Machine learning models are often susceptible to adversarial perturbations of their inputs. Even small perturbations can cause state-of-the-art classifiers with high "standard" accuracy to produce an incorrect prediction with high confidence. To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. We show that already in a simple natural data model, the sample complexity of robust learning can be significantly larger than that of "standard" learning. This gap is information theoretic and holds irrespective of the training algorithm or the model family. We complement our theoretical results with experiments on popular image classification datasets and show that a similar gap exists here as well. We postulate that the difficulty of training robust classifiers stems, at least partially, from this inherently larger sample complexity.

Journal of Parallel and Distributed Computing 104, 19-35.

Data Mining is the process of analyzing data from different perspectives and summarizing it into useful information that can be used to increase revenue, cut costs or both. Web Mining is the application of data mining techniques to discover patterns from the World Wide Web. It can be divided into three different types - Web usage mining, Web content mining and Web structure mining. Web usage mining itself can be classified further depending on the kind of usage data considered: Web Server Data, Application Server Data, and Application Level Data. Web log Mining includes three main stages: Data Pre-Processing, Pattern Discovery and Pattern Analysis. A) Data Pre-Processing: Web Server Data contains information such as who accessed the web site, what pages were accessed, Time of request etc. In pre-processing [3] stage, irrelevant data fields are removed and unique users are identified [4]. Transaction table is created through the user sessions.

High-intensity storms cause billions of pounds of damage every year, and climate change is set to make this worse in future. We already appear to be seeing more frequent and intense windstorms. Hurricane Ophelia and Storm Eleanor both wreaked havoc in the British Isles over the winter, including injuries, power cuts and severe travel delays. It's not only commuters and households that are affected. Every year across Europe, the number of trees that commercial forests lose to storms is equivalent to the annual amount of timber felled in Poland.

Warning: spoilers ahead for Netflix's Lost in Space. In the first episode of Netflix's new Lost in Space, Will Robinson (Maxwell Jenkins) discovers a robot (Brian Steele) and saves it from a spreading forest fire. As a result, it seems to imprint upon him, following him around and obeying him like a loyal pet. As Will is suddenly made responsible for another being's safety, he starts to mature. The robot starts to develop, too, becoming an integral part of the Robinson family as they struggle to adjust their biases and preconceptions about artificial intelligence.

Below is the closed-captioning text associated with this video. Since this uses automated speech to text spelling and grammar may not be accurate.

Just in time for Earth Day, Apple has unveiled a new recycling robot -- and it can disassemble 200 iPhones in a single hour. Daisy can successfully extract parts from nine types of iPhones -- and for every 100,000 devices it can salvage 1,900 kg of aluminum, 770 kg of cobalt, 710 kg of copper and 11 kg of rare earth elements. The robot represents a major step forward in Apple's mission to someday build its devices entirely from recycled materials. "We created Daisy to have a smaller footprint and the capability to disassemble multiple models of iPhones with higher variation compared to Liam" -- an earlier iteration of the company's recycling robotics -- Apple said in its 2018 Environmental Responsibility Report. Ultimately, Apple hopes to develop a closed-loop production system in which every reusable part of older devices is utilized in new ones.

The purpose of this paper is to re-investigate the estimation of multiple factor models by relaxing the convention that the number of factors is small. We first obtain the collection of all possible factors and we provide a simultaneous test, security by security, of which factors are significant. Since the collection of risk factors selected for investigation is large and highly correlated, we use dimension reduction methods, including the Least Absolute Shrinkage and Selection Operator (LASSO) and prototype clustering, to perform the investigation. For comparison with the existing literature, we compare the multi-factor model's performance with the Fama-French 5-factor model. We find that both the Fama-French 5-factor and the multi-factor model are consistent with the behavior of "large-time scale" security returns. In a goodness-of-fit test comparing the Fama-French 5-factor with the multi-factor model, the multi-factor model has a substantially larger adjusted $R^{2}$. Robustness tests confirm that the multi-factor model provides a reasonable characterization of security returns.

Scientists teamed up to use artificial intelligence to discover new alternatives to steel in record time. As a result, they discovered three new blends to form metallic glass and did this 200 times faster than it has ever been done before. Fang Ren, who developed algorithms to analyze data on the fly while a postdoctoral scholar at SLAC, at a Stanford Synchrotron Radiation Lightsource beamline where the system has been put to use. Metallic glass is essentially an alloy of the future. Normally, a few metals can be mixed together so that the ideal properties of each metal are'added' together to make a'super-metal'.

Officials say using the RadPiper robot is safer, tremendously faster and more accurate than the current method of workers taking external measurements. They also say it could save tens of millions of public dollars on cleanups of that site and one near Paducah, Kentucky.