Coarse-graining is a molecular modeling technique in which an atomistic system is represented in a simplified fashion that retains the most significant system features that contribute to a target output, while removing the degrees of freedom that are less relevant. This reduction in model complexity allows coarse-grained molecular simulations to reach increased spatial and temporal scales compared to corresponding all-atom models. A core challenge in coarse-graining is to construct a force field that represents the interactions in the new representation in a way that preserves the atomistic-level properties. Many approaches to building coarse-grained force fields have limited transferability between different thermodynamic conditions as a result of averaging over internal fluctuations at a specific thermodynamic state point. Here, we use a graph-convolutional neural network architecture, the Hierarchically Interacting Particle Neural Network with Tensor Sensitivity (HIP-NN-TS), to develop a highly automated training pipeline for coarse grained force fields which allows for studying the transferability of coarse-grained models based on the force-matching approach. We show that this approach not only yields highly accurate force fields, but also that these force fields are more transferable through a variety of thermodynamic conditions. These results illustrate the potential of machine learning techniques such as graph neural networks to improve the construction of transferable coarse-grained force fields.

"Jailbreak" is a major safety concern of Large Language Models (LLMs), which occurs when malicious prompts lead LLMs to produce harmful outputs, raising issues about the reliability and safety of LLMs. Therefore, an effective evaluation of jailbreaks is very crucial to develop its mitigation strategies. However, our research reveals that many jailbreaks identified by current evaluations may actually be hallucinations-erroneous outputs that are mistaken for genuine safety breaches. This finding suggests that some perceived vulnerabilities might not represent actual threats, indicating a need for more precise red teaming benchmarks. To address this problem, we propose the $\textbf{B}$enchmark for reli$\textbf{AB}$ilit$\textbf{Y}$ and jail$\textbf{B}$reak ha$\textbf{L}$l$\textbf{U}$cination $\textbf{E}$valuation (BabyBLUE). BabyBLUE introduces a specialized validation framework including various evaluators to enhance existing jailbreak benchmarks, ensuring outputs are useful malicious instructions. Additionally, BabyBLUE presents a new dataset as an augmentation to the existing red teaming benchmarks, specifically addressing hallucinations in jailbreaks, aiming to evaluate the true potential of jailbroken LLM outputs to cause harm to human society.

Structure-based drug design (SBDD) aims to generate potential drugs that can bind to a target protein and is greatly expedited by the aid of AI techniques in generative models. However, a lack of systematic understanding persists due to the diverse settings, complex implementation, difficult reproducibility, and task singularity. Firstly, the absence of standardization can lead to unfair comparisons and inconclusive insights. To address this dilemma, we propose CBGBench, a comprehensive benchmark for SBDD, that unifies the task as a generative heterogeneous graph completion, analogous to fill-in-the-blank of the 3D complex binding graph. By categorizing existing methods based on their attributes, CBGBench facilitates a modular and extensible framework that implements various cutting-edge methods. Secondly, a single task on \textit{de novo} molecule generation can hardly reflect their capabilities. To broaden the scope, we have adapted these models to a range of tasks essential in drug design, which are considered sub-tasks within the graph fill-in-the-blank tasks. These tasks include the generative designation of \textit{de novo} molecules, linkers, fragments, scaffolds, and sidechains, all conditioned on the structures of protein pockets. Our evaluations are conducted with fairness, encompassing comprehensive perspectives on interaction, chemical properties, geometry authenticity, and substructure validity. We further provide the pre-trained versions of the state-of-the-art models and deep insights with analysis from empirical studies. The codebase for CBGBench is publicly accessible at \url{https://github.com/Edapinenut/CBGBench}.

Cosine similarity is widely used to measure the similarity between two embeddings, while interpretations based on angle and correlation coefficient are common. In this study, we focus on the interpretable axes of embeddings transformed by Independent Component Analysis (ICA), and propose a novel interpretation of cosine similarity as the sum of semantic similarities over axes. To investigate this, we first show experimentally that unnormalized embeddings contain norm-derived artifacts. We then demonstrate that normalized ICA-transformed embeddings exhibit sparsity, with a few large values in each axis and across embeddings, thereby enhancing interpretability by delineating clear semantic contributions. Finally, to validate our interpretation, we perform retrieval experiments using ideal embeddings with and without specific semantic components.

Lablets are autonomous microscopic particles with programmable CMOS electronics that can control electrokinetic phenomena and electrochemical reactions in solution via actuator and sensor microelectrodes. In this paper, we describe the design and fabrication of optimized singulated lablets (CMOS3) with dimensions 140x140x50 micrometers carrying an integrated coplanar encapsulated supercapacitor as a rechargeable power supply. The lablets are designed to allow docking to one another or to a smart surface for interchange of energy, electronic information, and chemicals. The paper focusses on the digital and analog design of the lablets to allow significant programmable functionality in a microscopic footprint, including the control of autonomous actuation and sensing up to the level of being able to support a complete lablet self-reproduction life cycle, although experimentally this remains to be proven. The potential of lablets in autonomous sensing and control and for evolutionary experimentation are discussed.

