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Researcher profile

Xiao Shen

Xiao Shen contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
cond-mat.mtrl-sciMachine LearningSocial and Information Networksmath.PRArtificial Intelligencecond-mat.str-elcs.CYNeural and Evolutionary Computingphysics.comp-phphysics.optics

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Published work

14 published item(s)

preprint2026arXiv

AcademiClaw: When Students Set Challenges for AI Agents

Benchmarks within the OpenClaw ecosystem have thus far evaluated exclusively assistant-level tasks, leaving the academic-level capabilities of OpenClaw largely unexamined. We introduce AcademiClaw, a bilingual benchmark of 80 complex, long-horizon tasks sourced directly from university students' real academic workflows -- homework, research projects, competitions, and personal projects -- that they found current AI agents unable to solve effectively. Curated from 230 student-submitted candidates through rigorous expert review, the final task set spans 25+ professional domains, ranging from olympiad-level mathematics and linguistics problems to GPU-intensive reinforcement learning and full-stack system debugging, with 16 tasks requiring CUDA GPU execution. Each task executes in an isolated Docker sandbox and is scored on task completion by multi-dimensional rubrics combining six complementary techniques, with an independent five-category safety audit providing additional behavioral analysis. Experiments on six frontier models show that even the best achieves only a 55\% pass rate. Further analysis uncovers sharp capability boundaries across task domains, divergent behavioral strategies among models, and a disconnect between token consumption and output quality, providing fine-grained diagnostic signals beyond what aggregate metrics reveal. We hope that AcademiClaw and its open-sourced data and code can serve as a useful resource for the OpenClaw community, driving progress toward agents that are more capable and versatile across the full breadth of real-world academic demands. All data and code are available at https://github.com/GAIR-NLP/AcademiClaw.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Comparison of Update and Genetic Training Algorithms in a Memristor Crossbar Perceptron

Memristor-based computer architectures are becoming more attractive as a possible choice of hardware for the implementation of neural networks. However, at present, memristor technologies are susceptible to a variety of failure modes, a serious concern in any application where regular access to the hardware may not be expected or even possible. In this study, we investigate whether certain training algorithms may be more resilient to particular hardware failure modes, and therefore more suitable for use in those applications. We implement two training algorithms -- a local update scheme and a genetic algorithm -- in a simulated memristor crossbar, and compare their ability to train for a simple image classification task as an increasing number of memristors fail to adjust their conductance. We demonstrate that there is a clear distinction between the two algorithms in several measures of the rate of failure to train.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Ferroelectric 2D Antimony Oxides with Wide Bandgaps

The first two-dimensional (2D) polymorphs of antimony dioxide, namely, $γ$-Sb$_2$O$_4$ and $δ$-Sb$_2$O$_4$, are predicted using the evolutionary algorithm combined with first-principles density functional theory (DFT) calculations. Out-of-plane ferroelectricity is found in $γ$-Sb$_2$O$_4$, while in-plane ferroelectricity is found in $δ$-Sb$_2$O$_4$. The predicted dipole moments of $γ$-Sb$_2$O$_4$ and $δ$-Sb$_2$O$_4$ phases are 36.63 and 14.96 eÅ, respectively, implying that they can be good candidates for making ferroelectric devices. The calculations show that doping with other group V elements or applying strain can lower the switching energy barriers and thus facilitate switching. Results from GW calculations show indirect band gaps of 5.51 and 3.39 eV for $γ$-Sb$_2$O$_4$ and $δ$-Sb$_2$O$_4$ in their monolayers, respectively. Raman spectra are calculated to facilitate the experimental investigation of the predicted structures. The existence of both in-plane and out-of-plane 2D ferroelectricity and the large band gaps make this material system particularly interesting for potential applications.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution

