pwc-archive/papers-with-abstracts huggingface dataset:
Historical traffic flow data reconstrucion applying Wavelet Transform
Authors: E. R. Ribeiro, A. L. Cunha
Date: 2020-06-13 00:00:00

Despite the importance of fundamental parameters (traffic flow, density and speed) to describe the traffic behavior, there still are some difficulties in order to obtain and store this information. Furthermore, given the type of study or the project the resolution analysis interval can vary from less than one hour to annual. To create alternatives in database structures,this article aims to present a method to reconstruct disaggregated historical data from aggregated data using Wavelet Transform. From the proposed method, it is possible to reconstruct data in short intervals from data with longer intervals,since they have the same behavior, for example, data from the same or similar highway. For such, a Detail coefficient is generated through the Discrete Wavelet Transform (DWT) with the disaggregated data. The aggregated data was reconstructed through an Approximation coefficient. After establishing these coefficients, the Inverse Wavelet Transform (IWT) is applied. The results indicated an average correlation between the reconstructed the original data of 0.960; 0.974; 0.968 and 0.960 for data initially aggregated at 10, 20, 40 and 80 minutes interval, respectively. The results also indicated an absolute mean error of 7.13%; 8.74%; 9.83% and 11.23% for data initially aggregated in 10, 20, 40 and 80 minutes, respectively. In other words, results suggest that the reconstituted data have a high correlation and a low percentage of mean absolute error with the original signal. In conclusion, the reconstruction of data from aggregated data and Wavelet Transform presents a good correlation and low average absolute error rate, comparable to traffic estimation studies (CASTRO-NETO et al., 2009; CORIC et al. 2012, LAM et al., 2006; LIM, 2001).
Reconciling Kinetic and Equilibrium Models of Bacterial Transcription
Authors: Muir J. Morrison, Manuel Razo-Mejia, Rob Phillips
Date: 2020-06-14 00:00:00

The study of transcription remains one of the centerpieces of modern biology with implications in settings from development to metabolism to evolution to disease. Precision measurements using a host of different techniques including fluorescence and sequencing readouts have raised the bar for what it means to quantitatively understand transcriptional regulation. In particular our understanding of the simplest genetic circuit is sufficiently refined both experimentally and theoretically that it has become possible to carefully discriminate between different conceptual pictures of how this regulatory system works. This regulatory motif, originally posited by Jacob and Monod in the 1960s, consists of a single transcriptional repressor binding to a promoter site and inhibiting transcription. In this paper, we show how seven distinct models of this so-called simple-repression motif, based both on equilibrium and kinetic thinking, can be used to derive the predicted levels of gene expression and shed light on the often surprising past success of the equilbrium models. These different models are then invoked to confront a variety of different data on mean, variance and full gene expression distributions, illustrating the extent to which such models can and cannot be distinguished, and suggesting a two-state model with a distribution of burst sizes as the most potent of the seven for describing the simple-repression motif.
Nonparametric Tests of Tail Behavior in Stochastic Frontier Models
Date: 2020-06-14 00:00:00
Tasks: Diagnostic

This article studies tail behavior for the error components in the stochastic
frontier model, where one component has bounded support on one side, and the
other has unbounded support on both sides. Under weak assumptions on the error
components, we derive nonparametric tests that the unbounded component
distribution has thin tails and that the component tails are equivalent. The
tests are useful diagnostic tools for stochastic frontier analysis. A
simulation study and an application to a stochastic cost frontier for 6,100 US
banks from 1998 to 2005 are provided. The new tests reject the normal or
Laplace distributional assumptions, which are commonly imposed in the existing
literature.
A Computer Vision Aided Beamforming Scheme with EM Exposure Control in Outdoor LOS Scenarios
Authors: Tianqi Xiang, Huiwen Li, Boren Guo, Xin Zhang
Date: 2020-06-14 00:00:00
Tasks: Management

