Miracle-dz/UniFAR-FTUs huggingface dataset:
Regulation of Stroke Volume during Submaximal and Maximal Upright Exercise in Normal Man

To characterize the hemodynamic factors that regulate stroke volume during upright exercise in normal man, 24 asymptomatic male volunteers were evaluated by simultaneous right heart catheterization, radionuclide angiography, and expired gas analysis during staged upright bicycle exercise to exhaustion. From rest to peak exercise, oxygen consumption increased from 0.33 to 2.55 liters/min (7.7-fold), cardiac index increased from 3.0 to 9.7 liters/min per m2 (3.2-fold), and arteriovenous oxygen difference increased from 5.8 to 14.1 vol% (2.5-fold). The increase in cardiac index resulted from an increase in heart rate from 73 to 167 beats/min (2.5-fold), and an increase in left ventricular stroke volume index from 41 to 58 ml/m2 (1.4-fold). During low levels of exercise, there was a linear increase in cardiac index due to an increase in both heart rate and stroke volume index; stroke volume index increased as a result of an increase in left ventricular filling pressure and end-diastolic volume index and, to a much smaller extent, a decrease in end-systolic volume index. During high levels of exercise, further increases in cardiac index resulted entirely from an increase in heart rate, since stroke volume index increased no further. Left ventricular end-diastolic volume index decreased despite a linear increase in pulmonary artery wedge pressure; stroke volume index was maintained by a further decrease in endsystolic volume index. The degree to which stroke volume index increased during exercise in individuals correlated with the change in end-diastolic volume index (r = 0.66) but not with the change in end-systolic volume index (r = 0.07). Thus, the mechanism by which left ventricular stroke volume increases during upright exercise in man is dependent upon the changing relationship between heart rate, left ventricular filling, and left ventricular contractility. At low levels of exertion, an increase in left ventricular filling pressure and end-diastolic volume are important determinants of the stroke volume response through the Starling mechanism. At high levels of exertion, the exercise tachycardia is accompanied by a decrease in end-diastolic volume despite a progressive increase in filling pressure, so that stroke volume must be maintained by a decrease in end-systolic volume.
Observing scattered X-ray radiation from gamma-ray bursts: A way to measure their collimation angles

There are observational facts and theoretical arguments for an origin of gamma-ray bursts (GRBs) in molecular clouds in distant galaxies. If this is true, one could detect a significant flux of GRB prompt and early afterglow X-ray radiation scattered into our line of sight by the molecular and atomic matter located within tens of parsecs of the GRB site long after the afterglow has faded away. The scattered flux directly measures the typical density of the GRB ambient medium. Furthemore, if the primary emission is beamed, the scattered X-ray flux will be slowly decreasing for several months to years before falling off rapidly. Therefore, it should be possible to estimate the collimation angle of a burst from the light curve of its X-ray echo and a measured value of the line-of-sight absorption column depth. It is shown that detection of such an echo is for the brightest GRBs just within the reach of the Chandra and XMM-Newton observatories.
Demand Imbalances, Exchange Rate Misalignment and Monetary Policy1

In standard open macro models with incomplete markets, monetary policy geared towards price stability may result in (rather than correcting) misalignments in important asset prices like the exchange rate, even when the latter only reects fundamental-based valuation. We discuss instances in which optimal monetary policy redresses these ine¢ ciencies, achieving signi cant welfare gains relative to price stability. These gains are obtained by leaning against an overor under-valued exchange rate, associated with suboptimal cross-country demand and current account imbalances, consistent with exible ination targeting. JEL classi cation: F32, F33, F41
Time frame based link prediction in directed citation networks

Link prediction is a well-known problem in field of social network analysis which intends to guess the likelihood of the occurrences of connections between nodes. By using the structure of the network up to a given time appearance of links in future can be predict. In the most of previous studies, for performing the link prediction task just according to the exploration of the state of the network at a specific moment by applying the proximity metrics like topological based metrics to non-connected nodes has been used in order to predict new links. In those studies the behavior of links along the time or directed networks didn't considered which can be point as a limitation in link prediction studies. In this study we tried to overcome the above mentioned limitation by analyzing the development of topological measures in a citation network on a specific pried of time. For achieving this aim, chosen similarity matric deployed to all non-connected pairs of nodes in different frames of time in the network. Then, time frames are built for each pair to record their values which provided by the metric. Experiments on unsupervised prediction on a directed citation network show that the proposed method finds satisfactory results and is promising.
Application migration in HPC — A driver of the exascale era?