This paper studies the problem of cross-network node classification to overcome the insufficiency of labeled data in a single network. It aims to leverage the label information in a partially labeled source network to assist node classification in a completely unlabeled or partially labeled target network. Existing methods for single network learning cannot solve this problem due to the domain shift across networks. Some multi-network learning methods heavily rely on the existence of cross-network connections, thus are inapplicable for this problem. To tackle this problem, we propose a novel \textcolor{black}{graph} transfer learning framework AdaGCN by leveraging the techniques of adversarial domain adaptation and graph convolution. It consists of two components: a semi-supervised learning component and an adversarial domain adaptation component. The former aims to learn class discriminative node representations with given label information of the source and target networks, while the latter contributes to mitigating the distribution divergence between the source and target domains to facilitate knowledge transfer. Extensive empirical evaluations on real-world datasets show that AdaGCN can successfully transfer class information with a low label rate on the source network and a substantial divergence between the source and target domains. The source code for reproducing the experimental results is available at https://github.com/daiquanyu/AdaGCN.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

On the number and size of holes in the growing ball of first-passage percolation

First-passage percolation is a random growth model defined on $\mathbb{Z}^d$ using i.i.d. nonnegative weights $(τ_e)$ on the edges. Letting $T(x,y)$ be the distance between vertices $x$ and $y$ induced by the weights, we study the random ball of radius $t$ centered at the origin, $B(t) = \{x \in \mathbb{Z}^d : T(0,x) \leq t\}$. It is known that for all such $τ_e$, the number of vertices (volume) of $B(t)$ is at least order $t^d$, and under mild conditions on $τ_e$, this volume grows like a deterministic constant times $t^d$. Defining a hole in $B(t)$ to be a bounded component of the complement $B(t)^c$, we prove that if $τ_e$ is not deterministic, then a.s., for all large $t$, $B(t)$ has at least $ct^{d-1}$ many holes, and the maximal volume of any hole is at least $c\log t$. Conditionally on the (unproved) uniform curvature assumption, we prove that a.s., for all large $t$, the number of holes is at most $(\log t)^C t^{d-1}$, and for $d=2$, no hole in $B(t)$ has volume larger than $(\log t)^C$. Without curvature, we show that no hole has volume larger than $Ct \log t$.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Realization of A Strong Ferroelectric Metal by Nb-doping in Strained EuTiO$_{3}$

Ferroelectric (FE) metals have been attracting attention as they possess both metallicity and ferroelectricity, the two seemingly incompatible physical properties. An important problem for both fundamental research and potential applications is how to realize a strong FE metal. One strategy is to dope a strong FE insulator into an FE metal provided that the induced free electrons do not destroy FE distortion. Strained EuTiO$_{3}$ film is a strong FE ferromagnet and a promising candidate for such a strategy. Here, we calculate the structural, electronic, and polarization properties of Nb-doped strained EuTiO$_{3}$ film using the hybrid density-functional theory. The results show that the strained EuTi$_{0.875}$Nb$_{0.125}$O$_{3}$ film is a strong FE metal as the Nb doping induces metallicity without weakening the FE distortion. The underlying atomistic mechanism of the coexistence of metallicity and strong ferroelectricity is discussed. The findings show that combining doping and strain engineering is a promising way to realize new EuTiO$_{3}$-based strong FE metals and may be used in other materials as well.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Pressure-Induced Insulator-Metal Transition in Silicon Telluride from First-Principles Calculations

Silicon telluride (Si2Te3) is a two-dimensional semiconductor with unique structural properties due to the size contrast between Si and Te atoms. A recent experiment shows that the material turns metallic under hydrostatic pressure, while the lattice structure of the metallic phase remains to be identified. In this paper, we propose two metallic phases, M1 and M2, of Si2Te3 using the evolution algorithm and first-principles density functional theory (DFT) calculations. Unlike the presence of Si-Si dimers in the semiconducting (SC) phase, both M1 and M2 phases have individual Si atoms, which play important roles in the metallicity. Analysis of structural properties, electronic properties, dynamical as well as thermal stability is performed. The energies of these new structures are compared with the SC phase under the subsequent hydrostatic pressure up to 12 GPa. The results show that M1 and M2 phases have lower energies under high pressure, thus elucidating the appearance of the metallic phase of Si2Te3. In addition, the external pressure causes the SC phase to have an indirect-direct-indirect bandgap transition. Analysis of Raman spectra of the SC phase at a different pressure shows the shifting of the major Raman peaks, and finally disappearing confirms the phase transition. The results are in good agreement with the experimental observations. The understanding of the insulator-metal phase transition increases the potential usefulness of the material system.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Adversarial Deep Network Embedding for Cross-network Node Classification

