OVSNet : Towards One-Pass Real-Time Video Object Segmentation

Video object segmentation aims at accurately segmenting the target object regions across consecutive frames. It is technically challenging for coping with complicated factors (e.g., shape deformations, occlusion and out of the lens). Recent approaches have largely solved them by using backforth re-identification and bi-directional mask propagation. However, their methods are extremely slow and only support offline inference, which in principle cannot be applied in real time. Motivated by this observation, we propose a efficient detection-based paradigm for video object segmentation. We propose an unified One-Pass Video Segmentation framework (OVS-Net) for modeling spatial-temporal representation in a unified pipeline, which seamlessly integrates object detection, object segmentation, and object re-identification. The proposed framework lends itself to one-pass inference that effectively and efficiently performs video object segmentation. Moreover, we propose a maskguided attention module for modeling the multi-scale object boundary and multi-level feature fusion. Experiments on the challenging DAVIS 2017 demonstrate the effectiveness of the proposed framework with comparable performance to the state-of-the-art, and the great efficiency about 11.5 FPS towards pioneering real-time work to our knowledge, more than 5 times faster than other state-of-the-art methods.Comment: 10 pages, 6 figure

Local-to-Global Information Communication for Real-Time Semantic Segmentation Network Search

Neural Architecture Search (NAS) has shown great potentials in automatically designing neural network architectures for real-time semantic segmentation. Unlike previous works that utilize a simplified search space with cell-sharing way, we introduce a new search space where a lightweight model can be more effectively searched by replacing the cell-sharing manner with cell-independent one. Based on this, the communication of local to global information is achieved through two well-designed modules. For local information exchange, a graph convolutional network (GCN) guided module is seamlessly integrated as a communication deliver between cells. For global information aggregation, we propose a novel dense-connected fusion module (cell) which aggregates long-range multi-level features in the network automatically. In addition, a latency-oriented constraint is endowed into the search process to balance the accuracy and latency. We name the proposed framework as Local-to-Global Information Communication Network Search (LGCNet). Extensive experiments on Cityscapes and CamVid datasets demonstrate that LGCNet achieves the new state-of-the-art trade-off between accuracy and speed. In particular, on Cityscapes dataset, LGCNet achieves the new best performance of 74.0\% mIoU with the speed of 115.2 FPS on Titan Xp.Comment: arXiv admin note: text overlap with arXiv:1909.0679

Graph-guided Architecture Search for Real-time Semantic Segmentation

Designing a lightweight semantic segmentation network often requires researchers to find a trade-off between performance and speed, which is always empirical due to the limited interpretability of neural networks. In order to release researchers from these tedious mechanical trials, we propose a Graph-guided Architecture Search (GAS) pipeline to automatically search real-time semantic segmentation networks. Unlike previous works that use a simplified search space and stack a repeatable cell to form a network, we introduce a novel search mechanism with new search space where a lightweight model can be effectively explored through the cell-level diversity and latencyoriented constraint. Specifically, to produce the cell-level diversity, the cell-sharing constraint is eliminated through the cell-independent manner. Then a graph convolution network (GCN) is seamlessly integrated as a communication mechanism between cells. Finally, a latency-oriented constraint is endowed into the search process to balance the speed and performance. Extensive experiments on Cityscapes and CamVid datasets demonstrate that GAS achieves the new state-of-the-art trade-off between accuracy and speed. In particular, on Cityscapes dataset, GAS achieves the new best performance of 73.5% mIoU with speed of 108.4 FPS on Titan Xp.Comment: CVPR202

Predicting Continuous Locomotion Modes via Multidimensional Feature Learning from sEMG

Walking-assistive devices require adaptive control methods to ensure smooth transitions between various modes of locomotion. For this purpose, detecting human locomotion modes (e.g., level walking or stair ascent) in advance is crucial for improving the intelligence and transparency of such robotic systems. This study proposes Deep-STF, a unified end-to-end deep learning model designed for integrated feature extraction in spatial, temporal, and frequency dimensions from surface electromyography (sEMG) signals. Our model enables accurate and robust continuous prediction of nine locomotion modes and 15 transitions at varying prediction time intervals, ranging from 100 to 500 ms. In addition, we introduced the concept of 'stable prediction time' as a distinct metric to quantify prediction efficiency. This term refers to the duration during which consistent and accurate predictions of mode transitions are made, measured from the time of the fifth correct prediction to the occurrence of the critical event leading to the task transition. This distinction between stable prediction time and prediction time is vital as it underscores our focus on the precision and reliability of mode transition predictions. Experimental results showcased Deep-STP's cutting-edge prediction performance across diverse locomotion modes and transitions, relying solely on sEMG data. When forecasting 100 ms ahead, Deep-STF surpassed CNN and other machine learning techniques, achieving an outstanding average prediction accuracy of 96.48%. Even with an extended 500 ms prediction horizon, accuracy only marginally decreased to 93.00%. The averaged stable prediction times for detecting next upcoming transitions spanned from 28.15 to 372.21 ms across the 100-500 ms time advances.Comment: 10 pages,7 figure

Expectant treatment for angular pregnancy after assisted reproduction technology: a safe and patient-friendly treatment strategy

