Miracle-dz/UniFAR-FTUs huggingface dataset:
High resolution aluminum-27 and silicon-29 nuclear magnetic resonance spectroscopic study of layer silicates, including clay minerals

High-resolution solid-state magic-angle sample-spinning nuclear magnetic resonance (NMR) spectroscopy of layer silicates, including clay minerals, indicate the following relationships between the NMR parameters and the structures and compositions of the minerals. These relationships are potentially useful in understanding the crystal chemistry of layer silicates and the chemical and structural changes that take place in them during diagenesis and metamorphism. (1) There is a systematic deshielding at silicon (less negative chemical shift) with decreasing Si/(Si + A(4). (2) There is a trend of more positive chemical shifts for tetrahedrally-coordinated aluminum with decreasing Si/(Si + A(4)). (3) There is no systematic relationship between the chemical shift of octahedrally-coordinated aluminum and Si/ (Si + Al(4)). (4) There is moderately good agreement between A(4yA(6) ratios determined by NMR and by chemical analysis. (5) The silicon-29 NMR peak breadths of minerals with no Al(4) or with ordered A(4ySi arrang€ments are relatively narrow, while those of most of the other minerals are broader. (6) Paramagnetic impurities (probably iron) in the natural samples cause extensive spinning side-bands which complicate spectral analysis. (7) Higher magnetic field strengths greatly improve spectral resolution for aluminum-27.
Effect of Thermal Modification on Wood Cell Structures Observed by Pulsed-Field-Gradient Stimulated-Echo NMR

Thermal modification is an environment friendly method for increasing the lifetime and usability of timber products. In our previous work (J. Phys. Chem. B 2009, 113, 1080), we introduced a pulsed-field-gradient stimulated-echo (PGSTE) NMR based method that enables determining the highly anisotropic size distribution of voids (pores) inside wood cell structures in three orthogonal directions. Here, we demonstrate that the method can be used to quantify the effect of thermal modification on the pore dimensions in Pinus sylvestris pine wood. The results show that the modification decreases the dimensions of lumens inside tracheid cells both in the longitudinal and two transverse directions. However, the relative decrease becomes smaller at the highest modification temperature, implying partial destruction of the cell wall structure. The decrease is larger in the radial direction than in the tangential direction at all the modification temperatures.
Detection and Segmentation of Custom Objects using High Distraction Photorealistic Synthetic Data

We show a straightforward and useful methodology for performing instance segmentation using synthetic data. We apply this methodology on a basic case and derived insights through quantitative analysis. We created a new public dataset: The Expo Markers Dataset intended for detection and segmentation tasks. This dataset contains 5,000 synthetic photorealistic images with their corresponding pixel-perfect segmentation ground truth. The goal is to achieve high performance on manually-gathered and annotated real-world data of custom objects. We do that by creating 3D models of the target objects and other possible distraction objects and place them within a simulated environment. Expo Markers were chosen for this task, fitting our requirements of a custom object due to the exact texture, size and 3D shape. An additional advantage is the availability of this object in offices around the world for easy testing and validation of our results. We generate the data using a domain randomization technique that also simulates other photorealistic objects in the scene, known as distraction objects. These objects provide visual complexity, occlusions, and lighting challenges to help our model gain robustness in training. We are also releasing our manually-gathered datasets used for comparison and evaluation of our synthetic dataset. This white-paper provides strong evidence that photorealistic simulated data can be used in practical real world applications as a more scalable and flexible solution than manually-captured data. Code is available at the following address: this https URL
DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape Generation

3D shape generation is a fundamental operation in computer graphics. While significant progress has been made, especially with recent deep generative models, it remains a challenge to synthesize high-quality shapes with rich geometric details and complex structures, in a controllable manner. To tackle this, we introduce DSG-Net, a deep neural network that learns a disentangled structured & geometric mesh representation for 3D shapes, where two key aspects of shapes, geometry and structure, are encoded in a synergistic manner to ensure plausibility of the generated shapes, while also being disentangled as much as possible. This supports a range of novel shape generation applications with disentangled control, such as interpolation of structure (geometry) while keeping geometry (structure) unchanged. To achieve this, we simultaneously learn structure and geometry through variational autoencoders (VAEs) in a hierarchical manner for both, with bijective mappings at each level. In this manner, we effectively encode geometry and structure in separate latent spaces, while ensuring their compatibility: the structure is used to guide the geometry and vice versa. At the leaf level, the part geometry is represented using a conditional part VAE, to encode high-quality geometric details, guided by the structure context as the condition. Our method not only supports controllable generation applications, but also produces high-quality synthesized shapes, outperforming state-of-the-art methods.
Repurposing doxycyclinedihydroartemisinin-piperaquine-as an antimalarial drug : A study in infected mice Plasmodium berghei

