Miracle-dz/UniFAR-FTUs huggingface dataset:
What Have We Learned about Gender from Candidate Choice Experiments? A Meta-Analysis of Sixty-Seven Factorial Survey Experiments

Candidate choice survey experiments in the form of conjoint or vignette experiments have become a standard part of the political science toolkit for understanding the effects of candidate characteristics on vote choice. We collect 67 such studies from all over the world and reanalyze them using a standardized approach. We find that the average effect of being a woman (relative to a man) is a gain of approximately 2 percentage points. We find some evidence of heterogeneity across contexts, candidates, and respondents. The difference is somewhat larger for white (vs. black) candidates and among survey respondents who are women (vs. men) or, in the US context, identify as Democrats or Independents (vs. Republicans). Our results add to the growing body of experimental and observational evidence that voter preferences are not a major factor explaining the persistently low rates of women in elected office.
Drying process and Ghanaian green coffee quality crude protein, pH and caffeine levels

Green coffee beans were dried using three methods (drying on raised platform, drying on polythene sheet kept on ground and drying on concrete floor) at three different densities (20, 30 and 40 Kg/m≤). All drying methods and densities had no effect on % crude protein and caffeine levels while strongly affected the pH of dried beans. Drying on concrete floor at 40 kg/m≤ density yielded a significantly lower pH.
Analysis of Machine Learning Algorithms with Feature Selection for Intrusion Detection using UNSW-NB15 Dataset

In recent times, various machine learning classifiers are used to improve network intrusion detection. The researchers have proposed many solutions for intrusion detection in the literature. The machine learning classifiers are trained on older datasets for intrusion detection, which limits their detection accuracy. So, there is a need to train the machine learning classifiers on the latest dataset. In this paper, UNSW-NB15, the latest dataset is used to train machine learning classifiers. The selected classifiers such as K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), Random Forest (RF), Logistic Regression (LR), and Naïve Bayes (NB) classifiers are used for training from the taxonomy of classifiers based on lazy and eager learners. In this paper, Chi-Square, a filter-based feature selection technique, is applied to the UNSW-NB15 dataset to reduce the irrelevant and redundant features. The performance of classifiers is measured in terms of Accuracy, Mean Squared Error (MSE), Precision, Recall, F1-Score, True Positive Rate (TPR) and False Positive Rate (FPR) with or without feature selection technique and comparative analysis of these machine learning classifiers is carried out.
Characteristics of Chemical Composition and Particle Size Distribution from Real Coal-fired Circulating Fluidised Bed (CFB) Boiler

ABSTRACT The chemical component (element, ion, and carbonaceous aerosols) behaviour in particle size distributions (PSDs) was studied by determining the particulate matter (PM) emitted from the circulating fluidised-bed (CFB) combustion flue gas. Four CFB coal-fired boilers were employed in power plants. The actual particle mass percentages and PSDs were measured using an electrical low-pressure impactor (ELPI). Ion concentrations (SO42–, Cl–, NO3–, Na+, NH4+, Mg2+, K+ and Ca2+) were determined by ion chromatography (IC), elements (Al, Ca, Cr, Cu, Fe, K, Mg, Na, Ni, Pb, S, Si, Ti and Zn) were determined by inductively coupled plasma-mass spectrometry (ICP-AES), and the carbonaceous content (OC1, OC2, OC3, OC4, EC1, EC2 and EC3) was measured by thermal optical analysis. Significant differences were observed in the four boilers for PSD. Chemical species in particulate matter mainly concentrated in sizes 0.1–2.5 µm in aerodynamic diameter. In this work, the mass percentage of Ca, Ca2+, SO42–, NH4+ and OC were significantly high in different particle size regions. The mass concentrations of elements, ions, and carbonaceous aerosols in various sizes displayed trimodal or bimodal distributions; however, the peaks of mass concentrations of chemical species in different particle sizes varied greatly. Furthermore, the size distribution of chemical species was influenced by the pollution control device employed, and the same chemical species showed a marked difference among coal-fired boiler equipped with different pollution control devices. In addition, it was observed that the ratio of OC to EC was related to dust-cleaning method.
Comparative Study on the Technological Properties of Latex and Natural Rubber from Hancornia speciosa Gomes and Hevea brasiliensis

