pwc-archive/papers-with-abstracts huggingface dataset:
Multi-User Rate Splitting in Optical Wireless Networks
Authors: Ahmad Adnan Qidan, Khulood Alazwary, Taisir El-Gorashi, Majid Safari, Harald Haas, Richard V. Penty, Ian H. White, Jaafar M. H. Elmirghani
Date: 2022-07-23 00:00:00

Optical wireless communication (OWC) has recently received massive interest as a new technology that can support the enormous data traffic increasing on daily basis. Laser-based OWC networks can provide terabits per second (Tbps) aggregate data rates. However, the emerging OWC networks require clusters of optical transmitters to provide uniform coverage for multiple users. In this context, multi-user interference (MUI) is a crucial issue that must be managed efficiently to provide high spectral efficiency. Rate splitting (RS) is proposed as a transmission scheme to serve multiple users simultaneously by splitting the message of a given user into common and private messages, and then, each user decodes the desired message following a certain procedure. In radio frequency (RF) networks, RS provides higher spectral efficiency compared with orthogonal and non-orthogonal transmission schemes. Considering the high density of OWC networks, the performance of RS is limited by the cost of providing channel state information (CSI) at transmitters and by the noise resulting from interference cancellation. In this work, a user-grouping algorithm is proposed and used to form multiple groups, each group contains users spatially clustered. Then, an outer precoder is designed to manage inter-group interference following the methodology of blind interference alignment (BIA), which reduces the requirements of CSI at RF or optical transmitters. For intra-group interference, RS is applied within each group where the users belonging to a given group receive a unique common message on which their private messages are superimposed. Furthermore, an optimization problem is formulated to allocate the power among the private messages intended to all users such that the sum rate of the network is maximized.
Novel Improvement for Nonlinear Compatibility of Least Mean Square Adaptive Algorithm
Authors: Zhengyang Zhang
Date: 2022-07-23 00:00:00

In order to improve the least mean squares (LMS) adaptation algorithm to accommodate the nonlinear transfer function, and to adjust the coefficients of adaptive filter during the actual implement of bias voltage and signal amplitude, methods are proposed and simulated to develop a nonlinear adaptive filter. The inputs to LMS are replaced by the derivatives of traditional inputs, and the step for each training iteration is adaptively controlled by the difference between target signal and actual signal. The simulation utilizes the implementation of Nyquist pulses optical sampling and works as a digital signal processing pre-compensation to reduce influence of the frequency responses on wires and devices. The simulation result shows promising improvement with the modified adaptation algorithm method in tackling Mach Zehnder modulator's non-monotonic transfer function.
Perturbations in epidemiological models: When zombies attack, we can survive!
Authors: Robert F. Allen, Cassandra Jens, Theodore J. Wendt
Date: 2022-07-25 00:00:00

In this paper, we investigate the existence of stability-changing bifurcations in epidemiological models used to study the spread of zombiism through a human population. These bifurcations show that although linear instability of disease-free equilibria may exist in a model, perturbations of model parameters may result in stability. Thus, we show that humans can survive a zombie outbreak.
Multisymplectic Formulation of Deep Learning Using Mean--Field Type Control and Nonlinear Stability of Training Algorithm
Authors: Nader Ganaba
Date: 2022-07-07 00:00:00

As it stands, a robust mathematical framework to analyse and study various topics in deep learning is yet to come to the fore. Nonetheless, viewing deep learning as a dynamical system allows the use of established theories to investigate the behaviour of deep neural networks. In order to study the stability of the training process, in this article, we formulate training of deep neural networks as a hydrodynamics system, which has a multisymplectic structure. For that, the deep neural network is modelled using a stochastic differential equation and, thereby, mean-field type control is used to train it. The necessary conditions for optimality of the mean--field type control reduce to a system of Euler-Poincare equations, which has the a similar geometric structure to that of compressible fluids. The mean-field type control is solved numerically using a multisymplectic numerical scheme that takes advantage of the underlying geometry. Moreover, the numerical scheme, yields an approximated solution which is also an exact solution of a hydrodynamics system with a multisymplectic structure and it can be analysed using backward error analysis. Further, nonlinear stability yields the condition for selecting the number of hidden layers and the number of nodes per layer, that makes the training stable while approximating the solution of a residual neural network with a number of hidden layers approaching infinity.
A Bayesian method for estimating gene-level polygenicity under the framework of transcriptome-wide association study
Authors: Arunabha Majumdar, Bogdan Pasaniuc
Date: 2022-07-25 00:00:00
Tasks: Sensitivity, Specificity

