pwc-archive/papers-with-abstracts huggingface dataset:
Integrated information as a common signature of dynamical and information-processing complexity
Authors: Pedro A. M. Mediano, Fernando E. Rosas, Juan Carlos Farah, Murray Shanahan, Daniel Bor, Adam B. Barrett
Date: 2021-06-18 00:00:00

The apparent dichotomy between information-processing and dynamical approaches to complexity science forces researchers to choose between two diverging sets of tools and explanations, creating conflict and often hindering scientific progress. Nonetheless, given the shared theoretical goals between both approaches, it is reasonable to conjecture the existence of underlying common signatures that capture interesting behaviour in both dynamical and information-processing systems. Here we argue that a pragmatic use of Integrated Information Theory (IIT), originally conceived in theoretical neuroscience, can provide a potential unifying framework to study complexity in general multivariate systems. Furthermore, by leveraging metrics put forward by the integrated information decomposition ($\Phi$ID) framework, our results reveal that integrated information can effectively capture surprisingly heterogeneous signatures of complexity -- including metastability and criticality in networks of coupled oscillators as well as distributed computation and emergent stable particles in cellular automata -- without relying on idiosyncratic, ad-hoc criteria. These results show how an agnostic use of IIT can provide important steps towards bridging the gap between informational and dynamical approaches to complex systems.
Correlative approaches in single-molecule biophysics: a review of the progress in methods and applications
Authors: Mark C Leake
Date: 2021-06-18 00:00:00

Here, we discuss a collection of cutting-edge techniques and applications in use today by some of the leading experts in the field of correlative approaches in single-molecule biophysics. A key difference in emphasis, compared with traditional single-molecule biophysics approaches detailed previously, is on the emphasis of the development and use of complex methods which explicitly combine multiple approaches to increase biological insights at the single-molecule level. These so-called correlative single-molecule biophysics methods rely on multiple, orthogonal tools and analysis, as opposed to any one single driving technique. Importantly, they span both in vivo and in vitro biological systems as well as the interfaces between theory and experiment in often highly integrated ways, very different to earlier traditional non-integrative approaches. The first applications of correlative single-molecule methods involved adaption of a range of different experimental technologies to the same biological sample whose measurements were synchronised. However, now we find a greater flora of integrated methods emerging that include approaches applied to different samples at different times and yet still permit useful molecular-scale correlations to be performed. The resultant findings often enable far greater precision of length and time scales of measurements, and a more understanding of the interplay between different processes in the same cell. Many new correlative single-molecule biophysics techniques also include more complex, physiologically relevant approaches as well as increasing number that combine advanced computational methods and mathematical analysis with experimental tools. Here we review the motivation behind the development of correlative single-molecule microscopy methods, its history and recent progress in the field.
Set coverage and robust policy
Authors: Marc Henry, Alexei Onatski
Date: 2021-06-17 00:00:00

When conducting inference on partially identified parameters, confidence regions may cover the whole identified set with a prescribed probability, to which we will refer as set coverage, or they may cover each of its point with a prescribed probability, to which we will refer as point coverage. Since set coverage implies point coverage, confidence regions satisfying point coverage are generally preferred on the grounds that they may be more informative. The object of this note is to describe a decision problem in which, contrary to received wisdom, point coverage is clearly undesirable.
Active labour market policies for the long-term unemployed: New evidence from causal machine learning
Authors: Daniel Goller, Tamara Harrer, Michael Lechner, Joachim Wolff
Date: 2021-06-18 00:00:00

Active labor market programs are important instruments used by European employment agencies to help the unemployed find work. Investigating large administrative data on German long-term unemployed persons, we analyze the effectiveness of three job search assistance and training programs using Causal Machine Learning. Participants benefit from quickly realizing and long-lasting positive effects across all programs, with placement services being the most effective. For women, we find differential effects in various characteristics. Especially, women benefit from better local labor market conditions. We propose more effective data-driven rules for allocating the unemployed to the respective labor market programs that could be employed by decision-makers.
Introductory Economics: Gender, Majors, and Future Performance
Authors: Natsuki Arai, Shian Chang, Biing-Shen Kuo
Date: 2021-06-18 00:00:00

