Coping With Racism: Moderators of the Discrimination-Adjustment Link Among Mexican-Origin Adolescents

What strategies help ethnic minority adolescents to cope with racism? The present study addressed this question by testing the role of ethnic identity, social support, and anger expression and suppression as moderators of the discrimination-adjustment link among 269 Mexican-origin adolescents (Mage = 14.1 years), 12-17 years old from the Midwestern U.S. Results from multilevel moderation analyses indicated that ethnic identity, social support, and anger suppression, respectively, significantly attenuated the relations between discrimination and adjustment problems, whereas outward anger expression exacerbated these relations. Moderation effects differed according to the level of analysis. By identifying effective coping strategies in the discrimination-adjustment link at specific levels of analysis, the present findings can guide future intervention efforts for Latino youth

Wood-Inhabiting Microfungi of New York

The loss of biodiversity in tropical rain forests has commanded much attention and justifiably so. However, the loss of biodiversity in temperate regions is just as serious because every geographical area is home to unique species and ecological diversity. Microscopic fungi are understudied and their identities and distribution records are not well-documented. Assessing the loss of biodiversity in any ecosystem must first require an inventory of the diversity, abundance and scarcity of organisms. The versatile microscopic fungi can be plant, animal, and human pathogens as well as very effective decomposers of all types of substrates, e.g., toxic organic compounds, leaves, wood, etc. The present project focused on the wood-inhabiting microscopic fungi. More specifically the hyphomycetes (the conidial fungi or the anamorphic fungi) of New York. The basis of this project is nearly 2,500 specimens of microfungi collected from the northern hardwood forests and a few conifer plantations in 24 counties in New York from 1965 to 2004 and then in 2008. The accurate identification, inventory and distribution records of these microfungi are the first and only one of its kind for New York State. Data from this project are essential for my forthcoming book on the Lignicolous Hyphomycetes of New York. The book will provide better understanding of the morphology, species and generic delimitation, classification, and ecology of these important fungi. It will be an useful taxonomic guide for plant pathologists, wood technologists, aeromycologists, medical and pharmaceutical researchers and industrialists. It will stimulate more students to study microfungi. A preliminary report was published in the Harvard Papers in Botany (Wang 2001)

Generating Image Descriptions with Gold Standard Visual Inputs: Motivation, Evaluation and Baselines

In this paper, we present the task of generating image descriptions with gold standard visual detections as input, rather than directly from an image. This allows the Natural Language Generation community to focus on the text generation process, rather than dealing with the noise and complications arising from the visual detection process. We propose a fine-grained evaluation metric specifically for evaluating the content selection capabilities of image description generation systems. To demonstrate the evaluation metric on the task, several baselines are presented using bounding box information and textual information as priors for content selection. The baselines are evaluated using the proposed metric, showing that the fine-grained metric is useful for evaluating the content selection phase of an image description generation system

Cross-validating Image Description Datasets and Evaluation Metrics

The task of automatically generating sentential descriptions of image content has become increasingly popular in recent years, resulting in the development of large-scale image description datasets and the proposal of various metrics for evaluating image description generation systems. However, not much work has been done to analyse and understand both datasets and the metrics. In this paper, we propose using a leave-one-out cross validation (LOOCV) process as a means to analyse multiply annotated, human-authored image description datasets and the various evaluation metrics, i.e. evaluating one image description against other human-authored descriptions of the same image. Such an evaluation process affords various insights into the image description datasets and evaluation metrics, such as the variations of image descriptions within and across datasets and also what the metrics capture. We compute and analyse (i) human upper-bound performance; (ii) ranked correlation between metric pairs across datasets; (iii) lower-bound performance by comparing a set of descriptions describing one image to another sentence not describing that image. Interesting observations are made about the evaluation metrics and image description datasets, and we conclude that such cross-validation methods are extremely useful for assessing and gaining insights into image description datasets and evaluation metrics for image descriptions

Path relinking for the vertex separator problem

This paper presents the first population-based path relinking algorithm for solving the NP-hard vertex separator problem in graphs. The proposed algorithm employs a dedicated relinking procedure to generate intermediate solutions between an initiating solution and a guiding solution taken from a reference set of elite solutions (population) and uses a fast tabu search procedure to improve some selected intermediate solutions. Special care is taken to ensure the diversity of the reference set. Dedicated data structures based on bucket sorting are employed to ensure a high computational efficiency. The proposed algorithm is assessed on four sets of 365 benchmark instances with up to 20,000 vertices, and shows highly comparative results compared to the state-of-the-art methods in the literature. Specifically, we report improved best solutions (new upper bounds) for 67 instances which can serve as reference values for assessment of other algorithms for the problem

