Miracle-dz/UniFAR-FTUs huggingface dataset:
Singularities of Hermitian–Yang–Mills connections and Harder–Narasimhan–Seshadri filtrations

This is the first of two papers where we relate tangent cones of Hermitian-Yang-Mills connections at an isolated singularity to the complex algebraic geometry of the underlying reflexive sheaf, when the sheaf is locally modelled on the pull-back of a holomorphic vector bundle from the projective space. In this paper we shall impose an extra assumption that the graded sheaf determined by the Harder-Narasimhan-Seshadri filtration of the vector bundle is reflexive. In general we conjecture that the tangent cone is uniquely determined by the Harder-Narasimhan-Seshadri filtration of an algebraic tangent cone, which is a certain torsion-free sheaf on the projective space. In this paper we also prove this conjecture when there is an algebraic tangent cone which is locally free and stable.
How Far Has Our MSMEs Credit Underwriting Assessment in Indonesian Commercial Banks Progressed?

The literature on credit underwriting assessment is huge, but many are designed for consumer banking. Given that most banks in Indonesia got their revenue from commercial banking activities, it is critical to recall the main characteristics of current practice as recorded in published papers with cases in Indonesia's MSMEs credit underwriting risk assessment. This study discusses the industry standard of loan origination framework and the emerging trend of statistical and advanced analytic methods with their challenges, focusing on papers on credit underwriting risk assessment with MSMEs datasets leaning on Indonesian data whenever available and opening research questions in this topic. Examples given in this paper may indicate the methods used by Indonesian banks to assess the creditworthiness of MSMEs, although there is no clue if either the mentioned studies are implemented in the respective banks or just for academic purposes.
Efficient HPC and Energy-Aware Proactive Dynamic VM Consolidation in Cloud Computing

—The adoption of High-Performance Computing (HPC) applications has gained an extensive interest in the Cloud computing. Current cloud vendors utilize separate management tools for HPC and non-HPC applications, missing out on the consolidation benefits of virtualization. Non-HPC applications executed in the cloud may interfere with resource-hungry HPC applications, which is a key performance challenge. Furthermore, correlations between application major performance indicators, such as response time and throughput, with resource capaci- ties reveal that conventional placement strategies are impacting virtual machine efficiency, resulting in poor resource optimiza- tion, increased operating expenses, and longer wait times. Since applications often underutilized the hardware, smart execution of HPC and Non-HPC applications on the same node can boost system and energy efficiency. This research incorporates proactive dynamic VM consolidation to enhance the resource usage and performance while maintaining energy efficiency. The proposed algorithm generates a workload-aware fine-grained classification by employing machine learning techniques to generate compli- mentary profiles that alleviate cross-application interference by intelligently co-locating non-HPC and HPC applications. The research used CloudSim to simulate real HPC workloads. The results verified that the proposed algorithm outperforms all heuristic methods with respect to the metrics in key areas.
Neutrino transport and hydrodynamic stability of rotating proto-neutron stars

Aims. We study the stability of differentially rotating, non-magnetic proto-neutron stars. Methods. The stability is considered by making use of a linear analysis and taking neutrino transport into account. Results. When neutrino transport is efficient, the star can be subject to a diffusive instability that can occur even in the convectively stable region. The instability arises on a time scale that is comparable to the time scale of thermal diffusion. Hydrodynamic motions driven by the instability can lead to anisotropy in the neutrino flux since the instability is suppressed near the equator and rotation axis.
The $L^2$-metric on $C^\infty(M,N)$

Let $M$, $N$ be finite-dimensional manifolds with $M$ compact. This paper looks at the Riemnannian geometry on the space $C^\infty(M,N)$ of smooth maps equipped with the $L^2$-Riemannian metric. This metric was used by Ebin and Marsden in the proof of the well-posedness of the incompressible Euler equation and is related to the Wasserstein distance in optimal transport. The paper gives an introduction to the challenges of infinite-dimensional Riemannian geometry and shows how one use general connections to relate the geometry of $N$ and the geometry of $C^\infty(M,N)$.
The Pathway to Inclusive Education in Ethiopia: Perspectives of System- and School-Level Stakeholders

