pwc-archive/papers-with-abstracts huggingface dataset:
Modelling Large Dimensional Datasets with Markov Switching Factor Models
Authors: Matteo Barigozzi, Daniele Massacci
Date: 2022-10-18 00:00:00

We study a novel large dimensional approximate factor model with regime changes in the loadings driven by a latent first order Markov process. By exploiting the equivalent linear representation of the model, we first recover the latent factors by means of Principal Component Analysis. We then cast the model in state-space form, and we estimate loadings and transition probabilities through an EM algorithm based on a modified version of the Baum-Lindgren-Hamilton-Kim filter and smoother that makes use of the factors previously estimated. Our approach is appealing as it provides closed form expressions for all estimators. More importantly, it does not require knowledge of the true number of factors. We derive the theoretical properties of the proposed estimation procedure, and we show their good finite sample performance through a comprehensive set of Monte Carlo experiments. The empirical usefulness of our approach is illustrated through three applications to large U.S. datasets of stock returns, macroeconomic variables, and inflation indexes.
Party On: The Labor Market Returns to Social Networks in Adolescence
Authors: Adriana Lleras-Muney, Matthew Miller, Shuyang Sheng, Veronica Sovero
Date: 2022-10-17 00:00:00

We investigate the returns to adolescent friendships on earnings in adulthood using data from the National Longitudinal Study of Adolescent to Adult Health. Because both education and friendships are jointly determined in adolescence, OLS estimates of their returns are likely biased. We implement a novel procedure to obtain bounds on the causal returns to friendships: we assume that the returns to schooling range from 5 to 15% (based on prior literature), and instrument for friendships using similarity in age among peers. Having one more friend in adolescence increases earnings between 7 and 14%, substantially more than OLS estimates would suggest.
Electricity grid tariffs for electrification in households: Bridging the gap between cross-subsidies and fairness
Authors: Claire-Marie Bergaentzlé, Philipp Andreas Gunkel, Mohammad Ansarin, Yashar Ghiassi-Farrokhfal, Henrik Klinge Jacobsen
Date: 2022-10-18 00:00:00
Tasks: Fairness

Developing new electricity grid tariffs in the context of household electrification raises old questions about who pays for what and to what extent. When electric vehicles (EVs) and heat pumps (HPs) are owned primarily by households with higher financial status than others, new tariff designs may clash with the economic argument for efficiency and the political arguments for fairness. This article combines tariff design and redistributive mechanisms to strike a balance between time-differentiated signals, revenue stability for the utility, limited grid costs for vulnerable households, and promoting electrification. We simulate the impacts of this combination on 1.4 million Danish households (about 50% of the country's population) and quantify the cross-subsidization effects between groups. With its unique level of detail, this study stresses the spillover effects of tariffs. We show that a subscription-heavy tariff associated with a ToU rate and a low redistribution factor tackles all the above goals.
An Affective Video Database using Multimedia Content Analysis rated on Indian samples
Authors: Sudhakar Mishra, Narayanan Srinivasan, Uma Shanker Tiwary
Date: 2022-10-18 00:00:00

Availability of naturalistic affective stimuli is needed for creating the affective technological solution as well as making progress in affective science. Although a lot of progress in the collection of affective multimedia stimuli has been made in western countries, the technology and findings based on such monocultural datasets may not be scalable to other cultures. Moreover, the available dataset on affective multimedia content has some experimenter bias in the initial manual selection of affective multimedia content. Hence, in this work, we mainly tried to address two problems. The first problem relates to the experimenter's subjective bias, and the second relates to the non-availability of affective multimedia dataset validated on Indian population. We tried to address both problems by reducing the experimenter's bias as much as possible. We adopted the data science and multimedia content analysis techniques to perform our initial collection and a further selection of stimuli. Our method resulted in a dataset with a wide variety in content, stimuli from Western and Indian cinema, and symmetric presence of stimuli along valence, arousal and dominance dimensions. We conclude that using our method, more cross-cultural affective stimuli datasets can be created, which is essential to make progress in affective technology and science.
Scalable Framework For Deep Learning based CSI Feedback
Authors: Liqiang Jin, Qiuping Huang, Qiubin Gao, Yongqiang Fei, Shaohui Sun
Date: 2022-10-18 00:00:00
Tasks: Deep Learning

