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Predictive Cruise Control Using Radial Basis Function Network-Based Vehicle Motion Prediction and Chance Constrained Model Predictive Control

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Title: Predictive Cruise Control Using Radial Basis Function Network-Based Vehicle Motion Prediction and Chance Constrained Model Predictive Control Authors: Yoon, Seungje; Jeon, Hyeongseok; Kum, Dongsuk Abstract: Predicting future motions of surrounding vehicles and driver's intentions are essential to avoid future potential risks. The predicting future motions, however, is very challenging because the future cannot be deterministically known a priori and there are infinitely many possible future trajectories. Prediction becomes far more challenging when trying to foresee distant future. This paper proposes a probabilistic motion prediction algorithm that can accurately compute the likelihood of multiple target lanes and trajectories of surrounding vehicles by using the artificial neural network; more specifically radial base function network (RBFN). The RBFN prediction algorithm estimates the likelihood of each lane being the driver's target lane in categorical distributions and the corresponding future trajectories in parallel. In order to demonstrate the effectiveness of the proposed prediction algorithm, it is applied for the predictive cruise control problem. Chance-constrained model predictive control (CCMPC) is utilized because the chance constraints in CCMPC can handle collision uncertainties associated with future uncertainties from the proposed prediction algorithm. The RBFN-based CCMPC simulation is conducted for several risky cut-in scenarios and compared with the state-of-the-art Interactive Multiple Model (IMM)-based prediction algorithm. The simulation results show that the RBFN-based CCMPC achieves higher collision avoidance success rate than that of the IMM-based CCMPC while using smaller actuator inputs and providing higher passenger comforts. Furthermore, the RBFN-based CCMPC showed high robustness to false braking during near lane-change (lane-keeping) scenarios.
          

KDnuggets™ News 19:n42, Nov 6: 5 Statistical Traps Data Scientists Should Avoid; 10 Free Must-Read Books on AI

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Learn about statistical fallacies Data Scientists should avoid; New and quite amazing Deep Learning capabilities FB has been quietly open-sourcing; Top Machine Learning tools for Developers; How to build a Neural Network from scratch and more.
          

Designing Your Neural Networks

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Check out this step-by-step walk through of some of the more confusing aspects of neural nets to guide you to making smart decisions about your neural network architecture.
          

Multi-Level Neural Dynamics under Visual Perturbations and Motor Learning

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Composed by hundred billion neurons with nonlinearities, the brain consists explicit connection paradigms and exhibits collective dynamics. Also, both high computational flexibility and robustness are crucial for species. How then do biological systems balance the need for both variability and robustness?Mounting evidence suggests that evolution leads neural networks of cerebral cortexto a critical point between order and disorderand yield an optimal trade-off between the robustness and accuracy that biological machinery demands.A rigorous understanding of brain dynamics and function composes multiple levels of organization, suchas neural compartments, neural spiking and network-level population activity. To study thestability of trade-off between robustness and flexibility, we explored whether multi-level neural dynamicschangeor preserveunder visualperturbations and motor learning.We studied neural dynamics under various visual perturbations (visual stimuliand monocular deprivation), and across motor learning in motor cortex.We conducted statistical analysis and used model investigations. We found multiple dynamics changed under perturbation, such as neural connectivity, neural representations, et. al. On network level, criticality either serves as a set-point or constraint. Together, these results contribute to understanding of connection paradigms and collective dynamics in the brain under visual perturbation and motor learning.


          

Correlated SEM, FIB, and TEM Studies of Material Collected by the NASA Stardust Spacecraft

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The objective of this thesis is to describe the study of cometary materials returned by NASA’s Stardust mission. The majority of the research presented in this thesis focuses on improving our characterization and understanding of the fine (< 1 µm) component of comet Wild 2. Investigations of the Stardust foils are conducted with correlated Scanning Electron Microscopy (SEM), Focused Ion Beam (FIB) sample preparation, and Transmission Electron Microscopy (TEM). Investigations of the Stardust aerogels are conducted with plasma ashing sample preparation followed by detailed characterization of the material with TEM. Additional studies of the Stardust interstellar foils, as well as the use of a Convolutional Neural Network (CNN) to search images of the Stardust foils for impact features, are also presented. As a part of this thesis I have developed a new technique for analyzing the Stardust aerogels through the use of plasma ashing sample preparation. This technique is an improvement upon previous attempts to separate cometary materials from the aerogel through the use of HF vapor etching. Plasma ashing allows for cometary materials trapped within the Stardust aerogels to be deposited directly onto TEM grids allowing for detailed characterization of the cometary material with minimal interference from the aerogel itself. The correlated SEM/FIB/TEM studies of the Stardust foils demonstrated here nearly double the number of Stardust craters that have been elementally and structurally characterized in scientific literature. The crater impactor residues were largely composed of combinations of silicates and iron-nickel sulfides that, following impact, rapidly quenched into amorphous melt layers. Two craters were found to contain signatures of the refractory minerals spinel and taenite, indicating a component of the Wild 2 fines originated in the inner Solar System. However, the lack of crystalline material throughout the crater residues suggests that the fine component may largely be composed of amorphous silicates that likely formed in the outer Solar System. Additionally, the submicron Stardust craters appeared enriched in volatile elements relative to CI chondrites, further suggesting that the fine component of Wild 2 originated from a reservoir that was separate from the more refractory coarse (> 1 µm) component. The Stardust aerogel samples returned carbon-rich and potential oldhamite grains. Carbon-rich materials have not been previously observed in the Stardust foils, likely due to the violent collection methods, and the result suggests the ashing technique may be used to better characterize components of the Wild 2 fines that have been difficult to investigate. The presence of oldhamite in the Stardust aerogels would be scientifically significant as it is formed in highly reducing conditions and has only been identified in enstatite chondrites and enstatite achondrites. As a result, our results may call into question the Warren gap hypothesis, which would prohibit the presence of such highly reduced materials in the outer Solar System at the time that comet Wild 2 accreted.


