pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian Over-The-Air Computation
Authors: Yulin Shao, Deniz Gunduz, Soung Chang Liew
Date: 2021-09-08 00:00:00
Tasks: Edge-computing

As an important piece of the multi-tier computing architecture for future wireless networks, over-the-air computation (OAC) enables efficient function computation in multiple-access edge computing, where a fusion center aims to compute a function of the data distributed at edge devices. Existing OAC relies exclusively on the maximum likelihood (ML) estimation at the fusion center to recover the arithmetic sum of the transmitted signals from different devices. ML estimation, however, is much susceptible to noise. In particular, in the misaligned OAC where there are channel misalignments among received signals, ML estimation suffers from severe error propagation and noise enhancement. To address these challenges, this paper puts forth a Bayesian approach by letting each edge device transmit two pieces of statistical information to the fusion center such that Bayesian estimators can be devised to tackle the misalignments. Numerical and simulation results verify that, 1) For the aligned and synchronous OAC, our linear minimum mean squared error (LMMSE) estimator significantly outperforms the ML estimator. In the low signal-to-noise ratio (SNR) regime, the LMMSE estimator reduces the mean squared error (MSE) by at least 6 dB; in the high SNR regime, the LMMSE estimator lowers the error floor of MSE by 86.4%; 2) For the asynchronous OAC, our LMMSE and sum-product maximum a posteriori (SP-MAP) estimators are on an equal footing in terms of the MSE performance, and are significantly better than the ML estimator. Moreover, the SP-MAP estimator is computationally efficient, the complexity of which grows linearly with the packet length.
LuMaMi28: Real-Time Millimeter-Wave Massive MIMO Systems with Antenna Selection
Authors: MinKeun Chung, Liang Liu, Andreas Johansson, Sara Gunnarsson, Martin Nilsson, Zhinong Ying, Olof Zander, Kamal Samanta, Chris Clifton, Toshiyuki Koimori, Shinya Morita, Satoshi Taniguchi, Fredrik Tufvesson, Ove Edfors
Date: 2021-09-07 00:00:00

This paper presents LuMaMi28, a real-time 28 GHz massive multiple-input multiple-output (MIMO) testbed. In this testbed, the base station has 16 transceiver chains with a fully-digital beamforming architecture (with different pre-coding algorithms) and simultaneously supports multiple user equipments (UEs) with spatial multiplexing. The UEs are equipped with a beam-switchable antenna array for real-time antenna selection where the one with the highest channel magnitude, out of four pre-defined beams, is selected. For the beam-switchable antenna array, we consider two kinds of UE antennas, with different beam-width and different peak-gain. Based on this testbed, we provide measurement results for millimeter-wave (mmWave) massive MIMO performance in different real-life scenarios with static and mobile UEs. We explore the potential benefit of the mmWave massive MIMO systems with antenna selection based on measured channel data, and discuss the performance results through real-time measurements.
A Dual-Decoder Conformer for Multilingual Speech Recognition
Authors: Krishna D N
Date: 2021-08-22 00:00:00
Tasks: Decoder, Language Identification, Machine Translation, Multi-Task Learning, Phoneme Recognition, speech-recognition, Speech Recognition, Translation

Transformer-based models have recently become very popular for sequence-to-sequence applications such as machine translation and speech recognition. This work proposes a dual-decoder transformer model for low-resource multilingual speech recognition for Indian languages. Our proposed model consists of a Conformer [1] encoder, two parallel transformer decoders, and a language classifier. We use a phoneme decoder (PHN-DEC) for the phoneme recognition task and a grapheme decoder (GRP-DEC) to predict grapheme sequence along with language information. We consider phoneme recognition and language identification as auxiliary tasks in the multi-task learning framework. We jointly optimize the network for phoneme recognition, grapheme recognition, and language identification tasks with Joint CTC-Attention [2] training. Our experiments show that we can obtain a significant reduction in WER over the baseline approaches. We also show that our dual-decoder approach obtains significant improvement over the single decoder approach.
Cooperative Operation of the Fleet Operator and Incentive-aware Customers in an On-demand Delivery System: A Bi-level Approach
Authors: Canqi Yao, Shibo Chen, Zaiyue Yang
Date: 2021-09-08 00:00:00