This work reports a systematic comparative study of the technological properties of natural lattices and rubbers extracted from Hancornia speciosa Gomes and Hevea brasiliensis [(Willd. ex Adr. de Juss.) Muell. -Arg. ] (clone RRIM 600) trees from 11 collections in Brazil throughout 2004. Natural rubber latex particle sizes and distributions were quite similar with an average diameter around 1 μm. Molecular weight, Wallace plasticity, and Mooney viscosity values were approximately the same for both rubbers. Fourier transform infrared spectroscopy peaks characteristic of natural rubber were observed for both Hancornia and Hevea. The measured differences in technological properties included lower values for Hancornia dry rubber content, % ash, % nitrogen, and plasticity retention index but higher value for acetone extract. Interestingly, nitrogen and protein content were much lower in Hancornia, suggesting that it may have important applications in nonallergic rubber uses. This represents the first report of lacticifer-produced low-protein natural rubber. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2009
Effect of boundary layer evolution on nitrogen dioxide ( NO 2 ) and formaldehyde ( HCHO ) 1 concentrations at a high-altitude observatory in Western India 2

CE PT ED MA NU SC RI PT Effect of boundary layer evolution on nitrogen dioxide (NO2) and formaldehyde (HCHO) 1 concentrations at a high-altitude observatory in Western India 2 Mriganka Sekhar Biswas, G. Pandithurai, M. Y. Aslam, Rohit D. Patil, Anilkumar V. , 3 Shrikant D. Dudhambe, Christophe Lerot, Isebelle De Smedt, Michel Van Roozendael, and 4 Anoop S. Mahajan 5 1 Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India 6 2 Savitribai Phule Pune University, Pune, India 7 3 Belgian Institute for Space Aeronomy, Brussels, Belgium 8
Airborne Lidar Observations of Wind , Water Vapor , and Aerosol Profiles During The 1

Airborne Lidar Observations of Wind, Water Vapor, and Aerosol Profiles During The 1 NASA Aeolus Cal/Val Test Flight Campaign 2 3 Kristopher M. Bedka1, Amin R. Nehrir1, Michael Kavaya1, Rory Barton-Grimley1, Mark 4 Beaubien6, Brian Carroll5, James Collins2, John Cooney5, G. David Emmitt3, Steven Greco3, 5 Susan Kooi2, Tsengdar Lee4, Zhaoyan Liu1, Sharon Rodier2, Gail Skofronick-Jackson4 6 7 1NASA Langley Research Center, Hampton, VA 8 2Science Systems and Applications, Inc., Hampton, VA 9 3Simpson Weather Associates, Charlottesville, VA 10 4NASA Headquarters, Washington D.C. 11 5NASA Postdoctoral Fellowship Program, Universities Space Research Association at NASA Langley Research 12 Center, Hampton, VA 13 6Yankee Environmental Systems, Inc., Turners Falls, MA 14 15 Correspondence to: Kristopher M. Bedka (kristopher.m.bedka@nasa.gov) 16 17 ABSTRACT 18 Lidars are uniquely capable of collecting high precision and high spatio-temporal resolution observations that have 19
The relationship between brand trust and brand attachment : An applied study

This study aims to examine the relationship between brand trust and brand attachment and explore the impact of brand trust on brand attachment in the context of private car customers staying in Damietta Citie. To achieve the objectives of the study, a set of hypotheses were developed, and to verify the validity of these hypotheses, the researcher depended on a set of statistical methods: correlation and regression analysis. The results of statistical analysis indicate that: there is a significant relationship between brand trust and brand attachment. Brand trust positively affects brand attachment.
On Controllability and Persistency of Excitation in Data-Driven Control: Extensions of Willems’ Fundamental Lemma

