dataesr/scientific-paragraphs-categorization huggingface dataset:
INTRODUCTION Over the past decades data collection has become a priority in manufacturing, advertising, and many other large sectors of industry. As a direct consequence, we see the size of databases rapidly increasing by orders of magnitudes. At the same time, value-extraction from data is becoming more ambitious and aggressive, leading to larger and more complex queries. Together, these developments are quickly driving us to the limits of traditional query processing techniques and pose a critical challenge for the database systems of the near future. It is therefore vital to develop new techniques which are well-suited for these extreme scenarios. Various novel approaches for efficient query processing are being actively studied in theory and many promising theoretical results have been shown.
However, the effective implementation of these theoretical methods in mainstream database systems has seen little progress. One such method is the use of hypertree decompositions (HDs)  [15]. Hypertree decompositions group parts of a query together to produce a tree structure even for cyclic queries. This resulting tree of groups (the decomposition) then allows for very efficient evaluation using Yannakakis' algorithm  [21]. Furthermore, the size of intermediate results can be theoretically bounded from above by an expression that only depends on the size of the input relations and the size of the groups (the width of the decomposition). As a final benefit, a kind of local dependency constraint is enforced in the decompositions. Together with the tree structure of the decomposition this local dependency allows for highly parallelised evaluation of the query  [13, 14].
Implementations in specialised database systems have demonstrated large speedups in query evaluation by using HDs, especially on difficult instances  [1]. Nonetheless, one crucial challenge remains for more widespread use of HDs: it is computationally difficult to construct good hypertree decompositions. In general, finding a HD for a given query is NPhard  [15]  but finding a HD of some fixed width is tractable, i. e. , the problem is in XP in the terminology of parameterised complexity. In fact, a significantly stronger upper bound can be given. Finding a HD of fixed width is in the complexity class LogCFL  [15]  (contained in NC 2 ) and therefore, in theory, highly parallelisable  [4]. Related Work. The problem has received significant recent attention. Among other recent developments are the development of a large benchmark data set  [8] , novel algorithmic approaches  [6, 8, 17] , and being the subject of a recent PACE competition  [5].
IMPLEMENTATION AND EMPIRICAL EVALUATION We report now on the results of our empirical experiments. Our experiments are based on the HyperBench benchmark from  [8]. Our goal was to determine the exact hypertree width of as many instances as possible. We compare here the performance of three different decomposition methods, namely det--decomp from  [8, 18] , HtdSMT from  [19]  and our implementation of log--decomp. The full raw data of our experiments as well as the source code of our implementation of log--decomp is publicly available 2.
Benchmark Instances and Setting For the evaluation, we use the benchmark library HyperBench  [7]. It collects 3649 hypergraphs from various sources in industry and the literature. The instances are available at http://hyperbench. dbai. tuwien. ac. at. The instances are from CSPs and CQs, and in addition to sources from real-world applications, the benchmark also includes hypergraphs of randomly generated CSPs and CQs. Hardware and Software. Our implementation is based on Go 1. 14 and we will refer to it as log--decomp. We will give more detail below on how it was configured for the experiments reported in Section 6. 4. The hardware used for the evaluation was a cluster of 12 nodes, using Ubuntu 16. 04. 1 LTS, each with a 24 core Intel Xeon CPU E5-2650 v4, clocked at 2. 20Ghz and using 264 GB of RAM.
For each algorithm and instance group we list the number of solved instances (#solved) and statistics over the running times (avg, max, stdev). Times are all in seconds and rounded to a single digit after the comma. Overall results over all categories are given in the last row titled "Total". Note that while the CSP categories consist of hypergraphs obtained from CSP instances (industrial and random), they functionally are no different than database queries in our setting. For our purposes, the main difference between CSP and conjunctive query (CQ) instances in HyperBench is that the CSP instances are generally larger, while in particular the CQ Application set consists almost entirely of very small (≤ 15 edges) or acyclic instances. As mentioned above, some care is required when comparing times between algorithms.