IntroductionCurrently, the treatment strategies for angular pregnancy in the first trimester after assisted reproduction technology (ART) are unclear. Improper treatment will cause unnecessary losses to patients, especially infertile patients, after ART. The purpose of this study was to clarify the pregnancy outcomes of expectant treatment for angular pregnancy post-ART and to provide a basis for the formulation of clinical treatment strategies.MethodThis retrospective case series study was performed at the Reproductive Medicine Center of a university hospital. Maternal data and pregnancy outcomes were collected and analyzed for all patients diagnosed with angular pregnancies after ART between January 2016 and August 2021. The outcomes included live birth, term birth, premature birth, early pregnancy loss, fetal death, placental abruption, uterine rupture, maternal death, and hysterectomy.ResultsA total of 78 patients were analyzed in this study, of whom 54 (69.2%) had live births, 44 (56.4%) had term births, 21 (26.9%) had an early pregnancy loss, 1 (1.3%) had mid-trimester missed abortion, 1 (1.3%) underwent mid-trimester labor induction due to fetal malformation, and 1 (1.3%) underwent uterine rupture. There were no cases of maternal death, placental abruption, or hysterectomies.DiscussionAngular pregnancy after ART is not as dangerous as that described in previous studies; most cases could be treated expectantly under close-interval follow-up and obtain live birth

Complete genome sequence of Echinicola rosea JL3085, a xylan and pectin decomposer.

Marine Bacteroidetes are well known for their functional specialization on the decomposition of polysaccharides which results from a great number of carbohydrate-active enzymes. Here we represent the complete genome of a Bacteroitedes member Echinicola rosea JL3085T that was isolated from surface seawater of the South China Sea. The genome is 6.06 Mbp in size with a GC content of 44.1% and comprises 4613 protein coding genes. A remarkable genomic feature is that the number of glycoside hydrolase genes in the genome of E. rosea JL3085T is high in comparison with most of the sequenced members of marine Bacteroitedes. E. rosea JL3085T genome harbored multi-gene polysaccharide utilization loci (PUL) systems involved in the degradation of pectin, xylan and arabinogalactan. The large diversity of hydrolytic enzymes supports the use of E. rosea JL3085T as a candidate for biotechnological applications in enzymatic conversion of plant polysaccharides

Complete genome sequence of Echinicola rosea JL3085, a xylan and pectin decomposer

Abstract(#br)Marine Bacteroidetes are well known for their functional specialization on the decomposition of polysaccharides which results from a great number of carbohydrate-active enzymes. Here we represent the complete genome of a Bacteroitedes member Echinicola rosea JL3085 T that was isolated from surface seawater of the South China Sea. The genome is 6.06 Mbp in size with a GC content of 44.1% and comprises 4613 protein coding genes. A remarkable genomic feature is that the number of glycoside hydrolase genes in the genome of E. rosea JL3085 T is high in comparison with most of the sequenced members of marine Bacteroitedes . E. rosea JL3085 T genome harbored multi-gene polysaccharide utilization loci (PUL) systems involved in the degradation of pectin, xylan and arabinogalactan. The large diversity of hydrolytic enzymes supports the use of E. rosea JL3085 T as a candidate for biotechnological applications in enzymatic conversion of plant polysaccharides

Genome analysis of a plasmid-bearing myxobacterim Myxococcus sp. strain MxC21 with salt-tolerant property

Myxobacteria are widely distributed in various habitats of soil and oceanic sediment. However, it is unclear whether soil-dwelling myxobacteria tolerate a saline environment. In this study, a salt-tolerant myxobacterium Myxococcus sp. strain MxC21 was isolated from forest soil with NaCl tolerance >2% concentration. Under 1% salt-contained condition, strain MxC21 could kill and consume bacteria prey and exhibited complex social behaviors such as S-motility, biofilm, and fruiting body formation but adopted an asocial living pattern with the presence of 1.5% NaCl. To investigate the genomic basis of stress tolerance, the complete genome of MxC21 was sequenced and analyzed. Strain MxC21 consists of a circular chromosome with a total length of 9.13 Mbp and a circular plasmid of 64.3 kb. Comparative genomic analysis revealed that the genomes of strain MxC21 and M. xanthus DK1622 share high genome synteny, while no endogenous plasmid was found in DK1622. Further analysis showed that approximately 21% of its coding genes from the genome of strain MxC21 are predominantly associated with signal transduction, transcriptional regulation, and protein folding involved in diverse niche adaptation such as salt tolerance, which enables social behavior such as gliding motility, sporulation, and predation. Meantime, a high number of genes are also found to be involved in defense against oxidative stress and production of antimicrobial compounds. All of these functional genes may be responsible for the potential salt-toleration. Otherwise, strain MxC21 is the second reported myxobacteria containing indigenous plasmid, while only a small proportion of genes was specific to the circular plasmid of strain MxC21, and most of them were annotated as hypothetical proteins, which may have a direct relationship with the habitat adaptation of strain MxC21 under saline environment. This study provides an inspiration of the adaptive evolution of salt-tolerant myxobacterium and facilitates a potential application in the improvement of saline soil in future