SINGAPORE – A boom in consumer drone sales has spawned a counter-industry of startups aiming to stop drones flying where they shouldn't, by disabling them or knocking them out of the sky. Dozens of startup firms are developing techniques -- from deploying birds of prey to firing gas through a bazooka -- to take on unmanned aerial vehicles (UAVs) that are being used to smuggle drugs, drop bombs, spy on enemy lines or buzz public spaces. The arms race is fed in part by the slow pace of government regulation for drones. In Australia, for example, different agencies regulate drones and counter-drone technologies. "There are potential privacy issues in operating remotely piloted aircraft, but the Civil Aviation Safety Authority's role is restricted to safety. Privacy is not in our remit," a CASA official said.

Imagine a day when, from the comfort of your home and with a few mouse clicks, you can have a Michelin-starred chef's recipe prepared for you right there and then -- by a robotic kitchen. Not only would the robot cook up a scrumptious dinner, but it would also clean up after itself, leaving you with nothing to do besides eat. That dream is soon set to become reality thanks to a London-based company called Moley Robotics. Founded by Russian-born Mark Oleynik (who is CEO), the robo-chef will be launched on the market in 2018. It already exists in prototype form, with dozens of recipes in its library. It looks much like any other kitchen -- with a hob, a sink, an oven and hanging kitchen utensils -- only it also has two giant robotic arms with five-fingered hands that do all the work. In preparation for an upcoming issue of Frontier Tech Investor, we visited Oleynik in his lab. I thought I'd share a few insights from that conversation with you today. We were lucky enough to witness the robo-chef in action as it made BBC MasterChef winner Tim Anderson's crab bisque.

The Internet of Things (IoT) is touching every technology sector around the world, and it's having a significant impact on how enterprises and consumers interact with machines and devices. TechRepublic talked to IoT experts in a range of disciplines to find out what they think the biggest trends will be in 2017. Participants were Kevin Curran, IEEE senior member and senior lecturer in computer science at Ulster University; Francesco Cetraro, head of registrations, .cloud; Artificial intelligence, augmented reality, virtual reality, healthcare IoT, industrial IoT, and wearables are some of the topics of conversation about where the Internet of Things is headed in 2017. Diabetics have been waiting for years for better technology to manage their condition. Some got tired of waiting and hacked together an open source hardware and software solution.

More cities are adding smart city features so that Internet of Things (IoT) sensors and other connected technologies can improve the lives of citizens and visitors. As everyone knows, technology moves fast and finding out what's in store next is crucial to stay in the game. Diabetics have been waiting for years for better technology to manage their condition. Some got tired of waiting and hacked together an open source hardware and software solution. The concept of a smart city has been around for more than a decade, but it was only recently that the phrase "smart city" became part of the modern lexicon.

This is the 2-part blog version of a talk I've given at DOAG Conference this week. I've also uploaded the slides (no ppt; just pretty R presentation;-)) to the articles section, but if you'd like a little text I'm encouraging you to read on. That is, if you're in the target group for this post/talk. For this post, let me assume you're a SQL girl (or guy). With SQL you're comfortable (an expert, probably), you know how to get and manipulate your data, no nesting of subselects has you scared;-).

BEIRUT – An air raid has struck several cars in northwestern Syria, killing at least eight people, including al-Qaida-linked fighters and a senior commander with a Chinese Islamic militant faction, an activist group and a local jihadi commander said Monday. The attack occurred late Sunday on a road leading from the town of Sarmada to the Bab al-Hawa area on the border with Turkey, said the Britain-based Syrian Observatory for Human Rights and a local commander with the Fatah al-Sham Front, an al-Qaida-linked group. The militant spoke via text messages on condition of anonymity because of security concerns. It was not immediately clear who was behind the attack, but the Observatory's chief Rami Abdurrahman said it is widely believed to have been carried out by the U.S.-led coalition. The U.S. has killed some of al-Qaida's most senior commanders in Syria over the past two years in airstrikes.

Signed networks allow to model positive and negative relationships. We analyze existing extensions of spectral clustering to signed networks. It turns out that existing approaches do not recover the ground truth clustering in several situations where either the positive or the negative network structures contain no noise. Our analysis shows that these problems arise as existing approaches take some form of arithmetic mean of the Laplacians of the positive and negative part. As a solution we propose to use the geometric mean of the Laplacians of positive and negative part and show that it outperforms the existing approaches. While the geometric mean of matrices is computationally expensive, we show that eigenvectors of the geometric mean can be computed efficiently, leading to a numerical scheme for sparse matrices which is of independent interest.

In spite of the primal importance of discovering causal relations in science, the statistical analysis of empirical data has historically shied away from causality . Only releatively recently has a rigorous theory of causality emerged (see, for instance, [ 1, 2 ]), showing that empirical data indeed can contain information about causation rather than mere correlation. Since then, causal inference has quickly become influential. Examples range from applications to the inference of genetic [ 3] and social networks [ 4], to a better understanding of the role of causality within quantum physics [ 5-13]. T o formalize causal mechanisms it has become popular to use directed acyclic graphs (DAGs) where nodes denote random variables and directed edges (arrows) account for their causal relations. Central problems within this context include inferenceor model selection: 'Given samples from a number of observable variables, which DAG should we associate with them?', as well as hypothesis testing: 'Can the observed data be explained in terms of an assumed DAG?' Here, we concentrate on the latter problem and propose a novel solution based on the covariances that a given causal structure gives rise to.

The monitoring of sleep patterns without patient's inconvenience or involvement of a medical specialist is a clinical question of significant importance. To this end, we propose an automatic sleep stage monitoring system based on an affordable, unobtrusive, discreet, and long-term wearable in-ear sensor for recording the Electroencephalogram (ear-EEG). The selected features for sleep pattern classification from a single ear-EEG channel include the spectral edge frequency (SEF) and multi- scale fuzzy entropy (MSFE), a structural complexity feature. In this preliminary study, the manually scored hypnograms from simultaneous scalp-EEG and ear-EEG recordings of four subjects are used as labels for two analysis scenarios: 1) classification of ear-EEG hypnogram labels from ear-EEG recordings and 2) prediction of scalp-EEG hypnogram labels from ear-EEG recordings. We consider both 2-class and 4-class sleep scoring, with the achieved accuracies ranging from 78.5 % to 95.2 % for ear-EEG labels predicted from ear-EEG, and 76.8 % to 91.8 % for scalp-EEG labels predicted from ear-EEG. The corresponding kappa coefficients, which range from 0.64 to 0.83 for Scenario 1 and from 0.65 to 0.80 for Scenario 2, indicate a Substantial to Almost Perfect agreement, thus proving the feasibility of in-ear sensing for sleep monitoring in the community.

Personalized treatment of patients based on tissue-specific cancer subtypes has strongly increased the efficacy of the chosen therapies. Even though the amount of data measured for cancer patients has increased over the last years, most cancer subtypes are still diagnosed based on individual data sources (e.g. gene expression data). We propose an unsupervised data integration method based on kernel principal component analysis. Principal component analysis is one of the most widely used techniques in data analysis. Unfortunately, the straight-forward multiple-kernel extension of this method leads to the use of only one of the input matrices, which does not fit the goal of gaining information from all data sources. Therefore, we present a scoring function to determine the impact of each input matrix. The approach enables visualizing the integrated data and subsequent clustering for cancer subtype identification. Due to the nature of the method, no free parameters have to be set. We apply the methodology to five different cancer data sets and demonstrate its advantages in terms of results and usability.