pwc-archive/papers-with-abstracts huggingface dataset:
Extending Models Via Gradient Boosting: An Application to Mendelian Models
Authors: Theodore Huang, Gregory Idos, Christine Hong, Stephen Gruber, Giovanni Parmigiani, Danielle Braun
Date: 2021-05-13 00:00:00

Improving existing widely-adopted prediction models is often a more efficient and robust way towards progress than training new models from scratch. Existing models may (a) incorporate complex mechanistic knowledge, (b) leverage proprietary information and, (c) have surmounted barriers to adoption. Compared to model training, model improvement and modification receive little attention. In this paper we propose a general approach to model improvement: we combine gradient boosting with any previously developed model to improve model performance while retaining important existing characteristics. To exemplify, we consider the context of Mendelian models, which estimate the probability of carrying genetic mutations that confer susceptibility to disease by using family pedigrees and health histories of family members. Via simulations we show that integration of gradient boosting with an existing Mendelian model can produce an improved model that outperforms both that model and the model built using gradient boosting alone. We illustrate the approach on genetic testing data from the USC-Stanford Cancer Genetics Hereditary Cancer Panel (HCP) study.
Wav2KWS: Transfer Learning from Speech Representations for Keyword Spotting
Authors: D.J SEO, H.S OH, Y.C JUNG
Date: 2021-05-10 00:00:00
Tasks: Keyword Spotting, text-to-speech, Text to Speech, Transfer Learning

With the expanding development of on-device artificial intelligence, voice-enabled devices such as smart speakers, wearables,
and other on-device or edge processing systems have been proposed. However, building or obtaining large training datasets that
are essential for robust keyword spotting (KWS) remains cumbersome. To address this problem, we propose a deep neural
network that can rapidly establish a high-performance KWS system from arbitrary keyword instruction sets. We use an encoder
pretrained with a large-scale speech corpus as the backbone network and then design an effective transfer network for KWS. To
demonstrate the feasibility of the proposed network, various experiments were conducted on Google Speech Command Datasets
V1 and V2. In addition, to verify the applicability of the network for different languages, we conducted experiments using three
different Korean speech command datasets. The proposed network outperforms state-of-the-art deep neural networks in both
experiments. Furthermore, the proposed network can understand real human voice even when trained with synthetic text-to-speech
data.
Nonuniform Sampling Rate Conversion: An Efficient Approach
Authors: Pablo Martínez-Nuevo
Date: 2021-05-14 00:00:00

We present a discrete-time algorithm for nonuniform sampling rate conversion that presents low computational complexity and memory requirements. It generalizes arbitrary sampling rate conversion by accommodating time-varying conversion ratios, i.e., it can efficiently adapt to instantaneous changes of the input and output sampling rates. This approach is based on appropriately factorizing the time-varying discrete-time filter used for the conversion. Common filters that satisfy this factorization property are those where the underlying continuous-time filter consists of linear combinations of exponentials, e.g., those described by linear constant-coefficient differential equations. This factorization separates the computation into two parts: one consisting of a factor solely depending on the output sampling instants and the other consists of a summation -- that can be computed recursively -- whose terms depend solely on the input sampling instants and its number of terms is given by a relationship between input and output sampling instants. Thus, nonuniform sampling rates can be accommodated by updating the factors involved and adjusting the number of terms added. When the impulse response consists of exponentials, computing the factors can be done recursively in an efficient manner.
Sound Pressure Minimization at the Ear Drum for In-ear ANC Headphones using a Fixed Feedforward Remote Microphone Technique
Authors: Piero Rivera Benois, Reinhild Roden, Matthias Blau, Simon Doclo
Date: 2021-05-14 00:00:00

