pwc-archive/papers-with-abstracts huggingface dataset:
Style Transfer based Coronary Artery Segmentation in X-ray Angiogram
Authors: Supriti Mulay, Keerthi Ram, Balamurali Murugesan, Mohanasankar Sivaprakasam
Date: 2021-09-03 00:00:00
Tasks: Coronary Artery Segmentation, Deep Learning, Segmentation, Style Transfer

X-ray coronary angiography (XCA) is a principal approach employed for identifying coronary disorders. Deep learning-based networks have recently shown tremendous promise in the diagnosis of coronary disorder from XCA scans. A deep learning-based edge adaptive instance normalization style transfer technique for segmenting the coronary arteries, is presented in this paper. The proposed technique combines adaptive instance normalization style transfer with the dense extreme inception network and convolution block attention module to get the best artery segmentation performance. We tested the proposed method on two publicly available XCA datasets, and achieved a segmentation accuracy of 0.9658 and Dice coefficient of 0.71. We believe that the proposed method shows that the prediction can be completed in the fastest time with training on the natural images, and can be reliably used to diagnose and detect coronary disorders.
Mitosis Detection for Breast Cancer Pathology Images Using UV-Net
Authors: Seyed H. Mirjahanmardi, Samir Mitha, Salar Razavi, Susan Done, April Khademi
Date: 2021-09-02 00:00:00
Tasks: Mitosis Detection

The difficulty of detecting mitosis and its similarity to non-mitosis objects has remained a challenge in computational pathology. The lack of publicly available data has added more complexity. Deep learning algorithms have shown potentials in mitosis detection tasks. However, they face challenges when applied to pathology images with dense medium and diverse dataset. This paper introduces an optimized UV-Net architecture, developed to focus on mitosis details with high-resolution through feature preservation. Stain normalization methods are used to generalize the trained network. An F1 score of 0.6721 is achieved using this network.
Multi-source Domain Adaptation Using Gradient Reversal Layer for Mitotic Cell Detection
Authors: Satoshi Kondo
Date: 2021-09-02 00:00:00
Tasks: Cell Detection, Domain Adaptation, Domain Generalization

This is a write-up of our method submitted to Mitosis Domain Generalization (MIDOG 2021) Challenge held in MICCAI2021 conference.
Track Coalescence and Repulsion: MHT, JPDA, and BP
Authors: Thomas Kropfreiter, Florian Meyer, Stefano Coraluppi, Craig Carthel, Rico Mendrzik, Peter Willett
Date: 2021-09-03 00:00:00

Joint probabilistic data association (JPDA) and multiple hypothesis tracking (MHT) introduced in the 70s, are still widely used methods for multitarget tracking (MTT). Extensive studies over the last few decades have revealed undesirable behavior of JPDA and MHT type methods in tracking scenarios with targets in close proximity. In particular, JPDA suffers from the track coalescence effect, i.e., estimated tracks of targets in close proximity tend to merge and can become indistinguishable. Interestingly, in MHT, an opposite effect to track coalescence called track repulsion can be observed. In this paper, we review the estimation strategies of the MHT, JPDA, and the recently introduced belief propagation (BP) framework for MTT and we investigate if BP also suffers from these two detrimental effects. Our numerical results indicate that BP-based MTT can mostly avoid both track repulsion and coalescence.
A Scalable Track-Before-Detect Method With Poisson/Multi-Bernoulli Model
Authors: Thomas Kropfreiter, Jason L. Williams, Florian Meyer
Date: 2021-09-03 00:00:00

We propose a scalable track-before-detect (TBD) tracking method based on a Poisson/multi-Bernoulli model. To limit computational complexity, we approximate the exact multi-Bernoulli mixture posterior probability density function (pdf) by a multi-Bernoulli pdf. Data association based on the sum-product algorithm and recycling of Bernoulli components enable the detection and tracking of low-observable objects with limited computational resources. Our simulation results demonstrate a significantly improved tracking performance compared to a state-of-the-art TBD method.
Detailed analysis and comparison of different activity metrics
Authors: Bálint Maczák, Gergely Vadai, András Dér, István Szendi, Zoltán Gingl
Date: 2021-09-03 00:00:00

