pwc-archive/papers-with-abstracts huggingface dataset:
Some Pragmatic Prevention's Guidelines regarding SARS-CoV-2 and COVID-19 in Latin-America inspired by mixed Machine Learning Techniques and Artificial Mathematical Intelligence. Case Study: Colombia
Authors: Danny A. J. Gomez-Ramirez, Yoe A. Herrera-Jaramillo, Johana C. Ortega-Giraldo, Alex M. Ardila-Garcia
Date: 2021-05-12 00:00:00
Tasks: Opinion Mining

We use an enhanced methodology combining specific forms of AI techniques, opinion mining and artificial mathematical intelligence (AMI), with public data on the spread of the coronavirus SARS-CoV-2 and the incidence of COVID-19 disease in Colombia during the first three months since the first reported positive case. The results obtained, together with conceptual tools coming from the global taxonomy of fundamental cognitive mechanisms emerging in AMI and with suitable contextual information from Colombian public health and mainstream social media, allowed us to stating specific preventive guidelines for a better restructuring of initial safe and stable life conditions in Colombia, and in an extended manner in similar Latin American Countries. More specifically, we describe three major guidelines: 1) regular creative visualization and effective planning, 2) the continuous use of constructive linguistic frameworks, and 3) frequent and moderate use of kinesthetic routines. They should be understood as effective tools from a cognitive and behavioural perspective, rather than from a biological one. Even more, the first two guidelines should be acknowledged in integral cooperation with the third one regarding the global effect of COVID-19 in human beings as a whole, this includes the mind and body.
Inferring power system dynamics from synchrophasor data using Gaussian processes
Authors: Mana Jalali, Vassilis Kekatos, Siddharth Bhela, Hao Zhu, Virgilio Centeno
Date: 2021-05-26 00:00:00
Tasks: Gaussian Processes, Uncertainty Quantification

Synchrophasor data provide unprecedented opportunities for inferring power system dynamics, such as estimating voltage angles, frequencies, and accelerations along with power injection at all buses. Aligned to this goal, this work puts forth a novel framework for learning dynamics after small-signal disturbances by leveraging Gaussian processes (GPs). We extend results on learning of a linear time-invariant system using GPs to the multi-input multi-output setup. This is accomplished by decomposing power system swing dynamics into a set of single-input single-output linear systems with narrow frequency pass bands. The proposed learning technique captures time derivatives in continuous time, accommodates data streams sampled at different rates, and can cope with missing data and heterogeneous levels of accuracy. While Kalman filter-based approaches require knowing all system inputs, the proposed framework handles readings of system inputs, outputs, their derivatives, and combinations thereof collected from an arbitrary subset of buses. Relying on minimal system information, it further provides uncertainty quantification in addition to point estimates of system dynamics. Numerical tests verify that this technique can infer dynamics at non-metered buses, impute and predict synchrophasors, and locate faults under linear and non-linear system models under ambient and fault disturbances.
Application of the unified control and detection framework to detecting stealthy integrity cyber-attacks on feedback control systems
Authors: Steven X. Ding, Linlin Li, Dong Zhao, Chris Louen, Tianyu Liu
Date: 2021-02-27 00:00:00

This draft addresses issues of detecting stealthy integrity cyber-attacks on automatic control systems in the unified control and detection framework. A general form of integrity cyber-attacks that cannot be detected using the well-established observer-based technique is first introduced as kernel attacks. The well-known replay, zero dynamics and covert attacks are special forms of the kernel attacks. Existence conditions for the kernel attacks are presented. It is demonstrated, in the unified framework of control and detection, that all kernel attacks can be structurally detected when not only the observer-based residual, but also the control signal based residual signals are generated and used for the detection purpose. Based on the analytical results, two schemes for detecting the kernel attacks are then proposed, which allow reliable attack detection without loss of control performance. While the first scheme is similar to the well-established moving target method and auxiliary system aided detection scheme, the second detector is realised with encrypted transmissions of control and monitoring signals in the feedback control system that prevents adversary to gain system knowledge by means of eavesdropping attacks. Both schemes are illustrated by examples of detecting replay, zero dynamics and covert attacks and an experimental study on a three-tank control system.
Finite sample guarantees for quantile estimation: An application to detector threshold tuning
Authors: David Umsonst, Justin Ruths, Henrik Sandberg
Date: 2021-05-25 00:00:00
Tasks: Anomaly Detection

