Miracle-dz/UniFAR-FTUs huggingface dataset:
SVECV
 ‐f12: Benchmark of a composite scheme for accurate and cost effective evaluation of reaction barriers

A simple composite scheme is presented, based on a combination of density functional geometry and frequencies evaluation, valence energies obtained using the CCSD(T)-f12 method extrapolated to the complete basis set limit, and core-valence correlation corrections employing the MP2 method. The procedure was applied to the 38 reactions in Truhlar’s HTBH38/08 and NHTBH38/08 databases and the errors in the barriers with respect to their best values are presented. Mean unsigned deviation (MUD) for the complete set of 68 independent barriers is 0.40 kcal mol-1 , compared to 1.31 kcal/mol for G4 and 1.62 kcal/mol for the dispersion-corrected M06- 2X method. The accuracy of the procedure is also better that that of other calculations using composite methods of similar cost. The MUD of the new scheme on the barriers in the DBH24/08 subset (12 out of the 38 reactions in the other two sets) is 0.27 kcal mol-1 , better than that obtained at the expensive CCSD(T,full)/aug-cc-pCV(T+d)Z level (0.46 kcal mol-1 ) and comparable to the 2 most exact (and costly) Wn calculations (MUD=0.14 kcal mol-1 ). The maximum unsigned deviation (MaxUD) for all the reactions studied is 0.99 kcal/mol. G4 and M06-2X, on the other side, exhibit MaxUDs of 6.7 and 8.0 kcal/mol respectively. The method was further tested against a subset of the reactions in the databases, for which the geometry and energies of all species were determined at the much more demanding CCSD(T)-F12//pVQZ-F12 level. These results showed that Truhlar’s calculations in this subset are off the best values by a considerable amount, with an rmse of 0.56 kcal/mol. As a consequence, a new dataset of barrier heights, SV20, is presented. The SVECV-F12 procedure on this SV20 database results in rmse and MUD values of only 0.21 and 0.16 kcal/mol. The possible residual errors introduced by the approximations used for each component of the method are tested against more sophisticated calculations and shown to be accurate enough to obtain barriers well under the chemical precision limit at a reasonable cost for molecules of interest in atmospheric chemistry.
Government Transfers to Parents and Population Policy in a Global Perspective: An Economic Demographic Perspective

Abstract The world is rapidly converging towards lower fertility: in 2020, countries with a total fertility rate of less than 2.25 will encompass more than three-quarters of the world population. This implies that the determinants of childbearing will be increasingly similar in high-income and middle-income regions of the world. In this article, I discuss economic demography in relation to levels of childbearing. How do different societies distribute resources across the life course and between generations, and to what extent is this done through governmental transfers? The extent of such transfers varies considerably between low-income, middle-income, and high-income countries, which I explored through data from the National Transfers Account project. I argue that in low-fertility societies, the extent to which the costs of childrearing are socialised is important for fertility. The extent to which childrearing is socialised will be an important determinant of future fertility levels and, if used as a population policy, offers a straightforward pathway to achieve a desirable population size. As the global fertility decline continues, such policies will be relevant to most societies and a tool for governments to affect fertility levels across many contexts.
Evolution of hybrid distance based kNN classification

The evolution of classification of opinion mining and user review analysis span from decades reaching into ubiquitous computing in efforts such as movie review analysis. The performance of linear and non-linear models are discussed to classify the positive and negative reviews of movie data sets. The effectiveness of linear and non-linear algorithms are tested and compared in-terms of average accuracy. The performance of various algorithms is tested by implementing them on internet movie data base (IMDB). The hybrid kNN model optimizes the performance classification interns of accuracy. The accuracy of polarity prediction rate is improved with random-distance-weighted-kNN-ABC when compared with kNN algorithm applied alone.
Selective Conversion of Furfural to Cyclopentanone with CuZnAl Catalysts

