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Controlling Self-Organizing Dynamics on Networks Using Models that Self-Organize

Controlling Self-Organizing Dynamics on Networks Using Models that Self-Organize | Social Foraging | Scoop.it

Controlling self-organizing systems is challenging because the system responds to the controller. Here, we develop a model that captures the essential self-organizing mechanisms of Bak-Tang-Wiesenfeld (BTW) sandpiles on networks, a self-organized critical (SOC) system. This model enables studying a simple control scheme that determines the frequency of cascades and that shapes systemic risk. We show that optimal strategies exist for generic cost functions and that controlling a subcritical system may drive it to criticality. This approach could enable controlling other self-organizing systems.

 

Controlling Self-Organizing Dynamics on Networks Using Models that Self-Organize

Pierre-André Noël, Charles D. Brummitt, and Raissa M. D’Souza

Phys. Rev. Lett. 111, 078701 (2013)

http://dx.doi.org/10.1103/PhysRevLett.111.078701

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Dynamics of Social Interaction
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Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks

Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks | Social Foraging | Scoop.it
Traditional forecasting models fit a function approximation from dependent invariables to independent variables. However, they usually get into trouble when date are presented in various formats, such as text, voice and image. This study proposes a novel image-encoded forecasting method that input and output binary digital two-dimensional (2D) images are transformed from decimal data. Omitting any data analysis or cleansing steps for simplicity, all raw variables were selected and converted to binary digital images as the input of a deep learning model, convolutional neural network (CNN). Using shared weights, pooling and multiple-layer back-propagation techniques, the CNN was adopted to locate the nexus among variations in local binary digital images. Due to the computing capability that was originally developed for binary digital bitmap manipulation, this model has significant potential for forecasting with vast volume of data. The model was validated by a power loads predicting dataset from the Global Energy Forecasting Competition 2012.
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‘Differential Privacy’ Is About Collecting Your Data—But Not ​Your Data

‘Differential Privacy’ Is About Collecting Your Data—But Not ​Your Data | Social Foraging | Scoop.it
At WWDC, Apple name-checked the statistical science of learning as much as possible about a group while learning as little as possible about any individual in it.
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Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences | Social Foraging | Scoop.it
Author Summary From one moment to the next, in an ever-changing world, and awash in a deluge of sensory data, the brain fluidly guides our actions throughout an astonishing variety of tasks. Processing this ongoing bombardment of information is a fundamental problem faced by its underlying neural circuits. Given that the structure of our actions along with the organization of the environment in which they are performed can be intuitively decomposed into sequences of simpler patterns, an encoding strategy reflecting the temporal nature of these patterns should offer an efficient approach for assembling more complex memories and behaviors. We present a model that demonstrates how activity could propagate through recurrent cortical microcircuits as a result of a learning rule based on neurobiologically plausible time courses and dynamics. The model predicts that the interaction between several learning and dynamical processes constitute a compound mnemonic engram that can flexibly generate sequential step-wise increases of activity within neural populations.
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Near-Real Time Sessionization with Spark Streaming and Apache Hadoop

Near-Real Time Sessionization with Spark Streaming and Apache Hadoop | Social Foraging | Scoop.it
This Spark Streaming use case is a great example of how near-real-time processing can be brought to Hadoop.

Spark Streaming is one of the most interesting components within the Apache Spark stack. With Spark Streaming, you can create data pipelines that process streamed data using the same API that you use for processing batch-loaded data. Furthermore, Spark Steaming’s “micro-batching” approach provides decent resiliency should a job fail for some reason.

In this post, I will demonstrate and walk you through some common and advanced Spark Streaming functionality via the use case of doing near-real time sessionization of Website events, then load stats about that activity into Apache HBase, and then populate graphs in your preferred BI tool for analysis. (Sessionization refers to the capture of all clickstream activity within the timeframe of a single visitor’s Website session.) You can find the code for this demo here.

A system like this one can be super-useful for understanding visitor behavior (whether human or machine). With some additional work, it can also be designed to contain windowing patterns for detecting possible fraud in an asynchronous manner.
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Agent-based model of information spread in social networks

We propose evolution rules of the multiagent network and determine statistical patterns in life cycle of agents - information messages. The main discussed statistical pattern is connected with the number of likes and reposts for a message. This distribution corresponds to Weibull distribution according to modeling results. We examine proposed model using the data from Twitter, an online social networking service.
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Analysis of complex neural circuits with nonlinear multidimensional hidden state models

