We present a novel algorithm for overcomplete independent components analysis (ICA), where the number of latent sources k exceeds the dimension p of observed variables. Previous algorithms either suffer from high computational complexity or make strong assumptions about the form of the mixing matrix. Our algorithm does not make any sparsity assumption yet enjoys favorable computational and theoretical properties. Our algorithm consists of two main steps: (a) estimation of the Hessians of the cumulant generating function (as opposed to the fourth and higher order cumulants used by most algorithms) and (b) a novel semi-definite programming (SDP) relaxation for recovering a mixing component. We show that this relaxation can be efficiently solved with a projected accelerated gradient descent method, which makes the whole algorithm computationally practical. Moreover, we conjecture that the proposed program recovers a mixing component at the rate k < p^2/4 and prove that a mixing component can be recovered with high probability when k < (2 - epsilon) p log p when the original components are sampled uniformly at random on the hyper sphere. Experiments are provided on synthetic data and the CIFAR-10 dataset of real images.

Twitter is recently being used during crises to communicate with officials and provide rescue and relief operation in real time. The geographical location information of the event, as well as users, are vitally important in such scenarios. The identification of geographic location is one of the challenging tasks as the location information fields, such as user location and place name of tweets are not reliable. The extraction of location information from tweet text is difficult as it contains a lot of nonstandard English, grammatical errors, spelling mistakes, nonstandard abbreviations, and so on. This research aims to extract location words used in the tweet using a Convolutional Neural Network (CNN) based model. We achieved the exact matching score of 0.929, Hamming loss of 0.002, and F Our model was able to extract even three-to four-word long location references which is also evident from the exact matching score of over 92%. The findings of this paper can help in early event localization, emergency situations, real-time road traffic management, localized advertisement, and in various location-based services. Keywords: Location references, Tweets, Geo-locations, Named entity recognition, Gazetteer, Convolutional Neural Network 1. Introduction Tweets are very responsive to real-world events, and are sometimes even more immediate than traditional news channels. Therefore, it is possible to keep track of the latest information by following tweets. Several examples were seen when the news was first reported on Twitter, such as an airplane crash over the Hudson River in New York in the year 2009 (Sakaki et al., 2013), the death of former British Prime Minister Margaret Thatcher in April 2013 Preprint submitted to Elsevier January 25, 2019 Sakaki et al., 2013; Singh et al., 2017; Yuan & Liu, 2018). In an American Red Cross survey, a question was asked to individuals that "whom they contacted in an emergency?" The estimation and detection of location information of events and users from tweets are a major concern in relation to the above-mentioned tasks. Twitter provides three location information fields for sharing a user's location: (1) User location; (2) Place name; and (3) Geo-coordinate. The user location field has 140 character spaces (previously it was limited to 30 characters) in which the user can write his/her home location information while creating their profile. This field is optional to the user and the user can write any arbitrary words or leave it blank.

Nest Cam security cameras and the Nest Hello video doorbell can increase home security, but it's important to know if your house has enough Wi-Fi bandwidth to support them. For one Northern California family it was a terrifying experience: an emergency warning that came through a Nest surveillance camera of three intercontinental ballistic missiles, apparently from North Korea, headed straight to Los Angeles, Chicago and Ohio. Laura Lyons told the East Bay Times that the warning, "sounded completely legit, and it was loud and got our attention right off the bat.…It was five minutes of sheer terror and another 30 minutes trying to figure out what was going on." The warning proved to be a hoax, and according to Nest's parent Google, it wasn't a hack at all but rather the result of a compromised password. "Nest was not breached," Google said in a statement emailed to USA TODAY.

Much discussion of the future of work suggests it can only be a dystopian, robotic world. But the report of an ILO commission shows how humans, not algorithms, can be in charge. When the International Labour Organization (ILO) was founded 100 years ago in the aftermath of the first world war, governments, employers and workers came together convinced that lasting peace and stability depended on social justice. This is still true and, given the dramatic changes we are seeing, should encourage us to take bold and timely action. The constitution of the ILO of 1919, reinforced by the Philadelphia Declaration of 1944, remains the most ambitious global social contract in history.