A new catalytic system was developed for the selective conversion of biomass-derived furfural to cyclopentanone in aqueous solution. CuZnAl catalysts with different Cu/Zn molar ratios (0.5, 1, 2, and 3) and calcination temperatures (350, 500, and 700 °C) were investigated, and the CuZnAl-500–0.5 catalyst (Cu/Zn = 0.5, calcined at 500 °C) showed a remarkable catalytic performance in the reaction. A 62% yield of cyclopentanone was obtained at the optimized conditions (150 °C, 4 MPa H2, 6 h), and the TOF was 9.4 h–1. The catalysts were characterized by nitrogen adsorption, XRD, TEM, N2O titration, ICP, XPS, and a carbon–sulfur analyzer. The factors that influenced the activity of catalysts were also investigated. Additionally, the CuZnAl-500–0.5 was recycled five times and maintained good activity and stability. Hence, the current work presents a new and efficient catalytic system for the conversion of furfural to cyclopentanone. The low-cost nature of the CuZnAl makes it a potential catalyst for the product.
High Sensitive, Linear and Thermostable Pressure Sensor Utilizing Bipolar Junction Transistor for 5 kPa

Research of pressure sensor chip utilizing novel electrical circuit with bipolar-junction transistor-based (BJT) piezosensitive differential amplifier with negative feedback loop (PDA-NFL) for 5 kPa differential range was done. The significant advantages of developed chip PDA-NFL in the comparative analysis of advanced pressure sensor analogs, which are using the Wheatstone piezoresistive bridge, are clearly shown. The experimental results prove that pressure sensor chip PDA-NFL with 4.0x4. 0 mm chip area has sensitivity S = 11.2 ± 1.8 mV/V/kPa with nonlinearity of 2KNLback = 0.11 ± 0.09 %/FS (pressure is applied from the back chip side) and 2KNLtop = 0.18 ± 0.09 %/FS (pressure is applied from the top chip side). All temperature characteristics have low errors, because the precision elements balance of PDA-NFL electric circuit was used. Additionally, the burst pressure is 80 times higher than the working range.
Measuring Physiological Markers of Stress During Conversational Agent Interactions

Conversational agent (CA) technology is rapidly becoming ubiquitous. Understanding how CAs impact users on multiple levels, including physiology, thus becomes increasingly important. In this study, we examined the effects of a CA interaction on naive users’ physiological markers of stress i.e. heart rate (HR) and electrodermal activity (EDA). Participants (n = 21) prepared and executed a speech as part of a stressful interview, followed by a “Wizard-of-Oz” CA interaction. We expected the CA interactions to be mildly stressful. For a subset of participants with an initial resting period (n = 10), HR was elevated by 4.06 beats per minute (bpm) on average during the speech task, relative to the resting baseline. During the CA interaction however, HR was found to be 1.16 bpm lower on average. Moreover, HR and EDA values during the CA interaction were highly correlated with those during the resting period (Spearman’s rho: HR = 0.97, EDA = 0.96) with small differences (mean diff: HR = 1.16, EDA = 1.82). Contrary to initial expectations, users do not seem to exhibit a stress response during the CA interaction. We additionally performed similar analyses and compared our results with the Wearable Stress and Affect Detection (WESAD) dataset [1].
APPLICATION OF EDIBLE FILM AND COATING BASED ON ALOE VERA GEL FOR PRESERVATION OF PHYSICOCHEMICAL PROPERTIES OF PHYSALIS PERUVIANA L. FRUITS

The calyx removing of Physalis peruviana fruits for production of fresh cut fruit makes its shelf life shorter. So, Aloe vera gel (AV) used as edible film and coating for increasing the fruit shelf life of fresh cut Physalis fruit. The physical parameter of prepared AV films was investigated such as: Thickness, water vapor permeability (WVP) and water solubility. The current study evaluates the effect of AV film and coating for maintain the Physalis peruviana fruits without calyx at 5±1°C and 95 % RH for 30 days. AV gel film was effective in controlling the fungal decay. which recorded the lowest value (4%) and reduced deterioration in all other physicochemical properties of Physalis fruit comparing with control. This study showed that the AV gel could be suitable for producing an edible film and coating with good physical characteristics such as low WVP (0.0037g/m2.h.mmHg). AV's low WVP appeared to have a great impact on maintaining post-harvest quality especially reducing the percentage of weight loss of coated fruit during storage.
Comparison of Ferrous Ion Chelating Properties of Collagen Peptides from Dried and Fresh Cod Skin

