dataesr/scientific-paragraphs-categorization huggingface dataset:
(  7 ). In fact, these calculations are made only one time, at each frequency. In our simulator, using MATLAB  [9] , we use a matricial formalism, extending the impedance (Z) and the current (T) transmission from a layer l to its adjacent one (cf. (  9 ) or (17)), starting from a k layer: Where T addresses the derivation of the current (between two parallel regions) The ABCD-parameters are known variously as chain, cascade, or transmission line parameters. This cascade connection model is very suitable. As mentioned earlier, all the calculations are done in the reciprocal space. For instance, the potential is derived from the (kronecker) product of (sub-) matrix Z, by a DCT of the matrix of the injected current, accomplished using two consecutive FFTs. In our algorithm, the contact voltage is the mean value of the voltage of the discrete contact elements, calculated via Millman's theorem, as: Where φn is the substrate voltage at a node and N is the number of contact sub-areas.
This non uniform potential feature makes 3D-TLE very profitable when one calculates the impedance of two resistive regions rather than two metal contacts, for example, the oxide-insulated conductive regions (e. g. pads and coplanar waveguide) and junction-insulated well regions (e. g. n-well regions over a p-type substrate). In these cases, the substrate plates could not be considered as an equipotential plate. The simulation results obtained with Green/TLM (fig. 5 ) implemented in MATLAB and compared with COMSOL are found to be practically equal. Indeed, we choose, in MATLAB, layers as perfect parallelepipeds (uniform thickness layers); in this event, the so called eq. (  6 ) is an exact analytical solution. Otherwise, with refined meshes concerning the FEM methods, the solution convergence is unique.
A spiral inductance (Fig. 11 ) can be regarded as an assembly of several microstrip lines. These last are very widespread in the field them radio frequencies to carry out values of a few tens of nH. In order to adapt them to the applications requiring low powers, one must increase the section of the conductor to reduce his resistance. This type of inductance is very useful in ultra-high frequency (oscillator application). The simulation is very important because it determines the characteristics of inductance to use in this type of circuits For well characterizing an inductive bond, it is imperative to determine the value of inductance with accuracy; for this, we will model a spiral inductance in the static and frequential domains. The figure  (11)  shows a sight in prospect for a spiral designed with software COMSOL.
The thickness of inductance is 1 µm with a width of 40 µm, the distance from separation between the whorls is 2 µm. Figure  11. b. presents a cartography of the electrostatic potential on all the polarized structure. To calculate the value of the inductance of the model considered, it is initially necessary to solve the Poisson's equation to have the electrostatic potential on all the structure, then to solve the equation of the magnetic field for a current I cf. Ampere's theorem) We estimate the value of inductance for a thickness of 1 µm at 1. 102 nH, but using software HFSS [16] we find the value 1,31 nH, this comes from the lack of smoothness of the grid at the time of the passage in finite elements. We carry out a simulation of the parameters S and the admittance on a beach of frequency of 5 MHz up to 100 GHz.
Validation and evaluation methods The feedforward ONN is validated and evaluated for image edge detection in  [6]  using the digital design of Fig. 4. b) implemented on FPGA. In this work, the feedforward ONN configured for image edge detection is first emulated in python with a Hopfield-based feedforward network. It allows using the built-in functions of SIFT algorithm from Python libraries  [1]  combined with the ONN image edge detection. Also, it allows comparing precision and latency with other state-of-the-art feature detection and description algorithms available in Python libraries. However, in order to have a better latency assessment of the SIFT-ONN, we estimate the latency of the digital feedforward ONN on various image sizes. 4. 1. 1 Dataset. This work uses 36 standard 256x256 and 512x512 gray scale images from  [2].
Hopfield-based emulator in Python for precision evaluation. SIFT-ONN is validated in python using a Hopfield-based emulator of the digital feedforward ONN configured for image edge detection combined with built-in SIFT Python functions  [1]. Precision is obtained using images from the dataset applied to image relocation application. In feature detection and description algorithms, precision is evaluated by checking repeatability. An algorithm is repeatable if features can be correctly detected in images before and after transformation. In this work, to assess SIFT-ONN repeatability, the goal is to retrieve the position of parts of an image, the subimages from dataset, in the original image, the corresponding base image from dataset. To do so, feature detectors and descriptors are first generated from all base and sub-images using the SIFT-ONN feature detection and description algorithm and matched between the sub-images and the corresponding base image.
