Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of diet on release of dissolved organic and inorganic nutrients by the copepod Acartia tonsa

Acartia tonsa copepods are not limited to herbivory and can derive up to half their daily ration from predation on heterotrophic ciliates and dinoflagellates. The effects of an omnivorous diet on nutrient regeneration, however, remain unknown. In this study, we fed A. tonsa an exclusively carnivorous diet of either (1a) heterotrophic dinoflagellate Oxyrrhis marina or (1b) Gyrodinium dom- inans, (2) an exclusively herbivorous diet of Thalassiosira weissflogii diatoms, or (3) a mixed omnivo- rous diet. We measured the release rate, composition, and stoichiometry of dissolved organic carbon (DOC), dissolved organic phosphorus (DOP), and nitrogen (urea) in addition to the inorganic nutri- ents, ammonium (NH4 + ) and phosphate (PO4 3- ). Despite similar ingestion rates among treatments, as well as similar C:N ratios of food items, A. tonsa release rates of DOC and NH4 + were highest while feeding on a carnivorous diet and lowest while feeding omnivorously. In contrast, urea, on average, was a higher portion of total nitrogen released in the mixed diet treatment (32 to 59%). DOP release rates were only detectable in diets containing microzooplankton prey. Our results suggest that cope- pod diet plays an important role in determining the quantity and composition of regenerated C, N, and P available to phytoplankton and bacteria. Additionally, the uncoupling of ingestion and nutrient release rates and the variability in released ratios of dissolved C:N:P in our study suggests that stoi- chiometric models based exclusively on predator and prey C:N and N:P ratios may not be adequate in determining stoichiometry of total nutrient release.
Adaptive Network Segmentation and Channel Allocation in Large-Scale V2X Communication Networks

Mobility, node density, and the demand for large volumes of data exchange have aggravated competition for limited resources in the wireless communications environment. This paper proposes a novel MAC scheme called segmentation MAC (SMAC), which can be used in large-scale vehicle-to-everything (V2X) communication networks. SMAC functions to support the dynamical allocation of radio channels. It is compatible with the asynchronous multi-channel MAC sub-layer extension of the IEEE 802.11p standard. A key innovate feature of SMAC is that the segmentation of the network and channel allocations are dynamically adjusted according to the density of vehicles. We also propose a novel efficient forwarding mechanism to ensure inter-segment connectivity. To evaluate the performance of inter-segment connectivity, a rigorous analytical model is proposed to measure the multi-hop dissemination latency. The proposal is evaluated in network simulator NS2 as well as the standard IEEE 1609.4 and two asynchronous multi-channel MAC benchmarks. Both analytical and simulation results demonstrate better effectiveness of the proposed scheme compared with the existing similar schemes in the literature.
A geostatistical data-assimilation technique for enhancing macro-scale rainfall–runoff simulations

Abstract. Our study develops and tests a geostatistical technique for locally enhancing macro-scale rainfall–runoff simulations on the basis of observed streamflow data that were not used in calibration. We consider Tyrol (Austria and Italy) and two different types of daily streamflow data: macro-scale rainfall–runoff simulations at 11 prediction nodes and observations at 46 gauged catchments. The technique consists of three main steps: (1) period-of-record flow–duration curves (FDCs) are geostatistically predicted at target ungauged basins, for which macro-scale model runs are available; (2) residuals between geostatistically predicted FDCs and FDCs constructed from simulated streamflow series are computed; (3) the relationship between duration and residuals is used for enhancing simulated time series at target basins. We apply the technique in cross-validation to 11 gauged catchments, for which simulated and observed streamflow series are available over the period 1980–2010. Our results show that (1) the procedure can significantly enhance macro-scale simulations (regional LNSE increases from nearly zero to ≈0.7) and (2) improvements are significant for low gauging network densities (i.e. 1 gauge per 2000 km2).
Normalising orthographic and dialectal variants for the automatic processing of Swiss German