Deep nonparametric regression, characterized by the utilization of deep neural networks to learn target functions, has emerged as a focus of research attention in recent years. Despite considerable progress in understanding convergence rates, the absence of asymptotic properties hinders rigorous statistical inference. To address this gap, we propose a novel framework that transforms the deep estimation paradigm into a platform conducive to conditional mean estimation, leveraging the conditional diffusion model. Theoretically, we develop an end-to-end convergence rate for the conditional diffusion model and establish the asymptotic normality of the generated samples. Consequently, we are equipped to construct confidence regions, facilitating robust statistical inference. Furthermore, through numerical experiments, we empirically validate the efficacy of our proposed methodology.

Memes, which rapidly disseminate personal opinions and positions across the internet, also pose significant challenges in propagating social bias and prejudice. This study presents a novel approach to detecting harmful memes, particularly within the multicultural and multilingual context of Singapore. Our methodology integrates image captioning, Optical Character Recognition (OCR), and Large Language Model (LLM) analysis to comprehensively understand and classify harmful memes. Utilizing the BLIP model for image captioning, PP-OCR and TrOCR for text recognition across multiple languages, and the Qwen LLM for nuanced language understanding, our system is capable of identifying harmful content in memes created in English, Chinese, Malay, and Tamil. To enhance the system's performance, we fine-tuned our approach by leveraging additional data labeled using GPT-4V, aiming to distill the understanding capability of GPT-4V for harmful memes to our system. Our framework achieves top-1 at the public leaderboard of the Online Safety Prize Challenge hosted by AI Singapore, with the AUROC as 0.7749 and accuracy as 0.7087, significantly ahead of the other teams. Notably, our approach outperforms previous benchmarks, with FLAVA achieving an AUROC of 0.5695 and VisualBERT an AUROC of 0.5561.

In this study, it is demonstrated that Recurrent Neural Networks (RNNs), specifically those utilizing Gated Recurrent Unit (GRU) architecture, possess the capability to learn the complex dynamics of rate-and-state friction laws from synthetic data. The data employed for training the network is generated through the application of traditional rate-and-state friction equations coupled with the aging law for state evolution. A novel aspect of our approach is the formulation of a loss function that explicitly accounts for the direct effect by means of automatic differentiation. It is found that the RNN, with its GRU architecture, effectively learns to predict changes in the friction coefficient resulting from velocity jumps (with and without noise in the target data), thereby showcasing the potential of machine learning models in understanding and simulating the physics of frictional processes.

This paper introduces RoTipBot, a novel robotic system for handling thin, flexible objects. Different from previous works that are limited to singulating them using suction cups or soft grippers, RoTipBot can grasp and count multiple layers simultaneously, emulating human handling in various environments. Specifically, we develop a novel vision-based tactile sensor named RoTip that can rotate and sense contact information around its tip. Equipped with two RoTip sensors, RoTipBot feeds multiple layers of thin, flexible objects into the centre between its fingers, enabling effective grasping and counting. RoTip's tactile sensing ensures both fingers maintain good contact with the object, and an adjustment approach is designed to allow the gripper to adapt to changes in the object. Extensive experiments demonstrate the efficacy of the RoTip sensor and the RoTipBot approach. The results show that RoTipBot not only achieves a higher success rate but also grasps and counts multiple layers simultaneously -- capabilities not possible with previous methods. Furthermore, RoTipBot operates up to three times faster than state-of-the-art methods. The success of RoTipBot paves the way for future research in object manipulation using mobilised tactile sensors. All the materials used in this paper are available at \url{https://sites.google.com/view/rotipbot}.

The burgeoning utilization of Large Language Models (LLMs) in scientific research necessitates advanced benchmarks capable of evaluating their understanding and application of scientific knowledge comprehensively. To address this need, we introduce the SciKnowEval benchmark, a novel framework that systematically evaluates LLMs across five progressive levels of scientific knowledge: studying extensively, inquiring earnestly, thinking profoundly, discerning clearly, and practicing assiduously. These levels aim to assess the breadth and depth of scientific knowledge in LLMs, including knowledge coverage, inquiry and exploration capabilities, reflection and reasoning abilities, ethic and safety considerations, as well as practice proficiency. Specifically, we take biology and chemistry as the two instances of SciKnowEval and construct a dataset encompassing 50K multi-level scientific problems and solutions. By leveraging this dataset, we benchmark 20 leading open-source and proprietary LLMs using zero-shot and few-shot prompting strategies. The results reveal that despite achieving state-of-the-art performance, the proprietary LLMs still have considerable room for improvement, particularly in addressing scientific computations and applications. We anticipate that SciKnowEval will establish a comprehensive standard for benchmarking LLMs in science research and discovery, and promote the development of LLMs that integrate scientific knowledge with strong safety awareness. The dataset and code are publicly available at https://github.com/hicai-zju/sciknoweval .