Miracle-dz/UniFAR-FTUs huggingface dataset:
INFLUENCE OF VITAMIN D ON HUMAN HEALTH (REVIEW).

Objectives - the associations between vitamin D concentrations and respiratory diseases have been assessed in a large and rapidly expanding literature. Observational studies and numerous randomized trials. Data sources: - Medline, Embase, the Cochrane Central Register of Controlled Trials, Web of Science, ClinicalTrials.gov, and the International Standard Randomized Controlled Trials Number registry from 2011 to 2021. Vitamin D plays an essential role in maintaining bone health through regulating calcium concentrations in the body. The development of vitamin D deficiency is associated with deteriorating bone health and in severe cases, hypocalcemia, rickets, and osteomalacia in children and adults. Those at greatest risk of vitamin D deficiency include patients with chronic illnesses (e.g., chronic kidney disease, cystic fibrosis, asthma, and sickle cell disease), dark-pigmented skin, poor nutrition, and infants who are exclusively breastfed. The primary source of vitamin D is sunlight exposure with nutritional intake. However, the composite literature is often confusing and has led to heated debates about the optimal concentrations of vitamin D and related guidelines for supplementation. According to the last period of research, the impact of vitamin D is actively discussing the correct functioning of the immune system. It is established that it participates in the formation of the innate and adaptive immune response. In last years appeared data from controlled trials where there are confirmed D hypovitaminosis correlations with infections. The systemic review of the randomized controlled trials and meta analysis showed the effectiveness of vitamin D supplementation for reducing morbidity with respiratory diseases. In literary sources, the impact of Vitamin D is considered to influence the duration and severity of pneumonia. The authors note that in the cases of severe and complicated pneumonia, the concentrations of vitamin D was significantly lower than in control cases. It has been proposed that the activation of the vitamin D receptor (VDR) signaling pathway may generate beneficial effects in ARDS caused by SARS-CoV-2 with decreasing the cytokine/chemokine storm, regulating the renin‑angiotensin system, modulating neutrophil activity. The systemic review of the randomized controlled trials and meta analysis showed the effectiveness of vitamin D supplementation for reducing morbidity with respiratory diseases. But most of researchers have concluded that data remain uncertain and requires confirmation in farther well designed randomized controlled trials.
Chemical principles of instability and self-organization in reacting and diffusive systems

How patterns and structures undergo symmetry breaking and self-organize within biological systems from initially homogeneous states is a key issue for biological development. The activator-inhibitor (AI) mechanism, derived from reaction-diffusion (RD) models, has been widely believed to be the elementary mechanism for biological pattern formation. This mechanism generally requires activators to be self-enhanced and diffuse more slowly than inhibitors. Here, we identify the instability sources of biological systems and derive the self-organization conditions through solving eigenvalues (dispersion relation) of the generalized RD model for two chemicals. We show that both the single AI mechanisms with long-range inhibition and activation are enough to self-organize into fully-expressed domains without the involvement of the inhibitor-inhibitor (II) mechanism, through singly enhancing the difference in self-proliferation rates of activators and inhibitors or weakening the coupling degree between them. When cross diffusion involves, both the self-enhancement and the difference in diffusion coefficients of chemicals are no longer necessary for self-organization, and the patterning mechanism can be extended to semi-inhibitor and II mechanisms. However, we show that the single activator-activator (AA) mechanism is generally unable to self-organize, even if biological domain growth is additionally involved. Moreover, adding an II system after an AI one can produce discrete and bi-stable patterns. We also observe that a higher dimensional space can solely alter the patterning principles derived from a lower dimensional space, which may be due to the instability driven by the higher degree of spatial freedom. Such results provide new insights into biological pattern formation.
Neighbor2vec: an efficient and effective method for Graph Embedding

Graph embedding techniques have led to significant progress in recent years. However, present techniques are not effective enough to capture the patterns of networks. This paper propose neighbor2vec, a neighbor-based sampling strategy used algorithm to learn the neighborhood representations of node, a framework to gather the structure information by feature propagation between the node and its neighbors. We claim that neighbor2vec is a simple and effective approach to enhancing the scalability as well as equality of graph embedding, and it breaks the limits of the existing state-of-the-art unsupervised techniques. We conduct experiments on several node classification and link prediction tasks for networks such as ogbn-arxiv, ogbn-products, ogbn-proteins, ogbl-ppa,ogbl-collab and ogbl-citation2. The result shows that Neighbor2vec's representations provide an average accuracy scores up to 6.8 percent higher than competing methods in node classification tasks and 3.0 percent higher in link prediction tasks. The neighbor2vec's representations are able to outperform all baseline methods and two classical GNN models in all six experiments.
Chameleon-neutrino conformal coupling and MSW-mediated solar neutrino deficit