Swiss dialects of German are, unlike most dialects of well standardised languages, widely used in everyday communication. Despite this fact, they lack tools and resources for natural language processing. The main reason for this is the fact that the dialects are mostly spoken and that written resources are small and highly inconsistent. This paper addresses the great variability in writing that poses a problem for automatic processing. We propose an automatic approach to normalising the variants to a single representation intended for processing tools’ internal use (not shown to human users). We manually create a sample of transcribed and normalised texts, which we use to train and test three methods based on machine translation: word-by-word mappings, character-based machine translation, and language modelling. We show that an optimal combination of the three approaches gives better results than any of them separately.
3-D Model Generation from Single-view Image Using Object Classification

In order to generate a three-dimensional (3-D) scene from a single-view image, we need to estimate the depth map of the single-view image. In this paper, we propose a new method for object classification and depth assignment to generate a mesh-based 3-D model from a single-view image. After we classify video objects based on vanishing lines, we assign depth values to the objects. For objects hidden by other objects, we define a depth baseline and assign depth values from the baseline. After estimating the depth map, we generate a mesh-based 3-D model. Experimental results show that the proposed method generates a 3-D model successfully, reflecting properties of video objects pretty well.
Efficient Self-learning for Single Image Upsampling

Exploiting similarity of patches within multiple resolution versions of an image is often utilized to solve many vision problems. Particularly, for image upsampling, recently, there has been a slew of algorithms exploiting patch repetitions withinand acrossdifferent scales of an image, along with some priors to preserve the scene structure of the reconstructed image. One such method, self-learning algorithm [1], uses only one image to achieve high magnification factors. But, as the image resolution increases, the number of patches in dictionary increases dramatically, and makes the reconstruction computationally prohibitive. In this paper, we propose a method that removes the redundancies inherent in large self-learned dictionaries to upsample an image without using any regularization methods or priors, and drastically reduces time complexity. We further prove that any low-variance (low details) patch that does not find any match can be represented as a linear combination of only low-variance patches from dictionary. The same principle applies to high-variance (high details) patches. Images with high scaling factors can be obtained with this method without any regularization or prior information, which can be subjected to further regularization with necessary prior(s) to refine the reconstruction.
Temperature effects on pinpoint photopolymerization and polymerized micronanostructures

The spatial resolution in pinpoint two-photon photopolymerization of radical-type resins was found to be improved by varying the liquid sample temperature from a critical value Tc. For SCR-500 and NOA-61, the currently widely used resins, Tc is around room temperature. The improvement of spatial resolution by temperature decrease was attributable to restraint radicals diffusion; while the voxel size reduction versus temperature increase was considered as arising from enhanced chain termination. Furthermore, temperature plays an important role in size tuning of polymerized structures, for example, a photonic crystal blueshifted its working wavelength for 50 and 400nm when heated at 200 and 300°C, respectively, indicating the possibility to precisely tailor the photonic bandgap by means of thermal processing.
Semantic Documentation in Project Management

The existence of project management supporting tools did not eliminate the need of using desktop documents in this domain. However, retrieving information from documents is usually not trivial and depends on human effort. In this work we propose the use of semantic documentation in the project management domain as a way of getting useful information from data recorded in documents and spreadsheets. For this, we developed the Software Project Management Ontology and explored it to create features to support activities related to scope, time and cost management. The features were implemented in a specialization of the Infrastructure for Managing Semantic Documents, which was evaluated through an experimental study.
The case for a cold dark matter cusp in Draco