          

Data Scientist

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OVERVIEW

Are you a problem solver, explorer, and knowledge seeker always asking, What if*



If so, you may be the new team member we re looking for. Because at SAS, your curiosity matters whether you re developing algorithms, creating customer experiences, or answering critical questions. Curiosity is our code, and the opportunities here are endless.



What we do

We re the leader in analytics. Through our software and services, we inspire customers around the world to transform data into intelligence. Our curiosity fuels innovation, pushing boundaries, challenging the status quo and changing the way we live.



What you ll do

As a Data Scientist at SAS and a member of the analytics team, you will analyze customer data and build high-end analytical models for solving high-value business problems, such as credit and debit card fraud, online banking fraud, credit risk, network security, and other intriguing problems.



You will:

* Process and analyze large volumes of (customer) data.

* Build predictive models with advanced machine learning algorithms such as Neural Networks, Decision Trees, Boosting/Ensemble methods, Clustering, and Online learning.

* Interact with customers from the data analysis stage to the final report presentation.

* Assist in technical sales support as needed.

* Constantly innovate by building new variables; improve modeling techniques to boost model performance; maintain and refine the processes and procedures for building high-end analytic modeling solutions.

* Write coherent reports and make presentations on high-end analytical projects.



What we re looking for

* You re curious, passionate, authentic, and accountable. These are our values and influence everything we do.

* You have a master's degree in statistics, mathematics, computer science, engineering, the physical sciences, or any other quantitative field.

* 2+ years related experience such as analyzing data and/or building analytical models; in either an academic or professional setting.

* Knowledge of multiple operating systems (e.g. Windows, Unix/Linux).

* Proficiency with 1 or more of the following Programming or Scripting languages: R, SAS, Bash, Perl, Python, MATLAB.

* Thorough knowledge of at least some supervised and unsupervised modeling techniques such as Logistic/Linear Regression, SVMs, Neural Networks / Deep Networks, Boosting/Ensemble methods, Decision Trees, and/or Clustering.

* Ability to manage very large amounts of data.



The nice to haves

* Ph.D in applied statistics, mathematics, computer science, engineering, or the physical sciences.

* Industry experience in mathematical/statistical modeling, pattern recognition, or data mining/data analysis.

* Extensive experience specifying and building advanced analytic solutions for the financial services and related industries with large-scale transaction data.

* Extensive experience in data management, deployment and product support for advanced analytic solutions.

* Excellent programming skills and knowledge of SAS and scripting languages.

* Ability to translate model performance to financial benefit for the business by incorporating knowledge of customer business practices.



Other knowledge, skills, and abilities

* Excellent written and verbal communication skills.

* Ability to think analytically, write and edit technical material, and relate statistical concepts and applications to technical and business users.

* Ability to work both independently and in a team environment.

* Ability to travel as business requirements dictate.



Why SAS

* We love living the #SASlife and believe that happy, healthy people have a passion for life, and bring that energy to work. No matter what your specialty or where you are in the world, your unique contributions will make a difference.

* Our multi-dimensional culture blends our different backgrounds, experiences, and perspectives. Here, it isn t about fitting into our culture, it s about adding to it - and we can t wait to see what you ll bring.

#LI-TP1



SAS looks not only for the right skills, but also a fit to our core values. We seek colleagues who will contribute to the unique values that makes SAS such a great place to work. We look for the total candidate: technical skills, values fit, relationship skills, problem solvers, good communicators and, of course, innovators. Candidates must be ready to make an impact.



Additional Information:

To qualify, applicants must be legally authorized to work in the United States, and should not require, now or in the future, sponsorship for employment visa status. SAS is an equal opportunity employer. All qualified applicants are considered for employment without regard to race, color, religion, gender, sexual orientation, gender identity, age, national origin, disability status, protected veteran status or any other characteristic protected by law. Read more: Equal Employment Opportunity is the Law. Also view the supplement EEO is the Law, and the notice Pay Transparency



Equivalent combination of education, training and experience may be considered in place of the above qualifications. The level of this position will be determined based on the applicant's education, skills and experience. Resumes may be considered in the order they are received. SAS employees performing certain job functions may require access to technology or software subject to export or import regulations. To comply with these regulations, SAS may obtain nationality or citizenship information from applicants for employment. SAS collects this information solely for trade law compliance purposes and does not use it to discriminate unfairly in the hiring process.



