Miracle-dz/UniFAR-FTUs huggingface dataset:
CHLAMYDIA PNEUMONIAE INFECTION , INFLAMMATION AND HEAT SHOCK PROTEIN 60 IMMUNITY IN ASTHMA AND CORONARY HEART DISEASE TIINA SÄVYKOSKI

Chlamydia pneumoniae is a common respiratory pathogen worldwide. It does not only cause acute respiratory infections, but is also associated with chronic inflammatory diseases, such as asthma and coronary heart disease (CHD). Chlamydial heat shock protein 60 (Hsp60) is associated with the development of immunopathological damage following C. trachomatis infections, but the role of Hsp60 in C. pneumoniae infections is unclear. A slightly elevated level of C-reactive protein (CRP), as a marker of systemic inflammation, predicts cardiovascular events, but its role in asthma has not been studied. The aim of this study was to develop an EIA method for the measurement of Hsp60 antibodies and for studying the host immune responses to C. pneumoniae and chlamydial and human Hsp60 proteins, CRP levels and their interactions in asthma and CHD. Elevated levels of serum IgA antibodies to the Hsp60 protein of C. pneumoniae were associated with asthma and decreased pulmonary function. CRP levels were also higher in the asthma patients than in the controls. The patients with moderate asthma had higher CRP levels than those with mild asthma. The patients with a CRP level over 2 mg/l had higher levels of serum IgA antibodies to C. pneumoniae and chlamydial Hsp60 than the patients with lower CRP levels. A prospective nested case-control study was carried out, to study the role of Hsp60 antibodies as coronary risk predictors, and their association with C. pneumoniae infection and inflammation. The participants were obtained from the Helsinki Heart Study: 241 myocardial infarctions or coronary deaths occurred during the 8.5-year period among dyslipidemic middle-aged men. An elevated level of human Hsp60 IgA antibodies in baseline serum predicted the occurrence of a coronary event several years later, especially when present simultaneously with a high C. pneumoniae IgA antibody level and an elevated CRP level. Further studies showed that only persistently, not transiently, elevated levels predicted coronary events. The risk associated with elevated antibody levels increased markedly in the presence of an elevated CRP level, and vice versa. In conclusion, these results suggest that chlamydial Hsp60 is involved in the association between C. pneumoniae infection and asthma, while autoimmunity to human Hsp60 is implicated in the association between C. pneumoniae infection and CHD. Inflammation evidently plays an important role in these associations. It can also be concluded that IgA antibodies, compared to IgG antibodies, against C. pneumoniae and Hsp60 are better markers of chronicity, especially when they are persistently elevated.
Energy Detection of Multilevel PAM Signals with Systematic Threshold Mismatch

We address a symbol decision problem with spectrally efficient energy detected multilevel pulse amplitude modulated (PAM) signals. First, we analytically quantify the relationship between a systematic threshold mismatch and the required increase of the average signal-to-noise ratio to preserve a desired symbol error rate. For the case in which such an increase is not tolerable, we present a novel near-optimal multilevel threshold selection scheme, which is accurate for a wide range of system parameters.
Very-high-speed digital subscriber lines

VDSL services are overviewed with an emphasis on the basic architecture, applications, and data rates, as well as the technological challenges of design. Discussions of the telephone line environment, radio-band interference ingress and egress, impulse noise, and symmetric and asymmetric multiplexing are included, along with brief descriptions of two popular implementations.
The Game Theory Connection Between Boosting and Online Auctions