In this paper we consider an in-ear headphone equipped with an external microphone and aim to minimize the sound pressure at the ear drum by means of a fixed feedforward ANC controller. Based on measured acoustic paths to predict the sound pressure generated by external sources and the headphone at the ear drum, the FIR filter coefficients of the ANC controller are optimized for different sound fields. Due to the acoustic feedback path between the loudspeaker and the microphone, a stability constraint based on the Nyquist stability criterion is introduced. Performance degradations due to reinsertions of the headphone and intra-subject variations are addressed by simultaneously optimizing the controller for several measurement repetitions of the acoustic paths. Simulations show that the controller optimized for an ipsilateral excitation produces an attenuation of at least -10 dB that extends approximately to +45{\deg} and -65{\deg} from the ipsilateral DoA. The controller optimized for a diffuse-field excitation achieves an attenuation of at least -10 dB over a wider range of DoAs on the ipsilateral side, namely +90{\deg} to -90{\deg}. Optimizing the controller for several measurement repetitions is shown to be effective against performance degradations due to reinsertions and intra-subject variations.
A Primer on Techtile: An R&D Testbed for Distributed Communication, Sensing and Positioning
Authors: Gilles Callebaut, Jarne Van Mulders, Geoffrey Ottoy, Liesbet Van der Perre
Date: 2021-05-14 00:00:00
Tasks: Diversity, Edge-computing

The Techtile measurement infrastructure is a multi-functional, versatile testbed for new communication and sensing technologies relying on fine-grained distributed resources. The facility enables experimental research on hyper-connected interactive environments and validation of new wireless connectivity, sensing and positioning solutions. It consists of a data acquisition and processing equipment backbone and a fabric of dispersed edge computing devices, Software-Defined Radios, sensors, and LED sources. These bring intelligence close to the applications and can also collectively function as a massive, distributed resource. Furthermore, the infrastructure allows exploring more degrees and new types of diversity, i.e., scaling up the number of elements, introducing `3D directional diversity' by deploying the distributed elements with different orientations, and `interface diversity' by exploiting multiple technologies and hybrid signals (RF, acoustic, and visible light).
Mapping of Dynamics between Mechanical and Electrical Ports in SG-IBR Composite Grids
Authors: Yitong Li, Yunjie Gu, Timothy C. Green
Date: 2021-05-13 00:00:00

Power grids are traditionally dominated by synchronous generators (SGs) but are currently undergoing a major transformation through the increasing integration of inverter-based resources (IBRs). The SG-dominated grid is traditionally analyzed in a mechanical-centric view which ignores fast electrical dynamics and focuses on the torque-speed dynamics. By contrast, analysis of the emergent IBR-dominated grid usually takes the electrical-centric view which focuses on the voltage-current interaction. In this article, a port-mapping method is proposed to fill the gap between these approaches and combine them in a unified model. Specifically, the mechanical dynamics are mapped to the electrical impedance seen at the electrical port; and the electrical dynamics are also mapped to the torque coefficient seen at the mechanical port. The bidirectional mapping gives additional flexibility and insights to analyze the sub-system interactions in whole-system dynamics and guide the tuning of parameter. Application of the proposed method is illustrated in three cases with increasing scales, namely a single-SG-infinite-bus system, a single-IBR-weak-grid system, and a modified IEEE 14-bus SG-IBR composite system.
Safety-Constrained Learning and Control using Scarce Data and Reciprocal Barriers
Authors: Christos K. Verginis, Franck Djeumou, Ufuk Topcu
Date: 2021-05-13 00:00:00

We develop a control algorithm that ensures the safety, in terms of confinement in a set, of a system with unknown, 2nd-order nonlinear dynamics. The algorithm establishes novel connections between data-driven and robust, nonlinear control. It is based on data obtained online from the current trajectory and the concept of reciprocal barriers. More specifically, it first uses the obtained data to calculate set-valued functions that over-approximate the unknown dynamic terms. For the second step of the algorithm, we design a robust control scheme that uses these functions as well as reciprocal barriers to render the system forward invariant with respect to the safe set. In addition, we provide an extension of the algorithm that tackles issues of controllability loss incurred by the nullspace of the control-direction matrix. The algorithm removes a series of standard, limiting assumptions considered in the related literature since it does not require global boundedness, growth conditions, or a priori approximations of the unknown dynamics' terms.
Predicting the Spatially Varying Infection Risk in Indoor Spaces Using an Efficient Airborne Transmission Model
Authors: Zechariah Lau, Ian M. Griffiths, Aaron English, Katerina Kaouri
Date: 2020-12-22 00:00:00