In this paper, the task of cross-network node classification, which leverages the abundant labeled nodes from a source network to help classify unlabeled nodes in a target network, is studied. The existing domain adaptation algorithms generally fail to model the network structural information, and the current network embedding models mainly focus on single-network applications. Thus, both of them cannot be directly applied to solve the cross-network node classification problem. This motivates us to propose an adversarial cross-network deep network embedding (ACDNE) model to integrate adversarial domain adaptation with deep network embedding so as to learn network-invariant node representations that can also well preserve the network structural information. In ACDNE, the deep network embedding module utilizes two feature extractors to jointly preserve attributed affinity and topological proximities between nodes. In addition, a node classifier is incorporated to make node representations label-discriminative. Moreover, an adversarial domain adaptation technique is employed to make node representations network-invariant. Extensive experimental results demonstrate that the proposed ACDNE model achieves the state-of-the-art performance in cross-network node classification.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Coalescence estimates for the corner growth model with exponential weights

We establish estimates for the coalescence time of semi-infinite directed geodesics in the planar corner growth model with i.i.d. exponential weights. There are four estimates: upper and lower bounds on the probabilities of both fast and slow coalescence on the correct spatial scale with exponent $3/2$. Our proofs utilize a geodesic duality introduced by Pimentel and properties of the increment-stationary last-passage percolation process. For fast coalescence our bounds are new and they have matching optimal exponential order of magnitude. For slow coalescence we reproduce bounds proved earlier with integrable probability inputs, except that our upper bound misses the optimal order by a logarithmic factor.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Metallic ferroelectric-ferromagnetic multiferroics in strained EuTiO$_{3-x}$H$_x$

Polar metals are defined by the coexistence of metallicity and polar crystal structure. They have potential applications in non-linear optics, ferroelectric devices, and quantum devices. Meanwhile, ferroelectric-ferromagnetic (FE-FM) multiferroics display simultaneous ferroelectricity and ferromagnetism, leading to new technologies in information storage. It remains an open question whether metallicity, ferroelectricity, and ferromagnetism can coexist in a single domain of a material. EuTiO$_3$ is actively studied for potential applications in magnetic sensors, memories, magneto-optical devices, and energy conversion devices. It stabilizes at a multi-critical equilibrium and exhibits a rich range of intriguing properties. Here, using the results from hybrid density functional theory calculations, we report metallic FE-FM multiferroics in strain-engineered epitaxial EuTiO$_3$ with H doping. The emergence of the magnetism in polar metals provides a new degree of freedom to control these materials in applications. The underlying mechanism for the coexistence of metallicity, ferroelectricity, and ferromagnetism is discussed. The ferromagnetism in metallic EuTiO$_{3-x}$H$_x$ is explained by the Ruderman-Kittel-Kasuya- Yosida (RKKY) interaction, which agrees with experiments. The coexistence of metallicity and ferroelectricity is allowed because the electrons at the Fermi level are weakly coupled to the ferroelectric distortion. Our results suggest that the combined effect of strain and doping is responsible for achieving EuTiO$_3$-based metallic FE-FM multiferroics and may provide a new way for obtaining metallic FE-FM multiferroics in other materials.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Predicting A Novel Phase of 2D SiTe$_2$

