bluuebunny/crossref_metadata_2025_split huggingface dataset:
High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling
Authors: Chang Chih-Wei , Peng Junbo , Safari Mojtaba , Salari Elahheh , Pan Shaoyan , Roper Justin , Qiu Richard L J , Gao Yuan , Shu Hui-Kuo , Mao Hui , Yang Xiaofeng 
Published: January 2024

Abstract

Objective. High-resolution magnetic resonance imaging (MRI) can enhance lesion diagnosis, prognosis, and delineation. However, gradient power and hardware limitations prohibit recording thin slices or sub-1 mm resolution. Furthermore, long scan time is not clinically acceptable. Conventional high-resolution images generated using statistical or analytical methods include the limitation of capturing complex, high-dimensional image data with intricate patterns and structures. This study aims to harness cutting-edge diffusion probabilistic deep learning techniques to create a framework for generating high-resolution MRI from low-resolution counterparts, improving the uncertainty of denoising diffusion probabilistic models (DDPM). Approach. DDPM includes two processes. The forward process employs a Markov chain to systematically introduce Gaussian noise to low-resolution MRI images. In the reverse process, a U-Net model is trained to denoise the forward process images and produce high-resolution images conditioned on the features of their low-resolution counterparts. The proposed framework was demonstrated using T2-weighted MRI images from institutional prostate patients and brain patients collected in the Brain Tumor Segmentation Challenge 2020 (BraTS2020). Main results. For the prostate dataset, the bicubic interpolation model (Bicubic), conditional generative-adversarial network (CGAN), and our proposed DDPM framework improved the noise quality measure from low-resolution images by 4.4%, 5.7%, and 12.8%, respectively. Our method enhanced the signal-to-noise ratios by 11.7%, surpassing Bicubic (9.8%) and CGAN (8.1%). In the BraTS2020 dataset, the proposed framework and Bicubic enhanced peak signal-to-noise ratio from resolution-degraded images by 9.1% and 5.8%. The multi-scale structural similarity indexes were 0.970 ± 0.019, 0.968 ± 0.022, and 0.967 ± 0.023 for the proposed method, CGAN, and Bicubic, respectively. Significance. This study explores a deep learning-based diffusion probabilistic framework for improving MR image resolution. Such a framework can be used to improve clinical workflow by obtaining high-resolution images without penalty of the long scan time. Future investigation will likely focus on prospectively testing the efficacy of this framework with different clinical indications.
Conservative and nonconservative forces for Mie particles in acoustic trapping
Authors: Cheng Huimin , Zhang Xixi , Li Xiao , Ng Jack 
Published: January 2024

Abstract
A general acoustic force field can be decomposed into a conservative gradient force (GF) and a non-conservative scattering force (SF), which have very different physical and mathematical properties. However, the profiles of such forces for Mie particles are unknown, let alone their underlying physics. Here, by using a fast Fourier transform approach, we calculated the GF and SF for spherical particle of various sizes and various incident waves. For the same focused incident waves, the normalized GF and SF are similar for different particle sizes, while the total force can be quite different owing to the varying relative strength between the GF and SF. GF and SF possess symmetries that are not found in the incident waves, indicating that these physically and mathematically distinct forces have symmetries that are hidden from the beam profile. For a vortex beam carrying a well-defined topological charge, acoustic forces alone cannot trap particles.
Delayed and fractional-order dynamics of a model for earthquake induced by stress perturbations
Authors: Bisong M S , Mofor I A , Fomekong M , Tsasse L C , Mando A K , Manfouo F , Tanekou G B , Motchongom M T , Afungchui D 
Published: January 2024

