pwc-archive/papers-with-abstracts huggingface dataset:
Thermographic detection of internal defects using 2D photothermal super resolution reconstruction with sequential laser heating
Authors: Julien Lecompagnon, Samim Ahmadi, Philipp Hirsch, Christian Rupprecht, Mathias Ziegler
Date: 2022-03-01 00:00:00
Tasks: Super-Resolution

Thermographic photothermal super resolution reconstruction enables the resolution of internal defects/inhomogeneities below the classical limit which is governed by the diffusion properties of thermal wave propagation. Based on a combination of the application of special sampling strategies and a subsequent numerical optimization step in post-processing, thermographic super resolution has already proven to be superior to standard thermographic methods in the detection of one-dimensional defect/inhomogeneity structures. In our work, we report an extension of the capabilities of the method for efficient detection and resolution of defect cross sections with fully two-dimensional structured laser-based heating. The reconstruction is carried out using one of two different algorithms which are proposed within this work. Both algorithms utilize the combination of several coherent measurements using convex optimization and exploit the sparse nature of defects/inhomogeneities as is typical for most nondestructive testing scenarios. Finally, the performance of each algorithm is rated on reconstruction quality and algorithmic complexity. The presented experimental approach is based on repeated spatially structured heating by a high power laser. As a result, a two-dimensional sparse defect/inhomogeneity map can be obtained. In addition, the obtained results are compared with those of conventional thermographic inspection methods which make use of homogeneous illumination. Due to the sparse nature of the reconstructed defect/inhomogeneity map, this comparison is performed qualitatively.
Latent Unbalancedness in Three-Way Gravity Models
Authors: Daniel Czarnowske, Amrei Stammann
Date: 2022-03-04 00:00:00

Many panel data sets used for pseudo-poisson estimation of three-way gravity models are implicitly unbalanced because uninformative observations are redundant for the estimation. We show with real data as well as simulations that this phenomenon, which we call latent unbalancedness, amplifies the inference problem recently studied by Weidner and Zylkin (2021).
A Classifier-Lasso Approach for Estimating Production Functions with Latent Group Structures
Authors: Daniel Czarnowske
Date: 2022-03-04 00:00:00

I present a new estimation procedure for production functions with latent group structures. I consider production functions that are heterogeneous across groups but time-homogeneous within groups, and where the group membership of the firms is unknown. My estimation procedure is fully data-driven and embeds recent identification strategies from the production function literature into the classifier-Lasso. Simulation experiments demonstrate that firms are assigned to their correct latent group with probability close to one. I apply my estimation procedure to a panel of Chilean firms and find sizable differences in the estimates compared to the standard approach of classification by industry.
Gene-Environment Interplay in the Social Sciences
Authors: Rita Dias Pereira, Pietro Biroli, Titus Galama, Stephanie von Hinke, Hans van Kippersluis, Cornelius A. Rietveld, Kevin Thom
Date: 2022-03-04 00:00:00
Tasks: Decision Making

Nature (one's genes) and nurture (one's environment) jointly contribute to the formation and evolution of health and human capital over the life cycle. This complex interplay between genes and environment can be estimated and quantified using genetic information readily available in a growing number of social science data sets. Using genetic data to improve our understanding of individual decision making, inequality, and to guide public policy is possible and promising, but requires a grounding in essential genetic terminology, knowledge of the literature in economics and social-science genetics, and a careful discussion of the policy implications and prospects of the use of genetic data in the social sciences and economics.
Sparse InSAR Data 3D Inpainting for Ground Deformation Detection Along the Rail Corridor
Authors: Odysseas Pappas, Juliet Biggs, David Bull, Alin Achim, Nantheera Anantrasirichai
Date: 2022-03-04 00:00:00
Tasks: 3D Inpainting

