Question : Explain why every odd number can be written as the sum of two consecutive whole numbers? Related Answer. Which of the following statements are true and which are false? The sum of two odd numbers is an odd Explain whether the following statements are true or false. One square number can be written as the However, these contents do not necessarily represent the policy of the Department of Education, and you should not assume endorsement by the Federal Government.
Less - Screenshots App Trailer Your browser does not support this video player. Download the MP4 video here Need Help? We've put together some helpful hints to help resolve common issues. Visit our help section for more device assistance. Significance of study about effects of social networking sites to students. Q: How can you play with your squad vs your brother and his squad offline on ghosts?
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Hope this helps! The developer will be required to provide privacy details when they submit their next app update. With Family Sharing set up, up to six family members can use this app.
App Store Preview. Oct 27, Version 2. Ratings and Reviews. App Privacy. Size Category Education. Equipped with invisibility hand-held spotlights and laser beam pointers, we will navigate through information fields, with the help of films, recorded from the point of view of the explorer. An analytical expression of the area of the Mandelbrot set is unknown. So far, the most accurate numerical estimation of the area by R.
Munfao has a precision of 7 decimals and takes 8 days on a standard quad core PC. Practically, in a first approach 8 digits of precision were reached with about 50x the CPU load.
Mathematica with Open CL? A drawback of the first approach is that the numerical error is based on statistics and has limited theoretical significance. With the second approach precise upper and lower bounds of the area can be calculated on the same PC system. This second approach reaches a tessellation of depth 24 using Mathematica and Open CL? Depths of 25 or 26 are in reach, resulting in quad trees in the range of Terabytes of compressed data — the next bottleneck….
At last year's WTC, we introduced a long-term project to find and implement ways of using the Wolfram Technology Stack to provide protocols and tools for counting measuring and reporting progress on achieving the Sustainable Development Goals.
During the past year we have been working on the first phase of the project, using various functionalities of the Wolfram technology stack to model and prototype. The presentation will review three projects: 1. Historical data as raw material for modeling SDG data forms. One Indicator Deep dive into one indicator as proof-of-concept. Working with AVAC -- a global prevention advocacy organization and Cabrini Missions, an on-the-ground health and social services organization in rural Swaziland to model the functionality needed to measure results on one indicator.
This will be used as proof of concept for other indicators. Indicator 3. Emphasis is on cloud-based forms and dashboard design. Knowledge Connections Beginning use of feature detection and neural networks to understand how SDG goals, targets and indicators inter-relate.
Using text, pdf, narrative reports as raw material. Collecting samples of different types of documents and developing functions and modules to 'semanticize' and store them. This presentation will show how Wolfram Technology built a better data. It will show how Database Link? Several applications constructed using Mathematica and the data liberated from data.
The talk makes heavy use of Mathematica Dataset programming and Database Link? Little familiarity with details of the Affordable Care Act is required to understand the presentation. This presentation applies techniques of hierarchical distance clustering and machine learning to a famous and vexing problem of authorship attribution in medieval history. Sometime before , an anonymous author working in Venice, known as the Monk of Lido, wrote in Latin a contentious account of the First Crusade Around , another anonymous author working in Poland, known as the Gallus Anonymous, composed in Latin the earliest account of Polish history.
For decades, historians and philologists have argued whether or not Monk and Gallus are in fact one and the same anonymous author. I have used Mathematica to answer this question using hierarchical distance clustering and machine learning. First, I build a large corpus of representative Latin texts from the 11th and 12th centuries. Second, I convert each member text into a vector containing the frequencies of the most frequently occurring words MFW in the entire corpus.
Third, I use the eight distance measures in Mathematica's Hierarchical Clustering? Finally, I use machine learning algorithms to train a series of classifiers that can predict a text's authorship based on its MFW frequencies.
Cross-validation indicates that Gallus and Monk are very likely one and the same author. The results also reveal the especially high and hitherto underexplored effectiveness of the Bray Curtis Distance measure and of logistic regression in shedding light on questions of authorship attribution.
