dataesr/scientific-paragraphs-categorization huggingface dataset:
Identical qubits in a one-dimensional chain, with unitary dynamics In preparation for the study of long qubit chains, in this subsection we examine the dynamics of a 1D chain with open boundary conditions, as depicted with N = 9 qubits in Fig. 1. As a warmup we consider a coherent/unitary evolution (no dephasing, no dissipation), with qubits that have identical frequencies in the lab frame (ω i = ω 0 ), and we fix the driving amplitude of qubit 0 (at the left edge of the chain), such that the parameters of Eqs. (  24 )-(  25 ) take the values Ω = 2π × 1 , ∆ = 0 , (26) and g 0 = g 2 = 0. ( 27 ) We employ essentially exact, brute-force simulations based on storing the full 2 N × 2 N density matrix in memory. We evolve the dynamics with standard algorithms (e. g. , Runge-Kutta) using the Python packages qiskit-dynamics  [53]  and scipy  [61].
Summary and Outlook In this work we introduced a numerical solver for Lindblad dynamics that is capable of evolving the density matrix of tens of qubits in some physically relevant setups. Because it uses an MPO representations it is particularly adapted for systems where the qubits connected in 1D or quasi 1D geometries, but it can also be used in more general setups. The computational cost of a simulation depends on the amount of correlations in the system, as quantified by the OSEE. The solver consists of a high performance C++ core and a feature-rich Python interface. In addition to a comprehensive documentation and some tutorials, the source code used for generating the research presented in this work is available as an example within the solver repository  [43] , and can be used as a helpful starting point for conducting a research project managing hundreds of simulations using a local dataframe in Python.
Acknowledgements We thank  Gal Shmulovitz  and  Eldor Fadida  for significant contributions to the Python source code of the solver, and  Dekel Meirom  for significant contributions to its documentation. H. L. thanks  Gadi Aleksandrowicz ,  Eli Arbel ,  Daniel Puzzuoli ,  Chris Wood ,  Matthew Treinish ,  Moshe Goldstein ,  Noa Feldman , and  Matan Lotem  for very helpful feedback. Research by H. L. was partially sponsored by the  Army Research Office  and was accomplished under Grant Number  W911NF-21-1-0002. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the  Army Research Office  or the  U. S. Government. The U. S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
In the application process, we can utilize the GA-based model to generate the normalized design solution corresponding to a new garment style and fitness firstly. Next, the garment design solutions (garment ease allowances) can be calculated by combining the outputs of the GA model with the standard design solutions from the garment design knowledge base in a linear combination way. In the end, the obtained ease data are utilized for garment pattern making. The creation of the garment design solution generation model is given below: Let the fitness value f fitness SD j for the garment style demand SD j be denoted in Eq. (  1 ) and the fitness value f fitness FD j for the garment fitness demand FD j be denoted in Eq. (  2 ). where SF new j represents the generated new garment style features corresponding to the garment style demand SD j.
The output was the predicted real garment fitness. The detailed procedure for creating the garment fitness prediction model is presented as follows: Step 1: A series of real try-on-based sensory experiments were conducted to collect the real garment fitness data on various feature positions. Considering the target markets of the fashion companies involved in our study,  14   [49]. Meanwhile, we invited an expert with over 20 years of experience in sizing systems to join our research. With her indepth knowledge and rich experience, we can guarantee the recruited subjects were in line with the China National Standard. Step 2: Every subject performed his try-on with the experimental garments in static and dynamic scenarios. Meanwhile, they recorded the garment fitness data represented by {1, 2, 3, 4, 5} , corresponding to the semantics {εextremelytight(1)ε, εtight(2)ε, εneutral(3)ε, εloose(4)ε, ε extremelyloose(5)ε}.
