Ghost translation

Artificial intelligence has recently been widely used in computational imaging. The deep neural network (DNN) improves the signal-to-noise ratio of the retrieved images, whose quality is otherwise corrupted due to the low sampling ratio or noisy environments. This work proposes a new computational imaging scheme based on the sequence transduction mechanism with the transformer network. The simulation database assists the network in achieving signal translation ability. The experimental single-pixel detector's signal will be `translated' into a 2D image in an end-to-end manner. High-quality images with no background noise can be retrieved at a sampling ratio as low as 2%. The illumination patterns can be either well-designed speckle patterns for sub-Nyquist imaging or random speckle patterns. Moreover, our method is robust to noise interference. This translation mechanism opens a new direction for DNN-assisted ghost imaging and can be used in various computational imaging scenarios.Comment: 10 pages, 8 figure

3DGen: Triplane Latent Diffusion for Textured Mesh Generation

Latent diffusion models for image generation have crossed a quality threshold which enabled them to achieve mass adoption. Recently, a series of works have made advancements towards replicating this success in the 3D domain, introducing techniques such as point cloud VAE, triplane representation, neural implicit surfaces and differentiable rendering based training. We take another step along this direction, combining these developments in a two-step pipeline consisting of 1) a triplane VAE which can learn latent representations of textured meshes and 2) a conditional diffusion model which generates the triplane features. For the first time this architecture allows conditional and unconditional generation of high quality textured or untextured 3D meshes across multiple diverse categories in a few seconds on a single GPU. It outperforms previous work substantially on image-conditioned and unconditional generation on mesh quality as well as texture generation. Furthermore, we demonstrate the scalability of our model to large datasets for increased quality and diversity. We will release our code and trained models

CLIP-Layout: Style-Consistent Indoor Scene Synthesis with Semantic Furniture Embedding

Indoor scene synthesis involves automatically picking and placing furniture appropriately on a floor plan, so that the scene looks realistic and is functionally plausible. Such scenes can serve as homes for immersive 3D experiences, or be used to train embodied agents. Existing methods for this task rely on labeled categories of furniture, e.g. bed, chair or table, to generate contextually relevant combinations of furniture. Whether heuristic or learned, these methods ignore instance-level visual attributes of objects, and as a result may produce visually less coherent scenes. In this paper, we introduce an auto-regressive scene model which can output instance-level predictions, using general purpose image embedding based on CLIP. This allows us to learn visual correspondences such as matching color and style, and produce more functionally plausible and aesthetically pleasing scenes. Evaluated on the 3D-FRONT dataset, our model achieves SOTA results in scene synthesis and improves auto-completion metrics by over 50%. Moreover, our embedding-based approach enables zero-shot text-guided scene synthesis and editing, which easily generalizes to furniture not seen during training.Comment: Changed paper template and cleaned up table

The Role of Chain-of-Thought in Complex Vision-Language Reasoning Task

The study explores the effectiveness of the Chain-of-Thought approach, known for its proficiency in language tasks by breaking them down into sub-tasks and intermediate steps, in improving vision-language tasks that demand sophisticated perception and reasoning. We present the "Description then Decision" strategy, which is inspired by how humans process signals. This strategy significantly improves probing task performance by 50%, establishing the groundwork for future research on reasoning paradigms in complex vision-language tasks

A comparison of heavy mineral assemblage between the loess and theRed Clay sequences on the Chinese Loess Plateau