Abstract
Fractional calculus and time delay provide a powerful tool to model complex systems with memory and fractal systems and, the viscoelastic systems. Earthquakes are both complex systems with long-memory and some of their faults have fractal properties and, the rocks constituting faults have viscoelastic behavior. In this paper, we examined the dynamics of the spring-block considering the fractional viscous damping force and interaction between the blocks. The shear stress response is studied using the harmonic balance method and the numerical simulations are performed through Adams-Bashforth-Moulton scheme. The effects of the fractional-order and time delay on the amplitude-frequency curves and on the transition between steady state and seismic regime are investigated. The system response shows the existence of the resonance and anti-resonance. It is appears that the resonance amplitude and resonance frequency are strongly dependent on the time delay and fractional-order. This resonance phenomenon results in an accumulation of energy which can lead to the destabilization of the fault system. At the anti-resonance the system response has minimum amplitude, and the blocks move without accumulated energy. The results show that the delay and fractional viscous can affect the properties of the rock, which is characterized by the modified linear stiffness coefficient, modified linear damping coefficient and the modified linear friction coefficient. The stability and hopf bifurcation are investigated and the critical value of the time delay for the occurrence of Hopf bifurcation is determined. The transition from stationary state to the periodic orbit and vice-versa through the hopf bifurcation is observed in the system.
Analysis of misaligned optics effects on beam pointing stability in dual-pass optical system
Authors: Li Weiwei , Chen Fei , Zhang Kuo , Wang Jiamin , Lv Zhe 
Published: January 2024

Abstract
The high-power laser device utilizes a multi-pass amplification system to achieve elevated power laser output. Laser beam is transmitted several times through the amplifier, and the pointing stability is a critical factor affecting the efficiency of the system. Any perturbation of the optics during laser transmission can cause misalignment and further lead to beam pointing errors. Laser beam propagation simulation was conducted using the ray tracing method. Equivalent experiment was performed using a laser with a wavelength of 532 nm, and the centroid position data of the laser beam were collected to characterize the effect of the optics misalignment on laser pointing. A model evaluating the sensitivity of the optics was constructed, and the analysis of how various optics impact pointing accuracy was conducted. Results indicate that, a linear relationship exists between the misalignment of the optic and the pointing shift and angular deviation of the beam when a single optic is misaligned. If multiple optics are misaligned, the shift in centroid position of the laser beam can be calculated by adding up the shifts caused by each misaligned optic. Furthermore, the shift in the centroid position of the laser beam is the linear superposition of the misalignment of each optic.
Machine-learning-based mapping of blood oxygen saturation from dual-wavelength optoacoustic measurements
Authors: Kurakina D A , Kirillin M Yu , Khilov A V , Perekatova V V 
Published: January 2024

Abstract
We developed a novel machine-learning-based algorithm based on a gradient boosting regressor for three-dimensional pixel-by-pixel mapping of blood oxygen saturation based on dual-wavelength optoacoustic data. Algorithm training was performed on in silico data produced from Monte-Carlo-generated absorbed light energy distributions in tissue-like vascularized media for probing wavelengths of 532 and 1064 nm and the empirical instrumental function of the optoacoustic imaging setup with further validation of the independent in silico data. In vivo optoacoustic data for rabbit-ear vasculature was employed as a testing dataset. The developed algorithm allowed in vivo blood oxygen saturation mapping and showed clear differences in blood oxygen saturation values in veins at 15 °C and 43 °C due to functional arteriovenous anastomoses. These results indicated that dual-wavelength optoacoustic imaging could serve as a cost-effective alternative to complicated multiwavelength quantitative optoacoustic imaging.
Optimized operation scheme of flash-memory-based neural network online training with ultra-high endurance
Authors: Feng Yang , Sun Zhaohui , Qi Yueran , Zhan Xuepeng , Zhang Junyu , Liu Jing , Kobayashi Masaharu , Wu Jixuan , Chen Jiezhi 
Published: January 2024