A modified version of the MSW effect is studied within the framework of screening models through conformal couplings of a scalar field within matter. Such a coupling leads to a new, unknown interaction between the scalar field and the neutrino. We mainly look for the discrepancy in the total solar electron-neutrinos flux through this interaction. Since the scalar field behavior depends on the local density of matter, we observe an indirect effect of matter on flavor changes, and subsequently, on neutrino flux through neutrino decay. The paper ends by describing various probabilities and comparing the results with observational data.
Presentations and algebraic colimits of enriched monads for a subcategory of arities

We develop a general framework for studying signatures, presentations, and algebraic colimits of enriched monads for a subcategory of arities, even when the base of enrichment V is not locally presentable. When V satisfies the weaker requirement of local boundedness, the resulting framework is sufficiently general to apply to the Φ-accessible monads of Lack and Rosický and the J -ary monads of the first author, while even without local boundedness our framework captures in full generality the presentations of strongly finitary monads of Lack and Kelly as well as Wolff’s presentations of V -categories by generators and relations. Given any small subcategory of arities j : J →֒ C in an enriched category C , satisfying certain assumptions, we prove results on the existence of free J -ary monads, the monadicity of J -ary monads over J -signatures, and the existence of algebraic colimits of J -ary monads. We study a notion of presentation for J -ary monads and show that every such presentation presents a J -ary monad. Certain of our results generalize earlier results of Kelly, Power, and Lack for finitary enriched monads in the locally finitely presentable setting, as well as analogous results of Kelly and Lack for strongly finitary monads on cartesian closed categories. Our main results hold for a wide class of subcategories of arities in locally bounded enriched categories.
Mushrooms Detection, Localization and 3D Pose Estimation using RGB-D Sensor for Robotic-picking Applications

In this paper, we propose mushrooms detection, localization and 3D pose estimation algorithm using RGB-D data acquired from a low-cost consumer RGB-D sensor. We use the RGB and depth information for different purposes. From RGB color, we first extract initial contour locations of the mushrooms and then provide both the initial contour locations and the original image to active contour for mushrooms segmentation. These segmented mushrooms are then used as input to a circular Hough transform for each mushroom detection including its center and radius. Once each mushroom's center position in the RGB image is known, we then use the depth information to locate it in 3D space i.e. in world coordinate system. In case of missing depth information at the detected center of each mushroom, we estimate from the nearest available depth information within the radius of each mushroom. We also estimate the 3D pose of each mushroom using a pre-prepared upright mushroom model. We use a global registration followed by local refine registration approach for this 3D pose estimation. From the estimated 3D pose, we use only the rotation part expressed in quaternion as an orientation of each mushroom. These estimated (X,Y,Z) positions, diameters and orientations of the mushrooms are used for robotic-picking applications. We carry out extensive experiments on both 3D printed and real mushrooms which show that our method has an interesting performance.
GridTuner: Reinvestigate Grid Size Selection for Spatiotemporal Prediction Models

With the development of traffic prediction technology, spatiotemporal prediction models have attracted more and more attention from academia communities and industry. However, most existing researches focus on reducing model's prediction error but ignore the error caused by the uneven distribution of spatial events within a region. In this paper, we study a region partitioning problem, namely optimal grid size selection problem (OGSS), which aims to minimize the real error of spatiotemporal prediction models by selecting the optimal grid size. In order to solve OGSS, we analyze the upper bound of real error of spatiotemporal prediction models and minimize the real error by minimizing its upper bound. Through in-depth analysis, we find that the upper bound of real error will decrease then increase when the number of model grids increase from 1 to the maximum allowed value. Then, we propose two algorithms, namely Ternary Search and Iterative Method, to automatically find the optimal grid size. Finally, the experiments verify that the error of prediction has the same trend as its upper bound, and the change trend of the upper bound of real error with respect to the increase of the number of model grids will decrease then increase. Meanwhile, in a case study, by selecting the optimal grid size, the order dispatching results of a state-of-the-art prediction-based algorithm can be improved up to 13.6%, which shows the effectiveness of our methods on tuning the region partition for spatiotemporal prediction models.
The impact of life-history strategies on the stability of competitive ecological network

In natural ecosystems, species often compete with multiple species to survive and acquire resources. These species can be characterized by their different growth profile and life history strategies, namely r and K selected species (Williams, 2013). In this paper, we examine the role of different life-history strategies on the stability of the competitive community along with network complexity. We obtain that the r-selected species enhance community stability and resilience; on the other hand, K-selected species show the opposite trend. Network connectance shows a consistent trend of increasing stability; on the other side, the number of species shows a context-dependent effect on stability. Stability decreases with species number when the proportion of r-selected species is low or moderate and shows precisely the opposite trend when the ratio of r-selected species is high. We also show that our result is robust irrespective of network structure.
3-D Face Morphing Attacks: Generation, Vulnerability and Detection

