pwc-archive/papers-with-abstracts huggingface dataset:
Epidemic spreading on complex networks as front propagation into an unstable state
Authors: Ashley Armbruster, Matt Holzer, Noah Roselli, Lena Underwood
Date: 2021-09-24 00:00:00

We study epidemic arrival times in meta-population disease models through the lens of front propagation into unstable states. We demonstrate that several features of invasion fronts in the PDE context are also relevant to the network case. We show that the susceptible-infected-recovered model on a network is linearly determined in the sense that the arrival times in the nonlinear system are approximated by the arrival times of the instability in the system linearized near the disease free state. Arrival time predictions are extended to an susceptible-exposed-infected-recovered model. We then study a recent model of social epidemics where high order interactions of individuals lead to faster invasion speeds. For these pushed fronts we compute corrections to the estimated arrival time in this case. Finally, we show how inhomogeneities in local infection rates lead to faster average arrival times.
On the parametrization of epidemiologic models -- lessons from modelling COVID-19 epidemic
Authors: Yuri Kheifetz, Holger Kirsten, Markus Scholz
Date: 2021-09-23 00:00:00
Tasks: parameter estimation

A plethora of prediction models of SARS-CoV-2 pandemic were proposed in the past. Prediction performances not only depend on the structure and features of the model, but also on its parametrization. Official databases are often biased due to lag in reporting of cases, changing testing policy or incompleteness of data. Moreover, model parametrization is time-dependent e.g. due to changing age-structures, new emerging virus variants, non-pharmaceutical interventions and ongoing vaccination programs. To cover these aspects, we develop a principled approach to parametrize SIR-type epidemiologic models of different complexities by embedding the model structure as a hidden layer into a general Input-Output Non-Linear Dynamical System (IO-NLDS). Non-explicitly modelled impacts on the system are imposed as inputs of the system. Observable data are coupled to hidden states of the model by appropriate data models considering possible biases of the data. We estimate model parameters including their time-dependence by a Bayesian knowledge synthesis process considering parameter ranges derived from external studies as prior information. We applied this approach on a SIR-type model and data of Germany and Saxony demonstrating good prediction performances. By our approach, we can estimate and compare for example the relative effectiveness of non-pharmaceutical interventions and can provide scenarios of the future course of the epidemic under specified conditions. Our method of parameter estimation can be translated to other data sets, i.e. other countries and other SIR-type models even for other disease contexts.
Blood-derived lncRNAs as potential biomarkers for early cancer diagnosis: The Good, the Bad and the Beauty
Authors: Cedric Badowski, Bing He, Lana X Garmire
Date: 2021-09-24 00:00:00
Tasks: Diagnostic, Specificity

Cancer ranks as one of the deadliest diseases worldwide. The high mortality rate associated with cancer is partially due to the lack of reliable early detection methods and/or inaccurate diagnostic tools such as certain protein biomarkers. Cell-free nucleic acids (cfNA) such as circulating long non-coding RNAs (lncRNAs) have recently been proposed as a new class of potential biomarkers that could improve cancer diagnosis. The reported correlation between circulating lncRNA levels and the presence of tumors has triggered a great amount of interest among clinicians and scientists who have been actively investigating their potentials as reliable cancer biomarkers. In this report, we review the progress achieved (the Good) and challenges encountered (the Bad) in the development of circulating lncRNAs as potential biomarkers for early cancer diagnosis. We report and discuss the specificity and sensitivity issues of blood-based lncRNAs currently considered as promising biomarkers for various cancers such as hepatocellular carcinoma, colorectal cancer, gastric cancer and prostate cancer. We also emphasize the potential clinical applications (the Beauty) of circulating lncRNAs both as therapeutic targets and agents, on top of diagnostic and prognostic capabilities. Based on different published works, we finally provide recommendations for investigators who seek to investigate and compare the levels of circulating lncRNAs in the blood of cancer patients compared to healthy subjects by RT-qPCR or Next Generation Sequencing.
Meta-brain Models: biologically-inspired cognitive agents
Authors: Bradly Alicea, Jesse Parent
Date: 2021-08-31 00:00:00
Tasks: Decision Making, Specificity

