pwc-archive/papers-with-abstracts huggingface dataset:
Folding@home: achievements from over twenty years of citizen science herald the exascale era
Authors: Vincent A. Voelz, Vijay S. Pande, Gregory R. Bowman
Date: 2023-03-15 00:00:00
Tasks: Distributed Computing, GPU, Protein Folding

Simulations of biomolecules have enormous potential to inform our understanding of biology but require extremely demanding calculations. For over twenty years, the Folding@home distributed computing project has pioneered a massively parallel approach to biomolecular simulation, harnessing the resources of citizen scientists across the globe. Here, we summarize the scientific and technical advances this perspective has enabled. As the project's name implies, the early years of Folding@home focused on driving advances in our understanding of protein folding by developing statistical methods for capturing long-timescale processes and facilitating insight into complex dynamical processes. Success laid a foundation for broadening the scope of Folding@home to address other functionally relevant conformational changes, such as receptor signaling, enzyme dynamics, and ligand binding. Continued algorithmic advances, hardware developments such as GPU-based computing, and the growing scale of Folding@home have enabled the project to focus on new areas where massively parallel sampling can be impactful. While previous work sought to expand toward larger proteins with slower conformational changes, new work focuses on large-scale comparative studies of different protein sequences and chemical compounds to better understand biology and inform the development of small molecule drugs. Progress on these fronts enabled the community to pivot quickly in response to the COVID-19 pandemic, expanding to become the world's first exascale computer and deploying this massive resource to provide insight into the inner workings of the SARS-CoV-2 virus and aid the development of new antivirals. This success provides a glimpse of what's to come as exascale supercomputers come online, and Folding@home continues its work.
Physics Breakthrough Disproves Fundamental Assumptions of the Chicago School
Authors: Cortelyou C. Kenney
Date: 2023-03-14 00:00:00

Classical law and economics is foundational to the American legal system. Centered at the University of Chicago, its assumptions, most especially that humans act both rationally and selfishly, informs the thinking of legislatures, judges, and government lawyers, and has shaped nearly every aspect of the way commercial transactions are conducted. But what if the Chicago School, as I refer to this line of thinking, is wrong? Alternative approaches such as behavioral law and economics or law and political economy contend that human decisionmaking is based on emotions or should not be regulated as a social geometry of bargains. This Article proposes a different and wholly novel reason that the Chicago School is wrong: a fundamental assumption central to many of its game theory models has been disproven. This Article shows that a 2012 breakthrough from world famous physicist Freeman Dyson shocked the world of game theory. This Article shows that Chicago School game theorists are wrong on their own terms because these 2 x 2 games such as the Prisoner's Dilemma, Chicken, and Snowdrift, ostensibly based on mutual defection and corrective justice, in fact yield to an insight of pure cooperation. These new game theory solutions can be scaled to design whole institutions and systems that honor the pure cooperation insight, holding out the possibility of cracking large scale social dilemmas like the tragedy of the commons. It demonstrates that, in such systems, pure cooperation is the best answer in the right environment and in the long run. It ends by calling for a new legal field to redesign the structures based on the outdated assumptions of the Chicago School game theorists.
Economics of In-Space Industry and Competitiveness of Lunar-Derived Rocket Propellant
Authors: Philip Metzger
Date: 2023-03-16 00:00:00

