This paper aims to generate materials for 3D meshes from text descriptions. Unlike existing methods that synthesize texture maps, we propose to generate segment-wise procedural material graphs as the appearance representation, which supports high-quality rendering and provides substantial flexibility in editing. Instead of relying on extensive paired data, i.e., 3D meshes with material graphs and corresponding text descriptions, to train a material graph generative model, we propose to leverage the pre-trained 2D diffusion model as a bridge to connect the text and material graphs. Specifically, our approach decomposes a shape into a set of segments and designs a segment-controlled diffusion model to synthesize 2D images that are aligned with mesh parts. Based on generated images, we initialize parameters of material graphs and fine-tune them through the differentiable rendering module to produce materials in accordance with the textual description. Extensive experiments demonstrate the superior performance of our framework in photorealism, resolution, and editability over existing methods. Project page: https://zju3dv.github.io/MaPa <<<

<jats:p>Developing anode materials with high specific capacity and cycling stability is vital for improving thin‐film lithium‐ion batteries. Thin‐film zinc oxide (ZnO) holds promise due to its high specific capacity, but it suffers from volume changes and structural stress during cycling, leading to poor battery performance. In this research, we ingeniously combined polytetrafluoroethylene (PTFE) with ZnO using a radio frequency (RF) magnetron co‐sputtering method, ensuring a strong bond in the thin‐film composite electrode. PTFE effectively reduced stress on the active material and mitigated volume change effects during Li<jats:sup>+</jats:sup> ion intercalation and deintercalation. The composite thin films are thoroughly characterized using advanced techniques such as X‐ray diffraction, scanning electron microscopy, and X‐ray photoelectron spectroscopy for investigating correlations between material properties and electrochemical behaviors. Notably, the ZnO/PTFE thin‐film electrode demonstrated an impressive specific capacity of 1305 mAh g<jats:sup>−1</jats:sup> (=7116 mAh cm<jats:sup>−3</jats:sup>) at a 0.5C rate and a remarkable capacity retention of 82% from the 1st to the 100th cycle, surpassing the bare ZnO thin film (50%). This study provides valuable insights into using binders to stabilize active materials in thin‐film batteries, enhancing battery performance.</jats:p> <<<

Autonomous systems are soon to be ubiquitous, from manufacturing autonomy to agricultural field robots, and from health care assistants to the entertainment industry. The majority of these systems are developed with modular sub-components for decision-making, planning, and control that may be hand-engineered or learning-based. While these existing approaches have been shown to perform well under the situations they were specifically designed for, they can perform especially poorly in rare, out-of-distribution scenarios that will undoubtedly arise at test-time. The rise of foundation models trained on multiple tasks with impressively large datasets from a variety of fields has led researchers to believe that these models may provide common sense reasoning that existing planners are missing. Researchers posit that this common sense reasoning will bridge the gap between algorithm development and deployment to out-of-distribution tasks, like how humans adapt to unexpected scenarios. Large language models have already penetrated the robotics and autonomous systems domains as researchers are scrambling to showcase their potential use cases in deployment. While this application direction is very promising empirically, foundation models are known to hallucinate and generate decisions that may sound reasonable, but are in fact poor. We argue there is a need to step back and simultaneously design systems that can quantify the certainty of a model's decision, and detect when it may be hallucinating. In this work, we discuss the current use cases of foundation models for decision-making tasks, provide a general definition for hallucinations with examples, discuss existing approaches to hallucination detection and mitigation with a focus on decision problems, and explore areas for further research in this exciting field. <<<

<jats:title>Abstract</jats:title><jats:p>Automobile drive motor with flat wire winding has improved the slot fill factor and efficiency. However, under the current environment of drive motors development to high frequency and voltage, the increase in winding AC loss has weakened those advantages. Accurate analysis of AC loss and the design of high‐performance windings have become the key issues and difficulties in the design process of drive motors. Based on the winding AC loss generation mechanism, the authors propose a winding AC loss analysis method, which can effectively separate DC loss, eddy current loss, and circulating current loss. The method is used to analyse winding AC loss of 60 kW flat wire winding permanent magnet synchronous motor (PMSM). In addition, parallel multi‐strand flat wire windings are proposed to reduce the winding AC loss and eliminate the limitation of the magnetic load of the motor on the selection of winding layers. The AC loss of the parallel multi‐strand flat wire windings at different frequencies is calculated, which demonstrates the effectiveness of the proposed winding topology in reducing the winding AC loss. In addition, the correctness of the simulation model was verified through experiments, and the loss calculation method was verified through finite element analysis.</jats:p> <<<