By investigating the exam scores of introductory economics in a business school in Taiwan between 2008 and 2019, we find three sets of results: First, we find no significant difference between genders in the exam scores. Second, students' majors are significantly associated with their exam scores, which likely reflects their academic ability measured at college admission. Third, the exam scores are strong predictors of students' future academic performance.
Universal Risk Budgeting
Authors: Alex Garivaltis
Date: 2021-06-18 00:00:00

I juxtapose Cover's vaunted universal portfolio selection algorithm (Cover 1991) with the modern representation (Qian 2016; Roncalli 2013) of a portfolio as a certain allocation of risk among the available assets, rather than a mere allocation of capital. Thus, I define a Universal Risk Budgeting scheme that weights each risk budget (instead of each capital budget) by its historical performance record (a la Cover). I prove that my scheme is mathematically equivalent to a novel type of Cover and Ordentlich 1996 universal portfolio that uses a new family of prior densities that have hitherto not appeared in the literature on universal portfolio theory. I argue that my universal risk budget, so-defined, is a potentially more perspicuous and flexible type of universal portfolio; it allows the algorithmic trader to incorporate, with advantage, his prior knowledge (or beliefs) about the particular covariance structure of instantaneous asset returns. Say, if there is some dispersion in the volatilities of the available assets, then the uniform (or Dirichlet) priors that are standard in the literature will generate a dangerously lopsided prior distribution over the possible risk budgets. In the author's opinion, the proposed "Garivaltis prior" makes for a nice improvement on Cover's timeless expert system (Cover 1991), that is properly agnostic and open (from the very get-go) to different risk budgets. Inspired by Jamshidian 1992, the universal risk budget is formulated as a new kind of exotic option in the continuous time Black and Scholes 1973 market, with all the pleasure, elegance, and convenience that that entails.
Golos: Russian Dataset for Speech Research
Authors: Nikolay Karpov, Alexander Denisenko, Fedor Minkin
Date: 2021-06-18 00:00:00
Tasks: Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, Transfer Learning

This paper introduces a novel Russian speech dataset called Golos, a large corpus suitable for speech research. The dataset mainly consists of recorded audio files manually annotated on the crowd-sourcing platform. The total duration of the audio is about 1240 hours. We have made the corpus freely available to download, along with the acoustic model with CTC loss prepared on this corpus. Additionally, transfer learning was applied to improve the performance of the acoustic model. In order to evaluate the quality of the dataset with the beam-search algorithm, we have built a 3-gram language model on the open Common Crawl dataset. The total word error rate (WER) metrics turned out to be about 3.3% and 11.5%.
VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion
Authors: Disong Wang, Liqun Deng, Yu Ting Yeung, Xiao Chen, Xunying Liu, Helen Meng
Date: 2021-06-18 00:00:00
Tasks: Disentanglement, Quantization, Voice Conversion

One-shot voice conversion (VC), which performs conversion across arbitrary speakers with only a single target-speaker utterance for reference, can be effectively achieved by speech representation disentanglement. Existing work generally ignores the correlation between different speech representations during training, which causes leakage of content information into the speaker representation and thus degrades VC performance. To alleviate this issue, we employ vector quantization (VQ) for content encoding and introduce mutual information (MI) as the correlation metric during training, to achieve proper disentanglement of content, speaker and pitch representations, by reducing their inter-dependencies in an unsupervised manner. Experimental results reflect the superiority of the proposed method in learning effective disentangled speech representations for retaining source linguistic content and intonation variations, while capturing target speaker characteristics. In doing so, the proposed approach achieves higher speech naturalness and speaker similarity than current state-of-the-art one-shot VC systems. Our code, pre-trained models and demo are available at https://github.com/Wendison/VQMIVC.
Unsupervised Domain Adaptation for Dysarthric Speech Detection via Domain Adversarial Training and Mutual Information Minimization
Authors: Disong Wang, Liqun Deng, Yu Ting Yeung, Xiao Chen, Xunying Liu, Helen Meng
Date: 2021-06-18 00:00:00
Tasks: Domain Adaptation, Multi-Task Learning, Unsupervised Domain Adaptation