Without any radiation control measures, a large-scale mmWave antenna array at close range may lead to a large amount of electromagnetic exposure of human. In this paper, with the aid of pose detection in computer vision, a beamforming scheme using a novel exposure avoidance method is proposed in outdoor line of sight scenarios. Instead of reducing transmitted power, the proposed method can protect the vulnerable parts of human body from electromagnetic exposure during transmission by deviating the transmission beams from vulnerable parts. Besides, a finer beam management granularity is adopted to better balance the trade-off between exposure reduction and communication quality loss, because finer beams can provide more adjustability for finding the beam that reduces exposure without excessively reducing the link quality. The proposed exposure avoidance method is validated in simulations, and the results show that the finer beam management granularity can guarantee communication quality while reducing the electromagnetic exposure.
Dynamic Metasurface Antennas for 6G Extreme Massive MIMO Communications
Authors: Nir Shlezinger, George C. Alexandropoulos, Mohammadreza F. Imani, Yonina C. Eldar, David R. Smith
Date: 2020-06-14 00:00:00

Next generation wireless base stations and access points will transmit and receive using extremely massive numbers of antennas. A promising technology for realizing such massive arrays in a dynamically controllable and scalable manner with reduced cost and power consumption utilizes surfaces of radiating metamaterial elements, known as metasurfaces. To date, metasurfaces are mainly considered in the context of wireless communications as passive reflecting devices, aiding conventional transceivers in shaping the propagation environment. This article presents an alternative application of metasurfaces for wireless communications as active reconfigurable antennas with advanced analog signal processing capabilities for next generation transceivers. We review the main characteristics of metasurfaces used for radiation and reception, and analyze their main advantages as well as their effect on the ability to reliably communicate in wireless networks. As current studies unveil only a portion of the potential of metasurfaces, we detail a list of exciting research and implementation challenges which arise from the application of metasurface antennas for wireless transceivers.
Lateral land movement prediction from GNSS position time series in a machine learning aided algorithm
Authors: M. Kiani
Date: 2020-06-14 00:00:00
Tasks: BIG-bench Machine Learning, Gaussian Processes, Position, regression, Time Series, Time Series Analysis

We investigate the accuracy of conventional machine learning aided algorithms for the prediction of lateral land movement in an area using the precise position time series of permanent GNSS stations. The machine learning algorithms that are used are tantamount to the ones used in [1], except for the radial basis functions, i.e. multilayer perceptron, Bayesian neural network, Gaussian processes, k-nearest neighbor, generalized regression neural network, classification and regression trees, and support vector regression. A comparative analysis is presented in which the accuracy level of the mentioned machine learning methods is checked against each other. It is shown that the most accurate method for both of the components of the time series is the Gaussian processes, achieving up to 9.5 centimeters in accuracy.
The JHU Multi-Microphone Multi-Speaker ASR System for the CHiME-6 Challenge
Date: 2020-06-14 00:00:00
Tasks: Action Detection, Activity Detection, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper summarizes the JHU team's efforts in tracks 1 and 2 of the CHiME-6
challenge for distant multi-microphone conversational speech diarization and
recognition in everyday home environments. We explore multi-array processing
techniques at each stage of the pipeline, such as multi-array guided source
separation (GSS) for enhancement and acoustic model training data, posterior
fusion for speech activity detection, PLDA score fusion for diarization, and
lattice combination for automatic speech recognition (ASR). We also report
results with different acoustic model architectures, and integrate other
techniques such as online multi-channel weighted prediction error (WPE)
dereverberation and variational Bayes-hidden Markov model (VB-HMM) based
overlap assignment to deal with reverberation and overlapping speakers,
respectively. As a result of these efforts, our ASR systems achieve a word
error rate of 40.5% and 67.5% on tracks 1 and 2, respectively, on the
evaluation set. This is an improvement of 10.8% and 10.4% absolute, over the
challenge baselines for the respective tracks.
Comparative study of the biological activities of the aqueous extracts of two spontaneous plants harvested in the Algerian Sahara
Authors: Rekia Cherif
Date: 2020-06-14 00:00:00

