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Chuan Qin

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Machine Learning cond-mat.str-el Artificial Intelligence Computer Vision cond-mat.mtrl-sci Information Retrieval cond-mat.mes-hall cond-mat.supr-con Cryptography and Security Distributed, Parallel, and Cluster Computing math.OC math.PR Robotics

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preprint2026arXiv

SciHorizon-DataEVA: An Agentic System for AI-Readiness Evaluation of Heterogeneous Scientific Data

AI-for-Science (AI4Science) is increasingly transforming scientific discovery by embedding machine learning models into prediction, simulation, and hypothesis generation workflows across domains. However, the effectiveness of these models is fundamentally constrained by the AI-readiness of scientific data, for which no scalable and systematic evaluation mechanism currently exists. In this work, we propose SciHorizon-DataEVA, a novel agentic system to scalable AI-readiness evaluation of heterogeneous scientific data. At the evaluation-criteria level, we introduce the Sci-TQA2 principles, which organize AI-readiness into four complementary dimensions: Governance Trustworthiness, Data Quality, AI Compatibility, and Scientific Adaptability. Each dimension is decomposed into measurable atomic elements that enable fine-grained and executable assessment. To operationalize these principles at scale, we develop Sci-TQA2-Eval, a hierarchical multi-agent evaluation approach orchestrated through a directed, cyclic workflow. Our Sci-TQA2-Eval dynamically constructs dataset-aware evaluation specifications by combining lightweight dataset profiling, applicability-aware metric activation, and knowledge-augmented planning grounded in domain constraints and dataset-paper signals. These specifications are executed through an adaptive, tool-centric evaluation mechanism with built-in verification and self-correction, enabling scalable and reliable assessment across heterogeneous scientific data. Extensive experiments on scientific datasets spanning multiple domains demonstrate the effectiveness and generality of SciHorizon-DataEVA for principled AI-readiness evaluation.

preprint2025arXiv

Flowing from Reasoning to Motion: Learning 3D Hand Trajectory Prediction from Egocentric Human Interaction Videos

Prior works on 3D hand trajectory prediction are constrained by datasets that decouple motion from semantic supervision and by models that weakly link reasoning and action. To address these, we first present the EgoMAN dataset, a large-scale egocentric dataset for interaction stage-aware 3D hand trajectory prediction with 219K 6DoF trajectories and 3M structured QA pairs for semantic, spatial, and motion reasoning. We then introduce the EgoMAN model, a reasoning-to-motion framework that links vision-language reasoning and motion generation via a trajectory-token interface. Trained progressively to align reasoning with motion dynamics, our approach yields accurate and stage-aware trajectories with generalization across real-world scenes.

preprint2021arXiv

Adam revisited: a weighted past gradients perspective

Adaptive learning rate methods have been successfully applied in many fields, especially in training deep neural networks. Recent results have shown that adaptive methods with exponential increasing weights on squared past gradients (i.e., ADAM, RMSPROP) may fail to converge to the optimal solution. Though many algorithms, such as AMSGRAD and ADAMNC, have been proposed to fix the non-convergence issues, achieving a data-dependent regret bound similar to or better than ADAGRAD is still a challenge to these methods. In this paper, we propose a novel adaptive method weighted adaptive algorithm (WADA) to tackle the non-convergence issues. Unlike AMSGRAD and ADAMNC, we consider using a milder growing weighting strategy on squared past gradient, in which weights grow linearly. Based on this idea, we propose weighted adaptive gradient method framework (WAGMF) and implement WADA algorithm on this framework. Moreover, we prove that WADA can achieve a weighted data-dependent regret bound, which could be better than the original regret bound of ADAGRAD when the gradients decrease rapidly. This bound may partially explain the good performance of ADAM in practice. Finally, extensive experiments demonstrate the effectiveness of WADA and its variants in comparison with several variants of ADAM on training convex problems and deep neural networks.

preprint2021arXiv

Adversarial Examples Detection beyond Image Space

Deep neural networks have been proved that they are vulnerable to adversarial examples, which are generated by adding human-imperceptible perturbations to images. To defend these adversarial examples, various detection based methods have been proposed. However, most of them perform poorly on detecting adversarial examples with extremely slight perturbations. By exploring these adversarial examples, we find that there exists compliance between perturbations and prediction confidence, which guides us to detect few-perturbation attacks from the aspect of prediction confidence. To detect both few-perturbation attacks and large-perturbation attacks, we propose a method beyond image space by a two-stream architecture, in which the image stream focuses on the pixel artifacts and the gradient stream copes with the confidence artifacts. The experimental results show that the proposed method outperforms the existing methods under oblivious attacks and is verified effective to defend omniscient attacks as well.

preprint2020arXiv

A Survey on Knowledge Graph-Based Recommender Systems

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold start. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the abovementioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field and summarize them from two perspectives. On the one hand, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. On the other hand, we introduce datasets used in these works. Finally, we propose several potential research directions in this field.

