dataesr/scientific-paragraphs-categorization huggingface dataset:
NASA's Discovery Program enables more sophisticated (and less risky) missions with a PI-managed mission cost cap an order of magnitude above the cost cap for SIM-PLEx (e. g. , ∼$800 million or so), comparable to ESA's Medium (M-class) missions. Both the SIMPLEx and Discovery Programs are open to any mission that advances planetary science-obviously including Venus-targeting missions. Higher-cost NASA missions are typically chosen from a restricted list. Venus is on the list of allowed targets for the New Frontiers program, which includes missions that are more scientifically ambitious (and thus technically challenging) than Discovery-class missions. Specifically, the new decadal survey described a New Frontiers mission called the Venus In Situ Explorer (VISE), which must address at least two of these three scientific objectives: • Characterize past or present large-scale spatial and temporal (global, longitudinal and/or diurnal) processes within Venus's atmosphere.
• Investigate past or present surface-atmosphere interactions at Venus. • Establish past or present physical and chemical properties of the Venus surface and/or interior. VISE is anticipated to collect data that is not obtainable from an orbiter alone (e. g. , VERI-TAS and EnVision) or a single descent probe (e. g. , DAVINCI). Innovative teams can design missions with a wide range of modalities that are responsive to the VISE concept in the decadal survey. For example, missions proposed to the New Frontiers program under a previous definition of VISE have included landers (targeting the plains or tesserae) and a combined orbiter plus descent probe. This decadal survey commissioned a study at NASA's Goddard Space Flight Center of a new concept called ADVENTS (Assessment and Discovery of Venus's Past Evolution and Near-Term Climatic and Geophysical State, Fig.
g. ,  Lukco et al. 2018). The GEER facility is the staging ground for the team building the Long-Lived In Situ Solar System Explorer (LLISSE), which is a small lander that could serve as a technology demonstration on future Venus missions (e. g. ,  Beauchamp et al. 2021). LLISSE uses electronics built with silicon-carbide integrated circuits that can survive near-indefinitely on Venus's surface  (Kremic et al. 2021). Further work on LLISSE over the next ten years should enable the next decadal survey to treat a long-lived lander as technically feasible-capable of achieving the scientific promise intrinsic to such a mission modality. Increased investment in other NASA infrastructure is also needed to support future exploration of Venus-and planetary science in general. Spacecraft missions return data to scientists on Earth via the Deep Space Network (DSN), the international collection of giant radio antennas.
Acknowledgements JGO thanks the participants in the " Venus In Situ Sample Capture Mission " workshop organized by the  W. M. Keck Institute for Space Studies , especially  John Elliott  and  Francois Tissot , for sci-fi recommendations. Thanks also to all the participants of the "Venus: Evolution through Time" workshop and project through the  International Space Science Institute  for countless fruitful discussions. M. J. W. acknowledges support from the  GSFC Sellers Exoplanet Environments Collaboration (SEEC)  and  ROCKE -3D: The evolution of solar system worlds through time, funded by the  NASA Planetary and Earth Science Divisions Internal Scientist Funding Model. Thanks finally to two anonymous reviewers for providing helpful feedback. Funding Note Open access funding provided by  Swiss Federal Institute of Technology Zurich.
LENs only require their inputs to be human-understandable predicates, and they provide explanations in terms of simple First-Order Logic (FOL) formulas involving such predicates. LENs are general enough to cover a large number of scenarios. Amongst them, we consider the case in which LENs are directly used as special classifiers with the capability of being explainable, or when they act as additional networks with the role of creating the conditions for making a black-box classifier explainable by FOL formulas. Despite supervised learning problems are mostly emphasized, we also show that LENs can learn and provide explanations in unsupervised learning settings. Experimental results on several datasets and tasks show that LENs may yield better classifications than established white-box models, such as decision trees and Bayesian rule lists, while providing more compact and meaningful explanations.
