USE OF THE STATE-TRAIT ANXIETY INVENTORY WITH CHILDREN AND ADOLESCENTS IN CHINA: ISSUES WITH REACTION TIMES

The State-Trait Anxiety Inventory (Form Y; STAI-Y) is a balanced scale with a complex factor structure. Using survey data from children and adolescents in Jiangxi Province, China (N = 1,275), we conducted confirmatory factor analysis to clarify the number of factors in this instrument and to investigate the relationship between reaction time (RT) and anxiety. Results revealed the following 3 dimensions for the STAI-Y: anxiety absent, anxiety present, and general anxiety. Compared with those who answered all the questions (58%), those who missed questions (42%) had a lower education level, a longer RT, and higher scores for items indicating the presence of state or trait anxiety. Our results could provide innovative directions for the improvement and expansion of research using the STAI-Y with children and adolescents

Hawking Radiation of Black p-Branes from Gravitational Anomaly

We investigate the Hawking radiation of black $p$-branes of superstring theories using the method of anomaly cancelation, specially, we use the method of [S. Iso, H. Umetsu and F. Wilczek, {\sl Phys. Rev. Lett.} {\bf 96}, 151302 (2006); {\sl Phys. Rev. D} {\bf 74}, 044017 (2006)]. The metrics of black $p$-branes are spherically symmetric, but not the Schwarzschild type. In order to simplify the calculation, we first make a coordinate transformation to transform the metric to the Schwarzschild type. Then we calculate its energy-momentum flux from the method of anomaly cancelation of the above mentioned references. The obtained energy-momentum flux is equal to a black body radiation, the thermodynamic temperature of the radiation is equal to its Hawking temperature. And we find that the results are not changed for the original non-Schwarzschild type spherically symmetric metric.Comment: 19 pages Latex, some mistakes correcte

Distributionally Adversarial Attack

Recent work on adversarial attack has shown that Projected Gradient Descent (PGD) Adversary is a universal first-order adversary, and the classifier adversarially trained by PGD is robust against a wide range of first-order attacks. It is worth noting that the original objective of an attack/defense model relies on a data distribution $p(\mathbf{x})$, typically in the form of risk maximization/minimization, e.g., $\max/\min\mathbb{E}_{p(\mathbf(x))}\mathcal{L}(\mathbf{x})$ with $p(\mathbf{x})$ some unknown data distribution and $\mathcal{L}(\cdot)$ a loss function. However, since PGD generates attack samples independently for each data sample based on $\mathcal{L}(\cdot)$, the procedure does not necessarily lead to good generalization in terms of risk optimization. In this paper, we achieve the goal by proposing distributionally adversarial attack (DAA), a framework to solve an optimal {\em adversarial-data distribution}, a perturbed distribution that satisfies the $L_\infty$ constraint but deviates from the original data distribution to increase the generalization risk maximally. Algorithmically, DAA performs optimization on the space of potential data distributions, which introduces direct dependency between all data points when generating adversarial samples. DAA is evaluated by attacking state-of-the-art defense models, including the adversarially-trained models provided by {\em MIT MadryLab}. Notably, DAA ranks {\em the first place} on MadryLab's white-box leaderboards, reducing the accuracy of their secret MNIST model to $88.79\%$ (with $l_\infty$ perturbations of $\epsilon = 0.3$) and the accuracy of their secret CIFAR model to $44.71\%$ (with $l_\infty$ perturbations of $\epsilon = 8.0$). Code for the experiments is released on \url{https://github.com/tianzheng4/Distributionally-Adversarial-Attack}.Comment: accepted to AAAI-1

FID: Function Modeling-based Data-Independent and Channel-Robust Physical-Layer Identification

Trusted identification is critical to secure IoT devices. However, the limited memory and computation power of low-end IoT devices prevent the direct usage of conventional identification systems. RF fingerprinting is a promising technique to identify low-end IoT devices since it only requires the RF signals that most IoT devices can produce for communication. However, most existing RF fingerprinting systems are data-dependent and/or not robust to impacts from wireless channels. To address the above problems, we propose to exploit the mathematical expression of the physical-layer process, regarded as a function $\mathbf{\mathcal{F}(\cdot)}$, for device identification. $\mathbf{\mathcal{F}(\cdot)}$ is not directly derivable, so we further propose a model to learn it and employ this function model as the device fingerprint in our system, namely $\mathcal{F}$ID. Our proposed function model characterizes the unique physical-layer process of a device that is independent of the transmitted data, and hence, our system $\mathcal{F}$ID is data-independent and thus resilient against signal replay attacks. Modeling and further separating channel effects from the function model makes $\mathcal{F}$ID channel-robust. We evaluate $\mathcal{F}$ID on thousands of random signal packets from $33$ different devices in different environments and scenarios, and the overall identification accuracy is over $99\%$.Comment: Accepted to INFOCOM201

A MATHEMATICAL MODEL OF THE CORRUGATED PLATES PACKING OIL-WATER SEPARATOR

A new high-efficiency oil-water separator was developed by the authors and their coworkers [1]. The device appears like a horizontal container. Except for the parts of intake and outlet for water and the oil collecting chambers, the main body of this device is the separation chamber, in which the inclined corrugated plates are used as the separation medium

Detecting a set of entanglement measures in an unknown tripartite quantum state by local operations and classical communication

We propose a more general method for detecting a set of entanglement measures, i.e. negativities, in an \emph{arbitrary} tripartite quantum state by local operations and classical communication. To accomplish the detection task using this method, three observers, Alice, Bob and Charlie, do not need to perform the partial transposition maps by the structural physical approximation; instead, they are only required to collectively measure some functions via three local networks supplemented by a classical communication. With these functions, they are able to determine the set of negativities related to the tripartite quantum state.Comment: 16 pages, 2 figures, revte