Deep learning (DL) based channel state information (CSI) feedback in multiple-input multiple-output (MIMO) systems recently has attracted lots of attention from both academia and industrial. From a practical point of views, it is huge burden to train, transfer and deploy a DL model for each parameter configuration of the base station (BS). In this paper, we propose a scalable and flexible framework for DL based CSI feedback referred as scalable CsiNet (SCsiNet) to adapt a family of configured parameters such as feedback payloads, MIMO channel ranks, antenna numbers. To reduce model size and training complexity, the core block with pre-processing and post-processing in SCsiNet is reused among different parameter configurations as much as possible which is totally different from configuration-orienting design. The preprocessing and post-processing are trainable neural network layers introduced for matching input/output dimensions and probability distributions. The proposed SCsiNet is evaluated by metrics of squared generalized cosine similarity (SGCS) and user throughput (UPT) in system level simulations. Compared to existing schemes (configuration-orienting DL schemes and 3GPP Rel-16 Type-II codebook based schemes), the proposed scheme can significantly reduce mode size and achieve 2%-10% UPT improvement for all parameter configurations.
Probability-Reduction of Geolocation using Reconfigurable Intelligent Surface Reflections
Authors: Anders M. Buvarp, Daniel J. Jakubisin, William C. Headley, Jeffrey H. Reed
Date: 2022-10-18 00:00:00

With the recent introduction of electromagnetic meta-surfaces and reconfigurable intelligent surfaces, a paradigm shift is currently taking place in the world of wireless communications and related industries. These new technologies have enabled the inclusion of the wireless channel as part of the optimization process. This is of great interest as we transition from 5G mobile communications towards 6G. In this paper, we explore the possibility of using a reconfigurable intelligent surface in order to disrupt the ability of an unintended receiver to geolocate the source of transmitted signals in a 5G communication system. We investigate how the performance of the MUSIC algorithm at the unintended receiver is degraded by correlated reflected signals introduced by a reconfigurable intelligent surface in the wireless channel. We analyze the impact of the direction of arrival, delay, correlation, and strength of the reconfigurable intelligent surface signal with respect to the line-of-sight path from the transmitter to the unintended receiver. An effective method is introduced for defeating direction-finding efforts using dual sets of surface reflections. This novel method is called Geolocation-Probability Reduction using Dual Reconfigurable Intelligent Surfaces (GPRIS). We also show that the efficiency of this method is highly dependent on the geometry, that is, the placement of the reconfigurable intelligent surface relative to the unintended receiver and the transmitter.
Through Tissue Ultra-high-definition Video Transmission Using an Ultrasound Communication Channel
Authors: Zhengchang Kou, Andrew C. Singer, Michael L. Oelze
Date: 2022-10-18 00:00:00

Wireless capsule endoscopy (WCE) has been widely adopted as complementary to traditional wired gastroendoscopy, especially for small bowel diseases which are beyond the latter's reach. However, both the video resolution and frame rates are limited in current WCE solutions due to the limited wireless data rate. The reasons behind this are that the electromagnetic (EM), radio frequency (RF) based communication scheme used by WCE has strict limits on useable bandwidth and power, and the high attenuation in the human body compared to air. Ultrasound communication could be a potential alternative solution as it has access to much higher bandwidths and transmitted power with much lower attenuation. In this paper, we propose an ultrasound communication scheme specially designed for high data rate through tissue data transmission and validate this communication scheme by successfully transmitting ultra-high-definition (UHD) video (3840*2160 pixels at 60 FPS) through 5 cm of pork belly. Over 8.3 Mbps error free payload data rate was achieved with the proposed communication scheme and our custom-built field programmable gate array (FPGA) based test platform.
Hybrid mmWave MIMO Systems under Hardware Impairments and Beam Squint: Channel Model and Dictionary Learning-aided Configuration
Authors: Hongxiang Xie, Joan Palacios, Nuria González-Prelcic
Date: 2022-10-17 00:00:00
Tasks: Compressive Sensing, Dictionary Learning, parameter estimation

