Miracle-dz/UniFAR-FTUs huggingface dataset:
Daytime sky polarization calibration limitations

Abstract. The daytime sky has recently been demonstrated as a useful calibration tool for deriving polarization cross-talk properties of large astronomical telescopes. The Daniel K. Inouye Solar Telescope and other large telescopes under construction can benefit from precise polarimetric calibration of large mirrors. Several atmospheric phenomena and instrumental errors potentially limit the technique’s accuracy. At the 3.67-m AEOS telescope on Haleakala, we performed a large observing campaign with the HiVIS spectropolarimeter to identify limitations and develop algorithms for extracting consistent calibrations. Effective sampling of the telescope optical configurations and filtering of data for several derived parameters provide robustness to the derived Mueller matrix calibrations. Second-order scattering models of the sky show that this method is relatively insensitive to multiple-scattering in the sky, provided calibration observations are done in regions of high polarization degree. The technique is also insensitive to assumptions about telescope-induced polarization, provided the mirror coatings are highly reflective. Zemax-derived polarization models show agreement between the functional dependence of polarization predictions and the corresponding on-sky calibrations.
A Masked Engagement? The Influence of Mask-wearing on Students Learning Engagement

Educational system was dangerously challenged by COVID-19 without preparation. Schools and students cannot afford another strike. Mask-wearing has become a new normal. Scholars researched the potential effects of mask-wearing, but no direct study focuses on students learning engagement under mask-wearing conditions. In our study, we implemented a quantitative method to study students learning engagement in four aspects: emotional, behavioral, academic, and cognitive engagement. We received 218 questionnaire responses from native Chinese college students. Then, 90 valid responses were generated after exposure to three different stimuli: a native Chinese instructor lecturing English morphology in a quiet environment (SNR > 15 dB) without wearing a mask, the instructor lecturing the same content in the same acoustic environment (SNR > 15 dB) wearing a mask, and the instructor lecturing in the presence of noise (SNR < 15 dB) with mask-wearing. Our finding indicates that neither mask-wearing nor a noisy environment has a substantial influence on our participants emotional, behavioral, and cognitive engagement. However, our participants academic engagement was discouraged significantly after being lectured in noise after mask-wearing.
SEXTIC MOMENT PROBLEMS ON 3 PARALLEL LINES

Abstract. Sextic moment problems with an infinite algebraic variety are still widely open. We study the problem with a single cubic column relation associated to 3 parallel lines in which the variety is infinite. It turns out that this specific column relation has a strong connection with moment problems that have a symmetric algebraic variety. We present more concrete solutions to some sextic moment problems with a symmetric variety.
Massive Star Formation in the Sparsest Environments

Abstract There is growing evidence that massive stars sometimes form in extremely sparse environments. The RIOTS4 survey presents a variety of evidence supporting this scenario, including a sample of 14 OB stars in the Small Magellanic Cloud (SMC) that appear to have formed in situ as field stars. This is based on the presence of dense, symmetric HII regions hosting apparent non-runaway stars. We also present a spatially complete IMF of SMC field OB stars for masses > 7 M ⊙, showing that the slope is much steeper than the Salpeter value. The binary fraction among field OB stars is also the same as in clusters, based on a RIOTS4 subsample. These results suggest a relative, but incomplete, suppression of massive star formation in the sparsest regimes.
A New Model to Downscale Urban and Rural Surface and Air Temperatures Evaluated in Shanghai, China

A simple model, the Surface Temperature and Near-Surface Air Temperature (at 2 m) Model (TsT2m), is developed to downscale numerical model output (such as from ECMWF) to obtain higher-temporal- and higher-spatial-resolution surface and near-surface air temperature. It is evaluated in Shanghai, China. Surface temperature (Ts) and near-surface air temperature (Ta) submodels account for variations in land cover and their different thermal properties, resulting in spatial variations of surface and air temperature. The net all-wave radiation parameterization (NARP) scheme is used to compute net wave radiation for the surface temperature submodel, the objective hysteresis model (OHM) is used to calculate the net storage heat fluxes, and the surface temperature is obtained by the force-restore method. The near-surface air temperature submodel considers the horizontal and vertical energy changes for a column of well-mixed air above the surface. Modeled surface temperatures reproduce the general pattern of MODIS images well, while providing more detailed patterns of the surface urban heat island. However, the simulated surface temperatures capture the warmer urban land cover and are 10.3°C warmer on average than those derived from the coarser MODIS data. For other land-cover types, values are more similar. Downscaled, higher-temporal- and higher-spatial-resolution air temperatures are compared to observations at 110 automatic weather stations across Shanghai. After downscaling with TsT2m, the average forecast accuracy of near-surface air temperature is improved by about 20%. The scheme developed has considerable potential for prediction and mitigation of urban climate conditions, particularly for weather and climate services related to heat stress.
Fish Classification System Using YOLOv3-ResNet18 Model for Mobile Phones

