Electronic-Mechanical Coupling in Graphene from in situ Nanoindentation Experiments and Multiscale Atomistic Simulations

We present the in situ nanoindentation experiments performed on suspended graphene devices to introduce homogeneous tensile strain, while simultaneously carrying out electrical measurements. We find that the electrical resistance shows only a marginal change even under severe strain, and the electronic transport measurement confirms that there is no band gap opening for graphene under moderate uniform strain, which is consistent with our results from the first-principles informed molecular dynamics simulation

On the Trustworthiness Landscape of State-of-the-art Generative Models: A Comprehensive Survey

Diffusion models and large language models have emerged as leading-edge generative models and have sparked a revolutionary impact on various aspects of human life. However, the practical implementation of these models has also exposed inherent risks, highlighting their dual nature and raising concerns regarding their trustworthiness. Despite the abundance of literature on this subject, a comprehensive survey specifically delving into the intersection of large-scale generative models and their trustworthiness remains largely absent. To bridge this gap, This paper investigates both the long-standing and emerging threats associated with these models across four fundamental dimensions: privacy, security, fairness, and responsibility. In this way, we construct an extensive map outlining the trustworthiness of these models, while also providing practical recommendations and identifying future directions. These efforts are crucial for promoting the trustworthy deployment of these models, ultimately benefiting society as a whole.Comment: draft versio

Interfacial energy release rates of SiN/GaAs film/substrate systems determined using a cyclic loading dual-indentation method

Our previous study developed a dual-indentation method for testing the interfacial energy release rate, Gin, of the SiN/GaAs film/substrate systems. However, for the film/substrate systems with relatively high interfacial toughness, the dual-indentation method was unable to generate interfacial delamination. In this study, a cyclic loading dual-indentation method was proposed, in which the first monotonic loading in the dual-indentation method was replaced by cyclic loading. It was demonstrated that cyclic loading was effective at inducing delamination in relatively "tough" SiN/GaAs interfaces that were unable to be delaminated by dual-indentation method. The Gin values obtained from the cyclic loading indentation were in good agreement with those obtained from the dual-indentation method for the less tough interfaces. The delamination mechanism in the cyclic loading indentation was attributed to the hardening effect on the films induced by cyclic loading, permitting sufficient elastic strain energy to be accumulated to initiate the delamination

To Healthier Ethereum: A Comprehensive and Iterative Smart Contract Weakness Enumeration

With the increasing popularity of cryptocurrencies and blockchain technology, smart contracts have become a prominent feature in developing decentralized applications. However, these smart contracts are susceptible to vulnerabilities that hackers can exploit, resulting in significant financial losses. In response to this growing concern, various initiatives have emerged. Notably, the SWC vulnerability list played an important role in raising awareness and understanding of smart contract weaknesses. However, the SWC list lacks maintenance and has not been updated with new vulnerabilities since 2020. To address this gap, this paper introduces the Smart Contract Weakness Enumeration (SWE), a comprehensive and practical vulnerability list up until 2023. We collect 273 vulnerability descriptions from 86 top conference papers and journal papers, employing open card sorting techniques to deduplicate and categorize these descriptions. This process results in the identification of 40 common contract weaknesses, which are further classified into 20 sub-research fields through thorough discussion and analysis. SWE provides a systematic and comprehensive list of smart contract vulnerabilities, covering existing and emerging vulnerabilities in the last few years. Moreover, SWE is a scalable, continuously iterative program. We propose two update mechanisms for the maintenance of SWE. Regular updates involve the inclusion of new vulnerabilities from future top papers, while irregular updates enable individuals to report new weaknesses for review and potential addition to SWE

