How to Train Your Dragon: Tamed Warping Network for Semantic Video Segmentation

Real-time semantic segmentation on high-resolution videos is challenging due to the strict requirements of speed. Recent approaches have utilized the inter-frame continuity to reduce redundant computation by warping the feature maps across adjacent frames, greatly speeding up the inference phase. However, their accuracy drops significantly owing to the imprecise motion estimation and error accumulation. In this paper, we propose to introduce a simple and effective correction stage right after the warping stage to form a framework named Tamed Warping Network (TWNet), aiming to improve the accuracy and robustness of warping-based models. The experimental results on the Cityscapes dataset show that with the correction, the accuracy (mIoU) significantly increases from 67.3% to 71.6%, and the speed edges down from 65.5 FPS to 61.8 FPS. For non-rigid categories such as "human" and "object", the improvements of IoU are even higher than 18 percentage points

Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction

Accurate traffic prediction is a challenging task in intelligent transportation systems because of the complex spatio-temporal dependencies in transportation networks. Many existing works utilize sophisticated temporal modeling approaches to incorporate with graph convolution networks (GCNs) for capturing short-term and long-term spatio-temporal dependencies. However, these separated modules with complicated designs could restrict effectiveness and efficiency of spatio-temporal representation learning. Furthermore, most previous works adopt the fixed graph construction methods to characterize the global spatio-temporal relations, which limits the learning capability of the model for different time periods and even different data scenarios. To overcome these limitations, we propose an automated dilated spatio-temporal synchronous graph network, named Auto-DSTSGN for traffic prediction. Specifically, we design an automated dilated spatio-temporal synchronous graph (Auto-DSTSG) module to capture the short-term and long-term spatio-temporal correlations by stacking deeper layers with dilation factors in an increasing order. Further, we propose a graph structure search approach to automatically construct the spatio-temporal synchronous graph that can adapt to different data scenarios. Extensive experiments on four real-world datasets demonstrate that our model can achieve about 10% improvements compared with the state-of-art methods. Source codes are available at https://github.com/jinguangyin/Auto-DSTSGN

Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimation

Travel time estimation is one of the core tasks for the development of intelligent transportation systems. Most previous works model the road segments or intersections separately by learning their spatio-temporal characteristics to estimate travel time. However, due to the continuous alternations of the road segments and intersections in a path, the dynamic features are supposed to be coupled and interactive. Therefore, modeling one of them limits further improvement in accuracy of estimating travel time. To address the above problems, a novel graph-based deep learning framework for travel time estimation is proposed in this paper, namely Spatio-Temporal Dual Graph Neural Networks (STDGNN). Specifically, we first establish the node-wise and edge-wise graphs to respectively characterize the adjacency relations of intersections and that of road segments. In order to extract the joint spatio-temporal correlations of the intersections and road segments, we adopt the spatio-temporal dual graph learning approach that incorporates multiple spatial-temporal dual graph learning modules with multi-scale network architectures for capturing multi-level spatial-temporal information from the dual graph. Finally, we employ the multi-task learning approach to estimate the travel time of a given whole route, each road segment and intersection simultaneously. We conduct extensive experiments to evaluate our proposed model on three real-world trajectory datasets, and the experimental results show that STDGNN significantly outperforms several state-of-art baselines

Photoplethysmography Biometric Recognition Model Based on Sparse Softmax Vector and k-Nearest Neighbor

Photoplethysmography (PPG) biometric recognition has recently received considerable attention and is considered to be a promising biometric trait. Although some promising results on PPG biometric recognition have been reported, challenges in noise sensitivity and poor robustness remain. To address these issues, a PPG biometric recognition framework is presented in this article, that is, a PPG biometric recognition model based on a sparse softmax vector and k-nearest neighbor. First, raw PPG data are rerepresented by sliding window scanning. Second, three-layer features are extracted, and the features of each layer are represented by a sparse softmax vector. In the first layer, the features are extracted by PPG data as a whole. In the second layer, all the PPG data are divided into four subregions, then four subfeatures are generated by extracting features from the four subregions, and finally, the four subfeatures are averaged as the second layer features. In the third layer, all the PPG data are divided into 16 subregions, then 16 subfeatures are generated by extracting features from the 16 subregions, and finally, the 16 subfeatures are averaged as the third layer features. Finally, the features with first, second, and third layers are combined into three-layer features. Extensive experiments were conducted on three PPG datasets, and it was found that the proposed method can achieve a recognition rate of 99.95%, 97.21%, and 99.92% on the respective sets. The results demonstrate that the proposed method can outperform current state-of-the-art methods in terms of accuracy

Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction

Traffic congestion event prediction is an important yet challenging task in intelligent transportation systems. Many existing works about traffic prediction integrate various temporal encoders and graph convolution networks (GCNs), called spatio-temporal graph-based neural networks, which focus on predicting dense variables such as flow, speed and demand in time snapshots, but they can hardly forecast the traffic congestion events that are sparsely distributed on the continuous time axis. In recent years, neural point process (NPP) has emerged as an appropriate framework for event prediction in continuous time scenarios. However, most conventional works about NPP cannot model the complex spatio-temporal dependencies and congestion evolution patterns. To address these limitations, we propose a spatio-temporal graph neural point process framework, named STGNPP for traffic congestion event prediction. Specifically, we first design the spatio-temporal graph learning module to fully capture the long-range spatio-temporal dependencies from the historical traffic state data along with the road network. The extracted spatio-temporal hidden representation and congestion event information are then fed into a continuous gated recurrent unit to model the congestion evolution patterns. In particular, to fully exploit the periodic information, we also improve the intensity function calculation of the point process with a periodic gated mechanism. Finally, our model simultaneously predicts the occurrence time and duration of the next congestion. Extensive experiments on two real-world datasets demonstrate that our method achieves superior performance in comparison to existing state-of-the-art approaches