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Principal Data Scientist

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What you'll be doing. We are looking for a Principal Data Scientist who will be focused on delivering Customer Intelligence, as part of the System of Insights. You will drive profitable growth and business innovation by applying cutting edge machine learning techniques and AI technology. You will lead data science projects that drive customer intelligence, product personalization, marketing effectiveness, channel optimization, better customer experience, and operational efficiency. You will have to be adept at using large data sets to find opportunities for product and process optimization and using models to test the effectiveness of different courses of action. You must have strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms and creating/running simulations. You must also have a proven ability to drive business results with your data-based insights. You should have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes. Work on Advanced Analytics using Big Data, Data Warehousing, Cognitive and Heuristic platforms. Research, design, implement, and oversee high-end analytical/technology process and solutions with a focus on leveraging advanced machine learning, artificial intelligence and cognitive methods. Work with the business to understand the requirements of the digital challenges, heuristic, machine and cognitive analysis and communicate back the results. Build analytical solutions and models by manipulating large data sets and integrating diverse data sources. Perform ad-hoc analysis and develop reproducible analytical approaches to meet business requirements. Perform exploratory and targeted data analyses using descriptive statistics and other methods. machine learning and statistical techniques to large data sets to find actionable insights. Use complex algorithms to develop systems & applications that deliver business functions or architectural components. Present results and recommendations to senior management and business users. Responsible for providing line of sight to data quality and gaps where issues need to be addressed. Communicate the business value of technical solutions. Discover mutually beneficial solutions across customers while recognizing different styles. What we're looking for. You are a master at analyzing big data. You thrive in an environment where enormous volumes of data are generated at rapid speed. You're a creative thinker who likes to explore, and uncover the issues. You are decisive. You are great at influencing up, down, and across groups, and you take satisfaction in mentoring others; communicating what you've uncovered in a way that can be easily understood by others is one of your strengths. You'll need to have: Bachelor's degree or four or more years of work experience. Six or more years of relevant work experience. Experience using statistical computer languages (Python, Scala, PySpark, Java, SQL, etc.) to manipulate data and draw insights from large data sets. Even better if you have: A degree in mathematics, statistics, physics, engineering, computer science, economics, or relevantfield. Experience with Tableau or similar visual analysis tool, optimization, analytics and large data sets, project management, developing visually compelling interactive dashboards. Strong knowledgeof database concepts (Oracle, MS SQL, generic SQL, etc.) Strong knowledgeof data warehouse and data lake technology (Teradata, Hadoop). Strong knowledgeof third party analytic tools. Working experience with general purpose programming languages (Java, .Net, Python, Perl, etc.). Experience with shell scripting tools in Windows, Linux/Unix. Experience with data aggregating tools such as SPLUNK. Experience working with and creating data architectures. Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, XGBoost, Genetic Algorithms, etc. Strong knowledgeof advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications. Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, State Space, text mining, social network analysis, etc. Experience with distributed data/computing tools: Hadoop, Tez, Map/Reduce, Hive, Spark, PySpark, Scala, etc. Experience building semantic and feature engineering pipelines. Experience in adhoc-analysis and developing reproducible analytical approaches to meet business requirements. When you join Verizon. You'll have the power to go beyond - doing the work that's transforming how people, businesses and things connect with each other. Not only do we provide the fastest and most reliable network for our customers, but we were first to 5G - a quantum leap in connectivity. Our connected solutions are making communities stronger and enabling energy efficiency. Here, you'll have the ability to make an impact and create positive change. Whether you think in code, words, pictures or numbers, join our team of the best and brightest. We offer great pay, amazing benefits and opportunity to learn and grow in every role. Together we'll go far. Equal Employment Opportunity We're proud to be an equal opportunity employer - and celebrate our employees' differences, including race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, and Veteran status. Different makes us better.
          

Comment on Guest post by Julien Mairal: A Kernel Point of View on Convolutional Neural Networks, part I by indirim kodu

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hmm thaks you
          

Software Engineer

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Job Description:
  • Our Aerospace client is in need of a Software Engineer to support the development of NASA flight Software Systems. This candidate will be involved with the design, development, integration, test, and delivery of software systems for advanced space systems, atmospheric flight vehicles, science instruments, and ground support systems. The ideal candidate will be capable of supporting efforts within an integrated development environment at all phases of the project software life cycle.
    Qualifications:
    • --- Development of the flight software and other supporting software systems in the C/C++ programming language.
      --- Interfacing with both actual hardware and simulated hardware modules.
      --- Experience developing software requirements, operational concepts, system interfaces, test plans and procedures.
      --- Experience with software version control systems desired (i.e. GIT, Subversion, etc.)
      --- Utilizes SDKs, custom tools and COTs software in the overall development of software systems
      --- Specialized knowledge in areas critical to machine learning for autonomous systems is desired (i.e. neural networks, genetic algorithms, etc.)
      --- Other areas of knowledge such as human-machine interaction, computer vision and image processing techniques, and robust decision making under uncertainty in an aerospace context is helpful.
      --- C/C++ programming skills.
      --- Python
      --- LabWindows
      --- Java Scripting
      --- Code development in both Linux (i.e. Redhat7) and Windows operating systems
      --- Code development for real-time operating systems helpful (VxWorks, FreeRTOS, etc.)
      --- Familiar with embedded system/single board computing (i.e. BeagleBone, Raspberry Pi, etc.)
      --- BS degree or higher in Computer Science/Engineering or equivalent.
          

Deep Learning Market Top Key Vendors- IBM Corporation, Intel Corporation, NVIDIA Corporation, Alphabet Inc.

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Zion Market Research published a new 110+ pages industry research “Deep Learning Market: by Application (Speech Recognition, Image Recognition, Data Mining, Drug Discovery, Driver Assistance, and Others), by Components (Hardware and Software), by Architecture (Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Deep Belief Networks (DBN), Deep Stacking Networks (DSN), and Graphical Processing Units (GRU)), and […]
          

Multi - Disciplinary Algorithm Developer 3D Data

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Applied Research Associates, Inc. (ARA) is actively seeking a highly qualified scientist / engineer for the development of advanced 3D data analysis algorithms for the intelligence and defense communities. Applications include geolocation, navigation, image analysis, machine learning and point cloud analysis. The scientist / engineer will join a multi-disciplined collaborative team of engineers and scientists. This position is located in the Intelligence, Surveillance and Reconnaissance (ISR) Directorate of at the ARA Southeast Division in Raleigh, NC.

The ideal candidate will have an active interest in applying math/statistics/physics/engineering concepts to solve multi-disciplinary problems. The candidate should be familiar with improving/optimizing/tuning existing algorithms as well as development of new algorithms from scratch. This will include software design, software development, and debugging / issue resolution. The candidate should demonstrate a hands-on approach to problem solving and must be willing to actively participate in evaluation of algorithm and system performance. Other responsibilities include assisting in preparation of oral and written reports, supporting R&D business acquisition and customer briefings, present results of research at scientific / engineering conferences and publish in technical journals.

Scientists / engineers who are passionate about applying their expertise to solve problems of national importance, who have a strong entrepreneurial spirit, and who are seeking opportunities for personal and professional growth in a stable environment are strongly encouraged to apply.

Required Qualifications:

* MS Degree in Mathematics / Physics / Engineering along with 5-7 years' of experience or PhD Degree with 3-5 years' experience.

* Strong foundation in software development (i.e., experience with version control, at least 1 higher level language like Python, and at least 1 lower level language like C++).

* Firm understanding of 3D geometry and geospatial concepts.

