Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of β-adrenergic stimulation on the relationship between membrane potential, intracellular [Ca2+] and sarcoplasmic reticulum Ca2+ uptake in rainbow trout atrial myocytes

SUMMARY Long depolarizations cause a steady tonic contraction and induce sarcoplasmic reticulum (SR) Ca2+-uptake in trout atrial myocytes. Simultaneous measurements of cytosolic [Ca2+] ([Ca2+]i) and whole membrane current showed an elevated [Ca2+]i throughout the depolarization. Rapid caffeine (Caf) applications at –80 mV before and after a long depolarization were used to determine SR Ca2+ loading and its dependency on membrane potential and [Ca2+]i during depolarization. Following a 10 s depolarization, the maximal SR Ca2+ load was 597 μmol l–1 and loading was half-maximal at –12 mV. Theβ -adrenergic agonist isoproterenol (ISO) did not affect the maximal SR Ca2+ loading but shifted the potential for half-maximal loading by– 26 mV. Following a 3 s depolarization, the maximal SR Ca2+ uptake rate (V̇max) was 418μ mol l–1 s–1 in control conditions. ISO did not affect V̇max, but significantly lowered the average free Ca2+ transient during the depolarization and shifted the K0.5 for the relationship between SR Ca2+ uptake and [Ca2+]i from 1.27 in control to 0.8 μmol l–1 with ISO. Following repetitive 200 ms depolarizations, ISO increased the l-type Ca2+ current (ICa) amplitude by 91±29% and the peak Ca2+ transient by 41±10%, and decreased the half life of the Ca2+ transient from 151±12 to 111±6 ms. Using the relationship between [Ca2+]i and SR Ca2+ uptake to calculate the total SR Ca2+ uptake during a Ca2+ transient elicited by a 200 ms depolarization, a significant increase in the SR Ca2+ uptake from 37±6 μmol l–1 in control to 68±4 μmol l–1 with ISO was seen. When normalized to the total Ca2+ transport the contribution of the SR was not significantly different in the absence (35±6%) or presence of ISO (41±4%). Exposure of cells to ISO and low extracellular [Ca2+] increased ICa by 67±40% (N=5) but significantly reduced SR Ca2+ uptake at membrane potentials above –30 mV. Together, these results suggest that (i) ISO has a stimulatory effect on the SR Ca2+ pump that may contribute to the faster decay of the Ca2+ transient, and (ii) the relative contribution of the SR to the Ca2+ removal during relaxation is not altered by ISO in trout atrial myocytes.
Asymptotic distributions of principal components based on robust dispersions

Algebraically, principal components can be defined as the eigenvalues and eigenvectors of a covariance or correlation matrix, but they are statistically meaningful as successive projections of the multivariate data in the direction of maximal variability. An attractive alternative in robust principal component analysis is to replace the classical variability measure, i.e. variance, by a robust dispersion measure. This projection-pursuit approach was first proposed in Li & Chen (1985) as a method of constructing a robust scatter matrix. Recent unpublished work of C. Croux and A. Ruiz-Gazen provided the influence functions of the resulting principal components. The present paper focuses on the asymptotic distributions of robust principal components. In particular, we obtain the asymptotic normality of the principal components that maximise a robust dispersion measure. We also explain the need to use a dispersion functional with a continuous influence function. Copyright Biometrika Trust 2003, Oxford University Press.
An Exact Linearization Method for OLTC of Transformer in Branch Flow Model

The branch flow based optimal power flow (OPF) problem in radially operated distribution networks can be exactly relaxed to a second-order cone programming model without considering transformers. However, the introduction of nonlinear transformer models will make the OPF model nonconvex. This letter presents an exact linearized transformer's on-load tap-changer model to keep the OPF model convex via a binary expansion scheme and big-M method. Validity of the proposed method is verified using IEEE 33-bus test network.
Incorporating Incremental and Active Learning for Scene Classification

Scene classification is useful for automatic organization of personal digital photographs or visual guidance of robots, but it is a time consuming and labor-intensive task to label adequate examples to train robust classifiers. Active learning is a key technique to reduce human-labeling burden by exploring an optimal subset from unlabeled data. In this paper we use a batch mode incremental and active learning framework to construct scene classification models. In traditional batch examples selection methods, there often exists redundancy information between these top informative examples. To alleviate the impact of redundancy, we employ two effective batch selection strategies which one is called multi-pool based BvSB and the other is called K-centroid cluster BvSB, experimental results with widely used 15 scene and UIUC-sports datasets demonstrated that our scheme can get better results than that only using BvSB measurement which does not considering redundancy. In order to improve efficiency, batch mode incremental support vector machines are employed. With the incremental learning scheme, the training process of active learning is much more efficient than that using all the selected examples to retrain the classification models in each round.
Assessment for Excellence: The Philosophy and Practice of Assessment and Evaluation in Higher Education. American Council on Education/Macmillan Series on Higher Education.

