Miracle-dz/UniFAR-FTUs huggingface dataset:
Stellar Lyα Emission Lines in the Hubble Space Telescope Archive: Intrinsic Line Fluxes and Absorption from the Heliosphere and Astrospheres

We search the Hubble Space Telescope (HST) archive for previously unanalyzed observations of stellar H I Lyα emission lines, our primary purpose being to look for new detections of Lyα absorption from the outer heliosphere and to also search for analogous absorption from the astrospheres surrounding the observed stars. The astrospheric absorption is of particular interest because it can be used to study solar-like stellar winds that are otherwise undetectable. We find and analyze 33 HST Lyα spectra in the archive. All the spectra were taken with the E140M grating of the Space Telescope Imaging Spectrograph (STIS) instrument on board HST. The HST STIS spectra yield four new detections of heliospheric absorption (70 Oph, ξ Boo, 61 Vir, and HD 165185) and seven new detections of astrospheric absorption (EV Lac, 70 Oph, ξ Boo, 61 Vir, δ Eri, HD 128987, and DK UMa), doubling the previous number of heliospheric and astrospheric detections. When combined with previous results, 10 of 17 lines of sight within 10 pc yield detections of astrospheric absorption. This high detection fraction implies that most of the ISM within 10 pc must be at least partially neutral, since the presence of H I within the ISM surrounding the observed star is necessary for an astrospheric detection. In contrast, the detection percentage is only 9.7% (3 out of 31) for stars beyond 10 pc. Our Lyα analyses provide measurements of ISM H I and D I column densities for all 33 lines of sight, and we discuss some implications of these results. Finally, we measure chromospheric Lyα fluxes from the observed stars. We use these fluxes to determine how Lyα flux correlates with coronal X-ray and chromospheric Mg II emission, and we also study how Lyα emission depends on stellar rotation.
Survey on Outlier Detection in Data Mining

Data Mining is used to extract useful information from a collection of databases or data warehouses. In recent years, Data Mining has become an important field. This paper has surveyed upon data mining and its various techniques that are used to extract useful information such as clustering, and has also surveyed the techniques that are used to detect the outliers. This paper also presents various techniques used by different researchers to detect outliers and present the efficient result to the user.
Complexity effects in visuo-spatial working memory: Implications for the role of long-term memory

Several studies have shown that the capacity of visuo-spatial working memory is limited by complexity. Using a variant of the Corsi blocks task, this paper investigates the effect of complexity of the to-beremembered path on visuo-spatial memory span. Redundancy was determined by three Gestalt principles: symmetry, repetition, and continuation. Experiment 1 revealed an effect of path complexity. The subsequent experiments explored whether the superiority for recall of structured over complex paths can be attributed solely to the operation of visuo-spatial working memory, or whether it also reflects the use of long-term knowledge. Experiment 2 demonstrated that the effect of complexity remained, even when the mechanisms for visuo-spatial coding were removed by a secondary visuo-spatial task. In Experiments 3 and 4 subjects were trained in the recall of complex paths. This led to the creation of long-term memory representations for these paths, as shown by an improvement in their span, and a concomitant lack of transfer to new paths. Finally, Experiment 5 showed that one prior repetition of a complex path was sufficient to produce specific and long-term learning effects. These results point to the involvement of long-term memory processes in the temporary retention of visuo-spatial material for which representations exist in long-term memory. They also suggest that the effect of complexity may provide a tractable technique for investigating the mechanisms underlying the limits of visuo-spatial short-term storage.
Intelligent Advertising for User Generated Content through Sentiment Analysis

The tradeoff between revenue and market share triggers the emergence of targeted advertising. However, all previous related work only focuses on the topical relevance of ads and does not concern the attitudes of consumers. In our paper, we propose a novel advertising strategy DASA (Dissatisfaction-oriented Advertising based on Sentiment Analysis) which takes the attitudes of consumers into consideration and promotes ads according to what consumers are unsatisfied with. Our work is most suitable for User Generated Content which contains plenty of consumers’ opinionated information. The experiments show encouraging results.
Adrenaline Stimulates Glucagon Secretion in Pancreatic A-Cells by Increasing the Ca2+ Current and the Number of Granules Close to the L-Type Ca2+ Channels

