Miracle-dz/UniFAR-FTUs huggingface dataset:
The recent transfer of a homing endonuclease gene

The myxomycete Didymium iridis (isolate Panama 2) contains a mobile group I intron named Dir.S956-1 after position 956 in the nuclear small subunit (SSU) rRNA gene. The intron is efficiently spread through homing by the intron-encoded homing endonuclease I-DirI. Homing endonuclease genes (HEGs) usually spread with their associated introns as a unit, but infrequently also spread independent of introns (or inteins). Clear examples of HEG mobility are however sparse. Here, we provide evidence for the transfer of a HEG into a group I intron named Dir.S956-2 that is inserted into the SSU rDNA of the Costa Rica 8 isolate of D.iridis. Similarities between intron sequences that flank the HEG and rDNA sequences that flank the intron (the homing endonuclease recognition sequence) suggest that the HEG invaded the intron during the recent evolution in a homing-like event. Dir.S956-2 is inserted into the same SSU site as Dir.S956-1. Remarkably, the two group I introns encode distantly related splicing ribozymes with phylogenetically related HEGs inserted on the opposite strands of different peripheral loop regions. The HEGs are both interrupted by small spliceosomal introns that must be removed during RNA maturation.
Consistent Streaming Through Time: A Vision for Event Stream Processing

processing will play an increasingly important role in constructing enterprise applications that can immediately react to business critical events. Various technologies have been proposed in recent years, such as event processing, data streams and asynchronous messaging (e.g. pub/sub). We believe these technologies share a common processing model and differ only in target workload, including query language features and consistency requirements. We argue that integrating these technologies is the next step in a natural progression. In this paper, we present an overview and discuss the foundations of CEDR, an event streaming system that embraces a temporal stream model to unify and further enrich query language features, handle imperfections in event delivery, define correctness guarantees, and define operator semantics. We describe specific contributions made so far and outline next steps in developing the CEDR system.
Plenoptic image compression comparison between JPEG, JPEG2000 and SPITH

 Abstract — Plenoptic image is a novel visual representation that contains more information than traditional images. Different focal planes and different perspectives can be recovered by a rendering algorithm. In this work existing state of the art compression schemes are tested on plenoptic images, and its behavior is analyzed. The analysis is made not on the plenoptic image reconstruction, but on the rendered views. In this way the compression artifacts are analyzed in terms of the interested rendered view. The results showed that the existing compression schemes can be used with good results to compress plenoptic images, in general the JPEG and JPEG2000 had the best performance but for low bitrates the SPIHT had the advantage.
Degree Distribution of Competition-Induced Preferential Attachment Graphs

We introduce a family of one-dimensional geometric growth models, constructed iteratively by locally optimizing the trade-offs between two competing metrics, and show that this family is equivalent to a family of preferential attachment random graph models with upper cut-offs. This is the first explanation of how preferential attachment can arise from a more basic underlying mechanism of local competition. We rigorously determine the degree distribution for the family of random graph models, showing that it obeys a power law up to a finite threshold and decays exponentially above this threshold. We also rigorously analyse a generalized version of our graph process, with two natural parameters, one corresponding to the cut-off and the other a ‘fertility’ parameter. We prove that the general model has a power-law degree distribution up to a cut-off, and establish monotonicity of the power as a function of the two parameters. Limiting cases of the general model include the standard preferential attachment model without cut-off and the uniform attachment model.
Designing FlexRay-based automotive architectures: A holistic OEM approach

FlexRay is likely to become the de-facto standard for upcoming in-vehicle communication. Efficient scheduling of the static and dynamic segment of the communication cycle in combination with the determination of more than 60 parameters that are part of the FlexRay protocol is a challenging task. This paper provides a formal analysis for interdependencies between the parameters as well as a scheduling approach for the static and dynamic segment. Experimental results give evidence of a significant interdependency between the subtasks such that a holistic scheduling approach becomes mandatory to provide high-quality FlexRay schedules. As a solution, this work introduces a complete functional FlexRay scheduling approach that takes parameter selection, allocation of messages to the static and dynamic segment, and concurrent scheduling into account. A real-world case study from the automotive domain gives evidence of efficiency and applicability of the proposed approach.
Epistemic Communities: Description and Hierarchic Categorization

