2,859 research outputs found
Nighttime Thermal Infrared Image Colorization with Feedback-based Object Appearance Learning
Stable imaging in adverse environments (e.g., total darkness) makes thermal
infrared (TIR) cameras a prevalent option for night scene perception. However,
the low contrast and lack of chromaticity of TIR images are detrimental to
human interpretation and subsequent deployment of RGB-based vision algorithms.
Therefore, it makes sense to colorize the nighttime TIR images by translating
them into the corresponding daytime color images (NTIR2DC). Despite the
impressive progress made in the NTIR2DC task, how to improve the translation
performance of small object classes is under-explored. To address this problem,
we propose a generative adversarial network incorporating feedback-based object
appearance learning (FoalGAN). Specifically, an occlusion-aware mixup module
and corresponding appearance consistency loss are proposed to reduce the
context dependence of object translation. As a representative example of small
objects in nighttime street scenes, we illustrate how to enhance the realism of
traffic light by designing a traffic light appearance loss. To further improve
the appearance learning of small objects, we devise a dual feedback learning
strategy to selectively adjust the learning frequency of different samples. In
addition, we provide pixel-level annotation for a subset of the Brno dataset,
which can facilitate the research of NTIR image understanding under multiple
weather conditions. Extensive experiments illustrate that the proposed FoalGAN
is not only effective for appearance learning of small objects, but also
outperforms other image translation methods in terms of semantic preservation
and edge consistency for the NTIR2DC task.Comment: 14 pages, 14 figures. arXiv admin note: text overlap with
arXiv:2208.0296
MicroRNA-23a promotes myelination in the central nervous system.
Demyelinating disorders including leukodystrophies are devastating conditions that are still in need of better understanding, and both oligodendrocyte differentiation and myelin synthesis pathways are potential avenues for developing treatment. Overexpression of lamin B1 leads to leukodystrophy characterized by demyelination of the central nervous system, and microRNA-23 (miR-23) was found to suppress lamin B1 and enhance oligodendrocyte differentiation in vitro. Here, we demonstrated that miR-23a-overexpressing mice have increased myelin thickness, providing in vivo evidence that miR-23a enhances both oligodendrocyte differentiation and myelin synthesis. Using this mouse model, we explored possible miR-23a targets and revealed that the phosphatase and tensin homologue/phosphatidylinositol trisphosphate kinase/Akt/mammalian target of rapamycin pathway is modulated by miR-23a. Additionally, a long noncoding RNA, 2700046G09Rik, was identified as a miR-23a target and modulates phosphatase and tensin homologue itself in a miR-23a-dependent manner. The data presented here imply a unique role for miR-23a in the coordination of proteins and noncoding RNAs in generating and maintaining healthy myelin
BPhyOG: An interactive server for genome-wide inference of bacterial phylogenies based on overlapping genes
<p>Abstract</p> <p>Background</p> <p>Overlapping genes (OGs) in bacterial genomes are pairs of adjacent genes of which the coding sequences overlap partly or entirely. With the rapid accumulation of sequence data, many OGs in bacterial genomes have now been identified. Indeed, these might prove a consistent feature across all microbial genomes. Our previous work suggests that OGs can be considered as robust markers at the whole genome level for the construction of phylogenies. An online, interactive web server for inferring phylogenies is needed for biologists to analyze phylogenetic relationships among a set of bacterial genomes of interest.</p> <p>Description</p> <p>BPhyOG is an online interactive server for reconstructing the phylogenies of completely sequenced bacterial genomes on the basis of their shared overlapping genes. It provides two tree-reconstruction methods: Neighbor Joining (NJ) and Unweighted Pair-Group Method using Arithmetic averages (UPGMA). Users can apply the desired method to generate phylogenetic trees, which are based on an evolutionary distance matrix for the selected genomes. The distance between two genomes is defined by the normalized number of their shared OG pairs. BPhyOG also allows users to browse the OGs that were used to infer the phylogenetic relationships. It provides detailed annotation for each OG pair and the features of the component genes through hyperlinks. Users can also retrieve each of the homologous OG pairs that have been determined among 177 genomes. It is a useful tool for analyzing the tree of life and overlapping genes from a genomic standpoint.</p> <p>Conclusion</p> <p>BPhyOG is a useful interactive web server for genome-wide inference of any potential evolutionary relationship among the genomes selected by users. It currently includes 177 completely sequenced bacterial genomes containing 79,855 OG pairs, the annotation and homologous OG pairs of which are integrated comprehensively. The reliability of phylogenies complemented by annotations make BPhyOG a powerful web server for genomic and genetic studies. It is freely available at <url>http://cmb.bnu.edu.cn/BPhyOG</url>.</p
Can the Query-based Object Detector Be Designed with Fewer Stages?
Query-based object detectors have made significant advancements since the
publication of DETR. However, most existing methods still rely on multi-stage
encoders and decoders, or a combination of both. Despite achieving high
accuracy, the multi-stage paradigm (typically consisting of 6 stages) suffers
from issues such as heavy computational burden, prompting us to reconsider its
necessity. In this paper, we explore multiple techniques to enhance query-based
detectors and, based on these findings, propose a novel model called GOLO
(Global Once and Local Once), which follows a two-stage decoding paradigm.
Compared to other mainstream query-based models with multi-stage decoders, our
model employs fewer decoder stages while still achieving considerable
performance. Experimental results on the COCO dataset demonstrate the
effectiveness of our approach
Hierarchical TiO2 spheres assisted with graphene for a high performance lithium–sulfur battery
In this study, we report hierarchical TiO2 sphere–sulfur frameworks assisted with graphene as a cathode material for high performance lithium–sulfur batteries. With this strategy, the volume expansion and aggregation of sulfur nanoparticles can be effectively mitigated, thus enabling high sulfur utilization and improving the specific capacity and cycling stability of the electrode. Modification of the TiO2–S nanocomposites with graphene can trap the polysulfides via chemisorption and increase the electronic connection among various components. The graphene-assisted TiO2–S composite electrodes exhibit high specific capacity of 660 mA h g−1 at 5C with a capacity loss of only 0.04% per cycle in the prolonged charge–discharge processes at 1C
LORS: Low-rank Residual Structure for Parameter-Efficient Network Stacking
Deep learning models, particularly those based on transformers, often employ
numerous stacked structures, which possess identical architectures and perform
similar functions. While effective, this stacking paradigm leads to a
substantial increase in the number of parameters, posing challenges for
practical applications. In today's landscape of increasingly large models,
stacking depth can even reach dozens, further exacerbating this issue. To
mitigate this problem, we introduce LORS (LOw-rank Residual Structure). LORS
allows stacked modules to share the majority of parameters, requiring a much
smaller number of unique ones per module to match or even surpass the
performance of using entirely distinct ones, thereby significantly reducing
parameter usage. We validate our method by applying it to the stacked decoders
of a query-based object detector, and conduct extensive experiments on the
widely used MS COCO dataset. Experimental results demonstrate the effectiveness
of our method, as even with a 70\% reduction in the parameters of the decoder,
our method still enables the model to achieve comparable orComment: 9 pages, 5 figures, 11 tables, CVPR2024 accepte
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