arXiv subjects

Wen Deng

Publications and source records attributed to Wen Deng.

18 recordsLinked to original sources

arXiv2026

An in situ self-adaptive hydrogel coating enables seamless neural interfaces via okra mucilage polysaccharide and {\alpha}-helical peptide amphiphiles co-assembly

Long-term stability of neural interfaces is frequently compromised by mechanical mismatch and chronic neuroinflammation, often leading to electrode detachment and signal failure. While hydrogel coatings offer a solution, conventional designs typically rely on exogenous conductive fillers that can sacrifice mechanical flexibility or induce toxicity. Here, we report on a soft neural interface based on the supramolecular co-assembly of a renewable natural polysaccharide, okra mucilage polysaccharide (OMP), and an {\alpha}-helical peptide amphiphiles (APA). The resulting OMP-APA hydrogel (OP gel) exhibits environment-responsive enhancements in bioadhesion and charge-transport capability triggered by physiological pH and electrical stimulation. These properties arise from intrinsic, stimulus-responsive alterations in fibre architecture and orientation, eliminating the need for conductive fillers. Leveraging interfacial liquid-liquid phase separation, we demonstrate the in situ coating of ultra-thin OP-gel coating onto carbon fibre electrodes (CFE). The OP-gel-coated electrodes (OP-CFE) significantly mitigate foreign body responses and glial scarring, enabling stable, high-quality neural recordings in a mouse cortical in vivo model. Our findings provide a versatile strategy for constructing seamless, multifunctional bio-interfaces through supramolecular co-assembly, with broad implications for advancing neural prosthetics and neuroscience research.

physics.bio-ph↗

arXiv2026

S2SServiceBench: A Multimodal Benchmark for Last-Mile S2S Climate Services

Subseasonal-to-seasonal (S2S) forecasts play an essential role in providing a decision-critical weeks-to-months planning window for climate resilience and sustainability, yet a growing bottleneck is the last-mile gap: translating scientific forecasts into trusted, actionable climate services, requiring reliable multimodal understanding and decision-facing reasoning under uncertainty. Meanwhile, multimodal large language models (MLLMs) and corresponding agentic paradigms have made rapid progress in supporting various workflows, but it remains unclear whether they can reliably generate decision-making deliverables from operational service products (e.g., actionable signal comprehension, decision-making handoff, and decision analysis & planning) under uncertainty. We introduce S2SServiceBench, a multimodal benchmark for last-mile S2S climate services curated from an operational climate-service system to evaluate this capability. S2SServiceBenchcovers 10 service products with about 150+ expert-selected cases in total, spanning six application domains - Agriculture, Disasters, Energy, Finance, Health, and Shipping. Each case is instantiated at three service levels, yielding around 500 tasks and 1,000+ evaluation items across climate resilience and sustainability applications. Using S2SServiceBench, we benchmark state-of-the-art MLLMs and agents, and analyze performance across products and service levels, revealing persistent challenges in S2S service plot understanding and reasoning - namely, actionable signal comprehension, operationalizing uncertainty into executable handoffs, and stable, evidence-grounded analysis and planning for dynamic hazards-while offering actionable guidance for building future climate-service agents.

cs.LG↗

arXiv2025

CLIMATEAGENT: Multi-Agent Orchestration for Complex Climate Data Science Workflows

Climate science demands automated workflows to transform comprehensive questions into data-driven statements across massive, heterogeneous datasets. However, generic LLM agents and static scripting pipelines lack climate-specific context and flexibility, thus, perform poorly in practice. We present ClimateAgent, an autonomous multi-agent framework that orchestrates end-to-end climate data analytic workflows. ClimateAgent decomposes user questions into executable sub-tasks coordinated by an Orchestrate-Agent and a Plan-Agent; acquires data via specialized Data-Agents that dynamically introspect APIs to synthesize robust download scripts; and completes analysis and reporting with a Coding-Agent that generates Python code, visualizations, and a final report with a built-in self-correction loop. To enable systematic evaluation, we introduce Climate-Agent-Bench-85, a benchmark of 85 real-world tasks spanning atmospheric rivers, drought, extreme precipitation, heat waves, sea surface temperature, and tropical cyclones. On Climate-Agent-Bench-85, ClimateAgent achieves 100% task completion and a report quality score of 8.32, outperforming GitHub-Copilot (6.27) and a GPT-5 baseline (3.26). These results demonstrate that our multi-agent orchestration with dynamic API awareness and self-correcting execution substantially advances reliable, end-to-end automation for climate science analytic tasks.

