Miracle-dz/UniFAR-FTUs huggingface dataset:
Nitric oxide and phytohormone interactions: current status and perspectives

Nitric oxide (NO) is currently considered a ubiquitous signal in plant systems, playing significant roles in a wide range of responses to environmental and endogenous cues. During the signaling events leading to these plant responses, NO frequently interacts with plant hormones and other endogenous molecules, at times originating remarkably complex signaling cascades. Accumulating evidence indicates that virtually all major classes of plant hormones may influence, at least to some degree, the endogenous levels of NO. In addition, studies conducted during the induction of diverse plant responses have demonstrated that NO may also affect biosynthesis, catabolism/conjugation, transport, perception, and/or transduction of different phytohormones, such as auxins, gibberellins, cytokinins, abscisic acid, ethylene, salicylic acid, jasmonates, and brassinosteroids. Although still not completely elucidated, the mechanisms underlying the interaction between NO and plant hormones have recently been investigated in a number of species and plant responses. This review specifically focuses on the current knowledge of the mechanisms implicated in NO–phytohormone interactions during the regulation of developmental and metabolic plant events. The modifications triggered by NO on the transcription of genes encoding biosynthetic/degradative enzymes as well as proteins involved in the transport and signal transduction of distinct plant hormones will be contextualized during the control of developmental, metabolic, and defense responses in plants. Moreover, the direct post-translational modification of phytohormone biosynthetic enzymes and receptors through S-nitrosylation will also be discussed as a key mechanism for regulating plant physiological responses. Finally, some future perspectives toward a more complete understanding of NO–phytohormone interactions will also be presented and discussed.
From Natural Language to RDF Graphs with Pregroups

We define an algorithm translating natural language sentences to the formal syntax of RDF, an existential conjunctive logic widely used on the Semantic Web. Our translation is based on pregroup grammars, an efficient type-logical grammatical framework with a transparent syntax-semantics interface. We introduce a restricted notion of side effects in the semantic category of finitely generated free semimodules over {0, 1} to that end. The translation gives an intensional counterpart to previous extensional models. We establish a one-to-one correspondence between extensional models and RDF models such that satisfaction is preserved. Our translation encompasses the expressivity of the target language and supports complex linguistic constructions like relative clauses and unbounded dependencies.
Changing the responses of cortical neurons from sub- to suprathreshold using single spikes in vivo

Action Potential (APs) patterns of sensory cortex neurons encode a variety of stimulus features, but how can a neuron change the feature to which it responds? Here, we show that in vivo a spike-timing-dependent plasticity (STDP) protocol—consisting of pairing a postsynaptic AP with visually driven presynaptic inputs—modifies a neurons' AP-response in a bidirectional way that depends on the relative AP-timing during pairing. Whereas postsynaptic APs repeatedly following presynaptic activation can convert subthreshold into suprathreshold responses, APs repeatedly preceding presynaptic activation reduce AP responses to visual stimulation. These changes were paralleled by restructuring of the neurons response to surround stimulus locations and membrane-potential time-course. Computational simulations could reproduce the observed subthreshold voltage changes only when presynaptic temporal jitter was included. Together this shows that STDP rules can modify output patterns of sensory neurons and the timing of single-APs plays a crucial role in sensory coding and plasticity. DOI: http://dx.doi. org/10.7554/eLife.00012. 001
Slip Suppression of Electric Vehicles Using Sliding Mode Control Method

This paper presents a slip suppression controller using sliding mode control method for electric vehicles which aims to improve the control performance and the energy conservation by controlling the slip ratio of wheel. In this method, a robust sliding mode controller against the model uncertainties is designed to obtain the maximum driving force by suppressing the slip ratio. The numerical simulations for one wheel model under variations in mass of vehicle and road condition are performed and demonstrated to show the effectiveness of the proposed method.
ASPECTUAL ENTITIES AND TENSE IN DISCOURSE

Linguistic expressions form interesting patterns in discourse. In the domain of temporality, the morphemes of tense and aspectual viewpoint pattern significantly within a discourse. Shifts of viewpoint and tense are often associated with shifts of direction, and with the distinction between foreground and background. One important reason for this effect is that aspectual viewpoint and tense form closed sub-systems in language. In a closed system, choice of one term implies contrast with the other possibilities. The element of choice and its contrastive significance allows for more than one level of discourse meaning.
Traffic in Social Media II: Modeling Bursty Popularity

