pwc-archive/papers-with-abstracts huggingface dataset:
HuBeRo - a Framework to Simulate Human Behaviour in Robot Research
Authors: Karwowski, Jarosław and Dudek, Wojciech and Węgierek, Maciej and Winiarski, Tomasz
Date: 2021-07-22 00:00:00
Proceeding: Journal of Automation, Mobile Robotics and Intelligent Systems, 15(1), 31–38 2021 7
Tasks: Physical Simulations, Robot Navigation

Social robots’ software is commonly tested in a simulation due to the safety and convenience reasons as well as an environment configuration repeatability assurance. An interaction between a robot and a human requires taking a person presence and his movement abilities into consideration. The purpose of the article is to present the HuBeRo framework, which can be used to simulate human motion behaviour. The framework allows independent control of each individual’s activity, which distinguishes the presented approach from state-of-the-art, open-source solutions from the robotics domain. The article presents the framework assumptions, architecture and an example application with respect to presented scenarios.
An analysis of the semantic shifts of citations
Authors: Jiayue Xue
Date: 2021-08-06 00:00:00
Proceeding: ‌ 2021 8

The semantic shifts in natural language is a well established phenomenon and have been studied for many years. Similarly, the meanings of scientific publications may also change as time goes by. In other words, the same publication may be cited in distinct contexts. To investigate whether the meanings of citations have changed in different scenarios, which is also called in the semantic shifts in citations, we followed the same ideas of how researchers studied semantic shifts in language. To be more specific, we combined the temporal referencing model and the Word2Vec model to explore the semantic shifts of scientific citations in two aspects: their usages over time and their usages across different domains. By observing how citations themselves changed over time and comparing the closest neighbors of citations, we concluded that the semantics of scientific publications did shift in terms of cosine distances.
Citcom – Citation Recommendation
Authors: Melina Meyer, Jenny Frey, Tamino Laub, Marco Wrzalik, Dirk Krechel
Date: 2020-09-28 00:00:00
Proceeding: NFORMATIK 2020 2020 9
Tasks: Citation Recommendation, Feature Engineering

Citation recommendation aims to predict references based on a given text. In this paper, we focus on predicting references using small passages instead of a whole document. Besides using a search engine as baseline, we introduce two further more advanced approaches that are based on neural networks. The first one aims to learn an alignment between a passage encoder and reference embeddings while using a feature engineering approach including a simple feed forward network. The second model takes advantage of BERT, a state-of-the-art language representation model, to generate context-sensitive passage embeddings. The predictions of the second model are based on inter-passage similarities between the given text and indexed sentences, each associated with a set of references. For training and evaluation of our models, we prepare a large dataset consisting of English papers from various scientific disciplines.
Towards Low-distortion Multi-channel Speech Enhancement: The ESPNet-SE Submission to The L3DAS22 Challenge
Authors: Yen-Ju Lu, Samuele Cornell, Xuankai Chang, Wangyou Zhang, Chenda Li, Zhaoheng Ni, Zhong-Qiu Wang, Shinji Watanabe
Date: 2022-02-24 00:00:00
Tasks: Speech Enhancement

This paper describes our submission to the L3DAS22 Challenge Task 1, which consists of speech enhancement with 3D Ambisonic microphones. The core of our approach combines Deep Neural Network (DNN) driven complex spectral mapping with linear beamformers such as the multi-frame multi-channel Wiener filter. Our proposed system has two DNNs and a linear beamformer in between. Both DNNs are trained to perform complex spectral mapping, using a combination of waveform and magnitude spectrum losses. The estimated signal from the first DNN is used to drive a linear beamformer, and the beamforming result, together with this enhanced signal, are used as extra inputs for the second DNN which refines the estimation. Then, from this new estimated signal, the linear beamformer and second DNN are run iteratively. The proposed method was ranked first in the challenge, achieving, on the evaluation set, a ranking metric of 0.984, versus 0.833 of the challenge baseline.
Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation
Authors: Hemlata Tak, Massimiliano Todisco, Xin Wang, Jee-weon Jung, Junichi Yamagishi, Nicholas Evans
Date: 2022-02-24 00:00:00
Tasks: Audio Deepfake Detection, Data Augmentation, DeepFake Detection, Face Swapping, Self-Supervised Learning, Speaker Verification

