pwc-archive/papers-with-abstracts huggingface dataset:
A Wavelet Method for Panel Models with Jump Discontinuities in the Parameters
Authors: Oualid Bada, Alois Kneip, Dominik Liebl, Tim Mensinger, James Gualtieri, Robin C. Sickles
Date: 2021-09-22 00:00:00
Tasks: Algorithmic Trading, Time Series, Time Series Analysis

While a substantial literature on structural break change point analysis exists for univariate time series, research on large panel data models has not been as extensive. In this paper, a novel method for estimating panel models with multiple structural changes is proposed. The breaks are allowed to occur at unknown points in time and may affect the multivariate slope parameters individually. Our method adapts Haar wavelets to the structure of the observed variables in order to detect the change points of the parameters consistently. We also develop methods to address endogenous regressors within our modeling framework. The asymptotic property of our estimator is established. In our application, we examine the impact of algorithmic trading on standard measures of market quality such as liquidity and volatility over a time period that covers the financial meltdown that began in 2007. We are able to detect jumps in regression slope parameters automatically without using ad-hoc subsample selection criteria.
Ignorance is Bliss: A Game of Regret
Authors: Claudia Cerrone, Francesco Feri, Philip R. Neary
Date: 2021-09-22 00:00:00

An individual can only experience regret if she learns about an unchosen alternative. In many situations, learning about an unchosen alternative is possible only if someone else chose it. We develop a model where the ex-post information available to each regret averse individual depends both on their own choice and on the choices of others, as others can reveal ex-post information about what might have been. This implies that what appears to be a series of isolated single-person decision problems is in fact a rich multi-player behavioural game, the regret game, where the psychological payoffs that depend on ex-post information are interconnected. For an open set of parameters, the regret game is a coordination game with multiple equilibria, despite the fact that all individuals possess a uniquely optimal choice in isolation. We experimentally test this prediction and find support for it.
Persuasion with Ambiguous Receiver Preferences
Authors: Eitan Sapiro-Gheiler
Date: 2021-09-23 00:00:00

I describe a Bayesian persuasion problem where Receiver has a private type representing a cutoff for choosing Sender's preferred action, and Sender has maxmin preferences over all Receiver type distributions with known mean and bounds. This problem can be represented as a zero-sum game where Sender chooses a distribution of posterior mean beliefs that is a mean-preserving contraction of the prior over states, and an adversarial Nature chooses a Receiver type distribution with the known mean; the player with the higher realization from their chosen distribution wins. I formalize the connection between maxmin persuasion and similar games used to model political spending, all-pay auctions, and competitive persuasion. In both a standard binary-state setting and a new continuous-state setting, Sender optimally linearizes the prior distribution over states to create a distribution of posterior means that is uniform on a known interval with an atom at the lower bound of its support.
Unified Signal Compression Using a GAN with Iterative Latent Representation Optimization
Authors: Bowen Liu, Changwoo Lee, Ang Cao, Hun-Seok Kim
Date: 2021-09-23 00:00:00
Tasks: Generative Adversarial Network, Image Compression, MS-SSIM, SSIM

We propose a unified signal compression framework that uses a generative adversarial network (GAN) to compress heterogeneous signals. The compressed signal is represented as a latent vector and fed into a generator network that is trained to produce high quality realistic signals that minimize a target objective function. To efficiently quantize the compressed signal, non-uniformly quantized optimal latent vectors are identified by iterative back-propagation with alternating direction method of multipliers (ADMM) optimization performed for each iteration. The performance of the proposed signal compression method is assessed using multiple metrics including PSNR and MS-SSIM for image compression and also PESR, Kaldi, LSTM, and MLP performance for speech compression. Test results show that the proposed work outperforms recent state-of-the-art hand-crafted and deep learning-based signal compression methods.
A Lightweight dynamic filter for keyword spotting
Authors: Donghyeon Kim, Kyungdeuk Ko, Jeonggi Kwak, David K. Han, Hanseok Ko
Date: 2021-09-23 00:00:00
Tasks: Keyword Spotting, speech-recognition, Speech Recognition

