Miracle-dz/UniFAR-FTUs huggingface dataset:
Plant Promoters: Their Identification, Characterization, and Role in Gene Regulation

One of the strategies to overcome diseases or abiotic stress in crops is the use of improved varieties. Genetic improvement could be accomplished through different methods, including conventional breeding, induced mutation, genetic transformation, or gene editing. The gene function and regulated expression through promoters are necessary for transgenic crops to improve specific traits. The variety of promoter sequences has increased in the generation of genetically modified crops because they could lead to the expression of the gene responsible for the improved trait in a specific manner. Therefore, the characterization of the promoter activity is necessary for the generation of biotechnological crops. That is why several analyses have focused on identifying and isolating promoters using techniques such as reverse transcriptase-polymerase chain reaction (RT-PCR), genetic libraries, cloning, and sequencing. Promoter analysis involves the plant genetic transformation method, a potent tool for determining the promoter activity and function of genes in plants, contributing to understanding gene regulation and plant development. Furthermore, the study of promoters that play a fundamental role in gene regulation is highly relevant. The study of regulation and development in transgenic organisms has made it possible to understand the benefits of directing gene expression in a temporal, spatial, and even controlled manner, confirming the great diversity of promoters discovered and developed. Therefore, promoters are a crucial tool in biotechnological processes to ensure the correct expression of a gene. This review highlights various types of promoters and their functionality in the generation of genetically modified crops.
A Tale of Two Efficient Value Iteration Algorithms for Solving Linear MDPs with Large Action Space

Markov Decision Process (MDP) with large action space naturally occurs in many applications such as language processing, information retrieval, and recommendation system. There have been various approaches to solve these MDPs through value iteration (VI). Unfortunately, all VI algorithms require expensive linear scans over the entire action space for value function estimation during each iteration. To this end, we present two provable Least-Squares Value Iteration (LSVI) algorithms with runtime complexity sublinear in the number of actions for linear MDPs. We formulate the value function estimation procedure in VI as an approximate maximum inner product search problem and propose a Locality Sensitive Hashing ( LSH ) type data structure to solve this problem with sublinear time complexity. Our major contribution is combining the guarantees of approximate maximum inner product search with the regret analysis of reinforcement learning. We prove that, with the appropriate choice of approximation factor, there exists a sweet spot. Our proposed Sublinear LSVI algorithms maintain the same regret as the original LSVI algorithms while reducing the runtime complexity to sublinear in the number of actions. To the best of our knowledge, this is the first work that combines LSH with reinforcement learning that results in provable improvements. We hope that our novel way of combining data structures and the iterative al-gorithm will open the door for further study into the cost reduction in reinforcement learning.
Mediating Effect and Suppressing Effect: Intermediate Mechanism of Urban Land Use Efﬁciency and Economic Development

: The limited nature of land supply determines that improving land use efﬁciency is an inherent requirement for economic development. The aim of this study was to quantitatively examine its impact. Most current studies have explored the quantitative relationship between urban land use efﬁciency (ULUE) and economic development, but less attention has been given to the mechanism of action of both. In this study, we construct an analysis framework for the mechanism of ULUE in promoting economic development from three aspects: economic scale, economic structure, and economic quality, and we quantitatively investigate its impact on economic development and intermediate action mechanism through a mediating effect model on the basis of measuring ULUE by using a super-efﬁciency SBM-undesirable model. Based on the analysis of the panel data of 56 cities in the Yellow River Basin (YRB), the results show that, ﬁrst, ULUE has formed an ideal positive driving effect on economic development, and its inﬂuence mechanism has obvious heterogeneity in cities with different geographical locations and resource endowments. Second, ULUE affects economic development through three channels: economic scale-up effect, economic structure optimization effect, and economic quality enhancement effect, and there are two different mechanisms of mediating effect and suppressing effect. Finally, variables such as investment intensity of urban construction land and social beneﬁt act as suppressing effects, while variables such as economic output density of urban land, industrial structure, employment structure, economic beneﬁt, and environmental beneﬁt play partial mediating effects. These evidence-based ﬁndings can provide practical guidance for solving the dilemma of a lack of economic development momentum and inefﬁcient land use in a country or region.
Terrain appearance: Visual characterization using the right features

