dataesr/scientific-paragraphs-categorization huggingface dataset:
Researchers collected actual electricity demand data from grid operators in the northeastern and southwestern United States, determined daily, weekly, and seasonal demand. Oloyede also developed analysis and numerical models of nuclear, wind and solar power generation coupled with storage systems, and gave the functional structure and physical architecture of various design schemes. Reus et al. [11]  proposes a supply chain model of electrolytic hydrogen unit with seasonal hydrogen storage, which can bridge the gap between the fluctuations in renewable energy generation caused by excess electricity and the demand for fuel supply stations. Researchers have also developed a set of models that can calculate the cost of hydrogen supply, energy consumption and greenhouse gas emissions of fuel cell vehicles, and analyzed the potential impact of Liquid Organic Hydrogen Carriers (LOHC) on the future hydrogen mobility.
Gabrielli et al. [60]  designed a seasonal hydrogen storage operation model based on a multi-energy system near Zurich, Switzerland. The system is optimized in terms of total annual cost and carbon dioxide emissions, and is sensitive to different characteristics of the energy system. Analyze and view the topology of the energy hub to reveal the behavioral pattern of the energy system. Petkov et al. [13]  studied a residential area composed of five multistory buildings in Palermo, Italy. The residential area used renewable energy to produce and use hydrogen as an energy carrier, and was developed by NREL and the Midwest Research Institute in the United States. The power and renewable energy hybrid optimization model (HOMER) software developed to study the energy balance of the system and its components.
Firstly, to accelerate academic research as Open-Unmix provides implementations for the most popular deep learning frameworks, giving researchers a flexible way to reproduce results. Secondly, we provide a pre-trained model for end users and even artists to try and use source separation. Furthermore, we designed Open-Unmix to be one core component in an open ecosystem on music separation, where we already provide open datasets, software utilities, and open evaluation to foster reproducible research as the basis of future development.
Background Music separation is a problem which has fascinated researchers for over 50 years. This is partly because, mathematically, there exists no closed-form solution when many sources (instruments) are recorded in a mono or stereo signal. To address the problem, researchers exploited additional knowledge about the way the signals were recorded and mixed. A large number of these methods are centered around "classical" signal processing methods. For a more detailed overview see  (Rafii, Liutkus, Stöter, Mimilakis, & Bittner, 2017)  and  (Cano, FitzGerald, Liutkus, Plumbley, & Stöter, 2019). Many of these methods were hand-crafted and tuned to a small number of music recordings  (Araki et al. , 2012; Ono, Koldovsky, Miyabe, & Ito, 2013; Vincent et al. , 2012). Systematic objective evaluation of these methods, however, was hardly feasible as freely available datasets did not exist at that time.
In fact, for a meaningful evaluation, the ground truth separated stems are necessary. However, because commercial music is usually subject to copyright protection, and the separated stems are considered to be valuable assets in the music recording industry, they are usually unavailable. Nonetheless, thanks to some artists who choose licenses like Creative Commons, that allow sharing of the stems, freely available datasets were released in the past five years and have enabled the development of data-driven methods. Since then, progress in performance has been closely linked to the availability of more data that allowed the use of machine-learningbased methods. This led to a large performance boost similar to other audio tasks such as automatic speech recognition (ASR) where a large amount of data was available.
In fact, in 2016 the speech recognition community had access to datasets with more than 10000 hours of speech  (Amodei et al. , 2016). In contrast, at the same time, the MUSDB18 dataset was released  (Rafii et al. , 2017)  which comprises 150 full-length music tracks -a total of just 10 hours of music. To date, this is still the largest freely available dataset for source separation. Nonetheless, even with this small amount of data, deep neural networks (DNNs) were not only successfully used for music separation but they are now setting the state-of-the-art in this domain as can be seen by the results of the community-based signal separation evaluation campaign (SiSEC)  (Liutkus et al. , 2017; Ono, Rafii, Kitamura, Ito, & Liutkus, 2015; Stöter, Liutkus, & Ito, 2018). In these challenges, the proposed systems are compared to other methods.
