Business at the Comanche County Courthouse returned to normal on Friday, after it was evacuated Thursday afternoon. The Comanche County Courthouse will be opening at its regular time Friday following an evacuation order Thursday.

Find out where you should start your machine learning journey, what kinds of projects you can work on along the way, and how you can succeed in this complex field. The data era has arrived, and if you didn't prepare enough for it, don't worry we can still help you get on board. Money may have made the world go round, but all that's necessary is data and information in contemporary times. Harnessing these two basic concepts and analyzing and using such data will help us to truly collect and analyze valuable data that will keep us ahead of the competition. Analytics in business, tech, and finance is a must.

The Azure Machine Learning Algorithm Cheat Sheet helps you choose the right algorithm from the designer for a predictive analytics model. Azure Machine Learning has a large library of algorithms from the classification, recommender systems, clustering, anomaly detection, regression, and text analytics families. Each is designed to address a different type of machine learning problem. Start in the large blue box, "What do you want to do?" Then follow the lines out to match what you would like to solve. For example, maybe you have some data and you want to predict whether a customer will purchase or not.

Learn about the Nvidia Self-Paced Online Training from their Deep Learning Institute. By Nisha Arya, KDnuggets on July 29, 2022 in Data Science …

Machine learning software is a type of artificial intelligence (AI) that uses data to predict the outcomes of specific situations. Today machine learning technology is implemented across various business sectors to coordinate processes based on predicted outcomes. If you are looking to improve your organization's efficiency, consider leveraging machine learning tools. These platforms can help your team build machine learning models that will generate meaningful insights. In turn, this can lead to smarter business decisions and better outcomes across the organization.

Autonomous micromobility has been attracting the attention of researchers and practitioners in recent years. A key component of many micro-transport vehicles is the DC motor, a complex dynamical system that is continuous and non-linear. Learning to quickly control the DC motor in the presence of disturbances and uncertainties is desired for various applications that require robustness and stability. Techniques to accomplish this task usually rely on a mathematical system model, which is often insufficient to anticipate the effects of time-varying and interrelated sources of non-linearities. While some model-free approaches have been successful at the task, they rely on massive interactions with the system and are trained in specialized hardware in order to fit a highly parameterized controller. In this work, we learn to steer a DC motor via sample-efficient reinforcement learning. Using data collected from hardware interactions in the real world, we additionally build a simulator to experiment with a wide range of parameters and learning strategies. With the best parameters found, we learn an effective control policy in one minute and 53 seconds on a simulation and in 10 minutes and 35 seconds on a physical system.

Learning dexterous manipulation skills is a long-standing challenge in computer graphics and robotics, especially when the task involves complex and delicate interactions between the hands, tools and objects. In this paper, we focus on chopsticks-based object relocation tasks, which are common yet demanding. The key to successful chopsticks skills is steady gripping of the sticks that also supports delicate maneuvers. We automatically discover physically valid chopsticks holding poses by Bayesian Optimization (BO) and Deep Reinforcement Learning (DRL), which works for multiple gripping styles and hand morphologies without the need of example data. Given as input the discovered gripping poses and desired objects to be moved, we build physics-based hand controllers to accomplish relocation tasks in two stages. First, kinematic trajectories are synthesized for the chopsticks and hand in a motion planning stage. The key components of our motion planner include a grasping model to select suitable chopsticks configurations for grasping the object, and a trajectory optimization module to generate collision-free chopsticks trajectories. Then we train physics-based hand controllers through DRL again to track the desired kinematic trajectories produced by the motion planner. We demonstrate the capabilities of our framework by relocating objects of various shapes and sizes, in diverse gripping styles and holding positions for multiple hand morphologies. Our system achieves faster learning speed and better control robustness, when compared to vanilla systems that attempt to learn chopstick-based skills without a gripping pose optimization module and/or without a kinematic motion planner.

In the past few years, the emergence of pre-training models has brought uni-modal fields such as computer vision (CV) and natural language processing (NLP) to a new era. Substantial works have shown they are beneficial for downstream uni-modal tasks and avoid training a new model from scratch. So can such pre-trained models be applied to multi-modal tasks? Researchers have explored this problem and made significant progress. This paper surveys recent advances and new frontiers in vision-language pre-training (VLP), including image-text and video-text pre-training. To give readers a better overall grasp of VLP, we first review its recent advances from five aspects: feature extraction, model architecture, pre-training objectives, pre-training datasets, and downstream tasks. Then, we summarize the specific VLP models in detail. Finally, we discuss the new frontiers in VLP. To the best of our knowledge, this is the first survey focused on VLP. We hope that this survey can shed light on future research in the VLP field.

