pwc-archive/papers-with-abstracts huggingface dataset:
Spatio-temporal modelling of phenotypic heterogeneity in tumour tissues and its impact on radiotherapy treatment
Authors: Giulia L. Celora, Helen M. Byrne, P. G. Kevrekidis
Date: 2021-10-19 00:00:00

We present a mathematical model that describes how tumour heterogeneity evolves in a tissue slice that is oxygenated by a single blood vessel. Phenotype is identified with the stemness level of a cell, $s$, that determines its proliferative capacity, apoptosis propensity and response to treatment. Our study is based on numerical bifurcation analysis and dynamical simulations of a system of coupled non-local (in phenotypic space) partial differential equations that links the phenotypic evolution of the tumour cells to local oxygen levels in the tissue. In our formulation, we consider a 1D geometry where oxygen is supplied by a blood vessel located on the domain boundary and consumed by the tumour cells as it diffuses through the tissue. For biologically relevant parameter values, the system exhibits multiple steady states; in particular, depending on the initial conditions, the tumour is either eliminated ("tumour-extinction") or it persists ("tumour-invasion"). We conclude by using the model to investigate tumour responses to radiotherapy (RT), and focus on establishing which RT strategies can eliminate the tumour. Numerical simulations reveal how phenotypic heterogeneity evolves during treatment and highlight the critical role of tissue oxygen levels on the efficacy of radiation protocols that are commonly used clinically.
Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network
Authors: Nanzhe Wang, Haibin Chang, Dongxiao Zhang
Date: 2021-10-12 00:00:00

The theory-guided convolutional neural network (TgCNN) framework, which can incorporate discretized governing equation residuals into the training of convolutional neural networks (CNNs), is extended to two-phase porous media flow problems in this work. The two principal variables of the considered problem, pressure and saturation, are approximated simultaneously with two CNNs, respectively. Pressure and saturation are coupled with each other in the governing equations, and thus the two networks are also mutually conditioned in the training process by the discretized governing equations, which also increases the difficulty of model training. The coupled and discretized equations can provide valuable information in the training process. With the assistance of theory-guidance, the TgCNN surrogates can achieve better accuracy than ordinary CNN surrogates in two-phase flow problems. Moreover, a piecewise training strategy is proposed for the scenario with varying well controls, in which the TgCNN surrogates are constructed for different segments on the time dimension and stacked together to predict solutions for the whole time-span. For scenarios with larger variance of the formation property field, the TgCNN surrogates can also achieve satisfactory performance. The constructed TgCNN surrogates are further used for inversion of permeability fields by combining them with the iterative ensemble smoother (IES) algorithm, and sufficient inversion accuracy is obtained with improved efficiency.
Deep Learning to Estimate Permeability using Geophysical Data
Authors: M. K. Mudunuru, E. L. D. Cromwell, H. Wang, X. Chen
Date: 2021-10-08 00:00:00
Tasks: Deep Learning

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Subsurface process models based on hydrogeophysics are used to generate this synthetic data for deep learning analyses. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R2-score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(104) times faster than running a forward model. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings (O(105) - O(107)) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of the permeability field.
Towards Polarization-Insensitive Coherent Coded Phase OTDR
Authors: Sterenn Guerrier, Christian Dorize, Elie Awwad, Jérémie Renaudier
Date: 2021-10-12 00:00:00

We explore the alternatives for interrogating a fiber sensor from the polarization point of view, and demonstrate a better accuracy with dual polarization probing for coherent phi-OTDR compared with single polarization probing.
Revisiting identification concepts in Bayesian analysis
Authors: Jean-Pierre Florens, Anna Simoni
Date: 2021-10-19 00:00:00

This paper studies the role played by identification in the Bayesian analysis of statistical and econometric models. First, for unidentified models we demonstrate that there are situations where the introduction of a non-degenerate prior distribution can make a parameter that is nonidentified in frequentist theory identified in Bayesian theory. In other situations, it is preferable to work with the unidentified model and construct a Markov Chain Monte Carlo (MCMC) algorithms for it instead of introducing identifying assumptions. Second, for partially identified models we demonstrate how to construct the prior and posterior distributions for the identified set parameter and how to conduct Bayesian analysis. Finally, for models that contain some parameters that are identified and others that are not we show that marginalizing out the identified parameter from the likelihood with respect to its conditional prior, given the nonidentified parameter, allows the data to be informative about the nonidentified and partially identified parameter. The paper provides examples and simulations that illustrate how to implement our techniques.
In-Orbit Lunar Satellite Image Super Resolution for Selective Data Transmission
Authors: Atal Tewari, Chennuri Prateek, Nitin Khanna
Date: 2021-10-19 00:00:00
Tasks: Image Reconstruction, Image Super-Resolution, satellite image super-resolution, Super-Resolution