Actigraphic measurements are an important part of research in different disciplines, yet the procedure of determining activity values is unexpectedly not standardized in the literature. Although the measured raw acceleration signal can be diversely processed, and then the activity values can be calculated by different activity calculation methods, the documentations of them are generally incomplete or vary by manufacturer. These numerous activity metrics may require different types of preprocessing of the acceleration signal. For example, digital filtering of the acceleration signals can have various parameters; moreover, both the filter and the activity metrics can also be applied per axis or on the magnitudes of the acceleration vector. Level crossing-based activity metrics also depend on unclearly determined threshold level values. Due to the serious inconsistency of determining activity values, we created a detailed and comprehensive comparison of the different available activity calculation procedures because, up to the present, it was lacking in the literature. We assessed the different methods by analysing the triaxial acceleration signals measured during a 10-day movement of 42 subjects. We calculated 148 different activity signals for each subject's movement using the combinations of various types of preprocessing and 7 different activity metrics applied on both axial and magnitude data. We determined the linear relationship between the metrics by correlation analysis, while we also examined the effects of the preprocessing steps. Moreover, we established that the standard deviation of the data series can be used as an appropriate, adaptive and generalized threshold level for the level intersection-based metrics. On the basis of these results, our work also serves as a general guide on how to proceed if one wants to determine activity from the raw acceleration data.
Spectrum Learning-Aided Reconfigurable Intelligent Surfaces for 'Green' 6G Networks
Authors: Bo Yang, Xuelin Cao, Chongwen Huang, Yong Liang Guan, Chau Yuen, Marco Di Renzo, Dusit Niyato, Merouane Debbah, Lajos Hanzo
Date: 2021-09-03 00:00:00
Tasks: Autonomous Driving

In the sixth-generation (6G) era, emerging large-scale computing based applications (for example processing enormous amounts of images in real-time in autonomous driving) tend to lead to excessive energy consumption for the end users, whose devices are usually energy-constrained. In this context, energy-efficiency becomes a critical challenge to be solved for harnessing these promising applications to realize 'green' 6G networks. As a remedy, reconfigurable intelligent surfaces (RIS) have been proposed for improving the energy efficiency by beneficially reconfiguring the wireless propagation environment. In conventional RIS solutions, however, the received signal-to-interference-plus-noise ratio (SINR) sometimes may even become degraded. This is because the signals impinging upon an RIS are typically contaminated by interfering signals which are usually dynamic and unknown. To address this issue, `learning' the properties of the surrounding spectral environment is a promising solution, motivating the convergence of artificial intelligence and spectrum sensing, termed here as spectrum learning (SL). Inspired by this, we develop an SL-aided RIS framework for intelligently exploiting the inherent characteristics of the radio frequency (RF) spectrum for green 6G networks. Given the proposed framework, the RIS controller becomes capable of intelligently `{think-and-decide}' whether to reflect or not the incident signals. Therefore, the received SINR can be improved by dynamically configuring the binary ON-OFF status of the RIS elements. The energy-efficiency benefits attained are validated with the aid of a specific case study. Finally, we conclude with a list of promising future research directions.
Optimal Control applied to SIRD model of COVID 19
Authors: Amira Bouhali, Walid Ben Aribi, Slimane Ben Miled, Amira Kebir
Date: 2021-09-03 00:00:00

In this study, we present an epidemic-controlled SIRD model with two types of control strategies: mask wear and screening. The aim of this study is to minimize the number of deceased keeping a minimal cost of mask advertising and screening. The model is proved to be well-posed and to have an invariant region. Also, a thorough study of the dynamics is effected and the basic reproduction number is used to study the stability of the steady states of the model. As for the optimal control analysis, the existence of an optimal control was checked. Then its characterization was carried out using Pontryagin's minimum principle. Numerical simulations are conducted after that with different values of maximal screening for comparison. The findings of the optimal control analysis and numerical simulations both reveal that the optimal pair of strategies contribute enormously in lowering the number of infected and dead individuals. Although zero infection is not achieved in the population, this study implies that carrying an optimal approach constitutes a major step in controlling the spread of the disease to the barest minimum that can buy time for middle and low-income countries to carry on with their vaccination strategies.
Account for Neuronal Representations from the Perspective of Neurons
Authors: Chiyin Zheng
Date: 2021-09-03 00:00:00

Mounting evidence in neuroscience suggests the possibility of neuronal representations that individual neurons serve as the substrates of different mental representations in a point-to-point way. Combined with associationism, it can potentially address a range of theoretical problems and provide a straightforward explanation for our cognition. However, this idea is merely a hypothesis with many questions unsolved. In this paper, I will bring up a new framework to defend the idea of neuronal representations. The strategy is from micro- to macro-level. Specifically, in the micro-level, I first propose that our brain' prefers and preserves more active neurons. Yet as total chance of discharge, neurons must take strategies to fire more strongly and frequently. Then I describe how they take synaptic plasticity, inhibition, and synchronization as their strategies and demonstrate how the execution of these strategies during turn them into specialized neurons that selectively but strongly respond to familiar entities. In the macro-level, I further discuss how these specialized neurons underlie various cognitive functions and phenomena. Significantly, this paper, through defending neuronal representation, introduces a novel way to understand the relationship between brain and cognition.
The mean field approach for populations of spiking neurons
Authors: Giancarlo La Camera
Date: 2021-09-03 00:00:00