An integration of several fuel cell stacks into a single fuel cell system, a so-called fuel cell network, features a variety of possible network topologies for the corresponding fuel cells. Such topological configurations are well-known for the electrical and thermal architectures of electrochemical energy storage devices like secondary batteries or supercapacitors. It is a novel approach, however, to incorporate the reactant supply architectures of electrochemical energy conversion devices like fuel cells or redox-flow batteries while investigating their network topologies.
Using Wolfram System Modeler? This enables multiphysics simulation of the dynamical behavior of fuel cell networks. With the help of Wolfram Mathematica, fuel cell network models are analyzed and optimized, particularly with regard to model-based design of control systems. It is shown that fuel cell networks offer a higher overall system efficiency, compared to conventional fuel cell system possessing a single fuel cell stack.
Furthermore, it is illustrated that fuel cell network systems demonstrate an increased operating range and less dynamical transients. Fuel cells offer considerable benefits in sustainable energy conversion, but have not been widely developed for commercial applications, so far. Fuel cell networks could provide improvements in performance, longevity, reliability, and cost over conventional fuel cell systems.
The Wolfram Language is unique in that it combines its powerful programming language with real world knowledgebase. By using these features, we present here original music composed and interpreted by the WL and the code behind that. We have used different techniques, from simple lists manipulation to Machine Learning functions and connection to external services, to provide our framework the necessary minimum flexibility to create, what we think is, enjoyable music as a result of a collaboration between humans and machines.
Sports and eSports provide a rich challenge of communicating quantitative computational findings to largely qualitatively thinking people who are often on the road without access to powerful hardware. The Wolfram Language is uniquely suited to bridge the gap with its ability to quickly generate machine learning results, custom visualizations, interactive tools, and cloud-deployed solutions.
I present here a wide-ranging multi-project case study on how the Wolfram language has been deployed at NBA teams to explore and interact with optical player tracking data, project college player performance at the professional level, and evaluate trades and free agents, and how it has been deployed at Vantage Sports for eSports such as League of Legends to establish in-game win probabilities, provide unique data visualizations, and support professional teams in live drafting applications.
Alex, nicknamed after the library of Alexandria, is the combined effort of Assured Flow Solutions to create an all-encompassing set of tools for handling its vast array of internal calculations, workflows and data analysis techniques. The majority of its work involves studies to address certain production scenarios and the challenges associated with them. Assured Flow Solutions relies on a vast set of tools to solve these problems, which includes both academic and industry knowledge, as well as computer aided simulations.
A large portion of these calculations and workflows are captured in small utilities, one-off reports, and poorly manage spreadsheet macros. Grouping these tools together and leveraging the power of the Wolfram Language has allowed Assured Flow Solutions to create a truly unique set of tools not seen in the industry to date. Specifically, it will address two different areas: 1. The role Wolfram Language can play in the oil and gas industry 2.
Lessons learned in application development with the Wolfram Language. In a world where Hip Hop has influenced the sample-ability of culture and creative content, we see a similar repeatability in mathematical content and physical simulation.
Just as a media sample will appear the same if repeated unless altered such as by an artist , so too will mathematical relations such as those found in number theory appear the same to all observers unless acted upon by artistic sensibilities. Similarly, physical simulation set with the exact same ideal conditions and rule sets should evolve in the same predictable manners by recurring laws unless altered artistically. For example, the pioneering data equivalence philosophy of Stephan Wolfram, the mathematical cataloging of Eric Weisstein, and the pioneering 3D visual graphic design of Tom Wickham-Jones are all major influences to this system.
In addition to concept art and technical demos, Scantron will perform some mathematics-based art and music in order to display key points of the CJ system and philosophy. He will also discuss how current mathematical investigations such as the Langlands Correspondence, polytopes, and p-adic number relations may have much to say and share with modern mathematical art.
This talk is going to examine differences between doing Machine Leaning and Statistics. We assume that these two cultures tackle a large set of the same or similar problems hence a comparison is justified. The differences between the two cultures can be observed by comparing 1 what is considered important while participating in them, and 2 the methodologies employed for solving "real life", practical problems.
