bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gauss–Bonnet AdS planar and spherical black hole thermodynamics and holography
Authors: Paul Souvik , Gangopadhyay Sunandan , Saha Ashis 
Published: October 2024

Abstract
In this work, we extend the study in Bilic and Fabris (2022 J. High Energy Phys. JHEP11(2022)013) incorporating the AdS/CFT duality to establish a relationship between the local temperatures (Tolman temperatures) of a large (AdS) spherical and a (AdS) planar Schwarzschild black hole near the AdS boundary considering Gauss–Bonnet (GB) curvature correction in the gravitational action. We have shown that the higher curvature corrections appear in the local temperature relationship due to the inclusion of GB term in the bulk. By transforming the metric into Fefferman–Graham form, we have calculated the energy density of the conformal fluid at the boundary. The obtained result contains finite coupling corrections which are holographically induced by the GB curvature correction in the bulk theory. Following the well known approach of fluid/gravity duality, the energy density of the conformal fluid at the boundary is then compared with the black body radiation energy density. This comparison shows that the energy density is proportional to the temperature of the conformal fluid. The temperature of the conformal fluid is then shown to be related to the Tolman temperature of the black hole which then eventually helps us to establish both the Hawking temperature and Tolman temperature relationship between large spherically symmetric and planar Schwarzschild black holes in GB gravity near the AdS boundary.
Anisotropic hypocycloid inspired 3-bit digital coding metasurface for radar cross section reduction
Authors: Wang Chao , Wang Wen-Song , Li Yan-Ling , an Lei , Niu Fei , Wang Ru-Zhi , Zhang Sheng-Jun , Liu Jia-Qi 
Published: October 2024

Abstract
Recently, researchers have realized various exotic electromagnetic control devices using the coded metasurfaces, sparking a broad investigation into the phase or amplitude-based encoding method, as well as their combination, in the field of metasurface design. In this paper, to evaluate the influence of random mutual coupling between the adjacent element on the scattering performance of metasurface, and also to minimize the backward radar cross section (RCS) of metal plate targets, a novel encoding approach combining the reflection phase and element-form has been proposed. During the implementation process, an anisotropic hypocycloid inspired 3-bit digital coding metasurface was designed. It consists of 9 different element-forms, with each capable of providing 7 phase states. Simulation results demonstrate that the random mutual coupling introduced by the proposed elements does not significantly affect the RCS performance of the metasurface. With a good polarization insensitivity property for both linearly and circularly polarized waves, the designed 3-bit digital coding metasurface can achieve more than 20 dB RCS reduction at 10 GHz, while simultaneously transmitting additional information by encoding the element forms. The good consistency between theoretical simulation and sample testing unequivocally validates the precision of the design, this paper may serve as a useful reference for expanding the design methods of metasurfaces.
Weighted squared envelope nonlinear Gini index and its application to rotating machinery condition monitoring
Authors: Xie Xuyang , Yang Zichun , Zhang Lei , Xie Luotao , Wang Jintao , Chen Guobing 
Published: October 2024

Abstract
Sparsity measures (SMs) are effective tools for rotating machinery condition monitoring. However, under complex operating conditions, existing SMs often exhibit significant fluctuations, making it difficult to accurately detect early faults and monotonically represent the degradation processes. To tackle these challenges, a novel health indicator (HI) named the weighted squared envelope nonlinear Gini index (WSENGI) is proposed in this paper. Firstly, by introducing a nonlinearly increasing weight sequence inspired by the sigmoid function and the quadratic function-based quasi-arithmetic mean (QAM), a novel SM, the nonlinear Gini index (NGI), is developed based on the ratio of different QAMs framework. Building upon this foundation, the WSENGI is further constructed for condition monitoring by incorporating the weighted squared envelope into the NGI. When applied to two bearing run-to-failure datasets, the proposed HI shows improved sensitivity to early fault features and is able to depict the degradation processes monotonically, demonstrating notable advantages in condition monitoring.
Effective Faraday interaction between light and nuclear spins of helium-3 in its ground state: a semiclassical study
Authors: Fadel Matteo , Treutlein Philipp , Sinatra Alice 
Published: October 2024