cs.LG↗

arXiv2025

AtmosSci-Bench: Evaluating the Recent Advance of Large Language Model for Atmospheric Science

The rapid advancements in large language models (LLMs), particularly in their reasoning capabilities, hold transformative potential for addressing complex challenges and boosting scientific discovery in atmospheric science. However, leveraging LLMs effectively in this domain requires a robust and comprehensive evaluation benchmark. Toward this end, we present AtmosSci-Bench, a novel benchmark designed to systematically assess LLM performance across five core categories of atmospheric science problems: hydrology, atmospheric dynamics, atmospheric physics, geophysics, and physical oceanography. AtmosSci-Bench features a dual-format design comprising both multiple-choice questions (MCQs) and open-ended questions (OEQs), enabling scalable automated evaluation alongside deeper analysis of conceptual understanding. We employ a template-based MCQ generation framework to create diverse, graduate-level problems with symbolic perturbation, while OEQs are used to probe open-ended reasoning. We conduct a comprehensive evaluation of representative LLMs, categorized into four groups: instruction-tuned models, advanced reasoning models, math-augmented models, and domain-specific climate models. Our analysis provides some interesting insights into the reasoning and problem-solving capabilities of LLMs in atmospheric science. We believe AtmosSci-Bench can serve as a critical step toward advancing LLM applications in climate services by offering a standard and rigorous evaluation framework. Our source code is available at https://github.com/Relaxed-System-Lab/AtmosSci-Bench.

cs.LG↗

arXiv2024

Pressure Induced 18 K Superconductivity and Two Superconducting Phases in CuIr2S4

We report pressure-induced superconductivity in the spinel CuIr$_{2}$S$_{4}$ with a transition temperature ($T_{\text{c}}$) reaching \textbf{18.2 K}, establishing a new record for this class of materials and surpassing the decades-old limit of 13.7 K. Our electrical transport and synchrotron X-ray diffraction studies up to 224 GPa reveal the emergence of \textbf{two distinct superconducting phases} from a charge-ordered insulating state. The first phase (SC-I) appears around 18 GPa, and forms a dome-shaped superconducting region in which the resistivity exhibits a pronounced, field- and current-sensitive drop without reaching strict zero above our base temperature. Above 111.8 GPa, a second, lower-$T_{\text{c}}$ phase (SC-II) emerges and coexists with SC-I over a broad pressure range, and SC-II ultimately develops a true zero-resistance state above 122.2 GPa. These superconducting phases are intimately linked to a cascade of structural transitions that systematically distort the frustrated pyrochlore lattice of Ir atoms. Our results expand the potential for superconductivity in spinels and demonstrate a pathway to high-$T_{\text{c}}$ pairing directly from a correlated insulating state driven by lattice tuning.

cond-mat.supr-con↗

arXiv2024

MCSFF: Multi-modal Consistency and Specificity Fusion Framework for Entity Alignment

Multi-modal entity alignment (MMEA) is essential for enhancing knowledge graphs and improving information retrieval and question-answering systems. Existing methods often focus on integrating modalities through their complementarity but overlook the specificity of each modality, which can obscure crucial features and reduce alignment accuracy. To solve this, we propose the Multi-modal Consistency and Specificity Fusion Framework (MCSFF), which innovatively integrates both complementary and specific aspects of modalities. We utilize Scale Computing's hyper-converged infrastructure to optimize IT management and resource allocation in large-scale data processing. Our framework first computes similarity matrices for each modality using modality embeddings to preserve their unique characteristics. Then, an iterative update method denoises and enhances modality features to fully express critical information. Finally, we integrate the updated information from all modalities to create enriched and precise entity representations. Experiments show our method outperforms current state-of-the-art MMEA baselines on the MMKG dataset, demonstrating its effectiveness and practical potential.