In this detailed study, Astin examines why assessment activity has produced such meager results and, just as important, how existing activities can be improved. The author also discusses what new assessment practices can be implemented and shares specific and sometimes startling ideas on: --How assessment information can most effectively be used for evaluation; --How results can be used to enlighten and inform the practitioner; --How practical, technical, and political problems can be overcome when building an assessment database from student and faculty input; --How the movement of externally mandated assessments in various states is having a negative impact on higher education.
High precision document classication in over-abundant domains: A case study of web pornography search

We present a small, but important, set of techniques that were found to boost precision in web pornography search, as judged by volunteer annotators. Both structured and statistical approaches yielded statistically significant improvements in document classification. We detail the initial corpus-development process, as well as empirical trials that demonstrate the efficacy of our proposed techniques.
Region based image appeal metric for consumer photos

Image appeal may be defined as the interest that a photograph generates when viewed by human observers, incorporating subjective factors on top of the traditional objective quality measures. User studies were conducted in order to identify the right features to use in an image appeal measure; these studies also revealed that a photograph may be appealing even if only a region/area of the photograph is actually appealing. Extensive experimentation helped identify a good set of low level features, which were combined into a set of image appeal metrics. The appealing region detection and the image appeal metrics were validated with experiments conducted on 2000 ground truth images, each graded by three human observers. Two main image appeal metrics are presented: one that ranks the images based on image appeal with a high correlation with the human observations; and a second one which performs better at retrieving highly appealing images from an image collection.
An audio information hiding scheme with high — Rui Xu2, Xue-mei Li1 capacity based on compressed sensing and psychoacoustic model

Audio information hiding is a technique, which is used to hide secret message into the audio flows. In this paper, an audio information hiding scheme with high-capacity is proposed, which uses both Compressed Sensing (CS) and psychoacoustic model algorithms for improving the capacity of hidden message and the auditory imperceptibility. The scheme first applies CS technology to compress the secret speech and reduce the embedded information bit rate, and then employs psychoacoustic model algorithm for determining embedding strength of secret information. The scheme takes advantages of DWT and synchronization signal of m-sequence to realize blind extraction. Experiments results show that this scheme not only has good imperceptibility robustness, but also has high-capacity and blind extraction.
Selecting the optimal antithrombotic regimen for patients with acute coronary syndromes undergoing percutaneous coronary intervention

The wide variety of anticoagulant and antiplatelet agents available for clinical use has made choosing the optimal antithrombotic regimen for patients with acute coronary syndromes undergoing percutaneous coronary intervention a complex task. While there is no single best regimen, from a risk-benefit ratio standpoint, particular regimens may be considered optimal for different patients. We review the mechanisms of action for the commonly prescribed antithrombotic medications, summarize pertinent data from randomized trials on their use in acute coronary syndromes, and provide an algorithm (incorporating data from these trials as well as risk assessment instruments) that will help guide the decision-making process.
From Dots to Shapes: an auditory haptic game platform for teaching geometry to blind pupils

This paper describes "From Dots to Shapes" (FDTS), an auditory platform composed by three classic games ( Simon , Point Connecting and concentration game ) for blind and visually impaired pupils. Each game was adapted to work on a concept of the Euclidean geometry (e.g. ) The tool, , is based on sonic and haptic interaction, and therefore could be used by special educators as a help for teaching basic planar geometry.
Leadership Styles and Turnover Behavior in the US Federal Government: Does Span of Control Matter?

ABSTRACT: Even when leaders exhibit desirable leadership styles, the effectiveness of their behaviors on organizational outcomes may be influenced by the structural distance between them and their followers. Nonetheless, previous empirical research on the relationships between leadership styles and outcomes has largely ignored the role of leader distance in public administration. Using panel data drawn from the US federal government, this study explores how transformational and transactional leadership styles affect employee turnover behavior and how span of control as a proxy for leader distance moderates those relationships. The statistical results indicate that transformational leadership is negatively related to turnover behavior, whereas transactional leadership is positively related to turnover behavior. Further analysis shows that span of control significantly moderates the relationships between the two leadership styles and employee turnover behavior such that organizations benefit more from transformational leaders with a broad span of control than from those with a narrow span of control. Conversely, transactional leaders with a narrow span of control are better at reducing turnover than those with a broad span of control. The implications and contributions of these results for public administration are discussed.
Estimation of Place of Articulation During Stop Closures of Vowel–Consonant–Vowel Utterances

