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9 Best R Programming Certifications, Courses & Training

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Are you looking for the Best R Programming Certification Online? Here is the handpicked list of Best R Programming Course & Training to assist you to become an expert in programming in R. Before you start doing these courses we have included an article How to Start Programming in R? Go through this article you will get a brief idea about where and how to start learning r? Find out how attractive the r programming jobs are? Description: Learn R will help you gain expertise in R Programming, Data Manipulation, Exploratory Data Analysis, Data Visualization, Data Mining, Regression, Sentiment Analysis and using R Studio for real-life case studies on Retail, Social Media. "R" wins on Statistical Capability, Graphical capability, Cost, a rich set of packages and is the most preferred tool for Data Scientists. Description: Neurohacking describes how to use the R programming language and its associated package to perform manipulation, processing, and analysis of neuroimaging data.


Detecting Dynamic Community Structure in Functional Brain Networks Across Individuals: A Multilayer Apporach

arXiv.org Machine Learning

We present a unified statistical framework for characterizing community structure of brain functional networks that captures variation across individuals and evolution over time. Existing methods for community detection focus only on single-subject analysis of dynamic networks; while recent extensions to multiple-subjects analysis are limited to static networks. To overcome these limitations, we propose a multi-subject, Markov-switching stochastic block model (MSS-SBM) to identify state-related changes in brain community organization over a group of individuals. We first formulate a multilayer extension of SBM to describe the time-dependent, multi-subject brain networks. We develop a novel procedure for fitting the multilayer SBM that builds on multislice modularity maximization which can uncover a common community partition of all layers (subjects) simultaneously. By augmenting with a dynamic Markov switching process, our proposed method is able to capture a set of distinct, recurring temporal states with respect to inter-community interactions over subjects and the change points between them. Simulation shows accurate community recovery and tracking of dynamic community regimes over multilayer networks by the MSS-SBM. Application to task fMRI reveals meaningful non-assortative brain community motifs, e.g., core-periphery structure at the group level, that are associated with language comprehension and motor functions suggesting their putative role in complex information integration. Our approach detected dynamic reconfiguration of modular connectivity elicited by varying task demands and identified unique profiles of intra and inter-community connectivity across different task conditions. The proposed multilayer network representation provides a principled way of detecting synchronous, dynamic modularity in brain networks across subjects.


Interactions in information spread: quantification and interpretation using stochastic block models

arXiv.org Machine Learning

In most real-world applications, it is seldom the case that a given observable evolves independently of its environment. In social networks, users' behavior results from the people they interact with, news in their feed, or trending topics. In natural language, the meaning of phrases emerges from the combination of words. In general medicine, a diagnosis is established on the basis of the interaction of symptoms. Here, we propose a new model, the Interactive Mixed Membership Stochastic Block Model (IMMSBM), which investigates the role of interactions between entities (hashtags, words, memes, etc.) and quantifies their importance within the aforementioned corpora. We find that interactions play an important role in those corpora. In inference tasks, taking them into account leads to average relative changes with respect to non-interactive models of up to 150\% in the probability of an outcome. Furthermore, their role greatly improves the predictive power of the model. Our findings suggest that neglecting interactions when modeling real-world phenomena might lead to incorrect conclusions being drawn.


CoCoPIE: Making Mobile AI Sweet As PIE --Compression-Compilation Co-Design Goes a Long Way

arXiv.org Machine Learning

Assuming hardware is the major constraint for enabling real-time mobile intelligence, the industry has mainly dedicated their efforts to developing specialized hardware accelerators for machine learning and inference. This article challenges the assumption. By drawing on a recent real-time AI optimization framework CoCoPIE, it maintains that with effective compression-compiler co-design, it is possible to enable real-time artificial intelligence on mainstream end devices without special hardware. CoCoPIE is a software framework that holds numerous records on mobile AI: the first framework that supports all main kinds of DNNs, from CNNs to RNNs, transformer, language models, and so on; the fastest DNN pruning and acceleration framework, up to 180X faster compared with current DNN pruning on other frameworks such as TensorFlow-Lite; making many representative AI applications able to run in real-time on off-the-shelf mobile devices that have been previously regarded possible only with special hardware support; making off-the-shelf mobile devices outperform a number of representative ASIC and FPGA solutions in terms of energy efficiency and/or performance.


Health State Estimation

arXiv.org Artificial Intelligence

Life's most valuable asset is health. Continuously understanding the state of our health and modeling how it evolves is essential if we wish to improve it. Given the opportunity that people live with more data about their life today than any other time in history, the challenge rests in interweaving this data with the growing body of knowledge to compute and model the health state of an individual continually. This dissertation presents an approach to build a personal model and dynamically estimate the health state of an individual by fusing multi-modal data and domain knowledge. The system is stitched together from four essential abstraction elements: 1. the events in our life, 2. the layers of our biological systems (from molecular to an organism), 3. the functional utilities that arise from biological underpinnings, and 4. how we interact with these utilities in the reality of daily life. Connecting these four elements via graph network blocks forms the backbone by which we instantiate a digital twin of an individual. Edges and nodes in this graph structure are then regularly updated with learning techniques as data is continuously digested. Experiments demonstrate the use of dense and heterogeneous real-world data from a variety of personal and environmental sensors to monitor individual cardiovascular health state. State estimation and individual modeling is the fundamental basis to depart from disease-oriented approaches to a total health continuum paradigm. Precision in predicting health requires understanding state trajectory. By encasing this estimation within a navigational approach, a systematic guidance framework can plan actions to transition a current state towards a desired one. This work concludes by presenting this framework of combining the health state and personal graph model to perpetually plan and assist us in living life towards our goals.


