Continual Semantic Segmentation with Automatic Memory Sample Selection

Continual Semantic Segmentation (CSS) extends static semantic segmentation by incrementally introducing new classes for training. To alleviate the catastrophic forgetting issue in CSS, a memory buffer that stores a small number of samples from the previous classes is constructed for replay. However, existing methods select the memory samples either randomly or based on a single-factor-driven handcrafted strategy, which has no guarantee to be optimal. In this work, we propose a novel memory sample selection mechanism that selects informative samples for effective replay in a fully automatic way by considering comprehensive factors including sample diversity and class performance. Our mechanism regards the selection operation as a decision-making process and learns an optimal selection policy that directly maximizes the validation performance on a reward set. To facilitate the selection decision, we design a novel state representation and a dual-stage action space. Our extensive experiments on Pascal-VOC 2012 and ADE 20K datasets demonstrate the effectiveness of our approach with state-of-the-art (SOTA) performance achieved, outperforming the second-place one by 12.54% for the 6stage setting on Pascal-VOC 2012.Comment: Accepted to CVPR202

Integrated assessment on the implementation of sustainable heat technologies in the built environment in Harbin, China

Heating in built environments is an essential factor regarding energy consumption and CO2 emissions. Thus, the application of sustainable heating technologies is vital for reducing CO2 emissions. The literature indicates the requirement for a comprehensive assessment of the technical, economic, and environmental performances of various sustainable heating technologies and their implementation feasibility at the local level. Accordingly, this study presents a quantitative assessment relative to Harbin, a typical northern city with a coal-dominated heating system. Seven sustainable heating technologies were examined using current policy and future renewable scenarios. The results indicate that the examined heating technologies are technically feasible. Biomass heating saves costs and emissions (CO2 avoidance costs of 24–47 €/t), although fuel availability and storage management limit its implementation. Solar heating is a promising technology with reduced costs and low CO2 emissions (CO2 avoidance costs can decline by 50% from 2020 to 2050). However, its current resident acceptance is relatively low as lengthy investigations and periods for underground construction are required. Electric heating is preferable in terms of implementation feasibility; however, its economic competitiveness and environmental impact depend heavily on electricity prices and grid cleanliness (CO2 avoidance costs of 120–463 €/t). This study contributes to the existing literature on sustainable heat transition in China by providing informative local circumstances in Harbin and presenting assumption-making methods in detail when local data is not transparent. The integrated assessment provides solid evidence to facilitate decision-making in the clean heating transition in northern cities of China. The methods are applicable to other countries with similar heat-supply structures and climate conditions

Self adaptive global-local feature enhancement for radiology report generation

Automated radiology report generation aims at automatically generating a detailed description of medical images, which can greatly alleviate the workload of radiologists and provide better medical services to remote areas. Most existing works pay attention to the holistic impression of medical images, failing to utilize important anatomy information. However, in actual clinical practice, radiologists usually locate important anatomical structures, and then look for signs of abnormalities in certain structures and reason the underlying disease. In this paper, we propose a novel framework AGFNet to dynamically fuse the global and anatomy region feature to generate multi-grained radiology report. Firstly, we extract important anatomy region features and global features of input Chest X-ray (CXR). Then, with the region features and the global features as input, our proposed self-adaptive fusion gate module could dynamically fuse multi-granularity information. Finally, the captioning generator generates the radiology reports through multi-granularity features. Experiment results illustrate that our model achieved the state-of-the-art performance on two benchmark datasets including the IU X-Ray and MIMIC-CXR. Further analyses also prove that our model is able to leverage the multi-grained information from radiology images and texts so as to help generate more accurate reports

Deep3DSketch+: Obtaining Customized 3D Model by Single Free-Hand Sketch through Deep Learning

As 3D models become critical in today's manufacturing and product design, conventional 3D modeling approaches based on Computer-Aided Design (CAD) are labor-intensive, time-consuming, and have high demands on the creators. This work aims to introduce an alternative approach to 3D modeling by utilizing free-hand sketches to obtain desired 3D models. We introduce Deep3DSketch+, which is a deep-learning algorithm that takes the input of a single free-hand sketch and produces a complete and high-fidelity model that matches the sketch input. The neural network has view- and structural-awareness enabled by a Shape Discriminator (SD) and a Stroke Enhancement Module (SEM), which overcomes the limitations of sparsity and ambiguity of the sketches. The network design also brings high robustness to partial sketch input in industrial applications.Our approach has undergone extensive experiments, demonstrating its state-of-the-art (SOTA) performance on both synthetic and real-world datasets. These results validate the effectiveness and superiority of our method compared to existing techniques. We have demonstrated the conversion of free-hand sketches into physical 3D objects using additive manufacturing. We believe that our approach has the potential to accelerate product design and democratize customized manufacturing

Techno-economic-environmental analysis of seasonal thermal energy storage with solar heating for residential heating in China

Seasonal thermal energy storage (STES) of solar heat is an option of interest for clean heat transition, as residential heating is often fossil fuel-based. This study 1) proposes an integrated optimization criterion to examine how local context influences the optimal configuration planning, techno-economic-environmental performance, and feasibility of STES application; 2) identifies the position of STES in comparison to other sustainable heating options considering the local context; and 3) provides a comprehensive and transparent showcase highlighting the importance of the local context in determining the feasibility of STES in the clean heating transition. The TRNSYS modeling tool is adopted to analyze the performance, and Pareto optimization is applied to treat the multi-objective optimization. The solar fractions and storage efficiencies of the four case studies range between 58-67% and 57–69%, respectively. STES has significant potential to reduce CO2 emissions (52–72%) compared to conventional heating systems. However, the heating cost of the STES system (5.4–8.7 €-ct/kWh) is more than twice that of the conventional heating system. The CO2 avoidance cost of the four case studies ranges between 114 and 368 €/t. Properly reducing the borehole number in cold climate zones and increasing the solar collector area in warm climate zones help improve the system performance

Vascular niche IL-6 induces alternative macrophage activation in glioblastoma through HIF-2α.

