pwc-archive/papers-with-abstracts huggingface dataset:
Model of continuous random cascade processes in financial markets
Authors: Jun-ichi Maskawa, Koji Kuroda
Date: 2020-10-23 00:00:00
Tasks: Time Series, Time Series Analysis

This article present a continuous cascade model of volatility formulated as a stochastic differential equation. Two independent Brownian motions are introduced as random sources triggering the volatility cascade. One multiplicatively combines with volatility; the other does so additively. Assuming that the latter acts perturbatively on the system, then the model parameters are estimated by application to an actual stock price time series. Numerical calculation of the Fokker--Planck equation derived from the stochastic differential equation is conducted using the estimated values of parameters. The results reproduce the pdf of the empirical volatility, the multifractality of the time series, and other empirical facts.
A Modular Framework for Distributed Model Predictive Control of Nonlinear Continuous-Time Systems (GRAMPC-D)
Date: 2020-10-23 00:00:00
Tasks: Computational Efficiency, Model Predictive Control

The modular open-source framework GRAMPC-D for model predictive control of
distributed systems is presented in this paper. The modular concept allows to
solve optimal control problems (OCP) in a centralized and distributed fashion
using the same problem description. It is tailored to computational efficiency
with the focus on embedded hardware. The distributed solution is based on the
Alternating Direction Method of Multipliers (ADMM) and uses the concept of
neighbor approximation to enhance convergence speed. The presented framework
can be accessed through Cpp and Python and also supports plug-and-play and data
exchange between agents over a network.
Old Drugs for JAK-STAT Pathway Inhibition in COVID-19
Authors: Mohammad Reza Dayer
Date: 2020-10-23 00:00:00

The pandemic threat of COVID-19 with more than 37 million cases in which about 5 percent entering critical stage characterized by cytokine storm and hyperinflammatory condition, the state more often leads to admission to intensive care unit with rapid mortality. Janus kinase enzymes of Jak-1, Jak-2, Jak-3, and Tyk2 seem to be good targets for inhibition by medications to control cytokine storm in this context. In the present work, the inhibitory properties of different analgesic drugs on these targets are studied to assess their ability for clinical application from different points of view. Our docking results indicated that naproxen, methadone, and amitriptyline considering their higher binding energy, lower energy variance, and higher hydrophobicity, seem to express more inhibitory effects on Janus kinase enzymes than thats for approved inhibitors i.e. baricitinib and ruxolitinib. Accordingly, we suggest our wide list of candidate drugs including indomethacin, etodolac, buprenorphine, rofecoxib, duloxetine, valdecoxib, naproxen, methadone, and amitriptilin for clinical assessments for their usefulness in COVID-19 treatment, especially taking into account that up to now, there is no approved cure for this disease.
Abstracting the Traffic of Nonlinear Event-Triggered Control Systems
Authors: Giannis Delimpaltadakis, Manuel Mazo Jr
Date: 2020-10-23 00:00:00
Tasks: Scheduling

Scheduling communication traffic in networks of event-triggered control (ETC) systems is challenging, as their sampling times are unknown, hindering application of ETC in networks. In previous work, finite-state abstractions were created, capturing the sampling behaviour of LTI ETC systems with quadratic triggering functions. Offering an infinite-horizon look to all sampling patterns of an ETC system, such abstractions can be used for scheduling of ETC traffic. Here we significantly extend this framework, by abstracting perturbed uncertain nonlinear ETC systems with general triggering functions. To construct an ETC system's abstraction: a) the state space is partitioned into regions, b) for each region an interval is determined, containing all intersampling times of points in the region, and c) the abstraction's transitions are determined through reachability analysis. To determine intervals and transitions, we devise algorithms based on reachability analysis. For partitioning, we propose an approach based on isochronous manifolds, resulting into tighter intervals and providing control over them, thus containing the abstraction's non-determinism. Simulations showcase our developments.
Modeling the US-China trade conflict: a utility theory approach
Authors: Yuhan Zhang, Cheng Chang
Date: 2020-10-23 00:00:00

