arXiv subjects

Fan Zhang

Publications and source records attributed to Fan Zhang.

At least 685 records · Page 38Linked to original sources

arXiv2022

DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel Pathology Image Analysis

We present a novel weakly-supervised framework for classifying whole slide images (WSIs). WSIs, due to their gigapixel resolution, are commonly processed by patch-wise classification with patch-level labels. However, patch-level labels require precise annotations, which is expensive and usually unavailable on clinical data. With image-level labels only, patch-wise classification would be sub-optimal due to inconsistency between the patch appearance and image-level label. To address this issue, we posit that WSI analysis can be effectively conducted by integrating information at both high magnification (local) and low magnification (regional) levels. We auto-encode the visual signals in each patch into a latent embedding vector representing local information, and down-sample the raw WSI to hardware-acceptable thumbnails representing regional information. The WSI label is then predicted with a Dual-Stream Network (DSNet), which takes the transformed local patch embeddings and multi-scale thumbnail images as inputs and can be trained by the image-level label only. Experiments conducted on two large-scale public datasets demonstrate that our method outperforms all recent state-of-the-art weakly-supervised WSI classification methods.

cs.CV↗

arXiv2022

Mixed Reality Depth Contour Occlusion Using Binocular Similarity Matching and Three-dimensional Contour Optimisation

Mixed reality applications often require virtual objects that are partly occluded by real objects. However, previous research and commercial products have limitations in terms of performance and efficiency. To address these challenges, we propose a novel depth contour occlusion (DCO) algorithm. The proposed method is based on the sensitivity of contour occlusion and a binocular stereoscopic vision device. In this method, a depth contour map is combined with a sparse depth map obtained from a two-stage adaptive filter area stereo matching algorithm and the depth contour information of the objects extracted by a digital image stabilisation optical flow method. We also propose a quadratic optimisation model with three constraints to generate an accurate dense map of the depth contour for high-quality real-virtual occlusion. The whole process is accelerated by GPU. To evaluate the effectiveness of the algorithm, we demonstrate a time con-sumption statistical analysis for each stage of the DCO algorithm execution. To verify the relia-bility of the real-virtual occlusion effect, we conduct an experimental analysis on single-sided, enclosed, and complex occlusions; subsequently, we compare it with the occlusion method without quadratic optimisation. With our GPU implementation for real-time DCO, the evaluation indicates that applying the presented DCO algorithm can enhance the real-time performance and the visual quality of real-virtual occlusion.

cs.CV↗

arXiv2022

Double Thompson Sampling in Finite stochastic Games

We consider the trade-off problem between exploration and exploitation under finite discounted Markov Decision Process, where the state transition matrix of the underlying environment stays unknown. We propose a double Thompson sampling reinforcement learning algorithm(DTS) to solve this kind of problem. This algorithm achieves a total regret bound of $\tilde{\mathcal{O}}(D\sqrt{SAT})$in time horizon $T$ with $S$ states, $A$ actions and diameter $D$. DTS consists of two parts, the first part is the traditional part where we apply the posterior sampling method on transition matrix based on prior distribution. In the second part, we employ a count-based posterior update method to balance between the local optimal action and the long-term optimal action in order to find the global optimal game value. We established a regret bound of $\tilde{\mathcal{O}}(\sqrt{T}/S^{2})$. Which is by far the best regret bound for finite discounted Markov Decision Process to our knowledge. Numerical results proves the efficiency and superiority of our approach.

cs.LG↗

arXiv2022

Effects of Mesh Topology on MHD Solution Features in Coronal Simulations

Magnetohydrodynamic (MHD) simulations of the solar corona have become more popular with the increased availability of computational power. Modern computational plasma codes, relying upon Computational Fluid Dynamics (CFD) methods, allow for resolving the coronal features using solar surface magnetograms as inputs. These computations are carried out in a full 3D domain and thus selection of the right mesh configuration is essential to save computational resources and enable/speed up convergence. In addition, it has been observed that for MHD simulations close to the hydrostatic equilibrium, spurious numerical artefacts might appear in the solution following the mesh structure, which makes the selection of the grid also a concern for accuracy. The purpose of this paper is to discuss and trade off two main mesh topologies when applied to global solar corona simulations using the unstructured ideal MHD solver from the COOLFluiD platform. The first topology is based on the geodesic polyhedron and the second on UV mapping. Focus will be placed on aspects such as mesh adaptability, resolution distribution, resulting spurious numerical fluxes and convergence performance. For this purpose, firstly a rotating dipole case is investigated, followed by two simulations using real magnetograms from the solar minima (1995) and solar maxima (1999). It is concluded that the most appropriate mesh topology for the simulation depends on several factors, such as the accuracy requirements, the presence of features near the polar regions and/or strong features in the flow field in general. If convergence is of concern and the simulation contains strong dynamics, then grids which are based on the geodesic polyhedron are recommended compared to more conventionally used UV-mapped meshes.

