arXiv subjects

Shun Li

Publications and source records attributed to Shun Li.

16 recordsLinked to original sources

arXiv2026

GraRe: Grasp Candidate Re-Ranking for Frozen 6-DoF Grasp Detectors

Existing 6-DoF grasp detectors typically rank grasp candidates by detector confidence. However, our analysis on GraspNet-1Billion shows that detector confidence is often poorly aligned with grasp quality, causing successful grasp candidates to be ranked too low during execution. Motivated by this observation, we formulate grasp candidate re-ranking as a separate task for frozen detectors, aiming to improve candidate ordering without changing the detector or its grasp candidates. We propose GraRe, which estimates grasp quality from candidate attributes, shell-stratified local geometry, and object context. Candidate attributes condition the local geometric and object-context representations, and a Transformer fuses all three feature types. The predicted quality is combined with detector confidence to produce the final ranking. Experiments on GraspNet-1Billion with three frozen detectors show consistent improvements, with gains of up to 13.60 points in Average AP. Real-robot experiments further demonstrate robust grasping in cluttered scenes. These results show that improving candidate ranking provides a practical way to enhance frozen 6-DoF grasp detectors.

cs.RO↗

arXiv2026

An Asymptotic-Preserving Dynamical Low-Rank Semi-Lagrangian Method for Multiscale Linear Kinetic Transport Equations

In this paper, we develop an asymptotic-preserving (AP) dynamical low-rank semi-Lagrangian method for multiscale linear kinetic transport equations. The method combines the large-time-step capability of semi-Lagrangian discretizations with the storage and cost reduction provided by low-rank representations. The proposed scheme couples an approximate macroscopic density update with the basis update Galerkin integrator for the kinetic distribution. To retain the reduced complexity in the semi-Lagrangian flux evaluation, the flux derivative is computed through a sampled angular quadrature strategy. We establish an unconditional stability analysis of the full-quadrature low-rank scheme in the constant-coefficient case. The error induced by angular sampling in the flux derivative is quantified. The resulting scheme is shown to be AP in the diffusive limit. Numerical experiments, including high-dimensional test cases, demonstrate that the proposed method is AP, stable under large time steps, and computationally efficient across kinetic and diffusive regimes.

math.NA↗

arXiv2026

In-context modeling as a retrain-free paradigm for foundation models in computational science

Building models that generalize across physical systems without retraining remains a central challenge in computational science. Here we introduce In-Context Modeling (ICM), a retrain-free paradigm that infers physical relationships directly from observational fields. Rather than encoding system-specific behavior in fixed parameters, ICM assimilates measurements as physical context and performs inference through a single forward pass. Trained in a physics-informed, label-free manner using governing equations, a single model generalizes across unseen materials, geometries, and loading conditions. Demonstrated on hyperelasticity, ICM integrates with finite-element simulations and is validated using experimental full-field measurements. Moreover, performance improves with increasing data diversity and computational budget, exhibiting favorable scaling behavior analogous to foundation models. By recasting physical modeling as in-context inference, this work establishes a transferable paradigm for retrain-free scientific learning and a foundation for scalable modeling across computational science.

cs.CE↗

arXiv2026

ALIGN: Adversarial Learning for Generalizable Speech Neuroprosthesis

Intracortical brain-computer interfaces (BCIs) can decode speech from neural activity with high accuracy when trained on data pooled across recording sessions. In realistic deployment, however, models must generalize to new sessions without labeled data, and performance often degrades due to cross-session nonstationarities (e.g., electrode shifts, neural turnover, and changes in user strategy). In this paper, we propose ALIGN, a session-invariant learning framework based on multi-domain adversarial neural networks for semi-supervised cross-session adaptation. ALIGN trains a feature encoder jointly with a phoneme classifier and a domain classifier operating on the latent representation. Through adversarial optimization, the encoder is encouraged to preserve task-relevant information while suppressing session-specific cues. We evaluate ALIGN on intracortical speech decoding and find that it generalizes consistently better to previously unseen sessions, improving both phoneme error rate and word error rate relative to baselines. These results indicate that adversarial domain alignment is an effective approach for mitigating session-level distribution shift and enabling robust longitudinal BCI decoding.

