Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining Standard Occupational Classification Codes from Job Descriptions in Immigration Petitions

Accurate specification of standard occupational classification (SOC) code is critical to the success of many U.S. work visa applications. Determination of correct SOC code relies on careful study of job requirements and comparison to definitions given by the U.S. Bureau of Labor Statistics, which is often a tedious activity. In this paper, we apply methods from natural language processing (NLP) to computationally determine SOC code based on job description. We implement and empirically evaluate a broad variety of predictive models with respect to quality of prediction and training time, and identify models best suited for this task.
Variance of Twitter Embeddings and Temporal Trends of COVID-19 cases

The severity of the coronavirus pandemic necessitates the need of effective administrative decisions. Over 4 lakh people in India succumbed to COVID-19, with over 3 crore confirmed cases, and still counting. The threat of a plausible third wave continues to haunt millions. In this ever changing dynamic of the virus, predictive modeling methods can serve as an integral tool. The pandemic has further triggered an unprecedented usage of social media. This paper aims to propose a method for harnessing social media, specifically Twitter, to predict the upcoming scenarios related to COVID-19 cases. In this study, we seek to understand how the surges in COVID-19 related tweets can indicate rise in the cases. This prospective analysis can be utilised to aid administrators about timely resource allocation to lessen the severity of the damage. Using word embeddings to capture the semantic meaning of tweets, we identify Significant Dimensions (SDs). Our methodology predicts the rise in cases with a lead time of 15 days and 30 days with R2 scores of 0.80 and 0.62 respectively. Finally, we explain the thematic utility of the SDs.
Update in Unit Gradient

In Machine Learning, optimization mostly has been done by using a gradient descent method to find the minimum value of the loss. However, especially in deep learning, finding a global minimum from a nonconvex loss function across a high dimensional space is an extraordinarily difficult task. Recently, a generalization learning algorithm, Sharpness-Aware Minimization (SAM), has made a great success in image classification task. Despite the great performance in creating convex space, proper direction leading by SAM is still remained unclear. We, thereby, propose a creating a Unit Vector space in SAM, which not only consisted of the mathematical instinct in linear algebra but also kept the advantages of adaptive gradient algorithm. Moreover, applying SAM in unit gradient brings models competitive performances in image classification datasets, such as CIFAR - {10, 100}. The experiment showed that it performed even better and more robust than SAM.
Confounder importance learning for treatment effect inference

We address modelling and computational issues for multiple treatment effect inference under many potential confounders. A primary issue relates to preventing harmful effects from omitting relevant covariates (under-selection), while not running into over-selection issues that introduce substantial variance and a bias related to the non-random over-inclusion of covariates. We propose a novel empirical Bayes framework for Bayesian model averaging that learns from data the extent to which the inclusion of key covariates should be encouraged, specifically those highly associated to the treatments. A key challenge is computational. We develop fast algorithms, including an Expectation-Propagation variational approximation and simple stochastic gradient optimization algorithms, to learn the hyper-parameters from data. Our framework uses widely-used ingredients and largely existing software, and it is implemented within the R package mombf featured on CRAN. This work is motivated by and is illustrated in two applications. The first is the association between salary variation and discriminatory factors. The second, that has been debated in previous works, is the association between abortion policies and crime. Our approach provides insights that differ from previous analyses especially in situations with weaker treatment effects.
Learning from Demonstrations for Autonomous Soft-tissue Retraction *

The current research focus in Robot-Assisted Minimally Invasive Surgery (RAMIS) is directed towards increasing the level of robot autonomy, to place surgeons in a supervisory position. Although Learning from Demonstrations (LfD) approaches are among the preferred ways for an autonomous surgical system to learn expert gestures, they require a high number of demonstrations and show poor generalization to the variable conditions of the surgical environment. In this work, we propose an LfD methodology based on Generative Adversarial Imitation Learning (GAIL) that is built on a Deep Reinforcement Learning (DRL) setting. GAIL combines generative adversarial networks to learn the distribution of expert trajectories with a DRL setting to ensure generalisation of trajectories providing human-like behaviour. We consider automation of tissue retraction, a common RAMIS task that involves soft tissues manipulation to expose a region of interest. In our proposed methodology, a small set of expert trajectories can be acquired through the da Vinci Research Kit (dVRK) and used to train the proposed LfD method inside a simulated environment. Results indicate that our methodology can accomplish the tissue retraction task with human-like behaviour while being more sample-efficient than the baseline DRL method. Towards the end, we show that the learnt policies can be successfully transferred to the real robotic platform and deployed for soft tissue retraction on a synthetic phantom.
ebnm: An R Package for Solving the Empirical Bayes Normal Means Problem Using a Variety of Prior Families

