arXiv subjects

Jun Song

Publications and source records attributed to Jun Song.

At least 91 records · Page 5Linked to original sources

arXiv2024

Molecular dynamics simulations of interaction between a super edge dislocation and interstitial dislocation loops in irradiated L12-Ni3Al

The study employed MD simulations to investigate the interactions between a <110> super-edge dislocation, consisting of the four Shockley partials, and interstitial dislocation loops (IDLs) in irradiated L12-Ni3Al. Accounting for symmetry breakage in the L12 lattice, the superlattice planar faults with four distinct fault vectors have been considered for different IDL configurations. The detailed dislocation reactions and structural evolution events were identified as the four partials interacted with various IDL configurations. The slipping characteristics of Shockley partials within the IDLs and the resultant shearing and looping mechanisms were also clarified, revealing distinct energetic transition states determined by the fault vectors after the Shockley partials sweeping the IDL. Furthermore, significant variations in critical resolved shear stress (CRSS) required for the super-edge dislocation to move past the IDL were observed, attributed to various sizes and faulted vectors of enclosed superlattice planar faults in the IDLs. The current study extends the existing dislocation-IDL interaction theory from pristine FCC to L12 lattice, advances the understanding of irradiation hardening effects in L12-Ni3Al, and suggests potential applicability to other L12 systems.

cond-mat.mtrl-sci↗

arXiv2024

Modulating dislocation reactions through preferential hydrogen segregation in bcc metals

The interaction between dislocations is fundamental to plastic deformation, work hardening, and defect accumulation. While extensive research has focused on the impact of solutes on individual dislocations, how solutes affect dislocation-dislocation reactions remains largely unexplored. Here, using atomistic simulations of iron as a model bcc system, we demonstrate that hydrogen solutes enable two <111>/2 screw dislocations to react and form a <001> edge dislocation junction, a process that is otherwise unfavorable in hydrogen-free environments. This phenomenon arises from the preferential segregation of hydrogen around the <001> dislocation, which reduces the energy of the reaction product. The resulting <001> dislocation demonstrates remarkable stability and transforms into a <001> vacancy-type dislocation loop under strain. These vacancy-type dislocation loops can accumulate during continuous deformation and dislocation reactions, serving as precursors for the initiation of structural damage, such as cracking and blistering. Our findings highlight the pivotal role of hydrogen in dislocation reactions, uncover a novel defect accumulation mechanism crucial for interpreting recent experimental observations, and represent a significant advance in understanding hydrogen-induced damage in bcc metals.

cond-mat.mtrl-sci↗

arXiv2024

Can VLMs Play Action Role-Playing Games? Take Black Myth Wukong as a Study Case

Recently, large language model (LLM)-based agents have made significant advances across various fields. One of the most popular research areas involves applying these agents to video games. Traditionally, these methods have relied on game APIs to access in-game environmental and action data. However, this approach is limited by the availability of APIs and does not reflect how humans play games. With the advent of vision language models (VLMs), agents now have enhanced visual understanding capabilities, enabling them to interact with games using only visual inputs. Despite these advances, current approaches still face challenges in action-oriented tasks, particularly in action role-playing games (ARPGs), where reinforcement learning methods are prevalent but suffer from poor generalization and require extensive training. To address these limitations, we select an ARPG, ``Black Myth: Wukong'', as a research platform to explore the capability boundaries of existing VLMs in scenarios requiring visual-only input and complex action output. We define 12 tasks within the game, with 75% focusing on combat, and incorporate several state-of-the-art VLMs into this benchmark. Additionally, we will release a human operation dataset containing recorded gameplay videos and operation logs, including mouse and keyboard actions. Moreover, we propose a novel VARP (Vision Action Role-Playing) agent framework, consisting of an action planning system and a visual trajectory system. Our framework demonstrates the ability to perform basic tasks and succeed in 90% of easy and medium-level combat scenarios. This research aims to provide new insights and directions for applying multimodal agents in complex action game environments. The code and datasets will be made available at https://varp-agent.github.io/.

cs.AI↗

arXiv2024

Effect of particle oxidation, size and material on deformation, bonding and deposition during cold spray: a peridynamic investigation

