pwc-archive/papers-with-abstracts huggingface dataset:
Binary response model with many weak instruments
Authors: Dakyung Seong
Date: 2022-01-13 00:00:00
Tasks: model

This paper considers an endogenous binary response model with many weak instruments. We employ a control function approach and a regularization scheme to obtain better estimation results for the endogenous binary response model in the presence of many weak instruments. Two consistent and asymptotically normally distributed estimators are provided, each of which is called a regularized conditional maximum likelihood estimator (RCMLE) and a regularized nonlinear least squares estimator (RNLSE). Monte Carlo simulations show that the proposed estimators outperform the existing ones when there are many weak instruments. We use the proposed estimation method to examine the effect of family income on college completion.
Exit, Voice and Political Change: Evidence from Swedish Mass Migration to the United States; A Comment
Authors: Per Pettersson-Lidbom
Date: 2022-01-13 00:00:00

In this comment, I revisit the question raised in Karadja and Prawitz (2019) concerning a causal relationship between mass emigration and long-run political outcomes. I discuss a number of potential problems with their instrumental variable analysis. First, there are at least three reasons why their instrument violates the exclusion restriction: (i) failing to control for internal migration, (ii) insufficient control for confounders correlated with their instrument, and (iii) emigration measured with a nonclassical measurement error. Second, I also discuss two problems with the statistical inference, both of which indicate that the instrument does not fulfill the relevance condition, i.e., the instrument is not sufficiently correlated with the endogenous variable emigration. Correcting for any of these problems reveals that there is no relationship between emigration and political outcomes.
Power Control for Multigroup Multicast Cell-Free Massive MIMO Downlink
Authors: Muhammad Farooq, Markku Juntti, Le-Nam Tran
Date: 2022-01-13 00:00:00
Tasks: Fairness

We consider a multigroup multicast cell-free multiple-input multiple-output (MIMO) downlink system with short-term power constraints. In particular, the normalized conjugate beamforming scheme is adopted at each access point (AP) to keep the downlink power strictly under the power budget regardless of small scale fading. In the considered scenario, APs multicast signals to multiple groups of users whereby users in the same group receive the same message. Under this setup, we are interested in maximizing the minimum achievable rate of all groups, commonly known as the max-min fairness problem, which has not been studied before in this context. To solve the considered problem, we first present a bisection method which in fact has been widely used in previous studies for cell-free massive MIMO, and then propose an accelerated projected gradient (APG) method. We show that the proposed APG method outperforms the bisection method requiring lesser run time while still achieving the same objective value. Moreover, the considered power control scheme provides significantly improved performance and more fairness among the users compared to the equal power allocation scheme.
Recovery Conditions of Sparse Signals Using Orthogonal Least Squares-Type Algorithms
Authors: L. Lu, W. Xu, Y. Wang, Z. Tian
Date: 2022-01-13 00:00:00
Tasks: Vocal Bursts Type Prediction

Orthogonal least squares (OLS)-type algorithms are efficient in reconstructing sparse signals, which include the well-known OLS, multiple OLS (MOLS) and block OLS (BOLS). In this paper, we first investigate the noiseless exact recovery conditions of these algorithms. Specifically, based on mutual incoherence property (MIP), we provide theoretical analysis of OLS and MOLS to ensure that the correct nonzero support can be selected during the iterative procedure. Nevertheless, theoretical analysis for BOLS utilizes the block-MIP to deal with the block sparsity. Furthermore, the noiseless MIP-based analyses are extended to the noisy scenario. Our results indicate that for K-sparse signals, when MIP or SNR satisfies certain conditions, OLS and MOLS obtain reliable reconstruction in at most K iterations, while BOLS succeeds in at most (K/d) iterations where d is the block length. It is shown that our derived theoretical results improve the existing ones, which are verified by simulation tests.
On the Performance of Handover Mechanisms for Non-Terrestrial Networks
Authors: Yusuf Islam Demir, Muhammad Sohaib J. Solaija, Huseyin Arslan
Date: 2022-01-13 00:00:00

