Miracle-dz/UniFAR-FTUs huggingface dataset:
ARShadowGAN: Shadow Generative Adversarial Network for Augmented Reality in Single Light Scenes

Generating virtual object shadows consistent with the real-world environment shading effects is important but challenging in computer vision and augmented reality applications. To address this problem, we propose an end-to-end Generative Adversarial Network for shadow generation named ARShadowGAN for augmented reality in single light scenes. Our ARShadowGAN makes full use of attention mechanism and is able to directly model the mapping relation between the virtual object shadow and the real-world environment without any explicit estimation of the illumination and 3D geometric information. In addition, we collect an image set which provides rich clues for shadow generation and construct a dataset for training and evaluating our proposed ARShadowGAN. The extensive experimental results show that our proposed ARShadowGAN is capable of directly generating plausible virtual object shadows in single light scenes. Our source code is available at https://github.com/ldq9526/ARShadowGAN.
MEASURING WITHIN AND BETWEEN GROUP INEQUALITY IN INFANT MORTALITY OVER TIME: A BAYESIAN APPROACH WITH APPLICATION TO INDIA∗

Most studies on inequality in infant and child mortality compare average mortality rates between large groups of births, for example, comparing births from different countries, income groups, ethnicities, or different times. These studies do not measure within-group disparities. The few studies that have measured within-group variability in infant and child mortality have used tools from the income inequality literature, such as Gini indices. We show that the latter are inappropriate for infant and child mortality. We develop novel tools that are appropriate for analyzing infant and child mortality inequality, including inequality measures, covariate adjustments, and ANOVA methods. We illustrate how to handle uncertainty about complex inference targets, including ensembles of probabilities and kernel density estimates. We illustrate our methodology using a large data set from India, where we estimate infant and child mortality risk for over 400,000 births using a Bayesian hierarchical model. We show that most of the variance in mortality risk exists within groups of births, not between them, and thus that within-group mortality needs to be taken into account when assessing inequality in infant and child mortality. Our approach has broad applicability to many health indicators.
Mathematical modeling of drying kinetics of Bhimkol (Musa balbisiana) pulp using MATLAB

Bhimkol pulp was dried by spreading in a thin layer in a hot air dryer at 40°C, 50°C and 60°C respectively. The drying kinetics was accomplished by using a general code developed in MATLAB R2014a software. Among the five thin layer drying models used for the analysis, Midilli-Kucuk showed the best-fitted result with the greatest determination coefficient (R) value and lowest root mean square error (RMSE) and the sum of square error (SSE) values. For a better generalization of the drying model, master curve technique and generalized drying constant method were also applied. Master curve technique offered the best fit with R value of 0.997 as compared to generalized drying constant method and normal mathematical modeling technique. From the results, it was also observed that the value of drying constant k and effective diffusivity followed a rising trend with increasing temperature. The activation energy of the drying was 39.67 kJ mol.
Making Disk Failure Predictions SMARTer!

Disk drives are one of the most commonly replaced hardware components and continue to pose challenges for accurate failure prediction. In this work, we present analysis and findings from one of the largest disk failure prediction studies covering a total of 380,000 hard drives over a period of two months across 64 sites of a large leading data center operator. Our proposed machine learning based models predict disk failures with 0.95 F-measure and 0.95 Matthews correlation coefficient (MCC) for 10-days prediction horizon on average.
Objectifying Ride Comfort in Autonomous Driving An extended Model of the ISO-2631 Standard to Objectify the Ride Comfort of an Inattentive Occupant

In order to objectify driving comfort in autonomous driving, an extended model of the existing ISO-2631 standard has been developed, analyzed and tested in the presented work. Therefore the conventional measuring points are extended by the head. For the objectification of the driving comfort a further differentiation for the k-factors is suggested in the weighting. Subsequently the calculated effective values are assigned to a 6-step scale. A first review of the presented method is carried out in a driving simulator experiment with 50 persons. The subjects drove through 5 sections with different vertical dynamic road excitations in an inattentive state and rated the driving comfort on a 7-step Likert scale. Simultaneously a body measurement technology is used to record the movements of the occupants using initial sensors. The evaluation of the results shows that the extended model reproduces the occupant comfort ratings evaluated as ordinal data more accurately than the existing ISO-2631 standard.
An Enhanced Real-Time Deferrable Server Scheduler for Xen Virtualization Systems