The performance of spoofing countermeasure systems depends fundamentally upon the use of sufficiently representative training data. With this usually being limited, current solutions typically lack generalisation to attacks encountered in the wild. Strategies to improve reliability in the face of uncontrolled, unpredictable attacks are hence needed. We report in this paper our efforts to use self-supervised learning in the form of a wav2vec 2.0 front-end with fine tuning. Despite initial base representations being learned using only bona fide data and no spoofed data, we obtain the lowest equal error rates reported in the literature for both the ASVspoof 2021 Logical Access and Deepfake databases. When combined with data augmentation,these results correspond to an improvement of almost 90% relative to our baseline system.
Blind Reverberation Time Estimation in Dynamic Acoustic Conditions
Authors: Philipp Götz, Cagdas Tuna, Andreas Walther, Emanuël A. P. Habets
Date: 2022-02-23 00:00:00

The estimation of reverberation time from real-world signals plays a central role in a wide range of applications. In many scenarios, acoustic conditions change over time which in turn requires the estimate to be updated continuously. Previously proposed methods involving deep neural networks were mostly designed and tested under the assumption of static acoustic conditions. In this work, we show that these approaches can perform poorly in dynamically evolving acoustic environments. Motivated by a recent trend towards data-centric approaches in machine learning, we propose a novel way of generating training data and demonstrate, using an existing deep neural network architecture, the considerable improvement in the ability to follow temporal changes in reverberation time.
Exact Community Recovery over Signed Graphs
Authors: Xiaolu Wang, Peng Wang, Anthony Man-Cho So
Date: 2022-02-22 00:00:00
Tasks: Stochastic Block Model

Signed graphs encode similarity and dissimilarity relationships among different entities with positive and negative edges. In this paper, we study the problem of community recovery over signed graphs generated by the signed stochastic block model (SSBM) with two equal-sized communities. Our approach is based on the maximum likelihood estimation (MLE) of the SSBM. Unlike many existing approaches, our formulation reveals that the positive and negative edges of a signed graph should be treated unequally. We then propose a simple two-stage iterative algorithm for solving the regularized MLE. It is shown that in the logarithmic degree regime, the proposed algorithm can exactly recover the underlying communities in nearly-linear time at the information-theoretic limit. Numerical results on both synthetic and real data are reported to validate and complement our theoretical developments and demonstrate the efficacy of the proposed method.
Projected gradient-tracking in multi-cluster games and its application to power management
Authors: Jan Zimmermann, Tatiana Tatarenko, Volker Willert, Jürgen Adamy
Date: 2022-02-24 00:00:00
Tasks: Management

We are concerned with a distributed approach to solve multi-cluster games arising in multi-agent systems. In such games, agents are separated into distinct clusters. The agents belonging to the same cluster cooperate with each other to achieve a common cluster goal while a non-cooperative game is played between the clusters. To be able to deal with the sparsity of information, as each agent only knows a specific part of the problem, we combine gradient-tracking and consensus methods for information distribution into an algorithm that can solve both the cooperative and non-cooperative problem in a single run. The constraints of the problem are taken into account by the corresponding projection operators and linear convergence is proven given an appropriate constant step size. The algorithm is applied to a day-ahead power management problem, posed as a multi-cluster game, and its efficiency is demonstrated by simulations.
Positive Trigonometric Polynomials on the Stability of Spatially Interconnected Systems
Authors: Xiaokai Zhai
Date: 2022-02-24 00:00:00