QEMSCAN-based (Quantitative Evaluation of Minerals by Scanning Electron Microscopy) heavy mineral analysis has recently been demonstrated an efficient way to allow a rapid extraction of provenance information from sediments. However, one key issue to correctly obtain a provenance signal using this technique is to clearly separate effects of diagenetic alteration on heavy minerals in sediments, especially in fine-grained loess. Here we compare heavy mineral assemblages of bottom Quaternary loess (L33) and upper Pliocene Red Clay of three sites on the Chinese Loess Plateau (CLP). Two sites (Chaona and Luochuan) with similar modern climate conditions show similar heavy mineral assemblages but contain much less of the unstable heavy mineral amphibole than the drier Xifeng site. This result provides strong evidence supporting that climate-caused diagenesis is an important factor controlling heavy mineral assemblages of fine-grained loess. However, heavy mineral assemblages are similar for loess and paleosol layers deposited after 0.5 Ma on the Chinese Loess Plateau regardless of climate differences, suggesting that time is also a factor controlling heavy mineral assemblages of loess and Red Clay. Our high resolution sampling of the upper Miocene-Pliocene Chaona Red Clay sequence reveals similar heavy mineral compositions with a minor amphibole content, different from the drier Xifeng site results of the same age. This result indicates that the monsoonal climate pattern might have been maintained since the late Miocene. Furthermore, it indicates that the heavy mineral method is promising in tracing provenance for sites northwest of the Xifeng site on the Loess Plateau

Next Generation Sequencing and Comparative Genomic Analysis Reveal Extreme Plasticity of Two <i>Burkholderia glumae</i> Strains HN1 and HN2

Burkholderia glumae is an important rice pathogen, thus the genomic and evolutionary history may be helpful to control this notorious pathogen. Here, we present two complete genomes of the B. glumae strains HN1 and HN2, which were isolated from diseased rice seed in China. Average nucleotide identity (ANI) analysis shows greater than 99% similarity of the strains HN1 and HN2 with other published B. glumae genomes. Genomic annotation revealed that the genome of strain HN1 consists of five replicons (6,680,415 bp) with an overall G + C content of 68.06%, whereas the genome of strain HN2 comprises of three replicons (6,560,085 bp) with an overall G + C content of 68.34%. The genome of HN1 contains 5434 protein-coding genes, 351 pseudogenes, and 1 CRISPR, whereas the genome of HN2 encodes 5278 protein-coding genes, 357 pseudogenes, and 2 CRISPR. Both strains encode many pathogenic-associated genes (143 genes in HN1 vs. 141 genes in HN2). Moreover, comparative genomic analysis shows the extreme plasticity of B. glumae, which may contribute to its pathogenicity. In total, 259 single-copy genes were affected by positive selection. These genes may contribute to the adaption to different environments. Notably, six genes were characterized as virulence factors which may be an additional way to assist the pathogenicity of B. glumae

A-to-I RNA editing in bacteria increases pathogenicity and tolerance to oxidative stress.

Adenosine-to-inosine (A-to-I) RNA editing is an important posttranscriptional event in eukaryotes; however, many features remain largely unexplored in prokaryotes. This study focuses on a serine-to-proline recoding event (S128P) that originated in the mRNA of fliC, which encodes a flagellar filament protein; the editing event was observed in RNA-seq samples exposed to oxidative stress. Using Sanger sequencing, we show that the S128P editing event is induced by H2O2. To investigate the in vivo interaction between RNAs and TadA, which is the principal enzyme for A-to-I editing, genome-wide RNA immunoprecipitation-coupled high-throughput sequencing (iRIP-Seq) analysis was performed using HA-tagged TadA from Xanthomonas oryzae pv. oryzicola. We found that TadA can bind to the mRNA of fliC and the binding motif is identical to that previously reported by Bar-Yaacov and colleagues. This editing event increased motility and enhanced tolerance to oxidative stress due to changes in flagellar filament structure, which was modelled in 3D and measured by TEM. The change in filament structure due to the S128P mutant increased biofilm formation, which was measured by the 3D laser scanning confocal microscopy. RNA-seq revealed that a gene cluster that contributes to siderophore biosynthesis and Fe3+ uptake was upregulated in S128P compared with WT. Based on intracellular levels of reactive oxygen species and an oxidative stress survival assay, we found that this gene cluster can contribute to the reduction of the Fenton reaction and increases biofilm formation and bacterial virulence. This oxidative stress response was also confirmed in Pseudomonas putida. Overall, our work demonstrates that A-to-I RNA editing plays a role in bacterial pathogenicity and adaptation to oxidative stress