Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective Parametric Image Sequencing Technology with Aggregate Space Profound Training

That containing the full workflow of evaluation, patient stratification, treatment preparation, operation and follow-up is assisted by robot and quick solutions for target identification and segmentation. Present state-of-the-art describing approaches are generally established on computer teaching approaches that use supervised learning image databases. The reliability in processing large volumetric images and the need for powerful, representative image characteristics reflect two major challenges to be tackled. In high-dimensional spaces where the object of interest is parametric, traditional volume scanning methods do not scale to the vast spectrum of possible possibilities. The representativeness of the picture is subject to considerable manual engineering efforts. We suggest a process in the sense of volumetric image parsing for object identification and segmentation to solve a two-step study problem: structural position approximation and boundaries differentiation. To this end, we implement a new paradigm, MSDL, which uses the advantages of efficient object approximation in marginalised centralised environments and the automatic functional nature of the network architecture of Deep Learning (DL). Intelligent systems immediately define and detach explicative properties immediately from close to zero image data, but just the approximation’s high difficulty restricts their use in the volume context.
Deep Learning Algorithms for Tool Condition Monitoring in Milling: A Review

The 4th Industrial Revolution (Industry 4.0) necessitates implementing the prognostics and health management (PHM) practices in manufacturing processes. The traditional machine learning approach has well assisted the PHM practices within the same data distributions. However, when a high noise environment, versatile operating conditions, and cross-domain machining is considered, it still lacks key steps of generalizing unknown tool faults. In an attempt to address PHM practices under such domains, a generic Deep Learning-based scheme is gaining significant attention. In this paper, an inclusive review is presented in order to provide an insight into the application of DL in tool condition monitoring (TCM), particularly in milling. Commonly used DL algorithms and their applications toward TCM are initially discussed and number of illustrative DL models applied for TCM is presented. Later, emergent DL themes & their computational techniques are summarized with an intention to provide framework for domain generalization. Finally, challenges in further exploration and futuristic trends in TCM are discussed.
Photooxidative cyclization of 3-aryl/heteroaryl-4-heteroaryl coumarins: an experimental and semi-empirical study

Abstract Photochemical oxidation of substituted coumarins 1, 3 and 5 in benzene solution containing iodine gave the respective products 2, 4 and 6. The structures of the photoproducts have been confirmed by analyses of analytical and spectral data. The experimental results are further strengthened by their agreement with computationally calculated dipole moments (μ), minimum-energy values and heats of formations.
Mesoporous Co3O4 with Controlled Porosity: Inverse Micelle Synthesis and High-Performance Catalytic CO Oxidation at −60 °C

Crystalline mesoporous cobalt oxides with improved catalytic activity in CO oxidation were synthesized using an inverse surfactant micelle method. The prepared materials are monodispersed nanoparticle aggregates, and the mesopores are formed by connected intraparticle voids. Powder X-ray diffraction (PXRD), N2 sorption, field emission scanning electron microscope (FE-SEM) and high-resolution transmission electron microscopy (HR-TEM) revealed that both pore and nanoparticle sizes are enlarged with increasing thermal treatment temperatures (150–450 °C). Mesoporous cobalt oxide calcined at 350 °C exhibited the best oxidation activity and can achieve complete oxidization (100% conversion) of CO to CO2 at −60 °C under normal conditions (∼3–10 ppm of H2O) and at 80 °C under moisture rich conditions (∼3% H2O). The commercial Co3O4 reached 100% conversion at 220 °C under normal conditions. X-ray photoelectron spectroscopy (XPS), O2-temperature-programmed desorption (O2-TPD), H2-temperature-programmed reduction (H.
Grid Connected Bidirectional Converter Based Ems System with Model Predictive Control

In this paper, the bidirectional converter that performs DC-DC power conversion is integrated with the variable speed wind energy conversion system’s grid system. Generally, the voltage quality is reduced, and fluctuation in outputs is performed in renewable energy sources. The stable operation and power generation with fluctuation and power demand in the variable are ensured by developing the energy management system (EMS). In this proposed method, the bidirectional converter’s control is accomplished using the proportional-integral-derivative (PID) controller, which controls the power fluctuation from the WECS to maintain constant. The DC-AC voltage conversion is achieved using the model predictive controller (MPC), which controls both power and voltage. This proposed power and voltage controller enhances the stable AC voltage supply and improves the proper power flow. The proposed method, which is validated in simulation is simple, and the results are obtained using MATLAB/Simulink.
Performance Analysis of Retinex Based Algorithms for Enhancement of Low Light Images

