With the NFL season in full swing, I thought I would share a graphic that impressed me. It shows how Data Visualization and power of Data Science can help us understand more about our lives and things we enjoy. It was developed by Sean Taylor of the Facebook Data Science Team, and credits go to him and his team for this creative work, which leveraged data from over 35 million Facebook account holders . It shows the NFL team with the largest following in each county in the US. Data comes from Facebook likes. It also shows us a great example of how Data Science and Big Data can be used to answer questions about business in powerful ways.
According to the World Economic Forum, the diffusion of unsubstantiated rumors on online social media is one of the main threats for our society. The disintermediated paradigm of content production and consumption on online social media might foster the formation of homogeneous communities (echo-chambers) around specific worldviews. Such a scenario has been shown to be a vivid environment for the diffusion of false claim. Not rarely, viral phenomena trigger naive (and funny) social responses—e.g., the recent case of Jade Helm 15 where a simple military exercise turned out to be perceived as the beginning of the civil war in the US. In this work, we address the emotional dynamics of collective debates around distinct kinds of information—i.e., science and conspiracy news—and inside and across their respective polarized communities. We find that for both kinds of content the longer the discussion the more the negativity of the sentiment. We show that comments on conspiracy posts tend to be more negative than on science posts. However, the more the engagement of users, the more they tend to negative commenting (both on science and conspiracy). Finally, zooming in at the interaction among polarized communities, we find a general negative pattern. As the number of comments increases—i.e., the discussion becomes longer—the sentiment of the post is more and more negative.
Last week, I shared an interview conducted with the originator and creative producer of a project called My VirtualDream — an effort to develop a “crowdsourced” approach to neurological research. For her experiment, Dr. Natasha Kovacevic used an EEG device called the Muse. Since the technology employed for her research was as interesting as the experiment itself (and of course, since we at MDT love medical technologies and devices), I reached out to the company behind the “headband” technology to find out more.
Responding to my call was Trevor Coleman, co-founder of Interaxon, and Dr. Graeme Moffat, a neuroscientist and the director of science and regulatory for the company. Interaxon is the firm behind the development of the Muse EEG headband. Following is my interview with Coleman and Dr. Moffat on the Muse technology, its application areas, development, and what’s ahead.
Recently I learned about a project Google is working on Google Optimize. The replacement of Google Analytics Content Experiments that was replacing Google Website Optimizer with a new feature inside Google Tag Manager (GTM).
It’s unclear if this project will be in one of Google’s first paid versions of Google Tag Manager Premium of be a free addition to the platform, maybe we will hear on Google’s Unveil event February 29th . With Google Ventures’ current investment in Optimizely rumors says they might acquire and not finish developing it alone.
In the last decade, Guilio Tononi has developed the Integrated Information Theory (IIT) of consciousness. IIT postulates that consciousness is equal to integrated information (Φ). The goal of this paper is to show that IIT fails in its stated goal of quantifying consciousness. The paper will challenge the theoretical and empirical arguments in support of IIT. The main theoretical argument for the relevance of integrated information to consciousness is the principle of information exclusion. Yet, no justification is given to support this principle. Tononi claims there is significant empirical support for IIT, but this is called into question by the creation of a trivial theory of consciousness with equal explanatory power. After examining the theoretical and empirical evidence for IIT, arguments from philosophy of mind and epistemology will be examined. Since IIT is not a form of computational functionalism, it is vulnerable to fading/dancing qualia arguments. Finally, the limitations of the phenomenological approach to studying consciousness are examined, and it will be shown that IIT is a theory of protoconsciousness rather than a theory of consciousness.
Structured data, structured markup and rich snippets have always been about helping Google and search engines understand your content and potentially lead to Google making your search result snippet richer, with the goal of increasing your click-through rate from the organic results to your page.
Google has said, time and time again, that these are not used in the ranking algorithm to make a Web page rank better. They have said that adding structured markup to your pages won’t directly lead to your page ranking better in the Google search results.
Well, that may change in the future.
Google’s John Mueller said in a Google Hangout this morning (at the 21:40 minute mark) that “over time, I think it [structured markup] is something that might go into the rankings as well.”