In order to create these conditions in different types of datasets, generic continuous features can be either discretized into different bins or Booleanized by comparing them to a reference ground truth (e. g. for gene expression data, the gene signature obtained from control groups can be used as a reference). (i) An E2E LEN can perform a fully interpretable classification task, having the double role of main classifier and explanation provider (Fig. 4 , top), or (ii) it can work in parallel with another neural black-box classifier g, thus providing explanations of the predictions of g (Fig. 4 , bottom) -what we refer to as explaining a black-box. In both the cases (i) and (ii) we have E = Y. The first solution is preferred when a white-box classifier is of utmost importance, while a loss in terms of classification accuracy is acceptable, due to the constrained nature of the LENs.
(i) This two-level scenario can be implemented in a way that is coherent with the already discussed example of Fig. 2 , that is what we also show in  Fig. 5 (top). In this case, each example is labeled with q binary targets divided into two disjoint sets, where the black-box network g predicts the first z < q ones, and the LEN f predicts the remaining r = q -z, i. e. E = Y (z+1:q). The LEN will be both responsible of predicting the higher level concepts and of explaining them in function of the lower level concepts yielded by the black-box model, thus still falling withing the context of interpretable classification. (ii) We also consider the case in which the outcome of the LEN is not associated to any known categories. In this case, the. This is a realworld example from the MIMIC II dataset (see the experimental section), where patient features are used to classify if the patient will survive 28 days after hospital recovery.
Benchmarking out-of-the-box LENs In this section, we quantitatively assess the quality of the explanations and the performance of LENs, compared to state-of-the-art white-box models. We consider several tasks, covering multiple combinations of computational pipelines (Section 3. 1) and objectives (Section 3. 2). The summary of each task is reported in Table  3. A brief description of each task, the corresponding dataset and all the related experimental details are exposed in Section 6. 1. In Section 6. 2 six quantitative metrics are defined and used to compare LENs with the considered state-of-the-art methods. The Python code and the scripts used for the experiments, including parameter values and documentation, is freely available under Apache 2. 0 Public License from a GitHub repository  6.
Dataset and classification task details We considered five categorical learning problems ranging from computer vision to medicine and democracy. Some datasets (e. g. CUB) were already annotated with high-level concepts (e. g. bird attributes) which can be leveraged to train a concept bottleneck pipeline. For datasets without annotations for high-level concepts, we transformed the input space into a predicate space (i. e. R 9 → [0, 1] d ) to make it suitable for training LENs. According to the considered data type, we will evaluate and discuss the usage of different LEN pipelines and objectives, as already anticipated in Table  3. For the sake of consistency, in all the experiments based on supervised learning criteria (Section 4. 2) LENs were trained by optimizing Eq. 6, therefore extracting IFF rules, that better cope with the ground truth of the datasets.
MIMIC-II -E2E, INTERPRETABLE CLASSIFICATION. The Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II,  [Saeed et al. , 2011 , Goldberger et al. , 2000] ) is a public-access intensive care unit (ICU) database consisting of 32,536 subjects (with 40,426 ICU admissions) admitted to ICUs at a single tertiary care hospital. The dataset contains detailed description of a variety of clinical data classes: general, physiological, results of clinical laboratory tests, records of medications, fluid balance, and free text notes and reports of imaging studies (e. g. x-ray, CT, MRI, etc). In our experiments, we removed the text-based input features and we discretized blood pressure (BP) into five different categories (one-hot encoded): very low BP, low BP, normal BP, high BP, and very high BP.
MIMIC-II (EBB) -E2E, EXPLAINING A BLACK-BOX MODEL. On the same dataset, a second task is set up. A black-box model g is employed to solve the previously described classification task. The end-to-end LEN f instead is trained to mimic the behaviour of the black-box g, using the learning criterion of Eq. 11. MNIST E/O -CB, INTERPRETABLE CLASSIFICATION. The Modified National Institute of Standards and Technology database (MNIST,  [LeCun, 1998] ) contains a large collection of images representing handwritten digits. The input space X ⊂ R 28×28 is composed of 28x28 pixel images of digits from 0 to 9. However, the task we aim to solve is slightly different from the common digit-classification. We are interested in determining if a digit is either odd or even, and explain the assignment to one of these classes in terms of the specific digit categories from 0 to 9.