—Real-time deferrable server (RTDS) scheduler is an experimental CPU scheduler for Xen virtualization systems since version 4.5. Under RTDS, each virtual CPU (VCPU) is guaranteed to have a predeﬁned amount of physical CPU (PCPU) capacity so that the performance can be better predicted. However, the guaranteed capacity might not ﬁt the requirement of a VCPU at the run-time because it is deﬁned ofﬂine. Therefore, the performance of virtual machines (VMs) might be deteriorated at the run-time. In this paper, an RTDS-based CPU scheduler is proposed, called enhanced real-time deferrable server (ERTDS), to provide an additional amount of PCPU capacity to a VCPU when its run-time requirement is higher than expected. We have implemented ERTDS in Xen version 4.7 and a series of experiments has been conducted for which we have some encouraging results.
The Antioxidant and Hypoglycemic Properties and Phytochemical Profile of Clusia latipes Extracts

Diabetes mellitus has become one of the primary threats to human health, with its rapidly increasing prevalence, and gravely debilitating clinical complications1. Over the past decade, diabetes prevalence has risen faster in low and middle-income countries than in high-income countries. The World Health Organization (WHO) has projected that DM will be the 7th leading cause of death in 2030. Diabetes is a chronic disease that occurs when the pancreas does not produce sufficient insulin or when the body cannot effectively use the insulin it produces. Insulin is a hormone that regulates blood sugar. Hyperglycemia or high blood sugar is a common effect of uncontrolled diabetes, which might lead to serious heart, blood vessel, eye, kidney, and nerve damage over time 2,3. Type 2 diabetes mellitus (T2DM), which is the most common form of diabetes, is produced by the ineffective use of insulin in the body and is characterized by disorders of insulin action and secretion. Either one of these mechanisms could be the predominant feature, but generally, when diagnosed, both are manifested1. In T2DM, particularly, it is believed that oxidative stress caused by overproduction of reactive oxygen species (ROS) is the cause of vascular complication development4. Hyperglycemic condition of DM patients will produce tissue damage via the formation of ROS through five major mechanisms that have been shown to be activated by mitochondrial ROS overproduction: increased flux of glucose and other sugars through the polyol pathway; increased formation of advanced glycation end products; increased expression of the receptor for advanced glycation endproducts (RAGE) and its activating ligands; activation of protein kinase C isoforms; and over-activity of the hexosamine pathway5-7.
Dynamic Asymmetry and Fiscal Policy

We introduce a new time series model for public consumption expenditure, tax revenues and real income that is capable to incorporate oscillations characterized by asymmetric phase and duration (or dynamic asymmetry). A specific-to-general econometric strategy is implemented in order to exclude the null hypotheses that these variable are linear or symmetric and, consequently, to ensure that these can be parsimoniously modelled. The U.S. postwar data suggest that the dynamic asymmetry -- either in cycle, either in trend -- is effectively a reasonable hypothesis for government expenditure and tax revenue, but also that a simple vector model unifying the (different) nonlinearities of each single series is unfeasible. Such an \textquotedblleft Occam-razor\textquotedblright \hspace{1pt} failure hinders econometricians in building impulse responses for calculation of fiscal multiplier and is here circumvented via empirical indexes.
Hybridization of GRASP with Exterior Path Relinking for Identifying Critical Nodes in Graphs

—This paper deals a problem with detecting the critical nodes in order to achieve minimum pair-wise connectivity in the residual graph after removing a limited number of nodes from the graph. It is called the critical node detection problem (CNDP). In this paper, we propose a new hybridization scheme of greedy randomized adaptive search procedure (GRASP) with exterior path-relinking. Exterior path-relinking, a recently proposed metaheuristic method, creates paths of successive solutions starting from two high-quality solutions to other high quality solutions. A randomly selected solution from the elite solution pool collecting throughout GRASP iterations and the resulting solution from each GRASP iteration are relinked by exterior path-relinking for finding better solutions. The proposed algorithm is assisted on test instances from the literature. Computational experiments demonstrate that the proposed method outperforms other existing algorithms in the area of CNDP.
Informed initial pivot selection for approximating and eliminating search algorithms