Analysis of complex neural circuits with nonlinear multidimensional hidden state models | Social Foraging | Scoop.it
A universal need in understanding complex networks is the identification of individual information channels and their mutual interactions under different conditions. In neuroscience, our premier example, networks made up of billions of nodes dynamically interact to bring about thought and action. Granger causality is a powerful tool for identifying linear interactions, but handling nonlinear interactions remains an unmet challenge. We present a nonlinear multidimensional hidden state (NMHS) approach that achieves interaction strength analysis and decoding of networks with nonlinear interactions by including latent state variables for each node in the network. We compare NMHS to Granger causality in analyzing neural circuit recordings and simulations, improvised music, and sociodemographic data. We conclude that NMHS significantly extends the scope of analyses of multidimensional, nonlinear networks, notably in coping with the complexity of the brain.
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Gorilla: A fast, scalable, in-memory time series database

Error rates across one of Facebook’s sites were spiking. The problem had first shown up through an automated alert triggered by an in-memory time-series database called Gorilla a few minutes after the problem started. One set of engineers mitigated the immediate issue. A second group set out to find the root cause. They fired up Facebook’s time series correlation engine built on top of Gorilla, and searched for metrics showing a correlation with the errors. This showed that copying a release binary to Facebook’s web servers (a routine event) caused an anomalous drop in memory used across the site…
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Inferring causal impact using Bayesian structural time-series models

Inferring causal impact using Bayesian structural time-series models | Social Foraging | Scoop.it
An important problem in econometrics and marketing is to infer the causal impact that a designed market intervention has exerted on an outcome metric over time. In order to allocate a given budget optimally, for example, an advertiser must assess to what extent different campaigns have contributed to an incremental lift in web searches, product installs, or sales. This paper proposes to infer causal impact on the basis of a diffusion-regression state-space model that predicts the counterfactual market response that would have occurred had no intervention taken place. In contrast to classical difference-in-differences schemes, state-space models make it possible to (i) infer the temporal evolution of attributable impact, (ii) incorporate empirical priors on the parameters in a fully Bayesian treatment, and (iii) flexibly accommodate multiple sources of variation, including the time-varying influence of contemporaneous covariates, i.e., synthetic controls. Using a Markov chain Monte Carlo algorithm for model inversion, we illustrate the statistical properties of our approach on synthetic data. We then demonstrate its practical utility by evaluating the effect of an online advertising campaign on search-related site visits. We discuss the strengths and limitations of state-space models in enabling causal attribution in those settings where a randomised experiment is unavailable. The CausalImpact R package provides an implementation of our approach.
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JavaScript Conquered the Web. Now It’s Taking Over the Desktop

JavaScript Conquered the Web. Now It’s Taking Over the Desktop | Social Foraging | Scoop.it
Electron is a software development platform created by Github that lets developers use JavaScript along with other web technologies like HTML and CSS to create desktop applications that can run on Windows, Macintosh OS X, and Linux. The company released the first full version of Electron yesterday. But some of tech’s biggest names have already put the tool to work to push JavaScript beyond the browser.
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Apache Spark powers live SQL analytics in SnappyData

Apache Spark powers live SQL analytics in SnappyData | Social Foraging | Scoop.it
The team behind Pivotal's GemFire in-memory transactional data store recently unveiled a new database solution powered by GemFire and Apache Spark, called SnappyData.


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SnappyData is another recent example of Spark employed as a component in a larger database solution, with or without other pieces from Apache Hadoop.
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The Untold Story of Magic Leap, the World’s Most Secretive Startup

The Untold Story of Magic Leap, the World’s Most Secretive Startup | Social Foraging | Scoop.it
Virtual reality is posed to become a fundamental technology, and outfits like Magic Leap have an opportunity to become some of the largest companies ever.
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Amazon Kinesis Firehose adds Amazon Elasticsearch Data Ingestion and Enhanced Monitoring Features

Amazon Kinesis Firehose adds Amazon Elasticsearch Data Ingestion and Enhanced Monitoring Features | Social Foraging | Scoop.it
Amazon Kinesis Firehose, the easiest way to load streaming data into AWS, now supports Amazon Elasticsearch Service as a data delivery destination. You can now use Amazon Kinesis Firehose to stream data to your Amazon Elasticsearch domains continuously and in near real time. Amazon Kinesis Firehose automatically scales to match the throughput of your data and handles all the underlying stream management. For more information, see the Amazon Kinesis Firehose website and developer guide.
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Prediction of Cascading Failures in Spatial Networks

Prediction of Cascading Failures in Spatial Networks | Social Foraging | Scoop.it
Cascading overload failures are widely found in large-scale parallel systems and remain a major threat to system reliability; therefore, they are of great concern to maintainers and managers of different systems. Accurate cascading failure prediction can provide useful information to help control networks. However, for a large, gradually growing network with increasing complexity, it is often impractical to explore the behavior of a single node from the perspective of failure propagation. Fortunately, overload failures that propagate through a network exhibit certain spatial-temporal correlations, which allows the study of a group of nodes that share common spatial and temporal characteristics. Therefore, in this study, we seek to predict the failure rates of nodes in a given group using machine-learning methods.