Application migration is valuable for modern computing centers. Apart from a facilitation of the maintenance process, it enables dynamic load balancing for an improvement of the system's efficiency. Although, the concept is already wide-spread in cloud computing environments, it did not find huge adoption in HPC yet. As major challenges of future exascale systems are resiliency, concurrency, and locality, we expect migration of applications to be one means to cope with these challenges. In this paper we investigate its viability for HPC by deriving respective requirements for this specific field of application. In doing so, we sketch example scenarios demonstrating its potential benefits. Furthermore, we discuss challenges that result from the migration of OS-bypass networks and present a prototype migration mechanism enabling the seamless migration of MPI processes in HPC systems.
Verification of Multipair Copper-Cable Model by Measurements

Analysis, assessment, and design of advanced wireline transmission schemes over multipair copper cables require accurate knowledge of the channel properties. This paper investigates modeling of multiconductor cables based on interpair impedance measurements. A unified approach to the application of the "Cioffi" model is introduced. The direct measurement approach of the underlying interpair impedances yields a good match with the alternative approach suggested in the recent study of Cioffi et al. Crosstalk coupling functions derived from the model exhibit a good match with the corresponding direct measurements in the case in which the modeled length is close to the length of the interpair impedance measurements. However, the prediction power of this model with respect to termination impedance is limited.
Straight Lines Have to Be Straight Automatic Calibration and Removal of Distortion from Scenes of Structured Environments

Most algorithms in 3-D Computer Vision rely on the pin-hole camera model because of its simplicity, whereas video optics, especially low-cost wide-angle or ﬁsh-eye lens, generate a lot of non-linear distortion which can be critical. To ﬁnd the distortion parameters of a camera, we use the following fundamental property: a camera follows the pin-hole model if and only if the projection of every line in space onto the camera is a line. Consequently, if we ﬁnd the transformation on the video image so that every line in space is viewed in the transformed image as a line, then we know how to remove the distortion from the image. The algorithm consists of ﬁrst doing edge extraction on a possibly distorted video sequence, then doing polygonal approximation with a large tolerance on these edges to extract possible lines from the sequence, and then ﬁnding the parameters of our distortion model that best transform these edges to segments. Results are presented on real video images, compared with distortion calibration obtained by a full camera calibration method which uses a calibration grid.
Action-Gap Phenomenon in Reinforcement Learning

Many practitioners of reinforcement learning problems have observed that oftentimes the performance of the agent reaches very close to the optimal performance even though the estimated (action-)value function is still far from the optimal one. The goal of this paper is to explain and formalize this phenomenon by introducing the concept of the action-gap regularity. As a typical result, we prove that for an agent following the greedy policy \(\hat{\pi}\) with respect to an action-value function $\(\hat{Q}\)$, the performance loss $\(E[V^*(X) - V^{\hat{X}} (X)]\)$ is upper bounded by $\(O(|| \hat{Q} - Q^*||_\infty^{1+\zeta}\))$, in which ζ ≥ = 0) is the parameter quantifying the action-gap regularity. For ζ > 0, our results indicate smaller performance loss compared to what previous analyses had suggested. Finally, we show how this regularity affects the performance of the family of approximate value iteration algorithms.
Wireless sensor network virtualization: A survey

Wireless Sensor Networks (WSNs) are the key components of the emerging Internet-of-Things (IoT) paradigm. They are now ubiquitous and used in a plurality of application domains. WSNs are still domain specific and usually deployed to support a specific application. However, as WSNs' nodes are becoming more and more powerful, it is getting more and more pertinent to research how multiple applications could share a very same WSN infrastructure. Virtualization is a technology that can potentially enable this sharing. This paper is a survey on WSN virtualization. It provides a comprehensive review of the state-of-the-art and an in-depth discussion of the research issues. We introduce the basics of WSN virtualization and motivate its pertinence with carefully selected scenarios. Existing works are presented in detail and critically evaluated using a set of requirements derived from the scenarios. The pertinent research projects are also reviewed. Several research issues are also discussed with hints on how they could be tackled.
Impacts of Inter- and Intralaboratory Variations on the Reproducibility of Microbial Community Analyses

