pwc-archive/papers-with-abstracts huggingface dataset:
Influence of confinement on the spreading of bacterial populations
Authors: Daniel B. Amchin, Jenna A. Ott, Tapomoy Bhattacharjee, Sujit S. Datta
Date: 2021-08-05 00:00:00

The spreading of bacterial populations is central to processes in agriculture, the environment, and medicine. However, existing models of spreading typically focus on cells in unconfined settings--despite the fact that many bacteria inhabit complex and crowded environments, such as soils, sediments, and biological tissues/gels, in which solid obstacles confine the cells and thereby strongly regulate population spreading. Here, we develop an extended version of the classic Keller-Segel model of bacterial spreading that incorporates the influence of confinement in promoting both cell-solid and cell-cell collisions. Numerical simulations of this extended model demonstrate how confinement fundamentally alters the dynamics and morphology of spreading bacterial populations, in good agreement with recent experimental results. In particular, with increasing confinement, we find that cell-cell collisions increasingly hinder the initial formation and the long-time propagation speed of chemotactic pulses. Moreover, also with increasing confinement, we find that cellular growth and division plays an increasingly dominant role in driving population spreading--eventually leading to a transition from chemotactic spreading to growth-driven spreading via a slower, jammed front. This work thus provides a theoretical foundation for further investigations of the influence of confinement on bacterial spreading. More broadly, these results help to provide a framework to predict and control the dynamics of bacterial populations in complex and crowded environments.
Approximating Optimal Asset Allocations using Simulated Bifurcation
Authors: Thomas Bouquet, Mehdi Hmyene, François Porcher, Lorenzo Pugliese, Jad Zeroual
Date: 2021-08-06 00:00:00

This paper investigates the application of Simulated Bifurcation algorithms to approximate optimal asset allocations. It will provide the reader with an explanation of the physical principles underlying the method and a Python implementation of the latter applied to 441 assets belonging to the S&P500 index. In addition, the paper tackles the problem of the selection of an optimal sub-allocation; in this particular case, we find an adequate solution in an unrivaled timescale.
A Pomeranzian Growth Theory of the Great Divergence
Authors: Shuhei Aoki
Date: 2021-08-06 00:00:00

This study constructs a growth model of the Great Divergence that formalizes Pomeranz's (2000) hypothesis that the relief of land constraints in Europe has caused divergence in economic growth between Europe and China since the 19th century. The model consists of the agricultural and manufacturing sectors. The agricultural sector produces subsistence goods from land, intermediate goods from the manufacturing sector, and labor. The manufacturing sector produces goods from labor, and its productivity grows through the learning-by-doing of full-time manufacturing workers. Households make fertility decisions. In the model, a large exogenous positive shock in land supply causes the transition of the economy from the Malthusian state, in which all workers are engaged in agricultural production and per capita income is constant, to the non-Malthusian state, in which the share of workers engaged in agricultural production gradually decreases and per capita income grows at a roughly constant growth rate. The quantitative predictions of the model provide several insights into the causes of the Great Divergence.
Effectiveness of Anambra Broadcasting Service (ABS) Radio News on Teaching and Learning (a case study of Awka based Students)
Authors: Okechukwu Christopher Onuegbu
Date: 2021-08-06 00:00:00

This work sought to find out the effectiveness of Anambra Broadcasting Service (ABS) Radio news on teaching and learning. The study focused mainly on listeners of ABS radio news broadcast in Awka, the capital of Anambra State, Nigeria. Its objectives were to find out; if Awka based students are exposed to ABS radio; to discover the ABS radio program students favorite; the need gratification that drives students to listen to ABS radio news; the contributions of radio news to students teaching and learning; and effectiveness of ABS radio news on teaching and learning in Awka. The population of Awka students is 198,868. This is also the population of the study. But a sample size of 400 was chosen and administered with questionnaires. The study was hinged on the uses and gratification theory. It adopted a survey research design. The data gathered was analyzed using simple percentages and frequency of tables. The study revealed that news is very effective in teaching and learning. It was concluded that news is the best instructional media to be employed in teaching and learning. Among other things, it was recommended that teachers and students should listen to and make judicious use of news for academic purposes.
Complex-valued Spatial Autoencoders for Multichannel Speech Enhancement
Authors: Mhd Modar Halimeh, Walter Kellermann
Date: 2021-08-06 00:00:00
Tasks: Speech Enhancement

