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Fan Zhang

Publications and source records attributed to Fan Zhang.

At least 649 records · Page 36Linked to original sources

Andreev processes in mesoscopic multi-terminal graphene Josephson junctions

There is growing interest in using multi-terminal Josephson junctions (MTJJs) as a platform to artificially emulate topological phases and to investigate complex superconducting mechanisms such as quartet and multiplet Cooper pairings. Current experimental signatures in MTJJs have led to conflicting interpretations of the salient features. In this work, we report a collaborative experimental and theoretical investigation of graphene-based four-terminal Josephson junctions. We observe resonant features in the differential resistance maps that resemble those ascribed to multiplet Cooper pairings. To understand these features, we model our junctions using a circuit network of coupled two-terminal resistively and capacitively shunted junctions (RCSJs). Under appropriate bias current, the model predicts that a current flowing between two diagonal terminals in a four-terminal geometry may be represented as a sinusoidal function of a weighted sum of the superconducting phases. We show that starting from a semi-classical model with diffusive current-phase relations, the MTJJ effectively emulates a general form of the expected current-phase relation for multiplet Cooper pairings. Our study therefore suggests that differential resistance measurements alone are insufficient to conclusively distinguish resonant Andreev reflection processes from semi-classical circuit-network effects.

cond-mat.supr-con↗

Ultrafast carbon nanotube photodetectors with bandwidth over 60 GHz

The future interconnect links in intra- and inter-chip require the photodetector with high bandwidth, ultra-wide waveband, compact footprint, low-cost, and compatible integration process with silicon complementary metal-oxide-semiconductor (CMOS) technology. Here, we demonstrate a CMOS-compatible carbon nanotube (CNT) photodetector that exhibits high responsivity, high bandwidth and broad spectral operation over all optical telecommunication band based on high-purity CNT arrays. The ultrafast CNT photodetector demonstrates the 100 Gbit/s Nyquist-shaped on-off-keying (OOK) signal transmission, which can address the demand for high-speed optical interconnects in and between data centers. Furthermore, the photodetector exhibits a bandwidth over 60 GHz by scaling down the active area to 20 μm2. As the CNT photodetectors are fabricated by doping-free process, it also provides a cost-effective solution to integrate CNT photonic devices with CNT-based CMOS integrated circuits. Our work paves a way for future CNT-based high-speed optical interconnects and optoelectronic integrated circuits (OEICs).

physics.optics↗

COCONUT, a novel fast-converging MHD model for solar corona simulations: II. Assessing the impact of the input magnetic map on space-weather forecasting at minimum of activity

This paper is dedicated to the new implicit unstructured coronal code COCONUT, which aims at providing fast and accurate inputs for space weather forecast as an alternative to empirical models. We use all 20 available magnetic maps of the solar photosphere covering the date of the 2nd of July 2019 which corresponds to a solar eclipse on Earth. We use the same standard pre-processing on all maps, then perform coronal MHD simulations with the same numerical and physical parameters. In the end, we quantify the performance for each map using three indicators from remote-sensing observations: white-light total solar eclipse images for the streamers' edges, EUV synoptic maps for coronal holes and white-light coronagraph images for the heliospheric current sheet. We discuss the performance for space weather forecasts and we show that the choice of the input magnetic map has a strong impact. We find performances between 24% to 85% for the streamers' edges, 24% to 88% for the coronal hole boundaries and a mean deviation between 4 to 12 degrees for the heliospheric current sheet position. We find that the HMI runs are globally performing better on all indicators, with the GONG-ADAPT being the second-best choice. HMI runs perform better for the streamers' edges, GONG-ADAPT for polar coronal holes, HMI synchronic for equatorial coronal holes and for the streamer belt. We especially showcase the importance of the filling of the poles. This demonstrates that the solar poles have to be taken into account even for ecliptic plane previsions.

astro-ph.SR↗

GTAV-NightRain: Photometric Realistic Large-scale Dataset for Night-time Rain Streak Removal