Economic parameters are identified for an in-space industry where the capital is made on one planet, it is transported to and teleoperated on a second planet, and the product is transported off the second planet for consumption. This framework is used to model the long-run cost of lunar propellant production to help answer whether it is commercially competitive against propellant launched from Earth. The prior techno-economic analyses (TEAs) of lunar propellant production had disagreed over this. The "gear ratio on cost" for capital transport, G, and the production mass ratio of the capital, phi, are identified as the most important factors determining competitiveness. The prior TEAs are examined for how they handled these two metrics. This identifies crucial mistakes in some of the TEAs: choosing transportation architectures with high G, and neglecting to make choices for the capital that could achieve adequate phi. The tent sublimation technology has a value of phi that is an order of magnitude better than the threshold for competitiveness even in low Earth orbit (LEO). The strip mining technology is closer to the threshold, but technological improvements plus several years of operating experience will improve its competitiveness, according to the model. Objections from members of the aerospace community are discussed, especially the question whether the technology can attain adequate reliability in the lunar environment. The results suggest that lunar propellant production will be commercially viable and that it should lower the cost of doing everything else in space.
Economic Consequences of Online Tracking Restrictions: Evidence from Cookies
Authors: Klaus M. Miller, Bernd Skiera
Date: 2023-03-16 00:00:00
Tasks: Marketing

In recent years, European regulators have debated restricting the time an online tracker can track a user to protect consumer privacy better. Despite the significance of these debates, there has been a noticeable absence of any comprehensive cost-benefit analysis. This article fills this gap on the cost side by suggesting an approach to estimate the economic consequences of lifetime restrictions on cookies for publishers. The empirical study on cookies of 54,127 users who received 128 million ad impressions over 2.5 years yields an average cookie lifetime of 279 days, with an average value of EUR 2.52 per cookie. Only 13% of all cookies increase their daily value over time, but their average value is about four times larger than the average value of all cookies. Restricting cookies lifetime to one year (two years) decreases their lifetime value by 25% (19%), which represents a decrease in the value of all cookies of 9% (5%). In light of the EUR 10.60 billion cookie-based display ad revenue in Europe, such restrictions would endanger EUR 904 million (EUR 576 million) annually, equivalent to EUR 2.08 (EUR 1.33) per EU internet user. The article discusses these results' marketing strategy challenges and opportunities for advertisers and publishers.
Simplifying and Improving: Revisiting Bulgaria's Revenue Forecasting Models
Authors: Fabio Ashtar Telarico
Date: 2023-02-17 00:00:00

In the thirty years since the end of real socialism, Bulgaria's went from having a rather radically 'different' tax system to adopting flat-rate taxation with marginal tax rates that fell from figures as high as 40% to 10% for both the corporate-income tax and the personal-income tax. Crucially, the econometric forecasting models in use at the Bulgarian Ministry of Finance hinted at an increase in tax revenue compatible with the so-called 'Laffer curve'. Similarly, many economists held the view that revenues would have increased. However, reality fell short of those expectations based on forecasting models and rooted in mainstream economic theory. Thus, this paper asks whether there are betterperforming forecasting models for personal-and corporate-income tax-revenues in Bulgaria that are readily implementable and overperform the ones currently in use. After articulating a constructive critique of the current forecasting models, the paper offers readily implementable, transparent alternatives and proves their superiority.
Optimal Delegation in Markets for Matching with Signaling
Authors: Seungjin Han, Alex Sam, Youngki Shin
Date: 2023-03-16 00:00:00

This paper studies a delegation problem faced by the planner who wants to regulate receivers' reaction choices in markets for matching between receivers and senders with signaling. We provide a noble insight into the planner's willingness to delegate and the design of optimal (reaction) interval delegation as a solution to the planner's general mechanism design problem. The relative heterogeneity of receiver types and the productivity of the sender' signal are crucial in deriving optimal interval delegation in the presence of the trade-off between matching efficiency and signaling costs.
Subspace Hybrid Beamforming for Head-worn Microphone Arrays
Authors: Sina Hafezi, Alastair H. Moore, Pierre Guiraud, Patrick A. Naylor, Jacob Donley, Vladimir Tourbabin, Thomas Lunner
Date: 2023-03-15 00:00:00
Tasks: Denoising, Speech Enhancement

