It’s time to exercise, and the Apple Watch can help you track your workout sessions. In this excerpt from Apple Watch and iPhone Fitness Tips and Tricks, Jason Rich shows you how you can set a Caloric, Distance, or Time goal, and then have the watch display real-time data it collects as you pursue that goal during your workout.
The Workout app is somewhat similar to the Activity app, but instead of being designed for use at all times while you’re wearing the watch, this app allows you to collect and analyze data related to actual workouts.
To use this app, launch it from the Home screen of the Apple Watch (see Figure 5.23), and from the main menu, select the fitness-related activity you’re about to participate it. Options include Outdoor Walk, Outdoor Run, Outdoor Cycle, Indoor Run, Indoor Walk, Indoor Cycle, Elliptical, Rower, Stair Stepper, or Other.
When you’re ready to begin a workout, follow these steps to activate the Workout app on your watch:
- From any watch face you’ve selected to be displayed on the watch’s screen, press the Digital Crown to access the watch’s Home screen.
- Tap on the Workout app icon to launch the Workout app.
- When the main menu appears, tap on the type of workout you plan to engage in.
- Depending on the activity you select, a submenu screen enables you to Set Calories, Set Time, or Set Miles, or select Open (if you have no goal in mind, but simply want to track your workout-related data). If you select the Set Time screen, a timer appears, showing 0:00, with a negative sign (–) icon on the left and a plus sign (+) icon on the right. Tap the + icon to set the desired duration for your workout. Press the Start button, shown in Figure 5.24, to begin your workout.
One way to handle big data is to shrink it. If you can identify a small subset of your data set that preserves its salient mathematical relationships, you may be able to perform useful analyses on it that would be prohibitively time consuming on the full set.
The methods for creating such “coresets” vary according to application, however. Last week, at the Annual Conference on Neural Information Processing Systems, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory and the University of Haifa in Israel presented a new coreset-generation technique that’s tailored to a whole family of data analysis tools with applications in natural-language processing, computer vision, signal processing, recommendation systems, weather prediction, finance, and neuroscience, among many others.
“These are all very general algorithms that are used in so many applications,” says Daniela Rus, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT and senior author on the new paper. “They’re fundamental to so many problems. By figuring out the coreset for a huge matrix for one of these tools, you can enable computations that at the moment are simply not possible.”
As an example, in their paper the researchers apply their technique to a matrix — that is, a table — that maps every article on the English version of Wikipedia against every word that appears on the site. That’s 1.4 million articles, or matrix rows, and 4.4 million words, or matrix columns.
That matrix would be much too large to analyze using low-rank approximation, an algorithm that can deduce the topics of free-form texts. But with their coreset, the researchers were able to use low-rank approximation to extract clusters of words that denote the 100 most common topics on Wikipedia. The cluster that contains “dress,” “brides,” “bridesmaids,” and “wedding,” for instance, appears to denote the topic of weddings; the cluster that contains “gun,” “fired,” “jammed,” “pistol,” and “shootings” appears to designate the topic of shootings.
Joining Rus on the paper are Mikhail Volkov, an MIT postdoc in electrical engineering and computer science, and Dan Feldman, director of the University of Haifa’s Robotics and Big Data Lab and a former postdoc in Rus’s group.
The researchers’ new coreset technique is useful for a range of tools with names like singular-value decomposition, principal-component analysis, and latent semantic analysis. But what they all have in common is dimension reduction: They take data sets with large numbers of variables and find approximations of them with far fewer variables.
Today, loading a web page on a big website usually involves a database query — to retrieve the latest contributions to a discussion you’re participating in, a list of news stories related to the one you’re reading, links targeted to your geographic location, or the like.
But database queries are time consuming, so many websites store — or “cache” — the results of common queries on web servers for faster delivery.
If a site user changes a value in the database, however, the cache needs to be updated, too. The complex task of analyzing a website’s code to identify which operations necessitate updates to which cached values generally falls to the web programmer. Missing one such operation can result in an unusable site.
This week, at the Association for Computing Machinery’s Symposium on Principles of Programming Languages, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory presented a new system that automatically handles caching of database queries for web applications written in the web-programming language Ur/Web.
Although a website may be fielding many requests in parallel — sending different users different cached data, or even data cached on different servers — the system guarantees that, to the user, every transaction will look exactly as it would if requests were handled in sequence. So a user won’t, for instance, click on a link showing that tickets to an event are available, only to find that they’ve been snatched up when it comes time to pay.
In experiments involving two websites that had been built using Ur/Web, the new system’s automatic caching offered twofold and 30-fold speedups.