Bob drives to work every morning. On each day t, he picks one of many possible routes, and suffers some some delay dt. The traffic pattern changes from day to day, so some routes may be congested and others wide open, but Bob has no way of knowing this before hand. After returning home in the evening, he gossips with his neighbors/colleagues, each of whom has chosen a different path to work, and they compare their respective delays. After T days, Bob looks back at all the driving records and contrast it with his actual driving experience. He feels regret if there is a fixed route that he could have chosen to drive on everyday that would have saved time. How should Bob choose his routes in the morning in order to minimize this ”hindsight is 20-20” regret? Alice is a professional basketball scout. She traverses the country and watches thousands of college basketball players. In her worldview, a player is either an AllStar or a bust, and her job is to predict as accurately as possible whether a given prospect is going to be an All-Star. Basketball scouting is a notoriously difficult task. Alice has a set of simple rubrics when evaluating a player: height (tall or short), speed (fast or slow), handedness (right or left), geography (North or South), etc. Each rubric by itself is a very poor predictor (there are plenty of tall guys who can’t play) but it is better than nothing (a tall player is more likely to be good). She also has in her corner the profiles of past NBA players who are All-Stars or busts. How can she use all of her information to make the best prediction? These two situations may appear to be very different at first, but they all belong under the subject of learning. The first is a problem of online learning where the environment is dynamically changing and new information is revealed in time; the second is an example of boosting, where several weak predictors are to be combined to form a strong predictor. Ok, you say, but even as learning problems go, these
VIDEO PREPROCESSING BASED ON HUMAN PERCEPTION FOR TELESURGERY

Video transmission plays a critical role in robotic telesurgery because of the high bandwidth and high quality requirement. The goal of this dissertation is to find a preprocessing method based on human visual perception for telesurgical video, so that when preprocessed image sequences are passed to the video encoder, the bandwidth can be reallocated from non-essential surrounding regions to the region of interest, ensuring excellent image quality of critical regions (e.g. surgical region). It can also be considered as a quality control scheme that will gracefully degrade the video quality in the presence of network congestion. The proposed preprocessing method can be separated into two major parts. First, we propose a time-varying attention map whose value is highest at the gazing point and falls off progressively towards the periphery. Second, we propose adaptive spatial filtering and the parameters of which are adjusted according to the attention map. By adding visual adaptation to the spatial filtering, telesurgical video data can be compressed efficiently because of the high degree of visual redundancy removal by our algorithm. Our experimental results have shown that with the proposed preprocessing method, over half of the bandwidth can be reduced while there is no significant visual effect for the observer. We have also developed an optimal parameter selecting algorithm, so that when the network bandwidth is limited, the overall visual distortion after preprocessing is minimized.
A galaxy dynamo by Supernova-driven interstellar turbulence

Abstract Supernovae are the dominant energy source for driving turbulence within the interstellar plasma. Until recently, their effects on magnetic field amplification in disk galaxies remained a matter of speculation. By means of self-consistent simulations of supernova-driven turbulence, we find an exponential amplification of the mean magnetic field on timescales of a few hundred million years. The robustness of the observed fast dynamo is checked at different magnetic Reynolds numbers, and we find sustained dynamo action at moderate Rm. This indicates that the mechanism might indeed be of relevance for the real ISM. Sensing the flow via passive tracer fields, we infer that SNe produce a turbulent α effect which is consistent with the predictions of quasilinear theory. To lay a foundation for global mean-field models, we aim to explore the scaling of the dynamo tensors with respect to the key parameters of our simulations. Here we give a first account on the variation with the supernova rate.
Geometry of meromorphic functions and intersections onmoduli spaces of curves

We present a new approach to perform calculations with the certain standard classes in cohomology of the moduli spaces of curves. It is based on an important lemma of Ionel relating the intersection theoriy of the moduli space of curves and that of the space of admissible coverings. As particular results, we obtain expressions of Hurwitz numbers in terms of the intersections in the tautological ring, expressions of the simplest intersection numbers in terms of Hurwitz numbers, an algorithm of calculation of certain correlators which are the subject of the Witten conjecture, an improved algorithm for intersections related to the Boussinesq hierarchy, expressions for the Hodge integrals over two-pointed ramification cycles, cut-and-join type equations for a large class of intersection numbers, etc.
Data relaying with optimal resource management in wireless sensor networks