Abstract
With the rapid development of machine learning, the demand for high-efficient computing becomes more and more urgent. To break the bottleneck of the traditional Von Neumann architecture, computing-in-memory (CIM) has attracted increasing attention in recent years. In this work, to provide a feasible CIM solution for the large-scale neural networks (NN) requiring continuous weight updating in online training, a flash-based computing-in-memory with high endurance (109 cycles) and ultra-fast programming speed is investigated. On the one hand, the proposed programming scheme of channel hot electron injection (CHEI) and hot hole injection (HHI) demonstrate high linearity, symmetric potentiation, and a depression process, which help to improve the training speed and accuracy. On the other hand, the low-damage programming scheme and memory window (MW) optimizations can suppress cell degradation effectively with improved computing accuracy. Even after 109 cycles, the leakage current (I
off) of cells remains sub-10pA, ensuring the large-scale computing ability of memory. Further characterizations are done on read disturb to demonstrate its robust reliabilities. By processing CIFAR-10 tasks, it is evident that ~90% accuracy can be achieved after 109 cycles in both ResNet50 and VGG16 NN. Our results suggest that flash-based CIM has great potential to overcome the limitations of traditional Von Neumann architectures and enable high-performance NN online training, which pave the way for further development of artificial intelligence (AI) accelerators.
Electrical properties and structural optimization of GaN/InGaN/GaN tunnel junctions grown by molecular beam epitaxy
Authors: Fang Jun , Zhang Fan , Yang Wenxian , Tian Aiqin , Liu Jianping , Lu Shulong , Yang Hui 
Published: January 2024

Abstract
The InGaN films and GaN/InGaN/GaN tunnel junctions (TJs) were grown on GaN templates with plasma-assisted molecular beam epitaxy. As the In content increases, the quality of InGaN films grown on GaN templates decreases and the surface roughness of the samples increases. V-pits and trench defects were not found in the AFM images. p++-GaN/InGaN/n++-GaN TJs were investigated for various In content, InGaN thicknesses and doping concentration in the InGaN insert layer. The InGaN insert layer can promote good interband tunneling in GaN/InGaN/GaN TJ and significantly reduce operating voltage when doping is sufficiently high. The current density increases with increasing In content for the 3 nm InGaN insert layer, which is achieved by reducing the depletion zone width and the height of the potential barrier. At a forward current density of 500 A/cm2, the measured voltage was 4.31 V and the differential resistance was measured to be 3.75 × 10−3 Ω·cm2 for the device with a 3 nm p++-In0.35Ga0.65N insert layer. When the thickness of the In0.35Ga0.65N layer is closer to the “balanced” thickness, the TJ current density is higher. If the thickness is too high or too low, the width of the depletion zone will increase and the current density will decrease. The undoped InGaN layer has a better performance than n-type doping in the TJ. Polarization-engineered tunnel junctions can enhance the functionality and performance of electronic and optoelectronic devices.
Statistical mechanics of the maximum-average submatrix problem
Authors: Erba Vittorio , Krzakala Florent , Pérez Ortiz Rodrigo , Zdeborová Lenka 
Published: January 2024

Abstract
We study the maximum-average submatrix problem, wherein given an N × N matrix J, one needs to find the k × k submatrix with the largest average number of entries. We investigate the problem for random matrices J, whose entries are i.i.d. random variables, by mapping it to a variant of the Sherrington–Kirkpatrick spin-glass model at fixed magnetisation. We analytically characterise the phase diagram of the model as a function of the submatrix average and the size of the submatrix k in the limit

N
→
∞

. We consider submatrices of size

k
=
m
N

with

0
<
m
<
1

. We find a rich phase diagram, including dynamical, static one-step replica symmetry breaking (1-RSB) and full-step RSB. In the limit of m → 0, we find a simpler phase diagram featuring a frozen 1-RSB phase, where the Gibbs measure comprises exponentially many pure states each with zero entropy. We discover an interesting phenomenon, reminiscent of the phenomenology of the binary perceptron: there are efficient algorithms that provably work in the frozen 1-RSB phase.
Design and first experience of the prototype pixel layers for the ALICE FoCal
Authors: Bodova Tea , Alme Johan , Borshchov Viatcheslav , van den Brink Anthony , Groettvik Ola , Protsenko Maksym , Röhrich Dieter , Tymchuk Ihor , Ullaland Kjetil , Yang Shiming ,   the ALICE collaboration
Published: January 2024