After assessing the precision and repeatability of the SIFT-ONN, it is necessary to evaluate its computation time. Using parallelism for the ONN edge detection in Python is difficult, so for the Python tests with a Hopfield-based emulator, we consider sequential treatment, which makes the computation time really high, around hundreds of seconds. However, considering the digital feedforward ONN implemented on FPGA, it is possible to implement multiple ONNs in parallel to improve latency. Thus, to evaluate and compare the latency of SIFT-ONN with state-of-the-art algorithms, we implement the digital ONN with various sizes to measure latency of one computation and use it to estimate latency of a full-image scanning. Then, we extract post place and route implementation resource utilization to extract the maximum number of parallel ONNs which can be implemented in the Zybo Z7 board.
Furthermore, during tests, some divergences in the precision results were observed between two python simulations with equal parameters. Thus, we believe the generated dataset may not be large enough to clearly assess the precision of the SIFT-ONN algorithm and further tests are necessary. However, due to the large computation time, it was not possible to enlarge the dataset. Additionally, precision is tested on the image relocation application. However, it is also important to test feature detection and description algorithms on other applications, like image occlusion. Thus, additional tests on a larger dataset and on additional applications are also necessary to have a better assessment of the SIFT-ONN precision. Still, SIFT-ONN is close to ORB performances both in terms of precision and estimated latency.
ACKNOWLEDGMENTS This work is supported by the  European Union 's  Horizon 2020 research and innovation program ,  EU   H2020   NEURONN  project under Grant No. 871501  (www. neuronn. eu). We thank  T. Gil  ( LIRMM -CNRS ),  M. Jimenez ,  J. Nunez , and  M. J. Avedillo  ( IMSE -CSIC ) for fruitful exchanges and constructive suggestions on this research work.
Observational dataset For the past two decades, the SDSS has been the largest imaging and spectroscopic survey of the local Universe  (York et al. 2000). It uses a dedicated 2. 5-m wide field-of-view optical telescope, located at the Apache Point Observatory, and has provided astronomers with a tremendous amount of data. This wealth of data has consistently yielded cosmological constraints via the various SDSS Data Releases (DR), so far culminating in the 17th data release (DR17,  Abdurro'uf et al. 2022). To train and test the application of YOLO to cluster detection, we focus on the most complete and pure SDSS cluster catalog (see also Sect. 5), namely, the red-sequence Matched-filter Probabilistic Percolation (redMaPPer) DR8 (Data Release 8) catalog from  Rykoff et al. (2014). The redMaPPer algorithm is a red sequence cluster finder specifically designed for large photometric surveys.
2 that cover regions in the sky that are larger than the cutout images that we consider in this work because of calculation time efficiency. Ultimately, we worked with a final sample of 24 406 clusters, whose distribution is shown in Fig. 1. For each cluster, the algorithm provides its position, the richness λ 8  as a proxy for cluster mass and a list of cluster members  (Rozo & Rykoff 2014). For the network training and validation, we retrieved JPEG versions of the original SDSS DR16 raw images for each redMaPPer cluster, and constructed color images using the ImgCutout web service 9  by querying the SDSS Catalog Archive Server databases. These images are based on the g, r, and i-band FITS corrected frame files from the Science Archive Server, and the color images were built using the conversion algorithm 10  based on  Lupton et al.
Training and validation We trained YOLO-CL with about half (∼12 000) of our selected redMaPPer cluster images and the same number of random SDSS field images of the same size. The training sample is split into subsets (batches) that are simultaneously processed by the network. Our original SDSS color image cutouts are centered on the redMaPPer cluster positions. This original centering does not have an impact on cluster detection, which does not depend on the position of the cluster in the image. In fact, when we train the network, we apply data augmentation to the original cutouts, including translation, flipping, and cropping, which change the initial cluster position in the image. This prevents the network from learning that an object at the center of the image belongs to the "cluster" class and, instead, forces it to focus on the relevant features associated with clusters.
