Miracle-dz/UniFAR-FTUs huggingface dataset:
Nucleolin modulates compartmentalization and dynamics of histone 2B-ECFP in the nucleolus

ABSTRACT Eukaryotic cells have 2 to 3 discrete nucleoli required for ribosome synthesis. Nucleoli are phase separated nuclear sub-organelles. Here we examined the role of nuclear Lamins and nucleolar factors in modulating the compartmentalization and dynamics of histone 2B (H2B-ECFP) in the nucleolus. Live imaging and Fluorescence Recovery After Photobleaching (FRAP) of labelled H2B, showed that the depletion of Lamin B1, Fibrillarin (FBL) or Nucleostemin (GNL3), enhances H2B-ECFP mobility in the nucleolus. Furthermore, Nucleolin knockdown significantly decreases H2B-ECFP compartmentalization in the nucleolus, while H2B-ECFP residence and mobility in the nucleolus was prolonged upon Nucleolin overexpression. Co-expression of N-terminal and RNA binding domain (RBD) deletion mutants of Nucleolin or inhibiting 45S rRNA synthesis reduces the sequestration of H2B-ECFP in the nucleolus. Taken together, these studies reveal a crucial role of Nucleolin-rRNA complex in modulating the compartmentalization, stability and dynamics of H2B within the nucleolus.
Learning Text Component Features via Convolutional Neural Networks for Scene Text Detection

Reading the text embedded in natural scene images is essential to many applications. In this paper, we propose a method for detecting text in scene images based on multi-level connected component (CC) analysis and learning text component features via convolutional neural networks (CNN), followed by a graph-based grouping of overlapping text boxes. The multi-level CC analysis allows the extraction of redundant text and non-text components at multiple binarization levels to minimize the loss of any potential text candidates. The features of the resulting raw text/non-text components of different granularity levels are learned via a CNN. Those two modules eliminate the need for complex ad-hoc preprocessing steps for finding initial candidates, and the need for hand-designed features to classify such candidates into text or non-text. The components classified as text at different granularity levels, are grouped in a graph based on the overlap of their extended bounding boxes, then, the connected graph components are retained. This eliminates redundant text components and forms words or textlines. When evaluated on the "Robust Reading Competition" dataset for natural scene images, our method achieved better detection results compared to state-of-the-art methods. In addition to its efficacy, our method can be easily adapted to detect multi-oriented or multi-lingual text as it operates at low level initial components, and it does not require such components to be characters.
On Nondeterministic Derandomization of Freivalds' Algorithm: Consequences, Avenues and Algorithmic Progress

Motivated by studying the power of randomness, certifying algorithms and barriers for fine-grained reductions, we investigate the question whether the multiplication of two n x n matrices can be performed in near-optimal nondeterministic time O~(n^2). Since a classic algorithm due to Freivalds verifies correctness of matrix products probabilistically in time O(n^2), our question is a relaxation of the open problem of derandomizing Freivalds' algorithm. We discuss consequences of a positive or negative resolution of this problem and provide potential avenues towards resolving it. Particularly, we show that sufficiently fast deterministic verifiers for 3SUM or univariate polynomial identity testing yield faster deterministic verifiers for matrix multiplication. Furthermore, we present the partial algorithmic progress that distinguishing whether an integer matrix product is correct or contains between 1 and n erroneous entries can be performed in time O~(n^2) - interestingly, the difficult case of deterministic matrix product verification is not a problem of "finding a needle in the haystack", but rather cancellation effects in the presence of many errors. Our main technical contribution is a deterministic algorithm that corrects an integer matrix product containing at most t errors in time O~(sqrt{t} n^2 + t^2). To obtain this result, we show how to compute an integer matrix product with at most t nonzeroes in the same running time. This improves upon known deterministic output-sensitive integer matrix multiplication algorithms for t = Omega(n^{2/3}) nonzeroes, which is of independent interest.
Unsupervised Phase Extraction Using Dual Autoencoder

Phase assignment is usually a part of periodic time-series data processing which needs some manual labeling or a heuristic method for each specific type of signals. Our work uses an unsupervised learning method to make the phase identification process fully-automated and more universal. This method also allows flexibility of input data in the term of position variation and phase progression variation. Four synthetic periodic two-dimensional signals with different shapes are used to explore our method's learning capabilities and some limitations. The proposed method is also tested with an actual periodic movement sequence captured from a Kinect sensor. This method can learn the periodic pattern automatically from a noisy signal and assign correct phases.
New Intelligent AVR Controller Based on Particle Swarm Optimization for Transient Stability Enhancement

