Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Tip Leakage Vortex Dynamics in an Axial Compressor Cascade Using RANS Analyses

The tip leakage flow in turbine and compressor blade rows is responsible for a relevant fraction of the total loss. It contributes to unsteadiness, and have an important impact on the operability range of compressor stages. Experimental investigations and, more recently, scale-resolving CFD approaches have helped in clarifying the flow mechanism determining the dynamics of the tip leakage vortex. Due to their continuing fundamental role in design verifications, it is important to establish whether RANS/URANS approaches are able to reproduce the effects of such a flow feature, in order to correctly drive the design of the next generation of turbomachinery. Base studies are needed in order to accomplish this goal. In the present work the tip leakage flow in axial compressor rotor blade cascade have been studied. The cascade was tested experimentally in Virginia Tech Low Speed Cascade Wind Tunnel in both stationary and moving endwall configurations. Numerical analyses were performed using the TRAF code, a state-of-the-art in-house-developed 3D RANS/URANS flow solver. The impact of the numerical framework was investigated selecting different advection schemes including a central scheme with artificial dissipation and a high-resolution upwind strategy. In addition, two turbulence models have been used, the Wilcox linear k–ω model and a non-linear eddy viscosity model (Realizable Quadratic Eddy Viscosity Model), which accounts for turbulence anisotropy. The numerical results are scrutinized using the available measurements. A detailed discussion of the vortex evolution inside the blade passage and downstream of the blade trailing edge is presented in terms of streamwise velocity, streamwise vorticity, and turbulent kinetic energy contours. The purpose is to identify guidelines for obtaining the best representation of the vortex dynamics, with the methodologies usually employed in routine design iterations and, at the same time, evidence their weak aspects that need further modelling efforts.
Robust estimation of sparse precision matrix using adaptive weighted graphical lasso approach

Estimation of a precision matrix (i.e. inverse covariance matrix) is widely used to exploit conditional independence among continuous variables. The influence of abnormal observations is exacerbated in a high dimensional setting as the dimensionality increases. In this work, we propose robust estimation of the inverse covariance matrix based on an regularised objective function with a weighted sample covariance matrix. The robustness of the proposed objective function can be justified by a nonparametric technique of the integrated squared error criterion. To address the non-convexity of the objective function, we develop an efficient algorithm in a similar spirit of majorisation-minimisation. Asymptotic consistency of the proposed estimator is also established. The performance of the proposed method is compared with several existing approaches via numerical simulations. We further demonstrate the merits of the proposed method with application in genetic network inference.
Overspinning naked singularities in 
AdS3
 spacetime

The BTZ black hole belongs to a family of locally three-dimensional anti-de Sitter (AdS3) spacetimes labeled by their mass M and angular momentum J . The case M` ≥ |J |, where ` is the anti-de Sitter radius, provides the black hole. Extending the metric to other values of of M and J leads to geometries with the same asymptotic behavior and global symmetries, but containing a naked singularity at the origin. The case M` ≤ −|J | corresponds to spinning conical singularities that are reasonably well understood. Here we examine the remaining case, that is −|J | < M` < |J |. These naked singularities are mathematically acceptable solutions describing classical spacetimes. They are obtained by identifications of the covering pseudosphere in R and are free of closed timelike curves. Here we study the causal structure and geodesics around these overspinning geometries. We present a review of the geodesics for the entire BTZ family. The geodesic equations are completely integrated, and the solutions are expressed in terms of elementary functions. Special attention is given to the determination of circular geodesics, where new results are found. According to the radial bounds, eight types of noncircular geodesics appear in the BTZ spacetimes. For the case of overspinning naked singularity, null and spacelike geodesics can reach infinity passing by a point nearest to the singularity, others extend from the central singularity to infinity, and others still have a radial upper bound and terminate at the singularity. As expected for an anti-de Sitter spacetime, timelike geodesics cannot reach infinity; they either loop around the singularity or fall into it. The spatial projections of the geodesics (orbits) exhibit self-intersections, whose number is determined for null and spacelike geodesics, and it is found a special class of timelike geodesics whose spatial projections are closed. ar X iv :2 10 5. 06 48 8v 1 [ gr -q c] 1 3 M ay 2 02 1
Properties of the accretion ring in an X-ray binary, and accretion and excretion two-layer flows from it