cs.LG↗

arXiv2026

Temporal-Stability-Enhanced and Energy-Stable Dynamical Low-Rank Approximation for Multiscale Linear Kinetic Transport Equations

In this paper, we develop an asymptotic-preserving dynamical low-rank method for the multiscale linear kinetic transport equation. The proposed scheme is unconditionally stable in the diffusive regime while preserving the correct asymptotic behavior, and can achieve significant reductions in computational cost through a low-rank representation and large time step stability. A low-rank formulation consistent with the discrete energy is introduced under the discrete ordinates discretization, and energy stability of the resulting scheme is established. Numerical experiments confirm the energy stability and demonstrate that the method is efficient while maintaining accuracy across different regimes and capturing the correct asymptotic limits.

math.NA↗

arXiv2026

Phase Transition With Rapini-Papoular Surface Anchoring

We analyze the dynamical (in)stability of nematic liquid crystals in the presence of external magnetic fields and Rapini-Papoular surface potential. The P-HAN transition is investigated using a simplified 3D Ericksen-Leslie system. We find the thickness threshold of the P-HAN transition. If the thickness of the nematic layer exceeds this threshold, there is a global-in-time suitable weak solution converging exponentially to a nontrivial equilibrium state as time tends to infinity. If the thickness is no more than the threshold, the global-in-time suitable weak solution has a trivial long-time asymptotic limit. Our results rigorously justify the P-HAN transition discussed in the physics literature.

math.AP↗

arXiv2025

Thermal interaction-free ghost imaging

We propose an interaction-free ghost imaging scheme based on a thermal light source. By utilizing the quantum Zeno-like effect, our approach significantly reduces the light dose absorbed by the sample, thereby effectively preventing sample damage induced by light-matter interactions. Combined with the elimination of entangled photon sources and single-photon detectors, our approach enables significantly more photons to be utilized for image reconstruction, thereby markedly enhancing image quality compared to conventional ghost imaging. We further demonstrate active suppression of background noise via controllable photon loss. Our work offers a practical and cost-effective route to non-destructive, high-quality imaging for light-sensitive samples in fields such as life sciences.

quant-ph↗

arXiv2025

A Generalized $\chi_n$-Function

The mapping $\chi_n$ from $\F_{2}^{n}$ to itself defined by $y=\chi_n(x)$ with $y_i=x_i+x_{i+2}(1+x_{i+1})$, where the indices are computed modulo $n$, has been widely studied for its applications in lightweight cryptography. However, $\chi_n $ is bijective on $\F_2^n$ only when $n$ is odd, restricting its use to odd-dimensional vector spaces over $\F_2$. To address this limitation, we introduce and analyze the generalized mapping $\chi_{n, m}$ defined by $y=\chi_{n,m}(x)$ with $y_i=x_i+x_{i+m} (x_{i+m-1}+1)(x_{i+m-2}+1) \cdots (x_{i+1}+1)$, where $m$ is a fixed integer with $m\nmid n$. To investigate such mappings, we further generalize $\chi_{n,m}$ to $\theta_{m, k}$, where $\theta_{m, k}$ is given by $y_i=x_{i+mk} \prod_{\substack{j=1,\,\, m \nmid j}}^{mk-1} \left(x_{i+j}+1\right), \,\,{\rm for }\,\, i\in \{0,1,\ldots,n-1\}$. We prove that these mappings generate an abelian group isomorphic to the group of units in $\F_2[z]/(z^{\lfloor n/m\rfloor +1})$. This structural insight enables us to construct a broad class of permutations over $\F_2^n$ for any positive integer $n$, along with their inverses. We rigorously analyze algebraic properties of these mappings, including their iterations, fixed points, and cycle structures. Additionally, we provide a comprehensive database of the cryptographic properties for iterates of $\chi_{n,m}$ for small values of $n$ and $m$. Finally, we conduct a comparative security and implementation cost analysis among $\chi_{n,m}$, $\chi_n$, $\chi\chi_n$ (EUROCRYPT 2025 \cite{belkheyar2025chi}) and their variants, and prove Conjecture~1 proposed in~\cite{belkheyar2025chi} as a by-product of our study. Our results lead to generalizations of $\chi_n$, providing alternatives to $\chi_n$ and $\chi\chi_n$.

