BZPEERPapers, people, evidence and research paths
Menu

Discover

SearchFind papers, people, topics, institutions and opportunities from one place.GraphSee how papers, authors, topics and evidence connect.

Research tools

HomeContinue from the papers, people and topics that matter now.CollectionsTurn saved papers into reading lists, evidence maps and shareable context.ResearchReconstruct the path that led you from search to paper to question.CollaborationMove from a paper, topic or expert request into a focused thread.PublishCreate full-text research articles with metadata, formulas and versions.

Network

ExpertsFind specialists whose work and reviews explain why they can help.CommunitiesJoin research groups organized around papers, topics and shared evidence.

Opportunities

ListingsBrowse roles, calls and collaborations connected to real research context.

Account

Log inReturn to your saved papers, graph views and active threads.Create accountStart saving papers, building a research trail and joining teams.
Log inCreate account

Source author record

Chuan Li

Chuan Li appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
cond-mat.mes-hallastro-ph.SRComputer Visionhep-phcond-mat.mtrl-scinucl-thArtificial Intelligenceastro-ph.IMComputation and Languagecond-mat.supr-conhep-thMachine Learningmath-phmath.MPphysics.soc-phSocial and Information Networks

Catalog footprint

What is connected

18works
16topics
4close collaborators

Actions

Connect this record

Log in to claim
Open graphBrowse works

Research graph

See the researcher in context

Open full explorer

Inspect adjacent papers, topics, institutions and collaborators without losing the researcher page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

18 published item(s)

preprint2026arXiv

OpenJarvis: Personal AI, On Personal Devices

Personal AI stacks, like OpenClaw and Hermes Agent, are becoming central to daily work, yet they route nearly every query (often over sensitive local data) to cloud-hosted frontier models. Replacing frontier models with local models inside existing stacks does not work: swapping Claude Opus 4.6 for Qwen3.5-9B drops accuracy by 25-39 pp across personal AI tasks like PinchBench and GAIA. Existing stacks bundle agentic prompts, tool descriptions, memory configuration, and runtime settings around a specific cloud model. Only the prompts can be tuned, and state-of-the-art prompt optimizers close just 5 pp of the local-cloud gap on their own. This motivates a decomposed personal AI stack: one that exposes individual primitives which can be optimized individually or jointly to close the local-cloud gap. We present OpenJarvis, an architecture that represents a personal AI system as a typed spec over five primitives: Intelligence, Engine, Agents, Tools & Memory, and Learning. Each primitive is an independently editable field, making the stack end-to-end optimizable and measurable against accuracy, cost, and latency. Towards closing the local-cloud gap without surrendering local-model properties, OpenJarvis introduces LLM-guided spec search, a local-cloud collaboration in which frontier cloud models propose edits across the spec at search time, only non-regressing edits are accepted, and the resulting spec runs entirely on-device at inference time. With LLM-guided spec search, on-device specs match or exceed cloud accuracy on 4 of 8 benchmarks and land within 3.2 pp of the best cloud baseline on average. They also reduce marginal API cost by ~800x and end-to-end latency by 4x.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Calibration procedures for the CHASE/HIS science data

The Hα line is an important optical line in solar observations containing the information from the photosphere to the chromosphere. To study the mechanisms of solar eruptions and the plasma dynamics in the lower atmosphere, the Chinese Hα Solar Explorer (CHASE) was launched into a Sun-synchronous orbit on October 14, 2021. The scientific payload of the CHASE satellite is the Hα Imaging Spectrograph (HIS). The CHASE/HIS acquires, for the first time, seeing-free Hα spectroscopic observations with high spectral and temporal resolutions. It consists of two observational modes. The raster scanning mode provides full-Sun or region-of-interest spectra at Hα (6559.7-6565.9 Å) and Fe I (6567.8-6570.6 Å) wavebands. The continuum imaging mode obtains full-Sun photospheric images at around 6689 Å. In this paper, we present detailed calibration procedures for the CHASE/HIS science data, including the dark-field and flat-field correction, slit image curvature correction, wavelength and intensity calibration, and coordinate transformation. The higher-level data products can be directly used for scientific research.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The Chinese Hα Solar Explorer (CHASE) mission: An overview

