arXiv subjects

Jason Sun

Publications and source records attributed to Jason Sun.

7 recordsLinked to original sources

arXiv2026

Damage Prediction of Sintered {\alpha}-SiC Using Thermo-mechanical Coupled Fracture Model

A three-way coupled thermo-mechanical fracture model is presented to predict the damage of brittle ceramics, in particular {\alpha}-SiC, over a wide range of temperatures (20-1400 C). Predicting damage over such a range of temperatures is crucial for thermal protection systems for many systems such as spacecraft. The model, which has been implemented in MOOSE, is divided into three modules: elasticity, damage phase field, and heat conduction. Analytical approaches for determining crack length scales are presented for both simple tension and simple shear. Validation tests are conducted for both flexural strength and fracture toughness over the specified range of temperatures. Flexural strength simulation results fall within the uncertainty region of the experimental data, and mode I fracture toughness simulation results are also in agreement with the experimental data. Mode II and mixed mode fracture toughness simulations results are presented with the modified G-criterion. Finally, the parallel computing capabilities of the model is considered in various scalability tests.

cond-mat.mtrl-sci↗

arXiv2026

The effect of chemical vapor infiltration process parameters on flexural strength of porous {\alpha}-SiC: A numerical model

The flexural strength variability of {\alpha}-SiC based ceramics at elevated temperatures creates the need for an Integrated Computational Materials Engineering (ICME) framework that relates the strength of a specimen directly to its manufacturing process. To create this ICME framework a model must first be developed which establishes a relationship between the chemical vapor infiltration (CVI) process and parameters, the resulting mesoscale pores, and the overall macroscale flexural strength. Here a nonlinear single pore model of CVI is developed used in conjunction with a four-way coupled themo-mechanical damage model. The individual components of the model are tested and a sample system under a four-point bending test is explored. Results indicate that specimens with an initial porosity greater than 30% require temperatures below 1273 K to maintain structural integrity, while those with initial porosities less than 30% are temperature-independent, allowing for optimization of the CVI processing time without compromising strength.

cond-mat.mtrl-sci↗

arXiv2025

Navigating the Trade-off: A Synthesis of Defensive Strategies for Zero-Shot Adversarial Robustness in Vision-Language Models

This report synthesizes eight seminal papers on the zero-shot adversarial robustness of vision-language models (VLMs) like CLIP. A central challenge in this domain is the inherent trade-off between enhancing adversarial robustness and preserving the model's zero-shot generalization capabilities. We analyze two primary defense paradigms: Adversarial Fine-Tuning (AFT), which modifies model parameters, and Training-Free/Test-Time Defenses, which preserve them. We trace the evolution from alignment-preserving methods (TeCoA) to embedding space re-engineering (LAAT, TIMA), and from input heuristics (AOM, TTC) to latent-space purification (CLIPure). Finally, we identify key challenges and future directions including hybrid defense strategies and adversarial pre-training.

cs.CV↗

arXiv2025

Deep Optical Images of the Ejecta Nebula Around the Wolf-Rayet Star WR 8 (HD 62910)

We report the results of deep H$\alpha$ and [O III] images of the bright WN6/WC4 Wolf-Rayet star WR~8 (HD~62910). These data show considerably more surrounding nebulosity than seen in prior imaging. The brighter portions of the nebula span $\simeq6'$ in diameter and exhibit considerable fine-scale structure including numerous emission clumps and bright head-tail like features presumably due to the effects of the WR star's stellar winds. Due to the overlap of a relatively bright band of unrelated foreground diffuse interstellar H$\alpha$ emission, WR~8's nebula is best viewed via its [O III] emission. A faint $9' \times 13'$ diffuse outer nebulosity is detected surrounding the nebula's main ring of emission. Comparison of the nebula's optical structure with that seen in WISE 22 $\mu$m data shows a similarly clumpy structure but in a better defined emission shell of thermal continuum from dust. The infrared shell is coincident with the nebula's southern [O III] emissions but is mainly seen in the fainter outer portions of the northern [O III] emission clumps. It is this greater radial distance of dust emission in the nebula's northern areas that leads to a striking off-center position of the WR star in the IR shell.

astro-ph.SR↗

arXiv2022

Visual Representation Learning with Self-Supervised Attention for Low-Label High-data Regime

Self-supervision has shown outstanding results for natural language processing, and more recently, for image recognition. Simultaneously, vision transformers and its variants have emerged as a promising and scalable alternative to convolutions on various computer vision tasks. In this paper, we are the first to question if self-supervised vision transformers (SSL-ViTs) can be adapted to two important computer vision tasks in the low-label, high-data regime: few-shot image classification and zero-shot image retrieval. The motivation is to reduce the number of manual annotations required to train a visual embedder, and to produce generalizable and semantically meaningful embeddings. For few-shot image classification we train SSL-ViTs without any supervision, on external data, and use this trained embedder to adapt quickly to novel classes with limited number of labels. For zero-shot image retrieval, we use SSL-ViTs pre-trained on a large dataset without any labels and fine-tune them with several metric learning objectives. Our self-supervised attention representations outperforms the state-of-the-art on several public benchmarks for both tasks, namely miniImageNet and CUB200 for few-shot image classification by up-to 6%-10%, and Stanford Online Products, Cars196 and CUB200 for zero-shot image retrieval by up-to 4%-11%. Code is available at \url{https://github.com/AutoVision-cloud/SSL-ViT-lowlabel-highdata}.

cs.CV↗

arXiv2018

A neural interlingua for multilingual machine translation

We incorporate an explicit neural interlingua into a multilingual encoder-decoder neural machine translation (NMT) architecture. We demonstrate that our model learns a language-independent representation by performing direct zero-shot translation (without using pivot translation), and by using the source sentence embeddings to create an English Yelp review classifier that, through the mediation of the neural interlingua, can also classify French and German reviews. Furthermore, we show that, despite using a smaller number of parameters than a pairwise collection of bilingual NMT models, our approach produces comparable BLEU scores for each language pair in WMT15.

cs.CL↗

arXiv2017

A practical approach to dialogue response generation in closed domains

We describe a prototype dialogue response generation model for the customer service domain at Amazon. The model, which is trained in a weakly supervised fashion, measures the similarity between customer questions and agent answers using a dual encoder network, a Siamese-like neural network architecture. Answer templates are extracted from embeddings derived from past agent answers, without turn-by-turn annotations. Responses to customer inquiries are generated by selecting the best template from the final set of templates. We show that, in a closed domain like customer service, the selected templates cover $>$70\% of past customer inquiries. Furthermore, the relevance of the model-selected templates is significantly higher than templates selected by a standard tf-idf baseline.

cs.CL↗