Miracle-dz/UniFAR-FTUs huggingface dataset:
Online simulations of mineral dust aerosol distributions: Comparisons to NAMMA observations and sensitivity to dust emission parameterization

[1] A key uncertainty within Earth system modeling lies in the parameterization of the emission process for mineral aerosols, where emission scheme choice can have implications for emitted dust fluxes. For our study, we include versions of dust emission schemes from the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) and Dust Entrainment and Deposition (DEAD) models in the new NASA Goddard Earth Observing System version 4 model, to identify differences in simulated dust distributions caused by varying the emission scheme. The GOCART and DEAD schemes differ in their parameterization of the mobilization process, including their sensitivity to meteorological variables and the determination of the emitted particle size distribution. We focus on Saharan dust events during the NASA African Monsoon Multidisciplinary Analyses field campaign (August–September 2006) to compare with in situ, ground-based, and remote sensing observations. We find that the emission schemes produce comparable aerosol optical thickness and vertical extinction profiles, and their distributions compare well with observations from the space-based MODIS, OMI, MISR, and CALIPSO sensors, and the airborne LASE lidar. Neither emission scheme does especially well at capturing the variability or magnitude of specific dust events over the source region when compared to AERONET and MISR observations, but both improve downwind of the dust sources. Despite the similarities in the optical comparisons, the schemes differ in mass loadings owing to differences in their emitted dust particle size distributions over the source region. Our findings suggest that emission scheme choice for general circulation models is important only over the source region, where the emitted particle size distributions and corresponding mass budgets of emissions are influenced.
The Effect of Socioeconomic Status on Vitamin D Level in Children’s and Adolescents Living at Jeddah, Saudi Arabia

Objective: Due to the limited number of studies on the connection between Vitamin D deficiency and socioeconomic status (household income, number of family members, and parents’ education level) this research aimed to further investigate the relationship between socioeconomic state and vitamin D deficiency in Jeddah, Saudi Arabia. Methods: A cross-sectional study of 378 children and adolescents in an ambulatory clinic at the King Abdulaziz University Hospital, Jeddah, Saudi Arabia was performed from March to September 2015. The parents answered a questionnaire with guidance from the researchers and consent was obtained to take a blood sample to check each child’s vitamin D level. Results: The mean age was 9.5 ± 3, 9 years, and 1.9% had normal vitamin D levels. The mean vitamin D level was higher in low-income families than those with average and high incomes. The level was higher in families with less than 3 members than in families have 3 to 6 or more than 6 members. The mean vitamin D level was higher in families where the father was less educated or uneducated than in families where the father was highly educated. The mother’s education levels did not affect the mean vitamin D levels. On analysis with the Tukey test, we found that there was a significant difference in levels between families with low and medium income. The mean difference between low and average monthly income was 4.21 (p=0.034). Conclusion: Low-income families had the highest mean vitamin D level. No significant correlations were found between vitamin D level and number of family members or parents’ educational level.
Temperature and precipitation records from stalagmites grown under disequilibrium conditions: A first approach

To reconstruct past variations in Earth’s climate, a variety of climate archives are studied. During the last decades stalagmites came into focus due to their long, continuous growth and absolute dating techniques. In this study a numerical model was developed, which calculates variations in temperature and precipitation during the growth period of stalagmites grown under isotopic disequilibrium conditions using the isotope profiles both along the growth axis and individual growth layers as well as the growth depth relation.
A NOTE ON AN OPEN P R O B L E M IN LINEAR