In Ethiopia, commitment to promoting inclusive education for children with disabilities has remained high on the government’s agenda, yet challenges remain in achieving this goal. Since 2018, the government’s large-scale education reform programme – the General Education Quality Improvement Programme for Equity (GEQIP-E) – has had a specific focus on promoting inclusive education for children with disabilities through the provision of additional school grants, and the promotion of Inclusive Education Resource Centres. In this paper, we draw on data from the Research for Improving Systems of Education (RISE) Ethiopia, to explore the perspectives of system- and school-level stakeholders involved in supporting education for children with disabilities as part of the GEQIP-E programme. We explore the progress and challenges in this area, and highlight some of the factors that limit the implementation of strategies intended to support inclusive education at the system and school level. We show how children with disabilities continue to face significant challenges in accessing and benefiting from education. Based on our analysis, we put forward a number of recommendations including the need for better data and evidence on education for children with disabilities – including their wellbeing and experiences within the school context – and the need for more resources to address and support inclusive education.
Instability of Low-Velocity and High Water-Cut Oil–Water Two-Phase Flow Based on Microwave Signals

Abstract Accurately understanding the nonlinear and complex dynamics underlying low-velocity and high water-cut oil–water two-phase flow is a significant but challenging problem to the study of flow pattern boundary transformation and flow heat transfer. In this article, we propose a nonlinear analysis method to characterize and distinguish patterns from low-velocity and high water-cut oil–water two-phase flow based on the fractional entropy algorithm that have found great usage in complex science. First, we obtain the variable measurement and images of the oil–water two-phase flow by a microwave sensor and a high-speed camera, respectively. Second, we analyze the complexity of flow structure using recurrence algorithms and adaptive optimal kernel time–frequency representation. Finally, we propose a dynamic nonlinear analysis framework to characterize flow instability based on multiscale fractional weighted permutation entropy. The results show that the entropy rate and its mean value are sensitive to the flow structure. These interesting and significant findings suggest that the multiscale fractional weighted permutation entropy can potentially be a powerful tool for uncovering the underlying dynamics leading to the formation of two-phase flow.
ECA-MobileNetV3(Large)+SegNet Model for Binary Sugarcane Classification of Remotely Sensed Images

In recent years, several models based on fully convolutional neural networks have been proposed. These models mainly focused on improving accuracy but ignored computational efficiency. For this reason, this research proposes an innovative deep learning model, entitled “ECA-MobileNetV3(large)+Seg-Net model” for simultaneously concerning both. In the encoder, the structure-reduced MobileNetV3(large) is selected as the backbone network, which uses 11 network layers to replace 20 network layers of MobileNetV3(large). The hybrid dilated convolution with dilation rates of 1, 2, and 4 is introduced in the depthwise separable convolution to expand the local receptive field and enhance the connection of contextual information. Finally, the ECA module is fused with the bneck structure. In the decoder, the 18 network layers of SegNet’s decoder are replaced with nine layers to achieve a lightweight network with small parameters. When compared with the original SegNet, the overall accuracy (OA) of the model proposed in this research is averagely improved by 5.97%, 5.56%, and 4.13%, and the sugarcane identification accuracy is averagely improved by 7.16%, 4.01%, and 9.13%, respectively, in the three tested areas. In addition, the memory size, the number of parameters, and FLOPs are all reduced by 6/7.
Affine Projection Sign Subband Adaptive Filter Algorithm With Unbiased Estimation Under System Identification

The affine projection sign subband adaptive filter (AP-SSAF) was designed by utilizing the ${M}$ most recent input samples to further enhance the performance of the traditional SSAF. However, the abovementioned algorithms only concentrate on the noise interferences that exist in the output of system under system identification application. Actually, the input of adaptive filter could contain the noises as well. Thereby the performance of the above algorithms will inevitably deteriorate. Based on the excellent performance of AP-SSAF, in this brief, through incorporating unbiasedness criteria into the AP-SSAF, the bias-compensated AP-SSAF (BC-AP-SSAF) is devised for diminishing the impact of input noises via introduced bias compensation term, with some necessary assumptions. And the convergence is provided. Monte-Carlo simulations certify that the proposed BC-AP-SSAF holds stability under impulsive interferences and decreases the estimated bias markedly.
LIMIT THEOREMS FOR THE LEFT RANDOM WALK ON GLd (R)

