Lightweight yet reliable depth estimation models that can deployed on edge devices are crucial for the practical application of fields such as autonomous driving, robot navigation, and augmented reality. However, prev...
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Retrofitting projects play a critical role in enhancing the sustainability of existing structures, yet balancing time, cost, and environmental impact remains a significant challenge for decision-makers. This study int...
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Shoulder problem is a widely known musculoskeletal disorder that impacts heavily on the life of patients. Effective treatment for this must be preceded by accurate and timely diagnosis but traditional diagnostic techn...
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To improve the error correction performance, an innovative encoding structure with tail-biting for spinal codes is designed. Furthermore, an adaptive forward stack decoding(A-FSD) algorithm with lower complexity for s...
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To improve the error correction performance, an innovative encoding structure with tail-biting for spinal codes is designed. Furthermore, an adaptive forward stack decoding(A-FSD) algorithm with lower complexity for spinal codes is proposed. In the A-FSD algorithm, a flexible threshold parameter is set by a variable channel state to narrow the scale of nodes accessed. On this basis, a new decoding method of AFSD with early termination(AFSD-ET) is further proposed. The AFSD-ET decoder not only has the ability of dynamically modifying the number of stored nodes, but also adopts the early termination criterion to curtail complexity. The complexity and related parameters are verified through a series of simulations. The simulation results show that the proposed spinal codes with tail-biting and the AFSD-ET decoding algorithms can reduce the complexity and improve the decoding rate without sacrificing correct decoding performance.
The precise prediction of molecular properties is essential for advancements in drug development,particularly in virtual screening and compound *** recent introduction of numerous deep learningbased methods has shown ...
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The precise prediction of molecular properties is essential for advancements in drug development,particularly in virtual screening and compound *** recent introduction of numerous deep learningbased methods has shown remarkable potential in enhancing Molecular Property Prediction(MPP),especially improving accuracy and insights into molecular ***,two critical questions arise:does the integration of domain knowledge augment the accuracy of molecular property prediction and does employing multi-modal data fusion yield more precise results than unique data source methods?To explore these matters,we comprehensively review and quantitatively analyze recent deep learning methods based on various *** discover that integrating molecular information significantly improves Molecular Property Prediction(MPP)for both regression and classification ***,regression improvements,measured by reductions in Root Mean Square Error(RMSE),are up to 4.0%,while classification enhancements,measured by the area under the receiver operating characteristic curve(ROC-AUC),are up to 1.7%.Additionally,we discover that,as measured by ROC-AUC,augmenting 2D graphs with 3D information improves performance for classification tasks by up to 13.2%and enriching 2D graphs with 1D SMILES boosts multi-modal learning performance for regression tasks by up to 9.1%.The two consolidated insights offer crucial guidance for future advancements in drug discovery.
This paper devises the issue of machine learning rule-based methodology for uncovering the behavior-based rules of respective smart-phone users for purpose of providing context wise individualized notification service...
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Autism, a neurological disorder, manifests uniquely in areas such as verbal and nonverbal communication, social interactions, behavioral adaptability, and specific interests. The results collected indicate that health...
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In recent years, there has been growing interest in the use of the flexible membrane-type structures as floating breakwaters or wave energy absorption devices. Thus, it is of high importance to study the reliability a...
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Software security analysts typically only have access to the executable program and cannot directly access the source code of the *** poses significant challenges to security *** it is crucial to identify vulnerabilit...
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Software security analysts typically only have access to the executable program and cannot directly access the source code of the *** poses significant challenges to security *** it is crucial to identify vulnerabilities in such non-source code programs,there exists a limited set of generalized tools due to the low versatility of current vulnerability mining ***,these tools suffer from some *** terms of targeted fuzzing,the path searching for target points is not streamlined enough,and the completely random testing leads to an excessively large search ***,when it comes to code similarity analysis,there are issues with incomplete code feature extraction,which may result in information *** this paper,we propose a cross-platform and cross-architecture approach to exploit vulnerabilities using neural network obfuscation *** leveraging the Angr framework,a deobfuscation technique is introduced,along with the adoption of a VEX-IR-based intermediate language conversion *** combination allows for the unified handling of binary programs across various architectures,compilers,and compilation ***,binary programs are processed to extract multi-level spatial features using a combination of a skip-gram model with self-attention mechanism and a bidirectional Long Short-Term Memory(LSTM)***,the graph embedding network is utilized to evaluate the similarity of program *** on these similarity scores,a target function is determined,and symbolic execution is applied to solve the target *** solved content serves as the initial seed for targeted *** binary program is processed by using the de-obfuscation technique and intermediate language transformation method,and then the similarity of program functions is evaluated by using a graph embedding network,and symbolic execution is performed based on these similarity *** approach facilitates
While diffusion denoised smoothing (DDS) achieves excellent certified defense performance under l2 norm bounded perturbations, we find that DDS is vulnerable to image up-sampling. The bicubic interpolation used in DDS...
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