World Health Organization (WHO) estimates that nearly half of the world's population lived in malaria endemic areas (WHO, 2016). Malaria, a parasite infection is one Plasmodium of the greatest health challenges in tropical regions, despite the availability of antimalarial drugs, mosquito repellents and insecticide-treated nets. Malaria chemotherapy remains a major focus of research, and new molecules are being discovered prior to the emergence of drug-resistant strains of Plasmodium parasite (Gilliard et al., 2015). The use of antimalarial drugs is faced with resistance challenges from Plasmodium falciparum in primarily endemic areas. Other challenges include financial costs, contraindications, and clinical tolerance (Gilliard et al., 2015).
DETECTION ABOUT SARS-COV-2 PANDEMIC USING NEUTRAL NETWORKS AND DETECTION ALGORITHMS

Fake news has an extremely high impact on society, spreading quite simple and fast through social media, TV, internet, press, and other means of communication. The false news about the new coronavirus is blocked by the authorities, according to the decree for establishing a state of emergency. The misinformation of the population and the placement of fake news is two inevitable consequences in times of crisis, these being amplified by two other elements, which feed each other: fear of illness, which can cause deaths, but also uncertainty or lack of information on how to manage the crisis and what is involved. The need to stop the spreading of fake news, it's paramount and this paper proposes to recognizing truthful information from false information during the pandemic COVID-19 through a guide learning method. This guide implies a model for distinguishing false messages in the online environment, such as Machine Learning algorithms, which can have an accuracy of over 95%.
A UTOMATIC G ENERATION OF T EST C ASES FROM U SE -C ASE SPECIFICATION USING N ATURAL L ANGUAGE P ROCESSING

Software testing often targets natural language specification documents. The initial assumption is that automation of creating test cases from natural language specification is of benefit to speed up testing enabling a better coverage of all possible test scenarios. The proposed enabling principle for automatic generation of test cases is automatic retrieval of logic for t he interaction with an application. After retrieving, interaction logics is transformed into decision tables. Next, from decision tables it is possible to automatically generate test cases. Because in our research we target Croatian natural language, the assumption is that is it necessary to create a new approach to achieve the set goal. The main research questions posed in this paper are: "Is it possible to automatically generate test cases from use-case specifications written in the Croatian language? "; "Natural language processing tools for automatic test-generation save the tester's time and effort while improving the quality and coverage of the test cases? ". The expected results are to defining the method that using test-case generation tools reduce the time and effort for software testers and improve the test coverage of requirements.
Cardiovascular Disease in the Peruvian Highlands: Local Perceptions, Barriers, and Paths to Preventing Chronic Diseases in Andean Adults

Objectives: Public health interventions can be improved by understanding peoples' explanatory models of disease. We explore awareness and perceptions of cardiovascular diseases (CVD) and options for preventive actions in young rural adults highlanders. Methods: 46 men and women from communities in Cajamarca, Peru were purposively selected to participate in eight focus groups, where participants discussed their understanding and perceived causes of CVD as well as barriers and pathways to healthy lifestyles. Results: Fresh foods, physical activity, unpleasant emotions, and healthcare access were cited as important determinants of healthy lifestyles. Barriers to healthy diets included lacking nutritional knowledge, fluctuating food prices, and limited access to foodstuffs. Women felt particularly vulnerable to CVD and identified gendered barriers to manage stress and engage in leisure sports. Low health literacy, distance and poor doctor-patient relationships prevented participants from fully accessing healthcare. Conclusions: CVD prevention should consider local knowledge of these diseases and of healthy lifestyles and harness ongoing programmes that have successfully promoted good nutrition in children and pregnant women. In concert with public-private parterships, governments should include health prevention for the entire family.
Decomposing the Output Gap with Inflation Learning

We incorporate adaptive learning-based inflation expectations in an Unobserved Components model in order to study the link between inflation and the output gap. A modification of the hybrid New Keynesian Phillips curve serves as the backbone for modeling inflation dynamics. We find that learning based inflation forecasts not only shadow survey expectations in the pre-Volcker era, they also track inflation closely during the financial crisis and do not exhibit persistent overshooting. Our model also has good out of sample predictive performance both when compared with models that use survey expectations and when compared to an Unobserved Components Model without learning. We find evidence in favor of a relatively flat but significant Phillips curve relationship. The resulting output gap from our model has a lower amplitude than gaps obtained using proxy measures of expectations and other commonly used measures of the cycle. Furthermore, our results indicate that several recessions, including the Great Recession, were at least partially driven by large drops in the trend component of output.
The long-run effects of religious persecution: Evidence from the Spanish Inquisition