Artificial Intelligence (AI) systems based solely on neural networks or symbolic computation present a representational complexity challenge. While minimal representations can produce behavioral outputs like locomotion or simple decision-making, more elaborate internal representations might offer a richer variety of behaviors. We propose that these issues can be addressed with a computational approach we call meta-brain models. Meta-brain models are embodied hybrid models that include layered components featuring varying degrees of representational complexity. We will propose combinations of layers composed using specialized types of models. Rather than using a generic black box approach to unify each component, this relationship mimics systems like the neocortical-thalamic system relationship of the mammalian brain, which utilizes both feedforward and feedback connectivity to facilitate functional communication. Importantly, the relationship between layers can be made anatomically explicit. This allows for structural specificity that can be incorporated into the model's function in interesting ways. We will propose several types of layers that might be functionally integrated into agents that perform unique types of tasks, from agents that simultaneously perform morphogenesis and perception, to agents that undergo morphogenesis and the acquisition of conceptual representations simultaneously. Our approach to meta-brain models involves creating models with different degrees of representational complexity, creating a layered meta-architecture that mimics the structural and functional heterogeneity of biological brains, and an input/output methodology flexible enough to accommodate cognitive functions, social interactions, and adaptive behaviors more generally. We will conclude by proposing next steps in the development of this flexible and open-source approach.
Fetal oxygen delivery and consumption and blood gases in relation to gestational age
Authors: D. W. Rurak, M. Y. Shen, K. S. Joseph
Date: 2021-09-23 00:00:00
Tasks: Relation

Fetal oxygen delivery and consumption and blood gases in relation to gestational age. Oxygen crosses the placenta by diffusion and placental permeability to O$_2$ is high. Thus, the fetus receives adequate amounts, but vascular Po$_2$ is much lower than after birth. Studies of sustained fetal hypoxemia and acute 40-45% hemorrhage show that hypoxemia is not tolerated whereas hemorrhage is. This suggests that if fetal Po$_2$ falls markedly, O$_2$ diffusion from blood to tissue is impaired. Uterine blood and umbilical blood flows/fetal weight fall progressively with advancing gestation. This results in fetal hypoxemia, an increase in Pco$_2$, and decrease in pH. This decreases fetal O$_2$ delivery, and in fetal lambs and horses there is a decrease in fetal O$_2$ consumption. The decrease in O$_2$ demands is linked to a decrease in fetal breathing and body movements and growth rate. The decrease in fetal motility is due to an increase in fetal plasma PGE2 concentration, which begins at ~120 days GA in sheep and is due to the prepartum rise in fetal cortisol. Also, adenosine administration to fetal lambs decreases fetal breathing and REM sleep and the plasma adenosine concentration increases in late gestation. The fetal plasma levels of neurosteroids, which suppress fetal motility, increase with advancing gestation. The prepartum cortisol rise also inhibits fetal growth. In normal pregnancies, these mechanisms operate effectively to maintain an appropriate balance between fetal oxygen consumption and delivery. However, in pregnancies with either further reduce O$_2$ delivery or increase fetal O$_2$ demands, the mismatch between O$_2$ delivery and consumption may worsen leading to IUGR, hypoxic organ damage or stillbirth.
AraT5: Text-to-Text Transformers for Arabic Language Generation
Authors: El Moatez Billah Nagoudi, AbdelRahim Elmadany, Muhammad Abdul-Mageed
Date: 2021-08-31 00:00:00
Proceeding: ACL 2022 5
Tasks: Text Generation, Transfer Learning

