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.

As humans, we have given our best into critical thinking to improve our lives, bringing us into the age of information and communication technology. We have also devised means and ways to prolong and save human lives from unforeseen circumstances and cut back on response time to emergencies. This perhaps is why life expectancy has improved for people, especially in developed nations. In recent times, we are using technology to improve our healthcare and general wellbeing. So, this is to let you get a glimpse of some high-techs we [humans] have developed, already in the works, or need to establish that can save more lives and further improve our general wellbeing.

This article highlights a revolutionary new approach to offering Ultra-Personalised, Artificial Intelligence-Enabled Healthcare from Cortex Logic which is set to transform the way medical aid schemes, consumers and corporates offering employee benefits as part of their EAP (Employee Assistance Programmes) view their current healthcare offering. The Journey Wellness platform is perfectly timed to coincide with the current global shift in Healthcare, as the focus moves from a disease-management model to one that encourages optimum health and disease-avoidance, targeting younger as well as existing members with a holistic, pro-active offering. Journey Wellness was also recently introduced through the articles "Access To AI-Driven Platforms Can Improve Health And Financial Wellness – Cortex Group" and "This New AI-Enabled, Personalised Healthcare Platform Wants To Revolutionize Wellness & Healthcare In SA" as well as a number of presentations and interviews by Dr Jacques Ludik, Founder and CEO of Cortex Logic and President of the Machine Intelligence Institute of Africa MIIA, who has also recently written a book called'Democratizing Artificial Intelligence to Benefit Everyone'. He says that the Journey Wellness platform will provide more healthcare, to more people, faster. Dr Ludik explained that its AI-enabled, ultra-personalized health wellness platform, called Journey Wellness here in South Africa, provides real-time, relevant, on-demand and proactive guidance, advice, support and recommendations to help optimize a user's wellness whilst ensuring high standards of data privacy and choice. The system makes use of the Cortex AI engine to build a holistic and dynamic 360-degree wellness profile and has an AI-driven personalized wellness coach that provides information and personalized advice, coaching and guidance with respect to healthy eating and diet, exercise and fitness, mental wellness and health with a specific focus on chronic lifestyle diseases.

Much has been made about the rich and inclusive story in this year's edition of the popular Call of Duty video game franchise. That may be, but "Call of Duty: Vanguard," out Friday for PlayStation 5, PS4, Xbox Series X/S, Xbox One and PCs ($59.99-up, You are part of a team of special forces soldiers trying to commandeer a German train buzzing toward Hamburg. You must dodge gunfire from Nazis on both rail lines and German military trucks buzzing between the set of tracks. Your team, led by Sgt.

A Neyman-Scott process is a special case of a Cox process. The latent and observable stochastic processes are both Poisson processes. We consider a deep Neyman-Scott process in this paper, for which the building components of a network are all Poisson processes. We develop an efficient posterior sampling via Markov chain Monte Carlo and use it for likelihood-based inference. Our method opens up room for the inference in sophisticated hierarchical point processes. We show in the experiments that more hidden Poisson processes brings better performance for likelihood fitting and events types prediction. We also compare our method with state-of-the-art models for temporal real-world datasets and demonstrate competitive abilities for both data fitting and prediction, using far fewer parameters.

A modified commercial drone may have been responsible for an attempted attack on a Pennsylvania power substation last year, the first reported case of a drone assault on the U.S.'s energy infrastructure. Authorities believe a DJI Mavic 2 drone with a thick copper wire tethered to it was found in June 2020 was likely intended to disrupt operations'by creating a short circuit to cause damage to transformers or distribution lines,' according to a joint intelligence bulletin from the FBI, Department of Homeland Security, and the National Counterterrorism Center released October 28. If the wire had come into contact with any of the power plant's high-voltage equipment it could have resulted in a short circuit, power failure or even a fire, according to New Scientist. The Drive reported the drone was recovered by authorities from a substation near Hershey, Pennsylvania, about 100 miles from Philadelphia. No groups has claimed responsibility: The device's camera and internal memory card had been removed and identifying labels were removed, in a likely attempt to obscure its origins.