Rain is transparent, which reflects and refracts light in the scene to the camera. In outdoor vision, rain, especially rain streaks degrade visibility and therefore need to be removed. In existing rain streak removal datasets, although density, scale, direction and intensity have been considered, transparency is not fully taken into account. This problem is particularly serious in night scenes, where the appearance of rain largely depends on the interaction with scene illuminations and changes drastically on different positions within the image. This is problematic, because unrealistic dataset causes serious domain bias. In this paper, we propose GTAV-NightRain dataset, which is a large-scale synthetic night-time rain streak removal dataset. Unlike existing datasets, by using 3D computer graphic platform (namely GTA V), we are allowed to infer the three dimensional interaction between rain and illuminations, which insures the photometric realness. Current release of the dataset contains 12,860 HD rainy images and 1,286 corresponding HD ground truth images in diversified night scenes. A systematic benchmark and analysis are provided along with the dataset to inspire further research.

cs.CV↗

Composite Likelihoods with Bounded Weights in Extrapolation of Data

Among many efforts to facilitate timely access to safe and effective medicines to children, increased attention has been given to extrapolation. Loosely, it is the leveraging of conclusions or available data from adults or older age groups to draw conclusions for the target pediatric population when it can be assumed that the course of the disease and the expected response to a medicinal product would be sufficiently similar in the pediatric and the reference population. Extrapolation then can be characterized as a statistical mapping of information from the reference (adults or older age groups) to the target pediatric population. The translation, or loosely mapping of information, can be through a composite likelihood approach where the likelihood of the reference population is weighted by exponentiation and that this exponent is related to the value of the mapped information in the target population. The weight is bounded above and below recognizing the fact that similarity (of the disease and the expected response) is still valid despite variability of response between the cohorts. Maximum likelihood approaches are then used for estimation of parameters and asymptotic theory is used to derive distributions of estimates for use in inference. Hence, the estimation of effects in the target population borrows information from reference population. In addition, this manuscript also talks about how this method is related to the Bayesian statistical paradigm.

stat.ME↗

zkBridge: Trustless Cross-chain Bridges Made Practical

Blockchains have seen growing traction with cryptocurrencies reaching a market cap of over 1 trillion dollars, major institution investors taking interests, and global impacts on governments, businesses, and individuals. Also growing significantly is the heterogeneity of the ecosystem where a variety of blockchains co-exist. Cross-chain bridge is a necessary building block in this multi-chain ecosystem. Existing solutions, however, either suffer from performance issues or rely on trust assumptions of committees that significantly lower the security. Recurring attacks against bridges have cost users more than 1.5 billion USD. In this paper, we introduce zkBridge, an efficient cross-chain bridge that guarantees strong security without external trust assumptions. With succinct proofs, zkBridge not only guarantees correctness, but also significantly reduces on-chain verification cost. We propose novel succinct proof protocols that are orders-of-magnitude faster than existing solutions for workload in zkBridge. With a modular design, zkBridge enables a broad spectrum of use cases and capabilities, including message passing, token transferring, and other computational logic operating on state changes from different chains. To demonstrate the practicality of zkBridge, we implemented a prototype bridge from Cosmos to Ethereum, a particularly challenging direction that involves large proof circuits that existing systems cannot efficiently handle. Our evaluation shows that zkBridge achieves practical performance: proof generation takes less than 20 seconds, while verifying proofs on-chain costs less than 230K gas. For completeness, we also implemented and evaluated the direction from Ethereum to other EVM-compatible chains (such as BSC) which involves smaller circuits and incurs much less overhead.

cs.CR↗

Improving Primal Heuristics for Mixed Integer Programming Problems based on Problem Reduction: A Learning-based Approach

In this paper, we propose a Bi-layer Predictionbased Reduction Branch (BP-RB) framework to speed up the process of finding a high-quality feasible solution for Mixed Integer Programming (MIP) problems. A graph convolutional network (GCN) is employed to predict binary variables' values. After that, a subset of binary variables is fixed to the predicted value by a greedy method conditioned on the predicted probabilities. By exploring the logical consequences, a learning-based problem reduction method is proposed, significantly reducing the variable and constraint sizes. With the reductive sub-MIP problem, the second layer GCN framework is employed to update the prediction for the remaining binary variables' values and to determine the selection of variables which are then used for branching to generate the Branch and Bound (B&B) tree. Numerical examples show that our BP-RB framework speeds up the primal heuristic and finds the feasible solution with high quality.

