pwc-archive/papers-with-abstracts huggingface dataset:
Spillovers of Program Benefits with Missing Network Links
Authors: Lina Zhang
Date: 2020-09-21 00:00:00

The issue of missing network links in partially observed networks is frequently neglected in empirical studies. This paper addresses this issue when investigating the spillovers of program benefits in the presence of network interactions. Our method is flexible enough to account for non-i.i.d. missing links. It relies on two network measures that can be easily constructed based on the incoming and outgoing links of the same observed network. The treatment and spillover effects can be point identified and consistently estimated if network degrees are bounded for all units. We also demonstrate the bias reduction property of our method if network degrees of some units are unbounded. Monte Carlo experiments and a naturalistic simulation on real-world network data are implemented to verify the finite-sample performance of our method. We also re-examine the spillover effects of home computer use on children's self-empowered learning.
Detecting Sound Events Using Convolutional Macaron Net With Pseudo Strong Labels
Authors: Teck Kai Chan, Cheng Siong Chin
Date: 2020-09-21 00:00:00

In this paper, we propose addressing the lack of strongly labeled data by using pseudo strongly labeled data approximated using Convolutive Nonnegative Matrix Factorization. Using this set of data, we then train a novel architecture called the Convolutional Macaron Net (CMN), which combines Convolutional Neural Network (CNN) with MN, in a semi-supervised manner. Instead of training only a single model or using the Mean-teacher approach, we train two different CMNs synchronously using a curriculum consistency cost and a curriculum interpolated consistency cost. In the inference stage, one of the models will provide the frame-level prediction while the other model will provide the clip-level prediction. Our system outperforms the baseline system of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge Task 4 by a margin of over 10% based on our proposed framework. By comparing with the top submission of the DCASE 2019 challenge, our system accuracy is also higher by 1.8%. On the other hand, as compared to the top submission of DCASE 2020, our accuracy is also marginally higher by 0.3%, even with fewer Transformer encoding layers. Our system remains robust on unseen YouTube evaluation dataset and has a winning margin of 0.6% and 6.3% against the top submission of DCASE 2019 and the baseline system.
Experimental Investigation of Discrete Multitone Transmission in the Presence of Optical Noise and Chromatic Dispersion
Authors: Annika Dochhan, Laia Nadal, Helmut Griesser, Michael Eiselt, Michela Svaluto Moreolo, Jörg-Peter Elbers
Date: 2020-09-21 00:00:00

Enabled by channel adaptive bit and power loading, we experimentally demonstrate discrete multitone transmission at 56Gb/s with simple intensity modulation and direct detection and achieve 50 km reach in the 1.55um window.
A 3-Dimensional Simplex Modulation Format with Improved OSNR Performance Compared to DP-BPSK
Authors: Annika Dochhan, Helmut Grießer, Brian Teipen, Michael Eiselt
Date: 2020-09-21 00:00:00

The novel 3-dimensional modulation format 3D-Simplex offers potentially 1.2 dB higher OSNR tolerance than DP-DPSK while exhibiting the same spectral occupancy, modulating two bits per symbol. We verify this benefit experimen-tally and evaluate the transmission performance in a non-linear environment. All experimental results are confirmed by simulations. The benefit of 3D-Simplex is not maintained in the highly non-linear regime, but the performance is still comparable to that of DP-DPSK.
On the verge of life: Distribution of nucleotide sequences in viral RNAs
Authors: Mykola Husev, Andrij Rovenchak
Date: 2020-09-21 00:00:00
Tasks: General Classification

