Miracle-dz/UniFAR-FTUs huggingface dataset:
Nabo (Brassica Rapus L.): um tônico natural para a saúde

In addition to basic nutrition, plant-based foods provide substantial amounts of bioactive compounds which deliver desirable health benefits. During the last decade, secondary metabolites, also known as phytochemicals, obtained from plants, have aroused special attention by researchers. Amongst such plants, the turnip contains a few valuable components which not only endorse health benefits but also provide healing properties. Various bioactive components, for example peroxidase, kaempferol, phenolic compounds, sulforaphane, organic acids, vitamin K, glucosinolates etc are highlighted in this manuscript. Likewise, numerous minerals, such as copper, manganese and calcium, and organic acids, such as sinapic and ferulic acids and their derivatives, found in different amounts in fresh greens and turnip roots, are also discussed briefly. The current paper is focused on the phenolic compounds, which act as beneficial compounds for human health and can be isolated from plant foods, especially turnip. Due to the presence of bioactive constituents, turnip imparts a positive role with respect to the hepatic injury caused by diabetes, high antioxidant activity and a good hepatoprotective role. The impact of environmental conditions and processing mechanisms on the phenolic compound composition of Brassica vegetables, with special reference to turnip, was also briefly discussed.
Research on Classification and Prediction of Kiva Crowdfunding Projects Based on GBDT

Kiva is the first crowdfunding platform to provide non-profit private to private microfinance in the world, which is committed to providing microfinance to entrepreneurs in developing countries to achieve the goal of poverty eradication. In recent years, more and more enterprises have developed Kiva's operation mode in China. Predicting the success of crowdfunding projects before or after they are released will help to improve the success rate of crowdfunding projects and reduce unnecessary labor costs. In this paper, 213500 Kiva platform crowdfunding project data are collected and integrated, and the two classification prediction model of GBDT is constructed. The results show that the accuracy, precision, recall rate, F1 score and AUC value of the model are more than 0.95. Through comparative verification, it is concluded that the kiva crowdfunding project classification prediction model based on GBDT is better than LR, DT, KNN, RF, XGBoost and LightGBM methods in AUC value, and the prediction result is ideal. This prediction method is helpful for project sponsors, investors and intermediaries to predict crowdfunding projects The results of financing.
SAMPLE FETCHING ROVER-LIGHTWEIGHT ROVER CONCEPTS FOR MARS SAMPLE RETURN

As ESA and NASA are planning the Mars Sample Return (MSR) Programme, the 2018 ExoMars-MAX-C mission is being considered as the sample acquisition element in the overall MSR architecture. The MSR mission therefore requires a lightweight mobile platform dedicated to the collection of the sample cache, before returning it safely to the Mars Ascent Vehicle. Such a Sample Fetching Rover (SFR) concept is unlike any other past and current rover missions as it must traverse 15km over a 180-sols mission to retrieve a sample cache deposited by a previous mission and return it to the mars ascent vehicle. In addition, the mass allocation for the rover is currently aiming at a target of 60kg. This paper presents the challenges involved in the design and operation of the rover element, and especially the impact at system level of a lightweight design solution.
Wi-Fi Security protocols: A survey

Over the years, the wireless technology has evolved significantly and therefore the need to protect the wireless network becomes equally substantial. The choice of the best security protocol has always been an issue for the IT security staff. This paper illustrates various types of security mechanisms of wireless LAN. These security protocols are (802.11 Wired Equivalent Privacy, 802.11i Wi-Fi Protected Access and Wi-Fi Protected Access-2). The paper gives an overview of the key concepts and the working of the wireless security protocols, their limitations and the countermeasures introduced. The three protocols are also compared on the basis of the common features so as to give a deeper insight.
Deep Learning for Autonomous Cars

The current major paradigms for vision-based autonomous driving systems are: the mediated perception approach that parses the entire scene to make a driving decision, and the behavior reﬂex approach that directly maps an input image to a driving action through regression. A third paradigm Direct perception approach was proposed in [2], which maps the input image to a small number of key perception indicators that are necessary to drive safely. We use the power of transfer learning to test the direct perception approach by ﬁne-tuning a standard AlexNet architecture pre-trained using ImageNet data. The data for training and testing are taken from the TORCS game. It is observed that the ﬁne-tuned network performs similar to the network trained from scratch as provided by [2].
The Correlation between Migraine and some Biochemical Parameters in Saudi Females

