The federal government is planning to lift a moratorium on funding of controversial experiments that use human stem cells to create animal embryos that are partly human. The National Institutes of Health has unveiled a new policy to permit scientists to get federal money to make the embryos, known as chimeras, under certain carefully monitored conditions. The NIH imposed a moratorium on funding these experiments in September because they could raise ethical concerns. Scientists have started to grow human organs inside pigs, in an attempt to solve the worldwide shortage for transplants. Researchers are interested in growing human tissues and organs in animals by introducing pluripotent human cells into early animal embryos.

A software program dubbed'Mayhem' was poised to win the final round of a three-year contest to teach computers to launch and defend against cyber attacks, earning a 2 million prize for the team that wrote the winning code. The event, known as the Cyber Grand Challenge, concluded Thursday evening in a Las Vegas convention centre ballroom after a digital battle among software programs running on seven supercomputers on a stage in a Las Vegas ballroom. Thousands watched as announcers presented a play-by-play account of the competition. The event, known as the Cyber Grand Challenge, concluded Thursday evening in a Las Vegas convention centre ballroom after a digital battle among software programs. For almost 10 hours, competitors played the classic cybersecurity exercise of Capture the Flag in a specially created computer testbed laden with an array of bugs hidden inside custom, never-before-analyzed software.

A tiny remote-controlled aircraft modelled on an insect will become Britain's latest weapon against terror. The Dragonfly drone – which can fit in the palm of a hand - will spy on enemy positions and gather intelligence for the military and British agents. It is inspired by the biology of a dragonfly, with four flapping wings and four legs to enable it to fly through the air seamlessly and perch on a windowsill to spy on terrorists. The gadget could even fly into heavily guarded rooms full of jihadists and provide soldiers on the battlefield a picture of what is going on. It is one of the futuristic pieces of kit currently being developed for the Ministry of Defence and the UK's security forces as part of the MoD's new innovation project.

These are some of the government's sanitized terms for the "enhanced interrogation techniques" that the CIA used on captives held at secret locations overseas during the George W. Bush administration. They mask the brutality that many prisoners faced for months or years under the agency's interrogation program, as outlined in the landmark 2014 report from the Senate Intelligence Committee. That report is dense and riddled with redactions, but its findings have never been more relevant. Although President Obama signed an executive order ending "enhanced interrogation" two days after taking office in 2009, torture could easily find its way back on the table: Donald Trump has repeatedly endorsed waterboarding, which he's called "minimal, minimal, minimal torture," and has said he's prepared to do "much worse." What if there were a way to make sense of state-sanctioned torture in a more visceral way than by reading a news article or watching a documentary?

Have you ever wished getting in touch with the president was as easy as IMing your friends? Well, you can now send President Obama private messages through the White House's Facebook page. The Facebook messages will be among those considered for the 10 personal letters the president reads each day. White House chief digital officer Jason Goldman pitched the new Facebook messaging feature Wednesday as a way for the administration to "meet people where they are" online. The new feature relies on a chatbot that simulates a conversation but doesn't directly respond to questions.

Emerging high-performance architectures are anticipated to contain unreliable components that may exhibit soft errors, which silently corrupt the results of computations. Full detection and masking of soft errors is challenging, expensive, and, for some applications, unnecessary. For example, approximate computing applications (such as multimedia processing, machine learning, and big data analytics) can often naturally tolerate soft errors. We present Rely, a programming language that enables developers to reason about the quantitative reliability of an application--namely, the probability that it produces the correct result when executed on unreliable hardware. Rely allows developers to specify the reliability requirements for each value that a function produces. We present a static quantitative reliability analysis that verifies quantitative requirements on the reliability of an application, enabling a developer to perform sound and verified reliability engineering. The analysis takes a Rely program with a reliability specification and a hardware specification that characterizes the reliability of the underlying hardware components and verifies that the program satisfies its reliability specification when executed on the underlying unreliable hardware platform. We demonstrate the application of quantitative reliability analysis on six computations implemented in Rely. Reliability is a major concern in the design of computer systems. The current goal of delivering systems with negligible error rates restricts the available design space and imposes significant engineering costs.

I have an idea that would keep 100 percent of foreign-born terrorists out of the United States. All we have to do is this: Never let anybody in. Most of us find this idea ludicrous, of course, and rightly so. Keeping out terrorists is not the only goal of border policy; it's essential that the vast majority of people can come and go freely, whether for pleasure, business, or survival. Yet many of our decisions are based on similarly shoddy reasoning: We often fail to consider that there are two sides to the accuracy seesaw. For example, we celebrate mammograms for detecting 84 percent of breast cancers and bemoan when law enforcement can't access a phone, but we overlook how often mammograms detect fictitious cancers, or how many hackers were foiled by encryption.

There are multiple sides to every story, and while statistical topic models have been highly successful at topically summarizing the stories in corpora of text documents, they do not explicitly address the issue of learning the different sides, the viewpoints, expressed in the documents. In this paper, we show how these viewpoints can be learned completely unsupervised and represented in a human interpretable form. We use a novel approach of applying CorrLDA2 for this purpose, which learns topic-viewpoint relations that can be used to form groups of topics, where each group represents a viewpoint. A corpus of documents about the Israeli-Palestinian conflict is then used to demonstrate how a Palestinian and an Israeli viewpoint can be learned. By leveraging the magnitudes and signs of the feature weights of a linear SVM, we introduce a principled method to evaluate associations between topics and viewpoints. With this, we demonstrate, both quantitatively and qualitatively, that the learned topic groups are contextually coherent, and form consistently correct topic-viewpoint associations.

Consider a noisy linear observation model with an unknown permutation, based on observing $y = \Pi^* A x^* + w$, where $x^* \in \mathbb{R}^d$ is an unknown vector, $\Pi^*$ is an unknown $n \times n$ permutation matrix, and $w \in \mathbb{R}^n$ is additive Gaussian noise. We analyze the problem of permutation recovery in a random design setting in which the entries of the matrix $A$ are drawn i.i.d. from a standard Gaussian distribution, and establish sharp conditions on the SNR, sample size $n$, and dimension $d$ under which $\Pi^*$ is exactly and approximately recoverable. On the computational front, we show that the maximum likelihood estimate of $\Pi^*$ is NP-hard to compute, while also providing a polynomial time algorithm when $d =1$.

The dynamic nature of air quality chemistry and transport makes it difficult to identify the mixture of air pollutants for a region. In this study of air quality in the Houston metropolitan area we apply dynamic principal component analysis (DPCA) to a normalized multivariate time series of daily concentration measurements of five pollutants (O3, CO, NO2, SO2, PM2.5) from January 1, 2009 through December 31, 2011 for each of the 24 hours in a day. The resulting dynamic components are examined by hour across days for the 3 year period. Diurnal and seasonal patterns are revealed underlining times when DPCA performs best and two principal components (PCs) explain most variability in the multivariate series. DPCA is shown to be superior to static principal component analysis (PCA) in discovery of linear relations among transformed pollutant measurements. DPCA captures the time-dependent correlation structure of the underlying pollutants recorded at up to 34 monitoring sites in the region. In winter mornings the first principal component (PC1) (mainly CO and NO2) explains up to 70% of variability. Augmenting with the second principal component (PC2) (mainly driven by SO2) the explained variability rises to 90%. In the afternoon, O3 gains prominence in the second principal component. The seasonal profile of PCs' contribution to variance loses its distinction in the afternoon, yet cumulatively PC1 and PC2 still explain up to 65% of variability in ambient air data. DPCA provides a strategy for identifying the changing air quality profile for the region studied.