Longitudinal Analyses of Achievement Growth and Associated Kindergarten Factors for Subgroups of Children with Mathematics and Reading Difficulties

The present study used data from the Early Childhood Longitudinal Study - Kindergarten Class of 1998-1999 (ECLS-K) to explore the performance profiles of children with difficulties in mathematics. Two issues were particularly addressed in the present study- the longitudinal manifestation of math difficulties and the differential influence of early predictors on math growth rates and fifth-grade achievement for children with different subtypes of MD. The first issue was investigated by considering the stability and patterns of subgroup change for children with MD, MD-RD, RD, and TA, as well as by examining the math and reading achievement trajectories of children in different achievement subgroups. The second issue was explored by investigating how the identified kindergarten predictors influence progress in learning math and whether the effects of these kindergarten predictors vary among children in different achievement subgroups. Two main findings emerged: (a) children with MD-RD differed from children with MD and children in the comparison groups in the patterns of subtype change over time, math and reading IRT scale scores, and math and reading achievement trajectories; and (b) children's demographic characteristics, learning-related skills, math and reading performance at kindergarten entry, class size, and instructional time were all significantly predictive of their later math achievement and progress. Among the identified kindergarten predictors, only the effects of socioeconomic status and initial math knowledge vary across children in different kindergarten achievement subgroups. Despite some study limitations, the results of the present study add to the knowledge of academic development for children with difficulties in mathematics and have implications on early identification and intervention for this population

Rotating Black Holes and Coriolis Effect

In this work, we consider the fluid/gravity correspondence for general rotating black holes. By using the Petrov-like boundary condition in near horizon limit, we study the correspondence between gravitational perturbation and fluid equation. We find that the dual fluid equation for rotating black holes contains a Coriolis force term, which is closely related to the angular velocity of the black hole horizon. This can be seen as a dual effect for the frame-dragging effect of rotating black hole under the holographic picture.Comment: 5 page

Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning

Visual language grounding is widely studied in modern neural image captioning systems, which typically adopts an encoder-decoder framework consisting of two principal components: a convolutional neural network (CNN) for image feature extraction and a recurrent neural network (RNN) for language caption generation. To study the robustness of language grounding to adversarial perturbations in machine vision and perception, we propose Show-and-Fool, a novel algorithm for crafting adversarial examples in neural image captioning. The proposed algorithm provides two evaluation approaches, which check whether neural image captioning systems can be mislead to output some randomly chosen captions or keywords. Our extensive experiments show that our algorithm can successfully craft visually-similar adversarial examples with randomly targeted captions or keywords, and the adversarial examples can be made highly transferable to other image captioning systems. Consequently, our approach leads to new robustness implications of neural image captioning and novel insights in visual language grounding.Comment: Accepted by 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018). Hongge Chen and Huan Zhang contribute equally to this wor

ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models

Deep neural networks (DNNs) are one of the most prominent technologies of our time, as they achieve state-of-the-art performance in many machine learning tasks, including but not limited to image classification, text mining, and speech processing. However, recent research on DNNs has indicated ever-increasing concern on the robustness to adversarial examples, especially for security-critical tasks such as traffic sign identification for autonomous driving. Studies have unveiled the vulnerability of a well-trained DNN by demonstrating the ability of generating barely noticeable (to both human and machines) adversarial images that lead to misclassification. Furthermore, researchers have shown that these adversarial images are highly transferable by simply training and attacking a substitute model built upon the target model, known as a black-box attack to DNNs. Similar to the setting of training substitute models, in this paper we propose an effective black-box attack that also only has access to the input (images) and the output (confidence scores) of a targeted DNN. However, different from leveraging attack transferability from substitute models, we propose zeroth order optimization (ZOO) based attacks to directly estimate the gradients of the targeted DNN for generating adversarial examples. We use zeroth order stochastic coordinate descent along with dimension reduction, hierarchical attack and importance sampling techniques to efficiently attack black-box models. By exploiting zeroth order optimization, improved attacks to the targeted DNN can be accomplished, sparing the need for training substitute models and avoiding the loss in attack transferability. Experimental results on MNIST, CIFAR10 and ImageNet show that the proposed ZOO attack is as effective as the state-of-the-art white-box attack and significantly outperforms existing black-box attacks via substitute models.Comment: Accepted by 10th ACM Workshop on Artificial Intelligence and Security (AISEC) with the 24th ACM Conference on Computer and Communications Security (CCS

Link prediction for interdisciplinary collaboration via co-authorship network

We analyse the Publication and Research (PURE) data set of University of Bristol collected between $2008$ and $2013$. Using the existing co-authorship network and academic information thereof, we propose a new link prediction methodology, with the specific aim of identifying potential interdisciplinary collaboration in a university-wide collaboration network