* Team player with excellent presentation and written / oral communication skills.

* Hands-on approach to problem solving.

* US Citizenship (selected applicants will undergo a security investigation and must meet eligibility requirements at the time of employment).

* Ability to obtain a Secret Security Clearance.

Additional Desirable Qualifications:

* Experience in the use of MATLAB and/or Python.

* Experience in Android mobile app development.

* Background in image analysis.

* Background in machine learning (e.g., Convolutional Neural Networks).

* Background in cloud-based computing.

* Background in analysis of point clouds from LiDAR and other sources.

* Experience working on intelligence and DoD programs.

* Work in real-time, parallel and distributed computing (e.g., CUDA or OpenCL).

* Prior / existing security clearance.

About Us:

Applied Research Associates, Inc. is an employee-owned international research and engineering company recognized for providing technically superior solutions to complex and challenging problems in the physical sciences. The company, founded in Albuquerque, NM, in 1979, currently employs over 1,100 professionals and continues to grow. ARA offices throughout the United States and Canada provide a broad range of technical expertise in defense technologies, civil technologies, computer software and simulation, systems analysis, environmental technologies, and testing and measurement. The corporation also provides sophisticated technical products for environmental site characterization, pavement analysis, and robotics.

While this is all of the Year One and Beyond stuff, Day One is highly impressive too. These are things like our competitive salary (DOE), Employee Stock Ownership Plan (ESOP), benefits package, relocation opportunities, and a challenging culture where innovation & experimentation are the norm. At ARA, employees are our greatest assets so we give our employees the tools, training, and opportunities to take active roles as owners. The motto, "Engineering and Science for Fun and Profit" sums up the ARA experience. The corporation realizes that employee ownership spawns greater creativity and initiative along with higher performance and customer satisfaction levels.

ARA is passionate about inclusion and diversity in our workplace, in 2018 40% of our new employees voluntarily self-identified as protected veterans. (Source-AAP EOY 2018 Veterans Data Collection Report). Additionally, the Southeast Division looks not only for the right skills, but also for a cultural fit. We seek colleagues who will contribute to the unique culture that makes ARA such a great place to work. Some of the social impact aspects we have implemented at our division include monthly get-togethers, team outings to local baseball games in the summer, board game lunches, holiday party, corn hole tournaments, chili cook-offs and so on. We are also very proud of our Women's Initiative Network (WIN) whose purpose is to motivate, support, and encourage professional career development for women in order to maximize career and professional accomplishments. For additional information and an opportunity to join this unique workplace, please apply at careers.ara.com.

EqualOpportunityEmployerDescription

Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities

PayTransparencyPolicyStatement

The contractor will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor s legal duty to furnish information. 41 CFR 60-1.35(c)",

DegreeName: Doctorate

Education: Mathematics

MinimumRequiredYears: 5

MaximumRequiredYears: 7

Description: Relevant Work Experience (i.e. thesis, published research, industry)
          

Data Scientist I (Mid Level)

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PURPOSE OF JOB

Uses advanced techniques that integrate traditional and non-traditional datasets and method to enable analytical solutions; Applies predictive analytics, machine learning, simulation, and optimization techniques to generate management insights and enable customer-facing applications; participates in building analytical solutions leveraging internal and external applications to deliver value and create competitive advantage; Translates complex analytical and technical concepts to non-technical employees

JOB REQUIREMENTS

* Partners with other analysts across the organization to fully define business problems and research questions; Supports SME's on cross functional matrixed teams to solve highly complex work critical to the organization.

* Integrates and extracts relevant information from large amounts of both structured and unstructured data (internal and external) to enable analytical solutions.

* Conducts advanced analytics leveraging predictive modeling, machine learning, simulation, optimization and other techniques to deliver insights or develop analytical solutions to achieve business objectives.

* Supports Subject Matter Experts (SME's) on efforts to develop scalable, efficient, automated solutions for large scale data analyses, model development, model validation and model implementation.

* Works with IT to research architecture for new products, services, and features.

* Develops algorithms and supporting code such that research efforts are based on the highest quality data.

* Translates complex analytical and technical concepts to non-technical employees to enable understanding and drive informed business decisions.

MINIMUM REQUIREMENTS

* Master's degree in Computer Science, Applied Mathematics, Quantitative Economics, Statistics, or related field. 6 additional years of related experience beyond the minimum required may be substituted in lieu of a degree.

* 4 or more years of related experience and accountability for complex tasks and/or projects required.

* Proficient knowledge of the function/discipline and demonstrated application of knowledge, skills and abilities towards work products required.

* Proficient level of business acumen in the areas of the business operations, industry practices and emerging trends required.

Must complete 12 months in current position (from date of hire or date of placement), or must have manager's approval prior to posting.

*Qualifications may warrant placement in a different job level*

PREFERRED

* Expertise in experimental design, advanced statistical analysis, and modeling to discover key relationships in data and applying that information to predict likely future outcomes; fluent in regression, classification, tree-based models, clustering methods, text mining, and neural networks.

* Proven ability to enrich (add new information to) data, advise on appropriate course(s) of action to take based on results, summarize complex technical analysis for non-technical executive audiences, succinctly present visualizations of high dimensional data, and explain & justify the results of the analysis conducted.

* Highly competent at data wrangling and data engineering in SQL and SAS as well as advanced machine learning (ML) techniques using Python; comfortable in cloud computing environments (Azure, GCP, AWS).

* Hands-on experience developing products that utilize advanced machine learning techniques like deep learning in areas such as computer vision, Natural Language Processing (NLP), sensor data from the Internet of Things (IoT), and recommender systems; along with transitioning those solutions from the development environment into the production environment for full-time use.

* PhD in Computer Science, Applied Mathematics, Quantitative Economics, Operations Research, Statistics, or related field with coursework in advanced Machine Learning techniques (Natural Language Processing, Deep Neural Networks, etc).

* Fluent in deep learning frameworks and libraries (TensorFlow, Keras, PyTorch, etc).