Production of vowel-oral stop consonant-vowel utterances involves movement of articulators from the articulatory position of the initial vowel towards that of the oral stop closure, and then to that of the final vowel. As the closure segments have zero or low signal energy, linear predictive coding (LPC)-based estimation of vocal tract shape fails during stop closure. This paper reports a technique for estimation of place of articulation during stop closures by performing bivariate polynomial modeling on vocal tract area values during transition segments preceding and following the closure. The technique with second-degree polynomial modeling was found to be suitable for estimating the place of maximum constriction during stop closure segments of vowel-consonant-vowel utterances with bilabial, alveolar, and velar stops. The estimated places compared well with the actual constriction locations observed from the articulatory data. The technique may be useful for improving effectiveness of speech-training aids for production of stop consonants by providing visual feedback of the estimated place of articulation.
Multilevel Granularity Parallelism Synthesis on FPGAs

Recent progress in High-Level Synthesis (HLS) techniques has helped raise the abstraction level of FPGA programming. However implementation and performance evaluation of the HLS-generated RTL, involves lengthy logic synthesis and physical design flows. Moreover, mapping of different levels of coarse grained parallelism onto hardware spatial parallelism affects the final FPGA-based performance both in terms of cycles and frequency. Evaluation of the rich design space through the full implementation flow - starting with high level source code and ending with routed net list - is prohibitive in various scientific and computing domains, thus hindering the adoption of reconfigurable computing. This work presents a framework for multilevel granularity parallelism exploration with HLS-order of efficiency. Our framework considers different granularities of parallelism for mapping CUDA kernels onto high performance FPGA-based accelerators. We leverage resource and clock period models to estimate the impact of multi-granularity parallelism extraction on execution cycles and frequency. The proposed Multilevel Granularity Parallelism Synthesis (ML-GPS) framework employs an efficient design space search heuristic in tandem with the estimation models as well as design layout information to derive a performance near-optimal configuration. Our experimental results demonstrate that ML-GPS can efficiently identify and generate CUDA kernel configurations that can significantly outperform previous related tools whereas it can offer competitive performance compared to software kernel execution on GPUs at a fraction of the energy cost.
Heuristics and biases in autism: Less biased but not more logical.

In this study we compared the performance of autistic and typically developing adolescents on three well-known tasks from the heuristics and biases literature. As heuristic reasoning is based on the contextualisation of problems (e.g., Stanovich, 2003) and autistic people are known to be less sensitive to contextual features (e.g., Frith & Happe, 1994) we expected them to exhibit less bias on these tasks. In line with our predictions, autistic children were less susceptible to the conjunction fallacy, and they also gave more base rate responses in one version of the engineers and lawyers task. However, their performance on the control tasks indicated that they were not any more sensitive to probabilistic information than the control group (i.e., they were not more logical in a normative sense).
Impaired Fear Extinction Learning and Cortico-Amygdala Circuit Abnormalities in a Common Genetic Mouse Strain

Fear extinction is a form of new learning that results in the inhibition of conditioned fear. Trait deficits in fear extinction are a risk factor for anxiety disorders. There are few examples of naturally occurring animal models of impaired extinction. The present study compared fear extinction in a panel of inbred mouse strains. This strain survey revealed an impairment in fear extinction in 129/SvImJ (129S1). The phenotypic specificity of this deficit was evaluated by comparing 129S1 and C57BL/6J for one-trial and multitrial fear conditioning, nociception, and extinction of conditioned taste aversion and an appetitive instrumental response. 129S1 were tested for sensitivity to the extinction-facilitating effects of extended training, as well as d-cycloserine and yohimbine treatment. To elucidate the neural basis of impaired 129S1 fear extinction, c-Fos and Zif268 expression was mapped after extinction recall. Results showed that impaired fear extinction in 129S1 was unrelated to altered fear conditioning or nociception, and was dissociable from intact appetitive extinction. Yohimbine treatment facilitated extinction in 129S1, but neither extended extinction training nor d-cycloserine treatment improved 129S1 extinction. After extinction recall, 129S1 showed reduced c-Fos and Zif268 expression in the infralimbic cortex and basolateral amygdala, and elevated c-Fos or Zif268 expression in central nucleus of the amygdala and medial paracapsular intercalated cell mass, relative to C57BL/6J. Collectively, these data demonstrate a deficit in fear extinction in 129S1 associated with a failure to properly engage corticolimbic extinction circuitry. This common inbred strain provides a novel model for studying impaired fear extinction in anxiety disorders.
The star-forming content of the W3 giant molecular cloud

