Machine learning has given humanity the ability to run tasks in an automated manner. It enables us to improve what we already do by analyzing a continuous stream of data related to the same task. Machine learning has a wide range of applications in fields ranging from space research to digital marketing. Machine learning is also the foundation of artificial intelligence. We are not yet inundated with machines capable of making decisions on their own.

Apple's new-model, top-of-the-line MacBook Pro laptop computer could set you back nearly $4,000 before taxes. But that will seem like a Black Friday steal when a 45-year-old Apple computer goes on sale this week in Monrovia, where it may fetch six figures or more, even without a 16-inch, high-definition screen and the latest microprocessors. On Tuesday, John Moran Auctioneers will auction off a functioning Apple-1 computer hand-built by Steve Wozniak, Steve Jobs and others in a Los Altos, Calif., garage in 1976. The system was the rock upon which the trillion-dollar Apple empire was built. In his 2011 biography "Steve Jobs," Walter Isaacson quotes Wozniak as saying of the Apple-1: "We were participating in the biggest revolution that had ever happened, I thought. I was so happy to be a part of it."

A digital replica of Earth could help scientists better model the future of our planet and find solutions to problems wrought by climate change. The advanced model, dubbed Digital Twin Earth, is being developed by the European Space Agency (ESA) and its partners based on data and images from Earth-observation satellites and sensors on the ground. To run reliably, the project will require new advanced artificial intelligence algorithms and powerful supercomputers, which are currently being developed. ESA and its partners discussed their progress in the runup to the UN Climate Change Conference COP26, a two-week event that's currently taking place in Glasgow, Scotland. ESA launched the Digital Twin Earth project in 2020 and invited researchers and tech companies from across Europe to present their progress during an event called PhiWeek, which took place Oct. 11 to Oct. 15.

A rootkit is a program (or set of programs) that allows you to hide the presence of malware in the system. Rootkits are often part of multifunctional malware that could have multiple abilities, such as providing attackers with remote access to compromised hosts, intercepting network traffic, spying on users, recording keystrokes, stealing authentication information, or using the host as a base to mine cryptocurrencies and aid in DDoS attacks. The task of the rootkit is to mask this illegitimate activity on the compromised machine. Some rootkits, such as Necurs, Flame and DirtyMoe, are designed to combine both modes of operation and thus work at both levels. They accounted for 31% of the sample.

Higher-order tensor datasets are rising ubiquitously in modern data science applications, for instance, recommendation systems (Baltrunas et al., 2011; Bi et al., 2018), social networks (Bickel and Chen, 2009), genomics (Hore et al., 2016), and neuroimaging (Zhou et al., 2013). Tensor provides effective representation of data structure that classical vector-and matrix-based methods fail to capture. One example is music recommendation system (Baltrunas et al., 2011) that records ratings of songs from users on various contexts. This three-way tensor of user song context allows us to investigate interactions of users and songs in a context-specific manner. Another example is network dataset that records the connections among a set of nodes. Pairwise interactions are often insufficient to capture the complex relationships, whereas multi-way interactions improve the understanding of networks in molecular system (Young et al., 2018) and social networks (Han et al., 2020). In both examples, higher-order tensors represent multi-way interactions in an efficient way. Tensor estimation problem cannot be solved without imposing structures. An appropriate reordering of tensor entries often provides effective representation of the hidden salient structure.

Most existing classification methods aim to minimize the overall misclassification error rate, however, in applications, different types of errors can have different consequences. To take into account this asymmetry issue, two popular paradigms have been developed, namely the Neyman-Pearson (NP) paradigm and cost-sensitive (CS) paradigm. Compared to CS paradigm, NP paradigm does not require a specification of costs. Most previous works on NP paradigm focused on the binary case. In this work, we study the multi-class NP problem by connecting it to the CS problem, and propose two algorithms. We extend the NP oracle inequalities and consistency from the binary case to the multi-class case, and show that our two algorithms enjoy these properties under certain conditions. The simulation and real data studies demonstrate the effectiveness of our algorithms. To our knowledge, this is the first work to solve the multi-class NP problem via cost-sensitive learning techniques with theoretical guarantees. The proposed algorithms are implemented in the R package "npcs" on CRAN.

Unlike matrix completion, no algorithm for the tensor completion problem has so far been shown to achieve the information-theoretic sample complexity rate. This paper develops a new algorithm for the special case of completion for nonnegative tensors. We prove that our algorithm converges in a linear (in numerical tolerance) number of oracle steps, while achieving the information-theoretic rate. Our approach is to define a new norm for nonnegative tensors using the gauge of a specific 0-1 polytope that we construct. Because the norm is defined using a 0-1 polytope, this means we can use integer linear programming to solve linear separation problems over the polytope. We combine this insight with a variant of the Frank-Wolfe algorithm to construct our numerical algorithm, and we demonstrate its effectiveness and scalability through experiments.

Data on high-dimensional spheres arise frequently in many disciplines either naturally or as a consequence of preliminary processing and can have intricate dependence structure that needs to be understood. We develop exploratory factor analysis of the projected normal distribution to explain the variability in such data using a few easily interpreted latent factors. Our methodology provides maximum likelihood estimates through a novel fast alternating expectation profile conditional maximization algorithm. Results on simulation experiments on a wide range of settings are uniformly excellent. Our methodology provides interpretable and insightful results when applied to tweets with the $\#MeToo$ hashtag in early December 2018, to time-course functional Magnetic Resonance Images of the average pre-teen brain at rest, to characterize handwritten digits, and to gene expression data from cancerous cells in the Cancer Genome Atlas.

With the proliferation of knowledge graphs, modeling data with complex multirelational structure has gained increasing attention in the area of statistical relational learning. One of the most important goals of statistical relational learning is link prediction, i.e., predicting whether certain relations exist in the knowledge graph. A large number of models and algorithms have been proposed to perform link prediction, among which tensor factorization method has proven to achieve state-of-the-art performance in terms of computation efficiency and prediction accuracy. However, a common drawback of the existing tensor factorization models is that the missing relations and non-existing relations are treated in the same way, which results in a loss of information. To address this issue, we propose a binary tensor factorization model with probit link, which not only inherits the computation efficiency from the classic tensor factorization model but also accounts for the binary nature of relational data. Our proposed probit tensor factorization (PTF) model shows advantages in both the prediction accuracy and interpretability

Iraq's Prime Minister Mustafa al-Kadhemi said he was unhurt and appealed for "calm and restraint" after a drone attack on his residence early Sunday that heightened political tensions in the war-scarred country. The attack in Baghdad's Green Zone was the first to target the residence of Kadhemi, who has been in power since May 2020. It came as Iraq's political parties negotiate alliances over who will run the next government after elections last month. That vote saw the Conquest (Fatah) Alliance, the political arm of the pro-Iran Hashed al-Shaabi paramilitary network, suffer a substantial decline in its parliamentary seats, leading the group to denounce the outcome as "fraud". The big winner, with more than 70 seats according to the initial count, was the movement of Moqtada Sadr, a Shiite Muslim preacher who campaigned as a nationalist and critic of Iran.