In threshold-based anomaly detection, we want to tune the threshold of a detector to achieve an acceptable false alarm rate. However, tuning the threshold is often a non-trivial task due to unknown detector output distributions. A detector threshold that provides an acceptable false alarm rate is equivalent to a specific quantile of the detector output distribution. Therefore, we use quantile estimators based on order statistics to estimate the detector threshold. The estimation of quantiles from sample data has a more than a century long tradition and we provide three different distribution-free finite sample guarantees for a class of quantile estimators. The first is based on the Dworetzky-Kiefer-Wolfowitz inequality, the second utilizes the Vysochanskij-Petunin inequality, and the third is based on exact confidence intervals for a beta distribution. These guarantees are then compared and used in the detector threshold tuning problem. We use both simulated data as well as data obtained from an experimental setup with the Temperature Control Lab to validate the guarantees provided.
Large-Scale Scenarios of Electric Vehicle Charging with a Data-Driven Model of Control
Authors: Siobhan Powell, Gustavo Vianna Cezar, Elpiniki Apostolaki-Iosifidou, Ram Rajagopal
Date: 2021-05-25 00:00:00

Planning to support widespread transportation electrification depends on detailed estimates for the electricity demand from electric vehicles in both uncontrolled and controlled or smart charging scenarios. We present a modeling approach to rapidly generate charging estimates that include control for large-scale scenarios with millions of individual drivers. We model uncontrolled charging demand using statistical representations of real charging sessions. We model the effect of load modulation control on aggregate charging profiles with a novel machine learning approach that replaces traditional optimization approaches. We demonstrate its performance modeling workplace charging control with multiple electricity rate schedules, achieving small errors (2.5% to 4.5%), while accelerating computations by more than 4000 times. We illustrate the methodology by generating scenarios for California's 2030 charging demand including multiple charging segments and controls, with scenarios run locally in under 50 seconds, and for assisting rate design modeling the large-scale impact of a new workplace charging rate.
Effects of limited and heterogeneous memory in hidden-action situations
Authors: Patrick Reinwald, Stephan Leitner, Friederike Wall
Date: 2021-05-26 00:00:00

Limited memory of decision-makers is often neglected in economic models, although it is reasonable to assume that it significantly influences the models' outcomes. The hidden-action model introduced by Holmstr\"om also includes this assumption. In delegation relationships between a principal and an agent, this model provides the optimal sharing rule for the outcome that optimizes both parties' utilities. This paper introduces an agent-based model of the hidden-action problem that includes limitations in the cognitive capacity of contracting parties. Our analysis mainly focuses on the sensitivity of the principal's and the agent's utilities to the relaxed assumptions. The results indicate that the agent's utility drops with limitations in the principal's cognitive capacity. Also, we find that the agent's cognitive capacity limitations affect neither his nor the principal's utility. Thus, the agent bears all adverse effects resulting from limitations in cognitive capacity.
Multi-Dimensional Screening: Buyer-Optimal Learning and Informational Robustness
Authors: Rahul Deb, Anne-Katrin Roesler
Date: 2021-05-26 00:00:00

A monopolist seller of multiple goods screens a buyer whose type is initially unknown to both but drawn from a commonly known distribution. The buyer privately learns about his type via a signal. We derive the seller's optimal mechanism in two different information environments. We begin by deriving the buyer-optimal outcome. Here, an information designer first selects a signal, and then the seller chooses an optimal mechanism in response; the designer's objective is to maximize consumer surplus. Then, we derive the optimal informationally robust mechanism. In this case, the seller first chooses the mechanism, and then nature picks the signal that minimizes the seller's profits. We derive the relation between both problems and show that the optimal mechanism in both cases takes the form of pure bundling.
The Effect of Providing Peer Information on Evaluation for Gender Equalized and ESG Oriented Firms: An Internet Survey Experiment
Authors: Eiji Yamamura
Date: 2021-05-26 00:00:00

