In this paper, we construct a symplectic bigraded Toda hierarchy which contains an symplectic deformation of the original Toda lattice hierarchy. In particular, we give the rational solutions which are expressed by th...
详细信息
In this paper, we construct a symplectic bigraded Toda hierarchy which contains an symplectic deformation of the original Toda lattice hierarchy. In particular, we give the rational solutions which are expressed by the products of the symplectic Schur polynomials.
Monitoring and identifying sleep postures, states or stages is a prerequisite for diagnosing sleep disorders, and provides crucial information for the treatment of sleep disorders. At present, there is no universal mu...
详细信息
Existing action recognition methods based on event cameras have not fully exploited the advantages of event cameras,such as compressing event streams into frames for subsequent calculation,which greatly sacrifices the...
详细信息
Existing action recognition methods based on event cameras have not fully exploited the advantages of event cameras,such as compressing event streams into frames for subsequent calculation,which greatly sacrifices the time information of event ***,the conventional PointCloud-based methods suffer from large computational complexity while processing event data,which make it difficult to handle long-term *** tackle the above problems,we propose a dynamic graph memory-boosting recurrent neural network(DG-MBRNN).The proposed DG-MBRNN splits the event stream into sequential graph data for preserving structural information,then uses the recurrent neural network(RNN)with boosting spatiotemporal memory to handle long-term sequences of *** addition,the proposed method introduces a dynamic reorganization mechanism for the graph based on the distances of features,which can effectively increase the ability to extract local *** order to cope with the situation that the existing datasets have too simple actions and too limited categories,we propose a new event-based dataset containing 36 complex *** dataset will greatly promote the development of event-based action recognition *** results show the effectiveness of the proposed method in completing the event-based action recognition task.
Underwater target detection is an important method for detecting marine organisms. However, due to the image occlusion of underwater targets, blurred water quality, poor lighting conditions, small targets, and complex...
详细信息
Many practical time series forecasting (TSF) tasks are plagued by data limitations. To alleviate this challenge, we design a data-level augmentation framework. It involves a time series generation (TSG) module and a s...
详细信息
The risk of gas leaks has grown significantly as a life threatening issue in industrial activities, cooking, and heating. This system integrates automatic reaction mechanisms, real-time monitoring capabilities, and ad...
详细信息
In recent years, object detection (OD) has become essential in computer vision for identifying and localizing objects in digital images, prompting various sectors to adopt this technology. However, increased reliance ...
详细信息
In recent years, object detection (OD) has become essential in computer vision for identifying and localizing objects in digital images, prompting various sectors to adopt this technology. However, increased reliance on OD has also revealed vulnerabilities to attacks, highlighting the need for effective detection methods to mitigate potential risks. Therefore, the present paper primarily surveys existing studies on OD in the context of security and surveillance, highlighting its significance in these critical areas. The discussion includes an examination of conventional techniques such as HOG, DPM, and the Viola‒Jones detector. While these traditional methods have laid the groundwork for object detection, they are often considered inadequate because of their time-consuming and labor-intensive nature. Consequently, the focus shifts to DL (deep learning)-based OD models such as YOLO (you only look once), single shot detector (SSD), and Fast R-CNN. Among these, the present survey paper emphasizes YOLO models for their speed and efficiency, as they utilize a unified architecture for both region proposal and classification, making them particularly suitable for real-time applications. However, the distinguishing feature of the proposed survey lies in its comprehensive coverage, which not only encompasses YOLO models but also integrates an analysis of generative AI (GenAI) models and metaheuristic approaches. This multifaceted exploration allows for a richer understanding of the current landscape in computer vision and AI, highlighting the synergies and potential applications that arise from combining these diverse methodologies. Furthermore, the paper explores a wide range of applications for OD in real-time security and surveillance settings, illustrating its effectiveness in addressing contemporary security challenges. This highlights how advanced OD techniques can enhance situational awareness and response capabilities in various scenarios. By focusing on these aspect
Obtaining all perfect matchings of a graph is a tough problem in graph theory, and its complexity belongs to the #P-Complete class. The problem is closely related to combinatorics, marriage matching problems, dense su...
详细信息
Obtaining all perfect matchings of a graph is a tough problem in graph theory, and its complexity belongs to the #P-Complete class. The problem is closely related to combinatorics, marriage matching problems, dense subgraphs, the Gaussian boson sampling, chemical molecular structures, and dimer *** this paper, we propose a quadratic unconstrained binary optimization formula of the perfect matching problem and translate it into the quantum Ising model. We can obtain all perfect matchings by mapping them to the ground state of the quantum Ising Hamiltonian and solving it with the variational quantum eigensolver. Adjusting the model's parameters can also achieve the maximum or minimum weighted perfect matching. The experimental results on a superconducting quantum computer of the Origin Quantum Computing technology Company show that our model can encode 2~n dimensional optimization space with only O（n） qubits consumption and achieve a high success probability of the ground state corresponding to all perfect matchings. In addition, the further simulation results show that the model can support a scale of more than 14 qubits, effectively resist the adverse effects of noise, and obtain a high success probability at a shallow variational depth. This method can be extended to other combinatorial optimization problems.
Total ionizing dose(TID) radiation response of the custom bandgap voltage reference(BGR)fabricated with 65 nm, 40 nm and 28 nm commercial bulk CMOS technologies is investigated. TID response is assessed employing Co-6...
详细信息
Total ionizing dose(TID) radiation response of the custom bandgap voltage reference(BGR)fabricated with 65 nm, 40 nm and 28 nm commercial bulk CMOS technologies is investigated. TID response is assessed employing Co-60 gamma ray source. The measurements indicate that the voltage reference is reduced by5.67% in 28 nm, 0.56% in 40 nm and increased by 1.28% in65 nm devices under irradiation up to 1.2 Mrad(Si) *** 48 hours of annealing, the voltage reference changes are just-1.84% in 28 nm, 0.14% in 40 nm and 1.14% in 65nm. The obtained results demonstrate that the custom BGR has naturally superior TID response due to the circuit design margins.
Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and t...
详细信息
Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.
暂无评论