arXiv subjects

Xiao Shen

Publications and source records attributed to Xiao Shen.

At least 19 recordsLinked to original sources

arXiv2026

Coalescence and fluctuations of O'Connell$\unicode{x2013}$Yor polymers via the free-energy correlation profile

We establish an identity relating the spatial derivative of a two-point free-energy correlation to two fundamental geometric quantities: the annealed probability that two independently sampled polymers, in the same environment, meet before reaching the prescribed terminal level, and the exit-point location of a single polymer. Our result is inspired in part by recent integration-by-parts work of Gu and Quastel for the KPZ equation. A crucial step in their continuous setting relies on an ingenious application of It\^o's formula, which has no direct analogue in our semi-discrete setting. Instead, we exploit intrinsic symmetries of the polymer model together with the memoryless property. We also provide two applications: (i) we recover the horizontal Burke property by proving that the anchored stationary horizontal free-energy profile is a two-sided Brownian motion; (ii) in the zero-temperature limit, we obtain the corresponding identity for Brownian last-passage percolation.

math.PR↗

arXiv2026

Negative association of Busemann functions in exponential last-passage percolation

One hallmark of exactly solvable KPZ random growth models is product-form invariant measures. In the setting of exponential last-passage percolation (LPP), this corresponds to the independence of Busemann increments along any down-right path. However, this independence breaks down when multiple asymptotic directions are considered simultaneously, owing to the fact that jointly invariant measures are not jointly product-form. This paper shows that the failure of independence is one-sided: Busemann increments across arbitrary directions are negatively associated. As an application, we derive an exponential concentration inequality for sums of Busemann increments on the diffusive scale, even when the increments are not independent. While our argument relies on a Burke property that is special to exponential weights, all other proof ingredients$\unicode{x2014}$including hidden LPP monotonicities and braid relations for queueing maps$\unicode{x2014}$hold for arbitrary weights.

math.PR↗

arXiv2026

Data-Aided Target Localization in Multistatic ISAC Systems With Communication Constraints

Integrated sensing and communication (ISAC) enables future wireless networks to perform sensing and communication (S&C) over a shared waveform. In multistatic ISAC systems, however, the sensing receivers do not know the realizations of transmitted data symbols, making it challenging to exploit communication signals for sensing. In this paper, we propose a data-aided framework for target localization with two receiver strategies, namely statistical data-aided sensing and joint data-aided sensing and decoding, where the former marginalizes the random unknown data symbols and the latter reuses the reliably decoded data symbols as known virtual pilots. Under orthogonal frequency division multiplexing (OFDM) signaling, we derive the performance limits for target localization in both strategies and adopt the achievable ergodic data rate as the communication metric. Then, we formulate a joint time-allocation and transmit data-covariance design problem for target localization under communication constraints, which characterizes the joint S&C bound and quantifies the sensing gain provided by data symbols. In addition, we develop two target localization algorithms that implement the proposed data-aided receiver processing, and extend the framework to finite-alphabet signaling. Simulation results validate theoretical analysis and the effectiveness of the proposed data-aided schemes.

eess.SP↗

arXiv2026

In situ synchrotron X-ray diffraction study of flash austenitization and process design insights in medium-Manganese steels for energy applications

Medium Mn steels (MMnSs) are promising candidates for energy-related infrastructure because their multiphase microstructures and austenite stability can be tailored to improve failure resistance under demanding service conditions. Flash austenitization (FA) provides a rapid route to form austenite while limiting prior austenite grain coarsening and substitutional solute homogenization, but the related short-time transformation kinetics remain insufficiently quantified. In the present work, the effects of FA temperature and initial microstructure on austenitization kinetics were investigated in an Fe-6Mn-1.5Si-1Cr-0.3Mo-0.05Nb-0.2C (wt.%) MMnS using dilatometry-integrated in situ synchrotron X-ray diffraction. Two initial microstructures produced by austenite reversion treatment (ART) were heated at 100 degrees C/s to 850 degrees C, 900 degrees C, or 950 degrees C and then held isothermally. Rapid heating alone is insufficient for full austenitization, even above the reference Ac3 temperature determined under slow heating. Full austenitization, defined by bcc fraction (f_alpha) <= 1 wt.%, requires short holding, decreasing from about 8 s at 850 degrees C to about 2 s at 950 degrees C. The final stage of austenitization is less sensitive to FA temperature than the early holding stage. The initial ART state mainly shifts the starting austenite fraction, whereas both states show comparable kinetic trends at higher FA temperatures.

cond-mat.mtrl-sci↗

arXiv2026

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.

cs.AI↗

arXiv2026

Sensing-Constrained Diversity-Multiplexing Tradeoff in MIMO ISAC: A Geometric Approach