Allocating attentional resources to currently relevant information in a dynamically changing environment is critical to goal-directed behavior. Previous studies in nonhuman primates (NHPs) have demonstrated modulation of neural representations of stimuli, in particular visual categorizations, by behavioral significance in the lateral prefrontal cortex. In the human brain, a network of frontal and parietal regions, the “multiple demand” (MD) system, is involved in cognitive and attentional control. To test for the effect of behavioral significance on categorical discrimination in the MD system in humans, we adapted a previously used task in the NHP and used multivoxel pattern analysis for fMRI data. In a cued-detection categorization task, participants detected whether an image from one of two target visual categories was present in a display. Our results revealed that categorical discrimination is modulated by behavioral relevance, as measured by the distributed pattern of response across the MD network. Distinctions between categories with different behavioral status (e.g., a target and a nontarget) were significantly discriminated. Category distinctions that were not behaviorally relevant (e.g., between two targets) were not discriminated. Other aspects of the task that were orthogonal to the behavioral decision did not modulate categorical discrimination. In a high visual region, the lateral occipital complex, modulation by behavioral relevance was evident in its posterior subregion but not in the anterior subregion. The results are consistent with the view of the MD system as involved in top-down attentional and cognitive control by selective coding of task-relevant discriminations.
The data from mobile phones is revolutionizing our understanding of human activity. In recent years, it has revealed commuting patterns in major cities, wealth distribution in African countries, and even reproductive strategies in western societies. That has provided unprecedented insight for economists, sociologists, and city planners among others.
But this kind of advanced research is just a first step in a much broader trend. Phone data is set to become a standard resource that almost anyone can use to study and watch humanity continuously, much as they can now watch the weather unfold anywhere on the planet almost in real time.
If you are willing to pay the premium cost of delivery, and potentially miss out on some in-store specials, online grocery shopping may be a viable option for South Africans.
Two of the country’s major food retailers – Pick n Pay and Woolworths – offer online shopping services and deliveries.
While both groups have reported that online purchases account for a small portion of revenue approximately 1% to date, there has been an increase in consumers perusing the digital platforms to do their grocery shopping.
Semi-supervised clustering algorithms are increasingly employed for discovering hidden structure in data with partially labelled patterns. In order to make the clustering approach useful and acceptable to users, the information provided must be simple, natural and limited in number. To improve recognition capability, we apply an effective feature enhancement procedure to the entire data-set to obtain a single set of features or weights by weighting and discriminating the information provided by the user. By taking pairwise constraints into account, we propose a semi-supervised fuzzy clustering algorithm with feature discrimination (SFFD) incorporating a fully adaptive distance function. Experiments on several standard benchmark data sets demonstrate the effectiveness of the proposed method.
A fundamental challenge common to studies of animal movement, behavior, and ecology is the collection of high-quality datasets on spatial positions of animals as they change through space and time. Recent innovations in tracking technology have allowed researchers to collect large and highly accurate datasets on animal spatiotemporal position while vastly decreasing the time and cost of collecting such data. One technique that is of particular relevance to the study of behavioral ecology involves tracking visual tags that can be uniquely identified in separate images or movie frames. These tags can be located within images that are visually complex, making them particularly well suited for longitudinal studies of animal behavior and movement in naturalistic environments. While several software packages have been developed that use computer vision to identify visual tags, these software packages are either (a) not optimized for identification of single tags, which is generally of the most interest for biologists, or (b) suffer from licensing issues, and therefore their use in the study of animal behavior has been limited. Here, we present BEEtag, an open-source, image-based tracking system in Matlab that allows for unique identification of individual animals or anatomical markers. The primary advantages of this system are that it (a) independently identifies animals or marked points in each frame of a video, limiting error propagation, (b) performs well in images with complex backgrounds, and (c) is low-cost. To validate the use of this tracking system in animal behavior, we mark and track individual bumblebees (Bombus impatiens) and recover individual patterns of space use and activity within the nest. Finally, we discuss the advantages and limitations of this software package and its application to the study of animal movement, behavior, and ecology.