math.OC↗

Exploring Hydride Formation in Stainless Steel Revisits Theory of Hydrogen Embrittlement

Various mechanisms have been proposed for hydrogen embrittlement, but the causation of hydrogen-induced material degradation has remained unclear. This work shows hydrogen embrittlement due to phase instability (decomposition). In-situ diffraction measurements revealed metastable hydrides formed in stainless steel, typically declared as a non-hydride forming material. Hydride formation is possible by increasing the hydrogen chemical potential during electrochemical charging and low defect formation energy of hydrogen interstitials. Our findings demonstrate that hydrogen-induced material degradation can only be understood if measured in situ and in real-time during the embrittlement process.

cond-mat.mtrl-sci↗

Strong Dzyaloshinskii-Moriya Interaction in Monolayer CrI$_3$ on Metal Substrates

Dzyaloshinskii-Moriya interaction (DMI) is the primary mechanism for realizing real-space chiral spin textures, which are regarded as key components for the next-generation spintronics. However, DMI arises from a perturbation term of the spin-orbit interaction and is usually weak in pristine magnetic semiconductors. To date, large DMI and the resulting skyrmions are only realized in a few materials under stringent conditions. Using first-principles calculations, we demonstrate that significant DMI occurs between nearest-neighbor Cr atoms in two-dimensional (2D) magnetic semiconductor CrI$_3$ on Au or Cu substrates. This exceptionally strong DMI is generated by the interfacial charge transfer and weak chemical interactions between chromium halides and metal substrates, which break the spatial inversion symmetry. These findings highlight the significance of substrate effects in 2D magnets and expand the inventory of feasible materials with strong DMI.

cond-mat.mtrl-sci↗

Diversity beyond density: experienced social mixing of urban streets

Urban density, in the form of residents' and visitors' concentration, is long considered to foster diverse exchanges of interpersonal knowledge and skills, which are intrinsic to sustainable human settlements. However, with current urban studies primarily devoted to city and district-level analysis, we cannot unveil the elemental connection between urban density and diversity. Here we use an anonymized and privacy-enhanced mobile data set of 0.5 million opted-in users from three metropolitan areas in the U.S to show that at the scale of urban streets, density is not the only path to diversity. We represent the diversity of each street with the Experienced Social Mixing (ESM), which describes the chances of people meeting diverse income groups throughout their daily experience. We conduct multiple experiments and show that the concentration of visitors only explains 26% of street-level ESM. However, adjacent amenities, residential diversity, and income level account for 44% of the ESM. Moreover, using longitudinal business data, we show that streets with an increased number of food businesses have seen an increased ESM from 2016 to 2018. Lastly, although streets with more visitors are more likely to have crime, diverse streets tend to have fewer crimes. These findings suggest that cities can leverage many tools beyond density to curate a diverse and safe street experience for people.

physics.soc-ph↗

Room-temperature quantum spin Hall edge state in a higher-order topological insulator Bi$_4$Br$_4$

Room-temperature realization of macroscopic quantum phenomena is one of the major pursuits in fundamental physics. The quantum spin Hall state, a topological quantum phenomenon that features a two-dimensional insulating bulk and a helical edge state, has not yet been realized at room temperature. Here, we use scanning tunneling microscopy to visualize a quantum spin Hall edge state on the surface of the higher-order topological insulator Bi4Br4. We find that the atomically resolved lattice exhibits a large insulating gap of over 200meV, and an atomically sharp monolayer step edge hosts a striking in-gap gapless state, suggesting the topological bulk-boundary correspondence. An external magnetic field can gap the edge state, consistent with the time-reversal symmetry protection inherent to the underlying topology. We further identify the geometrical hybridization of such edge states, which not only attests to the Z2 topology of the quantum spin Hall state but also visualizes the building blocks of the higher-order topological insulator phase. Remarkably, both the insulating gap and topological edge state are observed to persist up to 300K. Our results point to the realization of the room-temperature quantum spin Hall edge state in a higher-order topological insulator.