At the same time, we also observed at least 3-fold speedup in the mean running time over these instances. In combination, our theoretical results and experiments demonstrate that log--decomp achieves our goal of effective parallel HD computation. We believe that the performance improvements, especially on large queries, as well as the good scaling behaviour lay a strong foundation for more widespread adoption of hypertree decomposition based query execution in high-performance database applications. With HD computation for large and complex queries becoming practically feasible, one of the key challenges that block the use of HDs is quickly becoming less problematic. We therefore consider full integration of hypertree decompositions into existing database systems to be a natural next step in this line of research.
ACKNOWLEDGEMENTS This work was supported by the  Austrian Science Fund (FWF) : P30930-N35. Georg Gottlob  is a  Royal Society Research Professor  and acknowledges support by the  Royal Society  for the present work in the context of the project " RAISON DATA " (Project reference:  RP\R1\201074 ). Matthias Lanzinger acknowledges support by the  Royal Society  project " RAISON DATA " (Project reference:  RP\R1\201074 ).
Recent advances in high-throughput microscopy imaging have made it easier to acquire large volumes of cell images. Thanks to electron microscopy (EM) imaging, they provide a high-resolution and sufficient field of view that suits imaging large cell types, including cardiomyocytes. A significant bottleneck with these large datasets is the time taken to collect, extract and statistically analyse 3D changes in cardiac ultrastructures. We address this bottleneck with CardioVinci. Recent deep learning (DL) findings have paved the way for much accessible data quantification and analysis tools. These methods have shown unprecedented performance in a wide range of data analysis tasks, including image analysis. In biology, DL has improved the accuracy of segmenting EM data and the efficiency by minimising the time required for quantifying such datasets.
Despite their successful applications in EM image analysis, these methods still represent some limitations, as reported in our previous works  [1, 2]. Finding the exemplary DL architecture, optimising, and fine-tuning to segment various ultrastructures sometimes need sophisticated techniques, including ensemble learning. This makes it challenging to collect a large sample of cardiomyocytes, obtain optimal images from the samples, develop and fine-tune deep neural networks for segmenting organelles, and finally report statistical measures. Generative adversarial networks (GANs) have been proposed to learn complex latent features from a dataset in an unsupervised manner. Simplistically speaking, GANs enable us to learn the original distributions of the data. For example, the EM images collected from dissected cardiac tissues only represent a sampled version from a complex data distribution representing the cardiomyocytes' organisation within the tissue population.
Then StyleGAN is utilised to optimise the error between generator and discriminator. After the images are generated using the trained StyleGAN, 3D statistical representation of cardiomyocytes are reconstructed. And finally, 3D shape and geometric statistics are retrieved using the generated volume. In addition to the above, we extracted surface area to volume ratio (SA:V) along with other 3D shape and geometrical statistics, including elongation, compactness, flatness, spareness and sphericity. These statistics are drawn based on the results of GAN and segmented images from the original dataset. The results suggest that GAN and segmented images share similar mean and median across these statistics; however, we observe relatively higher variance across extracted distributions using GAN. Our analysis suggests that Z-disks represent higher mean, median, and variance for SA:V, whereas mitochondria demonstrate a lower mean and variance range for SA:V.
CardioVinci exhibits phenomenal performance in the presence of limited data due to its scalability and would represent optimal performance when a large dataset is available. Our future aim is to utilise the style mixing capability of the StyleGAN to generate synthesized variations across different tissue blocks or even tissue types. We hope CardioVinci be utilised across other tissue types, and of course, other image modalities. We suggest the readers refer to the supplementary text and figures for detailed methods and analysis.
While studies tracking the movements of individual animals provide perspective on home range attributes, they are typically limited in terms of the fraction of the population sampled and the duration of study. Using longitudinal data from direct observations of wild Asian elephants at Udawalawe National Park, Sri Lanka (Fig. 1 ), we investigate PA-use according to separate considerations for the two sexes. For adult males, we examine whether individuals preferentially use the PA for foraging vs. mate search, hypothesizing that area use would be structured by age and motivational state. For adult females, we hypothesized that PA-use would be in uenced by the strength of associations among social a liates. We rst quantify the degree to which elephants revisit the PA over multiple years and then evaluate the in uences on area use strategies for each sex.