In this paper, we study the cooperative operation problem between the fleet operator and incentive-aware customers in an on-demand delivery system. Specifically, the fleet operator offers discounts on transportation costs in exchange of the delivery time flexibility of customers. In order to capture the interaction between the fleet operator and customers, a novel bi-level optimization framework is proposed. By exploiting the strong duality, and the KKT optimality condition of customer optimization problems, we can reformulate the bi-level optimization problem as a mixed integer nonlinear programming problem. Considering the inherent difficulties of MINLP, a computationally efficient algorithm, which combines the merits of Lagrangian dual decomposition and Benders decomposition, is devised to solve the resulting MINLP problem in a distributed manner. Finally, extensive numerical experiments demonstrate that the proposed cooperation scheme can decrease the delivery fees for the customers, and reduce the operation cost of the fleet operator at the same time, thus leading to a win-win situation for both sides.
Hierarchical Frequency and Voltage Control using Prioritized Utilization of Inverter Based Resources
Authors: Rahul Chakraborty, Aranya Chakrabortty, Evangelos Farantatos, Mahendra Patel, Hossein Hooshyar, Atena Darvishi
Date: 2021-09-08 00:00:00

We propose a novel hierarchical frequency and voltage control design for multi-area power system integrated with inverter-based resources (IBRs). The design is based on the idea of prioritizing the use of IBRs over conventional generator-based control in compensating for sudden and unpredicted changes in loads and generations, and thereby mitigate any undesired dynamics in the frequency or the voltage by exploiting their fast actuation time constants. A new sequential optimization problem, referred to as Area Prioritized Power Flow (APPF), is formulated to model this prioritization. It is shown that compared to conventional power flow APPF not only leads to a fairer balance between the dispatch of active and reactive power from the IBRs and the synchronous generators, but also limits the impact of any contingency from spreading out beyond its respective control area, thereby guaranteeing a better collective dynamic performance of the grid. This improvement, however, comes at the cost of adding an extra layer of communication needed for executing APPF in a hierarchical way. Results are validated using simulations of a 9-machine, 6-IBR, 33-bus, 3-area power system model, illustrating how APPF can mitigate a disturbance faster and more efficiently by prioritizing the use of local area-resources.
Analysis of Infinite Stiffness Using PID Controller
Authors: Rifat Choudhury
Date: 2021-09-07 00:00:00
Tasks: Position

The design of Proportional Integral-Derivative (PID) controller is involved to gain infinite stiffness using active vibration control system and it is a modification of previous work. The PID controller is a control loop feedback mechanism which steadily calculates a fallacy value as the difference between a desired set point and a variable process and applies correction based on proportional, integral, and derivative terms. In this paper to achieve infinite stiffness magnetic field has been created by passing current, the input of the electromagnet is the output voltage of the sensor. Depending upon the gap between electromagnet and suspended object vibration is isolated by the attractive force of the electromagnet. This paper also presents the application, limitation, and scope of this system. In our system current is controlled by Analog Circuit (PID). The current moves through the electromagnet which creates magnetic force. If the load increases the proximity sensor senses the distance and delivers signal to the controller circuit. After receiving the signal controller circuit increased current supply to the electromagnet through power amplifier circuit. Magnetic force is expanded then, and object is levitated in a stable position. If the load is decreased the proximity sensor senses the distance and delivers signal to the controller circuit. Controller circuit receives the signal and decreases current supply to the electromagnet through power amplifier circuit. Thus, magnetic force is increased, and object is levitated in a stable position. This paper reveals the feasibility of vibration control techniques for number of diverse applications. This paper is highly applicable for detecting direction that is magnetic levitation system and magnetic bearing.
In silico drug repositioning for COVID-19 using absolute binding free energy calculations
Authors: Théau Debroise, Rose Hoste, Quentin Chamayou, Hervé Minoux, Bruno Filoche-Rommé, Marc Bianciotto, Jean-Philippe Rameau, Laurent Schio, Maximilien Levesque
Date: 2021-09-08 00:00:00