Significance From Imperial Rome to North Korea, religious persecution entwined with various degrees of totalitarian control has caused conflict and bloodshed for millennia. In this paper, we ask the following: Can religious persecution have repercussions long after it has ceased? Using data on the Spanish Inquisition, we show that in municipalities where the Spanish Inquisition persecuted more citizens, incomes are lower, trust is lower, and education is markedly lower than in other comparable towns and cities. Nobody expects the Spanish Inquisition to still matter today, but it does. Religious persecution is common in many countries around the globe. There is little evidence on its long-term effects. We collect data from all across Spain, using information from more than 67,000 trials held by the Spanish Inquisition between 1480 and 1820. This comprehensive database allows us to demonstrate that municipalities of Spain with a history of a stronger inquisitorial presence show lower economic performance, educational attainment, and trust today. The effects persist after controlling for historical indicators of religiosity and wealth, ruling out potential selection bias.
Analysing the Interdependency Relationships among Drivers for Lean and Green Manufacturing Using ISM Approach

Customer awareness towards sustainability, environmental performance and government policies have enforced the manufacturing firms to implement best techniques and practices to fulfil customer expectations. In this study, the drivers for lean and green manufacturing have analyzed which will help the organizations to reduce waste emissions, improve environmental performance and increase profit. A total of sixteen drivers are identified from the literature and consulted with the experts’ team for further study. These drivers are further analyzed with interpretive structural modelling (ISM) approach to see the relationship among drivers. The study shows that cost reduction, increase in market share, government support and adequate technology development are the key drivers for lean and green manufacturing. Finally, the paper draws some conclusions and implications from the study. These implications will guide the management and policymakers to focus on the main drivers for lean and green implementation in the manufacturing industry.
Investigation of Nonstoichiometry in Oxide Thin Films by Simultaneous in Situ Optical Absorption and Chemical Capacitance Measurements: Pr-Doped Ceria, a Case Study

Simultaneous in situ optical absorption and electrochemical impedance spectroscopy measurements were performed, for the first time, at elevated temperature on a metal oxide thin film exhibiting oxygen nonstoichiometry, utilizing Pr0.1Ce0. 9O2−δ (10PCO) as a model system. Chemical capacitance measurements, capable of providing explicit values of δ, were used to determine the optically absorbing center (Pr4+) concentration and thereby the optical extinction coefficient for Pr4+. The absorption coefficient was found to exhibit a linear dependence on Pr4+ concentration, validating the use of optical absorption to examine defect concentration trends and allowing derivation of the extinction coefficient ePr4+ = 5.01 ± 0.14 × 10–18 cm2. Values of Pr4+ concentration derived from the chemical capacitance and corresponding trends in optical absorption were found to be self-consistent, validating the thin film defect model for 10PCO, thereby confirming that the oxygen reduction enthalpy in thin film 10PCO is lower th.
Doing It by the Book: Political Contestability and Public Contract Renegotiations

The frequent renegotiation of public contracts is variously attributed to collusion between officials and bidders, the desire to circumvent budgetary rules, and other factors. This column challenges the industrial organisation view of public contract renegotiation, showing that frequent renegotiations can be understood as a consequence of the non-remediable, rigid nature of contracts rather than an inherent consequence of opportunistic behaviour.
Rush to Raise: Does Fundraising Pressure Incentivize Strategic Venture Capital Deal Pricing?

We provide evidence that venture capitalists (VCs) strategically enhance their current funds’ interim performance around new fundraising events. Using novel investment-round-level pricing data, we document that, immediately prior to raising another fund, VCs tend to invest in follow-on financing rounds of their existing portfolio firms at abnormally high step-ups in valuation, relative to subsequent follow-on rounds at the same firms. This pattern cannot be explained by deal and investor characteristics, and strengthens when multiple VCs in the syndicate concurrently raise new funds. Investing at high round prices translates into higher quarterly portfolio IRRs reported at the fund level. Overall, our results question the veracity of portfolio valuations based on investment-round pricing, especially when the VC is under the pressure to raise a new fund.
A Systematic Literature Review on Evolution of Behavioral Finance.

Traditional finance is a knowledge base that summarizes the concepts and theories based on principle of rationality to take financial decisions. Standard finance theories are based on an underlying concept that investor act carefully and objectively while making their financial decisions. In addition, individual investor is assumed to behave rationally keeping in mind the risk and return involved. However, the researchers in psychology interpret that the financial decisions are often made in an irrational manner. Therefore, a new field of behavioral finance has evolved in past few decades to explain how personal, social and psychological factors help an individual to make their financial decisions. <br><br>Behavioral finance; an emerging and prospective field has been developed with the input taken from the field of psychology and finance which tries to explain the puzzling factor in stock market fluctuations. It is defined as “study of the influence of socio-psychological factors on asset price”. The behavioral and psychological insights have emerged as an application of economics<br><br>with psychology, which seeks to provide an explanation for people’s irrational financial decisions. It is a combination of psychology, sociology, and finance. The paper here tries to generate a procedural study to provide a systematic review of the evolution of behavioral finance theories and concepts. The study tries to explain how the assumptions of standard finance theories fail to provide an explanation of various anomalies, which led to an evolution of Behavioral Finance.
The influence of water on the structure of hydrous sodium tetrasilicate glasses

