Miracle-dz/UniFAR-FTUs huggingface dataset:
Convolutional Neural Networks With Intermediate Loss for 3D Super-Resolution of CT and MRI Scans

Computed Tomography (CT) scanners that are commonly-used in hospitals and medical centers nowadays produce low-resolution images, e.g. one voxel in the image corresponds to at most one-cubic millimeter of tissue. In order to accurately segment tumors and make treatment plans, radiologists and oncologists need CT scans of higher resolution. The same problem appears in Magnetic Resonance Imaging (MRI). In this paper, we propose an approach for the single-image super-resolution of 3D CT or MRI scans. Our method is based on deep convolutional neural networks (CNNs) composed of 10 convolutional layers and an intermediate upscaling layer that is placed after the first 6 convolutional layers. Our first CNN, which increases the resolution on two axes (width and height), is followed by a second CNN, which increases the resolution on the third axis (depth). Different from other methods, we compute the loss with respect to the ground-truth high-resolution image right after the upscaling layer, in addition to computing the loss after the last convolutional layer. The intermediate loss forces our network to produce a better output, closer to the ground-truth. A widely-used approach to obtain sharp results is to add Gaussian blur using a fixed standard deviation. In order to avoid overfitting to a fixed standard deviation, we apply Gaussian smoothing with various standard deviations, unlike other approaches. We evaluate the proposed method in the context of 2D and 3D super-resolution of CT and MRI scans from two databases, comparing it to related works from the literature and baselines based on various interpolation schemes, using <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> scaling factors. The empirical study shows that our approach attains superior results to all other methods. Moreover, our subjective image quality assessment by human observers reveals that both doctors and regular annotators chose our method in favor of Lanczos interpolation in 97.55% cases for an upscaling factor of <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> and in 96.69% cases for an upscaling factor of <inline-formula> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula>. In order to allow others to reproduce our state-of-the-art results, we provide our code as open source at <uri>https://github.com/lilygeorgescu/3d-super-res-cnn</uri>.
What gaze behavior do pedestrians take in interactions when they do not understand the intention of an automated vehicle?

This study focuses on establishing an external human-machine interface (eHMI) for automated vehicles (AVs) that can clearly and quickly convey driving intentions to pedestrians, thus improving the acceptability of AVs. Frist of all, this study seeks to evaluate whether pedestrians clearly receive the information from the AV. This paper proposes a hypothesis based on a decision-making model of the pedestrian. If a pedestrian does not accurately understand the driving intentions of AVs, then his/her gaze duration at AVs will increase. A pedestrian--vehicle interaction experiment was designed to verify the proposed hypothesis. An AV was used for interacting with pedestrians but whether the AV stops or not during interactions that was controlled by the experimenter. The gaze data of pedestrians and their subjective evaluations of the AV's driving intentions were observed. The experimental results supported the hypothesis, i.e., there was a correlation between the participants' gaze duration at an AV and their understanding of the AV's driving intention. Moreover, the gaze duration of most participants at a manually driving vehicle was shorter than that at an AV. Besides, we proposed two recommendations to the designers of eHMI: (1) when a pedestrian is engaged in an interaction with the AV, the driving intentions of the AV should be provided; (2) if the pedestrian still gazes at the AV after the AV displays its driving intentions, the AV should provide more clear information about its driving intentions.
Cross-Dataset Design Discussion Mining

Being able to identify software discussions that are primarily about design—which we call design mining—can improve documentation and maintenance of software systems. Existing design mining approaches have good classification performance using natural language processing (NLP) techniques, but the conclusion stability of these approaches is generally poor. A classifier trained on a given dataset of software projects has so far not worked well on different artifacts or different datasets. In this study, we replicate and synthesize these earlier results in a meta—analysis. We then apply recent work in transfer learning for NLP to the problem of design mining. However, for our datasets, these deep transfer learning classifiers perform no better than less complex classifiers. We conclude by discussing some reasons behind the transfer learning approach to design mining.
Pixel-Semantic Revise of Position Learning A One-Stage Object Detector with A Shared Encoder-Decoder

We analyze that different methods based channel or position attention mechanism give rise to different performance on scale, and some of state-of-the-art detectors applying feature pyramid are integrated with various variants convolutions with many mechanisms to enhance information, resulting in increasing runtime. This work addresses the problem by constructing an anchor-free detector with shared module consisting of encoder and decoder with attention mechanism. First, we consider different level features from backbone (e.g., ResNet-50) as the base features. Second, we feed the feature into a simple block, rather than various complex operations.Then, location and classification tasks are obtained by the detector head and classifier, respectively. At the same time, we use the semantic information to revise geometry locations. Additionally, we show that the detector is a pixel-semantic revise of position, universal, effective and simple to detect, especially, large-scale objects. More importantly, this work compares different feature processing (e.g.,mean, maximum or minimum) performance across channel. Finally,we present that our method improves detection accuracy by 3.8 AP compared to state-of-the-art MNC based ResNet-101 on the standard MSCOCO baseline.
Classification of Large-Scale High-Resolution SAR Images With Deep Transfer Learning