3A ). Males employing different strategies did not segregate spatially within the observation area (Fig. 3B ). Female PA-use There were 300 females identi ed, of which 230 were adults. Of these, 137 were seen consistently throughout the rst two years of the study, but seven females known to have died later and were removed from the dataset (n = 130 remaining). 50% of females were re-sighted in each year, while 0. 2% percent were not seen again and may represent additional mortality or dispersal events (Fig. 4A ). Social community size ranged from 2-22 individuals (Fig. 4B ). The largest social communities had the lowest median BSI values (Fig. 5A ), suggesting that these large communities have more residential members than smaller communities, but this cannot be statistically evaluated as individuals within communities are non-independent (see below) and community size is a variable with only 16 values.
De ning age classes and strategies for De ning social communities for females quantify adult female relationships, we created a group-by-individual matrix from all observations of adult females and calves between 2007-2015. The full dataset was then ltered to include only adult females that were present in the rst two years of the study to allow for the possibility that they were available for observation over the full duration of the study (n = 130). Next, we constructed an association matrix using simple ratio index (SRI) as edge weights in the R package asnipe  (Farine, 2013; Farine & Whitehead, 2015). SRI is a pairwise index of association that describes the proportion of observations where two individuals were seen together out of all possible observations  (Ginsberg & Young, 1992; Whitehead, 2008 ).
An association matrix and corresponding social network were constructed for each of the rst two years of the study separately. We then used a Louvain clustering algorithm (package igraph,  Csardi & Nepusz, 2014)  to detect communities within each of the social networks, and used a dynamic community detection algorithm to identify whether social communities were the same between the two years. This algorithm used a reciprocal majority method, i. e. , any network clusters that had more than half of the same members between subsequent years were considered the same social community (Liechti & Bonhoeffer 2020). This method allowed us to assign each female to one social community across the entire study period.
025. To test whether male BSI was in uenced by age class or musth state, we constructed a series of generalized linear mixed models (GLMM) using a gaussian logit link function in the R package lme4  (Bates et al. 2015). For each model, BSI was the dependent variable and the random effects were individual ID, month, and year. The possible covariates, motivational state and age class, were tested for correlation using a Fisher's Exact Test to determine how to structure models, i. e. , if age class and state should be included as covariates in models. We evaluated and selected the best tting model using an analysis of variance test (package stats, R Core Team 2021). To test whether social associations explain similarity in female PA-use, we used a multiple regression quadratic assignment procedure Dekker et al.
HST Data We used archival HST data for this cluster from the proposal ID:9719 (PI: T. Bridges). 9 The observations were performed with the Advanced Camera for Surveys/High-Resolution Camera (ACS/HRC) in the filters F814W and F606W. The exposure times were 2860 s and 2020 s, respectively. The ACS/HRC has a pixel scale of 0 025 pixel -1 and a field of view of 29″ × 26″. We downloaded the individual. flt files from the Mikulski archive for space telescopes (MAST) and drizzled them using Astrodrizzle  (Gonzaga et al. 2012)  to create the final image. The MDRIZSKY keyword was set to zero to avoid oversubtraction of the sky in the final drizzled image. The final color image (F606W-F814W) is shown in the right panel of Figure  1. The drizzled PSF in each band was obtained by inserting Tiny Tim PSFs into mock.
