Importance of Kernel Bandwidth in Quantum Machine Learning

Quantum kernel methods are considered a promising avenue for applying quantum computers to machine learning problems. However, recent results overlook the central role hyperparameters play in determining the performance of machine learning methods. In this work we identify the hyperparameter controlling the bandwidth of a quantum kernel and show that it controls the expressivity of the resulting model. We use extensive numerical experiments with multiple quantum kernels and classical datasets to show consistent change in the model behavior from underfitting (bandwidth too large) to overfitting (bandwidth too small), with optimal generalization in between. We draw a connection between the bandwidth of classical and quantum kernels and show analogous behavior in both cases. Furthermore, we show that optimizing the bandwidth can help mitigate the exponential decay of kernel values with qubit count, which is the cause behind recent observations that the performance of quantum kernel methods decreases with qubit count. We reproduce these negative results and show that if the kernel bandwidth is optimized, the performance instead improves with growing qubit count and becomes competitive with the best classical methods.

Active‐Set Newton Methods and Partial Smoothness

Diverse optimization algorithms correctly identify, in finite time, intrinsic constraints that must be active at optimality. Analogous behavior extends beyond optimization to systems involving partly smooth operators, and in particular to variational inequalities over partly smooth sets. As in classical nonlinear programming, such active‐set structure underlies the design of accelerated local algorithms of Newton type. We formalize this idea in broad generality as a simple linearization scheme for two intersecting manifolds.

DESEMPENHO DE CULTIVARES DE SOJA EM DIFERENTES AMBIENTES DE CULTIVO

Anualmente, inúmeros cultivares de soja são desenvolvidos por programas de melhoramento genético. Desse modo, é importante obter informações sobre o comportamento desses cultivares em distintos ambientes. Objetivou-se com a elaboração do trabalho avaliar o efeito da interação genótipo*ambiente no desempenho de cultivares de soja em diferentes ambientes de cultivo. O delineamento experimental utilizado foi de blocos ao acaso com três repetições. Durante a execução dos experimentos, foi avaliado o desempenho produtivo de seis cultivares de soja em seis ambientes. A variável considerada foi o rendimento de grãos (kg ha-1). As informações foram submetidas a análise de variância, análise de regressão linear simples e teste de comparação de médias. A média geral de produtividade de grãos foi de 2960 kg ha-1. Aanálise de regressão indicou dois cultivares com adaptabilidade ampla, três cultivares com adaptabilidade específica a ambientes desfavoráveis e um cultivar com adaptabilidade específica a ambientes favoráveis. Dentre os cultivares avaliados, quatro apresentaram comportamento esperado ao longo dos ambientes de cultivo. Os cultivares exibiram comportamento análogo quanto ao rendimento de grãos. Por meio da aplicação da metodologia da regressão linear, foi possível obter informações relevantes para cultivo de soja em ambientes subsequentes.Palavras-chave: Glicine max L.; interação genótipo*ambiente; adaptabilidade; estabilidade. PERFORMANCE OF SOYBEAN CULTIVARS IN DIFFERENT GROWING ENVIRONMENTS ABSTRACT:Annually, numerous soybean cultivars are developed by breeding programs. Thus, is important to obtain information about of these cultivars behavior in different environments. The objective of this work was to evaluate the effect of the genotype * environment interaction on the performance of soybean cultivars in different growing environments. The experimental design used was randomized blocks with three replications. During the execution of the experiments, was evaluated the productive performance of six soybean cultivars in six environments. The trait considered was grain yield (kg ha-1). The information was submitted to analysis of variance, simple linear regression analysis and means comparison test. The overall mean grain yield was 2960 kg ha-1. Regression analysis indicated two cultivars with broad adaptability, three cultivars with specific adaptability to unfavorable environments and one cultivar with specific adaptability to favorable environments. Among the evaluated cultivars, four showed prospective behavior throughout the cultivation environments. The cultivars exhibited analogous behavior regarding grain yield. The application of the linear regression methodology provided relevant information for soybean cultivation in subsequent environments.Keywords: Glicine max L.; genotype*environment interaction; adaptability; stability.

