bluuebunny/crossref_metadata_2025_split huggingface dataset:
Large scale analogue experiments in rock mechanics: new experimental facility, 3D-printed material and insertion of force transducers for stresses measurement
Authors: Faure-Catteloin Delphine , Deck Olivier , Bonnelye Audrey , Abi-Aad Emilio , Fenneteau Baptiste , Gaillard Laura , Conin Marianne 
Published: October 2024

Abstract
The aim of this paper is to present a new experimental device that was designed to perform analogue rock mechanics experiments with 3D-printed material. The device consists in the creation of two rectangular excavations (mining tunnels) in slices of 3D sand printed material. This is a true-triaxial test device that allows us to study the mechanical behaviour of reduced scale models of underground structures. The experimental protocol entails the horizontal loading of the specimen, followed by the excavation of two galleries while the specimen is maintained under a constant load. The results of the test carried out in DiMiTri show a generally logical evolution of the sensors with an increase in vertical stresses induced by the presence of excavations. This paper discusses the upcoming challenges in terms of instrumentation that will be encountered, together with the new opportunities for future research.
Optimization of the memristor fabrication based on graph convolutional network
Authors: Wu Qiyuan , Jia Han , Tang Wenchao , Dongale Tukaram D , Cai Hongling , Wu Xiaoshan 
Published: October 2024

Abstract
In this article, a machine learning model for accurately predicting the performance of unknown memristors is constructed by employing a graph convolutional network approach. Thickness and elemental composition are used to transform memristors into graph-structured data. This model exhibits high accuracy and, based on extensive training with a certain type of memristor data, can be applied to novel memristors and give rapid predictions of the performance with only a small-batch sample reported in the literature, showing the potential for excellent transfer learning. This model is also applied to predict the performance of halide memristors, which have received less attention in current research, and it is indeed that a halide perovskite memristor with potential high switching ratio is predicted.
Generalized kudryashov and extended auxiliary equation methods for novel solitons solutions to (1+1)-dimensional doubly dispersive equation of murnaghan’s rod
Authors: Adnan Mirza , Ahmed Naveed , Rani Mehwish , Mohsin Bandar Bin 
Published: October 2024

Abstract
In this study, the Generalized Kudryashov method and the Extended Auxiliary Equations method are employed to investigate the strongly nonlinear (1+1)-dimensional Doubly Dispersive equation model of inhomogeneous Murnaghan’s rod for developing novel soliton solutions. These symbolic methods are famous for solving various problems involving nonlinear partial differential equations. The study finds novel solitons like hyperbolic, exponential, rational, and trigonometric. Moreover, 2D, 3D, and contour plots under tunable parameters depict graphical representations of the solutions that furnish dynamic wave behavior and insights into the material’s elastic properties under strain and stress.
Deformable bodies in two dimensions and the gauge aspect of bottle flip
Authors: Hasan Arryan , Mohd Arif 
Published: October 2024

Abstract
The water-bottle flip demonstration, that has received quite a viewership on media, involves tossing a bottle filled (up to some fraction of its volume) with water up in the air while giving it a transverse spin in such a way that it lands perfectly upright on its base. A mathematical description of the actual dynamics of the bottle flip is bound to be very complicated due to the highly non-linear motion of the fluid. A model that is amenable to an analytical treatment has been proposed in the literature. Conservation of angular momentum in the center of mass frame allows the angular velocity of the bottle to change, and in particular it becomes very low right before the landing. This is thought to be the essential feature needed for an upright landing of the bottle. In this paper we study a mathematical formalism that would be useful to improve upon the analysis of this model. We point out that the proper mathematical setting to analyze such systems is to model the content of the bottle as a deformable body. This brings out the gauge dependence of the angular velocity and shows that it can even go to zero without violating the conservation of angular momentum. The mechanism is well studied in literature under the heading of the falling cat problem. Indeed, from our point of view the bottle-flip system is a two dimensional analog of the falling cat problem (modulo the self-control of the cat).
Advancing nonlinear problem solutions: Newton-like iterative techniques for varied convergence orders using homotopy perturbation
Authors: Raza Muhammad , Toheed Daanish , Rehman Najma Abdul 
Published: October 2024

Abstract
Iterative methods are crucial in numerical analysis for approximating solutions to nonlinear problems encountered in various fields, such as biomathematics, thermodynamics, chemical engineering, and fluid mechanics. This paper introduces innovative iterative methods of convergence orders three and six to eight, developed using the homotopy perturbation technique (HPT). We address the limitations of traditional derivative-based methods by incorporating divided differences, resulting in derivative-free schemes with optimal convergence properties. We present four newly designed algorithms (HPM1, HPM2, HPM3, and HPM4) and provide a comprehensive convergence analysis. Numerical simulations and basins of attraction demonstrate the superior performance of the proposed methods compared to existing methods of the same order. The proposed methods exhibit faster convergence and larger basins of attraction, making them highly effective for solving nonlinear equations. Our new numerical methods rely on approximations and iterative processes, with each small step bringing us closer to the exact solution. Exploring these numerical methods encourages us to apply mathematics to solve challenging problems in physics, engineering, finance, and more.
Cosmic strings from Tribrid inflation
Authors: Antusch Stefan , Trailović Katarina 
Published: October 2024

