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Office automation significantly enhances human productivity by automatically finishing routine tasks in the workflow. Beyond the basic information extraction studied in much of the prior document AI literature, the office automation research should be extended to more realistic office tasks which require to integrate various information sources in the office system and produce outputs through a series of decision-making processes. We introduce OfficeBench, one of the first office automation benchmarks for evaluating current LLM agents' capability to address office tasks in realistic office workflows. OfficeBench requires LLM agents to perform feasible long-horizon planning, proficiently switch between applications in a timely manner, and accurately ground their actions within a large combined action space, based on the contextual demands of the workflow. Applying our customized evaluation methods on each task, we find that GPT-4 Omni achieves the highest pass rate of 47.00%, demonstrating a decent performance in handling office tasks. However, this is still far below the human performance and accuracy standards required by real-world office workflows. We further observe that most issues are related to operation redundancy and hallucinations, as well as limitations in switching between multiple applications, which may provide valuable insights for developing effective agent frameworks for office automation., Comment: Preprint

Artificial intelligence (AI) is significantly transforming scientific research. Explainable AI methods, such as concept-based models (CMs), are promising for driving new scientific discoveries because they make predictions based on meaningful concepts and offer insights into the prediction process. In molecular science, however, explainable CMs are not as common compared to black-box models like Graph Neural Networks (GNNs), primarily due to their requirement for predefined concepts and manual label for each instance, which demand domain knowledge and can be labor-intensive. This paper introduces a novel framework for Automated Molecular Concept (AutoMolCo) generation and labeling. AutoMolCo leverages the knowledge in Large Language Models (LLMs) to automatically generate predictive molecular concepts and label them for each molecule. Such procedures are repeated through iterative interactions with LLMs to refine concepts, enabling simple linear models on the refined concepts to outperform GNNs and LLM in-context learning on several benchmarks. The whole AutoMolCo framework is automated without any human knowledge inputs in either concept generation, labeling, or refinement, thereby surpassing the limitations of extant CMs while maintaining their explainability and allowing easy intervention. Through systematic experiments on MoleculeNet and High-Throughput Experimentation (HTE) datasets, we demonstrate that the AutoMolCo-induced explainable CMs are beneficial and promising for molecular science research.

High energy electron radiography (HEER) is a promising tool for high energy density physics diagnostics, apart from other tools like X/{\gamma} ray shadowgraphy and high energy proton radiography. Impressive progresses have been made in development and application of HEER in past few years, and proved its potentials for high-resolution imaging of static opaque objects. By taking advantages of short pulse duration and tunable time structure of high energy electron probes, time-resolved imaging measurement of high energy density gold irradiated by ultrashort intense lasers has been performed. Phenomena of different time periods from picosecond to microsecond have been observed, thus proving feasibilities of this technique for imaging of static and dynamic objects.

Using a high energy electron beam for the imaging of high density matter with both high spatial-temporal and areal density resolution under extreme states of temperature and pressure is one of the critical challenges in high energy density physics . When a charged particle beam passes through an opaque target, the beam will be scattered with a distribution that depends on the thickness of the material. By collecting the scattered beam either near or off axis, so-called bright field or dark field images can be obtained. Here we report on an electron radiography experiment using 45 MeV electrons from an S-band photo-injector, where scattered electrons, after interacting with a sample, are collected and imaged by a quadrupole imaging system. We achieved a few micrometers (about 4 micrometers) spatial resolution and about 10 micrometers thickness resolution for a silicon target of 300-600 micron thickness. With addition of dark field images that are captured by selecting electrons with large scattering angle, we show that more useful information in determining external details such as outlines, boundaries and defects can be obtained., Comment: 7pages, 7 figures

A special beam line for high energy electron radiography is designed, including achromat and imaging systems. The requirement of the angle and position correction on the target from imaging system can be approximately realized by fine tuning the quadrupoles used in the achromat. The imaging system is designed by fully considering the limitation from the laboratory and beam diagnostics devices space. Two kinds of imaging system are designed and both show a good performance of imaging by beam trajectory simulation. The details of the beam optical requirement and optimization design are presented here. The beam line is designed and prepared to install in Tsinghua university linear electron accelerator laboratory for further precise electron radiography experiment study., Comment: 8 pages

The E-field of pulse line ion accelerator (PLIA) is unique with high frequency (~MHz), large magnitude (~MV/m) and limited measuring space (~cm). The integrated optical E-field sensor (IOES) has remarkable advantages and has been used for PLIA E-field measurement. Firstly, the transfer function of IOES has been calibrated to ensure the measurement accuracy. And the time-domain response illustrates that the sensor has a fast dynamic performance to effectively follow a 4 ns rising edge. Then, the E-field distribution along the axis and near the insulator surface of PLIA was measured, showing that the propagation of E-field is almost lossless and E-field near the insulation surface is about 1.1 times larger than that along the axis, which is in accordance with the simulation result., Comment: 10 pages,9 figures