We study properties of an accretion ring in a steady mass flow from a companion star to a compact object in an X-ray binary. The accretion ring is a place where matter inflowing from a companion star sojourns for a while to bifurcate to accretion and excretion flows due to angular momentum transfer in it. The matter in the accretion ring rotates along the Keplerian circular orbit determined by the intrinsic specific angular momentum of the inflowing matter and forms a thick ring-envelope. Two internal flows are expected to appear in the thick envelope. One is a mass spreading flow bifurcating to a thick accretion flow and a thick excretion flow, as a result of the angular momentum transfer within the ring-envelope. The other is a cooling flow toward the envelope center governed by radiative cooling under an effect of X-ray irradiation. This cooling flow eventually forms a core in the torus, from which a thin accretion disk and a thin excretion disk spread out as a result of the angular momentum transfer there again. Evaluating and comparing the time scales for the two internal flows, the accretion ring is shown to generally originate a two-layer accretion flow in which a thin accretion disk is sandwiched by a thick accretion flow, unless the accretion rate is very low. Properties of the thin excretion disk and the thick excretion flow are also investigated. The thin excretion disk is expected to terminate at a distance 4 times as large as the accretion ring radius and to form another ring there, unless tidal effects from the companion star exist. The thick excretion flow is, on the
SISPO: Space Imaging Simulator for Proximity Operations

This paper describes the architecture and demonstrates the capabilities of a newly developed, physically-based imaging simulator environment called SISPO, developed for small solar system body fly-by and terrestrial planet surface mission simulations. The image simulator utilises the open-source 3-D visualisation system Blender and its Cycles rendering engine, which supports physically based rendering capabilities and procedural micropolygon displacement texture generation. The simulator concentrates on realistic surface rendering and has supplementary models to produce realistic dust- and gas-environment optical models for comets and active asteroids. The framework also includes tools to simulate the most common image aberrations, such as tangential and sagittal astigmatism, internal and external comatic aberration, and simple geometric distortions. The model framework’s primary objective is to support small-body space mission design by allowing better simulations for characterisation of imaging instrument performance, assisting mission planning, and developing computer-vision algorithms. SISPO allows the simulation of trajectories, light parameters and camera’s intrinsic parameters.
Stroke Lesion Segmentation with Visual Cortex Anatomy Alike Neural Nets

Cerebrovascular accident, or commonly known as stroke, is an acute disease with extreme impact on patients and healthcare systems and is the second largest cause of death worldwide. Fast and precise stroke lesion detection and location is an extreme important process with regards to stroke diagnosis, treatment, and prognosis. Except from the manual segmentation approach, machine learning based segmentation methods are the most promising ones when considering efficiency and accuracy, and convolutional neural network based models are the first of its kind. However, most of these neural network models do not really align with the brain anatomical structures. Intuitively, this work presents a more brain alike model which mimics the anatomical structure of the human visual cortex. Through the preliminary experiments on the stroke lesion segmentation task, the proposed model is found to be able to perform equally well or better to the de-facto standard U-Net. Part of the implementation will be made available at https://github.com/DarkoBomer/VCA-Net.
Carrier localization induced by dynamic disorder in MAPbI3 described by first-principles based tight binding

