arXiv subjects

Qin Zhou

Publications and source records attributed to Qin Zhou.

At least 55 records · Page 3Linked to original sources

arXiv2021

Making Person Search Enjoy the Merits of Person Re-identification

Person search is an extended task of person re-identification (Re-ID). However, most existing one-step person search works have not studied how to employ existing advanced Re-ID models to boost the one-step person search performance due to the integration of person detection and Re-ID. To address this issue, we propose a faster and stronger one-step person search framework, the Teacher-guided Disentangling Networks (TDN), to make the one-step person search enjoy the merits of the existing Re-ID researches. The proposed TDN can significantly boost the person search performance by transferring the advanced person Re-ID knowledge to the person search model. In the proposed TDN, for better knowledge transfer from the Re-ID teacher model to the one-step person search model, we design a strong one-step person search base framework by partially disentangling the two subtasks. Besides, we propose a Knowledge Transfer Bridge module to bridge the scale gap caused by different input formats between the Re-ID model and one-step person search model. During testing, we further propose the Ranking with Context Persons strategy to exploit the context information in panoramic images for better retrieval. Experiments on two public person search datasets demonstrate the favorable performance of the proposed method.

cs.CV↗

arXiv2021

Dislocation induced energy dissipation in a tunable trilayer graphene resonator

In crystalline materials, the creation and modulation of dislocations are often associated with plastic deformation and energy dissipation. Here we report a study on the energy dissipation of a trilayer graphene ribbon resonator. The vibration of the ribbon generates cyclic mechanical loading to the graphene ribbon, during which mechanical energy is dissipated as heat. Measuring the quality factor of the graphene resonator provides a way to evaluate the energy dissipation. The graphene ribbon is integrated with silicon micro actuators, allowing its in-plane tension to be finely tuned. As we gradually increased the tension, we observed, in addition to the well-known resonance frequency increase, a large change in the energy dissipation. We propose that the dominating energy dissipation mechanism shifts over three regions. With small applied tension, the graphene is in elastic region, and the major energy dissipation is through graphene edge folding; as the tension increases, dislocations start to develop in the sample to gradually dominate the energy dissipation; finally, at large enough tension, graphene layers become decoupled and start to slide and cause friction, which induces the more severe energy dissipation. The generation and modulation of dislocations are modeled by molecular dynamics calculation and a method to count the energy loss is proposed and compared to the experiment.

cond-mat.mes-hall↗

arXiv2021

Random vector functional link neural network based ensemble deep learning for short-term load forecasting

Electricity load forecasting is crucial for the power systems' planning and maintenance. However, its un-stationary and non-linear characteristics impose significant difficulties in anticipating future demand. This paper proposes a novel ensemble deep Random Vector Functional Link (edRVFL) network for electricity load forecasting. The weights of hidden layers are randomly initialized and kept fixed during the training process. The hidden layers are stacked to enforce deep representation learning. Then, the model generates the forecasts by ensembling the outputs of each layer. Moreover, we also propose to augment the random enhancement features by empirical wavelet transformation (EWT). The raw load data is decomposed by EWT in a walk-forward fashion, not introducing future data leakage problems in the decomposition process. Finally, all the sub-series generated by the EWT, including raw data, are fed into the edRVFL for forecasting purposes. The proposed model is evaluated on twenty publicly available time series from the Australian Energy Market Operator of the year 2020. The simulation results demonstrate the proposed model's superior performance over eleven forecasting methods in three error metrics and statistical tests on electricity load forecasting tasks.

