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 VideoLectures.NET


Self-organizing principles in branching morphogenesis

VideoLectures.NET

The morphogenesis of branched organs remains a subject of abiding interest. Although much is known about the underlying signaling pathways, it remains unclear how macro-scopic features of branched organs, including their size, network topology, and spatial patterning, are encoded. Here, we show that, in mouse mammary gland, kidney, and hu-man prostate, these features can be explained quantitatively within a single unifying framework of branching and annihilating random walks. Based on quantitative analyses of large-scale organ reconstructions and proliferation kinetics measurements, we propose that morphogenesis follows from the proliferative activity of equipotent tips that stochas-tically branch and randomly explore their environment but compete neutrally for space, becoming proliferatively inactive when in proximity with neighboring ducts. These results show that complex branched epithelial structures develop as a self-organized process, reliant upon a strikingly simple but generic rule, without recourse to a rigid and determin-istic sequence of genetically programmed events.


Self-organizing principles in branching morphogenesis

VideoLectures.NET

Prof. Dr. Edouard Hannezo, Assistant Professor at the Institute of Science and Technology (IST) Austria, is interested in understanding how cells "know" how to make the right decisions at the right time and at the right place during development and normal tissue homeostasis, as well as how these decisions are dysregulated during cancer initiation.


A robotic avatar for deep-sea exploration

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The promise of oceanic discovery has intrigued scientists and explorers, whether to study underwater ecology and climate change, or to uncover natural resources and historic secrets buried deep at archaeological sites. To meet the challenge of accessing oceanic depths, Stanford University, working with KAUST's Red Sea Research Center and MEKA Robotics, developed Ocean One, a bimanual force-controlled humanoid robot that affords immediate and intuitive haptic interaction in oceanic environments.


Ocean One - A robotic avatar for deep-sea exploration

VideoLectures.NET

The discussion focuses on the development of Ocean One, a bimanual humanoid robotic diver that brings intuitive haptic physical interaction to oceanic environments. The robot was deployed during an expedition in the Mediterranean to Louis XIV's flagship Lune, lying off the coast of Toulon at 91 meters. Ocean One's demonstrated ability to distance humans physically from dangerous and unreachable spaces, while connecting their skills, intuition, and experience to the task, promises to fundamentally alter remote work. Robotic avatars will search for and acquire materials, build infrastructure, and perform disaster-prevention and recovery operations - be it deep in oceans and mines, on mountain tops, or in space.


Optimization of smart grids: Opportunities and directions

VideoLectures.NET

In this talk we will present the various optimization problems encountered in smart grids from the production, transmission and distribution of energy as well as the demand side management in smart homes and the pricing of energy. The optimization opportunities are highlighted for metaheuristics, multi-objective optimization, optimization under uncertainty, optimization-simulation, optimization-machine learning and multi-level optimization.


Analysing Dialogue for Diagnosis and Prediction in Mental Health

VideoLectures.NET

Conditions which affect our mental health often affect the way we use language; and treatment often involves linguistic interaction. This talk will present work on three related projects investigating the use of computational natural language processing (NLP) to help understand and improve diagnosis and treatment for such conditions. We will look at clinical dialogue between patient and doctor or therapist, in cases involving schizophrenia, depression and dementia; in each case, we find that diagnostic information and/or treatment outcomes are related to observable features of a patient's language and interaction with their conversational partner. We discuss the nature of these phenomena and the suitability and accuracy of NLP techniques for detecting them automatically.


Analysing Dialogue for Diagnosis and Prediction in Mental Health

VideoLectures.NET

Conditions which affect our mental health often affect the way we use language; and treatment often involves linguistic interaction. In his talk Dr Matthew Purver from Queen Mary University of London presents work on three related projects investigating the use of computational natural language processing (NLP) to help understand and improve diagnosis and treatment for such conditions.


5th Annual RavenPack Research Symposium: The Big Data & Machine Learning Revolution, New York 2017

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RavenPack's prestigious annual event has experienced growing interest, with attendance exceeding 260 buy-side professionals. Word on the street is RavenPack's research symposium is a "must attend event" for quantitative investors and financial professionals that are serious about Big Data. An excellent set of senior finance professionals shared their latest research and experience with big data and machine learning.


Data Mining in Unusual Domains with Information-rich Knowledge Graph Construction, Inference and Search

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The growth of the Web is a success story that has spurred much research in knowledge discovery and data mining. Data mining over Web domains that are unusual is an even harder problem. There are several factors that make a domain unusual. In particular, such domains have significant long tails and exhibit concept drift, and are characterized by high levels of heterogeneity. Notable examples of unusual Web domains include both illicit domains, such as human trafficking advertising, illegal weapons sales, counterfeit goods transactions, patent trolling and cyberattacks, and also non-illicit domains such as humanitarian and disaster relief.


Making Better Use of the Crowd

VideoLectures.NET

Over the last decade, crowdsourcing has been used to harness the power of human computation to solve tasks that are notoriously difficult to solve with computers alone, such as determining whether or not an image contains a tree, rating the relevance of a website, or verifying the phone number of a business. The machine learning and natural language processing communities were early to embrace crowdsourcing as a tool for quickly and inexpensively obtaining the vast quantities of labeled data needed to train systems. Once this data is collected, it can be handed off to algorithms that learn to make autonomous predictions or actions. Usually this handoff is where interaction with the crowd ends. The crowd provides the data, but the ultimate goal is to eventually take humans out of the loop.