In this work, we present a new intelligent control scheme to enhance the transient stability of multi-machine power system based on the generator automatic voltage regulation system. The algorithm consists on the estimation of the best parameters of voltage regulators to minimize an objective function. Particle swarm optimization and genetic algorithm methods are used to get the best performance of regulation systems. The control performances damp significantly oscillations and improve the stability of the system after a given disturbance which enhance the transient stability and obtain a new improved critical clearing time (CCT). The results are very promising and could be validated in practice.
Querying deep web data bases without accessing to data

Querying multiple deep web source information is an important issue that may be encountered in many applications. Deep Web is really treasury of new and complementary products offered by many service web providers. It contains more than 90% of information hidden behind web forms in remote databases, while only less than 10% of information in web reached by following hyperlinks called web of surface. In this paper, we address the issue of query answering without access to data. We than propose a new approach which guides interpretation of global query with graphical clues inspired from galaxy and cosmic universe. So that navigation around all semantic entities of global query (e.g. field, concept, query) is brings back to cosmic travel of astronaut around particles of cosmic universe (e.g. stars, galaxy, and super-galaxy). We have done virtual integration of deep web services, i.e. we have merged queries of a number of service web providers. The last step is to transform universal query to universal deep web form. The global query is a data structure which touches meaning of deep Web form, while universal web form touches the interpretation of universal query. These indicators guide visual perception of users through all semantic entities of query. So that, a user friendly web form is necessary to reach global query capability without complexity nor ambiguity, as global deep web form is designed for novice users (e.g. children) and all different ages on internet users. We have evaluated performance of our algorithm on two real-world and standard datasets, and have proved good performance.
Examining the Deviation From Balanced Time Perspective in the Dark Triad Throughout Adulthood

Individuals who score high on Dark Triad (DT) personality traits (i.e., Machiavellianism, subclinical narcissism and subclinical psychopathy) have been found to prefer a fast life strategy with enhanced motivation for immediate resource acquisition and short-term benefits. In line with these points, recent studies have found evidence showing that DT traits are associated with a biased, strongly present-oriented time perspective. In the current study, we aimed to examine whether the temporal attitude of individuals high in DT is deviant from a balanced time perspective (BTP) to a significant extent. To achieve this aim, we applied two operationalizations published in earlier studies to quantify BTP: the Deviation from Balanced Time Perspective coefficient (DBTP), calculated as the difference between individuals’ time perception and the optimal time perspective, as well as the person-oriented approach of identifying groups of individuals with similar time perception. Importantly, the age of participants (N = 346) covered a long and continuous period of adulthood—from the young adulthood to the elderly—in order to examine the moderating effect of age on the association of DT and BTP. Machiavellianism and psychopathy were both found to be clearly deviant from a BTP. In contrast, higher scores on narcissism were positively associated with a BTP profile. The DBTP analysis, however, suggested that this beneficial effect of narcissism was only prevalent among the elderly individuals.
Methods used to assess the performance of biomarkers for the diagnosis of acute kidney injury: a systematic review and meta-analysis

Abstract Purpose: Methods used to explore biomarkers for acute kidney injury (AKI) might have a major impact on the results and the use of these biomarkers. We evaluated the methods used to investigate biomarkers of AKI. Materials and methods: A systematic review and meta-analysis were performed using a computerized search of the MEDLINE and the EMBASE databases (PROSPERO CRD42017059618). Articles reporting biomarker’s performance to diagnose AKI were included. The outcome included a description of the methods used to assess the performance of biomarkers to diagnose AKI. Results: Among the 295 included studies, assessment of biomarkers was the primary endpoint in 284 with sample size calculation in only 8% of cases. Eighty-five percent of the studies summarized the performance of biomarkers with receiver operating characteristic (ROC) curves; however, 74 studies (25%) did not provide the threshold, sensibility or specificity. A total of 176 studies evaluated more than one biomarker, and only 25% combined biomarkers to increase diagnostic performance. We determined that the definition of AKI and study design impacted the diagnostic performance using uNGAL (urinary neutrophil gelatinase-associated lipocalin) as an example. Major publication bias was identified. Conclusions: Most articles that reported biomarkers of AKI performance present methodological weaknesses. Basic rules should be provided to increase the quality of reporting in this area.
Application of BIM technology in the construction management of shield tunnel

Aiming at the current situations of shield tunnel construction such as surrounding environment complexity, high risks, severe construction difficulties, high technical requirements, and urgent project schedules, innovatively using new technologies of BIM, 3D-based laser scanning, mobile internet, and WebGL to solve engineering problems is proposed. First, based on the BIM standard distributed by the international organization buildingSMART, a BIM application and implementation standard and an encoding specification for railway shield tunnel are proposed to build a visualization encoding system for tunnel model elements. Second, refined BIM-based modeling and information attachment of related shield tunnel majors have been achieved by utilizing the professional modeling software such as Bentley and Revit, as well as model assembling, model lightweighting, and separate storing of models and data. Last, a self-developed WebGL- based graphics engine is used to achieve the implementation of progress, quality and safety for the tunnel construction. It has been verified through practical engineering that the BIM technology can help to increase the collaboration efficiency and the informatization management level for tunnel engineering, also laying the data and technical foundation for exploring the BIM-based full digital archive of shield tunneling methodology.
Saliency-Based Detection of Identy Documents Captured by Smartphones