ABSTRACT Understanding the structure of knowledge communities, and particularly the organization of “epistemic communities”, or groups of agents sharing common knowledge concerns, is usually based on either social relationships or semantic similarity. To link social and semantic aspects, a formal framework based on Galois lattices (or concept lattices) categorizes epistemic communities in an automated and hierarchically structured way. The process rebuilds a whole community structure and taxonomy, and notably fields and subfields gathering a certain proportion of agents. It is applied to empirical data to exhibit these alleged structural properties, successfully compared with categories given by domain experts.
Constructing Event Templates from Written News

We propose a pipeline for learning event templates from a large corpus of textual news articles. An event template is a machine-usable semantic data structure, in our case a graph, describing a certain event type. Most earthquake news reports, for example, semantically fit the template "x people dead, town y shook, at time z". Such templates can be used as an input for information extraction tasks or automated ontology extension. We also present preliminary results in the form of sample extracted templates from Google News articles.
An Innovative Intelligent Expertise Classification Approach To Accelerate Network Data Visualization

In order to visualize huge network traffic, data visualization applications are being developed and used to complement network data visualization. With today’s network data visualization tools, it is only possible to view small portions of data and consuming lots of time to process the data. The network data process time can be reduced with the innovative artificial intelligence approach, which can effectively accelerate the network data visualization process and consequently classify network data into different level of details according different levels of computer users’ expertise on network. Previously, many visualization tools have been developed; either suffers from time to process the network data or the low understanding from the network data visualization efficiency. In this paper, we proposed an innovative intelligence approach, namely Intelligent Expertise Classification Algorithm (IECA) based on different computer users’ expertise level in order to improve the network data process time as well as the understanding level among computer users. The approach architecture details and its requirements such as expertise level from different levels of computer users and data from data procurement will be discussed. Numbers of experiments have been carried out on 100 computer users from different fields and different level of computer expertise to evaluate the approach effectiveness. It features fast intelligent expertise classification and support network data understanding performance.
Seismic time‐lapse imaging using interferometric least‐squares migration: Case study

Seismic time‐lapse surveys are susceptible to repeatability errors due to varying environmental conditions. To mitigate this problem, we propose the use of interferometric least‐squares migration to estimate the migration images for the baseline and monitor surveys. Here, a known reflector is used as the reference reflector for interferometric least‐squares migration, and the data are approximately redatumed to this reference reflector before imaging. This virtual redatuming mitigates the repeatability errors in the time‐lapse migration image. Results with synthetic and field data show that interferometric least‐squares migration can sometimes reduce or eliminate artifacts caused by non‐repeatability in time‐lapse surveys and provide a high‐resolution estimate of the time‐lapse change in the reservoir.
A Java-based formal development environment for factory communication systems

Reports on an ongoing project aimed at defining a development environment for factory communication systems which combines the OOA&D (object-oriented analysis and design) and OMT (Object Modeling Technique) reference methodologies with the use of formal methods for concurrent and distributed systems, and uses the Java language in various development phases, from specification to implementation. The proposed approach is exemplified by showing the modeling of a typical manufacturing control system: a quality control cell composed of a measurement unit and three handling robots.
Asymptotics of discrete MDL for online prediction

Minimum description length (MDL) is an important principle for induction and prediction, with strong relations to optimal Bayesian learning. This paper deals with learning processes which are independent and identically distributed (i.i.d.) by means of two-part MDL, where the underlying model class is countable. We consider the online learning framework, i.e., observations come in one by one, and the predictor is allowed to update its state of mind after each time step. We identify two ways of predicting by MDL for this setup, namely, a static and a dynamic one. (A third variant, hybrid MDL, will turn out inferior.) We will prove that under the only assumption that the data is generated by a distribution contained in the model class, the MDL predictions converge to the true values almost surely. This is accomplished by proving finite bounds on the quadratic, the Hellinger, and the Kullback-Leibler loss of the MDL learner, which are, however, exponentially worse than for Bayesian prediction. We demonstrate that these bounds are sharp, even for model classes containing only Bernoulli distributions. We show how these bounds imply regret bounds for arbitrary loss functions. Our results apply to a wide range of setups, namely, sequence prediction, pattern classification, regression, and universal induction in the sense of algorithmic information theory among others.
Antioxidant Levels in Cord Blood of Term Neonates and Its Association with Birth Weight