These data were used to form the out- put dataset to construct the proposed models. Step 3: 25 pieces of real experimental garments (leisure pants) of five sizes with five types of fabrics were selected in this research (see Tables  1  and 2 ). The inputs for creating the proposed models were procured using the software CLO 3D, thanks to its cutting-edge simulation techniques. First, 14 mannequins corresponding to the subjects were modelled. Next, we created 25 pieces of digital garments corresponding to the real experimental garments. Then, these digital garments were tried on the 14 digital mannequins one by one. For each virtual try-on, the garment ease allowances at each feature position were measured. Finally, all the collected ease data were aggregated to form the input dataset. Additionally, 11 indicators for characterizing the mechanical properties of the digital fabric were measured by the Fabric Kit of CLO 3D, including thickness (TH) , stretch weft (SWT) , stretch warp (SWP) , shear (SH) , bending weft (BWT) , bending warp (BWP) , bending ratio-weft (BRT) , bending ratio-warp (BRP) , buckling stiffness-weft (BST) , buckling stiffness-warp (BSP) , and density (DE).
Extensibility in the Development of Interactive Garment Pattern Recommendation System We further developed an interactive pants pattern recommendation system 2022 (IPPRS 2022) using MATLAB computer language. In the system, the user (consumer or designer) can intuitively input the feature body dimensions and determine the pants style and fitness demands using the interactive interfaces (see Fig. 8 ). The output of the system is a set of personalized pants production patterns. With the help of IPPRS 2022, the consumer can conveniently express his/her personalized demands that usually cannot be presented in words accurately. More than that, the proposed system can support the pattern designer (especially the novice) to put forward accurate design solutions quickly and intelligently, to fulfil consumers' personalized demands.
Point-by-point response to reviewers comments on "Expression of cell-wall related genes is highly variable and correlates with sepal morphology" Reviewers comments in blue, our response in black, new or modified text in red Reviewer #1: Anonymous Minor comments: -Why the clustering co-expression analysis was not performed with uniquely the HVGs? Did the authors make this analysis? The clustering co-expression analysis was performed for the entire gene expression dataset after filtering, that is, for 14085 genes. By running the WGCNA package on this data, we obtained 16 modules of co-expressed genes. These modules were tested for correlation with morphological data. Gene ontology enrichment analysis was then performed on these modules and cell-wall related genes were found to be overrepresented in modules highly correlated with morphology.
In parallel, cell-wall related genes happen to be highly variable. Given these results, at some point in our analysis, we were concerned that these two results could be confounded. Namely, we wondered if modules correlated with morphology had popped up because they were enriched in highly variable games. As can be observed in Supplementary Figure  6 , this is not the case. On the one hand, our top five correlated modules are not enriched in highly variable genes in general (it is true for the khaki and magenta modules, but not for the other three). Additionally, within modules, highly variable genes are not driving the correlation with morphology (their module membership and trait significance are not particularly high) nor are they driving the GO enrichment results. So, to answer the reviewer's question, we did not perform the co-expression clustering analysis on our group of highly variable genes because, in our view, we would be filtering our dataset a posteriori based on criteria which resulted from the analysis itself.
We believe that running WGCNA on HVGs would produce results that would be inevitably confounded. Additionally, WGCNA loses power to detect correlations for smaller datasets and for smaller modules so we do not expect more significant results a priori. We hope that this explanation convinces the reviewer. We consider that adding the analysis proposed by the reviewer to the current manuscript would not add relevant insight and would potentially make the reasoning within our manuscript circular, which we want to avoid. -Figure  2C  : add a color information for the variable contribution. codeLegend Supp figure1. Add a description of the color code D, E and F We are not sure what the reviewer is referring to here. We believe that the reviewer is referring to adding the color code for plants D, E and F in the legend for figure 2B (and not 2C).
org/10. 1038/nmeth. 2645) to identify HVG. We thank Dr. Cortijo for her suggestion. Indeed, following Dr. Cortijo's work in Cortijo et al. 2019, we had already performed a correction to account for the correlation between CV2 and average gene expression during an initial phase of the project when our dataset was slightly larger and included some lowly expressed genes. We then decided to impose stronger filtering criteria which made said correction irrelevant in practical terms (for example, it only modified one of the 718 genes in the top 5%). We, hence, removed the correction, and clarified so in the main text: "We then classified genes according to the CV 2 values of their expression. Given the higher variability (both technical and biological) found for lowly expressed genes, one can correct CV 2 to account for this association  (Cortijo et al.