The present study investigates the insecticidal and herbicidal effects of leaf extracts from two plants were harvested in the Northern Algerian Sahara. These are Cleome arabica (Capparaceae) and Pergularia tomentosa (Asclepiadaceae). The efficacy of the extracts from the plants was evaluated by the reflux extraction method. The phytochemical screening of the aqueous extracts of C. arabica shows a remarkable richness in active principles in comparison with the extract of P. tomentosa; including flavonoids, saponosides, glycosides, terpenes, sterols, deoxyose, polyphenols and total alkaloids. The imago of Tribolium confusum treated with aqueous extracts of C. arabica and P. tomentosa at doses of 80% to 100% respectively have mortality rates of 73.33% to 96.67%, and 36.67% to 86.67%. The lethal time 50 (TL50%) of the aqueous extract of C. arabica was estimated about 6.41 days, and 6.94 days for the extract P. tomentosa for the imago of T. confusum. The extracts of P. tomentosa is less toxic than the extracts of C. arabica. The allelopathic potentials of C. arabica and P. tomentosa tested on germination of the seeds of a weed Dactyloctenium aegyptium (Poaceae) and two cultivated species, including Hordeumvulgare and Triticumdurum (Poaceae), show that the inhibitory effect of extracts of C. arabica is very highly significant. It manifests itself in the growth of the aerial and underground part of the H. vulgar and T. durum. The inhibition rate is more than 84.44% for D. aegyptium seeds treated with the different concentrations. The inhibition rates range from 75.56% to 91.11% for T. durum wheat irrigate at 80% to 100%, but are only 55.56% to 77.78% for barley seeds treated with the same concentrations (80% to 100%).
The energy representation of world GDP
Date: 2020-06-14 00:00:00
Tasks: Form

The dependence of world GDP on current energy consumption and total energy
produced over the previous period and materialized in the form of production
infrastructure is studied. The dependence describes empirical data with high
accuracy over the entire observation interval 1965-2018.
Short-Range Ambient Backscatter Communication Using Reconfigurable Intelligent Surfaces
Authors: Mahyar Nemati, Jie Ding, Jinho Choi
Date: 2020-06-14 00:00:00
Tasks: TAG

Ambient backscatter communication (AmBC) has been introduced to address communication and power efficiency issues for short-range and low-power Internet-of-Things (IoT) applications. On the other hand, reconfigurable intelligent surface (RIS) has been recently proposed as a promising approach that can control the propagation environment especially in indoor communication environments. In this paper, we propose a new AmBC model over ambient orthogonal-frequency-division-multiplexing (OFDM) subcarriers in the frequency domain in conjunction with RIS for short-range communication scenarios. A tag transmits one bit per each OFDM subcarrier broadcasted from a WiFi access point. Then, RIS augments the signal quality at a reader by compensating the phase distortion effect of multipath channel on the incident signal. We also exploit the special spectrum structure of OFDM to transmit more data over its squeezed orthogonal subcarriers in the frequency domain. Consequently, the proposed method improves the bit-error-rate (BER) performance and provides a higher data rate compared to existing AmBC methods. Analytical and numerical evaluations show the superior performance of the proposed approach in terms of BER and data rate.
Suffocating Fire Sales
Authors: Nils Detering, Thilo Meyer-Brandis, Konstantinos Panagiotou, Daniel Ritter
Date: 2020-06-15 00:00:00

Fire sales are among the major drivers of market instability in modern financial systems. Due to iterated distressed selling and the associated price impact, initial shocks to some institutions can be amplified dramatically through the network induced by portfolio overlaps. In this paper, we develop a mathematical framework that allows us to investigate central characteristics that drive or hinder the propagation of distress. We investigate single systems as well as ensembles of systems that are alike, where similarity is measured in terms of the empirical distribution of all defining properties of a system. This asymptotic approach ensures a great deal of robustness to statistical uncertainty and temporal fluctuations. A characterization of those systems that are resilient to small shocks emerges, and we provide criteria that regulators might exploit in order to assess the stability of a financial system. We illustrate the application of these criteria for some exemplary configurations in the context of capital requirements and test the applicability of our results for systems of moderate size by Monte Carlo simulations.
Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons
Authors: Qianyi Li, Cengiz Pehlevan
Date: 2020-06-15 00:00:00
Proceeding: NeurIPS 2020 12

