pwc-archive/papers-with-abstracts huggingface dataset:
Shell Theory: A Statistical Model of Reality
Authors: Wen-Yan Lin, Siying Liu, Changhao Ren, Ngai-Man Cheung, Hongdong Li, Yasuyuki Matsushita
Date: 2021-05-28 00:00:00
Proceeding: IEEE Transactions on Pattern Analysis and Machine Intelligence 2021 5
Tasks: Anomaly Detection, BIG-bench Machine Learning, model, One-class classifier, Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly, Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly, Unsupervised Anomaly Detection with Specified Settings -- 1% anomaly, Unsupervised Anomaly Detection with Specified Settings -- 20% anomaly, Unsupervised Anomaly Detection with Specified Settings -- 30% anomaly

The foundational assumption of machine learning is that the data under consideration is separable into classes; while intuitively reasonable, separability constraints have proven remarkably difficult to formulate mathematically. We believe this problem is
rooted in the mismatch between existing statistical techniques and commonly encountered data; object representations are typically high dimensional but statistical techniques tend to treat high dimensions a degenerate case. To address this problem, we develop a dedicated statistical framework for machine learning in high dimensions. The framework derives from the observation that object relations form a natural hierarchy; this leads us to model objects as instances of a high dimensional, hierarchal generative processes. Using a distance based statistical technique, also developed in this paper, we show that in such generative processes, instances of each process in the hierarchy, are almost-always encapsulated by a distinctive-shell that excludes almost-all other instances. The result is shell theory, a statistical machine learning framework in which separability constraints (distinctive-shells) are formally derived from the assumed generative process.
Bitcoin's Crypto Flow Network
Authors: Yoshi Fujiwara, Rubaiyat Islam
Date: 2021-06-21 00:00:00

How crypto flows among Bitcoin users is an important question for understanding the structure and dynamics of the cryptoasset at a global scale. We compiled all the blockchain data of Bitcoin from its genesis to the year 2020, identified users from anonymous addresses of wallets, and constructed monthly snapshots of networks by focusing on regular users as big players. We apply the methods of bow-tie structure and Hodge decomposition in order to locate the users in the upstream, downstream, and core of the entire crypto flow. Additionally, we reveal principal components hidden in the flow by using non-negative matrix factorization, which we interpret as a probabilistic model. We show that the model is equivalent to a probabilistic latent semantic analysis in natural language processing, enabling us to estimate the number of such hidden components. Moreover, we find that the bow-tie structure and the principal components are quite stable among those big players. This study can be a solid basis on which one can further investigate the temporal change of crypto flow, entry and exit of big players, and so forth.
Sphynx: ReLU-Efficient Network Design for Private Inference
Authors: Minsu Cho, Zahra Ghodsi, Brandon Reagen, Siddharth Garg, Chinmay Hegde
Date: 2021-06-17 00:00:00

The emergence of deep learning has been accompanied by privacy concerns surrounding users' data and service providers' models. We focus on private inference (PI), where the goal is to perform inference on a user's data sample using a service provider's model. Existing PI methods for deep networks enable cryptographically secure inference with little drop in functionality; however, they incur severe latency costs, primarily caused by non-linear network operations (such as ReLUs). This paper presents Sphynx, a ReLU-efficient network design method based on micro-search strategies for convolutional cell design. Sphynx achieves Pareto dominance over all existing private inference methods on CIFAR-100. We also design large-scale networks that support cryptographically private inference on Tiny-ImageNet and ImageNet.
A New Channel Estimation Strategy in Intelligent Reflecting Surface Assisted Networks
Authors: Rui Wang, Liang Liu, Shuowen Zhang, Changyuan Yu
Date: 2021-06-22 00:00:00