Smartphones have became an easy and convenient mean to acquire documents. In this paper, we focus on the automatic segmentation of identity documents in smartphone photos or videos using visual saliency (VS). VS-based approaches, which pertain to computer vision, have not be considered yet for this particular task. Here we compare different VS methods, and we propose a new VS scheme, based on a recent distance belonging to the scope of mathematical morphology. We show that our resulting saliency maps are competitive with state-of-the-art visual saliency methods, and that such approaches are very promising for use in identity document detection and segmentation, even without taking into account any prior knowledge about document contents. In particular they can perform in real-time on smartphones.
Comparative analysis of the outcomes of differing time series forecasting strategies

Time series forecasting of data from various domains has become an increasingly interesting research subject in recent times. Prediction of future sample values is the main goal of time series forecasting. There are mainly two classes of time series forecasting, namely, single step and multi-step forecasting. There are various machine learning approaches that can be used along with various forecasting strategies which exist, some of which can be utilized in and by time series forecasting. However, inappropriate use of strategies can limit the applicability of the techniques to real world problems. This paper provides a review of time series forecasting, a classification of time series forecasting and also approaches and strategies of time series forecasting. It also demonstrates how inappropriate use of point to point rolling forecast strategy for forecasting could lead to unrealistic outcomes and how a multiple step forecasting strategy called Direct H step strategy could help to overcome this issue. Comparative analysis of two strategies using AutoRegressive Integrated Moving Average (ARIMA) approach are demonstrated.
Random Forest model for quality control of high resolution mass spectra from SILAC labeling experiments

Although plenty of chemical and electrical methods were employed to improve the performance of Liquid Chromatography-Mass Spectrometry(LC-MS) methods, the effective bioinformatics and statistical methods for the quantification of proteomics are still irreplaceable. A method for quality control of stable isotope labeling with amino acids in cell culture (SILAC) mass spectra acquired from high resolution mass spectrometers were described here. The mass profiles of light and heavy peptide pairs produced by SILAC and LC-MS are often affected by the collection instrument and the collection methods. Such influences may reduce the accuracy of further ratio estimations. So we applied Random Forest (RF) to remove the low quality mass spectra which deviated from expected theoretical isotopic distributions. Specifically, based on some features of the peptides, we trained a RF classifier and got the probability of positive or negative class for each mass profile via a training set obtained from some yeast samples. Then we tested the RF classifier on other profiles from yeast samples with different Light-Heavy ratios. We found that filtering mass profiles by RF classifier can improve the reliability of peptide ratio estimations significantly.
Dietary patterns and changes in body mass index and waist circumference in adults 1 – 3

Background: Obesity has increased > 20% in the past decade in the United States, and more than one-half of US adults are overweight or obese. Objective: Our objective was to further elucidate the nutritional etiology of changes in body mass index (BMI; in kg/m2) and waist circumference by dietary intake pattern. We hypothesized that a healthy dietary pattern would lead to smaller changes in BMI and waist circumference than would other dietary patterns. Design: Subjects were 459 healthy men and women participating in the ongoing Baltimore Longitudinal Study of Aging. Diet was assessed with the use of 7-d dietary records, from which 41 food groups were created and entered into a cluster analysis. Results: Five dietary patterns were derived (healthy, white bread, alcohol, sweets, and meat and potatoes). The mean annual change in BMI was 0.30 ± 0.06 for subjects in the meat-and-potatoes cluster and 0.05 ± 0.06 for those in the healthy cluster (P < 0.01). The mean annual change in waist circumference was more than 3 times as great for subjects in the white-bread cluster (1.32 ± 0.29 cm) as for those in the healthy cluster (0.43 ± 0.27 cm) (P < 0.05). Conclusions: Consuming a diet high in fruit, vegetables, reducedfat dairy, and whole grains and low in red and processed meat, fast food, and soda was associated with smaller gains in BMI and waist circumference. Because foods are not consumed in isolation, dietary pattern research based on natural eating behavior may be useful in understanding dietary causes of obesity and in helping individuals trying to control their weight. Am J Clin Nutr 2003;77:1417–25.
Embedding a Scheduler in Execution for a Planetary Rover