We have monitored electrical activity, voltage-gated Ca2+ currents, and exocytosis in single rat glucagon-secreting pancreatic A-cells. The A-cells were electrically excitable and generated spontaneous Na+- and Ca2+-dependent action potentials. Under basal conditions, exocytosis was tightly linked to Ca2+ influx through ω-conotoxin-GVIA–sensitive (N-type) Ca2+ channels. Stimulation of the A-cells with adrenaline (via β-adrenergic receptors) or forskolin produced a greater than fourfold PKA-dependent potentiation of depolarization-evoked exocytosis. This enhancement of exocytosis was due to a 50% enhancement of Ca2+ influx through L-type Ca2+ channels, an effect that accounted for <30% of the total stimulatory action. The remaining 70% of the stimulation was attributable to an acceleration of granule mobilization resulting in a fivefold increase in the number of readily releasable granules near the L-type Ca2+ channels.
Analysis of the results of lotteries using statistical methods and artificial neural networks

The popularity of lotteries has increased worldwide in the last few years, and so its revenues, making them an useful study case. This paper shows the research done for testing the randomness of the results of three lotteries and to check if there were any patterns, studying them as time series. This research was done using three methods, statistical randomness tests, autoregressive integrated moving averages (ARIMA), and artificial neural networks (ANN). At the end any sign of patterns or suspicious data was not found. Key-Words: Artificial neural networks, ARIMA, lottery, randomness tests, pattern recognition
Energy Efficient Routing for Multi-hop Underwater Acoustic Networks

Th e goal of this paper is to establish the optimal routing path that minimizes the total energy consumption for a given target signal-to-noise ratio (SNR) for a 2-dimensional multi-hop underwater acoustic network. The energy efficient routing paths for both variable transmission power (VTP) and fixed transmission power (FTP) schemes were investigated. An empirical closed-form expression of the optimal frequency-distance relationship was derived as an effective way to simplify the calculation. It was shown that the total energy consumption of the network is minimized only if all the hop distances are equal for a linear multi-hop network for both VTP and FTP sceneries. In the case of equispaced relays, the analytical results on the optimal number of hops that minimizes the total energy consumption and the optimal total energy consumption of the network were also provided. The results may provide theoretic foundation for the establishment of nearshore resource monitoring system.
Intelligent Support for Resource Quality Evaluation and Description in Health Information Portals

Quality control is a critical issue in online health information provision, where domain experts play an important role in evaluating and selecting quality and relevant resources to meet health consumers' individual knowledge needs. This research aims for a semi-automated approach to tackle challenges domain experts encounter in resource quality evaluation and description in the context of health information portals. This paper reports on the first stage of this research in which the domain experts' needs for intelligent supports are investigated. From this analysis, the architecture of a domain expert dashboard is proposed, where quality framework is distinguished among a suite of tools.
Quantifying the response of structural complexity and community composition to environmental change in marine communities

Habitat structural complexity is a key factor shaping marine communities. However, accurate methods for quantifying structural complexity underwater are currently lacking. Loss of structural complexity is linked to ecosystem declines in biodiversity and resilience. We developed new methods using underwater stereo‐imagery spanning 4 years (2010–2013) to reconstruct 3D models of coral reef areas and quantified both structural complexity at two spatial resolutions (2.5 and 25 cm) and benthic community composition to characterize changes after an unprecedented thermal anomaly on the west coast of Australia in 2011. Structural complexity increased at both resolutions in quadrats (4 m2) that bleached, but not those that did not bleach. Changes in complexity were driven by species‐specific responses to warming, highlighting the importance of identifying small‐scale dynamics to disentangle ecological responses to disturbance. We demonstrate an effective, repeatable method for quantifying the relationship among community composition, structural complexity and ocean warming, improving predictions of the response of marine ecosystems to environmental change.
Opportunistic Underlay Transmission in Multi-Carrier Cognitive Radio Systems

Underlay transmission in cognitive radio enables a secondary (unlicensed) system to utilize a frequency band of primary (licensed) system as long as the unlicensee interferes less than a certain threshold with the licensee. The secondary system needs to carefully consider not only its own channel to achieve a capacity gain by this sharing spectrum in multi-carrier systems, but also the interference channel to reduce interference at the primary receiver. In this paper, we formulate a capacity maximization problem of the secondary system under an interference-power constraint as well as a conventional transmit-power constraint, and propose an optimal power allocation policy in which we exploit a two-dimensional frequency-selectivity on both channels. Through extensive simulations, we compare the performance of optimal power allocation policy with that of equal power allocation policy and further investigate the effect of the primary's power allocation policy on the performance of the secondary system. Numerical results show that the optimal power allocation policy can achieve a higher capacity in more frequency-selective channels, compared to an equal power allocation policy. Interestingly, a water-filling policy for the primary system also gives additional opportunities to the secondary system than the equal power allocation policy.
Automatic edge matching across an image sequence based on reliable points

