dataesr/scientific-paragraphs-categorization huggingface dataset:
R i is executed as follows, where i =0 in the first iteration: fw i is run using cpu i with peripherals p j i and configuration data d i. Initial attempts to use R i will likely fail to sufficiently mirror S, but since rehosted systems are introspectable, the execution can be analyzed and various traces extracted to aid diagnostics. These traces may be collected by capturing the execution state at every instruction, system call, or at any other time of interest. Traces from R i have more diagnostic value if equivalent traces can be collected from the real device, S, via an output channel such as hardware debug support (e. g. , JTAG) or extractable software logs (e. g. , Linux ftrace). Evaluating the fidelity of R i with comparisons of ground-truth traces or expected behavior from S to observations of R i is essential for determining when the rehosting process has finished.
Despite the current focus on ARM and MIPS on Linux, the embedded systems in the wild are more diverse. While it is difficult to pinpoint the distribution of ISAs and OSs, prior studies provide an estimate by crawling the Internet for firmware images. For instance, in the dataset Chen et al. acquired for Firmadyne  [10] , 82% of firmware images ran on MIPS, 10% were ARM (neglecting endianness and bitwidth), and approximately 41% of all acquired images were based on Linux. Another large-scale study within the same order of magnitude  [19]  reports the acquisition of a dataset in which 63% of the firmware is for ARM devices, 7% for MIPS, and 86% for Linux. These numbers differ largely due to changes in source selection and processing of the datasets, but both show that a significant amount of firmware is not written for ARM and MIPS on Linux.
Given the significant number of embedded devices not running Linux on ARM or MIPS, an important research task is to find ways of making it easier to create fast and accurate VXEs. There has been some encouraging recent work on automated synthesis of semantic specifications for specific ISAs such as x86  [34, 39]. TaintInduce  [14]  shows that higher level semantics (i. e. , taint propagation rules) can be dynamically inferred for an ISA. However, these approaches focus on simple instructions, such as arithmetic and basic logical operations. To create fully-fledged VXEs for real-world CPUs, complex instructions that manipulate hidden CPU states (e. g. , instructions that change privilege levels) or perform complex highlevel tasks (e. g. , the AES-NI instructions on x86) may need to be handled.
Although various languages for describing and specifying CPU behaviour can create emulators and simulators (e. g. , Sleigh  [33] , Sled  [66] , and Verilog [40]), the process of creating these specifications is manual and error-prone. We believe research into extending automated synthesis to complex instructions and ISAs found in embedded systems could ease the difficulty of rehosting systems that use proprietary, legacy, or merely unpopular CPUs and architectures.
B. 2 Diversity: Unique Peripherals For our DTB corpus, we extract compatible strings from each system's description. The Device Tree Standard indicates that the compatible property of a node, a key whose value is a list of precedence-ordered strings in the format "manufacturer,model", should be used for device driver selection. For example, when the Linux kernel parses a device tree node with property compatible = "samsung,exynos3250pmu" it determines that it must load the device driver implemented in drivers/soc/samsung/exynos-pmu. c. If a node's compatible property lists multiple strings, we consider only the first. This reflects the kernel's selection precedence and ensures no physical peripheral is counted more than once. DTB nodes that do not contain a compatible property are ignored.
B. 3 Complexity: Driver SLOC as a Proxy Note that submission of Device Tree files to the Linux kernel does not obligate submission of source code for drivers named therein; we observed that 20-58% of drivers were open source, depending on architecture. When totaling SLOC per SoC, we use precise counts for open-source drivers and the average SLOC per driver count (architecturespecific) for closed-source drivers. Manual analysis of a small subset (n = 20) of QEMU-implemented peripherals demonstrated a positive correlation between driver and peripheral implementation SLOC as shown in Fig. 4. Unlike our tabulation of peripheral diversity, here we consider all compatible strings present, not just the first (preferred) per node. This aids completeness, as we do not miss the opportunity to count SLOC for any open drivers.