Since the rise of the SARS-CoV-2 pandemic in the winter of 2019, the need for an affordable and efficient drug has not yet been met. Leveraging its unique, fast and precise binding free energy prediction technology, Aqemia screened and ranked FDA-approved molecules against the 3ClPro protein. This protease is key to the post-translational modification of two polyproteins produced by the viral genome. We propose in our top 10 predicted molecules some drugs or prodrugs that could be repurposed and used in the treatment of COVID cases.
Optimal policy design to mitigate epidemics on networks using an SIS model
Authors: Carlo Cenedese, Lorenzo Zino, Michele Cucuzzella, Ming Cao
Date: 2021-09-08 00:00:00
Tasks: Model Predictive Control

Understanding how to effectively control an epidemic spreading on a network is a problem of paramount importance for the scientific community. The ongoing COVID-19 pandemic has highlighted the need for policies that mitigate the spread, without relying on pharmaceutical interventions, that is, without the medical assurance of the recovery process. These policies typically entail lockdowns and mobility restrictions, having thus nonnegligible socio-economic consequences for the population. In this paper, we focus on the problem of finding the optimum policies that "flatten the epidemic curve" while limiting the negative consequences for the society, and formulate it as a nonlinear control problem over a finite prediction horizon. We utilize the model predictive control theory to design a strategy to effectively control the disease, balancing safety and normalcy. An explicit formalization of the control scheme is provided for the susceptible--infected--susceptible epidemic model over a network. Its performance and flexibility are demonstrated by means of numerical simulations.
Approximate Factor Models with Weaker Loadings
Authors: Jushan Bai, Serena Ng
Date: 2021-09-08 00:00:00

Pervasive cross-section dependence is increasingly recognized as a characteristic of economic data and the approximate factor model provides a useful framework for analysis. Assuming a strong factor structure where $\Lop\Lo/N^\alpha$ is positive definite in the limit when $\alpha=1$, early work established convergence of the principal component estimates of the factors and loadings up to a rotation matrix. This paper shows that the estimates are still consistent and asymptotically normal when $\alpha\in(0,1]$ albeit at slower rates and under additional assumptions on the sample size. The results hold whether $\alpha$ is constant or varies across factor loadings. The framework developed for heterogeneous loadings and the simplified proofs that can be also used in strong factor analysis are of independent interest.
Eurasian Economic Union: Current Concept and Prospects
Authors: Larisa Kargina, Mattia Masolletti
Date: 2021-09-08 00:00:00

The authors of the article analyze the content of the Eurasian integration, from the initial initiative to the modern Eurasian Economic Union, paying attention to the factors that led to the transition from the Customs Union and the Single Economic Space to a stronger integration association. The main method of research is historical and legal analysis.
Principal Agent Problem as a Principled Approach to Electronic Counter-Countermeasures in Radar
Authors: Anurag Gupta, Vikram Krishnamurthy
Date: 2021-09-08 00:00:00

Electronic countermeasures (ECM) against a radar are actions taken by an adversarial jammer to mitigate effective utilization of the electromagnetic spectrum by the radar. On the other hand, electronic counter-countermeasures (ECCM) are actions taken by the radar to mitigate the impact of electronic countermeasures (ECM) so that the radar can continue to operate effectively. The main idea of this paper is to show that ECCM involving a radar and a jammer can be formulated as a principal-agent problem (PAP) - a problem widely studied in microeconomics. With the radar as the principal and the jammer as the agent, we design a PAP to optimize the radar's ECCM strategy in the presence of a jammer. The radar seeks to optimally trade-off signal-to-noise ratio (SNR) of the target measurement with the measurement cost: cost for generating radiation power for the pulse to probe the target. We show that for a suitable choice of utility functions, PAP is a convex optimization problem. Further, we analyze the structure of the PAP and provide sufficient conditions under which the optimal solution is an increasing function of the jamming power observed by the radar; this enables computation of the radar's optimal ECCM within the class of increasing affine functions at a low computation cost. Finally, we illustrate the PAP formulation of the radar's ECCM problem via numerical simulations. We also use simulations to study a radar's ECCM problem wherein the radar and the jammer have mismatched information.
Monotone Equilibrium in Matching Markets with Signaling
Authors: Seungjin Han, Alex Sam, Youngki Shin
Date: 2021-09-07 00:00:00

