Real-Time 6DOF Pose Relocalization for Event Cameras with Stacked Spatial LSTM Networks

We present a new method to relocalize the 6DOF pose of an event camera solely based on the event stream. Our method first creates the event image from a list of events that occurs in a very short time interval, then a Stacked Spatial LSTM Network (SP-LSTM) is used to learn the camera pose. Our SP-LSTM is composed of a CNN to learn deep features from the event images and a stack of LSTM to learn spatial dependencies in the image feature space. We show that the spatial dependency plays an important role in the relocalization task and the SP-LSTM can effectively learn this information. The experimental results on a publicly available dataset show that our approach generalizes well and outperforms recent methods by a substantial margin. Overall, our proposed method reduces by approx. 6 times the position error and 3 times the orientation error compared to the current state of the art. The source code and trained models will be released.Comment: 7 pages, 5 figure

Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior

Choosing a proper set of kernel functions is an important problem in learning Gaussian Process (GP) models since each kernel structure has different model complexity and data fitness. Recently, automatic kernel composition methods provide not only accurate prediction but also attractive interpretability through search-based methods. However, existing methods suffer from slow kernel composition learning. To tackle large-scaled data, we propose a new sparse approximate posterior for GPs, MultiSVGP, constructed from groups of inducing points associated with individual additive kernels in compositional kernels. We demonstrate that this approximation provides a better fit to learn compositional kernels given empirical observations. We also provide theoretically justification on error bound when compared to the traditional sparse GP. In contrast to the search-based approach, we present a novel probabilistic algorithm to learn a kernel composition by handling the sparsity in the kernel selection with Horseshoe prior. We demonstrate that our model can capture characteristics of time series with significant reductions in computational time and have competitive regression performance on real-world data sets.Comment: AAAI 202

Conditional Support Alignment for Domain Adaptation with Label Shift

Unsupervised domain adaptation (UDA) refers to a domain adaptation framework in which a learning model is trained based on the labeled samples on the source domain and unlabelled ones in the target domain. The dominant existing methods in the field that rely on the classical covariate shift assumption to learn domain-invariant feature representation have yielded suboptimal performance under the label distribution shift between source and target domains. In this paper, we propose a novel conditional adversarial support alignment (CASA) whose aim is to minimize the conditional symmetric support divergence between the source's and target domain's feature representation distributions, aiming at a more helpful representation for the classification task. We also introduce a novel theoretical target risk bound, which justifies the merits of aligning the supports of conditional feature distributions compared to the existing marginal support alignment approach in the UDA settings. We then provide a complete training process for learning in which the objective optimization functions are precisely based on the proposed target risk bound. Our empirical results demonstrate that CASA outperforms other state-of-the-art methods on different UDA benchmark tasks under label shift conditions

ROBUST DYNAMIC ID-BASED REMOTE MUTUAL AUTHENTICATION SCHEME

Dynamic ID based authentication scheme is more and more important in insecure wireless environment and system. Two of kinds of attack that authentication schemes must resist are stealing identity and reflection attack which is a potential way of attacking a challenge- response authentication system using the same protocol in both direc­tions. It must be guaranteed to prevent attackers from reusing informa­tion from authentication phase and the scheme of Yoon and Yoo satisfies those requirements. However, their scheme can not resist insider and impersonation attack by using lost or stolen smart card. In this paper, we demonstrate that Yoon and Yoo’s scheme is still vulnerable to those attacks. Then, we present an improvement to their scheme in order to isolate such problems

Synergic Effect of CaI and LiI on Ionic Conductivity of Solution-Based Synthesized Li7P3S11 Solid Electrolyte

Li7P3S11 doped with CaX2 (X = Cl, Br, I) and LiI solid electrolytes were successfully prepared by liquid-phase synthesis using acetonitrile as the reaction medium. Their structure was investigated using XRD, Raman spectroscopy and SEM-EDS. The data obtained from complex impedance spectroscopy was analyzed to study the ionic conductivity and relaxation dynamics in the prepared samples. The XRD results suggested that a part of CaX2 and LiI incorporated into the structure of Li7P3S11, while the remaining part existed at the grain boundary of the Li7P3S11 particle. The Raman peak positions of PS43- and P2S74- ions in samples 90Li7P3S11-5CaI2 and 90Li7P3S11-5CaI2-5LiI had shifted as compared to the Li7P3S11 sample, showing that CaI2 addition affected the vibration of PS43- and P2S74- ions. EDS results indicated that CaI2 and LiI were well dispersed in the prepared powder sample. The ionic conductivity at 25 °C of sample 90Li7P3S11-5CaI2-5LiI reached a very high value of 3.1 mS cm-1 due to the improvement of Li-ion movement at the grain boundary and structural improvement upon CaI2 and LiI doping. This study encouraged the application of Li7P3S11 in all-solid-state Li-ion batteries

NOVEL HPLC-UV METHOD USING VOLATILE BUFFER FOR SIMULTANEOUS DETERMINATION OF AMLODIPINE BESYLATE AND ATORVASTATIN CALCIUM

Objective: The purpose of this work was to develop and validate a novel HPLC-UV method using triethylamine (TEA) as a volatile buffer for simultaneous determination of amlodipine besylate (AML) and atorvastatin calcium (ATV).Methods: System suitability, linearity, limit of detection (LOD), limit of quantification (LOQ), selectivity, accuracy, and precision was validated using Hitachi L-2000 system with detector: DAD L-2455 at a detected wavelength of 245 nm. Stationary phase: Phenomenex Luna RP-C18 (250 mm x 4.6 mm, 5 µm) and mobile phase: acetonitrile-methanol-TEA pH 4.0 (ratio 52:18:30 v/v/v) were used. Samples' volume of 20 µl was run at room temperature with the flow rate at 1 ml/min.Results: The linearity demonstrated good correlation in the concentration range at 2-40 ppm and 4-80 ppm for AML and ATV, respectively. The method was repeatable with relative standard deviation (RSD) of the intermediate precision test less than 1%. The recovery rate was 100.03% and 99.58% for AML and ATV, respectively. The method was also validated for dissolution studies with excellent compatibility.Conclusion: A new, simple and easy HPLC-UV method was successfully developed and validated for the determination of AML and ATV in both quantification test and dissolution test.Keywords: Amlodipine, Atorvastatin, Simultaneous, Dissolution, HPLC, Quantification, Volatile buffe