In the notation of this paper, we can consider that each image comes with 12 attributes, where the first 10 ones are binary attributes about the specific digit type, i. e. from 0 to 9, while the last 2 ones are binary labels that encode whether the digit is even or odd (Y = [0, 1] 12 ). We consider a concept-bottleneck pipeline that consists of a concept space C that is about the attributes of the specific digit type. We focus on the objective of interpretable classification of the odd/even classes, so that the mapping X → C, with C = Y (1:10) is learned by a ResNet10 classifier g  [He et al. , 2016]  trained from scratch, while a LEN f is used to learn both the mapping and the explanation as a function C → E, with E = Y (11:12). MNIST E/O (ICLU) -CB, INTERPRETABLE CLUSTERING. The same dataset has been used with the objective of interpretable clustering.
In this case, we considered a concept space C = Y = [0, 1] 12 which comprise both the digits and the even/odd labels. The mapping X → C is learned again by a Resnet10 model g trained as a multi-label classifier, while the LEN f performs clustering from C → E, where E = [0, 1] 2 space, in order two extract two clusters that are expected to group data due to two properties of the concept space that are not-defined in advance. CUB -CB, INTERPRETABLE CLASSIFICATION. The Caltech-UCSD Birds-200-2011 dataset (CUB,  [Wah et al. , 2011] ) is a finegrained classification dataset. It includes 11,788 images representing 200 different bird species. Moreover, 312 lower-level binary attributes have been also attached to each image representing visual characteristics (color, pattern, shape) of particular parts  (beak, wings, tail, etc.
V-DEM -CASCADING, INTERPRETABLE CLASSIFICATION. Varieties of Democracy (V-Dem,  [Pemstein et al. , 2018 , Coppedge et al. , 2021] ) is a dataset containing a collection of indicators of latent regime characteristics over 202 countries from 1789 to 2020. The database includes 483 low-level indicators (e. g. media bias, party ban, high-court independence, initiatives permitted, etc), 82 mid-level indices (e. g. freedom of expression, freedom of association, equality before the law, etc), and 5 high-level indices of democracy principles (i. e. electoral, liberal, participatory, deliberative, and egalitarian). In the experiments, we considered each example to be paired with low/mid level indices and the information on electoral/non-electoral democracies taken from high level indices. Keeping the same order of the previous description, each sample is then paired with binary targets in Y = [0, 1] 483+82+2=567.
Decision trees are the fastest overall. In terms of consistency, LENs seem to provide more stable results, but overall there is not a dominant method (see Table  8 ). We can see, instead, how this result is clearly impacted by the considered dataset/task. Our intuition is that those datasets that are more coherently represented by the data in the different folds are expected to lead to more consistent behaviors. Fig. 10  shows a combined view of two of the main metrics considered so far, reporting the Pareto frontiers  [Marler and Arora, 2004]  for each experiment in terms of the explanation and model error (100 minus the explanation/model accuracy). The figure provides a broad perspective showing how the LEN framework is flexible enough to instantiate different models having different compromises between explanation and classification accuracy.
, 2021] , especially in case of multi-label classification. However this approach requires that the logic rules are already available for the considered task, so that a domain expert is expected to collect them. Interestingly, LENs can be applied to a set of clean data to automatically learn a set of FOL rules that may capture the important properties of the target domain. Then, these rules can be used to detect adversarial samples as in the framework introduced in  [Melacci et al. , 2021] , since they are exactly of the same type of the LEN ones. Without any attempts of being exhaustive on this topic, we briefly explored whether what we stated in the previous sentence would work in practice. We considered the bird-identification dataset (CUB 200), facing the classification problem in its original multi-label fashion, where a convolutional neural network g (ResNet18) is trained from scratch to classify both the 200 classes and the ones of the additional bird attributes.
Figure4: End-to-end (E2E) LENs directly work on input data that are interpretable per se and treated as concepts (C = X), while the output concepts are the activation scores of the classes of the dataset (r = q = 2). This is a realworld example from the MIMIC II dataset (see the experimental section), where patient features are used to classify if the patient will survive 28 days after hospital recovery. Top: the LEN solves the classification problem and provides explanation, also referred to as interpretable classification. Bottom: the LEN provides explanations of a black-box classifier. The universal quantifier and the argument of the predicates have been dropped for simplicity.
