Washington – The Trump administration plans to reinterpret a Cold War-era arms agreement between 34 nations with the goal of allowing U.S. defense contractors to sell more American-made drones to a wide array of nations, three defense industry executives and a U.S. official told Reuters. The policy change, which has not been previously reported, could open up sales of armed U.S. drones to less stable governments such as Jordan and the United Arab Emirates that in the past have been forbidden from buying them under the 33-year-old Missile Technology Control Regime (MTCR), said the U.S. official, a former U.S. official and one of the executives. It could also undermine longstanding MTCR compliance from countries such as Russia, said the U.S. official, who has direct knowledge of the policy shift. Reinterpreting the MTCR is part of a broader Trump administration effort to sell more weapons overseas. It has overhauled a broad range of arms export regulations and removed the U.S. from international arms treaties including the Intermediate-Range Nuclear Forces Treaty and the Open Skies Treaty.

Rainbow fish behave like matadors by darting away from their predators at the last moment to avoid being eaten, a new study reveals. The tiny fish, also known as a Trinidadian guppy, spans less than an inch in length. It initially draws the attention of its most common predator – the much larger pike cichlid – by turning its irises black, which makes its eyes very conspicuous. According to a British team of scientists who performed experiments in water tanks using robots, the rainbow fish then uses quick reflexes to whip its head out of the way, causing the predator to miss, before swimming away. The speed of the whole interaction is around three hundredths of a second, meaning it's only fully observable using a high-speed camera.

Many important decisions in societies such as school admissions, hiring, or elections are based on the selection of top-ranking individuals from a larger pool of candidates. This process is often subject to biases, which typically manifest as an under-representation of certain groups among the selected or accepted individuals. The most common approach to this issue is debiasing, for example via the introduction of quotas that ensure proportional representation of groups with respect to a certain, often binary attribute. Cases include quotas for women on corporate boards or ethnic quotas in elections. This, however, has the potential to induce changes in representation with respect to other attributes. For the case of two correlated binary attributes we show that quota-based debiasing based on a single attribute can worsen the representation of already underrepresented groups and decrease overall fairness of selection. We use several data sets from a broad range of domains from recidivism risk assessments to scientific citations to assess this effect in real-world settings. Our results demonstrate the importance of including all relevant attributes in debiasing procedures and that more efforts need to be put into eliminating the root causes of inequalities as purely numerical solutions such as quota-based debiasing might lead to unintended consequences.

A key challenge in understanding the sensory transformations of the visual system is to obtain a highly predictive model of responses from visual cortical neurons. Deep neural networks (DNNs) provide a promising candidate for such a model. However, DNNs require orders of magnitude more training data than neuroscientists can collect from real neurons because experimental recording time is severely limited. This motivates us to find images that train highly-predictive DNNs with as little training data as possible. We propose gaudy images---high-contrast binarized versions of natural images---to efficiently train DNNs. In extensive simulation experiments, we find that training DNNs with gaudy images substantially reduces the number of training images needed to accurately predict the simulated responses of visual cortical neurons. We also find that gaudy images, chosen before training, outperform images chosen during training by active learning algorithms. Thus, gaudy images overemphasize features of natural images, especially edges, that are the most important for efficiently training DNNs. We believe gaudy images will aid in the modeling of visual cortical neurons, potentially opening new scientific questions about visual processing, as well as aid general practitioners that seek ways to improve the training of DNNs.

Disney Parks have reportedly been urged to overhaul the theme of its famous Splash Mountain ride. The park's popular log flume ride has recently been getting calls for Disney to alter its whole attraction motif. In the wake of the protests against police brutality and racial injustices and amid the ongoing Black Lives Matter movement, fans and parkgoers have claimed that Disney's Splash Mountain is based on the 1946 Disney film "Song of the South" -- which has been dubbed one of the "most racist movies" for its stereotypes of black people. According to a report from CNN, the controversial live-action animated musical film has long been criticized for romanticizing the post-Civil War period in South America. "Song of the South" is set in a plantation in Georgia in the 1800s during the Reconstruction era.

