Multivariate Differential Association Analysis

Identifying how dependence relationships vary across different conditions plays a significant role in many scientific investigations. For example, it is important for the comparison of biological systems to see if relationships between genomic features differ between cases and controls. In this paper, we seek to evaluate whether the relationships between two sets of variables is different across two conditions. Specifically, we assess: do two sets of high-dimensional variables have similar dependence relationships across two conditions?. We propose a new kernel-based test to capture the differential dependence. Specifically, the new test determines whether two measures that detect dependence relationships are similar or not under two conditions. We introduce the asymptotic permutation null distribution of the test statistic and it is shown to work well under finite samples such that the test is computationally efficient, making it easily applicable to analyze large data sets. We demonstrate through numerical studies that our proposed test has high power for detecting differential linear and non-linear relationships. The proposed method is implemented in an R package kerDAA

Generalized Kernel Two-Sample Tests

Kernel two-sample tests have been widely used for multivariate data in testing equal distribution. However, existing tests based on mapping distributions into a reproducing kernel Hilbert space are mainly targeted at specific alternatives and do not work well for some scenarios when the dimension of the data is moderate to high due to the curse of dimensionality. We propose a new test statistic that makes use of a common pattern under moderate and high dimensions and achieves substantial power improvements over existing kernel two-sample tests for a wide range of alternatives. We also propose alternative testing procedures that maintain high power with low computational cost, offering easy off-the-shelf tools for large datasets. The new approaches are compared to other state-of-the-art tests under various settings and show good performance. The new approaches are illustrated on two applications: The comparison of musks and non-musks using the shape of molecules, and the comparison of taxi trips started from John F.Kennedy airport in consecutive months. All proposed methods are implemented in an R package kerTests

Preventing Failures by Dataset Shift Detection in Safety-Critical Graph Applications

Dataset shift refers to the problem where the input data distribution may change over time (e.g., between training and test stages). Since this can be a critical bottleneck in several safety-critical applications such as healthcare, drug-discovery, etc., dataset shift detection has become an important research issue in machine learning. Though several existing efforts have focused on image/video data, applications with graph-structured data have not received sufficient attention. Therefore, in this paper, we investigate the problem of detecting shifts in graph structured data through the lens of statistical hypothesis testing. Specifically, we propose a practical two-sample test based approach for shift detection in large-scale graph structured data. Our approach is very flexible in that it is suitable for both undirected and directed graphs, and eliminates the need for equal sample sizes. Using empirical studies, we demonstrate the effectiveness of the proposed test in detecting dataset shifts. We also corroborate these findings using real-world datasets, characterized by directed graphs and a large number of nodes

New Kernel-based Methods for High-dimensional Inferences

As we are entering the big data era with technological advances of data collection, high-dimensional and complex data is becoming prevalent and the development of effective analysis is gaining more attention to researchers in statistics and data science. Many approaches are usually parametric, but they are highly context specific.Kernel-based methods are widely used as a nonparametric approach and they have the potential to capture changes in the distribution. This dissertation aims to develop novel kernel-based methods for high- dimensional data on two problems: (i) two-sample testing and (ii) change-point analysis. Kernel two-sample tests have been widely used for high-dimensional data as an elegant nonparametric framework of testing equal distribution. However, existing tests based on kernel embeddings of probability distributions into reproducing kernel Hilbert spaces (RKHS) do not work well for a wide rage of alternatives when the dimension of the data is moderate to high due to the curse of dimensionality. We propose a new test statistic that makes use of patterns under high dimension and achieves substantial power improvement over existing kernel two-sample tests for general alternatives. We also propose an alternative testing procedure that maintains high power with little computational cost, offering easy off-the-shelf tools for large datasets. We also consider the testing and estimation of change-points, locations where the distribution abruptly changes in a sequence. Compared with two-sample testing problems, kernel-based methods in change- point analysis have not been well explored. We propose a new kernel-based framework that exhibits high power in detecting and estimating the location of the change-point under general alternatives. Analytic approximations to the significance of the new test statistics for both single change-point and changed-interval alternatives are derived and fast tests are proposed, offering easy off-the-shelf tools for large datasets

차량 내부 네트워크의 지연을 고려한 차선 유지 보조 시스템의 성능 평가

In-vehicle Networks (IVNs) , Controller Area Network (CAN), Automotive Ethernet, Latency, Advanced Driver Assistance System (ADAS), Lane Keeping Assistance System (LKAS), Data CompressionNⅠ. Introduction 1 II. In-vehicle Networks 6 2.1 Controller Area Network (CAN) 6 2.2 Automotive Ethernet 10 III. Effects on Latency on Lane Keeping Assistance System (LKAS) 13 3.1 Evaluation Metric for LKAS 13 3.2 Factors Affecting End-to-end Latency 17 ⅠV. Performance Evaluation 25 4.1 Simulation Setting 25 4.1.1 OMNeT++ Simulator 25 4.1.2 IVN Topology Design 27 4.2 Simulation Results 29 4.2.1 End-to-end Latency Analysis 29 4.2.2 Effects on LKAS 35 V. Conclusion 42MasterdCollectio

New graph-based multi-sample tests for high-dimensional and non-Euclidean data

Testing the equality in distributions of multiple samples is a common task in many fields. However, this problem for high-dimensional or non-Euclidean data has not been well explored. In this paper, we propose new nonparametric tests based on a similarity graph constructed on the pooled observations from multiple samples, and make use of both within-sample edges and between-sample edges, a straightforward but yet not explored idea. The new tests exhibit substantial power improvements over existing tests for a wide range of alternatives. We also study the asymptotic distributions of the test statistics, offering easy off-the-shelf tools for large datasets. The new tests are illustrated through an analysis of the age image dataset

High-Speed, Low-Latency In-Vehicle Network Based on the Bus Topology for Autonomous Vehicles: Automotive Networking and Applications

[No abstract available]FALS

Adaptive Controller Area Network Intrusion Detection System Considering Temperature Variations

Security threats increase as connectivity among vehicles increases. In particular, a lack of authentication, integrity, and confidentiality makes the controller area network (CAN) protocol, which is used in critical domains such as vehicle body and powertrain, vulnerable to threats. In this paper, we propose methods for CAN security enhancement that use a support vector machine (SVM) and the autocorrelation of the received signal to detect a malicious node. Robustness to temperature variation is also considered because autocorrelation is affected by temperature variation. There are two methods based on the degree of uniformity of the temperature distribution. If the temperature is uniformly distributed over the vehicle and the temperature sensor is embedded in the secure node, the first scheme (temperature measurement system) trains data in each segmented temperature range more precisely using multiple classifiers. If not (i.e., a nonuniform temperature distribution or an absence of a temperature sensor), the alternative scheme (all-temperature training system) trains data in all temperature ranges with a single classifier. The performances of the proposed systems are evaluated on a testbed. The proposed method can operate without modifying the CAN protocol because it is based on the characteristics of the physical layer. In addition, security can be enhanced redundantly by the system running independently without authentication protocols. IEEEFALS

Development of magnetic field measurement system for AMS cyclotron

A high-accuracy magnetic field measurement device based on a cyclotron is being developed for accelerator mass spectrometry (AMS). In this study, a magnetic field measurement device consisting of a Hall probe sensor, piezo-motor, and step motor was developed to measure the magnetic field of the AMS cyclotron magnet. The Hall probe sensor was calibrated to achieve positional accuracy by using polar coordinates. The measurement results between the ratchet gear and piezo-motor, which are the instruments used for driving the measurement device, were analyzed. The measurement result of the device with a piezo-motor exhibits a difference of 5 Gauss (0.04%) as compared with the simulation result