Mean field theory is a device to analyze the collective behavior of a dynamical system comprising many interacting particles. The theory allows to reduce the behavior of the system to the properties of a handful of parameters. In neural circuits, these parameters are typically the firing rates of distinct, homogeneous subgroups of neurons. Knowledge of the firing rates under conditions of interest can reveal essential information on both the dynamics of neural circuits and the way they can subserve brain function. The goal of this chapter is to provide an elementary introduction to the mean field approach for populations of spiking neurons. We introduce the general idea in networks of binary neurons, starting from the most basic results and then generalizing to more relevant situations. This allows to derive the mean field equations in a simplified setting. We then derive the mean field equations for populations of integrate-and-fire neurons. An effort is made to derive the main equations of the theory using only elementary methods from calculus and probability theory. The chapter ends with a discussion of the assumptions of the theory and some of the consequences of violating those assumptions. This discussion includes an introduction to balanced and metastable networks, and a brief catalogue of successful applications of the mean field approach to the study of neural circuits.
Dominance reversals: The resolution of genetic conflict and maintenance of genetic variation
Authors: Karl Grieshop, Eddie K. H. Ho, Katja R. Kasimatis
Date: 2021-09-03 00:00:00

Beneficial reversals of dominance reduce the costs of genetic trade-offs and can enable selection to maintain genetic variation for fitness. Beneficial dominance reversals are characterized by the beneficial allele for a given context (e.g. habitat, developmental stage, trait, or sex) being dominant in that context but recessive where deleterious. This context-dependence at least partially mitigates the fitness consequence of heterozygotes carrying one non-beneficial allele for their context and can result in balancing selection that maintains alternative alleles. Dominance reversals are theoretically plausible and are supported by mounting empirical evidence. Here we highlight the importance of beneficial dominance reversals as a mechanism for the mitigation of genetic conflict and review the theory and empirical evidence for them. We identify some areas in need of further research and development and outline three methods (dominance ordination, allele-specific expression, and allele-specific ATAC-Seq) that could facilitate the identification of antagonistic genetic variation. There is ample scope for the development of new empirical methods as well as reanalysis of existing data through the lens of dominance reversals. A greater focus on this topic will expand our understanding of the mechanisms that resolve genetic conflict and whether they maintain genetic variation.
Food webs and the principle of evolutionary adaptation
Authors: Alexander S. Bratus, Anastasiia V. Korushkina, Artem S. Novozhilov
Date: 2021-09-02 00:00:00

A principle of evolutionary adaptation is applied to the Lotka--Volterra models, in particular to the food webs. We present a relatively simple computational algorithm of optimization with respect to a given criterion. This algorithm boils down to a sequence of easy to solve linear programming problems. As a criterion for the optimization we use the total weighted population size of the given community and an ecological fitness, which is an analogue of the potential energy in physics. We show by computational experiments that it is almost always possible to substantially increase the total weighed population size for an especially simple food web -- food chain; we also show that food chains are evolutionary unstable under the given optimization criteria and, if allowed, evolve into more complicated structures of food webs.
Modelling silicosis: dynamics of a model with piecewise constant rate coefficients
Authors: Pedro R. S. Antunes, Fernando P. da Costa, João T. Pinto, Rafael Sasportes
Date: 2021-09-02 00:00:00
Tasks: Math

We study the dynamics about equilibria of an infinite dimension coagulation-fragmentation-death model for the silicosis disease mechanism introduced recently by da Costa, Drmota, and Grinfeld [Modelling silicosis: structure of equilibria, Euro. J. Appl. Math., 31 (6), (2020) 950-967] in the case where the rate coefficients are piecewise constant.
Efficacy versus abundancy: Comparing vaccination schemes
Authors: Omar El Deeb, Maya Jalloul
Date: 2021-08-31 00:00:00

We introduce a novel compartmental model accounting for the effects of vaccine efficacy, deployment rates and timing of initiation of deployment. We simulate different scenarios and initial conditions, and we find that higher abundancy and rate of deployment of low efficacy vaccines lowers the cumulative number of deaths in comparison to slower deployment of high efficacy vaccines. We also forecast that, at the same daily deployment rate, the earlier introduction of vaccination schemes with lower efficacy would also lower the number of deaths with respect to a delayed introduction of high efficacy vaccines, which can however, still achieve lower numbers of infections and better herd immunity.
What drives bitcoin? An approach from continuous local transfer entropy and deep learning classification models
Authors: Andrés García-Medina, Toan Luu Duc Huynh3
Date: 2021-09-02 00:00:00
Tasks: feature selection, Variable Selection