We develop a spatially dependent generalisation to the Wells-Riley model and its extensions applied to COVID-19, that determines the infection risk due to airborne transmission of viruses. We assume that the concentration of infectious particles is governed by an advection-diffusion-reaction equation with the particles advected by airflow, diffused due to turbulence, emitted by infected people and removed due to the room ventilation, inactivation of the virus and gravitational settling. We consider one asymptomatic or presymptomatic infectious person who breathes or talks, with or without a mask and model a quasi-3D setup that incorporates a recirculating air-conditioning flow. A semi-analytic solution is available and this enables fast simulations. We quantify the effect of ventilation and particle emission rate on the particle concentration, infection risk and the `time to probable infection' (TTPI). Good agreement with CFD models is achieved. Furthermore, we derive power laws that quantify the effect of ventilation, emission rate and infectiousness of the virus on the TTPI. The model can be easily updated to take into account modified parameter values. This work paves the way for establishing `safe occupancy times' at any location and has direct applicability in mitigating the spread of the COVID-19 pandemic.
Trends in the E-commerce and in the Traditional Retail Sectors During the Covid-19 Pandemic: an Evolutionary Game Approach
Authors: André Barreira da Silva Rocha, Matheus Oliveira Meirim, Lara Corrêa Nogueira
Date: 2021-05-14 00:00:00

An evolutionary game model is developed to study the interplay between consumers and producers when trade takes place on an e-commerce marketplace. The type of delivery service available and consumers' taste are particularly important regarding both game payoffs and players' strategies. The game payoff matrix is then adapted to analyse the different trading patterns that were developed during the COVID-19 pandemic in both the traditional retail and e-commerce sectors. In contrast to the former, investment in logistics and warehouses in the e-commerce sector allowed for the emergence of a trend in which fast delivery and eager consumers are becoming the norm.
Policy Evaluation during a Pandemic
Authors: Brantly Callaway, Tong Li
Date: 2021-05-14 00:00:00
Tasks: Epidemiology

National and local governments have implemented a large number of policies in response to the Covid-19 pandemic. Evaluating the effects of these policies, both on the number of Covid-19 cases as well as on other economic outcomes is a key ingredient for policymakers to be able to determine which policies are most effective as well as the relative costs and benefits of particular policies. In this paper, we consider the relative merits of common identification strategies that exploit variation in the timing of policies across different locations by checking whether the identification strategies are compatible with leading epidemic models in the epidemiology literature. We argue that unconfoundedness type approaches, that condition on the pre-treatment "state" of the pandemic, are likely to be more useful for evaluating policies than difference-in-differences type approaches due to the highly nonlinear spread of cases during a pandemic. For difference-in-differences, we further show that a version of this problem continues to exist even when one is interested in understanding the effect of a policy on other economic outcomes when those outcomes also depend on the number of Covid-19 cases. We propose alternative approaches that are able to circumvent these issues. We apply our proposed approach to study the effect of state level shelter-in-place orders early in the pandemic.
Dynamic Portfolio Allocation in High Dimensions using Sparse Risk Factors
Authors: Bruno P. C. Levy, Hedibert F. Lopes
Date: 2021-05-13 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We propose a fast and flexible method to scale multivariate return volatility predictions up to high-dimensions using a dynamic risk factor model. Our approach increases parsimony via time-varying sparsity on factor loadings and is able to sequentially learn the use of constant or time-varying parameters and volatilities. We show in a dynamic portfolio allocation problem with 452 stocks from the S&P 500 index that our dynamic risk factor model is able to produce more stable and sparse predictions, achieving not just considerable portfolio performance improvements but also higher utility gains for the mean-variance investor compared to the traditional Wishart benchmark and the passive investment on the market index.
Heterogeneously Perceived Incentives in Dynamic Environments: Rationalization, Robustness and Unique Selections
Authors: Evan Piermont, Peio Zuazo-Garin
Date: 2021-05-14 00:00:00