Internet survey experiment is conducted to examine how providing peer information of evaluation about progressive firms changed individual's evaluations. Using large sample including over 13,000 observations collected by two-step experimental surveys, I found; (1) provision of the information leads individuals to expect higher probability of rising of stocks and be more willing to buy it. (2) the effect on willingness to buy is larger than the expected probability of stock price rising, (3) The effect for woman is larger than for man. (4) individuals who prefer environment (woman's empowerment) become more willing to buy stock of pro-environment (gender-balanced) firms than others if they have the information. (5) The effect of the peer information is larger for individuals with "warm -glow" motivation.
Managing Manufacturing and Delivery of Personalised Medicine: Current and Future Models
Authors: Andreea Avramescu, Richard Allmendinger, Manuel López-Ibáñez
Date: 2021-05-21 00:00:00

With almost 50% of annual commercial drug approvals being Personalised Medicine (PM) and its huge potential to improve quality of life, this emerging medical sector has received increased attention from the industry and medical research, driven by health and care services, and us, the patients. Notwithstanding the power of Advanced Therapy Medicinal Products (ATMPs) to treat progressive illnesses and rare genetic conditions, their delivery on large scale is still problematic. The biopharmaceutical companies are currently struggling to meet timely delivery and, given high prices of up to $2 million per patient, prove the cost-effectiveness of their ATMP. The fragility of ATMPs combined with the impossibility for replacements due to the nature of the treatment and the advanced stages of the patient's condition are some of the bottlenecks added to a generally critical supply chain. As a consequence, ATMPs are currently used in most cases only as a last resort. ATMPs are at the intersection of multiple healthcare logistic networks and, due to their novelty, research around their commercialisation is still in its infancy from an operations research perspective. To accelerate technology adoption in this domain, we characterize pertinent practical challenges in a PM supply chain and then capture them in a holistic mathematical model ready for optimisation. The identified challenges and derived model will be contrasted with literature of related supply chains in terms of model formulations and suitable optimisation methods. Finally, needed technological advancements are discussed to pave the way to affordable commercialisation of PM.
Direct Implementation with Evidence
Authors: Soumen Banerjee, Yi-Chun Chen, Yifei Sun
Date: 2021-05-26 00:00:00

We study full implementation with evidence in an environment with bounded utilities. We show that a social choice function is Nash implementable in a direct revelation mechanism if and only if it satisfies the measurability condition proposed by <cite>BL2012</cite>. Building on a novel classification of lies according to their refutability with evidence, the mechanism requires only two agents, accounts for mixed-strategy equilibria and accommodates evidentiary costs. While monetary transfers are used, they are off the equilibrium and can be balanced with three or more agents. In a richer model of evidence due to <cite>KT2012</cite>, we establish pure-strategy implementation with two or more agents in a direct revelation mechanism. We also obtain a necessary and sufficient condition on the evidence structure for renegotiation-proof bilateral contracts, based on the classification of lies.
MMV-Net: A Multiple Measurement Vector Network for Multi-frequency Electrical Impedance Tomography
Authors: Zhou Chen, Jinxi Xiang, Pierre Bagnaninchi, Yunjie Yang
Date: 2021-05-26 00:00:00
Tasks: Image Reconstruction