This paper presents a new method for matching edges across a video image sequence. The method can deal with uncalibrated images acquired with a hand held camera. Compared to previous work, the method employs geometric constraints between edges based on reliable matched points, which reduces the search space for corresponding 2D edges in the frames. The 3D edge parameters are estimated from these matched 2D edges by using the Gauss-Markoff model with constraints. End points of each 3D edge are found by analyzing the end points of the corresponding 2D edges. The results show that the developed algorithms are able to efficiently and accurately reconstruct 3D edges from image sequences. * Corresponding author.
Minimal Data Needed for Valid & Accurate Image-Based fMRI Meta-Analysis

Meta-analysis is a powerful statistical tool to combine results from a set of studies. When image data is available for each study, a number of approaches have been proposed to perform such meta-analysis including combination of standardised statistics, just effect estimates or both effects estimates and their sampling variance. While the latter is the preferred approach in the statistical community, often only standardised estimates are shared, reducing the possible meta-analytic approaches. Given the growing interest in data sharing in the neuroimaging community there is a need to identify what is the minimal data to be shared in order to allow for future image-based meta-analysis. In this paper, we compare the validity and the accuracy of eight meta-analytic approaches on simulated and real data. In one-sample tests, combination of contrast estimates into a random-effects General Linear Model or non-parametric statistics provide a good approximation of the reference approach. If only standardised statistical estimates are shared, permutations of z-score is the preferred approach.
Service Oriented Load Balancing Framework in Computational Grid Environment

Grid computing or computational grid has become a vast research field in academics. It is a promising platform that provides resource sharing through multi-institutional virtual organizations for dynamic problem solving. Such platforms are much more cost-effective than traditional high performance computing systems. Due to the provision of scalability of resources, these days grid computing has become popular in industry as well. However, computational grid has different constraints and requirements to those of traditional high performance computing systems. In order to fully exploit such grid systems, resource management and scheduling are key challenges, where issues of task allocation and load balancing represent a common problem for most grid systems as because the load scenarios of individual grid resources are dynamic in nature. The objective of this paper is to review different existing load balancing algorithms or techniques applicable in grid computing and propose a layered service oriented framework for computational grid to solve the prevailing problem of dynamic load balancing.
2 Laminar and Inertial Scalings of the Drag

Experimental measurements show that turbulent flows can be decomposed into well-defined averages plus fluctuations, v = v̄ + v′. This is nicely illustrated in the Turbulence film available online at http://web.mit. edu/hml/ncfmf.html that was already mentioned in Lecture 1. In experiments, the average v̄ is often taken to be a time average since it is easier to measure the velocity at the same point for long times, while in theory it is usually an ensemble average — an average over many realizations of the same flow. For numerical simulations and mathematical analysis in simple geometries such as channels and pipes, the average is most easily defined as an average over the homogeneous or periodic directions. Given a velocity field v(x, y, z, t) we define a mean velocity for a shear flow with laminar flow U(y)x̂ by averaging over the directions (x, z) perpendicular to the shear direction y,
Cognition and hearing aids . 1 Cognition and hearing aids

The perceptual information transmitted from a damaged cochlea to the brain is more poorly specified than information from an intact cochlea and requires more processing in working memory before language content can be decoded. In addition to making sounds audible, current hearing aids include several technologies that are intended to facilitate language understanding for persons with hearing-impairment in challenging listening situations. These include directional microphones, noise reduction, and fast-acting amplitude compression systems. However, the processed signal itself may challenge listening to the extent that with specific types of technology, and in certain listening situations, individual differences in cognitive processing resources may determine listening success. Here, current and developing digital hearing aid signal processing schemes are reviewed in the light of individual working memory (WM) differences. It is argued that signal processing designed to improve speech understanding may have both positive and negative consequences, and that these may depend on individual WM capacity.
It Takes Two to Tango: Process Integration and Wages

This paper looks at the relationship between technological complexity and wages. Using a panel of UK establishments, we find that wages increase with the degree of complexity, measured both by the task ratio and by the presence of integrated process controls. Our findings are consistent with Kremer's (1993) "O-Ring" production function, which implies strong complementarities among the production activities. The ability to implement performance appraisal systems has a negative and significant effect on wages, consistent with the efficiency wage hypothesis. The use of minicomputers or the existence of a computer network increase the within-firm wage dispersion, which is also consistent with the presence of complementarities in the production process.
Robust ASR in Reverberant Environments using Temporal Cepstrum Smoothing for Speech Enhancement and an Amplitude Modulation Filterbank for Feature Extraction

