arXiv subjects

Jun Chen

Publications and source records attributed to Jun Chen.

At least 361 records · Page 20Linked to original sources

arXiv2021

Bayesian Optimisation for a Biologically Inspired Population Neural Network

We have used Bayesian Optimisation (BO) to find hyper-parameters in an existing biologically plausible population neural network. The 8-dimensional optimal hyper-parameter combination should be such that the network dynamics simulate the resting state alpha rhythm (8 - 13 Hz rhythms in brain signals). Each combination of these eight hyper-parameters constitutes a 'datapoint' in the parameter space. The best combination of these parameters leads to the neural network's output power spectral peak being constraint within the alpha band. Further, constraints were introduced to the BO algorithm based on qualitative observation of the network output time series, so that high amplitude pseudo-periodic oscillations are removed. Upon successful implementation for alpha band, we further optimised the network to oscillate within the theta (4 - 8 Hz) and beta (13 - 30 Hz) bands. The changing rhythms in the model can now be studied using the identified optimal hyper-parameters for the respective frequency bands. We have previously tuned parameters in the existing neural network by the trial-and-error approach; however, due to time and computational constraints, we could not vary more than three parameters at once. The approach detailed here, allows an automatic hyper-parameter search, producing reliable parameter sets for the network.

q-bio.QM↗

arXiv2021

Peierls transition driven ferroelasticity in two-dimensional $d$-$f$ hybrid magnet

For broad nanoscale applications, it is crucial to implement more functional properties, especially those ferroic orders, into two-dimensional materials. Here GdI$_3$ is theoretically identified as a honeycomb antiferromagnet with large $4f$ magnetic moment. The intercalation of metal atoms can dope electrons into Gd's $5d$-orbitals, which alters its magnetic state and lead to Peierls transition. Due to the strong electron-phonon coupling, the Peierls transition induces prominent ferroelasticity, making it a multiferroic system. The strain from undirectional stretching can be self-relaxed via resizing of triple ferroelastic domains, which can protect the magnet aganist mechnical breaking in flexible applications.

cond-mat.str-el↗

arXiv2021

Pattern formation during nonequilibrium crystallization by classical-density-functional-based approach

Solidification pattern during nonequilibrium crystallization is among the most important microstructures in the nature and technical realms. Phase field crystal (PFC) model could simulate the pattern formation during equilibrium crystallization at atom scale, but cannot grasp the nonequilibrium ones due to the absence of proper elastic-relaxation time scale. In this work, we propose a minimal classical-density-functional-theory-based model for crystal growth in supercooled liquid. Growth front nucleation (GFN) and various nonequilibrium patterns, including the faceting growth, spherulite, dendrite and the columnar-to-equiaxed transition (CET) among others, are grasped at atom scale. It is amazing that, except for undercooling and seed spacing, seed distribution is key factor that determines the CET. Overall, two-stage growth process, i.e., the diffusion-controlled growth and the GFN-dominated growth, are identified. But, compared with the second stage, the first stage becomes too short to be noticed under the high undercooling. The distinct feature for the second stage is the dramatic increments of dislocations, which explains the amorphous nucleation precursor in the supercooled liquid. Transition time between the two stages at different undercooling are investigated. Crystal growth of BCC structure further confirms our conclusions.

cond-mat.mtrl-sci↗

arXiv2021

Optimal False Discovery Rate Control for Large Scale Multiple Testing with Auxiliary Information

Large-scale multiple testing is a fundamental problem in high dimensional statistical inference. It is increasingly common that various types of auxiliary information, reflecting the structural relationship among the hypotheses, are available. Exploiting such auxiliary information can boost statistical power. To this end, we propose a framework based on a two-group mixture model with varying probabilities of being null for different hypotheses a priori, where a shape-constrained relationship is imposed between the auxiliary information and the prior probabilities of being null. An optimal rejection rule is designed to maximize the expected number of true positives when average false discovery rate is controlled. Focusing on the ordered structure, we develop a robust EM algorithm to estimate the prior probabilities of being null and the distribution of $p$-values under the alternative hypothesis simultaneously. We show that the proposed method has better power than state-of-the-art competitors while controlling the false discovery rate, both empirically and theoretically. Extensive simulations demonstrate the advantage of the proposed method. Datasets from genome-wide association studies are used to illustrate the new methodology.

