dataesr/scientific-paragraphs-categorization huggingface dataset:
In these radial bins of nullmigration, proto-planets of a particular size traversing the disk from inside-out stop and meet other objects migrating outside-in (see  Brož et al. 2021 , for an application of this concept to the solar system's terrestrial planets). In this letter, we revisit the  Raymond et al. (2016)  scenario, with the particular aim of understanding whether outward migration of embryos is a viable mechanism for reshaping the distribution of planetesimals within a terrestrial disk of more or less uniform surface density into one resembling the successful disk profiles identified in  Clement et al. (2021)  and  Clement & Chambers (2021). To accomplish this, we leverage a suite of new high-resolution numerical simulations employing the GPU-accelerated (graphics process unit) code GENGA  (Grimm & Stadel 2014).
ACKNOWLEDGMENTS The authors thank  Conel Alexander ,  Alan Jackson  and an anonymous reviewer for useful discussions and input that greatly improved the quality of this manuscript. S. N. R. acknowledges support from the  CNRS 's  PNP program. This work used the  Extreme Science and Engineering Discovery Environment (XSEDE) , which is supported by  National Science Foundation  grant number  ACI-1548562. Specifically, it used the Bridges2 system, which is supported by  NSF  award number  ACI-1445606 , at the  Pittsburgh Supercomputing Center (PSC: Nystrom  et al. 2015).
To distinguish proteins truly associating with ARHGAP45 from nonspecific contaminants, we compared our data with control AP-MS experiments involving wild-type (WT) Jurkat T cells. ARHGAP45 interactors were identified  (Fig 1B and Dataset EV1) , and the ARHGAP45-prey stoichiometry was also measured using intensity-based absolute quantification  (Voisinne et al, 2019). By determining the number of copies per cell of each protein expressed in Jurkat cells (Dataset EV2) and combining them with interaction stoichiometries, we organized the proteins interacting with ARHGAP45 (the "ARHGAP45 interactome") into a stoichiometry plot  (Fig 1C). Analysis of the GAP activity of ARHGAP45 in a cell-free system showed that it can catalyze RHOA, RAC1 and CDCD42 GTP hydrolysis  (de Kreuk et al, 2013). In contrast, our stoichiometry plot showed that RHOA was the major RHO GTPase capable of associating with ARHGAP45 in cellulo and constituted one of the most abundant ARHGAP45 interactors  (Fig 1C) , suggesting that in T cells ARHGAP45 primarily regulates the RHOA GTPase.
B Volcano plot showing proteins enrichment (fold change in log 10 scale) after affinity purification in Jurkat T cells expressing ARHGAP45 OST molecules compared to affinity purification in control Jurkat T cells expressing similar levels of WT (untagged) ARHGAP45 proteins prior to (unstimulated) and at 120 s after (stimulated) TCR stimulation. ARHGAP45 interacting proteins with a > 2-fold enrichment and a P-value < 0. 01 were selected as specific ARHGAP45 interactors (Dataset EV1) and some of them are specified in red. Red lines represent the thresholds set on P-value and enrichment to identify specific ARHGAP45 interactors. C ARHGAP45-specific interactors highlighted in the text are displayed in a stoichiometry plot  (Voisinne et al, 2019)  where the ratios of the prey to bait cellular abundances ("abundance stoichiometry") are plotted as a function of their maximal interaction stoichiometries ("interaction stoichiometry"), both using log 10 scale (see Datasets EV1 and EV2).
B The ratio of CTV/CMTPX-labeled cells present in each organ (Ro) was determined and normalized by dividing it with Ri, the ratio of CTV/CMTPX-labeled cells present in the cell mixtures prior to injection. Ro/Ri ratio corresponding to each of the specified mice are shown. Data shown for Arhgap45 À/À mice correspond to 4 independent experiments (E1-E4) involving a total of 15 individual mice whereas data shown for WT mice correspond to two independent experiments (E1 and E2) involving a total of five individual mice. The Arhgap45 À/À mice analyzed on the same day as the control mice corresponded to the E1 and E2 experiments. The specified cells (see lower right corner) were loaded into the upper chamber of a Transwell migration device without (no) or with the specified chemokines in the lower chamber.
