Miracle-dz/UniFAR-FTUs huggingface dataset:
DeLTA: GPU Performance Model for Deep Learning Applications with In-Depth Memory System Traffic Analysis

Training convolutional neural networks (CNNs) requires intense compute throughput and high memory bandwidth. Especially, convolution layers account for the majority of execution time of CNN training, and GPUs are commonly used to accelerate these layer workloads. GPU design optimization for efficient CNN training acceleration requires the accurate modeling of how their performance improves when computing and memory resources are increased. We present DeLTA, the first analytical model that accurately estimates the traffic at each GPU memory hierarchy level, while accounting for the complex reuse patterns of a parallel convolution algorithm. We demonstrate that our model is both accurate and robust for different CNNs and GPU architectures. We then show how this model can be used to carefully balance the scaling of different GPU resources for efficient CNN performance improvement.
Deformations of Polystable Sheaves on Surfaces: Quadraticity Implies Formality

We study relations between the quadraticity of the Kuranishi family of a coherent sheaf on a complex projective scheme and the formality of the DG-Lie algebra of its derived endomorphisms. In particular, we prove that for a polystable coherent sheaf on a smooth complex projective surface the DG-Lie algebra of derived endomorphisms is formal if and only if the Kuranishi family is quadratic.
Atomic-Scale Representation and Statistical Learning of Tensorial Properties

This chapter discusses the importance of incorporating three-dimensional symmetries in the context of statistical learning models geared towards the interpolation of the tensorial properties of atomic-scale structures. We focus on Gaussian process regression, and in particular on the construction of structural representations, and the associated kernel functions, that are endowed with the geometric covariance properties compatible with those of the learning targets. We summarize the general formulation of such a symmetry-adapted Gaussian process regression model, and how it can be implemented based on a scheme that generalizes the popular smooth overlap of atomic positions representation. We give examples of the performance of this framework when learning the polarizability and the ground-state electron density of a molecule.
Maslow’s Needs Hierarchy and the Effect of Income on Happiness Levels

Abraham Maslow’s theory of hierarchical needs has been employed by a large variety of conceptual frameworks. The theory can also offer additional insights to the research field which investigates the relationship between income and reported happiness levels. The incorporation of needs hierarchy into a happiness framework implies that individuals have a priority approach to happiness. This means that the most important needs must be satisfied first before the secondary needs come into the picture. In terms of income-happiness relationship, it suggests that income is very important for happiness up to a certain level of income. For higher income levels this effect becomes much weaker, given that the satisfaction of non-basic needs becomes important. The chapter tests this idea by using the European Foundation European Quality of Life Survey 2007 which contains data from 30 European countries and Turkey. In the proposed model, reported happiness is placed as a dependent variable and income level as an independent variable. The ordered probit model (with robust standard errors) is the main statistical tool of the work. The empirical results indicate that there is a strong positive relationship between income and happiness for low income households group, and a non-significant relationship between income and happiness for high income households group. This result supports the presence of hierarchical behaviour. The model also contains personal variables such as gender, age, marital status, educational level, number of children, working hours per week, country dummy variables and employment status. The relationship of these variables to reported happiness levels is also examined. Finally, there is a comparison of the empirical findings to results in the relevant literature.
FÜHR 3 D Tracking for an Augmented Reality System Trabalho

7 RESUMO . 8
V OLUMETRIC 3 D PRINTING OF ELASTOMERS BY TOMOGRAPHIC BACK-PROJECTION

Most additive manufacturing methods such as fused-deposition modelling, selective laser melting or stereolithography create objects sequentially one layer at a time. This type of process imposes limitations on the shapes and the materials that can be printed. For example, overhanging structures need additional supports during printing, and soft or elastic materials are difficult to print since they deform as new layers are added. While casting can be used instead to create certain elastic parts, design freedom is limited because cavities or tubes are difficult to unmold. Here we use a volumetric 3D printing method based on tomography, where the entire volume of a photopolymerizable resin is solidified at the same time. We demonstrate very rapid (<30s) printing of a variety of complex structures with acrylates and silicones.
Bioengineering Of Antibodies Marked With Atoms Of Different Elements For Simultaneous Localization Of Biomolecules With Electron Energy Loss Spectroscopic Imaging