The empirical Bayes normal means (EBNM) model is important to many areas of statistics, including (but not limited to) multiple testing, wavelet denoising, multiple linear regression, and matrix factorization. There are several existing software packages that can fit EBNM models under different prior assumptions and using different algorithms; however, the differences across interfaces complicate direct comparisons. Further, a number of important prior assumptions do not yet have implementations. Motivated by these issues, we developed the R package ebnm, which provides a unified interface for efficiently fitting EBNM models using a variety of prior assumptions, including nonparametric approaches. In some cases, we incorporated existing implementations into ebnm; in others, we implemented new fitting procedures with a focus on speed and numerical stability. To demonstrate the capabilities of the unified interface, we compare results using different prior assumptions in two extended examples: the shrinkage estimation of baseball statistics; and the matrix factorization of genetics data (via the new R package flashier). In summary, ebnm is a convenient and comprehensive package for performing EBNM analyses under a wide range of prior assumptions.
Vision-Only Robot Navigation in a Neural Radiance World

Neural Radiance Fields (NeRFs) have recently emerged as a powerful paradigm for the representation of natural, complex 3D scenes. NeRFs represent continuous volumetric density and RGB values in a neural network, and generate photo-realistic images from unseen camera viewpoints through ray tracing. We propose an algorithm for navigating a robot through a 3D environment represented as a NeRF using only an on-board RGB camera for localization. We assume the NeRF for the scene has been pre-trained offline, and the robot's objective is to navigate through unoccupied space in the NeRF to reach a goal pose. We introduce a trajectory optimization algorithm that avoids collisions with high-density regions in the NeRF based on a discrete time version of differential flatness that is amenable to constraining the robot's full pose and control inputs. We also introduce an optimization based filtering method to estimate 6DoF pose and velocities for the robot in the NeRF given only an onboard RGB camera. We combine the trajectory planner with the pose filter in an online replanning loop to give a vision-based robot navigation pipeline. We present simulation results with a quadrotor robot navigating through a jungle gym environment, through the inside of a church, and through Stonehenge using only an RGB camera. We also demonstrate an omnidirectional ground robot navigating through the church, requiring it to reorient to fit through the narrow gap. Videos of this work can be found at mikh3x4.github. io/nerf-navigation/.
Prediction of Carbon Nanostructure Mechanical Properties and Role of Defects Using Machine Learning

Carbon fiber and graphene-based nanostructures such as carbon nanotubes (CNTs) and defective structures have extraordinary potential as strong and lightweight materials. A longstanding bottleneck has been lack of understanding and implementation of atomic-scale engineering to harness the theoretical limits of modulus and tensile strength, of which only a fraction is routinely reached today. Here we demonstrate accurate and fast predictions of mechanical properties for CNTs and arbitrary 3D graphitic assemblies based on a training set of over 1000 stress-strain curves from cutting-edge reactive MD simulation and machine learning (ML). Several ML methods are compared and show that our newly proposed hierarchically structured graph neural networks with spatial information (HS-GNNs) achieve predictions in modulus and strength for any 3D nanostructure with only 5-10% error across a wide range of possible values. The reliability is sufficient for practical applications and a great improvement over off-the shelf ML methods with up to 50% deviation, as well as over earlier models for specific chemistry with 20% deviation. The algorithms allow more than 100 times faster mechanical property predictions than traditional molecular dynamics simulations, the identification of the role of defects and random 3D morphology, and high-throughput screening of 3D structures for enhanced mechanical properties. The algorithms can be scaled to morphologies up to 100 nm in size, expanded for chemically similar compounds, and trained to predict a broader range of properties.
Uncertainties due to hadronic production in FSI at LBNF

Recent neutrino oscillation experiments used high atomic number nuclear targets to attain sufficient interaction rates. The use of these complex targets introduced systematic uncertainties due to the nuclear effects in the experimental observable and need to be measured properly to pin down the discovery. Through this simulation work, we are trying to quantify the nuclear effects in the Ar target in comparison to H target which are proposed to be used at DUNE far detector and near detector respectively. GENIE and NuWro, two neutrino event generators, are used to construct final state kinematics. To quantify the systematic uncertainties in the observables, we present the ratio of the oscillation probability (P(Ar)/P(H)) as a function of reconstructed neutrino energy.
Confidence intervals for efficiencies in particle physics experiments

We compute bias and variance of an efficiency estimator for a random processes, in which the success probability is constant but the number of trials is drawn from a Poisson distribution. The standard estimator, although being a nonlinear function in this case, is unbiased. Compared to the case where the number of trials is fixed, the variance is increased or decreased depending on the expected number of trials. We further compute the variance for the case where the numbers of successes and failures have a variance which exceeds that of a Poisson process. This is the case, for example, when these numbers are obtained from a mixture of signal and background events in which the background is subtracted imperfectly. We compute generalised Wilson intervals based on these variances and study their coverage probability. We conclude that the standard Wilson interval is also suitable when the number of trials is Poisson distributed.
Dynamic Modeling and Simulation of a Four-wheel Skid-Steer Mobile Robot using Linear Graphs

