Miracle-dz/UniFAR-FTUs huggingface dataset:
β-Adrenoceptor Blocker Carvedilol Provides Cardioprotection via an Adenosine-Dependent Mechanism in Ischemic Canine Hearts

Background—Carvedilol is a β-adrenoceptor blocker with a vasodilatory action that is more effective for the treatment of congestive heart failure than other β-blockers. Recently, carvedilol has been reported to reduce oxidative stress, which may consequently reduce the deactivation of adenosine-producing enzymes and increase cardiac adenosine levels. Therefore, carvedilol may also have a protective effect on ischemia and reperfusion injury, because adenosine mediates cardioprotection in ischemic hearts. Methods and Results—In anesthetized dogs, the left anterior descending coronary artery was occluded for 90 minutes, followed by reperfusion for 6 hours. Carvedilol reduced the infarct size (15.0±2.8% versus 40.9±4.2% in controls), and this effect was completely reversed by the nonselective adenosine receptor antagonist 8-sulfophenyltheophylline (45.2±5.4%) or by an inhibitor of ecto-5′-nucleotidase (44.4±3.6%). There were no differences of either area at risk or collateral flow among the various groups. When the coronary perfusion pressure was reduced in other dogs so that coronary blood flow was decreased to 50% of the nonischemic level, carvedilol increased coronary blood flow (49.4±5.6 to 73.5±7.5 mL · 100 g−1 · min−1; P < 0.05) and adenosine release (112.3±22.2 to 240.6±57.1 nmol/L; P < 0.05) during coronary hypoperfusion. This increase of coronary blood flow was attenuated by either 8-sulfophenyltheophylline or superoxide dismutase. In human umbilical vein endothelial cells cultured with or without xanthine and xanthine oxidase, carvedilol caused an increase of ecto-5′-nucleotidase activity. Conclusions—Carvedilol shows a cardioprotective effect against ischemia and/or reperfusion injury via adenosine-dependent mechanisms.
International Journal of Parallel, Emergent and Distributed Systems

The development of radar systems on general-purpose off-the-shelf parallel hardware represents an effective means of providing efﬁcient implementations with reasonable realisation costs. However, the fulﬁlment of the required real-time constraints poses serious problems of performance and efﬁciency: parallel architectures need to be exploited at best, providing scalable parallelisations able to reach the desired throughput and latency levels. In this paper we discuss the implementation issues of the computational kernel of a well-known radar ﬁltering technique – the space–time adaptive processing – on today’s general-purpose parallel architectures (multi-/many-core platforms). In order to address the performance constraints imposed by the real-time implementation of this ﬁltering technique, we apply a structured approach (structured parallel programing) to develop parallel computations as instances and compositions of well-known parallelisation patterns. This paper provides a thorough description of the implementation issues and discusses the performance peaks achievable on a broad range of existing multi-core architectures.
The Integration of Ventilation and Locomotion in Archosaurs1

Synopsis. Movements of the pelvis have recently been found to contribute to ventilation in both crocodilians and birds. Alligators have a kinetic pelvis in which the ischiopubic and ischiotruncus muscles rotate the pubic bones ventrally to increase abdominal volume and thereby facilitate inspiration. In birds, the entire pelvis rocks on the axial skeleton to produce ventilation. Although the mechanisms of pelvic aspiration are very different in crocodilians and birds, it is unusual among vertebrates for the pelvic musculoskeletal system to play an active role in inspiration. This observation raises the possibility that the pelvic musculoskeletal system may have played an important role in the ventilation of basal archosaurs. Based on the mechanism of pelvic aspiration in crocodilians and the structure of gastralia in basal archosaurs, we suggest that an ischiotruncus muscle pulled the medial aspect of the gastralia caudally, and thereby helped to produce inspiration by increasing the volume of the cuirassal basket. The proposed mechanism of cuirassal breathing in non-avian theropods leads us to suggest that the phase relationship of the ventilatory and locomotor cycles in running theropods was the opposite of that observed in running birds. Furthermore, we suggest that the ventilatory cycle of flying pterosaurs was entrained to the locomotor cycle with a phase relationship that was the opposite of that observed during flight in modern birds.
Online Learning with Gaussian Payoffs and Side Observations