One hundred eighty-nine new packages made it to CRAN in June. Here are my “Top 40” selections in eleven categories: Computational Methods, Data, Ecology, Genomics, Machine Learning, Mathematics, Medicine, Statistics, Time Series, Utilities, and Visualizations. Computational Methods itp v1.2.0: Implements the interpolate, truncate, project root-finding algorithm developed by Oliveira & Takahashi (2021). The vignette provides an overview. QR v0..1.3: Provides a function to perform QR factorization without pivoting to a real or complex matrix. It is based on LAPACK. See the vignette. qsplines v1.0.0: Provides functions to create quaterion splines. See Barry & Goldman (1988) and Kochanek & Bartels (1984) for the details and look here for an example. VMDecomp v1.0.1: Implements the variational mode decomposition and two-dimensional variational mode decomposition algorithm. See Dragomiretskiy & Zosso (2014) for background and the vignette for examples. Data cmch v0.2.0: Implements a wrapper around the Canadian Mortgage and Housing Corporation web interface and enables programmatic and reproducible access to a wide variety of housing data. See the vignette for examples. EDIutils v1.0.1: Implements a client for the Environmental Data Initiative repository REST API and provides access to ecological data and metadata. There are five short vignettes: Evaluate & upload, Citation Metrics, Download Metrics, Search andaccess, and Tests. globaltrends v0.0.12: Provides functions to access global search volumes from the Google Trends portal. This working paper outlines the package’s methodological foundations and potential applications. See the vignette to get started. kaigiroku v0.5: Allows users to search and download data from the API for Japanese Diet proceedings. Look here for examples. NasdaqDataLink v1.0.0: Provides functions to interact directly with the Nasdaq Data Link API and obtain data in a number of formats. Look here for API documentation and here for package information. stortingscrape v0.1.1: Provides functions for retrieving data from the Norwegian Parliament, through the Norwegian Parliament API. See the vingette for an introduction. Ecology PointedSDMs v1.0.6: Provides tools to build integrated species distribution models and includes tools to run spatial cross-validation and plotting. See Issac et al. (2020) for and introduction to the methods. There is a Setophaga Example and an example for the Solitary Tinamou. restoptr v1.0.1: Implements a flexible framework for ecological restoration planning that aims to identify priority areas for restoration efforts using optimization algorithms described in Justeau-Allaire et al. 2021. See the vignette to get started. Genomics scapGNN v0.1.1: Implements a single cell active pathway analysis tool based on the graph neural network algorithm described in Scarselli et al. (2009) and Kipf & Welling (2017). This may be used to construct a gene-cell association network, infer pathway activity scores from different single cell modalities data and more. See the vignette for an overview and examples. SRTsim v0.99.2: Implements an independent, reproducible, and flexible Spatially Resolved Transcriptomics simulation framework that can be used to facilitate the development analytical methods and for a wide variety of SRT-specific analyses. See the vignette. xQTLbiolinks v1.1.1: Implements tools to query, download, and visualize of molecular quantitative trait locus and gene expression data from public resources through the GTEx API. There is a Quick Start Guide and vignettes on Colocalization, Specivicity, and Visualization. Machine Learning agua v0.0.1: Enables users to specify h2o as an engine for several tidymodels modeling methods. See README for examples. MagmaClustR V1.0.0: Implements two main algorithms, called Magma (Leroy et al. (2022) and MagmaClust (Leroy et al. (2020)), using a multi-task Gaussian processes (GP) model to perform predictions for supervised learning problems. See README for examples. openai v0.1.0: Provides a wrapper for OpenAI API endpoints including engines, completions, edits, files, fine-tunes, embeddings and legacy searches, classifications, and answers endpoints. See README to get started. sketching v0.1.0: Provides functions to construct sketches of data via random subspace embeddings. See Lee & Ng (2022) for the theory and the vignette for examples. webmorphR v0..1.1: Provides functions to create reproducible image stimuli, specialised for face images with psychomorph or webmorph templates. See README to get started. Mathematics GeneralizedWendland v0.5-2: Implements the fully parameterized generalized Wendland covariance function for use in Gaussian process models, as well as multiple methods for approximating it via covariance interpolation. The available methods are linear interpolation, polynomial interpolation, and cubic spline interpolation. See Bevilacqua et al. (2022) and the vignette for examples. jacobi v2.0.0: Evaluates Jacobi theta functions and related functions including the Weierstrass elliptic function, the Weierstrass sigma function, the Weierstrass zeta function, the Klein j-function, the