Objective Due to excessive production of free radicals and antioxidants evolved mechanisms against oxidative stress, infants are very vulnerable. As there was a significant relation between antioxidant levels and birth weight, we aimed verify this relationship. Materials & Methods In this descriptive analytical study we evaluated the antioxidant status of 40 healthy term newborns (gestation age 38-42 wk) with weight >2500 g (AGA) and 40 healthy term newborns (gestation age 38-42 wk) with LBW babies (weight < 2500 g) (SGA) in Ardabil Buali Hospital, Ardabil, northwest Iran in 2014. About 15 Ml of cord blood was collected after the second stage of labor. The levels of vitamin A, E, and C, catalase, glutathione peroxidase (GPX), bilirubin and serum uric acid were measured by standard methods. Informed consent was obtained from newborn mothers and study protocol was approved by university Ethics Committee. Data were analyzed using SPSS.19. Results The mean levels of bilirubin, vitamin C, E, catalase and GPX in AGA group were significantly higher than SGA group but the mean of serum uric acid in SGA group was more than AGA. In addition, the mean of vitamin A was similar in two groups. There was a significant relation between antioxidant levels and birth weight in term newborns. Conclusion In line with other studies the amounts of antioxidant levels except serum uric acid in AGA group was significantly more than SGA group.
Approximating the Volume of Deenable Sets

The rst part of this paper deals with nite-precision arithmetic. We give an upper bound on the precision that should be used in a Monte-Carlo integration method. Such bounds have been known only for convex sets; our bound applies to almost any \rea-sonable" set. In the second part of the paper, we show how to construct in polynomial time rst-order formulas that approximately deene the volume of deenable sets. This result is based on a VC dimension hypothesis , and is inspired from the well-known complexity-theoretic result \BPP 2 ". Finally, we show how these results can be applied to sets deened by systems of inequalities involving polynomial or exponential functions. In particular, section 5 contains an application to a problem of structural complexity in the Blum-Shub-Smale model of computation over the reals.
Poster Image Matching by Color Scheme and Layout Information

In this paper, we demonstrate a novel poster image matching system for wireless multimedia applications. We propose a method that incorporates both color and layout information of the poster image to achieve a robust performance in poster image matching. We apply both color compensation and background separation to extract a poster from an image effectively. Based on our experiment, we show that even under the effects of lighting, image rotation, scaling, and occlusion, our system can still maintain high recall and precision. We also show that our system can recognize the correct image from a database which contains several poster images with similar features. Finally, the promising performance of the poster image matching encourages us to further enrich the information retrieval for wireless environment
A compact CMOS UWB LNA using tunable active inductors for WLAN interference rejection

A compact 2.0–11.0GHz CMOS ultra-wideband (UWB) low-noise amplifier (LNA) using tunable active inductors for suppressing in-band (over 4.8–6.0GHz) WLAN interference signals is presented. In the proposed LNA, the active inductor in series with a small capacitor forms an active LC resonator which rejects or notches the undesired signals at the resonance frequency. Employing multiple resonators in the LNA increases the rejection depth. Moreover, the tunability of the active inductors allows for notching the signals over a wide frequency range. Designed and simulated in a 90nm digital CMOS process, the proposed LNA with such active inductors used in notch filters occupies a core chip-area of only 0.0182mm2. The LNA exhibits an average power gain of 16.5dB over 2.0–11.0GHz bandwidth while the rejection of unwanted WLAN interference signals is −44.8dB at 5.81GHz. The notch-frequency can be tuned in excess of 4.5–6.6GHz, and the rejection depth can be increased to −87.5dB, the highest rejection among the reported notch-filter UWB LNAs.
Under Consideration for Publication in Theory and Practice of Logic Programming Offline Specialisation in Prolog Using a Hand-written Compiler Generator

The so called " cogen approach " to program specialisation, writing a compiler generator instead of a specialiser, has been used with considerable success in partial evaluation of both functional and imperative languages. This paper demonstrates that the cogen approach is also applicable to the specialisation of logic programs (called partial deduction) and leads to effective specialisers. Moreover, using good binding-time annotations, the speed-ups of the specialised programs are comparable to the speed-ups obtained with online specialisers. The paper first develops a generic approach to offline partial deduction and then a specific offline partial deduction method, leading to the offline system lix for pure logic programs. While this is a usable specialiser by itself, it is used to develop the cogen system logen. Given a program, a specification of what inputs will be static, and an annotation specifying which calls should be unfolded, logen generates a specialised specialiser for the program at hand. Running this specialiser with particular values for the static inputs results in the specialised program. While this requires two steps instead of one, the efficiency of the specialisation process is improved in situations where the same program is specialised multiple times. The paper also presents and evaluates an automatic binding-time analysis that is able to derive the annotations. While the derived annotations are still suboptimal compared to hand-crafted ones, they enable non-expert users to use the logen system in a fully automated way. Finally, logen is extended so as to directly support a large part of Prolog's declarative and non-declarative features and so as to be able to perform so called mixline specialisa-tions.
Freedom, weakness, and determinism: from linear-time to branching-time