Motivated by a recent work of Benoist and Quint and extending results from the PhD thesis of the third author, we obtain limit theorems for products of independent and identically distributed elements of GLd (R), such as the Marcinkiewicz-Zygmund strong law of large numbers, the CLT (with rates in Wasserstein's distances) and almost sure invariance principles with rates.
A generalization of Kantorovich operators for convex compact subsets

In this paper we introduce and study a new sequence of positive linear operators acting on function spaces defined on a convex compact subset. Their construction depends on a given Markov operator, a positive real number and a sequence of probability Borel measures. By considering special cases of these parameters for particular convex compact subsets we obtain the classical Kantorovich operators defined in the one-dimensional and multidimensional setting together with several of their wide-ranging generalizations scattered in the literature. We investigate the approximation properties of these operators by also providing several estimates of the rate of convergence. Finally, the preservation of Lipschitz-continuity as well as of convexity are discussed
Exact Controllability of Nonlinear Heat Equations in Spaces of Analytic Functions.

It is by now well known that the use of Carleman estimates allows to establish the control-lability to trajectories of nonlinear parabolic equations. However, by this approach, it is not clear how to decide whether a given function is indeed reachable. In this paper, we pursue the study of the reachable states of parabolic equations based on a direct approach using control inputs in Gevrey spaces by considering a nonlinear heat equation in dimension one. The nonlinear part is assumed to be an analytic function of the spatial variable x, the unknown y, and its derivative $\partial$ x y. By investigating carefully a nonlinear Cauchy problem in the spatial variable and the relationship between the jet of space derivatives and the jet of time derivatives, we derive an exact controllability result for small initial and final data that can be extended as analytic functions on some ball of the complex plane. 2010 Mathematics Subject Classification: 35K40, 93B05
Dynamics of transcendental H\'enon maps

Transcendental Henon maps are the natural extensions of the well investigated complex polynomial Henon maps to the much larger class of holomorphic automorphisms. We prove here that transcendental Henon maps always have non-trivial dynamical behavior, namely that they always admit both periodic and escaping orbits, and that their Julia sets are non-empty and perfect.
Public versus private business school deans’ salary levels and structure in the United States

ABSTRACT Despite increased scrutiny of administrators’ salaries in higher education, little is known about the determinants of executive-level compensation at universities. This study examines the individual and institutional determinants of compensation of business school deans, in the United States, with a focus on differentials between private and public university deans, in the level of remuneration and the structure of compensation. Specifically, using a Oaxaca-decomposition, I estimate that despite managing smaller, less research intensive business schools, private university business school deans earn approximately 15% more than comparable public university deans, and that they are compensated more for managing finances than for managing students.
Computed Tomographic Thickness of Retrobulbar Optic Nerve Is Decreased in Glaucoma Patients and Is Negatively Correlated With Disease Severity