Abstract
We derive the semiclassical evolution equations for a system consisting of helium-3 atoms in the

2
3

S

metastable state interacting with a light field far-detuned from the

2
3

S
−

2
3

P

transition, in the presence of metastability exchange collisions with ground state helium atoms and a static magnetic field. For two configurations, each corresponding to a particular choice of atom–light detuning in which the contribution of either the metastable level

F
=
1

/

2

or

F
=
3

/

2

is dominant, we derive a simple model of three coupled collective spins from which we can analytically extract an effective coupling constant between the collective nuclear spin and light. In these two configurations, we compare the predictions of our simplified model with the full model.
Mirror-symmetry-induced pseudospin filtering of phonons in graphene nanoribbons
Authors: Chen Xiaobin , Cui Shiyu , Hu Yizhi , Yan Kun 
Published: October 2024

Abstract
Unlike electrons, phonons do not have the charge and spin degrees of freedom. Consequently, it is a big challenge for the control of phonon currents and the design of quantum phonon devices due to the lack of versatile degrees of freedom. In this work, we show that phonon bands of mirror-symmetric materials can be labeled by parities, which can act as a pseudospin of phonons. As a proof-of-concept, we show that perfect pseudospin filtering can be realized using mirror-symmetric graphene-nanoribbon heterojunctions. In addition, we show that the filtering takes place within a remarkably short length scale of just 3 nanometers. Our work reveals an intriguing new facet of phonon behavior and also underscores the immense potential of utilizing phonons as information carriers.
Dynamics of finite width Kernel and prediction fluctuations in mean field neural networks*
Authors: Bordelon Blake , Pehlevan Cengiz 
Published: October 2024

Abstract
We analyze the dynamics of finite width effects in wide but finite feature learning neural networks. Starting from a dynamical mean field theory description of infinite width deep neural network kernel and prediction dynamics, we provide a characterization of the

O

(
1

/

width

)

fluctuations of the dynamical mean field theory order parameters over random initializations of the network weights. Our results, while perturbative in width, unlike prior analyses, are non-perturbative in the strength of feature learning. We find that once the mean field/µP parameterization is adopted, the leading finite size effect on the dynamics is to introduce initialization variance in the predictions and feature kernels of the networks. In the lazy limit of network training, all kernels are random but static in time and the prediction variance has a universal form. However, in the rich, feature learning regime, the fluctuations of the kernels and predictions are dynamically coupled with a variance that can be computed self-consistently. In two layer networks, we show how feature learning can dynamically reduce the variance of the final tangent kernel and final network predictions. We also show how initialization variance can slow down online learning in wide but finite networks. In deeper networks, kernel variance can dramatically accumulate through subsequent layers at large feature learning strengths, but feature learning continues to improve the signal-to-noise ratio of the feature kernels. In discrete time, we demonstrate that large learning rate phenomena such as edge of stability effects can be well captured by infinite width dynamics and that initialization variance can decrease dynamically. For convolutional neural networks trained on CIFAR-10, we empirically find significant corrections to both the bias and variance of network dynamics due to finite width.
What can be learnt with wide convolutional neural networks?*
Authors: Cagnetta Francesco , Favero Alessandro , Wyart Matthieu 
Published: October 2024