Migraine is a common disabling primary headache disorder. It affects 6% of men and 18% of women worldwide and 12.1% in Saudi Arabia. The lack of many nutrients like vitamin B12 and magnesium is associated with migraine. Migraine may be socicated with cardiovascular risk factors such as a high level of cholesterol. Ninety-one Saudi female patients with a mean age of 32.91±1.187 and fifty-four healthy Saudi females (control) with a mean age of 30.00±1.32 years participated in this study. Serum lipid profile, vitamin B12, and magnesium levels were measured in both patients and controls. The results showed a significant increase in serum cholesterol and low-density lipoprotein (LDL), and lower levels of high-density lipoprotein (HDL) for migraine patients. However, no significant difference has been observed in triglyceride serum levels. No significant differences were found in vitamin B12 and magnesium (Mg) serum levels between the two groups (migraine patients and control). In conclusion, lipid profile was shown to be associated with Saudi female patients with migraine. Vitamin B12 serum levels and mg serum levels were not significantly correlated in migraine patientsand controls.
Bandits with partially observable confounded data

We study linear contextual bandits with access to a large, confounded, offline dataset that was sampled from some fixed policy. We show that this problem is closely related to a variant of the bandit problem with side information. We construct a linear bandit algorithm that takes advantage of the projected information, and prove regret bounds. Our results demonstrate the ability to take advantage of confounded offline data. Particularly, we prove regret bounds that improve current bounds by a factor related to the visible dimensionality of the contexts in the data. Our results indicate that confounded offline data can significantly improve online learning algorithms. Finally, we demonstrate various characteristics of our approach through synthetic simulations.
Serverless Architecture for Service Robot Management System

We have developed service robot management system to facilitate effective collaboration between multiple units and types of robots in operation. This system is implemented by serverless architecture on cloud and using cellular based IoT communication. So it has not only usual cloud system advantage that it is not necessary to prepare dedicated server and network equipment, but it reduces management efforts of servers. We have tested the system with robots in a public facility, and successfully confirmed its performance and functionality.
Generalized proof of the linearized second law in general quadric corrected Einstein-Maxwell gravity

Although the entropy of black holes in any diffeomorphism invariant theory of gravity can be expressed as the Wald entropy, the issue of whether the entropy always obeys the second law of black hole thermodynamics remains open. Since the non-minimal coupling interaction between gravity and the electromagnetic field in the general quadric corrected Einstein-Maxwell gravity can sufficiently influence the expression of the Wald entropy, we would like to check whether the Wald entropy of black holes in the quadric corrected gravity still satisfies the second law. A quasistationary accreting process of black holes is first considered, which describes that black holes is perturbed by matter fields and eventually settle down to a stationary state. Two assumptions that the matter fields should obey the null energy condition and that a regular bifurcation surface exists on the background spacetime are further proposed. According to the two assumptions and the Raychaudhuri equation, we demonstrate that the Wald entropy monotonically increases along the future event horizon under the linear order approximation of the perturbation. This result indicates that the Wald entropy of black holes in the quadric corrected gravity strictly obeys the linearized second law of thermodynamics. a xinyangwang@bnu.edu.cn b Corresponding author. jiejiang@mail.bnu.edu.cn 1
Deep learning for breast cancer classification: Enhanced tangent function

Recently, deep learning using convolutional neural network (CNN) has been used successfully to classify the images of breast cells accurately. However, the accuracy of manual classification of those histopathological images is comparatively low. This research aims to increase the accuracy of the classification of breast cancer images by utilizing a patch‐based classifier (PBC) along with deep learning architecture. The proposed system consists of a deep convolutional neural network that helps in enhancing and increasing the accuracy of the classification process. This is done by the use of the PBC. CNN has completely different layers where images are first fed through convolutional layers using hyperbolic tangent function together with the max‐pooling layer, drop out layers, and SoftMax function for classification. Further, the output obtained is fed to a PBC that consists of patch‐wise classification output followed by majority voting. The results are obtained throughout the classification stage for breast cancer images that are collected from breast‐histology datasets. The proposed solution improves the accuracy of classification whether or not the images had normal, benign, in‐situ, or invasive carcinoma from 87% to 94% with a decrease in processing time from 0.45 to 0.2 s on average. The proposed solution focused on increasing the accuracy of classifying cancer in the breast by enhancing the image contrast and reducing the vanishing gradient. Finally, this solution for the implementation of the contrast limited adaptive histogram equalization technique and modified tangent function helps in increasing the accuracy.
U-Net-and-a-half: Convolutional network for biomedical image segmentation using multiple expert-driven annotations