Online popularity has enormous impact on opinions, culture, policy, and profits, especially with the advent of the social Web and Web advertising. Yet the processes that drive popularity in our online world have only begun to be explored. We provide a quantitative, large scale, longitudinal analysis of the dynamics of online content popularity in two massive model systems, the Wikipedia and an entire country's Web space. In these systems, we track the change in the number of links to pages, and the number of times these pages are visited. We find that these changes occur in bursts, whose magnitude and time separation are very broadly distributed. This finding is in contrast with previous reports about news-driven content, and has profound implications for understanding collective attention phenomena in general, and Web trends in particular. To make sense of these empirical results, we offer a simple model that mimics the exogenous shifts of user attention and the ensuing non-linear perturbations in popularity rankings. While established models based on preferential attachment are insufficient to explain the observed dynamics, our stylized model is successful in recovering the key features observed in the empirical analysis of our systems.
On dangers of cross-validation in steganalysis On dangers of cross-validation in steganalysis

Modern steganalysis is a combination of a feature space design and a supervised binary classification. In this report, we assume that the feature space has been already constructed, i.e., the steganalyst has a set of training features and needs to train a binary classifier. Any machine learning tool can be used for this task and its parameters can be tuned through cross-validation, a standard automated model-selection procedure. However, classification problems arising in steganalysis have a very specific nature – individual training samples naturally form pairs of cover–stego feature vectors with opposite labels lying close to each other in the feature space. It is important to preserve these cover–stego pairs during cross-validation (prevent splitting each pair into different folds) otherwise the obtained error estimates may be misleading and lead to a suboptimal performance of the classifier. In this report, we demonstrate the sketched problem with cross-validation on a specific example of image steganalysis in the JPEG domain. As a classifier, we selected the support vector machine (SVM), a popular choice in steganalysis. In particular, we show that the implicit k-fold cross-validation as implemented in LIBSVM [2], a widely used implementaion of SVM, is not suitable for steganalysis and may result in a suboptimal performance and a striking discrepancy between the predicted and the real testing error. Instead of the implicit k-fold cross-validation, a steganalysis-aware cover-stego pair preserving cross-validation should be used. We stress that this is a steganalysis-specific issue and does not indicate any implementation flaw in LIBSVM. The issue with the standard cross-validation procedure in steganalysis has already been pointed out by Schwamberger and Franz in 2010 [14]. We believe, however, that the message may have been hidden to the reader in other experiments and conclusions presented in [14], as authors studied not only the cross-validation, but also different normalization techniques, and performed numerous experiments using different features and stego-algorithms. This technical report, on the other hand, is devoted solely to the problem of improper cross-validation. We go more in depth, provide explanation, and also study severity w.r. t. payload. Moreover, we point out a few examples of published work with results affected by the improper cross-validation.
Online Analysis of Community Evolution in Data Streams

This paper discusses the problem of online change detection in a large set of interacting entities. Such trends include the gradual formation and dissolution of different communities of interaction. Our results are focussed on the case where the interacting entities are received in the form of a fast data stream of interactions. In such cases, a user may wish to perform repeated exploratory querying of the data for different kinds of userdefined parameters. This is difficult to perform in a fast data stream because of the one-pass constraints on the computations. We propose an online analytical processing framework which separates out online data summarization from offline exploratory querying. The result is a method which provides the ability to perform exploratory querying without compromising on the quality of the results. The algorithms are tested over large sets of graph data streams with varying levels of evolution.
Finding People by their Shadows: Aerial Surveillance Using Body Biometrics Extracted from Ground Video