ABSTRACT With the advent of molecular biological techniques, especially next-generation sequencing and metagenomics, the number of microbial biogeography studies is rapidly increasing. However, these studies involve the synthesis of data generated by different laboratories using different protocols, chemicals, etc., all with inherent biases. The aim of this study was to assess inter- as well as intralaboratory variations in microbial community composition when standardized protocols are applied to a single soil sample. Aliquots from a homogenized soil sample from a rice field in Italy were sent to five participating laboratories. DNA was extracted by two investigators per laboratory using an identical protocol. All DNA samples were sent to one laboratory to perform DNA quantification, quantitative PCR (QPCR), and microarray and denaturing gradient gel electrophoresis (DGGE) analyses of methanotrophic communities. Yields, as well as purity of DNA, were significantly different between laboratories but in some cases also between investigators within the same laboratory. The differences in yield and quality of the extracted DNA were reflected in QPCR, microarray, and DGGE analysis results. Diversity indices (Shannon-Wiener, evenness, and richness) differed significantly between laboratories. The observed differences have implications for every project in which microbial communities are compared in different habitats, even if assessed within the same laboratory. To be able to make sensible comparisons leading to valid conclusions, intralaboratory variation should be assessed. Standardization of DNA extraction protocols and possible use of internal standards in interlaboratory comparisons may help in rendering a “quantifiable” bias.
The C. elegans homolog of the murine cystic kidney disease gene Tg737 functions in a ciliogenic pathway and is disrupted in osm-5 mutant worms.

Cilia and flagella are important organelles involved in diverse functions such as fluid and cell movement, sensory perception and embryonic patterning. They are devoid of protein synthesis, thus their formation and maintenance requires the movement of protein complexes from the cytoplasm into the cilium and flagellum axoneme by intraflagellar transport (IFT), a conserved process common to all ciliated or flagellated eukaryotic cells. We report that mutations in the Caenorhabditis elegans gene Y41g9a.1 are responsible for the ciliary defects in osm-5 mutant worms. This was confirmed by transgenic rescue of osm-5(p813) mutants using the wild-type Y41g9a.1 gene. osm-5 encodes a tetratricopeptide repeat (TPR)-containing protein that is the homolog of murine polaris (Tg737), a protein associated with cystic kidney disease and left-right axis patterning defects in the mouse. osm-5 is expressed in ciliated sensory neurons in C. elegans and its expression is regulated by DAF-19, an RFX-type transcription factor that governs the expression of other genes involved in cilia formation in the worm. Similar to murine polaris, the OSM-5 protein was found to concentrate at the cilium base and within the cilium axoneme as shown by an OSM-5::GFP translational fusion and immunofluorescence. Furthermore, time-lapse imaging of OSM-5::GFP fusion protein shows fluorescent particle migration within the cilia. Overall, the data support a crucial role for osm-5 in a conserved ciliogenic pathway, most likely as a component of the IFT process. http://www.biologists. com/Development/movies/dev3342.html
Topological properties of the drug targets regulated by microRNA in human protein–protein interaction network

The investigation of topological properties of proteins in protein–protein interaction network (PPIN) has great potentials to identify basic protein functions and mechanisms of action. Based on human PPIN, previous study has shown that the topological properties of drug targets are significantly distinguished from those of proteins that are not targeted by drugs (non-drug-targets). MicroRNAs (miRNAs) are known to regulate gene expression at the post-transcriptional level. To determine whether the differences in topological properties between drug targets and non-drug-targets are dominated by the proteins that are regulated by miRNA, we divided the drug targets into two sets: those are regulated by miRNA (mir-drug-targets) and those are not regulated by miRNA (non-mir-drug-targets). We compared the probability of interactions and five topological properties among the three types of proteins in human PPIN. Our results demonstrated that mir-drug-targets preferentially interact with other mir-drug-targets and tend to be hub-bottlenecks. However, there was no bias on topological properties between non-mir-drug-targets and non-drug-targets. The same topological features are observed among non-drug targets. These findings indicate that miRNA regulation has an important role in human PPIN, and may be useful in the development of novel drugs.
A low power localization architecture and system for wireless sensor networks

Localization (or locationing) is a central concern for ubiquitous self-configuring sensor networks. The implementation of a distributed, least-squares-based localization algorithm is presented. Low power and energy dissipation are key requirements for sensor networks. As part of the sensor network, the localization system must also conform to these requirements. An ultra-low-power and dedicated hardware implementation of the localization system is therefore presented. The cost of fixed-point implementation is also investigated. The design is implemented in a 0.13 /spl mu/ CMOS process. It dissipates 1.7 mW of active power and 0.122 nJ/op of active energy with a silicon area of 0.55 mm/sup 2/. The mean calculated location error due to fixed-point implementation is shown to be 6%.
Study of the accreting pulsar 4U 0115+63 using a bulk and thermal Comptonization model