Acknowledgements. We thank  Université Paris Cité (UPC) , which founded KG's Ph. D. research, and  University Paris Science & Lettres (PSL) , which founded SI postdoctoral research. We thank our PSL and UPC colleagues, and collaborators from the LightOn (https://lighton. ai/) company and the  École Normale Superieure (ENS) ,  Laurent Daudet ,  Florent Krzakala , and  Amelie Chatelain , for fruitful discussions. We thank  Jean-Baptiste Melin  and  James Bartlett  for useful discussions and help in choosing the X-ray catalog that we use in this paper. We thank  Alex Saro  for a insighful question that improved our comparison to the redMaPPer algorithm. We thank  Eli Rykoff  for proving the complete redMaPPer catalog. We gratefully acknowledge support from the  CNRS/IN2P3 Computing Center (Lyon -France)  for providing computing and data-processing resources needed for this work.
From the temperature behavior of the molecular volume for the 1:1 dDPPC:DLPC mixture, as well as the individual molecular volume of each lipid species, we inferred that the liquidus phase consists of only fluid-state lipids while the solidus phase consists of lipids that are in gel-like states. Using this solidus-liquidus phase model, the SANS data were analyzed with an unrestricted shape model analysis software: MONSA. The resulting fits show irregular domains with dendrite-like features as those previously observed on giant unilamellar vesicles (GUVs). The surface pair correlation function describes a characteristic domain size for the minority phase that decreases with temperature, a behavior found to be consistent with a concomitant decrease in membrane mismatch between the liquidus and solidus phases.
Differential Scanning Calorimetry (DSC) Using a micro differential scanning calorimeter, µDSC III from SETARAM Instrumentation, we obtained the phase diagram for dDPPC:DLPC. DSC data were taken for both 50 nm unilamellar vesicles (SUVs) and multilamellar vesicles (MLVs). dDPPC and DLPC in powder form were weighed into a glass vial and thoroughly mixed using chloroform (vortexed vigorously until the solution was clear). The removal of this chloroform was performed first with a stream of N 2 gas using a nitrogen evaporation system (N-EVAP) until a thin in-appearance dry lipid film remained on the vial's walls. The vials were then placed in vacuum at 60 • C overnight to remove any residual chloroform. Once dry, the sample was hydrated with 1 ml of H 2 O. MLVs were prepared by leaving the solution on a shaker overnight mixing in a 47 • C oven until the resulting mixture became a homogenous suspension.
Keeping DLPC constant (15 mg/mL), the amount of dDPPC was varied from 0 mol% to 90 mol%. The 100 mol% dDPPC was run at 15 mg/mL. SUVs were obtained from the MLV solutions after their calorimetry data were collected. Thermogram scans were obtained at 0. 2 • C/min. SetSoft 2000 provided by SETARAM was used to collect DSC data and exported as a text file to use with the program Mathematica™ for baseline subtraction and determination of onset and completion temperatures (details in Section S3 of the Supplementary Information).
Densitometry Densitometry data were obtained on samples having a total 10 mg/mL lipid concentration using the Density and Sound Velocity Meter: DMA 5000 M from Anton-Paar (Ashland, Virginia) at the Partnership for Soft Condensed Matter (PSCM) in Grenoble, France. Three samples were measured by cooling: dDPPC, DLPC and 1:1 dDPPC:DLPC. The lipid samples consisted of multilamellar vesicles (MLVs) in H 2 O, which had been incubated and gently rocked overnight at 40 • C. The samples were loaded at room temperature, and densitometry data were taken from 50 • C down to 5 • C in steps of 1 • C.
Small-Angle Neutron Scattering SANS measurements were taken on 50 nm SUV vesicle solutions using a 1:1 ratio of dDPPC:DLPC. The measurements were performed on the D22 instrument at Institut Laue-Langevin (Grenoble, France). Instrument configuration covered a Q range of 0. 003 ≤ Q ≤ 0. 5 Å -1. The wavelength used was 6 Å with a wavelength resolution of ∆λ/λ = 10%. Data were collected with a 2-D detector and reduced using GRASP, the reduction package provided by the ILL or the reduction package provided by NCNR in IGOR  [24].