Polygnicity refers to the phenomenon that multiple genetic variants have a non-zero effect on a complex trait. It is defined as the proportion of genetic variants that have a nonzero effect on the trait. Evaluation of polygenicity can provide valuable insights into the genetic architecture of the trait. Several recent works have attempted to estimate polygenicity at the SNP level. However, evaluating polygenicity at the gene level can be biologically more meaningful. We propose the notion of gene-level polygenicity, defined as the proportion of genes having a non-zero effect on the trait under the framework of transcriptome-wide association study. We introduce a Bayesian approach polygene to estimate this quantity for a trait. The method is based on spike and slab prior and simultaneously provides an optimal subset of non-null genes. Our simulation study shows that polygene efficiently estimates gene-level polygenicity. The method produces downward bias for small choices of trait heritability due to a non-null gene, which diminishes rapidly with an increase in the GWAS sample size. While identifying the optimal subset of non-null genes, polygene offers a high level of specificity and an overall good level of sensitivity -- the sensitivity increases as the sample size of the reference panel expression and GWAS data increase. We applied the method to seven phenotypes in the UK Biobank, integrating expression data. We find height to be most polygenic and asthma to be the least polygenic. Our analysis suggests that both HDL and triglycerides are more polygenic than LDL.
A New Approach in MRI Signal Processing for Detecting the Axonal Organization of the Brain
Authors: Ashishi Puri, Sanjeev Kumar
Date: 2022-07-24 00:00:00
Tasks: Diffusion  MRI

This article introduces a new methodology for reconstructing the white matter fiber pathways of brain in diffusion MRI. Usually, the signal intensity values will be lesser in the direction of higher diffusivity. The proposed approach picks the diffusion sensitivity gradient directions (dSGD), where the signal intensities are diminutive. Considering these as the directions of maximum diffusivity, we generate directions uniformly distributed around the picked dSGD. These newly computed uniformly spaced directions are considered gradient directions used in the reconstruction process. The state-of-art schemes like uniform gradient direction (UGD) have redundancy in the gradient direction, and adaptive gradient direction (AGD) has a constraint of solving linear system twice per voxel. These two limitations are turned down in this study simultaneously. Estimating gradient directions with the proposed scheme is employed in the multi-compartmental mixture models for calculating the fiber orientations. Simulation and experiments on the real data evaluate the feasibility of the proposed method.
Efficient population coding of sensory stimuli
Authors: Shuai Shao, Markus Meister, Julijana Gjorgjieva
Date: 2022-07-24 00:00:00

The efficient coding theory postulates that single cells in a neuronal population should be optimally configured to efficiently encode information about a stimulus subject to biophysical constraints. This poses the question of how multiple neurons that together represent a common stimulus should optimize their activation functions to provide the optimal stimulus encoding. Previous theoretical approaches have solved this problem with binary neurons that have a step activation function, and have assumed that spike generation is noisy and follows a Poisson process. Here we derive a general theory of optimal population coding with neuronal activation functions of any shape, different types of noise and heterogeneous firing rates of the neurons by maximizing the Shannon mutual information between a stimulus and the neuronal spiking output subject to a constrain on the maximal firing rate. We find that the optimal activation functions are discrete in the biological case of non-negligible noise and demonstrate that the information does not depend on how the population is divided into ON and OFF cells described by monotonically increasing vs. decreasing activation functions, respectively. However, the population with an equal number of ON and OFF cells has the lowest mean firing rate, and hence encodes the highest information per spike. These results are independent of the shape of the activation functions and the nature of the spiking noise. Finally, we derive a relationship for how these activation functions should be distributed in stimulus space as a function of the neurons' firing rates.
Where is the mind within the brain? Transient selection of subnetworks by metabotropic receptors and G protein-gated ion channels
Authors: Danko Nikolić
Date: 2022-07-11 00:00:00

