Miracle-dz/UniFAR-FTUs huggingface dataset:
An Exceptional Very High Energy Gamma-Ray Flare of PKS 2155–304

The high-frequency peaked BL Lac PKS 2155-304 at redshift z = 0.116 is a well-known VHE (>100 GeV) γ-ray emitter. Since 2002 its VHE flux has been monitored using the H.E. S.S. stereoscopic array of imaging atmospheric Cerenkov telescopes in Namibia. During the 2006 July dark period, the average VHE flux was measured to be more than 10 times typical values observed from the object. This article focuses solely on an extreme γ-ray outburst detected in the early hours of 2006 July 28 (MJD 53,944). The average flux observed during this outburst is I(>200 GeV) = (1.72 ± 0.05stat ± 0.34syst) × 10-9 cm-2 s-1, corresponding to ~7 times the flux, I(>200 GeV), observed from the Crab Nebula. Peak fluxes are measured with 1 minute timescale resolution at more than twice this average value. Variability is seen up to ~600 s in the Fourier power spectrum, and well-resolved bursts varying on timescales of ~200 s are observed. There are no strong indications for spectral variability within the data. Assuming the emission region has a size comparable to the Schwarzschild radius of a ~109 M☉ black hole, Doppler factors greater than 100 are required to accommodate the observed variability timescales.
Graded multiplicities in the exterior algebra of the little adjoint module

As a first application of the double affine Hecke algebra with unequal parameters on Weyl orbits to representation theory of semisimple Lie algebras, we find the graded multiplicities of the trivial module and of the little adjoint module in the exterior algebra of the little adjoint module of a simple Lie algebra $\,\mathfrak{g}\,$ with a non-simply laced Dynkin diagram. We prove that in type $\,B, C\,$ or $\,F\,$ these multiplicities can be expressed in terms of special exponents of positive long roots in the dual root system of $\,\mathfrak{g}. \,$
The quasi-optimality criterion in the linear functional strategy

The linear functional strategy for the regularization of inverse problems is considered. For selecting the regularization parameter therein, we propose the heuristic quasi-optimality principle and some modifications including the smoothness of the linear functionals. We prove convergence rates for the linear functional strategy with these heuristic rules taking into account the smoothness of the solution and the functionals and imposing a structural condition on the noise. Furthermore, we study these noise conditions in both a deterministic and stochastic setup and verify that for mildly-ill-posed problems and Gaussian noise, these conditions are satisfied almost surely, where on the contrary, in the severely-ill-posed case and in a similar setup, the corresponding noise condition fails to hold. Moreover, we propose an aggregation method for adaptively optimizing the parameter choice rule by making use of improved rates for linear functionals. Numerical results indicate that this method yields better results than the standard heuristic rule.
Fast inexact decomposition algorithms for large-scale separable convex optimization

In this paper, we propose a new inexact dual decomposition algorithm for solving separable convex optimization problems. This algorithm is a combination of three techniques: dual Lagrangian decomposition, smoothing and excessive gap. The algorithm has low computational complexity since it consists in only one primal step and two dual steps at each iteration and allows one to solve the subproblem of each component inexactly and in parallel. Moreover, the algorithmic parameters are updated automatically without any tuning strategy as it happens in augmented Lagrangian approaches. We analyse the convergence of the algorithm and estimate its analytical worst-case complexity for both the primal–dual suboptimality and the primal feasibility violation, where is a given accuracy. Extensive numerical tests confirm that our method is numerically more efficient than the classical decomposition methods from the literature.
Applications of Artiﬁcial Intelligence Models in Educational Analytics and Decision Making: A Systematic Review