Abstract
Tribrid inflation is a class of supersymmetric inflation models where the scalar component of a matter superfield, or a D-flat direction of matter fields, drives inflation. Similar to Hybrid inflation, the end of inflation is reached when a “waterfall field”, which was stabilized during inflation at a field value where the scalar potential features a large vacuum energy, starts rapidly rolling towards its minimum where a symmetry group G is spontaneously broken. In contrast to standard supersymmetric Hybrid inflation, where the inflaton is a gauge singlet, in Tribrid inflation it can be a gauge non-singlet, which, via its vacuum expectation value, already breaks the gauge symmetry. This raises the question whether topological defects can still form after inflation in this class of models, and if so, which types of defects are generated. We investigate this question systematically in realisations of Tribrid inflation where G = U(1) and we analyse under which conditions cosmic strings form. We find that in the considered cases where domain walls form, these are only temporary and do not invalidate the model realisations. We also discuss how our results can be used to analyse models of Tribrid inflation associated with the final step of SO(10) breaking, where cosmic strings can be metastable and provide a promising explanation of the recent PTA results hinting at a stochastic gravitational wave background at nanohertz frequencies.
Optical image watermarking based on orbital angular momentum holography
Authors: Zhu 朱 Jialong 家龙 , Ji 季 Jiaying 佳滢 , Wang 王 Le 乐 , Zhao 赵 Shengmei 生妹 
Published: October 2024

Abstract
We propose an optical image watermarking scheme based on orbital angular momentum (OAM) holography. Multiple topological charges (TCs, l) of OAM, as multiple cryptographic sub-keys, are embedded into the host image along with the watermark information. Moreover, the Arnold transformation is employed to further enhance the security and the scrambling time (m) is also served as another cryptographic key. The watermark image is embedded into the host image by using the discrete wavelet transformation (DWT) and singular value decomposition (SVD) methods. Importantly, the interference image is utilized to further enhance security. The imperceptibility of our proposed method is analyzed by using the peak signal-to-noise ratio (PSNR) and the histogram of the watermarked host image. To demonstrate robustness, a series of attack tests, including Gaussian noise, Poisson noise, salt-and-pepper noise, JPEG compression, Gaussian low-pass filtering, cropping, and rotation, are conducted. The experimental results show that our proposed method has advanced security, imperceptibility, and robustness, making it a promising option for optical image watermarking applications.
Spin fluctuations and orbital-selective superconductivity in Ba2CuO4−y: A FLEX study
Authors: Zheng 郑 Pei-Jun 裴俊 , Quan 全 Ya-Min 亚民 , Zou 邹 Liang-Jian 良剑 
Published: October 2024

Abstract
Recently discovered Ba2CuO4−y
provides new perspectives to the study of high-temperature superconductivity. Whereas, little is known about the spin dynamics of this material. In this work, we employ the fluctuation exchange (FLEX) approximation within the framework of spin-fluctuation mediated superconductivity to examine the behavior of the spin fluctuations of a two-orbital Hubbard model for Ba2CuO4−y
. Our calculations reveal an extraordinary spin resonance mode coupled to the superconducting state in the hole-underdoped regime. Furthermore, we confirm that the coupling between the electrons and this resonance mode can lead to a dip-like feature in the electronic spectrum as a feedback effect. In the hole-overdoped regime, by incorporating self energy into our calculations, we obtain orbital-dependent renormalizations and show how these self-energy effects can lead to the detailed gap structures and the orbital-selective superconductivity, which could not be obtained in a previous study using random phase approximation (RPA). This research may shed new light on searching for unconventional superconductors with higher transition temperatures.
Enhanced mechanical squeezing in an optomechanical system via backward stimulated Brillouin scattering
Authors: Chen 陈 Shan-Shan 珊珊 , Xie 谢 Yi-Long 亦龙 , Zhang 张 Jing-Jing 京京 , Zhang 张 Na-Na 娜娜 , Guo 郭 Yong-Rui 永瑞 , Yang 杨 Huan 桓 , Ma 马 Yong 勇 
Published: October 2024

