A Visual Representation-guided Framework with Global Affinity for Weakly Supervised Salient Object Detection

Fully supervised salient object detection (SOD) methods have made considerable progress in performance, yet these models rely heavily on expensive pixel-wise labels. Recently, to achieve a trade-off between labeling burden and performance, scribble-based SOD methods have attracted increasing attention. Previous scribble-based models directly implement the SOD task only based on SOD training data with limited information, it is extremely difficult for them to understand the image and further achieve a superior SOD task. In this paper, we propose a simple yet effective framework guided by general visual representations with rich contextual semantic knowledge for scribble-based SOD. These general visual representations are generated by self-supervised learning based on large-scale unlabeled datasets. Our framework consists of a task-related encoder, a general visual module, and an information integration module to efficiently combine the general visual representations with task-related features to perform the SOD task based on understanding the contextual connections of images. Meanwhile, we propose a novel global semantic affinity loss to guide the model to perceive the global structure of the salient objects. Experimental results on five public benchmark datasets demonstrate that our method, which only utilizes scribble annotations without introducing any extra label, outperforms the state-of-the-art weakly supervised SOD methods. Specifically, it outperforms the previous best scribble-based method on all datasets with an average gain of 5.5% for max f-measure, 5.8% for mean f-measure, 24% for MAE, and 3.1% for E-measure. Moreover, our method achieves comparable or even superior performance to the state-of-the-art fully supervised models

Expression levels of microRNAs are not associated with their regulatory activities

MicroRNAs (miRNAs) regulate their targets by triggering mRNA degradation or translational repression. The negative relationship between miRNAs and their targets suggests that the regulatory effect of a miRNA could be determined from the expression levels of its targets. Here, we investigated the relationship between miRNA activities determined by computational programs and miRNA expression levels by using data in which both mRNA and miRNA expression from the same samples were measured. We found that different from the intuitive expectation one might have, miRNA activity shows very weak correlation with miRNA expression, which indicates complex regulating mechanisms between miRNAs and their target genes

Optimal Design of Energy System Based on the Forecasting Data with Particle Swarm Optimization

Renewable energy source has developed rapidly and attracted considerable attention. The integration of renewable energy into the energy supply chain requires precise forecast of the output of energy supply chain, thereby reducing energy resource waste and greenhouse gas emissions. In this study, a coupled model system is developed to forecast energy supply chain for the design optimization of distributed energy system, which can be divided into two parts. In the first part, long short-term memory (LSTM) and particle swarm optimization algorithm (PSO) contribute to energy supply chain forecast considering time series, and particle swarm optimization is used to optimize the parameters of the long short-term memory model to improve the forecast accuracy. Results show that the mean absolute error and root mean squared error are 8.7 and 16.3 for the PSO-LSTM model, respectively. In the second part, the forecast results are used as input of the distributed energy system to further optimize the design and operation schemes, so as to achieve the coupling optimization of forecast and design. Finally, a case study is carried out to verify the effectiveness of the proposed method

Motion-aware Memory Network for Fast Video Salient Object Detection

Previous methods based on 3DCNN, convLSTM, or optical flow have achieved great success in video salient object detection (VSOD). However, they still suffer from high computational costs or poor quality of the generated saliency maps. To solve these problems, we design a space-time memory (STM)-based network, which extracts useful temporal information of the current frame from adjacent frames as the temporal branch of VSOD. Furthermore, previous methods only considered single-frame prediction without temporal association. As a result, the model may not focus on the temporal information sufficiently. Thus, we initially introduce object motion prediction between inter-frame into VSOD. Our model follows standard encoder--decoder architecture. In the encoding stage, we generate high-level temporal features by using high-level features from the current and its adjacent frames. This approach is more efficient than the optical flow-based methods. In the decoding stage, we propose an effective fusion strategy for spatial and temporal branches. The semantic information of the high-level features is used to fuse the object details in the low-level features, and then the spatiotemporal features are obtained step by step to reconstruct the saliency maps. Moreover, inspired by the boundary supervision commonly used in image salient object detection (ISOD), we design a motion-aware loss for predicting object boundary motion and simultaneously perform multitask learning for VSOD and object motion prediction, which can further facilitate the model to extract spatiotemporal features accurately and maintain the object integrity. Extensive experiments on several datasets demonstrated the effectiveness of our method and can achieve state-of-the-art metrics on some datasets. The proposed model does not require optical flow or other preprocessing, and can reach a speed of nearly 100 FPS during inference.Comment: 12 pages, 10 figure

DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization

Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has been successfully applied to various Combinatorial Optimization Problems (COPs). Traditionally, customizing ACO for a specific problem requires the expert design of knowledge-driven heuristics. In this paper, we propose DeepACO, a generic framework that leverages deep reinforcement learning to automate heuristic designs. DeepACO serves to strengthen the heuristic measures of existing ACO algorithms and dispense with laborious manual design in future ACO applications. As a neural-enhanced meta-heuristic, DeepACO consistently outperforms its ACO counterparts on eight COPs using a single neural model and a single set of hyperparameters. As a Neural Combinatorial Optimization method, DeepACO performs better than or on par with problem-specific methods on canonical routing problems. Our code is publicly available at https://github.com/henry-yeh/DeepACO.Comment: Accepted at NeurIPS 202

Optimal Planning for Deepwater Oilfield Development Under Uncertainties of Crude Oil Price and Reservoir

The development planning of deepwater oilfield directly influences production costs and benefits. However, the uncertainties of crude oil price and reservoir and the special production requirements make it difficult to optimize development planning of deepwater oilfield. Although there have been a number of scholars researching on this issue, previous models just focused on several special working conditions and few have considered energy supply of floating production storage and offloading (FPSO). In light of the normal deepwater production development cycles, in this paper, a multiscenario mixed integer linear programming (MS-MILP) method is proposed based on reservoir numerical simulation, considering the uncertainties of reservoir and crude oil price and the constraint of energy consumption of FPSO, to obtain the globally optimal development planning of deepwater oilfield. Finally, a real example is taken as the study objective. Compared with previous researches, the method proposed in this paper is testified to be practical and reliable