bluuebunny/crossref_metadata_2025_split huggingface dataset:
Data-driven discovery of ultraincompressible crystals from a universal correlation between bulk modulus and volumetric cohesive energy
Authors: Yuan Xiaoang , Gao Enlai 
Published: November 2023

Abstract
Bulk modulus and cohesive energy are two important quantities of condensed matter. From the interatomic energy landscape, we here derived a correlation between the bulk modulus (B) and the volumetric cohesive energy (ρ
e), i.e. B = 2(ln2)2
ρ
e/9ϵ
s
2 = kρ
e, where ϵ
s and k are the strain-to-failure of interatomic bonds and the factor of proportionality, respectively. By analyzing numerous crystals from first principles calculations, it was shown that this correlation is universally applicable to various crystals including simple substances and compounds. Most interestingly, it was found that ϵ
s of crystals with a similar structure are almost a constant, resulting in a linear relationship between B and ρ
e. Furthermore, we found that the value of k for any compound can be determined based on the rule of mixtures, i.e. k= ∑x
i
k
i, where x
i and k
i are the atomic fraction and the factor of proportionality for each element in this compound, respectively. Finally, this correlation was used to predict the bulk moduli for a vast number of crystals with known ρ
e in databases. After first principles verification of the top 50 crystals with the highest predicted bulk modulus, 25 ultraincompressible crystals with a bulk modulus greater than 400 GPa that can rival diamond (436 GPa) were discovered.
A fusion non-convex group sparsity difference method and its application in rolling bearing fault diagnosis
Authors: Wei Huiyong , Cai Gaigai , Liu Zeyu , Wang Shibin 
Published: November 2023

Abstract
Bearing fault is a common factor leading to machine failures. How to extract the periodic transient signal due to bearing faults submerged in strong noise is a challenging problem for bearing fault diagnosis. Total variation denoising is a method used to remove noise and extract features. However, it solely relies on the sparsity of the first-order difference of the signal, resulting in the loss of important features and underestimation of amplitude. Additionally, it fails to capture the periodicity of the signal. To address these issues, a novel approach called fusion non-convex group sparsity difference (FNC-GSD) method is proposed for bearing fault diagnosis. Firstly, it is recognized that noise does not exhibit sparsity in the difference domain, while transient signal exhibits group sparsity in the difference domain. This grouping property enhances the feature selection ability of sparse model. Inspired by this, the group sparsity of the transient signal in the difference domain is used to preserve the fault features as much as possible. Additionally, in order to promote sparsity of the signal itself in the time domain to preserve the potential impulse component, a l
1-norm regularization term is introduced. Furthermore, a non-convex sparsity-inducing penalty function strategy is employed to prevent amplitude underestimation. The proposed sparse model considers both the group sparsity in the difference domain and the sparsity in the time domain of the transient signal, and its solution is derived according to the majorization–minimization algorithm. And the particle swarm optimization algorithm is used to adaptively search the regularization parameters of FNC-GSD. Finally, multiple bearing fault diagnosis experiments are conducted to demonstrate the performance of the FNC-GSD. The results show that it has advantages in fault feature extraction compared with some other methods.
An interactive nuclei segmentation framework with Voronoi diagrams and weighted convex difference for cervical cancer pathology images
Authors: Yang Lin , Lei Yuanyuan , Huang Zhenxing , Geng Mengxiao , Liu Zhou , Wang Baijie , Luo Dehong , Huang Wenting , Liang Dong , Pang Zhifeng , Hu Zhanli 
Published: November 2023