In dynamic settings each economic agent's choices can be revealing of her private information. This elicitation via the rationalization of observable behavior depends each agent's perception of which payoff-relevant contingencies other agents persistently deem as impossible. We formalize the potential heterogeneity of these perceptions as disagreements at higher-orders about the set of payoff states of a dynamic game. We find that apparently negligible disagreements greatly affect how agents interpret information and assess the optimality of subsequent behavior: When knowledge of the state space is only 'almost common', strategic uncertainty may be greater when choices are rationalized than when they are not--forward and backward induction predictions, respectively, and while backward induction predictions are robust to small disagreements about the state space, forward induction predictions are not. We also prove that forward induction predictions always admit unique selections a la Weinstein and Yildiz (2007) (also for spaces not satisfying richness) and backward induction predictions do not.
Enhancing sparse representation of color images by cross channel transformation
Authors: Laura Rebollo-Neira, Aurelien Inacio
Date: 2021-05-14 00:00:00

Transformations for enhancing sparsity in the approximation of color images by 2D atomic decomposition are discussed. The sparsity is firstly considered with respect to the most significant coefficients in the wavelet decomposition of the color image. The discrete cosine transform is singled out as an effective transformation for this purpose. The enhanced feature is further exploited by approximating the transformed arrays using an effective greedy strategy with a separable highly redundant dictionary. The relevance of the achieved sparsity is illustrated by a simple encoding procedure. On a set of typical test images the compression at high quality recovery is shown to significantly improve upon JPEG and WebP formats. The results are competitive with those produced by the JPEG2000 standard.
CN-LBP: Complex Networks-based Local Binary Patterns for Texture Classification
Authors: Zhengrui Huang
Date: 2021-05-14 00:00:00
Tasks: Clustering, Texture Classification

To overcome the limitations of original local binary patterns (LBP), this article proposes a new texture descriptor aided by complex networks (CN) and LBP, named CN-LBP. Specifically, we first abstract a texture image (TI) as directed graphs over different bands with the help of pixel distance, intensity, and gradient (magnitude and angle). Second, several CN-based feature measurements (including clustering coefficient, in-degree centrality, out-degree centrality, and eigenvector centrality) are selected to further decipher the texture features, which generates four feature images that can retain the image information as much as possible. Third, given the original TIs, gradient images (GI), and generated feature images, we can obtain the discriminative representation of texture images based on uniform LBP (ULBP). Finally, the feature vector is obtained by jointly calculating and concatenating the spatial histograms. In contrast to original LBP, the proposed texture descriptor contains more detailed image information, and shows resistance to imaging and noise. Experiment results on four datasets demonstrate that the proposed texture descriptor can significantly improve the classification accuracies compared with the state-of-the-art LBP-based variants and deep learning-based methods.
Multi-Resolution Data Fusion for Super Resolution Imaging
Authors: Emma J Reid, Lawrence F Drummy, Charles A Bouman, Gregery T Buzzard
Date: 2021-05-13 00:00:00
Tasks: Computational Efficiency, Super-Resolution