cond-mat.mes-hall↗

An Insight-HXMT view of the mHz quasi-regular modulation phenomenon in the black hole X-ray binary 4U 1630-47

Here we report the spectral-timing results of the black hole X-ray binary 4U 1630-47 during its 2021 outburst using observations from the Hard X-ray Modulation Telescope. Type-C quasi-periodic oscillations (QPOs) in 1.6--4.2 Hz and quasi-regular modulation (QRM) near 60 mHz are detected during the outburst. The mHz QRM has a fractional rms of 10%--16% in the 8--35 keV energy band with a Q factor (frequency/width) of 2--4. Benefiting from the broad energy band of hxmt, we study the energy dependence of the 60 mHz QRM in 1--100 keV for the first time. We find that the fractional rms of the mHz QRM increases with photon energy, while the time lags of the mHz QRM are soft and decrease with photon energy. Fast recurrence of the mHz QRM, in a timescale of less than one hour, has been observed during the outburst. During this period, the corresponding energy spectra moderately change when the source transitions from the QRM state to the non-QRM state. The QRM phenomena also shows a dependence with the accretion rate. We suggest that the QRM could be caused by an unknown accretion instability aroused from the corona.

astro-ph.HE↗

Microreversibility, fluctuation relations, and response properties in 1D Kitaev Chain

We analytically calculate the cumulant generating function of energy and particle transport in an open 1D Kitaev chain by utilizing the Keldysh technique. The joint distribution of particle and energy currents obeys different fluctuation relations in different regions of the parameter space as a result of $U$(1) symmetry breaking and energy conservation. We discuss the thermoelectricity of the Kitaev chain as a three terminal system and derive an analytical expression of the maximum work power. The response theory up to the second order is explicitly checked, and the result is consistent with the relations derived from the fluctuation relation.

cond-mat.stat-mech↗

Enhancing HDR Video Compression through CNN-based Effective Bit Depth Adaptation

It is well known that high dynamic range (HDR) video can provide more immersive visual experiences compared to conventional standard dynamic range content. However, HDR content is typically more challenging to encode due to the increased detail associated with the wider dynamic range. In this paper, we improve HDR compression performance using the effective bit depth adaptation approach (EBDA). This method reduces the effective bit depth of the original video content before encoding and reconstructs the full bit depth using a CNN-based up-sampling method at the decoder. In this work, we modify the MFRNet network architecture to enable multiple frame processing, and the new network, multi-frame MFRNet, has been integrated into the EBDA framework using two Versatile Video Coding (VVC) host codecs: VTM 16.2 and the Fraunhofer Versatile Video Encoder (VVenC 1.4.0). The proposed approach was evaluated under the JVET HDR Common Test Conditions using the Random Access configuration. The results show coding gains over both the original VVC VTM 16.2 and VVenC 1.4.0 (w/o EBDA) on JVET HDR tested sequences, with average bitrate savings of 2.9% (over VTM) and 4.8% (against VVenC) based on the Bjontegaard Delta measurement. The source code of multi-frame MFRNet has been released at https://github.com/fan-aaron-zhang/MF-MFRNet.

eess.IV↗

Bulk and Surface Critical Behaviors of quantum Heisenberg antiferromagnet on a two-dimensional coupled diagonal ladders

Using Quantum Monte Carlo simulations, we study the spin-1/2 Heisenberg model on a two-dimensional lattice formed by coupling diagonal ladders. The model hosts an antiferromagnetic Néel phase, a rung singlet product phase, and a topological none trivial Haldane phase, separated by two quantum phase transitions. We show that the two quantum critical points are all in the three-dimensional O(3) universality class. The properties of the two gapped phases, including the finite-size behavior of the string orders in the Haldane phase, are studied. We show that the surface formed by the ladders ends is gapless, while the surface exposed along the ladders is gapful, in the Haldane phase. Conversely, in the gapped rung singlet phase, the former surface is gapped, and the latter is gapless. We demonstrate that, although mechanisms of the two gapless modes are different, nonordinary surface critical behaviors are realized at both critical points on the gapless surfaces exposed by simply cutting bonds without fine-tuning the surface coupling required to reach a multicritical point in classical models. We also show that, on the gapped surfaces, the surface critical behaviors are in the ordinary class.