For a g iven n -vec to r q and n x n matr ix M, we deno te the linear complementarity problem, of f inding an n -vec to r x such that q+Mx>-O, x>-O, x T ( q + M x ) = O by the pair (q, M). The feasible set of (q, M ) is defined as the po lyhedra l set X ( q , M ) = { x E R n : q + Mx >-O,x >-O}. The p rob lem (q, M) is feasible if X(q, M) # ft. Define 5g = {M E R "x" : for every q E R", X(q, M) ¢f t ~ (q, M) has a solution}. (1) It has long been an open p rob lem in the theory of the l inear c o m p l e m e n t a r i t y p rob lem to find a sui table cha rac te r i za t ion of Yl" which can be used to de t e rmine whe the r or no t an arb i t ra ry matr ix M is in Y{. In this note , we es tab l i sh a resul t (Theorem 3) which provides a usefu l nece s sa ry and sufficient cond i t ion for a matr ix to be long to 5f. We do not address the ques t ion of how to ver i fy if a matr ix satisfies this lat ter condi t ion . Recen t ly , Mangasa r i an [5] has es tab l i shed the fo l lowing r emarkab le theorem charac te r iz ing l inear c o m p l e m e n t a r i t y p rob lems as l inear programs.
Evaluation of the Effective Baking Conditions for Grade 3 Nigerian Acacia Species Bonded Foundry Sand Cores

The most effective economical baking conditions were determined for foundry cores bonded with the grade 3 Nigerian acacia species exudates. Silica sand base cores bonded with the material were oven baked at temperatures ranging f rom 160 to 250oC f or vary ing periods oven cooled and then subjected to ten sile strength tests to as certain the best bak ing conditions for different alloy ca stings. The experimental core specimens were in accordance with foundry test standard, shaped like figure number eight. They were tested with standard universal strength machine equipped with attachment for gripping the cores and an instantaneous meter from which the test values were read. The result were compared with established standard foundry core property table which showed that the class V iron/steel cores are best made with 3% acacia bonded silica sand baked at 160oC for 1 h. For magnesium cores, sand bonded with 4.5% acacia baked at 160oC for 1-2 h was optimum. For class IV iron/steel cores 4.5% acacia bonded sand baked at 180oC for 1 hour was optimal. Sand bonded with 4.5% grade 3 acacia baked at 200oC for 1.0-2.0 h was optimum for copper, aluminium, classes II and III iron/steel cores. Sand bonded with 8.0-13.0% grade 3 Nigerian acacia species baked at 200oC for 1.5-2 h was found most effective for class I iron and steel cores.
Bidirectional Buffer-Aided Relay Networks With Fixed Rate Transmission—Part II: Delay-Constrained Case

This is the second part of a two-part paper considering bidirectional relay networks with half-duplex nodes and block fading where the nodes transmit with a fixed transmission rate. In Part I, it was shown that a considerable gain in terms of sum throughput can be obtained by optimally selecting the transmission modes or, equivalently, the states of the nodes, i.e., the transmit, the receive, and the silent states, based on the qualities of the involved links. To enable adaptive transmission mode selection, the relay has to be equipped with two buffers for storage of the data received from the two users. The protocol proposed in Part I was delay unconstrained and provides an upper bound for the performance of practical delay-constrained protocols. In this paper, we propose two heuristic but efficient delay-constrained protocols, which can approach the performance upper bound reported in Part I, even in cases where only a small delay is permitted. The proposed protocols not only consider the instantaneous qualities of the involved links for adaptive mode selection but also take the states of the queues at the buffers into account, i.e., the number of packets in the queues. The average throughput and the average delay of the proposed delay-constrained protocols are evaluated by analyzing the Markov chain of the states of the queues. Numerical results show that the proposed protocols outperform existing bidirectional relaying protocols for delay-constrained transmission.
Cell Metabolism Molecular Markers for Feeding Neural Circuits

Cristian A. Pérez,1,3 Sarah A. Stanley,1 Robert W. Wysocki,1,2 Jana Havranova,1 Rebecca Ahrens-Nicklas,1 Frances Onyimba,1 and Jeffrey M. Friedman1,2,* 1Laboratory of Molecular Genetics, Rockefeller University, New York, NY 10065, USA 2Howard Hughes Medical Institute, New York, NY 10065, USA 3Present address: Monell Chemical Senses Center, Philadelphia, PA 19104, USA *Correspondence: friedj@mail.rockefeller. edu DOI 10.1016/j.cmet. 2010.12. 013
Cell Metabolism Role of CREB in Obesity active

