NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario

We introduce a novel visual question answering (VQA) task in the context of autonomous driving, aiming to answer natural language questions based on street-view clues. Compared to traditional VQA tasks, VQA in autonomous driving scenario presents more challenges. Firstly, the raw visual data are multi-modal, including images and point clouds captured by camera and LiDAR, respectively. Secondly, the data are multi-frame due to the continuous, real-time acquisition. Thirdly, the outdoor scenes exhibit both moving foreground and static background. Existing VQA benchmarks fail to adequately address these complexities. To bridge this gap, we propose NuScenes-QA, the first benchmark for VQA in the autonomous driving scenario, encompassing 34K visual scenes and 460K question-answer pairs. Specifically, we leverage existing 3D detection annotations to generate scene graphs and design question templates manually. Subsequently, the question-answer pairs are generated programmatically based on these templates. Comprehensive statistics prove that our NuScenes-QA is a balanced large-scale benchmark with diverse question formats. Built upon it, we develop a series of baselines that employ advanced 3D detection and VQA techniques. Our extensive experiments highlight the challenges posed by this new task. Codes and dataset are available at https://github.com/qiantianwen/NuScenes-QA

Genetic diversity assessment of sesame core collection in China by phenotype and molecular markers and extraction of a mini-core collection

<p>Abstract</p> <p>Background</p> <p>Sesame (<it>Sesamum indicum</it> L.) is one of the four major oil crops in China. A sesame core collection (CC) was established in China in 2000, but no complete study on its genetic diversity has been carried out at either the phenotypic or molecular level. To provide technical guidance, a theoretical basis for further collection, effective protection, reasonable application, and a complete analysis of sesame genetic resources, a genetic diversity assessment of the sesame CC in China was conducted using phenotypic and molecular data and by extracting a sesame mini-core collection (MC).</p> <p>Results</p> <p>Results from a genetic diversity assessment of sesame CC in China were significantly inconsistent at the phenotypic and molecular levels. A Mantel test revealed the insignificant correlation between phenotype and molecular marker information (<it>r</it> = 0.0043, <it>t</it> = 0.1320, <it>P</it> = 0.5525). The Shannon-Weaver diversity index (I) and Nei genetic diversity index (h) were higher (I = 0.9537, h = 0.5490) when calculated using phenotypic data from the CC than when using molecular data (I = 0.3467, h = 0.2218). A mini-core collection (MC) containing 184 accessions was extracted based on both phenotypic and molecular data, with a low mean difference percentage (MD, 1.64%), low variance difference percentage (VD, 22.58%), large variable rate of coefficient of variance (VR, 114.86%), and large coincidence rate of range (CR, 95.76%). For molecular data, the diversity indices and the polymorphism information content (PIC) for the MC were significantly higher than for the CC. Compared to an alternative random sampling strategy, the advantages of capturing genetic diversity and validation by extracting a MC using an advanced maximization strategy were proven.</p> <p>Conclusions</p> <p>This study provides a comprehensive characterization of the phenotypic and molecular genetic diversities of the sesame CC in China. A MC was extracted using both phenotypic and molecular data. Low MD% and VD%, and large VR% and CR% suggested that the MC provides a good representation of the genetic diversity of the original CC. The MC was more genetically diverse with higher diversity indices and a higher PIC value than the CC. A MC may aid in reasonably and efficiently selecting materials for sesame breeding and for genotypic biological studies, and may also be used as a population for association mapping in sesame.</p

Embedding NiCo₂O₄ Nanoparticles into a 3DHPC Assisted by CO₂‑Expanded Ethanol: A Potential Lithium-Ion Battery Anode with High Performance

A high-performance anode material, NiCo₂O₄/3DHPC composite, for lithium-ion batteries was developed through direct nanoparticles nucleation on a three-dimensional hierarchical porous carbon (3DHPC) matrix and cation substitution of spinel Co₃O₄ nanoparticles. It was synthesized via a supercritical carbon dioxide (scCO₂) expanded ethanol solution-assisted deposition method combined with a subsequent heat-treatment process. The NiCo₂O₄ nanoparticles were uniformly embedded into the porous carbon matrix and efficiently avoided free-growth in solution or aggregation in the pores even at a high content of 55.0 wt %. In particular, the 3DHPC was directly used without pretreatment or surfactant assistance. As an anode material for lithium-ion batteries, the NiCo₂O₄/3DHPC composite showed high reversible capacity and improved rate capability that outperformed those composites formed with single metal oxides (NiO/3DHPC, Co₃O₄/3DHPC), their physical mixture, and the composite prepared in pure ethanol (NiCo₂O₄/3DHPC-E). The superior performance is mainly contributed to the unique advantages of the scCO₂-expanded ethanol medium, and the combination of high utilization efficiency and improved electrical conductivity of NiCo₂O₄ as well as the electronic and ionic transport advantages of 3DHPC