This paper models the US-China trade conflict and attempts to analyze the (optimal) strategic choices. In contrast to the existing literature on the topic, we employ the expected utility theory and examine the conflict mathematically. In both perfect information and incomplete information games, we show that expected net gains diminish as the utility of winning increases because of the costs incurred during the struggle. We find that the best response function exists for China but not for the US during the conflict. We argue that the less the US coerces China to change its existing trade practices, the higher the US expected net gains. China's best choice is to maintain the status quo, and any further aggression in its policy and behavior will aggravate the situation.
Identification and Quantification of Aerosol Hot-spots over Lahore Region using MODIS Data
Authors: Safa~Ashraf, Zubair Khalid, Muhammad Tahir, Momin Uppal
Date: 2020-10-23 00:00:00

The increased concentration of aerosols in the air caused by ever-rising urbanization and the development of various industries has horrendous consequences on human health, environment and climate. The first step to counter adverse effects of air pollution in any region is to identify locations with a high concentration of aerosols, termed aerosol hot-spots. We specifically focus on the region of Lahore, Pakistan, a city that has consistently been ranked among the top ten most polluted cities in the world. In order to identify aerosol hot-spots in the city, we utilize two-year (2017-18) Aerosol Optical Thickness (AOT) data from MODerate resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite. We propose a method based on the Glowworm Swarm Optimization (GSO) algorithm that discovers several aerosol hot-spots over Lahore comprising of major highways and industrial areas. Furthermore, we formulate two quantification metrics to gauge the amount of aerosol content over each hot-spot. We also analyze the temporal variation of aerosol content that suggests the addition or suppression of pollution sources in the hot-spots. Unlike previous studies, our work provides novel insight into the regional aerosol concentration of Lahore and calls for maintenance of air quality of the regions falling under the identified aerosol hot-spots.
A Simulation-based Education Approach for the Electromagnetic and Electromechanical Transient Waves in Power Systems
Authors: Abdulelah Alharbi, Shutang You
Date: 2020-10-22 00:00:00

Power systems usually go through electromagnetic and electromechanical transient processes after different disturbances. Learning the characteristics and the differences between them are important but not easy for students majoring in power systems. This paper presents a simulation-based approach to comprehensively study the two types of transient waves, constituting the experimental part of the power system transient and stability course. In this approach, three models with different levels of complexity are developed to simultaneously show the two types of transient waves in the time domain. The developed models are then demonstrated as testbeds for investigating various aspects related to the two types of transient, such as waveforms induced by different disturbances, influencing factors on the propagation speed, and the interaction between incident waves and the reflective waves. In addition, a theory-to-practice engineering research process is demonstrated through developing a power system event-location application, which is inspired by electromechanical wave propagation study. The proposed education process and models at various complexity levels provide a creative and interactive way for power system transients and dynamics study.
Millimeter Wave MIMO Channel Estimation with 1-bit Spatial Sigma-delta Analog-to-Digital Converters
Authors: R. S. Prasobh Sankar, Sundeep Prabhakar Chepuri
Date: 2020-10-23 00:00:00
Tasks: Quantization

This paper focuses on channel estimation for mmWave MIMO systems with 1-bit spatial sigma-delta analog-to-digital converters (ADCs). The channel estimation performance with 1-bit spatial sigma-delta ADCs depends on the quantization noise modeling. Therefore, we present a new method for modeling the quantization noise by leveraging the deterministic input-output relation of the 1-bit spatial sigma-delta ADC. Using this new noise model, we propose an algorithm for channel estimation for a narrowband single-user mmWave line-of-sight MIMO system by determining the unknown angles and path attenuation that characterize the flat fading channel. Through simulations, we demonstrate that the performance of the developed method is comparable to the traditional analog systems and significantly better than the conventional 1-bit quantized systems.
Low-Rank Approximations of Nonseparable Panel Models
Authors: Iván Fernández-Val, Hugo Freeman, Martin Weidner
Date: 2020-10-23 00:00:00
Tasks: Matrix Completion