Every country in the world needs to report its fish production to the Food and Agriculture Organization of the United Nations (FAO) every year. In 2018, Indonesia ranked top five countries in fish production, with 8 million tons globally. Although it ranks top five, the fisheries in Indonesia are mostly dominated by traditional and small industries. Hence, a solution based on computer vision is needed to help detect and classify the fish caught every year. The research presents a method to detect and classify fish on mobile devices using the YOLOv3 model combined with ResNet18 as a backbone. For the experiment, the dataset used is four types of fish gathered from scraping across the Internet and taken from local markets and harbors with a total of 4,000 images. In comparison, two models are used: SSD-VGG and autogenerated model Huawei ExeML. The results show that the YOLOv3-ResNet18 model produces 98.45% accuracy in training and 98.15% in evaluation. The model is also tested on mobile devices and produces a speed of 2,115 ms on Huawei P40 and 3,571 ms on Realme 7. It can be concluded that the research presents a smaller-size model which is suitable for mobile devices while maintaining good accuracy and precision.
Exploring Representativity in Device Scheduling for Wireless Federated Learning

Existing device scheduling works in wireless federated learning (FL) mainly focused on selecting the devices with maximum gradient norm or loss function and require all devices to perform local training in each round. This may produce extra training costs and schedule devices with similar data statistics, thus degrading learning performance. To mitigate these problems, we first theoretically characterize the convergence behaviour of the considered FL system, finding that the learning performance is degraded by the difference between the aggregated gradient of scheduled devices and the full participation gradient. Inspired by this, we propose to find a subset of representative devices and the corresponding pre-device stepsizes to approximate the full participation aggregated gradient. Considering the limited wireless bandwidth, we formulate a problem to capture the trade-off between representativity and latency by optimizing device scheduling and bandwidth allocation policies. Our analysis reveals optimal bandwidth allocation is achieved when all scheduled devices have the same latency. Then, by proving the non-monotone submodularity of the problem, we develop a double greedy algorithm to solve the device scheduling policy. To avoid the local training of unscheduled devices, we utilize the historical gradient information of devices to estimate the current gradient for device scheduling design. Compared to existing scheduling algorithms, the proposed representativity-aware device scheduling algorithm improves 6.7% and 4.02% accuracies on two typical datasets under heterogeneous local data distributions, i.e., MNIST and CIFAR-10, respectively. In addition, the proposed latency- and representativity-aware scheduling algorithm saves over 16% and 12% training time for MNIST and CIFAR-10 datasets than the scheduling algorithms based on either latency and representativity individually.
The Permian-Triassic succession of the Montagnola Senese Ridge (Middle Tuscan Ridge, Italy): a perspective for late Palaeozoic magmatism and continentalisation in the western Tethys

ABSTRACT During the Permian-Triassic transition, dramatic environmental changes occurred, including the segmentation of Pangaea and the beginning of the Alpine orogenic cycle with widespread magmatism and tectonic activity. The Permian-Triassic succession deposited in the northern (palaeo-) Gondwana is now exposed in the inner Northern Apennines where it crops out discontinuously in the Middle Tuscan Ridge, a NS-oriented morphotectonic feature mainly formed by blue-schist and green-schist facies metamorphic rocks. In this paper, we describe a previously unreported late Palaeozoic succession exposed in the central part of the Middle Tuscan Ridge: the Montagnola Senese Ridge. This succession provides information concerning the enigmatic late Palaeozoic units exposed in southern Tuscany, and permits the age constraint of continentalization at the Permian-Triassic boundary with associated magmatism. Specifically, by integrating stratigraphic, structural, and petrographic data, we describe a siliciclastic, volcanoclastic-to-carbonate succession which recorded the transition from marine sedimentation to the typical continental, red-beds of the Verrucano tectofacies. Additionally, deformational features, metamorphism and age of the succession are discussed in terms of its tectono-sedimentary evolution and implications for the Palaeozoic-Triassic setting of the central Pangaea sector currently exposed in southern Tuscany. The results allow us to recognize a middle-late Permian pre-Verrucano succession, and to date the transition to continental facies as latest Permian. The microflora permits correlation of the pre-Verrucano succession to the late Palaeozoic units of the inner Northern Apennines, and establishes the diachronous nature of the Verrucano tectofacies exposed in the inner Northern Apennines. Finally, the thermal maturity of the organic matter indicates a maximum temperature of 387 ± 30°C attained during metamorphism.
Modeling thermal effects on bridge dynamic responses