Monitoring of ground movement close to the rail corridor, such as that associated with landslips caused by ground subsidence and/or uplift, is of great interest for the detection and prevention of possible railway faults. Interferometric synthetic-aperture radar (InSAR) data can be used to measure ground deformation, but its use poses distinct challenges, as the data is highly sparse and can be particularly noisy. Here we present a scheme for processing and interpolating noisy, sparse InSAR data into a dense spatio-temporal stack, helping suppress noise and opening up the possibility for treatment with deep learning and other image processing methods.
Distributed Online Learning for Coexistence in Cognitive Radar Networks
Authors: William Howard, Anthony Martone, R. Michael Buehrer
Date: 2022-03-04 00:00:00

This work addresses the coexistence problem for radar networks. Specifically, we model a network of cooperative, independent, and non-communicating radar nodes which must share resources within the network as well as with non-cooperative nearby emitters. We approach this problem using online Machine Learning (ML) techniques. Online learning approaches are specifically preferred due to the fact that each radar node has no prior knowledge of the environment nor of the positions of the other radar nodes, and due to the sequential nature of the problem. For this task we specifically select the multi-player multi-armed bandit (MMAB) model, which poses the problem as a sequential game, where each radar node in a network makes independent selections of center frequency and waveform with the same goal of improving tracking performance for the network as a whole. For accurate tracking, each radar node communicates observations to a fusion center on set intervals. The fusion center has knowledge of the radar node placement, but cannot communicate to the individual nodes fast enough for waveform control. Every radar node in the network must learn the behavior of the environment, which includes rewards, interferer behavior, and target behavior. Our contributions include a mathematical description of the MMAB framework adapted to the radar network scenario. We conclude with a simulation study of several different network configurations. Experimental results show that iterative, online learning using MMAB outperforms the more traditional sense-and-avoid (SAA) and fixed-allocation approaches.
Gamma-Shadowed Two-Ray with Diffuse Power Composite Fading Model
Authors: Pamela Njemcevic, Almir Maric
Date: 2022-03-04 00:00:00
Tasks: Vocal Bursts Valence Prediction

In this paper, a novel gamma-shadowed two-ray with diffuse power (GS-TWDP) composite fading model is proposed. The model is intended for modeling propagation in the emerging wireless networks working at millimeter wave (mmWave) frequencies, and is obtained as a combination of TWDP distribution for description of multipath effects and gamma distribution for modeling variations due to shadowing. After derivation of the exact probability density function (PDF), cumulative distribution function (CDF) and moment generating functions (MGF) expressions are obtained. Proposed model is verified by comparing the analytically obtained results with those measured at 28 GHz and reported in literature. Two upper bound average symbol error probability (ASEP) expressions are then derived for M-ary rectangular quadrature amplitude modulation (RQAM) by employing Chernoff and Chiani approximations of Gaussian Q-function, and are used to investigate relationship between GS-TWDP parameters and system performance. All the results are verified by Monte-Carlo simulation.
FewSense, Towards a Scalable and Cross-Domain Wi-Fi Sensing System Using Few-Shot Learning
Authors: Guolin Yin, Junqing Zhang, Guanxiong Shen, Yingying Chen
Date: 2022-03-03 00:00:00
Tasks: Domain Adaptation, Few-Shot Learning

Wi-Fi sensing can classify human activities because each activity causes unique changes to the channel state information (CSI). Existing WiFi sensing suffers from limited scalability as the system needs to be retrained whenever new activities are added, which cause overheads of data collection and retraining. Cross-domain sensing may fail because the mapping between activities and CSI variations is destroyed when a different environment or user (domain) is involved. This paper proposed a few-shot learning-based WiFi sensing system, named FewSense, which can recognise novel classes in unseen domains with only few samples. Specifically, a feature extractor was pre-trained offline using the source domain data. When the system was applied in the target domain, few samples were used to fine-tune the feature extractor for domain adaptation. Inference was made by computing the cosine similarity. FewSense can further boost the classification accuracy by collaboratively fusing inference from multiple receivers. We evaluated the performance using three public datasets, i.e., SignFi, Widar, and Wiar. The results show that FewSense with five-shot learning recognised novel classes in unseen domains with an accuracy of 90.3\%, 96.5\% ,82.7\% on SignFi, Widar, and Wiar datasets, respectively. Our collaborative sensing model improved system performance by an average of 30\%.
The starting dates of COVID-19 multiple waves
Authors: Paulo Roberto de Lima Gianfelice, Ricardo Sovek Oyarzabal, Americo Cunha Jr, Jose Mario Vicensi Grzybowski, Fernando da Conceição Batista, Elbert E. N. Macau
Date: 2022-03-03 00:00:00