Gemini/NIFS Data and Kinematics We obtained Gemini/NIFS laser-guide-star adaptive-optics observations of B023-G78 on 2014 October 7 and November 9 as part of program GN-2014B-DD-2 (PI: A. C. Seth). The data provide integral-field spectroscopy in the H band (1. 48-1. 79 μm) over a field of view of 3″ (11 pc at 0. 77 Mpc). For our final data cube with a spaxel size of 0 05, we combined the 6/8 900 s dithered exposures using the Gemini IRAF packages, with modifications as described in  Seth et al. (2010)  and  Ahn et al. (2018). Despite the use of offset sky exposures, additional on-chip sky subtraction was required before combination, using the corners of the chip; this makes our useful field of view ∼2″. The line spread function was measured from sky lines in each pixel with a median FWHM of 3.
27 Å. The kinematic maps are shown in Figure  2. Due to the data's high spatial resolution, we were able to resolve individual stars in the GC. To mitigate shot-noise effects due to the brightest cluster stars, we used the PampelMuse software  (Kamann et al. 2013)  to generate a star-subtracted cube of the GC. To describe the method in short, we fitted a single Sérsic image to the continuum image of the cluster and inspected the fit residuals. We then manually identified the locations where the residuals suggested the presence of resolved stars and the resulting list was used to recover their PSFs as a function of wavelength using PampelMuse  (Kamann et al. 2013 ). These PSF and the positions were used to extract the spectra of the input stellar sources. Finally, we combined the wavelength-dependent PSF model with the positions and the extracted spectra of the resolved stars to subtract their contributions from the NIFS data.
Deriving the Kinematic PSF To perform dynamical modeling with precision, understanding the PSF of the Gemini/NIFS kinematic data is critical. To determine the PSF, first we astrometrically aligned a continuum image created from the NIFS data cube (created without the additional on-chip sky subtraction) to the HST F814W image. Then, we used the HST F814W image within a 1″ radius and convolved it with a double Gaussian model of the PSF, varying the parameters of the Gaussians until the convolved HST image best matched the continuum image created from the data cube. We then convolved the resulting PSF model with the HST PSF to obtain the widths and relative strengths of the two Gaussian components. The best-fit FWHM of the inner and outer Gaussian component was found to be 0 127 containing 31.
We estimated the Bayesian information criterion (BIC) for the best-fit IMBH model and the no-BH model. The ΔBIC was 24, which provides strong evidence against the no-BH model. A ΔBIC > 10 supports strong evidence for one model over another  (Kass & Raftery 1995). For the best-fit BH mass and σ e as the integrated velocity dispersion at ∼0 5, the sphere of influence radius ( s = GM SOI BH e 2 ) is ∼0. 33 pc or ∼0 09; for comparison, the PSF core sigma (FWHM/2. 35) is 0 055, thus the SOI is resolved by our kinematic data as expected given the >3σ significance of the BH mass detection. The best-fit M/L F814W is -+ 1. 87 0. 04 0. 04 , giving a total dynamical mass of 6. 22 × 10 6 M e for this cluster. This total dynamical mass is similar to that found in  Strader et al. (2011)   6 M e ). We discuss the uncertainties in the M/L due to extinction in the next subsection but note that the dynamical mass is robust to changes in extinction.
The models also suggest moderate radial anisotropy with a best-fit β of -+ 0. 15 0. 03 0. 05. The inclination is not well constrained, but does not affect the estimates of other parameters. To visualize the models and data better, Figure  5  shows a 1D radial profile of the measured annular V rms and the V rms model prediction, as well as showing the best-fit model without a BH. The model 1D profiles are estimated by creating radial bins from the 2D model and taking the median for each bin along the major axis of the cluster. Every iteration of the MCMC simulation within 1σ is also plotted, which is the shaded region.
To explore (2), we performed tests by varying the M/L in our mass model instead of assuming a constant M/L. We assigned a M/L for the King and the Sérsic components based on their integrated colors using the theoretical color-M/L relations from  Roediger & Courteau (2015). These were then used as an input in our JAM models, and a single mass-scaling factor was used in place of the M/L (as in  Nguyen et al. 2018 Nguyen et al. , 2019)). This did not create much variation in the BH mass (shown as the pink line in Figure  5 ). Finally, we used the original kinematics data cube rather than the one after star subtraction to estimate the BH mass but there was not much variation observed (shown as a cyan line in Figure  5 ). Overall, these tests suggest that the dynamical signature of the IMBH in B023-G78 is robust.