This paper presents LEXR, a framework for explaining the decision making of recurrent neural networks (RNNs) using a formal description language called Linear Temporal Logic (LTL). LTL is the de facto standard for the specification of temporal properties in the context of formal verification and features many desirable properties that make the generated explanations easy for humans to interpret: it is a descriptive language, it has a variable-free syntax, and it can easily be translated into plain English. To generate explanations, LEXR follows the principle of counterexample-guided inductive synthesis and combines Valiant's probably approximately correct learning (PAC) with constraint solving. We prove that LEXR's explanations satisfy the PAC guarantee (provided the RNN can be described by LTL) and show empirically that these explanations are more accurate and easier-to-understand than the ones generated by recent algorithms that extract deterministic finite automata from RNNs.

Finding the factors contributing to criminal activities and their consequences is essential to improve quantitative crime research. To respond to this concern, we examine an extensive set of features from different perspectives and explanations. Our study aims to build data-driven models for predicting future crime occurrences. In this paper, we propose the use of streetlight infrastructure and Foursquare data along with demographic characteristics for improving future crime incident prediction. We evaluate the classification performance based on various feature combinations as well as with the baseline model. Our proposed model was tested on each smallest geographic region in Halifax, Canada. Our findings demonstrate the effectiveness of integrating diverse sources of data to gain satisfactory classification performance.

Federated learning (FL) is an emerging distributed machine learning paradigm that stands out with its inherent privacy-preserving advantages. Heterogeneity is one of the core challenges in FL, which resides in the diverse user behaviors and hardware capacity across devices who participate in the training. Heterogeneity inherently exerts a huge influence on the FL training process, e.g., causing device unavailability. However, existing FL literature usually ignores the impacts of heterogeneity. To fill in the knowledge gap, we build FLASH, the first heterogeneity-aware FL platform. Based on FLASH and a large-scale user trace from 136k real-world users, we demonstrate the usefulness of FLASH in anatomizing the impacts of heterogeneity in FL by exploring three previously unaddressed research questions: whether and how can heterogeneity affect FL performance; how to configure a heterogeneity-aware FL system; and what are heterogeneity's impacts on existing FL optimizations. It shows that heterogeneity causes nontrivial performance degradation in FL from various aspects, and even invalidates some typical FL optimizations.

In this paper, we consider the problem of optimizing the revenue a web publisher gets through real-time bidding (i.e. from ads sold in real-time auctions) and direct (i.e. from ads sold through contracts agreed in advance). We consider a setting where the publisher is able to bid in the real-time bidding auction for each impression. If it wins the auction, it chooses a direct campaign to deliver and displays the corresponding ad. This paper presents an algorithm to build an optimal strategy for the publisher to deliver its direct campaigns while maximizing its real-time bidding revenue. The optimal strategy gives a formula to determine the publisher bid as well as a way to choose the direct campaign being delivered if the publisher bidder wins the auction, depending on the impression characteristics. The optimal strategy can be estimated on past auctions data. The algorithm scales with the number of campaigns and the size of the dataset. This is a very important feature, as in practice a publisher may have thousands of active direct campaigns at the same time and would like to estimate an optimal strategy on billions of auctions. The algorithm is a key component of a system which is being developed, and which will be deployed on thousands of web publishers worldwide, helping them to serve efficiently billions of ads a day to hundreds of millions of visitors.

New York has always been known as the city that never sleeps. In this crisis time, this definition has never been clearer. Citizens of NYC have been subjected to the stress of Coronavirus pressures, which have hit Americans with fatigue and feelings of hopelessness as a result of our grim economic situation. NYC continues to fight back and be resilient, combating the crisis situation with innovative companies like IPsoft that provide unique AI solutions for clients. By leveraging AI solutions, we can bring America back to its former glory, and instead transform the cause of sleepless nights from recession anxiety to the exhaustion of a memorable night out in the city that never sleeps.