Cell proliferation was measured by luminescence after 48 h. Data are representative of 2 independent experiments with 3 mice per genotype (mean and SEM of triplicate are shown). B WT mice that received CTV-labeled WT or Arhgap45 À/À OT-I T cells were immunized using laser-assisted, dermal delivery of vaccibodies that target OVA to XCR1 + DC. Four days after antigen delivery, the extent of OT-I T-cell proliferation was determined by CTV dilution in ear-draining, auricular LNs, in LNs that do not drain the ear, and in the spleen. Data are representative of two experiments with 4 mice per group. C On the basis of the data shown in (B), the index of OT-I proliferation was calculated using FlowJo software. Each dot represents an individual mouse. Mean and SD are shown. ns, non-significant. ª 2021 The Authors EMBO reports 22: e52196 | 2021 the numbers of neutrophils, monocytes, and B cells of Arhgap45 À/À origin found in the blood of Arhgap45 À/À BM chimera showed a reduction commensurable to that of their BM precursors (Fig  8B ).
CRISPR-Cas9-based genome editing of Jurkat T cells The decision to introduce a Twin-Strep-tag coding sequences (OST) at the N-terminus of the ARHGAP45 protein was based on the presence of a putative PDZ-binding domain at its C-terminus (de Kreuk et al, 2013). ARHGAP45-deficient Jurkat T cells were first established using the following pairs of sgRNA-specifying oligonucleotide sequences to delete both critical exons 3 and 12 (transcript ID: ENST00000313093. 6; exon 3 deletion: 5 0 -GGGCTCATTTCGAG ACCGCGGGG-3 0 and 5 0 -CGAGGGCTCTCTGCCTAGAAGGG-3 0 ; exon 12 deletion: 5 0 -AGTAGGAGATCGTGGCCTGCGGG-3 0 and 5 0 -ATA GCTTCCTCCGCCTGCGG TGG-3'. A resulting clone (clone 12) that has deleted both exons 3 and 12 and lacked ARHGAP45 protein expression as assessed by Western Blot using an ARHGAP45-specific antibody (Sigma HPA019816) was selected for further study  (Fig 1A).
MS data analysis Raw MS files were processed with MaxQuant software (v. 1. 6. 3. 4) for database search with the Andromeda search engine and quantitative analysis. Data were searched against the Homo sapiens entries of the UniProtKB protein database (release Swiss-Prot reviewed 2019_01, 20,412 entries), and the set of common contaminants provided by MaxQuant. Carbamidomethylation of cysteines was set as a fixed modification, whereas oxidation of methionine and protein N-terminal acetylation were set as variable modifications. Specificity of trypsin digestion was set for cleavage after K or R residues, and two missed trypsin cleavage sites were allowed. The precursor mass tolerance was set to 20 ppm for the first search and 4. 5 ppm for the main Andromeda database search. The mass tolerance in tandem MS mode was set to 0.
Data analysis of Jurkat cell proteome Raw MS files were processed as previously described with MaxQuant software (v. 1. 6. 3. 4). Data were searched against the Homo sapiens entries of the UniProtKB protein database (release Swiss-Prot reviewed 2020_01, 20,367 entries). Analysis of the proteome of Jurkat cells identified 3,829 protein groups. Protein entries from the MaxQuant "proteinGroups. txt" output were first filtered to eliminate entries from reverse and contaminant databases. Cellular protein abundances were determined from raw intensities using the protein ruler methodology, using the following relationship: protein copies per cell = (protein MS signal × NA × DNA mass)/(M × histone MS signal), where NA is Avogadro's constant, M is the molar mass of the protein and the DNA mass of a diploid human cell is estimated to be 6.
Flow cytometry analysis and antibodies Stained cells were analyzed using a BD LSRII TM or a FACSymphony TM flow cytometer and a FACSDiva 9. 1 software (BD). Cell viability was evaluated using SYTOX TM Blue Dead Cell Stain (Invitrogen) or DAPI (Merck). The following antibodies were used. CD4 (RMA-5); CD8a (53-6. 7), CD5 (53-7. 3), CD16/CD32 (2. 4G2), CD19 (1D3), CD24 (M1/69), CD25 (PC61), CD27 (LG. 3A10), CD34 (RAM34), CD43 (S7), CD44 (IM7), CD45. 1(A20), CD45. 2 (104), CD45R (RA3-6B2), CD62L (Mel-14), BP-1 (6C3), IgM (R6-60. 2), IgD (11-26c. 2a), Ly-6G (clone 1A8), Ly-6C (AL-21), Sca-1 (Ly-6A/E, D7), TCRb (H57-597), TCRcd (GL3), Ter119 (TER-119), all from BD Biosciences. CD11b (M1/70), CD3 (17A2), CD150 (TC15-12F12. 2), CD135 (A2F10), CD161c (PK136), CD11c (N418), CD45 (30-F11), CD19 (6D5), and CD197 (CCR7, 4B12) all from BioLegend, CD127 (A7R34) and CD117 (2B8) both from eBioscience, and LFA-1 (CD11a, 121/7) from Invitrogen.
