dataesr/scientific-paragraphs-categorization huggingface dataset:
Then we performed the HCA classification using these 11 dimensions. ESD Figure 3. MCA Biplot showing the correlation between variables and main dimensions 1 and 2. A shows all variables, while B has enlarged the central part of the plot, highlighting the variables that are too close in A. ESD Figure 4. Fifteen most contributory categories along: A. Dimension 1, and B. Dimension 2 of MCA. The +sign indicates the positive coordinate of the variable along the dimension, whereas the -sign points to the negative coordinate of the variable along the dimension. ESD Figure 5. Socio-demographic characteristics (graphs 1-7) and geographical origin of the respondents (graphs 8-17) from the Survey A dataset. Graphs 1-4 show the number of people in each household (graph 1) as a function of age group (graphs 2-4).
ESD Figure 6. Hazard knowledge acquisition among the respondents from the Survey B dataset. Graphs 1-11. ESD Figure 7A. Level of risk perception and associated variables among the respondents from the Survey C dataset. Graphs 1-19. ESD Figure 7B. Level of risk perception and associated variables among the respondents from the Survey C dataset. Graphs 20-34. ESD Figure 8A. Socio-economic categories from the Survey D dataset: Graphs 1-10. ESD Figure 8B. Socio-economic categories from the Survey D dataset: Graphs 11-17. Structural typology of buildings from the Survey E dataset, graph 18, and physical vulnerability classes of buildings, graph 19 (see ESD Tables.
Acknowledgments The  ANR  ' RiskAdapt ' research project funded fieldwork and laboratory analyses. This research was also financed by the  French government IDEX-ISITE  initiative  16-IDEX-0001. We are grateful to  DIKTI (Directorate General of Higher Education, Ministry of National Education of Indonesia) , who bestowed two research permits to the first author. We acknowledge the scientific support from  Dr. A. -F. Yao-Lafourcade  ( Laboratory of Mathematics, UCA ),  MSc Isna Pujiastuti  ( Faculty of Geography, University Gadjah Mada, Yogyakarta ), and  MSc. Muhammad Syaif Habi ,  F. Muhammad A. W. Hasan  ( Faculty of Agriculture, IPB University, Bogor ). We thank  Mr. Mahjum  and  Pak Sam  for their logistical support. SFJ would like to acknowledge funding from the  National Research Foundation Singapore  and the  Singapore Ministry of Education  under the research centres of excellence initiative.
awareness, preparedness, and behaviour in case of imminent eruption and evacuation POPULATION SOCIO-DEMOGRAPHIC, ECONOMIC CHARACTERISTICS AND ADAPTIVE CAPACITY II. Livelihood and resources (Surveys D1 & E) APPROACH APPLIED TO SURVEY DATASETS STATISTICAL Univariate & Bivariate analyses, Multiple Correspondence Analysis, and Hierarchical Agglomerative Clustering Risk perception & associated variables (Surveys C & D2) I and II: Defining social vunerability and socio-economic categories 8. Building stock: use, structural types, vulnerability categories 1. House, land ownership III. Assessing 1. Location: village, dusun*, neigbourhood 3. Household composition (families, people) 4. Socio-professional category 5. Education degree 6. Resource person, monthly income threshold 7. Geographical origin (respondent, relatives) 8.
We were trying out tools that became much easier to use (for me at least) with the program Hyperbase. From 2001, Hyperbase became my main tool, which I have used on many corpora since, whereas Deconinck-Brossard applied different tools to her homiletic corpora. Hyperbase is the brainchild of Etienne Brunet, a French scholar and developer, who had started with published frequency indexes of an author or a period: Le vocabulaire de Jean Giraudoux: structure et évolution (1978), Le vocabulaire de Proust (1983), Le vocabulaire français de 1789 à nos jours (1981). 5 I invited him to my master's seminar in 2007, and my students presented computer-aided analyses they had prepared on a corpus of modernist short stories as a basis for discussion. In a few clicks, Hyperbase provides user-friendly graphs.