GNAWING ON HIS left index finger with his chipped old British teeth, temporal veins bulging and brow pensively squinched beneath the day-before-yesterday’s hair, the mathematician John Horton Conway unapologetically whiles away his hours tinkering and thinkering—which is to say he’s ruminating, although he will insist he’s doing nothing, being lazy, playing games.
In the selfish herd hypothesis, prey animals move toward each other to avoid the likelihood of being selected by a predator. However, many grouped animals move away from each other the moment before a predator attacks. Very little is known about this phenomenon, called flash expansion, such as whether it is triggered by one individual or a threshold and how information is transferred between group members. We performed a controlled experiment with whirligig beetles in which the ratio of sighted to unsighted individuals was systematically varied and emergent flash expansion was measured. Specifically, we examined: the percentage of individuals in a group that startled, the resulting group area, and the longevity of the flash expansion. We found that one or two sighted beetles in a group of 24 was not enough to cause a flash expansion after a predator stimulus, but four sighted beetles usually initiated a flash expansion. Also, the more beetles that were sighted the larger the resulting group area and the longer duration of the flash expansion. We conclude that flash expansion is best described as a threshold event whose adaptive value is to prevent energetically costly false alarms while quickly mobilizing an emergent predator avoidance response. This is one of the first controlled experiments of flash expansion, an important emergent property that has applications to understanding collective motion in swarms, schools, flocks, and human crowds. Also, our study is a convincing demonstration of social contagion, how the actions of one individual can pass through a group.
The organization in brain networks shows highly modular features with weak inter-modular interaction. The topology of the networks involves emergence of modules and sub-modules at different levels of constitution governed by fractal laws. The modular organization, in terms of modular mass, inter-modular, and intra-modular interaction, also obeys fractal nature. The parameters which characterize topological properties of brain networks follow one parameter scaling theory in all levels of network structure which reveals the self-similar rules governing the network structure. The calculated fractal dimensions of brain networks of different species are found to decrease when one goes from lower to higher level species which implicates the more ordered and self-organized topography at higher level species. The sparsely distributed hubs in brain networks may be most influencing nodes but their absence may not cause network breakdown, and centrality parameters characterizing them also follow one parameter scaling law indicating self-similar roles of these hubs at different levels of organization in brain networks.
GE Appliance’s global co-creation community, is launching a challenge that paves the way for a future where home appliances will have apps and app stores. The challenge illustrates how the Internet of Things (IoT) is changing the home. FirstBuild is leading the way to define an open industry standard enabling continuous updates to the most interactive part of a home appliance – the control panel.
EyeEm, the social photo-sharing community and marketplace, has unveiled a clever new technology that lets photographers upload their images and run them through an algorithm for instant keywording and aesthetic ranking.
The tool, called EyeVision, is a sweeping update to EyeEm’s aesthetic algorithm and is packaged with a new Web uploader that is being made available today to the company’s Market members — photographers who sell their photos through the service.
Debuting at the 2015 EyeEm Festival & Awards in New York City, “EyeVision is the computer vision framework for identifying great photography and for understanding what a photo contains and depicts,” EyeEm’s CTO Ramzi Rizk told TNW. “It’s not just the compositional aspects of the photo but emotions and abstract concepts represented in the imagery.”
Computational linguistics has dramatically changed the way researchers study and understand language. The ability to number-crunch huge amounts of words for the first time has led to entirely new ways of thinking about words and their relationship to one another.
This number-crunching shows exactly how often a word appears close to other words, an important factor in how they are used. So the word Olympics might appear close to words like running, jumping, and throwing but less often next to words like electron or stegosaurus. This set of relationships can be thought of as a multidimensional vector that describes how the word Olympics is used within a language, which itself can be thought of as a vector space.
And therein lies this massive change. This new approach allows languages to be treated like vector spaces with precise mathematical properties. Now the study of language is becoming a problem of vector space mathematics.
Today, Timothy Baldwin at the University of Melbourne in Australia and a few pals explore one of the curious mathematical properties of this vector space: that adding and subtracting vectors produces another vector in the same space.
The question they address is this: what do these composite vectors mean? And in exploring this question they find that the difference between vectors is a powerful tool for studying language and the relationship between words.