Purpose The purpose of the study was to quantitatively measure the width of the retrobulbar optic nerve-sheath complex by computed tomography (CT) in glaucoma and evaluate its relationship with optic nerve changes and visual field loss parameters. Methods Sixty-six eyes of 33 patients with bilateral asymmetric glaucomatous optic nerve damage and 20 eyes of 20 age- and sex-matched control subjects without glaucoma were included. Axial retrobulbar optic nerve-sheath complex was measured by CT in the eyes with advanced glaucomatous damage (group 1); in the fellow eyes of the same patients with moderate glaucomatous damage (group 2); and in control subjects (group 3). Measurements were obtained at three different points: just behind the globe, near the optic canal, and in the middle. Results Mean age and sex distribution between groups were comparable (p>0.05). The mean diameters of the retrobulbar optic nerve at three measurement points in group 1 (4.00±0.42mm, 3.49±0.44mm, 3.18±0.45mm) were significantly (for each, p<0.05) lower when compared with the corresponding points of group 2 eyes (4.24±0.41mm, 3.77±0.47nn, 3.47mm±0.44mm) and normal controls (4.58±0.44mm, 4.15±0.45mm, 3.92±0.48mm). Optic disc changes and visual field parameters were negatively correlated with retrobulbar optic nerve diameter (for each, p < 0.05). Conclusion Radiological alterations of the retrobulbar optic nerve in glaucomatous eyes revealed a decreased optic nerve diameter which correlated with disease severity. Optic nerve dimensions below the lower limit for normal individuals may be considered pathologically reduced and, therefore, CT measurements of the retrobulbar nerve may be additive to the traditional triad of raised intraocular pressure, field defects, and optic disc changes in some cases with opaque optic media preventing the fundus examination or with optic nerve anomalies.
Darboux transformations for a 6-point scheme

We introduce the Jonas transformation (a transformation of the Darboux type) for the general second-order differential equation in two independent variables. We present a discrete version of the transformation for a 6-point difference scheme. The scheme is appropriate for the solution of a hyperbolic-type initial-boundary value problem. We discuss several reductions and specifications of the transformations.
Pursuing multiple goals during the commute: A dynamic self-regulatory perspective

The current review theorizes that self-regulatory principles can be applied to the commute experience to better understand how spatial navigation and role transition processes interface with each other. Using a multiple goal framework, spatial navigation and role transitions are conceptualized as simultaneous goals, each with their own set of effort allocation processes. Furthermore, the review describes how dynamic and stable features of the commute and of the different roles individuals enact come together to impact spatial navigation and role transition effectiveness, along with well-being and effectiveness in individuals’ “home” and “workplace” roles. The proposed framework offers novel predictions about how and why these two activities impact each other in different ways, providing testable propositions that will help researchers begin to better understand the commute as a time for multiple goal regulation. The review concludes with suggestions for future research aimed at investigating these processes. Plain Language Summary The commute represents a time when individuals are focused on physically moving from one location to another to meet the goal of arriving at one's destination in a safe and timely manner (Calderwood & Mitropoulos, 2020). At the same time, individuals may also have the goal of transitioning between roles, which involves mentally detaching from one role (e.g., deactivating goals involved with being a spouse) and reattaching to a second role (e.g., activating goals relevant to being an employee). As such, the current review views the commute as a time and place in which individuals may be striving for multiple goals that involve shifting attention and energetic resources between goals (Louro et al., 2007). The review uses a multiple goal framework (e.g., Louro et al., 2007) to explain how dynamic and stable features of the commute and of individuals’ roles come together to influence commute and role transition effectiveness and work and home outcomes. Further, the review concludes with suggestions for future research aimed at investigating these dynamic processes.
Parallel Hough Transform based on Object Dual and Pymp Library

—Geometric shape detection in an image is a classical problem that leads to many applications, in cartography to highlight roads in a noisy image, in medical imaging to localize disease in a region and in agronomy to fight against weeds with pesticides. The Hough Transform method contributes effectively to the recognition of digital objects, as straight lines, circles and arbitrary objects. This paper deals with the theoretical comparisons of object dual based on the definition of Standard Hough Transform. It also focuses on parallelism of Hough Transform. A generic pseudo-code algorithm, using the Openmp library for the parallel computing of object dual is proposed in order to improve the execution time. In simulation, a triangular mesh superimposed on the image is implemented with the pymp library in python, in considering threads as inputs to read the image and to update the accumulator. The parallel computing presents reduction of the execution time accordingly to the rate of lit pixels in each virtual object and the number of threads. In perspectives, it will contribute to strenghen the developement of a toolkit for the Hough Transform method.
A 0.6–1.8-mW 3.4-dB NF Mixer-First Receiver With an N-Path Harmonic-Rejection Transformer-Mixer