ELK4 Promotes Cell Cycle Progression and Stem Cell-like Characteristics in HPV-associated Cervical Cancer by Regulating the FBXO22/PTEN Axis

Background:Cervical cancer (CC) is a prevalent gynecological carcinoma, and patients infected with human papillomavirus (HPV) have a higher morbidity rate.Aims:To explore the effects of ETS-like transcription factor 4 (ELK4) in patients with HPV+ CC.Study design:In vitro cell lines and human-sample study.Methods:The ELK4 levels in human tissue (65 HPV+ CC tissue and 25 HPV− normal cervical tissue) and cell lines (human cervical epithelial immortalized cell line H8 and CC cell lines HeLa [HPV18], CaSki [HPV16], and SiHa [HPV−]) were quantified using qRT-PCR and western blot assay. ELK4 knockdown transfection was effective and confirmed by western blotting. The MTT and EDU assays were used to evaluate cell viability and proliferation, respectively. Flow cytometry was used to detect the CC cell cycle stage. Stem cell markers, such as cluster of differentiation 133 (CD133), CD44, and aldehyde dehydrogenase 1, and the cervicospheres formed were measured. ChIP-qPCR and luciferase activity experiments were used to assess the bond between ELK4 and F-box protein 22 (FBXO22).Results:ELK4 was highly expressed in the HPV+ CC tissue. CC cells with ELK4 knockdown had lower viability and proliferation than the control cells. ELK4 knockdown blocked the progression of the cell cycle from G1 to S phase. ELK4 knockdown suppressed the stem cell-like characteristics of the HPV+ CC cells. ELK4 bonded with the FBXO22 promoter, inhibiting the levels of phosphatase and tensin homolog (PTEN).Conclusion:ELK4 facilitated cell cycle progression and stem cell-like characteristics by regulating the FBXO22/PTEN axis. Thus, ELK4 could be a potential therapeutic target to arrest the progress of HPV-associated CC

Genomic Comparative Analysis of <i>Cordyceps pseudotenuipes</i> with Other Species from <i>Cordyceps</i>

The whole genome of Cordyceps pseudotenuipes was sequenced, annotated, and compared with three related species to characterize the genome. The antibiotics and Secondary Metabolites Analysis Shell (antiSMASH) and local BLAST analysis were used to explore the secondary metabolites (SMs) and biosynthesis gene clusters (BGCs) of the genus Cordyceps. The genome-wide basic characteristics of C. pseudotenuipes, C. tenuipes, C. cicadae, and C. militaris revealed unequal genome size, with C. cicadae as the largest (34.11 Mb), followed by C. militaris (32.27 Mb). However, the total gene lengths of C. pseudotenuipes and C. tenuipes were similar (30.1 Mb and 30.06 Mb). The GC contents of C. pseudotenuipes, C. tenuipes, C. cicadae, and C. militaris genomes differed slightly (51.40% to 54.11%). AntiSMASH and local BLAST analysis showed that C. pseudotenuipes, C. tenuipes, C. cicadae, and C. militaris had 31, 28, 31, and 29 putative SM BGCs, respectively. The SM BGCs contained different quantities of polyketide synthetase (PKS), nonribosomal peptide synthetase (NRPS), terpene, hybrid PKS + NRPS, and hybrid NRPS + Other. Moreover, C. pseudotenuipes, C. tenuipes, C. cicadae, and C. militaris had BGCs for the synthesis of dimethylcoprogen. C. pseudotenuipes, C. tenuipes, and C. cicadae had BGCs for the synthesis of leucinostatin A/B, neosartorin, dimethylcoprogen, wortmanamide A/B, and beauvericin. In addition, the SM BGCs unique to C. pseudotenuipes were clavaric acid, communesin, and deoxynivalenol. Synteny analysis indicated that the scaffolds where the SM BGC was located were divided into more than 70 collinear blocks, and there might be rearrangements. Altogether, these findings improved our understanding of the molecular biology of the genus Cordyceps and will facilitate the discovery of new biologically active SMs from the genus Cordyceps using heterologous expression and gene knockdown methods

Characterizing N uptake and use efficiency in rice as influenced by environments

To compare N uptake and use efficiency of rice among different environments and quantify the contributions of indigenous soil and applied N to N uptake and use efficiency, field experiments were conducted in five sites in five provinces of China in 2012 and 2013. Four cultivars were grown under three N treatments in each site. Average total N uptake was 10–12 g m−2 in Huaiji, Binyang, and Haikou, 20 g m−2 in Changsha, and 23 g m−2 in Xingyi. Rice crops took up 54.6–61.7% of total plant N from soil in Huaiji, Binyang, and Haikou, 64.3% in Changsha, and 63.5% in Xingyi. Partial factor productivity of applied N and recovery efficiency of applied N in Changsha were higher than in Huaiji, Binyang, and Haikou, but were lower than in Xingyi. Physiological efficiency of soil N and fertilizer N were lower in Changsha than in Huaiji, Binyang, and Haikou, while the difference in them between Changsha and Xingyi were small or inconsistent. Average grain yields were 6.5–7.5 t ha−1 (medium yield) in Huaiji, Binyang, and Haikou, 9.0 t ha−1 (high yield) in Changsha, and 12.0 t ha−1 (super high yield) in Xingyi. Our results suggest that both indigenous soil and applied N were key factors for improving rice yield from medium to high level, while a further improvement to super high yield indigenous soil N was more important than fertilizer N, and a simultaneous increasing grain yield and N use efficiency can be achieved using SPAD-based practice in rice production