pwc-archive/papers-with-abstracts huggingface dataset:
3D CA model of tumor-induced angiogenesis
Authors: Monjoy Saha, Amit Kumar Ray, Swapan Kumar Basu
Date: 2020-05-25 00:00:00
Tasks: model

Tumor-induced angiogenesis is the formation of new sprouts from preexisting nearby parent blood vessels. Computationally, tumor-induced angiogenesis can be modeled using cellular automata (CA), partial differential equations, etc. In this present study, a realistic physiological approach has been made to model the process of angiogenesis by using 3D CA model. CA technique uses various neighborhoods like Von-Neumann neighborhood, Moore neighborhood, and Margolus neighborhood. In our model Von-Neumann neighborhood has used for distribution of some significant chemical and non-chemical tumor angiogenic factors like vascular endothelial growth factor, endothelial cells, O2, extracellular matrix, fibronectin, etc., and Moore neighborhood is used for distribution of matrix metalloproteinase. In vivo tumor environment all the factors are not distributed equally in the extracellular matrix. Distributions of those chemical and nonchemical factors depend on their source, nature and function. To keep similarity with the biological tumor environment, we have formulated initial distributions of the chemical and non-chemical factors accordingly. We have started the simulation in MATLAB with this initial distribution. Number of sprouts randomly varies from one run to another. We observed that sprouts are not originating from the same locations in each simulation. A sprout has high sensitivity of VEGF and fibronectin concentrations. sVEGFR-1 always tries to regress the sprout. When two or more sprouts come closer, they merge with each other leading to anastomosis. Sufficient number of tip cells may cause sprout towards tumor.
Recovery of surfaces and functions in high dimensions: sampling theory and links to neural networks
Authors: Qing Zou, Mathews Jacob
Date: 2020-05-26 00:00:00
Tasks: Denoising, Image Denoising, Time Series, Time Series Analysis

Several imaging algorithms including patch-based image denoising, image time series recovery, and convolutional neural networks can be thought of as methods that exploit the manifold structure of signals. While the empirical performance of these algorithms is impressive, the understanding of recovery of the signals and functions that live on manifold is less understood. In this paper, we focus on the recovery of signals that live on a union of surfaces. In particular, we consider signals living on a union of smooth band-limited surfaces in high dimensions. We show that an exponential mapping transforms the data to a union of low-dimensional subspaces. Using this relation, we introduce a sampling theoretical framework for the recovery of smooth surfaces from few samples and the learning of functions living on smooth surfaces. The low-rank property of the features is used to determine the number of measurements needed to recover the surface. Moreover, the low-rank property of the features also provides an efficient approach, which resembles a neural network, for the local representation of multidimensional functions on the surface.
Global sensitivity analysis informed model reduction and selection applied to a Valsalva maneuver model
Authors: E. Benjamin Randall, Nicholas Z. Randolph, Alen Alexanderian, Mette S. Olufsen
Date: 2020-05-26 00:00:00
Tasks: model, Model Selection, Sensitivity

In this study, we develop a methodology for model reduction and selection informed by global sensitivity analysis (GSA) methods. We apply these techniques to a control model that takes systolic blood pressure and thoracic tissue pressure data as inputs and predicts heart rate in response to the Valsalva maneuver (VM). The study compares four GSA methods based on Sobol' indices (SIs) quantifying the parameter influence on the difference between the model output and the heart rate data. The GSA methods include standard scalar SIs determining the average parameter influence over the time interval studied and three time-varying methods analyzing how parameter influence changes over time. The time-varying methods include a new technique, termed limited-memory SIs, predicting parameter influence using a moving window approach. Using the limited-memory SIs, we perform model reduction and selection to analyze the necessity of modeling both the aortic and carotid baroreceptor regions in response to the VM. We compare the original model to three systematically reduced models including (i) the aortic and carotid regions, (ii) the aortic region only, and (iii) the carotid region only. Model selection is done quantitatively using the Akaike and Bayesian Information Criteria and qualitatively by comparing the neurological predictions. Results show that it is necessary to incorporate both the aortic and carotid regions to model the VM.
Distributed Observers for LTI Systems with Finite Convergence Time: A Parameter Estimation-based Approach
Date: 2020-05-26 00:00:00
Tasks: parameter estimation, State Estimation