The classification of large-scale high-resolution synthetic aperture radar (SAR) land cover images acquired by satellites is a challenging task, facing several difficulties such as semantic annotation with expertise, changing data characteristics due to varying imaging parameters or regional target area differences, and complex scattering mechanisms being different from optical imaging. Given a large-scale SAR land cover data set collected from TerraSAR-X images with a hierarchical three-level annotation of 150 categories and comprising more than 100 000 patches, three main challenges in automatically interpreting SAR images of highly imbalanced classes, geographic diversity, and label noise are addressed. In this letter, a deep transfer learning method is proposed based on a similarly annotated optical land cover data set (NWPU-RESISC45). Besides, a top-2 smooth loss function with cost-sensitive parameters was introduced to tackle the label noise and imbalanced classes’ problems. The proposed method shows high efficiency in transferring information from a similarly annotated remote sensing data set, a robust performance on highly imbalanced classes, and is alleviating the overfitting problem caused by label noise. What is more, the learned deep model has a good generalization for other SAR-specific tasks, such as MSTAR target recognition with a state-of-the-art classification accuracy of 99.46%.
Error model and simulation for multisource fusion indoor positioning

Seamless positioning services are of a critical concern in building smart cities. In a multisource fusion indoor positioning system, providing the guidance information for the deployment of positioning sources is a key technology, which can optimize the infrastructure resources to provide higher positioning accuracy. The error models of single‐source positioning such as the received signal strength (RSS) fingerprint and the pedestrian dead reckoning (PDR) should be extended to meet the requirement of multisource indoor positioning for positioning error estimation. This paper proposes a model that combines the RSS fingerprint and PDR positioning error models for fusion positioning error simulation, which weights the PDR and RSS fingerprint positioning results and calculates the mean square error for the fusion positioning according to their positioning variances. This model is also used to establish an indoor positioning simulation system. To validate the proposed model, an experiment is performed which compared the actual positioning errors using the fusion positioning with the errors of the simulate model. The results show that the actual positioning error curves and the error curve predicted by the model are consistent. As a result, the proposed error model provides a solution for optimizing the deployment of positioning sources.
Geodesic rays and stability in the cscK problem

We prove that any finite energy geodesic ray with a finite Mabuchi slope is maximal in the sense of Berman-Boucksom-Jonsson, and reduce the proof of the uniform Yau-Tian-Donaldson conjecture for constant scalar curvature Kahler metrics to Boucksom-Jonsson's regularization conjecture about the convergence of non-Archimedean entropy functional. As further applications, we show that a uniform K-stability condition for model filtrations and the $\mathcal{J}^{K_X}$-stability are both sufficient conditions for the existence of cscK metrics. The first condition is also conjectured to be necessary. Our arguments also produce a different proof of the toric uniform version of YTD conjecture for all polarized toric manifolds. Another result proved here is that the Mabuchi slope of a geodesic ray associated to a test configuration is equal to the non-Archimedean Mabuchi invariant.
Discriminating multiple kernel learning for joint classification of optical and LiDAR data in urban area

In this paper, two contributions are made. Firstly, we propose a discriminating multiple kernel learning (DMKL) algorithm to solve the combination coefficient of basic kernels by maximizing the separability in the kernel Hilbert space in the process of MKL. The core idea of the proposed algorithm is to find the optimal projective direction, which projects the basic kernels to a discriminating kernel, by making the minimum intraclass scatter and maximum interclass scatter. Secondly, in order to make full use of the information provided by LiDAR and optical data, a strategy for fusion of heterogeneous features is proposed. DMKL is used to excavate information of different attributes in spectral, spatial and elevation level respectively. Then, composite kernel strategy is used to make a fusion for spectral, spatial and elevation information. The experiments were carried out on a hyperspectral and a multispectral image along with LiDAR data acquired over an urban area, and the experimental results show that DMKL algorithm provides the best performance among several the state-of-the-art algorithms and the proposed strategy of fusion for heterogeneous features is effective.
Intrinsic Quantum Anomalous Hall Effect in the Kagome Lattice Cs2LiMn3F12