* Highly skilled in handling Big Data (Hadoop, Hive, Spark, Kafka, etc).

* Experience in reinforcement learning, knowledge graphs and graph databases, Generative Adversarial Networks (GANs), semi-supervised learning, multi-task learning is a plus.

* Experience in publishing at top ML, computer vision, NLP, or AI conferences and/or contributing to ML/AI-related open source projects and/or converting ML/AI papers into code is a plus.

* Background in Property insurance operations with an understanding of claims, underwriting, and insurance pricing a plus.

* Additional Skills: Ability to translate business problems and requirements into technical solutions by building quick prototypes or proofs of concept with business and technical stakeholders.

* Ability to convert proofs of concept into scalable production solutions.

* Ability to lead teams by following best practices in development, automation, and continuous integration / continuous deployment (CI/CD) methods in an agile work environment.

* Ability to work in and with technical, multidisciplinary teams.

* Willingness to continuously learn and apply new analytical techniques

RELOCATION assistance is AVAILABLE for this position.

The above description reflects the details considered necessary to describe the principal functions of the job and should not be construed as a detailed description of all the work requirements that may be performed in the job.

Must complete 12 months in current position (from date of hire or date of placement), or must have manager s approval prior to posting.

LAST DAY TO APPLY TO THE OPENING IS 11/06/19 BY 11:59 PM CST TIME.

USAA is an equal opportunity and affirmative action employer and gives consideration for employment to qualified applicants without regard to race, color, religion, sex, national origin, age, disability, genetic information, sexual orientation, gender identity or expression, pregnancy, veteran status or any other legally protected characteristic. If you'd like more information about your EEO rights as an applicant under the law, please click here. For USAA s Affirmative Action and EEO statement, please click here. Furthermore, USAA makes hiring decisions compliant with the Fair Chance Initiative for Hiring Ordinance (LAMC 189.00).

USAA provides equal opportunity to qualified individuals with disabilities and disabled veterans. If you need a reasonable accommodation, please email HumanResources@usaa.com or call 1-800-210-USAA and select option 3 for assistance.
          

Book review: Deep learning with Python by François Chollet

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Deep learning with Python by Froncois Chollet is the third book I have reviewed on deep learning neural networks. Despite these reviews only spanning a couple of years it feels like the area is moving on rapidly. The biggest innovations I see from this book are in the use of pre-trained networks, and the dominance of …

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Assistant Professor - Data Science: Multiple Areas

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ASSISTANT PROFESSOR - DATA SCIENCE: MULTIPLE AREAS APPLY NOW TO ASSISTANT PROFESSOR - DATA SCIENCE: MULTIPLE AREAS

Job #JPF02319

* Hal c o lu Data Science Institute - HALICIO LU DATA SCIENCE INSTITUTE

RECRUITMENT PERIOD

Open date: October 28th, 2019
Next review date: Sunday, Dec 15, 2019 at 11:59pm (Pacific Time)
Apply by this date to ensure full consideration by the committee. Final date: Thursday, Dec 31, 2020 at 11:59pm (Pacific Time)
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.

DESCRIPTION

The University of California, San Diego invites applications from outstanding candidates for a tenure-track faculty position for primary appointment at the Halicioglu Data Science Institute with optional joint appointment in another academic department. The appointment will be at the Assistant level. Successful appointees will have a track record of scientific accomplishments, excellence in teaching, a commitment to university service and a commitment to support diversity, equity and inclusion at the university. The University of California, San Diego is committed to academic excellence and diversity within the faculty, staff and student body.

This search spans all areas of the data science including artificial intelligence, machine learning, data management and their applications and systems. For review purposes, candidates must submit an application to one of the following four broad areas of search that list topics of current interests, but are not limited to those listed.

a) Statistical Foundations of Data Science, Applied Statistics and Biostatistics
Statistical foundations of data science is one of the critical areas for hiring. Statistics (including Biostatistics) is the science of drawing inferences from data, thus forming a pillar of the emerging discipline of data science, together with Machine Learning. While both Statistics and Machine Learning are seeking optimal procedures for inference, e.g. prediction, the latter is more focused on algorithms and their computation/implementation, while the former is crucially entasked with quantifying the accuracy of such inference. Topics of current interest in Statistics include (but are not limited to): High-dimensional data, Large-scale Hypothesis Testing, Regularization and Sparsity, Functional Data, Causal inference, Complex Data, Dependent Data, Inference after model selection, Prediction Intervals, quantification of statistical significance and control of false discovery rate.

b)Digital and Data Infrastructure including Security
We invite candidates who build and study software systems and software-hardware integrated systems that serve as platforms to enable and amplify the impact of data science algorithms and applications. Our focus spans the whole lifecycle of digital data infrastructure, including (but not restricted to) systems for sensing and sourcing data, systems for storing, querying, learning, and analytics over data, streaming methods for analyzing very large datasets and systems for securely deploying data science methods in high-impact application verticals. Across this lifecycle, concerns of scalability, security, and usability will receive special attention. Examples of research areas that fit this focus include database and analytics systems, information integration and knowledge base construction, data mining and network/graph analytics, Internet of Things and cyber-physical systems, spatiotemporal information systems, cloud computing for data science, secure machine learning, database security. Examples of high-impact application verticals include smart and connected health, smart cities, e-commerce platforms, and social media platforms.

c) Artificial Intelligence, AI in Science Applications
We seek faculty candidates with a background in artificial intelligence or machine learning. This area includes, but is not exclusive to: natural language processing, computer vision, modeling high dimensional data with low intrinsic dimension, modeling dynamical systems, streaming methods for analyzing very large datasets, active learning, methods for incorporating machine learning into real-world systems that combine humans and machines, neural networks/deep-learning, reinforcement learning, optimization. We are interested in candidates with a strong applications emphasis as well as those that study the theoretical properties of the algorithms. Priority will be given to those who show a strong connection and relevance to data science.

d) Data Science in Public Policy
We seek faculty candidates with a background in statistics, computer science, economics, or public policy who are dedicated to exploring the use, risks, and benefits of data science for a well-defined vertical application area: such as democratic practice, media, trade in digital services, health, education, and societal infrastructure for energy, communication, transportation, and national security. Of particular interest will be candidates who are pursuing advances in theory, methods and tools that help us understand the opportunities and challenges that digital data pose to markets, organizations, society, and government; and (or) who are devising methods and evaluating policies to advance these opportunities and address these challenges, while considering both technical constraints and constraints related to social, political and economic feasibility.