Among the systems under test, classical signal processing based methods were clearly outperformed by machine learning methods. However they were still useful as a fast and often simple to understand baseline. In the following, we will describe a number of these reference implementations for source separation. While there are some commercial systems available, such as Audionamix XTRAX STEMS, IZOTOPE RX 7 or AudioSourceRE, we only considered tools that are available as open-source software, and are suitable for research. The first publicly available software for source separation was openBlissart, released in 2011  (Weninger, Lehmann, & Schuller, 2011). It is written in C++ and accounts for the class of systems that are based on non-negative matrix factorization (NMF). In 2012, the Flexible Audio Source Separation Toolbox (FASST) was presented in  (Ozerov, Vincent, & Bimbot, 2011; Salaün et al.
, 2014). It is written in MATLAB/C++ and is also based on NMF methods, but also includes other model-based methods. In 2016, the untwist library was proposed in  (Roma, Grais, Simpson, Sobieraj, & Plumbley, 2016). It comprises several methods, ranging from classical signal-processing-based methods to feed-forward neural networks. The library is written in Python 2. 7. Unfortunately, it has not been updated since 2017 and many of its methods are not subjected to automated testing. Nussl is a very recent framework, presented in  (Manilow, Seetharaman, & Pardo, 2018). It includes a large number of methods and generally focuses on classical signal processing methods rather than machine-learning-based techniques. It has built-in interfaces for common evaluation metrics and data sets. The library offers great modularity and a good level of abstraction.
"MNIST-like" Keeping in mind that the learning curve can be quite steep in audio processing, we did our best for Open-unmix to be: • simple to extend: The pre/post-processing, data-loading, training and models part of the code is isolated and easy to replace/update. In particular, a specific effort was done to make it easy to replace the model. • not a package: The software is composed of largely independent and self-containing parts, keeping it easy to use and easy to change. • hackable (MNIST like): Due to our objective of making it easier for machine-learning experts to try out music separation, we did our best to stick to the philosophy of baseline implementations for this community. In particular, Open-unmix mimics the famous MNIST example, including the ability to instantly start training on a dataset that is automatically downloaded.
Community Open-Unmix was developed by Fabian-Robert Stöter and Antoine Liutkus at Inria Montpellier. The research concerning the deep neural network architecture as well as the training process was done in close collaboration with Stefan Uhlich and Yuki Mitsufuji from Sony Corporation. In the future, we hope the software will be well received by the community. Open-Unmix is part of an ecosystem of software, datasets, and online resources: the sigsep community. First, we provide MUSDB18  (Rafii et al. , 2017)  and MUSDB18-HQ  (Rafii, Liutkus, Stöter, Mimilakis, & Bittner, 2019)  which are the largest freely available datasets; this comes with a complete toolchain to easily parse and read the datasets  (Stöter & Liutkus, 2019a). We maintain museval, the most used evaluation package for source separation  (Stöter & Liutkus, 2019b).
The prior distribution of the weights is modeled with a Dirichlet distribution, which is the generalization of the Beta distribution. Briefly, the Dirichlet distribution models probability distributions over realvalued vectors that sum 1 and, thus, it is particularly suitable to model the prior distribution of the weight vector. By representing the vector of weights as w and the data (the rankings of the algorithms) as R = {σ (1) ,. , σ (N ) }, the posterior distribution of the weights can be computed as: Pr(w|R) = Pr(R|w)Pr(w) Pr(R). (3) The above equation has no closed-form, but we can easily sample the posterior distribution of weights using Markov Chain Monte Carlo methods. The inference process is as follows. First, the performance data of the algorithms is collected, considering the scenario (the population) from which we want to draw our conclusions.