In a kagome lattice, the time reversal symmetry can be broken by a staggered magnetic ﬂux emerging from ferromagnetic ordering and intrinsic spin-orbit coupling, leading to several well-separated nontrivial Chern bands and intrinsic quantum anomalous Hall eﬀect. Based on this idea and ab initio calculations, we propose the realization of the intrinsic quantum anomalous Hall eﬀect in the single layer Cs 2 Mn 3 F 12 kagome lattice and on the (001) surface of a Cs 2 LiMn 3 F 12 single crystal by modifying the carrier coverage on it, where the band gap is around 20 meV. Moreover, a simpliﬁed tight binding model based on the in-plane ddσ antibonding states is constructed to understand the topological band structures of the system.
Approximation properties of shearlet frames for Sobolev spaces

In this paper, we study a newly developed shearlet system on bounded domains which yields frames for $H^s(\Omega)$ for some $s\in \mathbb{N}$, $\Omega \subset \mathbb{R}^2$. We will derive approximation rates with respect to $H^s(\Omega)$ norms for functions whose derivatives admit smooth jumps along curves and demonstrate superior rates to those provided by pure wavelet systems. These improved approximation rates demonstrate the potential of the novel shearlet system for the discretization of partial differential equations. Therefore, we implement an adaptive shearlet-based algorithm for the solution of an elliptic PDE and analyze its computational complexity and convergence properties.
Calculs 3D prédictifs pour la conception d'alvéoles de stockage de déchets radioactifs HAVL = 3D predictive calculation for the design of higly-active radioactive waste storage cells

Dans le cadre de l'experimentation "alveoles HA" au laboratoire souterrain de recherche de Bure (Andra), des modelisations mecaniques couplees (comportement differe ou hydromecanique) ont ete realisees pour predire le comportement attendu des argilites a l'excavation d'une alveole (contraintes, deplacements, EDZ). Elles sont ensuite comparees avec les mesures in situ (deplacements et pression interstitielle).
Cryptanalysis of the oil and vinegar signature scheme

Several multivariate algebraic signature schemes had been proposed in recent years, but most of them had been broken by exploiting the fact that their secret trapdoors are low rank algebraic structures. One of the few remaining variants is Patarin's Oil & Vinegar scheme, which is based on a system of n quadratic forms in 2n variables of two flavors (n oil variables and n vinegar variables). The security of the scheme depends on the difficulty of distinguishing between the two types, and does not seem to be susceptible to known low rank attacks. In this paper we describe two novel algebraic attacks which can efficiently separate the oil and vinegar variables, and thus forge arbitrary signatures.
B-parameters of the complete set of matrix elements of (ΔB = 2) operators from the lattice

We compute on the lattice the ``bag'' parameters of the five ΔB = 2 operators of the supersymmetric basis, by combining their values determined in full QCD and in the static limit of HQET. The extrapolation of the QCD results from the accessible heavy-light meson masses to the B-meson mass is constrained by the static result. The matching of the corresponding results in HQET and in QCD is for the first time made at NLO accuracy in the (NDR) renormalization scheme. All results are obtained in the quenched approximation.
Superscattering pattern shaping for radially anisotropic nanowires

We achieve efficient shaping of superscattering by radially anisotropic nanowires relying on resonant multipolar interferences. It is shown that the radial anisotropy of refractive index can be employed to resonantly overlap electric and magnetic multipoles of various orders, and as a result effective superscattering with different engineered angular patterns can be obtained. We further demonstrate that such superscattering shaping relying on unusual radial anisotropy parameters can be directly realised with isotropic multi-layered nanowires, which may shed new light to many fundamental researches and various applications related to scattering particles.
Perceptions of environmental sustainability held by students in a NSW primary school

The study aims to produce an insight into how young children perceive environmental sustainability and poses the research question: What perceptions of environmental sustainability do primary school students, in their final years of primary education, hold and express? Existing research (Phipps, 1991; Spork, 1992; Walker, 1995; Walker, 1997) indicates that the most significant work in environmental education in NSW is being done at the primary school level. However, evidence relating to the research question is limited. Therefore, it is important to gather and analyse data about these perceptions of primary school students if we are to develop and implement sustainability education curricula that best support the needs of students, is relevant to primary school teaching and learning, and provides insight into appropriate strategies for the professional development of teachers. The study involves students in their final years of primary school education and explores their perceptions of environmental sustainability. It provides insight into how their perceptions are informed and how they may be best nurtured. The data are gathered from primary school students in Years 4, 5 and 6 and their teachers in one NSW primary school. Qualitative methods are used to collect data. These include students responding to three differing types of stimulus material using an x open-ended questionnaire, focus group discussions, and individual interviews. The data are gathered using methodology that is influenced by Grounded Theory and analysed with reference to the existing literature. The conclusions report that the students in the study most frequently express notions of sustainability that are underpinned by homocentric views of sustainability; proximate cause and effect scenarios; and, proposed actions based on sharing natural resources fairly while maintaining the environmental balance and behaving responsibly. Finally, an emerging framework of primary school students’ notions of environmental sustainability is presented and discussed along with the implications for sustainability curricula.
Association of the C677T Polymorphism in the MTHFR Gene with Risk of Oral Squamous Cell Carcinoma in South Indian Population