This paper is devoted to the stability analysis of spatially interconnected systems (SISs) via the sum-of-squares (SOS) decomposition of positive trigonometric polynomials. For each spatial direction of SISs, three types of interconnected structures are considered. Inspired by the idea of rational parameterization and robust stabilizability function, necessary and sufficient conditions are derived for establishing the stability of SISs with two different combined topologies respectively. For these results, the primary issue concerns the global or local positivity of trigonometric polynomials. SOS decomposition and generalized trace parameterization of positive trigonometric polynomials are utilized so that the addressed problems can be quantified by two semidefinite programs (SDPs). The proposed methods are applicable to all possible interconnected structures due to the assumption of spatial reversibility. Numerical examples are given to illustrate the efficiency of the derived theoretical results.
Degradation-Reducing Control for Dynamically Reconfigurable Batteries
Authors: Tomas Kacetl, Jan Kacetl, Jinyang Fang, Malte Jaensch, Stefan Goetz
Date: 2022-02-23 00:00:00

Cascaded circuits such as modular multilevel con-verters (MMC) offer attractive qualities in reconfigurable battery applications. In contrast to conventional hard-wired dc battery packs, the MMC topology loads modules with ac current, which may lead to additional ageing of batteries. As recent studies reveal, such ageing of batteries occurs at low-frequency load ripple, and almost vanishes at high frequencies. State of the art in MMC bat-tery control focuses on state of charge and temperature balancing of individual modules. Previous methods to suppress ripple rely on slow feedback loops and low dynamics, which tends to form low-frequency patterns in the module load that negatively contribute to their ageing. This paper presents a novel module-current-oriented high-bandwidth control technique which minimizes low-frequency components in the module load spectrum. The control method respects limitations related to module data acquisition and enhanc-es the feedback bandwidth using observation techniques. We verify the proposed method experimentally on a laboratory setup and estimate the influence on the battery cells.
From evolution to folding of repeat proteins
Authors: Ezequiel A. Galpern, Jacopo Marchi, Thierry Mora, Aleksandra M. Walczak, Diego U. Ferreiro
Date: 2022-02-24 00:00:00
Tasks: Protein Folding

Repeat proteins are made with tandem copies of similar amino acid stretches that fold into elongated architectures. Due to their symmetry, these proteins constitute excellent model systems to investigate how evolution relates to structure, folding and function. Here, we propose a scheme to map evolutionary information at the sequence level to a coarse-grained model for repeat-protein folding and use it to investigate the folding of thousands of repeat-proteins. We model the energetics by a combination of an inverse Potts model scheme with an explicit mechanistic model of duplications and deletions of repeats to calculate the evolutionary parameters of the system at single residue level. This is used to inform an Ising-like model that allows for the generation of folding curves, apparent domain emergence and occupation of intermediate states that are highly compatible with experimental data in specific case studies. We analyzed the folding of thousands of natural Ankyrin-repeat proteins and found that a multiplicity of folding mechanisms are possible. Fully cooperative all-or-none transition are obtained for arrays with enough sequence-similar elements and strong interactions between them, while non-cooperative element-by-element intermittent folding arose if the elements are dissimilar and the interactions between them are energetically weak. In between, we characterised nucleation-propagation and multi-domain folding mechanisms. Finally, we showed that stability and cooperativity of a repeat-array can be quantitatively predicted from a simple energy score, paving the way for guiding protein folding design with a co-evolutionary model.
Competition among alliances of different sizes
Authors: Breno F. de Oliveira, Attila Szolnoki
Date: 2022-02-24 00:00:00

To understand the biodiversity of an ecosystem cannot be understood by solely analyzing the pair relations of competing species. Instead, we should consider multi-point interactions because the presence of a third party could change the original microscopic outcome significantly. In this way an alliance may emerge where species, who may have biased relations otherwise, can protect each other from an external invader. Such an alliance can be formed by two, three or even more species. By introducing a minimal model where six species compete for space we here study how the size of an alliance determines the vitality of a formation. We show that in the majority of parameter space the group of the smallest size prevails and other solutions can only be observed in a limited parameter range. These phases are separated by discontinuous phase transitions which can only be identified by intensive numerical efforts due to serious finite size effects and long relaxation processes.
A 2D Levy-flight model for the complex dynamics of real-life financial markets
Authors: Hediye Yarahmadi, Abbas Ali Saberi
Date: 2022-02-24 00:00:00