Solving large scale Max Cut problems via tabu search

In recent years many algorithms have been proposed in the literature for solving the Max-Cut problem. In this paper we report on the application of a new Tabu Search algorithm to large scale Max-cut test problems. Our method provides best known solutions for many well-known test problems of size up to 10,000 variables, although it is designed for the general unconstrained quadratic binary program (UBQP), and is not specialized in any way for the Max-Cut problem

Visual and semantic knowledge transfer for large scale semi-supervised object detection

Deep CNN-based object detection systems have achieved remarkable success on several large-scale object detection benchmarks. However, training such detectors requires a large number of labeled bounding boxes, which are more difficult to obtain than image-level annotations. Previous work addresses this issue by transforming image-level classifiers into object detectors. This is done by modeling the differences between the two on categories with both image-level and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations. We improve this previous work by incorporating knowledge about object similarities from visual and semantic domains during the transfer process. The intuition behind our proposed method is that visually and semantically similar categories should exhibit more common transferable properties than dissimilar categories, e.g. a better detector would result by transforming the differences between a dog classifier and a dog detector onto the cat class, than would by transforming from the violin class. Experimental results on the challenging ILSVRC2013 detection dataset demonstrate that each of our proposed object similarity based knowledge transfer methods outperforms the baseline methods. We found strong evidence that visual similarity and semantic relatedness are complementary for the task, and when combined notably improve detection, achieving state-of-the-art detection performance in a semi-supervised setting

Formation of an Edge Striped Phase in Fractional Quantum Hall Systems

We have performed an exact diagonalization study of up to N=12 interacting electrons on a disk at filling $\nu={1/3}$ for both Coulomb and $V_1$ short-range interaction for which Laughlin wave function is the exact solution. For Coulomb interaction and $N\geq 10$ we find persistent radial oscillations in electron density, which are not captured by the Laughlin wave function. Our results srongly suggest formation of a chiral edge striped phase in quantum Hall systems. The amplitude of the charge density oscillations decays slowly, perhaps as a square root of the distance from the edge; thus the spectrum of edge excitations is likely to be affected.Comment: 4 pages, 3 Figs. include

Large Scale Semi-supervised Object Detection using Visual and Semantic Knowledge Transfer

Deep CNN-based object detection systems have achieved remarkable success on several large-scale object detection benchmarks. However, training such detectors requires a large number of labeled bounding boxes, which are more difficult to obtain than image-level annotations. Previous work addresses this issue by transforming image-level classifiers into object detectors. This is done by modeling the differences between the two on categories with both imagelevel and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations. We improve this previous work by incorporating knowledge about object similarities from visual and semantic domains during the transfer process. The intuition behind our proposed method is that visually and semantically similar categories should exhibit more common transferable properties than dissimilar categories, e.g. a better detector would result by transforming the differences between a dog classifier and a dog detector onto the cat class, than would by transforming from the violin class. Experimental results on the challenging ILSVRC2013 detection dataset demonstrate that each of our proposed object similarity based knowledge transfer methods outperforms the baseline methods. We found strong evidence that visual similarity and semantic relatedness are complementary for the task, and when combined notably improve detection, achieving state-of-the-art detection performance in a semi-supervised setting

The application of componentised modelling techniques to catastrophe model generation

In this paper we show that integrated environmental modelling (IEM) techniques can be used to generate a catastrophe model for groundwater flooding. Catastrophe models are probabilistic models based upon sets of events representing the hazard and weights their likelihood with the impact of such an event happening which is then used to estimate future financial losses. These probabilistic loss estimates often underpin re-insurance transactions. Modelled loss estimates can vary significantly, because of the assumptions used within the models. A rudimentary insurance-style catastrophe model for groundwater flooding has been created by linking seven individual components together. Each component is linked to the next using an open modelling framework (i.e. an implementation of OpenMI). Finally, we discuss how a flexible model integration methodology, such as described in this paper, facilitates a better understanding of the assumptions used within the catastrophe model by enabling the interchange of model components created using different, yet appropriate, assumptions