Dedekind eta function, the lambda modular function, Jacobi elliptic functions, Neville theta functions, and the Eisenstein series for real and complex variables. Look here for some images. Medicine clinicalsignificance v1.0.0: Implements the clinical significance algorithm proposed by Jacobson et al. (1984) to determine if an intervention has a meaningful practical effect. There is a Getting Started Guide and vignettes on Cutoffs and Plots. PlatformDesign v1.0.1: Provides functions to calculate design parameters for an optimal two-period, multi-arm platform design allowing pre-planned deferred arms to be added during the trial. See Dunnett (1955) for background and the vignette for some theory and examples. Statistics bayesassurance v0.1.0: Provides functions to compute Bayesian assurance under various settings characterized by different assumptions and objectives, including precision-based conditions, credible intervals, and goal functions. See Pan & Banerjee (2021) for the theory. There are vignettes for using closed form solutions, the conjugate linear model, and precision based conditions. DSSP v0.1.1: Provides functions to draw samples from the direct sampling spatial prior model as described in White, Sun, & Speckman (2019). See the vignette for examples. edibble v0.1.0: Implements a system to facilitate designing comparative experiments using the grammar of experimental designs. See the edibble-book for documentation. mixgb v0.1.0: Implements a method for multiple imputation using XGBoost, bootstrapping and predictive mean matching as described in Deng and Lumley (2021). There is an Introduction and a vignette on Imputing new data with a saved imputer. outerbase v0.1.0: Implements in new method for high-dimensional regression using outer product models. See Plumlee (2014) and Plumlee et al. (2021) for background. There is a Getting started guide, a Base walkthrough, and vignettes on Learning from data and Speeding up inference. PFIM v5.0: Provides functions to evaluate or optimize designs for nonlinear mixed effects models using the Fisher Information matrix. See Malle & Baccar D (1997) and Retout et al. (2007) for background and the vignettes Design evaluation and optimixation (01), Design evaluation and optimixation (02), and Library of models for examples. VirtualPop v1.0.2: Provides functions to generate lifespans and fertility histories in continuous time using individual-level state transition (multi-state) models and data. See the vignettes on Simulation of life histories, Sampling from waiting time distributions, Simulation of individual fertility careers, and Validation. Time Series kssa v0.0.1: Implements the known sub-sequence algorithm described in Benavides et al. (2022), which helps to automatically identify and validate the best method for missing data imputation in a time series. Look here for examples. ts2net v0.1.0: Implements methods to transform time series into networks, a technique which may be useful for complex systems modeling, time series data mining, or time series analysis using networks. For an introduction to the topic and descriptions of the methods see Mitchell (2006), Silva & Zhao (2016), and Silva et al. (2021). See README to get started. Utilities cppchedkR Allows users to run Cppcheck on C/C++ files as an R command or an RStudio addin. See README. . gtExtras v0.4.1: Provides additional functions for creating tables with gt. See README for examples. . Visualization ggpie v0.2.2: Provides functions for creating pie, donut and rose pie plots with ggplot2. See the vignette. ggtrace v0.2.0: Provides ggplot2 geoms that allow groups of data points to be outlined or highlighted for emphasis. See the vignettes Trace lines and Trace points. Morphoscape v1.0.0: Implements adaptive landscape methods first described by Polly et al. (2016) for the integration, analysis and visualization of biological trait data on a phenotypic morphospace which are typically defined by shape metrics. See the vignette. r3js v0.0.1: Provides R and JavaScript functions to allow WebGL-based 3D plotting using the three.js library. See the vignettes: Getting Started, Creating a plot from scratch, and Grouping plot elements. rgl2gltf v1.0.0: Provides functions to work with glTF files which are used to describe 3D models. See the vignette for examples.. . shapviz v0.2.0: Provides functions to visualize SHapley Additive exPlanations (SHAP), such as waterfall plots, force plots, various types of importance plots, and dependence plots. See Lundberg & Lee (2017) for background and the vignette for examples.

Further specifics concerning the position and application procedures can be found on the Texas A&M Jobs Worksite. Texas A&M University is committed to enriching the learning and working environment for all visitors, students, faculty, and staff by promoting a culture that embraces inclusion, diversity, equity, and accountability. Diverse perspectives, talents, and identities are vital to accomplishing our mission and living our core values. The Texas A&M System is an Equal Opportunity / Affirmative Action / Veterans / Disability Employer committed to diversity.