We study the statistical decision process of detecting the low-rank signal from various signal-plus-noise type data matrices, known as the spiked random matrix models. We first show that the principal component analysis can be improved by entrywise pre-transforming the data matrix if the noise is non-Gaussian, generalizing the known results for the spiked random matrix models with rank-1 signals. As an intermediate step, we find out sharp phase transition thresholds for the extreme eigenvalues of spiked random matrices, which generalize the Baik-Ben Arous-P\'{e}ch\'{e} (BBP) transition. We also prove the central limit theorem for the linear spectral statistics for the spiked random matrices and propose a hypothesis test based on it, which does not depend on the distribution of the signal or the noise. When the noise is non-Gaussian noise, the test can be improved with an entrywise transformation to the data matrix with additive noise. We also introduce an algorithm that estimates the rank of the signal when it is not known a priori.

In this paper we present a first-order method that admits near-optimal convergence rates for convex/concave min-max problems while requiring a simple and intuitive analysis. Similarly to the seminal work of Nemirovski and the recent approach of Piliouras et al. in normal form games, our work is based on the fact that the update rule of the Proximal Point method (PP) can be approximated up to accuracy $\epsilon$ with only $O(\log 1/\epsilon)$ additional gradient-calls through the iterations of a contraction map. Then combining the analysis of (PP) method with an error-propagation analysis we establish that the resulting first order method, called Clairvoyant Extra Gradient, admits near-optimal time-average convergence for general domains and last-iterate convergence in the unconstrained case.

This idea of a data scientist who can work with textual data, signals, images, tabular data and legos is an old-fashioned way of seeing this profession. This book focuses on the core, fundamental principles of signal processing. The code corresponding to this book uses the core functionality of the scientific Python toolchain that should remain unchanged in the foreseeable future. For those looking to migrate their signal processing codes to Python, this book illustrates the key signal and plotting modules that can ease this transition. For those already comfortable with the scientific Python toolchain, this book illustrates the fundamental concepts in signal processing and provides a gateway to further signal processing concepts.

Machine learning has the potential to revolutionize the field of cybersecurity, automating many tasks that were previously done by human penetration testers. This has led to speculation that machine learning will replace a significant portion of the cybersecurity workforce overnight, just as it has done with other industries such as content graphic design. One of the main advantages of machine learning in cybersecurity is its ability to detect and respond to cyber threats in real time. Machine learning algorithms can process large amounts of data and identify patterns that may indicate a cyber attack, allowing them to quickly and efficiently respond to threats. This is in contrast to traditional cybersecurity methods, which rely on manual analysis and rule-based systems that can be slow and error-prone.

Orbiting at 250 miles above the Earth, the International Space Station (ISS) has been integral to a bucket load of research over the past 25 years. Surrounded by a dizzying number of controls and experiments, occupants get some shut eye in sleeping bags attached to walls that couldn't be further from luxury if you tried. But compared to the upcoming Lunar Gateway space station, which will orbit the moon when it is built later this decade, the ISS is decidedly roomy. That is according to one of the architects behind the design of Gateway, who said the living quarters will be so small that astronauts won't be able to stand upright inside them. Cramped: One of the architects behind the design of the new Lunar Gateway space station says the living quarters will be so small that astronauts won't be able to stand upright inside them.

Terrorist gangs smuggling Iranian missiles and drugs in the Middle East will soon have a fleet of 100 robo-ships hunting them down, Britain's most senior military commander in the Gulf has revealed. The new naval force, made up of drones that can operate around the clock, will be up and running by the summer, claimed Commodore Adrian Fryer. It comes as the top officer insisted he remained'alive' to the ever-present threat posed by Iran after a series of recent stand-offs, with the Commodore calling the Middle Eastern state the'main destabilising country' in the region. A small number of the unmanned vessels currently operating in the Gulf are already proving their worth, gathering invaluable data and information that's helping Royal Navy warships cut off smuggling routes and ambush gangs at sea. During the past 12 months, frigate HMS Montrose has seized her biggest haul of narcotics in its three years in the Gulf, bagging a whopping £46.8 million, bringing the ship's total during its time in the Middle East to a staggering £111.1 million.

Much of humanity's future privacy is reliant on the promise, potential, and caveats of how we handle the power of AI in the coming years. AI is a powerhouse tool with immense potential to help humanity. As we activate and enable this tool in it's many forms, let's make sure that we protect our right to privacy, permission, bias-removal, and more. So… Is privacy still possible? How does "outside access" to your data, including criminal access, affect your world?

Academic Earth More than 1,500 video lectures by professors from Harvard, Yale, broken down into single classes on topics like art, architecture, and astronomy.

We map the surface of Pluto using an unsupervised machine learning technique using the near-infrared observations of the LEISA/Ralph instrument onboard NASA's New Horizons spacecraft. The principal component reduced Gaussian mixture model was implemented to investigate the geographic distribution of the surface units across the dwarf planet. We also present the likelihood of each surface unit at the image pixel level. Average I/F spectra of each unit were analyzed -- in terms of the position and strengths of absorption bands of abundant volatiles such as N${}_{2}$, CH${}_{4}$, and CO and nonvolatile H${}_{2}$O -- to connect the unit to surface composition, geology, and geographic location. The distribution of surface units shows a latitudinal pattern with distinct surface compositions of volatiles -- consistent with the existing literature. However, previous mapping efforts were based primarily on compositional analysis using spectral indices (indicators) or implementation of complex radiative transfer models, which need (prior) expert knowledge, label data, or optical constants of representative endmembers. We prove that an application of unsupervised learning in this instance renders a satisfactory result in mapping the spatial distribution of ice compositions without any prior information or label data. Thus, such an application is specifically advantageous for a planetary surface mapping when label data are poorly constrained or completely unknown, because an understanding of surface material distribution is vital for volatile transport modeling at the planetary scale. We emphasize that the unsupervised learning used in this study has wide applicability and can be expanded to other planetary bodies of the Solar System for mapping surface material distribution.

Increasing the number of cyclists, whether for general transport or recreation, can provide health improvements and reduce the environmental impact of vehicular transportation. However, the public's perception of cycling may be driven by the ideologies and reporting standards of news agencies. For instance, people may identify cyclists on the road as "dangerous" if news agencies overly report cycling accidents, limiting the number of people that cycle for transportation. Moreover, if fewer people cycle, there may be less funding from the government to invest in safe infrastructure. In this paper, we explore the perceived perception of cyclists within news headlines. To accomplish this, we introduce a new dataset, "Bike Frames", that can help provide insight into how headlines portray cyclists and help detect accident-related headlines. Next, we introduce a multi-task (MT) regularization approach that increases the detection accuracy of accident-related posts, demonstrating improvements over traditional MT frameworks. Finally, we compare and contrast the perceptions of cyclists with motorcyclist-related headlines to ground the findings with another related activity for both male- and female-related posts. Our findings show that general news websites are more likely to report accidents about cyclists than other events. Moreover, cyclist-specific websites are more likely to report about accidents than motorcycling-specific websites, even though there is more potential danger for motorcyclists. Finally, we show substantial differences in the reporting about male vs. female-related persons, e.g., more male-related cyclists headlines are related to accidents, but more female-related motorcycling headlines about accidents. WARNING: This paper contains descriptions of accidents and death.