preprint2020arXiv

SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback

The recent development of online recommender systems has a focus on collaborative ranking from implicit feedback, such as user clicks and purchases. Different from explicit ratings, which reflect graded user preferences, the implicit feedback only generates positive and unobserved labels. While considerable efforts have been made in this direction, the well-known pairwise and listwise approaches have still been limited by various challenges. Specifically, for the pairwise approaches, the assumption of independent pairwise preference is not always held in practice. Also, the listwise approaches cannot efficiently accommodate "ties" due to the precondition of the entire list permutation. To this end, in this paper, we propose a novel setwise Bayesian approach for collaborative ranking, namely SetRank, to inherently accommodate the characteristics of implicit feedback in recommender system. Specifically, SetRank aims at maximizing the posterior probability of novel setwise preference comparisons and can be implemented with matrix factorization and neural networks. Meanwhile, we also present the theoretical analysis of SetRank to show that the bound of excess risk can be proportional to $\sqrt{M/N}$, where $M$ and $N$ are the numbers of items and users, respectively. Finally, extensive experiments on four real-world datasets clearly validate the superiority of SetRank compared with various state-of-the-art baselines.

preprint2016arXiv

Spin Hall effects in mesoscopic Pt films with high resistivity

The energy efficiency of the spin Hall effects (SHE) can be enhanced if the electrical conductivity is decreased without sacrificing the spin Hall conductivity. The resistivity of Pt films can be increased to 150-300 μΩ*cm by mesoscopic lateral confinement, thereby decreasing the conductivity. The SHE and inverse spin Hall effects (ISHE) in these mesoscopic Pt films are explored at 10 K by using the nonlocal spin injection/detection method. All relevant physical quantities are determined in-situ on the same substrate, and a quantitative approach is developed to characterize all processes effectively. Extensive measurements with various Pt thickness values reveal an upper limit for the Pt spin diffusion length: λ_pt<0.8 nm. The average product of λ_pt and the Pt spin Hall angle α_H is substantial: α_H*λ_pt=(0.142 +/- 0.040)nm for 4 nm thick Pt, though a gradual decrease is observed at larger Pt thickness. The results suggest enhanced spin Hall effects in resistive mesoscopic Pt films.

preprint2015arXiv

CDStore: Toward Reliable, Secure, and Cost-Efficient Cloud Storage via Convergent Dispersal

We present CDStore, which disperses users' backup data across multiple clouds and provides a unified multi-cloud storage solution with reliability, security, and cost-efficiency guarantees. CDStore builds on an augmented secret sharing scheme called convergent dispersal, which supports deduplication by using deterministic content-derived hashes as inputs to secret sharing. We present the design of CDStore, and in particular, describe how it combines convergent dispersal with two-stage deduplication to achieve both bandwidth and storage savings and be robust against side-channel attacks. We evaluate the performance of our CDStore prototype using real-world workloads on LAN and commercial cloud testbeds. Our cost analysis also demonstrates that CDStore achieves a monetary cost saving of 70% over a baseline cloud storage solution using state-of-the-art secret sharing.

preprint2014arXiv

(Sr3La2O5)(Zn1-xMnx)2As2: A Bulk Form Diluted Magnetic Semiconductor isostructural to the "32522" Fe-based Superconductors

A new diluted magnetic semiconductor system, (Sr3La2O5)(Zn1-xMnx)2As2, has been synthesized and characterized. 10% Mn substitution for Zn in bulk form (Sr3La2O5)Zn2As2 results in a ferromagnetic ordering below Curie temperature, TC ~ 40 K. (Sr3La2O5)(Zn1-xMnx)2As2 has a layered crystal structure identical to that of 32522-type Fe based superconductors, and represents the fifth DMS family that has a direct counterpart among the FeAs high temperature superconductor families.

preprint2014arXiv

Improved bounds for the mixing time of the random-to-random insertion shuffle

We prove an upper bound of $1.5324 n \log n$ for the mixing time of the random-to-random insertion shuffle, improving on the best known upper bound of $2 n \log n$. Our proof is based on the analysis of a non-Markovian coupling.

preprint2013arXiv

(La1-xBax)(Zn1-xMnx)AsO: A Two Dimensional "1111" Diluted Magnetic Semiconductor in Bulk Form

We report the synthesis and characterization of a bulk diluted magnetic semiconductor (La1-xBax)(Zn1-xMnx)AsO (0 <= x <= 0.2) with a layered crystal structure identical to that of the "1111" FeAs superconductors. No ferromagnetic order occurs for (Zn,Mn) substitution in the parent compound LaZnAsO without charge doping. Together with carrier doping via (La,Ba) sub- stitution, a small amount of Mn substituting for Zn results in ferromagnetic order with TC up to ~40 K, although the system remains semiconducting. Muon spin relaxation measurements confirm the development of ferromagnetic order in the entire volume, with the relationship between the internal field and TC consistent with the trend found in (Ga,Mn)As, the "111" Li(Zn,Mn)As, and the "122" (Ba,K)(Zn,Mn)2As2 systems.