Channel estimation is the main hurdle to reaping the benefits promised by the intelligent reflecting surface (IRS), due to its absence of ability to transmit/receive pilot signals as well as the huge number of channel coefficients associated with its reflecting elements. Recently, a breakthrough was made in reducing the channel estimation overhead by revealing that the IRS-BS (base station) channels are common in the cascaded user-IRS-BS channels of all the users, and if the cascaded channel of one typical user is estimated, the other users' cascaded channels can be estimated very quickly based on their correlation with the typical user's channel \cite{b5}. One limitation of this strategy, however, is the waste of user energy, because many users need to keep silent when the typical user's channel is estimated. In this paper, we reveal another correlation hidden in the cascaded user-IRS-BS channels by observing that the user-IRS channel is common in all the cascaded channels from users to each BS antenna as well. Building upon this finding, we propose a novel two-phase channel estimation protocol in the uplink communication. Specifically, in Phase I, the correlation coefficients between the channels of a typical BS antenna and those of the other antennas are estimated; while in Phase II, the cascaded channel of the typical antenna is estimated. In particular, all the users can transmit throughput Phase I and Phase II. Under this strategy, it is theoretically shown that the minimum number of time instants required for perfect channel estimation is the same as that of the aforementioned strategy in the ideal case without BS noise. Then, in the case with BS noise, we show by simulation that the channel estimation error of our proposed scheme is significantly reduced thanks to the full exploitation of the user energy.
Reinforcement Learning for Resource Allocation in Steerable Laser-based Optical Wireless Systems
Authors: Abdelrahman S. Elgamal, Osama Z. Alsulami, Ahmad Adnan Qidan, Taisir E. H. El-Gorashi, Jaafar M. H. Elmirghani
Date: 2021-06-21 00:00:00
Tasks: Management, Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

Vertical Cavity Surface Emitting Lasers (VCSELs) have demonstrated suitability for data transmission in indoor optical wireless communication (OWC) systems due to the high modulation bandwidth and low manufacturing cost of these sources. Specifically, resource allocation is one of the major challenges that can affect the performance of multi-user optical wireless systems. In this paper, an optimisation problem is formulated to optimally assign each user to an optical access point (AP) composed of multiple VCSELs within a VCSEL array at a certain time to maximise the signal to interference plus noise ratio (SINR). In this context, a mixed-integer linear programming (MILP) model is introduced to solve this optimisation problem. Despite the optimality of the MILP model, it is considered impractical due to its high complexity, high memory and full system information requirements. Therefore, reinforcement Learning (RL) is considered, which recently has been widely investigated as a practical solution for various optimization problems in cellular networks due to its ability to interact with environments with no previous experience. In particular, a Q-learning (QL) algorithm is investigated to perform resource management in a steerable VCSEL-based OWC systems. The results demonstrate the ability of the QL algorithm to achieve optimal solutions close to the MILP model. Moreover, the adoption of beam steering, using holograms implemented by exploiting liquid crystal devices, results in further enhancement in the performance of the network considered.
Robust EMRAN-aided Coupled Controller for Autonomous Vehicles
Authors: Sauranil Debarshi, Suresh Sundaram, Narasimhan Sundararajan
Date: 2021-06-22 00:00:00
Tasks: Autonomous Vehicles

This paper presents a coupled, neural network-aided longitudinal cruise and lateral path-tracking controller for an autonomous vehicle with model uncertainties and experiencing unknown external disturbances. Using a feedback error learning mechanism, an inverse vehicle dynamics learning scheme utilizing an adaptive Radial Basis Function (RBF) neural network, referred to as the Extended Minimal Resource Allocating Network (EMRAN) is employed. EMRAN uses an extended Kalman filter for online learning and weight updates, and also incorporates a growing/pruning strategy for maintaining a compact network for easier real-time implementation. The online learning algorithm handles the parametric uncertainties and eliminates the effect of unknown disturbances on the road. Combined with a self-regulating learning scheme for improving generalization performance, the proposed EMRAN-aided control architecture aids a basic PID cruise and Stanley path-tracking controllers in a coupled form. Its performance and robustness to various disturbances and uncertainties are compared with the conventional PID and Stanley controllers, along with a comparison with a fuzzy-based PID controller and an active disturbance rejection control (ADRC) scheme. Simulation results are presented for both slow and high speed scenarios. The root mean square (RMS) and maximum tracking errors clearly indicate the effectiveness of the proposed control scheme in achieving better tracking performance in autonomous vehicles under unknown environments.
Deep neural network Based Low-latency Speech Separation with Asymmetric analysis-Synthesis Window Pair
Authors: Shanshan Wang, Gaurav Naithani, Archontis Politis, Tuomas Virtanen
Date: 2021-06-22 00:00:00
Tasks: Clustering, Deep Clustering, Speech Enhancement, Speech Separation

