2,851 research outputs found
Simulation-Based Hypothesis Testing of High Dimensional Means Under Covariance Heterogeneity
In this paper, we study the problem of testing the mean vectors of high
dimensional data in both one-sample and two-sample cases. The proposed testing
procedures employ maximum-type statistics and the parametric bootstrap
techniques to compute the critical values. Different from the existing tests
that heavily rely on the structural conditions on the unknown covariance
matrices, the proposed tests allow general covariance structures of the data
and therefore enjoy wide scope of applicability in practice. To enhance powers
of the tests against sparse alternatives, we further propose two-step
procedures with a preliminary feature screening step. Theoretical properties of
the proposed tests are investigated. Through extensive numerical experiments on
synthetic datasets and an human acute lymphoblastic leukemia gene expression
dataset, we illustrate the performance of the new tests and how they may
provide assistance on detecting disease-associated gene-sets. The proposed
methods have been implemented in an R-package HDtest and are available on CRAN.Comment: 34 pages, 10 figures; Accepted for biometric
Two-Way Training for Discriminatory Channel Estimation in Wireless MIMO Systems
This work examines the use of two-way training to efficiently discriminate
the channel estimation performances at a legitimate receiver (LR) and an
unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless
system. This work improves upon the original discriminatory channel estimation
(DCE) scheme proposed by Chang et al where multiple stages of feedback and
retraining were used. While most studies on physical layer secrecy are under
the information-theoretic framework and focus directly on the data transmission
phase, studies on DCE focus on the training phase and aim to provide a
practical signal processing technique to discriminate between the channel
estimation performances at LR and UR. A key feature of DCE designs is the
insertion of artificial noise (AN) in the training signal to degrade the
channel estimation performance at UR. To do so, AN must be placed in a
carefully chosen subspace based on the transmitter's knowledge of LR's channel
in order to minimize its effect on LR. In this paper, we adopt the idea of
two-way training that allows both the transmitter and LR to send training
signals to facilitate channel estimation at both ends. Both reciprocal and
non-reciprocal channels are considered and a two-way DCE scheme is proposed for
each scenario. {For mathematical tractability, we assume that all terminals
employ the linear minimum mean square error criterion for channel estimation.
Based on the mean square error (MSE) of the channel estimates at all
terminals,} we formulate and solve an optimization problem where the optimal
power allocation between the training signal and AN is found by minimizing the
MSE of LR's channel estimate subject to a constraint on the MSE achievable at
UR. Numerical results show that the proposed DCE schemes can effectively
discriminate between the channel estimation and hence the data detection
performances at LR and UR.Comment: 1
Width-tuned magnetic order oscillation on zigzag edges of honeycomb nanoribbons
Quantum confinement and interference often generate exotic properties in
nanostructures. One recent highlight is the experimental indication of a
magnetic phase transition in zigzag-edged graphene nanoribbons at the critical
ribbon width of about 7 nm [G. Z. Magda et al., Nature \textbf{514}, 608
(2014)]. Here we show theoretically that with further increase in the ribbon
width, the magnetic correlation of the two edges can exhibit an intriguing
oscillatory behavior between antiferromagnetic and ferromagnetic, driven by
acquiring the positive coherence between the two edges to lower the free
energy. The oscillation effect is readily tunable in applied magnetic fields.
These novel properties suggest new experimental manifestation of the edge
magnetic orders in graphene nanoribbons, and enhance the hopes of graphene-like
spintronic nanodevices functioning at room temperature.Comment: 22 pages, 9 figure
2-(4-Fluorophenyl)-4-(4-methoxyphenyl)-5-(piperidin-1-ylmethyl)thiazole
In the title compound, C22H23FN2OS, the piperidine ring shows chair confirmation and the two benzene rings make a dihedral angle of 17.0 (6)°. The thiazole fragment is essentially planar with an r.m.s. deviation of 0.004 (2) Å and a maximum deviation of 0.006 (2) Å.. In the crystal, intermolecular C—H⋯π interactions lead to the formation of a layer structure
Sketch and Refine: Towards Fast and Accurate Lane Detection
Lane detection is to determine the precise location and shape of lanes on the
road. Despite efforts made by current methods, it remains a challenging task
due to the complexity of real-world scenarios. Existing approaches, whether
proposal-based or keypoint-based, suffer from depicting lanes effectively and
efficiently. Proposal-based methods detect lanes by distinguishing and
regressing a collection of proposals in a streamlined top-down way, yet lack
sufficient flexibility in lane representation. Keypoint-based methods, on the
other hand, construct lanes flexibly from local descriptors, which typically
entail complicated post-processing. In this paper, we present a
"Sketch-and-Refine" paradigm that utilizes the merits of both keypoint-based
and proposal-based methods. The motivation is that local directions of lanes
are semantically simple and clear. At the "Sketch" stage, local directions of
keypoints can be easily estimated by fast convolutional layers. Then we can
build a set of lane proposals accordingly with moderate accuracy. At the
"Refine" stage, we further optimize these proposals via a novel Lane Segment
Association Module (LSAM), which allows adaptive lane segment adjustment. Last
but not least, we propose multi-level feature integration to enrich lane
feature representations more efficiently. Based on the proposed "Sketch and
Refine" paradigm, we propose a fast yet effective lane detector dubbed
"SRLane". Experiments show that our SRLane can run at a fast speed (i.e., 278
FPS) while yielding an F1 score of 78.9\%. The source code is available at:
https://github.com/passerer/SRLane
Two-way training for discriminatory channel estimation in wireless MIMO systems
This work examines the use of two-way training to efficiently discriminate the channel estimation performances at a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. This work improves upon the original discriminatory channel estimation (DCE) scheme proposed by Chang where multiple stages of feedback and retraining were used. While most studies on physical layer secrecy are under the information-theoretic framework and focus directly on the data transmission phase, studies on DCE focus on the training phase and aim to provide a practical signal processing technique to discriminate between the channel estimation performances (and, thus, the effective received signal qualities) at LR and UR. A key feature of DCE designs is the insertion of artificial noise (AN) in the training signal to degrade the channel estimation performance at UR. To do so, AN must be placed in a carefully chosen subspace, based on the transmitter's knowledge of LR's channel, in order to minimize its effect on LR. In this paper, we adopt the idea of two-way training that allows both the transmitter and LR to send training signals to facilitate channel estimation at both ends. Both reciprocal and nonreciprocal channels are considered and a two-way DCE scheme is proposed for each scenario. For mathematical tractability, we assume that all terminals employ the linear minimum mean square error criterion for channel estimation. Based on the mean square error (MSE) of the channel estimates at all terminals, we formulate and solve an optimization problem where the optimal power allocation between the training signal and AN is found by minimizing the MSE of LR's channel estimate subject to a constraint on the MSE achievable at UR. Numerical results show that the proposed DCE schemes can effectively discriminate between the channel estimation and, hence, the data detection performances at LR and UR.This work was supported in part by the National Science Council, Taiwan, by Grant NSC 100-2628-E-007-025-MY3 and Grant NSC 101-2218-E-011-043, and in part by the Australian Research Council's Discovery Projects Funding Scheme (Project no.DP110102548)
- …