Cell tracking Cells were tracked with a home-made program developed with MATLAB software (The MathWorks, Natick, MA, USA) as previously described  (Valignat et al, 2013). Briefly, the program works on binarized images of a video microscopy time sequence. To analyze a large number of cells per image (~500 cells), bright field images with low magnification objective (Plan-Neofluar 10×/0. 3 objective, Carl Zeiss, Oberkochen, Germany) were collected every 10 s for 17 min. Each frame was divided by a background frame and intensity to homogenize all frames of a given stack, and finally binarized so that each individual cell appeared as a homogeneous white spot in a surrounding black background. Binarized images were then used to determine the trajectory and speed of each cell. In each experiment, the mean speed of crawling cells was calculated after exclusion of immobile cells (i.
e. , cells with curvilinear displacement below 10 µm during 1,000 s). To assess the precise morphology and the adhesion fingerprint of individual cells, a sequence of bright field and reflection interference contrast microscopy (RICM) images were taken every 2s for 14 min with a ×63 objective (Neofluar 63/1. 25 antiflex, Carl Zeiss, Oberkochen, Germany). All raw images were also treated by background division and intensity normalization. Then, bright field images were binarized using the Pixel classification workflow of the Ilastik software 79 and further analyzed using the image processing toolbox of Matlab to obtain the circularity and eccentricity of the cell contour. RICM images were inverted and thresholded. The adhesion fingerprint (appearing dark in raw images and white in treated images) was then detected for each cell.
Acknowledgements We thank  M. Bajenoff  and  S. Sarrazin  for discussion,  Rong Huang  for preparing Fig  1A ,  Raana Ramouz-Charpentier  for technical help, and the  EUCOMM Program  for Arhgap45 tm1a(EUCOMM)Wtsi mice. This work was supported by  CNRS ,  INSERM , the  European Union 's  Horizon 2020 research and innovation program  (grant agreement  n°787300  (BASILIC) to B. M. ), the  MSDAVENIR Fund  (to B. M. ), the  Investissement d'Avenir program PHENOMIN  ( French National Infrastructure for mouse Phenogenomics ;  ANR-10-INBS-07 , to B. M. ), the  National Natural Science Foundation of China  (grants  n°81471595  and  32070898  to Y. L. and  81901573  to L. H), the  111  project  D20036  (to Y. L. ), and by fellowships from  Fondation pour la Recherche M edicale  (L. G. ). the  Agence Nationale de la Recherche  ( RECRUTE :  ANR-15-CE15-0022  and  ILIAAD :  ANR-18-CE09-0029 , to O.
T), the  Excellence Initiative of Aix-Marseille University -A*MIDEX, a  French "Investissements d'Avenir" program  (to M. -P. V. ), The Marseille Proteomics facility is supported by  IBISA (Infrastructures Biologie Sant e et Agronomie) ,  Plan Cancer ,  Canceropôle PACA ,  R egion Sud Provence-Alpes-Côte d'Azur, Institut Paoli-Calmettes, Centre de Recherche en Canc erologie de Marseille ,  Plateforme Technologie Aix-Marseille  and  Fonds Europ een de D eveloppement R egional.
The approach can be used to approximate virtually all types of executable binaries (bare-metal applications, single-/multi-threaded user applications, OS/RTOS, etc. ). We introduce AxQEMU: a software based on the well known QEMU dynamic binary translator. We demonstrate how our approach can determine the effects of FP approximations on application-level Quality of Result (QoR), and how it interfaces with other tools from the literature. A hardware-level case study on a 28-nm FD-SOI implementation is presented, demonstrating how fine-grained energy/accuracy trade-offs can be made thanks to floating-point arbitrary reduced precision (ARP). For instance, considering the well-known arclength FP application, FPU computation energy savings of up to 19. 4% were achieved with an accuracy threshold of 10 significant digits, and up to 60.