A PhD in Computer Science, Math, Economics, Engineering or related discipline is required at the start of position.

Successful applicants will be expected to teach graduate and undergraduate students in the Data Science major/minor degree programs offered by the Institute. In case of a partial joint appointment with another department, the teaching workload would include appropriate course work in the participating department. All candidates are expected to establish a vigorous program of high-quality federally funded research that focuses on innovations in one of the targeted search areas.

The preferred candidate will have demonstrated strong leadership or a commitment to support diversity, equity and inclusion in an academic setting. The level of appointment and salary is commensurate with qualifications and based on UC pay schedules.

Applications must be submitted electronically through AP-Online Recruit website: https://apol-recruit.ucsd.edu/JPF02319

For applicants with interest in spousal/partner employment, please see the UC San Diego Partner Opportunities Program website.

UC San Diego is an Equal Opportunity/Affirmative Action Employer with a strong institutional commitment to excellence through diversity.

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.

JOB LOCATION

La Jolla, California

REQUIREMENTS

Document requirements

*

Curriculum Vitae - Your most recently updated C.V.

The University of California, San Diego is an Equal Opportunity/Affirmative Action Employer. You have the right to an equal employment opportunity.
https://www1.eeoc.gov/employers/upload/eeoc_self_print_poster.pdf
For more information about your rights, see the EEO is the Law Supplement.
https://www.dol.gov/ofccp/regs/compliance/posters/pdf/OFCCP_EEO_Supplement_Final_JRF_QA_508c.pdf
The University of California, San Diego is committed to providing reasonable accommodations to applicants with disabilities.
See our Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act Annual Security Reports.
https://www.ucop.edu/ethics-compliance-audit-services/compliance/clery-act/clery-act-details.html
          

Data Science Intern - Summer 2020

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At Cox Automotive, our data scientists are responsible for leading the development of advanced analytics models to solve our customers problems, improve our products and services, and inform internal business operations and strategy. They will work closely with business stakeholders as the subject matter expert on the application of statistics and modeling across multiple leading digital automotive brands. Day to day, the data scientist intern will:Gather and analyze data to solve and address highly complex business problems, make predictions on future outcomes, and provide prescriptive solutions that support decision making
Be involved in all phases of analytics projects including question formulation, research, development, implementation, testing, and maintenance
Explore data and build advanced analytical models, then present and discuss the resulting models to any level of audience

Qualifications:Education
Requirements



Pursuing a bachelor s or advanced degree; College
degree must be in quantitative discipline (e.g. Math, Statistics,
Economics, Biostatistics, Operations Research, Physics, or other
quantitative discipline). Advanced degree preferred.




Required
Experience and Qualifications



Hands-on working knowledge of Python/R and SQL required

Ability to apply statistical methodologies such as multiple
regression model, mixed models, time series models (Bayesian preferred),
neural networks, cluster analysis, text mining, and have prior experience
in optimization, simulation, marketing mix, multivariate testing, ensemble
modeling, graph algorithms

Exposure or experience with software development
practices preferred

A strong passion for empirical research and for
answering hard questions with data.

Curiosity, humility, and empathy



About Cox AutomotiveCox Automotive Inc. makes buying, selling and owning cars easier for everyone, while also enabling mobility services. The global company's 34,000-plus team members and family of brands, including Autotrader®, Clutch Technologies, Dealer.com®, Dealertrack®, Kelley Blue Book®, Manheim®, NextGear Capital®, VinSolutions®, vAuto® and Xtime®, are passionate about helping millions of car shoppers, tens of thousands of auto dealer clients across five continents and many others throughout the automotive industry thrive for generations to come. Cox Automotive is a subsidiary of Cox Enterprises Inc., a privately-owned, Atlanta-based company with revenues exceeding $20 billion. www.coxautoinc.comCox is an Equal Employment Opportunity employer - All qualified applicants/employees will receive consideration for employment without regard to that individual's age, race, color, religion or creed, national origin or ancestry, sex (including pregnancy), sexual orientation, gender, gender identity, physical or mental disability, veteran status, genetic information, ethnicity, citizenship, or any other characteristic protected by law.Statement to ALL Third-Party Agencies and Similar Organizations: Cox accepts resumes only from agencies with which we formally engage their services. Please do not forward resumes to our applicant tracking system, Cox employees, Cox hiring manager, or send to any Cox facility. Cox is not responsible for any fees or charges associated with unsolicited resumes.
          

Diseño experimental de vehículo autónomo utilizando redes neuronales

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Resumen : Este artículo está relacionado con el diseño experimental e implementación de un vehículo autónomo para el transporte de mercancías o materias primas en el interior de una industria o comercio. El proyecto fue desarrollado y coordinado por la Escuela de Ingeniería Eléctrica y Electrónica de ITCA-FEPADE. Este vehículo es accionado a través de un conjunto de sensores, tales como infrarrojos, ultrasónicos y sensor LIDAR; el vehículo es capaz de detectar su entorno, y basados en ellos, alcanzar su destino mediante decisiones de un Raspberry, que, ejecutando un programa basado en red neuronal da las instrucciones a un microcontrolador Arduino, el cual impulsa los motores eléctricos utilizando una etapa de potencia basada en transistores MOSFETs. La red neuronal es un tipo de control adaptativo, que viene a sustituir a los controladores tradicionales; al igual que el ser humano, la red neuronal debe ser entrenada para un funcionamiento óptimo utilizando inteligencia artificial, tal como el método de retropropagación, en la cual la red neuronal aprende de manera supervisada, en base a patrones de entrada y salidas conocidas. El vehículo es capaz de transportar un peso de hasta 30 Kg y las tareas de carga y descarga serán realizadas por un operador humano. Debido a los componentes electrónicos a bordo del vehículo, se recomienda su operación en ambientes secos y una superficie plana. El nivel de autonomía del vehículo, se refiere a transportar la carga de un punto a otro sin acción humana directa durante su desplazamiento. Entre los campos de aplicación, se puede considerar el área logística e industrial, para el transporte de materia prima, herramientas, componentes electrónicos, telas y alimentos enlatados, entre otros. Otros títulos : Experimental design of autonomous vehicle using neural networks Título : Diseño experimental de vehículo autónomo utilizando redes neuronales
          