Abstract

Objective. Nuclei segmentation is crucial for pathologists to accurately classify and grade cancer. However, this process faces significant challenges, such as the complex background structures in pathological images, the high-density distribution of nuclei, and cell adhesion. Approach. In this paper, we present an interactive nuclei segmentation framework that increases the precision of nuclei segmentation. Our framework incorporates expert monitoring to gather as much prior information as possible and accurately segment complex nucleus images through limited pathologist interaction, where only a small portion of the nucleus locations in each image are labeled. The initial contour is determined by the Voronoi diagram generated from the labeled points, which is then input into an optimized weighted convex difference model to regularize partition boundaries in an image. Specifically, we provide theoretical proof of the mathematical model, stating that the objective function monotonically decreases. Furthermore, we explore a postprocessing stage that incorporates histograms, which are simple and easy to handle and prevent arbitrariness and subjectivity in individual choices. Main results. To evaluate our approach, we conduct experiments on both a cervical cancer dataset and a nasopharyngeal cancer dataset. The experimental results demonstrate that our approach achieves competitive performance compared to other methods. Significance. The Voronoi diagram in the paper serves as prior information for the active contour, providing positional information for individual cells. Moreover, the active contour model achieves precise segmentation results while offering mathematical interpretability.
A comparative analysis of parallel SSHI and SEH for bistable vibration energy harvesters
Authors: Demouron Quentin , Morel Adrien , Gibus David , Benhemou Aya , Badel Adrien 
Published: November 2023

Abstract
The present work focuses on ambient vibration energy harvesting. Specifically, this article deals with bistable piezoelectric energy harvesters (PEHs) which exhibits a wider bandwidth than linear oscillators. These complex systems require an energy extraction circuit (EEC) to rectify their voltage to supply power to autonomous sensors. This EEC needs to be optimized in order to increase the harvested power and even the bandwidth of PEHs. Because of the complex dynamics of bistable PEHs, there is a lack of simple and physically-insightful models in the literature that would allow the understanding and optimization of the extraction circuit. To address this issue, the present work derives closed-form models of a bistable PEH coupled to a passive and an active synchronous EEC: respectively the standard energy harvesting (SEH) circuit and the parallel synchronized switch harvesting on inductor (P-SSHI) circuit. Experimental measurements conducted on a custom bistable PEH demonstrate the validity of the proposed models with a relative error lower than 15% on the harvested power and the bandwidth. The proposed models allow to easily understand the influence of the P-SSHI circuit on the dynamics of a bistable PEH. Moreover, a comparison of the performance of the SEH and the P-SSHI circuits, valid for any bistable generator, is proposed. The latter shows that under low electromechanical coupling and low acceleration amplitude the P-SSHI circuit leads to multiply the maximum harvested power up to 4.3 compared to the SEH circuit, and the bandwidth by a factor of 2.3.
Constructing artificial boundary condition of dispersive wave systems by deep learning neural network
Authors: Zheng Zijun , Shao Jiaru , Zhang Ziying 
Published: November 2023

Abstract
To solve one dimensional dispersive wave systems in an unbounded domain, a uniform way to establish localized artificial boundary conditions is proposed. The idea is replacing the half-infinite interval outside the region of interest with a super element which exhibits the same dynamics response. Instead of designing the detailed mechanical structures of the super element, we directly reconstruct its stiffness, mass, and damping matrices by matching its frequency-domain reaction force with the expected one. An artificial neural network architecture is thus specifically tailored for this purpose. It comprises a deep learning part to predict the response of generalized degrees of freedom under different excitation frequencies, along with a simple linear part for computing the external force vectors. The trainable weight matrices of the linear layers correspond to the stiffness, mass, and damping matrices we need for the artificial boundary condition. The training data consists of input frequencies and the corresponding expected frequency domain external force vectors, which can be readily obtained through theoretical means. In order to achieve a good result, the neural network is initialized based on an optimized spring-damper-mass system. The adaptive moment estimation algorithm is then employed to train the parameters of the network. Different kinds of equations are solved as numerical examples. The results show that deep learning neural networks can find some unexpected optimal stiffness/damper/mass matrices of the super element. By just introducing a few additional degrees of freedom to the original truncated system, the localized artificial boundary condition works surprisingly well.
Hybrid multilevel-multiwavelength signaling scheme for optical data transmission
Authors: Maximov Mikhail V , Shernyakov Yuri M , Gordeev Nikita Yu , Dubrovskii Vladimir G , Nadtochiy Alexey M , Zhukov Alexey E 
Published: November 2023