astro-ph.SR↗

arXiv2022

A CNN-based Post-Processor for Perceptually-Optimized Immersive Media Compression

In recent years, resolution adaptation based on deep neural networks has enabled significant performance gains for conventional (2D) video codecs. This paper investigates the effectiveness of spatial resolution resampling in the context of immersive content. The proposed approach reduces the spatial resolution of input multi-view videos before encoding, and reconstructs their original resolution after decoding. During the up-sampling process, an advanced CNN model is used to reduce potential re-sampling, compression, and synthesis artifacts. This work has been fully tested with the TMIV coding standard using a Versatile Video Coding (VVC) codec. The results demonstrate that the proposed method achieves a significant rate-quality performance improvement for the majority of the test sequences, with an average BD-VMAF improvement of 3.07 overall sequences.

eess.IV↗

arXiv2022

BVI-CC: A Dataset for Research on Video Compression and Quality Assessment

The video technology scenery has been very vivid over the past years, with novel video coding technologies introduced that promise improved compression performance over state-of-the-art technologies. Despite the fact that a lot of video datasets are available, representative content of the wide parameter space along with subjective evaluations of variations of encoded content from an unpartial end is required. In response to this requirement, this paper features a dataset, the BVI-CC. Three video codecs were deployed to create the variations of the encoded sequences: High Efficiency Video Coding (HEVC) Test Model (HM), AOMedia Video 1 (AV1), and Versatile Video Coding (VVC) Test Model (VTM). Nine source video sequences were carefully selected to offer both diversity and representativeness in the spatio-temporal domain. Different spatial resolution versions of the sequences were created and encoded by all three codecs at pre-defined target bit rates. The compression efficiency of the codecs was evaluated with commonly used objective quality metrics, and the subjective quality of their reconstructed content was also evaluated through psychophysical experiments. Additionally, an adaptive bit rate (convex hull rate-distortion optimization across spatial resolutions) test case was assessed using both objective and subjective evaluations. Finally, the computational complexities of the tested codecs were examined. All data have been made publicly available as part of the dataset, which can be used for coding performance evaluation and video quality metric development.

eess.IV↗

arXiv2022

Work statistics across a quantum critical surface

We study the universality of work statistics of a system quenched through a quantum critical surface. By using the adiabatic perturbation theory, we obtain the general scaling behavior for all cumulants of work. These results extend the studies of Kibble-Zurek mechanism scaling of work statistics from an isolated quantum critical point to a critical surface. As an example, we study the scaling behavior of work statistics in the two-dimensional (2D) Kitaev honeycomb model featured with a critical line. By utilizing the trace formula for quadratic fermionic Hamiltonian, we obtain the exact characteristic function of work of the 2D Kitaev model at zero temperature. The results confirm our prediction.

cond-mat.stat-mech↗

arXiv2022

Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification

Deep convolutional neural network (DCNN) models have been widely explored for skin disease diagnosis and some of them have achieved the diagnostic outcomes comparable or even superior to those of dermatologists. However, broad implementation of DCNN in skin disease detection is hindered by small size and data imbalance of the publically accessible skin lesion datasets. This paper proposes a novel single-model based strategy for classification of skin lesions on small and imbalanced datasets. First, various DCNNs are trained on different small and imbalanced datasets to verify that the models with moderate complexity outperform the larger models. Second, regularization DropOut and DropBlock are added to reduce overfitting and a Modified RandAugment augmentation strategy is proposed to deal with the defects of sample underrepresentation in the small dataset. Finally, a novel Multi-Weighted New Loss (MWNL) function and an end-to-end cumulative learning strategy (CLS) are introduced to overcome the challenge of uneven sample size and classification difficulty and to reduce the impact of abnormal samples on training. By combining Modified RandAugment, MWNL and CLS, our single DCNN model method achieved the classification accuracy comparable or superior to those of multiple ensembling models on different dermoscopic image datasets. Our study shows that this method is able to achieve a high classification performance at a low cost of computational resources and inference time, potentially suitable to implement in mobile devices for automated screening of skin lesions and many other malignancies in low resource settings.

cs.CV↗

arXiv2022

Model and predict age and sex in healthy subjects using brain white matter features: A deep learning approach