The aim of the study is to analyze viruses using parameters obtained from distributions of nucleotide sequences in the viral RNA. Seeking for the input data homogeneity, we analyze single-stranded RNA viruses only. Two approaches are used to obtain the nucleotide sequences; In the first one, chunks of equal length (four nucleotides) are considered. In the second approach, the whole RNA genome is divided into parts by adenine or the most frequent nucleotide as a "space". Rank--frequency distributions are studied in both cases. Within the first approach, the P\'olya and the negative hypergeometric distribution yield the best fit. For the distributions obtained within the second approach, we have calculated a set of parameters, including entropy, mean sequence length, and its dispersion. The calculated parameters became the basis for the classification of viruses. We observed that proximity of viruses on planes spanned on various pairs of parameters corresponds to related species. In certain cases, such a proximity is observed for unrelated species as well calling thus for the expansion of the set of parameters used in the classification. We also observed that the fourth most frequent nucleotide sequences obtained within the second approach are of different nature in case of human coronaviruses (different nucleotides for MERS, SARS-CoV, and SARS-CoV-2 versus identical nucleotides for four other coronaviruses). We expect that our findings will be useful as a supplementary tool in the classification of diseases caused by RNA viruses with respect to severity and contagiousness.
Flexible bandwidth 448 Gb/s DMT Transmission for Next Generation Data Center Inter-Connects
Authors: Annika Dochhan, Helmut Grießer, Michael Eiselt, Jörg-Peter Elbers
Date: 2020-09-21 00:00:00

We experimentally evaluate a flexible DMT system using 4 to 8 50-GHz-grid C-band channels to transmit 448 Gb/s over up to 240 km SSMF. VSB filtering enabled by detuned lasers significantly reduces the impact of chromatic dispersion.
Model-driven statistical arbitrage on LETF option markets
Authors: Sergey Nasekin, Wolfgang Karl Härdle
Date: 2020-09-21 00:00:00
Tasks: model

In this paper, we study the statistical properties of the moneyness scaling transformation by Leung and Sircar (2015). This transformation adjusts the moneyness coordinate of the implied volatility smile in an attempt to remove the discrepancy between the IV smiles for levered and unlevered ETF options. We construct bootstrap uniform confidence bands which indicate that the implied volatility smiles are statistically different after moneyness scaling has been performed. An empirical application shows that there are trading opportunities possible on the LETF market. A statistical arbitrage type strategy based on a dynamic semiparametric factor model is presented. This strategy presents a statistical decision algorithm which generates trade recommendations based on comparison of model and observed LETF implied volatility surface. It is shown to generate positive returns with a high probability. Extensive econometric analysis of LETF implied volatility process is performed including out-of-sample forecasting based on a semiparametric factor model and uniform confidence bands' study. It provides new insights into the latent dynamics of the implied volatility surface. We also incorporate Heston stochastic volatility into the moneyness scaling method for better tractability of the model.
Stochastic Learning-Based Robust Beamforming Design for RIS-Aided Millimeter-Wave Systems in the Presence of Random Blockages
Authors: Gui Zhou, Cunhua Pan, Hong Ren, Kezhi Wang, Maged Elkashlan, Marco Di Renzo
Date: 2020-09-21 00:00:00
Tasks: Stochastic Optimization

A fundamental challenge for millimeter wave (mmWave) communications lies in its sensitivity to the presence of blockages, which impact the connectivity of the communication links and ultimately the reliability of the entire network. In this paper, we analyze a reconfigurable intelligent surface (RIS)-aided mmWave communication system for enhancing the network reliability and connectivity in the presence of random blockages. To enhance the robustness of hybrid analog-digital beamforming in the presence of random blockages, we formulate a stochastic optimization problem based on the minimization of the sum outage probability. To tackle the proposed optimization problem, we introduce a low-complexity algorithm based on the stochastic block gradient descent method, which learns sensible blockage patterns without searching for all combinations of potentially blocked links. Numerical results confirm the performance benefits of the proposed algorithm in terms of outage probability and effective data rate.
Low-Complexity Massive MIMO Tensor Precoding
Authors: Lucas N. Ribeiro, Stefan Schwarz, André L. F. de Almeida, Martin Haardt
Date: 2020-09-21 00:00:00