Bitcoin has attracted attention from different market participants due to unpredictable price patterns. Sometimes, the price has exhibited big jumps. Bitcoin prices have also had extreme, unexpected crashes. We test the predictive power of a wide range of determinants on bitcoins' price direction under the continuous transfer entropy approach as a feature selection criterion. Accordingly, the statistically significant assets in the sense of permutation test on the nearest neighbour estimation of local transfer entropy are used as features or explanatory variables in a deep learning classification model to predict the price direction of bitcoin. The proposed variable selection methodology excludes the NASDAQ index and Tesla as drivers. Under different scenarios and metrics, the best results are obtained using the significant drivers during the pandemic as validation. In the test, the accuracy increased in the post-pandemic scenario of July 2020 to January 2021 without drivers. In other words, our results indicate that in times of high volatility, Bitcoin seems to autoregulate and does not need additional drivers to improve the accuracy of the price direction.
Super Neurons
Authors: Serkan Kiranyaz, Junaid Malik, Mehmet Yamac, Mert Duman, Ilke Adalioglu, Esin Guldogan, Turker Ince, Moncef Gabbouj
Date: 2021-08-03 00:00:00

Self-Organized Operational Neural Networks (Self-ONNs) have recently been proposed as new-generation neural network models with nonlinear learning units, i.e., the generative neurons that yield an elegant level of diversity; however, like its predecessor, conventional Convolutional Neural Networks (CNNs), they still have a common drawback: localized (fixed) kernel operations. This severely limits the receptive field and information flow between layers and thus brings the necessity for deep and complex models. It is highly desired to improve the receptive field size without increasing the kernel dimensions. This requires a significant upgrade over the generative neurons to achieve the non-localized kernel operations for each connection between consecutive layers. In this article, we present superior (generative) neuron models (or super neurons in short) that allow random or learnable kernel shifts and thus can increase the receptive field size of each connection. The kernel localization process varies among the two super-neuron models. The first model assumes randomly localized kernels within a range and the second one learns (optimizes) the kernel locations during training. An extensive set of comparative evaluations against conventional and deformable convolutional, along with the generative neurons demonstrates that super neurons can empower Self-ONNs to achieve a superior learning and generalization capability with a minimal computational complexity burden.
Instabilities in Plug-and-Play (PnP) algorithms from a learned denoiser
Authors: Abinash Nayak
Date: 2021-08-17 00:00:00

It's well-known that inverse problems are ill-posed and to solve them meaningfully, one has to employ regularization methods. Traditionally, popular regularization methods are the penalized Variational approaches. In recent years, the classical regularization approaches have been outclassed by the so-called plug-and-play (PnP) algorithms, which copy the proximal gradient minimization processes, such as ADMM or FISTA, but with any general denoiser. However, unlike the traditional proximal gradient methods, the theoretical underpinnings, convergence, and stability results have been insufficient for these PnP-algorithms. Hence, the results obtained from these algorithms, though empirically outstanding, can't always be completely trusted, as they may contain certain instabilities or (hallucinated) features arising from the denoiser, especially when using a pre-trained learned denoiser. In fact, in this paper, we show that a PnP-algorithm can induce hallucinated features, when using a pre-trained deep-learning-based (DnCNN) denoiser. We show that such instabilities are quite different than the instabilities inherent to an ill-posed problem. We also present methods to subdue these instabilities and significantly improve the recoveries. We compare the advantages and disadvantages of a learned denoiser over a classical denoiser (here, BM3D), as well as, the effectiveness of the FISTA-PnP algorithm vs. the ADMM-PnP algorithm. In addition, we also provide an algorithm to combine these two denoisers, the learned and the classical, in a weighted fashion to produce even better results. We conclude with numerical results which validate the developed theories.
Modeling strict age-targeted mitigation strategies for COVID-19
Authors: Maria Chikina, Wesley Pegden
Date: 2020-04-08 00:00:00

We use a simple SIR-like epidemic model which integrates known age-contact patterns for the United States to model the effect of age-targeted mitigation strategies for a COVID-19-like epidemic. We find that, among strategies which end with population immunity, strict age-targeted mitigation strategies have the potential to greatly reduce mortalities and ICU utilization for natural parameter choices.
Using generalized logistics regression to forecast population infected by Covid-19
Authors: Mario Villalobos-Arias
Date: 2020-04-06 00:00:00
Tasks: regression

In this work, a proposal to forecast the populations using generalized logistics regression curve fitting is presented. This type of curve is used to study population growth, in this case population of people infected with the Covid-19 virus; and it can also be used to approximate the survival curve used in actuarial and similar studies.