Corpus-based approach My next experiment was less convincing but an important step. Thanks to the digital turn taken in most universities and to a paper on my Sterne findings presented before the informal, international research English studies group within EUCOR -The European Campus (European Confederation of Upper-Rhine Universities), I was given access to the treasure trove of Chadwick-Healey's Eighteenth-Century Fiction Database, which Basel University had acquired but nobody was usingand which most French university libraries could not afford. After a frenzy of downloading material onto floppy disks, I came home with reliable electronic editions of nearly 100 texts that I could use on my personal computer. 6 However, overwhelmed by quantity, I did not at first adopt a corpus approach.
I was able to better explain this as Hyperbase provided easier-toread graphs, and a corpus comprising eight parts was more manageable than the 22 we had devised for Moll Flanders. A readily comprehensible paper had shown me the way: it used Hyperbase to compare Seneca's tragedies  (Mellet, 1998). In the same manner, the eight Defoe novels are positioned according to the vocabulary they share. 8 Colleagues in mathematics helped me by pointing out that if the first results made sense in terms of what I knew about the texts from literary history and traditional stylistic analysis, I could trust the data the software was producing and use it to develop further analyses. The texts were positioned in ways that made sense both in their groupings (the two Robinsons, the two female stories) and in their differentiations (the texts really considered as fiction vs.
I therefore set up a series of corpora to draw out the stylistic features of Roderick Random. The 1740s corpus comprises fictional texts of comparable length from that decade with a balance between male and female authors as well as between stories of male or female protagonists. Random being written as a first-person narrative, the 1STPERSON corpus is composed of fictional autobiographies from the eighteenth century, with some novels also present in the 1740s. The risk of bias caused by the prevalence of Defoe is balanced by the advantage of having several corpora of similar sizes, including the novel under study, as Random also belongs to the SMOLLETT corpus, which comprises all of Smollett's fiction. I therefore had three corpora of over a million words each, countering the objections of linguist colleagues that I did not have enough data with which to work.
One version of this study more focused on its use of Hyperbase got me into Brunet's session at JADT 2010. However, my strong stylistic bent put me in an awkward position among scholars like Jean-Marie Viprey, who had moved from their inspiring analyses of an author's style (1997) to demonstrating the software they were fine-tuning. Meanwhile my work was deemed too technical to be published in volumes derived from conferences primarily concerned with literature or cultural studies, at which I presented papers on the use of body words by female authors with my corpora as a backdrop (what hands do in Pamela, Evelina, Isabella, or Pride and Prejudice, for instance). I nearly gave corpus stylistics up. However, its use by a doctoral student of mine to study the expression of space in children's fiction over a century confirmed its potential.
We showcase its robustness and scalability by exploring the locomotion modes of a model microswimmer inspired by the diatom colony Bacillaria Paxillifer, and by simulating large suspensions of bacteria interacting near a no-slip boundary. Finally, we provide a Python implementation of our framework in a collaborative publicly available code, where the user can prescribe his own set of constraints through a single input file to study a wide spectrum of applications involving suspensions of articulated bodies.
Acknowledgments F. B. U. acknowledges support from  "la Caixa" Foundation  ( ID 100010434 ), fellowship  LCF/BQ/PI20/11760014 , and from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sk lodowska-Curie  grant agreement No  847648. B. D. acknowledges support from the  French National Research Agency (ANR) , under award  ANR-20-CE30-0006. B. D. also thanks the  NVIDIA Academic Partnership program  for providing GPU hardware for performing some of the simulations reported here.
Calibration with other tracers The estimates used as reference in Sect. 2. 3 are not the only option for calibrating our method. Relying on the same optical-to-FIR data, we can use alternate fitting codes (e. g. , Prospector) instead of LePhare. Besides that, owing to the wealth of ancillary data in COSMOS, other proxies for stellar mass and star formation can provide M label and SFR label. This is particularly evident for the latter, which can be derived, for instance, from Hα nebular emission or by the radio continuum. Other possibilities, such as the 21 cm atomic hydrogen or submm molecular gas transitions, are not included in the present discussion for sake of simplicity and we address the reader to  Kennicutt & Evans (2012)  for an exhaustive bibliography on the various star formation tracers.