The severe acute respiratory syndrome of coronavirus 2 spread globally very quickly, causing great concern at the international level due to the severity of the associated respiratory disease, the so-called COVID-19. Considering Rio de Janeiro city (Brazil) as an example, the first diagnosis of this disease occurred in March 2020, but the exact moment when the local spread of the virus started is uncertain as the Brazilian epidemiological surveillance system was not widely prepared to detect suspected cases of COVID-19 at that time. Improvements in this surveillance system occurred over the pandemic, but due to the complex nature of the disease transmission process, specifying the exact moment of emergence of new community contagion outbreaks is a complicated task. This work aims to propose a general methodology to determine possible start dates for the multiple community outbreaks of COVID-19, using for this purpose a parametric statistical approach that combines surveillance data, nonlinear regression, and information criteria to obtain a statistical model capable of describing the multiple waves of contagion observed. The dynamics of COVID-19 in the city of Rio de Janeiro is taken as a case study, and the results suggest that the original strain of the virus was already circulating in Rio de Janeiro city as early as late February 2020, probably being massively disseminated in the population during the carnival festivities.
Distributed Methods with Absolute Compression and Error Compensation
Authors: Marina Danilova, Eduard Gorbunov
Date: 2022-03-04 00:00:00
Tasks: Distributed Optimization

Distributed optimization methods are often applied to solving huge-scale problems like training neural networks with millions and even billions of parameters. In such applications, communicating full vectors, e.g., (stochastic) gradients, iterates, is prohibitively expensive, especially when the number of workers is large. Communication compression is a powerful approach to alleviating this issue, and, in particular, methods with biased compression and error compensation are extremely popular due to their practical efficiency. Sahu et al. (2021) propose a new analysis of Error Compensated SGD (EC-SGD) for the class of absolute compression operators showing that in a certain sense, this class contains optimal compressors for EC-SGD. However, the analysis was conducted only under the so-called $(M,\sigma^2)$-bounded noise assumption. In this paper, we generalize the analysis of EC-SGD with absolute compression to the arbitrary sampling strategy and propose the first analysis of Error Compensated Loopless Stochastic Variance Reduced Gradient method (EC-LSVRG) with absolute compression for (strongly) convex problems. Our rates improve upon the previously known ones in this setting. Numerical experiments corroborate our theoretical findings.
At least three xenon binding sites in the glycine binding domain of the N-methyl D-aspartate receptor
Authors: Yves-Henri Sanejouand
Date: 2022-03-04 00:00:00

Xenon can produce general anesthesia. Its main protein target is the N-methyl-D-aspartate receptor, a ionotropic channel playing a pivotal role in the function of the central nervous system. The molecular mechanisms allowing this noble gas to have such a specific effect remain obscure, probably as a consequence of the lack of structural data at the atomic level of detail. Herein, as a result of five independent molecular dynamics simulations, three different binding sites were found for xenon in the glycine binding domain of the N-methyl-D-aspartate receptor. The absolute binding free energy of xenon in these sites ranges between -8 and -14 kJ/mole. However, it depends significantly upon the protein conformer chosen for performing the calculation, suggesting that larger values could probably be obtained, if other conformers were considered. These three sites are next to each other, one of them being next to the glycine site. This could explain why the F758W and F758Y mutations can prevent competitive inhibition by xenon without affecting glycine binding.
Greedy Optimization of Electrode Arrangement for Epiretinal Prostheses
Authors: Ashley Bruce, Michael Beyeler
Date: 2022-03-04 00:00:00
Tasks: Dictionary Learning