In this contribution, we present a novel online approach to multichannel speech enhancement. The proposed method estimates the enhanced signal through a filter-and-sum framework. More specifically, complex-valued masks are estimated by a deep complex-valued neural network, termed the complex-valued spatial autoencoder. The proposed network is capable of exploiting as well as manipulating both the phase and the amplitude of the microphone signals. As shown by the experimental results, the proposed approach is able to exploit both spatial and spectral characteristics of the desired source signal resulting in a physically plausible spatial selectivity and superior speech quality compared to other baseline methods.
Deep Residual Echo Suppression and Noise Reduction: A Multi-Input FCRN Approach in a Hybrid Speech Enhancement System
Authors: Jan Franzen, Tim Fingscheidt
Date: 2021-08-06 00:00:00
Tasks: Acoustic echo cancellation, Speech Enhancement

Deep neural network (DNN)-based approaches to acoustic echo cancellation (AEC) and hybrid speech enhancement systems have gained increasing attention recently, introducing significant performance improvements to this research field. Using the fully convolutional recurrent network (FCRN) architecture that is among state of the art topologies for noise reduction, we present a novel deep residual echo suppression and noise reduction with up to four input signals as part of a hybrid speech enhancement system with a linear frequency domain adaptive Kalman filter AEC. In an extensive ablation study, we reveal trade-offs with regard to echo suppression, noise reduction, and near-end speech quality, and provide surprising insights to the choice of the FCRN inputs: In contrast to often seen input combinations for this task, we propose not to use the loudspeaker reference signal, but the enhanced signal after AEC, the microphone signal, and the echo estimate, yielding improvements over previous approaches by more than 0.2 PESQ points.
Accurate simulation of operating system updates in neuroimaging using Monte-Carlo arithmetic
Authors: Ali Salari, Yohan Chatelain, Gregory Kiar, Tristan Glatard
Date: 2021-08-06 00:00:00

Operating system (OS) updates introduce numerical perturbations that impact the reproducibility of computational pipelines. In neuroimaging, this has important practical implications on the validity of computational results, particularly when obtained in systems such as high-performance computing clusters where the experimenter does not control software updates. We present a framework to reproduce the variability induced by OS updates in controlled conditions. We hypothesize that OS updates impact computational pipelines mainly through numerical perturbations originating in mathematical libraries, which we simulate using Monte-Carlo arithmetic in a framework called "fuzzy libmath" (FL). We applied this methodology to pre-processing pipelines of the Human Connectome Project, a flagship open-data project in neuroimaging. We found that FL-perturbed pipelines accurately reproduce the variability induced by OS updates and that this similarity is only mildly dependent on simulation parameters. Importantly, we also found between-subject differences were preserved in both cases, though the between-run variability was of comparable magnitude for both FL and OS perturbations. We found the numerical precision in the HCP pre-processed images to be relatively low, with less than 8 significant bits among the 24 available, which motivates further investigation of the numerical stability of components in the tested pipeline. Overall, our results establish that FL accurately simulates results variability due to OS updates, and is a practical framework to quantify numerical uncertainty in neuroimaging.
The Influence of Age and Gender Information on the Diagnosis of Diabetic Retinopathy: Based on Neural Networks
Authors: Long Bai, Sihang Chen, Mingyang Gao, Leila Abdelrahman, Manal Al Ghamdi, Mohamed Abdel-Mottaleb
Date: 2021-08-06 00:00:00
Tasks: image-classification, Image Classification