Halide perovskites are strongly influenced by large amplitude anharmonic lattice fluctuations at room temperature. We develop a tight binding model for dynamically disordered MAPbI3 based on density functional theory (DFT) calculations to calculate electronic structure for finite temperature crystal structures at the length scale of thermal disorder and carrier localization. The model predicts individual Hamiltonian matrix elements and band structures with high accuracy, owing to the inclusion of additional matrix elements and descriptors for non-Coulombic interactions. We apply the model to electronic structure calculations in supercells up to 16 × 16 × 16, observing the onset of carrier localization induced by thermal disorder at 300 K, associated with decreasing band edge fluctuations, increasing carrier mass, and Rashba splitting approaching zero. We identify the length scale L∗ = 5 nm at which local electronic structure is affected by dynamic disorder.
An Identifying the Age of the Tiger Using Data Mining Techniques

Image mining is an astonishing to data mining concept. To understand the data mining concept prior knowledge is more important to image mining. Image mining deals with the extraction of implicit knowledge, image data relationships or other patterns not explicitly stored in the images. It is the process of analyzing large sets of domain-specific data and subsequently extracting information and knowledge in the form of new relationships, patterns, or clusters for the decision-making process. Tiger become a reserved animal. Conservation of tiger has been a challenging task. This work would add a small account to the herculean task of conserving the species. Several scientific researchers have carried out their research on the tiger reserve conservation. This research work proposes a method to find the age of the tiger, using color as a parameter. Color pixel-based image classification and clustering techniques has been used to identify the age of the tiger. This research work mainly focuses on RGB color spaces, which is implemented on the real time tiger images. The objective of the research work is to be done on assessing the age of the tiger using the color pixel-based image classification and clustering is the main task of the research work and to optimize the image filtering and enhancement methods that are used to remove the noise and to improve the quality of pixels or images and assessing the processing Time, Retrieval Time, Accuracy and Error Rate by generating the better results is real time tiger image database. 1
A HERO for Stroke Patients: a new hand exoskeleton 3D printed on textiles for rehabilitation

Background: Stroke survivors can be affected by motor deficits in the hand. Robotic equipment associated with brain-machine interfaces (BMI) may aid the motor rehabilitation of these patients. BMIs involving orthotic control by motor imagery practices have been successful in restoring stroke patients' movements. However, there is still little acceptance of the robotic devices available, either by patients and clinicians, mainly because of the high costs involved. Motivated by this context, the present work aims to design and construct the Hand Exoskeleton for Rehabilitation Objectives (HERO) to recover extension and flexion movements of the fingers. Methods: 3D printing technique in association with textiles was used to produce a lightweight and wearable device. 3D-printed actuators have also been designed to reduce equipment costs. The actuator transforms the torque of DC motors into linear force transmitted by Bowden cables to move the fingers passively. The exoskeleton was controlled by neuroelectric signal --- electroencephalography (EEG). Concept tests were performed to evaluate control performance. A healthy volunteer was submitted to a training block with the exoskeleton, according to the Graz-BCI protocol. Ergonomy was evaluated with a 2D tracking software. Results: The outcome of the applied manufacturing technique was aesthetically pleasing. HERO's glove can be compared to ordinary clothing. The weight over the hand was around 102 g. The volunteer was able to control the exoskeleton with 91.5\% accuracy. Conclusions: HERO's project resulted in a lightweight, simple, portable, ergonomic, and low-cost device. Its use is not restricted to a clinical setting. Thus, users will be able to execute motor training with the HERO at hospitals, rehabilitation clinics, and home, increasing the rehabilitation intervention time. This may favor motor rehabilitation and improve the life quality of stroke survivors.
Application of Deep Learning as a Noninvasive Tool to Determine Pathological Diagnosis of Enlarged Cervical Lymph Nodes with PET/CT