We report on the emergence of scaling laws in the temporal evolution of the daily closing values of the S\&P 500 index prices and its modeling based on the L\'evy flights in two dimensions (2D). The efficacy of our proposed model is verified and validated by using the extreme value statistics in random matrix theory. We find that the random evolution of each pair of stocks in a 2D price space is a scale-invariant complex trajectory whose tortuosity is governed by a $2/3$ geometric law between the gyration radius $R_g(t)$ and the total length $\ell(t)$ of the path, i.e., $R_g(t)\sim\ell(t)^{2/3}$. We construct a Wishart matrix containing all stocks up to a specific variable period and look at its spectral properties over 30 years. In contrast to the standard random matrix theory, we find that the distribution of eigenvalues has a power-law tail with a decreasing exponent over time -- a quantitative indicator of the temporal correlations. We find that the time evolution of the distance of a 2D L\'evy flights with index $\alpha=3/2$ from origin generates the same empirical spectral properties. The statistics of the largest eigenvalues of the model and the observations are in perfect agreement.
Oceanic frontal divergence alters phytoplankton competition and distribution
Authors: Abigail Plummer, Mara Freilich, Roberto Benzi, Chang Jae Choi, Lisa Sudek, Alexandra Z. Worden, Federico Toschi, Amala Mahadevan
Date: 2022-02-23 00:00:00

Ecological interactions among phytoplankton occur in a moving fluid environment. Oceanic flows can modulate the competition and coexistence between phytoplankton populations, which in turn can affect ecosystem function and biogeochemical cycling. We explore the impact of submesoscale velocity gradients on phytoplankton ecology using observations, simulations, and theory. Observations reveal that the relative abundance of Synechoccocus oligotypes varies on 1--10 km scales at an ocean front with submesoscale velocity gradients at the same scale. Simulations in realistic flow fields demonstrate that regions of divergence in the horizontal flow field can substantially modify ecological competition and dispersal on timescales of hours to days. Regions of positive (negative) divergence provide an advantage (disadvantage) to local populations, resulting in up to ~20% variation in community composition in our model. We propose that submesoscale divergence is a plausible contributor to observed taxonomic variability at oceanic fronts, and can lead to regional variability in community composition.
Climate-mediated shifts in temperature fluctuations promote extinction risk
Authors: Kate Duffy, Tarik C. Gouhier, Auroop R. Ganguly
Date: 2022-02-23 00:00:00

Climate-mediated changes in the spatiotemporal distribution of thermal stress can destabilize animal populations and promote extinction risk. Using quantile, spectral, and wavelet analyses of temperature projections from the latest generation of earth system models, we show that significant regional differences are expected to arise in the way that temperatures will increase over time. When integrated into empirically-parameterized mathematical models that simulate the dynamical and cumulative effects of thermal stress on the performance of 38 global ectotherm species, the projected spatiotemporal changes in temperature fluctuations are expected to give rise to complex regional changes in population abundance and stability over the course of the 21st century. However, despite their idiosyncratic effects on stability, projected temperatures universally increase extinction risk. These results show that population changes under future climate conditions may be more extensive and complex than the current literature suggests based on the statistical relationship between biological performance and average temperature.
Confidence Intervals of Treatment Effects in Panel Data Models with Interactive Fixed Effects
Authors: Xingyu Li, Yan Shen, Qiankun Zhou
Date: 2022-02-24 00:00:00
Tasks: Matrix Completion