This paper proposes the importance of age and gender information in the diagnosis of diabetic retinopathy. We utilized Deep Residual Neural Networks (ResNet) and Densely Connected Convolutional Networks (DenseNet), which are proven effective on image classification problems and the diagnosis of diabetic retinopathy using the retinal fundus images. We used the ensemble of several classical networks and decentralized the training so that the network was simple and avoided overfitting. To observe whether the age and gender information could help enhance the performance, we added the information before the dense layer and compared the results with the results that did not add age and gender information. We found that the test accuracy of the network with age and gender information was 2.67% higher than that of the network without age and gender information. Meanwhile, compared with gender information, age information had a better help for the results.
A Model-Agnostic Method for PMU Data Recovery Using Optimal Singular Value Thresholding
Authors: Shuchismita Biswas, Virgilio A. Centeno
Date: 2021-08-06 00:00:00

This paper presents a fast model-agnostic method for recovering noisy Phasor Measurement Unit (PMU) datastreams with missing entries. The measurements are first transformed into a Page matrix, and the original signals are reconstructed using low-rank matrix estimation based on optimal singular value thresholding. Two variations of the recovery algorithm are shown- a) an offline block-processing method for imputing past measurements, and b) an online method for predicting future measurements. Information within a PMU channel (temporal correlation) as well as from different PMUchannels in a network (spatial correlation) are utilized to recover degraded data. The proposed method is fast and needs no explicit knowledge of the underlying system model or measurement noise distribution. The performance of the recovery algorithms is illustrated using simulated measurements from the IEEE 39-bus test system as well as real measurements from an anonymized U.S. electric utility. Extensive numeric tests show that the original signals can be accurately recovered in the presence of additive noise, consecutive data drop as well as simultaneous data erasure across multiple PMU channels.
A Generalized Space-Frequency Index Modulation Scheme for Downlink MIMO Transmissions with Improved Diversity
Authors: Vasco Velez, Joao Pedro Pavia, Nuno Souto, Pedro Sebastiao, Americo Correia
Date: 2021-08-06 00:00:00
Tasks: Diversity

Multidimensional Index Modulations (IM) are a novel alternative to conventional modulations which can bring considerable benefits for future wireless networks. Within this scope, in this paper we present a new scheme, named as Precoding-aided Transmitter side Generalized Space-Frequency Index Modulation (PT-GSFIM), where part of the information bits select the active antennas and subcarriers which then carry amplitude and phase modulated symbols. The proposed scheme is designed for multiuser multiple-input multiple-output (MU-MIMO) scenarios and incorporates a precoder which removes multiuser interference (MUI) at the receivers. Furthermore, the proposed PT-GSFIM also integrates signal space diversity (SSD) techniques for tackling the typical poor performance of uncoded orthogonal frequency division multiplexing (OFDM) based schemes. By combining complex rotation matrices (CRM) and subcarrier-level interleaving, PT-GSFIM can exploit the inherent diversity in frequency selective channels and improve the performance without additional power or bandwidth. To support reliable detection of the multidimensional PT-GSFIM we also propose three different detection algorithms which can provide different tradeoffs between performance and complexity. Simulation results shows that proposed PT-GSFIM scheme, can provide significant gains over conventional MU-MIMO and GSM schemes.
Configuring Antenna System to Enhance the Downlink Performance of High Velocity Users in 5G MU-MIMO Networks
Authors: Md. Asif Ishrak Sarder, Fehima Tajrian, Moontasir Rafique, Mariea Anzum, Abdullah Bin Shams
Date: 2021-08-05 00:00:00
Tasks: Diversity

An exponential increase in the data rate demand prompted several technical innovations. Multi User Multiple Input Multiple Output (MU-MIMO) is one of the most promising schemes. This has been evolved into Massive MIMO technology in 5G to further stretch the network throughput. Massive MIMO tackles the rising data rate with the increase in the number of antenna. This comes at the price of a higher energy consumption. Moreover the high velocity users in MU-MIMO scheme experiences a frequent unpredictable change in the channel condition that degrade its downlink performance. Therefore a proper number of antenna selection is of paramount importance. This issue has been addressed using machine learning techniques and Channel State Information (CSI) but only for static users. In this study we propose to introduce antenna diversity in spatial multiplexing MU-MIMO transmission scheme by operating more number of reception antenna compare to the number of transmission antenna. The diversity improves the downlink performance of high velocity users. In general our results can be interpreted for large scale antenna systems like Massive MIMO. The proposed method can be easily implemented in the existing network architectures with minimal complexity. Also it has the potential for solving real-life problems like call drops and low data rate to be experienced by cellular users traveling through high-speed transportation systems like Dhaka MRT project
Respiratory Rate Estimation Based on WiFi Frame Capture
Authors: T. Kanda, T. Sato, H. Awano, S. Kondo, K. Yamamoto
Date: 2021-08-05 00:00:00
Tasks: Respiratory Rate Estimation