cs.AI↗

arXiv2023

Accelerating Maximal Clique Enumeration via Graph Reduction

As a fundamental task in graph data management, maximal clique enumeration (MCE) has attracted extensive attention from both academic and industrial communities due to its wide range of applications. However, MCE is very challenging as the number of maximal cliques may grow exponentially with the number of vertices. The state-of-the-art methods adopt a recursive paradigm to enumerate maximal cliques exhaustively, suffering from a large amount of redundant computation. In this paper, we propose a novel reduction-based framework for MCE, namely RMCE, that aims to reduce the search space and minimize unnecessary computations. The proposed framework RMCE incorporates three kinds of powerful reduction techniques including global reduction, dynamic reduction, and maximality check reduction. Global and dynamic reduction techniques effectively reduce the size of the input graph and dynamically construct subgraphs during the recursive subtasks, respectively. The maximality check reduction minimizes the computation for ensuring maximality by utilizing neighborhood dominance between visited vertices. Extensive experiments on 18 real graphs demonstrate the effectiveness of our proposed method. It achieves remarkable speedups up to 44.7x compared to existing approaches.

cs.DB↗

arXiv2021

Insulator-to-superconductor transition in quasi-one-dimensional HfS3 under pressure

Various transition metal trichalcogenides (TMTC) show the charge-density-wave and superconductivity, which provide an ideal platform to study the correlation between these two orderings and the mechanism of superconductivity. Currently, almost all metallic TMTC compounds can show superconductivity either at ambient pressure or at high pressure. However, most TMTC compounds are semiconductors and even insulators. Does the superconductivity exist in any non-metal TMTC compound? In this work, we managed to manipulate the electronic behavior of highly insulating HfS3 in term of pressure. HfS3 underwent an insulator-semiconductor transition near 17 GPa with a band gap reduce of ~1 eV. The optical absorption and Raman measurement provide the consistent results, suggesting the structural origin of the electronic transition. Upon further compression, HfS3 becomes a superconductor. The superconducting transition was initialized as early as 50.6 GPa and the zero-resistance is reached above 91.2 GPa. The superconducting behavior is further confirmed by both the magnetic field effect and current effect. This work sheds the light that all TMTC may be superconductors, and opens a new avenue to explore the abundant emergence phenomena in TMTC material family.

cond-mat.supr-con↗

arXiv2020

Stability of Couette flow for 2D Boussinesq system with vertical dissipation

This paper establishes the nonlinear stability of the Couette flow for the 2D Boussinesq equations with only vertical dissipation. The Boussinesq equations concerned here model buoyancy-driven fluids such as atmospheric and oceanographic flows. Due to the presence of the buoyancy forcing, the energy of the standard Boussinesq equations could grow in time. It is the enhanced dissipation created by the linear non-self-adjoint operator $y\partial_x -ν\partial_{yy}$ in the perturbation equation that makes the nonlinear stability possible. When the initial perturbation from the Couette flow $(y, 0)$ is no more than the viscosity to a suitable power (in the Sobolev space $H^b$ with $b>\frac43$), we prove that the solution of the 2D Boussnesq system with only vertical dissipation on $\mathbb T\times \mathbb R$ remains close to the Couette at the same order. A special consequence of this result is the stability of the Couette for the 2D Navier-Stokes equations with only vertical dissipation.