Next-generation wireless networks require massive connectivity and ubiquitous coverage, for which non-terrestrial networks (NTNs) are a promising enabler. However, NTNs, especially non-geostationary satellites bring about challenges such as increased handovers (HOs) due to the moving coverage area of the satellite on the ground. Accordingly, in this work, we compare the conventional measurement-based HO triggering mechanism with other alternatives such as distance, elevation angle, and timer-based methods in terms of the numbers of HOs, ping-pong HOs, and radio link failures. The system-level simulations, carried out in accordance with the 3GPP model, show that the measurement-based approach can outperform the other alternatives provided that appropriate values of hysteresis/offset margins and time-to-trigger parameters are used. Moreover, future directions regarding this work are also provided at the end.
Performance Analysis of Multi-user NOMA Wireless-Powered mMTC Networks: A Stochastic Geometry Approach
Authors: Thanh-Luan Nguyen, Tri Nhu Do, Georges Kaddoum
Date: 2022-01-13 00:00:00
Tasks: Vocal Bursts Type Prediction

In this paper, we aim to improve the connectivity, scalability, and energy efficiency of machine-type communication (MTC) networks with different types of MTC devices (MTCDs), namely Type-I and Type-II MTCDs, which have different communication purposes. To this end, we propose two transmission schemes called connectivity-oriented machine-type communication (CoM) and quality-oriented machine-type communication (QoM), which take into account the stochastic geometry-based deployment and the random active/inactive status of MTCDs. Specifically, in the proposed schemes, the active Type-I MTCDs operate using a novel Bernoulli random process-based simultaneous wireless information and power transfer (SWIPT) architecture. Next, utilizing multi-user power-domain non-orthogonal multiple access (PD-NOMA), each active Type-I MTCD can simultaneously communicate with another Type-I MTCD and a scalable number of Type-II MTCDs. In the performance analysis of the proposed schemes, we prove that the true distribution of the received power at a Type-II MTCD in the QoM scheme can be approximated by the Singh-Maddala distribution. Exploiting this unique statistical finding, we derive approximate closed-form expressions for the outage probability (OP) and sum-throughput of massive MTC (mMTC) networks. Through numerical results, we show that the proposed schemes provide a considerable sum-throughput gain over conventional mMTC networks.
Time-varying Identification of Guided Wave Propagation under Varying Temperature via Non-Stationary Time Series Models
Authors: Shabbir Ahmed, Fotis Kopsaftopoulos
Date: 2022-01-12 00:00:00
Tasks: Structural Health Monitoring, TAR, Time Series, Time Series Analysis

Modern-day civil, mechanical, and aeronautical structures are transitioning towards a continuous, online, and automated maintenance paradigm in order to ensure increased safety and reliability. The field of structural health monitoring (SHM) is playing a key role in this respect and active sensing acousto-ultrasound guided-wave based SHM techniques have shown great promise due to their potential sensitivity to small changes in the structure. However, the methods' robustness and diagnosis capability become limited in the presence of environmental and operational variability such as changing temperature. In order to circumvent this difficulty, in this paper, a novel stochastic time series-based framework was adopted to model guided wave propagation under varying temperatures. Different stochastic time-varying time series models, such as Recursive Maximum Likelihood Time-varying Auto-Regressive (RML-TAR) and Recursive Maximum Likelihood Time-varying Auto-Regressive with Exogenous Excitation (RML-TARX) models are put forward to model and capture the underlying dynamics of guided wave propagation under varying temperatures. The steps and facets of the identification procedure are presented and clearly explained. Then the identified models are used to perform one-step-ahead prediction as well as "simulation" of the guided wave signals. In order to gain insight from a physics perspective, high-fidelity finite element (FE) models were also established to model the effect of temperature variation on guided wave propagation. Finally, surrogate models are formulated through the use of stochastic time-dependent RML-TARX models and compared with the FE models under varying temperatures.
Deep Learning-based Link Configuration for Radar-aided Multiuser mmWave Vehicle-to-Infrastructure Communication
Authors: Andrew Graff, Yun Chen, Nuria González-Prelcic, Takayuki Shimizu
Date: 2022-01-12 00:00:00