: Education plays a critical role in society as it promotes economic development through human capital, reduces crime, and improves general well-being. In any country, especially in the developing ones, its presence on the political agenda is necessary. Despite recent educational advances, those developing countries have increased enrollments, but academic performance has fallen far short of expectations. According to international evaluations, Latin American countries have made little progress in recent years, considering the level of investment in education. Thus, Artiﬁcial Intelligence (AI) models, which deal with data differently from traditional analysis methods, can be an option to better understand educational dynamics and detect patterns. Through a literature review using the PRISMA methodology, we investigated how AI has been used to evaluate educational performance in basic education (elementary and high school) in several countries. We searched ﬁve platforms, resulting in a total of 19,114 works retrieved, and 70 articles included in the review. Among the main ﬁndings of this study, we can mention: (i) low adherence to the use of AI methodology in education for practical actions; (ii) restriction of analyzes to speciﬁc datasets; (iii) most studies focus on computational methodology and not on the meaning of the results for education; and (iv) a less trend to use AI methods, especially in Latin America. The COVID-19 pandemic has exacerbated educational challenges, highlighting the need for innovative solutions. Given the gap in the use of AI in education, we propose its methods for global academic evaluation as a means of supporting public policy-making and resource allocation. We estimate that these methods may yield better results more quickly, enabling us to better address the urgent needs of students and educators worldwide.
Driving altitude generation method with pseudo‐3D building model for unmanned aerial vehicles

Spatial information is geometrical information combined with the properties of an object. In city areas where unmanned aerial vehicle (UAV) usage demand is high, it is necessary to determine the appropriate driving altitude considering the height of buildings for safe driving. In this study, we propose a data‐provision method that generates the driving altitude of UAVs with a pseudo‐3D building model. The pseudo‐3D building model is developed using high‐precision spatial information provided by the National Geographic Information Institute. The proposed method generates the driving altitude of the UAV in terms of tile information, including the UAV's starting and arrival points and a straight line between the two points, and provides the data to users. To evaluate the efficacy of the proposed method, UAV driving altitude information was generated using data of 763 551 pseudo‐3D buildings in Seoul. Subsequently, the generated driving altitude data of the UAV was verified in AirSim. In addition, the execution time of the proposed method and the calculated driving altitude were analyzed.
On groups whose actions on finite-dimensional CAT(0) spaces have global fixed points

Abstract We give a criterion for group elements to have fixed points with respect to a semi-simple action on a complete CAT(0) space of finite topological dimension. As an application, we show that Thompson’s group T and various generalizations of Thompson’s group V have global fixed points when they act semi-simply on finite-dimensional complete CAT(0) spaces, while it is known that T and V act properly on infinite-dimensional CAT(0) cube complexes.
Stein approximation for multidimensional Poisson random measures by third cumulant expansions

We obtain Stein approximation bounds for stochastic integrals with respect to a Poisson random measure over ${\Bbb R}^d$, $d\geq 2$. This approach relies on third cumulant Edgeworth-type expansions based on derivation operators defined by the Malliavin calculus for Poisson random measures. The use of third cumulants can exhibit faster convergence rates than the standard Berry-Esseen rate for some sequences of Poisson stochastic integrals.
Grayscale Image Display Based on Nano-Polarizer Arrays

Optical metasurfaces have shown unprecedented capabilities to control the two-dimensional distributions of phase, polarization, and intensity profiles of optical waves. Here, a TiO2 nanostructure functioning as a nano-polarizer was optimized considering that an anisotropic nanostructure is sensitive to the polarization states of incident light. We demonstrate two metasurfaces consisting of nano-polarizer arrays featured with different orientations, which can continuously manipulate the intensity distribution of the output light cell by cell according to Malus law and clearly display the detailed information of the target image. These metasurfaces have potential application in ultracompact displays, high-density optical information storage, and many other related polarization optics fields.
Spanning tree generating functions and Mahler measures