We use a new mass modelling method, GRAVSPHERE, to measure the central dark matter density profile of the Draco dwarf spheroidal galaxy. Draco's star formation shut down long ago, making it a prime candidate for hosting a `pristine' dark matter cusp, unaffected by stellar feedback during galaxy formation. We first test GRAVSPHERE on a suite of tidally stripped mock `Draco'-like dwarfs. We show that we are able to correctly infer the dark matter density profile of both cusped and cored mocks within our 95 per cent confidence intervals. While we obtain only a weak inference on the logarithmic slope of these density profiles, we are able to obtain a robust inference of the amplitude of the inner dark matter density at 150 pc, ρ _DM(150 pc). We show that, combined with constraints on the density profile at larger radii, this is sufficient to distinguish a Λ Cold Dark Matter (ΛCDM) cusp - that has ρ _DM(150 pc) ≳ 1.8 × 10^8 M_☉ kpc^{-3} - from alternative dark matter models that have lower inner densities. We then apply GRAVSPHERE to the real Draco data. We find that Draco has an inner dark matter density of ρ _DM(150 pc) = 2. 4_{-0.6}^{+0.5} × 10^8 M_☉ kpc^{-3}, consistent with a ΛCDM cusp. Using a velocity-independent SIDM model, calibrated on ΛSIDM cosmological simulations, we show that Draco's high central density gives an upper bound on the SIDM cross-section of σ/m < 0.57 cm2 g-1 at 99 per cent confidence. We conclude that the inner density of nearby dwarf galaxies like Draco provides a new and competitive probe of dark matter models.
Phytochemical characterization and bioactive properties of Osyris quadripartita Salzm . ex Decne . from Algeria

Mountain Research Centre (CIMO), ESA, Polytechnic Institute of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal. Laboratory of Plant Biochemistry and Natural Products, Department of Biology, Faculty of nature and life sciences, University Ahmed Ben Bella Oran 1, 1524 ELM Naouer 31000 Oran, Algeria. Laboratory of Research in Arid Areas (LRZA), PO Box 32, El Alia Bab-Ezzouar, Algiers 16111, Algeria Center University Salhi Ahmed, Naama, Algeria. GIP-USAL, Faculty of Pharmacy, Universityof Salamanca, Campus Miguel de Unamuno, 37007 Salamanca, Spain.
Are Bug Reports Enough for Text Retrieval-Based Bug Localization?

Text Retrieval (TR) has been widely used to support many software engineering tasks, including bug localization (i.e., the activity of localizing buggy code starting from a bug report). Many studies show TR's effectiveness in lowering the manual effort required to perform this maintenance task; however, the actual usefulness of TR-based bug localization has been questioned in recent studies. These studies discuss (i) potential biases in the experimental design usually adopted to evaluate TRbased bug localization techniques and (ii) their poor performance in the scenario when they are needed most: when the bug report, which serves as the de facto query in most studies, does not contain localization hints (e.g., code snippets, method names, etc.) Fundamentally, these studies raise the question: do bug reports provide sufficient information to perform TR-based localization? In this work, we approach that question from two perspectives. First, we investigate potential biases in the evaluation of TR-based approaches which artificially boost the performance of these techniques, making them appear more successful than they are. Second, we analyze bug report text with and without localization hints using a genetic algorithm to derive a near-optimal query that provides insight into the potential of that bug report for use in TR-based localization. Through this analysis we show that in most cases the bug report vocabulary (i.e., the terms contained in the bug title and description) is all we need to formulate effective queries, making TR-based bug localization successful without supplementary query expansion. Most notably, this also holds when localization hints are completely absent from the bug report. In fact, our results suggest that the next major step in improving TR-based bug localization is the ability to formulate these near-optimal queries.
Computing the Umbrella Neighbourhood of a Vertex in the Delaunay Triangulation and a Single Voronoi Cell in Arbitrary Dimension

Delaunay triangulations and their geometric dual Voronoi diagrams are carefully studied topics in computational geometry and have numerous applications spanning fields such as physics, engineering, geographic information systems, and computer graphics. There are numerous efficient algorithms for computing both in two and three dimensional real space, but for higher dimensional space, the computational complexity grows exponentially. For many applications, it is only necessary to compute the star of simplices incident to (often called the umbrella neighbourhood of) a single vertex of the Delaunay triangulation, or equivalently, the Voronoi cell of a point. In practice, this may be a relatively small subset of the total Delaunay triangulation or Voronoi diagram. In this paper, an algorithm is proposed for computing the umbrella neighbourhood of a single vertex in the Delaunay triangulation.
Changes in deep reef benthic community composition across a latitudinal and environmental gradient in temperate Eastern Australia