Shadow analysis has been shown to enable the extension of gait biometrics to aerial surveillance. In past work the classifiers were both trained and tested on shadow features extracted by image processing. In real scenarios this requires imagery with shadows of people to be recognized. On the other hand one rarely has available the shadow information of the person sought, however direct body movement/information may be more easily obtained from ground surveillance cameras or video recordings. This paper proposes a scenario in which gait/dynamics features from body movement are obtained from a ground video and the search for matching dynamics of shadows takes place in aerial surveillance video. A common scenario would be the recording of people by ground/city surveillance cameras and the use of information to initiate a wide-area search for shadows from aerial platforms. Vice-versa, the shadow of a suspect leaving an incident area, detected by aerial surveillance, can trigger a city-wide search on body/gait biometrics as observed with city/ground surveillance cameras. To illustrate the feasibility of this approach the paper introduces a method that compares contours of bodies in ground image frames and contours of shadows in aerial image frames, for which an alignment is made and a distance is calculated, integrated over a normalized gait cycle. While the results are preliminary, for only 5 people, and using a specific walking arrangement to avoid compensation for changes in the viewing angles, the method obtains a 70% correct classification rate which is a first step in proving the feasibility of the approach.
Twitter as arena for the authentic outsider: exploring the social media campaigns of Trump and Clinton in the 2016 US presidential election

In the 2016 US presidential election campaign, social media platforms were increasingly used as direct sources of news, bypassing the editorial media. With the candidates’ millions of followers, Twitter has become a platform for mass communication and the candidate’s main online information channel. Likewise, social media has provided a platform for debating and critiquing the mainstream media by the campaigns and their networks. This article discusses the Twitter strategies of the democratic candidate Hillary Clinton and republican candidate Donald Trump during their US 2016 presidential election campaigns. While the Clinton campaign’s strategy confirms theories regarding the professionalisation of election campaigns, the Trump campaign’s more amateurish yet authentic style in social media points towards de-professionalisation and even amateurism as a counter-trend in political communication.
Global Elevation Ancillary Data for Land-use Classification Using Granular Neural Networks

The development of digital global databases containing data such as elevation and soil can greatly simplify and aid in the classification of remotely sensed data to create land-use classes. An efficient method that can simultaneously handle diverse input dimensions can be formed by merging fuzzy logic and neural networks. The so-called granular or fuzzy neural networks are able not only to achieve high classification levels, but at the same time produce compressed and transparent neural network skeletons. Compression results in reduced training times, while transparency is an aid for interpreting the structure of the neural network by translating it into meaningful rules and vice versa. The purpose of this paper is to provide some initial guidelines for the construction of granular neural networks in the remote sensing context, while using global elevation ancillary data within the classification process.
Browsed-Back Post Event Analyzer

Face recognition is considered as the integral part of the video surveillance or video security system. Most of the video surveillance systems recognize human on the basis of face, but the careful study shows that faces become vague from cameras either due to mask worn by terrorists or thieves, or due to distance from camera. This constraint makes thorny the investigation of an event after it has occurred. The objective of this paper is to propose a system that can recognize humans on the basis of their cloth color irrespective of their face ambiguity so that wearing of mask and distance of people from camera does not make any difference. The system is composed of two parts. One part detect the humans present in the video, segment the detected humans according to their trouser and shirt color and store the parameters of these clusters found with color based K-mean clustering. The second part is the query system. It provides an interface to the user to enter the query to get the required information from the video. By using this system we can convert a lot of available data (in the form of video) in to fruitful information. The system can also be used as image indexing with some modifications.
Microfluidic synthesis of monodisperse porous microspheres with size-tunable pores

We use a perfluorinated-dendrimer–dye complex that stabilizes microbubbles as a novel pore-forming agent. We use microfluidics to produce monodisperse emulsions containing a polymer matrix material, a model active, and the perfluorinated complex; upon drying, the emulsions form porous microspheres. This porosity causes the encapsulated model active to be released faster than from non-porous microspheres. Moreover, because of the fluorous features of the pores, we can also attach an additional guest molecule to the pores which is released with a profile that is distinct from that of the encapsulated active. These porous microspheres can encapsulate and controllably release multiple actives; this makes them valuable for applications such as drug delivery and imaging.
Genotypic and Allelic Variability in CYP19A1 among Populations of African and European Ancestry