Our goal in the present section is not to repeat the analysis with another sets of M SOM , SFR SOM estimates -but, rather, to show the capacity of different calibration samples to fill the SOM grid. A technique for deriving SFR Hα has been presented in Sect. 4. 2. Radio continuum emission is also correlated with galaxy star formation  (Condon 1992 ), but we do not calculate SFR radio here since we aim at discussing the availability of data rather than the final estimates. The latest, publicly available 21 radio data for almost the entire COSMOS field come from interferometry in the 3 GHz band with the ESO Very Large Array  (Smolčić et al. 2017). Instead of extracting individual sources, as done with both MIPS and spectroscopic tracers, we could stack 3 GHz map cutouts centered at the position of SOM galaxies belonging to the same cell.
40 < z < 1. 8 with an Hα flux >5 × 10 -17 erg s -1 cm -2  (Pozzetti et al. 2016). The third tracer, based on 3 GHz stacking, provides an SFR label for 510 cells, corresponds to ∼20% of the area actually sampled by previous calibrations. Very few cells (red pixels in Fig. 14 ) are newly labeled by the radio sample, partly due to the conservative S /N > 5 cut we imposed: if instead we also allow stacked images with 3 < S /N < 5, then the number of SOM cell probed by radio data nearly doubles. However, even in that case, the expansion with respect to the original MIPS calibration would be limited (only 65 additional SFR label ) due to the FIR-radio correlation and its strong dependency on stellar mass  (Delvecchio et al. 2021 ). Despite such a marginal expansion, it is still useful to have various tracers superimposed on the SOM for a better understanding of the galaxy parameter space.
2022)  up to z = 1. 8, together with the rich archive of complementary data available for the COSMOS field. Not only the quality, but also the large size of COSMOS observations make them an ideal training sample for the SOM. In our method, after the SOM dimensionality reduction created a grid of 80 × 80 cells, we labeled as many cells as possible with reference values of z, M, and SFR. These values are derived by means of ancillary data, in particular MIPS 24 µm from the S-COSMOS survey to constrain galaxy star formation. Once the SOM was calibrated, we projected other galaxies onto the grid, obtaining an estimate of their z SOM , M SOM , and SFR SOM depending on the cell to which each target has been associated. We used SXDF galaxies, but any other galaxy sample can be measured as long as the targets have the requisite   [(30, 30), (0, 50), (46, 34) ] on the 80 × 80 gird.
Besides the average in each cell, we can also inspect the reduced χ 2 (i. e. , χ 2 SOM /N dim , assuming N dim degrees of freedom) of individual galaxies to find which ones are misrepresented by their weight. This happens at edges of the grid, for instance, where the SOM is forced to pack together sparse objects from the outskirts of the data distribution. A bad match between certain galaxies and the weight linked to them can also concern cells in the interior of the grid (see the cells marked in yellow in Fig. B. 1) since a non-linear manifold such as our 0 < z < 1. 8 galaxy sample is hard to describe with a relatively limited network of weights. It is nonetheless reassuring that only 4. 4% of the COSMOS2020 galaxies overall have χ 2 SOM > 24. 7; this should be the 99th percentile threshold of a χ 2 distribution with N dim = 11 degrees of freedom: the fact that more than 1% of the sample exceeds the threshold can be ascribed partly to the SOM modeling, which is not expected to be a perfect description, but also to the fact photometric errors may not be entirely accurate in the COS-MOS2020 catalog 30.
Acknowledgements. The authors are grateful to the referee for the thorough and constructive comments they provided. I. D. would like to thank  Eric Bell ,  Micol Bolzonella ,  Rebecca Bowler ,  Ivan Delvecchio ,  Joel Leja ,  Vihang Mehta  for useful discussions. The authors are grateful to  Joel Leja  also for providing Prospector data in a convenient digital format. This work started during a meeting in Marseille, founded by the  French Agence Nationale de la Recherche  for the project " SAGACE ". This research is also partly supported by the  Centre National d'Etudes Spatiales (CNES). I. D. , J. R. W. , K. J. , and O. I. made use of the  CANDIDE Cluster at the Institut d'Astrophysique de Paris  and made possible by grants from the  PNCG, CNES  and the  DIM-ACAV. The Cosmic Dawn Center is funded by the  Danish National Research Foundation  under grant No.