Analysis' Considerations and Planning We illustrate the application of the Bayesian Plackett-Luce model by analyzing some of the performance data of the aforementioned benchmarking campaign. As described above, the numerical data encompass optimization results for 23 objective functions in 4 dimensions, each benchmarked over 11 instances by 11 competing algorithms. For each of these 23 × 4 × 11 × 11 = 11, 132 runs, the following information is recorded: whenever the algorithm has identified a solution of strictly better quality than the previous-best solution, a new data entry is created which stores the new incumbent fitness value and the number of the fitness evaluations elapsed before sampling it. 4 he analyses reported in Section 4 involve performance data of all eleven algorithms. Regarding the functions, we will show how to treat them either individually or collectively (in groups).
Importantly, mind should be given to the different interpretation of these two approaches (see  [16, p. 343] ). The plots in Fig. 2  show that all the algorithms have non-zero probabilities of being the best, even if some of them never arise as best in the data. One could think that this is due to the prior distribution, which assigns equal probabilities to all the algorithms before the data is incorporated into the model. However, the reason is not rooted in the prior, which has a negligible impact, as will be demonstrated in Section 4. 3. Rather, the Plackett-Luce model itself is responsible for this behavior, since its parameters (i. e. , the weights associated with each algorithm), reflect the relative ordering of all the algorithms, and not only the probability of being the best. So far we have focused on individual functions and, therefore, our conclusions are limited to the analyzed functions.
In this vein, we also suggest to include a Bayesian inference analysis module in IOHanalyzer, the data-analysis component of IOHprofiler. The Bayesian Plackett-Luce model described in this work has some strong points (e. g. , the ability to handle multiple algorithms), but -as with any statistical tool -also some weaknesses. It is therefore important to understand the limits of the model. An important  point to note is that by aggregating performances into rankings we lose information about the magnitude of the differences. Put differently, the model is capable of estimating the probability of an algorithm being the best, but it does not quantify the performance gap with respect to the first runner-up. Developing new statistical procedures to inspect the results from alternative points of view, forms an important line for future research.
ACKNOWLEDGMENTS This work has been partially supported by the  BERC 2018-2021  and  ELKARTEK programs (Basque Government) , and the project  TIN2016-78365-R  and  Severo Ochoa Program   SEV-2017-0718  ( Spanish Ministry of Economy, Industry and Competitiveness). Research was also supported by  COST Action CA15140  " Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO) ".
In the automata theoretic approach to explicit state LTL model checking, the synchronized product of the model and an automaton that represents the negated formula is checked for emptiness. In practice, a (transition-based generalized) Büchi automaton (TGBA) is used for this procedure. This paper investigates whether using a more general form of acceptance, namely a transition-based generalized Rabin automaton (TGRA), improves the model checking procedure. TGRAs can have significantly fewer states than TGBAs; however, the corresponding emptiness checking procedure is more involved. With recent advances in probabilistic model checking and LTL to TGRA translators, it is only natural to ask whether checking a TGRA directly is more advantageous in practice. We designed a multi-core TGRA checking algorithm and performed experiments on a subset of the models and formulas from the 2015 Model Checking Contest and generated LTL formulas for models from the BEEM database.
We extend that previous work in two ways. First, in this paper we also introduce a Fin-less acceptance condition, which is a disjunction of TGBAs. We show how a TGBA emptiness procedure can be trivially extended to support Fin-less acceptance and we empirically compare how automata with a Fin-less acceptance relate to TGBA and TGRA in terms of model checking performance. Second, we extend the set of experiments to include benchmark models from the BEEM database  [31] , accompanied by randomly generated LTL formulas (obtained from  [4] ). In total, more than 3000 model and formula combinations were added. We observed that the TGRAs generated from these formulas have a more complex structure compared to the ones from prior experiments. Experiments show how this affects the relative performance of checking TGRAs versus TGBAs.