In this paper, the concern is with the management of data traffic after node deployment. To address some of the shortcomings of both single and multihop communication, this paper work with a hybrid model. The WSNet architecture is comprised of three classes of sensor nodes, sensors, relay nodes, and relay gateways. Sensors - gather and send data values to one relay nodes. Relay nodes - receive data values from sensors and/or other relay nodes and forward them to relay nodes or relay gateways. Relay gateways - receive data values from RN and send them directly (in one hop) to the base station, possibly using a dedicated channel. Our goal is to select paths along which data packets can be relayed until they reach one or more relay gateways while satisfying several constraints. These decisions take place at the application level, are computed by a central algorithm running at the base station, and rely on routing protocols to deliver the messages. A fixed topology of directly communicating nodes were assumed and a long term data communication plan based on knowing the amount of data generated by sensor nodes. The optimality of the solution obtained was guaranteed
Fast Distance Metric Based Data Mining Techniques Using P-trees : k-Nearest-Neighbor Classification and k-Clustering

In this paper we consider the classification of spatial data streams, where the training dataset changes often. New training data arrive continuously and are added to the training set. For these types of data streams, building a new classifier each time can be very costly with most techniques. In this situation, k-nearest neighbor (KNN) classification is a very good choice, since no residual classifier needs to be built ahead of time. For that reason KNN is called a lazy classifier. KNN is extremely simple to implement and lends itself to a wide variety of variations. The traditional k-nearest neighbor classifier finds the k nearest neighbors based on some distance metric by finding the distance of the target data point from the training dataset, then finding the class from those nearest neighbors by some voting mechanism. There is a problem associated with KNN classifiers. They increase the classification time significantly relative to other non-lazy methods. To overcome this problem, in this paper we propose a new method of KNN classification for spatial data streams using a new, rich, datamining-ready structure, the Peano-count-tree or P-tree. In our method, we merely perform some logical AND/OR operations on P-trees to find the nearest neighbor set of a new sample and assign the class label. We have fast and efficient algorithms for AND/OR operations on P-trees, which reduce the classification time significantly, compared with traditional KNN classifiers. Instead of taking exactly the k nearest neighbors we form a closed-KNN set. Our experimental results show closed-KNN yields higher classification accuracy as well as significantly higher speed. 29
Some numerical aspects of nonlinear free surface motions by a Method of Fundamental Solutions

Since the pioneering works by Kupradze and Aleksidze (1963), the Method of Fundamental Solutions (MFS also known as Desingularized Methods) has received much attention, both in terms of numerical analysis or in terms of applications. This method applies as soon as there exist exact solutions of the Partial Differential Equation (PDE) driving the Boundary Value Problem (BVP). Here we focus on the twodimensional interior Dirichlet elliptic BVP as it is posed for the potential flow in a tank with a free surface. The two boundary conditions (kinematic and dynamic) form a time differential system to be solved so that the potential on the free surface is calculated while it deforms. The principle of MFS is to place singularities (represented by the Green function of the PDE) at a certain distance from the actual boundary. The potential which represents the flow is hence expressed as a finite sum of these singularities with unknown intensities. To compute these intensities, the obtained algebraic equations are classicaly solved via Gauss algorithm or possibly Least square algorithm and a Single Value Decomposition depending on the type of boundary conditions. Recently a special issue of Engineering Analysis with Boundary Elements (Volume 33, Issue 12, 2009) collects some papers in which numerical difficulties are still underlined. Among them, there is the arbitrary choice of the distance at which the singularities are located. It appears that this distance depends strongly on the application. But it has a crucial influence on the accuracy and (ill-)conditioning of linear system to be inverted. This abstract highlights some numerical aspects of the MFS applied to the standard nonlinear free surface motions. In particular we focus on the role of the additive constant to the velocity potential in the accuracy and stability of the computed solutions. Both weakly and highly nonlinear motions are examined ranging from a mild standing wave, up to the flip through phenomenon. 2) Numerical analysis
The analysis of the recovery of forest fire based on NDVI and NPP

