Miracle-dz/UniFAR-FTUs huggingface dataset:
CVD diamond with boron-doped delta-layers deposited by microwave plasma

The semiconductor single-crystal CVD diamond (obtained from the gas phase during homoepitaxial deposition) is a wide band gap semiconductor with a gap width of 5.5 eV. CVD diamond has unique characteristics – high mobility of charge carriers, high carrier saturation speed, high electric breakdown field, the greatest thermal conductivity, high radiation and chemical resistance. On a combination of properties the CVD diamond is superior to other wide band gap semiconductors and is considered a promising material for the creation of a new generation of high-power and high-frequency electronic devices. The main difficulty in realization of the potential of CVD diamond as an electronic material is the problem of creating charge carriers inside it. Compared with conventional semiconductors, dopants in diamond have deeper energy levels that significantly impede the activation of the dopant (the degree of ionization of the dopant at room temperature is less than 1%). Thus, in order to create an acceptable level of conductivity, it is necessary to increase the level of doping, but in case of boron doping this leads to a decrease of carriers (holes) mobility in diamond. To solve the problem of boron doping of CVD diamond, an approach based on delta-doping technology is known. A thin layer of diamond heavily doped with boron (having a thickness of 1–2 nm and concentration of boron atoms higher than 5×10 cm) is formed inside an undoped defect-free diamond of high quality. To achieve high electronic properties (obtaining high hole mobility and conductivity of the layer), it is necessary to realize sharp boundaries between the doped and undoped materials. Recently, this problem has been successfully solved [1, 2]. This report provides an overview of the results of studies on the growth of electronic-quality epitaxial layers of diamond, the production of heavily boron-doped layers and the study of their characteristics.
Why are verbal nouns more verbal than finite verbs? New insights into the interpretation of the P200 verbal signature

Traditionally, languages are assumed to minimally manifest a distinction between nouns and verbs. This assumption has occasionally been debated in the theoretical linguistic literature, in particular in the context of challenging verbal noun constructions that simultaneously manifest nominal and verbal features. From a psycholinguistic perspective, one of the most promising diagnostic criteria for determining whether a given word belongs to the category NOUN or VERB is an event-related brain potential (ERP) component, P200, whose amplitude is larger for verbs than for nouns. So far, a challenge for the interpretation of the P200 has been whether this component reflects verbal (e.g., action) semantics, lexical category or verb-related morphological operation. In the present study we report an ERP experiment whose goal was to contribute to a better understanding of the nature of the “verbal” P200 component by monitoring the comprehension of Polish morphologically related finite verbs, converbs, and verbal nouns. Thereby, we manipulated the syntactic category and morphological complexity of the critical words while keeping their semantics identical. The results show that finite verbs engender a smaller amplitude of the P200 component than less prototypical “verbs” such as verbal nouns and converbs. Based on this observation, we argue that the P200 component reflects the brain activation triggered by the demands of verb-related morphological integration processes performed on the verbal base of derived forms.
A . Implementation Details A . 1 . The Details of Commercial Services

2) Face anti-spoofing APIs: Currently, the mainstream face anti-spoofing solutions include cooperative living detection and non-cooperative living detection (silent living detection). Cooperative living detection requires the user to complete some specified action according to the prompt, and then output the live verification. As a comparison, silent live detection directly aims to directly judge whether the face in front of the machine is real or fake, which is widely used and studied due to convenience and practicability for users. Therefore, we mainly focus on silent living detection in this paper, e.g., FaceID, SenseID, considering their practicability and millions of API calls every day.
PREDICTING KEY FACTORS IN AGRICULTURAL DATABASES FOR THAI FARMERS TO MAKE THE RIGHT DECISION