Over the years, many techniques/tools/libraries have been proposed to explore the impact of using arbitrary precision FP arithmetic  [7] [8] [9] [10]  in computational kernels targeting many platforms (GPPs, GPUs, FPGAs, etc. ). The study presented here contributes to this goal by introducing an extension to  [11]. It presents a non-intrusive methodology enabling rapid design space exploration of reduced arbitrary precision FPUs in a CPU-based architecture context. The methodology proposed should help designers select the most optimized FPU configuration (exponent and mantissa bit-widths) satisfying a Quality of Result (QoR) threshold set for the application and input dataset provided by the designer, without the need to transform / rewrite / modify the source code. The approach led to the development of a software implementation which we called AxQEMU.
This implementation is based on the dynamic binary translation of assembly instructions, and is built on top of the wellknown functional simulator QEMU. The methodology was assessed through three case studies: 1) a direct application of AxQEMU alone, 2) using AxQEMU in conjunction with an FP variable type optimization tool from the literature, and 3) a hardware-level estimation of the energy savings provided by our methodology. Section 2 introduces our motivations, the terminology used in the remainder of the paper, and the problem statement. Section 3 presents a formalization of the idea. Section 4 shows the details of the software implementation. In Section 5, we assess the methodology developed through three detailed case studies. Section 6 presents a set of related works followed by a discussion.
Problem statement Given an application that takes as an input a dataset I, and providing a numerical output result O, our objective was to identify the optimal FPU configuration (E f , M f , E d , M d ) in terms of power consumption, execution time, and overall energy consumption, subject to a Quality of Result (QoR) constraint on the output O. To reach this goal, we adopted a three-step method: ① Starting from an application source code, steps 1 to 3 were performed using state-of-the-art tools  [12] [13] [14] [15] [16] [17] [18]  to generate a valid type configuration. ② Software design space exploration: for a given type configuration, the design space must be rapidly explored to select a set of FPU bit-width configurations satisfying the QoR constraint target. ③ FPU hardware assessments: for each configuration, an estimation of the overall energy savings was made to select the best final configuration.
QEMU overview QEMU supports most well-known architectures (RISC-V, ARM, x86-64, MicroBlaze, etc. ). We distinguish two machines: 1. the guest (or target) machine: the processor emulated, for which an application has been compiled, and 2. the host machine: the processor executing QEMU itself, which simulates the execution of the target code, even if the two processors have different architectures Fig. 1  depicts QEMU's operation principle. Efficient emulation is achieved thanks to the 'Tiny Code Generator' (TCG), which is a sort of run-time compiler embedded in QEMU. This generator dynamically 1 Please note that in contrast to our definition, the IEEE 2008-754 Standard's  [6]  computational instructions also include comparisons. 2 A state is defined by the content of the FPU registers r i ∈ A.
precise" section. Listing 1. Example of a C macro for function annotation. At run-time, the address of each assembly instruction instance is fetched from the Program Counter register (PC). If an assembly function belongs to the non-approximable address space (i. e. , the. precise memory section) then do_approx is set to zero and the instruction is emulated using the standard SoftFloat  [22]  FP emulation library. Otherwise, do_approx is asserted, and the instruction is emulated using an ARP library  [7, 9, 10] , taking the FPU configuration specified by the user into account. As for all similar techniques, the source code must be modified and re-compiled when using SA. However, when using our approach, the implementation is much simpler and light-weight than other current techniques, as well as being minimally-intrusive.
Case study 1: direct application of AxQEMU With this first use case, we demonstrate the direct use of AxQEMU alone to examine how the FP approximation affects a computationally heavy application, Jmeint  [24] , in the 3D gaming domain. This application detects the intersection of two triangles in space and takes as an input 100,000 random pairs of 3D triangle coordinates. For this application, all variables are declared as doubles, and the QoR metric is the intersection detection miss rate. The source code was instrumented with SA macros as explained in Section 4. 3. Fig. 2  shows the QoR variation as a function of the exponent E d and mantissa M d bit-widths. Fig. 1. AxQEMU overview. 3 The "d" in helper_fadd_d refers to double-precision, whereas the "s" in helper_fadd_s refers to single-precision.
Case study 2: AxQEMU applied to mixed-precision applications In this case study, we demonstrate how AxQEMU can be used in conjunction with other VTO tools. For this experiment, we selected the tool described in  [15]  which is briefly introduced in Section 5. 2. 1. The software type configurations described in  [15]  were instrumented with AxQEMU to select the best hardware configurations according to the specified QoR constraint (Phase ②).
The PROMISE tool PROMISE  [15]   4  was selected as the primary tool for VTO (Phase ①). Given an application and an input dataset, it applies a delta debugging algorithm  [25]  to find a configuration that minimizes the number of high-precision (double-precision) variables while nevertheless satisfying a given QoR constraint. Internally, PROMISE uses the CADNA library  [26] , which implements Discrete Stochastic Arithmetic (DSA)  [27] , a technique that estimates round-off error propagation and detects numerical instabilities in a program.