Time-frequency masking or spectrum prediction computed via short symmetric windows are commonly used in low-latency deep neural network (DNN) based source separation. In this paper, we propose the usage of an asymmetric analysis-synthesis window pair which allows for training with targets with better frequency resolution, while retaining the low-latency during inference suitable for real-time speech enhancement or assisted hearing applications. In order to assess our approach across various model types and datasets, we evaluate it with both speaker-independent deep clustering (DC) model and a speaker-dependent mask inference (MI) model. We report an improvement in separation performance of up to 1.5 dB in terms of source-to-distortion ratio (SDR) while maintaining an algorithmic latency of 8 ms.
A behavioural modelling approach to assess the impact of COVID-19 vaccine hesitancy
Authors: Bruno Buonomo, Rossella Della Marca, Alberto D'Onofrio, Maria Groppi
Date: 2021-06-22 00:00:00

In this paper we introduce a compartmental epidemic model describing the transmission of the COVID-19 disease in presence of non-mandatory vaccination. The model takes into account the hesitancy and refusal of vaccination. To this aim, we employ the information index, which mimics the idea that individuals take their decision on vaccination based not only on the present but also on the past information about the spread of the disease. Theoretical analysis and simulations show clearly as a voluntary vaccination can certainly reduce the impact of the disease but it is unable to eliminate it. We also show how the information-related parameters affect the dynamics of the disease. In particular, the hesitancy and refusal of vaccination is better contained in case of large information coverage and small memory characteristic time. Finally, the possible influence of seasonality is also investigated.
Stochastic Optimal Control for Multivariable Dynamical Systems Using Expectation Maximization
Authors: Prakash Mallick, Zhiyong Chen
Date: 2020-10-01 00:00:00

Trajectory optimization is a fundamental stochastic optimal control problem. This paper deals with a trajectory optimization approach for dynamical systems subject to measurement noise that can be fitted into linear time-varying stochastic models. Exact/complete solutions to these kind of control problems have been deemed analytically intractable in literature because they come under the category of Partially Observable Markov Decision Processes (POMDPs). Therefore, effective solutions with reasonable approximations are widely sought for. We propose a reformulation of stochastic control in a reinforcement learning setting. This type of formulation assimilates the benefits of conventional optimal control procedure, with the advantages of maximum likelihood approaches. Finally, an iterative trajectory optimization paradigm called as Stochastic Optimal Control - Expectation Maximization (SOC-EM) is put-forth. This trajectory optimization procedure exhibits better performance in terms of reduction of cumulative cost-to-go which is proved both theoretically and empirically. Furthermore, we also provide novel theoretical work which is related to uniqueness of control parameter estimates. Analysis of the control covariance matrix is presented, which handles stochasticity through efficiently balancing exploration and exploitation.
Dual-port grid-forming control of MMCs and its applications to grids of grids
Authors: Dominic Groß, Sánchez-Sánchez, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt
Date: 2021-06-21 00:00:00

This work focuses on grid-forming (GFM) control of Interconnecting Power Converters (IPCs) that are used to interconnect multiple HVAC and HVDC subgrids to form a grid of grids. We introduce the concept of dual-port GFM control that leverages the ability of Modular Multilevel Converters (MMCs) to simultaneously form its AC and DC terminal voltage and present two dual-port GFM MMC controls. We provide analytical results and high-fidelity simulations that demonstrate that (i) dual-port GFM control is more resilient to contingencies (i.e., line and generator outages) than state-of-the-art single-port GFM control, and (ii) unlike single-port GFM control, dual-port GFM control does not require assigning grid-forming and grid-following (GFL) roles to the IPC terminals in grids of grids. Finally, we provide an in-depth discussion and comparison of single-port GFM control and the proposed dual-port GFM controls.
Adaptive Sampling for Structure Preserving Model Order Reduction of Port-Hamiltonian Systems
Authors: Paul Schwerdtner, Matthias Voigt
Date: 2021-06-21 00:00:00