Deep reef assemblages in south-eastern Australia are poorly described, and have been surveyed by only a few studies conducted over small spatial scales. Here, we characterise the composition of deep (~30-90 m depth) sessile invertebrate communities from sub-tropical (27°S) to temperate eastern Australia (43°S). We estimated the cover of 51 preselected invertebrate types from over 1700 seafloor images collected by an autonomous underwater vehicle from >105 km of transects across the study region. Seafloor images were assessed using 3 alternative schemes reflecting different resolution of benthic invertebrate groupings, including the broad- level Collaborative and Automated Tools for Analysis of Marine Imagery project (‘CATAMI’) classification recently developed as a generic scoring approach for seafloor imagery. Ordination using canonical analysis of principal coordinates indicated a clear latitudinal gradient in benthic community composition and, particularly when based on individual morphotypes, 3 distinct community types (sub-tropical, warm temperate and cool temperate). Changes in community structure mostly correlated with primary productivity and the temperature climatology, while local-scale variability in community composition was most related to depth. Along with the gradual shift in deep reef community composition across latitudes, region-specific sessile invertebrates might serve as useful indicators of change in these deep benthic communities under future changes in ocean climate in the region, which has been identified as a global hotspot for ocean warming. Our methodological approach has general applicability for large-scale surveying and monitoring of benthic communities using underwater imagery.
FrequentPattern Mining in Multiple Trajectories of Football Players

Recently, it has been regarded as important in many sports fields to evaluate the tactics and athletes using actual play records. In this paper, as a first step towards a quantitative evaluation of the strategies in football games, we propose an algorithm for discovering frequent patterns on simultaneous trajectories of multiple football players. In the algorithm, given trajectories are firstly converted into a set of labeled sub-trajectories corresponding to the interval-based events. A pattern enumeration algorithm is then applied to the obtained interval-based events with a consideration of the order of events, the time difference and the spatial spread of subtrajectories. We introduce variables for subjects of events (subtrajectories) in the pattern. By using variables, we can recognize which events were played by the same player and which events were played differently. In addition, it is possible to extract a pattern which absorb the difference of concrete players. To evaluate the proposed algorithm, we conduct experiments using real trajectory datasets on nine matches in Japanese professional football league. The results on the computation time and the number of extracted patterns show the feasibility and effectiveness of the algorithm. In addition, we succeeded in extracting meaningful patterns representing certain offensive and defensive strategies formed by multiple football players.
International Conference on Semantic Systems Temporal Role Annotation for Named Entities

Natural language understanding tasks are key to extracting structured and semantic information from text. One of the most challenging problems in natural language is ambiguity and resolving such ambiguity based on context including temporal information. This paper, focuses on the task of extracting temporal roles from text, e.g. CEO of an organization or head of a state. A temporal role has a domain, which may resolve to different entities depending on the context and especially on temporal information, e.g. CEO of Microsoft in 2000. We focus on the temporal role extraction, as a precursor for temporal role disambiguation. We propose a structured prediction approach based on Conditional Random Fields (CRF) to annotate temporal roles in text and rely on a rich feature set, which extracts syntactic and semantic information from text. We perform an extensive evaluation of our approach based on two datasets. In the first dataset, we extract nearly 400k instances from Wikipedia through distant supervision, whereas in the second dataset, a manually curated ground-truth consisting of 200 instances is extracted from a sample of The New York Times (NYT) articles. Last, the proposed approach is compared against baselines where significant improvements are shown for both datasets. © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the SEMANTiCS 2018 – 14th International Conference on Semantic Systems.
Tidal amplification of seabed light.

