pwc-archive/papers-with-abstracts huggingface dataset:
A native chemical chaperone in the human eye lens
Authors: Eugene Serebryany, Sourav Chowdhury, Nicki E. Watson, Arthur McClelland, Eugene I. Shakhnovich
Date: 2020-12-17 00:00:00

Cataract is one of the most prevalent protein aggregation disorders and still the biggest cause of vision loss worldwide. The human lens, in its core region, lacks turnover of any cells or cellular components; it has therefore evolved remarkable mechanisms for resisting protein aggregation for a lifetime. We now report that one such mechanism relies on an unusually abundant metabolite, myo-inositol, to suppress light-scattering aggregation of lens proteins. We quantified aggregation suppression by in vitro turbidimetry and characterized both macroscopic and microscopic mechanisms of myo-inositol action using negative-stain electron microscopy, differential scanning fluorometry, and a thermal scanning Raman spectroscopy apparatus. Given recent metabolomic evidence that it is dramatically depleted in human cataractous lenses compared to age-matched controls, we suggest that maintaining or restoring healthy levels of myo-inositol in the lens may be a simple, safe, and widely available strategy for reducing the global burden of cataract.
Parallel WaveNet conditioned on VAE latent vectors
Authors: Jonas Rohnke, Tom Merritt, Jaime Lorenzo-Trueba, Adam Gabrys, Vatsal Aggarwal, Alexis Moinet, Roberto Barra-Chicote
Date: 2020-12-17 00:00:00
Tasks: Sentence, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Recently the state-of-the-art text-to-speech synthesis systems have shifted to a two-model approach: a sequence-to-sequence model to predict a representation of speech (typically mel-spectrograms), followed by a 'neural vocoder' model which produces the time-domain speech waveform from this intermediate speech representation. This approach is capable of synthesizing speech that is confusable with natural speech recordings. However, the inference speed of neural vocoder approaches represents a major obstacle for deploying this technology for commercial applications. Parallel WaveNet is one approach which has been developed to address this issue, trading off some synthesis quality for significantly faster inference speed. In this paper we investigate the use of a sentence-level conditioning vector to improve the signal quality of a Parallel WaveNet neural vocoder. We condition the neural vocoder with the latent vector from a pre-trained VAE component of a Tacotron 2-style sequence-to-sequence model. With this, we are able to significantly improve the quality of vocoded speech.
Geometric Brownian motion with affine drift and its time-integral
Authors: Runhuan Feng, Pingping Jiang, Hans Volkmer
Date: 2020-12-17 00:00:00

The joint distribution of a geometric Brownian motion and its time-integral was derived in a seminal paper by Yor (1992) using Lamperti's transformation, leading to explicit solutions in terms of modified Bessel functions. In this paper, we revisit this classic result using the simple Laplace transform approach in connection to the Heun differential equation. We extend the methodology to the geometric Brownian motion with affine drift and show that the joint distribution of this process and its time-integral can be determined by a doubly-confluent Heun equation. Furthermore, the joint Laplace transform of the process and its time-integral is derived from the asymptotics of the solutions. In addition, we provide an application by using the results for the asymptotics of the double-confluent Heun equation in pricing Asian options. Numerical results show the accuracy and efficiency of this new method.
Fast whole-brain imaging of seizures in zebrafish larvae by two-photon light-sheet microscopy
Authors: Giuseppe de Vito, Lapo Turrini, Caroline Müllenbroich, Pietro Ricci, Giuseppe Sancataldo, Giacomo Mazzamuto, Natascia Tiso, Leonardo Sacconi, Duccio Fanelli, Ludovico Silvestri, Francesco Vanzi, Francesco Saverio Pavone
Date: 2020-12-17 00:00:00