Abstract
Understanding how convolutional neural networks (CNNs) can efficiently learn high-dimensional functions remains a fundamental challenge. A popular belief is that these models harness the local and hierarchical structure of natural data such as images. Yet, we lack a quantitative understanding of how such structure affects performance, for example the rate of decay of the generalisation error with the number of training samples. In this paper, we study infinitely wide deep CNNs in the kernel regime. First, we show that the spectrum of the corresponding kernel inherits the hierarchical structure of the network, and we characterise its asymptotics. Then, we use this result together with generalisation bounds to prove that deep CNNs adapt to the spatial scale of the target function. In particular, we find that if the target function depends on low-dimensional subsets of adjacent input variables then the decay of the error is controlled by the effective dimensionality of these subsets. Conversely, if the target function depends on the full set of input variables then the error decay is controlled by the input dimension. We conclude by computing the generalisation error of a deep CNN trained on the output of another deep CNN with randomly initialised parameters. Interestingly, we find that, despite their hierarchical structure, the functions generated by infinitely wide deep CNNs are too rich to be efficiently learnable in high dimensions.
Universality laws for Gaussian mixtures in generalized linear models*
Authors: Dandi Yatin , Stephan Ludovic , Krzakala Florent , Loureiro Bruno , Zdeborová Lenka 
Published: October 2024

Abstract
Let

(

x

i

,

y

i

)

i
=
1
,
…
,
n

denote independent samples from a general mixture distribution

∑

c
∈

C

ρ

c

P

c

x

, and consider the hypothesis class of generalized linear models

y
^

=
F
(

Θ

⊤

x

)

. In this study, we investigate the asymptotic joint statistics of a family of generalized linear estimators

(

Θ

1

,
…
,

Θ

M

)

obtained either from (a) minimizing an empirical risk

R
^

n

(

Θ

;

X

,

y

)

or (b) sampling from the associated Gibbs measure

exp
⁡
(
−
β
n

R
^

n

(

Θ

;

X

,

y

)
)

. Our main contribution is to characterize under which conditions the asymptotic joint statistics of this family depends (in a weak sense) only on the means and covariances of the class conditional features distribution

P

c

x

. In particular, this allows us to prove the universality of different quantities of interest, such as the training and generalization errors, redeeming a recent line of work in high-dimensional statistics working under the Gaussian mixture hypothesis. Finally, we discuss the applications of our results in different machine learning tasks of interest, such as ensembling and uncertainty quantification.
Scaling properties of (2+1) directed polymers in the low-temperature limit
Authors: Dotsenko Victor 
Published: October 2024

Abstract
In terms of the replica method, we consider the low-temperature limit of (2+1) directed polymers in a random potential. The proposed approach allows us to compute the scaling exponent θ of the free energy fluctuations as well as the left tail of its probability distribution function. It is argued that

θ
=
1

/

4

, which is slightly different to the zero-temperature numerical value that is close to 0.241.
Error analysis of a coaxis five-bar parallel mechanism
Authors: Shen Guoxing , Liao Bin 
Published: October 2024

Abstract
With societal advancement, robots are increasingly being deployed in production processes. Currently, high-precision robots predominantly found in the market are delta parallel robots. Common parallel robots, including delta robots, generally suffer from the drawback of having a smaller workspace compared to serial robots. Contrasted with other types of parallel robots, coaxis parallel robots feature a larger workspace, thereby offering greater potential for application. This paper focuses on a coaxis five-bar parallel mechanism as the subject of investigation. Firstly, by employing the perturbation method to model the errors in the parallel mechanism, a mapping relationship between error sources and the errors of the mechanism’s end-effector was derived. Then a sensitivity analysis of each error source was performed statistically, yielding sensitivity metrics. Last, under conditions where the magnitudes of errors are identical, the end-effector error of a coaxis parallel mechanism is notably larger compared to that of a common parallel mechanism. This makes it more essential to conduct precision design on coaxis parallel robots in the future study.
Modeling and simulation study of acoustic response for dual-membrane capacitive MEMS microphone
Authors: Hu Siqi , Hu Haitao , Xue Wei , Kang Dianyu , Xiao Jing 
Published: October 2024