Keyword Spotting (KWS) from speech signals is widely applied to perform fully hands-free speech recognition. The KWS network is designed as a small-footprint model so it can continuously be active. Recent efforts have explored dynamic filter-based models in deep learning frameworks to enhance the system's robustness or accuracy. However, as a dynamic filter framework requires high computational costs, the implementation is limited to the computational condition of the device. In this paper, we propose a lightweight dynamic filter to improve the performance of KWS. Our proposed model divides the dynamic filter into two branches to reduce computational complexity: pixel level and instance level. The proposed lightweight dynamic filter is applied to the front end of KWS to enhance the separability of the input data. The experimental results show that our model is robustly working on unseen noise and small training data environments by using a small computational resource.
Source Printer Identification using Printer Specific Pooling of Letter Descriptors
Authors: Sharad Joshi, Yogesh Kumar Gupta, Nitin Khanna
Date: 2021-09-23 00:00:00

The digital revolution has replaced the use of printed documents with their digital counterparts. However, many applications require the use of both due to several factors, including challenges of digital security, installation costs, ease of use, and lack of digital expertise. Technological developments in the digital domain have also resulted in the easy availability of high-quality scanners, printers, and image editing software at lower prices. Miscreants leverage such technology to develop forged documents that may go undetected in vast volumes of printed documents. These developments mandate the research on creating fast and accurate digital systems for source printer identification of printed documents. We extensively analyze and propose a printer-specific pooling that improves the performance of printer-specific local texture descriptor on two datasets. The proposed pooling performs well using a simple correlation-based prediction instead of a complex machine learning-based classifier achieving improved performance under cross-font scenarios. The proposed system achieves an average classification accuracy of 93.5%, 94.3%, and 60.3% on documents printed in Arial, Times New Roman, and Comic Sans font types respectively, when documents printed in only Cambria font are available for training.
Integration of Backscatter Communication with Multi-cell NOMA: A Spectral Efficiency Optimization under Imperfect SIC
Authors: Wali Ullah Khan, Eva Lagunas, Asad Mahmood, Zain Ali, Symeon Chatzinotas, Björn Ottersten, Octavia A. Dobre
Date: 2021-09-23 00:00:00

Future wireless networks are expected to connect large-scale low-powered communication devices using the available spectrum resources. Backscatter communications (BC) is an emerging technology towards battery-free transmission in future wireless networks by leveraging ambient radio frequency (RF) waves that enable communications among wireless devices. Non-orthogonal multiple access (NOMA) has recently drawn significant attention due to its high spectral efficiency. The combination of these two technologies can play an important role in the development of future networks. This paper proposes a new optimization approach to enhance the spectral efficiency of nonorthogonal multiple access (NOMA)-BC network. Our framework simultaneously optimizes the power allocation of base station and reflection coefficient (RC) of the backscatter device in each cell under the assumption of imperfect signal decoding. The problem of spectral efficiency maximization is coupled on power and RC which is challenging to solve. To make this problem tractable, we first decouple it into two subproblems and then apply the decomposition method and Karush-Kuhn-Tucker conditions to obtain the efficient solution. Numerical results show the performance of the proposed NOMA-BC network over the pure NOMA network without BC.
Pairwise Distance and Position Estimators From Differences in UWB Channels to Observers
Authors: Gregor Dumphart, Robin Kramer, Armin Wittneben
Date: 2021-09-23 00:00:00
Tasks: Position