We present a novel and low-complexity massive multiple-input multiple-output (MIMO) precoding strategy based on novel findings concerning the subspace separability of Rician fading channels. Considering a uniform rectangular array at the base station, we show that the subspaces spanned by the channel vectors can be factorized as a tensor product between two lower dimensional subspaces. Based on this result, we formulate tensor maximum ratio transmit and zero-forcing precoders. We show that the proposed tensor precoders exhibit lower computational complexity and require less instantaneous channel state information than their linear counterparts. Finally, we present computer simulations that demonstrate the applicability of the proposed tensor precoders in practical communication scenarios.
Regularization Approach for Network Modeling of German Power Derivative Market
Authors: Shi Chen, Wolfgang Karl Härdle, Brenda López Cabrera
Date: 2020-09-21 00:00:00
Tasks: Variable Selection

In this paper we propose a regularization approach for network modeling of German power derivative market. To deal with the large portfolio, we combine high-dimensional variable selection techniques with dynamic network analysis. The estimated sparse interconnectedness of the full German power derivative market, clearly identify the significant channels of relevant potential risk spillovers. Our empirical findings show the importance of interdependence between different contract types, and identify the main risk contributors. We further observe strong pairwise interconnections between the neighboring contracts especially for the spot contracts trading in the peak hours, its implications for regulators and investors are also discussed. The network analysis of the full German power derivative market helps us to complement a full picture of system risk, and have a better understanding of the German power market functioning and environment.
Implied Basket Correlation Dynamics
Authors: Wolfgang Karl Härdle, Elena Silyakova
Date: 2020-09-21 00:00:00

Equity basket correlation can be estimated both using the physical measure from stock prices, and also using the risk neutral measure from option prices. The difference between the two estimates motivates a so-called "dispersion strategy''. We study the performance of this strategy on the German market and propose several profitability improvement schemes based on implied correlation (IC) forecasts. Modelling IC conceals several challenges. Firstly the number of correlation coefficients would grow with the size of the basket. Secondly, IC is not constant over maturities and strikes. Finally, IC changes over time. We reduce the dimensionality of the problem by assuming equicorrelation. The IC surface (ICS) is then approximated from the implied volatilities of stocks and the implied volatility of the basket. To analyze the dynamics of the ICS we employ a dynamic semiparametric factor model.
Weight Training Analysis of Sportsmen with Kinect Bioinformatics for Form Improvement
Authors: Khan Muhammad Umair, Saeed Khawar, Qadeer Sidra
Date: 2020-08-13 00:00:00
Tasks: Form

Sports franchises invest a lot in training their athletes. use of latest
technology for this purpose is also very common. We propose a system of
capturing motion of athletes during weight training and analyzing that data to
find out any shortcomings and imperfections. Our system uses Kinect depth image
to compute different parameters of athlete's selected joints. These parameters
are passed through certain algorithms to process them and formulate results on
their basis. Some parameters like range of motion, speed and balance can be
analyzed in real time. But for comparison to be performed between motions, data
is first recorded and stored and then processed for accurate results. Our
results depict that this system can be easily deployed and implemented to
provide a very valuable insight to dynamics of a work out and help an athlete
in improving his form.
Computation of Parameter Dependent Robust Invariant Sets for LPV Models with Guaranteed Performance
Authors: Ankit Gupta, Manas Mejari, Paolo Falcone, Dario Piga
Date: 2020-09-21 00:00:00
Tasks: Scheduling

This paper presents an iterative algorithm to compute a Robust Control Invariant (RCI) set, along with an invariance-inducing control law, for Linear Parameter-Varying (LPV) systems. As the real-time measurements of the scheduling parameters are typically available, in the presented formulation, we allow the RCI set description along with the invariance-inducing controller to be scheduling parameter dependent. The considered formulation thus leads to parameter-dependent conditions for the set invariance, which are replaced by sufficient Linear Matrix Inequality (LMI) conditions via Polya's relaxation. These LMI conditions are then combined with a novel volume maximization approach in a Semidefinite Programming (SDP) problem, which aims at computing the desirably large RCI set. In addition to ensuring invariance, it is also possible to guarantee performance within the RCI set by imposing a chosen quadratic performance level as an additional constraint in the SDP problem. The reported numerical example shows that the presented iterative algorithm can generate invariant sets which are larger than the maximal RCI sets computed without exploiting scheduling parameter information.
CRIX an index for cryptocurrencies
Authors: Simon Trimborn, Wolfgang Karl Härdle
Date: 2020-09-21 00:00:00
Tasks: Model Selection