Abstract
We suggest an idea of data encoding scheme based on the switching from the ground state (GS) to excited state (ES) lasing in quantum dot (QD) lasers with increase in injection current. The groups of two bits are assigned to lasing spectra that comprise either one or both GS and/or ES lasing lines depending on injection current. We expect that the proposed encoding scheme can potentially combine some advantages of multilevel signaling and wavelength division multiplexing.
Size effects in molecular dynamic simulations of fracture in bcc iron crystals
Authors: Pařík Petr , Machová Anna , Červ Jan , Uhnáková Alena , Hora Petr 
Published: November 2023

Abstract
Three-dimensional (3D) simulations via molecular dynamics (MD) show that the brittle or ductile behavior of the atomistic samples with the edge crack (001)[110] (crack plane/crack front) depend on size of the self-similar atomistic crystals. Since the basic continuum predictions concerning cracks do not consider the random thermal atomic motion, we are restricted in this study to MD simulations with initial temperature of 0 K. For all samples tested, the crack initiation is brittle. However, the subsequent crack growth can be inhibited by twin formation on oblique planes {112}, crack branching along {011} planes and new dislocation emissions on {123} slip planes and the final fracture can also be then ductile, which depends predominantly on the thickness of the atomistic sample. The representative quantity, the atomistic fracture toughness initially increases with increasing sample thickness and later saturates near Griffith level for plane strain state along the crack front. The tested loading rates are equivalent to a cross head speed of 0.833 · 10−4 m s−1 used in one our previous experiment. These new MD results comply with the stress analysis performed by the anisotropic linear fracture mechanics (LFM) and with some experimental observations.
Investigation of Ag/ZnO/p-Si heterostructure for diode and photodiode applications in visible spectrum
Authors: Yıldız Dilber Esra , Kocyigit Adem , Yıldırım Murat 
Published: November 2023

Abstract
Photodiodes have gained great attention for lightning control and optical communication over the last two decades. To obtain faster and more sensitive photodiodes are important for industrial applications. In this study, atomic layer deposition (ALD) technique was used to fabricate ZnO interlayer on p-Si, and thermal evaporation technique was employed to deposit Ag rectifying and Al ohmic contacts on ZnO and back surface of p-Si, respectively. The UV–Vis spectrometer was used to characterize optical behaviors of the ZnO interlayer. I-V measurements were conducted to characterize of Ag/ZnO/p-Si heterostructure for various solar light power intensities of dark, 20, 40, 60, 80 and 100 mW cm−2 and at various wavelengths from 351 nm to 800 nm by 50 nm intervals. According to I-V characteristics, the device exhibited increasing current at reverse biases depending on increasing light power intensity, and this confirmed photodiode behavior. Various diode parameters such as rectifying ratio, threshold voltage, series resistance, barrier height, etc. were determined and discussed in details from forward bias characteristics to investigate diode characteristics of the Ag/ZnO/p-Si heterostructure. The photodetection parameters such as responsivity, specific detectivity and external quantum efficiency (EQE) also were investigated. The Ag/ZnO/p-Si heterostructure exhibits good photodetection performance at all visible range of electromagnetic spectrum and can be good candidate for optoelectronic applications.
Electrochemical analysis of thermally treated two dimensional zinc sulphide hexagonal nano-sheets with reduced band gap
Authors: Phogat Peeyush , Shreya  , Jha Ranjana , Singh Sukhvir 
Published: November 2023