Configuring millimeter wave links following a conventional beam training protocol, as the one proposed in the current cellular standard, introduces a large communication overhead, specially relevant in vehicular systems, where the channels are highly dynamic. In this paper, we propose the use of a passive radar array to sense automotive radar transmissions coming from multiple vehicles on the road, and a radar processing chain that provides information about a reduced set of candidate beams for the links between the road-infrastructure and each one of the vehicles. This prior information can be later leveraged by the beam training protocol to significantly reduce overhead. The radar processing chain estimates both the timing and chirp rates of the radar signals, isolates the individual signals by filtering out interfering radar chirps, and estimates the spatial covariance of each individual radar transmission. Then, a deep network is used to translate features of these radar spatial covariances into features of the communication spatial covariances, by learning the intricate mapping between radar and communication channels, in both line-of-sight and non-line-of-sight settings. The communication rates and outage probabilities of this approach are compared against exhaustive search and pure radar-aided beam training methods (without deep learning-based mapping), and evaluated on multi-user channels simulated by ray tracing. Results show that: (i) the proposed processing chain can reliably isolate the spatial covariances for individual radars, and (ii) the radar-to-communications translation strategy based on deep learning provides a significant improvement over pure radar-aided methods in both LOS and NLOS channels.
Depth Normalization of Small RNA Sequencing: Using Data and Biology to Select a Suitable Method
Authors: Yannick Düren, Johannes Lederer, Li-Xuan Qin
Date: 2022-01-13 00:00:00

Deep sequencing has become one of the most popular tools for transcriptome profiling in biomedical studies. While an abundance of computational methods exists for "normalizing" sequencing data to remove unwanted between-sample variations due to experimental handling, there is no consensus on which normalization is the most suitable for a given data set. To address this problem, we developed "DANA" - an approach for assessing the performance of normalization methods for microRNA sequencing data based on biology-motivated and data-driven metrics. Our approach takes advantage of well-known biological features of microRNAs for their expression pattern and chromosomal clustering to simultaneously assess (1) how effectively normalization removes handling artifacts, and (2) how aptly normalization preserves biological signals. With DANA, we confirm that the performance of eight commonly used normalization methods vary widely across different data sets and provide guidance for selecting a suitable method for the data at hand. Hence, it should be adopted as a routine preprocessing step (preceding normalization) for microRNA sequencing data analysis. DANA is implemented in R and publicly available at https://github.com/LXQin/DANA.
Accurate identification of bacteriophages from metagenomic data using Transformer
Authors: Jiayu Shang, Xubo Tang, Ruocheng Guo, Yanni Sun
Date: 2022-01-13 00:00:00
Tasks: Language Modelling

Motivation: Bacteriophages are viruses infecting bacteria. Being key players in microbial communities, they can regulate the composition/function of microbiome by infecting their bacterial hosts and mediating gene transfer. Recently, metagenomic sequencing, which can sequence all genetic materials from various microbiome, has become a popular means for new phage discovery. However, accurate and comprehensive detection of phages from the metagenomic data remains difficult. High diversity/abundance, and limited reference genomes pose major challenges for recruiting phage fragments from metagenomic data. Existing alignment-based or learning-based models have either low recall or precision on metagenomic data. Results: In this work, we adopt the state-of-the-art language model, Transformer, to conduct contextual embedding for phage contigs. By constructing a protein-cluster vocabulary, we can feed both the protein composition and the proteins' positions from each contig into the Transformer. The Transformer can learn the protein organization and associations using the self-attention mechanism and predicts the label for test contigs. We rigorously tested our developed tool named PhaMer on multiple datasets with increasing difficulty, including quality RefSeq genomes, short contigs, simulated metagenomic data, mock metagenomic data, and the public IMG/VR dataset. All the experimental results show that PhaMer outperforms the state-of-the-art tools. In the real metagenomic data experiment, PhaMer improves the F1-score of phage detection by 27\%.
Neuronal cable equations derived from the hydrodynamic motion of charged particles
Authors: Davide Forcella, Alberto Romagnoni, Alain Destexhe
Date: 2022-01-13 00:00:00