Low overhead channel estimation based on compressive sensing (CS) has been widely investigated for hybrid wideband millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. The channel sparsifying dictionaries used in prior work are built from ideal array response vectors evaluated on discrete angles of arrival/departure. In addition, these dictionaries are assumed to be the same for all subcarriers, without considering the impacts of hardware impairments and beam squint. In this manuscript, we derive a general channel and signal model that explicitly incorporates the impacts of hardware impairments, practical pulse shaping functions, and beam squint, overcoming the limitations of mmWave MIMO channel and signal models commonly used in previous work. Then, we propose a dictionary learning (DL) algorithm to obtain the sparsifying dictionaries embedding hardware impairments, by considering the effect of beam squint without introducing it into the learning process. We also design a novel CS channel estimation algorithm under beam squint and hardware impairments, where the channel structures at different subcarriers are exploited to enable channel parameter estimation with low complexity and high accuracy. Numerical results demonstrate the effectiveness of the proposed DL and channel estimation strategy when applied to realistic mmWave channels.
AlignOT: An optimal transport based algorithm for fast 3D alignment with applications to cryogenic electron microscopy density maps
Authors: A. Tajmir Riahi, G. Woollard, F. Poitevin, A. Condon, K. Dao Duc
Date: 2022-10-17 00:00:00
Tasks: Cryogenic Electron Microscopy (cryo-EM)

Aligning electron density maps from Cryogenic electron microscopy (cryo-EM) is a first key step for studying multiple conformations of a biomolecule. As this step remains costly and challenging, with standard alignment tools being potentially stuck in local minima, we propose here a new procedure, called AlignOT, which relies on the use of computational optimal transport (OT) to align EM maps in 3D space. By embedding a fast estimation of OT maps within a stochastic gradient descent algorithm, our method searches for a rotation that minimizes the Wasserstein distance between two maps, represented as point clouds. We quantify the impact of various parameters on the precision and accuracy of the alignment, and show that AlignOT can outperform the standard local alignment methods, with an increased range of rotation angles leading to proper alignment. We further benchmark AlignOT on various pairs of experimental maps, which account for different types of conformational heterogeneities and geometric properties. As our experiments show good performance, we anticipate that our method can be broadly applied to align 3D EM maps.
Emerging dominant SARS-CoV-2 variants
Authors: Jiahui Chen, Rui Wang, Yuta Hozumi, Gengzhuo Liu, Yuchi Qiu, Xiaoqi Wei, Guo-Wei Wei
Date: 2022-10-18 00:00:00

Accurate and reliable forecasting of emerging dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants enables policymakers and vaccine makers to get prepared for future waves of infections. The last three waves of SARS-CoV-2 infections caused by dominant variants Omicron (BA.1), BA.2, and BA.4/BA.5 were accurately foretold by our artificial intelligence (AI) models built with biophysics, genotyping of viral genomes, experimental data, algebraic topology, and deep learning. Based on newly available experimental data, we analyzed the impacts of all possible viral spike (S) protein receptor-binding domain (RBD) mutations on the SARS-CoV-2 infectivity. Our analysis sheds light on viral evolutionary mechanisms, i.e., natural selection through infectivity strengthening and antibody resistance. We forecast that BA.2.10.4, BA.2.75, BQ.1.1, and particularly, BA.2.75+R346T, have high potential to become new dominant variants to drive the next surge.
Integrative Pan-Cancer Analysis of RNMT: a Potential Prognostic and Immunological Biomarker
Authors: Shuqiang Huang, Cuiyu Tan, Jinzhen Zheng, Zhugu Huang, Zhihong Li, Ziyin Lv, Wanru Chen
Date: 2022-10-18 00:00:00
Tasks: Prognosis, Survival Analysis

