GTC: Guided Training of CTC Towards Efficient and Accurate Scene Text Recognition

Connectionist Temporal Classification (CTC) and attention mechanism are two main approaches used in recent scene text recognition works. Compared with attention-based methods, CTC decoder has a much shorter inference time, yet a lower accuracy. To design an efficient and effective model, we propose the guided training of CTC (GTC), where CTC model learns a better alignment and feature representations from a more powerful attentional guidance. With the benefit of guided training, CTC model achieves robust and accurate prediction for both regular and irregular scene text while maintaining a fast inference speed. Moreover, to further leverage the potential of CTC decoder, a graph convolutional network (GCN) is proposed to learn the local correlations of extracted features. Extensive experiments on standard benchmarks demonstrate that our end-to-end model achieves a new state-of-the-art for regular and irregular scene text recognition and needs 6 times shorter inference time than attentionbased methods.Comment: Accepted by AAAI 202

Systematic identification of conserved motif modules in the human genome

Background: The identification of motif modules, groups of multiple motifs frequently occurring in DNA sequences, is one of the most important tasks necessary for annotating the human genome. Current approaches to identifying motif modules are often restricted to searches within promoter regions or rely on multiple genome alignments. However, the promoter regions only account for a limited number of locations where transcription factor binding sites can occur, and multiple genome alignments often cannot align binding sites with their true counterparts because of the short and degenerative nature of these transcription factor binding sites. Results: To identify motif modules systematically, we developed a computational method for the entire non-coding regions around human genes that does not rely upon the use of multiple genome alignments. First, we selected orthologous DNA blocks approximately 1-kilobase in length based on discontiguous sequence similarity. Next, we scanned the conserved segments in these blocks using known motifs in the TRANSFAC database. Finally, a frequent pattern mining technique was applied to identify motif modules within these blocks. In total, with a false discovery rate cutoff of 0.05, we predicted 3,161,839 motif modules, 90.8% of which are supported by various forms of functional evidence. Compared with experimental data from 14 ChIP-seq experiments, on average, our methods predicted 69.6% of the ChIP-seq peaks with TFBSs of multiple TFs. Our findings also show that many motif modules have distance preference and order preference among the motifs, which further supports the functionality of these predictions. Conclusions: Our work provides a large-scale prediction of motif modules in mammals, which will facilitate the understanding of gene regulation in a systematic way

JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models

Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents. Existing approaches can handle certain long-horizon tasks in an open world. However, they still struggle when the number of open-world tasks could potentially be infinite and lack the capability to progressively enhance task completion as game time progresses. We introduce JARVIS-1, an open-world agent that can perceive multimodal input (visual observations and human instructions), generate sophisticated plans, and perform embodied control, all within the popular yet challenging open-world Minecraft universe. Specifically, we develop JARVIS-1 on top of pre-trained multimodal language models, which map visual observations and textual instructions to plans. The plans will be ultimately dispatched to the goal-conditioned controllers. We outfit JARVIS-1 with a multimodal memory, which facilitates planning using both pre-trained knowledge and its actual game survival experiences. JARVIS-1 is the existing most general agent in Minecraft, capable of completing over 200 different tasks using control and observation space similar to humans. These tasks range from short-horizon tasks, e.g., "chopping trees" to long-horizon tasks, e.g., "obtaining a diamond pickaxe". JARVIS-1 performs exceptionally well in short-horizon tasks, achieving nearly perfect performance. In the classic long-term task of $\texttt{ObtainDiamondPickaxe}$, JARVIS-1 surpasses the reliability of current state-of-the-art agents by 5 times and can successfully complete longer-horizon and more challenging tasks. The project page is available at https://craftjarvis.org/JARVIS-1Comment: update project pag

A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects

Instant delivery services, such as food delivery and package delivery, have achieved explosive growth in recent years by providing customers with daily-life convenience. An emerging research area within these services is service Route\&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker. As one of the most crucial tasks in those service platforms, RTP stands central to enhancing user satisfaction and trimming operational expenditures on these platforms. Despite a plethora of algorithms developed to date, there is no systematic, comprehensive survey to guide researchers in this domain. To fill this gap, our work presents the first comprehensive survey that methodically categorizes recent advances in service route and time prediction. We start by defining the RTP challenge and then delve into the metrics that are often employed. Following that, we scrutinize the existing RTP methodologies, presenting a novel taxonomy of them. We categorize these methods based on three criteria: (i) type of task, subdivided into only-route prediction, only-time prediction, and joint route\&time prediction; (ii) model architecture, which encompasses sequence-based and graph-based models; and (iii) learning paradigm, including Supervised Learning (SL) and Deep Reinforcement Learning (DRL). Conclusively, we highlight the limitations of current research and suggest prospective avenues. We believe that the taxonomy, progress, and prospects introduced in this paper can significantly promote the development of this field

Effects of novel coronavirus Omicron variant infection on pregnancy outcomes: a retrospective cohort study from Guangzhou