Neuronal cable theory is usually derived from an electric analogue of the membrane, which contrasts with the slow movement of ions in aqueous media. We show here that it is possible to derive neuronal cable equations from a different perspective, based on the laws of hydrodynamic motion of charged particles (Navier-Stokes equations). This results in similar cable equations, but with additional contributions arising from nonlinear interactions inherent to fluid dynamics, and which may shape the integrative properties of the neurons.
Cognitive forces shape the dynamics of word usage across multiple languages
Authors: Alejandro Pardo Pintos, Diego E Shalom, Enzo Tagliazucchi, Gabriel Mindlin, Marcos A Trevisan
Date: 2022-01-12 00:00:00
Tasks: Time Series, Time Series Analysis

The analysis of thousands of time series in different languages reveals that word usage presents oscillations with a prevalence of 16-year cycles, mounted on slowly varying trends. These components carry different information: while similar oscillatory patterns gather semantically related words, similar trends group together keywords representative of cultural and historical periods. We interpreted the regular oscillations as cycles of interest and saturation, whose behavior could be captured using a simple mathematical model. Driving the model with the empirical trends, we were able to explain word frequency traces across multiple languages throughout the last three centuries. Our results suggest that word frequency usage is poised at dynamical criticality, close to a Hopf bifurcation which signals the emergence of oscillatory dynamics. Crucially, our model explains the oscillatory synchronization observed within groups of words and provides an interpretation of this phenomenon in terms of the cultural context driving collective cognition. These findings contribute to unravel how our use of language is shaped by the interplay between human cognition and sociocultural forces.
Modeling between-farm transmission dynamics of porcine epidemic diarrhea virus: characterizing the dominant transmission routes
Authors: Jason A. Galvis, Cesar A. Corzo, Joaquin M. Prada, Gustavo Machado
Date: 2022-01-13 00:00:00

The role of transportation vehicles, pig movement between farms, proximity to infected premises, and feed deliveries has not been fully considered in the dissemination dynamics of porcine epidemic diarrhea virus (PEDV). This has limited efforts for disease control and elimination restricting the development of risk-based resource allocation to the most relevant modes of PEDV dissemination. Here, we modeled nine modes of between-farm transmission pathways including farm-to-farm proximity (local transmission), contact network of pig farm movements between sites, four different contact networks of transportation vehicles (vehicles that transport pigs from farm-to-farm, pigs to markets, feed distribution and crew), the volume of animal by-products within feed diets (e.g. animal fat and meat and bone meal) to reproduce PEDV transmission dynamics. The model was calibrated in space and time with weekly PEDV outbreaks. We investigated the model performance to identify outbreak locations and the contribution of each route in the dissemination of PEDV. The model estimated that 42.7% of the infections in sow farms were related to vehicles transporting feed, 34.5% of infected nurseries were associated with vehicles transporting pigs to farms, and for both farm types, pig movements or local transmission were the next most relevant routes. On the other hand, finishers were most often (31.4%) infected via local transmission, followed by the vehicles transporting feed and pigs to farm networks. Feed ingredients did not significantly improve model calibration metrics. The proposed modeling framework provides an evaluation of PEDV transmission dynamics, ranking the most important routes of PEDV dissemination and granting the swine industry valuable information to focus efforts and resources on the most important transmission routes.
Novel Symmetry-preserving Neural Network Model for Phylogenetic Inference
Authors: Xudong Tang, Leonardo Zepeda-Nunez, Shengwen Yang, Zelin Zhao, Claudia Solis-Lemus
Date: 2022-01-12 00:00:00