The paper seeks to analyze the influence of temperature on the dynamic behavior of bridges. The objective is to propose a model that can be used to analyze the validity of damage detection algorithms that seek to separate variations in the modal properties due to damage or temperature. One first shows how temperature changes generate thermal stresses which, depending on the boundary conditions, can generate pre-stress levels that are sufficient to induce frequency shifts in the response. A procedure to compute these effects and generate low dimension reduced models with temperature appearing explicitly is proposed. This procedure is then illustrated for a simplified bridge and a realistic one for which test results are available.
Persistence in Engineering: Does Initial Mathematics Course Matter?

This study is situated within a larger project that seeks to understand how students that start in precalculus and struggle in their math courses persist and complete an engineering degree program. The specific aims of this study are to determine 1) the extent to which students that start in precalculus persist in engineering after one year, 2) correlations between the grade in engineering students’ first math course and/or the level of that course and persistence in engineering one year later, and 3) the relative number of students that graduated with an engineering degree who started in a non-college level (pass/fail) mathematics course. Data were collected and analyzed from academic records for all first semester engineering students at a southern land grant university, including the mathematics course for which the students were registered that semester, their grade in that course, and their major at the end of the following fall semester. The results of the analysis showed that both the level and grade in students’ first college mathematics course are significant predictors of retention in engineering: students starting out in a non-college level mathematics course and those making a D, F, or withdrawing from their first mathematics course are less likely to still be in an engineering program a year later than those that begin in a calculus course or those that made a C or better in their first mathematics course. Additionally, our results showed that fewer than 12% of graduating engineers during a single semester at our institution started in a non-college level mathematics course. In contrast, nearly 40% of graduating engineering students entered the university with AP or dual enrollment credit for single variable calculus. This quantitative study of trajectories of students who start out in non-college level mathematics was conducted to identify the subject(s) for a future qualitative study of the factors that contribute to students’ persistence in engineering when they encounter difficulties in their college mathematics courses.
Mirroring and switching authoritative personae: A ventriloquial analysis of shareholder engagement on carbon emissions

We examine how the authority of investors to speak about climate change with corporations is established. Leveraging the ‘communication as constitutive of organisations’ (CCO) perspective, we analyse who speaks on behalf of whom (or what) in shareholder engagement on corporate carbon emissions. Based on access to private dialogues between an engager acting on behalf of a pool of investors with 20 utility corporations, we identify how three authoritative personae—that of diplomat, advocate, and coach—convey climate change concerns. We find that the mirroring of these authoritative personae by corporations may lead to deliberation, evasion, or rejection of the suggested courses of action. We theorise how relational authority is communicatively constituted in shareholder engagement through a process of mirroring and switching between authoritative personae. Our framework contributes to the study of CCO and relational authority by highlighting how meta-figures are used by external actors in an attempt to author appropriate corporate actions. We discuss the implications of our framework for the role of shareholder engagement in current attempts at greening financial capitalism.
Weak Signal Detection based on Beta Divergence

Among different electromagnetic spectrum monitoring tasks, a fundamental one is detecting the presence of weak communication signal under non-cooperative setting. Various detection methods have been studied in the literature. These detection methods usually construct a proper hypothesis testing metric to distinguish the signal-plus-noise and noise-only signals. Information entropy based metric has recently been studied and shown its superiority over traditional energy based metrics. We find that it can be explained as a realization of the general Kullback-Leibler divergence. Inspired by that, in this paper, we propose to detect the signal using a statistic divergence called β-divergence. Different choices of β will lead to several well known divergences, e.g., Itakura-Saito divergence and Euclidean distance. We study its detection performance for non-cooperative signal sources. Both simulation and real experiments demonstrate that β-divergence can improve detection performance for various modulation types of communication signals.
Two-level designs constructed by concatenating orthogonal arrays of strenght three

Two-level orthogonal arrays of N runs, k factors and a strength of 3 provide suitable fractional factorial designs in situations where many of the main effects are expected to be active, as well as some two-factor interactions. If constructed using the fold-over technique, these designs are called even and allow at most N/2 - 1 interactions to be estimated. For k 48, we develop an algorithm for an optimal concatenation of strength-3 designs involving N/2 runs. Our approach involves column permutations of one of the concatenated designs, as well as sign switches of the elements of one or more columns of that design. We illustrate the potential of the algorithm by generating two-level even-odd designs with 64, 80, 96, 112 and 128 runs involving up to 33 factors. To the best of our knowledge, our designs with 80 and 96 runs involving fewer than 21 and 25 factors, respectively, are novel, just like our 112-run designs involving up to 29 factors. Our even-odd designs outperform or are competitive with the best known designs in terms of the aliasing of two-factor interactions and in terms of the number of estimable two-factor interactions.
Facial Micro-Expressions: An Overview