We define the notion of a spanning tree generating function (STGF) ∑anzn, which gives the spanning tree constant when evaluated at z = 1, and gives the lattice Green function (LGF) when differentiated. By making use of known results for logarithmic Mahler measures of certain Laurent polynomials, and proving new results, we express the STGFs as hypergeometric functions for all regular two and three dimensional lattices (and one higher-dimensional lattice). This gives closed form expressions for the spanning tree constants for all such lattices, which were previously largely unknown in all but one three-dimensional case. We show for all lattices that these can also be represented as Dirichlet L-series. Making the connection between STGFs and LGFs produces integral identities and hypergeometric connections, some of which appear to be new. This article is part of ‘Lattice models and integrability’, a special issue of Journal of Physics A: Mathematical and Theoretical in honour of F Y Wu's 80th birthday.
A Modified Newton Method for Multilinear PageRank

When studying the multilinear PageRank problem, a system of polynomial equations needs to be solved. In this paper, we develop convergence theory for a modified Newton method in a particular parameter regime. The sequence of vectors produced by Newton-like method is monotonically increasing and converges to the nonnegative solution. Numerical results illustrate the effectiveness of this procedure.
General Heuristics for Nonconvex Quadratically Constrained Quadratic Programming

We introduce the Suggest-and-Improve framework for general nonconvex quadratically constrained quadratic programs (QCQPs). Using this framework, we generalize a number of known methods and provide heuristics to get approximate solutions to QCQPs for which no specialized methods are available. We also introduce an open-source Python package QCQP, which implements the heuristics discussed in the paper.
The dynamics of dry and wet monsoon MCS formation over West Africa: Case assessment of February 13, 2018 and June 18, 2018

The increasing complexity of West African mesoscale convective systems (MCSs) has signified the need for consistent studies of their evolution. Currently, most studies have focused on wet‐season MCS dynamics, but studies of dry‐season storms are equally important to enhance their predictability. Here, two severe thunderstorm case studies in the dry (February 13, 2018) and wet (June 18, 2018) seasons were probed to understand their synoptics, cloud properties, thermodynamic anomalies, and kinematic environments. Storm cloud properties were analysed using the cloud‐top temperature (CTT), cloud‐top height (CTH), cloud water path (CWP), and cloud optical thickness (COT) from the Advanced Very High Resolution Radiometer. Storm kinematics was analysed through the low‐ and mid‐level wind shear and corresponding microburst activities. Finally, the Weather Research and Forecasting model was used to simulate the vertical motion during the storms. The results showed that both storms initiated in anomalous conditions of convective available potential energy and convection inhibition, mean sea‐level pressure, and sea‐surface temperature. The development and merging of the Malian and Niger lows were pivotal in attracting moisture to feed and sustain the June storm. In addition, the formation of a surface–mid‐level moist cyclonic vortex stagnated the June storm, allowing for continual moisture uplift for storm intensification and longevity. Enhanced microburst activities on June 17 were a possible trigger of night‐time convective storm initiation, whereas strong surface to mid‐level wind shear increased storm longevity. Both storms showed high CWP, COT, CTH, and colder CTT during evolution. The synoptic mechanism for the February 13 storm differed from the midlatitudinal trough interactions with the Inter‐Tropical Discontinuity, due to an anomalous extension of the Azores High over North Africa. The dynamics of these storms enhance understanding of MCS evolution over West Africa and have implications for the region's weather forecasting.
An Imbalanced Fault Diagnosis Method Based on TFFO and CNN for Rotating Machinery

Deep learning-based fault diagnosis usually requires a rich supply of data, but fault samples are scarce in practice, posing a considerable challenge for existing diagnosis approaches to achieve highly accurate fault detection in real applications. This paper proposes an imbalanced fault diagnosis of rotatory machinery that combines time-frequency feature oversampling (TFFO) with a convolutional neural network (CNN). First, the sliding segmentation sampling method is employed to primarily increase the number of fault samples in the form of one-dimensional signals. Immediately after, the signals are converted into two-dimensional time-frequency feature maps by continuous wavelet transform (CWT). Subsequently, the minority samples are expanded again using the synthetic minority oversampling technique (SMOTE) to realize TFFO. After such two-fold data expansion, a balanced data set is obtained and imported to an improved 2dCNN based on the LeNet-5 to implement fault diagnosis. In order to verify the proposed method, two experiments involving single and compound faults are conducted on locomotive wheel-set bearings and a gearbox, resulting in several datasets with different imbalanced degrees and various signal-to-noise ratios. The results demonstrate the advantages of the proposed method in terms of classification accuracy and stability as well as noise robustness in imbalanced fault diagnosis, and the fault classification accuracy is over 97%.
Pulsatile Bypass Flow by Means of Power Law and Newtonian Model: A Comparison Guided by Numerical Investigation