Testing the Stellar Mass BH Scenario with JAM Models A collection of stellar-mass BHs differs from an IMBH because its mass distribution is extended, and this extent may be resolvable by our observations. In ω Cen, the best-fit distribution of stellar-mass BHs from  Zocchi et al. (2019)  can be described as a Plummer density profile ( ( ) r p = + - M r r r 3 1 4 2 0 2 5 2 0 3 ) with the ratio of the BH subsystem Plummer radius (r 0 ) to the cluster half-light radius (r hl ), (r 0 /r hl ) ∼ 0. 3. This ratio would correspond to a r 0 of ∼1. 3 pc (0 3) in B023-G078; this is significantly broader than the core of our PSF and thus may result in measurable changes in our data relative to the point mass assumed in our IMBH models. However, we note that in the distribution function-based models of  Zocchi et al.
g. ,  Noyola et al. 2010) ; as noted previously, this IMBH detection has been contested (e. g. ,  Zocchi et al. 2017 Zocchi et al. , 2019;; Baumgardt et al. 2019). It is also more significant than the <3σ detection of a 2 × 10 4 M e BH in G1  (Gebhardt et al. 2005 ) derived from data with similar physical resolution. In comparison with the BHs previously found in other higher-mass UCDs, B023-G78 represents the first case in the IMBH regime, with all other BHs having both higher masses and mass fractions. Relative to central BHs in present-day galaxies, the mass is the lowest dynamical estimate apart from the ∼10 4 M e BH suggested in NGC 205  (Nguyen et al. 2019). The most comparable present-day NSC+BH system is NGC 4395, which hosts a ∼4 × 10 5 M e BH, inferred both dynamically (den Brok et al.
This paper presents the PEACEFULNESS software platform (Platform for transvErse evAluation of Control stra-tEgies For mULti-eNErgy Smart gridS), an open framework dedicated to multi-energy smart-grids, based on a techno-economic model that integrates economic considerations (contracts). As such, it is mainly oriented towards the evaluation of multi-energy grid supervision strategies, that is, energy management, and the corresponding policies and legal organization. The main goal is then to highlight the various possible behaviors and strategies to organize the probable future interconnections between the different energy carriers. In particular, it aims at investigating how to maximize the use of renewable energy sources (RES), using Demand Side Management (DSM) techniques and energy storage, in a shared economy context.
The open-source tool PEACE-FULNESS, written in Python, is described here in detail. It combines a top-down description of the energy networks and connections between the various agents (energy providers, distribution system operators, aggregators, consumers, producers, prosumers, etc. ), together with a techno-economic bottom-up description for all devices. Here, both public databases and users' data (basic heating demands or based on building modeling) can be used, as well as generic or more specific models (e. g. , PV panels with constant or temperature-dependent efficiency). One of its major unique features compared with other tools is that it extends the use of DSM techniques to various energy grids which can also interact together. Furthermore, different economic models can be set for both the aggregators and the customers, and even within these groups.
Concerning the numerical tools, they were not detailed all yet the most important ones were provided, and several tables were given for their availability and requirements (Table  1  pp. 441-442), their spatiotemporal scale (Table  2  pp. 446-447) and techno-economic possibilities (Table  3  pp. 448-451). As a final note, A very exhaustive review was recently published by Ref. [81]. Based on a survey questionnaire on SurveyXact, they analyzed 54 (over 137) tools, for which responses were complete. By cataloging them in 7 categories, they examined the corresponding software in term of simulation, optimization and equilibrium modeling. Moreover, a focus is made on their temporal, spatial, and technical resolution, and on the cross-sector coverage and demand representation. In spite of a large port-folio of tools, it was pointed out than only 36 models are free; and only 22 were in open source, with 8 requiring additional commercial software or solvers to run (which finally leads to 14 tools).