Ansrn^q.cr The structure of sodium tetrasilicate (NarSirOr) glasses containing 0 to 10 wtVo water was investigated by a combination of Raman, IR, and NMR methods. Both the "Si magic angle spinning NMR data and Raman spectra in the Si-O stretching region clearly show that water depolymerizes the silicate network of the glasses. Q-species distributions calculated from Raman spectra, assuming equal scattering cross sections of all bands in the Si-O stretching region, closely agree with results obtained from NMR data. At low total water contents, the silicate network is depolymerized mainly by breaking of Qa-Q" bonds' whereas breaking of Q:-q: bonds dominates at high water contents. Near IR spectra show the presence of both OH groups and molecular HrO in the glasses. The number of nonbridging O atoms per silicon atom, calculated from the near IR data, closely agrees with the results obtained from Raman and NMR, and confirms the assignment of the 4500 cm ' band in the near IR to a combination mode of Si-OH groups. Moreover, the intensity of the fundamental Si-OH stretching band at 910 cm I in the Raman spectra varies proportionally to the intensity of the 4500 cm-' near IR band. Both IR and Raman spectra show three main bands in the OH-stretching region, centered at 3580, 3000, and 2350 cm-', due to hydrous species with different hydrogen bond strengths. The relative intensities ofthese three bands are insensitive to total water content and OFI/H,O ratio, suggesting that both OH and HrO contribute to each of these bands. This is consistent with the fine structure of the HrO bending vibration in the IR spectra around 1640 cm ' and with the polarization dependence of the OH-stretching bands in the Raman spectra. Near IR spectra of hydrous sodium tetrasilicate glasses and hydrous aluminosilicate glasses are very similar and show a similar dependence of band intensity on total water content, suggesting that there is no fundamental difference in the dissolution mechanism of water in these systems.
Can Executives Predict How Firm News Affects Stock Price?

More than 650 U.S. public company executives predict the stock price response to their quarterly financial reports and share their prediction after under a nondisclosure agreement. Despite having full knowledge of the reports before their release, executives’ estimates differ from realized returns by roughly 100% of the average absolute one-day response (i.e., 600 to 700 basis points) and are directionally wrong in one-third of cases. We do not find evidence that executives learn from the unexpected price movements; rather, executives profitably trade against the market the greater the prediction error. Greater error also reduces window dressing of financial information.
SHD360: A Benchmark Dataset for Salient Human Detection in 360° Videos

Salient human detection (SHD) in dynamic 360{\deg} immersive videos is of great importance for various applications such as robotics, inter-human and human-object interaction in augmented reality. However, 360{\deg} video SHD has been seldom discussed in the computer vision community due to a lack of datasets with large-scale omnidirectional videos and rich annotations. To this end, we propose SHD360, the first 360{\deg} video SHD dataset which contains various real-life daily scenes. Since so far there is no method proposed for 360{\deg} image/video SHD, we systematically benchmark 11 representative state-of-the-art salient object detection (SOD) approaches on our SHD360, and explore key issues derived from extensive experimenting results. We hope our proposed dataset and benchmark could serve as a good starting point for advancing human-centric researches towards 360{\deg} panoramic data. The dataset is available at https://github.com/PanoAsh/SHD360.
Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks

Spiking Neural Networks (SNNs), as bio-inspired energy-efficient neural networks, have attracted great attentions from researchers and industry. The most efficient way to train deep SNNs is through ANN-SNN conversion. However, the conversion usually suffers from accuracy loss and long inference time, which impede the practical application of SNN. In this paper, we theoretically analyze ANN-SNN conversion and derive sufficient conditions of the optimal conversion. To better correlate ANN-SNN and get greater accuracy, we propose Rate Norm Layer to replace the ReLU activation function in source ANN training, enabling direct conversion from a trained ANN to an SNN. Moreover, we propose an optimal fit curve to quantify the fit between the activation value of source ANN and the actual firing rate of target SNN. We show that the inference time can be reduced by optimizing the upper bound of the fit curve in the revised ANN to achieve fast inference. Our theory can explain the existing work on fast reasoning and get better results. The experimental results show that the proposed method achieves near loss-less conversion with VGG-16, PreActResNet-18, and deeper structures. Moreover, it can reach 8.6× faster reasoning performance under 0.265× energy consumption of the typical method. The code is available at https://github.com/DingJianhao/OptSNNConvertion-RNL-RIL.