Light-sheet fluorescence microscopy (LSFM) enables real-time whole-brain functional imaging in zebrafish larvae. Conventional one photon LSFM can however induce undesirable visual stimulation due to the use of visible excitation light. The use of two-photon (2P) excitation, employing near-infrared invisible light, provides unbiased investigation of neuronal circuit dynamics. However, due to the low efficiency of the 2P absorption process, the imaging speed of this technique is typically limited by the signal-to-noise-ratio. Here, we describe a 2P LSFM setup designed for non-invasive imaging that enables quintuplicating state-of-the-art volumetric acquisition rate of the larval zebrafish brain (5 Hz) while keeping low the laser intensity on the specimen. We applied our system to the study of pharmacologically-induced acute seizures, characterizing the spatial-temporal dynamics of pathological activity and describing for the first time the appearance of caudo-rostral ictal waves (CRIWs).
Metabolic response to point mutations reveals principles of modulation of in vivo enzyme activity and phenotype
Authors: Sanchari Bhattacharyyaa, Shimon Bershtein, Bharat V. Adkara, Jaie Woodarda, Eugene I. Shakhnovich
Date: 2020-12-17 00:00:00

The relationship between sequence variation and phenotype is poorly understood. Here we use metabolomic analysis to elucidate the molecular mechanism underlying the filamentous phenotype of E. coli strains that carry destabilizing mutations in the Dihydrofolate Reductase (DHFR). We find that partial loss of DHFR activity causes SOS response indicative of DNA damage and cell filamentation. This phenotype is triggered by an imbalance in deoxy nucleotide levels, most prominently a disproportionate drop in the intracellular dTTP. We show that a highly cooperative (Hill coefficient 2.5) in vivo activity of Thymidylate Kinase (Tmk), a downstream enzyme that phosphorylates dTMP to dTDP, is the cause of suboptimal dTTP levels. dTMP supplementation in the media rescues filamentation and restores in vivo Tmk kinetics to almost perfect Michaelis-Menten, like its kinetics in vitro. Overall, this study highlights the important role of cellular environment in sculpting enzymatic kinetics with system level implications for bacterial phenotype.
Dynamic Reinsurance in Discrete Time Minimizing the Insurer's Cost of Capital
Authors: Alexander Glauner
Date: 2020-12-17 00:00:00

In the classical static optimal reinsurance problem, the cost of capital for the insurer's risk exposure determined by a monetary risk measure is minimized over the class of reinsurance treaties represented by increasing Lipschitz retained loss functions. In this paper, we consider a dynamic extension of this reinsurance problem in discrete time which can be viewed as a risk-sensitive Markov Decision Process. The model allows for both insurance claims and premium income to be stochastic and operates with general risk measures and premium principles. We derive the Bellman equation and show the existence of a Markovian optimal reinsurance policy. Under an infinite planning horizon, the model is shown to be contractive and the optimal reinsurance policy to be stationary. The results are illustrated with examples where the optimal policy can be determined explicitly.
A General Method for Calibrating Stochastic Radio Channel Models with Kernels
Authors: Ayush Bharti, Francois-Xavier Briol, Troels Pedersen
Date: 2020-12-17 00:00:00
Tasks: Clustering

Calibrating stochastic radio channel models to new measurement data is challenging when the likelihood function is intractable. The standard approach to this problem involves sophisticated algorithms for extraction and clustering of multipath components, following which, point estimates of the model parameters can be obtained using specialized estimators. We propose a likelihood-free calibration method using approximate Bayesian computation. The method is based on the maximum mean discrepancy, which is a notion of distance between probability distributions. Our method not only by-passes the need to implement any high-resolution or clustering algorithm, but is also automatic in that it does not require any additional input or manual pre-processing from the user. It also has the advantage of returning an entire posterior distribution on the value of the parameters, rather than a simple point estimate. We evaluate the performance of the proposed method by fitting two different stochastic channel models, namely the Saleh-Valenzuela model and the propagation graph model, to both simulated and measured data. The proposed method is able to estimate the parameters of both the models accurately in simulations, as well as when applied to 60 GHz indoor measurement data.
Deep Event Stereo Leveraged by Event-to-Image Translation
Authors: Soikat Hasan Ahmed, Hae Woong Jang, S. M. Nadim Uddin, Yong Ju Jung
Date: 2021-02-02 00:00:00
Tasks: 3D Reconstruction, Depth Estimation, Disparity Estimation, Event-based vision, Image Reconstruction, Translation