Perhaps the most important question posed by brain research is: How the brain gives rise to the mind. To answer this question, we have primarily relied on the connectionist paradigm: The brain's entire knowledge and thinking skills are thought to be stored in the connections; and the mental operations are executed by network computations. I propose here an alternative paradigm: Our knowledge and skills are stored in metabotropic receptors (MRs) and the G protein-gated ion channels (GPGICs). Here, mental operations are assumed to be executed by the functions of MRs and GPGICs. As GPGICs have the capacity to close or open branches of dendritic trees and axon terminals, their states transiently re-route neural activity throughout the nervous system. First, MRs detect ligands that signal the need to activate GPGICs. Next, GPGICs transiently selects a subnetwork within the brain. The process of selecting this new subnetwork is what constitutes a mental operation -- be it in a form of directed attention, perception of a grandmother or making a decision. Synaptic connections and network computations play only a secondary role, supporting MRs and GPGICs. According to this new paradigm, the mind emerges within the brain as the function of MRs and GPGICs whose primary function is to continually select the pathways over which neural activity will be allowed to pass. It is argued that MRs and GPGICs solve the scaling problem of intelligence from which the connectionism paradigm suffers.
Genetic similarity versus genetic ancestry groups as sample descriptors in human genetics
Authors: Graham Coop
Date: 2022-07-23 00:00:00

A common sample descriptor in human genomics studies is that of 'genetic ancestry group', with terms such as 'European genetic ancestry' or 'East Asian genetic ancestry' frequently used in publications to describe the genetics of groups of individuals based on the analysis of their genotypes. In this Perspective, I argue that these terms are imprecise and potentially misleading and that, for most applications, simple statements of genetic similarity represent a more accurate description.
PepSIRF + QIIME 2: software tools for automated, reproducible analysis of highly-multiplexed serology data
Authors: Annabelle M. Brown, Evan Bolyen, Isaiah Raspet, John A. Altin, Jason T. Ladner
Date: 2022-07-23 00:00:00

PepSIRF is a command-line, module-based open-source software package that facilitates the analysis of data from highly-multiplexed serology assays (e.g., PepSeq or PhIP-Seq). It has nine separate modules in its current release (v1.5.0): demux, info, subjoin, norm, bin, zscore, enrich, link, and deconv. These modules can be used together to conduct analyses ranging from demultiplexing raw high-throughput sequencing data to the identification of enriched peptides. QIIME 2 is an open-source, community-developed and plugin-based bioinformatics platform that focuses on data and analytical transparency. QIIME 2's features include integrated and automatic tracking of data provenance, a semantic type system, and built-in support for many types of user interfaces. Here, we describe three new QIIME 2 plugins that allow users to conduct PepSIRF analyses within the QIIME 2 environment and extend the core functionality of PepSIRF in two key ways: 1) enabling generation of interactive visualizations and 2) enabling automation of analysis pipelines that include multiple PepSIRF modules.
Patterns of protection, infection, and detection: Country-level effectiveness of COVID 19 vaccination in reducing mortality worldwide
Authors: Cosima Rughinis, Mihai Dima, Simona-Nicoleta Vulpe, Razvan Rughinis, Sorina Vasile
Date: 2022-07-08 00:00:00