cs.LG↗

arXiv2021

The impact and recovery of asteroid 2018 LA

The June 2, 2018, impact of asteroid 2018 LA over Botswana is only the second asteroid detected in space prior to impacting over land. Here, we report on the successful recovery of meteorites. Additional astrometric data refine the approach orbit and define the spin period and shape of the asteroid. Video observations of the fireball constrain the asteroid's position in its orbit and were used to triangulate the location of the fireball's main flare over the Central Kalahari Game Reserve. 23 meteorites were recovered. A consortium study of eight of these classifies Motopi Pan as a HED polymict breccia derived from howardite, cumulate and basaltic eucrite, and diogenite lithologies. Before impact, 2018 LA was a solid rock of about 156 cm diameter with high bulk density about 2.85 g/cm3, a relatively low albedo pV about 0.25, no significant opposition effect on the asteroid brightness, and an impact kinetic energy of about 0.2 kt. The orbit of 2018 LA is consistent with an origin at Vesta (or its Vestoids) and delivery into an Earth-impacting orbit via the nu_6 resonance. The impact that ejected 2018 LA in an orbit towards Earth occurred 22.8 +/- 3.8 Ma ago. Zircons record a concordant U-Pb age of 4563 +/- 11 Ma and a consistent 207Pb/206Pb age of 4563 +/- 6 Ma. A much younger Pb-Pb phosphate resetting age of 4234 +/- 41 Ma was found. From this impact chronology, we discuss what is the possible source crater of Motopi Pan and the age of Vesta's Veneneia impact basin.

astro-ph.EP↗

arXiv2021

The Sariçiçek howardite fall in Turkey: Source crater of HED meteorites on Vesta and impact risk of Vestoids

The Sariçiçek howardite meteorite shower consisting of 343 documented stones occurred on 2 September 2015 in Turkey and is the first documented howardite fall. Cosmogenic isotopes show that Sariçiçek experienced a complex cosmic ray exposure history, exposed during ~12-14 Ma in a regolith near the surface of a parent asteroid, and that an ca.1 m sized meteoroid was launched by an impact 22 +/- 2 Ma ago to Earth (as did one third of all HED meteorites). SIMS dating of zircon and baddeleyite yielded 4550.4 +/- 2.5 Ma and 4553 +/- 8.8 Ma crystallization ages for the basaltic magma clasts. The apatite U-Pb age of 4525 +/- 17 Ma, K-Ar age of ~3.9 Ga, and the U,Th-He ages of 1.8 +/- 0.7 and 2.6 +/- 0.3 Ga are interpreted to represent thermal metamorphic and impact-related resetting ages, respectively. Petrographic, geochemical and O-, Cr- and Ti- isotopic studies confirm that Sariçiçek belongs to the normal clan of HED meteorites. Petrographic observations and analysis of organic material indicate a small portion of carbonaceous chondrite material in the Sariçiçek regolith and organic contamination of the meteorite after a few days on soil. Video observations of the fall show an atmospheric entry at 17.3 +/- 0.8 kms-1 from NW, fragmentations at 37, 33, 31 and 27 km altitude, and provide a pre-atmospheric orbit that is the first dynamical link between the normal HED meteorite clan and the inner Main Belt. Spectral data indicate the similarity of Sariçiçek with the Vesta asteroid family spectra, a group of asteroids stretching to delivery resonances, which includes (4) Vesta. Dynamical modeling of meteoroid delivery to Earth shows that the disruption of a ca.1 km sized Vesta family asteroid or a ~10 km sized impact crater on Vesta is required to provide sufficient meteoroids <4 m in size to account for the influx of meteorites from this HED clan.

astro-ph.EP↗

arXiv2020

Signature of multilayer graphene strain-controlled domain walls in quantum Hall effect

Domain walls, topological defects that define the frontier between regions of different stacking in multilayer graphene, have proved to host exciting physics. The ability of tuning these topological defects in-situ in an electronic transport experiment brings a wealth of possibilities in terms of fundamental understanding of domain walls as well as for electronic applications. Here, we demonstrate through a MEMS (micro-electromechanical system) actuator and magnetoresistance measurements the effect of domain walls in multilayer graphene quantum Hall effect. Reversible and controlled uniaxial strain triggers these topological defects, manifested as new quantum Hall effect plateaus as well as a discrete and reversible modulation of the current across the device. Our findings are supported by theoretical calculations and constitute the first indication of the in-situ tuning of topological defects in multilayer graphene probed through electronic transport, opening the way to the use of reversible topological defects in electronic applications.

cond-mat.mes-hall↗

arXiv2020

Temporally semidiscrete approximation of a Dirichlet boundary control for a fractional/normal evolution equation with a final observation