10 Breakthrough Technologies 2020

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Here is our annual list of technological advances that we believe will make a real difference in solving important problems. We avoid the one-off tricks, the overhyped new gadgets. Instead we look for those breakthroughs that will truly change how we live and work. We're excited to announce that with this year's list we're also launching our very first editorial podcast, Deep Tech, which will explore the the people, places, and ideas featured in our most ambitious journalism. Later this year, Dutch researchers will complete a quantum internet between Delft and the Hague. An internet based on quantum physics will soon enable inherently secure communication. A team led by Stephanie Wehner, at Delft University of Technology, is building a network connecting four cities in the Netherlands entirely by means of quantum technology.


Network Clustering Via Kernel-ARMA Modeling and the Grassmannian The Brain-Network Case

arXiv.org Machine Learning

Background Network clustering is the task of assigning nodes to groups via user-defined (statistical) "similarities" among nodal time series (signals), and is ubiquitous across a plethora of disciplines such as computer vision [1], wireless-sensor [2], social [3] and brain networks [4]. In brain networks, the choice of scale and type of data determine how networks are built. At the microscopic level, network nodes might be neurons, and edges could represent anatomical connections such as synapses (structural connectivity), or statistical relationships between firing patterns of neurons (functional connectivity). Similarly, at the macroscopic level, nodes can represent brain regions. At this scale, in structural networks, edges might represent long range anatomical connections between brain regions or, in functional networks, statistical relationships between regional brain dynamics recorded via functional Magnetic Resonance Imaging (fMRI) or encephalopathy (EEG). Here, we are interested in functional brain networks in which network nodes represent brain regions whose activity can be represented by a time series describing the dynamic evolution of brain activity.[5];


Tensor denoising and completion based on ordinal observations

arXiv.org Machine Learning

Higher-order tensors arise frequently in applications such as neuroimaging, recommendation system, social network analysis, and psychological studies. We consider the problem of low-rank tensor estimation from possibly incomplete, ordinal-valued observations. Two related problems are studied, one on tensor denoising and another on tensor completion. We propose a multi-linear cumulative link model, develop a rank-constrained M-estimator, and obtain theoretical accuracy guarantees. Our mean squared error bound enjoys a faster convergence rate than previous results, and we show that the proposed estimator is minimax optimal under the class of low-rank models. Furthermore, the procedure developed serves as an efficient completion method which guarantees consistent recovery of an order-$K$ $(d,\ldots,d)$-dimensional low-rank tensor using only $\tilde{\mathcal{O}}(Kd)$ noisy, quantized observations. We demonstrate the outperformance of our approach over previous methods on the tasks of clustering and collaborative filtering.


Industry News

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Find here a listing of the latest industry news in genomics, genetics, precision medicine, and beyond. Updates are provided on a monthly basis. Sign-Up for our newsletter and never miss out on the latest news and updates. As 2019 came to an end, Veritas Genetics struggled to get funding due to concerns it had previously taken money from China. It was forced to cease US operations and is in talks with potential buyers. The GenomeAsia 100K Project announced its pilot phase with hopes to tackle the underrepresentation of non-Europeans in human genetic studies and enable genetic discoveries across Asia. Veritas Genetics, the start-up that can sequence a human genome for less than $600, ceases US operations and is in talks with potential buyers Veritas Genetics ceases US operations but will continue Veritas Europe and Latin America. It had trouble raising funding due to previous China investments and is looking to be acquired. Illumina loses DNA sequencing patents The European Patent ...


Artificial Intelligence for Social Good: A Survey

arXiv.org Artificial Intelligence

Its impact is drastic and real: Youtube's AIdriven recommendation system would present sports videos for days if one happens to watch a live baseball game on the platform [1]; email writing becomes much faster with machine learning (ML) based auto-completion [2]; many businesses have adopted natural language processing based chatbots as part of their customer services [3]. AI has also greatly advanced human capabilities in complex decision-making processes ranging from determining how to allocate security resources to protect airports [4] to games such as poker [5] and Go [6]. All such tangible and stunning progress suggests that an "AI summer" is happening. As some put it, "AI is the new electricity" [7]. Meanwhile, in the past decade, an emerging theme in the AI research community is the so-called "AI for social good" (AI4SG): researchers aim at developing AI methods and tools to address problems at the societal level and improve the wellbeing of the society.