bluuebunny/crossref_metadata_2025_split huggingface dataset:
Oxygen-vacancy-dependent high-performance α-Ga2O3 nanorods photoelectrochemical deep UV photodetectors
Authors: Qu Lihang , Ji Jie , Liu Xin , Shao Zhitao , Cui Mengqi , Zhang Yunxiao , Fu Zhendong , Huang Yuewu , Yang Guang , Feng Wei 
Published: February 2023

Abstract
Ga2O3 is a good candidate for deep ultraviolet photodetectors due to its wide-bandgap, good chemical, and thermal stability. Ga2O3-based photoelectrochemical (PEC) photodetectors attract increasing attention due to the simple fabrication and self-powered capability, but the corresponding photoresponse is still inferior. In this paper, the oxygen vacancy (Vo) engineering towards α-Ga2O3 was proposed to obtain high-performance PEC photodetectors. The α-Ga2O3 nanorods were synthesized by a simple hydrothermal method with an annealing process. The final samples were named as Ga2O3-400, Ga2O3-500, and Ga2O3-600 for annealing at 400 ℃, 500 ℃, and 600 ℃, respectively. Different annealing temperatures lead to different Vo concentrations in the α-Ga2O3 nanorods. The responsivity is 101.5 mA W−1 for Ga2O3-400 nanorod film-based PEC photodetectors under 254 nm illumination, which is 1.4 and 4.0 times higher than those of Ga2O3-500 and Ga2O3-600 nanorod film-based PEC photodetectors, respectively. The photoresponse of α-Ga2O3 nanorod film-based PEC photodetectors strongly depends on the Vo concentration and high Vo concentration accelerates the interfacial carrier transfer of Ga2O3-400, enhancing the photoresponse of Ga2O3-400 nanorod film-based PEC photodetectors. Furthermore, the α-Ga2O3 nanorod film-based PEC photodetectors have good multicycle, long-term stability, and repeatability. Our result shows that α-Ga2O3 nanorods have promising applications in deep UV photodetectors.
A novel Riemann–Hilbert approach via t-part spectral analysis for a physically significant nonlocal integrable nonlinear Schrödinger equation
Authors: Wu Jianping 
Published: February 2023

Abstract
In this paper, a novel Riemann–Hilbert (RH) approach is reported for a physically significant nonlocal integrable nonlinear Schrödinger equation. In this RH approach, the spectral analysis is performed from the t-part of the Lax pair rather than the x-part to formulate the desired RH problem. As a consequence, the resulting RH problem is determined by the t-part of the Lax pair with the x-part playing an auxiliary role. Compared with the traditional RH method, the novel RH approach in this paper has the merits that (a) the symmetry relations of the scattering data are found to be simple, (b) the general multi-soliton solutions of the equation can be easily obtained in the reflectionless cases. Additionally, to show the remarkable features of the obtained multi-soliton solutions, some special soliton dynamics are theoretically explored and then graphically illustrated by demonstrating their three-dimensional profiles.
Quaternion attention multi-scale widening network for endoscopy image super-resolution
Authors: Lin Junyu , Huang Guoheng , Huang Jun , Yuan Xiaochen , Zeng Yiwen , Shi Cheng 
Published: February 2023

Abstract

Objective. In the field of endoscopic imaging, Super-Resolution (SR) plays an important role in Manufactured Diagnosis, physicians and machine Automatic Diagnosis. Although many recent studies have been performed, by using deep convolutional neural networks on endoscopic SR, most of the methods have large parameters, which limits their practical application. In addition, almost all of these methods treat each channel equally based on the real-valued domain, without considering the difference among the different channels. Our objective is to design a SR model named Quaternion Attention Multi-scale Widening Network (QAMWN) for endoscopy images to address the above problem. Approach. QAMWN contains a stacked Quaternion Attention Multi-Scale Widening Block, that composed of Multi-scale Feature Widening Aggregation Module (MFWAM) and Quaternion Residual Channel Attention (QRCA). The MFWAM adopts multi-scale architecture with step-wise widening on feature channels for better feature extraction; and in QRCA, quaternion is introduced to construct Residual Channel Attention Mechanism, which obtains adaptively scales features by considering compact cross-channel interactions in the hyper-complex domain. Main results. To verify the efficacy of our method, it is performed on two public endoscopic datasets, CVC ClinicDB and Kvasir dataset. The experimental results show that our proposed method can achieve a better trade-off in model size and performance. More importantly, the proposed QAMWN outperforms previous state-of-the-art methods in both metrics and visualization. Significance. We propose a lightweight SR network for endoscopy and achieves better performance with fewer parameters, which helps in clinical diagnosis of endoscopy.
Memory efficient model based deep learning reconstructions for high spatial resolution 3D non-cartesian acquisitions
Authors: Miller Zachary , Pirasteh Ali , Johnson Kevin M 
Published: February 2023