CYP19A1 facilitates the bioconversion of estrogens from androgens. CYP19A1 intron single nucleotide polymorphisms (SNPs) may alter mRNA splicing, resulting in altered CYP19A1 activity, and potentially influencing disease susceptibility. Genetic studies of CYP19A1 SNPs have been well documented in populations of European ancestry; however, studies in populations of African ancestry are limited. In the present study, ten ‘candidate’ intronic SNPs in CYP19A1 from 125 African Americans (AA) and 277 European Americans (EA) were genotyped and their frequencies compared. Allele frequencies were also compared with HapMap and ASW 1000 Genomes populations. We observed significant differences in the minor allele frequencies between AA and EA in six of the ten SNPs including rs10459592 (p<0.0001), rs12908960 (p<0.0001), rs1902584 (p = 0.016), rs2470144 (p<0.0001), rs1961177 (p<0.0001), and rs6493497 (p = 0.003). While there were no significant differences in allele frequencies between EA and CEU in the HapMap population, a 1.2- to 19-fold difference in allele frequency for rs10459592 (p = 0.004), rs12908960 (p = 0.0006), rs1902584 (p<0.0001), rs2470144 (p = 0.0006), rs1961177 (p<0.0001), and rs6493497 (p = 0.0092) was observed between AA and the Yoruba (YRI) population. Linkage disequilibrium (LD) blocks and haplotype clusters that is unique to the EA population but not AA was also observed. In summary, we demonstrate that differences in the allele frequencies of CYP19A1 intron SNPs are not consistent between populations of African and European ancestry. Thus, investigations into whether CYP19A1 intron SNPs contribute to variations in cancer incidence, outcomes and pharmacological response seen in populations of different ancestry may prove beneficial.
Combined Category Visual Vocabulary: A new approach to visual vocabulary construction

Bag-of-words model (BoW), inspired by the problem of text representation and classification, has attracted intensive attention in object and scene categorization for its flexibility and good performance. In BOW model, a visual vocabulary is obtained by clustering local patches detected from training image set, and then an image can be represented by the histogram of visual words. However, how to construct an effective visual vocabulary is still a crucial and challenging step in BoW model. The conventional methods to construct a visual vocabulary are very time-consuming, and also the obtained vocabulary is not discriminative enough. We propose a novel approach to construct a visual vocabulary: Combined Category Visual Vocabulary (CCVV). Firstly, a category visual vocabulary for each image category is obtained. Then all these category visual vocabularies are combined together to form a general visual vocabulary, which is able to be used to represent images. The visual words in CCVV are related to one image category, so they have higher discriminative ability to separate the image category from others. The proposed approach also decreases the computational complexity by clustering local patches from only one category instead of all categories. Object interest local patches are obtained by means of the Harris-Affine detector and described by scale invariant feature transform (SIFT) descriptor. Support vector machine (SVM) is utilized to train a classifier in our experiment. The proposed approach is evaluated on the VOC 2006 database, and the experimental results demonstrate that the proposed approach is more computationally efficient and superior performance than conventional approaches.
Scene text detection images with pyramid image and MSER enhanced

Text detection from images in natural scene is one of the most active research areas. It still remains a challenge for researchers because of the complexity of the image in the wild specifically their background. The state of the text presents also different problems of localization such as size, font, color and orientation. This paper presents a new method based on the location of the concentration areas of text candidates in first step. This step allows a mask is applied, the major objective is to filter the maximum the complex background. We use Otsu technique and edge enhancement by pyramid image in different scales. The second step is to fine detection of candidate characters by maximally stable extremal regions (MSER) based on the luminance that gives more meaning to information merged with enhanced edges and connected to surpass the limits of MSER. Then non-text components are filtered out by the character candidate classification based on DTW using SIFT and HOG features. The false positives are eliminated by geometrical properties of text blocks. Finally we apply boundary box localization after a stage of word grouping. The proposed method has been evaluated on ICDAR 2013 scene text detection competition dataset and the encouraging experiments results can be compared with the latest published algorithms.
Graph-based recognition in photo collections using social semantics

In this paper, we show how to recognize people in Consumer Photo Collections by employing a graphical model together with a distance-based face description method. We devise a graph design and explore ways to further improve the recognition performance by incorporating context in the form of social semantics. Experiments on a public dataset demonstrate the effectiveness of our probabilistic approach compared to traditional nearest-neighbor matching.