Birth cohort differences in self-esteem, 1988–2008: A cross-temporal meta-analysis.

Three meta-analyses find increases over the generations in Rosenberg Self-Esteem scale (RSE) scores between 1988 and 2008 among American middle school (d = 0.78, n = 10,119), high school (d = 0.39, n = 16,669), and college students (d = 0.30, n = 28,918). The changes are consistent with an increasing emphasis on self-worth in American culture and, for high school students, with small increases in academic competence over time. College students' scores change only when the RSE is administered with a 4-point Likert scale with no midpoint. By 2008, a score of 40 (perfect self-esteem) was the modal response of college students, chosen by 18% of participants; 51% scored 35 or over. Given these shifts in responses, the possibility of revising the RSE is discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Focal-plane processing architectures for real-time hyperspectral image processing

Real-time image processing requires high computational and I/O throughputs obtained by use of optoelectronic system solutions. A novel architecture that uses focal-plane optoelectronic-area I/O with a fine-grain, low-memory, single-instruction-multiple-data (SIMD) processor array is presented as an efficient computational solution for real-time hyperspectral image processing. The architecture is evaluated by use of realistic workloads to determine data throughputs, processing demands, and storage requirements. We show that traditional store-and-process system performance is inadequate for this application domain, whereas the focal-plane SIMD architecture is capable of supporting real-time performances with sustained operation throughputs of 500-1500 gigaoperations/s. The focal-plane architecture exploits the direct coupling between sensor and parallel-processor arrays to alleviate data-bandwidth requirements, allowing computation to be performed in a stream-parallel computation model, while data arrive from the sensors.     

Heat transfer properties in a vertical channel filled with saturated and pressurized helium II

Various heat flow regimes were investigated in a vertical slit opened at both ends of a helium II bath, as a means of simulating the properties of a typical heat transfer channel in a superconducting device. In pressurized He II, the heat fluxes before the transition of He II of — He I are in agreement with the Gorter-Mellink model. In saturated He II, a heat transfer regime characterized by temperature oscillations superimposed on a Gorter-Mellink regime has been found.     

Segmentation for robust tracking in the presence of severe occlusion

Tracking an object in a sequence of images can fail due to partial occlusion or clutter. Robustness to occlusion can be increased by tracking the object as a set of "parts" such that not all of these are occluded at the same time. However, successful implementation of this idea hinges upon finding a suitable set of parts. In this paper we propose a novel segmentation, specifically designed to improve robustness against occlusion in the context of tracking. The main result shows that tracking the parts resulting from this segmentation outperforms both tracking parts obtained through traditional segmentations, and tracking the entire target. Additional results include a statistical analysis of the correlation between features of a part and tracking error, and identifying a cost function that exhibits a high degree of correlation with the tracking error.     

Layer-oriented simulation tool

The Layer-Oriented Simulation Tool (LOST) is a numerical simulation code developed for analysis of the performance of multiconjugate adaptive optics modules following a layer-oriented approach. The LOST code computes the atmospheric layers in terms of phase screens and then propagates the phase delays introduced in the natural guide stars' wave fronts by using geometrical optics approximations. These wave fronts are combined in an optical or numerical way, including the effects of wave-front sensors on measurements in terms of phase noise. The LOST code is described, and two applications to layer-oriented modules are briefly presented. We have focus on the Multiconjugate adaptive optics demonstrator to be mounted upon the Very Large Telescope and on the Near-IR-Visible Adaptive Interferometer for Astronomy (NIRVANA) interferometric system to be installed on the combined focus of the Large Binocular Telescope.     

Enhancing density-based clustering: Parameter reduction and outlier detection

Clustering is a widely used unsupervised data mining technique. It allows to identify structures in collections of objects by grouping them into classes, named clusters, in such a way that similarity of objects within any cluster is maximized and similarity of objects belonging to different clusters is minimized. In density-based clustering, a cluster is defined as a connected dense component and grows in the direction driven by the density. The basic structure of density-based clustering presents some common drawbacks: (i) parameters have to be set; (ii) the behavior of the algorithm is sensitive to the density of the starting object; and (iii) adjacent clusters of different densities could not be properly identified. In this paper, we address all the above problems. Our method, based on the concept of space stratification, efficiently identifies the different densities in the dataset and, accordingly, ranks the objects of the original space. Next, it exploits such a knowledge by projecting the original data into a space with one more dimension. It performs a density based clustering taking into account the reverse-nearest-neighbor of the objects. Our method also reduces the number of input parameters by giving a guideline to set them in a suitable way. Experimental results indicate that our algorithm is able to deal with clusters of different densities and outperforms the most popular algorithms DBSCAN and OPTICS in all the standard benchmark datasets.     