Visual neuroprostheses are the only FDA-approved technology for the treatment of retinal degenerative blindness. Although recent work has demonstrated a systematic relationship between electrode location and the shape of the elicited visual percept, this knowledge has yet to be incorporated into retinal prosthesis design, where electrodes are typically arranged on either a rectangular or hexagonal grid. Here we optimize the intraocular placement of epiretinal electrodes using dictionary learning. Importantly, the optimization process is informed by a previously established and psychophysically validated model of simulated prosthetic vision. We systematically evaluate three different electrode placement strategies across a wide range of possible phosphene shapes and recommend electrode arrangements that maximize visual subfield coverage. In the near future, our work may guide the prototyping of next-generation neuroprostheses.
Structural features of microvascular networks trigger blood-flow oscillations
Authors: Yaron Ben-Ami, George W. Atkinson, Joe M. Pitt-Francis, Philip K. Maini, Helen M. Byrne
Date: 2022-02-26 00:00:00

We analyse mathematical models in order to understand how microstructural features of vascular networks may affect blood-flow dynamics, and to identify particular characteristics that promote the onset of self-sustained oscillations. By focusing on a simple three-node motif, we predict that network "redundancy", in the form of a redundant vessel connecting two main flow-branches, together with differences in haemodynamic resistance in the branches, can promote the emergence of oscillatory dynamics. We use existing mathematical descriptions for blood rheology and haematocrit splitting at vessel branch-points to construct our flow model; we combine numerical simulations and stability analysis to study the dynamics of the three-node network and its relation to the system's multiple steady-state solutions. While, for the case of equal inlet-pressure conditions, a "trivial" equilibrium solution with no flow in the redundant vessel always exists, we find that it is not stable when other, stable, steady-state attractors exist. In turn, these "nontrivial" steady-state solutions may undergo a Hopf bifurcation into an oscillatory state. We use the branch diameter ratio, together with the inlet haematocrit rate, to construct a two-parameter stability diagram that delineates regimes in which such oscillatory dynamics exist. We show that flow oscillations in this network geometry are only possible when the branch diameters are sufficiently different to allow for a sufficiently large flow in the redundant vessel, which acts as the driving force of the oscillations. These microstructural properties, which were found to promote oscillatory dynamics, could be used to explore sources of flow instability in biological microvascular networks.
A Robust Statistical Analysis of the Role of Hydropower on the System Electricity Price and Price Volatility
Authors: Olukunle O. Owolabi, Kathryn Lawson, Sanhita Sengupta, Yingsi Huang, Lan Wang, Chaopeng Shen, Mila Getmansky Sherman, Deborah A. Sunter
Date: 2022-03-04 00:00:00
Tasks: quantile regression, regression

Hydroelectric power (hydropower) is unique in that it can function as both a conventional source of electricity and as backup storage (pumped hydroelectric storage) for providing energy in times of high demand on the grid. This study examines the impact of hydropower on system electricity price and price volatility in the region served by the New England Independent System Operator (ISONE) from 2014 - 2020. We perform a robust holistic analysis of the mean and quantile effects, as well as the marginal contributing effects of hydropower in the presence of solar and wind resources. First, the price data is adjusted for deterministic temporal trends, correcting for seasonal, weekend, and diurnal effects that may obscure actual representative trends in the data. Using multiple linear regression and quantile regression, we observe that hydropower contributes to a reduction in the system electricity price and price volatility. While hydropower has a weak impact on decreasing price and volatility at the mean, it has greater impact at extreme quantiles (> 70th percentile). At these higher percentiles, we find that hydropower provides a stabilizing effect on price volatility in the presence of volatile resources such as wind. We conclude with a discussion of the observed relationship between hydropower and system electricity price and volatility.
Bayesian community detection for networks with covariates
Authors: Luyi Shen, Arash Amini, Nathaniel Josephs, Lizhen Lin
Date: 2022-03-04 00:00:00
Tasks: Community Detection, Stochastic Block Model