Ling Qi,1,5,6 Maziyar Saberi,2,5 Erik Zmuda,3 Yiguo Wang,1 Judith Altarejos,1 Xinmin Zhang,4 Renaud Dentin,1 Susie Hedrick,1 Gautam Bandyopadhyay,2 Tsonwin Hai,3 Jerry Olefsky,2 and Marc Montminy1,* 1Laboratories for Peptide Biology, Salk Institute for Biological Studies, La Jolla, CA 92037, USA 2Division of Endocrinology and Metabolism, University of California, San Diego, La Jolla, CA 92037, USA 3Department of Molecular and Cellular Biochemistry, Ohio State University, Columbus, OH 43210, USA 4NimbleGen, Madison, WI 53719, USA 5These authors contributed equally to this work 6Present address: Division of Nutritional Sciences, Cornell University, Ithaca, NY 14853, USA *Correspondence: montminy@salk.edu DOI 10.1016/j.cmet. 2009.01. 006
Author ' s personal copy Adaptive load shedding and regional protection

The requirement for improved efficiency whilst maintaining system security necessitates the development of improved system analysis approaches and the development of advanced emergency control technologies. Load shedding is a type of emergency control that is designed to ensure system stability by curtailing system load to match generation supply. This paper presents a new adaptive load shedding scheme that provides emergency protection against excess frequency decline, whilst minimizing the risk of line overloading. The proposed load shedding scheme uses the local frequency rate information to adapt the load shedding behaviour to suit the size and location of the experienced disturbance. The proposed scheme is tested in simulation on a 3-region, 10-generator sample system and shows good performance. 2009 Elsevier Ltd. All rights reserved.
Parasitic infection levels by Anisakis spp. larvae (Nematoda: Anisakidae) in the black scabbardfish Aphanopus carbo (Osteichthyes: Trichiuridae) from Portuguese waters

Aphanopus carbo , black scabbardfish, specimens from 3 Portuguese regions (the mainland, Madeira and the Azores) during four (mainland and Madeira) or two (Azores) seasons of the year were examined for the presence of Anisakis . All the fish examined (n=287) were infected with Anisakis L3 larvae (prevalence = 100%). Significant geographical differences in intensity were found: the Azores showed the lowest mean intensity value (53.7) and Madeira the highest one (253.9). The intensity of infection was positively correlated with the host length in specimens of Sesimbra and Madeira. Significant seasonal differences in intensity were found in the studied regions. The very high values in prevalence and intensity strongly suggest that the consumption of raw or undercooked black scabbard fish is a potential risk for human health.
Model-driven Level 3 BLAS Performance Optimization on Loongson 3A Processor

Every mainstream processor vendor provides an optimized BLAS implementation for its CPU, as BLAS is a fundamental math library in scientific computing. The Loongson 3A CPU is a general-purpose 64-bit MIPS64 quad-core processor, developed by the Institute of Computing Technology, Chinese Academy of Sciences. To date, there has not been a sufficiently optimized BLAS on the Loongson 3A CPU. The purpose of this research is to optimize level 3 BLAS performance on the Loongson 3A CPU. We analyzed the Loongson 3A architecture and built a performance model to highlight the key point, L1 data cache misses, which is different from level 3 BLAS optimization on the mainstream x86 CPU. Therefore, we employed a variety of methods to avoid L1 cache misses in single thread optimization, including cache and register blocking, the Loongson 3A 128-bit memory accessing extension instructions, software prefetching, and single precision floating-point SIMD instructions. Furthermore, we improved parallel performance by reducing bank conflicts among multiple threads in the shared L2 cache. We created an open source BLAS project, OpenBLAS, to demonstrate the performance improvement on the Loongson 3A quad-core processor.
Fast Transformer Decoding: One Write-Head is All You Need