This paper presents a method that estimates the respiratory rate based on the frame capturing of wireless local area networks. The method uses beamforming feedback matrices (BFMs) contained in the captured frames, which is a rotation matrix of channel state information (CSI). BFMs are transmitted unencrypted and easily obtained using frame capturing, requiring no specific firmware or WiFi chipsets, unlike the methods that use CSI. Such properties of BFMs allow us to apply frame capturing to various sensing tasks, e.g., vital sensing. In the proposed method, principal component analysis is applied to BFMs to isolate the effect of the chest movement of the subject, and then, discrete Fourier transform is performed to extract respiratory rates in a frequency domain. Experimental evaluation results confirm that the frame-capture-based respiratory rate estimation can achieve estimation error lower than 3.2 breaths/minute.
Feature Augmented Hybrid CNN for Stress Recognition Using Wrist-based Photoplethysmography Sensor
Authors: Nafiul Rashid, Luke Chen, Manik Dautta, Abel Jimenez, Peter Tseng, Mohammad Abdullah Al Faruque
Date: 2021-08-02 00:00:00
Tasks: Photoplethysmography (PPG)

Stress is a physiological state that hampers mental health and has serious consequences to physical health. Moreover, the COVID-19 pandemic has increased stress levels among people across the globe. Therefore, continuous monitoring and detection of stress are necessary. The recent advances in wearable devices have allowed the monitoring of several physiological signals related to stress. Among them, wrist-worn wearable devices like smartwatches are most popular due to their convenient usage. And the photoplethysmography (PPG) sensor is the most prevalent sensor in almost all consumer-grade wrist-worn smartwatches. Therefore, this paper focuses on using a wrist-based PPG sensor that collects Blood Volume Pulse (BVP) signals to detect stress which may be applicable for consumer-grade wristwatches. Moreover, state-of-the-art works have used either classical machine learning algorithms to detect stress using hand-crafted features or have used deep learning algorithms like Convolutional Neural Network (CNN) which automatically extracts features. This paper proposes a novel hybrid CNN (H-CNN) classifier that uses both the hand-crafted features and the automatically extracted features by CNN to detect stress using the BVP signal. Evaluation on the benchmark WESAD dataset shows that, for 3-class classification (Baseline vs. Stress vs. Amusement), our proposed H-CNN outperforms traditional classifiers and normal CNN by 5% and 7% accuracy, and 10% and 7% macro F1 score, respectively. Also for 2-class classification (Stress vs. Non-stress), our proposed H-CNN outperforms traditional classifiers and normal CNN by 3% and ~5% accuracy, and ~3% and ~7% macro F1 score, respectively.
Specifics of formation tax revenues and ways to improve it in Georgia
Authors: George Abuselidze, Rusudan Zoidze
Date: 2021-08-06 00:00:00

In the research there is reviewed the peculiarities of the formation of tax revenues of the state budget, analysis of the recent past and present periods of tax system in Georgia, there is reviewed the influence of existing factors on the revenues, as well as the role and the place of direct and indirect taxes in the state budget revenues. In addition, the measures of stimulating action on formation of tax revenues and their impact on the state budget revenues are established. At the final stage, there are examples of foreign developed countries, where the tax system is perfectly developed, where various stimulating measures are successfully stimulating and consequently it promotes mobilization of the amount of money required in the state budget. The exchange of foreign experience is very important for Georgia, the existing tax model that is based on foreign experience is greatly successful. For the formation of tax policy, it is necessary to take into consideration all the factors affecting on it, a complex analysis of the tax system and the steps that will be really useful and perspective for our country.
Features of international taxation and its impact on business entities of Georgia
Authors: George Abuselidze, Mariam Msakhuradze
Date: 2021-08-06 00:00:00