The ever increasing amount of data and the growing diversity in data types requires eﬀective and eﬃcient retrieval techniques. With the diversity in data types also a variety of techniques for measuring the (dis)similarity between the data objects arises. Metric access methods (MAMs) make no assumption about the data representation. MAMs only require the dissimilarity function to be a metric and thus support a ﬂexible indexing paradigm. However, distance metrics can be expensive to compute. Thus, a main goal of MAMs is to reduce the number of distance computations. We replace the random initial pivot selection step of the approximating and eliminating search algorithm (AESA), a MAM capable to dramatically reduce the number of required distance computations. Our approach relies on the concept of the intrinsic dimension and comes with no additional cost during search. Besides showing that the performance of AESA can be improved, we also present a conceptually simple technique to measure the inﬂuence of intrinsic dimension deﬁnitions on search eﬃciency. This opens doors for the analysis of alternative intrinsic dimension deﬁ-nitions in future work.
An integrated model for aircraft routing and crew scheduling: Lagrangian Relaxation and metaheuristic algorithm

Airline optimization is a significant problem in recent researches and airline industrial as it can determine the level of service, profit and competition status of the airline. Aircraft and crew are expensive resources that need efficient utilization. This paper focuses simultaneously on two major issues including aircraft maintenance routing and crew scheduling. Several key issues such as aircraft replacement, fairly night flights assignment and long-life aircrafts are considered in this model. We used the flight hours as a new framework to control aircraft maintenance. At first, an integrated mathematical model for aircraft routing and crew scheduling problems is developed with the aim of cost minimization. Then, Lagrangian relaxation and Particle Swarm Optimization algorithm (PSO) are used as the solution techniques. To evaluate the efficiency of solution approaches, model is solved with different numerical examples in small, medium and large sizes and compared with GAMS output. The results show that Lagrangian relaxation method provides better solutions comparing to PSO and also has a small gap to optimum solution.
Studies on the influence of zinc on trace element status of neonatal piglets and copper concentration in kidney of weaned piglets including zinc-related genes

Increasing litter sizes in modern swine production have raised an urgent need for artificial rearing strategies and formula feeding. The current experiment was conducted to study the influence of formula trace element concentration according to recommendations for weaned piglets on mRNA concentration of zinc (Zn) -related genes in jejunum, liver and pancreas of neonatal piglets. Eight artificially reared piglets were fed a cow-milk based formula (FO) containing 100mg Zn/kg dry matter. Eight of their sow-reared (SM) littermates were used as control. After 14 days, all sixteen piglets were killed and jejunum, liver and pancreas evaluated for Zn, copper (Cu), manganese (Mn) and iron (Fe) concentration and mRNA concentration of metal and Zn specific transporters, metallothioneins (MTs) and interleukin 6 (IL-6). Feeding FO resulted in significantly higher Zn concentrations in liver tissue (p < 0.05). Furthermore, Fe and Mn concentrations in liver and jejunal tissue were higher (p < 0.05) in the FO group, whereas neither Zn transporters nor MTs in jejunal and pancreas tissue showed differences between both groups. MT mRNA concentration was higher (p < 0.05), whereas Zn transporter protein 1 (ZnT1) and divalent metal-ion transporter 1 (DMT1) mRNA concentration was lower (p < 0.05) in the liver of FO piglets. Besides Zn-induced expression of transporters and MTs, significantly increased IL-6 expression in the FO group suggests the involvement of cytokinemediated Mn and Fe sequestration in the liver and jejunum. The results reveal that dietary trace element concentration used in the study likely exceeded the requirements of the neonatal pig as reflected by homeostatic counter regulation in different organs.
Urgent need for coral demography in a world where corals are disappearing