math.AP↗

arXiv2019

Network Flows that Solve Sylvester Matrix Equations

In this paper, we study distributed methods for solving a Sylvester equation in the form of AX+XB=C for matrices A, B, C$\in R^{n\times n}$ with X being the unknown variable. The entries of A, B and C (called data) are partitioned into a number of pieces (or sometimes we permit these pieces to overlap). Then a network with a given structure is assigned, whose number of nodes is consistent with the partition. Each node has access to the corresponding set of data and holds a dynamic state. Nodes share their states among their neighbors defined from the network structure, and we aim to design flows that can asymptotically converge to a solution of this equation. The decentralized data partitions may be resulted directly from networks consisting of physically isolated subsystems, or indirectly from artificial and strategic design for processing large data sets. Natural partial row/column partitions, full row/column partitions and clustering block partitions of the data A, B and C are assisted by the use of the vectorized matrix equation. We show that the existing "consensus + projection" flow and the "local conservation + global consensus" flow for distributed linear algebraic equations can be used to drive distributed flows that solve this kind of equations. A "consensus + projection + symmetrization" flow is also developed for equations with symmetry constraints on the solution matrices. We reveal some fundamental convergence rate limitations for such flows regardless of the choices of node interaction strengths and network structures. For a special case with B=A$^T$, where the equation mentioned is reduced to a classical Lyapunov equation, we demonstrate that by exploiting the symmetry of data, we can obtain flows with lower complexity for certain partitions.

math.OC↗

arXiv2019

Distributed Computation for Solving the Sylvester Equation Based on Optimization

This paper solves the Sylvester equation in the form of AX+XB=C in a distributed way, and proposes three distributed continuous-time algorithms for three cases. We start with the basic algorithm for solving a least squares solution of the equation, and then give a simplified algorithm for the case when there is an exact solution to the equation, followed by an algorithm with regularization case. Based on local information and appropriate communication among neighbor agents, we solve the distributed computation problem of the Sylvester equation from the optimization viewpoint, and we prove the convergence of proposed algorithms to an optimal solution in three different cases, with help of the convex optimization and semi-stability.

math.OC↗

arXiv2017

Determining the direction of a nanowire's flexural vibrations by micro-lens optical fiber interferometer

Nanowires are perfect transducers for ultra-sensitive detections of force and mass. For small mass sensing, recent advances in detecting more properties than their masses with high sensitivity for absorbed particles onto a nanowire rely on identifications of two orthogonal flexural vibration modes of the nanowire. The directions of these orthogonal flexural vibrations with respect to measurement direction are crucial parameters of vibration modes. However, previous method, which determines a nanowire's vibration direction using thermal vibrations, requires simultaneously detecting of two orthogonal flexural vibrations of the nanowire with sufficient sensitivity. In this work, we propose and realize a method for the determination of directions of a nanowire's flexural vibrations by micro-lens optical fiber interferometer. Our method combines the light interference and light scattering of the nanowire. It does not require detecting a pair of degenerated orthogonal flexural vibration modes. Therefore, our method is expected to have wide usages in characterizing nanowire's vibrations and their applications.

cond-mat.mes-hall↗

arXiv2017

Large time behavior of solutions to 3-D MHD system with initial data near equilibrium

In \cite{ChCa}, Califano and Chiuderi conjectured that the energy of incompressible Magnetic hydrodynamical system is dissipated at a rate that is independent of the ohmic resistivity. The goal of this paper is to mathematically justify this conjecture in three space dimension provided that the initial magnetic field and velocity is a small perturbation of the equilibrium state $(e_3,0).$ In particular, we prove that for such data, 3-D incompressible MHD system without magnetic diffusion has a unique global solution. Furthermore, the velocity field and the difference between the magnetic field and $e_3$ decay to zero in both $L^\infty$ and $L^2$ norms with explicit rates. We point out that the decay rate in the $L^2$ norm is optimal in sense that this rate coincides with that of the linear system. The main idea of the proof is to exploit H$\ddot{o}$rmander's version of Nash-Moser iteration scheme, which is very much motivated by the seminar papers \cite{Kl80, Kl82, Kl84} by Klainerman on the long time behavior to the evolution equations.

math.AP↗

arXiv2015

Highly efficient intracellular chromobody delivery by mesoporous silica nanoparticles for antigen targeting and visualization in real time