We consider a sequential learning problem with Gaussian payoffs and side information: after selecting an action $i$, the learner receives information about the payoff of every action $j$ in the form of Gaussian observations whose mean is the same as the mean payoff, but the variance depends on the pair $(i,j)$ (and may be infinite). The setup allows a more refined information transfer from one action to another than previous partial monitoring setups, including the recently introduced graph-structured feedback case. For the first time in the literature, we provide non-asymptotic problem-dependent lower bounds on the regret of any algorithm, which recover existing asymptotic problem-dependent lower bounds and finite-time minimax lower bounds available in the literature. We also provide algorithms that achieve the problem-dependent lower bound (up to some universal constant factor) or the minimax lower bounds (up to logarithmic factors).
Automatic tracking of swimming microorganisms in 4D digital in-line holography data

Digital in-line holography is a microscopy technique which has gotten an increasing amount of attention over the last few years in the fields of microbiology, medicine and physics, as it provides an efficient way of measuring 3D microscopic data over time. In this paper we approach the challenges of a high throughput analysis of holographic microscopy data and present a system for detecting particles in 3D reconstructed holograms and their 3D trajectory estimation over time. Our main contribution is a robust method which evolves from the Hungarian bipartite weighted graph matching algorithm and allows us to deal with newly entering and leaving particles and compensate for missing data and outliers. In the experiments we compare our fully automatic system with manually labeled ground truth data and we can report an accuracy between 76% and 91%.
A Study on Feature Subsumption for Sentiment Classification in Social Networks using Natural Language Processing

In the past, whenever a customer wants to buy some product he used to consult his family members or friends. But this thing has been changed over the last few years where the users are generally finding out the reviews from the internet before purchasing the products. It is easy to process a review if the opinions are less, but for few popular products the reviews can be more that sometimes they will be in hundreds or thousands. It is a quite time taking process for the customers to go through all these reviews. So a system that could automatically summarize the opinions could be useful to the customers. This paper studies existing methods for sentiment classification and proposes new method Sentiment Classification for Dynamic Data Features (SCDDF) that not only considers many sites for sentiment classification but also aggregates the opinions using Bayesian Networks and Natural Language Processing techniques. We consider the various products and their features and classify them. Bulk amount of dynamic data is considered rather than the static one. It takes as input a collection of comments from the social networks and outputs ranks to the comments in the social networks, for each product, and also classifies the comments posted. Thus the user can evaluate the product and its features.
Hybrid Content-Based Collaborative-Filtering Music Recommendations

Recommendation of music is emerging with force nowadays due to the huge amount of music content and because users normally do not have the time to search through these collections looking for new items. The main purpose of a recommendation system is to estimate the user’s preferences and present him with some items that he does not know yet. Currently, most of the audio recommendation systems can be classified in two major kinds: recommendation systems based on collaborative filtering techniques and content based recommendation system. While both kinds of systems have good characteristics, they fail to provide good recommendation is specific situations. Recently a new kink of recommendation systems is emerging, hybrid content-based collaborative-filtering recommendation systems. The main objective of this thesis is to try to probe that the main disadvantages of content-based and collaborative filtering recommendation methods can be solver using hybrid methods. In order to do this we built a hybrid content-based collaborative filtering recommendation system. This hybrid system possesses the best characteristics of both methods and produce better results than each method individually. To achieve our goal we will also present a research on current content-based and collaborative methods in such a way that we will be able to fin out advantages and disadvantages of them
Immunohistochemical Characterization of the Chemosensory Pulmonary Neuroepithelial Bodies in the Naked Mole-Rat Reveals a Unique Adaptive Phenotype