The Importance of Space and Time for Signal Processing in Neuromorphic Agents: The Challenge of Developing Low-Power, Autonomous Agents That Interact With the Environment

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Artificial neural networks (ANNs) and computational neuroscience models have made tremendous progress, enabling us to achieve impressive results in artificial intelligence applications, such as image recognition, natural language processing, and autonomous driving. Despite this, biological neural systems consume orders of magnitude less energy than today's ANNs and are much more flexible and robust. This adaptivity and efficiency gap is partially explained by the computing substrate of biological neural processing systems that is fundamentally different from the way today?s computers are built. Biological systems use in-memory computing elements operating in a massively parallel way rather than time-multiplexed computing units that are reused in a sequential fashion. Moreover, the activity of biological neurons follows continuous-time dynamics in real, physical time instead of operating on discrete temporal cycles abstracted away from real time.
          

Neuroscience-Inspired Online Unsupervised Learning Algorithms: Artificial neural networks

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Inventors of the original artificial neural networks (ANNs) derived their inspiration from biology [1]. However, today, most ANNs, such as backpropagation-based convolutional deeplearning networks, resemble natural NNs only superficially. Given that, on some tasks, such ANNs achieve human or even superhuman performance, why should one care about such dissimilarity with natural NNs? The algorithms of natural NNs are relevant if one's goal is not just to outperform humans on certain tasks but to develop general-purpose artificial intelligence rivaling that of a human. As contemporary ANNs are far from achieving this goal and natural NNs, by definition, achieve it, natural NNs must contain some "secret sauce" that ANNs lack. This is why we need to understand the algorithms implemented by natural NNs.
          

Oracle Veritabanında SQL ile Deep Learning Modeli Geliştirmek : Predicting Boston House Prices

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Herkese Selam, Bu yazıda Oracle 18c’nin ileri analitik opsiyonlarına getirdiği yeni algoritmalardan biri olan Neural Networkleri kullanarak bir deep learning modeli kurup basit bir regresyon (Regression) analizi yapacağım. Umarım farkındalık anlamında faydalı bir yazı olur. Veri Bilimi ve Makine Öğrenmesi … Continue reading
          

Xilinx Alveo Accelerators Power Real-Time AI-based Intrusion Detection Service

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Today Xilinx announced that SK Telecom has adopted Xilinx Alveo Datacenter Accelerator cards to power a real-time AI-based physical intrusion and theft detection service. SK Telecom’s AI inference accelerator (AIX) implemented on Xilinx Alveo cards provides efficient and accurate physical intrusion detection using deep neural networks. "In the era of Artificial Intelligence where new services are being deployed at unprecedented rates, we keep pursuing to innovate our cloud systems to deliver more value to our customers with more reliable and efficient services across diverse segments.”

The post Xilinx Alveo Accelerators Power Real-Time AI-based Intrusion Detection Service appeared first on insideHPC.


          

Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks

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Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks

Feng, Z., Kittler, J., Awais, M., Huber, P. & Wu, X-J., 14 Dec 2018, Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018. IEEE Computer Society, p. 2235-2245 11 p. 8578336

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Publication details

Title of host publicationProceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
DatePublished - 14 Dec 2018
Pages2235-2245
Number of pages11
PublisherIEEE Computer Society
Original languageEnglish
ISBN (Electronic)978-1-5386-6420-9

Abstract

We present a new loss function, namely Wing loss, for robust facial landmark localisation with Convolutional Neural Networks (CNNs). We first compare and analyse different loss functions including L2, L1 and smooth L1. The analysis of these loss functions suggests that, for the training of a CNN-based localisation model, more attention should be paid to small and medium range errors. To this end, we design a piece-wise loss function. The new loss amplifies the impact of errors from the interval (-w, w) by switching from L1 loss to a modified logarithm function. To address the problem of under-representation of samples with large out-of-plane head rotations in the training set, we propose a simple but effective boosting strategy, referred to as pose-based data balancing. In particular, we deal with the data imbalance problem by duplicating the minority training samples and perturbing them by injecting random image rotation, bounding box translation and other data augmentation approaches. Last, the proposed approach is extended to create a two-stage framework for robust facial landmark localisation. The experimental results obtained on AFLW and 300W demonstrate the merits of the Wing loss function, and prove the superiority of the proposed method over the state-of-the-art approaches.

Bibliographical note

© 2018, The Author(s).


          

Line Learning In Neural Networks

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Line Learning In Neural Networks
          

Neural network fills in data gaps for spatial analysis of chromosomes

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Computational methods used to fill in missing pixels in low-quality images or video also can help scientists provide missing information for how DNA is organized in the cell, computational biologists have shown. Filling in this missing information will make it possible to more readily study the ... Reported by Science Daily 33 minutes ago.
          

AI Improves Quality and Usability of Optoacoustic Imaging

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In optoacoustics, image quality depends on the number and distribution of sensors used by the device; the more sensors and the more broadly they are arranged, the better the quality.