Corresponding Author: Guangrong Huang Key Lab of Marine Food Quality and Hazard Controlling Technology of Zhejiang Province,College of Life Sciences,China Jiliang University, Hangzhou310018, China E-mail: grhuang@126.com Abstract: Fish skin is a potential source of collagen and collagen is a protein source of bioactive peptides including iron chelating peptides. The aim of this study is to compare physicochemical properties of collagen extracted from fresh and dried cod skin and the ferrous ion chelating properties of collagen hydrolysates. Then the fresh and dried cod skin extracted collagen was hydrolyzed with collagenase to obtain collagen peptides. The collagen peptides chelated ferrous ion. The results showed that cod skin is a good source of collagen which is accounted for 95.17% of the total proteins. The collagen from fresh cod skin was slightly easier to be hydrolyzed than that from dried cod fish. Respectively, at the same hydrolysis conditions the maximum degree of hydrolysis were 13.64 and 12.57%. The collagen from fresh cod skin had better iron chelating ability than that from dried cod skin, the iron chelating abilities were 32.16 and 23.12%. The UV-Vis spectroscopy of collagen peptides obtained from fresh or dried cod skin showed the same maximum absorption peaks near 230 and 250 nm. When combined with ferrous ion, the maximum of absorption peak is migrated. SDS-PAGE proved that the molecular weight of collagen is 180k Da more or less. The electron microscopy and infrared spectra showed that collagen peptide could chelate with ferrous ion through -NH and C = O. Fresh cod skin extracted collagen has a higher degree of hydrolysis and is more favorable to chelate ferrous ion to produce peptide ferrous chelate, but they have a similar structure.
Hemodynamic Deconvolution Demystified: Sparsity-Driven Regularization at Work

Deconvolution of the hemodynamic response is an important step to access short timescales of brain activity recorded by functional magnetic resonance imaging (fMRI). Albeit conventional deconvolution algorithms have been around for a long time (e.g., Wiener deconvolution), recent state-of-the-art methods based on sparsity-pursuing regularization are attracting increasing interest to investigate brain dynamics and connectivity with fMRI. This technical note revisits the main concepts underlying two main methods, paradigm free mapping and total activation, in the most accessible way. Despite their apparent differences in the formulation, these methods are theoretically equivalent as they represent the synthesis and analysis sides of the same problem, respectively. We demonstrate this equivalence in practice with their best-available implementations using both simulations, with different signal-to-noise ratios, and experimental fMRI data acquired during a motor task and resting state. We evaluate the parameter settings that lead to equivalent results and showcase the potential of these algorithms compared to other common approaches. This note is useful for practitioners interested in gaining a better understanding of state-of-the-art hemodynamic deconvolution and aims to answer questions that practitioners often have regarding the differences between the two methods.
The Impact of Negative Sampling on Contrastive Structured World Models

World models trained by contrastive learning are a compelling alternative to autoencoder-based world models, which learn by reconstructing pixel states. In this paper, we describe three cases where small changes in how we sample negative states in the contrastive loss lead to drastic changes in model performance. In previously studied Atari datasets, we show that leveraging time step correlations can double the performance of the Contrastive Structured World Model. We also collect a full version of the datasets to study contrastive learning under a more diverse set of experiences.
Uncovering the structure of the French media ecosystem