In this paper, we obtained the changing curves of the meteorological elements (temperature, the percentage of sunshine, monthly precipitation) and NDVI of post-fire in Heilongjiang in 1987. Then through the light use efficiency model, we calculated NPP of post-fire in the recovery period by the data of remote sensing satellite and meteorological elements. Finally, we had summarized that the recovery cycle of the vegetation was long after fire based on the analysis of NDVI and NPP. It needed a few years or decades to recover. Specifically, NDVI and NPP significantly increased in the first few years after fire, since then became stable and showed a slow rise tendency.
PUNICA GRANATUM: A REVIEW ON PHARMACOLOGICAL AND THERAPEUTIC PROPERTIES

A wide range of medicinal plant parts is used as raw drugs and they possess varied medicinal properties. The different parts used as raw drugs include root, stem, flower, fruit, twigs exudates and modified plant organs. The Punica granatum is a native shrub of occidental Asia and Mediterranean Europe that has a rich history of traditional use in medicine. For centuries, the barks, leaves, flowers, fruits, and seeds of this plant have been used to treat various diseases. The aim of this present review is to throw light on the therapeutic utility of various parts of pomegranate.
Preferential Image Segmentation Using Level Set Methods Based On Color, Boundary and Texture

: The major key point of “preferential image segmentation,” is to preferentially segment objects of interests from an image and ignore the remaining portions of the image for the real world applications. These methods, however, tend to classify or parse a whole image instead of meaningful objects. It utilizes a tree of shapes to represent the image content. This representation provides a hierarchical tree for the objects contained in the level sets of the image. The hierarchical structure is utilized to select the candidate objects from the image. The Level set method provides a faster way to construct a tree of shapes. The boundaries of the selected objects are then compared with those of objects selected from prior images.
Study on the multi-modal data preprocessing for knowledge-converged super brain

Due to the ever-growing amount of data produced through versatile hyper-connected networks, in pursuit of the Internet of Everything (IoE), the challenges to derive useful knowledge from these big IoE data have received increasing attention. This research deals with fusing multi-modal data from the IoE networks, in order to efficiently provide a set of highly relevant input data for further high-level data analytics such as machine learning. We propose a functional structure of such a data fusion and implement a system prototype based on Apache Spark. Further, our concept is validated through the experiments using the KDD Cup 1999 data.
Fuzzy Role Based Expert System for Human Thermoregulation Model ME 290 M Final Project Spring 1999

This paper investigated the application of fuzzy rule based expert system (FRBES) to the identification of human thermoregulation system which has long been modeled as a crisp controller without adaptation by earlier approaches. In this project, human body is treated as an adaptive fuzzy logic system. An additional fuzzy controller is implemented and its effect studied. A fuzzy IF-THEN rule base consisting of a set of intuitive fuzzy rules is constructed and applied to the fuzzy thermal controller.
Face feature detection and normalization based on eyeball center and recognition

This paper presents an effective and efficient feature detection method based on eyes location. Here it is assumed that face is already detected. Then algorithm detects the position of pupils in the face image using geometric relation between the face and the eyes. Finally the algorithm normalizes the orientation and grayscale of the face image. The experimental results demonstrated that this algorithm can detect feature and normalize the face image efficiently and accurately. The algorithm can be used in face recognition because the normalized faces can improve the recognition rate.
The influence of playing video games as an attention rehabilitation technique in patients with traumatic brain injury

images 40 conjunctive search task images were created by R statistical software (A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org). Each image contained 59 red and green letter “d”s and green “b”s as distractors and one red letter “b” as the target that were randomly positioned on a white background. Each letter subtended approximately 0.6×0.1 degrees of visual angle.
Markov Logic Network : Unify Framework for Ontology Learning