In recent years, Artiﬁcial Intelligence (AI) has been widely adopted to real world applications on several domains. The Department of Agriculture Extension (DOAE) of Thailand has courageously developed a large AI-based system, namely, Personalized Data (PD), in order to deliver suggestions to millions of Thai farmer in a convenient, aﬀordable and timely fashion. While typical AI research expects experimental datasets and consumes a lot of time, this research investigates how such a system can deliver the required capabilities. There are three key factors, price, cost and yields of crops to be analyzed, in order to make it simple but meaningful to farmers. We found that artiﬁcial neural network with Multilayer Perceptron (MLP) and Random Forest (RF) models could eﬀectively predict yield, cost, and price of crops. Adjusting parameters such as learning rate and the number of hidden nodes aﬀect the accuracy of crop yield predictions. Smaller data sets required fewer hidden layers in model optimization. MLP models consistently produced more accurate yield predictions than RF models. MAPE (Mean Absolute Percentage Error) is used to measure both RF and MLP. It is found that MLP models produce accurate predictions. Similarly, RF is used to provide suggestions when time constraints are tight. We found that the accuracy is very high, and MAPE is around 5% in most cases. In some diﬃcult scenarios where data is not adequate, the results are still good, and MAPE is around 20%.
Tunable backflow in chiral nematic liquid crystals via twist-bend nematogens and surface-localised in-situ polymer protrusions

ABSTRACT Dynamic electro-optic response of the liquid crystal (LC) director shows a backflow effect that is manifested as an optical bounce in chiral nematic LCs (N*LC) during field-induced homeotropic-twisted transition. The bend elastic constant (K33) strongly influences the dynamics of backflow at the N*LC in homeotropic-twisted transition. The cyanobiphenyl LC dimers – CB7CB, CB9CB and CB11CB – possess a unique characteristic of inherent bend molecular configuration that lowers K33. With the modulation of the effective K33 in dimer-doped N*LCs, we report the tunability of the optical bounce that decreases with the increase in the length of flexible spacers in LC dimers. The doped LC dimers with short spacer lengths not only generate a strong backflow with an enhanced twist degeneracy of the LC director across the cell, but also prolong the time of disappearance of the optical bounce. Furthermore, we demonstrate the suppression of the optical bounce with surface localised polymer protrusions having 50–100 nm diameters, which allow faster dynamic relaxation process and reduced backflow. We envision a novel design of a tunable microfluidic device for precise flow control of organic or inorganic matter in LC medium that exploits the tunable backflow in LC dimer-doped N*LCs. Graphical Abstract
Continuous form-dependent focusing of non-spherical microparticles in a highly diluted suspension with the help of microfluidic spirals

Microfluidic spirals are able to focus non-spherical microparticles in diluted suspension due to the Dean effect. A secondary flow establishes in a curved channel, consisting of two counter-rotating vortices, which transport particles to an equilibrium position near the inner wall of the channel. The relevant size parameter, which is responsible for successful focusing, is the ratio between the particle diameter of a sphere and the hydraulic diameter, which is a characteristic of the microfluidic spiral. A non-spherical particle has not one but several different size parameters. This study investigated the minor and major axes, the equivalent spherical diameter, and the maximal rotational diameter as an equivalent to the spherical diameter. Using a polydimethylsiloxane (PDMS)-based microfluidic device with spirals, experiments were conducted with artificial peanut-shaped and ellipsoidal particles sized between 3 and 9 μm as well as with the bacteria Bacillus subtilis. Our investigations show that the equi.
Evaluation of Coastal Forests Recovery in Sendai Bay Area’s Iwanuma City Following the 2011 Tohoku Earthquake

—In the Sendai Bay Area of Japan’s Miyagi Prefecture, a 10-year coastal forest recovery project began in 2011 following the Tohoku Earthquake earlier that year. We should pay attention to the status of the restoration in order to maintain regional resilience in the aftermath of the earthquake and tsunami as well as to promote the recovery of agriculture and daily life. Therefore, we evaluated the recovery of coastal forests in Miyagi Prefecture following the 2011 Tohoku Earthquake. We analyzed satellite images taken by the Sentinel-2 satellite for several years following the disaster. Using the Google Earth Engine, we searched for and loaded Sentinel-2 images taken from 2016 to 2020. We concluded that the vegetation activity in the coastal forests in Iwanuma City gradually recovered during the 5-year research period. Therefore, we assume that the coastal forests in Miyagi Prefecture’s Iwanuma City have been recovering well following the 2011 Tohoku Earthquake. Nevertheless, some ﬁeld surveys or other proper methods would be needed to conﬁrm the computed results and to make a ﬁnal conclusion, because the normalized difference vegetation index (NDVI) is a relative rather than absolute value.
Block Compressed Sensing Image Reconstruction via Untrained Network Priors