Dysarthric speech detection (DSD) systems aim to detect characteristics of the neuromotor disorder from speech. Such systems are particularly susceptible to domain mismatch where the training and testing data come from the source and target domains respectively, but the two domains may differ in terms of speech stimuli, disease etiology, etc. It is hard to acquire labelled data in the target domain, due to high costs of annotating sizeable datasets. This paper makes a first attempt to formulate cross-domain DSD as an unsupervised domain adaptation (UDA) problem. We use labelled source-domain data and unlabelled target-domain data, and propose a multi-task learning strategy, including dysarthria presence classification (DPC), domain adversarial training (DAT) and mutual information minimization (MIM), which aim to learn dysarthria-discriminative and domain-invariant biomarker embeddings. Specifically, DPC helps biomarker embeddings capture critical indicators of dysarthria; DAT forces biomarker embeddings to be indistinguishable in source and target domains; and MIM further reduces the correlation between biomarker embeddings and domain-related cues. By treating the UASPEECH and TORGO corpora respectively as the source and target domains, experiments show that the incorporation of UDA attains absolute increases of 22.2% and 20.0% respectively in utterance-level weighted average recall and speaker-level accuracy.
Leveraging Google Earth Engine platform to characterize and map small seasonal wetlands in the semi-arid environments of South Africa
Authors: Siyamthanda Gxokwe, Timothy Dube, Dominic Mazvimavi
Date: 2021-06-18 00:00:00
Tasks: Management

Small seasonal wetlands are a distinctive feature of the semi-arid environments. Although these systems are small and ephemeral in nature, they provide habitats to aquatic flora and fauna, and play a critical role in sustaining livelihoods through the provision of ecological services. There is a great concern that these wetlands are not routinely monitored, which has contributed to their poor management. So far, scientific research strides have been noted in mapping and understanding the spatial coverage and ecohydrological dynamics of wetlands in semi-arid areas using broadband multispectral data. However, because of the sensing characteristics of these platforms, mapping and monitoring of small wetlands remains a challenge.
Fast & Robust Image Interpolation using Gradient Graph Laplacian Regularizer
Authors: Fei Chen, Gene Cheung, Xue Zhang
Date: 2021-01-25 00:00:00
Tasks: Image Restoration

In the graph signal processing (GSP) literature, it has been shown that signal-dependent graph Laplacian regularizer (GLR) can efficiently promote piecewise constant (PWC) signal reconstruction for various image restoration tasks. However, for planar image patches, like total variation (TV), GLR may suffer from the well-known "staircase" effect. To remedy this problem, we generalize GLR to gradient graph Laplacian regularizer (GGLR) that provably promotes piecewise planar (PWP) signal reconstruction for the image interpolation problem -- a 2D grid with random missing pixels that requires completion. Specifically, we first construct two higher-order gradient graphs to connect local horizontal and vertical gradients. Each local gradient is estimated using structure tensor, which is robust using known pixels in a small neighborhood, mitigating the problem of larger noise variance when computing gradient of gradients. Moreover, unlike total generalized variation (TGV), GGLR retains the quadratic form of GLR, leading to an unconstrained quadratic programming (QP) problem per iteration that can be solved quickly using conjugate gradient (CG). We derive the means-square-error minimizing weight parameter for GGLR, trading off bias and variance of the signal estimate. Experiments show that GGLR outperformed competing schemes in interpolation quality for severely damaged images at a reduced complexity.
M-ary Aggregate Spread Pulse Modulation in LPWANs for IoT applications
Authors: Alexei V. Nikitin, Ruslan L. Davidchack
Date: 2021-06-18 00:00:00