SLOC is computed with pygount, a Python library that supports C syntax and does not count comments or empty lines. Automating SLOC measurement for QEMU peripheral implementations is infeasible as they can be incomplete, tightly coupled with QEMU-internal objects, or spread across hierarchical source files. For example, hw/cpu/a9mpcore. c extends QEMU's CPU class to  add support for multiple Cortex-A9 peripherals (GIC, SCU, timers, etc. ).
ACKNOWLEDGMENTS The authors wish to thank the following individuals for their contributions and support:  Lindsey Wang ,  John Wilkinson ,  Douglas E. Stetson ,  William Hedberg , and  Greta Lepore. This work was in part funded by  ONR  Awards  N00014-15-1-2180  and  N00014-19-1-2364 ; the  National Science Foundation  under Grants No. CNS-1916398  and  CNS-1942793 ;  NWO 628. 001. 030 " Tropics " and  NWO NWA-ORC  InterSect; and a research contract with  Siemens AG. DISTRIBUTION STATEMENT   A. Approved  for public release. Distribution is unlimited. This material is based upon work supported by the  Under Secretary of Defense for Research and Engineering under Air Force  Contract No. FA8702-15-D-0001. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the  Under Secretary of Defense for Research and Engineering, Office of Naval Research , or the  National Science Foundation.
DESIGN, SETTING, AND PARTICIPANTS This cohort study included US women who participated in the longitudinal, population-based Nurses' Health Study II and had not previously or subsequently been diagnosed with celiac disease. Dietary data were collected from 1991 to 2015, and data on cognitive function were collected from 2014 to 2019. Data analysis was conducted from October 2020 to April 2021. EXPOSURES Energy-adjusted gluten intake, cumulatively averaged across questionnaire cycles prior to cognitive assessment. MAIN OUTCOMES AND MEASURES Three standardized cognitive scores assessed by the validated Cogstate Brief Battery: (1) psychomotor speed and attention score, (2) learning and working memory score, and (3) global cognition score. Higher scores indicated better performance.
Statistical Analysis We used age-and multivariable-adjusted linear regression to examine the associations between gluten intake quintiles and each cognitive score. Consistent with prior analyses of data from our cohort,  22, 23, 32, 33    We conducted several secondary analyses. First, because grains are major sources of gluten and are correlated with cognition,  41  we additionally adjusted for refined grains (servings per day) and whole grains (servings per day) in the multivariable model. Then, we conducted stratified analyses by age (<65 vs Ն65 years), smoking (never vs ever), BMI (<25 vs Ն25), current depression status, and history of diabetes, hypertension, and hypercholesterolemia to investigate potential effect modification by testing the interaction of gluten and these factors. Next, given that it is possible that only extreme intake of gluten affects cognition, we compared cognitive scores by gluten intake deciles.
Last, we modeled the continuous gluten variable in grams per day to test for linear associations. We also conducted several sensitivity analyses to assess the robustness of our findings. First, we additionally excluded participants who (1) had ever reported cancer diagnosis (n = 1761), (2) had every reported dementia diagnosis (n = 8), (3) or had not completed all dietary assessments preceding the cognitive assessment (n = 1848). Second, to assess temporal associations, we modeled gluten intake during each 4-year questionnaire cycle before the cognitive assessment (Յ4, We used SAS version 9. 4 (SAS Institute) to perform all analyses. All tests were 2 sided and had a significance level of P <. 05. The eMethods in the Supplement provide more details on measurements and analysis.