The pulmonary neuroepithelial bodies (NEBs) constitute polymodal airway chemosensors for monitoring and signaling ambient gas concentrations (pO2, pCO2/H+) via complex innervation to the brain stem controlling breathing. NEBs produce the bioactive amine, serotonin (5-HT), and a variety of peptides with multiple effects on lung physiology and other organ systems. NEBs in mammals appear prominent and numerous during fetal and neonatal periods, and decline in the post-natal period suggesting an important role during perinatal adaptation. The naked mole-rat (NMR), Heterocephalus glaber, has adapted to the extreme environmental conditions of living in subterranean burrows in large colonies (up to 300 colony mates). The crowded, unventilated burrows are environments of severe hypoxia and hypercapnia. However, NMRs adjust readily to above ground conditions. The chemosensory NEBs of this species were characterized and compared to those of the conventional Wistar rat (WR) to identify similarities and differences that could explain the NMR’s adaptability to environments. A multilabel immunohistochemical analysis combined with confocal microscopy revealed that the expression patterns of amine, peptide, neuroendocrine, innervation markers and chemosensor component proteins in NEBs of NMR were similar to that of WR. However, we found the following differences: 1) NEBs in both neonatal and adult NMR lungs were significantly larger and more numerous as compared to WR; 2) NEBs in NMR had a more variable compact cell organization and exhibited significant differences in the expression of adhesion proteins; 3) NMR NEBs showed a significantly greater ratio of 5-HT positive cells with an abundance of 5-HT; 4) NEBs in NMR expressed the proliferating cell nuclear antigen (PCNA) and the neurogenic gene (MASH1) indicating active proliferation and a state of persistent differentiation. Taken together our findings suggest that NEBs in lungs of NMR are in a hyperactive, functional and developmental state, reminiscent of a persistent fetal state that extends postnatally.
Facilitating post-surgical complication detection through sublanguage analysis

Identification of postsurgical complications is the first step towards improving patient safety and health care quality as well as reducing heath care cost. Existing NLP-based approaches for retrieving postsurgical complications are based on search strategies. Here, we conduct a sublanguage analysis study using free text reports available for a cohort of patients with postsurgical complications identified manually to compare the keywords identified by subject matter experts with words/phrases automatically identified by sublanguage analysis. The results suggest that search-based approaches may miss some cases and the sublanguage analysis results can be used as a base to develop an information extraction system or support search-based NLP approaches by augmenting search queries.
BUILDING BOUNDARY EXTRACTION BASED ON LIDAR POINT CLOUDS DATA

LIDAR (Light Detection And Ranging) is an active remote sensing system, which could provide fleetly three dimensional information of earth surface with high vertical accuracy. Now the building boundary extraction and normalization is the key approach for 3D modeling of building and city mapping. In this paper, a new algorithm named Alpha Shapes is developed to extract the building boundary. Compared with other algorithms, Alpha Shapes algorithm works effectively in inner and outer boundaries extraction from point clouds data with convex and concave polygon shape. Moreover it can keep fine features of buildings adaptively and filter the footprints of non-building. In addition, an improved boundary simplifying algorithm is suggested to refine the extracted building boundary. And two regularization algorithms are developed to make the refined boundary regular. The experiments proved that the normalized building boundary is generated perfectly by these algorithms.
Molecular Epidemiology of HIV-1 in Panama: Origin of Non-B Subtypes in Samples Collected from 2007 to 2013