Abstract
The University of Bergen is involved in developing two calorimeters: the pixel section of
the Electromagnetic Forward Calorimeter for the ALICE Upgrade to be installed during LS3 for
data-taking in the period 2029–2032 and the Digital Tracking Calorimeter for the proton Computed
Tomography prototype. Both designs utilize pixel sensors and require a reliable connection to the
readout which is positioned approximately 5 meters away. Furthermore, the structural design of
these calorimeters calls for a compact layer assembly. This paper provides the strategies
deployed to meet these critical requirements, focusing on the implementation of the chip-on-flex
assembly, and the ultrasonic welding techniques. It further describes other design considerations
essential for maintaining good signal integrity. Finally, it gives insight into the experiences
and challenges faced while working with the prototypes, both in the laboratory and test beam
setups.
Measurement of secondary neutron spectra induced by 480 MeV proton and 430 MeV/u 4He beams with a thick aluminum target
Authors: Di Chicco A. , Boscolo D. , Luoni F. , Kozlova E. , Weber U. , Durante M. , Zboril M. 
Published: January 2024

Abstract
Knowledge of the characteristics of secondary neutrons produced by the interaction of
Galactic Cosmic Radiation with spacecraft shielding materials is becoming increasingly important
for predicting and mitigating biological risks of space explorers during deep-space travel. Hadron
accelerators for medical applications are well suited to reproduce part of the conditions found in
deep-space in terms of ion species and energies. The objectives of this work are to measure the
secondary neutron spectra produced by proton and helium ion beams hitting an aluminum target with
energies that correspond to the Galactic Cosmic Radiation peak during solar minimal activity and
to validate and compare physical models of Monte Carlo simulations. Neutron spectra were measured
with the extended-range Bonner sphere system NEMUS at two positions, 0° and 90° relative to the
direction of the primary ion beam. The experimental setup consisted of 480 MeV proton and 430 MeV/u 4He beams colliding with a 30×30×63.5 cm3 aluminum
target. The experimental neutron spectra were analyzed using the MAXED unfolding code and compared
to several Monte Carlo simulation codes. The results show deviations in terms of the shape of the
neutron energy distributions ranging between 1% and 14% and of the integral quantities of
fluence and ambient dose equivalent ranging between 1% and 5.2%.
Ionizing radiation influence on 28-nm MOS transistor's low-frequency noise characteristics
Authors: Apro M. , Michalowska-Forsyth A. 
Published: January 2024

Abstract
In this paper, we explore the transitions of low-frequency noise characteristics in high-k metal-gate bulk CMOS transistors induced by Total Ionizing Dose (TID). Due to the strong bias dependence of the noise characteristics, differentiating between noise shifts caused by the effective biasing change and the contribution of the newly generated traps becomes extremely challenging. In order to better understand the effects of irradiation, transistor noise had to be characterized at several biasing points, both in linear and saturation regions, before and after exposure to 1 Grad (SiO2) of TID. Correlation between shifts in time and frequency domain is presented in this work, along with possible explanations for each variation that occurs. We present examples of irradiation-generated Random Telegraph Noise (RTN) defects as well as various TID effects on noise Power Spectral Density (PSD) curves with pre-existing RTN sources.
Bionic study of distance-azimuth discrimination of multi-scattered point objects in bat bio-sonar
Authors: Wang Feng , Chen Ming 
Published: January 2024

Abstract
This paper presents a novel approach to enhance the discrimination capacity of multi-scattered point objects in bat bio-sonar. A broadband interferometer mathematical model is developed, incorporating both distance and azimuth information, to simulate the transmitted and received signals of bats. The Fourier transform is employed to simulate the preprocessing step of bat information for feature extraction. Furthermore, the bat bio-sonar model based on convolutional neural network (BS-CNN) is constructed to compensate for the limitations of conventional machine learning and CNN networks, including three strategies: Mix-up data enhancement, joint feature and hybrid atrous convolution module. The proposed BS-CNN model emulates the perceptual nerves of the bat brain for distance-azimuth discrimination and compares with four conventional classifiers to assess its discrimination efficacy. Experimental results demonstrate that the overall discrimination accuracy of the BS-CNN model is 93.4%, surpassing conventional CNN networks and machine learning methods by at least 5.9%. This improvement validates the efficacy of the BS-CNN bionic model in enhancing the discrimination accuracy in bat bio-sonar and offers valuable references for radar and sonar target classification.
Climate, fire, and anthropogenic disturbance determine the current global distribution of tropical forest and savanna
Authors: Williamson Grant J , Tng David YP , Bowman David MJS 
Published: January 2024