Background: The C677T mutation in the methylenetetrahydrofolate reductase (MTHFR) gene is associated with reduced enzyme activity, and a significant increase in serum homocysteine (Hcy) concentration. MTHFR C677T polymorphism was likely to play an important role in the susceptibility to OSCC. Methods: In this case-control study, we genotyped 150 OSCC patients and 150 controls, using a PCR-RFLP method. The serum total homocysteine levels were quantitatively detected using commercial enzyme immunoassay (EIA) kits from AxisShied Diagnostics Ltd, UK. Results: In the present study heterozygous CT genotype was found statistically significantly more prevalent in patients than in controls [OR 3.38, 95%CI 146-7.79, p-value 0.004], whereas TT genotype was not statistically significant [OR 2.01, 95%CI 0.18-22.4, p-value 0.05]. CC genotype may be protective for OSCC [OR 0.30, 95%CI 0.13-0.67, P-value 0.003]. The mean serum tHcy levels (mmol/L) 15.0 were higher in cases in all three CC (26.81±7.51), CT (29.26±7.67) and TT (48.85±28.07) genotypes when compared to CC (17.85±2.18), CT (17±1.0) and TT (0) with controls. However, significant variation of homocysteine levels was not observed between the genotypes of OSCC patients and controls. Conclusion: The results of the present study indicate that the frequency of MTHFR 677 ‘T’ allele is rare and CT genotype may be associated with OSCC risk. However, the polymorphisms in the MTHFR gene (677C/T) that are reported to modulate homocysteine levels were not associated with OSCC.
Cardiac Output in Coronary Occlusion Studied by the Wezler-Boeger Physical Method.∗

Summary In 4 of 5 cases of coronary occlusion the cardiac output fell rapidly, reaching its lowest level between the second and third day. The output began to rise soon after the third day and maintained a relatively constant level after the tenth day. In the 2 hypertensive patients (Cases 1 and 2) the cardiac output rose in spite of the fact that the blood pressure remained low.
Continuous-time finite element analysis of multiphase flow in groundwater hydrology

Summary. A nonlinear differential system for describing an air-water system in ground water hydrology is given. The system is written in a fractional fl o w formulation, i.e., in terms o f a saturation and a gl oba l pressure. A continuous-time version o f the finite element method is developed and analyzed for the approximation o f the saturation and pressure. The saturation equation i s treated by a Galerkin finite element method, w h il e the pressure equation is treated by a mixed fi n i te element method. The analysis is carried out first for the case where the capillary diffusion coefficient is assumed to be uniformly positive, and is then extended to a degenerate case where the diffusion coefficient can be zero. It is shown that error estimates o f optimal order in the L 2 -norm and almost optimal order in the L °°- nor m can be obtained i n the nondegenerate case. In the degenerate case w e consider a regularization o f the saturation equation by perturbing the diffusion coefficient. The norm o f error estimates depends on the severity o f the degeneracy in diffusivity, with almost optimal order convergence for non-severe degeneracy. Existence and uniqueness o f the approximate solution is also proven.
Evaluating African-Derived mtDNA Haplotype Diversity Via Independent Sample Collections

ABSTRACT Since the sample sizes for forensic cases, as well as studies of African ethnic groups, are usually low, and independent samples are rare, it has been difficult to determine whether small samples contain an accurate representation of a sampled population. In this study, two independent samples of the Bamileke had similar values with regard to standard and molecular diversity indices and selective neutrality. The two Fulbe samples were also similar, but they differed with respect to selective neutrality values. In both ethnic groups, shared haplotypes were present at low frequencies. However, the Fulbe had fewer exclusive matches with outside ethnic groups compared to the Bamileke. Nevertheless, in both ethnic groups, within-group matches were more common than matches to any other Cameroonian ethnic group. Only a small percentage of the observed mtDNA haplotypes have the potential for being ethnic and/or region specific. To assess this potential, sample sizes will have to be orders of magnitude larger in order to observe significant numbers of those relatively rare haplotypes. However, as database size is increased, haplotype sharing will correspondingly increase; and many haplotypes that are common in a single ethnic group will also be found in multiple ethnic groups.