We provide estimation methods for nonseparable panel models based on low-rank factor structure approximations. The factor structures are estimated by matrix-completion methods to deal with the computational challenges of principal component analysis in the presence of missing data. We show that the resulting estimators are consistent in large panels, but suffer from approximation and shrinkage biases. We correct these biases using matching and difference-in-differences approaches. Numerical examples and an empirical application to the effect of election day registration on voter turnout in the U.S. illustrate the properties and usefulness of our methods.
Performance Analysis of Intelligent Reflective Surface Aided Wireless Communications
Authors: Dulaj Gunasinghe, Dhanushka Kudathanthirige, Gayan Amarasuriya Aruma Baduge
Date: 2020-10-23 00:00:00
Tasks: Diversity

The fundamental performance metrics of an intelligent reflective surface (IRS)-aided wireless system are presented. By optimizing the IRS phase-shift matrix, the received signal-to-noise ratio (SNR) is maximized at the destination in the presence of both reflected and direct channels. The probability distributions of this maximum SNR are tightly approximated for the moderate-to-large reflective element regime. Thereby, the probability density function and cumulative distribution function of this tight SNR approximation are derived in closed-form for Nakagami-m fading to facilitate a statistical characterization of the performance metrics. The outage probability, average symbol error probability, and achievable rate bounds are derived. By virtue of an asymptotic analysis in the high SNR regime, the diversity order is quantified. Thereby, we reveal that the overall diversity order can be scaled as a function of the number of reflective elements (N) such that Gd = mv + min(mg;mh)N, where mv, mh and mg are the Nakagami-m parameters of the direct, source-to-IRS and IRS-to-destination channels, respectively. The asymptotic achievable rate is derived, and thereby, it is shown that the transmit power can be scaled inversely proportional to N2. The impact of quantized IRS phase-shifts is investigated by deriving the achievable rate bounds. Useful insights are obtained by analyzing the system performance for different severity of fading cases including spatially correlated fading. Our analysis and numerical results reveal that IRS is a promising technology for boosting the performance of wireless communications by intelligently controlling the propagation channels without employing additional active radio-frequency chains.
Determinants of Lending to Small and Medium Enterprises by Commercial Banks in Kenya
Authors: David Haritone Shikumo, Mwangi Mirie
Date: 2020-10-23 00:00:00
Tasks: Descriptive

Small and Medium Enterprises (SMEs) access to external finance is an issue of significant research interest to academicians. Commercial banks consider many SMEs not to be credit worthy because of their inability to meet some banking requirements. Hence, the objective of this study was to investigate what determines lending to SMEs by commercial banks in Kenya. To achieve the study objectives, a descriptive research design was employed. The study undertook a census of the 43 commercial banks in Kenya, with full data being obtained for 36 institutions. The study used secondary data from the annual published reports of commercial banks in Kenya for a period of 5 years from 2010-2014. The data collected was analyzed through the multiple linear regression using the Statistical Package for Social Studies version 20.The study established that bank size and liquidity significantly influences (positively and negatively, respectively) lending to SMEs by commercial banks in Kenya while credit risk and interest rates have no significant influence on lending to SMEs by commercial banks in Kenya. The study recommends that lending to SMEs by commercial banks in Kenya be enhanced by adopting policies that grow the commercial banks.
Determinants of Financial Performance of Microfinance Banks in Kenya
Authors: King'ori S. Ngumo, Kioko W. Collins, Shikumo H. David
Date: 2020-10-23 00:00:00
Tasks: Descriptive