Abstract

Objective. Model based deep learning (MBDL) has been challenging to apply to the reconstruction of 3D non-Cartesian MRI due to GPU memory demand because the entire volume is needed for data-consistency steps embedded in the model. This requirement makes holding even a single unroll in GPU memory difficult meaning memory efficient techniques used to increase unroll number like gradient checkpointing and deep equilibrium learning will not work well for high spatial resolution 3D non-Cartesian reconstructions without modification. Here we develop a memory efficient method called block-wise learning that combines gradient checkpointing with patch-wise training to overcome this obstacle and allow for fast and high-quality 3D non-Cartesian reconstructions using MBDL. Approach. Block-wise learning applied to a single unroll decomposes the input volume into smaller patches, gradient checkpoints each patch, passes each patch iteratively through a neural network regularizer, and then rebuilds the full volume from these output patches for data-consistency. This method is applied across unrolls during training. Block-wise learning significantly reduces memory requirements by tying GPU memory for a single unroll to user selected patch size instead of the full volume. This algorithm was used to train a MBDL architecture to reconstruct highly undersampled, 1.25 mm isotropic, pulmonary magnetic resonance angiography volumes with matrix sizes varying from 300–450 × 200–300 × 300–450 on a single GPU. We compared block-wise learning reconstructions against L1 wavelet compressed reconstructions and proxy ground truth images. Main results. MBDL with block-wise learning significantly improved image quality relative to L1 wavelet compressed sensing while simultaneously reducing average reconstruction time 38x. Significance. Block-wise learning allows for MBDL to be applied to high spatial resolution, 3D non-Cartesian datasets with improved image quality and significant reductions in reconstruction time relative to traditional iterative methods.
Unification based on the mysterious cubic-structure grouping of quarks and leptons
Authors: Mehrafarin Mohammad 
Published: February 2023

Abstract
We present a unification model based on the well-known but mysterious cubic-structure grouping of quarks and leptons that suggests an underlying symmetry connection deemed explainable by a unified theory. It results in an extension of the Pati-Salam model that consolidates the fermions into two sixteen-dimensional chiral representations of the gauge group. Moreover, the discrete cubic flavor symmetry arbitrarily conjectured in the literature is found here to be implied by the gauge symmetry. Furthermore, the gauge algebra also describes an eleven-dimensional spacetime decomposed into the usual spacetime and the seven-sphere, which is the manifold of unit octonions. This suggests such a spacetime is apt for embracing all elementary particles and their interactions.
Dynamic diffusion of water inside graphene-reinforced PU/PTFE coatings: A molecular dynamics approach
Authors: Sharma Amit , Sharma Sumit 
Published: February 2023

Abstract
Polymers are extensively used in various applications due to their excellent properties. Recently, graphene-based polymer composites have gotten significant attention due to their superior barrier properties to corrosive media. Although there are lots of experimental studies on graphene-based composites, there are very few works available at the atomistic level using molecular dynamics. The present work investigates the role of temperature on the diffusion of water and the effect of graphene-reinforcement in the polyurethane (PU) and polytetrafluoroethylene (PTFE). The results showed an increase in the diffusion at higher temperatures due to an increase in the mobility of polymeric chains at higher temperatures and hence a smooth path for the travel of water molecules. There is a significant decrease in the diffusion of water molecules in the graphene-reinforced systems when compared with neat PU and PTFE systems. The decrement in the diffusion of water in the range of 273 K to 350 K was around 79–93% for graphene-reinforce PU systems and around 58–62% in the case of graphene-reinforced PTFE systems.
Asymmetry-driven reconfigurability of magnetic vortices in hemispherical shells
Authors: Mary Anija , Edathumkandy Yadhu Krishnan , Thomas Senoy 
Published: February 2023