, 2019). However, the gene expression threshold that we have set has eliminated most lowly expressed genes from our dataset and essentially, there is no correlation between CV 2 values and average gene expression (Supplementary Figure  2 ). " We agree that this is not clear from the data, in particular Supplementary Figure  2. We have now included a 2d density visualization as suggested by Dr. Cortijo and added the results of a Pearson correlation test (r=-0. 00189, p-value=0. 823). We have added the following to the figure legend: "Point density in the 2 dimensional space (CV 2 against μ) is shown in the bottom plot with the Pearson correlation coefficient (r) and the corresponding p-value shown highlighting no correlation between the two variables. " -The Supplementary Figure  3  is very difficult to understand.
Gene expression CV2 mean values for the cell-wall related gene list is clearly above the 95% confidence interval of the distribution of mean CV2 values for 1000 random subsets of the whole 14085 gene set. We have added this paragraph in the main text: "Gene expression CV2 values of these 718 genes is higher compared to those of the entire gene dataset (Supplementary Figure  5 ). We confirmed this through a bootstrapping approach by calculating gene expression CV2 means for 1000 random samples of 718 genes from the entire dataset to have gene sets of equal size. The gene expression CV2 mean of our CWRG list falls clearly above the 95% confidence interval of the distribution corresponding to the entire dataset (Supplementary Figure  5 ). " We have also modified the Supplementary Figure  5  legend accordingly: "Supplementary Figure  5 : Left: violin plots of gene expression CV 2 values for our entire 14,085-gene set (purple) compared to that of the 718 cell-wall related gene (CWRG) list found in the dataset (blue).
We have additionally defined both module membership and gene significance in the Materials & Methods WGCNA section with the following text: "Each gene within a module has a module membership value, calculated as the correlation between that gene's expression and the expression of the module eigengene across all samples. It is representative of the gene's intramodular connectivity. Each gene also has a gene significance for any trait, calculated as the correlation between the gene's expression values and the morphological parameter values across all samples. " -About the discussion on cell-wall related genes, I would like to add that this category of GO is also enriched among HVG detected between plants in  Cortijo et al. 2019. The authors' observations might thus be more general and not specific to sepals, making it a phenomenon of wide interest.
Mesh adaptive updating strategy and UBFELA-RTME As stated in  Yang et al. (2015a) , rigorous solutions to the ultimate failure characteristics of geotechnical problems and their associated collapse mechanisms can be obtained by using mesh-liked rigid block systems in the UBFELA-RTME. In this method, a refined initial mesh is beneficial for obtaining more rigorous upper-bound solutions and the associated failure modes. Nevertheless, it would greatly increase the computational effort, making the solving process of nonlinear programming become quite difficult. This is particularly for soils with high friction angles, where the dilatancy effects cannot be ignored and the assumption of associated flow rule of the upper bound theorem becomes unrealistic to evaluate  (Sloan, 2013). To overcome these difficulties and improve the accuracy of upper-bound solutions, a mesh adaptive updating strategy is proposed and implemented in MATLAB.
The introduction of Renewable and Recoverable Energies (R&R) into the energy mix is one of the major levers for reducing CO2 emissions, particularly for heat production. For To this end, energy systems must be put in place based in particular on innovative thermal storage technologies. The purpose of this communication is to present the MERLIN modelling/optimization environment (Hétreux G. , 2022) which offers software components allowing the development of applications to help with dimensioning and operational management of multi-energy systems. By way of illustration, these tools are implemented through the study of a system composed of a concentrated solar power plant and a storage used to supply hot air to a food drying oven.