P x∈½1;N� jx〉. In the case where N = 2 n , this is done by applying H ⊗n to the input state j0〉 ⊗n. At this point, the measurement of the state of the system would yield a marked element with probability M/N. Grover's algorithm will repeatedly act on the state to increase those odds. Note that Grover's search is often described as searching a database, which fits in the definition above if we think of f as performing a memory access at a given index. However, in order to implement this efficiently, one may need the quantum random-access model presented in Section 2. 10. The first building block of Grover's algorithm is an oracle that implements the inspection function f (See Figure  1 ). Intuitively, this state acts on a superposition of basis states by multiplying the phase of all the component jx〉 corresponding to the index x of a marked element by -1.
This is the Grover-meet-Simon approach of Ref. [82] ; a superposition attack so far, since each iteration needs to call FX in superposition. In Ref. [80] , the authors introduced the offline-Simon algorithm, which performs the same attack, but makes only O(n) superposition queries to the FX quantum oracle at the beginning of the algorithm. Intuitively, the queries done at each iteration in Grover-meet-Simon are actually redundant and can be removed, leaving us only with a single layer of offline queries. These queries are stored using polyðnÞ qubits only, kept in a database and reused at each iteration. F I G U R E 6 The Even-Mansour cipher  [85]  F I G U R E 7 The FX cipher  [123]  Since only O(n) superposition queries are now made, it becomes possible to replace them entirely with classical queries.
P x∈f0;1g n jx; 0〉, then for each i, XORs FX(i) to the second register if the first one is equal to i. Note that the memory used is still polyðnÞ qubits, since the classical queries to FX are consumed online and do not need to be stored. There is also no memory access here, since the database is not accessed in a classical sense; it is only reused to perform instances of Simon's algorithm. Then, the resulting algorithm runs in quantum time Õð2 n Þ, with a first step of Õð2 n Þ computations to prepare the database, and a Grover search of time Õð2 n Þ. It does not have a classical equivalent, though a classical attack in time O(2 2n ) does exist. In Ref. [81] , the offline-Simon algorithm was extended to target more constructions and in particular, constructions of the form: x ↦ E 0 k ðk 2 ⊕ E k ðk 1 ⊕ xÞÞ; with two independent block cipher calls keyed by k.
Therefore, Question 2 what are potential quantum speed-ups for 'easy' lattice-reductions like LLL or BKZ with small block-size? Turning to the average-case problem, like LWE and SIS, the currently most efficient approach to solve these problems for cryptographically relevant parameters is to run the appSVP solvers, that is, BKZ reduction. So, Question 3 are there any non-lattice-based approaches (classical or quantum) to solve LWE/SIS that are faster, may be even for some limited parameter ranges, than the approach via lattice-reduction? The last question is not limited to the ℓ 2 -norm: LWE is also interesting when the error vector comes form a uniform distribution. Respectively, SIS is of importance as well  [90]  when the solution is required to be bounded in ℓ ∞ -norm. Associated to a linear code C are its generator matrix G ∈ F k�n and its parity-check matrix H ∈ F n-k�n.
ACKNOWLEDGEMENTS We are grateful to  Martin Ekerå  for providing detailed material on Shor's algorithms and on the various recent derivatives of these algorithms. We also thank the anonymous reviewers for their many insightful comments that allowed significant improvements of this work. Elena Kirshanova  is supported by the  Young Russian Mathematics scholarship  and by the  Russian Science Foundation  grant  N 1-71-00114 , https://rscf. ru/project /22-41-04411/.
Many pathway databases with data extracted from the literature, and manually curated, exist and can be used as a source to infer a specific type of networks from a list of genes  [2] [3] [4] [5] [6]. A real community effort is conducted to create the networks in standard format in order to ensure their readability, their classification, their homogeneity, their exchange and their analysis by making them understandable to machines (Systems Biology Graphical Notations, SBGN  [7] ). Many of these databases have joined this effort and provide the networks in one of the standard formats. Most pathway databases include annotations about the nature of the interactions, where they take place, in which cell type and for which types of experimental conditions the data were generated. Nonetheless, one issue when constructing these networks is that they can very easily reach a large dimension with a high number of nodes and numerous (sometimes heterogeneous types of) interactions on which it becomes more and more difficult to reason intuitively.