Abstract
Inhomogeneous magnetic configurations like vortices attract tremendous appeal as an emerging candidate in understanding nanoscale spin behaviours and utilizing their spin configurations for advanced technological applications. For vortex-driven practical applications, independent control and manipulation of both the circularity and polarity of the magnetic vortex is a prerequisite. In this study, we have shown that both the circularity and polarity of the magnetic vortex in an asymmetric hemispherical shell can be controlled by changing a single parameter - the direction of the in-plane external magnetic field. Furthermore, our results demonstrate the influence of geometrical asymmetry on the characteristics of magnetic vortices in ferromagnetic permalloy shells. These findings are expected to be helpful while designing vortex-based advanced technologies.
Tunable nonradiative recombination dynamics and charge injection of graphene quantum dots for energy conversion applications by controllable functionalization
Authors: Cui Peng , Xue Yuan 
Published: February 2023

Abstract
Understanding the dependence of optoelectronic properties and charge transfer processes on the specified functionalization pattern of the graphene quantum dot (GQD) surface is key to deciphering the photovoltaic and photocatalytic mechanisms. In the present work, the photophysical properties and energy conversion efficiency of OCH3-functionalized GQDs are investigated using first-principle calculations. Furthermore, the nonradiative electron–hole recombination dynamics is analyzed using Fermi’s golden rule. Our results show that the OCH3 group has different binding energies on the GQD surface depending on its binding configuration and forms different oxidation patterns of the GQD controlled by the reaction temperature. Both basal and edge oxidation reduce the bandgaps of GQDs due to the electron localization effect, resulting in differing chemical stability. In addition, basal oxidation provides more degrees of freedom with which to tune the wavelengths and oscillator strengths of the low absorption peaks. Although edge oxidation provides a stronger electron-injection driving force from the GQDs into the TiO2 and facilitates charge separation, it also leads to faster nonradiative recombination, which reduces charge separation. Overall, our work reveals a detailed mechanistic picture of energy conversion in oxidized GQDs.
Quantum tunneling of ultralight dark matter out of satellite galaxies
Authors: Hertzberg Mark P. , Loeb Abraham 
Published: February 2023

Abstract
The idea of ultralight scalar (axion) dark matter is theoretically appealing and may
resolve some small-scale problems of cold dark matter; so it deserves careful attention. In this
work we carefully analyze tunneling of the scalar field in dwarf satellites due to the tidal
gravitational force from the host halo. The tidal force is far from spherically symmetric; causing
tunneling along the axis from the halo center to the dwarf, while confining in the orthogonal
plane. We decompose the wave function into a spherical term plus higher harmonics, integrate out
angles, and then numerically solve a residual radial Schrödinger-Poisson system. By demanding
that the core of the Fornax dwarf halo can survive for at least the age of the universe places a
bound on the dark matter particle mass 2 × 10-22 eV ≲ m ≲ 6 ×
10-22eV. Interestingly, we show that if another very low density halo is seen, then it rules
out the ultralight scalar as core proposal completely. Furthermore, the non-condensed particles
likely impose an even sharper lower bound. We also determine how the residual satellites could be
distributed as a function of radius.
Baryogenesis, primordial black holes and MHz–GHz gravitational waves
Authors: Gehrman Thomas C. , Shams Es Haghi Barmak , Sinha Kuver , Xu Tao 
Published: February 2023