On the Robustness of Split Learning against Adversarial Attacks

Split learning enables collaborative deep learning model training while preserving data privacy and model security by avoiding direct sharing of raw data and model details (i.e., sever and clients only hold partial sub-networks and exchange intermediate computations). However, existing research has mainly focused on examining its reliability for privacy protection, with little investigation into model security. Specifically, by exploring full models, attackers can launch adversarial attacks, and split learning can mitigate this severe threat by only disclosing part of models to untrusted servers.This paper aims to evaluate the robustness of split learning against adversarial attacks, particularly in the most challenging setting where untrusted servers only have access to the intermediate layers of the model.Existing adversarial attacks mostly focus on the centralized setting instead of the collaborative setting, thus, to better evaluate the robustness of split learning, we develop a tailored attack called SPADV, which comprises two stages: 1) shadow model training that addresses the issue of lacking part of the model and 2) local adversarial attack that produces adversarial examples to evaluate.The first stage only requires a few unlabeled non-IID data, and, in the second stage, SPADV perturbs the intermediate output of natural samples to craft the adversarial ones. The overall cost of the proposed attack process is relatively low, yet the empirical attack effectiveness is significantly high, demonstrating the surprising vulnerability of split learning to adversarial attacks.Comment: accepted by ECAI 2023, camera-ready versio

Progressive Denoising Model for Fine-Grained Text-to-Image Generation

Recently, vector quantized autoregressive (VQ-AR) models have shown remarkable results in text-to-image synthesis by equally predicting discrete image tokens from the top left to bottom right in the latent space. Although the simple generative process surprisingly works well, is this the best way to generate the image? For instance, human creation is more inclined to the outline-to-fine of an image, while VQ-AR models themselves do not consider any relative importance of each component. In this paper, we present a progressive denoising model for high-fidelity text-to-image image generation. The proposed method takes effect by creating new image tokens from coarse to fine based on the existing context in a parallel manner and this procedure is recursively applied until an image sequence is completed. The resulting coarse-to-fine hierarchy makes the image generation process intuitive and interpretable. Extensive experiments demonstrate that the progressive model produces significantly better results when compared with the previous VQ-AR method in FID score across a wide variety of categories and aspects. Moreover, the text-to-image generation time of traditional AR increases linearly with the output image resolution and hence is quite time-consuming even for normal-size images. In contrast, our approach allows achieving a better trade-off between generation quality and speed.Comment: Technique report. arXiv admin note: text overlap with arXiv:2206.10789 by other author

Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning

Recent works have brought attention to the vulnerability of Federated Learning (FL) systems to gradient leakage attacks. Such attacks exploit clients' uploaded gradients to reconstruct their sensitive data, thereby compromising the privacy protection capability of FL. In response, various defense mechanisms have been proposed to mitigate this threat by manipulating the uploaded gradients. Unfortunately, empirical evaluations have demonstrated limited resilience of these defenses against sophisticated attacks, indicating an urgent need for more effective defenses. In this paper, we explore a novel defensive paradigm that departs from conventional gradient perturbation approaches and instead focuses on the construction of robust data. Intuitively, if robust data exhibits low semantic similarity with clients' raw data, the gradients associated with robust data can effectively obfuscate attackers. To this end, we design Refiner that jointly optimizes two metrics for privacy protection and performance maintenance. The utility metric is designed to promote consistency between the gradients of key parameters associated with robust data and those derived from clients' data, thus maintaining model performance. Furthermore, the privacy metric guides the generation of robust data towards enlarging the semantic gap with clients' data. Theoretical analysis supports the effectiveness of Refiner, and empirical evaluations on multiple benchmark datasets demonstrate the superior defense effectiveness of Refiner at defending against state-of-the-art attacks.Comment: under revie

Selective laser melting of alumina: a single track study

Ceramics-based additive manufacturing is a complex process and the solidification mechanism and microstructural evolution are currently not fully understood. In this work, AlO single tracks were formed using a customised selective laser melting (SLM) system equipped with a high power diode laser. The effects of laser energy density (LED) on geometry, microstructure and micro-mechanical properties of AlO tracks were investigated. To better understand the solidification mechanism, a transient three-dimensional thermal model was developed for predicting the thermal behaviour of the melt pool. The results indicated the use of high LED gave rise to decreased viscosity and surface tension of the molten alumina and led to localized melting of the substrate. Both, in turn, enabled the formation of a continuous solidified track. The solidified tracks were primarily composed of columnar dendrite. When relatively high LED (≥ 25.7 kJ/m) was applied, equiaxed dendrite appeared along the central line near the track surface. The size of dendritic grains decreased with the decreased LED, attributed to the increased cooling rate at solidification interface. The micro-hardness of the solidified track was found to be inversely proportional to the grain size owning to grain boundary strengthening effect