stat.ME↗

arXiv2021

Towards a Unified Approach to Single Image Deraining and Dehazing

We develop a new physical model for the rain effect and show that the well-known atmosphere scattering model (ASM) for the haze effect naturally emerges as its homogeneous continuous limit. Via depth-aware fusion of multi-layer rain streaks according to the camera imaging mechanism, the new model can better capture the sophisticated non-deterministic degradation patterns commonly seen in real rainy images. We also propose a Densely Scale-Connected Attentive Network (DSCAN) that is suitable for both deraining and dehazing tasks. Our design alleviates the bottleneck issue existent in conventional multi-scale networks and enables more effective information exchange and aggregation. Extensive experimental results demonstrate that the proposed DSCAN is able to deliver superior derained/dehazed results on both synthetic and real images as compared to the state-of-the-art. Moreover, it is shown that for our DSCAN, the synthetic dataset built using the new physical model yields better generalization performance on real images in comparison with the existing datasets based on over-simplified models.

cs.CV↗

arXiv2021

Video Frame Interpolation via Generalized Deformable Convolution

Video frame interpolation aims at synthesizing intermediate frames from nearby source frames while maintaining spatial and temporal consistencies. The existing deep-learning-based video frame interpolation methods can be roughly divided into two categories: flow-based methods and kernel-based methods. The performance of flow-based methods is often jeopardized by the inaccuracy of flow map estimation due to oversimplified motion models, while that of kernel-based methods tends to be constrained by the rigidity of kernel shape. To address these performance-limiting issues, a novel mechanism named generalized deformable convolution is proposed, which can effectively learn motion information in a data-driven manner and freely select sampling points in space-time. We further develop a new video frame interpolation method based on this mechanism. Our extensive experiments demonstrate that the new method performs favorably against the state-of-the-art, especially when dealing with complex motions.

cs.CV↗

arXiv2021

Manipulation of magnetic domain wall by ferroelectric switching: Dynamic magnetoelectricity at the nanoscale

Controlling magnetism using voltage is highly desired for applications, but remains challenging due to fundamental contradiction between polarity and magnetism. Here we propose a mechanism to manipulate magnetic domain walls in ferrimagnetic or ferromagnetic multiferroics using electric field. Different from those studies based on static domain-level couplings, here the magnetoelectric coupling relies on the collaborative spin dynamics around domain walls. Accompanying the reversal of spin chirality driven by polarization switching, a "rolling-downhill"-like motion of domain wall is achieved at the nanoscale, which tunes the magnetization locally. Our mechanism opens an alternative route to pursuit practical and fast converse magnetoelectric functions via spin dynamics.

cond-mat.mtrl-sci↗

arXiv2021

The configuration and failed eruption of a complex magnetic flux rope above a δ sunspot region

Aims. We investigate the configuration of a complex flux rope above a δ sunspot region in NOAA AR 11515, and its eruptive expansion during a confined M5.3-class flare. Methods. We study the formation of the δ sunspot using continuum intensity images and photospheric vector magnetograms provided by SDO/HMI. We use EUV and UV images provided by SDO/AIA, and hard X-ray emission recorded by RHESSI to investigate the eruptive details. The coronal magnetic field is extrapolated with a non-linear force free field (NLFFF) method, based on which the flux rope is identified by calculating the twist number Tw and squashing factor Q. We search the null point via a modified Powell hybrid method. Results. The collision between two emerging spot groups form the δ sunspot. A bald patch (BP) forms at the collision location, above which a complex flux rope is identified. The flux rope has multiple layers, with one compact end and one bifurcated end, having Tw decreasing from the core to the boundary. A null point is located above the flux rope. The eruptive process consists of precursor flaring at a 'v'-shaped coronal structure, rise of the filament, and flaring below the filament, corresponding well with the NLFFF topological structures, including the null point and the flux rope with BP and hyperbolic flux tube (HFT). Two sets of post-flare loops and three flare ribbons support the bifurcation configuration of the flux rope. Conclusions. The precursor reconnection, which occurs at the null point, weakens the overlying confinement to allow the flux rope to rise, fitting the breakout model. The main phase reconnection, which may occur at the BP or HFT, facilitates the flux rope rising. The results suggest that the δ spot configuration presents an environment prone to the formation of complex magnetic configurations which will work together to produce activities.

astro-ph.SR↗

arXiv2021

Evidence of Andreev blockade in a double quantum dot coupled to a superconductor

We design and investigate an experimental system capable of entering an electron transport blockade regime in which a spin-triplet localized in the path of current is forbidden from entering a spin-singlet superconductor. To stabilize the triplet a double quantum dot is created electrostatically near a superconducting lead in an InAs nanowire. The dots are filled stochastically with electrons of either spin. The superconducting lead is a molecular beam epitaxy grown Al shell. The shell is etched away over a wire segment to make room for the double dot and the normal metal gold lead. The quantum dot closest to the normal lead exhibits Coulomb diamonds, the dot closest to the superconducting lead exhibits Andreev bound states and an induced gap. The experimental observations compare favorably to a theoretical model of Andreev blockade, named so because the triplet double dot configuration suppresses Andreev reflections. Observed leakage currents can be accounted for by finite temperature. We observe the predicted quadruple level degeneracy points of high current and a periodic conductance pattern controlled by the occupation of the normal dot. Even-odd transport asymmetry is lifted with increased temperature and magnetic field. This blockade phenomenon can be used to study spin structure of superconductors. It may also find utility in quantum computing devices that utilize Andreev or Majorana states.