Objective: To construct a deep-learning convolution neural network (DL-CNN) system for pathological diagnosis of cervical lymph nodes by using computed tomography (CT), fluorine-18 fluorodeoxyglucose (18F-FDG) positron emission tomography (PET), and fused PET/CT images.Materials and methods: A total of 1020 cross-sectional images for each imaging modality was obtained from 211 patients (153 patients with lymphomas and 116 patients with metastases) with enlarged cervical lymph nodes from January 2014 to June 2018. All eligible images were distributed randomly into the training, validation, and testing cohorts with ratios of 70%, 15%, and 15%. We applied eight DL-CNN algorithms with pretrained bases from ImageNet dataset on CT, PET, and fused PET/CT imaging datasets to differentiate lymphomatous nodes from metastatic nodes, respectively. Attention heatmaps of PET and fused PET/CT images generated by class activation mapping (CAM) were used in visualization of class specific regions recognized by the prediction model with best performance. Results: The accuracy of eight deep learning algorithms with pretrained base ranged from 0.650 to 0.981 on PET testing cohort, and from 0.738 to 0.981 on fused PET/CT testing cohort. The VGG16 model on PET images and DenseNet121 model on fused PET/CT images had the best diagnostic performance among all eight algorithms with sensitivity and specificity of 1.000 and 0.963. Class-specific discriminative subregions were highlighted by attention maps for clinical review.Conclusion: A DL-CNN system was developed for classifying metastatic and lymphomatous involvement with favorable diagnostic performance on PET and PET/CT images in patients with enlarged cervical lymph nodes. The further clinical practice of this system may improve quality of the following therapeutic interventions and optimize patients’ outcomes.
Threshold Martingales and the Evolution of Forecasts

This paper introduces a martingale that characterizes two properties of evolving forecast distributions. Ideal forecasts of a future event behave as martingales, sequen- tially updating the forecast to leverage the available information as the future event approaches. The threshold martingale introduced here measures the proportion of the forecast distribution lying below a threshold. In addition to being calibrated, a threshold martingale has quadratic variation that accumulates to a total determined by a quantile of the initial forecast distribution. Deviations from calibration or to- tal volatility signal problems in the underlying model. Calibration adjustments are well-known, and we augment these by introducing a martingale filter that improves volatility while guaranteeing smaller mean squared error. Thus, post-processing can rectify problems with calibration and volatility without revisiting the original forecast- ing model. We apply threshold martingales first to forecasts from simulated models and then to models that predict the winner in professional basketball games.
How FDI attracted? A Configurational Analysis for Regional Competitiveness in Mechanism of FDI Entry and Exit: Evidence from China

The economic benefits and spillover effects from FDI could lead accelerating the innovation of local economy and technological concept, which make it welcome in underdeveloped countries' authorities. This paper focuses on the entry and exit mechanism of FDI in 31 provinces of China from 2001 to 2016. The optimized diamond model associated with multi-theoretical cooperation is adopted to set up the regional competitiveness configuration for attracting FDI. It revealed that FDI attempts to enter regions with certain economic basis and favorable government conditions; but if the market fluctuates, even if there are favorable policy conditions, FDI will restructure because of their profit-seeking. Based on the conclusions, local governments can use the minimum resources to attract FDI in appropriate circumstances, and can optimize policies in time when it is not suitable for the entry and maintenance of FDI, so as to focus on develop local markets.
Large-scale clustering amongst Fermi blazars; evidence for axis alignments?

We find evidence for large-scale clustering amongst Fermi-selected BL Lac objects but not amongst Fermi-selected flat spectrum radio quasars (FSRQs). Using two-point correlation functions, we have investigated the clustering properties of different classes of objects from the Fermi–LAT(Large Area Telescope) 4FGL catalogue. We wanted to test the idea based on optical polarization observations that there might be large volumes of space in which AGN axes are aligned. To do this, we needed a clean sample of blazars as these are objects with their jet axes pointing towards the observer and Fermi sources provide such a sample. We find that high latitude Fermi sources taken as a whole show a significant clustering signal on scales up to 30°. To investigate if all blazars behave in the same way we used he machine learning classifications from the literature, which are based only on gamma-ray information, to separate BL Lac-like objects from FSRQ-like objects. A possible explanation for the clustering signal we find amongst the BL Lac-like objects is that there are indeed large volumes of space in which AGN axes are aligned. This signal might be washed out in FSRQs since they occupy a much larger volume of space. Thus, our results support the idea that large scale polarization alignments could originate from coherent alignments of AGN axes. We speculate that these axis alignments may be related to the well-known intrinsic alignments of galaxy optical position angles.
Shattering as a source of small grains in the circum-galactic medium