In low-power wide-area networks (LPWANs), various trade-offs among the bandwidth, data rates, and energy per bit have different effects on the quality of service under different propagation conditions (e.g. fading and multipath), interference scenarios, multi-user requirements, and design constraints. Such compromises, and the manner in which they are implemented, further affect other technical aspects, such as system's computational complexity and power efficiency. At the same time, this difference in trade-offs also adds to the technical flexibility in addressing a broader range of IoT applications. This paper addresses a physical layer LPWAN approach based on the Aggregate Spread Pulse Modulation (ASPM) and provides a brief assessment of its properties in additive white Gaussian noise (AWGN) channel. In the binary ASPM the control of the quality of service is performed through the change in the spectral efficiency, i.e., the data rate at a given bandwidth. Implementing M-ary encoding in ASPM further enables controlling service quality through changing the energy per bit (in about an order of magnitude range) as an additional trade-off parameter. Such encoding is especially useful for improving the ASPM's energy per bit performance, thus increasing its range and overall energy efficiency, and making it more attractive for use in LPWANs for IoT applications.
Spatial Distribution of Distortion due to Nonlinear Power Amplification in Distributed Massive MIMO
Authors: François Rottenberg, Gilles Callebaut, Liesbet Van der Perre
Date: 2021-06-18 00:00:00

Due to the nonlinearity of power amplifiers (PAs), the transmit signal is distorted. Previous works have studied the spatial distribution of this distortion for a central massive MIMO array. In this work, we extend the analysis for distributed massive MIMO in a line-of-sight scenario. We show that the distortion is not always uniformly distributed in space. In the single-user case, it coherently combines at the user location. In the few users case, the signals will add up at the user locations plus several others locations. As the number of users increases, it becomes close to uniformly distributed. As a comparison with a central massive MIMO system, having the same total number of antennas, the distortion in distributed massive MIMO is considerably more uniformly distributed in space. Moreover, the potential coherent combining is contained in a zone rather than in generic directions, i.e., in a beamspot. Furthermore, a small-scale fading effect is observed at unintended locations due to the non-coherent combining of the signals. As a result, one can expect that going towards distributed systems allows working closer to saturation, increasing the PA operating efficiency and/or using low-cost PAs.
A Weak Monotonicity Based Muscle Fatigue Detection Algorithm for a Short-Duration Poor Posture Using sEMG Measurements
Authors: Xinliang Guo, Lei Lu, Mark Robinson, Ying Tan, Kusal Goonewardena, Denny Oetomo
Date: 2021-06-18 00:00:00

Muscle fatigue is usually defined as a decrease in the ability to produce force. The surface electromyography (sEMG) signals have been widely used to provide information about muscle activities including detecting muscle fatigue by various data-driven techniques such as machine learning and statistical approaches. However, it is well-known that sEMG signals are weak signals (low amplitude of the signals) with a low signal-to-noise ratio, data-driven techniques cannot work well when the quality of the data is poor. In particular, the existing methods are unable to detect muscle fatigue coming from static poses. This work exploits the concept of weak monotonicity, which has been observed in the process of fatigue, to robustly detect muscle fatigue in the presence of measurement noises and human variations. Such a population trend methodology has shown its potential in muscle fatigue detection as demonstrated by the experiment of a static pose.
Moment-based Parameter Estimation for $Γ$-parameterized TWDP Model
Authors: Pamela Njemcevic, Almir Maric, Enio Kaljic
Date: 2021-06-18 00:00:00
Tasks: parameter estimation

In this paper, parameterization of a two-wave with diffuse power (TWDP) fading model is revised. Anomalies caused by using conventional TWDP parameters $K$ and $\Delta$ are first identified, indicating that the existing moment-based estimators of a tuple $(K, \Delta)$ are not able to provide accurate estimations for various combinations of their values. Therefore, a moment-based estimators for a newly proposed parameters $K$ and $\Gamma$ are derived and analyzed through asymptotic variance (AsV) and Cramer-Rao bound (CRB) metrics. The results are qualitatively compared to those obtained for a $(K, \Delta)$ tuple, showing that moment-based estimators of the improved $\Gamma$-based parameterization managed to overcome all anomalies observed within the conventional TWDP parameterization.
LEO Satellite Constellations for 5G and Beyond: How Will They Reshape Vertical Domains?
Authors: Shicong Liu, Zhen Gao, Yongpeng Wu, Derrick Wing Kwan Ng, Xiqi Gao, Kai-Kit Wong, Symeon Chatzinotas, Bjorn Ottersten
Date: 2021-06-18 00:00:00

