COMPARISON OF MARKER AND MARKERLESS MOTION CAPTURE SYSTEM FOR GAIT KINEMATICS

The purpose of this study was to compare gait kinematics measured with a markerless motion capture system against data measured with a marker-based motion capture system. A sample of 14 over-ground walking trials were captured simultaneously with two camcorders (60Hz) and an 8-camera marker system. The markerless data was further processed to landmarks using markerless human movement automatic capture system (FastMove). Body landmarks data of X and Z coordinates were highly consistent between the two systems, while data of Y coordinate showed low consistency. The Bland-Altman plots’ results showed low agreement between individual measurements of the maximum and minimum of knee and ankle flexion angles from both systems against the average of the measurement

CELLULASE6 and MANNANASE7 affect cell differentiation and silique dehiscence in Arabidopsis

Cellulases, hemicellulases and pectinases play important roles in fruit development and maturation, but mutants with defects in the fruit have not been reported for cellulase or hemicellulase genes. Here we report the functional characterization of cellulase gene CEL6 and hemicellulase gene MAN7 in silique development and dehiscence in Arabidopsis. These genes were found to be expressed in vegetative and reproductive organs, and their expression in the silique partially depended on the IND and ALC transcriptional factors. Mutant alleles of cel6 and man7 exhibited delayed secondary cell wall thickening and altered cell morphology in the valve margin and impaired silique dehiscence. Cells in the separation layer in nearly mature siliques of the single mutants and the cel6-1 man7-3 double mutant remained intact whereas they degenerated in the wild-type control. Phenotypic studies of single, double, triple and quadruple mutants revealed that the higher-order mutant combinations of the cel6-1, man7-3, and pectinase adpg1-1 and adpg2- 1 mutations produced more severe silique indehiscent phenotypes than the corresponding lower-order mutant combinations, except for some combinations involving cel6-1, man7-3, and adpg2-1. Our results demonstrate that the ability of the silique to dehisce can be manipulated to different degrees by altering the activities of proteins of different types.Plant Biology, Ecology and Evolutio

Learning Universal Policies via Text-Guided Video Generation

A goal of artificial intelligence is to construct an agent that can solve a wide variety of tasks. Recent progress in text-guided image synthesis has yielded models with an impressive ability to generate complex novel images, exhibiting combinatorial generalization across domains. Motivated by this success, we investigate whether such tools can be used to construct more general-purpose agents. Specifically, we cast the sequential decision making problem as a text-conditioned video generation problem, where, given a text-encoded specification of a desired goal, a planner synthesizes a set of future frames depicting its planned actions in the future, after which control actions are extracted from the generated video. By leveraging text as the underlying goal specification, we are able to naturally and combinatorially generalize to novel goals. The proposed policy-as-video formulation can further represent environments with different state and action spaces in a unified space of images, which, for example, enables learning and generalization across a variety of robot manipulation tasks. Finally, by leveraging pretrained language embeddings and widely available videos from the internet, the approach enables knowledge transfer through predicting highly realistic video plans for real robots.Comment: NeurIPS 2023, Project Website: https://universal-policy.github.io

Video Timeline Modeling For News Story Understanding

In this paper, we present a novel problem, namely video timeline modeling. Our objective is to create a video-associated timeline from a set of videos related to a specific topic, thereby facilitating the content and structure understanding of the story being told. This problem has significant potential in various real-world applications, such as news story summarization. To bootstrap research in this area, we curate a realistic benchmark dataset, YouTube-News-Timeline, consisting of over $12$k timelines and $300$k YouTube news videos. Additionally, we propose a set of quantitative metrics as the protocol to comprehensively evaluate and compare methodologies. With such a testbed, we further develop and benchmark exploratory deep learning approaches to tackle this problem. We anticipate that this exploratory work will pave the way for further research in video timeline modeling. The assets are available via https://github.com/google-research/google-research/tree/master/video_timeline_modeling.Comment: Accepted as a spotlight by NeurIPS 2023, Track on Datasets and Benchmark