Multi-frequency Electrical Impedance Tomography (mfEIT) is an emerging biomedical imaging modality to reveal frequency-dependent conductivity distributions in biomedical applications. Conventional model-based image reconstruction methods suffer from low spatial resolution, unconstrained frequency correlation and high computational cost. Deep learning has been extensively applied in solving the EIT inverse problem in biomedical and industrial process imaging. However, most existing learning-based approaches deal with the single-frequency setup, which is inefficient and ineffective when extended to address the multi-frequency setup. In this paper, we present a Multiple Measurement Vector (MMV) model based learning algorithm named MMV-Net to solve the mfEIT image reconstruction problem. MMV-Net takes into account the correlations between mfEIT images and unfolds the update steps of the Alternating Direction Method of Multipliers (ADMM) for the MMV problem. The non-linear shrinkage operator associated with the weighted l2_1 regularization term is generalized with a cascade of a Spatial Self-Attention module and a Convolutional Long Short-Term Memory (ConvLSTM) module to capture intra- and inter-frequency dependencies. The proposed MMVNet was validated on our Edinburgh mfEIT Dataset and a series of comprehensive experiments. All reconstructed results show superior image quality, convergence performance and noise robustness against the state of the art.
Gated Fusion Network for SAO Filter and Inter Frame Prediction in Versatile Video Coding
Authors: Shiba Kuanar, Dwarikanath Mahapatra, Vassilis Athitsos, K. R Rao
Date: 2021-05-25 00:00:00
Tasks: Blocking, Decoder, Denoising

To achieve higher coding efficiency, Versatile Video Coding (VVC) includes several novel components, but at the expense of increasing decoder computational complexity. These technologies at a low bit rate often create contouring and ringing effects on the reconstructed frames and introduce various blocking artifacts at block boundaries. To suppress those visual artifacts, the VVC framework supports four post-processing filter operations. The interoperation of these filters introduces extra signaling bits and eventually becomes overhead at higher resolution video processing. In this paper, a novel deep learning-based model is proposed for sample adaptive offset (SAO) nonlinear filtering operation and substantiated the merits of intra-inter frame quality enhancement. We introduced a variable filter size multi-scale CNN (MSCNN) to improve the denoising operation and incorporated strided deconvolution for further computation improvement. We demonstrated that our deconvolution model can effectively be trained by leveraging the high-frequency edge features learned in a parallel fashion using feature fusion and residual learning. The simulation results demonstrate that the proposed method outperforms the baseline VVC method in BD-BR, BD-PSNR measurements and achieves an average of 3.762 % bit rate saving on the standard video test sequences.
AI for Calcium Scoring
Authors: Sanne G. M. van Velzen, Nils Hampe, Bob D. de Vos, Ivana Išgum
Date: 2021-05-24 00:00:00

Calcium scoring, a process in which arterial calcifications are detected and quantified in CT, is valuable in estimating the risk of cardiovascular disease events. Especially when used to quantify the extent of calcification in the coronary arteries, it is a strong and independent predictor of coronary heart disease events. Advances in artificial intelligence (AI)-based image analysis have produced a multitude of automatic calcium scoring methods. While most early methods closely follow standard calcium scoring accepted in clinic, recent approaches extend this procedure to enable faster or more reproducible calcium scoring. This chapter provides an introduction to AI for calcium scoring, and an overview of the developed methods and their applications. We conclude with a discussion on AI methods in calcium scoring and propose potential directions for future research.
Imaging in random media by two-point coherent interferometry
Authors: Liliana Borcea, Josselin Garnier
Date: 2021-05-21 00:00:00
Tasks: Retrieval, Vocal Bursts Valence Prediction

This paper considers wave-based imaging through a heterogeneous (random) scattering medium. The goal is to estimate the support of the reflectivity function of a remote scene from measurements of the backscattered wave field. The proposed imaging methodology is based on the coherent interferometric (CINT) approach that exploits the local empirical cross correlations of the measurements of the wave field. The standard CINT images are known to be robust (statistically stable) with respect to the random medium, but the stability comes at the expense of a loss of resolution. This paper shows that a two-point CINT function contains the information needed to obtain statistically stable and high-resolution images. Different methods to build such images are presented, theoretically analyzed and compared with the standard imaging approaches using numerical simulations. The first method involves a phase-retrieval step to extract the reflectivity function from the modulus of its Fourier transform. The second method involves the evaluation of the leading eigenvector of the two-point CINT imaging function seen as the kernel of a linear operator. The third method uses an optimization step to extract the reflectivity function from some cross products of its Fourier transform. The presentation is for the synthetic aperture radar data acquisition setup, where a moving sensor probes the scene with signals emitted periodically and records the resulting backscattered wave. The generalization to other imaging setups, with passive or active arrays of sensors, is discussed briefly.
Reconfigurable Architecture for Spatial Sensing in Wideband Radio Front-End
Authors: M. Gupta, S. Sharma, H. Joshi, S. J. Darak
Date: 2021-05-26 00:00:00