Identification of components in macromolecular assemblies is an essential step toward the determination of their interactions and functions. The goal for such an effort is to create a bridge between biochemistry and molecular biology data with images of molecular architecture showing distribution and co-localization of specific molecular species. Bioengineered antibodies carrying reporter elements either as atoms individually incorporated into or as small colloidal particles attached to these antibodies can be used to attain such a goal. We have previously pioneered development of labels, which permit, by means of spectroscopy, simultaneous detection and localization of several different molecules within one frame [1-3]. In order to confirm the capabilities of the system, we have employed a model using a molecule of alpha-actinin. It serves as anchoring site for actin and has a precisely known periodic pattern of distribution within myofibril architecture. Antibodies against alpha-actinin were derivatized with boron [1,2]. Rabbit psoas muscle was prepared via high-pressure freezing, followed by freeze-substitution, epon-embedding, and sectioning [4]. Alpha-actinin in sections was labeled with boronated antibodies [2]. The samples were viewed with the energy filtering transmission electron microscope (LEO 912 EFTEM) equipped with the omega energy filter and EELS calculation software (SIS 2.2). Distribution of boronated antibodies against alpha-actinin was determined based upon EELS imaging maps for boron at 20l eV edge. The pattern of alpha-actinin could be clearly demonstrated (Fig. 1). That pattern was compared to the overall ultrastructure revealed through the contrast tuning near the carbon edge (Fig.2). The label distribution can be superimposed onto myofibril architecture and the distribution of alpha-actinin within myofibrillar architecture concluded. The distribution of boronated anti-alpha-actinin antibodies is restricted to the Z-line validating the known to be a pattern of alpha-actinin localization. This demonstrates both the accuracy and fidelity attainable using labeled bioengineered antibodies in conjunction with EELS imaging. [5]
WENO–WOMBAT: Scalable Fifth-order Constrained-transport Magnetohydrodynamics for Astrophysical Applications

Owing to increases in computing power, high-order Eulerian schemes will likely become instrumental in simulations of turbulence and magnetic field amplification in astrophysical fluids in the next years. We present the implementation of a fifth-order weighted essentially non-oscillatory scheme for constrained-transport magnetohydrodynamics in the code WOMBAT. We establish the correctness of our implementation with extensive number tests. We find that the fifth-order scheme performs as accurately as a common second-order scheme at half the resolution. We argue that for a given solution quality, the new scheme is more computationally efficient than lower order schemes in three dimensions. We also establish the performance characteristics of the solver in the WOMBAT framework. Our implementation fully vectorizes using flattened arrays in thread-local memory. It performs at about 0.6 million zones per second per node on Intel Broadwell. We present scaling tests of the code on up to 98,000 cores on the Cray XC40 machine “Hazel Hen,” with a sustained performance of about 5% of peak at scale.
Relativistic shock breakout from a stellar wind

We construct an analytic model for the breakout of a relativistic radiation mediated shock from a stellar wind, and exploit it to calculate the observational diagnostics of the breakout signal. The model accounts for photon escape through the finite optical depth wind, and treats the fraction of downstream photons escaping to infinity as an adiabatic parameter that evolves in a quasi-steady manner. It is shown that the shock is mediated by radiation even when a large fraction of the downstream photons escape, owing to self-generation and adjustment of opacity through accelerated pair creation. Relativistic breakout occurs at radii at which the total optical depth of the wind ahead of the shock is $\sim (m_e/m_p)\Gamma_{sh}$, provided that the local shock Lorentz factor $\Gamma_{sh}$ exceeds unity at this location. Otherwise the breakout occurs in the Newtonian regime. A relativistic breakout is expected in a highly energetic spherical explosion ($10^{52}-10^{53}$ erg) of a Wolf-Rayet star, or in cases where a smaller amount of energy ($\sim 10^{51}$ erg) is deposited by a jet in the outer layers of the star. The properties of the emission observed in such explosions during the relativistic breakout are derived. We find that for typical parameters about $10^{48}$ ergs are radiated in the form of MeV gamma-rays over a duration that can range from a fraction of a second to an hour. Such a signal may be detectable out to 10-100 Mpc by current gamma-ray satellites.
Internationalization of family firms-challenges and opportunities in Russia