Observed reddening in the circum-galactic medium (CGM) indicates a significant abundance of small grains, of which the origin is still to be clarified. We examine a possible path of small-grain production through shattering of pre-existing large grains in the CGM. Possible sites where shattering occurs on a reasonable time-scale are cool clumps with hydrogen number density =H ∼ 0.1 cm−3 and gas temperature )gas ∼ 104 K, which are shown to exist through observations of Mg ii absorbers. We calculate the evolution of grain size distribution in physical conditions appropriate for cool clumps in the CGM, starting from a large-grain-dominated distribution suggested from theoretical studies. With an appropriate gas turbulence model expected from the physical condition of cold clumps (maximum eddy size and velocity of ∼100 pc and 10 km s−1, respectively), together with the above gas density and temperature and the dust-to-gas mass ratio inferred from observations (0.006), we find that small-grain production occurs on a time-scale (a few ×108 yr) comparable to the lifetime of cool clumps derived in the literature. Thus, the physical conditions of the cool clouds are favrourable for small-grain production. We also confirm that the reddening becomes significant on the above time-scale. Therefore, we conclude that small-grain production by shattering is a probable cause for the observed reddening in the CGM. We also mention the effect of grain materials (or their mixtures) on the reddening at different redshifts (1 and 2).
Building Affordance Relations for Robotic Agents - A Review

Affordances describe the possibilities for an agent to perform actions with an object. While the significance of the affordance concept has been previously studied from varied perspectives, such as psychology and cognitive science, these approaches are not always sufficient to enable direct transfer, in the sense of implementations, to artificial intelligence (AI)-based systems and robotics. However, many efforts have been made to pragmatically employ the concept of affordances, as it represents great potential for AI agents to effectively bridge perception to action. In this survey, we review and find common ground amongst different strategies that use the concept of affordances within robotic tasks, and build on these methods to provide guidance for including affordances as a mechanism to improve autonomy. To this end, we outline common design choices for building representations of affordance relations, and their implications on the generalisation capabilities of an agent when facing previously unseen scenarios. Finally, we identify and discuss a range of interesting research directions involving affordances that have the potential to improve the capabilities of an AI agent.
REGINA—Reasoning Graph Convolutional Networks in Human Action Recognition

It is known that the kinematics of the human body skeleton reveals valuable information in action recognition. Recently, modeling skeletons as spatio-temporal graphs with Graph Convolutional Networks (GCNs) has been reported to solidly advance the state-of-the-art performance. However, GCN-based approaches exclusively learn from raw skeleton data, and are expected to extract the inherent structural information on their own. This paper describes REGINA, introducing a novel way to REasoning Graph convolutional networks IN Human Action recognition. The rationale is to provide to the GCNs additional knowledge about the skeleton data, obtained by handcrafted features, in order to facilitate the learning process, while guaranteeing that it remains fully trainable in an end-to-end manner. The challenge is to capture complementary information over the dynamics between consecutive frames, which is the key information extracted by state-of-the-art GCN techniques. Moreover, the proposed strategy can be easily integrated in the existing GCN-based methods, which we also regard positively. Our experiments were carried out in well-known action recognition datasets and enabled to conclude that REGINA contributes for solid improvements in performance when incorporated to other GCN-based approaches, without any other adjustment regarding the original method. For reproducibility, the REGINA code and all the experiments carried out will be publicly available at https://github.com/DegardinBruno.