To improve image quality in low-cost optoacoustic devices with only a small number of ultrasonic sensors, researchers at ETH Zurich and the University of Zurich turned to machine learning. They developed a framework for the efficient recovery of image quality from sparse optoacoustic data using a deep convolutional neural network and demonstrated their approach with whole body mouse imaging in vivo.

To generate accurate, high-resolution reference images for training, the team began by developing a high-end optoacoustic scanner with 512 sensors. An...
          

Supervised learning for distribution of centralised multiagent patrolling strategies. , lundi 18 novembre à 14h.

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For nearly two decades, patrolling has received significant attention from the multiagent community. Multiagent patrolling (MAP) consists in modelling a patrol task to optimise as a multiagent system. The problem of optimising a patrol task is to distribute agents over the area to patrol in space and time the most efficiently, which constitutes a decision-making problem. A range of algorithms based on reactive, cognitive, reinforcement learning, centralised and decentralised strategies, among others, have been developed to make such a task ever more efficient. However, the existing patrolling-specific approaches based on supervised learning were still at preliminary stages, although a few works addressed this issue. Central to supervised learning, which is a set of methods and tools that allow inferring new knowledge, is the idea of learning a function mapping any input to an output from a sample of data composed of input-output pairs; learning, in this case, enables the system to generalise to new data never observed before. Until now, the best online MAP strategy, namely without precalculation, has turned out to be a centralised strategy with a coordinator. However, as for any centralised decision process in general, such a strategy is hardly scalable. The purpose of this work is then to develop and implement a new methodology aimed at turning any high-performance centralised strategy into a distributed strategy. Indeed, distributed strategies are by design resilient, more adaptive to changes in the environment, and scalable. In doing so, the centralised decision process, generally represented in MAP by a coordinator, is distributed into patrolling agents by means of supervised learning methods, so that agents of the resultant distributed strategy tend to capture each a part of the algorithm executed by the centralised decision process. The outcome is a new distributed decision-making algorithm based on machine learning. In this thesis therefore, such a procedure of distribution of centralised strategy is established, then concretely implemented using some artificial neural networks architectures.
          

Přednáška - Ariel Shamir - The Face Of Art: Landmark Detection & Geometric Style in Portraits

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Krátká anotace přednášky: Neural networks abilities have been utilised in many fields in science and engineering. Recently, they have also been used in Art and design to stylise photographs guided by artworks of various artists. However, such stylisation mostly concentrates on the texture and the colour of the artworks. In this work we try to capture the Geometric style of artists using neural networks. We concentrate on portraits and propose a new method for landmark detection in paintings that allow us to analyse and capture geometric deformations in face paintings, and define a geometric style for artists. We demonstrate our technique by creating average portraits for Artists as well as defining a geometry-aware portrait stylisation algorithm. This work was published in SIGGRAPH 2019 and is joint work with Jordan Yaniv and Yael Newman. See http://www.faculty.idc.ac.il/arik/site/foa/face-of-art.asp Bio: Ariel Shamir is the Dean of the Efi Arazi School of Computer Science at the Interdisciplinary Center in Israel. He received his Ph.D. in computer science in 2000 from the Hebrew University in Jerusalem. He spent two years as PostDoc at the University of Texas in Austin. Shamir has numerous publications and a number of patents. He is currently an associate editor for ACM Transactions on Graphics, Graphical Models and Computational Visual Media, and was an associate editor for Computers and Graphics journal (2010-2014), IEEE Transactions on Visualization and Computer Graphics (2015-2017), He also served on the program committee of many leading international conferences, including SIGGRAPH, SIGGRAPH Asia, and Eurographics. Shamir was named one of the most highly cited researchers on the Thomson Reuters list in 2015. He has a broad commercial experience consulting various companies including Disney research, Mitsubishi Electric, PrimeSense (now Apple), Verisk and more. Shamir specializes in geometric modeling, computer graphics, image processing and machine learning. He is a member of the ACM SIGGRAPH, IEEE Computer, AsiaGraphics and EuroGraphics associations.

19.11.2019


          

07 November 2019: The fossil of an upright ape, science in 150 years, and immunization progress around the world

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This week, insights into the evolution of walking upright, how science needs to change in the next 150 years, and the unfinished agenda for vaccines.


In this episode:


00:50 Early ape locomotion

The discovery of a fossil of a new species of ape gives new insights on how bipedalism may have evolved. Research Article: Böhme et al.News and Views: Fossil ape hints at how walking on two feet evolvedNews: Fossil ape offers clues to evolution of walking on two feet


07:24 Research Highlights

Women lacking olfactory bulbs can somehow still smell, and telling whiskies apart through evaporation patterns. Research Highlight: The women who lack an odour-related brain area — and can still smell a roseResearch Highlight: Bourbon or Scotch? A droplet’s dynamics reveal the truth


09:44 How should science evolve?

This year is Nature’s 150th anniversary. Science has made huge strides during this time, but what needs to change to continue this progress for the next 150 years? Comment: Science must move with the times


17:52 The state of vaccination in 2019

Researchers assess the differences in immunization levels worldwide and identify the bottlenecks in developing new vaccines. Research article: Piot et al.


23:54 News Chat

An AI figures out the sun’s place in the Solar System, and reassessing the size of the proton. News article: AI Copernicus: Neural network ‘discovers’ that Earth orbits the Sun; News: Puzzle over size of proton leaps closer to resolution

For information regarding your data privacy, visit acast.com/privacy
          

Deep Learning for Detection of Cancerous and Precancerous Esophagus Tissue

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This diagnostic study describes a novel attention-based deep neural network framework for classifying microscopy images to identify Barrett esophagus and esophageal adenocarcinoma.
          

Manual Of Neural Networks Simon

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Manual Of Neural Networks Simon
          

Predictive Model Ensembles: Pros and Cons

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Many recent machine learning challenges winners are predictive model ensembles. We have seen this in the news. Data science challenges are hosted on many platforms. Techniques included decision trees, regression, and neural networks. And, winning ensembles used these in concert. But, let’s understand the pros and cons of an ensemble approach. Pros of Model Ensembles […]


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