Willems’ fundamental lemma asserts that all trajectories of a linear time-invariant system can be obtained from a finite number of measured ones, assuming that controllability and a persistency of excitation condition hold. We show that these two conditions can be relaxed. First, we prove that the controllability condition can be replaced by a condition on the controllable subspace, unobservable subspace, and a certain subspace associated with the measured trajectories. Second, we prove that the persistency of excitation requirement can be relaxed if the degree of a certain minimal polynomial is tightly bounded. Our results show that data-driven predictive control using online data is equivalent to model predictive control, even for uncontrollable systems. Moreover, our results significantly reduce the amount of data needed in identifying homogeneous multi-agent systems.
On the Observational Difference between the Accretion Disk–Corona Connections among Super- and Sub-Eddington Accreting Active Galactic Nuclei

We present a systematic X-ray and multiwavelength study of a sample of 47 active galactic nuclei (AGNs) with reverberation mapping measurements. This sample includes 21 super-Eddington accreting AGNs and 26 sub-Eddington accreting AGNs. Using high-state observations with simultaneous X-ray and UV/optical measurements, we investigate whether super-Eddington accreting AGNs exhibit different accretion disk–corona connections compared to sub-Eddington accreting AGNs. We find tight correlations between the X-ray-to-UV/optical spectral slope parameter (α OX) and the monochromatic luminosity at 2500 Å (L 2500Å) for both the super- and sub-Eddington subsamples. The best-fit α OX–L 2500Å relations are consistent overall, indicating that super-Eddington accreting AGNs are not particularly X-ray weak in general compared to sub-Eddington accreting AGNs. We find dependences of α OX on both the Eddington ratio (L Bol/L Edd) and black hole mass (M BH) parameters for our full sample. A multivariate linear regression analysis yields , with a scatter similar to that of the α OX–L 2500Å relation. The hard (rest-frame >2 keV) X-ray photon index (Γ) is strongly correlated with L Bol/L Edd for the full sample and the super-Eddington subsample, but these two parameters are not significantly correlated for the sub-Eddington subsample. A fraction of super-Eddington accreting AGNs show strong X-ray variability, probably due to small-scale gas absorption, and we highlight the importance of employing high-state (intrinsic) X-ray radiation to study the accretion disk–corona connections in AGNs.
Numerical Simulation of Hot Accretion Flows. IV. Effects of Black Hole Spin and Magnetic Field Strength on the Wind and the Comparison between Wind and Jet Properties

This is the fourth paper of our series studying winds from hot accretion flows around black holes. In the first two papers, we showed the existence of strong winds in hot accretion flows using hydrodynamical and magnetohydrodynamical (MHD) simulations. In the third paper, by using three-dimensional general relativity MHD numerical simulation data of hot accretion flows and adopting a “virtual particle trajectory” data analysis approach, we calculated the properties of wind, such as its mass flux and velocity. However, that paper focuses only on a nonspinning black hole and standard and normal accretion. In the present paper, we extend the third paper by including cases of a rapidly rotating black hole and magnetically arrested disk. We focus on investigating the effect of spin and magnetic field on the properties of the wind and jet. It is found that a larger spin and stronger magnetic field usually enhance the wind and jet. The formulae describing the mass flux, poloidal velocity, and fluxes of momentum, kinetic energy, and total energy of the wind and jet are presented. One interesting finding, among others, is that even in the case of a very rapidly spinning black hole, where the jet is supposed to be the strongest, the momentum flux of the jet is smaller than that of the wind, while the total energy flux of the jet is larger than that of the wind by at most a factor of 10. This result suggests that the wind potentially plays a more important role than the jet, at least for some problems in active galactic nucleus feedback.
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training