 Abstract —In this paper, we consider image reconstruction methods for block compressed sensing (BCS). We model images to lie in the range of an untrained network for every subblock and combine the projected gradient descent algorithm to improve the performance. This proposed method is named BCS_PGDNET. Meanwhile, sufficient condition is provided for algorithmic convergence. We compare with the following algorithm: OMP, COSAMP, IRLS, BCS_SPL_DDWT, BCS_ FOCUSS, BCS_SPL_DCT. Experimental simulations show that BCS_PGDNET method have better recovery performance than main-stream CS methods and BCS methods with relative higher PSNR and SSIM, and tradeoffs between block size and the improved reconstruction performance are discussed. This is generation of compressed sensing (CS) with untrained network priors.
Utilizing Deep Learning Techniques to Diagnose Nodules in Lung Computed Tomography (CT) Scan Images

—There are different methods available for detecting lung cancer including CT (Computed Tomography) scan, MRI(Magnetic Resonance Imaging) scan etc. Among all methods, CT scan images are preferred more because they can detect a very small nodule in the lungs. Early treatment can be given to patients if it is diagnosed at early stages, hence reducing the number of deaths. This paper shows that Median Filter outperformed the Average, Gaussian, Laplacian, and Wiener Filters in the preprocessing stage for the removal of noise from images. Additionally, a study has been conducted on several image segmentation algorithms, such as clustering, watershed, and Thresholding segmentation. This was followed by the extraction and classification of nodules. Different performance parameters have been calculated to validate the results of the model and it is discovered that proposed model has greatest performance.
Identifying crash risk factors and high risk locations on an interstate network

Highway safety improvement projects are identified by using either (i) a site-specific or (ii) a systemic approach. In the site-specific approach, locations for improvements are ranked according to different performance measures such as critical crash rate, expected crash rate or equivalent property damage only. Alternatively, in the systemic approach, roadway characteristics such as number of lanes, shoulder width, etc. are flagged as a ‘risk’ (or ‘preventative’) feature that increases (decreases) the risk of negative outcomes. Using the Highway Safety Information System database, we seek to merge the two approaches by, first, identifying roadway factors associated with an increased occurrence of car crashes (features we call ‘risk factors’) and, subsequently, identifying roadway segments with a higher crash risk. Specifically, we model the locations of crashes as a realization from a spatial point process. We then parameterize the associated intensity surface of this spatial point process as the sum of a regression on roadway characteristics and spatially correlated error terms. Thus, through the regression piece, we identify hazardous roadway features and through the spatially correlated error terms, we identify locations of high risk.
Minmax Approach: An Efficient Technique For Image De-Noising

: This study proposes a robust wavelet shrinkage approach for image denoising. Removal of noise is critical in the image reconstruction process, but denoising of image is still a challenging task. Denoising is used to remove the noise from corrupted image, while preserving the edges and other detailed features to a larger extent. This noise is added during acquisition, transmission & reception and even in storage & retrieval processes. Wavelet shrinkage is an image denoising technique based on the concept of thresholding the wavelet coefficients. Thresholds can be of either hard or soft estimates. Hard thresholding is a keep or kill rule whereas soft thresholding shrinks the coefficients above the threshold in absolute value. We are emphasizing on soft thresholds. The key challenge of wavelet shrinkage is to find an appropriate threshold value, which is typically controlled by the signal variance .
Portfolio Optimization under Cardinality Constraints: A Comparative Study

The Cardinality Constraint-Based Optimization problem is investigated in this note. In portfolio optimization problem, the cardinality constraint allows one to invest in assets out of a universe of N assets for a prespecified value of K. It is generally agreed that choosing a “small” value of K forces the implementation of diversification in small portfolios. However, the question of how small must be K has remained unanswered. In the present work, using a comparative approach we show computationally that optimal portfolio selection with a relatively small or large number of assets, K, may produce similar results with differentiated reliabilities.
A New Sieving Approach for Solving the HNP with One Bit of Nonce by Using Built-in Modulo Arithmetic

