arXiv subjects

Yuan Yue

Publications and source records attributed to Yuan Yue.

8 recordsLinked to original sources

arXiv2026

Compact Stars Sourced by Perfect Fluid Dark Matter Halos

Recent studies have shown that dark matter halos can support regular black holes or compact stars by assuming an anisotropic energy-momentum tensor. In this paper, we extend the analysis to the dark matter halo as an isotropic perfect fluid. By employing galactic dark matter profiles-specifically the Einasto and Dehnen models-as the mass-energy density source, we numerically solve the Einstein field equations and find a class of non-singular, horizonless compact star solutions. Moreover, these configurations remain stable against axial perturbations while satisfying the dominant energy condition.

gr-qc↗

arXiv2026

Compact Stars Sourced by Dark Matter Halos and Their Frozen States

Inspired by regular black holes (RBHs) sourced by dark matter halos, we generalize the anisotropic energy-momentum tensor by relaxing the $P_r = -ρ$ condition between radial pressure and density. We demonstrate that while RBHs are a unique special case, a broader class of relations yields horizonless compact stars. Under specific parameter limits, these objects approach a ``frozen state," mimicking black hole features without an event horizon. These compact star solutions could satisfy weak energy conditions and provide a robust mechanism for dark matter-sourced black hole mimickers.

gr-qc↗

arXiv2026

Does Motion Intensity Impair Cognition in HCI? The Critical Role of Physical Motion-Visual Target Directional Congruency

Human-computer interaction (HCI) increasingly occurs in motion-rich environments. The ability to accurately and rapidly respond to directional visual cues is critical in these contexts. How whole-body motion and individual differences affect human perception and reaction to these directional cues is therefore a key, yet an underexplored question for HCI. This study used a 6-DOF motion platform to measure task performance on a visual direction judgment task. We analyzed performance by decomposing the complex motion into two distinct components: a task-irrelevant lateral interference component and a task-aligned directional congruency component. Results indicate that increased motion intensity lengthened reaction times. This effect was primarily driven by the lateral interference component, and this detrimental impact was disproportionately amplified for individuals with high motion sickness susceptibility. Conversely, directional congruency, where motion direction matched the visual cue, improved performance for all participants. These findings suggest that motion's impact on cognition is not monolithic, and that system design for mobile HCI can be informed by strategies that actively shape motion, such as minimizing lateral interference while maximizing directional congruency.

cs.HC↗

arXiv2023

Frozen Hayward-boson stars

Recently, the model of the Einstein-Bardeen theory minimally coupled to a complex, massive, free scalar field was investigated in arXiv:2305.19057. The introduction of a scalar field disrupts the formation of an event horizon, leaving only a type of solution referred to as a Bardeen-boson star. When the magnetic charge $q$ exceeds a certain critical value, the frozen Bardeen-boson star can be obtained with $ω\rightarrow 0$. In this paper, we extend to the investigation of Einstein-Hayward-scalar model, and obtain the solution of frozen Hayward-boson star, including the ground and excited states. Furthermore, under the same parameters, it is interesting to observe that both the ground state and the excited states frozen stars have the same critical horizon and mass.

gr-qc↗

arXiv2023

Finding neural signatures for obesity through feature selection on source-localized EEG

Obesity is a serious issue in the modern society and is often associated to significantly reduced quality of life. Current research conducted to explore obesity-related neurological evidences using electroencephalography (EEG) data are limited to traditional approaches. In this study, we developed a novel machine learning model to identify brain networks of obese females using alpha band functional connectivity features derived from EEG data. An overall classification accuracy of 0.937 is achieved. Our finding suggests that the obese brain is characterized by a dysfunctional network in which the areas that responsible for processing self-referential information and environmental context information are impaired.

cs.LG↗

arXiv2023

Boson star with parity-odd symmetry in wormhole spacetime

In this paper, we revisit the model of bosonic matter in the form of a free complex scalar field with a nontrivial wormhole spacetime topology supported by a free phantom field. We obtain a new type of boson star with wormhole solutions, in which the complex scalar field possess full parity-odd symmetry with respect to the two asymptotically flat spacetime regions. When the size of the throat is small, The behavior of boson stars with wormhole approaches that of boson stars. When the size of the throat is intermediate, the typical spiraling dependence of the mass and the particle number on the frequency of the boson stars is replaced by a loop structure. However, as the size becomes relatively large, the loop structure will also disappear. In particular, The complex scalar field could form two boson stars with opposite phase differences with respect to the two spacetime regions in the limit of vanishing throat size. We analyze the properties of this new type of boson stars with wormhole and further show that the wormhole spacetime geometry.

gr-qc↗

arXiv2023

Variational Autoencoder Learns Better Feature Representations for EEG-based Obesity Classification

Obesity is a common issue in modern societies today that can lead to various diseases and significantly reduced quality of life. Currently, research has been conducted to investigate resting state EEG (electroencephalogram) signals with an aim to identify possible neurological characteristics associated with obesity. In this study, we propose a deep learning-based framework to extract the resting state EEG features for obese and lean subject classification. Specifically, a novel variational autoencoder framework is employed to extract subject-invariant features from the raw EEG signals, which are then classified by a 1-D convolutional neural network. Comparing with conventional machine learning and deep learning methods, we demonstrate the superiority of using VAE for feature extraction, as reflected by the significantly improved classification accuracies, better visualizations and reduced impurity measures in the feature representations. Future work can be directed to gaining an in-depth understanding regarding the spatial patterns that have been learned by the proposed model from a neurological view, as well as improving the interpretability of the proposed model by allowing it to uncover any temporal-related information.

cs.LG↗

arXiv2015

Optimal Size for Maximal Energy Efficiency in Information Processing of Biological Systems Due to Bistability

Energy efficiency is closely related to the evolution of biological systems and is important to their information processing. In this paper, we calculated the excitation probability of a simple model of a bistable biological unit in response to pulsatile inputs, and its spontaneous excitation rate due to noise perturbation. Then we analytically calculated the mutual information, energy cost, and energy efficiency of an array of these bistable units. We found that the optimal number of units could maximize this array's energy efficiency in encoding pulse inputs, which depends on the fixed energy cost. We conclude that demand for energy efficiency in biological systems may strongly influence the size of these systems under the pressure of natural selection.

physics.bio-ph↗