The cryptocurrency market is unique on many levels: Very volatile, frequently changing market structure, emerging and vanishing of cryptocurrencies on a daily level. Following its development became a difficult task with the success of cryptocurrencies (CCs) other than Bitcoin. For fiat currency markets, the IMF offers the index SDR and, prior to the EUR, the ECU existed, which was an index representing the development of European currencies. Index providers decide on a fixed number of index constituents which will represent the market segment. It is a challenge to fix a number and develop rules for the constituents in view of the market changes. In the frequently changing CC market, this challenge is even more severe. A method relying on the AIC is proposed to quickly react to market changes and therefore enable us to create an index, referred to as CRIX, for the cryptocurrency market. CRIX is chosen by model selection such that it represents the market well to enable each interested party studying economic questions in this market and to invest into the market. The diversified nature of the CC market makes the inclusion of altcoins in the index product critical to improve tracking performance. We have shown that assigning optimal weights to altcoins helps to reduce the tracking errors of a CC portfolio, despite the fact that their market cap is much smaller relative to Bitcoin. The codes used here are available via www.quantlet.de.
Active Contrastive Learning of Audio-Visual Video Representations
Authors: Shuang Ma, Zhaoyang Zeng, Daniel McDuff, Yale Song
Date: 2020-08-31 00:00:00
Proceeding: ICLR 2021 1
Tasks: Contrastive Learning, Representation Learning, Video Classification

Contrastive learning has been shown to produce generalizable representations of audio and visual data by maximizing the lower bound on the mutual information (MI) between different views of an instance. However, obtaining a tight lower bound requires a sample size exponential in MI and thus a large set of negative samples. We can incorporate more samples by building a large queue-based dictionary, but there are theoretical limits to performance improvements even with a large number of negative samples. We hypothesize that \textit{random negative sampling} leads to a highly redundant dictionary that results in suboptimal representations for downstream tasks. In this paper, we propose an active contrastive learning approach that builds an \textit{actively sampled} dictionary with diverse and informative items, which improves the quality of negative samples and improves performances on tasks where there is high mutual information in the data, e.g., video classification. Our model achieves state-of-the-art performance on challenging audio and visual downstream benchmarks including UCF101, HMDB51 and ESC50.\footnote{Code is available at: \url{https://github.com/yunyikristy/CM-ACC}}
Game Level Clustering and Generation using Gaussian Mixture VAEs
Authors: Yang Zhihan, Sarkar Anurag, Cooper Seth
Date: 2020-08-22 00:00:00
Tasks: Clustering

Variational autoencoders (VAEs) have been shown to be able to generate game
levels but require manual exploration of the learned latent space to generate
outputs with desired attributes. While conditional VAEs address this by
allowing generation to be conditioned on labels, such labels have to be
provided during training and thus require prior knowledge which may not always
be available. In this paper, we apply Gaussian Mixture VAEs (GMVAEs), a variant
of the VAE which imposes a mixture of Gaussians (GM) on the latent space,
unlike regular VAEs which impose a unimodal Gaussian. This allows GMVAEs to
cluster levels in an unsupervised manner using the components of the GM and
then generate new levels using the learned components. We demonstrate our
approach with levels from Super Mario Bros., Kid Icarus and Mega Man. Our
results show that the learned components discover and cluster level structures
and patterns and can be used to generate levels with desired characteristics.
Grasp-type Recognition Leveraging Object Affordance
Authors: Wake Naoki, Sasabuchi Kazuhiro, Ikeuchi Katsushi
Date: 2020-08-26 00:00:00
Tasks: Object, Vocal Bursts Type Prediction