The Hidden Number Problem (HNP) has been extensively used in the side-channel attacks against (EC)DSA and Diﬃe-Hellman. The lattice approach is a primary method of solving the HNP. In EURO-CRYPT 2021, Albrecht and Heninger constructed a new lattice to solve the HNP, which converts the HNP to the SVP. After that, their approach became the state-of-the-art lattice method of solving the HNP. But Al-brecht and Heninger’s approach has a high failure rate for solving the HNP with one bit of nonce ( 1 -bit HNP) because there are enormous vectors related to the modulus q shorter than the target vector in Albrecht and Heninger’s Lattice. To decrease the number of vectors that are shorter than the target vector and avoid the duplicated reduction, we introduce the modulo-q lattice, a residue class ring of the general lattice modulo q , where q is the modulus of the HNP. We present a new sieving algorithm to search for the shortest vectors in the modulo-q lattice. Our algorithm uses built-in modulo q arithmetic and many optimization techniques. As a result, we can solve a general 1-bit HNP ( q = 2 120 ) within 5 days and solve a general 1-bit HNP ( q = 2 128 ) within 17 days.
Comparing performance of classification algorithms to use for grading coffee’s raw quality by using image processing techniques

More specifically, this study emphases on comparing performance of classification algorithms to use for grading coffee raw quality by using image processing methods. To ease experimentation image processing phases are followed, including image acquisition, image preprocessing (image filtering and attribute selection), image analysis (segmentation, feature extraction and classification), and image understanding for raw quality image grading. Artificial Neural
Improved RepVGG ground-based cloud image classification with attention convolution

Abstract. Atmospheric clouds greatly impact Earth's radiation, hydrological cycle, and climate change. Accurate automatic recognition of cloud shape based on a ground-based cloud image is helpful for analyzing solar irradiance, water vapor content, and atmospheric motion and then predicting photovoltaic power, weather trends, and severe weather changes. However, the appearance of clouds is changeable and diverse, and their classification is still challenging. In recent years, convolution neural networks (CNNs) have made great progress in ground-based cloud image classification. However, traditional CNNs poorly associate long-distance clouds, making the extraction of global features of cloud images quite problematic. This study attempts to mitigate this problem by elaborating on a ground-based cloud image classification method based on the improved RepVGG convolution neural network and attention mechanism. Firstly, the proposed method increases the RepVGG residual branch and obtains more local detail features of cloud images through small convolution kernels. Secondly, an improved channel attention module is embedded after the residual branch fusion, effectively extracting the global features of cloud images. Finally, the linear classifier is used to classify the ground cloud images. Finally, the warm-up method is applied to optimize the learning rate in the training stage of the proposed method, making it lightweight in the inference stage and thus avoiding overfitting and accelerating the model's convergence. The proposed method is validated on the multimodal ground-based cloud dataset (MGCD) and the ground-based remote sensing cloud database (GRSCD) containing seven cloud categories, with the respective classification accuracy rate values of 98.15 % and 98.07 % outperforming those of the 10 most advanced methods used as the reference. The results obtained are considered instrumental in ground-based cloud image classification.
Portable implementation model for CFD simulations. Application to hybrid CPU/GPU supercomputers

ABSTRACT Nowadays, high performance computing (HPC) systems experience a disruptive moment with a variety of novel architectures and frameworks, without any clarity of which one is going to prevail. In this context, the portability of codes across different architectures is of major importance. This paper presents a portable implementation model based on an algebraic operational approach for direct numerical simulation (DNS) and large eddy simulation (LES) of incompressible turbulent flows using unstructured hybrid meshes. The strategy proposed consists in representing the whole time-integration algorithm using only three basic algebraic operations: sparse matrix–vector product, a linear combination of vectors and dot product. The main idea is based on decomposing the nonlinear operators into a concatenation of two SpMV operations. This provides high modularity and portability. An exhaustive analysis of the proposed implementation for hybrid CPU/GPU supercomputers has been conducted with tests using up to 128 GPUs. The main objective consists in understanding the challenges of implementing CFD codes on new architectures.
Exploring the potential mechanisms of Ferulic Acid for treating COVID-19 based on Network Pharmacology and Molecular Docking as well as experimental verification