We investigated the negative relationship between mortality and COVID-19 vaccination at ecological level, which has been established through clinical trials and other investigations at the individual level. We conducted an exploratory, correlational, country-level analysis of open data centralized by Our World in Data concerning the cumulative COVID-19 mortality for the winter wave of the pandemic as function of the vaccination rate in October 2021. In order to disentangle the protective relationship from confounding processes, we controlled variables that capture country-level social development and level of testing. We also deployed three segmentation tactics, distinguishing among countries based on their level of COVID-19 testing, age structure, and types of vaccines used. Controlling for confounding factors did not highlight a statistically significant global relationship between vaccination and cumulative mortality in the total country sample. As suggested by previous estimates at country level, a strong, significant, negative relationship between cumulative mortality and vaccination was highlighted through segmentation analysis for countries positioned at the higher end of the social development spectrum. The strongest estimate for vaccine effectiveness at ecological level was obtained for countries that use Western-only vaccines. This may partly reflect the higher effectiveness of Western vaccines in comparison with the average of all vaccines in use; it may also derive from the lower social heterogeneity of countries included in this segment. COVID-19 testing has a significant and positive relationship with cumulative mortality for all subsamples. This indicates that testing intensity should be controlled as a potential confounder in future ecological analyses of COVID-19 mortality.
A Study on Impact of Downsizing on Profitability of Construction Industries listed in Bombay Stock Exchange (BSE) India
Authors: D Reshma, Sudharani R, Suresh N
Date: 2022-07-23 00:00:00

The study investigates the impact of downsizing layoffs on the profitability of construction industries listed in BSE India. In India, construction industries have adopted downsizing long back in the organization to improve the firms performance. For the purpose of the study, Secondary data of 15 Construction companies listed in BSE India have been considered for a period of 10 years from FY.2010 to FY2019. Data has been taken from the companys official website. The variable considered for the analysis is Other Expenses, Returns on Net Worth, Employee Expenses, Number of Employees, and Profit Per Employee. The study has used the Co-integration test to see co-integration between the variables, Ordinary Least Square (OLS) and Vector Auto Regression (VAR) the model used for estimating the impact of downsizing on the profitability of construction companies. OLS and VAR model has been used to draw a conclusion based on the P values and R square. From the result, it can be concluded that, Expect Profit Per Employees are the downsizing variable that has no significant impact on the profitability of the firms performance. Whereas the other Downsizing variables Employee Expenses and the Number of Employee has a significant impact on the profitability of the firms performance
Linear and Nonlinear Partial Integro-Differential Equations arising from Finance
Authors: Jose Cruz, Maria Grossinho, Daniel Sevcovic, Cyril Izuchukwu Udeani
Date: 2022-07-23 00:00:00

The purpose of this review paper is to present our recent results on nonlinear and nonlocal mathematical models arising from modern financial mathematics. It is based on our four papers written jointly by J. Cruz, M. Grossinho, D. Sevcovic, and C. Udeani, as well as parts of PhD thesis by J. Cruz. We investigated linear and nonlinear partial integro-differential equations (PIDEs) arising from option pricing and portfolio selection problems and studied the systematic relationships between the PIDEs with option pricing theory and Black--Scholes models. First, we relax the liquid and complete market assumptions and extend the models that study the market's illiquidity to the case where the underlying asset price follows a L\'evy stochastic process with jumps. Then, we establish the corresponding PIDE for option pricing under suitable assumptions. The qualitative properties of solutions to nonlocal linear and nonlinear PIDE are presented using the theory of abstract semilinear parabolic equation in the scale of Bessel potential spaces. The existence and uniqueness of solutions to the PIDE for a general class of the so-called admissible L\'evy measures satisfying suitable growth conditions at infinity and origin are also established in the multidimensional space.
Phase-type representations of stochastic interest rates with applications to life insurance
Authors: Jamaal Ahmad, Mogens Bladt
Date: 2022-07-22 00:00:00