Figure6: Cascading LENs, an example taken from the V-Dem classification dataset (see the experimental section). The final classification on the status of the democracy is divided into two steps. First, the input data/concepts C 1 are mapped into high-level concepts C 2. High-level concepts are then used to compute the final classification. Cascading LENs can provide explanations at different levels of granularity, implementing multiple interpretable classifications. The universal quantifier and the argument of the predicates have been dropped for simplicity.
A selection of some of the best rules extracted for each experiment. Each rule involves concept names that are taken from the respective dataset. We dropped the argument (c) from each predicate to make the notation more compact, and each rule is intended to hold ∀c ∈ C. Rule from decision trees are not shown since they involve a very large number of terms. (Zero ∧ ¬One ∧ ¬Two ∧ ¬Three ∧ ¬Four ∧ ¬Five ∧ ¬Six ∧ ¬Seven ∧ ¬Eight ∧ ¬Nine) ∨ (Two ∧ ¬Zero ∧ ¬One ∧ ¬Three ∧ ¬Four ∧ ¬Five ∧ ¬Six ∧ ¬Seven ∧ ¬Eight ∧ ¬Nine) ∨ (Four ∧ ¬Zero ∧ ¬One ∧ ¬Two ∧ ¬Three ∧ ¬Five ∧ ¬Six ∧ ¬Seven ∧ ¬Eight ∧ ¬Nine) ∨ (Six ∧ ¬Zero ∧ ¬One ∧ ¬Two ∧ ¬Three ∧ ¬Four ∧ ¬Five ∧ ¬Seven ∧ ¬Eight ∧ ¬Nine) ∨ (Eight ∧ ¬Zero ∧ ¬One ∧ ¬Two ∧ ¬Three ∧ ¬Four ∧ ¬Five ∧ ¬Six ∧ ¬Seven ∧ ¬Nine).
Acknowledgments We thank  Ben Day ,  Dobrik Georgiev ,  Dmitry Kazhdan , and  Alberto Tonda  for useful feedback and suggestions. This work was partially supported by the  GO - DS21  project funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  848077  and by the  PRIN 2017 project RexLearn (Reliable and Explainable Adversarial Machine Learning ), funded by the  Italian Ministry of Education, University and Research  (grant no. 2017TWNMH2 ). This work was also partially supported by  TAILOR , a project funded by  EU   Horizon 2020 research and innovation programme under GA No 952215.
other more recent studies have been developed to study the efficiency of the stack either by optimizing its geometry or by using a different working fluid  [27] [28] [29] [30]. Most of above researches are carried out with a view to optimize the performance of thermoacoustic devices. In fact, the majority of the numerical studies conducted to understand the behavior of thermoacoustic systems are about simple simulations that presenting the effect of some parameters on thermoacoustic energy conversion. However, none of these studies shows the time-dependent evolution from the self-exited process until reaching the steady-state. In this work, a 2D model, based on CFD simulation carried out using the software Comsol Multiphysics, is considered to simulate and investigate the influence of various stack parameters and boundary conditions on thermoacoustic energy conversion of a standing-wave thermoacoustic engine designed to drive a thermally driven thermoacoustic refrigerator  [31, 32].
The parameters studied are examined by solving Navier-Stokes and energy equations using the finite element method. The parameters considered are stack length, position, plate thickness, gap between two plates and temperature gradient along the stack. The thermo-physical properties of the stack studied are thermal conductivity and heat capacity, considered by variant the regenerative unit material. The time-dependent of the self-excited-process from the initial disturbance to the steady state of the acoustic wave generated by the engine is showed at each case. In addition, an optimization approach is performed by the use of an optimization algorithm based on the simplified linear thermoacoustic theory, carried out using MATLAB code, to design and optimize the parameters investigated by the CFD analysis.