Depth estimation in real-world applications requires precise responses to fast motion and challenging lighting conditions. Event cameras use bio-inspired event-driven sensors that provide instantaneous and asynchronous information of pixel-level log intensity changes, which makes them suitable for depth estimation in such challenging conditions. However, as the event cameras primarily provide asynchronous and spatially sparse event data, it is hard to provide accurate dense disparity map in stereo event camera setups - especially in estimating disparities on local structures or edges. In this study, we develop a novel deep event stereo network that reconstructs spatial intensity image features from embedded event streams and leverages the event features using the reconstructed image features to compute dense disparity maps. To this end, we propose a novel event-to-image translation network with a cross-semantic attention mechanism that calculates the global semantic context of the event features for the intensity image reconstruction. In addition, a feature aggregation module is developed for accurate disparity estimation, which modulates the event features with the reconstructed image features by a stacked dilated spatially-adaptive denormalization mechanism. Experimental results reveal that our method can outperform the state-of-the-art methods by significant margins both in quantitative and qualitative measures.
KwARG: Parsimonious reconstruction of ancestral recombination graphs with recurrent mutation
Authors: Anastasia Ignatieva, Rune B. Lyngsø, Paul A. Jenkins, Jotun Hein
Date: 2020-12-17 00:00:00

The reconstruction of possible histories given a sample of genetic data in the presence of recombination and recurrent mutation is a challenging problem, but can provide key insights into the evolution of a population. We present KwARG, which implements a parsimony-based greedy heuristic algorithm for finding plausible genealogical histories (ancestral recombination graphs) that are minimal or near-minimal in the number of posited recombination and mutation events. Given an input dataset of aligned sequences, KwARG outputs a list of possible candidate solutions, each comprising a list of mutation and recombination events that could have generated the dataset; the relative proportion of recombinations and recurrent mutations in a solution can be controlled via specifying a set of 'cost' parameters. We demonstrate that the algorithm performs well when compared against existing methods. The software is made available on GitHub.
Denoising Text to Speech with Frame-Level Noise Modeling
Authors: Chen Zhang, Yi Ren, Xu Tan, Jinglin Liu, Kejun Zhang, Tao Qin, Sheng Zhao, Tie-Yan Liu
Date: 2020-12-17 00:00:00
Tasks: Denoising, text-to-speech, Text to Speech

While neural-based text to speech (TTS) models can synthesize natural and intelligible voice, they usually require high-quality speech data, which is costly to collect. In many scenarios, only noisy speech of a target speaker is available, which presents challenges for TTS model training for this speaker. Previous works usually address the challenge using two methods: 1) training the TTS model using the speech denoised with an enhancement model; 2) taking a single noise embedding as input when training with noisy speech. However, they usually cannot handle speech with real-world complicated noise such as those with high variations along time. In this paper, we develop DenoiSpeech, a TTS system that can synthesize clean speech for a speaker with noisy speech data. In DenoiSpeech, we handle real-world noisy speech by modeling the fine-grained frame-level noise with a noise condition module, which is jointly trained with the TTS model. Experimental results on real-world data show that DenoiSpeech outperforms the previous two methods by 0.31 and 0.66 MOS respectively.
Hiring from a pool of workers
Authors: Azar Abizada, Inácio Bó
Date: 2020-12-17 00:00:00
Tasks: Fairness

In many countries and institutions around the world, the hiring of workers is made through open competitions. In them, candidates take tests and are ranked based on scores in exams and other predetermined criteria. Those who satisfy some eligibility criteria are made available for hiring from a "pool of workers." In each of an ex-ante unknown number of rounds, vacancies are announced, and workers are then hired from that pool. When the scores are the only criterion for selection, the procedure satisfies desired fairness and independence properties. We show that when affirmative action policies are introduced, the established methods of reserves and procedures used in Brazil, France, and Australia, fail to satisfy those properties. We then present a new rule, which we show to be the unique rule that extends static notions of fairness to problems with multiple rounds while satisfying aggregation independence, a consistency requirement. Finally, we show that if multiple institutions hire workers from a single pool, even minor consistency requirements are incompatible with variations in the institutions' rules.
Low-Complexity Steered Response Power Mapping based on Nyquist-Shannon Sampling
Authors: Thomas Dietzen, Enzo De Sena, Toon van Waterschoot
Date: 2020-12-17 00:00:00