We remark that this selection is in favour of the TGBA checking algorithm, since all cases where timeouts and memory errors occurred in the TGBA algorithm were filtered out as a result of our selection criteria. For each pair of model M and formula ϕ, we solved the model checking task L (M ⊗ A ¬ϕ ) = ∅ using 5 configurations that were repeated 10 times. We take the mean of the results to mitigate randomness introduced by the multi-core search procedure. The configurations were: ltl2tgba using the TGBA checking algorithm, LTL3DRA, Rabinizer 3, and LTL3TELA translated to TGRA, where the latter three cases used the TGRA checking algorithm introduced in this paper. Every task was run with a timeout of 10 min. In total, the MCC experiments took approximately 5 days to complete. The second set of experiments consists of 3200 formulas over 16 models extracted from the BEEM database  [4, 31].
In total, the BEEM experiments also took approximately 5 days to complete. All our results and means to reproduce them are available on https://github. com/utwente-fmt/Rabin-STTT. The numbers between parentheses denote how many times faster the TGBA checking algorithm is compared to the other configuration. We only used the experiments that were checked in all TGRA and TGBA configurations without running out of memory or time (39 in total for the MCC experiments, with 13 counterexamples, and 1167 in total for the BEEM experiments, with 699 counterexamples)  We notice that there is quite some difference between the results from the MCC experiments and the BEEM experiments. We argue that the LTL formulas for the MCC experiments are more realistic, but also less complex. The effect is that the produced TGRAs are simpler in structure and are therefore similar to the TGBAs.
A reason for why a TFLA might be checked faster than a TGBA is that a counterexample may be detected earlier as there are multiple acceptance sets that can be checked simultaneously. Another Here, |Pairs| denotes the number of Inf sets in the TFLA. We only used data from experiments without a counterexample and that were checked in the TFLA, LTL3TELA-TGRA, and TGBA configurations without running out of memory or time (106 in total for BEEM) reason is because the acceptance condition is more lenient than a TGBA. This allows a TFLA generator to construct more complex automata without significantly impacting the emptiness checking algorithm. In Table  7  we see that a TFLA has significantly more states than the corresponding TGBA, which holds as well for its product automaton. The effect of the TFLA construction procedure is visible in Fig.
Acknowledgements This work is supported by the  3TU. BSR  project. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecomm ons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Due to the complex nature of regulatory actions that take place after transcription, this information alone is often not as enlightening as one would hope. Answering questions such as 'what metabolic reorganization followed from an external change' based on expression levels alone remains thus difficult. Where transcriptomics data serves as a hint as to the actual functioning of a cell, the metabolic network represents the catalogue of possible functions and their relationships. It is then a natural choice to combine gene expression data with the knowledge provided by a metabolic network. The simplest representation of a metabolic network is by means of an ordinary graph that connects together either reactions or their associated genes based on shared metabolites. Because these connections do not necessarily correspond to a flow of matter, such simple model is perhaps best described as a knowledge graph where edges represent associations rather than physical structures.
The earliest approaches to incorporating the metabolic network into the analysis of 'omics data thus correspond to searching for subsets of interest amongst metabolic genes or reactions by way of subgraph extraction: starting from a set of nodes of interest (i. e. genes or reactions implicated by the data), find a subgraph that connects the observations  (Alcaraz et al. , 2011 (Alcaraz et al. , , 2012;; Antonov et al. , 2008; Baumbach et al. , 2012; Noirel et al. , 2008; van Helden et al. , 2000). Similar methods have also been used with other networks, such as proteinprotein  (Segal et al. , 2003)  or gene interaction networks  (Ideker et al. , 2002). A richer way of representing and analyzing metabolic networks is through the framework of constraint-based analysis. Here, the network is given as a stoichiometric matrix that not only contains a more accurate graphical representation of the network structure, but also includes the reaction stoichiometry.