A novel approach to solve the problem of distributed state estimation of
linear time-invariant systems is proposed in this paper. It relies on the
application of parameter estimation-based observers, where the state
observation task is reformulated as a parameter estimation problem. In contrast
with existing results our solution achieves convergence in finite-time, without
injection of high gain, and imposes very weak assumptions on the communication
graph---namely the existence of a Hamiltonian walk. The scheme is shown to be
robust vis-\'a-vis external disturbances and communication delays.
Estimating the Number of Infected Cases in COVID-19 Pandemic
Authors: Donghui Yan, Ying Xu, Pei Wang
Date: 2020-05-24 00:00:00

The COVID-19 pandemic has caused major disturbance to human life. An important reason behind the widespread social anxiety is the huge uncertainty about the pandemic. A fundamental uncertainty is how many or what percentage of people have been infected. There are published and frequently updated data on various statistics of the pandemic, at local, country or global level. However, due to various reasons, many cases were not included in those reported numbers. We propose a structured approach for the estimation of the number of unreported cases, where we distinguish cases that arrive late in the reported numbers and those who had mild or no symptoms and thus were not captured by any medical system at all. We use post-report data for the estimation of the former and population matching to the latter. We estimate that the reported number of infected cases in the US should be corrected by multiplying a factor of 220.54% as of Apr 20, 2020, while the infection ratio out of the US population is estimated to be 0.53%, implying a case mortality rate at 2.85% which is close to the 3.4% suggested by the WHO in Mar 2020. Towards the end of the summer of 2020, the overall infection ratio of the US rises to 2.49% while the case mortality decreases to 2.09%, and the ratio of asymptomatic cases out of all infected cases reduces from the pre-summer 35-40% to around 20-25%.
Daily Middle-Term Probabilistic Forecasting of Power Consumption in North-East England
Authors: Roberto Baviera, Giuseppe Messuti
Date: 2020-05-26 00:00:00
Tasks: Time Series, Time Series Analysis

Probabilistic forecasting of power consumption in a middle-term horizon (months to a year) is a main challenge in the energy sector. It plays a key role in planning future generation plants and transmission grid. We propose a new model that incorporates trend and seasonality features as in traditional time-series analysis and weather conditions as explicative variables in a parsimonious machine learning approach, known as Gaussian Process. Applying to a daily power consumption dataset in North East England provided by one of the largest energy suppliers, we obtain promising results in Out-of-Sample density forecasts up to one year, even using a small dataset, with only a two-year In-Sample data. In order to verify the quality of the achieved power consumption probabilistic forecast we consider measures that are common in the energy sector as pinball loss and Winkler score and backtesting conditional and unconditional tests, standard in the banking sector after the introduction of Basel II Accords.
Impact of the State of Emergency Declaration for COVID-19 on Preventive Behaviors and Mental Conditions in Japan: Difference in Difference Analysis using Panel Data
Date: 2020-05-23 00:00:00

During the COVID-19 epidemic in Japan between March and April 2020, Internet
surveys were conducted to construct panel data to investigate changes at the
individual level regarding preventive behaviors and mental conditions by
surveying the same respondents at different times. Specifically, the
difference-in-difference (DID) method was used to explore the impact of the
COVID-19 state of emergency declared by the government. Key findings were: (1)
the declaration led people to stay home, while also generating anger, fear, and
anxiety. (2) The effect of the declaration on the promotion of preventive
behaviors was larger than the detrimental effect on mental conditions. (3)
Overall, the effect on women was larger than that on men.
Stocks and Cryptocurrencies: Anti-fragile or Robust?
Authors: Darío Alatorre, Carlos Gershenson, José L. Mateos
Date: 2020-05-26 00:00:00

