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A First Approach to the Asymptotics of Low-Mach-Number Flows
In this chapter, we describe an initial (rather ‘naive’) approach to the asymptotics of low-Mach-number flows, based mainly on consideration of the...
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A Multi-modal Framework for Robots to Learn Manipulation Tasks from Human Demonstrations
Enabling robots to learn manipulation tasks by observing human demonstrations remains a major challenge. Recent advances in video captioning tasks...
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Machines Learn Better with Better Data Ontology: Lessons from Philosophy of Induction and Machine Learning Practice
As scientists start to adopt machine learning (ML) as one research tool, the security of ML and the knowledge generated become a concern. In this...
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QuILTs: Validated Teaching–Learning Sequences for Hel** Students Learn Quantum Mechanics
We have been develo** and validating teaching–learning sequences for use in quantum mechanics courses, called Quantum Interactive Learning...
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Slow Atmospheric Motion as a Low-Mach-Number Flow
First, in Sect. 6.1, we discuss in detail various facets of the applicability of the Boussinesq approximation to slow atmospheric motion. In Sect....
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Is academic anxiety good or bad for students? Investigating the moderating effects of anxiety on the reciprocal relations between self-efficacy and achievement in mathematics
This longitudinal research, grounded in Bandura’s social cognitive theory, examined the cross-lagged relations between mathematics self-efficacy...
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Gender differences in mathematical achievement development: a family psychobiosocial model
This study proposes a family psychobiosocial model on gender differences in cognitive development. Specifically, the aim is to investigate how family...
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Water jet angle prediction in supersonic crossflows: Euler–Lagrange and machine learning approaches
This study presents a comprehensive investigation into water jet injection dynamics in supersonic crossflows, employing a hybrid approach that...
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Modeling and realization of photonic biosensor for hazardous virus detection using ML approach
The broad range of sexually transmitted viruses are infections generally attained through uncertain sexual contact and can lead to serious health...
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Fast Aerodynamics Prediction of Wedge Tail Airfoils Using Multi-head Perceptron Network
Wedge tail airfoils refer to the addition of a wedge section to the tail of an airfoil. This modification has demonstrated greater efficiency in...
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Uncertainty involved drag divergence characteristic predicting method based on VAE
Effective access to obtain the drag divergence characteristic of an airfoil is crucial for improving the economy, safety, and comfort of the...
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Controllable image generation based on causal representation learning
Artificial intelligence generated content (AIGC) has emerged as an indispensable tool for producing large-scale content in various forms, such as...
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A deep reinforcement learning control approach for high-performance aircraft
This research introduces a flight controller for a high-performance aircraft, able to follow randomly generated sequences of waypoints, at varying...
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Logit prototype learning with active multimodal representation for robust open-set recognition
Robust open-set recognition (OSR) performance has become a prerequisite for pattern recognition systems in real-world applications. However, the...
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Adversarial data splitting for domain generalization
Domain generalization aims to learn a model that is generalizable to an unseen target domain, which is a fundamental and challenging task in machine...
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Develo** an advanced neural network and physics solver coupled framework for accelerating flow field simulations
Computational fluid dynamics simulation accounts for a large number of workloads in the numerical design optimization of aerodynamics problems. In...
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A Machian Interpretation of the Theory of Relativity? Joseph Petzoldt’s Reading of Einstein
Even though the relationship between Einstein and Mach is well studied, the literature on the topic often overlooks the fact that Mach never provided...
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Granger causal representation learning for groups of time series
Discovering causality from multivariate time series is an important but challenging problem. Most existing methods focus on estimating the Granger...
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Learning from experience: what the emerging global marine assessment community can learn from the social processes of other global environmental assessments
In recent decades, international assessments of the ocean have evolved from specialized, technical evaluations of the state of the marine environment...
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Wire rope damage detection based on a uniform-complementary binary pattern with exponentially weighted guide image filtering
In response to the problem of unclear texture structure in steel wire rope images caused by complex and uncertain lighting conditions, resulting in...