Excitation-inhibition (E-I) balance is ubiquitously observed in the cortex. Recent studies suggest an intriguing link between balance on fast timescales, tight balance, and efficient information coding with spikes. We further this connection by taking a principled approach to optimal balanced networks of excitatory (E) and inhibitory (I) neurons. By deriving E-I spiking neural networks from greedy spike-based optimizations of constrained minimax objectives, we show that tight balance arises from correcting for deviations from the minimax optima. We predict specific neuron firing rates in the network by solving the minimax problem, going beyond statistical theories of balanced networks. Finally, we design minimax objectives for reconstruction of an input signal, associative memory, and storage of manifold attractors, and derive from them E-I networks that perform the computation. Overall, we present a novel normative modeling approach for spiking E-I networks, going beyond the widely-used energy minimizing networks that violate Dale's law. Our networks can be used to model cortical circuits and computations.
Transactive Control of Electric Railways Using Dynamic Market Mechanisms
Date: 2020-06-15 00:00:00

Electricity demand of electric railways is a relatively unexplored source of
flexibility in demand response applications in power systems. In this paper, we
propose a transactive control based optimization framework for coordinating the
power grid network and the train network. This is accomplished by coordinating
dispatchable distributed energy resources and demand profiles of trains using a
two-step optimization. A railway based dynamic market mechanism (rDMM) is
proposed for the dispatch of distributed energy resources (DER) in the power
network along the electric railway using an iterative negotiation process, and
generates profiles of electricity prices, and constitutes the first step. The
train dispatch attempts minimize the operational costs of trains that ply along
the railway, while subject to constraints on their acceleration profiles, route
schedules, and the train dynamics, and generates demand profiles of trains and
constitutes the second step. The rDMM seeks to optimize the operational costs
of the underlying DERs while ensuring power balance. Together, they form an
overall framework that yields the desired transactions between the railway and
power grid infrastructures. This overall optimization approach is validated
using simulation studies of the Southbound Amtrak service along the Northeast
Corridor (NEC) in the United States, which shows a 25% reduction in energy
costs when compared to standard trip optimization based on minimum work, and a
75% reduction in energy costs when compared to the train cost calculated using
a field dataset.
Dimensionality reduction via path integration for computing mRNA distributions
Authors: Jaroslav Albert
Date: 2020-06-15 00:00:00
Tasks: Dimensionality Reduction

Inherent stochasticity in gene expression leads to distributions of mRNA copy numbers in a population of identical cells. These distributions are determined primarily by the multitude of states of a gene promoter, each driving transcription at a different rate. In an era where single-cell mRNA copy number data are more and more available, there is an increasing need for fast computations of mRNA distributions. In this paper, we present a method for computing separate distributions for each species of mRNA molecules, i. e. mRNAs that have been either partially or fully processed post-transcription. The method involves the integration over all possible realizations of promoter states, which we cast into a set of linear ordinary differential equations of dimension $M\times n_j$, where $M$ is the number of available promoter states and $n_j$ is the mRNA copy number of species $j$ up to which one wishes to compute the probability distribution. This approach is superior to solving the Master equation (ME) directly in two ways: a) the number of coupled differential equations in the ME approach is $M\times\Lambda_1\times\Lambda_2\times ...\times\Lambda_L$, where $\Lambda_j$ is the cutoff for the probability of the $j^{\text{th}}$ species of mRNA; and b) the ME must be solved up to the cutoffs $\Lambda_j$, which are {\it ad hoc} and must be selected {\it a priori}. In our approach, the equation for the probability to observe $n$ mRNAs of any species depends only on the the probability of observing $n-1$ mRNAs of that species, thus yielding a correct probability distribution up to an arbitrary $n$. To demonstrate the validity of our derivations, we compare our results with Gillespie simulations for ten randomly selected system parameters.
Joint Optimization of the Deployment and Resource Allocation of UAVs in Vehicular Edge Computing and Networks
Authors: Yuke Zheng, Bo Yang, Cailian Chen
Date: 2020-06-15 00:00:00
Tasks: CPU, Edge-computing