Diversity and multiplexing are the two fundamental gains of multiple-input and multiple-output (MIMO) communications, enabling systems to simultaneously achieve increased reliability and higher data rates. The intricate interplay between these two metrics is captured by the celebrated diversity-multiplexing tradeoff (DMT). With the rapid evolution of wireless technologies, low-latency integrated sensing and communication (ISAC) has emerged as a key enabler for 6G applications, including extended reality (XR) and massive digital twins. Consequently, understanding the DMT within MIMO ISAC systems becomes critical. In this paper, we investigate the communication DMT in a mono-static MIMO ISAC system under Rayleigh fading, specifically when the transmitter is constrained to emit sensing-optimal waveforms. By unveiling the geometric properties of generalized Stiefel manifolds and employing large-deviation analysis, we characterize the asymptotic outage probability of this typical ISAC channel. This formulation yields an elegant converse bound on the sensing-constrained DMT. Ultimately, our work provides an answer to a pivotal unanswered question in ISAC system design: How much MIMO gain is fundamentally sacrificed in communication to integrate optimal sensing capabilities?

eess.SP↗

arXiv2026

Non-Equilibrium Thermodynamic Extremal Principles During Filament Formation in ECM Memristors

Electrochemical metallization (ECM) memristors have potential applications in future neuromorphic computing hardware. The set, reset, and variable-resistance features of these devices originate in the formation and breakup of metal filaments in a solid-state electrolyte. While the performance characteristics of these devices are widely investigated, the driving principles behind the morphology of the filament formation process remain unclear. In this study, we propose an approach motivated by the extremal principles found in non-equilibrium thermodynamics and observe an entropy production and energy dissipation rate minimization during the filament-forming process in kinetic Monte Carlo simulations.

cond-mat.mes-hall↗

arXiv2026

negMIX: Negative Mixup for OOD Generalization in Open-Set Node Classification

Open-set node classification (OSNC) allows unlabeled test data to contain novel classes previously unseen in the labeled data. The goal is to classify in-distribution (ID) nodes into corresponding known classes and reject out-of-distribution (OOD) nodes as unknown class. Despite recent notable progress in OSNC, two challenges remain less explored, i.e., how to enhance generalization to OOD nodes, and promote intra-class compactness and inter-class separability. To tackle such challenges, we propose a novel Negative Mixup with Cross-Layer Graph Contrastive Learning (negMIX) model. Firstly, we devise a novel negative Mixup method purposefully crafted for the open-set scenario with theoretical justification, to enhance the model's generalization to OOD nodes and yield clearer ID/OOD boundary. Additionally, a unique cross-layer graph contrastive learning module is developed to maximize the prototypical mutual information between the same class nodes across different topological distance neighborhoods, thereby facilitating intra-class compactness and inter-class separability. Extensive experiments validate significant outperformance of the proposed negMIX over state-of-the-art methods in various scenarios and settings.

cs.SI↗

arXiv2026

Near-atomic investigation on the elemental redistribution during co-precipitation of nano-sized kappa phase and B2 phase in an Al-alloyed lightweight steel

In the present study, correlative transmission Kikuchi diffraction transmission electron microscopy (TKD-TEM) measurements, atom probe tomography (APT), and density functional theory (DFT) calculations are used to reveal the elemental redistribution during co-precipitation of nanosized kappa and B2 phases in an FCC matrix of an Al alloyed Fe-10Al-7Mn-6Ni-1C (wt.%) steel. Upon ageing at 800 C for 15 min, two co-nanoprecipitation modes are observed: B2 forming together with kappa and B2 forming separately from kappa in the FCC matrix. APT reveals that the B2 precipitate next to kappa (referred to as B2I) is close to an FeAl type phase, while the isolated B2 precipitate (referred to as B2II) is close to a NiAl type phase. The kappa precipitates maintain a nearly constant Al content of approximately 18.4 at.% regardless of their precipitation position. DFT confirms that kappa may accommodate limited Ni substitution at Fe sites without losing structural stability, and that Fe Ni atomic exchange between kappa and B2 is thermodynamically favorable at 800 C. This exchange drives the B2 phase to evolve from a NiAl type towards an FeAl type, improving the stability of both phases during co-precipitation. These results provide understanding of kappa B2 interactions and offer insights for designing nanosized intermetallic strengthened microstructures in Al alloyed lightweight steels.