A two-stage multi-channel speech enhancement method is proposed which consists of a novel adaptive beamformer, Hybrid Minimum Variance Distortionless Response (MVDR), Isotropic-MVDR (Iso), and a novel multi-channel spectral Principal Components Analysis (PCA) denoising. In the first stage, the Hybrid-MVDR performs multiple MVDRs using a dictionary of pre-defined noise field models and picks the minimum-power outcome, which benefits from the robustness of signal-independent beamforming and the performance of adaptive beamforming. In the second stage, the outcomes of Hybrid and Iso are jointly used in a two-channel PCA-based denoising to remove the 'musical noise' produced by Hybrid beamformer. On a dataset of real 'cocktail-party' recordings with head-worn array, the proposed method outperforms the baseline superdirective beamformer in noise suppression (fwSegSNR, SDR, SIR, SAR) and speech intelligibility (STOI) with similar speech quality (PESQ) improvement.
Diffusion-Shock Inpainting
Authors: Kristina Schaefer, Joachim Weickert
Date: 2023-03-16 00:00:00

We propose diffusion-shock (DS) inpainting as a hitherto unexplored integrodifferential equation for filling in missing structures in images. It combines two carefully chosen components that have proven their usefulness in different applications: homogeneous diffusion inpainting and coherence-enhancing shock filtering. DS inpainting enjoys the complementary synergy of its building blocks: It offers a high degree of anisotropy along an eigendirection of the structure tensor. This enables it to connect interrupted structures over large distances. Moreover, it benefits from the sharp edge structure generated by the shock filter, and it exploits the efficient filling-in effect of homogeneous diffusion. The second order equation that underlies DS inpainting inherits a continuous maximum-minimum principle from its constituents. In contrast to other attractive second order inpainting equations such as edge-enhancing anisotropic diffusion, we can guarantee this property also for the proposed discrete algorithm. Our experiments show a performance that is comparable to or better than many linear or nonlinear, isotropic or anisotropic processes of second or fourth order. They include homogeneous diffusion, biharmonic interpolation, TV inpainting, edge-enhancing anisotropic diffusion, the methods of Tschumperl\'e and of Bornemann and M\"arz, Cahn-Hilliard inpainting, and Euler's elastica.
VDPVE: VQA Dataset for Perceptual Video Enhancement
Authors: Yixuan Gao, Yuqin Cao, Tengchuan Kou, Wei Sun, Yunlong Dong, Xiaohong Liu, Xiongkuo Min, Guangtao Zhai
Date: 2023-03-16 00:00:00
Tasks: Deblurring, valid, Video Enhancement, Video Quality Assessment, Visual Question Answering (VQA)

Recently, many video enhancement methods have been proposed to improve video quality from different aspects such as color, brightness, contrast, and stability. Therefore, how to evaluate the quality of the enhanced video in a way consistent with human visual perception is an important research topic. However, most video quality assessment methods mainly calculate video quality by estimating the distortion degrees of videos from an overall perspective. Few researchers have specifically proposed a video quality assessment method for video enhancement, and there is also no comprehensive video quality assessment dataset available in public. Therefore, we construct a Video quality assessment dataset for Perceptual Video Enhancement (VDPVE) in this paper. The VDPVE has 1211 videos with different enhancements, which can be divided into three sub-datasets: the first sub-dataset has 600 videos with color, brightness, and contrast enhancements; the second sub-dataset has 310 videos with deblurring; and the third sub-dataset has 301 deshaked videos. We invited 21 subjects (20 valid subjects) to rate all enhanced videos in the VDPVE. After normalizing and averaging the subjective opinion scores, the mean opinion score of each video can be obtained. Furthermore, we split the VDPVE into a training set, a validation set, and a test set, and verify the performance of several state-of-the-art video quality assessment methods on the test set of the VDPVE.
End-to-End Learning-Based Wireless Image Recognition Using the PyramidNet in Edge Intelligence
Authors: Kyubihn Lee, Nam Yul Yu
Date: 2023-03-16 00:00:00
Tasks: Decoder