The steered response power (SRP) approach to acoustic source localization computes a map of the acoustic scene from the frequency-weighted output power of a beamformer steered towards a set of candidate locations. Equivalently, SRP may be expressed in terms of time-domain generalized cross-correlations (GCCs) at lags equal to the candidate locations' time-differences of arrival (TDOAs). Due to the dense grid of candidate locations, each of which requires inverse Fourier transform (IFT) evaluations, conventional SRP exhibits a high computational complexity. In this paper, we propose a low-complexity SRP approach based on Nyquist-Shannon sampling. Noting that on the one hand the range of possible TDOAs is physically bounded, while on the other hand the GCCs are bandlimited, we critically sample the GCCs around their TDOA interval and approximate the SRP map by interpolation. In usual setups, the number of sample points can be orders of magnitude less than the number of candidate locations and frequency bins, yielding a significant reduction of IFT computations at a limited interpolation cost. Simulations comparing the proposed approximation with conventional SRP indicate low approximation errors and equal localization performance. MATLAB and Python implementations are available online.
Differential dissipativity analysis of reaction-diffusion systems
Authors: Felix Miranda-Villatoro, Rodolphe Sepulchre
Date: 2020-05-02 00:00:00

This note shows how classical tools from linear control theory can be leveraged to provide a global analysis of nonlinear reaction-diffusion models. The approach is differential in nature. It proceeds from classical tools of contraction analysis and recent extensions to differential dissipativity.
Optimal switch from a fossil-fueled to an electric vehicle
Authors: Paolo Falbo, Giorgio Ferrari, Giorgio Rizzini, Maren Diane Schmeck
Date: 2020-12-17 00:00:00

In this paper we propose and solve a real options model for the optimal adoption of an electric vehicle. A policymaker promotes the abeyance of fossil-fueled vehicles through an incentive, and the representative fossil-fueled vehicle's owner decides the time at which buying an electric vehicle, while minimizing a certain expected cost. This involves a combination of various types of costs: the stochastic opportunity cost of driving one unit distance with a traditional fossil-fueled vehicle instead of an electric one, the cost associated to traffic bans, and the net purchase cost. After determining the optimal switching time and the minimal cost function for a general diffusive opportunity cost, we specialize to the case of a mean-reverting process. In such a setting, we provide a model calibration on real data from Italy, and we study the dependency of the optimal switching time with respect to the model's parameters. Moreover, we study the effect of traffic bans and incentive on the expected optimal switching time. We observe that incentive and traffic bans on fossil-fueled transport can be used as effective tools in the hand of the policymaker to encourage the adoption of electric vehicles, and hence to reduce air pollution.
Revisit 1D Total Variation restoration problem with new real-time algorithms for signal and hyper-parameter estimations
Authors: Zhanhao Liu, Marion Perrodin, Thomas Chambrion, Radu Stoica
Date: 2020-12-17 00:00:00
Tasks: Denoising

1D Total Variation (TV) denoising, considering the data fidelity and the Total Variation (TV) regularization, proposes a good restored signal preserving shape edges. The main issue is how to choose the weight $\lambda$ balancing those two terms. In practice, this parameter is selected by assessing a list of candidates (e.g. cross validation), which is inappropriate for the real time application. In this work, we revisit 1D Total Variation restoration algorithm proposed by Tibshirani and Taylor. A heuristic method is integrated for estimating a good choice of $\lambda$ based on the extremums number of restored signal. We propose an offline version of restoration algorithm in O(n log n) as well as its online implementation in O(n). Combining the rapid algorithm and the automatic choice of $\lambda$, we propose a real-time automatic denoising algorithm, providing a large application fields. The simulations show that our proposition of $\lambda$ has a similar performance as the states of the art.
Uncertainty Quantification in Case of Imperfect Models: A Review
Authors: Sebastian Kersting, Michael Kohler
Date: 2020-12-17 00:00:00
Tasks: Uncertainty Quantification