Applications in materials and biological imaging are limited by the ability to collect high-resolution data over large areas in practical amounts of time. One solution to this problem is to collect low-resolution data and interpolate to produce a high-resolution image. However, most existing super-resolution algorithms are designed for natural images, often require aligned pairing of high and low-resolution training data, and may not directly incorporate a model of the imaging sensor. In this paper, we present a Multi-resolution Data Fusion (MDF) algorithm for accurate interpolation of low-resolution data at multiple resolutions up to 8x. Our approach uses small quantities of unpaired high-resolution data to train a neural network prior model denoiser and then uses the Multi-Agent Consensus Equilibrium (MACE) problem formulation to balance this denoiser with a forward model agent that promotes fidelity to measured data. A key theoretical novelty is the analysis of mismatched back-projectors, which modify typical forward model updates for computational efficiency or improved image quality. We use MACE to prove that using a mismatched back-projector is equivalent to using a standard back-projector and an appropriately modified prior model. We present electron microscopy results at 4x and 8x interpolation factors that exhibit reduced artifacts relative to existing methods while maintaining fidelity to acquired data and accurately resolving sub-pixel-scale features.
Surface Detection for Sketched Single Photon Lidar
Authors: Michael P. Sheehan, Julián Tachella, Mike E. Davies
Date: 2021-05-14 00:00:00

Single-photon lidar devices are able to collect an ever-increasing amount of time-stamped photons in small time periods due to increasingly larger arrays, generating a memory and computational bottleneck on the data processing side. Recently, a sketching technique was introduced to overcome this bottleneck which compresses the amount of information to be stored and processed. The size of the sketch scales with the number of underlying parameters of the time delay distribution and not, fundamentally, with either the number of detected photons or the time-stamp resolution. In this paper, we propose a detection algorithm based solely on a small sketch that determines if there are surfaces or objects in the scene or not. If a surface is detected, the depth and intensity of a single object can be computed in closed-form directly from the sketch. The computational load of the proposed detection algorithm depends solely on the size of the sketch, in contrast to previous algorithms that depend at least linearly in the number of collected photons or histogram bins, paving the way for fast, accurate and memory efficient lidar estimation. Our experiments demonstrate the memory and statistical efficiency of the proposed algorithm both on synthetic and real lidar datasets.
Two-dimensional multi-target detection: an autocorrelation analysis approach
Authors: Shay Kreymer, Tamir Bendory
Date: 2021-05-14 00:00:00
Tasks: Vocal Bursts Valence Prediction

We consider the two-dimensional multi-target detection problem of recovering a target image from a noisy measurement that contains multiple copies of the image, each randomly rotated and translated. Motivated by the structure reconstruction problem in single-particle cryo-electron microscopy, we focus on the high noise regime, where the noise hampers accurate detection of the image occurrences. We develop an autocorrelation analysis framework to estimate the image directly from a measurement with an arbitrary spacing distribution of image occurrences, bypassing the estimation of individual locations and rotations. We conduct extensive numerical experiments, and demonstrate image recovery in highly noisy environments. The code to reproduce all numerical experiments is publicly available at https://github.com/krshay/MTD-2D.
Estimation of Parameters for an Archetypal Model of Cardiomyocyte Membrane Potentials
Authors: Muhamad H. N. Aziz, Radostin D. Simitev
Date: 2021-05-14 00:00:00
Tasks: Computational Efficiency

Contemporary realistic mathematical models of single-cell cardiac electrical excitation are immensely detailed. Model complexity leads to parameter uncertainty, high computational cost and barriers to mechanistic understanding. There is a need for reduced models that are conceptually and mathematically simple but physiologically accurate. To this end, we consider an archetypal model of single-cell cardiac excitation that replicates the phase-space geometry of detailed cardiac models, but at the same time has a simple piecewise-linear form and a relatively low-dimensional configuration space. In order to make this archetypal model practically applicable, we develop and report a robust method for estimation of its parameter values from the morphology of single-stimulus action potentials derived from detailed ionic current models and from experimental myocyte measurements. The procedure is applied to five significant test cases and an excellent agreement with target biomarkers is achieved. Action potential duration restitution curves are also computed and compared to those of the target test models and data, demonstrating conservation of dynamical pacing behaviour by the fine-tuned archetypal model. An archetypal model that accurately reproduces a variety of wet-lab and synthetic electrophysiology data offers a number of specific advantages such as computational efficiency, as also demonstrated in the study. Open-source numerical code of the models and methods used is provided.