A key challenge in robot teaching is grasp-type recognition with a single RGB
image and a target object name. Here, we propose a simple yet effective
pipeline to enhance learning-based recognition by leveraging a prior
distribution of grasp types for each object. In the pipeline, a convolutional
neural network (CNN) recognizes the grasp type from an RGB image. The
recognition result is further corrected using the prior distribution (i.e.,
affordance), which is associated with the target object name. Experimental
results showed that the proposed method outperforms both a CNN-only and an
affordance-only method. The results highlight the effectiveness of
linguistically-driven object affordance for enhancing grasp-type recognition in
robot teaching.
Trading multiple mean reversion
Authors: E. Boguslavskaya, M. Boguslavsky, D. Muravey
Date: 2020-09-21 00:00:00
Tasks: Management, Position

How should one construct a portfolio from multiple mean-reverting assets? Should one add an asset to portfolio even if the asset has zero mean reversion? We consider a position management problem for an agent trading multiple mean-reverting assets. We solve an optimal control problem for an agent with power utility, and present a semi-explicit solution. The nearly explicit nature of the solution allows us to study the effects of parameter mis-specification, and derive a number of properties of the optimal solution.
Are mouse and cat the missing link in the COVID-19 outbreaks in seafood markets?
Authors: Daniel H. Tao, Weitao Sun
Date: 2020-09-18 00:00:00

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus caused the novel coronavirus disease-2019 (COVID-19) affecting the whole world. Like SARS-CoV and MERS-CoV, SARS-CoV-2 are thought to originate in bats and then spread to humans through intermediate hosts. Identifying intermediate host species is critical to understanding the evolution and transmission mechanisms of COVID-19. However, determining which animals are intermediate hosts remains a key challenge. Virus host-genome similarity (HGS) is an important factor that reflects the adaptability of virus to host. SARS-CoV-2 may retain beneficial mutations to increase HGS and evade the host immune system. This study investigated the HGSs between 399 SARS-CoV-2 strains and 10 hosts of different species, including bat, mouse, cat, swine, snake, dog, pangolin, chicken, human and monkey. The results showed that the HGS between SARS-CoV-2 and bat was the highest, followed by mouse and cat. Human and monkey had the lowest HGS values. In terms of genetic similarity, mouse and monkey are halfway between bat and human. Moreover, given that COVID-19 outbreaks tend to be associated with live poultry and seafood markets, mouse and cat are more likely sources of infection in these places. However, more experimental data are needed to confirm whether mouse and cat are true intermediate hosts. These findings suggest that animals closely related to human life, especially those with high HGS, need to be closely monitored.
Coupling between COVID-19 and seasonal influenza leads to synchronization of their dynamics
Date: 2020-09-17 00:00:00

Interactions between COVID-19 and other pathogens may change their dynamics.
Specifically, this may hinder the modelling of empirical data when the symptoms
of both infections are hard to distinguish. We introduce a model coupling the
dynamics of COVID-19 and seasonal influenza, simulating cooperation,
competition and asymmetric interactions. We find that the coupling synchronizes
both infections, with a strong influence on the dynamics of influenza, reducing
its time extent to a half.
A Time Series Data Analysis of Indian Commercial Dynamism
Authors: Samsul Alam
Date: 2020-09-21 00:00:00
Tasks: Time Series, Time Series Analysis

In this report it is analyzed the focuses of the commercial dynamism of India, covering the fundamentals of growth rate, trade balance, coverage rate, openness rate, share of world indicators and then present each of them in detail.
On the Existence of Conditional Maximum Likelihood Estimates of the Binary Logit Model with Fixed Effects
Date: 2020-09-29 00:00:00

By exploiting McFadden (1974)'s results on conditional logit estimation, we
show that there exists a one-to-one mapping between existence and uniqueness of
conditional maximum likelihood estimates of the binary logit model with fixed
effects and the configuration of data points. Our results extend those in
Albert and Anderson (1984) for the cross-sectional case and can be used to
build a simple algorithm that detects spurious estimates in finite samples. As
an illustration, we exhibit an artificial dataset for which the STATA's command
\texttt{clogit} returns spurious estimates.