Chromobodies have recently drawn great attention as bioimaging nanotools. They offer antigen binding specificity and affinity comparable to conventional antibodies, but much smaller size and higher stability. Importantly, chromobodies can be used in live cell imaging for highly specific spatio-temporal visualization of cellular processes. To date, functional application of chromobodies requires lengthy genetic manipulation of the target cell. Here, we developed multifunctional large-pore mesoporous silica nanoparticles (MSNs) as nanocarriers to directly transport chromobodies into living cells for antigen-visualization in real time. The multifunctional large-pore MSNs feature high loading capacity for chromobodies, and are efficiently taken up by cells. By functionalizing the internal MSN surface with nitrilotriacetic acid-metal ion complexes, we could control the release of His6-tagged chromobodies from MSNs in acidified endosomes. When chromobodies escape from the endosomes through the proton sponge effect generated by their built-in His6-tags, co-localization of signals from fluorescent endogenous antigen and organic dye-labeled chromobodies can be detected. Hence, by combining the two nanotools, chromobodies and MSNs, we established a new powerful approach for chromobody applications in living cells.

physics.bio-ph↗

arXiv2015

Generating Giant and Tunable Nonlinearity in a Macroscopic Mechanical Resonator from Chemical Bonding Force

Nonlinearity in macroscopic mechanical system plays a crucial role in a wide variety of applications, including signal transduction and processing, synchronization, and building logical devices. However, it is difficult to generate nonlinearity due to the fact that macroscopic mechanical systems follow the Hooke's law and response linearly to external force, unless strong drive is used. Here we propose and experimentally realize a record-high nonlinear response in macroscopic mechanical system by exploring the anharmonicity in deforming a single chemical bond. We then demonstrate the tunability of nonlinear response by precisely controlling the chemical bonding interaction, and realize a cubic elastic constant of \mathversion{bold}$2 \times 10^{18}~{\rm N}/{\rm m^3}$, many orders of magnitude larger in strength than reported previously. This enables us to observe vibrational bistate transitions of the resonator driven by the weak Brownian thermal noise at 6~K. This method can be flexibly applied to a variety of mechanical systems to improve nonlinear responses, and can be used, with further improvements, to explore macroscopic quantum mechanics.

quant-ph↗

arXiv2013

Defects and hyperfine interactions in binary Fe-Al alloys studied by positron annihilation and Mossbauer spectroscopies

The defects, the behavior of 3d electrons and the hyperfine interactions in binary Fe-Al alloys with different Al contents have been studied by the measurements of positron lifetime spectra, coincidence Doppler broadening spectra of positron annihilation radiation and Mossbauer spectra. The results show that on increasing the Al content in Fe-Al alloys, the mean positron lifetime of the alloys increase, while the mean electron density of the alloys decrease. The increase of Al content in binary Fe-Al alloys will decrease the amount of unpaired 3d electrons; as a consequence the probability of positron annihilation with 3d electrons and the hyperfine field decrease rapidly. Mossbauer spectra of binary Fe-Al alloys with Al content less than 25at% show discrete sextets, these alloys give ferromagnetic contribution at room temperature. The Mössbauer spectrum of Fe70Al30 shows a broad singlet. As Al content higher than 40 at%, the Mössbauer spectra of these alloys are singlet, that is, the alloys are paramagnetic. The behavior of 3d electron and its effect on the hyperfine field of the binary Fe-Al alloy has been discussed.

cond-mat.mtrl-sci↗

arXiv2011

Structure constants of the Weyl calculus

We find some explicit bounds on the ${\mathcal L}(L^2)$-norm of pseudo-differential operators with symbols defined by a metric on the phase space. In particular, we prove that this norm depends only on the "structure constants" of the metric and a fixed semi-norm of the symbol. Analogous statements are made for the Fefferman-Phong inequality.

math.FA↗

arXiv2011

Pseudospectrum for Oseen vortices operators

In this paper, we give resolvent estimates for the linearized operator of the Navier-Stokes equation in $\R^2$ around the Oseen vortices, in the fast rotating limit $α\to+\infty$.

math.AP↗