Uncertainty quantification of complex technical systems is often based on a computer model of the system. As all models such a computer model is always wrong in the sense that it does not describe the reality perfectly. The purpose of this article is to give a review of techniques which use observed values of the technical systems in order to take into account the inadequacy of a computer model in uncertainty quantification. The techniques reviewed in this article are illustrated and compared by applying them to applications in mechanical engineering.
Self-Fulfilling Prophecies, Quasi Non-Ergodicity and Wealth Inequality
Authors: Jean-Philippe Bouchaud, Roger Farmer
Date: 2020-12-17 00:00:00

We construct a model of an exchange economy in which agents trade assets contingent on an observable signal, the probability of which depends on public opinion. The agents in our model are replaced occasionally and each person updates beliefs in response to observed outcomes. We show that the distribution of the observed signal is described by a quasi-non-ergodic process and that people continue to disagree with each other forever. These disagreements generate large wealth inequalities that arise from the multiplicative nature of wealth dynamics which make successful bold bets highly profitable.
Making experimental data tables in the life sciences more FAIR: a pragmatic approach
Authors: Daniel Jacob, Romain David, Sophie Aubin, Yves Gibon
Date: 2020-12-17 00:00:00
Tasks: Management

Making data compliant with the FAIR Data principles (Findable, Accessible, Interoperable, Reusable) is still a challenge for many researchers, who are not sure which criteria should be met first and how. Illustrated from experimental data tables associated with a Design of Experiments, we propose an approach that can serve as a model for a research data management that allows researchers to disseminate their data by satisfying the main FAIR criteria without insurmountable efforts. More importantly, this approach aims to facilitate the FAIRification process by providing researchers with tools to improve their data management practices.
On-board Electrical, Electronics and Pose Estimation System for Hyperloop Pod Design
Authors: Nihal Singh, Jay Karhade, Ishika Bhattacharya, Prathamesh Saraf, Plava Kattamuri, Alivelu Manga Parimi
Date: 2020-12-17 00:00:00
Tasks: Fault Detection, Management, Pose Estimation

Hyperloop is a high-speed ground-based transportation system utilizing sealed tubes, with the aim of ultimately transporting passengers between metropolitan cities in efficiently designed autonomous capsules. In recent years, the design and development of sub-scale prototypes for these Hyperloop pods has set the foundation for realizing more practical and scalable pod architectures. This paper proposes a practical, power and space optimized on-board electronics architecture, coupled with an end-to-end computationally efficient pose estimation algorithm. Considering the high energy density and discharge rate of on-board batteries, this work additionally presents a robust system for fault detection, protection and management of batteries, along with the design of the surrounding electrical system. Performance evaluation and verification of proposed algorithms and circuits has been carried out by software simulations using both Python and Simulink.
Robust Phase Retrieval with Green Noise Binary Masks
Authors: Qiuliang Ye, Yuk-Hee Chan, Michael G. Somekh, Daniel P. K. Lun
Date: 2020-12-17 00:00:00
Tasks: Retrieval

Phase retrieval with pre-defined optical masks can provide extra constraint and thus achieve improved performance. The recent progress in optimization theory demonstrates the superiority of random masks in phase retrieval algorithms. However, traditional approaches just focus on the randomness of the masks but ignore their non-bandlimited nature. When using these masks in the reconstruction process for phase retrieval, the high frequency part of the masks is often removed in the process and thus leads to degraded performance. Based on the concept of digital halftoning, this paper proposes a green noise binary masking scheme which can greatly reduce the high frequency content of the masks while fulfilling the randomness requirement. The experimental results show that the proposed green noise binary masking scheme outperform the traditional ones when using in a DMD-based coded diffraction pattern phase retrieval system.