Cold spray (CS) has emerged as an important additive manufacturing technology over the past decade. This study investigates the effect of oxide layers on the CS process, focusing on the deformation behavior of copper (Cu) and iron (Fe) particles upon collision with a matching substrate. Using a peridynamics-based approach, we examine the effects of oxide thickness, particle size, and particle/substrate material on material deformation and oxide fracture processes. Our results show that thicker oxide films restrict particle deformation, delay oxide discontinuities and material jetting, and increase the critical velocity required for metal to metal contact. Larger particles, despite uniform deformation across sizes, require lower velocities to initiate jetting and oxide separation because of their higher kinetic energy, leading to metallurgical bonding at lower velocities. Soft to soft impacts induce oxide film cracking at lower velocities, resulting in larger interface areas and more oxide-free contact zones, thereby reducing the critical velocity. Furthermore, the volume of residual oxide has a power-law relationship with the particle size, indicating that the oxide-cleaning ability of the particles affects the critical velocity. This study highlights the importance of oxide deformation and fracture during CS processes and provides valuable insights into the breakage and removal of oxides and subsequent metallic bond formation. These findings offer beneficial new knowledge for the rational design and optimization of CS processes.

physics.app-ph↗

arXiv2024

RW-NSGCN: A Robust Approach to Structural Attacks via Negative Sampling

Node classification using Graph Neural Networks (GNNs) has been widely applied in various practical scenarios, such as predicting user interests and detecting communities in social networks. However, recent studies have shown that graph-structured networks often contain potential noise and attacks, in the form of topological perturbations and weight disturbances, which can lead to decreased classification performance in GNNs. To improve the robustness of the model, we propose a novel method: Random Walk Negative Sampling Graph Convolutional Network (RW-NSGCN). Specifically, RW-NSGCN integrates the Random Walk with Restart (RWR) and PageRank (PGR) algorithms for negative sampling and employs a Determinantal Point Process (DPP)-based GCN for convolution operations. RWR leverages both global and local information to manage noise and local variations, while PGR assesses node importance to stabilize the topological structure. The DPP-based GCN ensures diversity among negative samples and aggregates their features to produce robust node embeddings, thereby improving classification performance. Experimental results demonstrate that the RW-NSGCN model effectively addresses network topology attacks and weight instability, increasing the accuracy of anomaly detection and overall stability. In terms of classification accuracy, RW-NSGCN significantly outperforms existing methods, showing greater resilience across various scenarios and effectively mitigating the impact of such vulnerabilities.

cs.LG↗

arXiv2024

Production characteristics of light nuclei, hypertritons and $Ω$-hypernuclei in Pb+Pb collisions at $\sqrt{s_{NN}}=5.02$ TeV

We extend an analytical nucleon coalescence model with hyperons to study productions of light nuclei, hypertritons and $Ω$-hypernuclei in Pb+Pb collisions at $\sqrt{s_{NN}}=5.02$ TeV. We derive the formula of the momentum distribution of two bodies coalescing into dibaryon states and that of three bodies coalescing into tribaryon states. We explain the available data of the coalescence factors $B_2$ and $B_3$, the transverse momentum spectra, the averaged transverse momenta, the yield rapidity densities, yield ratios of the deuteron, antihelium-3, antitriton, hypertriton measured by the ALICE collaboration, and give predictions of different $Ω$-hypernuclei, e.g., $H(pΩ^-)$, $H(nΩ^-)$ and $H(pnΩ^-)$. We find two groups of interesting observables, the averaged transverse momentum ratios of light (hyper-)nuclei to protons (hyperons) and the centrality-dependent yield ratios of theirs. The former group exhibits a reverse-hierarchy of the nucleus size, and the latter is helpful for the judgements of the nucleus production mechanism as well as the nucleus own size.

hep-ph↗

arXiv2024

Understanding the Influence of Hydrogen on BCC Iron Grain Boundaries using the Kinetic Activation Relaxation technique (k-ART)

