PALF: Pre-Annotation and Camera-LiDAR Late Fusion for the Easy Annotation of Point Clouds

3D object detection has become indispensable in the field of autonomous driving. To date, gratifying breakthroughs have been recorded in 3D object detection research, attributed to deep learning. However, deep learning algorithms are data-driven and require large amounts of annotated point cloud data for training and evaluation. Unlike 2D image labels, annotating point cloud data is difficult due to the limitations of sparsity, irregularity, and low resolution, which requires more manual work, and the annotation efficiency is much lower than 2D image.Therefore, we propose an annotation algorithm for point cloud data, which is pre-annotation and camera-LiDAR late fusion algorithm to easily and accurately annotate. The contributions of this study are as follows. We propose (1) a pre-annotation algorithm that employs 3D object detection and auto fitting for the easy annotation of point clouds, (2) a camera-LiDAR late fusion algorithm using 2D and 3D results for easily error checking, which helps annotators easily identify missing objects, and (3) a point cloud annotation evaluation pipeline to evaluate our experiments. The experimental results show that the proposed algorithm improves the annotating speed by 6.5 times and the annotation quality in terms of the 3D Intersection over Union and precision by 8.2 points and 5.6 points, respectively; additionally, the miss rate is reduced by 31.9 points

Current Performance and On-Going Improvements of the 8.2 m Subaru Telescope

An overview of the current status of the 8.2 m Subaru Telescope constructed and operated at Mauna Kea, Hawaii, by the National Astronomical Observatory of Japan is presented. The basic design concept and the verified performance of the telescope system are described. Also given are the status of the instrument package offered to the astronomical community, the status of operation, and some of the future plans. The status of the telescope reported in a number of SPIE papers as of the summer of 2002 are incorporated with some updates included as of 2004 February. However, readers are encouraged to check the most updated status of the telescope through the home page, http://subarutelescope.org/index.html, and/or the direct contact with the observatory staff.Comment: 18 pages (17 pages in published version), 29 figures (GIF format), This is the version before the galley proo

Effect of oil droplet sizes of oil‐in‐water emulsion on the taste impressions of added tastants

By conducing organoleptic analysis, the authors investigated the effect of oil droplet size on human preference to the taste of oil‐in‐water (o/w) emulsions to which several tastants were added. Two singly dispersed o/w emulsions, different in oil droplet sizes, consisting of 10% triacilglycerol purified from tuna, sardine or soybean oil, 0.5% emulsifier and water were prepared by ceramic membrane filtration. Organoleptic analysis showed that the impressions of sweetness, bitterness and umami, which were developed by addition of certain taste substances, were different between o/w emulsions with different oil droplet sizes. The results of two‐bottle choice tests, which were carried out using olfactory‐blocked mice, showed that o/w emulsions with 1.00‐µm droplets, with added sweet or bitter substances, were preferred to emulsion with 5.50‐µm droplets. These results suggest that the droplet size of o/w emulsion remarkably influences certain taste impressions created by added taste substances

Spatio-temporal Dynamics and Mechanisms of Stress Granule Assembly

<div><p>Stress granules (SGs) are non-membranous cytoplasmic aggregates of mRNAs and related proteins, assembled in response to environmental stresses such as heat shock, hypoxia, endoplasmic reticulum (ER) stress, chemicals (e.g. arsenite), and viral infections. SGs are hypothesized as a loci of mRNA triage and/or maintenance of proper translation capacity ratio to the pool of mRNAs. In brain ischemia, hippocampal CA3 neurons, which are resilient to ischemia, assemble SGs. In contrast, CA1 neurons, which are vulnerable to ischemia, do not assemble SGs. These results suggest a critical role SG plays in regards to cell fate decisions. Thus SG assembly along with its dynamics should determine the cell fate. However, the process that exactly determines the SG assembly dynamics is largely unknown. In this paper, analyses of experimental data and computer simulations were used to approach this problem. SGs were assembled as a result of applying arsenite to HeLa cells. The number of SGs increased after a short latent period, reached a maximum, then decreased during the application of arsenite. At the same time, the size of SGs grew larger and became localized at the perinuclear region. A minimal mathematical model was constructed, and stochastic simulations were run to test the modeling. Since SGs are discrete entities as there are only several tens of them in a cell, commonly used deterministic simulations could not be employed. The stochastic simulations replicated observed dynamics of SG assembly. In addition, these stochastic simulations predicted a gamma distribution relative to the size of SGs. This same distribution was also found in our experimental data suggesting the existence of multiple fusion steps in the SG assembly. Furthermore, we found that the initial steps in the SG assembly process and microtubules were critical to the dynamics. Thus our experiments and stochastic simulations presented a possible mechanism regulating SG assembly.</p></div

Comparative Analysis of Physical Maps of Four Bacillus subtilis (natto) Genomes

The complete SfiI and I-CeuI physical maps of four Bacillus subtilis (natto) strains, which were previously isolated as natto (fermented soybean) starters, were constructed to elucidate the genome structure. Not only the similarity in genome size and organization but also the microheterogeneity of the gene context was revealed. No large-scale genome rearrangements among the four strains were indicated by mapping of the genes, including 10 rRNA operons (rrn) and relevant genes required for natto production, to the loci corresponding to those of the B. subtilis strain Marburg 168. However, restriction fragment length polymorphism and the presence or absence of strain-specific DNA sequences, such as the prophages SPβ, skin element, and PBSX, as well as the insertion element IS4Bsu1, could be used to identify one of these strains as a Marburg type and the other three strains as natto types. The genome structure and gene heterogeneity were also consistent with the type of indigenous plasmids harbored by the strains

Simulation results of SG assembly.

<p>At 0 min, TIA₁ (blue dots) distributed diffusely in the cytoplasm. At 6.7 min, several small SGs were assembled (red circles, one of which is indicated by a red arrowhead surrounded by white line). TIA₂ (green dots, one of which is indicated by a green arrowhead), TIA₃ (yellow dots, one on which is indicated by an yellow arrowhead), and TIA* (red dots, one of which is indicated by a red arrowhead) are also shown. At 16.7 min, SG distributed with no spatial preference. At 26.7 and 40 min, the size of SGs increased, and they gradually localized to the perinuclear region. At 60 min, the number of SG decreased, and the localization around nucleus became evident.</p

Analysis of the dynamics of SG assembly.

<p>(A) Time courses of SG, TIA₁, TIA₂, and TIA₃ are shown. TIA₁ decreased monotonically as the increase in the number of SG. TIA₂ and TIA₃ increased quickly just after the beginning of SS, and soon they decreased. The peak number of TIA₂ was small, and that of TIA₃ was 9, which was smaller than the peak number of SG. (B) The decrease in the number of SG after the peak was caused by the fusion between SGs. If we aligned the occurrences of fusions (black pulsatile vertical lines) to the time course of the number of SGs (red line), the occurrences of the decrement in the time course and those of fusion events coincided perfectly. Two SGs at 3200 sec (white arrowheads in the bottom left panel) fused together forming one larger SG at 3210 sec (white arrowhead in the bottom right panel). (C) SG distribution at 60 min was changed by the change in <i>p</i>_<i>m</i>/<i>p</i>_<i>n</i>. Small change in <i>p</i>_<i>m</i>/<i>p</i>_<i>n</i> changed the SG distribution.</p