This paper compares the performance of a selection of texture feature extraction methods. The goal is to find the best performing method to distinguish between eight predefined terrain types, captured during operation of the CRAB rover. The procedure applied to the texture images includes transformation of the perspective angle to a bird-eye view as well as conversion to gray-scale and scale adaptations where each image is split up into smaller patches. An overall performance measure based on a classification attempt allows comparing the texture feature extraction methods to each other. A second approach measures the distinguishability of any combination of two terrains by the overlapping area of the Gaussian distribution curves. The best overall performance is obtained using the 2nd Fourier coefficient of a frequency domain image as a texture feature since it is able to capture the changing resolution within the close and distant image patches. First order statistics show good results whereas the second order statistics as gray level co-occurrence matrix (GLCM) and gray level run length matrix (GLRLM) do not perform as expected since they react sensitively to changing resolution. The GLCM is strongly influenced by the chosen offset. Methods based on discrete Fourier transformation prove to be the most promising approaches within the implemented texture feature extraction methods. Terrain appearance CONTENTS
Designing and developing a literature-derived, population-based dietary inﬂammatory index

Objective: To design and develop a literature-derived, population-based dietary inﬂammatory index (DII) to compare diverse populations on the inﬂammatory potential of their diets. Design: Peer-reviewed primary research articles published through December 2010 on the effect of diet on inﬂammation were screened for possible inclusion in the DII scoring algorithm. Qualifying articles were scored according to whether each dietary parameter increased ( 1 1), decreased ( 2 1) or had no (0) effect on six inﬂammatory biomarkers: IL-1 b , IL-4, IL-6, IL-10, TNF- a and C-reactive protein. Setting: The Dietary Inﬂammatory Index Development Study was conducted in the Cancer Prevention and Control Program of the University of South Carolina in Columbia, SC, USA from 2011 to 2012. Results: A total of E 6500 articles published through December 2010 on the effect of dietary parameters on the six inﬂammatory markers were screened for inclusion in the DII scoring algorithm. Eleven food consumption data sets from countries around the world were identiﬁed that allowed individuals’ intakes to be expressed relative to the range of intakes of the forty-ﬁve food parameters observed across these diverse populations. Qualifying articles ( n 1943) were read and scored based on the forty-ﬁve pro- and anti-inﬂammatory food parameters identiﬁed in the search. When ﬁt to this composite global database, the DII score of the maximally pro-inﬂammatory diet was 1 7 ? 98, the maximally anti-inﬂammatory DII score was 2 8 ? 87 and the median was 1 0 ? 23. Conclusions: The DII reﬂects both a robust literature base and standardization of individual intakes to global referent values. The success of this ﬁrst-of-a-kind attempt at relating intakes of inﬂammation-modulating foods relative to global norms sets the stage for use of the DII in a wide variety of epidemiological and clinical studies.
Tensor-based Kernel Machines with Structured Inducing Points for Large and High-Dimensional Data

Kernel machines are one of the most studied family of methods in machine learning. In the exact setting, training requires to instantiate the kernel matrix, thereby prohibiting their application to large-sampled data. One popular kernel approximation strategy which allows to tackle large-sampled data consists in interpolating product kernels on a set of grid-structured inducing points. However, since the number of model parameters increases exponentially with the dimensionality of the data, these methods are limited to small-dimensional datasets. In this work we lift this limitation entirely by placing inducing points on a grid and constraining the primal weights to be a low-rank Canonical Polyadic Decomposition. We derive a block coordinate descent algorithm that efficiently exploits grid-structured inducing points. The computational complexity of the algorithm scales linearly both in the number of samples and in the dimensionality of the data for any product kernel. We demonstrate the performance of our algorithm on large-scale and high-dimensional data, achieving state-of-the art results on a laptop computer. Our results show that grid-structured approaches can work in higher-dimensional problems.
Dietary Supplementation of a Mixture of Nucleotides, β-Glucan and Vitamins C and E Improved the Growth and Health Performance of Olive Flounder, Paralichthys olivaceus

A twelve-week feeding trial was conducted to investigate the effect of dietary supplementation of a functional immunostimulant mixture (FIM; nucleotides, β-glucan and vitamins C and E) on growth, feed utilization, innate immunity, digestive enzyme activity, hematological parameters, intestinal morphology and inflammatory gene expressions of olive flounder, Paralichthys olivaceus. A fish meal-based basal diet (control) was formulated, and three other diets were prepared by incorporating 0.5, 1.0 and 1.5% FIM into the basal diet (HB0.5, HB1.0 and HB1.5, respectively). Five replicate groups of fish (26.3 ± 0.1 g) were fed one of the experimental diets. Inclusion of 1.5% FIM in the diet significantly enhanced growth and feed utilization. Significantly higher plasma hemoglobin, hematocrit, total protein levels and lower plasma glucose level were observed in the HB1.5 group. Total immunoglobulin content, lysozyme, nitroblue tetrazolium, myeloperoxidase, superoxide dismutase, glutathione peroxidase and catalase activities and immune-related gene expressions of toll-like receptor and perforin were significantly increased at the highest inclusion level. FIM supplementation significantly increased villus height and goblet cell counts. Anti-inflammatory gene expressions were significantly upregulated at a 1.5% level. Dietary supplementation of 1.5% of FIM could improve growth and feed utilization, immune, hematological and intestinal histomorphological parameters of olive flounder.
Coexistence of Thread and Sheet Chaotic Attractors for Three-Dimensional Lozi Map