The images captured under low and poor light conditions may suffer low contrast and blurring problems which will affect its interpretation and recognition. Hence it is important to enhance and restore such low contrast images. The enhancement of the captured images in the low light environment through various image enhancement algorithms is discussed in this work. The set of algorithms tested includes single scale retinex, multiscale retinex color preservation, multiscale retinex color restoration, contrast limited enhanced adaptive histogram equalization, dark channel prior, and gamma correction algorithms. This paper discusses the various variants of retinex models, which are considered powerful methods for image enhancement and restoration. The proposed work involves the study of various image enhancement algorithms and their comparison on the basis of a reference original low light image and human eye perception models. The performance of various retinex derivative algorithms is experimented with using the LIME dataset, and the histogram equalization is analyzed for metrics such as peak signal to noise ratio, structural similarity index, and discrete entropy.
A comparative study of K-rich and Na/Ca-rich feldspar ice-nucleatingparticles in a nanoliter droplet freezing assay

Abstract. A recently designed droplet freezing assay was used to study the freezing of up to 1500 identical 0.2 nL water droplets containing suspensions of one Na/Ca-rich feldspar and three K-rich and one Na/Ca-rich feldspar particles. Three types of experiments have been conducted: cooling ramp, isothermal freezing at a constant temperature, and freeze–thaw cycles. The observed freezing behavior has been interpreted with the help of a model based on the classical nucleation theory (soccer ball model (SBM); Niedermeier et al., 2015). By applying the model to the different freezing experiments conducted with the same ice-nucleating material, the unique sets of model parameters for specific feldspar suspensions could be derived. The SBM was shown to adequately describe the observed cooling rate dependence, the ice-nucleating active sites (INAS) surface density ns(T) in a wide temperature range, and the shift of the freezing curves towards lower temperature with dilution. Moreover, the SBM was capable of reproducing the variation of INAS surface density ns(T) with concentration of ice-nucleating particles in the suspension droplets and correctly predicting the leveling-off of ns(T) at low temperature. The freeze–thaw experiments have clearly shown that the heterogeneous freezing induced even by very active ice-nucleating species still possesses a stochastic nature, with the degree of randomness increasing towards homogeneous nucleation. A population of the high-temperature INAS has been identified in one of the K-rich feldspar samples. The freezing of 0.8 wt % suspension droplets of this particular feldspar was observed already at −5 °C. These high-temperature active sites could be deactivated by treating the sample with hydrogen peroxide but survived heating up to 90 °C. Given a high mass concentration of these high-temperature active sites (2.9 × 108 g−1) and a very low value of contact angle (0.56 rad) the possibility of biological contamination of the sample was concluded to be unlikely but could not be completely ruled out. The freezing efficacy of all feldspar samples has been shown to reduce only slightly after suspension in water for over 5 months.
Strategic decisions have “major” consequences: Gender differences in college major choices

We study the academic choices of applicants to the largest public university in Colombia. In this setting, access to public university college slots is limited by capacity constraints, admissions are solely based on a college entrance exam score, and cutoffs for every major are determined endogenously by applicants’ scores and the demand for slots in different majors. We use rich administrative data on applicants’ exam scores, ranking of preferences and enrollment data to leverage the fact that applicants just below and above the major-specific cutoffs have essentially identical characteristics. We perform a RD-DD analysis to study gender differentials in college major choices comparing the choices made by applicants who just miss the cutoff for their top choice major —i.e., marginal applicants. We find that marginal women follow diversification and rush in strategies consisting in reporting a larger number of college options to be considered for in the slot allocation mechanism and rushing in to enroll in a less preferred major in the first admission cycle we observe. Marginal men are more likely to retake the exam in two subsequent admission cycles and end up selecting majors that have a better income potential than those selected by marginal women. The gender wage gap of potential income in our sample is about 7%, which suggests that strategic decisions in college major choices could explain over half of the gender wage gap among college educated workers in Colombia. Together, our findings suggest that occupational segregation an the gender wage gap are not only determined by unconstrained preferences, as previous work has suggested, but that it can be magnified by strategic decisions, even when there are no initial substantial gender differences in stated preference ranking of majors. Norwegian School of Economics, Department of Economics and FAIR, Helleveien 30, 5042 Bergen, Norway. Email: catalina.franco@nhh.no Department of Economics, Brandeis University. Email: aaehawki@brandeis.edu We thank Universidad Nacional de Colombia for generously sharing their administrative data with us. We obtained useful valuable feedback from Kjetil Bjorvatn, Sandy Black, Siri Isaksson, Amma Panin, Tanya Rosenblat, Bertil Tungodden, Melanie Wasserman and Basit Zafar. This paper is part of a project financially supported by the FAIR and Sm̊aforsk grants at the Norwegian School of Economics.
Bilingual Cognition and Growth Mindset: A Review of Cognitive Flexibility and Its Implications for Dual-Language Education