We have surveyed a ∼0.9 square degree area of the W3 giant molecular cloud (GMC) and star-forming region in the 850-μm continuum, using the Submillimetre Common-User Bolometer Array on the James Clerk Maxwell Telescope. A complete sample of 316 dense clumps were detected with a mass range from around 13 to 2500 M⊙. Part of the W3 GMC is subject to an interaction with the H II region and fast stellar winds generated by the nearby W4 OB association. We find that the fraction of total gas mass in dense, 850-μm traced structures is significantly altered by this interaction, being around 5-13 per cent in the undisturbed cloud but ∼25-37 per cent in the feedback-affected region. The mass distribution in the detected clump sample depends somewhat on assumptions of dust temperature and is not a simple, single power law but contains significant structure at intermediate masses. This structure is likely to be due to crowding of sources near or below the spatial resolution of the observations. There is little evidence of any difference between the index of the high-mass end of the clump mass function in the compressed region and in the unaffected cloud. The consequences of these results are discussed in terms of current models of triggered star formation.
Lensing reconstruction from Planck sky maps: inhomogeneous noise

We discuss the effects of inhomogeneous sky coverage on cosmic microwave background lens reconstruction, focusing on application to the recently launched Planck satellite. We discuss the 'mean field' which is induced by noise inhomogeneities, as well as three approaches to lens reconstruction in this context: an optimal maximum-likelihood approach which is computationally expensive to evaluate and two suboptimal approaches which are less intensive. The first of these is only suboptimal at the 5 per cent level for Planck, and the second prevents biasing due to uncertainties in the noise model.
Multi-sensor input path planning for an autonomous ground vehicle

Autonomous Unmanned Ground Vehicles (UGV's) are mobile platforms that serve a wide range of specialized applications in urban, military, domestic, and industrial settings. UGV's can be remotely operated or autonomous and usually include a variety of sensors and manipulators that are used to solve specific investigation tasks. They also include sensory input for use in autonomous navigation algorithms. For example, radio activity or explosive sensors can help the remote assessment of a dangerous area. In the case of autonomous navigation, a UGV usually employs Light Detection and Ranging (LIDAR) sensors a. k. a. laser range finders, ultra sonic sensors, cameras, and Global Positioning System (GPS) receivers to avoid obstacles and follow a set of GPS waypoints that define a path for the UGV to cover. This paper presents the development of autonomous path planning in a UGV that uses various sensors including, a laser range finder, a digital compass, a GPS receiver, and computer vision. The sensor data is fused in a “cost matrix” that assigns positive numerical values to obstacles detected using the various sensors. Negative value contributions are added to the cost matrix is areas corresponding to the desired heading dictated by GPS waypoint navigation. A cost function is implemented by adding cost matrix values over several possible paths crossing the matrix, causing the lowest-cost path to be selected as the UGV's next heading. The algorithm was tested on a grassy path with white lines defining an allowable path that includes various physical obstacles.
Efficiency enhancement of genetic algorithms via building-block-wise fitness estimation

This paper studies fitness inheritance as an efficiency enhancement technique for a class of competent genetic algorithms called estimation distribution algorithms. Probabilistic models of important sub-solutions are developed to estimate the fitness of a proportion of individuals in the population, thereby avoiding computationally expensive function evaluations. The effect of fitness inheritance on the convergence time and population sizing are modeled and the speed-up obtained through inheritance is predicted. The results show that a fitness-inheritance mechanism which utilizes information on building-block fitnesses provides significant efficiency enhancement. For additively separable problems, fitness inheritance reduces the number of function evaluations to about half and yields a speed-up of about 1.75-2.25.
Multi-sensor Visual Analytics Supported by Machine-Learning Models

Machines, such as engines, vehicles, or even aircraft, go through extensive controlled trials during their development. Each machine is typically instrumented with hundreds of sensors that produce voluminous time-series data. Engineers analyze suchdata to improve their understanding of how machines are used in practice, which in turn helps them in taking design decisions. Most often they study operational profiles various sensors fora given day of operation using histograms, or examine time-series from multiple sensors together. However, when confrontedwith data from dozens of sensors, over many years of operation, they are challenged by the large number of histograms toanalyze, and the sheer length of time-series' to explore. Traditional approaches such as hierarchical histograms, time-series semantic zooming etc. often cannot cope with the volume of data encountered in practice. We augment basic data visualizations such as histograms, heat-maps and basic time-series visualizations with machine-learning models that aid in summarizing, querying, searching, and interactively linking visualizations derived fromlarge volumes of multi-sensor data. In this paper we describe our machine-learning augmented approach to visual analytics in thecontext of its actual use in practice for answering questions ofinterest to engineers analyzing large-scale multi-sensor data.
The Rsr1/Bud1 GTPase Interacts with Itself and the Cdc42 GTPase during Bud-Site Selection and Polarity Establishment in Budding Yeast

Bimolecular fluorescence complementation assays allow the visualization of the homotypic and heterotypic GTPase interactions in vivo. The Rsr1 homotypic interaction involves its polybasic region and depends on its GDP-GTP exchange factor. Dimerization of GTPases may be an efficient mechanism to set up cellular asymmetry.