Amazon Comprehend Medical is a new HIPAA-eligible service that uses machine learning (ML) to extract medical information with high accuracy. This reduces the cost, time, and effort of processing large amounts of unstructured medical text. You can extract entities and relationships like medication, diagnosis, and dosage, and you can also extract protected health information (PHI). Using Amazon Comprehend Medical allows end users to get value from raw clinical notes that is otherwise largely unused for analytical purposes because it's difficult to parse. There is immense value associated with extracting information from these notes and integrating it with other medical systems like an Electronic Health Record (EHR) and a Clinical Trial Management System (CTMS).

The Port Authority of New York and New Jersey, which operates the Newark airport, said in a statement that agency officials met last week with counterparts from the FAA, FBI and Homeland Security Department "to review and enhance protocols for the rapid detection and interdiction of drones." A spokesman would not provide specifics and declined to say whether the airport has any anti-drone technology.

A Project Task Assignment for the Teaming-Enabled Architectures for Manned-Unmanned Systems (TEAMS) prototype program was recently awarded to GE Aviation. The project is under the authority of the Base Vertical Lift Consortium Project Agreement and is sponsored by the U.S. Air Force Research Lab (AFRL). "The TEAMS program is a tremendous opportunity for GE to work closely with AFRL and our industry partners to prototype architectures that will enable the next generation of Manned-Unmanned Teaming capabilities," says John Kormash, director of Advanced & Special Programs for GE Aviation. "GE's experience and investments in the areas of architecture, modeling, simulation, and system instantiations will enhance the AFRL's objectives of developing open, flexible, and scalable solutions for tomorrow's autonomous vehicles." TEAMS is an architectural modeling and prototyping effort under the AFRL's Flexible, Assured Manned-Unmanned Systems (FAMUS) program.

Having visited more than 40 countries, 200 cities and lived in six countries in the last 10 years as well as working in the disruptive Internet of Things (IoT) space, the subject of how to make cities smarter and more citizen friendly is of particular interest to me for both professional and personal reasons. More than half of the world's population now live in cities -- and the figure will rise to more than two thirds by 2050, according to a United Nations forecast. Growing numbers of city residents put pressure on energy and water resources, transport networks, environment, national healthcare budgets as well as many more aspects of the city. In the last few weeks I have been thinking about the most important problems that the most cities around the world may face, but they can be solved or reduced by Internet of Things and artificial intelligence (AI) enabled solutions. By IoT we simply mean when objects are connected to the Internet and exchange data.

Increasingly sophisticated cyberthreats regularly overwhelm traditional security solutions -- but adding AI to the mix changes the game. To learn how AI-powered security solutions let you identify, analyze, and eliminate new and evolving threats in real time, don't miss this VB Live event. Every year malicious cyber activity rips off the U.S. alone anywhere between $57 billion and $109 billion, and the average cost of a cyber attack is $3.85 million but can reach $350 million for megabreaches. And the cyberthiefs are getting smarter, more aggressive, and harder to stop, since their attacks evolve as quickly as traditional security methods find a way to spot the intrusions and stop them. It's a consequence of the widespread penetration of internet connectivity, which also increases the potential impact of a threat, the growth in the number of black-hat programmers dedicating themselves to cyber warfare, and the increasing sophistication of attack tools, techniques, and strategies designed to ramp up the speed and power of cyber assaults.

China is taking its Big Brother approach to government one step further with plans to use CCTV cameras and artificial intelligence to follow people across the country. The plan from the Chinese People's Liberation Army (PLA) would use the nation's network of surveillance to find wanted civilians. Known as EnsembleNet, the programme was trained using 2,000 clips from CCTV footage and is 90 per cent accurate, the firm claims. Chinese People's Liberation Army (PLA) is hoping to use the nation's network of surveillance to find wanted civilians. Body shapes and discernible features are spotted, remembered and scanned for in other footage in the database.