Rapid technological advancements have tremendously increased the data acquisition capabilities of remote sensing satellites. However, the data utilization efficiency in satellite missions is very low. This growing data also escalates the cost required for data downlink transmission and post-processing. Selective data transmission based on in-orbit inferences will address these issues to a great extent. Therefore, to decrease the cost of the satellite mission, we propose a novel system design for selective data transmission, based on in-orbit inferences. As the resolution of images plays a critical role in making precise inferences, we also include in-orbit super-resolution (SR) in the system design. We introduce a new image reconstruction technique and a unique loss function to enable the execution of the SR model on low-power devices suitable for satellite environments. We present a residual dense non-local attention network (RDNLA) that provides enhanced super-resolution outputs to improve the SR performance. SR experiments on Kaguya digital ortho maps (DOMs) demonstrate that the proposed SR algorithm outperforms the residual dense network (RDN) in terms of PSNR and block-sensitive PSNR by a margin of +0.1 dB and +0.19 dB, respectively. The proposed SR system consumes 48% less memory and 67% less peak instantaneous power than the standard SR model, RDN, making it more suitable for execution on a low-powered device platform.
Spatial-temporal water area monitoring of Miyun Reservoir using remote sensing imagery from 1984 to 2020
Authors: Chang Liu, Hairong Tang, Luyan Ji, Yongchao Zhao
Date: 2021-10-14 00:00:00
Tasks: Management

Miyun Reservoir has produced huge benefits in flood control, agricultural irrigation, power generation, aquaculture, tourism, and urban water supply. Accurately water mapping is of great significance to the ecological environment monitoring of the Miyun Reservoir and the management of the South-to-North Water Diversion Project. On the 60th anniversary of the completion of the Miyun Reservoir, we took the Miyun Reservoir as the study area and collected all the Landsat-5 and Landsat-8 remote sensing images from 1984 to 2020 for water mapping. Based on the spectral, topographical and temporal-spatial characteristics of water, we proposed an automated method for long-term researvoir mapping, which can solve the problems caused by cloud, shadow, ice and snow pixels. Moreover, it can also deal with 'the same objects with different spectra' and spectral mixed problems. The overall accuracy is as high as 98.2% for the case with no cloud or snow/ice cover. The landscape division index is introduced to analyze the morphological changes of Miyun Reservoir. Based on the mapping results, we analyzed the changes of Miyun Reservoir from 1984 to 2020 and the driving factors of them.
Low Complexity Single Source DOA Estimation Based on Reduced Dimension SVR
Authors: Md Imrul Hasan, Mohammad Saquib
Date: 2021-10-19 00:00:00
Tasks: regression

Conventional direction of arrival (DOA) estimation algorithms suffer from performance degradation due to antenna pattern distortion and substantial computational complexity in real-time execution. The support vector regression (SVR) approach is a possible solution to overcome those limitations. In this work, we propose a sequential DOA estimation technique that combines the reduced dimension SVR (for the azimuthal plane) with a closed form approach (for the elevation plane). Thus, the training and testing are only required for the azimuthal angles which makes it very attractive from the implementation complexity point of view. Our analysis demonstrates that the proposed algorithm offers significant complexity gain over the popular MUSIC algorithm while exhibiting similar root-mean-square error performance.
SleepPriorCL: Contrastive Representation Learning with Prior Knowledge-based Positive Mining and Adaptive Temperature for Sleep Staging
Authors: Hongjun Zhang, Jing Wang, Qinfeng Xiao, Jiaoxue Deng, Youfang Lin
Date: 2021-10-15 00:00:00
Tasks: Representation Learning, Self-Supervised Learning, Sleep Staging, Time Series, Time Series Analysis

The objective of this paper is to learn semantic representations for sleep stage classification from raw physiological time series. Although supervised methods have gained remarkable performance, they are limited in clinical situations due to the requirement of fully labeled data. Self-supervised learning (SSL) based on contrasting semantically similar (positive) and dissimilar (negative) pairs of samples have achieved promising success. However, existing SSL methods suffer the problem that many semantically similar positives are still uncovered and even treated as negatives. In this paper, we propose a novel SSL approach named SleepPriorCL to alleviate the above problem. Advances of our approach over existing SSL methods are two-fold: 1) by incorporating prior domain knowledge into the training regime of SSL, more semantically similar positives are discovered without accessing ground-truth labels; 2) via investigating the influence of the temperature in contrastive loss, an adaptive temperature mechanism for each sample according to prior domain knowledge is further proposed, leading to better performance. Extensive experiments demonstrate that our method achieves state-of-the-art performance and consistently outperforms baselines.
Positional-Spectral-Temporal Attention in 3D Convolutional Neural Networks for EEG Emotion Recognition
Authors: Jiyao Liu, Yanxi Zhao, Hao Wu, Dongmei Jiang
Date: 2021-10-13 00:00:00
Tasks: EEG, EEG Emotion Recognition, Electroencephalogram (EEG), Emotion Recognition

