First attempt to build realistic driving scenes using video-to-video synthesis in OpenDS framework

Existing programmable simulators enable researchers to customize different driving scenarios to conduct in-lab automotive driver simulations. However, software-based simulators for cognitive research generate and maintain their scenes with the support of 3D engines, which may affect users' experiences to a certain degree since they are not sufficiently realistic. Now, a critical issue is the question of how to build scenes into real-world ones. In this paper, we introduce the first step in utilizing video-to-video synthesis, which is a deep learning approach, in OpenDS framework, which is an open-source driving simulator software, to present simulated scenes as realistically as possible. Off-line evaluations demonstrated promising results from our study, and our future work will focus on how to merge them appropriately to build a close-to-reality, real-time driving simulator

Roles of plant growth substance in callus induction of Achyranthes bidentata

    In this research, callus from leaves, petioles and stems of Achyranthes bidentata was evidently initiated by plant growth substance, in which 2,4-dichlorophenoxyacetic acid (2,4-D) was very important to callus induction, but effects of other plant growth substances were various, and the optimum combination of plant growth substances for callus induction from leaves, petioles and stems was respectively obtained. Compared with callus induction from leaves and petioles, callus induction from stems was easier, and the higher induction rate and bigger mass of callus from stems were obtained. This study showed that the dedifferentiation capacity of various explants from Achyranthes bidentata was obviously different, and effects of plant growth substance on callus induction from various explants of Achyranthes bidentata were significantly diverse

Drill String Mechanics and Extension Capacity of Extended-Reach Well

For the design and construction of the extended-reach well, the ultimate elongation capacity of the extended-reach well was studied. Given the drilling practice of extended-reach well, the extension limit prediction criterion of extended-reach well is established. In this paper, based on the drilling practice of extended-reach well, by the finite element method, the gap element method and the dynamic finite element method of the whole drill string, static analysis model of the extended-reach well and the mechanicsꞌ analysis model of drill string vibration are established. The frictional resistance condition and strength condition of the limit extension of the extended-reach well are solved respectively, and the extended limit prediction criterion of the extended-reach well is established. The software was prepared based on the model and theory, the model and software were validated with the example of drilling, and the average error of the calculated value is 6.94% when compared with the measured values in the field. It can meet the needs of drilling engineering and study of the extension capability of the extended-reach wells

From Indeterminacy to Determinacy: Augmenting Logical Reasoning Capabilities with Large Language Models

Recent advances in LLMs have revolutionized the landscape of reasoning tasks. To enhance the capabilities of LLMs to emulate human reasoning, prior works focus on modeling reasoning steps using specific thought structures like chains, trees, or graphs. However, LLM-based reasoning continues to encounter three challenges: 1) Selecting appropriate reasoning structures for various tasks; 2) Exploiting known conditions sufficiently and efficiently to deduce new insights; 3) Considering the impact of historical reasoning experience. To address these challenges, we propose DetermLR, a novel reasoning framework that formulates the reasoning process as a transformational journey from indeterminate premises to determinate ones. This process is marked by the incremental accumulation of determinate premises, making the conclusion progressively closer to clarity. DetermLR includes three essential components: 1) Premise identification: We categorize premises into two distinct types: determinate and indeterminate. This empowers LLMs to customize reasoning structures to match the specific task complexities. 2) Premise prioritization and exploration: We leverage quantitative measurements to assess the relevance of each premise to the target, prioritizing more relevant premises for exploring new insights. 3) Iterative process with reasoning memory: We introduce a reasoning memory module to automate storage and extraction of available premises and reasoning paths, preserving historical reasoning details for more accurate premise prioritization. Comprehensive experimental results show that DetermLR outperforms all baselines on four challenging logical reasoning tasks: LogiQA, ProofWriter, FOLIO, and LogicalDeduction. DetermLR can achieve better reasoning performance while requiring fewer visited states, highlighting its superior efficiency and effectiveness in tackling logical reasoning tasks.Comment: Code repo: https://github.com/XiaoMi/DetermL

HairBrush for Immersive Data-Driven Hair Modeling

International audienceWhile hair is an essential component of virtual humans, it is also one of the most challenging digital assets to create. Existing automatic techniques lack the generality and flexibility to create rich hair variations, while manual authoring interfaces often require considerable artistic skills and efforts, especially for intricate 3D hair structures that can be difficult to navigate. We propose an interactive hair modeling system that can help create complex hairstyles in minutes or hours that would otherwise take much longer with existing tools. Modelers, including novice users, can focus on the overall hairstyles and local hair deformations, as our system intelligently suggests the desired hair parts. Our method combines the flexibility of manual authoring and the convenience of data-driven automation. Since hair contains intricate 3D structures such as buns, knots, and strands, they are inherently challenging to create using traditional 2D interfaces. Our system provides a new 3D hair author-ing interface for immersive interaction in virtual reality (VR). Users can draw high-level guide strips, from which our system predicts the most plausible hairstyles via a deep neural network trained from a professionally curated dataset. Each hairstyle in our dataset is composed of multiple variations, serving as blend-shapes to fit the user drawings via global blending and local deformation. The fitted hair models are visualized as interactive suggestions that the user can select, modify, or ignore. We conducted a user study to confirm that our system can significantly reduce manual labor while improve the output quality for modeling a variety of head and facial hairstyles that are challenging to create via existing techniques

When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks

In-context learning (ICL) has become the default method for using large language models (LLMs), making the exploration of its limitations and understanding the underlying causes crucial. In this paper, we find that ICL falls short of handling specification-heavy tasks, which are tasks with complicated and extensive task specifications, requiring several hours for ordinary humans to master, such as traditional information extraction tasks. The performance of ICL on these tasks mostly cannot reach half of the state-of-the-art results. To explore the reasons behind this failure, we conduct comprehensive experiments on 18 specification-heavy tasks with various LLMs and identify three primary reasons: inability to specifically understand context, misalignment in task schema comprehension with humans, and inadequate long-text understanding ability. Furthermore, we demonstrate that through fine-tuning, LLMs can achieve decent performance on these tasks, indicating that the failure of ICL is not an inherent flaw of LLMs, but rather a drawback of existing alignment methods that renders LLMs incapable of handling complicated specification-heavy tasks via ICL. To substantiate this, we perform dedicated instruction tuning on LLMs for these tasks and observe a notable improvement. We hope the analyses in this paper could facilitate advancements in alignment methods enabling LLMs to meet more sophisticated human demands.Comment: Under revie