Multi-head attention layers, as used in the Transformer neural sequence model, are a powerful alternative to RNNs for moving information across and between sequences. While training these layers is generally fast and simple, due to parallelizability across the length of the sequence, incremental inference (where such paralleization is impossible) is often slow, due to the memory-bandwidth cost of repeatedly loading the large "keys" and "values" tensors. We propose a variant called multi-query attention, where the keys and values are shared across all of the different attention "heads", greatly reducing the size of these tensors and hence the memory bandwidth requirements of incremental decoding. We verify experimentally that the resulting models can indeed be much faster to decode, and incur only minor quality degradation from the baseline.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning

With the advent of deep generative models in computational chemistry, in silico anticancer drug design has undergone an unprecedented transformation. While state-of-the-art deep learning approaches have shown potential in generating compounds with desired chemical properties, they disregard the genetic profile and properties of the target disease. Here, we introduce the first generative model capable of tailoring anticancer compounds for a specific biomolecular profile. Using a RL framework, the transcriptomic profiles of cancer cells are used as a context for the generation of candidate molecules. Our molecule generator combines two separately pretrained variational autoencoders (VAEs) - the first VAE encodes transcriptomic profiles into a smooth, latent space which in turn is used to condition a second VAE to generate novel molecular structures on the given transcriptomic profile. The generative process is optimized through PaccMann, a previously developed drug sensitivity prediction model to obtain effective anticancer compounds for the given context (i.e., transcriptomic profile). We demonstrate how the molecule generation can be biased towards compounds with high predicted inhibitory effect against individual cell lines or specific cancer sites. We verify our approach by investigating candidate drugs generated against specific cancer types and find the highest structural similarity to existing compounds with known efficacy against these cancer types. We envision our approach to transform in silico anticancer drug design by leveraging the biomolecular characteristics of the disease in order to increase success rates in lead compound discovery.
Dynamic Analysis of Buck-Based Photovoltaic Array Model

PV applications regularly require the control of the input voltage of DC-DC converters. Unlike conventional converter, in this study the input voltage is controlled and the output voltage is maintained constant. A linearized photovoltaic (PV) array model and buck converter with constant output voltage model were used in this paper to get the transfer function of the electronic converter and to design a voltage compensator to regulate the input voltage of the converter. Results were obtained and illustrated verifying the efficiency of the compensator used in enhancing the time-response performance of the buck-based PV system by reducing the over-shoot, settling and rising time. Nonetheless, enhancing the stability of the buck-based PV system. 
Warm CO Gas Generated by Possible Turbulent Shocks in a Low-mass Star-forming Dense Core in Taurus

We report ALMA Cycle 3 observations in CO isotopes toward a dense core, MC27/L1521F in Taurus, which is considered to be at an early stage of multiple star formation in a turbulent environment. Although most of the high-density parts of this core are considered to be as cold as ∼10 K, high-angular resolution (∼20 au) observations in 12CO (J = 3–2) revealed complex warm (>15–60 K) filamentary/clumpy structures with the sizes from a few tens of astronomical units to ∼1000 au. The interferometric observations of 13CO and C18O show that the densest part with arc-like morphologies associated with the previously identified protostar and condensations are slightly redshifted from the systemic velocity of the core. We suggest that the warm CO clouds may be consequences of shock heating induced by interactions among the different density/velocity components that originated from the turbulent motions in the core. However, such a small-scale and fast turbulent motion does not correspond to a simple extension of the line–width–size relation (i.e., Larson's law), and thus the actual origin remains to be studied. The high-angular resolution CO observations are expected to be essential in detecting small-scale turbulent motions in dense cores and to investigate protostar formation therein.
Deep Learning for Stock Selection Based on High Frequency Price-Volume Data