A more coarse-grain approach consists in translating the information into a logical model where variables are discrete and there are no parameters to fit. The approach is simpler and more versatile but the conclusions that can be drawn from this type of models are not quantitative and not all questions can be answered (dosage of drug treatments, precise timing of events, etc. ). We present, here, an alternative approach for mathematical modeling of these signaling pathways and its implementation in a software, MaBoSS (Markovian Boolean Stochastic Simulator)  [13, 14]. The main hypothesis of MaBoSS relies on the definition of genes or proteins' activities by discrete levels, within a continuous time dependency framework. Initially, MaBoSS was designed to model signaling pathways in a single cell type, but over time and to answer the evolution of the biological questions that were formulated, MaBoSS was extended to tackle a broader biological context, including heterogeneous and dynamic cell populations.
Over the past decade, our contribution towards more comprehensive and complex approaches has been extended in several directions. We first started modeling the signaling pathways of a single cell with MaBoSS (https://github. com/sysbio-curie/ MaBoSS-env-2. 0) [13, 14]  and WebMaBoSS, a user-friendly web interface (https://github. com/sysbio-curie/WebMaBoSS) [35]. We then explored the possibility to consider families of resembling models that fit biological constraints with the simulation of ensembles of MaBoSS models  [36]. We further allowed the cells to interact with each other and other cell types, die and divide with UPMaBoSS (https://github. com/sysbio-curie/UPMaBoSS-docker) [37]. Finally, we included spatial considerations in the interactions among cells and cell types and with the microenvironment with PhysiBoSS (https://github.
Qualitative modeling with MaBoSS and its extensions MaBoSS primary tool is a C++ software. It requires a. bnd file (Boolean Network Descriptor) and a. cfg file (configuration) as inputs. These files can be written and edited manually, but very often, models are constructed with a different software (BoolNet or GINsim), imported, handled, modified and simulated within a python interface called PyMaBoSS ( https://github. com/colomoto/ pyMaBoSS) developed by the CoLoMoTo consortium  [39]. All the model files and the python notebooks used to simulate the models in this review are provided in the Supplementary material and in the dedicated GitHub available at https://github. com/sysbiocurie/MaBoSS-Review/.
For instance, a mutation in ROS-related genes (overactivation of the node ROS (reactive oxygen species) in the model) strongly affects the proportion of surviving versus dying cells to 1. 6% and 28. 1%, respectively, highlighting the role of ROSrelated genes in cell death (Fig. 4B ). The same simulations can be performed using a user-friendly web interface, WebMaBoSS  [35]  available at this address: https://maboss. curie. fr/webmaboss/). The user, after login in, can import any model from public databases or from local MaBoSS files and simulate models without any extensive knowledge of the software.
When modeling tumorigenesis, these challenges should be tackled. The stochasticity of MaBoSS tool suite can partly solve this question: with MaBoSS, each trajectory is a cell and each cell can follow its own path. It is possible to simulate a certain percentage of cells with different sets of mutations, reproducing to a certain extent the clonality reported in the tumor. We developed further a methodology to integrate omics data into these models and create a model per patient with different mutations and transition rates inferred from discretized data (mutations of copy number variation data), and continuous data (RNAseq or proteomics data), respectively  [58]. As we already showed, PhysiBoSS is particularly suited for modeling and simulating clonality and the impact that a treatment may have on the composition and proportion of different clones inside a tumor.
This type of effects could be observed experimentally with an increase of surviving cells after activation of an oncogene, or as the result of a statistical analysis which identified some genes as significantly differentially expressed in two experiments. Another interesting situation is related to population sizes: UPMaBoSS can compute the proportions of sub-populations defined by over/under-expression of surface markers, such as CD4 or CD8 markers for T cells, and this output could be compared to flow cytometry data if available. As a general comment, the comparison between logical model simulations and data outputs is not straightforward and remains challenging (see tentative table in the Supplementary material summarizing some possible correspondence). Finally, on top of the technical difficulties of writing the logical rules, the choice of the parameters (transition rates) can be mentioned.