Obviously, the cultural differences stem from practitioners backgrounds and schooling and we are going to point major differences of educational and experience backgrounds. Also, jobs. General observations will be stated and an overview of concrete examples in Mathematica is going to be presented. The concrete examples are from the sufields of data analysis, prediction, recommendations, time series, gambling mathematics.
Two examples are going to be discussed in more detail: 1 using collaborative filtering instead of regression, and 2 using natural language processing techniques in time series prediction. As a conclusion we are going to consider how to look for or come up with the new examples showing the cultural differences.
Some of the major approaches and tricks of each culture are going to be identified and listed. This talk is a continuation with more advanced projects of general interest that are used to compare further Mathematica and R.
The topics covered are: data summarization and reports, importance of variables investigation, dimension reduction algorithms comparison, natural language processing, programming utilization of functional parsers, sparse linear algebra applications to recommenders, graph algorithms, handling collections of time series, detecting components in time series getting insight into personal finances, making of dynamic interfaces. As a year-old, computational thinking seems to be far from my reach.
But my perspective has changed over the last 6 months. I have had the opportunity to learn the Wolfram Language with the guidance of my mentors as a participant in the Wolfram Mentorship Program, and work on a project of my own using the Wolfram Language and NYC Open Data. My presentation will discuss my journey as I learned the Wolfram Language using Dr. Stephen Wolfram's book "An Elementary Introduction to the Wolfram Language", how the Wolfram Language can be greatly leveraged in projects such as mine, and the next steps I plan to take.
My presentation will share how my learning experience was different for the Wolfram Language as opposed to the previous ones I have learned, such as Java and Swift, the challenges I faced as I learned computational thinking for the very first time using the Wolfram Language and how I overcame them, but more importantly, how I discovered the broad range of possibilities the language has to offer, unlike any other.
The Wolfram Mentorship Program has also been a cornerstone to my learning. My presentation will discuss how the structure of the Wolfram Mentorship Program has helped me through the process, and what I found critical to my learning the language.
I hope that it will provide a unique perspective on the process of learning the Wolfram Language through the eyes of a young programmer, and on what I think is "A New Kind of Learning". It simulated a Rogerian psychotherapist. I expanded on the code that appeared in Creative Computing in , and it was effective enough that it 'fooled' some graduate students into thinking it was 'intelligent'.
It was, arguably, the first chat-bot. In its format, it resembled any number of messaging applications, and at times, it is hard to tell whether the 'person' at the other end is real or a computer.
Up to date, there have been many methods developed to estimate the parameters for nonlinear curve fitting or nonlinear least square fitting.
Some of these include method of partial sums, Hotelling' s method, method of successive approximations, and method of moments. On the other hand, parameter estimations in nonlinear differential equation models can be done by solving the nonlinear differential equation with numerical approximations and then applying one of the above method for parameter estimations to the solution for least squares fitting.
The models the mathematicians develop are unlikely to have analytical solutions in the early stage of their research. The numerical methods need to be applied to solve them. In this work, we develop a parameter estimation algorithm in Mathematica. This algorithm can be used as a least square fitting method for both solutions i. There are two reasons behind exploring a new algorithm for data fitting: First, we want to keep the model as it be for fitting, i.
Second, we want to keep the number of parameters as small as possible. If we solve the models, one more parameter will be added to the set of parameters.
Economic and regulatory constraints, need for scientific controls, and human factors like job satisfaction and professional development, make research workflows especially challenging and compelling targets for efforts to promote organizational innovation. Typically, work is supported in large part by monolithic institutional systems that require users to be trained in basic use, and to wait too long for modifications necessary for them to work more efficiently and effectively.
While these systems are a great way to store and secure data, they are not as helpful in application of data resources.
This begs the question: what performance benefits might accrue through relaxing these constraints? We show how Mathematica is used to rapidly build, test and productionize user-designed computational artifacts, information appliances, that support individual and group tasks in a variety of research workflows. Our talk details these accomplishments, and the particular applications of Mathematica critical to the large financial return to these methods. They simply need to pick a topic they are interested in and create a Wolfram Demonstration on the topic.
The reason I am hoping to give this talk is to share with other educators how they can use Mathematica to enrich the educational experiences of their students. I will share the practical details of assigning a project like this. This includes making sure the students have the technical skills and offering the support they need to realize their vision in Mathematica.