This article presents a low-power passive mixer-first receiver (MFRX) with an N-path harmonic-rejection (HR) transformer-mixer for narrowband Internet-of-Everything (IoE) applications. Composed of only switches and capacitors, the N-path HR transformer-mixer integrates the behavior of a transformer and harmonic recombination into the passive mixer itself, providing voltage gain, HR, and high- $Q$ filtering before any active components. A prototype RX in 22-nm fully-depleted silicon-on-insulator (FD-SOI) occupies 0.064 mm2 and operates from 300 MHz to 3.0 GHz while consuming 0.6–1.8 mW. It achieves 3.4–4.8-dB noise figure (NF), 14–18-dBm out-of-band (OB)-input-referred third-order intercept point (IIP3), −5–0-dBm OB-B1dB, −8/−3-dBm H3/H5-B1dB, and < 1-dB NF degradation with a −15-dBm OB blocker.
Thermal gradient-induced forces on geodesic reference masses for LISA

The low frequency sensitivity of space-borne gravitational wave observatories will depend critically on the geodesic purity of the trajectories of orbiting test masses. Fluctuations in the temperature difference across the enclosure surrounding the free-falling test mass can produce noisy forces through several processes, including the radiometric effect, radiation pressure, and outgassing. We present here a detailed experimental investigation of thermal gradient-induced forces for the Laser Interferometer Space Antenna (LISA) gravitational wave mission and the LISA Pathfinder, employing high resolution torsion pendulum measurements of the torque on a LISA-like test mass suspended inside a prototype of the LISA gravitational reference sensor that will surround the test mass in orbit. The measurement campaign, accompanied by numerical simulations of the radiometric and radiation pressure effects, allows a more accurate and representative characterization of thermal-gradient forces in the specific geometry and environment relevant to LISA free-fall. The pressure dependence of the measured torques allows clear identification of the radiometric effect, in quantitative agreement with the model developed. In the limit of zero gas pressure, the measurements are most likely dominated by outgassing, but at a low level that does not threaten the current LISA noise estimate, which assumes a maximum net force per degreemore » of temperature difference of 100(pN/K) for the overall thermal gradient-induced effects. « less
Three Improvements to Multi-Level Monte Carlo Simulation of SDE Systems

We introduce three related but distinct improvements to multilevel Monte Carlo (MLMC) methods for the solution of systems of stochastic differential equations (SDEs). Firstly, we show that when the payoff function is twice continuously differentiable, the computational cost of the scheme can be dramatically reduced using a technique we call `Ito linearization'. Secondly, by again using Ito's lemma, we introduce an alternative to the antithetic method of Giles et. al [M.B. Giles, L. Szpruch. arXiv preprint arXiv:1202.6283, 2012] that uses an approximate version of the Milstein discretization requiring no Levy area simulation to obtain the theoretically optimal cost-to-error scaling. Thirdly, we generalize the antithetic method of Giles to arbitrary refinement factors. We present numerical results and compare the relative strengths of various MLMC-type methods, including each of those presented here.
Quintic periods and stability conditions via homological mirror symmetry

For the Fermat Calabi-Yau threefold and the theory of stability conditions [Bri07], there have been two mathematical aims given by physical reasoning. One is that we should define stability conditions by central charges of quintic periods [Hos04,Kon12,KonSoi13], which extend the Gamma class [KKP,Iri09,Iri11]. The other is that for well-motivated stability conditions on a derived Fukaya-type category, each stable object should be a Lagrangian [ThoYau]. We answer affirmatively to these aims with the simplest homological mirror symmetry (HMS for short) of the Fermat Calabi-Yau threefold [Oka09,FutUed] and stability conditions of Bridgeland type, which we introduce. With HMS, we naturally obtain stability conditions of Bridgeland type by the monodromy around the Gepner point. As consequences, we obtain bases of quintic periods and the mirror map [CdGP] categorically, wall-crossings by quintic periods, and a quasimodular form [KanZag] attached to quintic periods by motivic Donaldson-Thomas invariants [KonSoi08]. The quasimodular form is of the quantum dilogarithm and of a mock modular form [Zag06].