Hydrogen embrittlement (HE) poses a significant challenge in the mechanical integrity of iron and its alloys. This study explores the influence of hydrogen atoms on two distinct grain boundaries (GBs), $\Sigma37$ and $\Sigma3$, in body-centered-cubic (BCC) iron. Using the kinetic activation-relaxation technique (k-ART), an off-lattice kinetic Monte Carlo approach, we examine diffusion barriers and mechanisms associated with these GBs. Our findings reveal distinct behaviors of hydrogen in different GB environments, emphasizing the elastic deformation that arises around the GB in the presence of H that leads to either the predominance of new pathways and diffusion routes or a pinning effect of H atoms. We find that, for these systems, while GB is energetically favorable for H, this element diffuses more slowly at the GBs than in the bulk. Moreover, with detailed information about the evolution landscape around GB, we find that the saturation of a GB with hydrogen both stabilizes the GB by shifting barriers associated with Fe diffusion to higher energies and smooths the energy landscape, reducing the number of diffusion events. This comprehensive analysis enhances our understanding of hydrogen's role in GB behavior, contributing valuable insights for the design and optimization of materials in hydrogen-related applications.

cond-mat.mtrl-sci↗

arXiv2024

Kinetics of hydrogen and vacancy diffusion in iron: A Kinetic Activation Relaxation technique (k-ART) study

We investigate hydrogen (H) and mono and divacancy-hydrogen complexes (VH$_x$ and V$_2$H$_x$) diffusion in body-centered-cubic (BCC) iron using the kinetic Activation-Relaxation Technique (k-ART), an off-lattice kinetic Monte Carlo approach with on-the-fly event catalog building, to explore diffusion barriers and associated mechanisms for these defects. K-ART uncovers complex diffusion pathways for the bound complexes, with important barrier variations that depend on the geometrical relations between the position of the inserting Fe atom and that of the bound H. Since H is small and brings little lattice deformation around itself, these bound complexes are compact, and H is fully unbound at the second neighbor site already. As more H are added, however, vacancies deform and affect the lattice over longer distances, contributing to increasing the VH$_x$ complex diffusion barrier and its impact on its local environment. We find, moreover, that the importance of this trapping decreases when going from mono to divacancy complexes, although diffusion barriers for these complexes increase with the number of trapped H.

cond-mat.mtrl-sci↗

arXiv2024

GeoGPT4V: Towards Geometric Multi-modal Large Language Models with Geometric Image Generation

Large language models have seen widespread adoption in math problem-solving. However, in geometry problems that usually require visual aids for better understanding, even the most advanced multi-modal models currently still face challenges in effectively using image information. High-quality data is crucial for enhancing the geometric capabilities of multi-modal models, yet existing open-source datasets and related efforts are either too challenging for direct model learning or suffer from misalignment between text and images. To overcome this issue, we introduce a novel pipeline that leverages GPT-4 and GPT-4V to generate relatively basic geometry problems with aligned text and images, facilitating model learning. We have produced a dataset of 4.9K geometry problems and combined it with 19K open-source data to form our GeoGPT4V dataset. Experimental results demonstrate that the GeoGPT4V dataset significantly improves the geometry performance of various models on the MathVista and MathVision benchmarks. The code is available at https://github.com/Lanyu0303/GeoGPT4V_Project

cs.CV↗

arXiv2024

Enhancing the Resilience of Graph Neural Networks to Topological Perturbations in Sparse Graphs

Graph neural networks (GNNs) have been extensively employed in node classification. Nevertheless, recent studies indicate that GNNs are vulnerable to topological perturbations, such as adversarial attacks and edge disruptions. Considerable efforts have been devoted to mitigating these challenges. For example, pioneering Bayesian methodologies, including GraphSS and LlnDT, incorporate Bayesian label transitions and topology-based label sampling to strengthen the robustness of GNNs. However, GraphSS is hindered by slow convergence, while LlnDT faces challenges in sparse graphs. To overcome these limitations, we propose a novel label inference framework, TraTopo, which combines topology-driven label propagation, Bayesian label transitions, and link analysis via random walks. TraTopo significantly surpasses its predecessors on sparse graphs by utilizing random walk sampling, specifically targeting isolated nodes for link prediction, thus enhancing its effectiveness in topological sampling contexts. Additionally, TraTopo employs a shortest-path strategy to refine link prediction, thereby reducing predictive overhead and improving label inference accuracy. Empirical evaluations highlight TraTopo's superiority in node classification, significantly exceeding contemporary GCN models in accuracy.

cs.LG↗

arXiv2024

Enhancing Sufficient Dimension Reduction via Hellinger Correlation

In this work, we develop a new theory and method for sufficient dimension reduction (SDR) in single-index models, where SDR is a sub-field of supervised dimension reduction based on conditional independence. Our work is primarily motivated by the recent introduction of the Hellinger correlation as a dependency measure. Utilizing this measure, we develop a method capable of effectively detecting the dimension reduction subspace, complete with theoretical justification. Through extensive numerical experiments, we demonstrate that our proposed method significantly enhances and outperforms existing SDR methods. This improvement is largely attributed to our proposed method's deeper understanding of data dependencies and the refinement of existing SDR techniques.

