pwc-archive/papers-with-abstracts huggingface dataset:
Detection of Structural Regimes and Analyzing the Impact of Crude Oil Market on Canadian Stock Market: Markov Regime-Switching Approach
Authors: Mohammadreza Mahmoudi, Hana Ghaneei
Date: 2021-08-31 00:00:00

This study aims to analyze the impact of the crude oil market on the Toronto Stock Exchange Index (TSX)c based on monthly data from 1970 to 2021 using Markov-switching vector autoregressive (MSI-VAR) model. The results indicate that TSX return contains two regimes, including: positive return (regime 1), when growth rate of stock index is positive; and negative return (regime 2), when growth rate of stock index is negative. Moreover, regime 1 is more volatile than regime 2. The findings also show the crude oil market has negative effect on the stock market in regime 1, while it has positive effect on the stock market in regime 2. In addition, we can see this effect in regime 1 more significantly in comparison to regime 2. Furthermore, two period lag of oil price decreases stock return in regime 1, while it increases stock return in regime 2.
Analysis of taste heterogeneity in commuters travel decisions using joint parking and mode choice model: A case from urban India
Authors: Janak Parmar, Gulnazbanu Saiyed, Sanjaykumar Dave
Date: 2021-08-26 00:00:00
Tasks: Management

The concept of transportation demand management (TDM) upholds the development of sustainable mobility through the triumph of optimally balanced transport modal share in cities. The modal split management directly reflects on TDM of each transport subsystem, including parking. In developing countries, the policy-makers have largely focused on supply-side measures, yet demand-side measures have remained unaddressed in policy implications. Ample literature is available presenting responses of TDM strategies, but most studies account mode choice and parking choice behaviour separately rather than considering trade-offs between them. Failing to do so may lead to biased model estimates and impropriety in policy implications. This paper seeks to fill this gap by admitting parking choice as an endogenous decision within the model of mode choice behaviour. This study integrates attitudinal factors and built-environment variables in addition to parking and travel attributes for developing comprehensive estimation results. A mixed logit model with random coefficients is estimated using hierarchical Bayes approach based on the Markov Chain Monte Carlo simulation method. The results reveal significant influence of mode/parking specific attitudes on commuters choice behaviour in addition to the built-environment factors and mode/parking related attributes. It is identified that considerable shift is occurring between parking-types in preference to switching travel mode with hypothetical changes in parking attributes. Besides, study investigates the heterogeneity in the willingness-to-pay through a follow-up regression model, which provides important insights for identifying possible sources of this heterogeneity among respondents. The study provides remarkable results which may be beneficial to planning authorities for improving TDM strategies especially in developing countries.
Compromise, Don't Optimize: Generalizing Perfect Bayesian Equilibrium to Allow for Ambiguity
Authors: Karl Schlag, Andriy Zapechelnyuk
Date: 2020-03-05 00:00:00

We introduce a solution concept for extensive-form games of incomplete information in which players need not assign likelihoods to what they do not know about the game. This is embedded in a model in which players can hold multiple priors. Players make choices by looking for compromises that yield a good performance under each of their updated priors. Our solution concept is called perfect compromise equilibrium. It generalizes perfect Bayesian equilibrium. We show how it deals with ambiguity in Cournot and Bertrand markets, public good provision, Spence's job market signaling, bilateral trade with common value, and forecasting.
Physiological-Physical Feature Fusion for Automatic Voice Spoofing Detection
Authors: Junxiao Xue, Hao Zhou, Yabo Wang
Date: 2021-09-01 00:00:00
Tasks: Speaker Verification, Speech Synthesis, Voice Conversion

Speaker verification systems have been used in many production scenarios in recent years. Unfortunately, they are still highly prone to different kinds of spoofing attacks such as voice conversion and speech synthesis, etc. In this paper, we propose a new method base on physiological-physical feature fusion to deal with voice spoofing attacks. This method involves feature extraction, a densely connected convolutional neural network with squeeze and excitation block (SE-DenseNet), multi-scale residual neural network with squeeze and excitation block (SE-Res2Net) and feature fusion strategies. We first pre-trained a convolutional neural network using the speaker's voice and face in the video as surveillance signals. It can extract physiological features from speech. Then we use SE-DenseNet and SE-Res2Net to extract physical features. Such a densely connection pattern has high parameter efficiency and squeeze and excitation block can enhance the transmission of the feature. Finally, we integrate the two features into the SE-Densenet to identify the spoofing attacks. Experimental results on the ASVspoof 2019 data set show that our model is effective for voice spoofing detection. In the logical access scenario, our model improves the tandem decision cost function (t-DCF) and equal error rate (EER) scores by 4% and 7%, respectively, compared with other methods. In the physical access scenario, our model improved t-DCF and EER scores by 8% and 10%, respectively.
Analysis of Intra-Operative Physiological Responses Through Complex Higher-Order SVD for Long-Term Post-Operative Pain Prediction
Authors: Raheleh Baharloo, Jose C. Principe, Parisa Rashidi, Patrick J. Tighe
Date: 2021-09-02 00:00:00