ObjectiveSince 2022, Omicron has been circulating in China as a major variant of the novel coronavirus, but the effects of infection with Omicron variants on pregnant women and newborns are unknown. The purpose of this study was to determine the clinical characteristics of Omicron infection during pregnancy and its effect on pregnancy outcomes.MethodsThis study retrospectively analyzed the data of 93 confirmed cases of novel coronavirus infection and 109 non-infected patients admitted to the isolation ward of Guangdong Maternal and Child Health Hospital from December 1, 2022 to January 31, 2023, and statistically analyzed the clinical features of Omicron variant infection during pregnancy and its impact on pregnancy outcomes. Further effects of underlying diseases on Omicron infection in pregnant women were analyzed.ResultsThe incubation period of COVID-19 infection was 0.99±0.86 days, 94.38% of patients had fever or other respiratory symptoms, the lymphocyte count in the infected group was lower than that in the uninfected group, and the lymphocyte count was further reduced in the patients with pregnancy complications or complications. Compared with the uninfected group, APTT and PT were prolonged, platelet count and fibrinogen were decreased in the infected group, all of which had statistical significance. COVID-19 infection during pregnancy increased the rate of cesarean section compared to uninfected pregnant patients, and COVID-19 infection in gestational diabetes resulted in a 4.19-fold increase in cesarean section rate. There was no statistically significant difference in gestational age between the two groups. The incidence of intrauterine distress, turbidity of amniotic fluid and neonatal respiratory distress were higher in the infection group. No positive cases of neonatal COVID-19 infection have been found.ConclusionThe patients infected with omicron during pregnancy often have febrile respiratory symptoms with lymphocyopenia, but the incidence of severe disease is low. Both Omicron infection and gestational diabetes further increase the incidence of cesarean section, and this study found no evidence of vertical transmission of Omicron

Research progress on extraction, purification, structure and biological activity of Dendrobium officinale polysaccharides

Dendrobium officinale Kimura et Migo (D. officinale) is a traditional medicinal and food homologous plant that has been used for thousands of years in folk medicine and nutritious food. Recent studies have shown that polysaccharide is one of the main biologically active components in D. officinale. D. officinale polysaccharides possess several biological activities, such as anti-oxidant, heptatoprotective, immunomodulatory, gastrointestinal protection, hypoglycemic, and anti-tumor activities. In the past decade, polysaccharides have been isolated from D. officinale by physical and enzymatic methods and have been subjected to structural characterization and activity studies. Progress in extraction, purification, structural characterization, bioactivity, structure-activity relationship, and possible bioactivity mechanism of polysaccharides D. officinale were reviewed. In order to provide reference for the in-depth study of D. officinale polysaccharides and the application in functional food and biomedical research

Genomic Characterization Provides New Insights for Detailed Phage- Resistant Mechanism for Brucella abortus

As the causative agent of cattle brucellosis, Brucella abortus commonly exhibits smooth phenotype (by virtue of colony morphology) that is characteristically sensitive to specific Brucella phages, playing until recently a major role in taxonomical classification of the Brucella species by the phage typing approach. We previously reported the discrepancy between traditional phenotypic typing and MLVA results of a smooth phage-resistant (SPR) strain Bab8416 isolated from a 45-year-old custodial worker with brucellosis in a cattle farm. Here, we performed whole genome sequencing and further obtained a complete genome sequence of strain Bab8416 by a combination of multiple NGS technologies and routine PCR sequencing. The detailed genetic differences between B. abortus SPR Bab8416 and large smooth phage-sensitive (SPS) strains were investigated in a comprehensively comparative genomic study. The large indels between B. abortus SPS strains and Bab8416 showed possible divergence between two evolutionary branches at a far phylogenetic node. Compared to B. abortus SPS strain 9-941 (Bab9-941), the specific re-arrangement event in Bab8416 displaying a closer linear relationship with B. melitensis 16M than other B. abortus strains resulted in the truncation of c-di-GMP synthesis, and 3 c-di-GMP-metabolizing genes, were present in Bab8416 and B. melitensis 16M, but absent in Bab9-941 and other B. abortus strains, indicating potential SPR-associated key determinants and novel molecular mechanisms. Moreover, despite almost completely intact smooth LPS related genes, only one mutated OmpA family protein of Bab8416, functionally related to flagellar and efflux pump, was newly identified. Several point mutations were identified to be Bab8416 specific while a majority of them were verified to be B. abortus ST2 characteristic. In conclusion, our study therefore identifies new SPR-associated factors that could play a role in refining and updating Brucella taxonomic schemes and provides resources for further detailed analysis of mechanism for Brucella phage resistance

PeHVA22 gene family in passion fruit (Passiflora edulis): initial characterization and expression profiling diversity

Passion fruit, an economically valuable fruit crop, is highly vulnerable to adverse climate conditions. The HVA22 genes, recognized as abscisic acid (ABA) and stress-inducible, play vital roles in stress response and growth regulation in diverse eukaryotic organisms. Here, six HVA22 genes were firstly identified in passion fruit genome and all predicted to be localized within the endoplasmic reticulum. Phylogenetic analyses showed that all PeHVA22s were divided into four subgroups. The gene structural features of PeHVA22 genes clustered in the same subgroup were relatively conserved, while the gene structure characteristics of PeHVA22s from different subgroups varied significantly. PeHVA22A and PeHVA22C closely clustered with barley HVA22 in Group II, were also induced by ABA and drought stress treatment, suggesting conserved roles similar to barley HVA22. Meanwhile, most PeHVA22s exhibited induced expression post-drought treatment but were suppressed under salt, low and high-temperature conditions, indicating a unique role in drought response. Additionally, PeHVA22s displayed tissue-specific expression patterns across diverse tissues, except for PeHVA22B which maybe a pseudogene. Notably, PeHVA22C, PeHVA22E, and PeHVA22F predominantly expressed in fruit, indicating their involvement in fruit development. Almost all PeHVA22s showed variable expression at different developmental stages of stamens or ovules, implying their roles in passion fruit’s sexual reproduction. The intricate roles of PeHVA22s may result from diverse regulatory factors including transcription factors and CREs related to plant growth and development, hormone and stress responsiveness. These observations highlighted that PeHVA22s might play conserved roles in ABA response and drought stress tolerance, and also be participated in the regulation of passion fruit growth and floral development