The Chinese Hα Solar Explorer (CHASE), dubbed "Xihe" - Goddess of the Sun, was launched on October 14, 2021 as the first solar space mission of China National Space Administration (CNSA). The CHASE mission is designed to test a newly developed satellite platform and to acquire the spectroscopic observations in the Hα waveband. The Hα Imaging Spectrograph (HIS) is the scientific payload of the CHASE satellite. It consists of two observational modes: raster scanning mode and continuum imaging mode. The raster scanning mode obtains full-Sun or region-of-interest spectral images from 6559.7 to 6565.9 Å and from 6567.8 to 6570.6 Å with 0.024 Å pixel spectral resolution and 1 minute temporal resolution. The continuum imaging mode obtains photospheric images in continuum around 6689 Å with the full width at half maximum of 13.4 Å. The CHASE mission will advance our understanding of the dynamics of solar activity in the photosphere and chromosphere. In this paper, we present an overview of the CHASE mission including the scientific objectives, HIS instrument overview, data calibration flow, and first results of on-orbit observations.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

NPRportrait 1.0: A Three-Level Benchmark for Non-Photorealistic Rendering of Portraits

Despite the recent upsurge of activity in image-based non-photorealistic rendering (NPR), and in particular portrait image stylisation, due to the advent of neural style transfer, the state of performance evaluation in this field is limited, especially compared to the norms in the computer vision and machine learning communities. Unfortunately, the task of evaluating image stylisation is thus far not well defined, since it involves subjective, perceptual and aesthetic aspects. To make progress towards a solution, this paper proposes a new structured, three level, benchmark dataset for the evaluation of stylised portrait images. Rigorous criteria were used for its construction, and its consistency was validated by user studies. Moreover, a new methodology has been developed for evaluating portrait stylisation algorithms, which makes use of the different benchmark levels as well as annotations provided by user studies regarding the characteristics of the faces. We perform evaluation for a wide variety of image stylisation methods (both portrait-specific and general purpose, and also both traditional NPR approaches and neural style transfer) using the new benchmark dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Reducing electronic transport dimension to topological hinge states by increasing geometry size of Dirac semimetal Josephson junctions

The notion of topological phases has been extended to higher-order and has been generalized to different dimensions. As a paradigm, Cd3As2 is predicted to be a higher-order topological semimetal, possessing three-dimensional (3D) bulk Dirac fermions, two-dimensional (2D) Fermi arcs, and one-dimensional (1D) hinge states. These topological states have different characteristic length scales in electronic transport, allowing to distinguish their properties when changing sample size. Here, we report an anomalous dimensional reduction of supercurrent transport by increasing the size of Dirac semimetal Cd3As2-based Josephson junctions. An evolution of the supercurrent quantum interferences from a standard Fraunhofer pattern to a superconducting quantum interference device (SQUID)-like one is observed when the junction channel length is increased. The SQUID-like interference pattern indicates the supercurrent flowing through the 1D hinges. The identification of 1D hinge states should be valuable for deeper understanding the higher-order topological phase in a 3D Dirac semimetal.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis

This paper studies a combination of generative Markov random field (MRF) models and discriminatively trained deep convolutional neural networks (dCNNs) for synthesizing 2D images. The generative MRF acts on higher-levels of a dCNN feature pyramid, controling the image layout at an abstract level. We apply the method to both photographic and non-photo-realistic (artwork) synthesis tasks. The MRF regularizer prevents over-excitation artifacts and reduces implausible feature mixtures common to previous dCNN inversion approaches, permitting synthezing photographic content with increased visual plausibility. Unlike standard MRF-based texture synthesis, the combined system can both match and adapt local features with considerable variability, yielding results far out of reach of classic generative MRF methods.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Full range of proximity effect probed with Superconductor/Graphene/Superconductor junctions