Scientists world-wide are putting together massive efforts to understand how the biodiversity that we see on Earth evolved from single-cell organisms at the origin of life and this diversification process is represented through the Tree of Life. Low sampling rates and high heterogeneity in the rate of evolution across sites and lineages produce a phenomenon denoted "long branch attraction" (LBA) in which long non-sister lineages are estimated to be sisters regardless of their true evolutionary relationship. LBA has been a pervasive problem in phylogenetic inference affecting different types of methodologies from distance-based to likelihood-based. Here, we present a novel neural network model that outperforms standard phylogenetic methods and other neural network implementations under LBA settings. Furthermore, unlike existing neural network models, our model naturally accounts for the tree isomorphisms via permutation invariant functions which ultimately result in lower memory and allows the seamless extension to larger trees.
Analysis of a five-factor capital market model
Authors: Søren Fiig Jarner, Michael Preisel
Date: 2022-01-13 00:00:00
Tasks: model

In this paper we analyse the five-factor capital market model of Munk et al.(2004). The model features a Vasicek interest rate model, an equity index with mean-reverting excess return and an index for realized inflation with mean-reverting expectation. The primary aim of the analysis is to facilitate so-called exact simulation from the model on a set of discrete time points. It turns out that this can be achieved by sampling from a (degenerate) seven-dimensional normal distribution. We derive the distributional results necessary and describe how to overcome the rank deficiency of the variance-covariance matrix in practice. The tradeable assets in the original model consist of cash, nominal bonds and stocks. We extend the investment universe to also include inflation bonds by deriving the arbitrage free break-even inflation (BEI) curve for a three-parameter specification of the two market prices of inflation risk. Finally, we provide a number of auxiliary results regarding the dynamics of constant-maturity nominal and inflation bond indices, the distribution of the stock index in nominal and real terms, and the distribution of the Sharpe ratio for individual assets and portfolios with an application to factor investing.
Pricing Time-to-Event Contingent Cash Flows: A Discrete-Time Survival Analysis Approach
Authors: Jackson P. Lautier, Vladimir Pozdnyakov, Jun Yan
Date: 2022-01-13 00:00:00
Tasks: Survival Analysis

Prudent management of insurance investment portfolios requires competent asset pricing of fixed-income assets with time-to-event contingent cash flows, such as consumer asset-backed securities (ABS). Current market pricing techniques for these assets either rely on a non-random time-to-event model or may not utilize detailed asset-level data that is now available with most public transactions. We first establish a framework capable of yielding estimates of the time-to-event random variable from securitization data, which is discrete and often subject to left-truncation and right-censoring. We then show that the vector of discrete-time hazard rate estimators is asymptotically multivariate normal with independent components, which has not yet been done in the statistical literature in the case of both left-truncation and right-censoring. The time-to-event distribution estimates are then fed into our cash flow model, which is capable of calculating a formulaic price of a pool of time-to-event contingent cash flows vis-\'{a}-vis calculating an expected present value with respect to the estimated time-to-event distribution. In an application to a subset of 29,845 36-month leases from the Mercedes-Benz Auto Lease Trust 2017-A (MBALT 2017-A) bond, our pricing model yields estimates closer to the actual realized future cash flows than the non-random time-to-event model, especially as the fitting window increases. Finally, in certain settings, the asymptotic properties of the hazard rate estimators allow investors to assess the potential uncertainty of the price point estimates, which we illustrate for a subset of 493 24-month leases from MBALT 2017-A.
Using Computational Intelligence for solving the Ornstein-Zernike equation
Authors: Edwin Bedolla
Date: 2021-11-17 00:00:00
Tasks: Relation

The main goal of this thesis is to provide an exploration of the use of computational intelligence techniques to study the numerical solution of the Ornstein-Zernike equation for simple liquids. In particular, a continuous model of the hard sphere fluid is studied. There are two main proposals in this contribution. First, the use of neural networks as a way to parametrize closure relation when solving the Ornstein-Zernike equation. It is explicitly shown that in the case of the hard sphere fluid, the neural network approach seems to reduce to the so-called Hypernetted Chain closure. For the second proposal, we explore the fact that if more physical information is incorporated into the theoretical formalism, a better estimate can be obtained with the use of evolutionary optimization techniques. When choosing the modified Verlet closure relation, and leaving a couple of free parameters to be adjusted, the results are as good as those obtained from molecular simulations. The thesis is then closed with a brief summary of the main findings and outlooks on different ways to improve the proposals presented here.