Optimal Dirichlet boundary control for a fractional/normal evolution with a final observation is considered. The unique existence of the solution and the first-order optimality condition of the optimal control problem are derived. The convergence of a temporally semidiscrete approximation is rigorously established, where the control is not explicitly discretized and the state equation is discretized by a discontinuous Galerkin method in time. Numerical results are provided to verify the theoretical results.

math.NA↗

arXiv2020

Weighted Bilinear Coding over Salient Body Parts for Person Re-identification

Deep convolutional neural networks (CNNs) have demonstrated dominant performance in person re-identification (Re-ID). Existing CNN based methods utilize global average pooling (GAP) to aggregate intermediate convolutional features for Re-ID. However, this strategy only considers the first-order statistics of local features and treats local features at different locations equally important, leading to sub-optimal feature representation. To deal with these issues, we propose a novel weighted bilinear coding (WBC) framework for local feature aggregation in CNN networks to pursue more representative and discriminative feature representations, which can adapt to other state-of-the-art methods and improve their performance. In specific, bilinear coding is used to encode the channel-wise feature correlations to capture richer feature interactions. Meanwhile, a weighting scheme is applied on the bilinear coding to adaptively adjust the weights of local features at different locations based on their importance in recognition, further improving the discriminability of feature aggregation. To handle the spatial misalignment issue, we use a salient part net (spatial attention module) to derive salient body parts, and apply the WBC model on each part. The final representation, formed by concatenating the WBC encoded features of each part, is both discriminative and resistant to spatial misalignment. Experiments on three benchmarks including Market-1501, DukeMTMC-reID and CUHK03 evidence the favorable performance of our method against other outstanding methods.

cs.CV↗

arXiv2019

Distribution Context Aware Loss for Person Re-identification

To learn the optimal similarity function between probe and gallery images in Person re-identification, effective deep metric learning methods have been extensively explored to obtain discriminative feature embedding. However, existing metric loss like triplet loss and its variants always emphasize pair-wise relations but ignore the distribution context in feature space, leading to inconsistency and sub-optimal. In fact, the similarity of one pair not only decides the match of this pair, but also has potential impacts on other sample pairs. In this paper, we propose a novel Distribution Context Aware (DCA) loss based on triplet loss to combine both numerical similarity and relation similarity in feature space for better clustering. Extensive experiments on three benchmarks including Market-1501, DukeMTMC-reID and MSMT17, evidence the favorable performance of our method against the corresponding baseline and other state-of-the-art methods.

cs.CV↗

arXiv2019

Learning to Self-Train for Semi-Supervised Few-Shot Classification

Few-shot classification (FSC) is challenging due to the scarcity of labeled training data (e.g. only one labeled data point per class). Meta-learning has shown to achieve promising results by learning to initialize a classification model for FSC. In this paper we propose a novel semi-supervised meta-learning method called learning to self-train (LST) that leverages unlabeled data and specifically meta-learns how to cherry-pick and label such unsupervised data to further improve performance. To this end, we train the LST model through a large number of semi-supervised few-shot tasks. On each task, we train a few-shot model to predict pseudo labels for unlabeled data, and then iterate the self-training steps on labeled and pseudo-labeled data with each step followed by fine-tuning. We additionally learn a soft weighting network (SWN) to optimize the self-training weights of pseudo labels so that better ones can contribute more to gradient descent optimization. We evaluate our LST method on two ImageNet benchmarks for semi-supervised few-shot classification and achieve large improvements over the state-of-the-art method. Code is at https://github.com/xinzheli1217/learning-to-self-train.

cs.CV↗

arXiv2019

Barrier Inhomogeneity of Schottky Diode on Nonpolar AlN Grown by Physical Vapor Transport

An aluminum nitride (AlN) Schottky barrier diode (SBD) was fabricated on a nonpolar AlN crystal grown on tungsten substrate by physical vapor transport. The Ni/Au-AlN SBD features a low ideality factor n of 3.3 and an effective Schottky barrier height (SBH) of 1.05 eV at room temperature. The ideality factor n decreases and the effective SBH increases at high temperatures. The temperature dependences of n and SBH were explained using an inhomogeneous model. A mean SBH of 2.105 eV was obtained for the Ni-AlN Schottky junction from the inhomogeneity analysis of the current-voltage characteristics. An equation in which the parameters have explicit physical meanings in thermionic emission theory is proposed to describe the current-voltage characteristics of inhomogeneous SBDs.