cond-mat.mtrl-sci↗

arXiv2025

An upper bound on geodesic length in 2D critical first-passage percolation

We consider i.i.d. first-passage percolation (FPP) on the two-dimensional square lattice, in the critical case where edge-weights take the value zero with probability $\tfrac{1}{2}$. Critical FPP is unique in that the Euclidean lengths of geodesics are superlinear -- rather than linear -- in the distance between their endpoints. This fact was speculated by Kesten in 1986 but not confirmed until 2019 by Damron and Tang, who showed a lower bound on geodesic length that is polynomial with degree strictly greater than $1$. In this paper, we establish the first nontrivial upper bound. Namely, we prove that for a large class of critical edge-weight distributions, the shortest geodesic from the origin to a box of radius $R$ uses at most $R^{2+ε}π_3(R)$ edges with high probability, for any $ε> 0$. Here $π_3(R)$ is the polychromatic 3-arm probability from classical Bernoulli percolation; upon inserting its conjectural asymptotic, our bound converts to $R^{4/3 + ε}$. In any case, it is known that $π_3(R) \lesssim R^{-δ}$ for some $δ> 0$, so our bound gives an exponent strictly less than $2$. In the special case of Bernoulli($\tfrac{1}{2}$) edge-weights, we replace the additional factor of $R^ε$ with a constant and give an expectation bound.

math.PR↗

arXiv2025

Temporal correlation in the inverse-gamma polymer

Understanding the decay of correlations in time for (1+1)-dimensional polymer models in the KPZ universality class has been a challenging topic. Following numerical studies by physicists, concrete conjectures were formulated by Ferrari and Spohn (Ferrari-Spohn '16) in the context of planar exponential last passage percolation. These have mostly been resolved by various authors. In the context of positive temperature lattice models, however, these questions have remained open. We consider the time correlation problem for the exactly solvable inverse-gamma polymer in $\mathbb{Z}^2$. We establish, up to constant factors, upper and lower bounds on the correlation between free energy functions for two polymers rooted at the origin (droplet initial condition) when the endpoints are either close together or far apart. We find the same exponents as predicted in (Ferrari-Spohn '16). Our arguments rely on the understanding of stationary polymers, coupling, and random walk comparison. We use recently established moderate deviation estimates for the free energy. In particular, we do not require asymptotic analysis of complicated exact formulae.

math.PR↗

arXiv2025

Estimates for the empirical distribution along a geodesic in first-passage percolation

In first-passage percolation, we assign i.i.d.~nonnegative weights $(t_e)$ to the nearest-neighbor edges of $\mathbb{Z}^d$ and study the induced pseudometric $T = T(x,y)$. In this paper, we focus on geodesics, or optimal paths for $T$, and estimate the empirical distribution of weights along them. We prove an upper bound for the expected number of edges with weight $\geq M$ in the union of all geodesics from $0$ to $x$ of the form $q(M) \mathbb{P}(t_e \geq M)|x|$, where $q(M) \leq e^{-cM}$. This shows that the tail of the expected empirical distribution along a geodesic is lighter than that of the original weight distribution by an exponential factor. We also give a lower bound for the expected minimal number of edges with weight $\geq M$ in any geodesic from $0$ to $x$ in terms of $\mathbb{P}(t_e \geq M)$ and $\mathbb{P}(t_e \in [M,2M])$. For example, these two imply that if $t_e$ has a power law tail of the form $\mathbb{P}(t_e \geq M) \sim M^{-α}$, then the tail of the expected empirical distribution asymptotically lies between $e^{-CM \log M}$ and $e^{-cM}$. We also provide estimates for the expected number of edges in a geodesic with weight in a set $A$ for (a) arbitrary $A$, (b) $A$ an interval separated from the infimum of the support of $t_e$ and (c) $A=[0,a]$ for some $a \geq 0$.

math.PR↗

arXiv2025

Open-Set Cross-Network Node Classification via Unknown-Excluded Adversarial Graph Domain Alignment

Existing cross-network node classification methods are mainly proposed for closed-set setting, where the source network and the target network share exactly the same label space. Such a setting is restricted in real-world applications, since the target network might contain additional classes that are not present in the source. In this work, we study a more realistic open-set cross-network node classification (O-CNNC) problem, where the target network contains all the known classes in the source and further contains several target-private classes unseen in the source. Borrowing the concept from open-set domain adaptation, all target-private classes are defined as an additional unknown class. To address the challenging O-CNNC problem, we propose an unknown-excluded adversarial graph domain alignment (UAGA) model with a separate-adapt training strategy. Firstly, UAGA roughly separates known classes from unknown class, by training a graph neural network encoder and a neighborhood-aggregation node classifier in an adversarial framework. Then, unknown-excluded adversarial domain alignment is customized to align only target nodes from known classes with the source, while pushing target nodes from unknown class far away from the source, by assigning positive and negative domain adaptation coefficient to known class nodes and unknown class nodes. Extensive experiments on real-world datasets demonstrate significant outperformance of the proposed UAGA over state-of-the-art methods on O-CNNC.

