Iran vows retaliation; Lt. Col. Daniel Davis, Walid Phares, and Rep. Mark Green react. A giant question mark hangs over the Middle East as the world waits to see what action Iran will take to retaliate for the long-overdue killing Friday morning of Iranian Gen. Qassem Soleimani in a drone strike ordered by President Trump. President Trump made the right decision in ordering Soleimani killed in Iraq. I've been arguing for four years that we ought to take out this dangerous enemy of the United States, who was responsible for the deaths of hundreds of Americans and wanted to kill many more. Thankfully, his killing days are over.

BAGHDAD – A fresh airstrike hit pro-Iran fighters in Iraq early Saturday, as fears grew of a proxy war erupting between Washington and Tehran a day after an American drone strike killed a top Iranian general. The killing of Quds Force commander Gen. Qassem Soleimani in Baghdad on Friday was the most dramatic escalation yet in spiralling tensions between Iran and the United States, which pledged to send more troops to the region -- even as President Donald Trump insisted he did not want war. Iran's ambassador to the United Nations, Majid Takht Ravanchi, told CNN that the killing was an "act of war on the part of the United States." A new strike on Saturday targeted a convoy belonging to the Hashed al-Shaabi, an Iraqi paramilitary network dominated by Shiite factions with close ties to Iran. The Hashed did not say who it held responsible but Iraqi state television reported it was a U.S. airstrike.

The Artificial Intelligence market has witnessed growth from USD XX million to USD XX million from 2014 to 2019. With the CAGR of X.X%, this market is estimated to reach USD XX million in 2026. The report mainly studies the size, recent trends and development status of the Artificial Intelligence market, as well as investment opportunities, government policy, market dynamics (drivers, restraints, opportunities), supply chain and competitive landscape. Technological innovation and advancement will further optimize the performance of the product, making it more widely used in downstream applications. Moreover, Porter's Five Forces Analysis (potential entrants, suppliers, substitutes, buyers, industry competitors) provides crucial information for knowing the Artificial Intelligence market.

Radiology artificial intelligence (AI) was again the hottest topic at the 2019 Radiological Society of North America (RSNA) annual meeting in December. AI was a primary theme in the larger booths in the north and south expo floors, as well as on the new third expo floor dedicated AI showcase. The separate AI show floor did not make many AI vendors happy. Many wanted the artificial intelligence showcase on the same level as the other expo halls to reduce the shuttling between the floors for meetings. RSNA organizers pointed out to one startup that due to the sheer number of AI exhibitors, they had to give the showcase its own space.

Artificial intelligence (AI) is now receiving unprecedented global attention as it finds widespread practical application in multiple spheres of activity. But what are the human rights, social justice and development implications of AI when used in areas such as health, education and social services, or in building "smart cities"? How does algorithmic decision making impact on marginalised people and the poor? This edition of Global Information Society Watch (GISWatch) provides a perspective from the global South on the application of AI to our everyday lives. It includes 40 country reports from countries as diverse as Benin, Argentina, India, Russia and Ukraine, as well as three regional reports.

Seattle-based smart home appliance maker Wyze, which is popular for selling its products cheaper than its competitors, has been accused of a data breach and trafficking the data to Alibaba Cloud servers in China. In response to the alleged data breach against its production database, Wyze logged outfits users out of their accounts and has strengthened security for its servers. "Customers endured a lengthy reauthentication process as the company responded to a series of reports claiming that the company stored sensitive information about people's security cameras, local networks, and email addresses in exposed databases.", Texas-based Twelve Security, a self-described "boutique" consulting firm, claimed of a data breach against Wyze's two Elasticsearch databases on Medium yesterday. The data has come from 2.4 million users from the United States, United Kingdom, the United Arab Emirates, Egypt, and parts of Malaysia.

Reservoir computing, a highly successful neuromorphic computing scheme used to filter, predict, classify temporal inputs, has entered an era of microchips for several other engineering and biological applications. A basis for reservoir computing is memory-loss or the echo state property. It is an open problem on how design parameters of the reservoir can be optimized to maximize reservoir freedom to map an input robustly and yet have its close-by-variants represented in the reservoir differently. We present a framework to analyze stability due to input and parameter perturbations and make a surprising fundamental conclusion, that the echo state property is \emph{equivalent} to robustness to input in any nonlinear recurrent neural network that may or may not be in the gambit of reservoir computing. Further, backed by theoretical conclusions, we define and find the difficult-to-describe \emph{input specific} edge-of-criticality or the echo state property threshold, which defines the boundary between parameter related stability and instability.

Tourism information is scattered around nowadays. To search for the information, it is usually time consuming to browse through the results from search engine, select and view the details of each accommodation. In this paper, we present a methodology to extract particular information from full text returned from the search engine to facilitate the users. Then, the users can specifically look to the desired relevant information. The approach can be used for the same task in other domains. The main steps are 1) building training data and 2) building recognition model. First, the tourism data is gathered and the vocabularies are built. The raw corpus is used to train for creating vocabulary embedding. Also, it is used for creating annotated data. The process of creating named entity annotation is presented. Then, the recognition model of a given entity type can be built. From the experiments, given hotel description, the model can extract the desired entity,i.e, name, location, facility. The extracted data can further be stored as a structured information, e.g., in the ontology format, for future querying and inference. The model for automatic named entity identification, based on machine learning, yields the error ranging 8%-25%.

The study and development of autonomous vehicles have seen an increasing interest in recent years, becoming hot topics in both academia and industry. One of the main reasearch areas in this field is related to control systems, in particular planning and decision-making problems. The basic approaches for scheduling high-level maneuver execution modules are based on the concepts of time-to-collision (van der Horst and Hogema, 1994) and headway control (Hatipoglu et al., 1996). In order to add interpretation capabilities to the system, several approaches model the driving decision-making problem as a Partially Observable Markov Decision Process (POMDP, (Spaan, 2012)), as in (Liu et al., 2015) for urban scenarios and in (Song et al., 2016) for intersection handling. A further extension is proposed in (Bandyopadhyay et al., 2012) where a Mixed Observability Markov Decision Process (MOMDP) (Ong et al., 2010) is used to model uncertainties in agents intentions. However, since vehicles are assumed to behave in a deterministic way, the aforementioned approaches handle many situations with excessive prudence and would not be able to enter in a busy roundabout.

Consider the massive size of real estate lending. The Fed's latest report shows mortgage debt topping $9 trillion. When including mortgages from businesses, it tops $15 trillion. Over 10 million homes and commercial properties sell each year. Equally staggering is how much data exists on the borrowers.