The family firms (FFs), especially the small and medium-sized enterprises (SMEs), play an instrumental role in the economic spectrum of the Russian economy with respect to their contribution to income, output, and employment, ceteris paribus. The FFs not only contribute the domestic business activities but also make a significant contribution to international business. Ever since the launch of the mass privatization program (MPP) in Russia during 1992–1994 numerous disruptions on the business and economic landscape of Russia have emerged, and as a result, the FFs in Russia have been experiencing several new opportunities and challenges in the international market. However, it is noticeable that corporate regulatory, and corporate governance systems do not even clearly define the FFs. The current study explores the following research objectives. First, the motivation of internationalization of FFs in Russia; second, their process of internationalization, and third, the problems and challenges faced by the FFs. Different theoretical perspectives have been discussed to problematize and analyze the research objectives of the study. The current qualitative study is based on the semi-structured interview method. As many as ten FF entrepreneurs, representing five different industries, have been analyzed. The findings show that there is neither clarity nor unanimity of the very meaning and understanding of FFs in Russia. The lack of regulated bank credit and the existence of a complex taxation system dissuade the FFs from investing in new ventures and undertaking innovative activities. Similarly, the government’s directives to set up business operations at certain specified business facilities, at the exorbitant costs though, has created downward pressure on the profitability of FFs. Many FFs have initiated their international business activities owing to their growing linkages with the external contingencies, developed over time. Similarly, internationalization has increased the competitiveness of the FFs in the Russian domestic market too.
Determination of folic acid by capillary zone electrophoresis with indirect chemiluminescence detection

A capillary electrophoresis method with on-line inhibited chemiluminescence (CL) detection was first used to determine folic acid (FA). This method was established based on the quenching effect of FA on the CL reaction of luminol with a Ag(iii) complex in alkaline medium. The separation was conducted with a 20.0 mM sodium borate buffer containing 1.0 mmol L−1 luminol. Under optimized conditions, FA was baseline separated and detected in less than 10 min. The limit of detection of FA was 1.3 mg L−1, with a linear range of 5.0–150.0 mg L−1 (r = 0.9953). The RSD value was 2.8% for intra-day precision and 5.4% inter-day precision. The recoveries of the standard addition of tablets and human urine ranged from 90.3% to 107.5% and from 82.0 to 105.7%, respectively. The proposed method was successfully applied to determine FA contents in commercial pharmaceutical tablets and human urine samples. Results suggested that this method was simple and robust.
Neural Autonomous Navigation with Riemannian Motion Policy

End-to-end learning for autonomous navigation has received substantial attention recently as a promising method for reducing modeling error. However, its data complexity, especially around generalization to unseen environments, is high. We introduce a novel image-based autonomous navigation technique that leverages in policy structure using the Riemannian Motion Policy (RMP) framework for deep learning of vehicular control. We design a deep neural network to predict control point RMPs of the vehicle from visual images, from which the optimal control commands can be computed analytically. We show that our network trained in the Gibson environment can be used for indoor obstacle avoidance and navigation on a real RC car, and our RMP representation generalizes better to unseen environments than predicting local geometry or predicting control commands directly.
ALMA study of the HD 100453 AB system and the tidal interaction of the companion with the disk

Context. The complex system HD 100453 AB with a ring-like circumprimary disk and two spiral arms, one of which is pointing to the secondary, is a good laboratory in which to test spiral formation theories. Aims. We aim to resolve the dust and gas distribution in the disk around HD 100453 A and to quantify the interaction of HD 100453 B with the circumprimary disk. Methods. Using ALMA band 6 dust continuum and CO isotopologue observations we have studied the HD 100453 AB system with a spatial resolution of 0′′. 09 × 0′′. 17 at 234 GHz. We used smoothed particle hydrodynamics (SPH) simulations and orbital fitting to investigate the tidal influence of the companion on the disk. Results. We resolve the continuum emission around HD 100453 A into a disk between 0′′. 22 and 0′′. 40 with an inclination of 29.5° and a position angle of 151.0°, an unresolved inner disk, and excess mm emission cospatial with the northern spiral arm which was previously detected using scattered light observations. We also detect CO emission from 7 au (well within the disk cavity) out to 1′′. 10, overlapping with HD 100453 B at least in projection. The outer CO disk position angle (PA) and inclination differ by up to 10° from the values found for the inner CO disk and the dust continuum emission, which we interpret as due to gravitational interaction with HD 100453 B. Both the spatial extent of the CO disk and the detection of mm emission at the same location as the northern spiral arm are in disagreement with the previously proposed near co-planar orbit of HD 100453 B. Conclusions. We conclude that HD 100453 B has an orbit that is significantly misaligned with the circumprimary disk. Because it is unclear whether such an orbit can explain the observed system geometry we highlight an alternative scenario that explains all detected disk features where another, (yet) undetected, low mass close companion within the disk cavity, shepherds a misaligned inner disk whose slowly precessing shadows excite the spiral arms.
Bank Branch Closure Assessment Using DEA