Abstract
As the micro-electro-mechanical systems (MEMS) technology matures, MEMS sensors have found widespread applications in mechatronics, robotics, and voice control. The high stability, high integration, and radio frequency interference resistance of MEMS microphones have rapidly led to their adoption in these domains, displacing electret condenser microphones.This article focuses on modeling and analyzing dual-membrane capacitive MEMS microphone, utilizing an lumped equivalent circuit model to calculate the microphone’s acoustic frequency response. Furthermore, the article employs the finite element method (FEM) to conduct a detailed analysis of the resonant frequency of the membrane, as well as to explore the effects of changes in the volume of the front and back chamber on the packaging performance of MEMS microphones. Finally, physical tests were conducted, and the simulation results of the two models showed considerable consistency with the curves obtained from the physical tests, verifying the accuracy of the models.
Improved traffic sign detection algorithm for lightweight YOLOv8
Authors: Qin Hualu , Yin Zhe 
Published: October 2024

Abstract
In robotics and automated control systems, effective utilization of resources and real-time performance are critical to system performance. For traffic sign detection existing Convolutional Neural Networks (CNN), You Only Look Once (YOLO), etc, the former performs end-to-end detection, more senior feature representations can be learned, and performs better in complex scenarios, but it needs a lot of labeled data for training; the latter performs better in terms of accuracy and speed, and is able to quickly detect traffic signs and give location information, but requires higher computational resources. To overcome the problems of poor real-time transportation sign detection and mini-goals, a lightweight traffic sign proposed algorithm for the detection of devices based on YOLOv8. In this study, on the YOLOv8n model, instead of the normal convolution in BottleNeck, we propose to replace the Faster Implementation of CSP Bottleneck with 2 convolutions (C2f) structure in the backbone network with a partial convolution (PConv), resulting in a structure (CSPPC); Introduction of Cross-Scale Feature Fusion Module (CCFM) proposed by Real-Time Detection, Embedding, and Tracking (RT-DETR) modeling; Use Weighted Interpolation of Sequential Evidence for Intersection over Union (Wise-IoU) loss function. Modified method was trained on the road sign dataset and the number of parameters was reduced by 49.83% compared to the initial model, Mean Average Precision (mAP) increase of 3.92%, and the decrease in floating-point operations is 34.57%. Results show that YOLOv8n model meets the requirements of high detection accuracy and lightweight modeling with great advantages in robotics and automated control systems to accelerate the recognition and decision-making process and improve the response speed of the system.
Effect of friction layer thickness on energy conversion efficiency of traveling wave rotary ultrasonic motor
Authors: Wei Deqiang , Wan Aohui , Guo Lianghong , Nie Rui , Chen Hucheng 
Published: October 2024

Abstract
Due to its compact size and resistance to electromagnetic interference. Applications for traveling wave rotary ultrasonic motors (TRUM) include robotic joint drives and aerospace.The motor’s energy conversion efficiency is impacted by the characteristics of the friction layer, yet the underlying mechanism influencing the efficiency remains unreported. Using the previously created finite element model, the mechanism via which the amount of thickness within the friction layer influences the motor output characteristics is examined.Analysis is done on the variations in motor loss, energy conversion efficiency, and input-output characteristics. The computation results show that when the friction layer is less than 0.4 mm, the machine’s output speed increases as the friction layer’s thickness decreases.The results of the calculation indicate that when the friction layer thickness is between 0.05 and 0.4 mm, the motor’s output torque and speed would drop. The findings indicate that the output torque and motor torque decrease as the friction layer thickness increases when it is between 0.05 and 0.4 mm. Both the viscoelastic losses and the motor’s energy conversion efficiency rise.Research shows that the friction layer thickness has a huge impact on the motor and cannot be ignored, providing a reference for the application of motors in industries such as robot joints.
Flow field analysis of newtonian fluid flow induced by planetary and radial motion of drill string
Authors: Wang Zhigang , Zhang Liye , Wang Qi , Xi Zheng , An Di , Zheng Jun , Guo Yongxuan 
Published: October 2024