We consider the problem of obtaining relative location information between two wireless nodes from the differences in their ultra-wideband (UWB) channels to observer nodes. Our approach focuses on the delays of multipath components (MPCs) extracted from the observed channels. For the two different cases of known and unknown MPC association between these channels, we present estimators for the distance and for the relative position vector between the two nodes. The position estimators require both MPC directions and MPC delays as input. All presented estimators exhibit very desirable technological properties: they do not require line-of-sight conditions, precise synchronization, or knowledge about the observer locations or about the environment. These advantages could enable low-cost wireless network localization in dynamic multipath environments. The exposition is complemented by a numerical evaluation of the estimation accuracy using random sampling, where especially the position estimators show the potential for great accuracy.
Comments on Combining Switched TMAs and FDAs to Synthesize Dot-Shaped Beampatterns
Authors: Mahdi Fartookzadeh
Date: 2021-09-20 00:00:00

The idea of frequency diverse array (FDA) has been invigorated in the recent years by supposing to have the ability of stopping the electromagnetic wave propagation and producing secretive direct connections between distant points. However, it was based on a theoretical mistake in a series of published papers beginning from 2014. In fact, the issue was not very clear since 2018; hence the researchers should be very careful in relying on the papers of this topic.
Proteomics Standards Initiatives ProForma 2.0 Unifying the encoding of Proteoforms and Peptidoforms
Authors: Richard D. LeDuc, Eric W. Deutsch, Pierre-Alain Binz, Ryan T. Fellers, Anthony J. Cesnik, Joshua A. Klein, Tim Van Den Bossche, Ralf Gabriels, Arshika Yalavarthi, Yasset Perez-Riverol, Jeremy Carver, Wout Bittremieux, Shin Kawano, Benjamin Pullman, Nuno Bandeira, Neil L. Kelleher, Paul M. Thomas, Juan Antonio Vizcaíno
Date: 2021-09-23 00:00:00

There is the need to represent in a standard manner all the possible variations of a protein or peptide primary sequence, including both artefactual and post-translational modifications of peptides and proteins. With that overall aim, here, the Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has developed a notation, called ProForma 2.0, which is a substantial extension of the original ProForma notation, developed by the Consortium for Top-Down Proteomics (CTDP). ProForma 2.0 aims to unify the representation of proteoforms and peptidoforms. Therefore, this notation supports use cases needed for bottom-up and middle/topdown proteomics approaches and allows the encoding of highly modified proteins and peptides using a human and machine-readable string. ProForma 2.0 covers encoding protein modification names and accessions, cross-linking reagents including disulfides, glycans, modifications encoded using mass shifts and/or via chemical formulas, labile and C or N-terminal modifications, ambiguity in the modification position and representation of atomic isotopes, among other use cases. Notational conventions are based on public controlled vocabularies and ontologies. Detailed information about the notation and existing implementations are available at http://www.psidev.info/proforma and at the corresponding GitHub repository (https://github.com/HUPO-PSI/proforma).
Routing brain traffic through the von Neumann bottleneck: Parallel sorting and refactoring
Authors: Jari Pronold, Jakob Jordan, Brian J. N. Wylie, Itaru Kitayama, Markus Diesmann, Susanne Kunkel
Date: 2021-09-23 00:00:00

Generic simulation code for spiking neuronal networks spends the major part of time in the phase where spikes have arrived at a compute node and need to be delivered to their target neurons. These spikes were emitted over the last interval between communication steps by source neurons distributed across many compute nodes and are inherently irregular with respect to their targets. For finding the targets, the spikes need to be dispatched to a three-dimensional data structure with decisions on target thread and synapse type to be made on the way. With growing network size a compute node receives spikes from an increasing number of different source neurons until in the limit each synapse on the compute node has a unique source. Here we show analytically how this sparsity emerges over the practically relevant range of network sizes from a hundred thousand to a billion neurons. By profiling a production code we investigate opportunities for algorithmic changes to avoid indirections and branching. Every thread hosts an equal share of the neurons on a compute node. In the original algorithm all threads search through all spikes to pick out the relevant ones. With increasing network size the fraction of hits remains invariant but the absolute number of rejections grows. An alternative algorithm equally divides the spikes among the threads and sorts them in parallel according to target thread and synapse type. After this every thread completes delivery solely of the section of spikes for its own neurons. The new algorithm halves the number of instructions in spike delivery which leads to a reduction of simulation time of up to 40 %. Thus, spike delivery is a fully parallelizable process with a single synchronization point and thereby well suited for many-core systems. Our analysis indicates that further progress requires a reduction of the latency instructions experience in accessing memory.
Robust models of SARS-CoV-2 heterogeneity and control
Authors: Kory D. Johnson, Annemarie Grass, Daniel Toneian, Mathias Beiglböck, Jitka Polechová
Date: 2021-09-23 00:00:00