Recognizing the feelings of human beings plays a critical role in our daily communication. Neuroscience has demonstrated that different emotion states present different degrees of activation in different brain regions, EEG frequency bands and temporal stamps. In this paper, we propose a novel structure to explore the informative EEG features for emotion recognition. The proposed module, denoted by PST-Attention, consists of Positional, Spectral and Temporal Attention modules to explore more discriminative EEG features. Specifically, the Positional Attention module is to capture the activate regions stimulated by different emotions in the spatial dimension. The Spectral and Temporal Attention modules assign the weights of different frequency bands and temporal slices respectively. Our method is adaptive as well as efficient which can be fit into 3D Convolutional Neural Networks (3D-CNN) as a plug-in module. We conduct experiments on two real-world datasets. 3D-CNN combined with our module achieves promising results and demonstrate that the PST-Attention is able to capture stable patterns for emotion recognition from EEG.
Minimal Compression of a Radio-Frequency Pulse
Authors: W. J. Szajnowski
Date: 2021-10-12 00:00:00

Quadrature amplitude modulation (QAM) and a complementary representation of a causal waveform have been used to develop a sidelobe-free pulse-compression technique. Envelopes of radio-frequency (RF) pulses under study include both unimodal (Laplacian, Gaussian, rectangular) and bimodal (Hermite-Gaussian) shapes. Although the achievable compression gain is small (~2), the generation and phase-invariant correlation processing of such created compressible pulses can be accomplished with the use of low-complexity systems.
Analysis of False Data Injection Impact on AI based Solar Photovoltaic Power Generation Forecasting
Authors: S. Sarp, M. Kuzlu, U. Cali, O. Elma, O. Guler
Date: 2021-10-12 00:00:00
Tasks: regression

The use of solar photovoltaics (PV) energy provides additional resources to the electric power grid. The downside of this integration is that the solar power supply is unreliable and highly dependent on the weather condition. The predictability and stability of forecasting are critical for the full utilization of solar power. This study reviews and evaluates various machine learning-based models for solar PV power generation forecasting using a public dataset. Furthermore, The root mean squared error (RMSE), mean squared error (MSE), and mean average error (MAE) metrics are used to evaluate the results. Linear Regression, Gaussian Process Regression, K-Nearest Neighbor, Decision Trees, Gradient Boosting Regression Trees, Multi-layer Perceptron, and Support Vector Regression algorithms are assessed. Their responses against false data injection attacks are also investigated. The Multi-layer Perceptron Regression method shows robust prediction on both regular and noise injected datasets over other methods.
A review of commonly used compartmental models in epidemiology
Authors: Mohamed Mehdaoui
Date: 2021-10-18 00:00:00
Tasks: Epidemiology

In order to model an epidemic, different approaches can be adopted. Mainly, the deterministic approach and the stochastic one. Recently, a large amount of literature has been published using the two approaches. The aim of this paper is to illustrate the usual framework used for commonly adopted compartmental models in epidemiology and introduce variant analytic and numerical tools that interfere on each one of those models, as well as the general related types of existing, ongoing and future possible contributions.
Probing the relationship between linear dynamical systems and low-rank recurrent neural network models
Authors: Adrian Valente, Srdjan Ostojic, Jonathan Pillow
Date: 2021-10-19 00:00:00

A large body of work has suggested that neural populations exhibit low-dimensional dynamics during behavior. However, there are a variety of different approaches for modeling low-dimensional neural population activity. One approach involves latent linear dynamical system (LDS) models, in which population activity is described by a projection of low-dimensional latent variables with linear dynamics. A second approach involves low-rank recurrent neural networks (RNNs), in which population activity arises directly from a low-dimensional projection of past activity. Although these two modeling approaches have strong similarities, they arise in different contexts and tend to have different domains of application. Here we examine the precise relationship between latent LDS models and linear low-rank RNNs. When can one model class be converted to the other, and vice versa? We show that latent LDS models can only be converted to RNNs in specific limit cases, due to the non-Markovian property of latent LDS models. Conversely, we show that linear RNNs can be mapped onto LDS models, with latent dimensionality at most twice the rank of the RNN.
Quantitative relations among causality measures with applications to nonlinear pulse-output network reconstruction
Authors: Zhong-qi K. Tian, Kai Chen, Songting Li, David W. McLaughlin, Douglas Zhou
Date: 2021-10-17 00:00:00