The deployment of cellular spectrum in licensed, shared and unlicensed spectrum demands wideband sensing over non-contiguous sub-6 GHz spectrum. To improve the spectrum and energy efficiency, beamforming and massive multi-antenna systems are being explored which demand spatial sensing i.e. blind identification of vacant frequency bands and direction-of-arrival (DoA) of the occupied bands. We propose a reconfigurable architecture to perform spatial sensing of multi-band spectrum digitized via wideband radio front-end comprising of the sparse antenna array (SAA) and Sub-Nyquist Sampling (SNS). The proposed architecture comprises SAA pre-processing and algorithms to perform spatial sensing directly on SNS samples. The proposed architecture is realized on Zynq System on Chip (SoC), consisting of the ARM processor and FPGA, via hardware-software co-design (HSCD). Using the dynamic partial reconfiguration (DPR), on-the-fly switching between algorithms depending on the number of active signals in the sensed spectrum is enabled. The functionality, resource utilization, and execution time of the proposed architecture are analyzed for various HSCD configurations, word-length, number of digitized samples, signal-to-noise ratio (SNR), and antenna array (sparse/non-sparse).
Full-Duplex meets Reconfigurable Surfaces: RIS-assisted SIC for Full-Duplex Radios
Authors: Simon Tewes, Markus Heinrichs, Paul Staat, Rainer Kronberger, Aydin Sezgin
Date: 2021-05-26 00:00:00

Reconfigurable intelligent surfaces (RIS) are a key enabler of various new applications in 6G smart radio environments. By utilizing an RIS prototype system, this paper aims to enhance self-interference (SI) cancellation for in-band full-duplex (FD) communication systems. SI suppression is a crucial requirement for FD communication as the SI severely limits the performance of a node by shadowing the received signal from a distant node with its own transmit signal. To this end, we propose to assist SI cancellation by exploiting an RIS to form a suitable cancellation signal in the analog domain. Building upon a 256-element RIS prototype, we present results of RIS-assisted SI cancellation from a practical testbed. Given an initial analog isolation of 44 dB provided by the antenna design, we are able to cancel the leaked signal by an additional 59 dB in the narrowband case, resulting in an overall SI suppression of 103 dB without additional digital cancellation. The presented case study shows promising performance to build an FD communication system on this foundation.
Experimental Exploration of Unlicensed Sub-GHz Massive MIMO for Massive Internet-of-Things
Authors: Gilles Callebaut, Sara Gunnarsson, Andrea P. Guevara, Anders J Johansson, Liesbet Van der Perre, Fredrik Tufvesson
Date: 2021-05-26 00:00:00

Due to the increase of Internet-of-Things (IoT) devices, IoT networks are getting overcrowded. Networks can be extended with more gateways, increasing the number of supported devices. However, as investigated in this work, massive MIMO has the potential to increase the number of simultaneous connections, while also lowering the energy expenditure of these devices. We present a study of the channel characteristics of massive MIMO in the unlicensed sub-GHz band. The goal is to support IoT applications with strict requirements in terms of number of devices, power consumption, and reliability. The assessment is based on experimental measurements using both a uniform linear and a rectangular array. Our study demonstrates and validates the advantages of deploying massive MIMO gateways to serve IoT nodes. While the results are general, here we specifically focus on static nodes. The array gain and channel hardening effect yield opportunities to lower the transmit-power of IoT nodes while also increasing reliability. The exploration confirms that exploiting large arrays brings great opportunities to connect a massive number of IoT devices by separating the nodes in the spatial domain. In addition, we give an outlook on how static IoT nodes could be scheduled based on partial channel state information.