Long-term pain conditions after surgery and patients' responses to pain relief medications are not yet fully understood. While recent studies developed an index for nociception level of patients under general anesthesia, based on multiple physiological parameters, it remains unclear whether and how dynamics of these parameters indicate long-term post-operative pain (POP). To extract unbiased and interpretable descriptions of how physiological parameters dynamics change over time and across patients in response to surgical procedures, we employed a multivariate-temporal analysis. We demonstrate the main features of intra-operative physiological responses can be used to predict long-term POP. We propose to use a complex higher-order SVD method to accurately decompose the patients' physiological responses into multivariate structures evolving in time. We used intra-operative vital signs of 175 patients from a mixed surgical cohort to extract three interconnected, low-dimensional complex-valued descriptions of patients' physiological responses: multivariate factors, reflecting sub-physiological parameters; temporal factors reflecting common intra-surgery temporal dynamics; and patients factors, describing patient to patient changes in physiological responses. Adoption of complex-HOSVD allowed us to clarify the dynamic correlation structure included in intra-operative physiological responses. Instantaneous phases of the complex-valued physiological responses within the subspace of principal descriptors enabled us to discriminate between mild versus severe levels of long-term POP. By abstracting patients into different surgical groups, we identified significant surgery-related principal descriptors: each of them potentially encodes different surgical stimulation. The dynamics of patients' physiological responses to these surgical events are linked to long-term post-operative pain development.
Transmit Design for Joint MIMO Radar and Multiuser Communications with Transmit Covariance Constraint
Authors: Xiang Liu, Tianyao Huang, Yimin Liu
Date: 2021-09-02 00:00:00

In this paper, we consider the design of a multiple-input multiple-output (MIMO) transmitter which simultaneously functions as a MIMO radar and a base station for downlink multiuser communications. In addition to a power constraint, we require the covariance of the transmit waveform be equal to a given optimal covariance for MIMO radar, to guarantee the radar performance. With this constraint, we formulate and solve the signal-to-interference-plus-noise ratio (SINR) balancing problem for multiuser transmit beamforming via convex optimization. Considering that the interference cannot be completely eliminated with this constraint, we introduce dirty paper coding (DPC) to further cancel the interference, and formulate the SINR balancing and sum rate maximization problem in the DPC regime. Although both of the two problems are non-convex, we show that they can be reformulated to convex optimizations via the Lagrange and downlink-uplink duality. In addition, we propose gradient projection based algorithms to solve the equivalent dual problem of SINR balancing, in both transmit beamforming and DPC regimes. The simulation results demonstrate significant performance improvement of DPC over transmit beamforming, and also indicate that the degrees of freedom for the communication transmitter is restricted by the rank of the covariance.
60 GHz Outdoor Propagation Measurements and Analysis Using Facebook Terragraph Radios
Authors: Kairui Du, Omkar Mujumdar, Ozgur Ozdemir, Ender Ozturk, Ismail Guvenc, Mihail L. Sichitiu, Huaiyu Dai, Arupjyoti Bhuyan
Date: 2021-09-02 00:00:00

The high attenuation of millimeter-wave (mmWave) would significantly reduce the coverage areas, and hence it is critical to study the propagation characteristics of mmWave in multiple deployment scenarios. In this work, we investigated the propagation and scattering behavior of 60 GHz mmWave signals in outdoor environments at a travel distance of 98 m for an aerial link (rooftop to rooftop), and 147 m for a ground link (light-pole to light-pole). Measurements were carried out using Facebook Terragraph (TG) radios. Results include received power, path loss, signal-to-noise ratio (SNR), and root mean square (RMS) delay spread for all beamforming directions supported by the antenna array. Strong line-of-sight (LOS) propagation exists in both links. We also observed rich multipath components (MPCs) due to edge scatterings in the aerial link, while only LOS and ground reflection MPCs in the other link.
Similarity Preserving Unsupervised Feature Selection based on Sparse Learning
Authors: Hadi Zare, Mohsen Ghassemi Parsa, Mehdi Ghatee, Sasan H. Alizadeh
Date: 2020-12-15 00:00:00
Proceeding: 10th International Symposium on Telecommunications (IST) 2020 12
Tasks: feature selection, Sparse Learning