Face Recognition systems (FRS) have been found to be vulnerable to morphing attacks, where the morphed face image is generated by blending the face images from contributory data subjects. This work presents a novel direction for generating face-morphing attacks in 3D. To this extent, we introduced a novel approach based on blending 3D face point clouds corresponding to contributory data subjects. The proposed method generates 3D face morphing by projecting the input 3D face point clouds onto depth maps and 2D color images, followed by image blending and wrapping operations performed independently on the color images and depth maps. We then back-projected the 2D morphing color map and the depth map to the point cloud using the canonical (fixed) view. Given that the generated 3D face morphing models will result in holes owing to a single canonical view, we have proposed a new algorithm for hole filling that will result in a high-quality 3D face morphing model. Extensive experiments were conducted on the newly generated 3D face dataset comprising 675 3D scans corresponding to 41 unique data subjects and a publicly available database (Facescape) with 100 data subjects. Experiments were performed to benchmark the vulnerability of the proposed 3D morph-generation scheme against automatic 2D, 3D FRS, and human observer analysis. We also presented a quantitative assessment of the quality of the generated 3D face-morphing models using eight different quality metrics. Finally, we propose three different 3D face Morphing Attack Detection (3D-MAD) algorithms to benchmark the performance of 3D face morphing attack detection techniques.
Multi-Mode Spatial Signal Processor With Rainbow-Like Fast Beam Training and Wideband Communications Using True-Time-Delay Arrays

Initial access in millimeter-wave (mmW) wireless is critical toward successful realization of the fifth-generation (5G) wireless networks and beyond. Limited bandwidth in existing standards and use of phase-shifters in analog/hybrid phased-antenna arrays (PAAs) are not suited for these emerging standards demanding low-latency direction finding. This work proposes a reconfigurable true-time-delay (TTD)-based spatial signal processor (SSP) with frequency-division beam training methodology and wideband beam-squint less data communications. Discrete-time delay compensated clocking technique is used to support 800-MHz bandwidth with a large unity-gain bandwidth ring-amplifier (RAMP)-based signal combiner. To extensively characterize the proposed SSP across different SSP modes and frequency–angle pairs, an automated testbed is developed using computer vision techniques that significantly speeds up the testing progress and minimizes possible human errors. Using seven levels of time-interleaving for each of the four antenna elements, TTD SSP has a delay range of 3.8 ns over 800 MHz and achieves unique frequency-to-angle mapping in the beam training mode with nearly 12-dB frequency-independent gain in the beamforming mode. The SSP is prototyped in 65-nm CMOS with an area of 1.98 mm2 consuming only 29 mW excluding buffers. Furthermore, an error vector magnitude (EVM) of 7.3% is realized for 16-QAM modulation at a speed of 614.4 Mb/s.
Continuous-Curvature Target Tree Algorithm for Path Planning in Complex Parking Environments

Rapidly-exploring random tree (RRT) has been applied for autonomous parking due to quickly solving high-dimensional motion planning and easily reflecting constraints. However, planning time increases by the low probability of extending toward narrow parking spots without collisions. To reduce the planning time, the target tree algorithm was proposed, substituting a parking goal in RRT with a set (target tree) of backward parking paths. However, it consists of circular and straight paths, and an autonomous vehicle cannot park accurately because of curvature-discontinuity. Moreover, the planning time increases in complex environments; backward paths can be blocked by obstacles. Therefore, this paper introduces the continuous-curvature target tree algorithm for complex parking environments. First, a target tree includes clothoid paths to address such curvature-discontinuity. Second, to reduce the planning time further, a cost function is defined to construct a target tree that considers obstacles. Integrated with optimal-variant RRT and searching for the shortest path among the reached backward paths, the proposed algorithm obtains a near-optimal path as the sampling time increases. Experiment results in real environments show that the vehicle more accurately parks, and continuous-curvature paths are obtained more quickly and with higher success rates than those acquired with other sampling-based algorithms.
Coherence-Based Distributed Document Representation Learning for Scientific Documents