Fitting of the SANS data requires the SLD of the liquidus and solidus domains given by: SLD s = v dDPPC s SLD dDPPC g + (1 -v dDPPC s )SLD DLPC g (5a) SLD l = v dDPPC l SLD dDPPC f + 1 -v dDPPC l SLD DLPC f (5b) where the indexes s and l indicate solidus and liquidus, respectively, and g and f indicate gel and fluid, respectively. The terminology difference between solidus and gel or liquidus and fluid is meant to distinguish between the state of a lipid mixture versus the state of a lipid species. As will be shown below, this distinction is necessary. Because there is a gap in the solidus boundary, the value of v dDPPC s is not known. Fits to the SANS data required varying this parameter-or, equivalently, the value of SLD s -as will be detailed below. Since the temperature dependence of the molecular volumes of the lipids is critical information to properly calculate SLDs, we additionally considered the temperaturedependent SLD of the solvent.
Our bead vesicles were beads on a coordinate grid producing vesicles of a desired radius and membrane thickness and with the beads forming a near-single layer of beads representing the membrane (see Figure  S7. 1 in Section S7 of the Supplementary Information). The beads provide a mechanism for local variations in SLD and collectively are not constrained to any particular shape on the vesicle. The software we used to vary the beads' SLD and fit the SANS data was MONSA  [37]. MONSA calculates a scattering intensity from the real-space bead model; however, since extruded vesicles are polydisperse in size (~0. 25), MONSA cannot properly generate the scattering of this system. Nevertheless, in this work, it is the presence domains that generate the excess scattering because the vesicles are basically contrast matched.
Indeed, this suggests that this behavior is due to the unique melting profile of DLPC (see Figure  S3. 1C,F in Section S3 of the Supplementary Information). In Section S8 of the Supplemental Information, Figure  S8. 3A shows the family of molecular volumes obtained, highlighting the effect of a sliding value of α for the higher-temperature data and, in Figure  S8. 3B, their concomitant position marking the solidus boundary in the phase diagram. Figure  4B  shows the molecular volume for the 1:1 mixture obtained by densitometry and calorimetry with the corresponding best-fit molecular volumes obtained from the SANS analysis, and in Figure  4C  are their loci in the phase diagram. Figure  4A  shows the corresponding best fits against the scattering data. SANS is a technique that detects nanoscale structures, and using real-space bead vesicle models allowed us to extract this information.
6) of the solidus boundary continues smoothly-without a sharp turn where 0. 6 < χ dDPPC < 0. 7to χ dDPPC = 1, thus not coinciding with the upper branch described by the calorimetry result. Interestingly, consistency between molecular volumes and SANS fits resulted in a solidus boundary that connects the lower and upper solidus branches (see Figure  4C ). Knoll and collaborators, in their exploration of the DSPC:DMPC phase diagram using a similar combination of approaches, calorimetry and densitometry  [27]  and SANS  [45] , found a similar discontinuity region suggested by densitometry and had a near-constant composition solidus boundary connecting the lower and upper solidus branches as well. SANS, however, was applied differently than was presented here. In their case, they performed solvent contrast variation experiments (varying D 2 O/H 2 O ratios) for two mixtures with different initial compositions  [45]  from which they were able to deduce the solidus and liquidus boundaries for the system, albeit for only a small number of data points.
As in the present study, their goal was to extract structural information. They approached the analysis using analytical models such as round domains and stripes but were unable to show consistency between their analysis and the boundaries from the phase diagram reported by Van Dijk et al. Thus, using analytical shapes such as circular domains and stripes, which, according to GUV imaging (see References  [22, 23]  and Figure  S9. 1 in Section S9 of the Supplementary Information), were unlikely, was an important limitation of their analysis. Using a bead vesicle as a real-space model to fit the SANS data from vesicles exhibiting phase separation allowed for a free-form analysis tool that yielded domains showing a phase (liquidus) evolving.
Acknowledgments: The authors thank the Institute Laue Langevin for providing neutron beam time under proposals 9-13-428 and 9-13-510 U. P. -S. acknowledges travel support from the ILL to promote scientific collaboration with L. Porcar, as well as support for density measurements of lipids mixtures studied herein through equipment made available by the Partnership for Soft Condensed Matter (PSCM). U. P. -S. gratefully acknowledges the support from the NSF, CAREER award DMR-1753238.