Various feature selection methods have been recently proposed on different applications to reduce the computational burden of machine learning algorithms as well as the complexity of learned models. Preserving sample similarities and selecting discriminative features are two major factors should be satisfied, especially by unsupervised feature selection methods. This paper aims to propose a novel unsupervised feature selection approach which employs an ℓ 2,1 -norm regularization model to preserve global and local similarities by minimizing an objective function. Cluster analysis is also incorporated in this framework to take the inherent structure of the data into account. The experimental results show the strength of the proposed approach as compared with the earlier well-known methods on a variety of standard datasets.
A review of computational tools for generating metagenome-assembled genomes from metagenomic sequencing data
Authors: Chao Yang, Debajyoti Chowdhury, Zhenmiao Zhang, William K. Cheung, Aiping Lu, Zhao Xiang Bian, Lu Zhang
Date: 2021-09-02 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Microbes are essentially yet convolutedly linked with human lives on the earth. They critically interfere in different physiological processes and thus influence overall health status. Studying microbial species is used to be constrained to those that can be cultured in the lab. But it excluded a huge portion of the microbiome that could not survive on lab conditions. In the past few years, the culture-independent metagenomic sequencing enabled us to explore the complex microbial community coexisting within and on us. Metagenomics has equipped us with new avenues of investigating the microbiome, from studying a single species to a complex community in a dynamic ecosystem. Thus, identifying the involved microbes and their genomes becomes one of the core tasks in metagenomic sequencing. Metagenome-assembled genomes are groups of contigs with similar sequence characteristics from de novo assembly and could represent the microbial genomes from metagenomic sequencing. In this paper, we reviewed a spectrum of tools for producing and annotating metagenome-assembled genomes from metagenomic sequencing data and discussed their technical and biological perspectives.
Stoechiometric and dynamical autocatalysis for diluted chemical reaction networks
Authors: Jeremie Unterberger, Philippe Nghe
Date: 2021-09-02 00:00:00

Autocatalysis underlies the ability of chemical and biochemical systems to replicate. Recently, Blokhuis et al. gave a stoechiometric definition of autocatalysis for reaction networks, stating the existence of a combination of reactions such that the balance for all autocatalytic species is strictly positive, and investigated minimal autocatalytic networks, called {\em autocatalytic cores}. By contrast, spontaneous autocatalysis -- namely, exponential amplification of all species internal to a reaction network, starting from a diluted regime, i.e. low concentrations -- is a dynamical property. We introduce here a topological condition (Top) for autocatalysis, namely: restricting the reaction network description to highly diluted species, we assume existence of a strongly connected component possessing at least one reaction with multiple products (including multiple copies of a single species). We find this condition to be necessary and sufficient for stoechiometric autocatalysis. When degradation reactions have small enough rates, the topological condition further ensures dynamical autocatalysis, characterized by a strictly positive Lyapunov exponent giving the instantaneous exponential growth rate of the system. The proof is generally based on the study of auxiliary Markov chains. We provide as examples general autocatalytic cores of Type I and Type III in the typology of Blokhuis et al. In a companion article, Lyapunov exponents and the behavior in the growth regime are studied quantitatively beyond the present diluted regime .
Nested canalizing functions minimize sensitivity and simultaneously promote criticality
Authors: H. Coban, A. Kabakcioglu
Date: 2021-09-02 00:00:00
Tasks: Sensitivity

We prove that nested canalizing functions are the minimum-sensitivity Boolean functions for any given activity ratio and we characterize the sensitivity boundary which has a nontrivial fractal structure. We further observe, on an extensive database of regulatory functions curated from the literature, that this bound severely constrains the robustness of biological networks. Our findings suggest that the accumulation near the "edge of chaos" in these systems is a natural consequence of a drive towards maximum stability while maintaining plasticity in transcriptional activity.
Cross-time functional connectivity analysis
Authors: Ze Wang
Date: 2021-09-01 00:00:00
Tasks: Functional Connectivity

A large body of literature has shown the substantial inter-regional functional connectivity in the mammal brain. One important property remaining un-studied is the cross-time interareal connection. This paper serves to provide a tool to characterize the cross-time functional connectivity. The method is extended from the temporal embedding based brain temporal coherence analysis. Both synthetic data and in-vivo data were used to evaluate the various properties of the cross-time functional connectivity matrix, which is also called the cross-regional temporal coherence matrix.
Master Stability Functions for metacommunities with two types of habitats
Authors: Alexander Krauß, Thilo Gross, Barbara Drossel
Date: 2021-09-02 00:00:00
Tasks: Vocal Bursts Valence Prediction

