work is also included in the attached GitHub repository.
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design
Authors: Wengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi Jaakkola
Date: 2021-10-09 00:00:00
Proceeding: ICLR 2022 4
Tasks: Graph Neural Network, Protein Design, Specificity

Antibodies are versatile proteins that bind to pathogens like viruses and stimulate the adaptive immune system. The specificity of antibody binding is determined by complementarity-determining regions (CDRs) at the tips of these Y-shaped proteins. In this paper, we propose a generative model to automatically design the CDRs of antibodies with enhanced binding specificity or neutralization capabilities. Previous generative approaches formulate protein design as a structure-conditioned sequence generation task, assuming the desired 3D structure is given a priori. In contrast, we propose to co-design the sequence and 3D structure of CDRs as graphs. Our model unravels a sequence autoregressively while iteratively refining its predicted global structure. The inferred structure in turn guides subsequent residue choices. For efficiency, we model the conditional dependence between residues inside and outside of a CDR in a coarse-grained manner. Our method achieves superior log-likelihood on the test set and outperforms previous baselines in designing antibodies capable of neutralizing the SARS-CoV-2 virus.
Quantifying the noise in bursty gene expression under regulation by small RNAs
Authors: Shigang Qiu, Tao Jia
Date: 2021-10-10 00:00:00

Gene expression is a fundamental process in a living system. The small RNAs (sRNAs) is widely observed as a global regulator in gene expression. The inherent nonlinearity in this regulatory process together with the bursty production of messenger RNA (mRNA), sRNA and protein make the exact solution for this stochastic process intractable. This is particularly the case when quantifying the protein noise level, which has great impact on multiple cellular processes. Here we propose an approximate yet reasonably accurate solution for the gene expression noise with infrequent burst and strong regulation by sRNAs. This analytical solution allows us to better analyze the noise and stochastic deviation of protein level. We find that the regulation amplifies the noise, reduces the protein level. The stochasticity in the regulation generates more proteins than what if the stochasticity is removed from the system. The sRNA level is most important to the relationship between the noise and stochastic deviation. The results provide analytical tools for more general studies of gene expression and strengthen our quantitative understandings of post-transcriptional regulation in controlling gene expression processes.
Guided assembly of cellular network models from knowledge in literature
Authors: Yasmine Ahmed, Natasa Miskov-Zivanov
Date: 2021-10-09 00:00:00

Computational modeling is crucial for understanding and analyzing complex systems. In biology, model creation is a human dependent task that requires reading hundreds of papers and conducting wet lab experiments, which would take days or months. To overcome this hurdle, we propose a novel automated method, that utilizes the knowledge published in literature to suggest model extensions by selecting most relevant and useful information in few seconds. In particular, our novel approach organizes the events extracted from the literature as a collaboration graph with additional metric that relies on the event occurrence frequency in literature. Additionally, we show that common graph centrality metrics vary in the assessment of the extracted events. We have demonstrated the reliability of the proposed method using three different selected models, namely, T cell differentiation, T cell large granular lymphocyte, and pancreatic cancer cell. Our proposed method was able to find high percent of the desired new events with an average recall of 82%.