Spatiotemporal regulation of tumor immunity remains largely unexplored. Here we identify a vascular niche that controls alternative macrophage activation in glioblastoma (GBM). We show that tumor-promoting macrophages are spatially proximate to GBM-associated endothelial cells (ECs), permissive for angiocrine-induced macrophage polarization. We identify ECs as one of the major sources for interleukin-6 (IL-6) expression in GBM microenvironment. Furthermore, we reveal that colony-stimulating factor-1 and angiocrine IL-6 induce robust arginase-1 expression and macrophage alternative activation, mediated through peroxisome proliferator-activated receptor-γ-dependent transcriptional activation of hypoxia-inducible factor-2α. Finally, utilizing a genetic murine GBM model, we show that EC-specific knockout of IL-6 inhibits macrophage alternative activation and improves survival in the GBM-bearing mice. These findings illustrate a vascular niche-dependent mechanism for alternative macrophage activation and cancer progression, and suggest that targeting endothelial IL-6 may offer a selective and efficient therapeutic strategy for GBM, and possibly other solid malignant tumors

Network pharmacology-based exploration identified the antiviral efficacy of Quercetin isolated from mulberry leaves against enterovirus 71 via the NF-κB signaling pathway

Introduction: Mulberry leaf (ML) is known for its antibacterial and anti-inflammatory properties, historically documented in “Shen Nong’s Materia Medica”. This study aimed to investigate the effects of ML on enterovirus 71 (EV71) using network pharmacology, molecular docking, and in vitro experiments.Methods: We successfully pinpointed shared targets between mulberry leaves (ML) and the EV71 virus by leveraging online databases. Our investigation delved into the interaction among these identified targets, leading to the identification of pivotal components within ML that possess potent anti-EV71 properties. The ability of these components to bind to the targets was verified by molecular docking. Moreover, bioinformatics predictions were used to identify the signaling pathways involved. Finally, the mechanism behind its anti-EV71 action was confirmed through in vitro experiments.Results: Our investigation uncovered 25 active components in ML that targeted 231 specific genes. Of these genes, 29 correlated with the targets of EV71. Quercetin, a major ingredient in ML, was associated with 25 of these genes. According to the molecular docking results, Quercetin has a high binding affinity to the targets of ML and EV71. According to the KEGG pathway analysis, the antiviral effect of Quercetin against EV71 was found to be closely related to the NF-κB signaling pathway. The results of immunofluorescence and Western blotting showed that Quercetin significantly reduced the expression levels of VP1, TNF-α, and IL-1β in EV71-infected human rhabdomyosarcoma cells. The phosphorylation level of NF-κB p65 was reduced, and the activation of NF-κB signaling pathway was suppressed by Quercetin. Furthermore, our results showed that Quercetin downregulated the expression of JNK, ERK, and p38 and their phosphorylation levels due to EV71 infection.Conclusion: With these findings in mind, we can conclude that inhibiting the NF-κB signaling pathway is a critical mechanism through which Quercetin exerts its anti-EV71 effectiveness

A New Time–Frequency Feature Extraction Method for Action Detection on Artificial Knee by Fractional Fourier Transform

With the aim of designing an action detection method on artificial knee, a new time−frequency feature extraction method was proposed. The inertial data were extracted periodically using the microelectromechanical systems (MEMS) inertial measurement unit (IMU) on the prosthesis, and the features were extracted from the inertial data after fractional Fourier transform (FRFT). Then, a feature vector composed of eight features was constructed. The transformation results of these features after FRFT with different orders were analyzed, and the dimensions of the feature vector were reduced. The classification effects of different features and different orders are analyzed, according to which order and feature of each sub-classifier were designed. Finally, according to the experiment with the prototype, the method proposed above can reduce the requirements of hardware calculation and has a better classification effect. The accuracies of each sub-classifier are 95.05%, 95.38%, 91.43%, and 89.39%, respectively; the precisions are 78.43%, 98.36%, 98.36%, and 93.41%, respectively; and the recalls are 100%, 93.26%, 86.96%, and 86.68%, respectively

Preimpact Fall Detection for Elderly Based on Fractional Domain

The aging population has become a growing worldwide problem. Every year, deaths and injuries caused by elderly people's falls bring huge social costs. To reduce the rate of injury and death caused by falls among the elderly and the following social cost, the elderly must be monitored. In this context, falls detecting has become a hotspot for many research institutions and enterprises at home and abroad. This paper proposes an algorithm framework to prealarm the fall based on fractional domain, using inertial data sensor as motion data collection devices, preprocessing the data by axis synthesis and mean filtering, and using fractional-order Fourier transform to convert the collected data from time domain to fractional domain. Based on the above, a multilayer dichotomy classifier is designed, and each node parameter selection method is given, which constructed a preimpact fall detection system with excellent performance. The experiment result demonstrates that the algorithm proposed in this paper can guarantee better warning effect and classification accuracy with fewer features