In contrast with robust systems that resist noise or fragile systems that break with noise, antifragility is defined as a property of complex systems that benefit from noise or disorder. Here we define and test a simple measure of antifragility for complex dynamical systems. In this work we use our antifragility measure to analyze real data from return prices in the stock and cryptocurrency markets. Our definition of antifragility is the product of the return price and a perturbation. We explore different types of perturbations that typically arise from within the system. Our results suggest that for both the stock market and the cryptocurrency market, the tendency among the 'top performers' is to be robust rather than antifragile. It would be important to explore other possible definitions of antifragility to understand its role in financial markets and in complex dynamical systems in general.
Embedded System to Detect, Track and Classify Plankton Using a Lensless Video Microscope
Date: 2020-05-26 00:00:00
Tasks: Raspberry Pi 3

Plankton provide the foundation for life on earth. To advance our
understanding of the marine ecosystem, for scientific, commercial and survival
purposes, more in situ continuous monitoring and analysis of plankton is
required. Cost, complexity, power and data communication demands are barriers
to widespread deployment of in situ plankton microscopes. We address these
barriers by building and characterizing a lensless microscope with a data
pipeline optimized for the Raspberry Pi 3. The pipeline records 1080p video of
multiple plankton swimming in a sample well while simultaneously detecting,
tracking and selecting salient cropped images for classification @ 5.1 frames
per second. Thirteen machine learning classifiers and combinations of nine sets
of features are evaluated on nine plankton classes, optimized for speed
(F1=0.74 @ 1 msec. per image prediction) and accuracy (F1=0.81 @ .80 sec.).
System performance results confirm that performing the entire data pipeline
from image capture to classification is possible on a low-cost open-source
embedded computer.
Allostery and Kinetic Proofreading
Authors: Vahe Galstyan, Rob Phillips
Date: 2020-05-26 00:00:00

Kinetic proofreading is an error correction mechanism present in the processes of the central dogma and beyond, and typically requires the free energy of nucleotide hydrolysis for its operation. Though the molecular players of many biological proofreading schemes are known, our understanding of how energy consumption is managed to promote fidelity remains incomplete. In our work, we introduce an alternative conceptual scheme called 'the piston model of proofreading' where enzyme activation through hydrolysis is replaced with allosteric activation achieved through mechanical work performed by a piston on regulatory ligands. Inspired by Feynman's ratchet and pawl mechanism, we consider a mechanical engine designed to drive the piston actions powered by a lowering weight, whose function is analogous to that of ATP synthase in cells. Thanks to its mechanical design, the piston model allows us to tune the 'knobs' of the driving engine and probe the graded changes and trade-offs between speed, fidelity and energy dissipation. It provides an intuitive explanation of the conditions necessary for optimal proofreading and reveals the unexpected capability of allosteric molecules to beat the Hopfield limit of fidelity by leveraging the diversity of states available to them. The framework that we built for the piston model can also serve as a basis for additional studies of driven biochemical systems.
Spatiotemporal motion prediction in free-breathing liver scans via a recurrent multi-scale encoder decoder
Date: 2020-05-28 00:00:00
Proceeding: MIDL 2019 7
Tasks: Decoder, Image Registration, motion prediction

In this work we propose a multi-scale recurrent encoder-decoder architecture
to predict the breathing induced organ deformation in future frames. The model
was trained end-to-end from input images to predict a sequence of motion
labels. Targets were created by quantizing the displacement fields obtained
from deformable image registration. We report results using MRI free-breathing
acquisitions from 12 volunteers. Experiments were aimed at investigating the
proposed multi-scale design and the effect of increasing the number of
predicted frames on the overall accuracy of the model. The proposed model was
able to predict vessel positions in the next temporal image with a mean
accuracy of 2.07 (2.95) mm showing increased performance in comparison with
state-of-the-art approaches.
Different eigenvalue distributions encode the same temporal tasks in recurrent neural networks
Authors: Cecilia Jarne
Date: 2020-05-26 00:00:00