Coral reefs have long attracted attention because of their biological and economic importance, but this interest now has turned to examining the possibility of functional extirpation. Widespread declines in coral abundances have fueled the shift in motivation for studying reefs and catalyzed the proliferation of monitoring to record the changes underway. Despite appreciation of monitoring as a scientific endeavor, its primary use has continued to be the quantification of cover of coral, macroalgae, and a few other space holders. The limitations of coral cover in evaluating the consequences of changing coral abundance were highlighted decades ago. Yet neglect of the tools most appropriate for this task (demographic approaches) and continuing emphasis on a tool (coral cover) that is not ideal, indicates that these limitations are not widely appreciated. Reef monitoring therefore continues to underperform with respect to its potential, thus depriving scientists of the approaches necessary to project the fate of coral reefs and test hypotheses focused on the proximal causes of declining coral cover. We make the case that the coral reef crisis creates a need for coral demography that is more acute now than 4 decades ago. Modern demographic approaches are well suited to meet this need, but to realize their potential, consideration will need to be given to the possibility of expanding ecological monitoring of coral reefs to provide the data necessary for demographic analyses of their foundation taxon, the Scleractinia.
Automatic Continuous CRT Optimization to Improve Hemodynamic Response: An Italian Single-Center Experience

Background. Optimization of cardiac resynchronization therapy (CRT) settings after implant can improve response to therapy. In this Italian single-center experience, we investigated the rate of hemodynamic and clinical response in heart failure patients treated with continuously and automatically optimized CRT. Methods. Patients were selected from June 2015 to April 2017 according to the most recent CRT guidelines; all were in sinus rhythm at implant and received a CRT-defibrillator system equipped with SonR, which automatically optimizes AV and VV delays every week. SonR was activated just after implant and remained active during follow-up. The rate of hemodynamic response (R-HR) was defined as ΔLVEF > 5%, super-response (R-HSR) as Δ LVEF > 15%, and clinical response as a negative transition of NYHA class≥−1 at 6 months follow-up vs. baseline (preimplant). Results. Mean follow-up for the 31 patients (aged 69:9 ± 9:4 years; 61% male; NYHA class II/III 19%/81%; ischemic etiology 65%) was 6 ± 0:7 months. At baseline, LVEF was 29:1% ± 4:7% and QRS duration 146 ± 13ms. LBBB morphology was observed in 65%. At 6 months, R-HR was 74% (23/31), R-HSR 32% (10/31), and clinical response rate 77% (24/31). Hemodynamically, patients with ischemic etiology benefited more than those without ischemic etiology, both in terms of response (80% versus 64%) and super-response (35% versus 27%). Conclusions. Continuous automatic weekly optimization of CRT over 6 months consistently improved R-HR, R-HSR, and clinical response in NYHA class II/III heart failure patients versus baseline. Patients with ischemic etiology in particular may benefit hemodynamically from this type of CRT optimization.
Blob Extraction Algorithm in Detection of Convective Cells for Data Fusion

Earth’s atmosphere is monitored by a multitude of sensors. It is the troposphere that is of crucial importance for human activity, as it is there that the weather phenomena take place. Weather observations are performed by surface sensors monitoring, inter alia, humidity, temperature and winds. In order to observe the developments taking place in the atmosphere, especially in the clouds, weather radars are commonly used. They monitor severe weather that is associated with storm clouds, cumulonimbuses, which create precipitation visible on radar screens. Therefore, radar images can be utilized to track storm clouds in a data fusion system. In this paper an algorithm is developed for the extraction of blobs (interesting areas in radar imagery) used within data fusion systems to track storm cells. The algorithm has been tested with the use of real data sourced from a weather radar network. 100% of convection cells were detected, with 90% of them being actual thunderstorms.
Predicting the Structural Performance of the Wings of an Unmanned Aircraft Vehicle Using Fluid-structure Interaction