stat.ML↗

arXiv2024

ConvLLaVA: Hierarchical Backbones as Visual Encoder for Large Multimodal Models

High-resolution Large Multimodal Models (LMMs) encounter the challenges of excessive visual tokens and quadratic visual complexity. Current high-resolution LMMs address the quadratic complexity while still generating excessive visual tokens. However, the redundancy in visual tokens is the key problem as it leads to more substantial compute. To mitigate this issue, we propose ConvLLaVA, which employs ConvNeXt, a hierarchical backbone, as the visual encoder of LMM to replace Vision Transformer (ViT). ConvLLaVA compresses high-resolution images into information-rich visual features, effectively preventing the generation of excessive visual tokens. To enhance the capabilities of ConvLLaVA, we propose two critical optimizations. Since the low-resolution pretrained ConvNeXt underperforms when directly applied on high resolution, we update it to bridge the gap. Moreover, since ConvNeXt's original compression ratio is inadequate for much higher resolution inputs, we train a successive stage to further compress the visual tokens, thereby reducing redundancy. These optimizations enable ConvLLaVA to support inputs of 1536x1536 resolution generating only 576 visual tokens, capable of handling images of arbitrary aspect ratios. Experimental results demonstrate that our method achieves competitive performance with state-of-the-art models on mainstream benchmarks. The ConvLLaVA model series are publicly available at https://github.com/alibaba/conv-llava.

cs.CV↗

arXiv2024

Critical Assessment of Mass and Lattice Disorder in Thermal Conductivity Prediction for Medium and High Entropy Ceramics

Medium and high entropy ceramics, with their distinctive disordered structures, exhibit ultra-low thermal conductivity and high temperature stability. These properties make them strong contenders for next generation thermal barrier coating (TBC) materials. However, predicting their thermal conductivity has been challenging, primarily due to their unique phonon scattering mechanisms. Apart from the conventional phonon-phonon scattering mechanism, the phonon-disorder scattering, comprising both mass and force constant disorder, are also expected to make significant contribution in determining the thermal conductivity of medium and high entropy ceramics. However, it remains challenging to quantify the phonon-disorder contribution, particular in the aspect of force constant disorder. Here we demonstrated a relationship between the lattice disorder, a quantity more readily calculable, with force constant disorder, rendering it possible to substitute the force constant disorder by lattice disorder. Based on this relationship and drawing inspiration from Klement's equation of static imperfection, we have developed a model that quantitatively assesses the connection between disorder and thermal conductivity. Applying our model to the medium/high entropy rocksalt and pyrochlore oxides as representatives, we found good alignment between the theoretical predictions and experimental measurements of thermal conductivities, confirming the validity of our model. The model developed offers a critical predictive tool for rapid screening of TBC materials based on medium and high entropy ceramics.

cond-mat.mtrl-sci↗

arXiv2024

Dreamer XL: Towards High-Resolution Text-to-3D Generation via Trajectory Score Matching

In this work, we propose a novel Trajectory Score Matching (TSM) method that aims to solve the pseudo ground truth inconsistency problem caused by the accumulated error in Interval Score Matching (ISM) when using the Denoising Diffusion Implicit Models (DDIM) inversion process. Unlike ISM which adopts the inversion process of DDIM to calculate on a single path, our TSM method leverages the inversion process of DDIM to generate two paths from the same starting point for calculation. Since both paths start from the same starting point, TSM can reduce the accumulated error compared to ISM, thus alleviating the problem of pseudo ground truth inconsistency. TSM enhances the stability and consistency of the model's generated paths during the distillation process. We demonstrate this experimentally and further show that ISM is a special case of TSM. Furthermore, to optimize the current multi-stage optimization process from high-resolution text to 3D generation, we adopt Stable Diffusion XL for guidance. In response to the issues of abnormal replication and splitting caused by unstable gradients during the 3D Gaussian splatting process when using Stable Diffusion XL, we propose a pixel-by-pixel gradient clipping method. Extensive experiments show that our model significantly surpasses the state-of-the-art models in terms of visual quality and performance. Code: \url{https://github.com/xingy038/Dreamer-XL}.