The rapid development of communication technologies in the past decades has provided immense vertical opportunities for individuals and enterprises. However, conventional terrestrial cellular networks have unfortunately neglected the huge geographical digital divide, since high bandwidth wireless coverage is concentrated to urban areas. To meet the goal of ``connecting the unconnected'', integrating low Earth orbit (LEO) satellites with the terrestrial cellular networks has been widely considered as a promising solution. In this article, we first introduce the development roadmap of LEO satellite constellations (SatCons), including early attempts in LEO satellites with the emerging LEO constellations. Further, we discuss the unique opportunities of employing LEO SatCons for the delivery of integrating 5G networks. Specifically, we present their key performance indicators, which offer important guidelines for the design of associated enabling techniques, and then discuss the potential impact of integrating LEO SatCons with typical 5G use cases, where we engrave our vision of various vertical domains reshaped by LEO SatCons. Technical challenges are finally provided to specify future research directions.
Robust deep hedging
Authors: Eva Lütkebohmert, Thorsten Schmidt, Julian Sester
Date: 2021-06-18 00:00:00

We study pricing and hedging under parameter uncertainty for a class of Markov processes which we call generalized affine processes and which includes the Black-Scholes model as well as the constant elasticity of variance (CEV) model as special cases. Based on a general dynamic programming principle, we are able to link the associated nonlinear expectation to a variational form of the Kolmogorov equation which opens the door for fast numerical pricing in the robust framework. The main novelty of the paper is that we propose a deep hedging approach which efficiently solves the hedging problem under parameter uncertainty. We numerically evaluate this method on simulated and real data and show that the robust deep hedging outperforms existing hedging approaches, in particular in highly volatile periods.
A symmetry-inclusive algebraic approach to genome rearrangement
Authors: Venta Terauds, Joshua Stevenson, Jeremy Sumner
Date: 2021-06-18 00:00:00
Tasks: Position

Of the many modern approaches to calculating evolutionary distance via models of genome rearrangement, most are tied to a particular set of genomic modelling assumptions and to a restricted class of allowed rearrangements. The "position paradigm", in which genomes are represented as permutations signifying the position (and orientation) of each region, enables a refined model-based approach, where one can select biologically plausible rearrangements and assign to them relative probabilities/costs. Here, one must further incorporate any underlying structural symmetry of the genomes into the calculations and ensure that this symmetry is reflected in the model. In our recently-introduced framework of {\em genome algebras}, each genome corresponds to an element that simultaneously incorporates all of its inherent physical symmetries. The representation theory of these algebras then provides a natural model of evolution via rearrangement as a Markov chain. Whilst the implementation of this framework to calculate distances for genomes with `practical' numbers of regions is currently computationally infeasible, we consider it to be a significant theoretical advance: one can incorporate different genomic modelling assumptions, calculate various genomic distances, and compare the results under different rearrangement models. The aim of this paper is to demonstrate some of these features.
Artificial Perception Meets Psychophysics, Revealing a Fundamental Law of Illusory Motion
Authors: Taisuke Kobayashi, Eiji Watanabe
Date: 2021-06-18 00:00:00

Rotating Snakes is a visual illusion in which a stationary design is perceived to move dramatically. In the current study, the mechanism that generates perception of motion was analyzed using a combination of psychophysics experiments and deep neural network models that mimic human vision. We prepared three- and four-color illusion-like designs with a wide range of luminance and measured their strength of induced rotational motion. As a result, we discovered the fundamental law that the effect of the four-color snake rotation illusion was successfully enhanced by the combination of two perceptual motion vectors produced by the two three-color designs. In years to come, deep neural network technology will be one of the most effective tools not only for engineering applications but also for human perception research.