The high tunability of the density of states of graphene makes it an ideal probe of quantum transport in different regimes. In particular, the supercurrent that can flow through a non-superconducting (N) material connected to two superconducting electrodes, crucially depends on the lenghth of the N relative to the superconducting coherence length. Using graphene as the N material we have investigated the full range of the superconducting proximity effect, from short to long diffusive junctions. By combining several S/graphene/S samples with different contacts and lengths, and measuring their gate-dependent critical currents ($ I_c $) and normal state resistance $ R_N $, we compare the product $eR_NI_c$ to the relevant energies, the Thouless energy in long junctions and the superconducting gap of the contacts in short junctions, over three orders of magnitude of Thouless energy. The experimental variations strikingly follow a universal law, close to the predictions of the proximity effect both in the long and short junction regime, as well as in the crossover region, thereby revealing the interplay of the different energy scales. Differences in the numerical coefficients reveal the crucial role played by the interfacial barrier between graphene and the superconducting electrodes, which reduces the supercurrent in both short and long junctions. Surprisingly the reduction of supercurrent is independent of the gate voltage and of the nature of the electrodes. A reduced induced gap and Thouless energy are extracted, revealing the role played by the dwell time in the barrier in the short junction, and an effective increased diffusion time in the long junction. We compare our results to the theoretical predictions of Usadel equations and numerical simulations which better reproduce experiments with imperfect NS interfaces.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks

This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative neural networks for efficient texture synthesis. While deep neural network approaches have recently demonstrated remarkable results in terms of synthesis quality, they still come at considerable computational costs (minutes of run-time for low-res images). Our paper addresses this efficiency issue. Instead of a numerical deconvolution in previous work, we precompute a feed-forward, strided convolutional network that captures the feature statistics of Markovian patches and is able to directly generate outputs of arbitrary dimensions. Such network can directly decode brown noise to realistic texture, or photos to artistic paintings. With adversarial training, we obtain quality comparable to recent neural texture synthesis methods. As no optimization is required any longer at generation time, our run-time performance (0.25M pixel images at 25Hz) surpasses previous neural texture synthesizers by a significant margin (at least 500 times faster). We apply this idea to texture synthesis, style transfer, and video stylization.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Signature of gate-tunable magnetism in graphene grafted with Pt-porphyrins

Inducing magnetism in graphene holds great promises, such as controlling the exchange interaction with a gate electrode and generating exotic magnetic phases. Coating graphene with magnetic molecules or atoms has so far mostly lead to decreased graphene mobility. In the present work, we show that Pt-porphyrins adsorbed on graphene lead to an enhanced mobility and to gate-dependent magnetism. We report that porphyrins can be donor or acceptor, depending on graphene s initial doping. The porphyrins transfer charge and ionize around the charged impurities on graphene, decreasing the graphene doping and increasing its mobility. In addition, ionized porphyrins carry a magnetic moment. Using the sensitivity of mesoscopic transport to magnetism, in particular the superconducting proximity effect and conductance fluctuations, we explore the magnetic order induced in graphene by the interacting magnetic moments of the ionized porphyrins. Among the signatures of magnetism, we find two-terminal-magnetoresistance fluctuations with an odd component, a tell-tale sign of time reversal symmetry breaking at zero field, that does not exist in uncoated graphene sample. When graphene is connected to superconducting electrodes, the induced magnetism leads to a gate-voltage-dependent suppression of the supercurrent, modified magnetic interference patterns, and gate-voltage-dependent magnetic hysteresis. The magnetic signatures are greatest for long superconductor graphene superconductor junctions and for samples with the highest initial doping, compatible with a greater number of ionized and thus magnetic porphyrins. Our findings suggest that long-range magnetism is induced through graphene by the ionized porphyrins magnetic moment. This magnetic interaction is controlled by the density of carriers in graphene, a tunability that could be exploited in spintronic applications.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Predicting Neighbor Distribution in Heterogeneous Information Networks