With the development of smart vehicles, computing-intensive tasks are widely and rapidly generated. To alleviate the burden of on-board CPU, connected vehicles can offload tasks to or make request from nearby edge server thanks to the emerging Mobile Edge Computing (MEC). However, such approach may sharply increase the workload of an edge server, and cause network congestion, especially in rural and mountain areas where there are few edge servers. To this end, a UAV-assisted MEC system is proposed in this paper, and joint optimization algorithm of the deployment and resource allocation of UAVs (JOAoDR) is proposed to decide the location and balance the resource and rewards of the UAVs. We solve a long-term profit maximization problem in terms of the operator. Numerical results demonstrated that our algorithm outperforms other benchmarks algorithm, and validated our solution.
A Generalized Gaussian Extension to the Rician Distribution for SAR Image Modeling
Authors: Oktay Karakuş, Ercan E. Kuruoglu, Alin Achim
Date: 2020-06-15 00:00:00

In this paper, we present a novel statistical model, $\textit{the generalized-Gaussian-Rician}$ (GG-Rician) distribution, for the characterization of synthetic aperture radar (SAR) images. Since accurate statistical models lead to better results in applications such as target tracking, classification, or despeckling, characterizing SAR images of various scenes including urban, sea surface, or agricultural, is essential. The proposed statistical model is based on the Rician distribution to model the amplitude of a complex SAR signal, the in-phase and quadrature components of which are assumed to be generalized-Gaussian distributed. The proposed amplitude GG-Rician model is further extended to cover the intensity SAR signals. In the experimental analysis, the GG-Rician model is investigated for amplitude and intensity SAR images of various frequency bands and scenes in comparison to state-of-the-art statistical models that include $\mathcal{K}$, Weibull, Gamma, and Lognormal. In order to decide on the most suitable model, statistical significance analysis via Kullback-Leibler divergence and Kolmogorov-Smirnov statistics are performed. The results demonstrate the superior performance and flexibility of the proposed model for all frequency bands and scenes and its applicability on both amplitude and intensity SAR images. The Matlab package is available at https://github.com/oktaykarakus/GG-Rician-SAR-Image-Modelling.
Efficient Algorithms for Convolutional Inverse Problems in Multidimensional Imaging
Authors: Didem Dogan, Figen S. Oktem
Date: 2020-06-15 00:00:00
Tasks: Image Reconstruction

Multidimensional imaging, capturing image data in more than two dimensions, has been an emerging field with diverse applications. Due to the limitation of two-dimensional detectors in obtaining the high-dimensional image data, computational imaging approaches have been developed to pass on some of the burden to a reconstruction algorithm. In various image reconstruction problems in multidimensional imaging, the measurements are in the form of superimposed convolutions. In this paper, we introduce a general framework for the solution of these problems, called here convolutional inverse problems, and develop fast image reconstruction algorithms with analysis and synthesis priors. These include sparsifying transforms, as well as convolutional or patch-based dictionaries that can adapt to correlations in different dimensions. The resulting optimization problems are solved via alternating direction method of multipliers with closed-form, efficient, and parallelizable update steps. To illustrate their utility and versatility, the developed algorithms are applied to three-dimensional image reconstruction problems in computational spectral imaging for cases with or without correlation along the third dimension. As the advent of multidimensional imaging modalities expands to perform sophisticated tasks, these algorithms are essential for fast iterative reconstruction in various large-scale problems.