Different brain areas, such as the cortex and, more specifically, the prefrontal cortex, show great recurrence in their connections, even in early sensory areas. {Several approaches and methods based on trained networks have been proposed to model and describe these regions. It is essential to understand the dynamics behind the models because they are used to build different hypotheses about the functioning of brain areas and to explain experimental results. The main contribution here is the description of the dynamics through the classification and interpretation carried out with a set of numerical simulations. This study sheds light on the multiplicity of solutions obtained for the same tasks and shows the link between the spectra of linearized trained networks and the dynamics of the counterparts. The patterns in the distribution of the eigenvalues of the recurrent weight matrix were studied and properly related to the dynamics in each task.
Extração e Classificação de Características Radiômicas em Gliomas de Baixo Grau para Análise da Codeleção 1p/19q
Authors: Tony Alexandre Medeiros Silva, Guilherme Sousa Cassia, João Luiz Azevedo Carvalho
Date: 2020-05-26 00:00:00
Tasks: Dimensionality Reduction

Radiomics is an emerging area, which presents a large set of computational methods and techniques to extract quantitative characteristics from magnetic resonance images. In the feature extraction stage, its outputs must be well defined and carefully evaluated, to provide imaging diagnostics, prognoses and responses to treatment therapies. In this study, we present the extraction of quantitative characteristics from magnetic resonance images in low-grade gliomas using the Pyradiomics library and, using a multilayer perceptron neural network, we will show the prediction of the deletion of the 1p / 19q chromosomes in these gliomas. Several studies show that 1p / 19q chromosomal codelection is a positive prognostic factor in low-grade gliomas, as they are more sensitive to chemotherapy. Due to the large number of extracted characteristics, it was necessary to use a dimensionality reduction technique, the analysis principal components, which proved to be efficient in this study. After training and testing the characteristics performed by the multilayer perceptron neural network, the results showed to be very promising in detecting the deletion status of chromosomes 1p / 19q, mainly taking into account the possibility of avoiding surgical biopsies for this diagnosis.
State Estimation-Based Robust Optimal Control of Influenza Epidemics in an Interactive Human Society
Date: 2020-11-11 00:00:00
Tasks: State Estimation

This paper presents a state estimation-based robust optimal control strategy
for influenza epidemics in an interactive human society in the presence of
modeling uncertainties. Interactive society is influenced by the random
entrance of individuals from other human societies whose effects can be modeled
as a non-Gaussian noise. Since only the number of exposed and infected humans
can be measured, states of the influenza epidemics are first estimated by an
extended maximum correntropy Kalman filter (EMCKF) to provide a robust state
estimation in the presence of the non-Gaussian noise. An online quadratic
program (QP) optimization is then synthesized subject to a robust control
Lyapunov function (RCLF) to minimize susceptible and infected humans, while
minimizing and bounding the rates of vaccination and antiviral treatment. The
joint QP-RCLF-EMCKF meets multiple design specifications such as state
estimation, tracking, pointwise control optimality, and robustness to parameter
uncertainty and state estimation errors that have not been achieved
simultaneously in previous studies. The uniform ultimate boundedness
(UUB)/convergence of error trajectories is guaranteed using a Lyapunov
stability argument. The soundness of the proposed approach is validated on the
influenza epidemics of an interactive human society with a population of 16000.
Simulation results show that the QP-RCLF-EMCKF achieves appropriate tracking
and state estimation performance. The robustness of the proposed controller is
finally illustrated in the presence of modeling error and non-Gaussian noise.
Organ size increases with obesity and correlates with cancer risk
Authors: Haley Grant Yifan Zhang, Lu Li, Yan Wang, Satomi Kawamoto, Sophie Pénisson, Daniel F. Fouladi, Shahab Shayesteh, Alejandra Blanco, Saeed Ghandili, Eva Zinreich, Jefferson S. Graves, Seyoun Park, Scott Kern, Jody Hooper, Alan L. Yuille, Elliot K Fishman, Linda Chu, Cristian Tomasetti
Date: 2020-05-27 00:00:00