In edge intelligence, deep learning~(DL) models are deployed at an edge device and an edge server for data processing with low latency in the Internet of Things~(IoT). In this letter, we propose a new end-to-end learning-based wireless image recognition scheme using the PyramidNet in edge intelligence. We split the PyramidNet carefully into two parts for an IoT device and the edge server, which is to pursue low on-device computation. Also, we apply a squeeze-and-excitation block to the PyramidNet for the improvement of image recognition. In addition, we embed compression encoder and decoder at the splitting point, which reduces communication overhead by compressing the intermediate feature map. Simulation results demonstrate that the proposed scheme is superior to other DL-based schemes in image recognition, while presenting less on-device computation and fewer parameters with low communication overhead.
Enhancing Vital Sign Estimation Performance of FMCW MIMO Radar by Prior Human Shape Recognition
Authors: Hadi Alidoustaghdam, Min Chen, Ben Willetts, Kai Mao, André Kokkeler, Yang Miao
Date: 2023-03-16 00:00:00

Radio technology enabled contact-free human posture and vital sign estimation is promising for health monitoring. Radio systems at millimeter-wave (mmWave) frequencies advantageously bring large bandwidth, multi-antenna array and beam steering capability. \textit{However}, the human point cloud obtained by mmWave radar and utilized for posture estimation is likely to be sparse and incomplete. Additionally, human's random body movements deteriorate the estimation of breathing and heart rates, therefore the information of the chest location and a narrow radar beam toward the chest are demanded for more accurate vital sign estimation. In this paper, we propose a pipeline aiming to enhance the vital sign estimation performance of mmWave FMCW MIMO radar. The first step is to recognize human body part and posture, where we exploit a trained Convolutional Neural Networks (CNN) to efficiently process the imperfect human form point cloud. The CNN framework outputs the key point of different body parts, and was trained by using RGB image reference and Augmentative Ellipse Fitting Algorithm (AEFA). The next step is to utilize the chest information of the prior estimated human posture for vital sign estimation. While CNN is initially trained based on the frame-by-frame point clouds of human for posture estimation, the vital signs are extracted through beamforming toward the human chest. The numerical results show that this spatial filtering improves the estimation of the vital signs in regard to lowering the level of side harmonics and detecting the harmonics of vital signs efficiently, i.e., peak-to-average power ratio in the harmonics of vital signal is improved up to 0.02 and 0.07dB for the studied cases.
Robust Direction-of-Arrival Estimation using Array Feedback Beamforming in Low SNR Scenarios
Authors: Parth Mehta, Kumar Appaiah, Rajbabu Velmurugan
Date: 2023-03-16 00:00:00
Tasks: Direction of Arrival Estimation

A new spatial IIR beamformer based direction-of-arrival (DoA) estimation method is proposed in this paper. We propose a retransmission based spatial feedback method for an array of transmit and receive antennas that improves the performance parameters of a beamformer, viz. half-power beamwidth (HPBW), side-lobe suppression, and directivity. Through quantitative comparison, we show that our approach outperforms the previous feedback beamforming approach with a single transmit antenna, and the conventional beamformer. We then incorporate a retransmission based minimum variance distortionless response (MVDR) beamformer with the feedback beamforming setup. We propose two approaches, show that one approach is superior in terms of lower estimation error, and use that as the DoA estimation method. We then compare this approach with Multiple Signal Classification (MUSIC), Estimation of Parameters using Rotation Invariant Technique (ESPRIT), robust MVDR, nested-array MVDR, and reduced-dimension MVDR methods. The results show that at SNR levels of -60 dB to -10 dB, the angle estiation error of the proposed method is 20 degree less compared to that of prior methods.
A Framework for the Automated Parameterization of a Sensorless Bearing Fault Detection Pipeline
Authors: Tobias Wagner, Alexander Gepperth, Elmar Engels
Date: 2023-03-15 00:00:00
Tasks: AutoML, Fault Detection, Hyperparameter Optimization