This paper presents techniques aiming at improving automatic speech recognition (ASR) in single channel scenarios in the context of the REVERB (REverberant Voice Enhancement and Recognition Benchmark) challenge. System improvements range from speech enhancement over robust feature extraction to model adaptation and word-based integration of multiple classiﬁers. The selective temporal cepstrum smoothing (TCS) technique is applied to enhance the reverberant speech signal at moderate noise levels, based on a statistical model of room impulse responses (RIRs) and minimum statistics (MS), considering estimates of late reverberations and the noise power spectrum densities (PSDs). Robust feature extraction is performed by amplitude modulation ﬁltering of the cepstrogram to extract its temporal modulation information. As an alternative classiﬁer, the acoustic models have been adopted using different RIRs and a RIR selection scheme based on a multi-layer perceptron (MLP) system that uses spectro-temporal features as the input. In the ﬁnal stage, a system combination approach achieved by recognizer output voting error reduction (ROVER) is employed to obtain a jointly optimal recognized transcription. The proposed system has been evaluated in two different processing modes, i.e. utterance-based batch processing and full batch processing , which results in an overall average absolute improvement of 11% under variant reverberant conditions compared to the baseline system
Comparing Bioenergetics Models of Double-Crested Cormorant (Phalacrocorax auritus) Fish Consumption

Abstract. Double-crested Cormorants (Phalacrocorax auritus) began to recolonize Leech Lake, Minnesota, in the 1990s and reached 2,524 breeding pairs before control measures started in 2005. Walleye (Sander vitreus) recruitment concurrently declined, creating concern in the local community. To better understand the impacts of cormorants on fish, a cormorant diet analysis was conducted and two models were compared: Madenjian and Gabrey (1995) and Niche Mapper™ for estimating cormorant consumption of fish. Stomach content analysis revealed Yellow Perch (Perca flavescens) to be the most dominant species, providing 80% and 40% of the overall diet in 2005 and 2006, respectively. The Madenjian and Gabrey and the Niche Mapper™ models showed similar results in estimating daily food consumption of cormorants despite their different approaches and complexity levels. In both models, consumption estimates were greater in 2005 than in 2006, with 26 to 33% of their total body mass in 2005 and 18 to 22% of their total body mass in 2006. Likely the variation was caused by the variation in diet found in cormorant stomachs and the difference in the caloric densities of their diet between the two years, with Lake Herring (or Cisco, Coregonus artedi) being more prevalent in the diet in 2006. The most sensitive parameters in the Madenjian and Gabrey model were daily energy expenditure, assimilation efficiency, caloric density of fish and weight of cormorants. In the Niche Mapper™ model, core body temperature of cormorants and caloric density of fish were the most sensitive parameters. The strengths and weaknesses of both models are discussed, while providing guidance for researchers to select the method most applicable to their specific site and available data.
The Real-Time Scheduling Strategy Based on Traffic and Load Balancing in Storm

The era of big data has led to the emergence of new systems for real-time distributed stream processing. Apache Storm is one of the most popular stream processing systems today. However, Storm, as many other stream processing systems, lacks an intelligent scheduling mechanism. The default round-robin scheduling which disregards inter-node traffic and worker nodes load balancing may be inefficient sometimes. This paper proposed a real-time scheduling algorithm based on inter-node traffic and worker nodes load balancing within Storm. Algorithm is divided into two steps: In the first step according to the topology structure and inter-node traffic, executors are assigned to slots to ensure the minimum interaction traffic. The second step, we consider the worker nodes load, to choose the lowest load node for slots assignment. Experiments demonstrate that this scheduling algorithm compared to the default scheduling algorithm, performance of average latency and average inter-node traffic in the system improved above 50%, and compared the traffic-based scheduling algorithm, improved about 10%.
Abnormal tissue detection of breast ultrasound image using combination of morphological technique

Breast cancer is the most common killer in women worldwide, which is originating from breast tissue. Early detection of breast cancer plays an important role in its therapy. Existing breast cancer detection method like ultrasound imaging can not be used at all ages, also quality of obtained images affected by speckle noise. Thus, it is essential to introduce new method to overcome these problems.This paper proposes an approach of detecting abnormal tissue in ultrasound breast image. The objective is to detect the abnormal tissue such as cyst or tumor inside breast tissues using image segmentation and combination of morphological technique. Image was processed using threshold segmentation method followed by dilation and erosion. The region of abnormalities tissue were found and has been differentiating from other tissue through several step of method applied. Besides, the same method was applied to the other different type of breast abnormalities tissue and the necessary result was obtained. The finding results show that this method can be used for early detection of breast cancer since it can detect several types of abnormalities in breast tissues.