S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process

We present a super-resolution model for an advection-diffusion process with limited information. While most of the super-resolution models assume high-resolution (HR) ground-truth data in the training, in many cases such HR dataset is not readily accessible. Here, we show that a Recurrent Convolutional Network trained with physics-based regularizations is able to reconstruct the HR information without having the HR ground-truth data. Moreover, considering the ill-posed nature of a super-resolution problem, we employ the Recurrent Wasserstein Autoencoder to model the uncertainty.Comment: 9 pages, 8 figure

BatmanNet: Bi-branch Masked Graph Transformer Autoencoder for Molecular Representation

Although substantial efforts have been made using graph neural networks (GNNs) for AI-driven drug discovery (AIDD), effective molecular representation learning remains an open challenge, especially in the case of insufficient labeled molecules. Recent studies suggest that big GNN models pre-trained by self-supervised learning on unlabeled datasets enable better transfer performance in downstream molecular property prediction tasks. However, they often require large-scale datasets and considerable computational resources, which is time-consuming, computationally expensive, and environmentally unfriendly. To alleviate these limitations, we propose a novel pre-training model for molecular representation learning, Bi-branch Masked Graph Transformer Autoencoder (BatmanNet). BatmanNet features two tailored and complementary graph autoencoders to reconstruct the missing nodes and edges from a masked molecular graph. To our surprise, BatmanNet discovered that the highly masked proportion (60%) of the atoms and bonds achieved the best performance. We further propose an asymmetric graph-based encoder-decoder architecture for either nodes and edges, where a transformer-based encoder only takes the visible subset of nodes or edges, and a lightweight decoder reconstructs the original molecule from the latent representation and mask tokens. With this simple yet effective asymmetrical design, our BatmanNet can learn efficiently even from a much smaller-scale unlabeled molecular dataset to capture the underlying structural and semantic information, overcoming a major limitation of current deep neural networks for molecular representation learning. For instance, using only 250K unlabelled molecules as pre-training data, our BatmanNet with 2.575M parameters achieves a 0.5% improvement on the average AUC compared with the current state-of-the-art method with 100M parameters pre-trained on 11M molecules.Comment: 11 pages, 3 figure

FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data

Federated learning (FL) allows agents to jointly train a global model without sharing their local data. However, due to the heterogeneous nature of local data, it is challenging to optimize or even define fairness of the trained global model for the agents. For instance, existing work usually considers accuracy equity as fairness for different agents in FL, which is limited, especially under the heterogeneous setting, since it is intuitively "unfair" to enforce agents with high-quality data to achieve similar accuracy to those who contribute low-quality data, which may discourage the agents from participating in FL. In this work, we propose a formal FL fairness definition, fairness via agent-awareness (FAA), which takes different contributions of heterogeneous agents into account. Under FAA, the performance of agents with high-quality data will not be sacrificed just due to the existence of large amounts of agents with low-quality data. In addition, we propose a fair FL training algorithm based on agent clustering (FOCUS) to achieve fairness in FL measured by FAA. Theoretically, we prove the convergence and optimality of FOCUS under mild conditions for linear and general convex loss functions with bounded smoothness. We also prove that FOCUS always achieves higher fairness in terms of FAA compared with standard FedAvg under both linear and general convex loss functions. Empirically, we show that on four FL datasets, including synthetic data, images, and texts, FOCUS achieves significantly higher fairness in terms of FAA while maintaining competitive prediction accuracy compared with FedAvg and state-of-the-art fair FL algorithms

Multiscale modelling and experimental analysis of ultrasonic-assisted drilling of GLARE fibre metal laminates

This study aims to evaluate the effectiveness of Ultrasonic-assisted drilling (UAD) of Glass laminate aluminium reinforced epoxy (GLARE) at high cutting speeds (Spindle speeds: 3000–7500 rpm; feed rates 300–750 mm/min) by analysing the thrust force and hole quality metrics (surface roughness, hole size, and burr formations. The research also presents numerical modelling of FMLs under conventional and UAD regimes to predict thrust force using ABAQUS/SIMULIA. The thrust force and exit burrs were reduced by up to 40.83 % and 80 %, respectively. The surface roughness metrics (Ra and Rz) were slightly higher using UAD but remained within the desirable limits of surface roughness for machined aeronautical structures. The discrepancy between the simulation and experimental results was adequate and did not exceed 15 %. The current study shows that it is feasible to drill holes in GLARE using higher cutting parameters and maintain excellent hole quality, which means increased productivity and reduced costs

FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and LLMs

This paper introduces FedMLSecurity, a benchmark that simulates adversarial attacks and corresponding defense mechanisms in Federated Learning (FL). As an integral module of the open-sourced library FedML that facilitates FL algorithm development and performance comparison, FedMLSecurity enhances the security assessment capacity of FedML. FedMLSecurity comprises two principal components: FedMLAttacker, which simulates attacks injected into FL training, and FedMLDefender, which emulates defensive strategies designed to mitigate the impacts of the attacks. FedMLSecurity is open-sourced 1 and is customizable to a wide range of machine learning models (e.g., Logistic Regression, ResNet, GAN, etc.) and federated optimizers (e.g., FedAVG, FedOPT, FedNOVA, etc.). Experimental evaluations in this paper also demonstrate the ease of application of FedMLSecurity to Large Language Models (LLMs), further reinforcing its versatility and practical utility in various scenarios

Effects of seawater acidification and solar ultraviolet radiation on photosynthetic performances and biochemical compositions of Rhodosorus sp. SCSIO-45730

Ocean acidification (OA) caused by rising atmospheric CO2 concentration and solar ultraviolet radiation (UVR) resulting from ozone depletion may affect marine organisms, but little is known regarding how unicellular Rhodosorus sp. SCSIO-45730, an excellent species resource containing various biological-active compounds, responds to OA and UVR. Therefore, we conducted a factorial coupling experiment to unravel the combined effects of OA and UVR on the growth, photosynthetic performances, biochemical compositions and enzyme activities of Rhodosorus sp. SCSIO-45730, which were exposed to two levels of CO2 (LC, 400 μatm, current CO2 level; HC, 1000 μatm, future CO2 level) and three levels of UVR (photosynthetically active radiation (PAR), PAR plus UVA, PAR plus UVB) treatments in all combinations, respectively. Compared to LC treatment, HC stimulated the relative growth rate (RGR) due to higher optimum and effective quantum yields, photosynthetic efficiency, maximum electron transport rates and photosynthetic pigments contents regardless of UVR. However, the presence of UVA had no significant effect but UVB markedly reduced the RGR. Additionally, higher carbohydrate content and lower protein and lipid contents were observed when Rhodosorus sp. SCSIO-45730 was cultured under HC due to the ample HCO3− applications and active stimulation of metabolic enzymes of carbonic anhydrase and nitrate reductase, thus resulting in higher TC/TN. OA also triggered the production of reactive oxygen species (ROS), and the increase of ROS coincided approximately with superoxide dismutase and catalase activities, as well as phenols contents. However, UVR induced photochemical inhibition and damaged macromolecules, making algal cells need more energy for self-protection. Generally, these results revealed that OA counteracted UVR-related inhibition on Rhodosorus sp. SCSIO-45730, adding our understanding of the red algae responding to future global climate changes

DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Generative Pre-trained Transformer (GPT) models have exhibited exciting progress in capabilities, capturing the interest of practitioners and the public alike. Yet, while the literature on the trustworthiness of GPT models remains limited, practitioners have proposed employing capable GPT models for sensitive applications to healthcare and finance - where mistakes can be costly. To this end, this work proposes a comprehensive trustworthiness evaluation for large language models with a focus on GPT-4 and GPT-3.5, considering diverse perspectives - including toxicity, stereotype bias, adversarial robustness, out-of-distribution robustness, robustness on adversarial demonstrations, privacy, machine ethics, and fairness. Based on our evaluations, we discover previously unpublished vulnerabilities to trustworthiness threats. For instance, we find that GPT models can be easily misled to generate toxic and biased outputs and leak private information in both training data and conversation history. We also find that although GPT-4 is usually more trustworthy than GPT-3.5 on standard benchmarks, GPT-4 is more vulnerable given jailbreaking system or user prompts, potentially due to the reason that GPT-4 follows the (misleading) instructions more precisely. Our work illustrates a comprehensive trustworthiness evaluation of GPT models and sheds light on the trustworthiness gaps. Our benchmark is publicly available at https://decodingtrust.github.io/