Weakly Supervised Video Salient Object Detection via Point Supervision

Video salient object detection models trained on pixel-wise dense annotation have achieved excellent performance, yet obtaining pixel-by-pixel annotated datasets is laborious. Several works attempt to use scribble annotations to mitigate this problem, but point supervision as a more labor-saving annotation method (even the most labor-saving method among manual annotation methods for dense prediction), has not been explored. In this paper, we propose a strong baseline model based on point supervision. To infer saliency maps with temporal information, we mine inter-frame complementary information from short-term and long-term perspectives, respectively. Specifically, we propose a hybrid token attention module, which mixes optical flow and image information from orthogonal directions, adaptively highlighting critical optical flow information (channel dimension) and critical token information (spatial dimension). To exploit long-term cues, we develop the Long-term Cross-Frame Attention module (LCFA), which assists the current frame in inferring salient objects based on multi-frame tokens. Furthermore, we label two point-supervised datasets, P-DAVIS and P-DAVSOD, by relabeling the DAVIS and the DAVSOD dataset. Experiments on the six benchmark datasets illustrate our method outperforms the previous state-of-the-art weakly supervised methods and even is comparable with some fully supervised approaches. Source code and datasets are available.Comment: accepted by ACM MM 202

Variety-driven rhizosphere microbiome bestows differential salt tolerance to alfalfa for coping with salinity stress

Soil salinization is a global environmental issue and a significant abiotic stress that threatens crop production. Root-associated rhizosphere microbiota play a pivotal role in enhancing plant tolerance to abiotic stresses. However, limited information is available concerning the specific variations in rhizosphere microbiota driven by different plant genotypes (varieties) in response to varying levels of salinity stress. In this study, we compared the growth performance of three alfalfa varieties with varying salt tolerance levels in soils with different degrees of salinization. High-throughput 16S rRNA and ITS sequencing were employed to analyze the rhizosphere microbial communities. Undoubtedly, the increasing salinity significantly inhibited alfalfa growth and reduced rhizosphere microbial diversity. However, intriguingly, salt-tolerant varieties exhibited relatively lower susceptibility to salinity, maintaining more stable rhizosphere bacterial community structure, whereas the reverse was observed for salt-sensitive varieties. Bacillus emerged as the dominant species in alfalfa's adaptation to salinity stress, constituting 21.20% of the shared bacterial genera among the three varieties. The higher abundance of Bacillus, Ensifer, and Pseudomonas in the rhizosphere of salt-tolerant alfalfa varieties is crucial in determining their elevated salt tolerance. As salinity levels increased, salt-sensitive varieties gradually accumulated a substantial population of pathogenic fungi, such as Fusarium and Rhizoctonia. Furthermore, rhizosphere bacteria of salt-tolerant varieties exhibited increased activity in various metabolic pathways, including biosynthesis of secondary metabolites, carbon metabolism, and biosynthesis of amino acids. It is suggested that salt-tolerant alfalfa varieties can provide more carbon sources to the rhizosphere, enriching more effective plant growth-promoting bacteria (PGPB) such as Pseudomonas to mitigate salinity stress. In conclusion, our results highlight the variety-mediated enrichment of rhizosphere microbiota in response to salinity stress, confirming that the high-abundance enrichment of specific dominant rhizosphere microbes and their vital roles play a significant role in conferring high salt adaptability to these varieties

Transcriptome Phase Distribution Analysis Reveals Diurnal Regulated Biological Processes and Key Pathways in Rice Flag Leaves and Seedling Leaves

Plant diurnal oscillation is a 24-hour period based variation. The correlation between diurnal genes and biological pathways was widely revealed by microarray analysis in different species. Rice (Oryza sativa) is the major food staple for about half of the world's population. The rice flag leaf is essential in providing photosynthates to the grain filling. However, there is still no comprehensive view about the diurnal transcriptome for rice leaves. In this study, we applied rice microarray to monitor the rhythmically expressed genes in rice seedling and flag leaves. We developed a new computational analysis approach and identified 6,266 (10.96%) diurnal probe sets in seedling leaves, 13,773 (24.08%) diurnal probe sets in flag leaves. About 65% of overall transcription factors were identified as flag leaf preferred. In seedling leaves, the peak of phase distribution was from 2:00am to 4:00am, whereas in flag leaves, the peak was from 8:00pm to 2:00am. The diurnal phase distribution analysis of gene ontology (GO) and cis-element enrichment indicated that, some important processes were waken by the light, such as photosynthesis and abiotic stimulus, while some genes related to the nuclear and ribosome involved processes were active mostly during the switch time of light to dark. The starch and sucrose metabolism pathway genes also showed diurnal phase. We conducted comparison analysis between Arabidopsis and rice leaf transcriptome throughout the diurnal cycle. In summary, our analysis approach is feasible for relatively unbiased identification of diurnal transcripts, efficiently detecting some special periodic patterns with non-sinusoidal periodic patterns. Compared to the rice flag leaves, the gene transcription levels of seedling leaves were relatively limited to the diurnal rhythm. Our comprehensive microarray analysis of seedling and flag leaves of rice provided an overview of the rice diurnal transcriptome and indicated some diurnal regulated biological processes and key functional pathways in rice