physics.app-ph↗

arXiv2018

Robust and Efficient Graph Correspondence Transfer for Person Re-identification

Spatial misalignment caused by variations in poses and viewpoints is one of the most critical issues that hinders the performance improvement in existing person re-identification (Re-ID) algorithms. To address this problem, in this paper, we present a robust and efficient graph correspondence transfer (REGCT) approach for explicit spatial alignment in Re-ID. Specifically, we propose to establish the patch-wise correspondences of positive training pairs via graph matching. By exploiting both spatial and visual contexts of human appearance in graph matching, meaningful semantic correspondences can be obtained. To circumvent the cumbersome \emph{on-line} graph matching in testing phase, we propose to transfer the \emph{off-line} learned patch-wise correspondences from the positive training pairs to test pairs. In detail, for each test pair, the training pairs with similar pose-pair configurations are selected as references. The matching patterns (i.e., the correspondences) of the selected references are then utilized to calculate the patch-wise feature distances of this test pair. To enhance the robustness of correspondence transfer, we design a novel pose context descriptor to accurately model human body configurations, and present an approach to measure the similarity between a pair of pose context descriptors. Meanwhile, to improve testing efficiency, we propose a correspondence template ensemble method using the voting mechanism, which significantly reduces the amount of patch-wise matchings involved in distance calculation. With aforementioned strategies, the REGCT model can effectively and efficiently handle the spatial misalignment problem in Re-ID. Extensive experiments on five challenging benchmarks, including VIPeR, Road, PRID450S, 3DPES and CUHK01, evidence the superior performance of REGCT over other state-of-the-art approaches.

cs.CV↗

arXiv2018

Graph Correspondence Transfer for Person Re-identification

In this paper, we propose a graph correspondence transfer (GCT) approach for person re-identification. Unlike existing methods, the GCT model formulates person re-identification as an off-line graph matching and on-line correspondence transferring problem. In specific, during training, the GCT model aims to learn off-line a set of correspondence templates from positive training pairs with various pose-pair configurations via patch-wise graph matching. During testing, for each pair of test samples, we select a few training pairs with the most similar pose-pair configurations as references, and transfer the correspondences of these references to test pair for feature distance calculation. The matching score is derived by aggregating distances from different references. For each probe image, the gallery image with the highest matching score is the re-identifying result. Compared to existing algorithms, our GCT can handle spatial misalignment caused by large variations in view angles and human poses owing to the benefits of patch-wise graph matching. Extensive experiments on five benchmarks including VIPeR, Road, PRID450S, 3DPES and CUHK01 evidence the superior performance of GCT model over other state-of-the-art methods.

cs.CV↗

arXiv2017

Hole doping, hybridization gap, and electronic correlation in graphene on a platinum substrate

The interaction between graphene and substrates provides a viable routes to enhance functionality of both materials. Depending on the nature of electronic interaction at the interface, the electron band structure of graphene is strongly influenced, allowing us to make use the intrinsic properties of graphene or to design additional functionality in graphene. Here, we present an angle-resolved photoemission study on the interaction between graphene and a platinum substrate. The formation of an interface between graphene and platinum leads to a strong deviation in the electronic structure of graphene not only from its freestanding form but also from the behavior observed on typical metals. The combined study on the experimental and theoretical electron band structure unveils the unique electronic properties of graphene on a platinum substrate, which singles out graphene/platinum as a model system investigating graphene on a metallic substrate with strong interaction.