Scheduling often takes place in the context of execution. This reality drives several key design decisions: (1) when to invoke (re) scheduling, (2) what to do when the scheduler is running, and (3) how to use the schedule to execute scheduled activities. We define these design decisions theoretically in the context of the embedded scheduler and practically in the context of the design of an embedded scheduler for a planetary rover. We use the concept of a commit window to enable execution to use the previously generated schedule while (re) scheduling. We define the concepts of fixed cadence, event driven, and hybrid scheduling to control invocation of (re) scheduling. We define the concept of flexible execution to enable execution of the generated schedule to be adaptive within the response cycle of the scheduler. We present empirical results from both synthetic and planetary rover scheduling and execution model data that documents the effectiveness of these techniques at enabling the scheduler to take advantage of execution opportunities to complete activities earlier.
A Generalized Evaluation Scheme for Comparing Temperature Products from Satellite Observations, Numerical Weather Model, and Ground Measurements Over the Tibetan Plateau

Ground surface temperature (GST) measurements are scarce in Tibetan plateau (TP), whereas the satellite observations and numerical weather model outputs are good alternatives to fill the spatial gaps among ground stations. However, the evaluation of different temperature products is challenging due to the distinct temporal and spatial dimensions in their acquisition methods. This paper intended to develop an evaluation framework for comparing the performances of various temperature data, including the Advanced Along-Track Scanning Radiometer (AATSR) satellite land surface temperature (LST) data, the high Asia refined (HAR) analysis numerical outputs, and the GST. In the proposed framework, we introduce a diurnal temperature cycle model and an aggregated weighted method to solve the temporal and spatial mismatch problem between different data sets. The results over TP show that the evaluation framework solves the temporal and spatial matching among different data sets. AATSR LST and HAR outputs are consistent regardless of the heterogeneous and weather conditions at Linzhi site indicating that the fully homogeneous land surface conditions are not the only way for the satellite/simulation validations. Our results suggest that the proposed framework of time normalization and spatial aggregation method is appropriate for evaluating satellite thermal infrared retrieved data sets and numerical simulations even when the proper ground measurements are insufficient. Since it performs well in the high elevation and complex land surface-conditioned TP region, it will be easy to be adopted in the other regions with a variety of data sets.
Study of global cloud droplet number concentration with A-Train satellites

Abstract. Cloud droplet number concentration (CDNC) is an important microphysical property of liquid clouds that impacts radiative forcing, precipitation and is pivotal for understanding cloud–aerosol interactions. Current studies of this parameter at global scales with satellite observations are still challenging, especially because retrieval algorithms developed for passive sensors (i.e., MODerate Resolution Imaging Spectroradiometer (MODIS)/Aqua) have to rely on the assumption of cloud adiabatic growth. The active sensor component of the A-Train constellation (i.e., Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)/CALIPSO) allows retrievals of CDNC from depolarization measurements at 532 nm. For such a case, the retrieval does not rely on the adiabatic assumption but instead must use a priori information on effective radius (re), which can be obtained from other passive sensors. In this paper, re values obtained from MODIS/Aqua and Polarization and Directionality of the Earth Reflectance (POLDER)/PARASOL (two passive sensors, components of the A-Train) are used to constrain CDNC retrievals from CALIOP. Intercomparison of CDNC products retrieved from MODIS and CALIOP sensors is performed, and the impacts of cloud entrainment, drizzling, horizontal heterogeneity and effective radius are discussed. By analyzing the strengths and weaknesses of different retrieval techniques, this study aims to better understand global CDNC distribution and eventually determine cloud structure and atmospheric conditions in which they develop. The improved understanding of CDNC can contribute to future studies of global cloud–aerosol–precipitation interaction and parameterization of clouds in global climate models (GCMs).
A novel rumor detection method based on labeled cascade propagation tree

Nowadays Sina Weibo has become a fashionable social media platform in China. Meanwhile, the public and anonymous environment provides rumors a perfect hotbed to breed and spread. The negative influence on society from rumors cannot be ignored. Traditional rumor detection methods based on features always focus on static or flat features coming from content, users, propagation and etc. but it often ignores the effect of information's propagation structure. Aiming at this problem, first we introduce information's propagation cascade model into LPT (Labeled Propagation Tree) and propose an improved model — Labeled Cascade Propagation Tree (CA-LPT) which allows us to consider the effect of information's propagation structure. Second, we investigate users' influence assessment by a dynamic method. Finally, we predict whether a microblog post is a rumor by proposing 10 new features and combine with hybrid kernel SVM based on random walk graph kernel. Experiment results on real-world data from Sina Weibo demonstrate the efficacy of CA-LPT related new features. We conclude that CA-LPT can help building a more exact propagation tree and provide clues for a better classification accuracy.