Layered IV-VI$_2$ compounds often exist in the CdI$_2$ structure. Using the evolution algorithm and first-principles calculations, we predict a novel layered structure of silicon ditelluride (SiTe$_2$) that is more stable than the CdI$_2$ phase. The structure has a triclinic unit cell in its bulk form and exhibits the competition between the Si atoms' tendency to form tetrahedral bonds and the Te atoms' tendency to form hexagonal close-packing. The electronic and vibrational properties of the predicted phase are investigated. The effective mass of electron is small among 2D semiconductors, which is beneficial for applications such as field-effect transistors. The vibrational Raman and IR spectra are calculated to facilitate future experimental investigations

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Temperature- and Polarization- Dependent Optical Properties of Single Si2Te3 Nanoplates

We report a combined experimental and computational study of the optical properties of individual silicon telluride (Si2Te3) nanoplates. The p-type semiconductor Si2Te3 has a unique layered crystal structure with hexagonal closed-packed Te sublattices and Si-Si dimers occupying octahedral intercalation sites. The orientation of the silicon dimers leads to unique optical and electronic properties. Two-dimensional Si2Te3 nanoplates with thicknesses of hundreds of nanometers and lateral sizes of tens of micrometers are synthesized by a chemical vapor deposition technique. At temperatures below 150 K, the Si2Te3 nanoplates exhibit a direct band structure with a band gap energy of 2.394 eV at 7 K and an estimated free exciton binding energy of 150 meV. Polarized reflection measurements at different temperatures show anisotropy in the absorption coefficient due to an anisotropic orientation of the silicon dimers, which is in excellent agreement with theoretical calculations of the dielectric functions. Polarized Raman measurements of single Si2Te3 nanoplates at different temperatures reveal various vibrational modes, which agree with density functional perturbation theory calculations. The unique structural and optical properties of nanostructured Si2Te3 hold great potential applications in optoelectronics and chemical sensing.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Deep Network Embedding for Graph Representation Learning in Signed Networks

Network embedding has attracted an increasing attention over the past few years. As an effective approach to solve graph mining problems, network embedding aims to learn a low-dimensional feature vector representation for each node of a given network. The vast majority of existing network embedding algorithms, however, are only designed for unsigned networks, and the signed networks containing both positive and negative links, have pretty distinct properties from the unsigned counterpart. In this paper, we propose a deep network embedding model to learn the low-dimensional node vector representations with structural balance preservation for the signed networks. The model employs a semi-supervised stacked auto-encoder to reconstruct the adjacency connections of a given signed network. As the adjacency connections are overwhelmingly positive in the real-world signed networks, we impose a larger penalty to make the auto-encoder focus more on reconstructing the scarce negative links than the abundant positive links. In addition, to preserve the structural balance property of signed networks, we design the pairwise constraints to make the positively connected nodes much closer than the negatively connected nodes in the embedding space. Based on the network representations learned by the proposed model, we conduct link sign prediction and community detection in signed networks. Extensive experimental results in real-world datasets demonstrate the superiority of the proposed model over the state-of-the-art network embedding algorithms for graph representation learning in signed networks.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Ultra-High Mechanical Flexibility of 2D Silicon Telluride

Silicon telluride (Si2Te3) is a two-dimensional material with a unique variable structure where the silicon atoms form Si-Si dimers to fill the "metal" sites between the Te layers. The Si-Si dimers have four possible orientations: three in-plane and one out-of-the plane directions. The structural variability of Si2Te3 allows unusual properties especially the mechanical properties. Using results from first-principles calculations, we show that the Si2Te3 monolayer can sustain a uniaxial tensile strain up to 38%, highest among all two-dimensional materials reported. The high mechanical flexibility allows applying mechanical strain to reduce the band gap by 1.4 eV. With increasing strain, the band gap undergoes an unusual indirect-direct-indirect-direct transition. We also show that the uniaxial strain can effectively control the Si-Si dimer alignment, which is beneficial for practical applications.

0 citations0 reviewsNo code linkedOpen access