“Most very popular websites backed by databases don’t actually ask the database over and over again for each request,” says Adam Chlipala, an associate professor of electrical engineering and computer science at MIT and senior author on the conference paper. “They notice that, ‘Oh, I seem to have asked this question quite recently, and I saved the result, so I’ll just pull that out of memory.’”
“But the tricky part here is that you have to realize when you make changes to the database that some of your saved answers are no longer necessarily correct, and you have to do what’s called ‘invalidating’ them. And in the mainstream way of implementing this, the programmer needs to manually add invalidation logic. For every line of code that changes the database, the programmer has to sit down and think, ‘Okay, for every other line of code that reads the database and saves the result in a cache, which ones of those are going to be broken by the change I just made?’”
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Using a lighter is often very easy to accomplish, yet not invariably. Anytime a person is actually camping outdoors and they’ll want to light something but it is breezy outside the house, they may have trouble getting their particular lighter to be effective. The same goes for virtually any situation that demands a lighter for use yet where it might be too breezy to in fact get it to light.
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Every day, countless products are stolen. From mobile phones to bikes and pcs, most of them are expensive and also difficult to replace. Even in case they could be replaced conveniently, they could have critical info in them that a person can’t very easily replace. Often, when the product might be stolen, there’s no approach to get it back because there isn’t any method for a person to demonstrate it’s theirs. Even in case the Police find the stolen goods, they won’t have a method of matching the products to the proper owner, meaning they’re not brought back.
The issue is many products aren’t registered, even if it’s easy to register them. There isn’t any way of knowing who actually owned the product, therefore there is not any method of contacting the owner in order to return the merchandise to them. What this means is the individual will most likely never see the stolen product, even if it is located and an individual who stole it is actually caught. As an example, a thief could be caught soon after stealing a single item, but when his residence is searched for the stolen product, they find some other items that were stolen. Those items are recovered, however there is no solution to tell who exactly they belonged to.
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Right now, the main wished-for big ticket purchase for nearly all young adults is going to be that relating to a mobile phone, if possible an iPhone. An iPhone is actually the equal of the superior identify running sneakers or even backpack … the item is really a real status symbol. Sensible fathers and mothers use the chance to purchase a mobile phone as one to enable youngsters decide things like options as opposed to price, the worth of a fantastic sale, and also exactly why, whenever expending pertaining to an essential purchase it is imperative that you consider things such as artistry, top quality and the time-span associated with the maker’s warranties. One of the reasons that it may be so good for little ones to take part in the purchase of their own telephones is because the likelihood is great that they may find themselves buying more than just one telephone via their life-time.
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Plenty of people, whenever they consider any sort of artist, visualize a gentleman standing up before hos canvas and easel with a tray of paints in one hand and a brush within the other. Yet, designers are occasionally found in astonishing places, and so are masters of an shocking range of materials. People have witnessed the gorgeous creations that men and women leave behind about the beach front, or maybe define from blocks of ice, or even make from scrap metal or even with an old hardwood trunk area, utilizing nothing more than his chainsaw for a resource. More often than not, the main necessity for the performer is actually a vision. Nevertheless, finely-detailed often factors straight into workmanship at the same time. Just check with any person that operates in the arena connected with scientific molding! The littlest miscalculations can create weaknesses which usually make a product as waste.
That is why companies like Paulson Training Programs, Inc. (www.paulsontraining.com) go to these measures to supply the entire industry injection molding seminars. The field as a whole and every manufacturing unit especially benefits from uniform injection molding training. These Paulson courses are really simple to comprehend, perhaps as they have a tendency to present a great deal of the pertinent data with the viewpoint within the plastic-type material as it were. Something relating to this type of display tends to spark the actual creativity. Maybe it is so individuals will then picture themselves getting compressed inside the cavity, exactly what the strain and heat feel like, along with what may make it so it will be unable to achieve that absolute last small crevice. An excellent benefit of these types of presentations is that they typically are made available in-house, at individualized stations where staff members are in a position to advance at his / her individual rate.
Folks come to materials manufacturing coming from a wide array of backdrops. A number of facilities have got staff that acquired his / her original training via competitor industrial facilities. Other folks emerged by way of technological university packages, or even from backyard businesses. Lots of individuals really originated from other countries. This brings together an enormous array of various kinds of experience and even troubles fixing capabilities, however causes communicating problems when they are not almost all employees utilize terminology and terminology to actually indicate the very same factors. Employee in-house instruction fixes this problem and also puts all people on precisely the same page.
Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory have developed a new computational model of a neural circuit in the brain, which could shed light on the biological role of inhibitory neurons — neurons that keep other neurons from firing.
The model describes a neural circuit consisting of an array of input neurons and an equivalent number of output neurons. The circuit performs what neuroscientists call a “winner-take-all” operation, in which signals from multiple input neurons induce a signal in just one output neuron.
Using the tools of theoretical computer science, the researchers prove that, within the context of their model, a certain configuration of inhibitory neurons provides the most efficient means of enacting a winner-take-all operation. Because the model makes empirical predictions about the behavior of inhibitory neurons in the brain, it offers a good example of the way in which computational analysis could aid neuroscience.
The researchers will present their results this week at the conference on Innovations in Theoretical Computer Science. Nancy Lynch, the NEC Professor of Software Science and Engineering at MIT, is the senior author on the paper. She’s joined by Merav Parter, a postdoc in her group, and Cameron Musco, an MIT graduate student in electrical engineering and computer science.
For years, Lynch’s group has studied communication and resource allocation in ad hoc networks — networks whose members are continually leaving and rejoining. But recently, the team has begun using the tools of network analysis to investigate biological phenomena.
“There’s a close correspondence between the behavior of networks of computers or other devices like mobile phones and that of biological systems,” Lynch says. “We’re trying to find problems that can benefit from this distributed-computing perspective, focusing on algorithms for which we can prove mathematical properties.”
In recent years, artificial neural networks — computer models roughly based on the structure of the brain — have been responsible for some of the most rapid improvement in artificial-intelligence systems, from speech transcription to face recognition software.
An artificial neural network consists of “nodes” that, like individual neurons, have limited information-processing power but are densely interconnected. Data are fed into the first layer of nodes. If the data received by a given node meet some threshold criterion — for instance, if it exceeds a particular value — the node “fires,” or sends signals along all of its outgoing connections.
Each of those outgoing connections, however, has an associated “weight,” which can augment or diminish a signal. Each node in the next layer of the network receives weighted signals from multiple nodes in the first layer; it adds them together, and again, if their sum exceeds some threshold, it fires. Its outgoing signals pass to the next layer, and so on.
The age of big data has seen a host of new techniques for analyzing large data sets. But before any of those techniques can be applied, the target data has to be aggregated, organized, and cleaned up.
That turns out to be a shockingly time-consuming task. In a 2016 survey, 80 data scientists told the company CrowdFlower that, on average, they spent 80 percent of their time collecting and organizing data and only 20 percent analyzing it.
An international team of computer scientists hopes to change that, with a new system called Data Civilizer, which automatically finds connections among many different data tables and allows users to perform database-style queries across all of them. The results of the queries can then be saved as new, orderly data sets that may draw information from dozens or even thousands of different tables.
“Modern organizations have many thousands of data sets spread across files, spreadsheets, databases, data lakes, and other software systems,” says Sam Madden, an MIT professor of electrical engineering and computer science and faculty director of MIT’s bigdata@CSAIL initiative. “Civilizer helps analysts in these organizations quickly find data sets that contain information that is relevant to them and, more importantly, combine related data sets together to create new, unified data sets that consolidate data of interest for some analysis.”
The researchers presented their system last week at the Conference on Innovative Data Systems Research. The lead authors on the paper are Dong Deng and Raul Castro Fernandez, both postdocs at MIT’s Computer Science and Artificial Intelligence Laboratory; Madden is one of the senior authors. They’re joined by six other researchers from Technical University of Berlin, Nanyang Technological University, the University of Waterloo, and the Qatar Computing Research Institute. Although he’s not a co-author, MIT adjunct professor of electrical engineering and computer science Michael Stonebraker, who in 2014 won the Turing Award — the highest honor in computer science — contributed to the work as well.
Pairs and permutations
Data Civilizer assumes that the data it’s consolidating is arranged in tables. As Madden explains, in the database community, there’s a sizable literature on automatically converting data to tabular form, so that wasn’t the focus of the new research. Similarly, while the prototype of the system can extract tabular data from several different types of files, getting it to work with every conceivable spreadsheet or database program was not the researchers’ immediate priority. “That part is engineering,” Madden says.
The system begins by analyzing every column of every table at its disposal. First, it produces a statistical summary of the data in each column. For numerical data, that might include a distribution of the frequency with which different values occur; the range of values; and the “cardinality” of the values, or the number of different values the column contains. For textual data, a summary would include a list of the most frequently occurring words in the column and the number of different words. Data Civilizer also keeps a master index of every word occurring in every table and the tables that contain it.