In light of the continuing emergence of new SARS-CoV-2 variants and vaccines, we create a simulation framework for exploring possible infection trajectories under various scenarios. The situations of primary interest involve the interaction between three components: vaccination campaigns, non-pharmaceutical interventions (NPIs), and the emergence of new SARS-CoV-2 variants. Additionally, immunity waning and vaccine boosters are modeled to account for their growing importance. New infections are generated according to a hierarchical model in which people have a random, individual infectiousness. The model thus includes super-spreading observed in the COVID-19 pandemic. Our simulation functions as a dynamic compartment model in which an individual's history of infection, vaccination, and possible reinfection all play a role in their resistance to further infections. We present a risk measure for each SARS-CoV-2 variant, $\rho^\V$, that accounts for the amount of resistance within a population and show how this risk changes as the vaccination rate increases. Furthermore, by considering different population compositions in terms of previous infection and type of vaccination, we can learn about variants which pose differential risk to different countries. Different control strategies are implemented which aim to both suppress COVID-19 outbreaks when they occur as well as relax restrictions when possible. We demonstrate that a controller that responds to the effective reproduction number in addition to case numbers is more efficient and effective in controlling new waves than monitoring case numbers alone. This is of interest as the majority of the public discussion and well-known statistics deal primarily with case numbers.
Nordic vaccination strategies face/off via age range comparative analysis on key indicators of COVID-19 severity and healthcare stress level
Authors: Anna Sigridur Islind, María Óskarsdóttir, Corentin Cot, Giacomo Cacciapaglia, Francesco Sannino
Date: 2021-09-15 00:00:00

In this paper we analyze the impact of vaccinations on spread of the COVID-19 virus for different age groups. More specifically we examine the deployment of vaccines in the Nordic countries in a comparative analysis where we analyze factors such as healthcare stress level and severity of disease through new infections, hospitalizations, intensive care unit (ICU) occupancy and deaths. Moreover, we analyze the impact of the various vaccine types, vaccination rate on the spread of the virus in each age group for Denmark, Finland, Iceland, Norway and Sweden from the start of the vaccination period in December 2020 until the end of August 2021. We perform a three-fold analysis: i) frequency analysis of infections and vaccine rates by age groups, ii) rolling correlations between vaccination strategies, severity of COVID-19 and healthcare stress level and; iii) we also employ the epidemic Renormalization Group (eRG) framework. The latter is used to mathematically model wave structures, as well as the impact of vaccinations on wave dynamics. We further compare the Nordic countries with England. Our main results are the quantification of the impact of the vaccination campaigns on age groups epidemiological data, across countries with high vaccine uptake. The data clearly shows that vaccines markedly reduce the number of new cases and the risk of serious illness.
Projecting the Impact of Covid-19 Variants and Vaccination Strategies in Disease Transmission using a Multilayer Network Model in Costa Rica
Authors: Yury E. García, Gustavo Mery, Paola Vásquez, Juan G. Calvo, Luis A. Barboza, Tania Rivas, Fabio Sanchez
Date: 2021-09-07 00:00:00
Tasks: ICU Admission