Development of deep learning systems for biomedical segmentation often requires access to expert-driven, manually annotated datasets. If more than a single expert is involved in the annotation of the same images, then the inter-expert agreement is not necessarily perfect, and no single expert annotation can precisely capture the so-called ground truth of the regions of interest on all images. Also, it is not trivial to generate a reference estimate using annotations from multiple experts. Here we present a deep neural network, defined as U-Net-and-a-half, which can simultaneously learn from annotations performed by multiple experts on the same set of images. U-Net-and-a-half contains a convolutional encoder to generate features from the input images, multiple decoders that allow simultaneous learning from image masks obtained from annotations that were independently generated by multiple experts, and a shared low-dimensional feature space. To demonstrate the applicability of our framework, we used two distinct datasets from digital pathology and radiology, respectively. Specifically, we trained two separate models using pathologist-driven annotations of glomeruli on whole slide images of human kidney biopsies (10 patients), and radiologist-driven annotations of lumen cross-sections of human arteriovenous fistulae obtained from intravascular ultrasound images (10 patients), respectively. The models based on U-Net-and-a-half exceeded the performance of the traditional U-Net models trained on single expert annotations alone, thus expanding the scope of multitask learning in the context of biomedical image segmentation.
Visual SLAM with Graph-Cut Optimized Multi-Plane Reconstruction

This paper presents a semantic planar SLAM system that improves pose estimation and mapping using cues from an instance planar segmentation network. While the mainstream approaches are using RGB-D sensors, employing a monocular camera with such a system still faces challenges such as robust data association and precise geometric model fitting. In the majority of existing work, geometric model estimation problems such as homography estimation and piece-wise planar reconstruction (PPR) are usually solved by standard (greedy) RANSAC separately and sequentially. However, setting the inlier-outlier threshold is difficult in absence of information about the scene (i.e. the scale). In this work, we revisit these problems and argue that two mentioned geometric models (homographies/3D planes) can be solved by minimizing an energy function that exploits the spatial coherence, i.e. with graph-cut optimization, which also tackles the practical issue when the output of a trained CNN is inaccurate. Moreover, we propose an adaptive parameter setting strategy based on our experiments, and report a comprehensive evaluation on various open-source datasets.
Tropical precipitation clusters as islands on a rough water‐vapor topography

Tropical precipitation clusters exhibit power‐law frequency distributions in area and volume (integrated precipitation), implying a lack of characteristic scale in tropical convective organization. However, it remains unknown what gives rise to the power laws and how the power‐law exponents for area and volume are related to one another. Here, we explore the perspective that precipitation clusters are islands above a convective threshold on a rough column‐water‐vapor (CWV) topography. This perspective is supported by the agreement between the precipitation clusters and CWV islands in their frequency distributions as well as fractal dimensions. Power laws exist for CWV islands at different thresholds through the CWV topography, suggesting that the existence of power laws is not specifically related to local precipitation dynamics, but is rather a general feature of CWV islands. Furthermore, the frequency distributions and fractal dimensions of the clusters can be reproduced when the CWV field is modeled to be self‐affine with a roughness exponent of 0.3. Self‐affine scaling theory relates the statistics of precipitation clusters to the roughness exponent; it also relates the power‐law slopes for area and volume without involving the roughness exponent. Thus, the perspective of precipitation clusters as CWV islands provides a useful framework to consider many statistical properties of these precipitation clusters, particularly given that CWV is well‐observed over a wide range of length‐scales in the Tropics. However, the statistics of CWV islands at the convective threshold imply a smaller roughness than is inferred from the power spectrum of the bulk CWV field, and further work is needed to understand the scaling of the CWV field.
TBNet: Two-Stream Boundary-aware Network for Generic Image Manipulation Localization

Finding tampered regions in images is a hot research topic in machine learning and computer vision. Although many image manipulation location algorithms have been proposed, most of them only focus on the RGB images with different color spaces, and the frequency information that contains the potential tampering clues is often ignored. In this work, a novel end-to-end two-stream boundary-aware network (abbreviated as TBNet) is proposed for generic image manipulation localization in which the RGB stream, the frequency stream, and the boundary artifact location are explored in a unified framework. Specifically, we first design an adaptive frequency selection module (AFS) to adaptively select the appropriate frequency to mine inconsistent statistics and eliminate the interference of redundant statistics. Then, an adaptive cross-attention fusion module (ACF) is proposed to adaptively fuse the RGB feature and the frequency feature. Finally, the boundary artifact location network (BAL) is designed to locate the boundary artifacts for which the parameters are jointly updated by the outputs of the ACF, and its results are further fed into the decoder. Thus, the parameters of the RGB stream, the frequency stream, and the boundary artifact location network are jointly optimized, and their latent complementary relationships are fully mined. The results of extensive experiments performed on four public benchmarks of the image manipulation localization task, namely, CASIA1.0, COVER, Carvalho, and In-The-Wild, demonstrate that the proposed TBNet can significantly outperform state-of-the-art generic image manipulation localization methods in terms of both MCC and F1.
Suppression of Crosstalk in Superconducting Qubits Using Dynamical Decoupling