We introduce a notion of competitive signaling equilibrium (CSE) in one-to-one matching markets with a continuum of heterogeneous senders and receivers. We then study monotone CSE where equilibrium outcomes - sender actions, receiver reactions, beliefs, and matching - are all monotone in the stronger set order. We show that if the sender utility is monotone-supermodular and the receiver's utility is weakly monotone-supermodular, a CSE is stronger monotone if and only if it passes Criterion D1 (Cho and Kreps (1987), Banks and Sobel (1987)). Given any interval of feasible reactions that receivers can take, we fully characterize a unique stronger monotone CSE and establishes its existence with quasilinear utility functions.
Application of Ghost-DeblurGAN to Fiducial Marker Detection
Authors: Yibo Liu, Amaldev Haridevan, Hunter Schofield, Jinjun Shan
Date: 2021-09-08 00:00:00
Tasks: Deblurring, Generative Adversarial Network, Pose Estimation

Feature extraction or localization based on the fiducial marker could fail due to motion blur in real-world robotic applications. To solve this problem, a lightweight generative adversarial network, named Ghost-DeblurGAN, for real-time motion deblurring is developed in this paper. Furthermore, on account that there is no existing deblurring benchmark for such task, a new large-scale dataset, YorkTag, is proposed that provides pairs of sharp/blurred images containing fiducial markers. With the proposed model trained and tested on YorkTag, it is demonstrated that when applied along with fiducial marker systems to motion-blurred images, Ghost-DeblurGAN improves the marker detection significantly. The datasets and codes used in this paper are available at: https://github.com/York-SDCNLab/Ghost-DeblurGAN.
Contrastive Learning with Temporal Correlated Medical Images: A Case Study using Lung Segmentation in Chest X-Rays
Authors: Dewen Zeng, John N. Kheir, Peng Zeng, Yiyu Shi
Date: 2021-09-07 00:00:00
Tasks: Contrastive Learning, Image Segmentation, Medical Image Segmentation, object-detection, Object Detection, Representation Learning, Segmentation, Semantic Segmentation, Transfer Learning

Contrastive learning has been proved to be a promising technique for image-level representation learning from unlabeled data. Many existing works have demonstrated improved results by applying contrastive learning in classification and object detection tasks for either natural images or medical images. However, its application to medical image segmentation tasks has been limited. In this work, we use lung segmentation in chest X-rays as a case study and propose a contrastive learning framework with temporal correlated medical images, named CL-TCI, to learn superior encoders for initializing the segmentation network. We adapt CL-TCI from two state-of-the-art contrastive learning methods-MoCo and SimCLR. Experiment results on three chest X-ray datasets show that under two different segmentation backbones, U-Net and Deeplab-V3, CL-TCI can outperform all baselines that do not incorporate any temporal correlation in both semi-supervised learning setting and transfer learning setting with limited annotation. This suggests that information among temporal correlated medical images can indeed improve contrastive learning performance. Between the two variations of CL-TCI, CL-TCI adapted from MoCo outperforms CL-TCI adapted from SimCLR in most settings, indicating that more contrastive samples can benefit the learning process and help the network learn high-quality representations.
PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction
Authors: Qiang Ma, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert, Amir Alansary
Date: 2021-09-06 00:00:00
Tasks: Surface Reconstruction

Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI). In this work, we introduce Pial Neural Network (PialNN), a 3D deep learning framework for pial surface reconstruction. PialNN is trained end-to-end to deform an initial white matter surface to a target pial surface by a sequence of learned deformation blocks. A local convolutional operation is incorporated in each block to capture the multi-scale MRI information of each vertex and its neighborhood. This is fast and memory-efficient, which allows reconstructing a pial surface mesh with 150k vertices in one second. The performance is evaluated on the Human Connectome Project (HCP) dataset including T1-weighted MRI scans of 300 subjects. The experimental results demonstrate that PialNN reduces the geometric error of the predicted pial surface by 30% compared to state-of-the-art deep learning approaches.
Leveraging Multiple Transmissions and Receptions for Channel-Agnostic Deep Learning-Based Network Device Classification
Authors: Nora Basha, Bechir Hamdaoui
Date: 2021-09-08 00:00:00

