Constructing Sample-to-Class Graph for Few-Shot Class-Incremental Learning

Few-shot class-incremental learning (FSCIL) aims to build machine learning model that can continually learn new concepts from a few data samples, without forgetting knowledge of old classes. The challenges of FSCIL lies in the limited data of new classes, which not only lead to significant overfitting issues but also exacerbates the notorious catastrophic forgetting problems. As proved in early studies, building sample relationships is beneficial for learning from few-shot samples. In this paper, we promote the idea to the incremental scenario, and propose a Sample-to-Class (S2C) graph learning method for FSCIL. Specifically, we propose a Sample-level Graph Network (SGN) that focuses on analyzing sample relationships within a single session. This network helps aggregate similar samples, ultimately leading to the extraction of more refined class-level features. Then, we present a Class-level Graph Network (CGN) that establishes connections across class-level features of both new and old classes. This network plays a crucial role in linking the knowledge between different sessions and helps improve overall learning in the FSCIL scenario. Moreover, we design a multi-stage strategy for training S2C model, which mitigates the training challenges posed by limited data in the incremental process. The multi-stage training strategy is designed to build S2C graph from base to few-shot stages, and improve the capacity via an extra pseudo-incremental stage. Experiments on three popular benchmark datasets show that our method clearly outperforms the baselines and sets new state-of-the-art results in FSCIL

Dynamic V2X Autonomous Perception from Road-to-Vehicle Vision

Vehicle-to-everything (V2X) perception is an innovative technology that enhances vehicle perception accuracy, thereby elevating the security and reliability of autonomous systems. However, existing V2X perception methods focus on static scenes from mainly vehicle-based vision, which is constrained by sensor capabilities and communication loads. To adapt V2X perception models to dynamic scenes, we propose to build V2X perception from road-to-vehicle vision and present Adaptive Road-to-Vehicle Perception (AR2VP) method. In AR2VP,we leverage roadside units to offer stable, wide-range sensing capabilities and serve as communication hubs. AR2VP is devised to tackle both intra-scene and inter-scene changes. For the former, we construct a dynamic perception representing module, which efficiently integrates vehicle perceptions, enabling vehicles to capture a more comprehensive range of dynamic factors within the scene.Moreover, we introduce a road-to-vehicle perception compensating module, aimed at preserving the maximized roadside unit perception information in the presence of intra-scene changes.For inter-scene changes, we implement an experience replay mechanism leveraging the roadside unit's storage capacity to retain a subset of historical scene data, maintaining model robustness in response to inter-scene shifts. We conduct perception experiment on 3D object detection and segmentation, and the results show that AR2VP excels in both performance-bandwidth trade-offs and adaptability within dynamic environments

Multi-Label Continual Learning using Augmented Graph Convolutional Network

Multi-Label Continual Learning (MLCL) builds a class-incremental framework in a sequential multi-label image recognition data stream. The critical challenges of MLCL are the construction of label relationships on past-missing and future-missing partial labels of training data and the catastrophic forgetting on old classes, resulting in poor generalization. To solve the problems, the study proposes an Augmented Graph Convolutional Network (AGCN++) that can construct the cross-task label relationships in MLCL and sustain catastrophic forgetting. First, we build an Augmented Correlation Matrix (ACM) across all seen classes, where the intra-task relationships derive from the hard label statistics. In contrast, the inter-task relationships leverage hard and soft labels from data and a constructed expert network. Then, we propose a novel partial label encoder (PLE) for MLCL, which can extract dynamic class representation for each partial label image as graph nodes and help generate soft labels to create a more convincing ACM and suppress forgetting. Last, to suppress the forgetting of label dependencies across old tasks, we propose a relationship-preserving constrainter to construct label relationships. The inter-class topology can be augmented automatically, which also yields effective class representations. The proposed method is evaluated using two multi-label image benchmarks. The experimental results show that the proposed way is effective for MLCL image recognition and can build convincing correlations across tasks even if the labels of previous tasks are missing

Modeling the effect of air temperature and pressure on the reliability of a passive containment cooling system

Passive systems safety is a key design aspect of new generation Nuclear Power Plants (NPPs). The Passive Containment Cooling System (PCCS) of the AP1000 NPP is a typical passive safety system, by which the heat produced in the containment is transferred to the environment through natural circulation and atmosphere is used as ultimate heat sink, making the climatic conditions of the plant location influencing the system reliability. In this paper, the effect of air temperature and pressure on the system reliability is analyzed by the variance decomposition sensitivity method. Results show the importance of considering the joint effect of the air pressure and temperature for the system reliability assessment

Experimental Investigation on Heat Transfer and Pressure Drop of Supercritical Carbon Dioxide in a Mini Vertical Upward Flow