In this work, the comparison between the Newtonian and the Power Law models was pointed out to determine the flow field within a system that includes the bypass of a narrowing zone (Stenosis) of an ideal artery. The simulations were conducted in a transient regime considering the pulsed effect inside the veins. The results show that the Power Law model does not sufficiently coincide with the Newtonian model, especially for describing the wall stresses. More specifically, the Power Law model predicts a velocity profile that remains parabolic for the entire time range analyzed, while the Newtonian model captures vorticities.
SyncSignature: A Simple, Efficient, Parallelizable Framework for Tree Similarity Joins

This paper introduces SyncSignature, the first fully parallelizable algorithmic framework for tree similarity joins under edit distance. SyncSignature makes use of implicit-synchronized signature generation schemes, which allow for an efficient and parallelizable candidate-generation procedure via hash join. Our experiments on large real-world datasets show that the proposed algorithms under the SyncSignature framework significantly outperform the state-of-the-art algorithm in the parallel computation environment. For datasets with big trees, they also exceed the state-of-the-art algorithms by a notable margin in the centralized/single-thread computation environment. To complement and guide the experimental study, we also provide a thorough theoretical analysis for all proposed signature generation schemes.
Schizophrenia Assessment, Referral and Awareness Training for Health Auxiliaries (SARATHA): Protocol for a Mixed-Methods Pilot Study in Rural India

Background: Workforce shortages pose major obstacles to the timely detection and treatment of schizophrenia, particularly in low-income and middle-income countries. The SARATHA (Schizophrenia Assessment, Referral, and Awareness Training for Health Auxiliaries) project involves the systematic development, iterative refinement, and pilot testing of a digital program for training community health workers in the early detection and referral of schizophrenia in primary care settings in rural India. Methods: SARATHA is a three-phase study. Phase 1 involves consulting with experts and clinicians, and drawing from existing evidence to inform the development of a curriculum for training community health workers. Phase 2 consists of designing and digitizing the training content for delivery on a smartphone app. Design workshops and focus group discussions will be conducted to seek input from community health workers and service users living with schizophrenia to guide revisions and refinements to the program content. Lastly, Phase 3 entails piloting the training program with a target sample of 20 community health workers to assess feasibility and acceptability. Preliminary effectiveness will be explored, as measured by community health workers’ changes in knowledge about schizophrenia and the program content after completing the training. Discussion: If successful, this digital training program will offer a potentially scalable approach for building capacity of frontline community health workers towards reducing delays in early detection of schizophrenia in primary care settings in rural India. This study can inform efforts to improve treatment outcomes for persons living with schizophrenia in low-resource settings.
Gingival Crevicular Fluid-An Eos of Biomarkers

GCF is a serum transudate or inflammatory exudate that is derived from the periodontal tissues and can be collected at the orifice or from within the gingival crevice. (1) The potential diagnostic importance of gingival fluid was recognized more than 60 years ago and serious investigation of the dynamics of GCF production did not begin until the late 1950s with the reports of Brill and co-workers. (2,3) The fluid component of GCF derives primarily from microvascular leakage. Additions to the fluid volume are made from the intercellular fluid and cellular cytoplasm. The gingival tissue is constantly subjected to mechanical and bacterial aggregation.Resistance to these actions is provided by saliva, sulcular fluid, epithelial surface keratinisation and initial stages of inflammation. (4) The origin,composition and clinical significance of gingival crevicular fluid have significantly helped us in understanding the pathogenesis of periodontal disease.The need to find a non-invasive test method in periodontitis drew attention to the sulcus fluid that is produced in small quantities even in a completely healthy periodontium, and its composition is near similar to that of blood plasma.
Dielectric Radome Analysis with a Simplified Nonstandard Path Integral Model