cond-mat.mes-hall↗

arXiv2021

Twist-induced control of near-field heat radiation between magnetic Weyl semimetals

Due to the large anomalous Hall effect, magnetic Weyl semimetals can support nonreciprocal surface plasmon polariton modes in the absence of an external magnetic field. This implies that magnetic Weyl semimetals can find novel application in (thermal) photonics. In this work, we consider the near-field radiative heat transfer between two magnetic Weyl semimetal slabs and show that the heat transfer can be controlled with a relative rotation of the parallel slabs. Thanks to the intrinsic nonreciprocity of the surface modes, this so-called twisting method does not require surface structuring like periodic gratings. The twist-induced control of heat transfer is due to the mismatch of the surface modes from the two slabs with a relative rotation.

physics.optics↗

arXiv2021

Unzipping chemical bond of non-layered bulk structures to form ultrathin nanocrystals

The rich electronic and band structures of monolayered crystals distinguished from their layered bulk counterparts offer versatile physical/chemical properties and applications.1-5 Their fabrications, particularly the top-down "exfoliations", are successful promised by the weak Van der Waals force between monolayers.6-9 Differentially, un-zipping ultra-thin crystals (e.g. with only one layer of crystal plane) from non-layered structures is highly challenging due to the strong chemical bond between planes and atoms. Alterative finely controlled growth of these ultra-thin materials is not really successful. This work demonstrates how a technique can be used to unzip and disintegrate ultra-thin crystal plane (e.g. monolayered nanocrystals and nanosheets) from bulk non-layered structures (ZnO, alpha/belta-MnO2, TiO2, alpha-TiB2), and present how the basic optical properties changed to distinguish from their bulk phases. The work here gives a strong tool kit to various novel 2D non-layered nanomaterials, providing significant contributions to the family of two-dimensional materials, potentially paving the way for various practical applications.

cond-mat.mtrl-sci↗

arXiv2021

Edge-Featured Graph Attention Network

Lots of neural network architectures have been proposed to deal with learning tasks on graph-structured data. However, most of these models concentrate on only node features during the learning process. The edge features, which usually play a similarly important role as the nodes, are often ignored or simplified by these models. In this paper, we present edge-featured graph attention networks, namely EGATs, to extend the use of graph neural networks to those tasks learning on graphs with both node and edge features. These models can be regarded as extensions of graph attention networks (GATs). By reforming the model structure and the learning process, the new models can accept node and edge features as inputs, incorporate the edge information into feature representations, and iterate both node and edge features in a parallel but mutual way. The results demonstrate that our work is highly competitive against other node classification approaches, and can be well applied in edge-featured graph learning tasks.

cs.LG↗

arXiv2021

Pre-eruption Splitting of the Double-Decker Structure in a Solar Filament

Solar filaments often erupt partially. Although how they split remains elusive, the splitting process has the potential of revealing the filament structure and eruption mechanism. Here we investigate the pre-eruption splitting of an apparently single filament and its subsequent partial eruption on 2012 September 27. The evolution is characterized by three stages with distinct dynamics. During the quasi-static stage, the splitting proceeds gradually for about 1.5 hrs, with the upper branch rising at a few kilometers per second and displaying swirling motions about its axis. During the precursor stage that lasts for about 10 min, the upper branch rises at tens of kilometers per second, with a pair of conjugated dimming regions starting to develop at its footpoints; with the swirling motions turning chaotic, the axis of the upper branch whips southward, which drives an arc-shaped EUV front propagating in the similar direction. During the eruption stage, the upper branch erupts with the onset of a C3.7-class two-ribbon flare, while the lower branch remains stable. Judging from the well separated footpoints of the upper branch from those of the lower one, we suggest that the pre-eruption filament processes a double-decker structure composed of two distinct flux bundles, whose formation is associated with gradual magnetic flux cancellations and converging photospheric flows around the polarity inversion line.