Transfer learning with a unified Transformer framework (T5) that converts all language problems into a text-to-text format was recently proposed as a simple and effective transfer learning approach. Although a multilingual version of the T5 model (mT5) was also introduced, it is not clear how well it can fare on non-English tasks involving diverse data. To investigate this question, we apply mT5 on a language with a wide variety of dialects--Arabic. For evaluation, we introduce a novel benchmark for ARabic language GENeration (ARGEN), covering seven important tasks. For model comparison, we pre-train three powerful Arabic T5-style models and evaluate them on ARGEN. Although pre-trained with ~49 less data, our new models perform significantly better than mT5 on all ARGEN tasks (in 52 out of 59 test sets) and set several new SOTAs. Our models also establish new SOTA on the recently-proposed, large Arabic language understanding evaluation benchmark ARLUE (Abdul-Mageed et al., 2021). Our new models are publicly available. We also link to ARGEN datasets through our repository: https://github.com/UBC-NLP/araT5.
Spatial modeling of mid-infrared spectral data with thermal compensation using integrated nested Laplace approximation
Authors: Bernardo Aquino, Stefano Castruccio, Vijay Gupta, Scott Howard
Date: 2021-09-20 00:00:00
Proceeding: Applied Optics 2021 9

The problem of analyzing substances using low-cost sensors with a low signal-to-noise ratio (SNR) remains challenging. Using accurate models for the spectral data is paramount for the success of any classification task. We demonstrate that the thermal compensation of sample heating and spatial variability analysis yield lower modeling errors than non-spatial modeling. Then, we obtain the inference of the spectral data probability density functions using the integrated nested Laplace approximation (INLA) on a Bayesian hierarchical model. To achieve this goal, we use the fast and user-friendly R-INLA package in R for the computation. This approach allows affordable and real-time substance identification with fewer SNR sensor measurements, thereby potentially increasing throughput and lowering costs.
Critical community size for COVID-19 -- a model based approach to provide a rationale behind the lockdown
Authors: Sarmistha Das, Pramit Ghosh, Bandana Sen, Indranil Mukhopadhyay
Date: 2020-04-07 00:00:00

Background: Restrictive mass quarantine or lockdown has been implemented as the most important controlling measure to fight against COVID-19. Many countries have enforced 2 - 4 weeks' lockdown and are extending the period depending on their current disease scenario. Most probably the 14-day period of estimated communicability of COVID-19 prompted such decision. But the idea that, if the susceptible population drops below certain threshold, the infection would naturally die out in small communities after a fixed time (following the outbreak), unless the disease is reintroduced from outside, was proposed by Bartlett in 1957. This threshold was termed as Critical Community Size (CCS). Methods: We propose an SEIR model that explains COVID-19 disease dynamics. Using our model, we have calculated country-specific expected time to extinction (TTE) and CCS that would essentially determine the ideal number of lockdown days required and size of quarantined population. Findings: With the given country-wise rates of death, recovery and other parameters, we have identified that, if at a place the total number of susceptible population drops below CCS, infection will cease to exist after a period of TTE days, unless it is introduced from outside. But the disease will almost die out much sooner. We have calculated the country-specific estimate of the ideal number of lockdown days. Thus, smaller lockdown phase is sufficient to contain COVID-19. On a cautionary note, our model indicates another rise in infection almost a year later but on a lesser magnitude.
A Comparative Analysis on Bangla Handwritten Digit Recognition with Data Augmentation and Non-Augmentation Process
Authors: MD Abdullah Al Nasim, Refat E Ferdous, Mahim Anzum Haque Pantho, Atiqul Islam Chowdhury
Date: 2020-06-26 00:00:00
Proceeding: International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) 2020 6
Tasks: Data Augmentation, Handwritten Digit Recognition, image-classification, Image Classification, Object Recognition

