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Pseudorandom Correlation Functions from Variable-Density LPN, Revisited
Pseudorandom correlation functions (PCF), introduced in the work of (Boyle et al., FOCS 2020), allow two parties to locally generate, from short...
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A distributed variable density path search and simplification method for industrial manipulators with end-effector’s attitude constraints
In many robot operation scenarios, the end-effector’s attitude constraints of movement are indispensable for the task process, such as robotic...
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Multi-density crime predictor: an approach to forecast criminal activities in multi-density crime hotspots
The increasing pervasiveness of ICT technologies and sensor infrastructures is enabling police departments to gather and store increasing volumes of...
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Latent Variable Model Selection
Latent variable models are important knowledge representations for machine learning. This chapter introduces two information-theoretic criteria for...
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Density-Based Clustering
The flat, hierarchical, and GMM are incapable of handling outliers. DBSCAN is an algorithm that handles large density-based spatial data containing...
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A smooth single-variable-based interpolation function for multi-material topology optimization
This article presents a novel single-variable-based material interpolation scheme for multi-material density-based topology optimization. In the...
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OSTNet: overlap** splitting transformer network with integrated density loss for vehicle density estimation
Vehicle density estimation plays a crucial role in traffic monitoring, providing the traffic management department with the traffic volume and...
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Effective Density-Based Concept Drift Detection for Evolving Data Streams
Concept drift is a common phenomenon appearing in evolving data streams of a wide range of applications including credit card fraud protection,...
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Density peaks clustering algorithm with connected local density and punished relative distance
Density peaks clustering (DPC) algorithm has been widely applied in many fields due to its innovation and efficiency. However, the original DPC...
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Cross-layer analysis of clock glitch fault injection while fetching variable-length instructions
With the increasing complexity of embedded systems, the use of variable-length instruction sets has become essential, so that higher code density and...
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Causal Interpretability and Uncertainty Estimation in Mixture Density Networks
Neural network implementations have predominantly been a black box lacking both in interpretability and estimation of uncertainty. In this study, we...
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An efficient non-iterative smoothed particle hydrodynamics fluid simulation method with variable smoothing length
In classical smoothed particle hydrodynamics (SPH) fluid simulation approaches, the smoothing length of Lagrangian particles is typically constant....
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Design and experiment of a variable stiffness soft manipulator for non-destructive gras**
With the advantages of high flexibility, high safety, and good adhesion and wrap**, soft robots have a wide range of application prospects in...
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A powerful method for interactive content-based image retrieval by variable compressed convolutional info neural networks
There is a need for efficient methods to retrieve and obtain the visual data that a client need. New methods for content-based image retrieval (CBIR)...
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A Multi-level Density-Based Crowd Simulation Architecture
Large-scale crowd phenomena are complex to model as the behaviour of pedestrians needs to be described at both strategic, tactical, and operational...
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Variable-wise generative adversarial transformer in multivariate time series anomaly detection
Multiple variable time series anomaly detection plays a significant role in fields such as AIOps and intelligent healthcare. However, time series...
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Higher Fault Detection Through Novel Density Estimators in Unit Test Generation
Many-objective evolutionary algorithms (MOEAs) have been applied in the software testing literature to automate the generation of test cases. While...
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The impact of variable ordering on Bayesian network structure learning
Causal Bayesian Networks (CBNs) provide an important tool for reasoning under uncertainty with potential application to many complex causal systems....
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High-dimensional local polynomial regression with variable selection and dimension reduction
Variable selection and dimension reduction have been considered in nonparametric regression for improving the precision of estimation, via the...
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Clustering Brain Connectomes Through a Density-Peak Approach
The density-peak (DP) algorithm is a mode-based clustering method that identifies cluster centers as data points being surrounded by neighbors with...