Since its original publication in 1978, Lozi’s chaotic map has been thoroughly explored and continues to be. Hundreds of publications have analyzed its particular structure or applied its properties in many fields (electronic devices such as memristors, A.I. with swarm intelligence, etc.). Several generalizations have been proposed, transforming the initial two-dimensional map into a multidimensional one. However, they do not respect the original constraint that allows this map to be one of the few strictly hyperbolic: a constant Jacobian. In this paper, we introduce a three-dimensional piece-wise linear extension respecting this constraint and we explore a special property never highlighted for chaotic mappings: the coexistence of thread chaotic attractors (i.e., attractors that are formed by a collection of lines) and sheet chaotic attractors (i.e., attractors that are formed by a collection of planes). This new three-dimensional mapping can generate a large variety of chaotic and hyperchaotic attractors. We give five examples of such behavior in this article. In the first three examples, there is the coexistence of thread and sheet chaotic attractors. However, their shapes are different and they are constituted by a different number of pieces. In the last two examples, the blow up of the attractors with respect to parameter a and b is highlighted.
Integration of Polynomials Times Double Step Function in Quadrilateral Domains for XFEM Analysis

The numerical integration of discontinuous functions is an abiding problem addressed by various authors. This subject gained even more attention in the context of the extended finite element method (XFEM), in which the exact integration of discontinuous functions is crucial to obtaining reliable results. In this scope, equivalent polynomials represent an effective method to circumvent the problem while exploiting the standard Gauss quadrature rule to exactly integrate polynomials times step function. Certain scenarios, however, might require the integration of polynomials times two step functions (i.e., problems in which branching cracks, kinking cracks or crack junctions within a single finite element occur). In this context, the use of equivalent polynomials has been investigated by the authors, and an algorithm to exactly integrate arbitrary polynomials times two Heaviside step functions in quadrilateral domains has been developed and is presented in this paper. Moreover, the algorithm has also been implemented into a software library (DD_EQP) to prove its precision and effectiveness and also the proposed method’s ease of implementation into any existing computational software or framework. The presented algorithm is the first step towards the numerical integration of an arbitrary number of discontinuities in quadrilateral domains. Both the algorithm and the library have a wide application range, in addition to fracture mechanics, from mathematical computing of complex geometric regions, to computer graphics and computational mechanics.
Impact of a Nonlocal Closure Scheme in a Simulation of a Monsoon System over India

Abstract The impact of two parameterization schemes for the atmospheric boundary layer in predicting monsoon circulation over the Indian region has been studied using a Global Spectral Model. The performance of the nonlocal closure scheme for the boundary layer has been tested in the operational global model of the National Centre for Medium Range Weather Forecasting (NCMRWF) for its possible implementation and operational use. Keeping the parameterization schemes for all other physical processes the same, the performance of the nonlocal closure scheme is studied and compared with the performance of the operational local closure scheme of the boundary layer processes. Incorporation of the nonlocal closure scheme shows marginal impact in the prediction of the flow pattern. However, systematic improvement in the precipitation distribution over the Indian region is seen with the incorporation of a nonlocal closure scheme during the month of August 1999. Location of the precipitation maximum along the west co.
Adaptive Bandwidth Mean Shift for Video Image Segmentation

A new nonparametric estimation method called adaptive bandwidth mean shift (ABMS) algorithm is applied to realizing image smoothing and segmentation,which removes noises and trivial texture and preserves the structural features of the image objects.The intersection of confidence intervals (ICI) rule and the sample point density estimator in ABMS algorithm were used to determine the smoothing bandwidth and obtain the mean shift vector of each pixel in the video image.Along the direction of this vector,image was smoothed and the pixels were clustered to several blocks.At the same time,the motion features of the image blocks were fused and the object was extracted from the video sequence.The experiments and simulations verify the efficiency of the proposed method.
Scale-Dependent Priors for Variance Parameters in Structured Additive Distributional Regression