This study combines computational fluid dynamics (CFD) and finite element analysis (FEA) to predict the impact of a wind gust on the wings and the wing-fuselage connectors of an Unmanned Aircraft Vehicle (UAV). The wind gust is modelled as a sudden increase in airspeed, 10 m/s above the UAV cruise velocity of 13 m/s, and compared to normal wind conditions. For the CFD simulations, Spalart-Allmaras and k-ω SST are employed in ANSYS FLUENT and the numerical results validated against XFLR5 (a semi-empirical software based on Massachusetts Institute of Technology’s famous low Reynolds number CFD program, XFOIL). During the wind gust condition, the lift increases to 244 N and drag to 13.2 N compared to the normal wind condition of 77.2 N and 4.34 N, in lift and drag respectively. The CFD calculated aerodynamic loads were coupled to ANSYS MECHANICAL to determine the resulting structural response on the wings. The maximum stress was observed along a slender component connecting the back-wing’s spar and shell, with a magnitude of 23.8 MPa and 75.0 MPa for the normal and wind gust flight conditions, respectively. The numerically calculated stresses on the wings obtained from ANSYS MECHANICAL are then used to analytically predict the structural response of the wing-fuselage connectors. The investigation, on the aerodynamic loads and resulting mechanical stresses produced in the wings and transferred to the wing-fuselage connectors, identified that despite an increase in loading during wind gust condition, the resulting stresses are still way below the critical levels. Keywords— Aerodynamic loads, CFD, fluid-structure interaction, turbulence, UAV
Breast Cancer Detection Using Image Processing Techniques and Classification Algorithms

Breast cancer is the top cancer in women worldwide. Early detection of this disease and its classiﬁcation into cases can improve the prognosis and even save lives by promoting timely clinical management to patients. An accurate diagnosis of breast cancer and its classiﬁcation into benign, malignant and normal cases is a challenge in cancer research. Because of the ability to enable the computer to learn from past samples to detect and classify patterns, in machine learning, classiﬁcation algorithms are widely used for cancer identiﬁcation. However, many of them are focused on binary classiﬁcation (cancer and no-cancer; benign and malignant). In this work, we present a Computer-Aided Diagnosis (CAD) approach for diagnosis and classiﬁcation of patients into three conditions (malignant, benign and normal) from pixel mammogram images. For the classiﬁcation task, we explore and compare three outstanding classiﬁers: Support Vector Machine (SVM), k-Nearest Neighbors (K-NN), and Random Forest (RF) to analyze their accuracy in decision making. In addition, we discuss the eﬀects of pre-processed mammogram images before entering the classiﬁer, which results in higher eﬀective classiﬁcation.
Interference in Multi-beam Antenna System of 5G Network

Massive multiple-input-multiple-output (MIMO) and beamforming are key technologies, which significantly influence on increasing effectiveness of emerging fifth-generation (5G) wireless communication systems, especially mobile-cellular networks. In this case, the increasing effectiveness is understood mainly as the growth of network capacity resulting from better diversification of radio resources due to their spatial multiplexing in macro- and micro-cells. However, using the narrow beams in lieu of the hitherto used cell-sector brings occurring interference between the neighboring beams in the massive-MIMO antenna system, especially, when they utilize the same frequency channel. An analysis of this effect is the aim of this paper. In this case, it is based on simulation studies, where a multi-elliptical propagation model and standard 3GPP model are used. We present the impact of direction and width of the neighboring beams of 5G new radio gNodeB base station equipped with the multi-beam antenna system on the interference level between these beams. The simulations are carried out for line-of-sight (LOS) and non-LOS conditions of a typical urban environment.
Neutron Capture Rates For Strongly Interacting Neutrons

NEUTRON CAPTURE RATES FOR STRONGLY INTERACTING NEUTRONS Bryn Knight University of Guelph, 2020 Advisors: Professor Liliana Caballero At the low temperature and high density conditions of a neutron star crust, neutrons are degenerate. In this work, we study the effect of this degeneracy on neutron capture rates by several rp-process ashes and neutron-rich nuclei within accreting neutron stars. We consider strongly interacting asymmetric nuclear matter and its effect on the neutron chemical potential and therefore on the capture rates. We investigate variations in the mass model, optical potential and gamma strength functions used to calculate the cross section and their effects on the reaction rate in the context of degenerate neutron capture. Lastly, we generalize the reaction rate behavior of exothermic and endothermic neutron capture reactions when compared to their Maxwellian counterparts. Our results provide an insight to abundances’ evolution of rp-process ashes and energy generation in the neutron star crust.