We present an adaptive sampling strategy for the optimization-based structure preserving model order reduction (MOR) algorithm developed in [Schwerdtner, P. and Voigt, M. (2020). Structure preserving model order reduction by parameter optimization, Preprint arXiv:2011.07567]. This strategy reduces the computational demand and the required a priori knowledge about the given full order model, while at the same time retaining a high accuracy compared to other structure preserving but also unstructured MOR algorithms. A numerical study with a port-Hamiltonian benchmark system demonstrates the effectiveness of our method combined with its new adaptive sampling strategy. We also investigate the distribution of the sample points.
Port-Hamiltonian System Identification from Noisy Frequency Response Data
Authors: Paul Schwerdtner
Date: 2021-06-21 00:00:00

We present a new method for the identification of linear time-invariant passive systems from noisy frequency response data. In particular, we propose to fit a parametrized port-Hamiltonian (pH) system, which is automatically passive, to supplied data with respect to a least-squares objective function. In a numerical study, we assess the accuracy of the resulting identified models by comparing our method to two other frequency domain system identification methods. One of the methods being compared is a recently published identification procedure that also computes pH systems and the other one is the well-known vector-fitting algorithm, which provides unstructured models. The numerical evaluation demonstrates a substantial increase in accuracy of our method compared to the other pH identification procedure and a slightly improved accuracy compared to vector-fitting. This underlines the suitability of our method for the estimation of passive or pH systems - in particular from noisy frequency response data.
Reconfigurable Intelligent Surface-Aided Wireless Power Transfer Systems: Analysis and Implementation
Authors: Nguyen Minh Tran, Muhammad Miftahul Amri, Je Hyeon Park, Dong In Kim, Kae Won Choi
Date: 2021-06-12 00:00:00

Reconfigurable intelligent surface (RIS) is a promising technology for RF wireless power transfer (WPT) as it is capable of beamforming and beam focusing without using active and power-hungry components. In this paper, we propose a multi-tile RIS beam scanning (MTBS) algorithm for powering up internet-of-things (IoT) devices. Considering the hardware limitations of the IoT devices, the proposed algorithm requires only power information to enable the beam focusing capability of the RIS. Specifically, we first divide the RIS into smaller RIS tiles. Then, all RIS tiles and the phased array transmitter are iteratively scanned and optimized to maximize the receive power. We elaborately analyze the proposed algorithm and build a simulator to verify it. Furthermore, we have built a real-life testbed of RIS-aided WPT systems to validate the algorithm. The experimental results show that the proposed MTBS algorithm can properly control the transmission phase of the transmitter and the reflection phase of the RIS to focus the power at the receiver. Consequently, after executing the algorithm, about 20 dB improvement of the receive power is achieved compared to the case that all unit cells of the RIS are in OFF state. By experiments, we confirm that the RIS with the MTBS algorithm can greatly enhance the power transfer efficiency.
Lite-Sparse Hierarchical Partial Power Processing for Second-Use Battery Energy Storage Systems
Authors: Xiaofan Cui, Alireza Ramyar, Peyman Mohtat, Veronica Contreras, Jason Siegel, Anna Stefanopoulou, Al-Thaddeus Avestruz
Date: 2021-06-07 00:00:00