Image analysis The two photon images were processed using custom python scripts to align and segment the embryos, and to extract the apical and basal myosin profiles. See the Supporting Information for a full description of the procedure. time frames. For extracting the apical and basal contour of the epithelium, we manually segmented a large number of frames to precisely define the respective apical and basal contours. The separation of the apical contour from the vitelline membrane is a non-trivial segmentation task, we therefore used these initially segmented frames to train a Deep Learning algorithm (namely a U-NET (  46 )) to do similar segmentation automatically for future movies. To align the embryos in space, an ellipse (Fig. 1  C dashed blue ellipse) with direction of the principal axis (dashed blue line) towards the posterior side, was fitted on the apical contour of the epithelium at Tcell = 0 min (reference time when cellular front of epithelial cells passes the nucleus).
The intersection (indicated by green star) of the principal axis with the midline (in red) of the epithelium is defined as the "zero (s = 0)" reference of the arc-length coordinate. For myosin quantification, we developed a python script to identify the segmented apical and basal contours as closed polynomials. These polynomials were then discretized by 100 evenly spaced nodes, such that each apical node has a correspondence to the nearest basal node. For each node, a myosin-mask was defined by a quadrilateral with height approximately 10 pixels (determined by the thickness of the myosin signal) and variable width determined by the distance between the adjacent nodes. Myosin intensity at a given node was calculated by averaging the pixel intensities within the respective mask. To extract model inputs, we constructed a midline contour by a new set of nodes defined by the average of each pair of respective apical and basal nodes.
At each midline node, we computed tissue velocity via particle image velocimetry (PIV, using python library openpiv), total myosin intensity (sum of the myosin intensity at the apical and basal nodes), active-moment (product of the difference in myosin intensity at the apical and basal nodes with the distance of the midline node from either the apical or the basal node) and curvature (spatial derivative of the angle between the adjacent pair of midline edges).
Multiphysics tools developed and/or used at CEA/IRESNE Multiphysics simulation is discussed in this paper in its widest definition, i. e. , it concerns any software combining different physics fields. IRESNE develops and/or uses various multiphysics software programs or platforms. Among them, the main tools and associated references are summarized in Table  1. An initial observation is that, in general, each tool is dedicated to a given multiphysics application field. Numerical coupling tool platforms generally gather various applications corresponding to various reactor types, see for example PLEIADES  (Marelle et al. , 2013; Michel et al. , 2021)  fuel behavior platform including ALCYONE (PWR)  (Michel et al. , 2021) , GERMINAL (SFR)  (Michel et al. , 2021; Lainet et al. , 2019) , MAIA (MTR) or LICOS (design)  (Helfer et al.
, 2015)  codes; and PROCOR  (Le Tellier et al. , 2015)  corium behavior platform for severe accidents simulation. The CORPUS/C3PO coupling platforms  (Le Pallec et al. , 2016; C3PO, 2023)  are dedicated to reactor behavior and perform partitioned multidisciplinary simulations between neutronics (CRONOS2  (Lautard et al. , 1992)  or APOLLO3  (Schneider, 2016)  codes), thermal-hydraulics (FLICA4  (Toumi et al. , 2000) , CATHARE2  (Geffraye et al. , 2011)  or CATHARE3) and fuel behavior (ALCYONE2 and in the near future GERMINAL3). We should also mention the recent computational platform SEASON dedicated to nuclear core degradation integrating the SIMMER  (Kedzierska et al. , 2021)  software possibly coupled with other advanced disciplinary tools such as GERMINAL2, CATHARE2, ERANOS2  (Ruggieri, 2006)  or APOLLO3.
These numerical coupling platforms embed advanced numerical methods used to perform efficient coupling schemes. Only limited use of commercial multiphysics tools (namely FLUENT, COMSOL) is made at IRESNE. Indeed, these software programs are often too generic to efficiently deal with the tricky physics and coupling systems tackled at the institute. It is worth underlying that, at the international level, some multiphysics modelling frameworks such as MOOSE  (Permann et al. , 2020)  and OpenFOAM  (Jasak, 2009)  aim at gathering simulation tools covering various nuclear physical fields (neutronics, thermal-hydraulic, fuel behavior,…) within the same computational framework. The IRE-SNE's approach, and more generally DES' approach (Energy Division of the CEA), is to develop dedicated numerical frameworks for each application field and to use coupling platforms (such as C3PO recently developed in open source (C3PO, 2023) in order to yield multidisciplinary simulations.