Multiclass classification with bandit feedback using adaptive regularization

We present a new multiclass algorithm in the bandit framework, where after making a prediction, the learning algorithm receives only partial feedback, i.e., a single bit indicating whether the predicted label is correct or not, rather than the true label. Our algorithm is based on the second-order Perceptron, and uses upper-confidence bounds to trade-off exploration and exploitation, instead of random sampling as performed by most current algorithms. We analyze this algorithm in a partial adversarial setting, where instances are chosen adversarially, while the labels are chosen according to a linear probabilistic model which is also chosen adversarially. We show a regret of $\mathcal{O}(\sqrt{T}\log T)$ , which improves over the current best bounds of $\mathcal{O}(T^{2/3})$ in the fully adversarial setting. We evaluate our algorithm on nine real-world text classification problems and on four vowel recognition tasks, often obtaining state-of-the-art results, even compared with non-bandit online algorithms, especially when label noise is introduced.     

Regulatory Noncoding and Predicted Pathogenic Coding Variants of CCR5 Predispose to Severe COVID-19

Genome-wide association studies (GWAS) found locus 3p21.31 associated with severe COVID-19. CCR5 resides at the same locus and, given its known biological role in other infection diseases, we investigated if common noncoding and rare coding variants, affecting CCR5, can predispose to severe COVID-19. We combined single nucleotide polymorphisms (SNPs) that met the suggestive significance level (P ≤ 1 × 10⁻⁵) at the 3p21.31 locus in public GWAS datasets (6406 COVID-19 hospitalized patients and 902,088 controls) with gene expression data from 208 lung tissues, Hi-C, and Chip-seq data. Through whole exome sequencing (WES), we explored rare coding variants in 147 severe COVID-19 patients. We identified three SNPs (rs9845542, rs12639314, and rs35951367) associated with severe COVID-19 whose risk alleles correlated with low CCR5 expression in lung tissues. The rs35951367 resided in a CTFC binding site that interacts with CCR5 gene in lung tissues and was confirmed to be associated with severe COVID-19 in two independent datasets. We also identified a rare coding variant (rs34418657) associated with the risk of developing severe COVID-19. Our results suggest a biological role of CCR5 in the progression of COVID-19 as common and rare genetic variants can increase the risk of developing severe COVID-19 by affecting the functions of CCR5.     

The use of activated charcoal in combination with other fining agents and its influence on the organoleptic properties of sherry wine

Fining experiments have been conducted on fino sherry wine from the Jerez-Xérès-Sherry region (southern Spain), in which the combined use of activated charcoal with proteinaceous fining agents (casein, potassium caseinate, albumin, and gelatin) has been studied. The effect of these fining agents on the polyphenolic content, the aromatic profile, and the resistance to browning of the treated wine has been determined. The polyphenolic content suffers significant decreases following the use of activated charcoal; these decreases are only increased slightly by the subsequent use of the other fining agents. The aromatic profile was not found to be altered by the clarification agents used. Despite the reduction in the polyphenolic content, the treated wines show a tendency to suffer browning similar to that observed in non-clarified wine.     

Judgment of contingency: Cognitive biases in depressed and nondepressed subjects.

In this research I investigated whether the use of relevant affective outcomes influences depressed and nondepressed subjects' judgment of contingency. Similar to previous studies (Alloy & Abramson, 1979, Experiments 1 and 2), Experiments 1 and 2 confirmed that when the outcome is affectively neutral (i.e., the onset of a light) depressed subjects make accurate judgments of contingency, whereas nondepressed subjects show (in noncontingent situations) a significant illusion of control. In Experiments 3 and 4 (a contingency situation and a noncontingency situation, respectively) different types of sentences (negative self-referent, negative other-referent, positive self-referent, positive other-referent) were used as outcomes. Although depressed subjects were more reluctant to show biased judgments than were the nondepressed subjects, in noncontingency situations depressed subjects made overestimated judgments of contingency when the outcomes were negative self-referent sentences. Results are discussed with regard to current cognitive theories of depression, particularly the learned helplessness model. (PsycINFO Database Record (c) 2010 APA, all rights reserved)