The explosive growth of electric vehicles (EVs) is leading to a surge in retired EV batteries, which are typically recycled despite having nearly 80% available capacity. Repurposing automotive batteries for second-use battery energy storage systems (2-BESS) has both economical and environmental benefits. The challenge with second-use batteries is the heterogeneity in their state of health. This paper introduces a new strategy to optimize 2-BESS performance despite the heterogeneity of individual batteries while reducing the cost of power conversion. In this paper, the statistical distribution of the power heterogeneity in the supply of batteries is used to optimize the choice of power converters and design the power flow within the battery energy storage system (BESS) to optimize power capability. By leveraging a new lite-sparse hierarchical partial power processing (LS-HiPPP) approach, we study how a hierarchy in partial power processing (PPP) partitions power converters to significantly reduce converter ratings, process less power to achieve high system efficiency with lower cost (lower efficiency) converters, and take advantage of economies of scale by requiring only a minimal number of sets of identical converters. Our results demonstrate that LS-HiPPP architectures offer the best tradeoff between battery utilization and converter cost and have higher system efficiency than conventional partial power processing (C-PPP) in all cases.
Surrogate-based variational data assimilation for tidal modelling
Authors: Rem-Sophia Mouradi, Cédric Goeury, Olivier Thual, Fabrice Zaoui, Pablo Tassi
Date: 2021-06-08 00:00:00

Data assimilation (DA) is widely used to combine physical knowledge and observations. It is nowadays commonly used in geosciences to perform parametric calibration. In a context of climate change, old calibrations can not necessarily be used for new scenarios. This raises the question of DA computational cost, as costly physics-based numerical models need to be reanalyzed. Reduction and metamodelling represent therefore interesting perspectives, for example proposed in recent contributions as hybridization between ensemble and variational methods, to combine their advantages (efficiency, non-linear framework). They are however often based on Monte Carlo (MC) type sampling, which often requires considerable increase of the ensemble size for better efficiency, therefore representing a computational burden in ensemble-based methods as well. To address these issues, two methods to replace the complex model by a surrogate are proposed and confronted : (i) PODEn3DVAR directly inspired from PODEn4DVAR, relies on an ensemble-based joint parameter-state Proper Orthogonal Decomposition (POD), which provides a linear metamodel ; (ii) POD-PCE-3DVAR, where the model states are POD reduced then learned using Polynomial Chaos Expansion (PCE), resulting in a non-linear metamodel. Both metamodels allow to write an approximate cost function whose minimum can be analytically computed, or deduced by a gradient descent at negligible cost. Furthermore, adapted metamodelling error covariance matrix is given for POD-PCE-3DVAR, allowing to substantially improve the metamodel-based DA analysis. Proposed methods are confronted on a twin experiment, and compared to classical 3DVAR on a measurement-based problem. Results are promising, in particular superior with POD-PCE-3DVAR, showing good convergence to classical 3DVAR and robustness to noise.
Information of income position and its impact on perceived tax burden and preference for redistribution: An Internet Survey Experiment
Authors: Eiji Yamamura
Date: 2021-06-22 00:00:00
Tasks: Position, Survey

A customized internet survey experiment is conducted in Japan to examine how individuals' relative income position influences preferences for income redistribution and individual perceptions regarding income tax burden. I first asked respondents about their perceived income position in their country and their preferences for redistribution and perceived tax burden. In the follow-up survey for the treatment group, I provided information on their true income position and asked the same questions as in the first survey. For the control group, I did not provide their true income position and asked the same questions. I gathered a large sample that comprised observations of the treatment group (4,682) and the control group (2,268). The key findings suggest that after being informed of individuals' real income position, (1) individuals who thought their income position was higher than the true one perceived their tax burden to be larger, (2) individuals' preference for redistribution hardly changes, and (3) irreciprocal individuals perceive their tax burden to be larger and are more likely to prefer redistribution. However, the share of irreciprocal ones is small. This leads Japan to be a non-welfare state.
Retractions: Updating from Complex Information
Authors: Duarte Gonçalves, Jonathan Libgober, Jack Willis
Date: 2021-06-21 00:00:00
Tasks: Experimental Design, Misinformation

We modify a canonical experimental design to identify the effectiveness of retractions. Comparing beliefs after retractions to beliefs (a) without the retracted information and (b) after equivalent new information, we find that retractions result in diminished belief updating in both cases. We propose this reflects updating from retractions being more complex, and our analysis supports this: we find longer response times, lower accuracy, and higher variability. The results -- robust across diverse subject groups and design variations -- enhance our understanding of belief updating and offer insights into addressing misinformation.