Abstract
We investigate theoretically the enhancement of mechanical squeezing in a multimode optomechanical system by introducing a coherent phonon–photon interaction via the backward stimulated Brillouin scattering (BSBS) process. The coherent photon–phonon interaction where two optical modes couple to a Brillouin acoustic mode with a large decay rate provides an extra channel for the cooling of a Duffing mechanical oscillator. The squeezing degree and the robustness to the thermal noises of the Duffing mechanical mode can be enhanced greatly. When the Duffing nonlinearity is weak, the squeezing degree of the mechanical mode in the presence of BSBS can be improved by more than one order of magnitude compared with that in the absence of BSBS. Our scheme may be extended to other quantum systems to study novel quantum effects.
Heliospheric Effect on Solar Activity Parameters during Maximum Phase of Solar Cycle 24 (2012–2015)
Authors: Singh P. R. , Kushwaha Upendra Kr. Singh , Singh A. K. , Pant T. K. 
Published: October 2024

Abstract
The time series of daily data on solar activity proxies, namely the sunspot number (SSN), sunspot area (SSA), solar radio flux (F10.7), modified coronal index (MCI), solar flare index (FI), and cosmic ray intensity (CRI), were analyzed to understand the solar activity modulations and short-term periodicities therein. Rieger-type and other short-term periods include the solar rotational period that covers the maximum activity phase period (maximum phase of solar cycle 24). The wavelet power spectra and Periodogram of SSN, SSA, F10.7, MCI, FI, and CRI exhibited a significant short-term period. The heliospheric effects exist for a particular period (∼27 days) and they are related to the solar activity phenomena. The cross-correlation coefficients and time lags between the CRI and solar activity parameters were estimated to be ∼200, 46, 281, 39, and 47 days for SSN, SSA, F10.7, MCI, and FI respectively during the time series 2012–2015 (maximum phase of solar cycle 24).
Six lectures on linearized neural networks
Authors: Misiakiewicz Theodor , Montanari Andrea 
Published: October 2024

Abstract
This tutorial examines what can be learnt about the behavior of multi-layer neural networks from the analysis of linear models. While there are important gaps between neural networks and their linear counterparts, many useful lessons can be learnt by studying the latter.
A few preliminary remarks, before diving into the math:

•
We will not assume specific background in machine learning, let alone neural networks. On the other hand, we will assume some graduate-level mathematics, in particular probability theory (however, we will refer to the literature for complete proofs.)

•
Some of the notations that are used throughout the text will be summarized in appendix A.

•
We will keep bibliographic references in the main text to a minimum. A short guide to the literature is given in appendix B.
Kernels, data & physics
Authors: Cagnetta Francesco , Oliveira Deborah , Sabanayagam Mahalakshmi , Tsilivis Nikolaos , Kempe Julia 
Published: October 2024

Abstract
Lecture notes from the course given by Professor Julia Kempe at the summer school ‘Statistical physics of Machine Learning’ in Les Houches. The notes discuss the so-called NTK approach to problems in machine learning, which consists of gaining an understanding of generally unsolvable problems by finding a tractable kernel formulation. The notes are mainly focused on practical applications such as data distillation and adversarial robustness, examples of inductive bias are also discussed.
Les Houches lectures on deep learning at large and infinite width*
Authors: Bahri Yasaman , Hanin Boris , Brossollet Antonin , Erba Vittorio , Keup Christian , Pacelli Rosalba , Simon James B 
Published: October 2024

Abstract
These lectures, presented at the 2022 Les Houches Summer School on Statistical Physics and Machine Learning, focus on the infinite-width limit and large-width regime of deep neural networks. Topics covered include the various statistical and dynamical properties of these networks. In particular, the lecturers discuss properties of random deep neural networks, connections between trained deep neural networks, linear models, kernels and Gaussian processes that arise in the infinite-width limit, and perturbative and non-perturbative treatments of large but finite-width networks, at initialization and after training.
Computational complexity of deep learning: fundamental limitations and empirical phenomena
Authors: Barak Boaz , Carrell Annabelle , Favero Alessandro , Li Weiyu , Stephan Ludovic , Zlokapa Alexander 
Published: October 2024

Abstract
This manuscript is the lecture notes of B. Barak’s course in the Les Houches ‘Statistical Physics and Machine Learning’ summer school in 2022. It surveys various proxies for computational hardness in random planted problems, from the low-degree likelihood ratio to statistical query complexity and the Franz–Parisi criterion, as well as the various relationships between those criteria. We also present a few aspects of the study of deep learning, from both a theoretical and empirical point of view.
Research on Mixed Reality Hand Interaction Technology in Flight Simulation Teaching
Authors: Li Guang , Liu Jifang , Chang Zhiqiang , Luo Jing , Wang Yanzhao 
Published: October 2024