The United States has seen an increase in cultural and linguistic diversity of student populations. Policy makers have looked toward existing research in dual language education, alternative curriculum, and bilingualism to support the needs of dual-language learners. In this paper, we review two areas of research that have implications for educational policy, and also have theoretical implications for early cognitive development. The first area focuses on cognitive flexibility in bilingual populations. The second area focuses on growth mindset. We highlight the parallels in these constructs, arguing that bilinguals may be uniquely receptive to growth mindset interventions due to their increased cognitive flexibility. We identify specific ways that growth mindset interventions could be applied to support dual-language learners. Lastly, we argue that future research in both areas may provide researchers and educators with a better understanding of early cognitive development in bilingual populations and the emergence of growth mindset in all populations.
GazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules

ABSTRACT Extracting precise location information from microblogs is a crucial task in many applications, particularly in disaster response, revealing where damages are, where people need assistance, and where help can be found. A crucial prerequisite to location extraction is place name extraction. In this paper, we present GazPNE: a hybrid approach to place name extraction which fuses rules, gazetteers, and deep learning techniques without requiring any manually annotated data. The core of the approach is to learn the intrinsic characteristics of multi-word place names with deep learning from gazetteers. Specifically, GazPNE consists of a rule-based system to select n-grams from the microblogs that potentially contain place names, and a C-LSTM model that decides if the selected n-gram is a place name or not. The C-LSTM is trained on 388.1 million examples containing 6.8 million positive examples with US and Indian place names extracted from OpenStreetMap and 381.3 million negative examples synthesized by rules. We evaluate GazPNE against the SoTA on a manually annotated 4,500 tweet dataset which contains 9,026 place names from three foods: 2016 in Louisiana (US), 2016 in Houston (US), and 2015 in Chennai (India). GazPNE achieves SotA performance on the test data with an F1 of 0.84.
The configuration analysis of the adsorption isotherm of nitrogen in low temperature with the lignite char produced under fast pyrolysis

Char samples prepared using Yuanbaoshan lignite, different in particle size and pyrolysis time, were analyzed with Micromeritics ASAP2020 auto-adsorption analyzer. All these samples were made under the condition of fast pyrolysis in the drop-tube furnace apparatus, which provides the similar situation to the real pulverized coal fired boilers. The analysis of adsorption isotherms shows that the isotherms of char samples, different in particle size and pyrolysis time, are all type Ⅱ with hysteresis loops; and the result indicates that those samples possess continuous and integral system of pores, ranging from molecular scalar (about 0.86 nm in diameter) to infinite large scalar (contrast to normal situations). Based on the analysis of hysteresis loops, pores of samples are consist of large quantity of blind holes (circular blind ones, planar blind ones, wedge blind ones or taper blind ones), and some split ones.
Soundy Automated Parallelization of Test Execution

Regression Testing is an important quality assurance practice widely adopted today. Optimizing regression testing is important. Test parallelization has the potential to leverage the power of multi-core architectures to accelerate regression testing. Unfortunately, it is not possible to directly use parallelization options available in build systems and testing frameworks without introducing test flakiness. Tests can fail because of data races or broken test dependencies. Although it is possible to safely circumvent those problems with the assistance of an automated tool to collect test dependencies (e.g., PRADET), the cost of that solution is prohibitive, defeating the purpose of test parallelization. This paper proposes PASTE an approach to automatically parallelize the execution of test suites. PASTE alternates parallel and sequential execution of test cases and test classes to circumvent provoked test failures. PASTE does not provide the safety guarantee that flakiness will not be manifested, but our results indicate that the strategy is sufficient to avoid them. We evaluated PASTE on 25 projects mined from GitHub using an objective selection criteria. Results show that (i) PASTE could circumvent flakiness introduced with parallelization in all projects that manifested them and (ii) 52% of the projects benefited from test-parallelization with a median speedup of 1.59x (best: 2.28x, average: 1.47x, worst: 0.93x).
In Search of the Best Response Scale in a Mixed-mode Survey (Web and Mail). Evidence from MTMM Experiments in the GESIS Panel