Abstract
At present, in the process of oil drilling, the drill string is an essential downhole drilling tool. It is immersed in the hole filled with drilling fluid for a long time, and it is driven by the rotation of the rotary table to drill down. In this process, the drill string will not only rotate around its axis but also may rotate around the axis of the hole and produce radial motion. It is important to study the flow law of Newtonian fluid flow induced by drill string movement, which can effectively reduce drilling accidents caused by drill string vortex and buckling, thus reducing economic losses. After research and analysis, the finite volume method, which has good applicability and obvious advantages, is used to discretize the established mathematical model, and the mathematical equation after discretization is obtained. By using MATLAB software, the flow field of Newtonian fluid induced by drill string rotation motion is analyzed, the axial velocity distribution diagram and tangential velocity distribution diagram are obtained, and the velocity flow diagram under different eccentricity conditions is drawn. It is found that secondary flow occurs only when the eccentricity reaches a certain distance requirement.
Numerical simulation of complex slope dike overtopping wave flow under isolated wave action based on OpenFOAM
Authors: Hou Yadong , Ye Junwei , Wang Shiwen 
Published: October 2024

Abstract
In recent years, a significant number of compound slope dikes have been developed, but research on the over-wave flow of their viewing platforms remains relatively sparse. In this paper, isolated waves are selected to simulate storm surges. Numerical flumes are constructed based on OpenFOAM to explore the thickness and flow velocity of overtopping wave flow impacts and the thickness and flow velocity of backwash flow on viewing platforms on a compound slope dike with three different foreslope gradients. The study finds that both the impact thickness and return thickness of the cross-wave flow show a tendency to decay along the course, and the smaller the relative wave height is, the slower the impact thickness decays along the course, and the faster the return thickness decays along the course.
Study on flow regime characteristics in 3×3 rod bundle channels based on wire mesh sensor
Authors: Long Pengchen , Shi Haopeng , Zhao Meng , Cheng Yixuan 
Published: October 2024

Abstract
The characteristics and evolution of two-phase flow are of critical importance to the safety of pressurized water reactors. Based on the double-layer wire mesh sensors in this paper, the air-water two-phase flow experiment of the 3×3 rod bundle channels is carried out at room temperature and pressure. The results show that the critical bubble diameter range for the reversal of lateral lift direction is 4 to 5.8 mm. In addition, the time-averaged void fraction for bubbly flow reveals a wall peak distribution at lower superficial gas velocities and shifts to a core peak as these velocities increase. For cap flow, the cross-distribution of cap bubbles within adjacent subchannels triggers large-scale mixing of the liquid phase between adjacent subchannels. For slug flow, large-sized bubbles develop along the axis and cross subchannel gaps to aggregate into slug-shaped bubbles, with a more pronounced distribution of the central peak of void fraction. The drift-flux models are evaluated against the experimental data, and the Ozaki model demonstrates higher precision in estimating void fraction, exhibiting a deviation of 9.8% on average.
Study on flood level forecasting of tidal reach in Puyang River basin
Authors: Zhen Yiwei , Guo Ming , Li Penghui , Chen Jianzheng , Liu Yucheng 
Published: October 2024

Abstract
The downstream section of the Puyang River is a tidal river reach subject to both upstream floods and downstream tides. This combined impact results in an unstable relationship between water level and discharge at the forecast station, making flood level forecasting challenging. This paper took the Linpu station on the Puyang River in Zhejiang Province as the research object. Based on the historical flood data, the cause of high water levels at Linpu station was analyzed. The flood levels at Linpu station were decomposed into flood increments and basic tidal levels. Using the multiple regression method, the prediction formula for upstream flood-induced water level increments was obtained. A flood level forecasting method based on the flood increment was developed. The result of the scheme accuracy evaluation indicates that this method has good forecasting performance. The flood level forecasting method for the tidal channel proposed in this paper has a simple structure and high accuracy, which provides a scientific basis for the prevention and control of flood and drought hazards and the decision-making of project scheduling in the tidal river.