Training a practical and effective model for stock selection has been a greatly concerned problem in the field of artificial intelligence. Even though some of the models from previous works have achieved good performance in the U.S. market by using low-frequency data and features, training a suitable model with high-frequency stock data is still a problem worth exploring. Based on the high-frequency price data of the past several days, we construct two separate models-Convolution Neural Network and Long Short-Term Memory-which can predict the expected return rate of stocks on the current day, and select the stocks with the highest expected yield at the opening to maximize the total return. In our CNN model, we propose improvements on the CNNpred model presented by E. Hoseinzade and S. Haratizadeh in their paper which deals with low-frequency features. Such improvements enable our CNN model to exploit the convolution layer's ability to extract high-level factors and avoid excessive loss of original information at the same time. Our LSTM model utilizes Recurrent Neural Network'advantages in handling time series data. Despite considerable transaction fees due to the daily changes of our stock position, annualized net rate of return is 62.27% for our CNN model, and 50.31% for our LSTM model.
Spatial Feature Extraction in Airborne Hyperspectral Images Using Local Spectral Similarity

Local spectral similarity (LSS) algorithm has been developed for detecting homogeneous areas and edges in hyperspectral images (HSIs). The proposed algorithm transforms the 3-D data cube (within a spatial window) into a spectral similarity matrix by calculating the vector-similarity between the center pixel-spectrum and the neighborhood spectra. The final edge intensity is derived upon order statistics of the similarity matrix or spatial convolution of the similarity matrix with the spatial kernels. The LSS algorithm facilitates simultaneous use of spectral-spatial information for the edge detection by considering the spatial pattern of similar spectra within a spatial window. The proposed edge-detection method is tested on benchmark HSIs as well as the image obtained from Airborne-Visible-and-Infra-RedImaging-Spectrometer-Next-Generation (AVIRIS-NG). Robustness of the LSS method against multivariate Gaussian noise and low spatial resolution scenarios were also verified with the benchmark HSIs. Figure-of-merit, false-alarm-count and miss-count were applied to evaluate the performance of edge detection methods. Results showed that Fractional distance measure and Euclidean distance measure were able to detect the edges in HSIs more precisely as compared to other spectral similarity measures. The proposed method can be applied to radiance and reflectance data (whole spectrum) and it has shown good performance on principal component images as well. In addition, the proposed algorithm outperforms the traditional multichannel edge detectors in terms of both fastness, accuracy and the robustness. The experimental results also confirm that LSS can be applied as a pre-processing approach to reduce the errors in clustering as well as classification outputs.
FINANCIAL AND ECONOMIC MECHANISM OF ENSURING INVESTMENT ACTIVITY OF ENTERPRISES WITHIN INSTITUTIONAL MODELS OF FINANCING THE REAL SECTOR OF ECONOMICS

The subject matter of the article is economic relations arising among subjects of investment activity within institutional models for financing the real sector of the economy. The goal of the work is the development of a financial and economic mechanism for ensuring the investment activity of enterprises within institutional models for financing the real sector of the economy with determining the optimal ratio of sources for financing investment resources on macro and micro levels when implementing the "state-regulator" and "state-investor" models of financing. The following tasks were solved in the article – the essence of institutional models for financing the real sector of the economy was determined and their main characteristics were compared; the financial and economic mechanism to ensure the investment activity of enterprises within institutional models "state-regulator" and "state-investor" was developed. The following methods were used: the logical generalization and comparison, analysis and synthesis, induction and deduction, system analysis. The following results were obtained. The basic institutional models of financing the real sector of the economy were studied; basing on the comparison of the characteristics of models where the X and Y-economies predominate, the elements of the financial and economic mechanism for ensuring the investment activity of enterprises were determined; the elements of the mechanism were systematized in accordance with the prevailing interests of the subjects of ensuring investment activity depending on the investment goal on the basis of the selected model of the economic development and financial policy in the country. A set of methods and tools that actuate economic laws in financial relations under two institutional models of financing is emphasized. The order for determining the optimum interrelation of sources of financing of capital investments on macro and micro level is offered. Conclusions. The developed financial and economic mechanism includes the procedure for selecting methods and instruments of indirect stimulation (state-regulator) or direct financial support for investment activity of enterprises (state-investor) on the basis of the selected model of economic development and financial policy in the country. At the present stage of the development of Ukraine, a financial mechanism is needed to ensure the investment activity which includes the elements of two models, where the state simultaneously acts as both a regulator and an investor (mixed type).