SemanticLock: An authentication method for mobile devices using semantically-linked images

We introduce SemanticLock, a single factor graphical authentication solution for mobile devices. SemanticLock uses a set of graphical images as password tokens that construct a semantically memorable story representing the user`s password. A familiar and quick action of dragging or dropping the images into their respective positions either in a \textit{continous flow} or in \textit{discrete} movements on the the touchscreen is what is required to use our solution. The authentication strength of the SemanticLock is based on the large number of possible semantic constructs derived from the positioning of the image tokens and the type of images selected. Semantic Lock has a high resistance to smudge attacks and it equally exhibits a higher level of memorability due to its graphical paradigm. In a three weeks user study with 21 participants comparing SemanticLock against other authentication systems, we discovered that SemanticLock outperformed the PIN and matched the PATTERN both on speed, memorability, user acceptance and usability. Furthermore, qualitative test also show that SemanticLock was rated more superior in like-ability. SemanticLock was also evaluated while participants walked unencumbered and walked encumbered carrying "everyday" items to analyze the effects of such activities on its usage

An Efficient Algorithm for Deep Stochastic Contextual Bandits

In stochastic contextual bandit (SCB) problems, an agent selects an action based on certain observed context to maximize the cumulative reward over iterations. Recently there have been a few studies using a deep neural network (DNN) to predict the expected reward for an action, and the DNN is trained by a stochastic gradient based method. However, convergence analysis has been greatly ignored to examine whether and where these methods converge. In this work, we formulate the SCB that uses a DNN reward function as a non-convex stochastic optimization problem, and design a stage-wise stochastic gradient descent algorithm to optimize the problem and determine the action policy. We prove that with high probability, the action sequence chosen by this algorithm converges to a greedy action policy respecting a local optimal reward function. Extensive experiments have been performed to demonstrate the effectiveness and efficiency of the proposed algorithm on multiple real-world datasets.Comment: Accepted by AAAI 202

Metal-poor stars observed with the automated planet finder telescope. I. Discovery of five carbon-enhanced metal-poor stars from LAMOST

We report on the discovery of five carbon-enhanced metal-poor (CEMP) stars in the metallicity range of $-3.3<$ [Fe/H] $<-2.4$. These stars were selected from the LAMOST DR3 low-resolution (R$\sim$ 2,000) spectroscopic database as metal-poor candidates and followed-up with high-resolution spectroscopy (R$\sim$110,000) with the LICK/APF. Stellar parameters and individual abundances for 25 chemical elements (from Li to Eu) are presented for the first time. These stars exhibit chemical abundance patterns that are similar to those reported in other literature studies of very and extremely metal-poor stars. One of our targets, J2114$-$0616, shows high enhancement in carbon ([C/Fe]=1.37), nitrogen ([N/Fe]= 1.88), barium ([Ba/Fe]=1.00), and europium ([Eu/Fe]=0.84). Such chemical abundance pattern suggests that J2114$-$0616 can be classified as CEMP-r/s star. In addition, the star J1054+0528 can be classified as a CEMP-rI star, with [Eu/Fe]=0.44 and [Ba/Fe]=$-$0.52. The other stars in our sample show no enhancements in neutron-capture elements and can be classified as CEMP-no stars. We also performed a kinematic and dynamical analysis of the sample stars based on Gaia DR2 data. The kinematic parameters, orbits, and binding energy of these stars, show that J2114$-$0616 is member of the outer halo population, while the remaining stars belong to the inner halo population but with an accreted origin. Collectively, these results add important constraints on the origin and evolution of CEMP stars as well as on their possible formation scenarios

Gamma-Glutamyl Transpeptidase to Platelet Ratio Is a Novel and Independent Prognostic Marker for Resectable Lung Cancer: A Propensity Score Matching Study.

BACKGROUND We report this propensity score matching (PSM) analysis to assess prognostic roles of preoperative gamma-glutamyl transpeptidase to platelet ratio (GPR) in video-assisted thoracoscopic (VATS) lobectomy for stage I-II non-small-cell lung cancer (NSCLC). METHODS The PSM-based study conducted on our single-center prospectively collected database from January 2014 to August 2015 provided Kaplan-Meier survival analyses using the log-rank test to discriminate differences in overall survival (OS) and disease-free survival (DFS) between patients stratified by preoperative GPR. RESULTS Our study includes 379 patients diagnosed with operable primary stage I-II NSCLC. A GPR value at 0.16 was recognized as the optimal cutoff point for prognostic prediction. Both OS and DFS of patients with GPR ≥0.16 were significantly shortened when compared to those of patients with GPR <0.16. Patients with GPR ≥0.16 had significantly lower 5-year rates of OS and DFS than those of patients with GPR <0.16 (P <0.001). Significant associations between GPR and unfavorable survival still are validated in the PSM analysis. Multivariable Cox regression models on both the entire cohort and the PSM cohort consistently demonstrated that an elevated preoperative GPR could be an independent prognostic marker for both OS and DFS of resectable NSCLC. CONCLUSIONS GPR may be an effective and noninvasive prognostic biomarker in VATS lobectomy for surgically resectable NSCLC

ISA-Net: Improved spatial attention network for PET-CT tumor segmentation

Achieving accurate and automated tumor segmentation plays an important role in both clinical practice and radiomics research. Segmentation in medicine is now often performed manually by experts, which is a laborious, expensive and error-prone task. Manual annotation relies heavily on the experience and knowledge of these experts. In addition, there is much intra- and interobserver variation. Therefore, it is of great significance to develop a method that can automatically segment tumor target regions. In this paper, we propose a deep learning segmentation method based on multimodal positron emission tomography-computed tomography (PET-CT), which combines the high sensitivity of PET and the precise anatomical information of CT. We design an improved spatial attention network(ISA-Net) to increase the accuracy of PET or CT in detecting tumors, which uses multi-scale convolution operation to extract feature information and can highlight the tumor region location information and suppress the non-tumor region location information. In addition, our network uses dual-channel inputs in the coding stage and fuses them in the decoding stage, which can take advantage of the differences and complementarities between PET and CT. We validated the proposed ISA-Net method on two clinical datasets, a soft tissue sarcoma(STS) and a head and neck tumor(HECKTOR) dataset, and compared with other attention methods for tumor segmentation. The DSC score of 0.8378 on STS dataset and 0.8076 on HECKTOR dataset show that ISA-Net method achieves better segmentation performance and has better generalization. Conclusions: The method proposed in this paper is based on multi-modal medical image tumor segmentation, which can effectively utilize the difference and complementarity of different modes. The method can also be applied to other multi-modal data or single-modal data by proper adjustment

Four-hundred Very Metal-poor Stars Studied with LAMOST and Subaru. I. Survey Design, Follow-up Program, and Binary Frequency

The chemical abundances of very metal-poor stars provide important constraints on the nucleosynthesis of the first generation of stars and early chemical evolution of the Galaxy. We have obtained high-resolution spectra with the Subaru Telescope for candidates of very metal-poor stars selected with a large survey of Galactic stars carried out with LAMOST. In this series of papers, we report on the elemental abundances of about 400 very metal-poor stars and discuss the kinematics of the sample obtained by combining the radial velocities measured in this study and recent astrometry obtained with Gaia. This paper provides an overview of our survey and follow-up program, and reports radial velocities for the whole sample. We identify seven double-lined spectroscopic binaries from our high-resolution spectra, for which radial velocities of the components are reported. We discuss the frequency of such relatively short-period binaries at very low metallicity.Comment: 24 pages, 9 figures, 5 tables, to appear in Ap