But I am also hoping to communicate a philosophy: Students can and will do amazing things when they are given freedom to experiment, express themselves, pursue their interests, take ownership over their work, and be creative.
The bi-directional reflectance distribution function BRDF defines anisotropy of the reflective properties of a surface. It is required to specify a boundary condition for radiative transfer RT modeling which is used in various applications such as aerosol retrievals, cloud retrievals, and atmospheric modeling. Ground based measurements of reflected radiance draw increasing attention as a source of information about anisotropy of surface reflection.
However, experimental data need to be corrected for atmospheric effects to derive surface BRDF. This study develops a new method of retrieving BRDF on its whole domain making it immediately suitable for further atmospheric RT modeling applications.
It was shown that surface reflected radiance satisfies an integral equation which kernel and source depend on BRDF provided it is known and solutions of atmosphere-only RT problems. If surface reflected radiance is known that equation can be solved with respect to unknown BRDF. In particular, the equation has the form suitable for successive iterations.
The key feature of the method is that RT modeling has to be done only one time before the start of iterative process. This differs the method from previously developed methods Martonchik , Remote Sens.
In this talk I will share that experience and highlight how Mathematica helped us. We will discuss applications of some specific visualization techniques and their implementation in the Wolfram Language and as well as the third party libraries and other programming languages that assisted in making our visuals.
Mathematica sometimes cannot determine an exact closed-form expression for a constant that is an integral, an infinite series, or the solution to an equation, but Mathematica can determine a float approximation.
Do you wonder if that float approaches some expressible closed form as the precision increases? You can use the Ask Constants? This package supplements Wolfram Alpha by offering different expression models, an alternative presentation, and a corresponding Propose [ In this talk I'll demonstrate how to use BEST DB Editor to migrate data a text file and a spreadsheet file with several tabs to a database in the cloud. The database created will contain text, numbers, dates, Mathematica expressions and images.
Thomas Young was the first to describe astigmatism in the his! The reduction of astigmatism at the time of modern day cataract surgery is usually achieved through one of two techniques: Through the implantation of a toric intraocular lens or through incisions in the limbus or cornea, with each technique leaving a degree of residual astigmatism.
Recently the femtosecond laser has allowed for corneal incisions to be more accurately placed and nomograms for the corresponding astigmatism reduction are constantly being refined. We illustrate the central role of Mathematica in the development and implementation of these techniques.
Last spring I taught a course on computational engineering using Wolfram Mathematica as the software and text. I covered a variety of topics including using computational engineering. Next spring I will teach a follow on course using these techniques to teach a course on advanced decision processes.
The talk will summarize lessons learned along with examples used in the course from this last spring and ideas on how to address the more challenging course for next spring. Multiple approaches have been developed to automatically identify specific features of the retina and optic disk from digital fundus photos with the presumptive goal of assisting in the screening and diagnosing of advanced ocular conditions.
These approaches have used a variety of languages, databases, algorithms for automatic retinal image analysis ARIA. In parallel a queryable atlas of the retina, Retin ASK? We present some of the elements of our current effort to integrate the methods of ARIA into a unified environment, thus providing the foundation for extending Retin ASK?
This is made possible by the very extensive number of functions available in Mathematica and the integrated environment that it provides. There micro-services leverage the wolfram math libraries and allow for full API decoupling from other production code using their algorithm offerings.
The services are written in Wolfram language and deployed in AWS with full scalability and monitoring. Results and analysis for scaling of these various services are presented across hundreds of thousands and more API calls per day.
The power of leveraging the Wolfram Private Cloud technology to accelerate the research to production pipeline is discussed in detail, allowing clear understanding of the tradeoffs and advantages to using this technology for algorithm development, especially machine learning. Although Manipulate is great for quick-and-dirty interactive interfaces, non-trivial interfaces require migrating towards Dynamic Module?
The Evolved Analytics' Data Modeler? In this talk we will address the implementation best practices which have emerged over the years as well as some of the more subtle functional forms and option settings for dynamics and notebooks which contribute to success. System Modeler?
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