The purpose of the present paper is to incorporate stochastic interest rates into a matrix-approach to multi-state life insurance, where formulas for reserves, moments of future payments and equivalence premiums can be obtained as explicit formulas in terms of product integrals or matrix exponentials. To this end we consider the Markovian interest model, where the rates are piecewise deterministic (or even constant) in the different states of a Markov jump process, and which is shown to integrate naturally into the matrix framework. The discounting factor then becomes the price of a zero-coupon bond which may or may not be correlated with the biometric insurance process. Another nice feature about the Markovian interest model is that the price of the bond coincides with the survival function of a phase-type distributed random variable. This, in particular, allows for calibrating the Markovian interest rate models using a maximum likelihood approach to observed data (prices) or to theoretical models like e.g. a Vasicek model. Due to the denseness of phase-type distributions, we can approximate the price behaviour of any zero-coupon bond with interest rates bounded from below by choosing the number of possible interest rate values sufficiently large. For observed data models with few data points, lower dimensions will usually suffice, while for theoretical models the dimensionality is only a computational issue.
Rich Feature Distillation with Feature Affinity Module for Efficient Image Dehazing
Authors: Sai Mitheran, Anushri Suresh, Nisha J. S., Varun P. Gopi
Date: 2022-07-13 00:00:00
Tasks: Contrastive Learning, image-classification, Image Classification, Image Dehazing, Knowledge Distillation, Single Image Haze Removal, Super-Resolution

Single-image haze removal is a long-standing hurdle for computer vision applications. Several works have been focused on transferring advances from image classification, detection, and segmentation to the niche of image dehazing, primarily focusing on contrastive learning and knowledge distillation. However, these approaches prove computationally expensive, raising concern regarding their applicability to on-the-edge use-cases. This work introduces a simple, lightweight, and efficient framework for single-image haze removal, exploiting rich "dark-knowledge" information from a lightweight pre-trained super-resolution model via the notion of heterogeneous knowledge distillation. We designed a feature affinity module to maximize the flow of rich feature semantics from the super-resolution teacher to the student dehazing network. In order to evaluate the efficacy of our proposed framework, its performance as a plug-and-play setup to a baseline model is examined. Our experiments are carried out on the RESIDE-Standard dataset to demonstrate the robustness of our framework to the synthetic and real-world domains. The extensive qualitative and quantitative results provided establish the effectiveness of the framework, achieving gains of upto 15\% (PSNR) while reducing the model size by $\sim$20 times.
Online Adaptive Personalization for Face Anti-spoofing
Authors: Davide Belli, Debasmit Das, Bence Major, Fatih Porikli
Date: 2022-07-04 00:00:00
Tasks: Face Anti-Spoofing

Face authentication systems require a robust anti-spoofing module as they can be deceived by fabricating spoof images of authorized users. Most recent face anti-spoofing methods rely on optimized architectures and training objectives to alleviate the distribution shift between train and test users. However, in real online scenarios, past data from a user contains valuable information that could be used to alleviate the distribution shift. We thus introduce OAP (Online Adaptive Personalization): a lightweight solution which can adapt the model online using unlabeled data. OAP can be applied on top of most anti-spoofing methods without the need to store original biometric images. Through experimental evaluation on the SiW dataset, we show that OAP improves recognition performance of existing methods on both single video setting and continual setting, where spoof videos are interleaved with live ones to simulate spoofing attacks. We also conduct ablation studies to confirm the design choices for our solution.
Action Quality Assessment using Transformers
Authors: Abhay Iyer, Mohammad Alali, Hemanth Bodala, Sunit Vaidya
Date: 2022-07-20 00:00:00
Tasks: Action Quality Assessment

Action quality assessment (AQA) is an active research problem in video-based applications that is a challenging task due to the score variance per frame. Existing methods address this problem via convolutional-based approaches but suffer from its limitation of effectively capturing long-range dependencies. With the recent advancements in Transformers, we show that they are a suitable alternative to the conventional convolutional-based architectures. Specifically, can transformer-based models solve the task of AQA by effectively capturing long-range dependencies, parallelizing computation, and providing a wider receptive field for diving videos? To demonstrate the effectiveness of our proposed architectures, we conducted comprehensive experiments and achieved a competitive Spearman correlation score of 0.9317. Additionally, we explore the hyperparameters effect on the model's performance and pave a new path for exploiting Transformers in AQA.