The increasing prevalence of network data in a vast variety of fields and the need to extract useful information out of them have spurred fast developments in related models and algorithms. Among the various learning tasks with network data, community detection, the discovery of node clusters or "communities," has arguably received the most attention in the scientific community. In many real-world applications, the network data often come with additional information in the form of node or edge covariates that should ideally be leveraged for inference. In this paper, we add to a limited literature on community detection for networks with covariates by proposing a Bayesian stochastic block model with a covariate-dependent random partition prior. Under our prior, the covariates are explicitly expressed in specifying the prior distribution on the cluster membership. Our model has the flexibility of modeling uncertainties of all the parameter estimates including the community membership. Importantly, and unlike the majority of existing methods, our model has the ability to learn the number of the communities via posterior inference without having to assume it to be known. Our model can be applied to community detection in both dense and sparse networks, with both categorical and continuous covariates, and our MCMC algorithm is very efficient with good mixing properties. We demonstrate the superior performance of our model over existing models in a comprehensive simulation study and an application to two real datasets.
Adaptive Discounting of Implicit Language Models in RNN-Transducers
Authors: Vinit Unni, Shreya Khare, Ashish Mittal, Preethi Jyothi, Sunita Sarawagi, Samarth Bharadwaj
Date: 2022-02-21 00:00:00
Tasks: Language Modeling, Language Modelling

RNN-Transducer (RNN-T) models have become synonymous with streaming end-to-end ASR systems. While they perform competitively on a number of evaluation categories, rare words pose a serious challenge to RNN-T models. One main reason for the degradation in performance on rare words is that the language model (LM) internal to RNN-Ts can become overconfident and lead to hallucinated predictions that are acoustically inconsistent with the underlying speech. To address this issue, we propose a lightweight adaptive LM discounting technique AdaptLMD, that can be used with any RNN-T architecture without requiring any external resources or additional parameters. AdaptLMD uses a two-pronged approach: 1) Randomly mask the prediction network output to encourage the RNN-T to not be overly reliant on it's outputs. 2) Dynamically choose when to discount the implicit LM (ILM) based on rarity of recently predicted tokens and divergence between ILM and implicit acoustic model (IAM) scores. Comparing AdaptLMD to a competitive RNN-T baseline, we obtain up to 4% and 14% relative reductions in overall WER and rare word PER, respectively, on a conversational, code-mixed Hindi-English ASR task.
A Comparative Study of the Performance for Predicting Biodegradability Classification: The Quantitative Structure–Activity Relationship Model vs the Graph Convolutional Network
Authors: Myeonghun Lee, Kyoungmin Min
Date: 2022-01-14 00:00:00
Proceeding: ACS Omega 2022 1
Tasks: Prediction, Specificity

The prediction and evaluation of the biodegradability of molecules with computational methods are becoming increasingly important. Among the various methods, quantitative structure–activity relationship (QSAR) models have been demonstrated to predict the ready biodegradation of chemicals but have limited functionality owing to their complex implementation. In this study, we employ the graph convolutional network (GCN) method to overcome these issues. A biodegradability dataset from previous studies was trained to generate prediction models by (i) the QSAR models using the Mordred molecular descriptor calculator and MACCS molecular fingerprint and (ii) the GCN model using molecular graphs. The performance comparison of the methods confirms that the GCN model is more straightforward to implement and more stable; the specificity and sensitivity values are almost identical without specific descriptors or fingerprints. In addition, the performance of the models was further verified by randomly dividing the dataset into 100 different cases of training and test sets and by varying the test set ratio from 20 to 80%. The results of the current study clearly suggest the promise of the GCN model, which can be implemented straightforwardly and can replace conventional QSAR prediction models for various types and properties of molecules.