The human brain's white matter (WM) structure is of immense interest to the scientific community. Diffusion MRI gives a powerful tool to describe the brain WM structure noninvasively. To potentially enable monitoring of age-related changes and investigation of sex-related brain structure differences on the mapping between the brain connectome and healthy subjects' age and sex, we extract fiber-cluster-based diffusion features and predict sex and age with a novel ensembled neural network classifier. We conduct experiments on the Human Connectome Project (HCP) young adult dataset and show that our model achieves 94.82% accuracy in sex prediction and 2.51 years MAE in age prediction. We also show that the fractional anisotropy (FA) is the most predictive of sex, while the number of fibers is the most predictive of age and the combination of different features can improve the model performance.

eess.IV↗

arXiv2022

Minimal non-abelian nodal braiding in ideal metamaterials

Exploring new topological phases and phenomena has become a vital topic in condensed matter physics and material sciences. It is generally believed that a pair of band nodes with opposite topological charges will annihilate after collision. Recent studies reveal that a braided colliding nodal pair can be stabilized in a multi-gap system with PT or C_2z T symmetry. Beyond the conventional single-gap abelian band topology, this intriguing phenomenon exemplifies non-abelian topological charges. Here, we construct ideal acoustic metamaterials to realize non-abelian braiding with the fewest band nodes. We experimentally observe an elegant but nontrivial nodal braiding process, including nodes creation, braiding, collision, and repulsion (i.e., failure to annihilate), and measure the mirror eigenvalues to elucidate the essential braiding consequence. The latter, at the level of wavefunctions, is of prime importance since the braiding physics essentially aims to entangle multi-band wavefunctions. Furthermore, we experimentally unveil the highly intricate correspondence between the edge responses and the bulk non-abelian charges. Notably, all our experimental data perfectly reproduce our numerical simulations. Our findings pave the way for developing non-abelian topological physics that is still in its infancy.

cond-mat.other↗

arXiv2022

SupWMA: Consistent and Efficient Tractography Parcellation of Superficial White Matter with Deep Learning

White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts to enable quantification and visualization. Most parcellation methods focus on the deep white matter (DWM), while fewer methods address the superficial white matter (SWM) due to its complexity. We propose a deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. A point-cloud-based network is modified for our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers. We perform evaluation on a large tractography dataset with ground truth labels and on three independently acquired testing datasets from individuals across ages and health conditions. Compared to several state-of-the-art methods, SupWMA obtains a highly consistent and accurate SWM parcellation result. In addition, the computational speed of SupWMA is much faster than other methods.

cs.CV↗

arXiv2022

Tensor-Network Approach to Work Statistics for 1D Quantum Lattice Systems

We introduce a numerical approach to calculate the statistics of work done on 1D quantum lattice systems initially prepared in thermal equilibrium states. This approach is based on two tensor-network techniques: Time Evolving Block Decimation (TEBD) and Minimally Entangled Typical Thermal States (METTS). The former is an efficient algorithm used to simulate the dynamics of 1D quantum lattice systems, while the latter a finite-temperature algorithm for generating a set of typical states representing the Gibbs canonical ensemble. As an illustrative example, we apply this approach to the Ising chain in mixed transverse and longitudinal fields. Under an arbitrary protocol, the moment generating function of the work can be obtained, from which the work moments are numerically calculated and the quantum Jarzynski equality can be tested. Moreover, the numerical approach is also adapted to test the universal quantum work relation involving a functional of an arbitrary observable.

cond-mat.stat-mech↗

arXiv2021

Evidence for Flat Band Dirac Superconductor Originating from Quantum Geometry

In a flat band superconductor, the charge carriers' group velocity vF is extremely slow, quenching their kinetic energy. The emergence of superconductivity thus appears paradoxical, as conventional BCS theory implies a vanishing coherence length, superfluid stiffness, and critical current. Here, using twisted bilayer graphene (tBLG), we explore the profound effect of vanishingly small vF in a Dirac superconducting flat band system Using Schwinger-limited non-linear transport studies, we demonstrate an extremely slow vF ~ 1000 m/s for filling fraction nu between -1/2 and -3/4 of the moire superlattice. In the superconducting state, the same velocity limit constitutes a new limiting mechanism for the critical current, analogous to a relativistic superfluid. Importantly, our measurement of superfluid stiffness, which controls the superconductor's electrodynamic response, shows that it is not dominated by the kinetic energy, but instead by the interaction-driven superconducting gap, consistent with recent theories on a quantum geometric contribution. We find evidence for small pairs, characteristic of the BCS to Bose-Einstein condensation (BEC) crossover, with an unprecedented ratio of the superconducting transition temperature to the Fermi temperature exceeding unity, and discuss how this arises for very strong coupling superconductivity in ultra-flat Dirac bands.