Currently available superconducting quantum processors with interconnected transmon qubits are noisy and prone to various errors. The errors can be attributed to sources such as open quantum system effects and spurious inter-qubit couplings (crosstalk). The ZZ-coupling between qubits in fixed frequency transmon architectures is always present and contributes to both coherent and incoherent crosstalk errors. Its suppression is therefore a key step towards enhancing the fidelity of quantum computation using transmons. Here we propose the use of dynamical decoupling to suppress the crosstalk, and demonstrate the success of this scheme through experiments performed on several IBM quantum cloud processors. In particular, we demonstrate improvements in quantum memory as well as the performance of single-qubit and two-qubit gate operations. We perform open quantum system simulations of the multi-qubit processors and find good agreement with the experimental results. We analyze the performance of the protocol based on a simple analytical model and elucidate the importance of the qubit drive frequency in interpreting the results. In particular, we demonstrate that the XY4 dynamical decoupling sequence loses its universality if the drive frequency is not much larger than the system-bath coupling strength. Our work demonstrates that dynamical decoupling is an effective and practical way to suppress crosstalk and open system effects, thus paving the way towards higher-fidelity logic gates in transmon-based quantum computers.
FoodLogoDet-1500: A Dataset for Large-Scale Food Logo Detection via Multi-Scale Feature Decoupling Network

Food logo detection plays an important role in the multimedia for its wide real-world applications, such as food recommendation of the self-service shop and infringement detection on e-commerce platforms. A large-scale food logo dataset is urgently needed for developing advanced food logo detection algorithms. However, there are no available food logo datasets with food brand information. To support efforts towards food logo detection, we introduce the dataset FoodLogoDet-1500, a new large-scale publicly available food logo dataset, which has 1,500 categories, about 100,000 images and about 150,000 manually annotated food logo objects. We describe the collection and annotation process of FoodLogoDet-1500, analyze its scale and diversity, and compare it with other logo datasets. To the best of our knowledge, FoodLogoDet-1500 is the first largest publicly available high-quality dataset for food logo detection. The challenge of food logo detection lies in the large-scale categories and similarities between food logo categories. For that, we propose a novel food logo detection method Multi-scale Feature Decoupling Network (MFDNet), which decouples classification and regression into two branches and focuses on the classification branch to solve the problem of distinguishing multiple food logo categories. Specifically, we introduce the feature offset module, which utilizes the deformation-learning for optimal classification offset and can effectively obtain the most representative features of classification in detection. In addition, we adopt a balanced feature pyramid in MFDNet, which pays attention to global information, balances the multi-scale feature maps, and enhances feature extraction capability. Comprehensive experiments on FoodLogoDet-1500 and other two popular benchmark logo datasets demonstrate the effectiveness of the proposed method. The code and FoodLogoDet-1500 can be found at https://github.com/hq03/FoodLogoDet-1500-Dataset.
Local Morphometry of Closed, Implicit Surfaces

Anatomical structures such as the hippocampus, liver, and bones can be analyzed as orientable, closed surfaces. This permits the computation of volume, surface area, mean curvature, Gaussian curvature, and the Euler–Poincaré characteristic as well as comparison of these morphometrics between structures of different topology. The structures are commonly represented implicitly in curve evolution problems as the zero level set of an embedding. Practically, binary images of anatomical structures are embedded using a signed distance transform. However, quantization prevents the accurate computation of curvatures, leading to considerable errors in morphometry. This paper presents a fast, simple embedding procedure for accurate local morphometry as the zero crossing of the Gaussian blurred binary image. The proposed method was validated based on the femur and fourth lumbar vertebrae of 50 clinical computed tomography datasets. The results show that the signed distance transform leads to large quantization errors in the computed local curvature. Global validation of morphometry using regression and Bland-Altman analysis revealed that the coefficient of determination for the average mean curvature is improved from 93.8%with the signed distance transform to 100%with the proposed method. For the surface area, the proportional bias is improved from -5.0% for the signed distance transform to +0.6% for the proposed method. The Euler–Poincaré characteristic is improved from unusable in the signed distance transform to 98% accuracy for the proposed method. The proposed method enables an improved local and global evaluation of curvature for purposes of morphometry on closed, implicit surfaces.