The causal connectivity of a network is often inferred to understand the network function. It is arguably acknowledged that the inferred causal connectivity relies on causality measure one applies, and it may differ from the network's underlying structural connectivity. However, the interpretation of causal connectivity remains to be fully clarified, in particular, how causal connectivity depends on causality measures and how causal connectivity relates to structural connectivity. Here, we focus on nonlinear networks with pulse signals as measured output, $e.g.$, neural networks with spike output, and address the above issues based on four intensively utilized causality measures, $i.e.$, time-delayed correlation, time-delayed mutual information, Granger causality, and transfer entropy. We theoretically show how these causality measures are related to one another when applied to pulse signals. Taking the simulated Hodgkin-Huxley neural network and the real mouse brain network as two illustrative examples, we further verify the quantitative relations among the four causality measures and demonstrate that the causal connectivity inferred by any of the four well coincides with the underlying network structural connectivity, therefore establishing a direct link between the causal and structural connectivity. We stress that the structural connectivity of networks can be reconstructed pairwisely without conditioning on the global information of all other nodes in a network, thus circumventing the curse of dimensionality. Our framework provides a practical and effective approach for pulse-output network reconstruction.
FaxPlainAC: A Fact-Checking Tool Based on EXPLAINable Models with HumAn Correction in the Loop
Authors: Zijian Zhang, Koustav Rudra, Avishek Anand
Date: 2021-09-12 00:00:00
Tasks: Explainable Models, Fact Checking

Fact-checking on the Web has become the main mechanism through which we detect the credibility of the news or information. Existing fact-checkers verify the authenticity of the information (support or refute the claim) based on secondary sources of information. However, existing approaches do not consider the problem of model updates due to constantly increasing training data due to user feedback. It is therefore important to conduct user studies to correct models' inference biases and improve the model in a life-long learning manner in the future according to the user feedback. In this paper, we present FaxPlainAC, a tool that gathers user feedback on the output of explainable fact-checking models. FaxPlainAC outputs both the model decision, i.e., whether the input fact is true or not, along with the supporting/refuting evidence considered by the model. Additionally, FaxPlainAC allows for accepting user feedback both on the prediction and explanation. Developed in Python, FaxPlainAC is designed as a modular and easily deployable tool. It can be integrated with other downstream tasks and allowing for fact-checking human annotation gathering and life-long learning.
Private Language Model Adaptation for Speech Recognition
Authors: Zhe Liu, Ke Li, Shreyan Bakshi, Fuchun Peng
Date: 2021-09-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Federated Learning, Language Modeling, Language Modelling, model, speech-recognition, Speech Recognition

Speech model adaptation is crucial to handle the discrepancy between server-side proxy training data and actual data received on local devices of users. With the use of federated learning (FL), we introduce an efficient approach on continuously adapting neural network language models (NNLMs) on private devices with applications on automatic speech recognition (ASR). To address the potential speech transcription errors in the on-device training corpus, we perform empirical studies on comparing various strategies of leveraging token-level confidence scores to improve the NNLM quality in the FL settings. Experiments show that compared with no model adaptation, the proposed method achieves relative 2.6% and 10.8% word error rate (WER) reductions on two speech evaluation datasets, respectively. We also provide analysis in evaluating privacy guarantees of our presented procedure.
Clinical Trial Information Extraction with BERT
Authors: Xiong Liu, Greg L. Hersch, Iya Khalil, Murthy Devarakonda
Date: 2021-09-11 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Natural language processing (NLP) of clinical trial documents can be useful in new trial design. Here we identify entity types relevant to clinical trial design and propose a framework called CT-BERT for information extraction from clinical trial text. We trained named entity recognition (NER) models to extract eligibility criteria entities by fine-tuning a set of pre-trained BERT models. We then compared the performance of CT-BERT with recent baseline methods including attention-based BiLSTM and Criteria2Query. The results demonstrate the superiority of CT-BERT in clinical trial NLP.
Attitude Optimal Backstepping Controller Based Quaternion for a UAV
Authors: Kaddouri Djamel
Date: 2016-03-03 00:00:00
Proceeding: Advanced Control of Complex Dynamical Systems with Applications 2016 3

A hierarchical controller design based on nonlinear  theory and backstepping technique is developed for a nonlinear and coupled dynamic attitude system using conventional quaternion based method. The derived controller combines the attractive features of  optimal controller and the advantages of the backstepping technique leading to a control law which avoids winding phenomena. Performance issues of the controller are illustrated in a simulation study made for a four-rotor vertical take-off and landing (VTOL) aerial robot prototype known as the quadrotor aircraft.