Complex behaviour in many systems arises from the stochastic interactions of spatially distributed particles or agents. Stochastic reaction-diffusion processes are widely used to model such behaviour in disciplines ranging from biology to the social sciences, yet they are notoriously difficult to simulate and calibrate to observational data. Here we use ideas from statistical physics and machine learning to provide a solution to the inverse problem of learning a stochastic reactiondiffusion process from data. Our solution relies on a nontrivial connection between stochastic reaction-diffusion processes and spatiotemporal Cox processes, a well-studied class of models from computational statistics. This connection leads to an efficient and flexible algorithm for parameter inference and model selection. Our approach shows excellent accuracy on numeric and real data examples from systems biology and epidemiology. Our work provides both insights into spatiotemporal stochastic systems, and a practical solution to a longstanding problem in computational modelling. Many complex behaviours in several disciplines originate from a common mechanism: the dynamics of locally interacting, spatially distributed agents. Examples arise at all spatial scales and in a wide range of scientific fields, from microscopic interactions of low-abundance molecules within cells, to ecological and epidemic phenomena at the continental scale. Frequently, stochasticity and spatial heterogeneity play a crucial role in determining the process dynamics and the emergence of collective behaviour [1]-[8]. Stochastic reaction-diffusion processes (SRDPs) constitute a convenient mathematical framework to model such systems. SRDPs were originally introduced in statistical physics [10, 11] to describe the collective behaviour of populations of point-wise agents performing Brownian diffusion in space and stochastically interacting with other, nearby agents according to predefined rules. The flexibility afforded by the local interaction rules has led to a wide application of SRDPs in many different scientific disciplines where complex spatiotemporal behaviours arise, from molecular biology [4, 9, 12], to ecology [13], to the social sciences [14]. Despite their popularity, SRDPs pose considerable challenges, as analytical computations are only possible for a handful of systems [8].

Quantum-enhanced metrology aims to estimate an unknown parameter such that the precision scales better than the shot-noise bound. Single-shot adaptive quantum-enhanced metrology (AQEM) is a promising approach that uses feedback to tweak the quantum process according to previous measurement outcomes. Techniques and formalism for the adaptive case are quite different from the usual non-adaptive quantum metrology approach due to the causal relationship between measurements and outcomes. We construct a formal framework for AQEM by modeling the procedure as a decision-making process, and we derive the imprecision and the Cram\'{e}r-Rao lower bound with explicit dependence on the feedback policy. We also explain the reinforcement learning approach for generating quantum control policies, which is adopted due to the optimal policy being non-trivial to devise. Applying a learning algorithm based on differential evolution enables us to attain imprecision for adaptive interferometric phase estimation, which turns out to be SQL when non-entangled particles are used in the scheme.

One of my regular news sources surfaced an article about how IBM's Watson (discussed as part of my Smart Algorithms post) where AI Saves Woman's Life By Identifying Her Disease When Other Methods (humans) Failed. In just 10 minutes the AI (machine learning variety) compared the woman's genetic information with 20 million clinical oncology studies. A human doctor couldn't do that much in a lifetime let alone the short periods doctors get with patients in modern'medicine.' This is where computer algorithms have an edge over humans. The machine learning algorithm needs training, which is currently human guided, but once trained is so much faster at the tasks trained for than any human ever could be.

Germany's Interior Minister says he wants to introduce facial recognition software at train stations and airports to help identify suspects following two attacks in the country last month. In a report published on Sunday in the German newspaper Bild am Sonntag, Thomas de Maiziere said internet software was able to determine whether persons shown in photographs were celebrities or politicians. "I would like to use this kind of facial recognition technology in video cameras at airports and train stations. Then, if a suspect appears and is recognised, it will show up in the system," he told the paper. Germany's Thomas de Maiziere takes aim at face veils He said a similar system was already being tested for unattended luggage, which the camera reports after a certain number of minutes.

DNA Database Offers Disease Insights A giant DNA database that pulled data from over 60,000 people from diverse parts of the world is helping scientists pinpoint the causes of disease. Analyzing the huge amount of DNA allowed an international team of researchers to newly identify 3,000 genes that may cause disease. They were also able to conclude that 160 genetic mutations previously thought to be connected to disease are in fact harmless. The researchers focused on a number of different diseases ranging from muscular dystrophy and cystic fibrosis to some types of heart disease. Self-Driving Uber Fleet Arriving in Pittsburgh This month, the ride-sharing company Uber will unleash a new fleet of self-driving cars in Pittsburgh, Pennsylvania.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Concrete is one of the most prevalent materials in our built environment, but it's hasn't seen much innovation. That's why it's so exciting that a team of Singapore researchers developed a new flexible concrete that's lighter and tougher than existing mixtures. In other design news, a developer in Germany is building the world's largest passive housing complex with a total of 162 units. Stanford researchers created a tiny black rectangle that uses sunlight to purify water in minutes instead of hours. In a stunning example of biomimicry this week, scientists studied squids to invent a high-tech fabric that repair itself and neutralizes toxins.

One of the speakers was Perry Timms, Founder & Director of PTHR. In this blog post, Perry explains about what robotics, automation, machine learning and AI means for the world of work, jobs and human endeavours. There is a certain technophobia present in HR. Often HR professionals do not get the technology that's being used. Being tech savvy nowadays means more than having a self-service cloud-based HR system.

Visualise and explore the work of 44,579 superstar Researchers from 8,437 Research Institutions in 118 countries on every sport in the Olympics 2016 Research Dashboard by wizdom.ai, the world's largest research knowledge graph powered by big data analytics, machine learning and artificial intelligence. With all eyes set on the Rio Olympics 2016, the world witnesses the greatest display of human strength, endurance, dexterity and performance by participants from across the globe. Everyday over the course of the two weeks, over 11,400 athletes compete for the gold medal in their game. They have undertaken intensive training for months and years to reach the epitome of physical fitness and to optimise their performance, making every millisecond count. Backing the Olympians that make it to the podium in every field, all along through their training there are thousands of researchers around the world who have extensively studied the games to raise the bar for human performance.

A day in the life of a Star Citizen!! My Artificial Intelligence and I grow a special bond while we are away on a special retrieval mission. JavaScript is currently disabled, this site works much better if youenable JavaScript in your browser. An exhibition dedicated to art stars Andy Warhol and Ai Weiwei guarantees a visual and conceptual feast. But two other qualities make "Andy Warhol/ Ai Weiwei" one of the finest exhibitions The Andy Warhol Museum has mounted in its illustrious 22 years.