Background: RNA guanine-7 methyltransferase (RNMT) is one of the main regulators of N7-methylguanosine, and the deregulation of RNMT correlated with tumor development and immune metabolism. However, the specific function of RNMT in pan-cancer remains unclear. Methods: RNMT expression in different cancers was analyzed using multiple databases, including Cancer Cell Line Encyclopedia (CCLE), Genotype-Tissue Expression Project (GTEx), and The Cancer Genome Atlas (TCGA). Cox regression analysis and Kaplan-Meier analysis were used to estimate the correlation of RNMT expression to prognosis. The data was also used to research the relationship between RNMT expression and common immunoregulators, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), and DNA methyltransferase (DNMT). Additionally, the cBioPortal website was used to evaluate the characteristics of RNMT alteration. The TISDB database was used to obtain the expression of different subtypes. The Tumor Immune Estimation Resource (TIMER) database was used to analyze the association between RNMT and tumor immune infiltration. Gene set enrichment analysis (GSEA) was used to identify the relevant pathways. Results: RNMT was ubiquitously highly expressed across cancers and survival analysis revealed that its expression was highly associated with the clinical prognosis of various cancer types. Remarkably, RNMT participates in immune regulation and plays a crucial part in the tumor microenvironment. A positive association was found between RNMT expression and six immune cell types expression in colon adenocarcinoma, kidney renal clear cell carcinoma, and liver hepatocellular carcinoma. Moreover, RNMT expression was highly associated with immunoregulators in most cancer types, and correlated to TMB, MSI, MMR, and DNMT. Finally, GSEA indicated that RNMT may correlate with tumor immunity.
MVP: Detection of motif-making and -breaking mutations
Authors: Afif Elghraoui, Faramarz Valafar
Date: 2022-10-15 00:00:00

Background: DNA, RNA, and protein sequence motifs can be recognition sites for biological functions such as regulation, DNA base modification, and molecular binding in general. The gain and loss of such motifs can carry important consequences. When comparing sequences, the analysis of individual variants does not impart an understanding of the impact on these sites. Rather, only when these variants considered together with their neighbors and the original sequence context does this become possible. Results: The motif-variant probe (mvp) makes this consideration, counting instances of user-specified sequence motifs before and after mutation and reports those that result in motif gain or loss. mvp can perform a similar analysis for proteins with amino acid variant data. The software is freely available at https://lpcdrp.gitlab.io/mvp and also installable with the conda package manager. Conclusions: The ability to easily search for variants affecting any motif, together with the simultaneous consideration of neighboring variants makes mvp a versatile tool to aid in a less-frequented dimension of comparative genomics.
Rare-Event Sampling Analysis Uncovers the Fitness Landscape of the Genetic Code
Authors: Yuji Omachi, Nen Saito, Chikara Furusawa
Date: 2022-10-18 00:00:00

The genetic code refers to a rule that maps 64 codons to 20 amino acids. Nearly all organisms, with few exceptions, share the same genetic code, the standard genetic code (SGC). While it remains unclear why this universal code has arisen and been maintained during evolution, it may have been preserved under selection pressure. Theoretical studies comparing the SGC and numerically created hypothetical random genetic codes have suggested that the SGC has been subject to strong selection pressure for being robust against translation errors. However, these prior studies have searched for random genetic codes in only a small subspace of the possible code space due to limitations in computation time. Thus, how the genetic code has evolved, and the characteristics of the genetic code fitness landscape, remain unclear. By applying multicanonical Monte Carlo, an efficient rare-event sampling method, we efficiently sampled random codes from a much broader random ensemble of genetic codes than in previous studies, estimating that only one out of every $10^{20}$ random codes is more robust than the SGC. This estimate is significantly smaller than the previous estimate, one in a million. We also characterized the fitness landscape of the genetic code that has four major fitness peaks, one of which includes the SGC. Furthermore, genetic algorithm analysis revealed that evolution under such a multi-peaked fitness landscape could be strongly biased toward a narrow peak, in an evolutionary path-dependent manner.
Generalised Gillespie Algorithms for Simulations in a Rule-Based Epidemiological Model Framework
Authors: David Alonso, Steffen Bauer, Markus Kirkilionis, Lisa Maria Kreusser, Luca Sbano
Date: 2022-10-17 00:00:00
Tasks: Epidemiology