Determination of Bangla handwritten digit is a
momentous image classification task. Though object recognition
technology is getting smarter day by day, still Bangla handwritten
digit recognition remains inconclusive. Researchers are becoming
more concerned about handwritten digit recognition for it’s
educational and advantageous importance. But it is a matter
of trouble that the improvement in Bangla handwritten digit
recognition is significantly less as compared to the other languages. To improve the performance of the Bangla handwritten
digit recognition system, we have designed a model, in which
all basic Bangla digits have been classified. Furthermore, we
have also demonstrated Densenet121 architecture in our system.
For recognizing Bangla handwriting digits, we proposed CNN
(Convolution Neural Network) model. Our system has been
experimented on the NumtaDB dataset for recognizing Bangla
digit both with augmentation and non-augmentation.
Brain Tumor Detection using Convolutional Neural Network
Authors: Tonmoy Hossain, Fairuz Shadmani Shishir, Mohsena Ashraf, MD Abdullah Al Nasim, Faisal Muhammad Shah
Date: 2019-05-03 00:00:00
Proceeding: international conference on advances in science, engineering and robotics technology (ICASERT) 2019 5
Tasks: Brain Tumor Segmentation, Tumor Segmentation

Brain Tumor segmentation is one of the most
crucial and arduous tasks in the terrain of medical image
processing as a human-assisted manual classification can result
in inaccurate prediction and diagnosis. Moreover, it is an
aggravating task when there is a large amount of data present
to be assisted. Brain tumors have high diversity in appearance
and there is a similarity between tumor and normal tissues and
thus the extraction of tumor regions from images becomes
unyielding. In this paper, we proposed a method to extract brain
tumor from 2D Magnetic Resonance brain Images (MRI) by
Fuzzy C-Means clustering algorithm which was followed by
traditional classifiers and convolutional neural network. The
experimental study was carried on a real-time dataset with
diverse tumor sizes, locations, shapes, and different image
intensities. In traditional classifier part, we applied six
traditional classifiers namely Support Vector Machine (SVM),
K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP),
Logistic Regression, Naïve Bayes and Random Forest which was
implemented in scikit-learn. Afterward, we moved on to
Convolutional Neural Network (CNN) which is implemented
using Keras and Tensorflow because it yields to a better
performance than the traditional ones. In our work, CNN
gained an accuracy of 97.87%, which is very compelling. The
main aim of this paper is to distinguish between normal and
abnormal pixels, based on texture based and statistical based
features.
Multi-Objective Digital PID Controller Design in Parameter Space
Authors: Haoan Wang, Levent Guvenc
Date: 2020-12-04 00:00:00
Tasks: Sensitivity

This paper presents a multi-objective digital PID controller design method using the parameter space approach of robust control. Absolute stability is treated first by finding the digital PID controller gain parameter space corresponding to closed loop poles being inside the unit circle. Phase margin, gain margin and a mixed sensitivity bound are treated as frequency domain constraints. Determination of digital PID controller parameter space regions satisfying these constraints is presented. All of these regions are superimposed to obtain a multi-objective digital PID controller gain parameter space solution region. The path following controller design of an automated driving vehicle is used as an example to illustrate the method.
FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding
Authors: Yanan Zheng, Jing Zhou, Yujie Qian, Ming Ding, Chonghua Liao, Jian Li, Ruslan Salakhutdinov, Jie Tang, Sebastian Ruder, Zhilin Yang
Date: 2021-09-27 00:00:00
Proceeding: ACL 2022 5
Tasks: Benchmarking, Natural Language Understanding

The few-shot natural language understanding (NLU) task has attracted much recent attention. However, prior methods have been evaluated under a disparate set of protocols, which hinders fair comparison and measuring progress of the field. To address this issue, we introduce an evaluation framework that improves previous evaluation procedures in three key aspects, i.e., test performance, dev-test correlation, and stability. Under this new evaluation framework, we re-evaluate several state-of-the-art few-shot methods for NLU tasks. Our framework reveals new insights: (1) both the absolute performance and relative gap of the methods were not accurately estimated in prior literature; (2) no single method dominates most tasks with consistent performance; (3) improvements of some methods diminish with a larger pretrained model; and (4) gains from different methods are often complementary and the best combined model performs close to a strong fully-supervised baseline. We open-source our toolkit, FewNLU, that implements our evaluation framework along with a number of state-of-the-art methods.
MIIDL: a Python package for microbial biomarkers identification powered by interpretable deep learning
Authors: Jian Jiang
Date: 2021-09-24 00:00:00