The downward trend in the number of commercial bank offices is driven by the bank's efforts to shift banking transactions from physical branch to digital channels in order to improve efficiency. In prioritizing the branch closure, bank needs to define the appropriate method used in the analysis. This case study is intended to identify the parameter to determine the prioritization of bank branch office closure. This study uses a non-parametric approach of Data Envelopment Analysis (DEA) to examine the efficiency and productivity change of branch offices at one of the large bank in Indonesia. The one-stage DEA was used to generate the relative efficiency score, and the input-oriented Variable Return to Scale (VRS) assumption is adopted in data analysis based on the production approach. The Malmquist Productivity Index was also adopted to measure the total factor productivity change. The DEA result shows that a number of closed branches in 2019 and 2020 were actually considered efficient, with increasing productivity, compared to many other inefficient branches. The efficiency and productivity score can be further used by the bankâ€™s management to evaluate the upcoming branch closure as well as the overall branches efficiency.
Perceived Competence and Skill Development in Physical Education: The Effect of Teacher Feedback

This study examined the effects of feedback on perceived competence and student performance in physical education. The sample (N = 113) consisted of fifth-grade physical education students, recruited from two elementary schools and randomly assigned into small groups. Participants within each group were randomly assigned to receive positive general, corrective informational, or no feedback and completed preand post-task questionnaires. Groups were given a lacrosse shot task presentation and then completed a lacrosse shot pre-test, practice session, and post-test, including a preand post-test. During the lacrosse task, teachers administered feedback treatments to participants at the rate of every other attempt for a total of six feedback statements, excluding the control group. Lacrosse experience was found to be 2.45 on a 9-point Likert-type scale, demonstrating that the sample was unfamiliar with the lacrosse skill. Three separate split plot 2 × 3 analysis of variance (ANOVA) tests with repeated measures were conducted to determine whether feedback treatment type influenced participants’ perceived competence, product performance skill, and process performance skill when considering preand post-task measures. The findings demonstrated that feedback appears to have a limited effect on perceived competence and performance during unfamiliar tasks in elementary school physical education.
Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce

Nowadays, more and more customers browse and purchase products in favor of using mobile E-Commerce Apps such as Taobao and Amazon. Since merchants are usually inclined to describe redundant and over-informative product titles to attract attentions from customers, it is important to concisely display short product titles on limited screen of mobile phones. To address this discrepancy, previous studies mainly consider textual information of long product titles and lacks of human-like view during training and evaluation process. In this paper, we propose a Multi-Modal Generative Adversarial Network (MM-GAN) for short product title generation in E-Commerce, which innovatively incorporates image information and attribute tags from product, as well as textual information from original long titles. MM-GAN poses short title generation as a reinforcement learning process, where the generated titles are evaluated by the discriminator in a human-like view. Extensive experiments on a large-scale E-Commerce dataset demonstrate that our algorithm outperforms other state-of-the-art methods. Moreover, we deploy our model into a real-world online E-Commerce environment and effectively boost the performance of click through rate and click conversion rate by 1.66% and 1.87%, respectively.
Dark photon dark matter from a network of cosmic strings

We study the production of ultra-light dark photons from a network of near-global, Abelian-Higgs cosmic strings. We find that dark photons produced in this way are nonrelativistic today and can make up all of the dark matter for dark photon masses as small as $m_A \sim 10^{-22} \, \mathrm{eV}$.
Location-Based Saliency Maps from a Fully Connected Layer using Multi-Shapes

Recently, with the development of technology, computer vision research based on the human visual system has been actively conducted. Saliency maps have been used to highlight areas that are visually interesting within the image, but they can suffer from low performance due to external factors, such as an indistinct background or light source. In this study, existing color, brightness, and contrast feature maps are subjected to multiple shape and orientation filters and then connected to a fully connected layer to determine pixel intensities within the image based on location-based weights. The proposed method demonstrates better performance in separating the background from the area of interest in terms of color and brightness in the presence of external elements and noise. Location-based weight normalization is also effective in removing pixels with high intensity that are outside of the image or in non-interest regions. Our proposed method also demonstrates that multi-filter normalization can be processed faster using parallel processing.
Unifying neutron star sub-populations in the supernova fallback accretion model

We employ the supernova fallback disk model to simulate the spin evolution of isolated young neutron stars (NSs). We consider the submergence of the NS magnetic fields during the supercritical accretion stage and its succeeding reemergence. It is shown that the evolution of the spin periods and the magnetic fields in this model is able to account for the relatively weak magnetic fields of central compact objects and the measured braking indices of young pulsars. For a range of initial parameters, evolutionary links can be established among various kinds of NS sub-populations including magnetars, central compact objects and young pulsars. Thus, the diversity of young NSs could be unified in the framework of the supernova fallback accretion model.