cs.CV↗

arXiv2024

First Principles Studies of Stacking Fault Energies in Ternary Magnesium Alloys

Magnesium (Mg) alloys have emerged as promising materials due to their low density and high strength-to-weight ratio, offering a wide range of applications across multiple industries. Nevertheless, the inherent brittleness of Mg alloys poses a significant hurdle, necessitating innovative approaches to enhance their mechanical performance. Among the various strategies, manipulating stacking fault energy (SFE) has been a key focus, although primarily within the realm of binary alloys. This study investigates SFE in Mg alloys, focusing on ternary compositions. Utilizing first-principles DFT calculations, we analyze solute interactions and their influence on SFE, particularly in Mg-Al-X and Mg-Zn-X configurations. Predictive models are developed for estimating SFE effects, revealing solute pairs that mimic rare earth elements and show potential for improved ductility. The findings contribute to fundamental insights into Mg alloy behavior, offering practical directions for designing advanced materials with superior mechanical properties.

cond-mat.mtrl-sci↗

arXiv2023

A calculation method to estimate thermal conductivity of high entropy ceramic for thermal barrier coatings

High entropy ceramics are highly promising as next generation thermal barrier coatings due to their unique disorder structure, which imparts ultra-low thermal conductivity and good high temperature stability. Unlike traditional ceramic materials, the thermal resistance in high entropy ceramics predominantly arises from phonon-disorder scattering rather than phonon-phonon interactions. In this study, we propose a calculation method based on the supercell phonon unfolding (SPU) technique to predict the thermal conductivity of high entropy ceramics, specially focusing on rocksalt oxides structures. Our prediction method relies on using the reciprocal value of SPU phonon spectra linewidth as an indicator of phonon lifetime. The obtained results demonstrate a strong agreement between the predicted thermal conductivities and the experimental measurements, validating the feasibility of our calculation method. Furthermore, we extensively investigate and discuss the atomic relaxation and lattice distortion effects in 5-dopants and 6-dopants rocksalt structures during the process.

cond-mat.mtrl-sci↗

arXiv2023

Different Coalescence Sources of Light Nuclei Production in Au-Au Collisions at $\sqrt{s_{NN}}=3$ GeV

We study the production of light nuclei in the coalescence mechanism in Au-Au collisions at midrapidity at $\sqrt{s_{NN}}=3$ GeV. We derive analytic formulas of momentum distributions of two bodies, three bodies and four nucleons coalescing into light nuclei, respectively. We naturally explain the transverse momentum spectra of the deuteron ($d$), triton ($t$), helium-3 ($^3$He) and helium-4 ($^4$He). We reproduce the data of yield rapidity densities and averaged transverse momenta of $d$, $t$, $^3$He and $^4$He. We give proportions of contributions from different coalescence sources for $t$, $^3$He and $^4$He in their productions. We find that besides nucleon coalescence, nucleon$+$nucleus coalescence and nucleus$+$nucleus coalescence may play requisite roles in light nuclei production in Au-Au collisions at $\sqrt{s_{NN}}=3$ GeV.

hep-ph↗

arXiv2023

Production properties of deuterons, helions and tritons via an analytical nucleon coalescence method in Pb-Pb collisions at $\sqrt{s_{NN}}=2.76$ TeV

We improve a nucleon coalescence model to include the coordinate-momentum correlation in nucleon joint distributions, and apply it to Pb-Pb collisions at $\sqrt{s_{NN}}=2.76$ TeV to study production properties of deuterons ($d$), helions ($^3$He) and tritons ($t$). We give formulas of the coalescence factors $B_2$ and $B_3$, and naturally explain their behaviors as functions of the collision centrality and the transverse momentum per nucleon $p_T/A$. We reproduce the transverse momentum spectra, averaged transverse momenta and yield rapidity densities of $d$, $^3$He and $t$, and find the system effective radius obtained in the coalescence production of light nuclei behaves similarly to Hanbury Brown-Twiss interferometry radius. We particularly give expressions of yield ratios $d/p$, $^3$He$/d$, $t/p$, $^3$He$/p$, $d/p^{2}$, $^3$He$/p^3$, $t/^3$He and argue their nontrivial behaviors can be used to distinguish production mechanisms of light nuclei.

nucl-th↗