Experiments on the convection heat transfer and pressure drop of supercritical carbon dioxide in a mini vertical upward flow were investigated in a smooth tube with an inner diameter of 2 mm. The experiments were conducted with pressures ranging from 7.62 to 8.44 MPa, mass fluxes ranging from 600 to 1600 kg·m−2·s−1, and heat flux ranging from 49.3 to 152.3 kW·m−2. Results show that the peak of heat transfer occurs when the bulk fluid temperature is below the proposed critical temperature and the wall temperature is above the proposed critical temperature. For the 2 mm vertical upward flow, the radial buoyancy effects are relatively weak, and the axial thermal acceleration effect cannot be negligible. In this study, a new modified Jackson correlation for the supercritical carbon dioxide is proposed for convective heat transfer. To reflect the effect of flow acceleration on heat transfer, a dimensionless heat flux was introduced to construct a new semi-correlation of heat transfer. The new correlation of friction factor taking into account the variation of density and dynamic viscosity was proposed with 146 experimental data within a ±20% error band

Experimental Investigation on Heat Transfer and Pressure Drop of Supercritical Carbon Dioxide in a Mini Vertical Upward Flow

Experiments on the convection heat transfer and pressure drop of supercritical carbon dioxide in a mini vertical upward flow were investigated in a smooth tube with an inner diameter of 2 mm. The experiments were conducted with pressures ranging from 7.62 to 8.44 MPa, mass fluxes ranging from 600 to 1600 kg·m−2·s−1, and heat flux ranging from 49.3 to 152.3 kW·m−2. Results show that the peak of heat transfer occurs when the bulk fluid temperature is below the proposed critical temperature and the wall temperature is above the proposed critical temperature. For the 2 mm vertical upward flow, the radial buoyancy effects are relatively weak, and the axial thermal acceleration effect cannot be negligible. In this study, a new modified Jackson correlation for the supercritical carbon dioxide is proposed for convective heat transfer. To reflect the effect of flow acceleration on heat transfer, a dimensionless heat flux was introduced to construct a new semi-correlation of heat transfer. The new correlation of friction factor taking into account the variation of density and dynamic viscosity was proposed with 146 experimental data within a ±20% error band

A host-guest approach to fabricate metallic cobalt nanoparticles embedded in silk-derived N-doped carbon fibers for efficient hydrogen evolution

Hydrogen evolution reaction (HER) plays a key role in generating clean and renewable energy. As the most effective HER electrocatalysts, Pt group catalysts suffer from severe problems such as high price and scarcity. It is highly desirable to design and synthesize sustainable HER electrocatalysts to replace the Pt group catalysts. Due to their low cost, high abundance and high activities, cobalt-incorporated N-doped nanocarbon hybrids are promising candidate electrocatalysts for HER. In this report, we demonstrated a robust and eco-friendly host-guest approach to fabricate metallic cobalt nanoparticles embedded in N-doped carbon fibers derived from natural silk fibers. Benefiting from the one-dimensional nanostructure, the well-dispersed metallic cobalt nanoparticles and the N-doped thin graphitized carbon layer coating, the best Co-based electrocatalyst manifests low overpotential (61 mV@10 mA/cm2) HER activity that is comparable with commercial 20% Pt/C, and good stability in acid. Our findings provide a novel and unique route to explore high-performance noble-metal-free HER electrocatalysts. Keywords: Silk, Carbon fibers, Cobalt nanoparticles, Hydrogen evolution, Nitrogen dopin

Electrochemical CO2 reduction catalyzed by organic/inorganic hybrids

Electroreduction of CO2 into value-added chemicals and fuels utilizing renewable electricity offers a sustainable way to meet the carbon-neutral goal and a viable solution for the storage of intermittent green energy sources. At the core of this technology is the development of electrocatalysts to accelerate the redox kinetics of CO2 reduction reactions (CO2RR) toward high targeted-product yield at minimal energy input. This perspective focuses on a unique category of CO2RR electrocatalysts embodying both inorganic and organic components to synergistically promote the reaction activity, selectivity and stability. First, we summarize recent progress on the design and fabrication of organic/inorganic hybrids CO2RR electrocatalysts, with special attention to the assembly protocols and structural configurations. We then carry out a comprehensive discussion on the mechanistic understanding of CO2RR processes tackled jointly by the inorganic and organic phases, with respect to the regulation of mass and charge transport, modification of double-layer configuration, tailoring of intermediates adsorption, and establishment of tandem pathways. At the end, we outline future challenges in the rational design of organic/inorganic hybrids for CO2RR and further extend the scope to the device level. We hope this work could incentivize more research interests to construct organic/inorganic hybrids for mobilizing electrocatalytic CO2RR towards industrialization