In this paper, we introduce a new perspective on training deep neural networks capable of state-of-the-art performance without the need for the expensive over-parameterization by proposing the concept of In-Time Over-Parameterization (ITOP) in sparse training. By starting from a random sparse network and continuously exploring sparse connectivities during training, we can perform an Over-Parameterization in the space-time manifold, closing the gap in the expressibility between sparse training and dense training. We further use ITOP to understand the underlying mechanism of Dynamic Sparse Training (DST) and indicate that the benefits of DST come from its ability to consider across time all possible parameters when searching for the optimal sparse connectivity. As long as there are sufficient parameters that have been reliably explored during training, DST can outperform the dense neural network by a large margin. We present a series of experiments to support our conjecture and achieve the state-of-the-art sparse training performance with ResNet-50 on ImageNet. More impressively, our method achieves dominant performance over the overparameterization-based sparse methods at extreme sparsity levels. When trained on CIFAR-100, our method can match the performance of the dense model even at an extreme sparsity (98%). Code can be found this https URL.
Function-Correcting Codes

Motivated by applications in machine learning and archival data storage, we introduce function-correcting codes, a new class of codes designed to protect a function evaluation on the data against errors. We show that function-correcting codes are equivalent to irregular-distance codes, i.e., codes that obey some given distance requirement between each pair of codewords. Using these connections, we study irregular-distance codes and derive general upper and lower bounds on their optimal redundancy. Since these bounds heavily depend on the specific function, we provide simplified, suboptimal bounds that are easier to evaluate. We further employ our general results to specific functions of interest and we show that function-correcting codes can achieve significantly less redundancy than standard error-correcting codes which protect the whole data.
Irreducibility and monicity for representations of $k$-graph $C^*$-algebras

The representations of a $k$-graph $C^*$-algebra $C^*(\Lambda)$ which arise from $\Lambda$-semibranching function systems are closely linked to the dynamics of the $k$-graph $\Lambda$. In this paper, we undertake a systematic analysis of the question of irreducibility for these representations. We provide a variety of necessary and sufficient conditions for irreducibility, as well as a number of examples indicating the optimality of our results. We also explore the relationship between irreducible $\Lambda$-semibranching representations and purely atomic representations of $C^*(\Lambda)$. Throughout the paper, we work in the setting of row-finite source-free $k$-graphs; this paper constitutes the first analysis of $\Lambda$-semibranching representations at this level of generality.
Characterization of Geological Certified Reference Materials by Inductively Coupled Plasma-Mass Spectrometry

A rapid, safe, and efficient microwave-assisted acid digestion method is reported to determine thirty-six elements in three international geological reference materials and two in-house reference materials using high resolution inductively coupled plasma mass spectrometry. This method was also employed to determine the radiogenic isotopic composition of Sr and Nd in two geological reference materials by multicollector inductively coupled plasma mass spectrometry. In order to ascertain the most suitable preparation method, four microwave-assisted digestion procedures were compared. The presence of insoluble fluorides on the recoveries of Ba, Rb, Sr and rare earth elements was resolved through sequential evaporation at 80 degrees Celsius followed by microwave digestion. Digestion methods without using HClO4 or H3BO3 and using only H3BO3 were unsuitable due to lower recoveries and higher procedural blanks, respectively. Digestion methods that involved the use of HCl-HNO3-HF and HCl-HNO3-HF-HClO4 were most suitable, resulting in better recoveries of the elements. However, the HCl-HNO3-HF method was preferred compared to HCl-HNO3-HF-HClO4 because the former avoids the use of HClO4 and thus was used in measurements of the radiogenic isotopes of Sr and Nd. The optimized method was used to verify the reference values for the isotopic compositions of Sr and Nd in geological standard reference materials.
REVIEW AND CALCULATION OF ACTIVITY CO EFFICIENT OF TEA POLYPHENOLS AND ITS SOLUBILITY IN DIFFERENT SOLVENTS

Tea, one of the most popular beverages, has been co nsumed for thousands of years for their flavors and health benefits. Polyphenols are the flavanoid and phenolic acids in tea that co ntribute to health benefits. There are more than 45 polyphenols in tea. In this review, the structure, health benefits and suitable solvent selection for extraction of these polyphen ols is covered. Further the calculation of activity coefficient of tea polyphen ol is also done. This study helps us to understand the suitability of a solvent and its temperature dependency in its extraction process f rom tea leaves.
Optimizing Consensus-based Multi-target Tracking with Multiagent Rollout Control Policies