Abstract
Gravitational waves (GWs) in the MHz–GHz frequency range are motivated by a host of
early Universe phenomena such as oscillons, preheating, and cosmic strings. We point out that
baryogenesis too serves as a motivation to probe GWs in this frequency range. The connection is
through primordial black holes (PBHs): on the one hand, PBHs induce baryogenesis by Hawking
evaporating into a species that has baryon number and CP violating decays; on the other, PBHs
induce GWs through second order effects when the scalar fluctuations responsible for their
formation re-enter the horizon. We describe the interplay of the parameters responsible for
successful baryogenesis on the plane of the strain and frequency of the induced GWs, being careful
to delineate regimes where PBH domination or washout effects occur. We provide semi-analytic
scalings of the GW strain with the baryon number to entropy ratio and other parameters important
for baryogenesis. Along the way, we sketch a solution to the dark matter-baryogenesis coincidence
problem with two populations of PBHs, which leads to a double-peaked GW signal. Our results
underscore the importance of probing the ultra high frequency GW frontier.
Non-minimally coupled Natural Inflation: Palatini and Metric formalism with the recent BICEP/Keck
Authors: Bostan Nilay 
Published: February 2023

Abstract
In this work, we show the effect of the non-minimal coupling ξϕ
2
R on the
inflationary parameters by considering the single-field inflation and present the inflationary
predictions of the appealing potential for the particle physics viewpoint: Natural Inflation, an
axion-like inflaton which has a cosine-type periodic potential and the inflaton naturally emerges
as a pseudo-Nambu-Goldstone boson with a spontaneously broken global symmetry. We present the
inflationary predictions for this potential, ns
, r, and α = dns
/d ln
k. In addition, we assume standard thermal history after inflation, and using this, for
considered potential, we show compatible regions for the ns
, r within the recent BICEP/Keck
results.
Multimodal motor imagery decoding method based on temporal spatial feature alignment and fusion
Authors: Zhang Yukun , Qiu Shuang , He Huiguang 
Published: February 2023

Abstract

Objective. A motor imagery-based brain-computer interface (MI-BCI) translates spontaneous movement intention from the brain to outside devices. Multimodal MI-BCI that uses multiple neural signals contains rich common and complementary information and is promising for enhancing the decoding accuracy of MI-BCI. However, the heterogeneity of different modalities makes the multimodal decoding task difficult. How to effectively utilize multimodal information remains to be further studied. Approach. In this study, a multimodal MI decoding neural network was proposed. Spatial feature alignment losses were designed to enhance the feature representations extracted from the heterogeneous data and guide the fusion of features from different modalities. An attention-based modality fusion module was built to align and fuse the features in the temporal dimension. To evaluate the proposed decoding method, a five-class MI electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) dataset were constructed. Main results and significance. The comparison experimental results showed that the proposed decoding method achieved higher decoding accuracy than the compared methods on both the self-collected dataset and a public dataset. The ablation results verified the effectiveness of each part of the proposed method. Feature distribution visualization results showed that the proposed losses enhance the feature representation of EEG and fNIRS modalities. The proposed method based on EEG and fNIRS modalities has significant potential for improving decoding performance of MI tasks.
Confused or not: decoding brain activity and recognizing confusion in reasoning learning using EEG
Authors: Xu Tao , Wang Jiabao , Zhang Gaotian , Zhang Ling , Zhou Yun 
Published: February 2023

Abstract

Objective. Confusion is the primary epistemic emotion in the learning process, influencing students’ engagement and whether they become frustrated or bored. However, research on confusion in learning is still in its early stages, and there is a need to better understand how to recognize it and what electroencephalography (EEG) signals indicate its occurrence. The present work investigates confusion during reasoning learning using EEG, and aims to fill this gap with a multidisciplinary approach combining educational psychology, neuroscience and computer science. Approach. First, we design an experiment to actively and accurately induce confusion in reasoning. Second, we propose a subjective and objective joint labeling technique to address the label noise issue. Third, to confirm that the confused state can be distinguished from the non-confused state, we compare and analyze the mean band power of confused and unconfused states across five typical bands. Finally, we present an EEG database for confusion analysis, together with benchmark results from conventional (Naive Bayes, Support Vector Machine, Random Forest, and Artificial Neural Network) and end-to-end (Long Short Term Memory, Residual Network, and EEGNet) machine learning methods. Main results. Findings revealed: 1. Significant differences in the power of delta, theta, alpha, beta and lower gamma between confused and non-confused conditions; 2. A higher attentional and cognitive load when participants were confused; and 3. The Random Forest algorithm with time-domain features achieved a high accuracy/F1 score (88.06%/0.88 for the subject-dependent approach and 84.43%/0.84 for the subject-independent approach) in the binary classification of the confused and non-confused states. Significance. The study advances our understanding of confusion and provides practical insights for recognizing and analyzing it in the learning process. It extends existing theories on the differences between confused and non-confused states during learning and contributes to the cognitive-affective model. The research enables researchers, educators, and practitioners to monitor confusion, develop adaptive systems, and test recognition approaches.
A machine-learning-based tool for last closed-flux surface reconstruction on tokamaks
Authors: Wan Chenguang , Yu Zhi , Pau Alessandro , Sauter Olivier , Liu Xiaojuan , Yuan Qiping , Li Jiangang 
Published: February 2023

