SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments

Different environments pose a great challenge to the outdoor robust visual perception for long-term autonomous driving and the generalization of learning-based algorithms on different environmental effects is still an open problem. Although monocular depth prediction has been well studied recently, there is few work focusing on the robust learning-based depth prediction across different environments, e.g. changing illumination and seasons, owing to the lack of such a multi-environment real-world dataset and benchmark. To this end, the first cross-season monocular depth prediction dataset and benchmark SeasonDepth is built based on CMU Visual Localization dataset. To benchmark the depth estimation performance under different environments, we investigate representative and recent state-of-the-art open-source supervised, self-supervised and domain adaptation depth prediction methods from KITTI benchmark using several newly-formulated metrics. Through extensive experimental evaluation on the proposed dataset, the influence of multiple environments on performance and robustness is analyzed qualitatively and quantitatively, showing that the long-term monocular depth prediction is still challenging even with fine-tuning. We further give promising avenues that self-supervised training and stereo geometry constraint help to enhance the robustness to changing environments. The dataset is available on https://seasondepth.github.io, and benchmark toolkit is available on https://github.com/SeasonDepth/SeasonDepth.Comment: 19 pages, 13 figure

Direct imaging and chemical analysis of unstained DNA origami performed with a transmission electron microscope

Here, we report a simple and rapid characterisation technique combining physical and chemical analysis for DNA origami with conventional TEM.close4

Zinc-blende and wurtzite GaAs quantum dots in nanowires studied using hydrostatic pressure

We report both zinc-blende (ZB) and wurtzite (WZ) crystal phase self-assembled GaAs quantum dots (QDs) embedding in a single GaAs/AlGaAs core-shell nanowires (NWs). Optical transitions and single-photon characteristics of both kinds of QDs have been investigated by measuring photoluminescence (PL) and time-resolved PL spectra upon application of hydrostatic pressure. We find that the ZB QDs are of direct band gap transition with short recombination lifetime (~1 ns) and higher pressure coefficient (75-100 meV/GPa). On the contrary, the WZ QDs undergo a direct-to-pseudodirect bandgap transition as a result of quantum confinement effect, with remarkably longer exciton lifetime (4.5-74.5 ns) and smaller pressure coefficient (28-53 meV/GPa). These fundamentally physical properties are further examined by performing state-of-the-art atomistic pseudopotential calculations

Nucleic acid nanostructure for delivery of CRISPR/Cas9‐based gene editing system

Abstract CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats/CRISPR‐associated protein 9)‐based gene editing system has aroused great interest in many research fields. However, the efficient and safe delivery of this gene editing system into the target tissues and cells remain a major challenge. During the past decades, nucleic acid nanostructures have been widely developed for drug delivery. In this perspective, we will introduce and discuss the recent progress in the design of multifunctional nucleic acid nanostructures, including RCA‐derived DNA, branched DNA, and hybrid DNA, for delivery of the CRISPR/Cas9‐based gene editing system. Furthermore, we prospect the challenges and future opportunities of nucleic acid nanotechnology in the delivery of gene editing systems

Anticancer Activities of Tumor-killing Nanorobots

Pharmaceutical uses of cancer therapeutics, such as intravenous thrombin to elicit blood coagulation, have been hampered by lack of tumor specificity. Based on rapid progress in DNA origami-based machines capable of transporting molecular payloads, DNA nanorobots have been constructed to specifically deliver therapeutic agents into tumor vessels

Surface modification of calcium carbonate nanoparticles as hydraulic oil additives friction performance research

In this experiment, calcium carbonate nanopartilces were prepared by metathesis method. The calcium carbonate powders were modified by sodium dodecyl sulfonate. The characteristics of raw and modified calcium carbonate powders were analyzed and characterized by various methods. The size of the unmodified nano-calcium carbonate is about 2.7 μm, and the particle size of the modified particles is about several hundred nanometers. The CFT-1 material performance tester was utilized to evaluate the tribological characteristics of the additive. The data show that the modified nano-calcium carbonate can improve the anti-friction and anti-friction performance of hydraulic oil