Targeting 5α-reductase with 99mTc labeled dutasteride derivatives for prostate imaging

To assess the suitability of 99mTc labeled 5α-reductase (5α-Rds) inhibitors for non-invasive targeting of prostate cancer (PCa) using Male Sprague Dawely Rat (MSDR) model. In this work, dutasteride (Cpd-1) a 5α-Rds inhibitor was derivatized to its dithiocarbamate analogue (Cpd-2) for subsequent synthesis of 99mTc(CO)3-dutasteride dithiocarbamate (Cpd-3) using tricarbonyl technique. To determine the structure of Cpd-3, for the first time a reference Re(CO)3-dutasteride dithiocarbamate (Cpd-4) was synthesized and characterized with NMR, ESIMS, HPLC and elemental analysis. HPLC was used to establish the identity of Cpd-3 using Cpd-4 as a reference standard. The suitability of Cpd-3 as a new 5α-Rds targeting agent was investigated, both in vitro and vivo. The Cpd-3 has shown ≥98 % in vitro stability at room temperature and was remained ≥90 % stable up to 6 h. In serum Cpd-3 has revealed an analogous behavior with a small decrease in stability after 16 h. High uptake (26.25 ± 1.10 %, after 4 h of i.v.) of Cpd-3 was observed in the prostate (target tissue) of MSDR model with reasonably good target to non-target ratio. Blocking the target site with excess Cpd-2 considerably decreased the uptake of Cpd-3 to 4.10 ± 0.75 % in PCa. High in vitro stability in saline and serum and in vivo uptake in prostate of MSDR model plausibly attracts the opportunity of using Cpd-3 as a novel radiopharmaceutical for non invasive targeting of prostate.

Bullying Victimization as a Strain: Examining Changes in Bullying Victimization and Delinquency among Korean Students from a Developmental General Strain Theory Perspective

Objectives: Using a developmental extension of Agnew’s general strain theory (GST), the current study aims to assess the within-individual associations between bullying victimization and delinquency, as well as the recent versus enduring effects of bullying victimization experiences and the moderating influences of several risk factors. Method: Random effects Tobit models are conducted to examine the relationship between changes in bullying victimization and five forms of delinquency using five waves from the Korean Youth Panel Survey. A measure calculating the duration of consecutive bullying experiences is introduced into these models to capture the enduring effects of this strain, and margins analyses are used to assess moderating influences. Results: There is a positive relationship between experiencing a bullying event and delinquency, and this relationship is stronger at higher levels of risk factors. Moreover, bullying victimization over consecutive years has a consistent harmful effect with regard to analogous behavior and violence and theft, as opposed to a diminishing effect for overall delinquency, substance use, and bullying. Conclusions: While there is support for many of the propositions of GST, there are some inconsistences regarding the duration effects. The findings suggest the need for further assessments of the temporal patterns of strains.

Formation dynamics of mesocrystals composed of organically modified CeO2 nanoparticles: analogy to a particle formation model

Mesocrystals, non-classical crystalline nanostructured materials composed of aligned nanoparticles, present analogous behavior to ordinary particle formation.

Bioinspired passive variable recruitment of fluidic artificial muscles

This article presents a novel, passive approach to creating variable actuator recruitment in bundles of fluidic artificial muscles. The passive recruitment control approach is inspired by the functionality of mammalian muscle tissues, in which a single activation signal from the nervous system sequentially triggers contraction of progressively larger actuation elements until the required force is generated. Biologically, this behavior is encoded by differences in electrical resistance properties between smaller and larger muscle-fiber groups. The approach presented here produces analogous behavior using a uniform applied pressure to all fluidic artificial muscles while creating differential pressure responses and threshold pressures among the fluidic artificial muscles via tailored bladder elasticity parameters. A model for using elastic bladder stiffness to control an artificial muscle bundle with a single valve is explored and used to compare a bundle of fluidic artificial muscles with both low and high threshold pressure units to a single fluidic artificial muscle of equivalent displacement and force capability. The results of this analysis indicate the efficacy of using this control method; it is advantageous in cases where a wide range of displacements and forces are necessary and can increase efficiency when the system primarily operates in a low-force regime but requires occasional bursts of high-force capability.