The pursuit of autonomous driving technology hinges on the sophisticated integration of perception, decision-making, and control systems. Traditional approaches, both data-driven and rule-based, have been hindered by their inability to grasp the nuance of complex driving environments and the intentions of other road users. This has been a significant bottleneck, particularly in the development of common sense reasoning and nuanced scene understanding necessary for safe and reliable autonomous driving. The advent of Visual Language Models (VLM) represents a novel frontier in realizing fully autonomous vehicle driving. This report provides an exhaustive evaluation of the latest state-of-the-art VLM, GPT-4V(ision), and its application in autonomous driving scenarios. We explore the model's abilities to understand and reason about driving scenes, make decisions, and ultimately act in the capacity of a driver. Our comprehensive tests span from basic scene recognition to complex causal reasoning and real-time decision-making under varying conditions. Our findings reveal that GPT-4V demonstrates superior performance in scene understanding and causal reasoning compared to existing autonomous systems. It showcases the potential to handle out-of-distribution scenarios, recognize intentions, and make informed decisions in real driving contexts. However, challenges remain, particularly in direction discernment, traffic light recognition, vision grounding, and spatial reasoning tasks. These limitations underscore the need for further research and development. Project is now available on GitHub for interested parties to access and utilize: \url{https://github.com/PJLab-ADG/GPT4V-AD-Exploration} <<<

Deep learning in computer vision has achieved great success with the price of large-scale labeled training data. However, exhaustive data annotation is impracticable for each task of all domains of interest, due to high labor costs and unguaranteed labeling accuracy. Besides, the uncontrollable data collection process produces non-IID training and test data, where undesired duplication may exist. All these nuisances may hinder the verification of typical theories and exposure to new findings. To circumvent them, an alternative is to generate synthetic data via 3D rendering with domain randomization. We in this work push forward along this line by doing profound and extensive research on bare supervised learning and downstream domain adaptation. Specifically, under the well-controlled, IID data setting enabled by 3D rendering, we systematically verify the typical, important learning insights, e.g., shortcut learning, and discover the new laws of various data regimes and network architectures in generalization. We further investigate the effect of image formation factors on generalization, e.g., object scale, material texture, illumination, camera viewpoint, and background in a 3D scene. Moreover, we use the simulation-to-reality adaptation as a downstream task for comparing the transferability between synthetic and real data when used for pre-training, which demonstrates that synthetic data pre-training is also promising to improve real test results. Lastly, to promote future research, we develop a new large-scale synthetic-to-real benchmark for image classification, termed S2RDA, which provides more significant challenges for transfer from simulation to reality. The code and datasets are available at this https URL. <<<

Large language models (LLMs) are shown to possess a wealth of actionable knowledge that can be extracted for robot manipulation in the form of reasoning and planning. Despite the progress, most still rely on pre-defined motion primitives to carry out the physical interactions with the environment, which remains a major bottleneck. In this work, we aim to synthesize robot trajectories, i.e., a dense sequence of 6-DoF end-effector waypoints, for a large variety of manipulation tasks given an open-set of instructions and an open-set of objects. We achieve this by first observing that LLMs excel at inferring affordances and constraints given a free-form language instruction. More importantly, by leveraging their code-writing capabilities, they can interact with a visual-language model (VLM) to compose 3D value maps to ground the knowledge into the observation space of the agent. The composed value maps are then used in a model-based planning framework to zero-shot synthesize closed-loop robot trajectories with robustness to dynamic perturbations. We further demonstrate how the proposed framework can benefit from online experiences by efficiently learning a dynamics model for scenes that involve contact-rich interactions. We present a large-scale study of the proposed method in both simulated and real-robot environments, showcasing the ability to perform a large variety of everyday manipulation tasks specified in free-form natural language. Project website: this https URL <<<