Microfinance provides strength to boost the economic activities of low-income earners and thus contributes to eradication of poverty. However, microfinance institutions face stringent competition from commercial banks; the growth of microloan activities of commercial banks may confront microfinance institutions with increased competition for borrowers. In Kenya, the micro finance sector has extremely high competition indicated by the shifting market share and profitability. This study sought to examine the determinants of financial performance of Microfinance banks in Kenya. The study adopted a descriptive research design and used secondary data from 7 Microfinance banks for a period of 5 years from 2011 to 2015. The data collected was analyzed using correlation and regression analysis. The study found a positive and statistically significant relationship between operational efficiency, capital adequacy, firm size and financial performance of microfinance banks in Kenya. However, the study found an insignificant negative relationship between liquidity risk, credit risk and financial performance of microfinance banks in Kenya. The study concluded that there is direct relationship between operational efficiency, capital adequacy, firm size and financial performance of microfinance banks in Kenya.
Feasibility Assessment of an Optically Powered Digital Retinal Prosthesis Architecture for Retinal Ganglion Cell Stimulation
Authors: William Lemaire, Maher Benhouria, Konin Koua, Wei Tong, Gabriel Martin-Hardy, Melanie Stamp, Kumaravelu Ganesan, Louis-Philippe Gauthier, Marwan Besrour, Arman Ahnood, David John Garrett, Sébastien Roy, Michael Ibbotson, Steven Prawer, Réjean Fontaine
Date: 2020-10-23 00:00:00

Clinical trials previously demonstrated the notable capacity to elicit visual percepts in blind patients affected with retinal diseases by electrically stimulating the remaining neurons on the retina. However, these implants restored very limited visual acuity and required transcutaneous cables traversing the eyeball, leading to reduced reliability and complex surgery with high postoperative infection risks. To overcome the limitations imposed by cables, a retinal implant architecture in which near-infrared illumination carries both power and data through the pupil to a digital stimulation controller is presented. A high efficiency multi-junction photovoltaic cell transduces the optical power to a CMOS stimulator capable of delivering flexible interleaved sequential stimulation through a diamond microelectrode array. To demonstrate the capacity to elicit a neural response with this approach while complying with the optical irradiance limit at the pupil, fluorescence imaging with a calcium indicator is used on a degenerate rat retina. The power delivered by the laser at the permissible irradiance of 4 mW/mm2 at 850 nm is shown to be sufficient to both power the stimulator ASIC and elicit a response in retinal ganglion cells (RGCs), with the ability to generate of up to 35 000 pulses per second at the average stimulation threshold. This confirms the feasibility of generating a response in RGCs with an infrared-powered digital architecture capable of delivering complex sequential stimulation patterns at high repetition rates, albeit with some limitations.
Spectral folding and two-channel filter-banks on arbitrary graphs
Authors: Eduardo Pavez, Benjamin Girault, Antonio Ortega, Philip A. Chou
Date: 2020-10-23 00:00:00
Tasks: Computational Efficiency, Vocal Bursts Valence Prediction

In the past decade, several multi-resolution representation theories for graph signals have been proposed. Bipartite filter-banks stand out as the most natural extension of time domain filter-banks, in part because perfect reconstruction, orthogonality and bi-orthogonality conditions in the graph spectral domain resemble those for traditional filter-banks. Therefore, many of the well known orthogonal and bi-orthogonal designs can be easily adapted for graph signals. A major limitation is that this framework can only be applied to the normalized Laplacian of bipartite graphs. In this paper we extend this theory to arbitrary graphs and positive semi-definite variation operators. Our approach is based on a different definition of the graph Fourier transform (GFT), where orthogonality is defined with the respect to the Q inner product. We construct GFTs satisfying a spectral folding property, which allows us to easily construct orthogonal and bi-orthogonal perfect reconstruction filter-banks. We illustrate signal representation and computational efficiency of our filter-banks on 3D point clouds with hundreds of thousands of points.
Multilevel Monte-Carlo for computing the SCR with the standard formula and other stress tests
Authors: Aurélien Alfonsi, Adel Cherchali, Jose Arturo Infante Acevedo
Date: 2020-10-23 00:00:00