Abstract
Significantly reduced band gap (2.0 eV) zinc sulphide (ZnS) nanoparticles are synthesized using microwave/ultraviolet/ultrasonic assisted hydrothermal route in a two step process. Initially, ZnS nanospheres are synthesized, showing a cubic structural phase with a band gap of 3.2 eV, which is further annealed at 1000 °C to get the final product. Following annealing, the cubic ZnS undergoes a phase change to wurtzite ZnS, in the form of hexagonal nanosheets showing polymorphism phenomena, along with a reduced band gap of 2 eV. The optical analysis reveals a red shift in the absorbance region, transitioning from the absorption of UV radiations in cubic ZnS to visible radiations in wurtzite ZnS. Thermogravimetric (TGA) measurements and its analysis also revealed the phase change of ZnS (cubic) to ZnS (Wurtzite) when heated at 1000 °C. Microstructural analysis reveals the formation of sheets oriented along (100) plane, which is evidenced by the interplanar spacing and lattice fringes. The photoluminscence spectra highlights quantum energy states present between the highest occupied molecular orbital (HOMO), which is 2.36 eV for cubic phase and 1.76 eV for the hexagonal phase, and the lowest unoccupied molecular orbital (LUMO), with values of −0.84 eV for cubic while −0.24 for hexagonal ZnS. The CIE coordinates for wurtzite ZnS, at X= 0.55 and Y= 0.23, corresponds to red light emission. The suitability of wurtzite phase ZnS for solar cell applications has been demonstrated through electrochemical studies using Nyquist plot and cyclic voltrammetry (CV) techniques. CV demonstrates the presence of redox peaks and reversibility of the material during the redox process. The diffusive behaviour is also confirmed by observing the variation of peak current with scan rate, following Rendle Sevick equation. The presence of Warburg diffusion in Nyquist plot indicates the efficient charge transfer dynamics of the material, suggesting a high potential for exciton formation in energy production. Consequently, this material stands as a promising candidate for efficient solar cells.
Development of a quasi-ring airy vortex beam using an all-dielectric geometric phase metasurface
Authors: Cheng Kaixiang , Hu Zheng Da , Wu Jingjing , Wang Jicheng , Khakhomov Sergei 
Published: November 2023

Abstract
The quasi-ring Airy vortex beam (QRAVB) is a special type of vortex beam was recently proposed, which performs efficiently in turbulent environments. However, bulky optical elements, such as the spatial light modulator, used for generating the QRAVB hinder its application in modern integrated optical communication devices. Here, we present a type of compact optical element developed for generating the QRAVB by using an all-dielectric geometric phase metasurface. We simulate the performance of the designed metasurface and discuss several properties, such as autofocusing and self-healing of the metasurface-generated beams, are similar to Airy beams. This type of metasurface has potential applications in integrated free-space optical communication and on-chip optical information processing in the future.
Role of morphology and composition in the transport properties of highly conducting CVD grown PtTex
Authors: Nidhi  , Singh Hardeep , Das Samaresh , Nautiyal Tashi 
Published: November 2023

Abstract
Platinum telluride (PtTex), a metallic noble transition-metal dichalcogenide, has emerged as a central candidate for magnetic and optoelectronic applications. Recently, PtTex has drawn great attention because of its large positive magnetoresistance and broadband photodetection owing to its astounding electron transport properties. Here, we report the role of morphology, texture, and composition in the transport properties of CVD-grown highly conducting PtTex. Two different compositions of Te atoms, named tellurium stoichiometric (TS) and tellurium rich (TR), with different morphology have been obtained in the PtTex films. The highest longitudinal conductivity was found to be ∼ 3.57 × 107 and 3.83 × 106 S m−1 at 200 K in the TR and TS-PtTex samples, respectively. The maximum carrier density in TR and TS-PtTex samples was found to be of the order of 1022 and 1021 cm−3, respectively. Further, the negative magnetoresistance has also been found to be ∼ −2.2% at 300 K in the presence of an in-plane magnetic field of 9 T. Our results, reporting extremely high conductivity along with negative magnetoresistance, promise exciting applications of PtTex for nanoelectronic devices.
The Atacama Cosmology Telescope: map-based noise simulations for DR6
Authors: Atkins Zachary , Duivenvoorden Adriaan J. , Coulton William R. , Qu Frank J. , Aiola Simone , Calabrese Erminia , Chesmore Grace E. , Choi Steve K. , Devlin Mark J. , Dunkley Jo , Hervías-Caimapo Carlos , Guan Yilun , Posta Adrien La , Li Zack , Louis Thibaut , Madhavacheril Mathew S. , Moodley Kavilan , Naess Sigurd , Nati Federico , Niemack Michael D. , Page Lyman , Puddu Roberto , Salatino Maria , Sifón Cristóbal , Staggs Suzanne T. , Vargas Cristian , Vavagiakis Eve M. , Wollack Edward J. 
Published: November 2023