This study provides a large-scale mapping of the French media space using digital methods to estimate political polarization and to study information circuits. We collect data about the production and circulation of online news stories in France over the course of one year, adopting a multi-layer perspective on the media ecosystem. We source our data from websites, Twitter and Facebook. We also identify a certain number of important structural features. A stochastic block model of the hyperlinks structure shows the systematic rejection of counter-informational press in a separate cluster which hardly receives any attention from the mainstream media. Counter-informational sub-spaces are also peripheral on the consumption side. We measure their respective audiences on Twitter and Facebook and do not observe a large discrepancy between both social networks, with counter-information space, far right and far left media gathering limited audiences. Finally, we also measure the ideological distribution of news stories using Twitter data, which also suggests that the French media landscape is quite balanced. We therefore conclude that the French media ecosystem does not suffer from the same level of polarization as the US media ecosystem. The comparison with the American situation also allows us to consolidate a result from studies on disinformation: the polarization of the journalistic space and the circulation of fake news are phenomena that only become more widespread when dominant and influential actors in the political or journalistic space spread topics and dubious content originally circulating in the fringe of the information space.
Continental-Scale Building Detection from High Resolution Satellite Imagery

Identifying the locations and footprints of buildings is vital for many practical and scientific purposes. Such information can be particularly useful in developing regions where alternative data sources may be scarce. In this work, we describe a model training pipeline for detecting buildings across the entire continent of Africa, using 50 cm satellite imagery. Starting with the U-Net model, widely used in satellite image analysis, we study variations in architecture, loss functions, regularization, pre-training, self-training and post-processing that increase instance segmentation performance. Experiments were carried out using a dataset of 100k satellite images across Africa containing 1.75M manually labelled building instances, and further datasets for pre-training and self-training. We report novel methods for improving performance of building detection with this type of model, including the use of mixup (mAP +0.12) and self-training with soft KL loss (mAP +0.06). The resulting pipeline obtains good results even on a wide variety of challenging rural and urban contexts, and was used to create the Open Buildings dataset of 516M Africa-wide detected footprints.
Efficient Video Object Segmentation with Compressed Video

We propose an efﬁcient inference framework for semi-supervised video object segmentation by exploiting the temporal redundancy of the video. Our method performs inference on selected keyframes and makes predictions for other frames via propagation based on motion vectors and residuals from the compressed video bitstream. Speciﬁcally, we propose a new motion vector-based warping method for propagating segmentation masks from keyframes to other frames in a multi-reference manner. Additionally, we propose a residual-based reﬁnement module that can correct and add detail to the block-wise propagated segmentation masks. Our approach is ﬂexible and can be added on top of existing video object segmentation algorithms. With STM with top-k ﬁltering as our base model, we achieved highly competitive results on DAVIS16 and YouTube-VOS with substantial speedups of up to 4.9X with little loss in accuracy.
Accelerated MRI Reconstruction With Separable and Enhanced Low-Rank Hankel Regularization

Magnetic resonance imaging serves as an essential tool for clinical diagnosis, however, suffers from a long acquisition time. Sparse sampling effectively saves this time but images need to be faithfully reconstructed from undersampled data. Among the existing reconstruction methods, the structured low-rank methods have advantages in robustness to the sampling patterns and lower error. However, the structured low-rank methods use the 2D or higher dimension k-space data to build a huge block Hankel matrix, leading to considerable time and memory consumption. To reduce the size of the Hankel matrix, we proposed to separably construct multiple small Hankel matrices from rows and columns of the k-space and then constrain the low-rankness on these small matrices. This separable model can significantly reduce the computational time but ignores the correlation existed in inter- and intra-row or column, resulting in increased reconstruction error. To improve the reconstructed image without obviously increasing the computation, we further introduced the self-consistency of k-space and virtual coil prior. Besides, the proposed separable model can be extended into other imaging scenarios which hold exponential characteristics in the parameter dimension. The in vivo experimental results demonstrated that the proposed method permits the lowest reconstruction error with a fast reconstruction. The proposed approach requires only 4% of the state-of-the-art STDLR-SPIRiT runtime for parallel imaging reconstruction, and achieves the fastest computational speed in parameter imaging reconstruction.
Spectral signatures of axionlike dark matter