Design optimization The linear theory of thermoacoustics  [Swift, Rott]  allows a first estimate of the performances of thermoacoustic systems. However, in the case of high power systems where nonlinear phenomena become significant, this theory has been limited. These nonlinear phenomena are responsible in particular for the appearance of nonperiodic flows. Some are of thermo-convective origin, dynamic (turbulence), others of purely acoustic origin (streaming). These flows are sometimes the source of convective heat transfer, which can modify the temperature gradient both in the wave generator and in the refrigerator, thus influence the efficiency of the systems and limit the possibilities of optimization of thermoacoustic devices. The optimization procedure adapted in this work is based on a MATLAB code.
Conclusion In order to explore the performance of a small standingwave thermoacoustic engine two analogies have been presented in this work. The first analogy is based on a numerical modeling of standing-wave thermoacoustic engine to access all instantaneous information (velocity, pressure, temperature) for each point of the calculation domain based on CFD simulations. The second analogy is based on the use of an optimization algorithm carried out using the programming MATLAB language. The algorithm is built based on the simplified linear thermoacoustic theory and applied for designing thermoacoustic engines with different stack parameters and operating conditions. For the CFD simulation, a 2D simulation model is implemented after to visualize the temporal evolution of the process of acoustic wave excitation and to examine the engine efficiency in term of the acoustic pressure and the oscillations frequency.
Notes taken during these interviews helped the authors interpret the factors revealed by the statistical analysis. The Q-sorts were then recorded and coded with the help of the PQMethod software  (Schmolck and Atkinson, 2014). Internally, the software codes the Q-sorts as a matrix where the columns correspond to a statement and the rows correspond to the respondents. Each cell corresponds to the rank given to the statement 𝑆𝑇 𝑥 by the respondent 𝑟. Therefore, a line corresponds to all the ranks given by one respondent.
Because of their complex structure and their biochemical properties, the functional characterization of ADAMTS-TSL using classical experimental approaches remains a challenge  [6]. In addition, performing specific predictions using computational methods is complicated by the lack of structural data, the complex multidomain organization of these proteins and the presence of paralogs. To date, functional predictions mainly consist in recognizing known conserved domains/motifs thanks to pre-computed signatures available in databases such as PROSITE  [7]  or Pfam  [8]. While this approach provides a first approximation of ADAMTS-TSL functions, it fails to account for specific activities. For instance, the closely related paralogs ADAMTS-8 and ADAMTS-5 share the same domain organization but ADAMTS-5 cleaves aggrecan/versican highly efficiently  [9] [10] [11]  while ADAMTS-8 does not  [12].
These observations suggest that more refined methods are needed to identify the specific sequence regions associated with the functional specificity of ADAMTS-TSL proteins. Instead of relying on external generic domains/motifs, we propose here to consider all local conservation of sequences within the ADAMTS-TSL family as potential evolutionary traces of their functional significance. The detection of all the conserved loci from at least two proteins of the family is made possible by partial and local multiple alignment of their sequences. Introduced by the Protomata suite  [13, 14] , this kind of alignment is composed of sets of local alignments involving each a subset of the sequences to align. These local alignments enable the identification of all the conserved sequence modules maintained during evolution, at least in a subset of sequences, by selective pressures, and thus likely to be important functionally.
Species selection and sequence retrieval To build the set of input sequences, we selected 9 species: Homo sapiens, Mus musculus, Bos taurus, Gallus gallus, Xenopus tropicalis, Danio rerio, Ciona intestinalis, Caenorhabditis elegans, and Drosophila melanogaster, because they are representative of the evolutionary history of the ADAMTS-TSL family from the bilateria ancestor to Homo sapiens and they have complete proteome and genome annotations in the Gene Feature Format (GFF) datasets (S1 Table ). Providing the whole proteomes of the nine species as input, we used the Orthofinder software  [25]  (v2. 5. 2, default settings) to cluster the sequences into 31,229 orthogroups. An orthogroup The different steps of the method, illustrated here for a dummy set of sequences containing two paralogs p1 and p2 (from one species) and their ortholog p3 (from another species), are: 1) Inference of the reference gene tree from protein sequences by a standard pipeline (PASTA, RAxML, TreeFix); 2) Identification of conserved sequence modules (i.