The pervasive uncertainty and dynamic nature of real-world environments present significant challenges for the widespread implementation of machine-driven Intelligent Decision-Making (IDM) systems. Consequently, IDM should possess the ability to continuously acquire new skills and effectively generalize across a broad range of applications. The advancement of Artificial General Intelligence (AGI) that transcends task and application boundaries is critical for enhancing IDM. Recent studies have extensively investigated the Transformer neural architecture as a foundational model for various tasks, including computer vision, natural language processing, and reinforcement learning. We propose that a Foundation Decision Model (FDM) can be developed by formulating diverse decision-making tasks as sequence decoding tasks using the Transformer architecture, offering a promising solution for expanding IDM applications in complex real-world situations. In this paper, we discuss the efficiency and generalization improvements offered by a foundation decision model for IDM and explore its potential applications in multi-agent game AI, production scheduling, and robotics tasks. Lastly, we present a case study demonstrating our FDM implementation, DigitalBrain (DB1) with 1.3 billion parameters, achieving human-level performance in 870 tasks, such as text generation, image captioning, video game playing, robotic control, and traveling salesman problems. As a foundation decision model, DB1 represents an initial step toward more autonomous and efficient real-world IDM applications. <<<

A high-throughput lithographic method with 25-nanometer resolution and smooth vertical sidewalls is proposed and demonstrated. The technique uses compression molding to create a thickness contrast pattern in a thin resist film carried on a substrate, followed by anisotropic etching to transfer the pattern through the entire resist thickness. Metal patterns with a feature size of 25 nanometers and a period of 70 nanometers were fabricated with the use of resist templates created by imprint lithography in combination with a lift-off process. With further development, imprint lithography should allow fabrication of sub-10-nanometer structures and may become a commercially viable technique for manufacturing integrated circuits and other nanodevices. <<<

An ultrasonic imaging technique has been developed to investigate the internal changes of pouch cells nondestructively. The local ultrasonic transmittance of pouch cells has been measured and used for imaging with a new ultrasonic scanning machine designed and built in-house. The wetting process of the cells is clearly observed via such ultrasonic imaging techniques. Furthermore, ultrasonic transmission images of fresh cells and aged cells with different electrolytes and cycling conditions exhibit very different ultrasonic transmittance, which can be caused by electrolyte dry-out or “unwetting” due to cell swelling. The ultrasonic imaging technique is a very sensitive method to probe failure mechanisms in Li-ion pouch cells. <<<

The magnetoelectric effects in multiferroics have a great potential in creating next-generation memory devices. We use an alternative concept of nonvolatile memory based, on a type of nonlinear magnetoelectric effects showing a butterfly-shaped hysteresis loop. The principle is to utilize the states of the magnetoelectric coefficient, instead of magnetization, electric polarization, or resistance, to store binary information. Our experiments in a device made of the PMN-PT/Terfenol- D multiferroic heterostructure clearly demonstrate that the sign of the magnetoelectric coefficient can be repeatedly switched between positive and negative by applying electric fields, confirming the feasibility of this principle. This kind of nonvolatile memory has outstanding practical virtues such as simple structure, easy operation in writing and reading, low power, fast speed, and diverse materials available. <<<

<jats:p> Recent findings indicate that the Japanese IT sector increasingly lags the U.S. IT sector in software innovation and that this underlies Japan's weakening competitive performance vis-à-vis U.S. IT. This article explores alternative explanations for this outcome and analyzes what explains the Japanese software industry's trajectory. The sources are found in the late understanding of the transformational role of software and its value-creating potential as well as in the evolution of the industry's structure. Finally, this article considers what policy makers in other nations might learn from the Japanese experience in building a more vibrant software industry. </jats:p> <<<

<jats:p>Computation-intensive design problems are becoming increasingly common in manufacturing industries. The computation burden is often caused by expensive analysis and simulation processes in order to reach a comparable level of accuracy as physical testing data. To address such a challenge, approximation or metamodeling techniques are often used. Metamodeling techniques have been developed from many different disciplines including statistics, mathematics, computer science, and various engineering disciplines. These metamodels are initially developed as “surrogates” of the expensive simulation process in order to improve the overall computation efficiency. They are then found to be a valuable tool to support a wide scope of activities in modern engineering design, especially design optimization. This work reviews the state-of-the-art metamodel-based techniques from a practitioner’s perspective according to the role of metamodeling in supporting design optimization, including model approximation, design space exploration, problem formulation, and solving various types of optimization problems. Challenges and future development of metamodeling in support of engineering design is also analyzed and discussed.</jats:p> <<<