Recently, considerable attention has been devoted to the prediction problems arising from heterogeneous information networks. In this paper, we present a new prediction task, Neighbor Distribution Prediction (NDP), which aims at predicting the distribution of the labels on neighbors of a given node and is valuable for many different applications in heterogeneous information networks. The challenges of NDP mainly come from three aspects: the infinity of the state space of a neighbor distribution, the sparsity of available data, and how to fairly evaluate the predictions. To address these challenges, we first propose an Evolution Factor Model (EFM) for NDP, which utilizes two new structures proposed in this paper, i.e. Neighbor Distribution Vector (NDV) to represent the state of a given node's neighbors, and Neighbor Label Evolution Matrix (NLEM) to capture the dynamics of a neighbor distribution, respectively. We further propose a learning algorithm for Evolution Factor Model. To overcome the problem of data sparsity, the learning algorithm first clusters all the nodes and learns an NLEM for each cluster instead of for each node. For fairly evaluating the predicting results, we propose a new metric: Virtual Accuracy (VA), which takes into consideration both the absolute accuracy and the predictability of a node. Extensive experiments conducted on three real datasets from different domains validate the effectiveness of our proposed model EFM and metric VA.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Magnetic field resistant quantum interferences in bismuth nanowires based Josephson junctions

We investigate proximity induced superconductivity in micrometer-long bismuth nanowires con- nected to superconducting electrodes with a high critical field. At low temperature we measure a supercurrent that persists in magnetic fields as high as the critical field of the electrodes (above 11 T). The critical current is also strongly modulated by the magnetic field. In certain samples we find regular, rapid SQUID-like periodic oscillations occurring up to high fields. Other samples ex- hibit less periodic but full modulations of the critical current on Tesla field scales, with field-caused extinctions of the supercurrent. These findings indicate the existence of low dimensionally, phase coherent, interfering conducting regions through the samples, with a subtle interplay between orbital and spin contributions. We relate these surprising results to the electronic properties of the surface states of bismuth, strong Rashba spin-orbit coupling, large effective g factors, and their effect on the induced superconducting correlations.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Proton activity of the Sun in current solar cycle 24

We present a study of 7 large solar proton events (SPEs) of current solar cycle 24 (from 2009 January up to date). They were recorded by GOES spacecraft with highest proton fluxes over 200 pfu for energies $>$10 MeV. In situ particle measurements show that: (1) The profiles of the proton fluxes are highly dependent of the locations of their solar sources, namely flares or coronal mass ejections (CMEs); (2) The solar particle release (SPR) times fall in the decay phase of the flare emission, and are in accordance with the times when the CMEs travel to an average height of 7.9 solar radii; (3) The time differences between the SPR and the flare peak are also dependent of the locations of the solar active regions (ARs). The results tend to support the concept of proton acceleration by the CME-driven shock, even though there exists a possibility of particle acceleration at flare site with subsequent perpendicular diffusion of accelerated particles in the interplanetary magnetic field (IMF). We derive the integral time-of-maximum (TOM) spectra of solar protons in two forms: a single power-law distribution and a power law broken with an exponential tail. It is found that the unique Ground Level Enhancement (GLE) event on 2012 May 17 displays a hardest spectrum and a largest broken energy that may explain why the this event could extend to relativistic energy.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Strain superlattices and macroscale suspension of Graphene induced by corrugated substrates

We investigate the organized formation of strain, ripples and suspended features in macroscopic CVD-prepared graphene sheets transferred onto a corrugated substrate made of an ordered arrays of silica pillars of variable geometries. Depending on the aspect ratio and sharpness of the corrugated array, graphene can conformally coat the surface, partially collapse, or lay, fakir-like, fully suspended between pillars over tens of micrometers. Upon increase of pillar density, ripples in collapsed films display a transition from random oriented pleats emerging from pillars to ripples linking nearest neighboring pillars organized in domains of given orientation. Spatially-resolved Raman spectroscopy, atomic force microscopy and electronic microscopy reveal uniaxial strain domains in the transferred graphene, which are induced and controlled by the geometry. We propose a simple theoretical model to explain the transition between suspended and collapsed graphene. For the arrays with high aspect ratio pillars, graphene membranes stays suspended over macroscopic distances with minimal interaction with pillars tip apex. It offers a platform to tailor stress in graphene layers and open perspectives for electron transport and nanomechanical applications.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Waiting time distribution of solar energetic particle events modeled with a non-stationary Poisson process