The nonstandard finite-difference time-domain (NS-FDTD) method is a highly accurate numerical tool, however, it is not suitable for curved-surface objects, as it employs discrete points on orthogonal grids. To this objective, a path integral (PI) model can be considered as a useful means to drastically improve the efficiency of the NS-FDTD algorithm. It has been previously shown that the featured scheme can greatly improve the NS-FDTD competence for curved surface on rough orthogonal grids and PEC objects. In this paper, we analyze a radome with curved dielectric parts and confirm the effectivity of the PI model.
A Verification Theorem for Indexability of Discrete Time Real State Discounted Restless Bandits

This paper presents sufficient conditions for indexability (existence of the Whittle index) of general real state discrete time restless bandit projects under the discounted optimality criterion, with possibly unbounded reward and resource consumption functions. The main contribution is a verification theorem establishing that, if performance metrics and an explicitly defined marginal productivity (MP) index satisfy three conditions, then the project is indexable with its Whittle index being given by the MP index, in a form implying optimality of threshold policies for dynamic project control. The proof is based on partial conservation laws and infinite-dimensional linear programming duality. Further contributions include characterizations of the index as a Radon--Nikod\'ym derivative and as a shadow price, and a recursive index-computing scheme.
Extent of Capital Flight and Its Impact on Economic Growth: The Case of WAEMU countries

This paper estimates the magnitude of capital flight and analyzes its impact on economic growth in the West African Economic and Monetary Union countries. Over the period from 1970 to 2019, total real capital flight from these countries is positive and significant with a magnitude that amounts to $31,075.26 million in constant dollars, or 17.40 percent of investment. Six countries have experienced significant real capital flight over the past four decades: Ivory Coast, Guinea Bissau, Mali, Niger, Burkina Faso, and Senegal. Using dynamic fixed-effects estimation, the paper finds that, in the long run, capital flight significantly reduces economic growth in countries where capital flight is positive and that the negative effect does not appear to be cumulative with investment in the case of these groups of countries. In addition, the paper recommends that the authorities commit to reducing capital flight by improving governance and strengthening the quality of institutions.
A Robust Vision-based Lane Detection using RANSAC Algorithm

In a pursuit to reduce ever increasing road accidents by developing an advanced driver assistance system (ADAS), this paper proposes a vision-based lane detection algorithm. This paper incorporates a framework constituting of color space conversion, region of interest (ROI), adaptive histogram equalization, clustering of lane pixels, and RANdom SAmple Consensus (RANSAC) to develop a lane detection algorithm. The advantage of adaptive histogram equalization is to adjust the pixel intensity of Shadow and illumination regions in the road image using a contrast limit function. Further, clustering of a lane pixels is used to count and accumulate lane pixels above certain threshold. Finally, a RANSAC algorithm is applied to remove outliers and fit the lane lines model. The advantage of proposed framework is to detect the ego-lane and also all the lane boundaries in the image plane. Moreover, based on visual analysis, algorithm reveals a superior lane detection performance suitable for illumination variation, shadow, and lane variant width.
Discretization and superintegrability all rolled into one

Abelian integrals appear in mathematical descriptions of various physical processes. According to Abel’s theorem these integrals are related to motion of a set of points along a plane curve around fixed points, which are rarely used in physical applications. We propose to interpret coordinates of the fixed points either as parameters of exact discretization or as additional first integrals for equations of motion reduced to Abelian quadratures on a symmetric product of algebraic curve.