Distributed document representation is one of the basic problems in natural language processing. Currently distributed document representation methods mainly consider the context information of words or sentences. These methods do not take into account the coherence of the document as a whole, e.g., a relation between the paper title and abstract, headline and description, or adjacent bodies in the document. The coherence shows whether a document is meaningful, both logically and syntactically, especially in scientific documents (papers or patents, etc.). In this paper, we propose a coupled text pair embedding (CTPE) model to learn the representation of scientific documents, which maintains the coherence of the document with coupled text pairs formed by segmenting the document. First, we divide the document into two parts (e.g., title and abstract, etc) which construct a coupled text pair. Then, we adopt negative sampling to construct uncoupled text pairs whose two parts are from different documents. Finally, we train the model to judge whether the text pair is coupled or uncoupled and use the obtained embedding of coupled text pairs as the embedding of documents. We perform experiments on three datasets for one information retrieval task and two recommendation tasks. The experimental results verify the effectiveness of the proposed CTPE model.
Agricultural plant cataloging and establishment of a data framework from UAV-based crop images by computer vision

Abstract Background Unmanned aerial vehicle (UAV)–based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of individual plants over several images and the extraction of relevant information tremendously. Results In this work, we present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs abbreviated as “cataloging” based on comprehensible computer vision methods. We evaluate the workflow on 2 real-world datasets. One dataset is recorded for observation of Cercospora leaf spot—a fungal disease—in sugar beet over an entire growing cycle. The other one deals with harvest prediction of cauliflower plants. The plant catalog is utilized for the extraction of single plant images seen over multiple time points. This gathers a large-scale spatiotemporal image dataset that in turn can be applied to train further machine learning models including various data layers. Conclusion The presented approach improves analysis and interpretation of UAV data in agriculture significantly. By validation with some reference data, our method shows an accuracy that is similar to more complex deep learning–based recognition techniques. Our workflow is able to automatize plant cataloging and training image extraction, especially for large datasets.
Backstepping Design Embedded With Time-Varying Command Filters

If embedded with command filter properly, the implementation of backstepping design could be dramatically simplified. In this brief, we introduce a command filter with time-varying gain and integrate it with backstepping design, resulting in a new set of backstepping control algorithms with low complexity even for high-order strict-feedback systems. Furthermore, with the aid of “softening” sign function based compensator, zero-error output tracking is ensured while at the same time maintaining prescribed transient performance. Numerical simulation is carried out to verify the effectiveness and benefits of the proposed method.
Homologous Coronal Mass Ejections Caused by Recurring Formation and Disruption of Current Sheet within a Sheared Magnetic Arcade

The Sun often produces coronal mass ejections with similar structure repeatedly from the same source region, and how these homologous eruptions are initiated remains an open question. Here, by using a new magnetohydrodynamic simulation, we show that homologous solar eruptions can be efficiently produced by recurring formation and disruption of a coronal current sheet as driven by the continuous shearing of the same polarity inversion line within a single bipolar configuration. These eruptions are initiated by the same mechanism, in which an internal current sheet forms slowly in a gradually sheared bipolar field and reconnection of the current sheet triggers and drives the eruption. Each of the eruptions does not release all of the free energy, leaving a large amount in the post-flare arcade below the erupting flux rope. Thus, a new current sheet can be more easily formed by further shearing of the post-flare arcade than by shearing a potential field arcade, and this is favorable for producing the next eruption. Furthermore, it is found that the new eruption is stronger since the newly formed current sheet has a larger current density and a lower height. In addition, our results also indicate the existence of a magnetic energy threshold for a given flux distribution, and eruption occurs once this threshold is approached.
M* of Two-dimensional Electron Gas: a Neural Canonical Transformation Study

The quasiparticle effective mass m^\astm* of interacting electrons is a fundamental quantity in the Fermi liquid theory. However, the precise value of the effective mass of uniform electron gas is still elusive after decades of research. The newly developed neural canonical transformation approach [Xie et al., J. Mach. Learn. 1, (2022)] offers a principled way to extract the effective mass of electron gas by directly calculating the thermal entropy at low temperature. The approach models a variational many-electron density matrix using two generative neural networks: an autoregressive model for momentum occupation and a normalizing flow for electron coordinates. Our calculation reveals a suppression of effective mass in the two-dimensional spin-polarized electron gas, which is more pronounced than previous reports in the low-density strong-coupling region. This prediction calls for verification in two-dimensional electron gas experiments.
Evaluation of histological findings with severity grade, to analyze toxicology in-vivo studies

In-vivo toxicological studies are characterized by multiple primary endpoints with quite different scales. Whereas guidelines and publications provide various statistical tests for normally distributed endpoints (such as organ weights) and proportions (such as tumor rates), few approaches are available for graded histopathological findings, such as 0, +, ++, +++. This represents a basic contradiction of the statistical analysis because these graded findings sometimes show a high predictive value for potential toxic effects. Here we discuss different methods comparatively, especially from the viewpoints of i) designs for very small sample sizes and ii) interpretability by toxicologists. A new approach is recommended where a simultaneous test is performed over all class combinations of score levels, such as (0, +) vs (++, +++). Corresponding R code is provided by way of a data example.