cond-mat.str-el↗

TractoFormer: A Novel Fiber-level Whole Brain Tractography Analysis Framework Using Spectral Embedding and Vision Transformers

Diffusion MRI tractography is an advanced imaging technique for quantitative mapping of the brain's structural connectivity. Whole brain tractography (WBT) data contains over hundreds of thousands of individual fiber streamlines (estimated brain connections), and this data is usually parcellated to create compact representations for data analysis applications such as disease classification. In this paper, we propose a novel parcellation-free WBT analysis framework, TractoFormer, that leverages tractography information at the level of individual fiber streamlines and provides a natural mechanism for interpretation of results using the attention mechanism of transformers. TractoFormer includes two main contributions. First, we propose a novel and simple 2D image representation of WBT, TractoEmbedding, to encode 3D fiber spatial relationships and any feature of interest that can be computed from individual fibers (such as FA or MD). Second, we design a network based on vision transformers (ViTs) that includes: 1) data augmentation to overcome model overfitting on small datasets, 2) identification of discriminative fibers for interpretation of results, and 3) ensemble learning to leverage fiber information from different brain regions. In a synthetic data experiment, TractoFormer successfully identifies discriminative fibers with simulated group differences. In a disease classification experiment comparing several methods, TractoFormer achieves the highest accuracy in classifying schizophrenia vs control. Discriminative fibers are identified in left hemispheric frontal and parietal superficial white matter regions, which have previously been shown to be affected in schizophrenia patients.

eess.IV↗

FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for Doctor Recommendation Using EHR

In the past decade, with the development of big data technology, an increasing amount of patient information has been stored as electronic health records (EHRs). Leveraging these data, various doctor recommendation systems have been proposed. Typically, such studies process the EHR data in a flat-structured manner, where each encounter was treated as an unordered set of features. Nevertheless, the heterogeneous structured information such as service sequence stored in claims shall not be ignored. This paper presents a doctor recommendation system with time embedding to reconstruct the potential connections between patients and doctors using heterogeneous graph attention network. Besides, to address the privacy issue of patient data sharing crossing hospitals, a federated decentralized learning method based on a minimization optimization model is also proposed. The graph-based recommendation system has been validated on a EHR dataset. Compared to baseline models, the proposed method improves the AUC by up to 6.2%. And our proposed federated-based algorithm not only yields the fictitious fusion center's performance but also enjoys a convergence rate of O(1/T).

cs.IR↗

White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning

White matter tract microstructure has been shown to influence neuropsychological scores of cognitive performance. However, prediction of these scores from white matter tract data has not been attempted. In this paper, we propose a deep-learning-based framework for neuropsychological score prediction using microstructure measurements estimated from diffusion magnetic resonance imaging (dMRI) tractography, focusing on predicting performance on a receptive vocabulary assessment task based on a critical fiber tract for language, the arcuate fasciculus (AF). We directly utilize information from all points in a fiber tract, without the need to average data along the fiber as is traditionally required by diffusion MRI tractometry methods. Specifically, we represent the AF as a point cloud with microstructure measurements at each point, enabling adoption of point-based neural networks. We improve prediction performance with the proposed Paired-Siamese Loss that utilizes information about differences between continuous neuropsychological scores. Finally, we propose a Critical Region Localization (CRL) algorithm to localize informative anatomical regions containing points with strong contributions to the prediction results. Our method is evaluated on data from 806 subjects from the Human Connectome Project dataset. Results demonstrate superior neuropsychological score prediction performance compared to baseline methods. We discover that critical regions in the AF are strikingly consistent across subjects, with the highest number of strongly contributing points located in frontal cortical regions (i.e., the rostral middle frontal, pars opercularis, and pars triangularis), which are strongly implicated as critical areas for language processes.

cs.CV↗