cs.CR↗

arXiv2024

High-Order Implicit Low-Rank Method with Spectral Deferred Correction for Matrix Differential Equations

In this paper, we develop a low-rank method with high-order temporal accuracy using spectral deferred correction (SDC) to compute linear matrix differential equations. In [1], a low rank numerical method is proposed to correct the modeling error of the basis update and the Galerkin (BUG) method, which is a computational approach for DLRA. This method (merge-BUG/mBUG method) has been demonstrated to be first order convergent for general advection-diffusion problems. In this paper, we explore using SDC to elevate the convergence order of the mBUG method. In SDC, we start by computing a first-order solution by mBUG, and then perform successive updates by computing low-rank solutions to the Picard integral equation. Rather than a straightforward application of SDC with mBUG, we propose two aspects to improve computational efficiency. The first is to reduce the intermediate numerical rank by detailed analysis of dependence of truncation parameter on the correction levels. The second aspect is a careful choice of subspaces in the successive correction to avoid inverting large linear systems (from the K- and L-steps in BUG). We prove that the resulting scheme is high-order accurate for the Lipschitz continuous and bounded dynamical system. We consider numerical rank control in our framework by comparing two low-rank truncation strategies: the hard truncation strategy by truncated singular value decomposition and the soft truncation strategy by soft thresholding. We demonstrate numerically that soft thresholding offers better rank control in particular for higher-order schemes for weakly (or non-)dissipative problems.

math.NA↗

arXiv2024

Traditional Transformation Theory Guided Model for Learned Image Compression

Recently, many deep image compression methods have been proposed and achieved remarkable performance. However, these methods are dedicated to optimizing the compression performance and speed at medium and high bitrates, while research on ultra low bitrates is limited. In this work, we propose a ultra low bitrates enhanced invertible encoding network guided by traditional transformation theory, experiments show that our codec outperforms existing methods in both compression and reconstruction performance. Specifically, we introduce the Block Discrete Cosine Transformation to model the sparsity of features and employ traditional Haar transformation to improve the reconstruction performance of the model without increasing the bitstream cost.

eess.IV↗

arXiv2019

Linear Dependence of Post-irradiation Input Bias Currents on Pre-irradiation Values in Silicon Bipolar Microcircuits

We find in experiments a linear dependence of ionization irradiation-induced degradations on pre-irradiation values of the input bias current in bipolar devices with simple input stages. The dependence is found to generally exist in all studied cases of different device types and different irradiation conditions. A unique behavior of the energy distribution of the interface states ($D_{it}$) under irradiation is suggested as the origin of the observed phenomenon: the generation of interface traps through the depassivation of Si-H bonds located near the pre-irradiation interface traps displays a $D_{it}$ as an enlarge of the initial $D_{it}$ and results in the general linear dependence. A more accurate damage prediction method by using the pre-irradiation values is proposed based on the observed phenomenon.

physics.space-ph↗

arXiv2019

Driving positron beam acceleration with coherent transition radiation

Positron acceleration in plasma wakefield faces significant challenges since the positron beam must be pre-generated and precisely coupled into the wakefield, and most critically, suffers from defocusing issues. Here we propose a scheme that utilizes laser-driven electrons to produce, inject and accelerate positrons in a single set-up. The high-charge electron beam from wakefield acceleration creates copious electron-positron pairs via the Bethe-Heitler process, followed by enormous coherent transition radiation due to the electrons' exiting from the metallic foil. Simulation results show that the coherent transition radiation field reaches up to 10's GV m-1, which captures and accelerates the positrons to cut-off energy of 1.5 GeV with energy peak of 500 MeV and energy spread is about 24.3%. An external longitudinal magnetic field of 30 T is also applied to guide the electrons and positrons during the acceleration process. This proposed method offers a promising way to obtain GeV fast positron sources.