Using network pharmacology, we aimed to examine the underlying mechanisms of FA in treating COVID-19. GeneCards and OMIM were queried for information on FA treatment and COVID-19 targets. The PPI network was obtained using the software Cytoscape. GO and KEGG enrichment analyses were used to identify biological pathways associated with the target proteins. Using AutoDock Tools, simulate molecular docking and predict the extent of FA binding to key targets. In in vitro cell experiments, A549 served as the object, various concentrations of FA were used to prepare the cell model, and qRT-PCR was used to detect the expression of TLR4, ACE, and EGFR genes. Network pharmacology screened 18 FA and COVID-19 intersection targets, enriched 1594 GO entries, and targeted the regulation of the inflammatory response. 29 KEGG pathways involving COVID-19, NF-B, and Toll-like receptor signalling were significantly enriched. Cell experiments confirm that FA can effectively inhibit the expression of TLR4, ACE, and EGFR. This study aims to shed new light on the search for potential COVID-19 treatments.
Searching for new gamma-ray sources using earth occultation imaging with the CGRO/Burst and Transient Source Experiment

xiv Chapter 1: Introduction . 1 1.1 Astrophysical Motivation . 1 1.2 Burst and Transient Source Experiment (BATSE) . 3 1.3 Gamma-Ray Burst Monitor (GBM) . 8 1.4 Plan of the Dissertation . 9 Chapter 2: BATSE and Earth Occultation Technique . 11 2.1 Earth Occultation Technique with BATSE . 12 2.1.1 Step Fitting . 14 2.1.2 Occultation Transition Duration . 15 2.1.3 Sensitivity . 17 Chapter 3: Imaging with Earth Occultation . 20 3.
Segmentasi Pembuluh Darah Pada Citra Fundus Menggunakan Isotrophic Undecimate Wavelet Transform dan Fuzzy-Region Growing

Segmentasi pembuluh darah pada citra retina menjadi hal yang substansial dalam dunia kedokteran, karena citra retina dapat digunakan untuk mendeteksi beberapa penyakit, seperti: diabetic retinopathy, hypertension, dan cardiovascular. Dokter membutuhkan waktu yang lama untuk mendeteksi pembuluh darah retina terutama pembuluh darah tipis karena diproses secara manual, sehingga diperlukan metode yang dapat membantu proses diagnosis agar lebih cepat. Pada penelitian ini menggabungkan metode isotropic undecimated wavelet transform (IUWT) dan fuzzy-region growing (FRG) untuk segmentasi pada citra fundus. Metode IUWT digunakan sebagai segmentasi awal, sedangkan FRG digunakan untuk segmentasi akhir pada pembuluh darah tebal dan tipis. Input awal yaitu citra fundus yang diambil green channel selanjutnya diproses dengan IUWT dan FRG. Output hasil segmentasi IUWT dan FRG yaitu pembuluh darah tebal dan tipis. Skenario uji coba menggunakan dua skenario yang pertama uji coba perbandingan dengan citra groundthruth, skenario kedua perbandingan hasil segmentasi IUWT dan fuzzy-region growing. Rata-rata metode IUWT dan FRG lebih tinggi yaitu 92 % daripada metode IUWT dan thresholding sebesar 83 %. ================================================================= Segmentation of the blood vessels in the retinal image becomes substantially in medicine, because the retinal image can be used to detect several diseases, such as diabetic retinopathy, hypertension, and cardiovascular. The doctor take a long time to detect retinal blood vessels especially thin blood vessels due to be processed manually, so we need automatic method that can help the diagnostic process to make it faster. In this research combines methods isotropic undecimated wavelet transform (IUWT) and fuzzy region growing (FRG) for segmenting the image of the fundus. IUWT used to obtain the first segmentation, then the FRG used to finish the segmentation in the thick and thin blood vessels. The initial input is green channel. The result of segmentation are thick and thin blood vessels. There are two scenario: the first scenario is used to trial the groundthruth comparison with the image, second scenario is used to comparison IUWT segmentation and fuzzy-region growing with the previous method IUWT and thresholding. Accuracy results show a better average yield of 92% for FRG and 83% for thresholding. The IUWT and FRG methods have been shown to segment blood vessels thick and thin both visually and accurately.