cond-mat.mtrl-sci↗

arXiv2016

Extended Eckart Theorem and New Variation Method for Excited States of Atoms

We extend the Eckart theorem, from the ground state to excited statew, which introduces an energy augmentation to the variation criterion for excited states. It is shown that the energy of a very good excited state trial function can be slightly lower than the exact eigenvalue. Further, the energy calculated by the trial excited state wave function, which is the closest to the exact eigenstate through Gram-Schmidt orthonormalization to a ground state approximant, is lower than the exact eigenvalue as well. In order to avoid the variation restrictions inherent in the upper bound variation theory based on Hylleraas, Undheim, and McDonald [HUM] and Eckart Theorem, we have proposed a new variation functional Omega-n and proved that it has a local minimum at the eigenstates, which allows approaching the eigenstate unlimitedly by variation of the trial wave function. As an example, we calculated the energy and the radial expectation values of Triplet-S(even) Helium atom by the new variation functional, and by HUM and Eckart theorem, respectively, for comparison. Our preliminary numerical results reveal that the energy of the calculated excited states 3rd Triplet-S(even) and 4th Triplet-S(even) may be slightly lower than the exact eigenvalue (inaccessible by HUM theory) according to the General Eckart Theorem proved here, while the approximate wave function is better than HUM.

physics.comp-ph↗

arXiv2014

Evolution of Interlayer Coupling in Twisted MoS2 Bilayers

Van der Waals (vdW) coupling is emerging as a powerful method to engineer and tailor physical properties of atomically thin two-dimensional (2D) materials. In graphene/graphene and graphene/boron-nitride structures it leads to interesting physical phenomena ranging from new van Hove singularities1-4 and Fermi velocity renormalization5, 6 to unconventional quantum Hall effects7 and Hofstadter's butterfly pattern8-12. 2D transition metal dichalcogenides (TMDCs), another system of predominantly vdW-coupled atomically thin layers13, 14, can also exhibit interesting but different coupling phenomena because TMDCs can be direct or indirect bandgap semiconductors15, 16. Here, we present the first study on the evolution of interlayer coupling with twist angles in as-grown MoS2 bilayers. We find that an indirect bandgap emerges in bilayers with any stacking configuration, but the bandgap size varies appreciably with the twist angle: it shows the largest redshift for AA- and AB-stacked bilayers, and a significantly smaller but constant redshift for all other twist angles. The vibration frequency of the out-of-plane phonon in MoS2 shows similar twist angle dependence. Our observations, together with ab initio calculations, reveal that this evolution of interlayer coupling originates from the repulsive steric effects, which leads to different interlayer separations between the two MoS2 layers in different stacking configurations.

cond-mat.mes-hall↗

arXiv2013

High-throughput Imaging and Spectroscopy of Individual Carbon Nanotubes in Devices with Light Microscopy

Two paramount challenges in carbon nanotube research are achieving chirality-controlled synthesis and understanding chirality-dependent device physics. High-throughput and in-situ chirality and electronic structural characterization of individual carbon nanotubes is crucial for addressing these challenges. Optical imaging and spectroscopy has unparalleled throughput and specificity, but its realization for single nanotubes on substrates or in devices has long been an outstanding challenge. Here we demonstrate video-rate imaging and in-situ spectroscopy of individual carbon nanotubes on various substrates and in functional devices using a novel high-contrast polarization-based optical microscopy. Our technique enables the complete chirality profiling of hundreds of as-grown carbon nanotubes. In addition, we in-situ monitor nanotube electronic structure in active field-effect devices, and observe that high-order nanotube optical resonances are dramatically broadened by electrostatic doping. This unexpected behaviour points to strong interband electron-electron scattering processes that can dominate ultrafast dynamics of excited states in carbon nanotubes.

cond-mat.mes-hall↗

arXiv2013

Imprint of transition metal d-orbitals on graphene Dirac cone

We investigate the influence of SiO2, Au, Ag, Cu, and Pt substrates on the Raman spectrum of graphene. Experiments reveal particularly strong modifications to the intensity, position, width, and shape of the Raman signal of graphene on platinum, compared to that of suspended graphene. The modifications strongly depend on the relative orientation of the graphene and platinum lattices. These observations are theoretically investigated and shown to originate from hybridization of electronic states in graphene and d-orbitals in platinum. It is expected that, quite generally, hybridization between graphene and any material with d-orbitals near the Fermi level will result in an imprint on the graphene Dirac cone which depends sensitively on the relative orientation of the respective lattices.

cond-mat.mtrl-sci↗