cs.SI↗

arXiv2025

Large deviations of geodesic midpoint fluctuations in last-passage percolation with general i.i.d. weights

The study of transversal fluctuations of the optimal path is a crucial aspect of the Kardar-Parisi-Zhang (KPZ) universality class. In this work, we establish the large deviation limit for the midpoint transversal fluctuations in a general last-passage percolation (LPP) model with mild assumption on the i.i.d. weights. The rate function is expressed in terms of the right tail large deviation rate function of the last-passage value and the shape function. When the weights are chosen to be i.i.d. exponential random variables, our result verifies a conjecture communicated to us by Liu [Liu'22], showing the asymptotic probability of the geodesic from $(0,0)$ to $(n,n)$ following the corner path $(0,0) \to (n,0) \to (n,n)$ is $({4}/{e^2})^{n+o(n)}$.

math.PR↗

arXiv2024

Roles of Structural Coordination and Strain Orientation in the Phase Stability of Ferroelectric HfO$_2$

Phase stabilization continues to be a critical issue in hafnium oxide (HfO$_2$) due to the interdependence of various contributing factors. Using first-principles calculations, we analyze the effects of strain and doping on stabilizing the ferroelectric phase. We found that combining Y-doping, O-vacancy, and compressive biaxial strain, particularly in the (111) orientation, offers an optimal pathway for stabilizing the ferroelectric phase of HfO$_2$. Analysis of structural coordination reveals how compressive strain affects phase competition. Crystallography analysis provides insights into the advantage of the (111) strain orientation compared to the (001) orientation. The impact of dopants is discussed in the context of these findings.

cond-mat.mtrl-sci↗

arXiv2024

Phosphorus Nanotubes from Chemical Cleavage

We propose a strategy to make phosphorus nanotubes from two well-known phosphorus allotropes: violet phosphorus and fibrous red phosphorus. First-principles calculations show that doping with sulfur dissociates the covalent bonds between tubular phosphorus structures that form bilayers in these allotropes, resulting in free-standing 1D nanotubes. Due to the substitutional nature of the sulfur dopant, the resulting 1D structure is linear, unlike the helical ring structure studied previously. The sulfur sites are situated periodically along the 1D nanotubes and can be further functionalized. Our results show that the S-doped phosphorus nanotube can sustain a tensile strain of up to 18%. The strain also substantially modifies the electronic band gap and the effective mass of carriers. Calculations using the many-body Green's functions (GW) and the Bethe-Salpeter equation (BSE) approaches reveal a large exciton binding energy of 1.57 eV. The one-dimensional nature, linearity, functionalizability, mechanical flexibility, tunability of electronic properties, and large exciton binding energy make this material interesting for applications in optoelectronic devices, solar cells, chemical sensors, and quantum computing.

cond-mat.mtrl-sci↗

arXiv2024

Atomic-scale study on core-shell Cu precipitation in steels: atom probe tomography and ab initio calculations

The present work investigates the atomic interactions among Cu, Al, and Ni elements in bcc-iron matrix, focusing on the formation mechanism of nano-sized core-shell Cu precipitates. Using a combination of atom probe tomography (APT), density functional theory (DFT) cal-culations, and molecular dynamics (MD) simulations, the study provides insights into the atomic-scale migration tendencies of these elements in the supersaturated solid solution sur-rounding Cu precipitate in the martensite phase of a medium-Mn steel. The results show that Ni and Al atoms were not expelled by Cu atoms but were instead attracted to the bcc iron matrix, forming a stable co-segregation in the outer shell. This phase effectively surrounded the nano-sized Cu precipitate and prevented its rapid growth, contributing to improved me-chanical properties. The findings offer a theoretical method for developing Cu-contaminated circular steels by utilizing DFT calculations to unravel bonding preferences and assess the po-tential for forming a stable precipitation phase around nano-sized Cu precipitates.

cond-mat.mtrl-sci↗

arXiv2024

Time correlations in the inverse-gamma polymer with flat initial condition

Temporal correlations in the KPZ universality class have gained significant attention, following the conjectures in [Ferr-Spoh'16]. Building on prior work in the zero temperature setting [Basu-Gang-Zhan'21], we address the time correlation problem with flat initial conditions in the positive temperature regime. Our study focuses on the inverse-gamma polymer, where we establish an upper bound for the correlation between two free energies whose endpoints are far apart in time. In contrast to the previous work [Basu-Gang-Zhan'21], our work not only extends the result to positive temperatures but also eliminates the reliance on integrable probability inputs related to the Airy process. This advancement allows us to address local scales, where the short time remains fixed while the large time grows arbitrarily, a scenario beyond the reach of the Airy scaling limit.

math.PR↗