This paper considers a multiagent, connected, robotic fleet where the primary functionality of the agents is sensing. A distributed multi-sensor control strategy maximizes the value of the collective sensing capability of the fleet, using an information-driven approach. Each agent individually performs sensor processing (Kalman Filtering and Joint Probabilistic Data Association) to identify trajectories (and associated distributions). Using communication with neighbors, the agent enhances the prediction of the trajectories by a Consensus of Information approach that iteratively calculates the Kullback-Leibler average of trajectory distributions, which enables the calculation of the collective information for the fleet. The dynamics of the agents, the evolution of the identified trajectories for each agent, and the dynamics of individual observed objects are captured as a Partially Observable Markov Decision Process (POMDP). Using this POMDP and applying rollout with receding horizon control, an optimized non-myopic control policy that maximizes the collective fleet information value is synthesized. Simulations are presented for a scenario with three heterogeneous UAVs performing coordinated target tracking that illustrate the proposed methodology and compare the centralized approach with a contemporary sequential multiagent distributed decision technique.
Accurate Evaluation on the Interactions of SARS-CoV-2 with Its Receptor ACE2 and Antibodies CR3022/CB6*

The spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has become a global health crisis The binding affinity of SARS-CoV-2 (in particular the receptor binding domain, RBD) to its receptor angiotensin converting enzyme 2 (ACE2) and the antibodies is of great importance in understanding the infectivity of COVID-19 and evaluating the candidate therapeutic for COVID-19 We propose a new method based on molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) to accurately calculate the free energy of SARS-CoV-2 RBD binding to ACE2 and antibodies The calculated binding free energy of SARS-CoV-2 RBD to ACE2 is -13 3 kcal/mol, and that of SARS-CoV RBD to ACE2 is -11 4 kcal/mol, which agree well with the experimental results of -11 3 kcal/mol and -10 1 kcal/mol, respectively Moreover, we take two recently reported antibodies as examples, and calculate the free energy of antibodies binding to SARS-CoV-2 RBD, which is also consistent with the experimental findings Further, within the framework of the modified MM/PBSA, we determine the key residues and the main driving forces for the SARS-CoV-2 RBD/CB6 interaction by the computational alanine scanning method The present study offers a computationally efficient and numerically reliable method to evaluate the free energy of SARS-CoV-2 binding to other proteins, which may stimulate the development of the therapeutics against the COVID-19 disease in real applications
Hyperparameter Optimization Is Deceiving Us, and How to Stop It

Recent empirical work shows that inconsistent results based on choice of hyperparameter optimization (HPO) configuration are a widespread problem in ML research. When comparing two algorithms J and K searching one subspace can yield the conclusion that J outperforms K, whereas searching another can entail the opposite. In short, the way we choose hyperparameters can deceive us. We provide a theoretical complement to this prior work, arguing that, to avoid such deception, the process of drawing conclusions from HPO should be made more rigorous. We call this process epistemic hyperparameter optimization (EHPO), and put forth a logical framework to capture its semantics and how it can lead to inconsistent conclusions about performance. Our framework enables us to prove EHPO methods that are guaranteed to be defended against deception, given bounded compute time budget t. We demonstrate our framework's utility by proving and empirically validating a defended variant of random search.
Superradiance in deformed Kerr black holes

Ongoing observations in the strong-field regime are in optimal agreement with general relativity, although current errors still leave room for small deviations from Einstein's theory. Here we summarise our recent results on superradiance of scalar and electromagnetic test fields in Kerr-like spacetimes, focusing mainly on the Konoplya--Zhidenko metric. We observe that, while for large deformations with respect to the Kerr case superradiance is suppressed, it can be nonetheless enhanced for small deformations. We also study the superradiant instability caused by massive scalar fields, and we provide a first estimate of the effect of the deformation on the instability timescale.