Abstract
Tokamaks allow to confine fusion plasma with magnetic fields. The prediction/reconstruction of the last closed-flux surface (LCFS) is one of the primary challenges in the control of the magnetic configuration. The evolution in time of the LCFS is determined by the interaction between the actuator coils and the internal tokamak plasma. This task requires real-time capable tools to deal with high-dimensional data and high resolution at same time, where the interaction between a wide range of input actuator coils with internal plasma state responses adds an additional layer of complexity. In this work, we present the application of a novel state-of-the-art machine learning model to LCFS reconstruction in an experimental advanced superconducting tokamak (EAST) that learns automatically from the experimental data of EAST. This architecture allows not only offline simulation and testing of a particular control strategy but can also be embedded in a real-time control system for online magnetic equilibrium reconstruction and prediction. In real-time modeling tests, our approach achieves very high accuracies, with an average similarity of over 99% in the LCFS reconstruction of the entire discharge process.
Wall stabilization of high-beta anisotropic plasmas in an axisymmetric mirror trap
Authors: Kotelnikov Igor , Prikhodko Vadim , Yakovlev Dmitri 
Published: February 2023

Abstract
The stabilization of the ‘rigid’ flute and ballooning modes both with and without the effect of additional MHD anchors in an axisymmetric mirror trap, with the help of a perfectly conducting lateral wall, is studied using a model pressure distribution of a plasma with a steep-angle neutral beam injection at the magnetic field minimum. The calculations were performed for an anisotropic plasma in a model that simulates the pressure distribution during the injection of beams of fast neutral atoms into the magnetic field minimum. It is assumed that the lateral wall is an axisymmetric shell surrounding the plasma that follows the shape of the magnetic field line and is placed at a certain distance to the plasma border. It has been found that for the effective stabilization of the modes by the lateral wall only the parameter beta (β, ratio of plasma pressure to the magnetic field pressure) must exceed some critical value β
crit. When combined with the conducting end plates imitating the MHD end stabilizers, there are two critical beta values and two stability zones

0
<
β
<

β

crit

1

and

β

crit

2

<
β
<
1

that can merge, making the entire range of allowable beta values

0
<
β
<
1

stable. The dependence of the critical betas on the degree of plasma anisotropy, the mirror ratio, and the width of the vacuum gap between the plasma and the lateral wall is studied. In contrast to the previous studies focusing on a plasma model with a sharp boundary, we calculated the stability zones for a number of diffuse radial pressure profiles and several axial magnetic field profiles.
Summary report of the 4th IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis (FDPVA)
Authors: Gonzalez de Vicente S.M. , Mazon D. , Xu M. , Pinches S. , Churchill M. , Dinklage A. , Fischer R. , Murari A. , Rodriguez-Fernandez P. , Stillerman J. , Vega J. , Verdoolaege G. 
Published: February 2023

Abstract
The objective of the Fourth Technical Meeting on Fusion Data Processing, Validation and Analysis was to provide a platform during which a set of topics relevant to fusion data processing, validation and analysis are discussed with the view of extrapolating needs to next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge-based understanding of the physical processes governing the dynamics of these plasmas. This paper presents the recent progress and achievements in the domain of plasma diagnostics and synthetic diagnostics data analysis (including image processing, regression analysis, inverse problems, deep learning, machine learning, big data and physics-based models for control) reported at the meeting. The progress in these areas highlight trends observed in current major fusion confinement devices. A special focus is dedicated on data analysis requirements for ITER and DEMO with a particular attention paid to artificial intelligence for automatization and improving reliability of control processes.