Abstract
The integration of mixed reality simulation training in institutional education and teaching processes can yield significant quality benefits. Non-wearable interactive methods represent a more natural form of hand interaction within flight cockpits, covering various emerging fields such as mixed reality, computer vision, and human-computer interaction. This paper is guided by a “human-centered” approach to natural human-computer interaction and proposes a virtual hand mixed reality interaction scheme based on hand natural feature points. This study introduces a hand detection, segmentation, and recognition algorithm based on skin colour and key hand feature points. Initially, the algorithm uses the Otsu adaptive threshold algorithm in the YCbCr space for skin colour detection to accommodate varying image brightness levels. Subsequently, it employs a hand Keypoint model for rapid hand detection within skin-like areas while excluding interference from other regions. During the process of hand tracking, optimization prediction is performed using the particle filter algorithm combined with the artificial fish swarm algorithm to achieve tracking accuracy within six pixels in both horizontal and vertical directions. Gesture recognition involves extracting Fourier descriptive contour features of the hand bone structure through identification of feature points. Furthermore, this study combines the optimization capabilities of the artificial fish swarm algorithm to enhance support vector machine model parameter optimization for improved recognition rates. Testing involved 800 processed images as part of a test set for gesture recognition; only five commonly used gestures within flight cockpits were recognized and classified with an accuracy rate reaching up to 98%. Finally, this research presents virtual representations of common operational gestures including natural hand states, control stick manipulation, and throttle handling positions, button activations, and rotational movements via head-mounted displays or screens. The proposed mixed reality-based hand gesture interaction system addresses challenges related to complex background interference during hand detection in cockpit environments as well as issues arising from lighting disturbances. Additionally it resolves problems associated with insufficient particles leading to tracking failures while significantly enhancing registration effects during tracking processes. Moreover it mitigates issues related to Fourier descriptive features being influenced by background variations or changes in pose along with limitations pertaining to expressing singular forms of gestures thereby improving overall classification accuracy.
Research on Power Grid Load Forecasting Method Based on Support Vector Machine
Authors: Chen Rong , Zheng Yan 
Published: October 2024

Abstract
In order to improve the accuracy of power load forecasting, a power load forecasting model with the support vector machine (SVM) was proposed in the existing load forecasting methods. Firstly, establish the basic analysis on the SVM theoretical foundation. Through the establishment of SVM prediction model, the real power data is sent into the support vector machine prediction model for learning and forecasting once it has been processed. Think about the electricity grid load change rule states, our enterprises accurately understand the local power grid operation and scheduling measures by estimating the electricity uses. Meanwhile, it optimizes the power grid management through careful scheduling means, prevent excessive consumption of resources and large energy consumption. To achieve the prediction effect, this paper builds the prediction model and analyses the data with the MATLAB. In the experiment, the real value is compared with the anticipated value. Through the analysis of more than 8000 load data, the accuracy of the training set is 96.184%, and the accuracy of the test set is 96.6134%. The outcomes demonstrate the great accuracy of the SVM prediction model in predicting electricity load.
Research on Digital Twin-Oriented UAV Data Chain Communication
Authors: Chen Zhang , Liu Lijun , Zhang Hui , Dai Xueya , Fu Yanfang 
Published: October 2024

Abstract
In this paper, for the problems of low utilization of link resources, poor flexibility, poor adaptability, and low accuracy and difficulty in verification and assessment of traditional network modelling technology in Link-16 data chain time slot allocation research, digital twin technology is introduced to combine digital twin with data chain modelling to build a digital twin-oriented data chain verification platform. And through the OPNET simulation platform, the UAV data chain simulation model of multiple terrain scenarios is established. The influence of terrain on UAV communication is explored, focusing on the irregular terrain model, including the consideration of electromagnetic wave propagation and multipath effect, and the data chain performance indexes under different terrain conditions are compared through simulation. It is found that marine terrain has a significant impact on latency and communication, while mountainous terrain results in higher latency due to complex terrain structure and vegetation cover. Digital twin oriented UAV data chain modelling is characterized by high accuracy and low latency. Therefore, the research in this paper can provide guidance for designing a more reliable and efficient Link-16 data chain communication system and provide substantial insights for military communication network planning.
Face Recognition with Siamese Networks
Authors: Niu Yuxin , Wang Zhongsheng 
Published: October 2024

Abstract
Face recognition method is a common problem in modern Internet life. In this paper, face recognition will be designed based on Siamese network. The traditional methods have some problems, such as being greatly affected by environmental factors, insufficient processing of individual differences, lack of robustness and adaptability. However, the existing Siamese network based method has better recognition effect. In this paper, a face recognition technology based on Siamese network will be proposed. In this method, the features of two face images are extracted by the same neural network, and then these feature vectors are input into a distance metric model to calculate the similarity or distance between them, which is used to compare the similarity between faces. This method reduces the training parameters of the model, improves the utilization of parameters, and the average accuracy can reach 98%. Contrasted with traditional face recognition techniques, it offers enhanced accuracy and resilience, delivering favourable outcomes in real-world applications.