Introduction: Extreme weather or climate, including heat waves and cold waves, is considered a health issue causing adverse effects on health, such as cardiovascular diseases (CVDs), mortality and morbidity. Thus, this systematic review aimed to study the impacts of extreme ambient temperature on cardiovascular outcomes. Material and Methods: This study was carried out based on the Preferred R...

We show that, for the space of Borel probability measures on a Borel subset of a Polish metric space, the extreme points of the Prokhorov, Monge-Wasserstein and Kantorovich metric balls about a measure whose support has at most n points, consist of measures whose supports have at most n+2 points. Moreover, we use the Strassen and Kantorovich-Rubinstein duality theorems to develop representation...

Extreme learning machines are single-hidden layer feed-forward neural networks, where the training is restricted to the output weights in order to achieve fast learning with good performance. The success of learning strongly depends on the random parameter initialization. To overcome the problem of unsuited initialization ranges, a novel and efficient pretraining method to adapt extreme learnin...

Extreme classification problems are multiclass and multilabel classification problems where the number of outputs is so large that straightforward strategies are neither statistically nor computationally viable. One strategy for dealing with the computational burden is via a tree decomposition of the output space. While this typically leads to training and inference that scales sublinearly with...

We investigated the plasma conditions for obtaining highly efficient extreme ultraviolet light from laserproduced tin plasmas for lithography of next generation semiconductors. Based on accurate atomic data tables calculated using the detailed configuration accounting code, we conducted 1-D radiation hydrodynamic simulations to calculate the dynamics of tin plasma and its emission of extreme ul...

This paper proposes a specific type of Local Linear Model, the Shuffled Linear Model (SLM), that can be used as a universal approximator. Local operating points are chosen randomly and linear models are used to approximate a function or system around these points. The model can also be interpreted as an extension to Extreme Learning Machines with Radial Basis Function nodes, or as a specific wa...

We present an algorithm to compute a complete set of efficient solutions for the biobjective integer minimum cost flow problem. We use the two phase method with a parametric network simplex algorithm in phase 1 to compute all supported non-dominated extreme points. In phase 2, the remaining nondominated points (non-extreme supported and non-supported) are computed using a k best flow algorithm ...

This paper addresses the problem of designing supply chains that are resilient to natural or human-induced extreme events. It focuses on the development of efficient restoration strategies that aid the supply chain in recovering from a disruption, thereby limiting the impact on its customers. The proposed restoration model takes into account possible disruptions to infrastructures, e.g., transp...

This paper depicts algorithms for solving the decision Boolean Satisfiability Problem. An extreme problem is formulated to analyze the complexity of algorithms and the complexity for solving it. A novel and easy reformulation as a lottery for an extreme case is presented to determine a stable complexity around 2n. The reformulation point out that the decision Boolean Satisfiability Problem can ...

We examine information aggregation regarding industry capital productivity from privately informed managers in a dynamic model with optimal incentive contracts. Information cascades always occur if managers enjoy limited liability: when beliefs regarding productivity become endogenously extreme (optimistic or pessimistic), learning stops. There is no learning if initial beliefs are extreme, or ...