Micro-expression (ME) is an involuntary, fleeting, and subtle facial expression. It may occur in high-stake situations when people attempt to conceal or suppress their true feelings. Therefore, MEs can provide essential clues to people’s true feelings and have plenty of potential applications, such as national security, clinical diagnosis, and interrogations. In recent years, ME analysis has gained much attention in various fields due to its practical importance, especially automatic ME analysis in computer vision as MEs are difficult to process by naked eyes. In this survey, we provide a comprehensive review of ME development in the field of computer vision, from the ME studies in psychology and early attempts in computer vision to various computational ME analysis methods and future directions. Four main tasks in ME analysis are specifically discussed, including ME spotting, ME recognition, ME action unit detection, and ME generation in terms of the approaches, advance developments, and challenges. Through this survey, readers can understand MEs in both aspects of psychology and computer vision, and apprehend the future research direction in ME analysis.
The Role of Green Budgeting on Environmental Quality on Indonesia

Law Number 32 of 2009 requires Regional Governments to allocate an adequate environmental protection and management budget. However, the allocation of green budgeting is less than 1% of the Regional Revenue and Expenditure Budget. This study aims to determine the effect of green budgeting, Human Development Index (HDI), Foreign Direct Investment (FDI), and population density on environmental quality in Indonesia from 2011-2020. The research method used is descriptive quantitative with panel data regression analysis techniques. Data were taken from 34 provinces in Indonesia from 2011 – 2020. The research variables are Environmental Quality, Green Budgeting, Human Development Index, Foreign Direct Investment, and Population Density. The data were obtained from the Central Agency of Statistics and the Ministry of Environment and Forestry Republic Indonesia using literature study data collection techniques. The data was processed using the Eviews 9.0 analysis tool, with the Fixed Effects Model as the best model. The finding shows that green budgeting has a positive but insignificant effect, Human Development Index (HDI) has a significant positive effect. In contrast, Foreign Direct Investment (FDI) and population density significantly negatively affect environmental quality in Indonesia.
A Deep Learning Method for Predicting Low-Cost and High-Accuracy Designing of Anticipatory Layered Nanostructures

The production-learning-research-based process is considered as a systematic approach that generates significant scientific and economic values. However, the scientific value often receives greater attention than the economic value, which can render it impractical when the cost of mass production is high. To comprehensively address the requirements of high accuracy and low cost in simulation and fabrication, a deep inverse learning method based on the deep convolutional neural network (CNN) is proposed. This method aims to identify an optimal solution that intelligently utilizes low-cost materials instead of high-cost materials during the training process. By developing an improved training model capable of replacing noble metals with ordinary materials, the proposed approach successfully facilitates the design of lower cost layered nanostructures based on the user requirements. Eventually, it is concluded that the cost of predicted structures by the proposed algorithm is much lower than that of the standard deep CNN. Furthermore, the average relative error of the spectrum is less than 5%.
A Bayesian Approach for Estimating Multilevel Latent Contextual Models

In many applications of multilevel modeling, group-level (L2) variables for assessing group-level effects are generated by aggregating variables from a lower level (L1). However, the observed group mean might not be a reliable measure of the unobserved true group mean. In this article, we propose a Bayesian approach for estimating a multilevel latent contextual model that corrects for measurement error and sampling error (i.e., sampling only a small number of L1 units from a L2 unit) when estimating group-level effects of aggregated L1 variables. Two simulation studies were conducted to compare the Bayesian approach with the maximum likelihood approach implemented in Mplus. The Bayesian approach showed fewer estimation problems (e.g., inadmissible solutions) and more accurate estimates of the group-level effect than the maximum likelihood approach under problematic conditions (i.e., small number of groups, predictor variable with a small intraclass correlation). An application from educational psychology is used to illustrate the different estimation approaches.
Political Extremism in a Global Perspective

Examining data from the World Value Survey about left-right political orientation, the paper explores political extremism among common people worldwide. Our analysis reveals (i) a positive correlation between left-wing and right-wing extremism across countries, (ii) an average rise in political extremism globally in the last decade, (iii) greater political extremism in less developed countries, (iv) and a surge, during the last decade, in political extremism for less developed countries and for countries where development has not met expectations. Besides offering a picture of how successful political extremism is globally, our investigation provides insight into the driving forces behind this phenomenon.