Abstract
The increasing statistical power of cosmic microwave background (CMB) datasets requires a commensurate effort in understanding their noise properties. The noise in maps from ground-based instruments is dominated by large-scale correlations, which poses a modeling challenge. This paper develops novel models of the complex noise covariance structure in the Atacama Cosmology Telescope Data Release 6 (ACT DR6) maps. We first enumerate the noise properties that arise from the combination of the atmosphere and the ACT scan strategy. We then prescribe a class of Gaussian, map-based noise models, including a new wavelet-based approach that uses directional wavelet kernels for modeling correlated instrumental noise. The models are empirical, whose only inputs are a small number of independent realizations of the same region of sky. We evaluate the performance of these models against the ACT DR6 data by drawing ensembles of noise realizations. Applying these simulations to the ACT DR6 power spectrum pipeline reveals a ∼ 20% excess in the covariance matrix diagonal when compared to an analytic expression that assumes noise properties are uniquely described by their power spectrum. Along with our public code, mnms, this work establishes a necessary element in the science pipelines of both ACT DR6 and future ground-based CMB experiments such as the Simons Observatory (SO).
Control of laser-plasma instabilities by non-collinear polychromatic light
Authors: Zhao Yao , Yin Hongwei , Zhao Bin , Cui Zijian 
Published: November 2023

Abstract
Normal broadband lasers with collinear polychromatic components have immense potential for mitigating laser plasma instabilities (LPIs). However, the projection complexity of collinear polychromatic light (CPL) is a significant challenge owing to the demand for a large bandwidth and beamlet number. Here, we propose a theoretical LPI model and optical design for non-collinear polychromatic light (NCPL), which has a small angle

∼

4
∘

and large frequency difference ∼1% between the double-color beamlets. LPI models of the NCPL demonstrate a decoupling threshold for the shared daughter waves under a multibeam configuration. Compared with the CPL, the wavevector couplings of LPIs are further reduced by the introduced angle. Therefore, both the growth rate and saturation level of LPIs are greatly mitigated by using the NCPL. The two- and three-dimensional simulation results indicate that the NCPL reduces the absolute and convective decoupling thresholds of the CPL and is sufficient to effectively mitigate the reflectivity, hot-electron generation, and intensity of cross-beam energy transfer. An optical design for the efficient generation of ultraviolet NCPL has been presented based on the unsaturated optical parametric amplification and non-collinear sum-frequency generation.
Two-layer neural network on infinite-dimensional data: global optimization guarantee in the mean-field regime
*
Authors: Nishikawa Naoki , Suzuki Taiji , Nitanda Atsushi , Wu Denny 
Published: November 2023

Abstract
The analysis of neural network optimization in the mean-field regime is important as the setting allows for feature learning. The existing theory has been developed mainly for neural networks in finite dimensions, i.e. each neuron has a finite-dimensional parameter. However, the setting of infinite-dimensional input naturally arises in machine learning problems such as nonparametric functional data analysis and graph classification. In this paper, we develop a new mean-field analysis of a two-layer neural network in an infinite-dimensional parameter space. We first give a generalization error bound, which shows that the regularized empirical risk minimizer properly generalizes when the data size is sufficiently large, despite the neurons being infinite-dimensional. Next, we present two gradient-based optimization algorithms for infinite-dimensional mean-field networks, by extending the recently developed particle optimization framework to the infinite-dimensional setting. We show that the proposed algorithms converge to the (regularized) global optimal solution, and moreover, their rates of convergence are of polynomial order in the online setting and exponential order in the finite sample setting, respectively. To the best of our knowledge, this is the first quantitative global optimization guarantee of a neural network on infinite-dimensional input and in the presence of feature learning.