In this paper, we propose a lightweight approach to address the problems of noise amplification, detail loss, and edge blurring encountered in low-light image enhancement, which typically hinder subsequent computer vi...
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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...
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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.
In response to the issues of polysemy in word vectors and inadequate contextual comprehension in traditional text summarization algorithms, this paper proposes a text summarization generation algorithm based on an imp...
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Fault detection of High-speed train based on maintenance data is very important for the maintenance work of High-speed train and is the basis for the safe operation of High-speed train. In order to fill the gaps in th...
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This study presents an innovative approach to probabilistic time series forecasting, emphasizing the principle of justifiable granularity coupled with fuzzy set theory to address the intricacies of time series forecas...
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Pedestrian re-identification algorithms are crucial in personnel localization tasks in underground coal mines. The high similarity in attire among personnel in this environment renders general pedestrian re-identifica...
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In the application scenario of automatic segmentation diagnosis for dental lesions, the focus of semantic segmentation tasks lies in how to design a lightweight segmentation network enabling model deployment on edge d...
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Multi-target tracking is facing the difficulties of modeling uncertain motion and observation *** tracking algorithms are limited by specific models and priors that may mismatch a real-world *** this paper,considering...
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Multi-target tracking is facing the difficulties of modeling uncertain motion and observation *** tracking algorithms are limited by specific models and priors that may mismatch a real-world *** this paper,considering the model-free purpose,we present an online Multi-Target Intelligent Tracking(MTIT)algorithm based on a Deep Long-Short Term Memory(DLSTM)network for complex tracking requirements,named the MTIT-DLSTM ***,to distinguish trajectories and concatenate the tracking task in a time sequence,we define a target tuple set that is the labeled Random Finite Set(RFS).Then,prediction and update blocks based on the DLSTM network are constructed to predict and estimate the state of targets,***,the prediction block can learn the movement trend from the historical state sequence,while the update block can capture the noise characteristic from the historical measurement ***,a data association scheme based on Hungarian algorithm and the heuristic track management strategy are employed to assign measurements to targets and adapt births and *** results manifest that,compared with the existing tracking algorithms,our proposed MTIT-DLSTM algorithm can improve effectively the accuracy and robustness in estimating the state of targets appearing at random positions,and be applied to linear and nonlinear multi-target tracking scenarios.
Methane accidents are one of the most dangerous accidents that threaten the safety and production of coal mines, therefore, how to accurately predict the methane concentration in the future period by utilizing the spa...
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Energetic structural materials(ESMs)are a new type of structural materials with bearing and damage *** this work the microstructure,mechanical properties and energy release characteristics of multi-element Ti-Zr-Ta al...
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Energetic structural materials(ESMs)are a new type of structural materials with bearing and damage *** this work the microstructure,mechanical properties and energy release characteristics of multi-element Ti-Zr-Ta alloys with good casting performance were *** microstructure of the Ti_(x)ZrTa alloys gradually change from BCC+HCP to single BCC structure with the increase of *** the Ti_(2)Zr_(y)Ta alloys was still uniform and single BCC structure with the increase of *** evolution of microstructure and composition then greatly affect the mechanical properties and energy-release characteristics of Ti-Zr-Ta *** synergistic effect of dual phase structure increases the fracture strain of Ti_(x)ZrTa(x=0.2,0.5)with the Ti content decreases,while the fracture strain of Ti_(x)ZrTa(x=2.0,3.0,4.0)gradually increase with the Ti content increases caused by the annihilation of the obstacles for dislocation *** as Zr content increases,the fracture strain of Ti_(2)Zr_(y)Ta alloys decrease,then the oxidation reaction rate and fragmentation degree gradually *** higher oxidation rate and the lager exposed oxidation area jointly leads the higher releasing energy efficiency of Ti_(x)ZrTa alloys with low Ti content and Ti_(2)Zr_(y)Ta alloys with high Zr content.
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