Resource-Adaptive Newton's Method for Distributed Learning

Distributed stochastic optimization methods based on Newton's method offer significant advantages over first-order methods by leveraging curvature information for improved performance. However, the practical applicability of Newton's method is hindered in large-scale and heterogeneous learning environments due to challenges such as high computation and communication costs associated with the Hessian matrix, sub-model diversity, staleness in training, and data heterogeneity. To address these challenges, this paper introduces a novel and efficient algorithm called RANL, which overcomes the limitations of Newton's method by employing a simple Hessian initialization and adaptive assignments of training regions. The algorithm demonstrates impressive convergence properties, which are rigorously analyzed under standard assumptions in stochastic optimization. The theoretical analysis establishes that RANL achieves a linear convergence rate while effectively adapting to available resources and maintaining high efficiency. Unlike traditional first-order methods, RANL exhibits remarkable independence from the condition number of the problem and eliminates the need for complex parameter tuning. These advantages make RANL a promising approach for distributed stochastic optimization in practical scenarios

Exploring differences in injury severity between occupant groups involved in fatal rear-end crashes: A correlated random parameter logit model with mean heterogeneity

Rear-end crashes are one of the most common crash types. Passenger cars involved in rear-end crashes frequently produce severe outcomes. However, no study investigated the differences in the injury severity of occupant groups when cars are involved as following and leading vehicles in rear-end crashes. Therefore, the focus of this investigation is to compare the key factors affecting the injury severity between the front- and rear-car occupant groups in rear-end crashes. First, data is extracted from the Fatality Analysis Reporting System (FARS) for two types of rear-end crashes from 2017 to 2019, including passenger cars as rear-end and rear-ended vehicles. Significant injury severity difference between front- and rear-car occupant groups is found by conducting likelihood ratio test. Moreover, the front- and rear-car occupant groups are modelled by the correlated random parameter logit model with heterogeneity in means (CRPLHM) and the random parameter logit model with heterogeneity in means (RPLHM), respectively. From the modeling, the significant factors are occupant positions, driver age, overturn, vehicle type, etc. For instance, the driving and front-right positions significantly increase the probability of severe injury when struck by another vehicle. Large truck-strike-car tends to cause severe outcomes compared to car-strike-large truck. This study provides an insightful knowledge of mechanism of occupant injury severity in rear-end crashes, and propose some effective countermeasures to mitigate the crash severity, such as implementing stricter seat belt laws, improving the coverage of the streetlights, strengthening car driver's emergency response ability

Study on the microstructural evolution of different component alloys consisting of B2-NiSc intermetallics

Ni-50%Sc and Ni-51%Sc alloy were prepared with a vacuum arc smelting and water cooled copper mold suction-casting machine. The results showed that the two component alloys consisted of the primary phase B2-NiSc and lamellar (Ni2Sc+NiSc)eutectic due to the loss of Sc duringmelting. Two groups of alloys underwent (970 ℃, 72 h) homogenization heat treatment, and spherical or plate shape Ni2Sc particles were dispersed on the B2-NiSc matrix. With the increase of Sc content from50% to 51%, the amount of the second phase in the alloy decreases, the microstructure becomes uniform, and the grain gradually changes from long bar to a spherical particle. According to the Jackson boundary theory, the Jackson factor α of B2-NiSc =0.5 < 2, so the interface is rough, which explains that the growth pattern of the B2-NiSc phase is anon-faceted growth. It is consistent with the dendritic growth pattern of the B2-NiSc phase, which is observed from the experiment. After a longheat treatment, the number of vacancies decreases and the microstructure became uniform. The loss rate of Sc in rapidly quenched solidification was higher than that after the heat treatment

Zooarchaeological and Genetic Evidence for the Origins of Domestic Cattle in Ancient China

This article reviews current evidence for the origins of domestic cattle in China. We describe two possible scenarios: 1) domestic cattle were domesticated indigenously in East Asia from the wild aurochs ( Bos primigenius), and 2) domestic cattle were domesticated elsewhere and then introduced to China. We conclude that the current zooarchaeological and genetic evidence does not support indigenous domestication within China, although it is possible that people experimented with managing wild aurochs in ways that did not lead to complete domestication. Most evidence indicates that domestic taurine cattle ( Bos taurus) were introduced to China during the third millennium b.c., and were related to cattle populations first domesticated in the Near East. Zebu cattle ( Bos indicus) entered China sometime between 2000 and 200 b.c., but much less is known about this species. The role of cattle as ritual and wealth animals seems to have been critical to their initial introduction

A Region-Shrinking-Based Acceleration for Classification-Based Derivative-Free Optimization

Derivative-free optimization algorithms play an important role in scientific and engineering design optimization problems, especially when derivative information is not accessible. In this paper, we study the framework of classification-based derivative-free optimization algorithms. By introducing a concept called hypothesis-target shattering rate, we revisit the computational complexity upper bound of this type of algorithms. Inspired by the revisited upper bound, we propose an algorithm named "RACE-CARS", which adds a random region-shrinking step compared with "SRACOS" (Hu et al., 2017).. We further establish a theorem showing the acceleration of region-shrinking. Experiments on the synthetic functions as well as black-box tuning for language-model-as-a-service demonstrate empirically the efficiency of "RACE-CARS". An ablation experiment on the introduced hyperparameters is also conducted, revealing the mechanism of "RACE-CARS" and putting forward an empirical hyperparameter-tuning guidance