Rule-based models have been successfully used to represent different aspects of the COVID-19 pandemic, including age, testing, hospitalisation, lockdowns, immunity, infectivity, behaviour, mobility and vaccination of individuals. These rule-based approaches are motivated by chemical reaction rules which are traditionally solved numerically with the standard Gillespie algorithm proposed in the context of molecular dynamics. When applying reaction system type of approaches to epidemiology, generalisations of the Gillespie algorithm are required due to the time-dependency of the problems. In this article, we present different generalisations of the standard Gillespie algorithm which address discrete subtypes (e.g., incorporating the age structure of the population), time-discrete updates (e.g., incorporating daily imposed change of rates for lockdowns) and deterministic delays (e.g., given waiting time until a specific change in types such as release from isolation occurs). These algorithms are complemented by relevant examples in the context of the COVID-19 pandemic and numerical results.
Sector-wise analysis of Indian stock market: Long and short-term risk and stability analysis
Authors: Suchetana Sadhukhan, Poulomi Sadhukhan
Date: 2022-10-18 00:00:00

This paper, for the first time, focuses on the sector-wise analysis of a stock market through multifractal analysis. We have considered Bombay Stock Exchange, India, and identified two time scales, short ($<200$ days) and long time-scale ($>200$ days) for investment. We infer that long-term investment will be more profitable. For long time scale, sectors can be separated into two categories based on the Hurst exponent values; one corresponds to stable sectors with small fluctuations, and the other with dominance of large fluctuations leading to possible downturns in those sectors.
Measure-valued processes for energy markets
Authors: Christa Cuchiero, Luca Di Persio, Francesco Guida, Sara Svaluto-Ferro
Date: 2022-10-17 00:00:00

We introduce a framework that allows to employ (non-negative) measure-valued processes for energy market modeling, in particular for electricity and gas futures. Interpreting the process' spatial structure as time to maturity, we show how the Heath-Jarrow-Morton approach can be translated to this framework, thus guaranteeing arbitrage free modeling in infinite dimensions. We derive an analog to the HJM-drift condition and then treat in a Markovian setting existence of non-negative measure-valued diffusions that satisfy this condition. To analyze mathematically convenient classes we build on Cuchiero et al. (2021) and consider measure-valued polynomial and affine diffusions, where we can precisely specify the diffusion part in terms of continuous functions satisfying certain admissibility conditions. For calibration purposes these functions can then be parameterized by neural networks yielding measure-valued analogs of neural SPDEs. By combining Fourier approaches or the moment formula with stochastic gradient descent methods, this then allows for tractable calibration procedures which we also test by way of example on market data. We also sketch how measure-valued processes can be applied in the context of renewable energy production modeling.
Double Attention-based Lightweight Network for Plant Pest Recognition
Authors: Sivasubramaniam Janarthan, Selvarajah Thuseethan, Sutharshan Rajasegarar, John Yearwood
Date: 2022-10-04 00:00:00

Timely recognition of plant pests from field images is significant to avoid potential losses of crop yields. Traditional convolutional neural network-based deep learning models demand high computational capability and require large labelled samples for each pest type for training. On the other hand, the existing lightweight network-based approaches suffer in correctly classifying the pests because of common characteristics and high similarity between multiple plant pests. In this work, a novel double attention-based lightweight deep learning architecture is proposed to automatically recognize different plant pests. The lightweight network facilitates faster and small data training while the double attention module increases performance by focusing on the most pertinent information. The proposed approach achieves 96.61%, 99.08% and 91.60% on three variants of two publicly available datasets with 5869, 545 and 500 samples, respectively. Moreover, the comparison results reveal that the proposed approach outperforms existing approaches on both small and large datasets consistently.