Abstract
Tropical forest and savanna biomes are pivotal in the functioning of the Earth system. Both are biodiverse and under increasing threat due to land clearing and anthropogenic climate change, and play important roles in the global carbon cycle, through maintenance of a large carbon pool in tropical forests, and exchange in savannas through extensive landscape fires. Reliable mapping of tropical forest and savanna is essential to understand how the current distribution of these vegetation types is controlled by climate land clearing and fire. Using Google Maps satellite imagery, we manually classified 24 239 random points as forest, savanna, or anthropogenic landscapes within the tropics and applied this novel dataset to defining the climatic zone where forest and savanna exist as alternative states. Because fire and climate are correlated, we developed separate geospatial models to rank the importance of climate, topography, and human influence on vegetation present. This modeling confirmed that those areas with more fires had lower probabilities of tropical forest, that forest was most likely in areas with high mean annual rainfall with little seasonal variation in precipitation, and that anthropogenic factors disrupt this environmental predictability. We also identified areas where tropical forest and savanna both co-occur, but these were relatively uncommon. These relationships suggest that future drier climates projected under anthropogenic climate change, combined with clearing and burning that have reduced tropical forest extent to a subset of its theoretical distribution, will lead to irreversible loss of tropical forests. Our modeling provides global mapping that can be used track further changes to distribution of tropical forests.
On the hierarchy and fine structure of blowups and gradient catastrophes for multidimensional homogeneous Euler equation
*
Authors: Konopelchenko B G , Ortenzi G 
Published: January 2024

Abstract
Blowups of derivatives and gradient catastrophes for the n-dimensional homogeneous Euler equation are discussed. It is shown that, in the case of generic initial data, the blowups exhibit a fine structure in accordance with the admissible ranks of certain matrix generated by the initial data. Blowups form a hierarchy composed by n + 1 levels with the singularity of derivatives given by

∂

u
i

/

∂

x
k

∼

|

δ

x

|

−
(
m
+
1
)

/

(
m
+
2
)

,

m
=
1
,
…
,
n

along certain critical directions. It is demonstrated that in the multi-dimensional case there is certain bounded linear superposition of blowup derivatives. Particular results for the potential motion are presented too. Hodograph equations are basic tools of the analysis.
Enhancement of lacrimal gland cell function by decellularized lacrimal gland derived hydrogel
Authors: Wiebe-Ben Zakour Katharina E , Kaya Sema , Matros Julia C , Hacker Michael C , Cheikh-Rouhou Amina , Spaniol Kristina , Geerling Gerd , Witt Joana 
Published: January 2024

Abstract
Sustainable treatment of aqueous deficient dry eye (ADDE) represents an unmet medical need and therefore requires new curative and regenerative approaches based on appropriate in vitro models. Tissue specific hydrogels retain the individual biochemical composition of the extracellular matrix and thus promote the inherent cell´s physiological function. Hence, we created a decellularized lacrimal gland (LG) hydrogel (dLG-HG) meeting the requirements for a bioink as the basis of a LG model with potential for in vitro ADDE studies. Varying hydrolysis durations were compared to obtain dLG-HG with best possible physical and ultrastructural properties while preserving the original biochemical composition. A particular focus was placed on dLG-HG´s impact on viability and functionality of LG associated cell types with relevance for a future in vitro model in comparison to the unspecific single component hydrogel collagen type-I (Col) and the common cell culture substrate Matrigel. Proliferation of LG epithelial cells (EpC), LG mesenchymal stem cells, and endothelial cells cultured on dLG-HG was enhanced compared to culture on Matrigel. Most importantly with respect to a functional in vitro model, the secretion capacity of EpC cultured on dLG-HG was higher than that of EpC cultured on Col or Matrigel. In addition to these promising cell related properties, a rapid matrix metalloproteinase-dependent biodegradation was observed, which on the one hand suggests a lively cell–matrix interaction, but on the other hand limits the cultivation period. Concluding, dLG-HG possesses decisive properties for the tissue engineering of a LG in vitro model such as cytocompatibility and promotion of secretion, making it superior to unspecific cell culture substrates. However, deceleration of biodegradation should be addressed in future experiments.