Mixed-mode surveys allow researchers to combine the advantages of multiple modes, for example, the low cost of the web mode with the higher coverage of offline modes. One drawback of combining modes is that there might be systematic differences in measurement across modes. Thus, it would be useful to know which measurement methods work best in all employed modes. This study sets out to find a method that results in the highest measurement quality across self-administered web mode questionnaires (web mode) and self-administered paper questionnaires sent out by mail (mail mode). Two Multitrait-Multimethod (MTMM) experiments employing questions on environmental attitudes and supernatural beliefs were implemented in the GESIS Panel, a probability-based panel in Germany. The experiments were designed to estimate the measurement quality of three different response scales: A seven-point fully labelled scale, a 101-point numerical openended scale and an eleven-point partially labelled scale. Our results show that the elevenpoint partially labelled scale consistently leads to the highest measurement quality across both modes. We thus recommend using eleven-point partially labelled scales when measuring attitudes or beliefs in mixed-mode surveys combining web and mail mode.
Snowcap: synthesizing network-wide configuration updates

Large-scale reconfiguration campaigns tend to be nerve-racking for network operators as they can lead to significant network downtimes, decreased performance, and policy violations. Unfortunately, existing reconfiguration frameworks often fall short in practice as they either only support a small set of reconfiguration scenarios or simply do not scale. We address these problems with Snowcap, the first network reconfiguration framework which can synthesize configuration updates that comply with arbitrary hard and soft specifications, and involve arbitrary routing protocols. Our key contribution is an efficient search procedure which leverages counter-examples to efficiently navigate the space of configuration updates. Given a reconfiguration ordering which violates the desired specifications, our algorithm automatically identifies the problematic commands so that it can avoid this particular order in the next iteration. We fully implemented Snowcap and extensively evaluated its scalability and effectiveness on real-world topologies and typical, large-scale reconfiguration scenarios. Even for large topologies, Snowcap finds a valid reconfiguration ordering with minimal side-effects (i.e., traffic shifts) within a few seconds at most.
A Method for Recovering Near Infrared Information from RGB Measurements with Application in Precision Agriculture

In this work we develop a cost-efficient coupled dictionary learning based method for reconstructing multispectral images using only a single RGB commercial camera, without requiring the sensitivity function of the camera sensor. Considering the very high cost, the acquisition time and reduced mobility of multispectral cameras we claim that this is a very attractive option. In contrast to other approaches, the proposed method is not limited only to spectral bands inside the visible spectrum, but it also considers an even more challenging task, that is the reconstruction of spectral bands outside the visible range closer to the near-infrared wavelengths of the spectrum. Extensive experiments with real data demonstrate the effectiveness and applicability of the proposed method in the precision agriculture domain. To this end, we calculate one of the most widely used vegetation indices, the normalized difference vegetation index (NVDI), which may be used for plant health monitoring.
Getting high on the market: Stock price movements, drug abuse and health implications

<p class="MsoNormal" style="margin-top: 12pt;"><span lang="EN-US" style="font-family: arial, helvetica, sans-serif;">Using an international dataset, this letter finds that high stock returns are associated with increased death rates from drug use disorders. Although the out-of-pocket expenditure on healthcare also rises following a stock market surge, the net effect on life expectancy is significantly negative. </span></p>
High-dimensional Cross-market Dependence Modeling and Portfolio Forecasting by Copula Variational LSTM

In the increasingly connected world, many systems are more or less coupled with each other in various ways. A typical example is the cross-market portfolio management, where the products of heterogeneous markets are selected and configured for investment. In such cross-market problems, one market is coupled with and influenced by others, and the financial variables of a market are coupled over time. This work makes the first attempt to model both the observations-based and latent dependence degrees and structures of highdimensional financial variables in cross-market portfolios by integrating variational recurrent neural networks. It integrates the distribution-based sequential modeling of multivariate time series and the regular vine copula-based dependence structures for modeling variable dependencies. Our method addresses the needs and gaps of modeling non-normal and long-range distributional interactions across market variables. We verify the model in terms of both technical significance and portfolio investment performance against benchmarks including linear models, stochastic volatility models, deep neural networks, and variational recurrent networks for portfolio forecasting.
The Bank of Korea Watch

Traders closely watch the Bank of Korea (BOK) base rate decisions since the short rate is the primary factor in bond and currency valuations. The survey of professional forecasters (SPF) has been widely used as the most reliable BOK base rate decision forecaster. In this paper, we investigate whether the SPF's prediction ability can be improved further. To this end, we use a dynamic multinomial ordered probit prediction model of the BOK base rate with a large number of predictors, and apply a Bayesian variable selection algorithm. Through an empirical exercise, we show that our approach substantially outperforms the SPF in terms of out-of-sample prediction. The key predictors are found to be the SPF, short-term bond yields, lagged base rate, federal funds rate, and inflation expectation survey data. Further, allowing for the prediction abilities to change over time is essential for improving predictive accuracy.