Obesity increases significantly cancer risk in various organs. Although this has been recognized for decades, the mechanism through which this happens has never been explained. Here, we show that the volumes of kidneys, pancreas, and liver are strongly correlated (median correlation = 0.625; P-value<10-47) with the body mass index (BMI) of an individual. We also find a significant relationship between the increase in organ volume and the increase in cancer risk (P-value<10-12). These results provide a mechanism explaining why obese individuals have higher cancer risk in several organs: the larger the organ volume the more cells at risk of becoming cancerous. These findings are important for a better understanding of the effects obesity has on cancer risk and, more generally, for the development of better preventive strategies to limit the mortality caused by obesity.
Multiple Access in Aerial Networks: From Orthogonal and Non-Orthogonal to Rate-Splitting
Authors: Wael Jaafar, Shimaa Naser, Sami Muhaidat, Paschalis C. Sofotasios, Halim Yanikomeroglu
Date: 2020-05-27 00:00:00

Recently, interest on the utilization of unmanned aerial vehicles (UAVs) has aroused. Specifically, UAVs can be used in cellular networks as aerial users for delivery, surveillance, rescue search, or as an aerial base station (aBS) for communication with ground users in remote uncovered areas or in dense environments requiring prompt high capacity. Aiming to satisfy the high requirements of wireless aerial networks, several multiple access techniques have been investigated. In particular, space-division multiple access(SDMA) and power-domain non-orthogonal multiple access (NOMA) present promising multiplexing gains for aerial downlink and uplink. Nevertheless, these gains are limited as they depend on the conditions of the environment. Hence, a generalized scheme has been recently proposed, called rate-splitting multiple access (RSMA), which is capable of achieving better spectral efficiency gains compared to SDMA and NOMA. In this paper, we present a comprehensive survey of key multiple access technologies adopted for aerial networks, where aBSs are deployed to serve ground users. Since there have been only sporadic results reported on the use of RSMA in aerial systems, we aim to extend the discussion on this topic by modelling and analyzing the weighted sum-rate performance of a two-user downlink network served by an RSMA-based aBS. Finally, related open issues and future research directions are exposed.
ACGAN-based Data Augmentation Integrated with Long-term Scalogram for Acoustic Scene Classification
Date: 2020-05-27 00:00:00
Tasks: Acoustic Scene Classification, Data Augmentation, Scene Classification

In acoustic scene classification (ASC), acoustic features play a crucial role
in the extraction of scene information, which can be stored over different time
scales. Moreover, the limited size of the dataset may lead to a biased model
with a poor performance for records from unseen cities and confusing scene
classes. In order to overcome this, we propose a long-term wavelet feature that
requires a lower storage capacity and can be classified faster and more
accurately compared with classic Mel filter bank coefficients (FBank). This
feature can be extracted with predefined wavelet scales similar to the FBank.
Furthermore, a novel data augmentation scheme based on generative adversarial
neural networks with auxiliary classifiers (ACGANs) is adopted to improve the
generalization of the ASC systems. The scheme, which contains ACGANs and a
sample filter, extends the database iteratively by splitting the dataset,
training the ACGANs and subsequently filtering samples. Experiments were
conducted on datasets from the Detection and Classification of Acoustic Scenes
and Events (DCASE) challenges. The results on the DCASE19 dataset demonstrate
the improved performance of the proposed techniques compared with the classic
FBank classifier. Moreover, the proposed fusion system achieved first place in
the DCASE19 competition and surpassed the top accuracies on the DCASE17
dataset.
An Overview of Signal Processing Techniques for Terahertz Communications
Authors: Hadi Sarieddeen, Mohamed-Slim Alouini, Tareq Y. Al-Naffouri
Date: 2020-05-27 00:00:00

Terahertz (THz)-band communications are a key enabler for future-generation wireless communications systems that promise to integrate a wide range of data-demanding applications. Recent advances in photonic, electronic, and plasmonic technologies are closing the gap in THz transceiver design. Consequently, prospect THz signal generation, modulation, and radiation methods are converging, and corresponding channel model, noise, and hardware-impairment notions are emerging. Such progress establishes a foundation for well-grounded research into THz-specific signal processing techniques for wireless communications. This tutorial overviews these techniques, emphasizing ultra-massive multiple-input multiple-output (UM-MIMO) systems and reconfigurable intelligent surfaces, vital for overcoming the distance problem at very high frequencies. We focus on the classical problems of waveform design and modulation, beamforming and precoding, index modulation, channel estimation, channel coding, and data detection. We also motivate signal processing techniques for THz sensing and localization.