This study proposes a framework for the automated hyperparameter optimization of a bearing fault detection pipeline for permanent magnet synchronous motors (PMSMs) without the need of external sensors. A automated machine learning (AutoML) pipeline search is performed by means of a genetic optimization to reduce human induced bias due to inappropriate parameterizations. For this purpose, a search space is defined, which includes general methods of signal processing and manipulation as well as methods tailored to the respective task and domain. The proposed framework is evaluated on the bearing fault detection use case under real world conditions. Considerations on the generalization of the deployed fault detection pipelines are also taken into account. Likewise, attention was paid to experimental studies for evaluations of the robustness of the fault detection pipeline to variations of the motors working condition parameters between the training and test domain. The present work contributes to the research of fault detection on rotating machinery in the following terms: (1) Reduction of the human induced bias to the data science process, while still considering expert and task related knowledge, ending in a generic search approach (2) tackling the bearing fault detection task without the need for external sensors (sensorless) (3) learning a domain robust fault detection pipeline applicable to varying motor operating parameters without the need of re-parameterizations or fine-tuning (4) investigations on working condition discrepancies with an excessive degree to determine the pipeline limitations regarding the abstraction of the motor parameters and the pipeline hyperparameters
Text-to-ECG: 12-Lead Electrocardiogram Synthesis conditioned on Clinical Text Reports
Authors: Hyunseung Chung, Jiho Kim, Joon-Myoung Kwon, Ki-Hyun Jeon, Min Sung Lee, Edward Choi
Date: 2023-03-09 00:00:00
Tasks: text-to-speech, Text to Speech

Electrocardiogram (ECG) synthesis is the area of research focused on generating realistic synthetic ECG signals for medical use without concerns over annotation costs or clinical data privacy restrictions. Traditional ECG generation models consider a single ECG lead and utilize GAN-based generative models. These models can only generate single lead samples and require separate training for each diagnosis class. The diagnosis classes of ECGs are insufficient to capture the intricate differences between ECGs depending on various features (e.g. patient demographic details, co-existing diagnosis classes, etc.). To alleviate these challenges, we present a text-to-ECG task, in which textual inputs are used to produce ECG outputs. Then we propose Auto-TTE, an autoregressive generative model conditioned on clinical text reports to synthesize 12-lead ECGs, for the first time to our knowledge. We compare the performance of our model with other representative models in text-to-speech and text-to-image. Experimental results show the superiority of our model in various quantitative evaluations and qualitative analysis. Finally, we conduct a user study with three board-certified cardiologists to confirm the fidelity and semantic alignment of generated samples. our code will be available at https://github.com/TClife/text_to_ecg
Optimizing antimicrobial treatment schedules: some fundamental analytical results
Authors: Guy Katriel
Date: 2023-03-16 00:00:00

This work studies fundamental questions regarding the optimal design of antimicrobial treatment protocols, using standard pharmacodynamic and pharmacokinetic mathematical models. We consider the problem of designing an antimicrobial treatment schedule to achieve eradication of a microbial infection, while minimizing the area under the time-concentration curve (AUC). We first solve this problem under the assumption that an arbitrary antimicrobial concentration profile may be chosen, and prove that the 'ideal' concentration profile consists of a constant concentration over a finite time duration, where explicit expressions for the optimal concentration and the time duration are given in terms of the pharmacodynamic parameters. Since antimicrobial concentration profiles are induced by a dosing schedule and the antimicrobial pharmacokinetics, the ideal concentration profile is not strictly feasible. We therefore also investigate the possibility of achieving outcomes which are close to those provided by the ideal concentration profile,using a bolus+continuous dosing schedule, which consists of a loading dose followed by infusion of the antimicrobial at a constant rate. We explicitly find the optimal bolus+continuous dosing schedule, and show that, for realistic parameter ranges, this schedule achieves results which are nearly as efficient as those attained by the ideal concentration profile. The optimality results obtained here provide a baseline and reference point for comparison and evaluation of antimicrobial treatment plans.