We simulated overload failure propagations in a weighted lattice network that start with a center attack and predicted the failure percentages of different groups of nodes that are separated by a given distance. The experimental results of a feedforward neural network (FNN), a recurrent neural network (RNN) and support vector regression (SVR) all show that these different models can accurately predict the similar behavior of nodes in a given group during cascading overload propagation.
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Nielsen : Consumer Neuroscience Unveils Trailblazing Ad Testing Solution

Nielsen Consumer Neuroscience, the leader in measuring non-conscious responses to deliver consumer insights, today announced the launch of an advertising research solution that will set a new standard for marketers looking to elevate their advertising creative and optimize in-market performance.

Video Ad Explorer, which was shown to predict in-market consumer sales behavior in a ground-breaking study with CBS, integrates the most comprehensive suite of neuroscience technologies. It helps brands unlock consumer insights and unravel the complexities of advertising creative development with unprecedented predictive power.

While individual neuroscience measures provide some level of prediction to in-market sales, Video Ad Explorer employs unique analyses using a rich combination of neuroscience tools for the highest level of prediction on a global scale. With analysis and feedback on a second by second basis, the results and insights can help optimize ideas and turn good advertising into great advertising.

By evaluating creative with measures from electroencephalography (EEG), core biometrics (which includes skin conductance response and heart rate), facial coding, eye tracking and self-report, brands can access their unique, complementary insights into the complexity of the consumer brain. The integrated use of these tools improves the ability of ad creative to drive-in-market success.

"Over the years, brands have had to settle for incomplete tools and processes for understanding creative development, but Video Ad Explorer changes that," said Dr. Carl Marci, Chief Neuroscientist for Nielsen Consumer Neuroscience. "By integrating these tools, we're providing brand teams with a full picture of their consumers' thinking and emotional response that will create greater confidence and understanding about how their creative will perform."
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Jelel Ezzine's curator insight, June 17, 4:05 AM
non=conscious responses.
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Could a neuroscientist understand a microprocessor?

There is a popular belief in neuroscience that we are primarily data limited, that producing large, multimodal, and complex datasets will, enabled by data analysis algorithms, lead to fundamental insights into the way the brain processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. Here we take a simulated classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the processor. This suggests that current approaches in neuroscience may fall short of producing meaningful models of the brain.

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A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of In Vivo Activity

A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of  In Vivo  Activity | Social Foraging | Scoop.it
The prefrontal cortex is centrally involved in a wide range of cognitive functions and their impairment in psychiatric disorders. Yet, the computational principles that govern the dynamics of prefrontal neural networks, and link their physiological, biochemical and anatomical properties to cognitive functions, are not well understood. Computational models can help to bridge the gap between these different levels of description, provided they are sufficiently constrained by experimental data and capable of predicting key properties of the intact cortex. Here, we present a detailed network model of the prefrontal cortex, based on a simple computationally efficient single neuron model (simpAdEx), with all parameters derived from in vitro electrophysiological and anatomical data. Without additional tuning, this model could be shown to quantitatively reproduce a wide range of measures from in vivo electrophysiological recordings, to a degree where simulated and experimentally observed activities were statistically indistinguishable. These measures include spike train statistics, membrane potential fluctuations, local field potentials, and the transmission of transient stimulus information across layers. We further demonstrate that model predictions are robust against moderate changes in key parameters, and that synaptic heterogeneity is a crucial ingredient to the quantitative reproduction of in vivo-like electrophysiological behavior. Thus, we have produced a physiologically highly valid, in a quantitative sense, yet computationally efficient PFC network model, which helped to identify key properties underlying spike time dynamics as observed in vivo, and can be harvested for in-depth investigation of the links between physiology and cognition.
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Mixtape Application: Last.fm Data Characterization

This report analyses data collected from Last.fm and used to create a real-time recommendation system. We collected over 2M songs and 1M tags and 372K user's listening habits. We characterize users' profiles: age, playcount, friends, gender and country. We characterized song, artist and tag popularity, genres of songs. Additionally we evaluated the co-occurrence of songs in users' histories, which can be used to compute similarity between songs.
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How the hidden mathematics of living cells could help us decipher the brain

How the hidden mathematics of living cells could help us decipher the brain | Social Foraging | Scoop.it
Given how much they can actually do, computers have a surprisingly simple basis. Indeed, the logic they use has worked so well that we have even started to think of them as analogous to the human brain. Current computers basically use two basic values – 0 (false) and 1 (true) – and apply simple operations like “and”, “or” and “not” to compute with them. These operations can be combined and scaled up to represent virtually any computation.