We consider the construction of confidence intervals for treatment effects estimated using panel models with interactive fixed effects. We first use the factor-based matrix completion technique proposed by Bai and Ng (2021) to estimate the treatment effects, and then use bootstrap method to construct confidence intervals of the treatment effects for treated units at each post-treatment period. Our construction of confidence intervals requires neither specific distributional assumptions on the error terms nor large number of post-treatment periods. We also establish the validity of the proposed bootstrap procedure that these confidence intervals have asymptotically correct coverage probabilities. Simulation studies show that these confidence intervals have satisfactory finite sample performances, and empirical applications using classical datasets yield treatment effect estimates of similar magnitudes and reliable confidence intervals.
Semiparametric Estimation of Dynamic Binary Choice Panel Data Models
Authors: Fu Ouyang, Thomas Tao Yang
Date: 2022-02-24 00:00:00

We propose a new approach to the semiparametric analysis of panel data binary choice models with fixed effects and dynamics (lagged dependent variables). The model we consider has the same random utility framework as in Honore and Kyriazidou (2000). We demonstrate that, with additional serial dependence conditions on the process of deterministic utility and tail restrictions on the error distribution, the (point) identification of the model can proceed in two steps, and only requires matching the value of an index function of explanatory variables over time, as opposed to that of each explanatory variable. Our identification approach motivates an easily implementable, two-step maximum score (2SMS) procedure -- producing estimators whose rates of convergence, in contrast to Honore and Kyriazidou's (2000) methods, are independent of the model dimension. We then derive the asymptotic properties of the 2SMS procedure and propose bootstrap-based distributional approximations for inference. Monte Carlo evidence indicates that our procedure performs adequately in finite samples.
The Long-Term Effects of Early-Life Pollution Exposure: Evidence from the London Smog
Authors: Stephanie von Hinke, Emil N. Sørensen
Date: 2022-02-23 00:00:00

This paper uses a large UK cohort to investigate the impact of early-life pollution exposure on individuals' human capital and health outcomes in older age. We compare individuals who were exposed to the London smog in December 1952 whilst in utero or in infancy to those born after the smog and those born at the same time but in unaffected areas. We find that those exposed to the smog have substantially lower fluid intelligence and worse respiratory health, with some evidence of a reduction in years of schooling.
Co-occurring Diseases Heavily Influence the Performance of Weakly Supervised Learning Models for Classification of Chest CT
Authors: Fakrul Islam Tushar, Vincent M. D'Anniballe, Geoffrey D. Rubin, Ehsan Samei, Joseph Y. Lo
Date: 2022-02-23 00:00:00
Tasks: Binary Classification, Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Weakly-supervised Learning

Despite the potential of weakly supervised learning to automatically annotate massive amounts of data, little is known about its limitations for use in computer-aided diagnosis (CAD). For CT specifically, interpreting the performance of CAD algorithms can be challenging given the large number of co-occurring diseases. This paper examines the effect of co-occurring diseases when training classification models by weakly supervised learning, specifically by comparing multi-label and multiple binary classifiers using the same training data. Our results demonstrated that the binary model outperformed the multi-label classification in every disease category in terms of AUC. However, this performance was heavily influenced by co-occurring diseases in the binary model, suggesting it did not always learn the correct appearance of the specific disease. For example, binary classification of lung nodules resulted in an AUC of < 0.65 when there were no other co-occurring diseases, but when lung nodules co-occurred with emphysema, the performance reached AUC > 0.80. We hope this paper revealed the complexity of interpreting disease classification performance in weakly supervised models and will encourage researchers to examine the effect of co-occurring diseases on classification performance in the future.
Single Image Super-Resolution Methods: A Survey
Authors: Bahattin Can Maral
Date: 2022-02-17 00:00:00
Tasks: Image Super-Resolution, Super-Resolution, Survey

Super-resolution (SR), the process of obtaining high-resolution images from one or more low-resolution observations of the same scene, has been a very popular topic of research in the last few decades in both signal processing and image processing areas. Due to the recent developments in Convolutional Neural Networks, the popularity of SR algorithms has skyrocketed as the barrier of entry has been lowered significantly. Recently, this popularity has spread into video processing areas to the lengths of developing SR models that work in real-time. In this paper, we compare different SR models that specialize in single image processing and will take a glance at how they evolved to take on many different objectives and shapes over the years.