Alexander V. Gramolin, Arne Wickenbrock, 3 Deniz Aybas, 4 Hendrik Bekker, Dmitry Budker, 3, 5 Gary P. Centers, 3 Nataniel L. Figueroa, 3 Derek F. Jackson Kimball, and Alexander O. Sushkov 4, 7 Department of Physics, Boston University, Boston, MA 02215, USA Helmholtz-Institut, GSI Helmholtzzentrum für Schwerionenforschung, 55128 Mainz, Germany Johannes Gutenberg-Universität Mainz, 55128 Mainz, Germany Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA Department of Physics, University of California, Berkeley, CA 94720, USA Department of Physics, California State University – East Bay, Hayward, CA 94542, USA Photonics Center, Boston University, Boston, MA 02215, USA (Dated: July 27, 2021)
Crowdsourcing Strong Labels for Sound Event Detection

Strong labels are a necessity for evaluation of sound event detection methods, but often scarcely available due to the high resources required by the annotation task. We present a method for estimating strong labels using crowdsourced weak labels, through a process that divides the annotation task into simple unit tasks. Based on estimations of annotators' competence, aggregation and processing of the weak labels results in a set of objective strong labels. The experiment uses synthetic audio in order to verify the quality of the resulting annotations through comparison with ground truth. The proposed method produces labels with high precision, though not all event instances are recalled. Detection metrics comparing the produced annotations with the ground truth show 80% F-score in 1 s segments, and up to 89.5% intersection-based F1-score calculated according to the polyphonic sound detection score metrics.
Second Moment Closure Modeling and DNS of Stratified Shear Layers

Buoyant shear layers are encountered in many engineering and environmental applications, and have been studied by researchers in the context of experiments and modeling for decades. Often, these flows have high Reynolds and Richardson numbers, and this leads to significant/intractable space-time resolution requirements for DNS or LES modeling. On the other hand, many of the important physical mechanisms in these systems, such as stress anisotropy, wake stabilization, and regime transition, inherently render eddy viscosity-based RANS modeling inappropriate. Accordingly, we pursue second-moment closure (SMC), i.e., full Reynolds stress/flux/variance modeling, for moderate Reynolds number non-stratified, and stratified shear layers for which DNS is possible. A range of sub-model complexity is pursued for the diffusion of stresses, density fluxes and variance, pressure strain and scrambling, and dissipation. These sub-models are evaluated in terms of how well they are represented by DNS in comparison to the exact Reynolds averaged terms, and how well they impact the accuracy of the full RANS closure. For the non-stratified case, the SMC model predicts the shear layer growth rate and Reynolds shear stress profiles accurately. Stress anisotropy and budgets are captured only qualitatively. Comparing DNS of exact and modeled terms, inconsistencies in model performance and assumptions are observed, including inaccurate prediction of individual statistics, non-negligible pressure diffusion, and dissipation anisotropy. For the stratified case, shear layer and gradient Richardson number growth rates, and stress, flux and variance decay rates, are captured with less accuracy than corresponding flow parameters in the non-stratified case. These studies lead to several recommendations for model improvement.
Comprehensive Studies for Arbitrary-shape Scene Text Detection

Numerous scene text detection methods have been proposed in recent years. Most of them declare they have achieved state-of-the-art performances. However, the performance comparison is unfair, due to lots of inconsistent settings (e.g., training data, backbone network, multi-scale feature fusion, evaluation protocols, etc.). These various settings would dissemble the pros and cons of the proposed core techniques. In this paper, we carefully examine and analyze the inconsistent settings, and propose a unified framework for the bottom-up based scene text detection methods. Under the unified framework, we ensure the consistent settings for non-core modules, and mainly investigate the representations of describing arbitrary-shape scene texts, e.g., regressing points on text contours, clustering pixels with predicted auxiliary information, grouping connected components with learned linkages, etc. With the comprehensive investigations and elaborate analyses, it not only cleans up the obstacle of understanding the performance differences between existing methods, but also reveals the advantages and disadvantages of previous models under fair comparisons.