In another work,  Samal et al. (2017)  used extreme currents (EC,  Clarke, 1988)  to find sets of genes whose activity correlated with a certain condition. The authors first calculated the ECs of a network leading to sets of genes. Then, given a gene expression dataset spanning several conditions, a statistical model was fitted to find sets of genes associated with certain conditions. Finally,  Zhu et al. (2017)  first defined a set of reactions that changed in expression from one condition to another by looking for changes in the average expression levels of all associated genes. They then formulated a MILP-problem which minimizes an inconsistency score between two flux vectors, one for each condition and the observed changes in expression levels. For a review of combining 'omics data and metabolic networks, we refer the reader to  (Kim and Lun, 2014; Machado and Herrga ˚rd, 2014; Opdam et al.
, 2017; Rezola et al. , 2015; Saha et al. , 2014; Vivek-Ananth and Samal, 2016). In this paper, we propose a new method called MOOMIN, for Mathematical explOration of 'Omics data on a MetabolIc Network, that we believe offers several improvements in the integration of transcriptomic data with metabolic networks in relation to previous methods. The first improvement is that our method does not require a biological objective function (as opposed to, for example,  Jensen and Papin, 2011) , which sometimes might be problematic. Take the biomass objective function for instance, which corresponds to the production of biomass as an approximation of growth: frequently this function is not tailor-made for the organism in question, but is rather a reproduction of objective functions for model species such as Escherichia coli or human.
We shall therefore present in the next sections the method we developed and the approaches we chose to improve the state-of-the-art algorithms as well as two case-studies where our method was able to detect results that were not evident from the differential expression data. Finally, we provide an implementation as a collection of MATLAB scripts to be used in concert with the COBRA Toolbox  (Heirendt et al. , 2019). This is particularly interesting because the closest approach in terms of methodology  (Zhu et al. , 2017)  does not have an implementation available, as far as we are aware. Moreover, the method of Zhu et al. does not take as input the results of a DE analysis but rather the authors have created their own pipeline that directly identifies changes at the level of the reactions.
However, since it does not guarantee full stoichiometric balance, artifacts such as infeasible cycles can be produced. The topological formulation is described in more detail in the Supplementary Materials. To test and demonstrate MOOMIN, we chose three previously published DE datasets. For all three sets, we started the analysis from raw counts and performed the DE analysis using the R package EBSeq  (Leng et al. , 2013). For the last two datasets, we also performed a standard gene ontology (GO) enrichment analysis using the EBSeq output. The results are shown in Table  1  and demonstrate some differences between 'traditional' DE analysis and our algorithm (for full results of the GO enrichment, see Supplementary Tables ). The first dataset analyzed compared Saccharomyces cerevisiae growth in a chemostat versus a batch culture, and is meant as a proof of concept as the associated metabolic shift is both well understood and clearly reflected in the expression of genes.
These results are discussed in Section 4. 1. The other two datasets studied Escherichia coli exposure to mercury and S. cerevisiae exposure to ethanol, and offer more biologically interesting results. They are discussed in Sections 4. 2 and 4. 3 respectively. For all datasets, we also compared the outputs from the original formulation [Equation (  8 )] and the topological approximation (see Supplementary Materials). These were largely in agreement, with only minor differences in reaction colours. A closer look at how the two solutions might diverge is provided for the S. cerevisiae ethanol stress data in Section 4. 3. 1. The S. cerevisiae model used was the iMM904 published by  Mo et al. (2009). It contains 1226 metabolites and 1577 reactions. The E. coli model chosen was the iJO1366 published by  Orth et al.
(2011). It contains 1805 metabolites and 2583 reactions. Both models were downloaded from the BiGG database  (King et al. , 2016). MOOMIN was run on an early 2015 MacBook Pro with a two-core 2, 9 GHz Intel Core i5 processor, and solving the problem in Equation (8) took 446 s for the S. cerevisiae chemostat versus batch dataset, 72 s for the E. coli mercury exposure set, and 5182 s for the S. cerevisiae ethanol exposure set.