astro-ph.SR↗

arXiv2021

Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference

To the best of our knowledge, the existing deep-learning-based Video Super-Resolution (VSR) methods exclusively make use of videos produced by the Image Signal Processor (ISP) of the camera system as inputs. Such methods are 1) inherently suboptimal due to information loss incurred by non-invertible operations in ISP, and 2) inconsistent with the real imaging pipeline where VSR in fact serves as a pre-processing unit of ISP. To address this issue, we propose a new VSR method that can directly exploit camera sensor data, accompanied by a carefully built Raw Video Dataset (RawVD) for training, validation, and testing. This method consists of a Successive Deep Inference (SDI) module and a reconstruction module, among others. The SDI module is designed according to the architectural principle suggested by a canonical decomposition result for Hidden Markov Model (HMM) inference; it estimates the target high-resolution frame by repeatedly performing pairwise feature fusion using deformable convolutions. The reconstruction module, built with elaborately designed Attention-based Residual Dense Blocks (ARDBs), serves the purpose of 1) refining the fused feature and 2) learning the color information needed to generate a spatial-specific transformation for accurate color correction. Extensive experiments demonstrate that owing to the informativeness of the camera raw data, the effectiveness of the network architecture, and the separation of super-resolution and color correction processes, the proposed method achieves superior VSR results compared to the state-of-the-art and can be adapted to any specific camera-ISP. Code and dataset are available at https://github.com/proteus1991/RawVSR.

eess.IV↗

arXiv2020

Covariate Adaptive Family-wise Error Rate Control for Genome-Wide Association Studies

The family-wise error rate (FWER) has been widely used in genome-wide association studies. With the increasing availability of functional genomics data, it is possible to increase the detection power by leveraging these genomic functional annotations. Previous efforts to accommodate covariates in multiple testing focus on the false discovery rate control while covariate-adaptive FWER-controlling procedures remain under-developed. Here we propose a novel covariate-adaptive FWER-controlling procedure that incorporates external covariates which are potentially informative of either the statistical power or the prior null probability. An efficient algorithm is developed to implement the proposed method. We prove its asymptotic validity and obtain the rate of convergence through a perturbation-type argument. Our numerical studies show that the new procedure is more powerful than competing methods and maintains robustness across different settings. We apply the proposed approach to the UK Biobank data and analyze 27 traits with 9 million single-nucleotide polymorphisms tested for associations. Seventy-five genomic annotations are used as covariates. Our approach detects more genome-wide significant loci than other methods in 21 out of the 27 traits.

stat.ME↗

arXiv2020

Secure Location-Aware Authentication and Communication for Intelligent Transportation Systems

Intelligent transportation systems (ITS) are expected to effectively create a stand-alone network for secure communication among autonomous agents. In such a dynamic and fast-changing network with high-speed agents, verifying the authenticity and integrity of messages while taking preventive action (e.g., applying brakes) within tens of milliseconds is one of the main challenges. In such a brief moment after receiving a message, the agent not only must verify the integrity and authenticity of the received message but also needs to perform extra computations to localize the sender of the message for taking appropriate action (e.g., an immediate stop warning from a vehicle in front vs. rear). In this paper, we present an inherently location-aware and lightweight authentication protocol by exploiting in situ visual localization (i.e., SLAM). In this protocol, each agent displays its public key using visual authentication beacons (e.g., QR codes). Thus, receiving agents not only can verify and authenticate the messages but also can easily localize the sender by keeping a shortlist of observed visual beacons within their visual localization system with no additional computation cost. Compared to prior work, our location-aware protocol is scalable, does not depend on any infrastructure, removes the high cost of post-message-delivery localization, and provides trustworthiness guarantees for information that are beyond the reach of each agent sensors.

cs.CR↗

arXiv2020

Indium Antimonide, constraints on practicality as a magneto-optical platform for topological surface plasmon polaritons

Magnetic-field-biased indium antimonide (InSb) is one of the most widely-discussed materials for supporting nonreciprocal surface plasmon polaritons (SPPs), which have recently been shown to be topological. In this work, we provide a critical assessment of InSb as a magneto-optical SPP platform, and show that it is only viable under a narrow set of conditions.

physics.optics↗

arXiv2020

AIM 2020 Challenge on Learned Image Signal Processing Pipeline

This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results. The participating teams were solving a real-world RAW-to-RGB mapping problem, where to goal was to map the original low-quality RAW images captured by the Huawei P20 device to the same photos obtained with the Canon 5D DSLR camera. The considered task embraced a number of complex computer vision subtasks, such as image demosaicing, denoising, white balancing, color and contrast correction, demoireing, etc. The target metric used in this challenge combined fidelity scores (PSNR and SSIM) with solutions' perceptual results measured in a user study. The proposed solutions significantly improved the baseline results, defining the state-of-the-art for practical image signal processing pipeline modeling.

cs.CV↗