Social network analysis (SNA) has become a widespread tool for the study of animal social organisation. However despite this broad applicability, SNA is currently limited by both an overly strong focus on pattern analysis as well as a lack of dynamic interaction models. Here, we use a dynamic modelling approach that can capture the responses of social networks to changing environments. Using the guppy, Poecilia reticulata, we identified the general properties of the social dynamics underlying fish social networks and found that they are highly robust to differences in population density and habitat changes. Movement simulations showed that this robustness could buffer changes in transmission processes over a surprisingly large density range. These simulation results suggest that the ability of social systems to self-stabilise could have important implications for the spread of infectious diseases and information. In contrast to habitat manipulations, social manipulations (e.g. change of sex ratios) produced strong, but short-lived, changes in network dynamics. Lastly, we discuss how the evolution of the observed social dynamics might be linked to predator attack strategies. We argue that guppy social networks are an emergent property of social dynamics resulting from predator–prey co-evolution. Our study highlights the need to develop dynamic models of social networks in connection with an evolutionary framework.
Cloudera, a company that helped popularize Hadoop as a platform for analyzing huge amounts of data when it was founded in 2008, is overhauling its core technology. The One Platform Initiative the company announced Wednesday lays out Cloudera’s plan to officially replace MapReduce with Apache Spark as the default processing engine for Hadoop.
Cloudera chief technologist Eli Collins said the company is “at best” halfway through the process from a technology standpoint and should be done in about a year. When complete, Spark should have similar levels of security, manageability, and scalability as MapReduce, and should be equally integrated with the rest of the technologies that comprise the ever-expanding Hadoop platform.
A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body “pose” of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics.
In the process, Google will challenge start-up Instacart Inc. and Amazon, and the latter, will take on established firms like GrubHub Inc. as well as newer rivals such as Uber, which has expanded its own meal delivery service in cities like San Francisco and Austin.
Interestingly, both groceries and restaurant delivery (called food tech in Indian venture capital lingo) are crowded and popular businesses in India, with a clutch of start-ups competing for business.
Google will start testing a delivery service for fresh food and groceries in two US cities later this year.
Currently, users and consumers can review and rate products through online services, which provide huge databases that can be used to explore people’s preferences and unveil behavioral patterns. In this work, we investigate patterns in movie ratings, considering IMDb (the Internet Movie Database), a highly visited site worldwide, as a source. We find that the distribution of votes presents scale-free behavior over several orders of magnitude, with an exponent very close to 3/2, with exponential cutoff. It is remarkable that this pattern emerges independently of movie attributes such as average rating, age and genre, with the exception of a few genres and of high-budget films. These results point to a very general underlying mechanism for the propagation of adoption across potential audiences that is independent of the intrinsic features of a movie and that can be understood through a simple spreading model with mean-field avalanche dynamics.
A two-player quantum game is considered in the presence of thermal decoherence. It is shown how the thermal environment modeled in terms of rigorous Davies approach affects payoffs of the players. The conditions for either beneficial or pernicious effect of decoherence are identified. The general considerations are exemplified by the quantum version of Prisoner Dilemma.
Large quantities of data have been generated from multiple sources at exponential rates in the last few years. These data are generated at high velocity as real time and streaming data in variety of formats. These characteristics give rise to challenges in its modeling, computation, and processing. Hadoop MapReduce (MR) is a well known data-intensive distributed processing framework using the distributed file system (DFS) for Big Data. Current implementations of MR only support execution of a single algorithm in the entire Hadoop cluster. In this paper, we propose MapReducePack (MRPack), a variation of MR that supports execution of a set of related algorithms in a single MR job. We exploit the computational capability of a cluster by increasing the compute-intensiveness of MapReduce while maintaining its data-intensive approach. It uses the available computing resources by dynamically managing the task assignment and intermediate data. Intermediate data from multiple algorithms are managed using multi-key and skew mitigation strategies. The performance study of the proposed system shows that it is time, I/O, and memory efficient compared to the default MapReduce. The proposed approach reduces the execution time by 200% with an approximate 50% decrease in I/O cost. Complexity and qualitative results analysis shows significant performance improvement.
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