Phylogenetic studies have suggested that the HIV-1 epidemic in the Americas is mainly dominated by HIV subtype B. However, countries of South America and the Caribbean have recently reported changes in their circulating HIV-1 genetic profiles. The aim of this study was to characterize the molecular profile of the HIV-1 epidemic in Panama by the analysis of 655 polymerase gene (pol) sequences that were obtained from HIV-infected Panamanians diagnosed between 1987 and 2013. Blood samples were collected from recently infected, antiretroviral drug-naïve and treatment-experienced subjects since mid-2007 to 2013. Viral RNA from plasma was extracted and sequences of HIV protease and reverse transcriptase genes were obtained. Bootscanning and phylogenetic methods were used for HIV subtyping and to trace the putative origin of non-B subtype strains. Our results showed that HIV-1 infections in Panama are dominated by subtype B (98.9%). The remaining 1.1% is represented by a diverse collection of recombinant variants including: three URFs_BC, one CRF20_BG, and one CRF28/29_BF, in addition to one subtype F1 and one subtype C, none of which were previously reported in Panama. The non-B subtype variants detected in Panama were probably introduced from Brazil (subtype F1 and CRF28/29_BF), Cuba (CRF20_BG), Dominican Republic (URFs_BC) and India (subtype C). Panama is the geographical vertex that connects the North with South America and the Caribbean through trade and cultural relations, which may explain the observed introductions of non-B subtype HIV-1 variants from both the Caribbean and South America into this Central American country.
JouleQuest: An Accurate Power Model for the StarCore DSP Platform

This paper describes the design, validation and integration of JouleQuest: a comprehensive power estimation framework for the StarCore DSP platform. The goal of this work is to provide a power model coupled to a fast platform simulator, that can accurately predict the power variability on the platform. The power consumption model for the DSP core is an instruction level model while the power models for the peripheral components are based on functional operations executed by the blocks. The DSP core instruction level power model is a generic model applicable to any VLIW DSP core and improves on existing VLIW models by reducing the model complexity to O(n) where n is the number of processor instructions. The peripheral power models are based on a novel technique of characterizing the power of operations and operation sequences executed on the peripheral block. This allows accurate power modeling for complex peripheral blocks like cache controllers, arbiters etc. having significant parallel operation execution. The platform power has been verified to give an average error of approximately 5% across a large suite of DSP and control benchmarks having high power variability. The paper concludes with a case study demonstrating the usage of the power simulator for energy optimization of ITU-T G.729 speech-codec software
A Profile of the Demographics, Psychological Predispositions, and Social/Behavioral Patterns of Computer Hacker Insiders and Outsiders

This chapter looks at the literature—myths and realities—surrounding the demographics, psychological predispositions, and social/behavioral patterns of computer hackers, to better understand the harms that can be caused to targeted persons and property by online breaches. The authors suggest that a number of prevailing theories regarding those in the computer underground (CU)—such as those espoused by the psychosexual theorists—may be less accurate than theories based on gender role socialization, given recent empirical studies designed to better understand those in the CU and why they engage in hacking and cracking activities. The authors conclude the chapter by maintaining that online breaches and online concerns regarding privacy, security, and trust will require much more complex solutions than currently exist, and that teams of experts in psychology, criminology, law, and information technology security need to collaborate to bring about more effective real-world solutions for the virtual world.
Determination of Path Loss Model based on Measurements for IEEE 802. 11n in Large Rectangular Room

In this paper, path loss (PL) model for IEEE 802.11n in large rectangular room is determined on basis of PL measurements. It is found that PL model can be described by a TGn channel model and free-space model at small distances with PL exponent of 2.67 and 2. At large distances PL model can be described by one-slope model with PL exponent of 1.6. Further, it is determined PL exponent increases in the presence of humans. Finally we investigate that our PL model has less PL so it is better than one-slope model, free-space model and TGn channel model.
Verification and Fault Localization for VHDL Programs

Introduction During the last decades, hardware-design languages like Verilog and VHDL have become very common for designing digital circuits. These languages allow designers to specify and test the behavior of circuits before they are produced. This helps to avoid incorrect designs and saves money, providing that the designs are rigorously tested. If faulty behavior is identified through verification, the location of the error in the source code is of interest. Finding this root cause of the misbehavior is not always as easy as expected. The source code is usually huge. Furthermore, it is often written by one team of hardware designers and tested by another team. Because hardware designs are becoming ever more complex, verification is becoming a bottleneck in the design flow. Hence, verification and fault localization are both important issues for hardware designers and their companies. Both are imperative for ensuring quality of the hardware designs, reducing time to market, and avoiding costly re-designs. As a consequence, they reduce the overall design costs.
Exact conserved quantities on the cylinder I: conformal case