This paper presents the application of the concepts and approaches of linear graph (LG) theory in the modeling and simulation of a four-wheel skid-steer mobile robotic system. An LG representation of the system is proposed, and the accompanying state-space model of the dynamics of a mobile robot system is evaluated using the associated LGtheory MATLAB toolbox, which was developed in our lab. A genetic algorithm (GA)-based parameter estimation method is employed to determine the system parameters, which leads to a very accurate simulation of the model. The developed model is then evaluated and validated by comparing the simulated LG model trajectory with the trajectory of an ROS Gazebo-simulated robot and experimental data obtained from the physical robotic system. The obtained results demonstrate that the proposed LG model, combined with the GA parameter estimation process, produces a highly accurate method of modeling and simulating a mobile robotic system.
A survey on active noise control techniques - Part II: Nonlinear systems

Part I of this paper reviewed the development of the linear active noise control (ANC) technique in the past decade. However, ANC systems might have to deal with some nonlinear components and the performance of linear ANC techniques may degrade in this scenario. To overcome this limitation, nonlinear ANC (NLANC) algorithms were developed. In Part II, we review the development of NLANC algorithms during the last decade. The contributions of heuristic ANC algorithms are outlined. Moreover, we emphasize recent advances of NLANC algorithms, such as spline ANC algorithms, kernel adaptive filters, and nonlinear distributed ANC algorithms. Then, we present recent applications of ANC technique including linear and nonlinear perspectives. Future research challenges regarding ANC techniques are also discussed.
Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation

Segmentation of Prostate Cancer (PCa) tissues from Gleason graded histopathology images is vital for accurate diagnosis. Although deep learning (DL) based segmentation methods achieve state-of-the-art accuracy, they rely on large datasets with manual annotations. We propose a method to synthesize for PCa histopathology images by learning the geometrical relationship between different disease labels using self-supervised learning. We use a weakly supervised segmentation approach that uses Gleason score to segment the diseased regions and the resulting segmentation map is used to train a Shape Restoration Network (ShaRe-Net) to predict missing mask segments in a self-supervised manner. Using DenseUNet as the backbone generator architecture we incorporate latent variable sampling to inject diversity in the image generation process and thus improve robustness. Experiments on multiple histopathology datasets demonstrate the superiority of our method over competing image synthesis methods for segmentation tasks. Ablation studies show the benefits of integrating geometry and diversity in generating high-quality images, and our self-supervised approach with limited class-labeled data achieves similar performance as fully supervised learning.
Information Rate Optimization for Non-Regenerative MIMO Relay Networks with a Direct Link

We consider the optimization of a two-hop relay network based on an amplify-and-forward Multiple-Input Multiple-Output (MIMO) relay. The relay is assumed to derive the output signal by a Relay Transform Matrix (RTM) applied to the input signal. Assuming perfect channel state information about the network at the relay, the RTM is optimized according to two different criteria: {\bf\em i)} network capacity; {\bf\em ii)} network capacity based on Orthogonal Space--Time Block Codes. The two assumptions have been addressed in part in the literature. The optimization problem is reduced to a manageable convex form, whose KKT equations are explicitly solved. Then, a parametric solution is given, which yields the power constraint and the capacity achieved with uncorrelated transmitted data as functions of a positive indeterminate. The solution for a given average power constraint at the relay is amenable to a \emph{water-filling-like} algorithm, and extends earlier literature results addressing the case without the direct link. Simulation results are reported concerning a Rayleigh relay network and the role of the direct link SNR is precisely assessed.
PFPN: Continuous Control of Physically Simulated Characters using Particle Filtering Policy Network

Data-driven methods for physics-based character control using reinforcement learning have been successfully applied to generate high-quality motions. However, existing approaches typically rely on Gaussian distributions to represent the action policy, which can prematurely commit to suboptimal actions when solving high-dimensional continuous control problems for highly-articulated characters. In this paper, to improve the learning performance of physics-based character controllers, we propose a framework that considers a particle-based action policy as a substitute for Gaussian policies. We exploit particle filtering to dynamically explore and discretize the action space, and track the posterior policy represented as a mixture distribution. The resulting policy can replace the unimodal Gaussian policy which has been the staple for character control problems, without changing the underlying model architecture of the reinforcement learning algorithm used to perform policy optimization. We demonstrate the applicability of our approach on various motion capture imitation tasks. Baselines using our particle-based policies achieve better imitation performance and speed of convergence as compared to corresponding implementations using Gaussians, and are more robust to external perturbations during character control. Related code is available at: https://motion-lab.github. io/PFPN.
X2CT-FLOW: Maximum a posteriori reconstruction using a progressive flow-based deep generative model for ultra sparse-view computed tomography in ultra low-dose protocols