cond-mat.supr-con↗

arXiv2021

The Design and Performance of Charged Particle Detector onboard the GECAM Mission

The Gravitational Wave highly energetic Electromagnetic Counterpart All-sky Monitor (GECAM) is dedicated to detecting gravitational wave gamma-ray bursts. It is capable of all-sky monitoring over and discovering gamma-ray bursts and new radiation phenomena. GECAM consists of two microsatellites, each equipped with 8 charged particle detectors (CPDs) and 25 gamma-ray detectors (GRDs). The CPD is used to measure charged particles in the space environment, monitor energy and flow intensity changes, and identify between gamma-ray bursts and space charged particle events in conjunction with GRD. CPD uses plastic scintillator as the sensitive material for detection, silicon photomultiplier (SiPM) array as the optically readable device, and the inlaid Am-241 radioactive source as the onboard calibration means. In this paper, we will present the working principle, physical design, functional implementation and preliminary performance test results of the CPD.

astro-ph.IM↗

arXiv2021

Dedicated SiPM array for GRD of GECAM

The discovery of gravitational waves and gamma-ray bursts heralds the era of multi-messenger astronomy. With the adoption of two small satellites to achieve the all-sky monitoring of gamma-ray bursts, the gravitational wave high-energy electromagnetic counterpart all-sky monitor (GECAM) possesses a quasi-real-time early warning ability and plays an important role in positioning the sources of gravitational waves and in subsequent observations.

hep-ex↗

arXiv2021

Ground-based calibration and characterization of GRD of GECAM: 8-160 keV

As the main detector of the GECAM satellite, the calibration of the energy response and detection efficiency of the GRD detector is the main content of the ground-based calibration. The calibration goal requires the calibrated energy points to sample the full energy range (8 keV-2 MeV) as much as possible. The low energy band (8-160 keV) is calibrated with the X-ray beam, while the high energy band (>160 keV) with radioactive sources. This article mainly focuses on the calibration of the energy response and detection efficiency in the 8-160 keV with a refined measurement around the absorption edges of the lanthanum bromide crystal. The GRD performances for different crystal types, data acquisition modes, working modes, and incident positions are also analyzed in detail. We show that the calibration campaign is comprehensive, and the calibration results are generally consistent with simulations as expected.

astro-ph.IM↗

arXiv2021

The In-Flight Realtime Trigger and Localization Software of GECAM

Realtime trigger and localization of bursts are the key functions of GECAM, which is an all-sky gamma-ray monitor launched in Dec 10, 2020. We developed a multifunctional trigger and localization software operating on the CPU of the GECAM electronic box (EBOX). This onboard software has the following features: high trigger efficiency for real celestial bursts with a suppression of false triggers caused by charged particle bursts and background fluctuation, dedicated localization algorithm optimized for short and long bursts respetively, short time latency of the trigger information which is downlinked throught the BeiDou satellite navigation System (BDS). This paper presents the detailed design and deveopment of this trigger and localization software system of GECAM, including the main functions, general design, workflow and algorithms, as well as the verification and demonstration of this software, including the on-ground trigger tests with simulated gamma-ray bursts made by a dedicated X-ray tube and the in-flight performance to real gamma-ray bursts and magnetar bursts.

astro-ph.IM↗

arXiv2021

The design and performance of GRD onboard the GECAM satellite

Background: Each GECAM satellite payload contains 25 gamma-ray detectors (GRDs), which can detect gamma-rays and particles and can roughly localize the Gamma-Ray Bursts (GRBs). GRD was designed using lanthanum bromide (LaBr3) crystal as the sensitive material with the rear end coupled with silicon photomultiplier (SiPM) array for readout. Purpose: In aerospace engineering design of GRD, there are many key points to be studied. In this paper, we present the specific design scheme of GRD, the assembly and the performance test results of detectors. Methods: Based on Monte Carlo simulation and experimental test results, the specific schematic design and assembling process ofGRDwere optimized. After being fully assembled, theGRDswere conducted performance tests by using radioactive source and also conducted random vibration tests. Result and conclusion: The test results show that all satellite-borne GRDs have energy resolution <16% at 59.5 keV, meeting requirements of satellite in scientific performance. The random vibration test shows that GRD can maintain in a stable performance, which meets the requirement of spatial application.

astro-ph.IM↗