Turning Delay Stochastic User Equilibrium Model based on the Weibull Distribution

With the continuous expansion of urban scales and the constant growth of traffic demands, it has become important to accurately predict the distribution of traffic flow so as to relieve the traffic jams and lower the energy consumption. This research mainly focuses on the distribution problem of traffic flow in the urban traffic network. A minimization program has been provided as an alternative formulation for the turning delay stochastic user equilibrium problem. The paper derives the Weibull distribution-based node-link random loading mechanism of turning delay for direct calculation of link and turning flows that are consistent with the path flow, thus avoiding the enumeration of turning paths. Numerical examples are provided to illustrate the turning delay stochastic user equilibrium (SUE) model and the nodelink- based algorithm. The experiment demonstrates that the present method can reflect the relative performance of link and turning costs well, while presenting its advantages in the simulation of large-scale turning delay flow  ssignment

Performance Assessment of Algorithms for Building Energy Optimization Problems with Different Properties

Assessing the performance of algorithms in solving building energy optimization (BEO) problems with different properties is essential for selecting appropriate algorithms to achieve the best design solution. This study begins with a classification of the properties of BEO problems from three perspectives, namely, design variables, objective functions, and constraints. An analytical approach and a numerical approach are proposed to determine the properties of BEO problems. Six BEO test problems with different properties, namely, continuous vs. discrete, convex vs. non-convex, linear vs. non-linear, uni-modal vs. multimodal, and single-dimensional vs. multi-dimensional, are composed to evaluate the performance of algorithms. The selected optimization algorithms for performance assessment include the discrete Armijo gradient, Particle Swarm Optimization (PSO), Hooke-Jeeves, and hybrid PSO and Hooke-Jeeves. The assessment results indicate that multimodality can cause Hooke-Jeeves and discrete Armijo gradient algorithms to fall into local optima traps. The convex, non-convex, linear and non-linear properties of uni-modal BEO problems have little impact on the performance behavior of the algorithms. The discrete Armijo gradient and Hooke-Jeeves are not recommended for solving discrete and multi-dimensional BEO problems

A Comparison of Negative Pressure and Conventional Therapy in Spine Infections: A Single-Center Retrospective Study

Purpose: To investigate the effectiveness and safety of negative-pressure wound therapy (NPWT) in treating primary spinal infections. Methods: Patients who underwent surgical treatment for primary spinal infection between January 2018 and June 2021 were retrospectively evaluated. They were divided into two groups based on the type of surgery: one that underwent negative-pressure wound therapy (NPWT) and another that underwent conventional surgery (CVSG-Posterior debridement, bone grafting, fusion, and internal fixation in one stage). The two groups were compared in terms of the total operation time, total blood loss, total postoperative drainage, postoperative pain score, time for the postoperative erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) to return to normal, postoperative complications, treatment time, and recurrence rate. Results: A total of 43 cases of spinal infection were evaluated, with 19 in the NPWT group and 24 in the CVSG group. The NPWT group had a superior postoperative drainage volume, antibiotic use time, erythrocyte sedimentation rate and CRP recovery times, VAS score at 3 months after the operation, and cure rate at 3 months after operation compared with the CVSG group. There were no significant variations in the total hospital stay and intraoperative blood loss between the two groups. Conclusions: This study supports the use of negative pressure in the treatment of a primary spinal infection and indicates that it has a notably better short-term clinical effect than conventional surgery. Additionally, its mid-term cure rate and recurrence rate are more desirable than those of conventional treatments

Crosstalk Defect Detection Method Based on Salient Color Channel Frequency Domain Filtering

Display crosstalk defect detection is an important link in the display quality inspection process. We propose a crosstalk defect detection method based on salient color channel frequency domain filtering. Firstly, the salient color channel in RGBY is selected by the maximum relative entropy criterion, and the color quaternion matrix of the displayed image is formed with the Lab color space. Secondly, the image color quaternion matrix is converted into the logarithmic spectrum in the frequency domain through the hyper-complex Fourier transform. Finally, Gaussian threshold band-pass filtering and hyper-complex inverse Fourier transform are used to separate the low-contrast defects and background of the display image. The experimental results show that the accuracy of the proposed algorithm reaches 96% for a variety of crosstalk defect detection. Compared with the current advanced defect detection algorithms, the effectiveness of the proposed method for low-contrast crosstalk defect detection is confirmed