This paper studies the multilevel Monte-Carlo estimator for the expectation of a maximum of conditional expectations. This problem arises naturally when considering many stress tests and appears in the calculation of the interest rate module of the standard formula for the SCR. We obtain theoretical convergence results that complements the recent work of Giles and Goda and gives some additional tractability through a parameter that somehow describes regularity properties around the maximum. We then apply the MLMC estimator to the calculation of the SCR at future dates with the standard formula for an ALM savings business on life insurance. We compare it with estimators obtained with Least Square Monte-Carlo or Neural Networks. We find that the MLMC estimator is computationally more efficient and has the main advantage to avoid regression issues, which is particularly significant in the context of projection of a balance sheet by an insurer due to the path dependency. Last, we discuss the potentiality of this numerical method and analyze in particular the effect of the portfolio allocation on the SCR at future~dates.
Multi-Channel Speaker Verification for Single and Multi-talker Speech
Authors: Saurabh Kataria, Shi-Xiong Zhang, Dong Yu
Date: 2020-10-23 00:00:00
Tasks: Action Detection, Activity Detection, Speaker Verification, Speech Enhancement, Triplet

To improve speaker verification in real scenarios with interference speakers, noise, and reverberation, we propose to bring together advancements made in multi-channel speech features. Specifically, we combine spectral, spatial, and directional features, which includes inter-channel phase difference, multi-channel sinc convolutions, directional power ratio features, and angle features. To maximally leverage supervised learning, our framework is also equipped with multi-channel speech enhancement and voice activity detection. On all simulated, replayed, and real recordings, we observe large and consistent improvements at various degradation levels. On real recordings of multi-talker speech, we achieve a 36% relative reduction in equal error rate w.r.t. single-channel baseline. We find the improvements from speaker-dependent directional features more consistent in multi-talker conditions than clean. Lastly, we investigate if the learned multi-channel speaker embedding space can be made more discriminative through a contrastive loss-based fine-tuning. With a simple choice of Triplet loss, we observe a further 8.3% relative reduction in EER.
Improving Noise Robustness of an End-to-End Neural Model for Automatic Speech Recognition
Date: 2020-10-23 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, speech-recognition, Speech Recognition

We present our experiments in training robust to noise an end-to-end
automatic speech recognition (ASR) model using intensive data augmentation. We
explore the efficacy of fine-tuning a pre-trained model to improve noise
robustness, and we find it to be a very efficient way to train for various
noisy conditions, especially when the conditions in which the model will be
used, are unknown. Starting with a model trained on clean data helps establish
baseline performance on clean speech. We carefully fine-tune this model to both
maintain the performance on clean speech, and improve the model accuracy in
noisy conditions. With this schema, we trained robust to noise English and
Mandarin ASR models on large public corpora. All described models and training
recipes are open sourced in NeMo, a toolkit for conversational AI.
X-TaSNet: Robust and Accurate Time-Domain Speaker Extraction Network
Date: 2020-10-24 00:00:00
Tasks: Speech Separation

Extracting the speech of a target speaker from mixed audios, based on a
reference speech from the target speaker, is a challenging yet powerful
technology in speech processing. Recent studies of speaker-independent speech
separation, such as TasNet, have shown promising results by applying deep
neural networks over the time-domain waveform. Such separation neural network
does not directly generate reliable and accurate output when target speakers
are specified, because of the necessary prior on the number of speakers and the
lack of robustness when dealing with audios with absent speakers. In this
paper, we break these limitations by introducing a new speaker-aware speech
masking method, called X-TaSNet. Our proposal adopts new strategies, including
a distortion-based loss and corresponding alternating training scheme, to
better address the robustness issue. X-TaSNet significantly enhances the
extracted speech quality, doubling SDRi and SI-SNRi of the output speech audio
over state-of-the-art voice filtering approach. X-TaSNet also improves the
reliability of the results by improving the accuracy of speaker identity in the
output audio to 95.4%, such that it returns silent audios in most cases when
the target speaker is absent. These results demonstrate X-TaSNet moves one
solid step towards more practical applications of speaker extraction
technology.