For countries starting to receive steady supplies of vaccines against SARS-CoV-2, the course of Covid-19 for the following months will be determined by the emergence of new variants and successful roll-out of vaccination campaigns. To anticipate this scenario, we used a multilayer network model developed to forecast the transmission dynamics of Covid-19 in Costa Rica, and to estimate the impact of the introduction of the Delta variant in the country, under two plausible vaccination scenarios, one sustaining Costa Rica's July 2021 vaccination pace of 30,000 doses per day and with high acceptance from the population and another with declining vaccination pace to 13,000 doses per day and with lower acceptance. Results suggest that the introduction and gradual dominance of the Delta variant would increase Covid-19 hospitalizations and ICU admissions between $35\%$ and $33.25\%$, from August 2021 to December 2021, depending on vaccine administration and acceptance. In the presence of the Delta variant, new Covid-19 hospitalizations and ICU admission would experience an average increase of $24.26\%$ and $27.19\%$ respectively in the same period if the vaccination pace drops. Our results can help decision-makers better prepare for the COVID-19 pandemic in the months to come.
Marginals Versus Copulas: Which Account For More Model Risk In Multivariate Risk Forecasting?
Authors: Simon Fritzsch, Maike Timphus, Gregor Weiss
Date: 2021-09-22 00:00:00

Copulas. We study the model risk of multivariate risk models in a comprehensive empirical study on Copula-GARCH models used for forecasting Value-at-Risk and Expected Shortfall. To determine whether model risk inherent in the forecasting of portfolio risk is caused by the candidate marginal or copula models, we analyze different groups of models in which we fix either the marginals, the copula, or neither. Model risk is economically significant, is especially high during periods of crisis, and is almost completely due to the choice of the copula. We then propose the use of the model confidence set procedure to narrow down the set of available models and reduce model risk for Copula-GARCH risk models. Our proposed approach leads to a significant improvement in the mean absolute deviation of one day ahead forecasts by our various candidate risk models.
IE-GAN: An Improved Evolutionary Generative Adversarial Network Using a New Fitness Function and a Generic Crossover Operator
Authors: Junjie Li, Jingyao Li, Wenbo Zhou, Shuai Lü
Date: 2021-07-25 00:00:00
Tasks: Evolutionary Algorithms, Generative Adversarial Network, Knowledge Distillation

The training of generative adversarial networks (GANs) is usually vulnerable to mode collapse and vanishing gradients. The evolutionary generative adversarial network (E-GAN) attempts to alleviate these issues by optimizing the learning strategy with multiple loss functions. It uses a learning-based evolutionary framework, which develops new mutation operators specifically for general deep neural networks. However, the evaluation mechanism in the fitness function of E-GAN cannot truly reflect the adaptability of individuals to their environment, leading to an inaccurate assessment of the diversity of individuals. Moreover, the evolution step of E-GAN only contains mutation operators without considering the crossover operator jointly, isolating the superior characteristics among individuals. To address these issues, we propose an improved E-GAN framework called IE-GAN, which introduces a new fitness function and a generic crossover operator. In particular, the proposed fitness function, from an objective perspective, can model the evolutionary process of individuals more accurately. The crossover operator, which has been commonly adopted in evolutionary algorithms, can enable offspring to imitate the superior gene expression of their parents through knowledge distillation. Experiments on various datasets demonstrate the effectiveness of our proposed IE-GAN in terms of the quality of the generated samples and time efficiency.
An Algorithm for Generating Gap-Fill Multiple Choice Questions of an Expert System
Authors: Pornpat Sirithumgul, Pimpaka Prasertsilp, Lorne Olfman
Date: 2021-09-17 00:00:00
Tasks: Multiple-choice, software testing, valid

This research is aimed to propose an artificial intelligence algorithm comprising an ontology-based design, text mining, and natural language processing for automatically generating gap-fill multiple choice questions (MCQs). The simulation of this research demonstrated an application of the algorithm in generating gap-fill MCQs about software testing. The simulation results revealed that by using 103 online documents as inputs, the algorithm could automatically produce more than 16 thousand valid gap-fill MCQs covering a variety of topics in the software testing domain. Finally, in the discussion section of this paper we suggest how the proposed algorithm should be applied to produce gap-fill MCQs being collected in a question pool used by a knowledge expert system.