The accurate identification of wireless devices is critical for enabling automated network access monitoring and authenticated data communication in large-scale networks; e.g., IoT. RF fingerprinting has emerged as a solution for device identification by leveraging the transmitter unique manufacturing impairments. Although deep learning is proven efficient in classifying devices based on the hardware impairments fingerprints, DL models perform poorly due to channel variations. That is, although training and testing neural networks using data generated during the same period achieve reliable classification, testing them on data generated at different times degrades the accuracy substantially, an already well recognized problem within the community. To the best of our knowledge, we are the first to propose to leverage MIMO capabilities to mitigate the channel effect and provide a channel-resilient device classification. We show that for AWGN channels, combining multiple received signals improves the testing accuracy by up to $30\%$. We also show that for Rayleigh channels, blind channel estimation enabled by MIMO increases the testing accuracy by up to $40\%$ when the models are trained and tested over the same channel, and by up to $60\%$ when the models are tested on a channel that is different from that used for training.
Optimal Sensor Placement for Source Localization: A Unified ADMM Approach
Authors: Nitesh Sahu, Linlong Wu, Prabhu Babu, Bhavani Shankar M. R., Björn Ottersten
Date: 2021-09-08 00:00:00

Source localization plays a key role in many applications including radar, wireless and underwater communications. Among various localization methods, the most popular ones are Time-Of-Arrival (TOA), Time-Difference-Of-Arrival (TDOA), and Received Signal Strength (RSS) based. Since the Cram\'{e}r-Rao lower bounds (CRLB) of these methods depend on the sensor geometry explicitly, sensor placement becomes a crucial issue in source localization applications. In this paper, we consider finding the optimal sensor placements for the TOA, TDOA and RSS based localization scenarios. We first unify the three localization models by a generalized problem formulation based on the CRLB-related metric. Then a unified optimization framework for optimal sensor placement (UTMOST) is developed through the combination of the alternating direction method of multipliers (ADMM) and majorization-minimization (MM) techniques. Unlike the majority of the state-of-the-art works, the proposed UTMOST neither approximates the design criterion nor considers only uncorrelated noise in the measurements. It can readily adapt to to different design criteria (i.e. A, D and E-optimality) with slight modifications within the framework and yield the optimal sensor placements correspondingly. Extensive numerical experiments are performed to exhibit the efficacy and flexibility of the proposed framework.
A Computationally Efficient EK-PMBM Filter for Bistatic mmWave Radio SLAM
Authors: Yu Ge, Ossi Kaltiokallio, Hyowon Kim, Fan Jiang, Jukka Talvitie, Mikko Valkama, Lennart Svensson, Sunwoo Kim, Henk Wymeersch
Date: 2021-09-08 00:00:00
Tasks: Simultaneous Localization and Mapping, State Estimation

Millimeter wave (mmWave) signals are useful for simultaneous localization and mapping (SLAM), due to their inherent geometric connection to the propagation environment and the propagation channel. To solve the SLAM problem, existing approaches rely on sigma-point or particle-based approximations, leading to high computational complexity, precluding real-time execution. We propose a novel low-complexity SLAM filter, based on the Poisson multi-Bernoulli mixture (PMBM) filter. It utilizes the extended Kalman (EK) first-order Taylor series based Gaussian approximation of the filtering distribution, and applies the track-oriented marginal multi-Bernoulli/Poisson (TOMB/P) algorithm to approximate the resulting PMBM as a Poisson multi-Bernoulli (PMB). The filter can account for different landmark types in radio SLAM and multiple data association hypotheses. Hence, it has an adjustable complexity/performance trade-off. Simulation results show that the developed SLAM filter can greatly reduce the computational cost, while it keeps the good performance of mapping and user state estimation.