physics.plasm-ph↗

arXiv2016

Spin-Valley Beam Splitter in Graphene

The fourfold spin-valley degenerate degrees of freedom in bulk graphene can support rich physics and novel applications associated with multicomponent quantum Hall effects and linear conductance filtering. In this work, we study how to break the spin-valley degeneracy of electron beams spatially. We propose a spin-valley beam splitter in a gated ferromagnetic/pristine/strained graphene structure. We demonstrate that, in a full resonant tunneling regime for all spin-valley beam components, the formation of quasi-standing waves can lead four giant lateral Goos-Hänchen shifts as large as the transverse beam width, while the interplay of the two modulated regions can lead difference of resonant angles or energies for the four spin-valley flavors, manifesting an effective spin-valley beam splitting effect. The beam splitting effect is found to be controllable by the gating and strain.

cond-mat.mes-hall↗

arXiv2016

MeV Argon ion beam generation with narrow energy spread

Laser driven particle acceleration has shown remarkable progresses in generating multi-GeV electron bunches and 10s of MeV ion beams based on high-power laser facilities. Intense laser pulse offers the acceleration field of 1012 Volt per meter, several orders of magnitude larger than that in conventional accelerators, enabling compact devices. Here we report that a highly-collimated argon ion beam with narrow energy spread is produced by irradiating a 45-fs fully-relativistic laser pulse onto an argon cluster target. The highly-charged (Argon ion with charge state of 16+) heavy ion beam has a minimum absolute energy spread of 0.19 MeV per nucleon at the energy peak of 0.39 MeV per nucleon. we identify a novel scheme from particle-in-cell simulations that greatly reduces the beam energy spread. The laser-driven intense plasma wakefield has a strong modulation on the ion beam in a way that the low energy part is cut off. The pre-accelerated argon ion beam from Coulomb explosion thus becomes more mono-energetic and collimated.

physics.plasm-ph↗

arXiv2015

Identifying Emotion from Natural Walking

Emotion identification from gait aims to automatically determine persons affective state, it has attracted a great deal of interests and offered immense potential value in action tendency, health care, psychological detection and human-computer(robot) interaction.In this paper, we propose a new method of identifying emotion from natural walking, and analyze the relevance between the traits of walking and affective states. After obtaining the pure acceleration data of wrist and ankle, we set a moving average filter window with different sizes w, then extract 114 features including time-domain, frequency-domain, power and distribution features from each data slice, and run principal component analysis (PCA) to reduce dimension. In experiments, we train SVM, Decision Tree, multilayerperception, Random Tree and Random Forest classification models, and compare the classification accuracy on data of wrist and ankle with respect to different w. The performance of emotion identification on acceleration data of ankle is better than wrist.Comparing different classification models' results, SVM has best accuracy of identifying anger and happy could achieve 90:31% and 89:76% respectively, and identification ratio of anger-happy is 87:10%.The anger-neutral-happy classification reaches 85%-78%-78%.The results show that it is capable of identifying personal emotional states through the gait of walking.

cs.CV↗

arXiv2015

Recognition of Emotions using Kinects

Psychological studies indicate that emotional states are expressed in the way people walk and the human gait is investigated in terms of its ability to reveal a person's emotional state. And Microsoft Kinect is a rapidly developing, inexpensive, portable and no-marker motion capture system. This paper gives a new referable method to do emotion recognition, by using Microsoft Kinect to do gait pattern analysis, which has not been reported. $59$ subjects are recruited in this study and their gait patterns are record by two Kinect cameras. Significant joints selecting, Coordinate system transforming, Slider window gauss filter, Differential operation, and Data segmentation are used in data preprocessing. Feature extracting is based on Fourier transformation. By using the NaiveBayes, RandomForests, libSVM and SMO classification, the recognition rate of natural and unnatural emotions can reach above 70%.It is concluded that using the Kinect system can be a new method in recognition of emotions.

cs.CY↗