We present a study of the waiting time distributions (WTDs) of solar energetic particle (SEP) events observed with the spacecraft $WIND$ and $GOES$. Both the WTDs of solar electron events (SEEs) and solar proton events (SPEs) display a power-law tail $\sim Δt^{-γ}$. The SEEs display a broken power-law WTD. The power-law index is $γ_{1} =$ 0.99 for the short waiting times ($<$70 hours) and $γ_{2} =$ 1.92 for large waiting times ($>$100 hours). The break of the WTD of SEEs is probably due to the modulation of the corotating interaction regions (CIRs). The power-law index $γ\sim$ 1.82 is derived for the WTD of SPEs that is consistent with the WTD of type II radio bursts, indicating a close relationship between the shock wave and the production of energetic protons. The WTDs of SEP events can be modeled with a non-stationary Poisson process which was proposed to understand the waiting time statistics of solar flares (Wheatland 2000; Aschwanden $\&$ McTiernan 2010). We generalize the method and find that, if the SEP event rate $λ= 1/Δt$ varies as the time distribution of event rate $f(λ) = A λ^{-α}exp(-βλ)$, the time-dependent Poisson distribution can produce a power-law tail WTD $\sim Δt^{α- 3}$, where $0 \leq α< 2$.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Amending the Vafa-Witten Theorem

The strong version of the Vafa-Witten theorem is shown may not to hold because the zero condensate from a direct computation of the order parameter is found to be a result on the symmetric vacuum. The validity of the Vafa-Witten theorem relies then on its weak version, that the Goldstone boson is absent in vector-like gauge theories with vanishing θ-angle. The existence of a charged ρ-meson condensate, which violates electromagnetic gauge symmetry, is consistent with this weak version of the Vafa-Witten theorem when applied to strong magnetic fields in QCD.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Minimal Ward-Takahashi Vertices and Pion Light Cone Distribution Amplitudes from Gauge Invariant, Nonlocal, Dynamical Quark Model

The gauge-invariant, nonlocal, dynamical quark model is proved to generate the minimal vertices which satisfy the Ward-Takahashi identities. In the chiral limit, the momentum-dependent quark self-energy results in a flat-like form with some end point delta-funtions for the light-cone pion distribution amplitudes, similarly found in the Nambu Jona-Lasino model with constant constituent mass. The leading order nonzero pion and current quark masses corrections lead concave type asymptotic-like form modifications to twist-2 pion distribution amplitude with end point pillars and twist-3 tensor pion distribution amplitude above the flat-like form backgrounds. A by-product of our investigation shows that the variable u appearing in pion light-cone distribution amplitudes is just the standard Feynman parameter in the Feynman parameter integrals; also chiral perturbation works well for these amplitudes.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Superconducting proximity effect through graphene from zero field to the Quantum Hall regime

We investigate the superconducting proximity effect through graphene in the long diffusive junction limit, at low and high magnetic field. The interface quality and sample phase coherence lead to a zero resistance state at low temperature, zero magnetic field, and high doping. We find a striking suppression of the critical current near graphene\rq{}s charge neutrality point, which we attribute to specular reflexion of Andreev pairs at the interface of charge puddles. This type of reflexion, specific to the Dirac band structure, had up to now remained elusive. At high magnetic field the use of superconducting electrodes with high critical field enables the investigation of the proximity effect in the Quantum Hall regime. Although the supercurrent is not directly detectable in our two wire configuration, interference effects are visible which may be attributed to the injection of Cooper pairs into edge states.

0 citations0 reviewsNo code linkedOpen access
preprint2009arXiv

Computation of the p6 order chiral Lagrangian coefficients from the underlying theory of QCD

We present results of computing the p6 order low energy constants in the normal part of chiral Lagrangian both for two and three flavor pseudo-scalar mesons. This is a generalization of our previous work on calculating the p4 order coefficients of the chiral Lagrangian in terms of the quark self energy Sigma(p2) approximately from QCD. We show that most of our results are consistent with those we can find in the literature.

0 citations0 reviewsNo code linkedOpen access