These coupling platforms handle any software based on a framework that is encapsulated in the ICoCo (Interface for Code Coupling) supervision API  (Salome, 2023). This is the case for the PLEIADES and PROCOR platforms. In the nuclear reactor scientific community, multiphysics coupling very often refers to the traditional neutronics and thermal-hydraulics coupling  (Ivanov et al. , 2016)  for the analysis of the behavior of the nuclear reactor and components in normal and accidental situations. In IRESNE's research activity framework, many physics are coupled: neutronics, solid mechanics, thermal physics, (physical) chemistry, fluid mechanics (potentially restricted to transport problems), electromagnetism, … In Table  2 , we summarize the different physics which are coupled in the main multiphysics tools developed or used at IRESNE.
Within the IRESNE framework, the single physics features of the main multiphysics tools have been listed (not provided here for the purpose of brevity). Some of these tools are composed of single physics software programs and correspond to chained/coupled multiphysics tools, often through a numerical multiphysics platform (see CORPUS/ C3PO for example for the reactor behavior field). Others are intrinsically multiphysics and combine various physics models and different coupling algorithms in one software program. This is the case with the CATHARE software; it couples a 1D (or possibly 3D) core two-phase thermal-hydraulics solver with a 0D neutronics point kinetics solver. In the field of corium and FPs behavior, the goal of the PROCOR platform is to create a simulation of the molten radioactive materials (i.
e. , corium including molten fuel, steel and other core materials like boron carbide for example) located at the bottom of the vessel (characteristic distribution of molten materials in case of In-Vessel-Retention Safety Strategy) in a hypothetical case of severe accident. This platform is composed, for PWR applications, of several thermal-hydraulics models in which the material behavior is linked to thermodynamics data. This data could be derived either from thermochemical results at thermodynamics equilibrium or thermochemical kinetics models. For SFR applications, the SEASON platform couples (but only in one direction) thermal-mechanics and thermal-hydraulics software to define the initial core state before the accident; then the multiphysics SIMMER software, which handles complex core degradation models, is coupled with neutronics software.
The software of the PLEIADES numerical platform, dedicated to fuel behavior analysis from normal towards accidental conditions (but also during long term storage), couples advanced thermal-mechanics, physical chemistry and thermochemistry models as well as simplified neutronics and thermal-hydraulics models  (Michel et al. , 2021). By carrying out this detailed modeling review on IRESNE's multiphysics tools, it appears that many physical issues are transversal to the various IRESNE research fields and require the same kind of models, e. g. , in multiphase thermal-hydraulics, thermochemistry, etc. Some possibilities of pooling the development of models between application fields have been highlighted. For instance, fields of sodium  technology and corium behavior share the need to simulate multicomponent compressible hydraulic flows, with phase changes (mass) and thermal exchanges linked, in particular, to inclusions fragmentation and coalescence.
Simulation process Regarding the multiphysics simulation process, the two main points are discussed below: coupling algorithms and data transfer of (nonmatching) meshes. Multiphysics tools involve multiple physical models exchanging information by means of coupling schemes. The multiphysics tools developed and/or used at IRESNE are essentially based on partitioned coupling schemes. Hence, they are based on single physics solvers (or software programs), which are coupled in time through an iterative process. Most of the multiphysics tools are based on an explicit coupling (sometimes called "loose numerical coupling"), which can be defined as a one-way coupling at each time step. This solution is convenient for phenomena involving different time-scales (managed by time substepping). However, this type of coupling is often used for practical reasons (calculation time, field exchange, convergence problems, …) more than mathematical requirements.