Model checking is a method for the verification of systems with respect to their specifications. Symbolic model-checking, which enables the verification of large systems, proceeds by calculating fixed-point expressions over the system's set of states. The /spl mu/-calculus is a branching-time temporal logic with fixed-point operators. As such, it is a convenient logic for symbolic model-checking tools. In particular, the alternation-free fragment of /spl mu/-calculus has a restricted syntax, making the symbolic evaluation of its formulas computationally easy. Formally, it takes time that is linear in the size of the system. On the other hand, specifiers find the /spl mu/-calculus inconvenient. In addition, specifiers often prefer to use Linear-time formalisms. Such formalisms, however, cannot in general be translated to the alternation-free CL-calculus, and their symbolic evaluation involves nesting of fixed-points, resulting in time complexity that is quadratic in the size of the system. In this paper we characterize linear-time properties that can be specified in the alternation-free /spl mu/-calculus. We show that a linear-time property can be specified in the alternation-free /spl mu/-calculus if it can be recognized by a deterministic Buchi automation. We study the problem of deciding whether a linear-time property, specified by either an automaton or an LTL formula, can be translated to an alternation-free /spl mu/-calculus formula, and describe the translation, when exists.
An exploratory study of Google Scholar

Purpose – The purpose of this paper is to discuss the new scientific search service Google Scholar (GS). It aims to discuss this search engine, which is intended exclusively for searching scholarly documents, and then empirically test its most important functionality. The focus is on an exploratory study which investigates the coverage of scientific serials in GS.Design/methodology/approach – The study is based on queries against different journal lists: international scientific journals from Thomson Scientific (SCI, SSCI, AH), open access journals from the DOAJ list and journals from the German social sciences literature database SOLIS as well as the analysis of result data from GS. All data gathering took place in August 2006.Findings – The study shows deficiencies in the coverage and up‐to‐dateness of the GS index. Furthermore, the study points out which web servers are the most important data providers for this search service and which information sources are highly represented. The paper can show tha.
Detection and Absolute Quantification Porcine DNA in Sausages Using Quantitative Polymerase Chain Reaction (qPCR) Method

Identification of adulteration of processed meat products with unwanted ingredients is a crucial issue. These meat products are prone to forgery and mix with porcine. Meat source authentication is important for Muslim consumers to whom comsumption of products containing pork and its derivatives in an products is prohibited. This present study aims at development of detection and quantification method of porcine DNA (Deoxyribo Nucleic Acid) in processed meat products, sausages. Two novel primer pairs were designed specifically targeting fragment of Displacement Loop (D-Loop) and cytochrome b (cyt b) of porcine mitochondrial DNA and to generate 139 bp and 143 bp amplicons, respectively. Detection and quantification were accomplished by Quantitative Polymerase Chain Reaction (qPCR). Porcine DNA standard curves and cycle threshold were used for quantification. The detection limit of porcine DNA was as little as 0.05 pg. Of all sausages tested (n=13), four of them contained porcine DNA as much 3.1 pg; 0.160 pg; 0.294 pg; and 0.110 pg in 0.001 mg of sausages for J, G, I, and L samples respectively. The specific qPCR assay method can be used for the detection of porcine DNA in minute amounts, which can be used for the halal authentication of food and pharmaceutical products.
Semantic Role Labeling Using Complete Syntactic Analysis

In this paper we introduce a semantic role labeling system constructed on top of the full syntactic analysis of text. The labeling problem is modeled using a rich set of lexical, syntactic, and semantic attributes and learned using one-versus-all AdaBoost classifiers. Our results indicate that even a simple approach that assumes that each semantic argument maps into exactly one syntactic phrase obtains encouraging performance, surpassing the best system that uses partial syntax by almost 6%.
The number of matchings in random graphs

We study matchings on sparse random graphs by means of the cavity method. We first show how the method reproduces several known results about maximum and perfect matchings in regular and Erdös–Rényi random graphs. Our main new result is the computation of the entropy, i.e. the leading order of the logarithm of the number of solutions, of matchings with a given size. We derive both an algorithm to compute this entropy for an arbitrary graph with a girth that diverges in the large size limit, and an analytic result for the entropy in regular and Erdös–Rényi random graph ensembles.