The nonlinear integral equations describing the spectra of the left and right (continuous) quantum KdV equations on the cylinder are derived from integrable lattice field theories, which turn out to allow the Bethe Ansatz equations of a twisted ``spin -1/2'' chain. A very useful mapping to the more common nonlinear integral equation of the twisted continuous spin +1/2 chain is found. The diagonalization of the transfer matrix is performed. The vacua sector is analysed in detail detecting the primary states of the minimal conformal models and giving integral expressions for the eigenvalues of the transfer matrix. Contact with the seminal papers [1,2] by Bazhanov, Lukyanov and Zamolodchikov is realised. General expressions for the eigenvalues of the infinite-dimensional abelian algebra of local integrals of motion are given and explicitly calculated at the free fermion point.
Development of FCM based algorithm for the delineation of QRS-complexes in Electrocardiogram

This paper proposes a simple technique for automatic detection and delineation of cardiac beat (QRS-complex) in Electrocardiogram (ECG) using Fuzzy C-Means (FCM) clustering algorithm. The power line interference and baseline wander present in the ECG signal is removed using digital filtering techniques. Absolute slope of the filtered ECG signal is calculated to enhance the QRS-complexes in the ECG signal. FCM technique is then used to delineate QRS and non-QRS-regions. Algorithm performance is validated using original single lead ECG recordings from the dataset-3 of the CSE multi-lead measurement library. Detection rate of 98.74% is achieved. The percentage of false positive and false negative detection is very low. The delineation performance of the algorithm is validated by calculating the mean and standard deviation of the errors between automatic and manual annotations by the referee's cardiologists/combined program median in the CSE study.
Using articulatory information for speaker adaptation

Articulatory features (AF) have proven beneficial for automatic speech recognition (ASR) in noisy environments, for hyper-articulated speech or in multilingual settings. A stream setup can combine standard sub-phone Gaussian mixture models with feature GMM; the weights assigned to each feature stream such as VOICED or BILABIAL could intuitively be used for adaptation to speaker or text. In this paper, we investigate this stream setup, which allows us to add articulatory information to a baseline CD-HMM recognizer, on a database containing several speakers in a number of recordings of spontaneous speech. Our findings indicate that articulatory features as we use them are not entirely a speaker-dependent property, but when using them for speaker adaptation, we find their performance to be comparable to that of constrained MLLR.
Influence of Machine Learning vs. Ranking Algorithm on the Critical Dimension

critical dimension is the minimum number of features required for a learning machine to perform with " high " accuracy, which for a specific dataset is dependent upon the learning machine and the ranking algorithm. Discovering the critical dimension, if one exists for a dataset, can help to reduce the feature size while maintaining the learning machine's performance. It is important to understand the influence of learning machines and ranking algorithms on critical dimension to reduce the feature size effectively. In this paper we experiment with three ranking algorithms and three learning machines on several datasets to study their combined effect on the critical dimension. Results show the ranking algorithm has greater influence on the critical dimension than the learning machine.
A robust technique for image descreening based on the wavelet transform

In this correspondence, a novel wavelet-based approach to recover continuous-tone (contone) images from halftone images is presented. Wavelet decomposition of the halftone image facilitates a series of spatial and frequency selective processing to preserve most of the original image contents while eliminating the halftone noise. Furthermore, optional nonlinear filtering can be applied as a postprocessing stage to create the final aesthetic contone image. This approach lends itself to practical applications since it is independent of parameter estimation and, hence, universal to all types of halftoned images, including those obtained by scanning printed halftones.