[1] Because solar irradiance decreases approximately exponentially with depth in the sea, the increase in irradiance at the seabed from mid to low tide is greater than the decrease from mid to high tide. Summed over a day, this can lead to a net amplification of seabed irradiance in tidal waters compared to nontidal waters with the same mean depth and transparency. In this paper, this effect is quantified by numerical and analytical integration of the Lambert-Beer equation to derive the ratio of daily total seabed irradiance with and without a tide. Greatest amplification occurs in turbid water with large tidal range and low tide occurring at noon. The theoretical prediction is tested against observations of seabed irradiance in the coastal waters of North Wales where tidal amplification of seabed light by up to a factor of 7 is both observed and predicted. Increasing the strength of tidal currents tends to increase the turbidity of the water and hence reduce the light reaching the seabed, but this effect is made less by increasing tidal amplification, especially when low water is in the middle of the day. The ecological implications of tidal amplification are discussed. The productivity of benthic algae will be greater than that predicted by simple models which calculate seabed irradiance using the mean depth of water alone. Benthic algae are also able to live at greater depths in tidal waters than in nontidal waters with the same transparency.
Segue: Overviewing Evolution Patterns of Egocentric Networks by Interactive Construction of Spatial Layouts

Getting the overall picture of how a large number of ego-networks evolve is a common yet challenging task. Existing techniques often require analysts to inspect the evolution patterns of ego-networks one after another. In this study, we explore an approach that allows analysts to interactively create spatial layouts in which each dot is a dynamic ego-network. These spatial layouts provide overviews of the evolution patterns of ego-networks, thereby revealing different global patterns such as trends, clusters and outliers in evolution patterns. To let analysts interactively construct interpretable spatial layouts, we propose a data transformation pipeline, with which analysts can adjust the spatial layouts and convert dynamic ego-networks into event sequences to aid interpretations of the spatial positions. Based on this transformation pipeline, we develop Segue, a visual analysis system that supports thorough exploration of the evolution patterns of ego-networks. Through two usage scenarios, we demonstrate how analysts can gain insights into the overall evolution patterns of a large collection of ego-networks by interactively creating different spatial layouts.
Cultural Values and Gender Equity on National Olympic Committee Boards

International Journal of Exercise Science 10(6): 857-874, 2017. Women are underrepresented in leadership positions throughout sport, and researchers have largely explored organizational, group, and individual antecedents of this phenomenon. The purpose of the current study was to expand on this understanding by investigating the influence of a country’s cultural values on the representation of women on National Olympic Committees. Drawing from Hofstede’s cultural dimensions theory, the authors included five cultural values: power distance, uncertainty avoidance, individualism-collectivism, masculinity-femininity, and long-term orientation. Results indicate that women constituted only 19.7 percent of the positions on the boards. Regression analysis, controlling for size of the Olympics program in the country, indicate that cultural values accounted for 41.8 percent of the variance in board gender diversity. Countries with lower power distance, lower masculinity, and lower uncertainty avoidance all had a higher proportion of women on the board. The authors discuss practical and theoretical implications.
SUPERVISED NONNEGATIVE MATRIX FACTORIZATION FOR ACOUSTIC SCENE CLASSIFICATION

This report describes our contribution to the 2016 IEEE AASP DCASE challenge for the acoustic scene classiﬁcation task. We propose a feature learning approach following the idea of decomposing time-frequency representations with nonnegative matrix factorization. We aim at learning a common dictionary representing the data and use projections on this dictionary as features for classiﬁcation. Our system is based on a novel supervised extension of nonnegative matrix factorization. In the approach we propose, the dictionary and the classiﬁer are optimized jointly in order to ﬁnd a suited representation to minimize the classiﬁcation cost. The proposed method signiﬁcantly outperforms the baseline and provides improved results compared to unsupervised nonnegative matrix factorization.