The work "International Taxation and its impact on Georgian Business Subjects" discusses the essence, types of international taxation and ways to prevent it. Object of international taxation, taxable base and rates, features based on the taxpayer. The approaches of states and its impact on the activities of business entities. The aim of the work was to study the theoretical and methodological bases of international taxation in the tax system of Georgia and to present the existing problems. To get acquainted with the activities of the free industrial zones in our country and to evaluate them. Sharing opinions and expressing one's attitude towards it. The work presents the opinion on the impact of the approaches and recommendations of our country's legislation on international taxation on the business sector of Georgia to correct the current situation.
Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations
Authors: Philippe Tillet, H. T. Kung, David Cox
Date: 2019-06-22 00:00:00
Proceeding: MAPL 2019 6
Tasks: GPU

The validation and deployment of novel research ideas in the field of Deep Learning is often limited by the availability of efficient compute kernels for certain basic primitives. In particular, operations that cannot leverage existing vendor libraries (e.g., cuBLAS, cuDNN) are at risk of facing poor device utilization unless custom implementations are written by experts – usually at the expense of portability. For this reason, the development of new programming abstractions for specifying custom Deep Learning workloads at a minimal performance cost has become crucial.
We present Triton, a language and compiler centered around the concept of tile, i.e., statically shaped multi-dimensional sub-arrays. Our approach revolves around (1) a C-based language and an LLVM-based intermediate representation (IR) for expressing tensor programs in terms of operations on parametric tile variables and (2) a set of novel tile-level optimization passes for compiling these programs into efficient GPU code. We demonstrate how Triton can be used to build portable implementations of matrix multiplication and convolution kernels on par with hand-tuned vendor libraries (cuBLAS / cuDNN), or for efficiently implementing recent research ideas such as shift convolutions.
On Tree Based Phylogenetic Networks
Authors: Louxin Zhang
Date: 2015-09-05 00:00:00

A large class of phylogenetic networks can be obtained from trees by the addition of horizontal edges between the tree edges. These networks are called tree based networks. Reticulation-visible networks and child-sibling networks are all tree based. In this work, we present a simply necessary and sufficient condition for tree-based networks and prove that there is a universal tree based network for each set of species such that every phylogenetic tree on the same species is a base of this network. The existence of universal tree based network implies that for any given set of phylogenetic trees (resp. clusters) on the same species there exists a tree base network that display all of them.
MOST: Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning
Authors: Tuan Nguyen, Trung Le, He Zhao, Quan Hung Tran, Truyen Nguyen, Dinh Phung
Date: 2021-05-13 00:00:00
Proceeding: UAI 2021 5
Tasks: Domain Adaptation, Imitation Learning, Multi-Source Unsupervised Domain Adaptation, Unsupervised Domain Adaptation

Multi-source domain adaptation (DA) is more challenging than conventional DA because the knowledge is transferred from several source domains to a target domain. To this end, we propose in this paper a novel model for multi-source DA using the theory of optimal transport and imitation learning. More specifically, our approach consists of two cooperative agents: a teacher classifier and a student classifier. The teacher classifier is a combined expert that leverages knowledge of domain experts that can be theoretically guaranteed to handle perfectly source examples, while the student classifier acting on the target domain tries to imitate the teacher classifier acting on the source domains. Our rigorous theory developed based on optimal transport makes this cross-domain imitation possible and also helps to mitigate not only the data shift but also the label shift, which are inherently thorny issues in DA research. We conduct comprehensive experiments on real-world datasets to demonstrate the merit of our approach and its optimal transport based imitation learning viewpoint. Experimental results show that our proposed method achieves state-of-the-art performance on benchmark datasets for multi-source domain adaptation including Digits-five, Office-Caltech10, and Office-31 to the best of our knowledge.