This “binary "or "Boolean” logic was introduced by George Boole in 1854 to describe what he called “the laws of thought”. But the brain is far from a binary logic device. And while programmes such as the Human Brain Project seek to model the brain using computers, the notion of what computers are is also constantly changing.
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Building artificial intelligence models with Internet-of-Things data

Building artificial intelligence models with Internet-of-Things data | Social Foraging | Scoop.it
You might ask what the difference is between most artificial intelligence (AI) companies and SparkCognition. Here it is: while at other firms, humans build models; SparkCognition puts them together with algorithms. Rather than roughing out one model and then doing a bunch of testing, SparkCognition continually tests and fits models to data accumulating in real time, an architecture that allows it to deal with big data.
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CausalImpact: An R package for causal inference in time series

The CausalImpact R package implements an approach to estimating the causal effect of a designed intervention on a time series. For example, how many additional daily clicks were generated by an advertising campaign? Answering a question like this can be difficult when a randomized experiment is not available. The package aims to address this difficulty using a structural Bayesian time-series model to estimate how the response metric might have evolved after the intervention if the intervention had not occurred.
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Amazon’s Giving Away the AI Behind Its Product Recommendations

Amazon’s Giving Away the AI Behind Its Product Recommendations | Social Foraging | Scoop.it
Amazon has become the latest tech giant that's giving away some of its most sophisticated technology.
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Deep Neural Networks as a Computational Model for Human Shape Sensitivity

Deep Neural Networks as a Computational Model for Human Shape Sensitivity | Social Foraging | Scoop.it
Author Summary Shape plays an important role in object recognition. Despite years of research, no models of vision could account for shape understanding as found in human vision of natural images. Given recent successes of deep neural networks (DNNs) in object recognition, we hypothesized that DNNs might in fact learn to capture perceptually salient shape dimensions. Using a variety of stimulus sets, we demonstrate here that the output layers of several DNNs develop representations that relate closely to human perceptual shape judgments. Surprisingly, such sensitivity to shape develops in these models even though they were never explicitly trained for shape processing. Moreover, we show that these models also represent categorical object similarity that follows human semantic judgments, albeit to a lesser extent. Taken together, our results bring forward the exciting idea that DNNs capture not only objective dimensions of stimuli, such as their category, but also their subjective, or perceptual, aspects, such as shape and semantic similarity as judged by humans.
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A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data

A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data | Social Foraging | Scoop.it
Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life science and medical domain. Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets. These shortcomings are addressed in this study, where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains. By publishing the source code and the datasets, this paper aims to be a new well-funded basis for unsupervised anomaly detection research. Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time. Besides the anomaly detection performance, computational effort, the impact of parameter settings as well as the global/local anomaly detection behavior is outlined. As a conclusion, we give an advise on algorithm selection for typical real-world tasks.
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Lenovo is using machine learning to analyse unstructured data from YouTube and Instagram

Lenovo is using machine learning to analyse unstructured data from YouTube and Instagram | Social Foraging | Scoop.it
The world's biggest PC maker Lenovo is analysing unstructured data from social channels including YouTube and Instagram to help the firm build products with customers' feedback in mind.

Mohammed Chaara, director of the Customer Insight Center of Excellence, Strategy & Analytics at Lenovo, explained that four years ago the firm had been an engineering and product-focused company.
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SA neuroscientists launch world’s first NeuroWine

SA neuroscientists launch world’s first NeuroWine | Social Foraging | Scoop.it
A South African team of neuromarketers and neuroscientists have announced the launch of the world’s first ever NeuroWine, a wine that was developed by taking the tools and technologies that are traditionally used in neuroscience and applying them to the art of wine-making.

Neural Sense, a local neuromarketing consultancy, partnered with Pieter Walser, a Cape winemaker from the BLANKBottle label, and using neuroscience and biometric technologies, tested 21 different white wine and 20 different red wine varietals from a number of different vineyards across the country. They assessed Walser’s emotional and cognitive responses to each taste testing experience to create the world’s first NeuroWine (one bottle of red and one white).

Dr David Rosenstein, from Neural Sense, explains. “One of the pieces of technology we used – known as electroencephalography or EEG – is a device which fits around the head and picks up the electrical activity on the surface of one’s scalp. It looks at how the brain is functioning and the associated brain waves, which in turn tells us various things about brain activity.
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