Detecting microbial biomarkers used to predict disease phenotypes and clinical outcomes is crucial for disease early-stage screening and diagnosis. Most methods for biomarker identification are linear-based, which is very limited as biological processes are rarely fully linear. The introduction of machine learning to this field tends to bring a promising solution. However, identifying microbial biomarkers in an interpretable, data-driven and robust manner remains challenging. We present MIIDL, a Python package for the identification of microbial biomarkers based on interpretable deep learning. MIIDL innovatively applies convolutional neural networks, a variety of interpretability algorithms and plenty of pre-processing methods to provide a one-stop and robust pipeline for microbial biomarkers identification from high-dimensional and sparse data sets.
Using neural networks to solve the 2D Poisson equation for electric field computation in plasma fluid simulations
Authors: Lionel Cheng, Ekhi Ajuria Illarramendi, Guillaume Bogopolsky, Michael Bauerheim, Benedicte Cuenot
Date: 2021-09-27 00:00:00
Tasks: GPU

The Poisson equation is critical to get a self-consistent solution in plasma fluid simulations used for Hall effect thrusters and streamer discharges, since the Poisson solution appears as a source term of the unsteady nonlinear flow equations. As a first step, solving the 2D Poisson equation with zero Dirichlet boundary conditions using a deep neural network is investigated using multiple-scale architectures, defined in terms of number of branches, depth and receptive field. One key objective is to better understand how neural networks learn the Poisson solutions and provide guidelines to achieve optimal network configurations, especially when coupled to the time-varying Euler equations with plasma source terms. Here, the Receptive Field is found critical to correctly capture large topological structures of the field. The investigation of multiple architectures, losses, and hyperparameters provides an optimal network to solve accurately the steady Poisson problem. The performance of the optimal neural network solver, called PlasmaNet, is then monitored on meshes with increasing number of nodes, and compared with classical parallel linear solvers. Next, PlasmaNet is coupled with an unsteady Euler plasma fluid equations solver in the context of the electron plasma oscillation test case. In this time-evolving problem, a physical loss is necessary to produce a stable simulation. PlasmaNet is finally tested on a more complex case of discharge propagation involving chemistry and advection. The guidelines established in previous sections are applied to build the CNN to solve the same Poisson equation in cylindrical coordinates with different boundary conditions. Results reveal good CNN predictions and pave the way to new computational strategies using modern GPU-based hardware to predict unsteady problems involving a Poisson equation.
Emergent behavior and neural dynamics in artificial agents tracking turbulent plumes
Authors: Satpreet Harcharan Singh, Floris van Breugel, Rajesh P. N. Rao, Bingni Wen Brunton
Date: 2021-09-25 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Tracking a turbulent plume to locate its source is a complex control problem because it requires multi-sensory integration and must be robust to intermittent odors, changing wind direction, and variable plume statistics. This task is routinely performed by flying insects, often over long distances, in pursuit of food or mates. Several aspects of this remarkable behavior have been studied in detail in many experimental studies. Here, we take a complementary in silico approach, using artificial agents trained with reinforcement learning to develop an integrated understanding of the behaviors and neural computations that support plume tracking. Specifically, we use deep reinforcement learning (DRL) to train recurrent neural network (RNN) agents to locate the source of simulated turbulent plumes. Interestingly, the agents' emergent behaviors resemble those of flying insects, and the RNNs learn to represent task-relevant variables, such as head direction and time since last odor encounter. Our analyses suggest an intriguing experimentally testable hypothesis for tracking plumes in changing wind direction -- that agents follow local plume shape rather than the current wind direction. While reflexive short-memory behaviors are sufficient for tracking plumes in constant wind, longer timescales of memory are essential for tracking plumes that switch direction. At the level of neural dynamics, the RNNs' population activity is low-dimensional and organized into distinct dynamical structures, with some correspondence to behavioral modules. Our in silico approach provides key intuitions for turbulent plume tracking strategies and motivates future targeted experimental and theoretical developments.