Current questions in ecology revolve around instabilities in the dynamics on spatial networks and particularly the effect of node heterogeneity. We extend the Master Stability Function formalism to inhomogeneous biregular networks having two types of spatial nodes. Notably, this class of systems also allows the investigation of certain types of dynamics on higher-order networks. Combined with the Generalized Modelling approach to study the linear stability of steady states, this is a powerful tool to numerically asses the stability of large ensembles of systems. We analyze the stability of ecological metacommunities with two distinct types of habitats analytically and numerically in order to identify several sets of conditions under which the dynamics can become stabilized by dispersal. Our analytical approach allows general insights into stabilizing and destabilizing effects in metapopulations. Specifically, we show that maladaptive dispersal may be stable under certain conditions.
Seizure Classification of EEG based on Wavelet Signal Denoising Using a Novel Channel Selection Algorithm
Authors: Niamh McCallan, Scot Davidson, Kok Yew Ng, Pardis Biglarbeigi, Dewar Finlay, Boon Leong Lan, James McLaughlin
Date: 2021-09-02 00:00:00
Tasks: channel selection, Denoising, EEG, Electroencephalogram (EEG), Seizure Detection

Epilepsy is a disorder of the nervous system that can affect people of any age group. With roughly 50 million people worldwide diagnosed with the disorder, it is one of the most common neurological disorders. The EEG is an indispensable tool for diagnosis of epileptic seizures in an ideal case, as brain waves from an epileptic person will present distinct abnormalities. However, in real world situations there will often be biological and electrical noise interference, as well as the issue of a multichannel signal, which introduce a great challenge for seizure detection. For this study, the Temple University Hospital (TUH) EEG Seizure Corpus dataset was used. This paper proposes a novel channel selection method which isolates different frequency ranges within five channels. This is based upon the frequencies at which normal brain waveforms exhibit. A one second window was selected, with a 0.5 second overlap. Wavelet signal denoising was performed using Daubechies 4 wavelet decomposition, thresholding was applied using minimax soft thresholding criteria. Filter banking was used to localise frequency ranges from five specific channels. Statistical features were then derived from the outputs. After performing bagged tree classification using 500 learners, a test accuracy of 0.82 was achieved.
There are no equal opportunity infectors: Epidemiological modelers must rethink our approach to inequality in infection risk
Authors: Jon Zelner, Nina B. Masters, Ramya Naraharisetti, Sanyu Mojola, Merlin Chowkwanyun
Date: 2021-09-01 00:00:00
Tasks: Decision Making

Mathematical models have come to play a key role in global pandemic preparedness and outbreak response: helping to plan for disease burden, hospital capacity, and inform non-pharmaceutical interventions. Such models have played a pivotal role in the COVID-19 pandemic, with transmission models and, by consequence, modelers guiding global, national, and local responses to SARS-CoV-2. However, these models have systematically failed to account for the social and structural factors which lead to socioeconomic, racial, and geographic health disparities. Why do epidemiologic models of emerging infections ignore known structural drivers of disparate health outcomes? What have been the consequences of this limited framework? What should be done to develop a more holistic approach to modeling-based decision-making during pandemics? In this Perspective, we evaluate potential historical and political explanations for the exclusion of drivers of disparity in infectious disease models for emerging infections, which have often been characterized as "equal opportunity infectors" despite ample evidence to the contrary. We look to examples from other disease systems (HIV, STIs) as a potential blueprint for how social connections, environmental, and structural factors can be integrated into a coherent, rigorous, and interpretable modeling framework. We conclude by outlining principles to guide modeling of emerging infections in ways that represent the causes of inequity in infection as central rather than peripheral mechanisms.
Precise option pricing by the COS method--How to choose the truncation range
Authors: Gero Junike, Konstantin Pankrashkin
Date: 2021-09-02 00:00:00

The Fourier cosine expansion (COS) method is used for pricing European options numerically very fast. To apply the COS method, a truncation range for the density of the log-returns need to be provided. Using Markov's inequality, we derive a new formula to obtain the truncation range and prove that the range is large enough to ensure convergence of the COS method within a predefined error tolerance. We also show by several examples that the classical approach to determine the truncation range by cumulants may lead to serious mispricing. Usually, the computational time of the COS method is of similar magnitude in both cases.
Vietnamese end-to-end speech recognition using wav2vec 2.0
Authors: Thai Binh Nguyen
Date: 2021-09-02 00:00:00
Proceeding: https://github.com/vietai/ASR 2021 9
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Handwriting Recognition, speech-recognition, Speech Recognition

Our models are pre-trained on 13k hours of Vietnamese youtube audio (un-label data) and fine-tuned on 250 hours labeled of VLSP ASR dataset on 16kHz sampled speech audio. We use wav2vec2 architecture for the pre-trained model. For fine-tuning phase, wav2vec2 is fine-tuned using Connectionist Temporal Classification (CTC), which is an algorithm that is used to train neural networks for sequence-to-sequence problems and mainly in Automatic Speech Recognition and handwriting recognition. On the Vivos dataset, we achieved a WER score of 6.15