Discussion and perspectives We have presented a new variational model to compute a UV parameterization of a triangle mesh with a joint optimization of distortion and cuts. It is a discrete variant of the Ambrosio-Tortorelli functional, which offers theoretical guarantees for solving problems involving a penalization in the length of discontinuities. We have shown that this approach is effective in practice, faster than other simultaneous optimization methods, with quality often close to the state-of-the-art Optcuts method, and incorporates easily other constraints in its formulation like border injectivity, assisted cutting or compact texture maps. We can also note that the loci of deformable faces may have an arbitrary topology, so cuts can take various shapes. Finally, the code is available at https://github.
The exponential increase in publicly available sequencing data and genomic resources necessitates the development of highly efficient methods for data processing and analysis. Locality-sensitive hashing techniques have successfully transformed large datasets into smaller, more manageable sketches while maintaining comparability using metrics such as Jaccard and containment indices. However, fixed-size sketches encounter difficulties when applied to divergent datasets. Scalable sketching methods, such as sourmash, provide valuable solutions but still lack resource-efficient, tailored indexing. Our objective is to create lighter sketches with comparable results while enhancing efficiency. We introduce the concept of Fractional Hitting Sets, a generalization of Universal Hitting Sets, which uniformly cover a specified fraction of the k-mer space.
In theory and practice, we demonstrate the feasibility of achieving such coverage with simple but highly efficient schemes. By encoding the covered k-mers as super-k-mers, we provide a space-efficient exact representation that also enables optimized comparisons. Our novel tool, SuperSampler, implements this scheme, and experimental results with real bacterial collections closely match our theoretical findings. In comparison to sourmash, SuperSampler achieves similar outcomes while utilizing an order of magnitude less space and memory and operating several times faster. This highlights the potential of our approach in addressing the challenges presented by the ever-expanding landscape of genomic data. SuperSampler is an open-source software and can be accessed at github. com/TimRouze/supersampler.
Introduction The field of genomics has exploded in recent years, driven by the availability of cheap and easy sequencing data generation. The Sequence Read Archive (SRA) is a vast and to some extent under-exploited goldmine of genomic data, containing an enormous amount of genetic information. However, one of the biggest challenges in utilizing this data is the lack of efficient indexing and querying tools. The GenBank database, for example, already contains 1. 2 million bacterial genomes, totaling over 5 Terabases of data. In the face of such vast genetic information, a crucial need is to promptly and precisely determine the most similar (or contained) known entry for a given query document (assembled or unassembled reads). Specifically, in this work we focus on the metagenomic assessment problem, which entails characterizing microbial communities in a specific environment using DNA sequencing data and potentially large amounts of reference entries.
Such a property makes fixed-size sketches ill-suited for comparing dissimilar contents, in particular in a resource-frugal context since the sketch size to reach an acceptable precision would be too high to handle. Happily, scaled sketches that grow linearly with the input size do not present such drawback. sourmash is the leading tool that implements such scheme and provides sketches composed of k-mers represented as 32 bits hashes. To do so, k-mers are selected if their hash is below a specific threshold calculated in regard to the desired subsampling rate. Our goal is to improve the space efficiency of such sketch using efficient k-mer encoding dubbed SPSS  [16]  exploiting the fact that k-mers share overlaps. The most convenient yet relatively space efficient SPSS are super-k-mers and our work relies on such efficient k-mer representation using the notion of minimizers.
Minimizers and super-k-mers Definition 1 (minimizer). Given a total order O and a k-mer u, the minimizer of u is the smallest m-mer of u according to O. The method to select minimizers is referred to as a minimizer scheme. In practice, O is defined on integers by hashing m-mers using a random hash function h and selecting the smallest hash value. We assume that the hash function is chosen such that the hashes are independent and uniformly distributed. From now on, we use w = k -m + 1 to denote the number of m-mers inside a k-mer. Definition 2 (super-k-mer). A super-k-mer is a maximal substring of a string s (|s| ≥ k) in which each consecutive k-mers have the same minimizer. Super-k-mers are an interesting SPSS because a super-k-mer containing x k-mers is composed of k + x -1 bases which incur a cost of 2(k+x-1)   x bits per k-mer.