The selection of appropriate hyperpriors for variance parameters is an important and sensible topic in all kinds of Bayesian regression models involving the speciﬁcation of (conditionally) Gaussian prior structures where the variance parameters determine a data-driven, adaptive amount of prior variability or precision. We consider the special case of structured additive distributional regression where Gaussian priors are used to enforce speciﬁc properties such as smoothness or shrinkage on various eﬀect types combined in predictors for multiple parameters related to the distribution of the response. Relying on a recently proposed class of penalised complexity priors motivated from a general set of construction principles, we derive a hyperprior structure where prior elicitation is facilitated by assumptions on the scaling of the diﬀerent eﬀect types. The posterior distribution is assessed with an adaptive Markov chain Monte Carlo scheme and conditions for its propriety are studied theoretically. We investigate the new type of scale-dependent priors in simulations and two challenging applications, in particular in comparison to the standard inverse gamma priors but also alternatives such as half-normal, half-Cauchy and proper uniform priors for standard deviations.
Transmission Control in NB-IoT With Model-Based Reinforcement Learning

In Narrowband Internet of Things (NB-IoT), the control of uplink transmissions is a complex task involving device scheduling, resource allocation in the carrier, and the configuration of link-adaptation parameters. Existing heuristic proposals partially address the problem, but reinforcement learning (RL) seems to be the most effective approach a priori, given its success in similar control problems. However, the low sample efficiency of conventional (model-free) RL algorithms is an important limitation for their deployment in real systems. During their initial learning stages, RL agents need to explore the policy space selecting actions that are, in general, highly ineffective. In an NB-IoT access network this implies a disproportionate increase in transmission delays. In this paper, we make two contributions to enable the adoption of RL in NB-IoT: first, we present a multi-agent architecture based on the principle of task division. Second, we propose a new model-based RL algorithm for link adaptation characterized by its high sample efficiency. The combination of these two strategies results in an algorithm that, during the learning phase, is able to maintain the transmission delay in the order of hundreds of milliseconds, whereas model-free RL algorithms cause delays of up to several seconds. This allows our approach to be deployed, without prior training, in an operating NB-IoT network and learn to control it efficiently without degrading its performance.
Comparison and Analysis among Four Objective and Quantificational Blocking Indexes

Existing objective,quantificational blocking indexes can be summed up by four: Departure method,TM method,PV-θ index and circumfluent type method.Taking blocking highs in durative weather disaster including low temperature,snowfall and freezing in China from January 1 to February 2,2008 as an instance,the merits and demerits of these four objective means are researched one by one according to the daily reanalysis data provided by NCEP/NCAR.The results are as follows: Every method has its merits and ineluctable demerits because of algebra operational conformation for objective blocking index.These reflect that it′s difficult to identify blocking accurately in some sense created by earth′s atmosphere with its almost infinite degrees of freedom.Based on what merits and demerits of these blocking indexes have,investigator and operation department could apply blocking indexes better if they select compatible one or improve and innovate a method according to their selves practice.
Front Propagation into Unstable States II : Linear versus Nonlinear Marginal Stability and Rate of Convergence

On montre que la transition de la stabilite lineaire marginale a la stabilite non lineaire marginale peut etre decrite dans une representation dynamique de la propagation du front dans des etats instables. L'approche resultante donne une interpretation physique et une extension des resultats d'Aronson et Weinberger et de Ben-Jacob
Orthogonal Empirical Mode Decomposition

For the problem that the intrinsic mode functions(IMF) decomposed by the empirical mode decomposition(EMD) in HilbertHuang transform are not orthogonal in mathematical rigor,a new method based on the Gram-Schmidt method is proposed and the complete orthogonal intrinsic mode functions are attained.As a result,Hilbert-Huang transform is improved.The method is validated through the decomposition of a typical time history,and the application is proved to be opromising by the decomposition of a real earthquake record.
Filtering method for medical images based on median filtering and anisotropic diffusion

Medical image filtering process should retain the edge details of diagnostic significance. For Perona-Malik( PM) anisotropic diffusion model experienced failure when dealing with strong noise and choosing parameter K of diffusion threshold relies on experience, this paper proposed an improved anisotropic diffusion algorithm. First, PM was combined with the median filter algorithm, and then the gradient mode of the original image was replaced with the gradient mode from the image which was smoothed by the median filter to control the process of diffusion. While applying the adaptive diffusion threshold( Median Absolute Deviation( MAD) of the gradient in current neighborhood) and iteration termination criteria, the algorithm improved robustness and efficiency of the algorithm. The experiment was operated respectively on echocardiography,CT images and Lena image to denoise, and used Peak Signal-to-Noise Ratio( PSNR) and Edge Preservation Index( EPI) as evaluation criterion. The experimental results show that the improves algorithm outperforms PM algorithm and Catte-PM method for improving PSNR while preserving image detail information, and meets the requirements for application in medical images more effectively.