Real-world data is most often presented in inconsistent, noisy, or incomplete state. Probability is suitable framework for uncertainty and Logic theory is suitable for complexity. But Statistical Related learning based Markov Logic is suitable for both uncertainty and Complexity. The Markov logic is an advanced and encouraging method to handle this kind of uncertainty presented in the structured data. Markov logic joints the gap between the first order logic and then the probabilistic theory. A Markov logic network (MLN) is collection of first-order logic formulas called rules. This each rule is connected with a numerical weight. Markov logic provides facility to specify probability distributions among complex relational domains and Deterministic dependencies are specified by formulas with infinite weight.
Information Theoretical and Statistical Features for Intrinsic Plagiarism Detection

In this paper we present some information theoretical and statistical features including function word skip n-grams for detecting plagiarism intrinsically. We train a binary classifier with different feature sets and observe their performances. Basically, we propose a set of 36 features for classifying plagiarized and non-plagiarized texts in suspicious documents. Our experiment finds that entropy, relative entropy and correlation coefficient of function word skip n-gram frequency profiles are very effective features. The proposed feature set achieves F-Score of 85.10%.
An Extended Consideration of Joint Exploration and Tracking: JET

Autonomous exploration and multi-object tracking by a team of agents have traditionally been considered as two separate, yet related, problems which are usually solved in two phases: an exploration phase then a tracking phase. The exploration problem is usually viewed through an information theoretic framework where a robotic agent attempts to gather as much information about the environment or an Object of Interest (OI). Conversely, the tracking problem attempts to maintain precise location information about an OI over time. This work proposes a single framework which enables the multi-robot multi-object problem to be solved simultaneously. A hierarchical architecture is used to coordinate robotic agents in the tracking of multiple OIs while simultaneously allowing the task to remain computationally efficient. The primary contributions of this work are a probabilistic constraint on the tracked OIs' covariances guarantees tracking performance throughout the entire mission. The automatic discovery of new OIs, a seamless transition to guaranteed tracking of discovered OIs, and the automatic balancing of exploration with the requirements of tracking.
REST: An alternative to RPC for Web services architecture

Currently most Web services architectures adopt RPC as their architectural style. But because of the complexity of RPC, there are bottlenecks of RPC-style Web services in Web-scale applications. REST not only can make full use of Web features, but also has the advantage of simplicity. So REST becomes a new alternative to RPC for Web services architecture. In this paper, at first the brief introductions of RPC and REST are provided. Then two kinds of architectural styles are analyzed and compared from the perspectives of scalability, coupling, and security. In the end the development trend of Web services architecture is prospected.
Glial cell line‐derived neurotrophic factor up‐regulates GTP‐cyclohydrolase I activity and tetrahydrobiopterin levels in primary dopaminergic neurones

Glial cell line‐derived neurotrophic factor (GDNF) protects dopaminergic neurones against toxic and physical damage. In addition, GDNF promotes differentiation and structural integrity of dopaminergic neurones. Here we show that GDNF can support the function of primary dopaminergic neurones by triggering activation of GTP‐cyclohydrolase I (GTPCH I), a key enzyme in catecholamine biosynthesis. GDNF stimulation of primary dopaminergic neurones expressing both tyrosine 3‐monooxygenase and GTPCH I resulted in a dose‐dependent doubling of GTPCH I activity, and a concomitant increase in tetrahydrobiopterin levels whereas tyrosine 3‐monooxygenase activity was not altered. Actinomycin D, asan inhibitor of de novo biosynthesis, abolished any GDNF‐mediated up‐regulation of GTPCH I activity. However, GTPCH I mRNA levels in primary dopaminergic neurones were not altered by GDNF treatment, suggesting that the mode of action for that up‐regulation is not directly connected to the regulation of GTPCH I transcription. We conclude that GDNF, in addition to its action in structural differentiation, also promotes differentiation regarding expression and enzymatic activity of a crucial component in the dopaminergic biosynthetic pathway.