Ultra sparse-view computed tomography (CT) algorithms can reduce radiation exposure of patients, but those algorithms lack an explicit cycle consistency loss minimization and an explicit log-likelihood maximization in testing. Here, we propose X2CT-FLOW for the maximum a posteriori (MAP) reconstruction of a three-dimensional (3D) chest CT image from a single or a few two-dimensional (2D) projection images using a progressive flow-based deep generative model, especially for ultra low-dose protocols. The MAP reconstruction can simultaneously optimize the cycle consistency loss and the log-likelihood. The proposed algorithm is built upon a newly developed progressive flow-based deep generative model, which is featured with exact log-likelihood estimation, efficient sampling, and progressive learning. We applied X2CT-FLOW to reconstruction of 3D chest CT images from biplanar projection images without noise contamination (assuming a standard-dose protocol) and with strong noise contamination (assuming an ultra low-dose protocol). With the standard-dose protocol, our images reconstructed from 2D projected images and 3D ground-truth CT images showed good agreement in terms of structural similarity (SSIM, 0.7675 on average), peak signal-to-noise ratio (PSNR, 25.89 dB on average), mean absolute error (MAE, 0.02364 on average), and normalized root mean square error (NRMSE, 0.05731 on average). Moreover, with the ultra low-dose protocol, our images reconstructed from 2D projected images and the 3D ground-truth CT images also showed good agreement in terms of SSIM (0.7008 on average), PSNR (23.58 dB on average), MAE (0.02991 on average), and NRMSE (0.07349 on average).
Landscape Planning and Image Analysis Based on Multipopulation Coevolution Particle Swarm Radial Basis Function Neural Network Algorithm

Urban landscape planning and design is not only closely related to people's living environment, but also has an important impact on urban planning and development. However, there are some problems in landscape planning and design, such as excellent cases, low reuse rate of data, discrepancy between design scheme and actual situation, and serious shortage of relevant professionals. The artificial neural network can give corresponding ways to improve and solve these problems. Therefore, this paper proposes a research on garden planning and design based on multipopulation coevolution particle swarm radial basis function neural network algorithm. Based on multipopulation coevolution particle swarm radial basis function neural network algorithm, the error between the predicted evaluation value and the actual evaluation value in the simulation experiment is less than 5%, which shows good accuracy and generalization ability in performance. And in the plant configuration simulation experiment, it can effectively evaluate the urban planning and design and put forward the corresponding adjustment scheme according to the analysis results, which is more in line with the actual needs of urban planning.
Study of the Effect of Nutrient Environment on the Biological Characteristics of Fusarium solani Clinical Corneal Isolates

Purpose The biological characteristics of Fusarium solani clinical corneal isolates growing in different nutritional conditions in vitro were studied in order to find out the key point of pathogenicity. Methods Five kinds of media with different glucose and nitrogen concentrations were prepared as the liquid and solid forms. The clinical isolates were as follows: 2 Fusarium solani strains. The clinical corneal isolates and the standard strains were inoculated in the solid and liquid media. They were all incubated at 296 for 96 h and observed at defined time points. The optical density was recorded to generate the growth curves in liquid media. Morphologic changes of colonies in the solid media were determined under the light microscope. Results The clinical isolates of Fusarium solani showed stronger reproductive capacity in the abominable nutritional condition. Besides, when the glucose concentration in the medium was consistent with the glucose concentration of aqueous in diabetic patients, the clinical isolates would show the biological features of quicker growth rate and stronger reproductive capacity. Conclusions Nitrogen source is essential for fungus reproduction. The clinical isolates showed stronger environmental adaptability under different nutritional conditions and more sensitive to environmental changes.
Genome-wide phenotypic RNAi screen in the Drosophila wing: phenotypic description of functional classes

Abstract The Drosophila genome contains approximately 14,000 protein-coding genes encoding all the necessary information to sustain cellular physiology, tissue organization, organism development, and behavior. In this manuscript, we describe in some detail the phenotypes in the adult fly wing generated after knockdown of approximately 80% of Drosophila genes. We combined this phenotypic description with a comprehensive molecular classification of the Drosophila proteins into classes that summarize the main expected or known biochemical/functional aspect of each protein. This information, combined with mRNA expression levels and in situ expression patterns, provides a simplified atlas of the Drosophila genome, from housekeeping proteins to the components of the signaling pathways directing wing development, that might help to further understand the contribution of each gene group to wing formation.