Design space exploration (DSE) with AxQEMU An exhaustive DSE was performed for each type configuration and each application. Let us denote C = {c 0 , c 1 , …} a set of FPU configurations to be studied. Our objective was to determine the most optimized FPU configuration c i ∈ C satisfying the target QoR threshold. The DSE is a two-step process: 5. 2. 4. 1. Simulation. First the source code corresponding to a type configuration (e. g. the V10 configuration of arclength) is compiled. Then, the binary is executed using AxQEMU for each FPU configuration c i ∈ C. The raw QoR values (i. e. , number of significant digits) for all c i are stored in an array M for processing in the next step. This simulation step can be more or less time consuming depending on the size of the input dataset, the execution time for the application itself, and the number of FPU configurations to be studied.
FP Variable type optimization Tools such as  [15] [16] [17]  are designed to find, given an {application, input dataset, QoR constraint}, a variable type configuration of the original application that minimizes the number of high-precision variables and maximizes the number of low-precision variables. For some of these tools  [16, 17] , the objective is to optimize speed, whereas for others  [15] , the goal is to maximize the number of single-precision variables. As a consequence, results produced by  [15]  are more reproducible. All these tools are based on delta-debugging heuristics  [25] , which constitutes a scalability bottleneck when dealing with programs with numerous FP variables. Although  [17]  is relatively more scalable, the search space still depends on the number of variables in the application, and the transformations are specific to the x86 architecture.
Non-standard/arbitrary format support The previous tools only support standard IEEE 754  [6]  formats, although  [17]  also supports extended precision formats (i. e. Intel's 80bit format implemented as long double in C). As explained in Section 2. 2, our approach is complementary with these and other works  [12] [13] [14] , since it provides further computation power/energy/execution time optimization thanks to ARP and it can reuse their results to determine a first coarse-grained memory footprint and computation optimization. [18]  proposes an arbitrary precision impact simulation methodology and introduces an automatic source code transformation tool. However, since it exploits the MPFR C library  [7] , it only allows variation in the mantissa bit-width. Other libraries such as FlexFloat  [9]  (written in C) and FloatX  [10]  (its C++ successor) were developed to make it possible to express variable precision behavior in software, to simulate the effects of arbitrary FP bit-width reduction on the QoR provided by applications.
Non-intrusive impact simulation One of the most closely related works to the one described here is VPREC  [28] , a back-end system that enables non-intrusive variable precision simulation in the Verificarlo  [29]  software toolchain. This tool was introduced to simulate in-time variable precision, specifically for iterative algorithms which benefit from adaptive gradually-increasing precision rather than fixed precision. Although the idea is very similar to ours, there are some key differences: 1) Verificarlo requires a specific instrumentation toolchain, making recompiling mandatory, and as a consequence the instrumented application is 2) Instrumentation is performed on Valgrind, which means that the application was studied in a high-level operating system context. In contrast, here, we support virtually all contexts (including bare-metal).
Conclusion This work introduces a hardware-friendly and code non-intrusive approach to optimize FPU bit-width sizing for a given embedded application, dataset, and quality of result level (QoR). A software implementation (AxQEMU) based on the well-known QEMU dynamic binary translator made it possible to perform a rapid evaluation of the impact of arbitrary reduced precision (ARP) on an application's QoR, without modifying the software stack. Compared to other approaches, our methodology allows fine-grained bit-width tuning to obtain the best QoR vs. energy consumption trade-off. Our approach can be used either as a standalone process or in conjunction with variable type optimization tools for fine-grained tuning. The approach was tested on several benchmarks from the literature through case studies.
The paper describes problems with the current additive manufacturing chain before considering additive manufacturing as part of a modern manufacturing chain. Additive manufacturing can be used for near net-shape for finishing, for repair or for adding special features which cannot be made with metal cutting. Recently ISO TC184/SC1/WG7 published ISO 14649-17 for proess data of additive manufacturing. This paper describes how STEP-NC deals with these different scenarios in terms of accuracy, multi-material and variation of slice direction. The possibilities of multi-material objects also raises questions about the design of such objects and how these need to be handled by an advanced controller. The paper also describes non-planar slicing. Curved direction and cylindrical direction are shown to improve the accuracy of curved structure additive manufacturing.