Context. Highly magnetized pulsars accreting matter in a binary system are bright sources in the X-ray band (0.1–100 keV). Despite the early comprehension of the basic emission mechanism, their spectral energy distribution is generally described by phenomenological or simplified models. Aims. We propose a study of the spectral emission from the high mass X-ray binary pulsar 4U 0115+63 by means of thermal and bulk Comptonization models based on the physical properties of such objects. Methods. For this purpose, we analyze the BeppoSAX data in the energy range 0.7–100 keV of the 1999 giant outburst, 12 days after the maximum. We focus first on the phase averaged emission, and then on the phase resolved spectra, by modeling the system using a two-component continuum. Results. At higher energy, above ∼7 keV, the emission is due to thermal and bulk Comptonization of the seed photons produced by cyclotron cooling of the accretion column, and at lower energy, the emission is due to thermal Comptonization of a blackbody source in a diffuse halo close to the stellar surface. Phase resolved analysis establishes that most of the emission in the main peak comes from the column, while the low energy component gives a nearly constant contribution throughout the phase. Conclusions. From the best fit parameters, we argue that the cyclotron emission is produced ∼1.7 km above the stellar surface, and escapes from the column near its base, where the absorption features are generated by the interaction with the magnetic field in the halo. We find that in 4U 0115+63, the observed spectrum is dominated by reprocessed cyclotron radiation, whereas in other bright sources with stronger magnetic fields such as Her X-1, the spectrum is dominated by reprocessed bremsstrahlung.
A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Samples

Adversarial samples are maliciously created inputs that lead a learning-based classifier to produce incorrect output labels. An adversarial sample is often generated by adding adversarial perturbation (AP) to a normal test sample. Recent studies that tried to analyze classifiers under such AP are mostly empirical and provide little understanding of why. To fill this gap, we propose a theoretical framework for analyzing learning-based classifiers, especially deep neural networks (DNN) in the face of such AP. By using concepts from topology, this framework brings forth the key reasons why an adversarial can fool a classifier (f1) and suggests a new focus on its oracle (f2, like human annotators of that specific task). By investigating the topology relationship between two (pseudo)metric spaces corresponding to predictor f1 and oracle f2, we develop several ideal conditions that can determine if f1 is always robust (strong-robust) against adversarial samples according to its f2. The theoretical framework leads to a set of novel and complementary insights that have not been uncovered by the literature. Surprisingly our theorems find that just one extra irrelevant feature can make a classifier not strong-robust, and the right feature representation learning is the key to getting a classifier that is both accurate and strong robust. Empirically we find that "Siamese architecture" can be used to help DNN models get close to the desired topological relationship for strong-robustness, which in turn effectively improves its performance against AP.
Remote sensing of swell waves in the North Sea with Sentinel-1 Synthetic Aperture Radar

The North Sea is known for its marine activities, which need ocean data for safe operations. The ocean surface is described using the two-dimensional wave spectrum. This ocean wave spectrum can be estimated using wave buoys, wave models or remote sensing techniques. Wave buoys and numerical models are known to contain inaccuracies, which stresses the importance of other observation techniques. The solution of this thesis is using a remote sensing technique from Sentinel-1: Synthetic Aperture Radar. In total 11 case studies were analyzed in the North sea and 3 case studies in Portugal. Sentinel-1 SAR cross-spectra were verified and validated using wave buoy measurements and cross-spectra from simulated SAR images from OCEANSAR. In total, 6 North Sea case studies showed a positive result, where a swell peak was visible and the peak matched spectral data from wave buoys. Case studies from the Portuguese coast showed the best results, where SAR cross-spectra agreed well with buoy measurements. Further improvements include the removal of non-linear contribution from cross-spectra, mapping of wave buoy measurements on the quasi-linear swell spectrum and calculating the full Modulation Transfer Function. This thesis showed it is possible to measure low-frequency waves in the North Sea using Sentinel-1 Synthetic Aperture Radar. SAR images lead to a better understanding of the movements of swells and provide additional information for marine activities. A combination of wave buoy data, Sentinel-1 SAR data and OCEANSAR data showed measuring swell waves is possible with this state-of-the-art remote sensing technique.
On optimal degree selection for polynomial kernel with support vector machines: Theoretical and empirical investigations

The key challenge in kernel based learning algorithms is the choice of an appropriate kernel and its optimal parameters. Selecting the optimal degree of a polynomial kernel is critical to ensure good generalisation of the resulting support vector machine model. In this paper we propose Bayesian and Laplace approximation methods to estimate the polynomial degree. A rule based meta-learning approach is then proposed for automatic polynomial kernel and its optimal degree selection. The new approach is constructed and tested on different sizes of 112 datasets with binary class as well as multi class classification problems. An extensive computational evaluation of these methods is conducted, and rules are generated to determine when these approximation methods are appropriate.