This was a noninterventional study with no additional sampling or change in the sampling procedures, and data were completely anonymized. According to the French Health Public Law (CSP Art L 1121-1. 1), such protocol is exempted from informed consent application. Aspergillus+ BALF were classified as (i) invasive pulmonary aspergillosis (IPA) according to EORTC/MSG consensus criteria (4), (ii) chronic pulmonary aspergillosis (CPA) according to ESCMID/ERS guidelines  (5)  or (iii) colonization if classification criteria were not met. Finally, all BALF from infected and colonized patients (n=10) and all control BALF (n=10) were pooled as Aspergillus+ and control (Aspergillus-) groups, respectively; BALF from each individual were pooled in equal volume for a group. Protein concentrations in both pools were measured by Bradford assay and divided into three technical replicates for proteomics.
7 min. One unique peptide to the protein group was required for the protein identification. A false discovery rate cut-off of 1% was applied at the peptide and protein levels. The statistical analysis of the proteomics data was performed as described previously  (6). Functional annotation of BALF proteomes was conducted using database for annotation, visualization, and integrated discovery (DAVID) v6. 8  (7). Overrepresented functional categories among proteins enriched in each subject population were identified relative to total unique protein found in BALF from our study using a permutation-based false discovery rate analysis (FDR). Processes with at least three protein members and FDR <5% were deemed significant. Proteinprotein interactions were analyzed and sketched with STRING database v11.
System and data We used a dataset from a previous study  (Edeline et al. 2013)  which consisted in 4 145 234 individual body size measurements of fish, representing 50 fish species, collected during 20 809 fishing operations of the French Office of Water and Aquatic Ecosystems (formerly ONEMA, now ONB) from 1980 to 2008, at 7024 stream stations distributed across eight hydrographic basins in France (see  Edeline et al. 2013  for further details on sampling scheme). Mean air temperature during the two years prior to sampling were available for each fishing operation. Monthly mean air temperature was obtained by interpolating monthly temperature averages recorded at 1085 meteorological stations (see  Edeline et al. 2013  for details). The temperature was averaged over two years prior to sampling to better capture the average thermal conditions at a given site.
Global trophic networks Global networks can be viewed as maximum possible networks as they feature all nodes and links that we expect to find in a community containing all 50 fish species in our dataset. Such a network is hypothetical in that the 50 fish species may not necessarily co-occur in reality. Global networks consist of 1) resource nodes (R) and fish nodes (F), and 2) an interaction matrix representing links between nodes. We built two global networks: 1) a size-structured global network comprising nine size classes per species corresponding to nine quantiles of the empirical body size distribution of each species in the dataset, and thereby 50 × 9 fish nodes, and 2) an unstructured global network featuring 1 size class per species, thus containing 50 × 1 fish nodes. We refer to each type of network either by 'size-structured' or 'species-level' thereafter.
2  top and bottom, Fig. 1a ). In order to establish nodes and interactions in global networks, we identified the diet of each species, their ontogenetic diet shifts (i. e. changes in diet with size, thereafter termed 'diet shifts') and size-dependency in piscivory. For this purpose, we searched reviews and articles that contained information about fish diet and ontogeny (89 articles in total), as well as the Fishbase database  (Froese and Pauly. 2015). General literature resources were acquired by querying google Scholar and Web of Science with all possible combinations of keywords such as 'ontogenetic diet shifts', 'size-dependence', 'piscivory', 'freshwater fish diet' and relevant synonyms (from February to March 2016). For species that were not featured in these general resources, we narrowed down our search to articles that contained the Latin and/or vernacular name of the given species and looked for evidence of information on their diet.
Defining nodes We defined resource nodes as seven primary non-fish food sources (R = {R1, …, R7}) of the 50 fish species  (Allan and Castillo 2007, Hart and Reynolds 2008) : Nutrients and detritus (R1, i. e. dead organic matter and inorganic nutrients), biofilms (R2, i. e. assemblages of micro-organisms), phytobenthos (R3, i. e. small plants growing on the substrate), macrophyte (R4, i. e. plants extending in the water column), phytoplankton (R5, i. e. drifting algae), zooplankton (R6, i. e. drifting micro-organisms) and zoobenthos (R7, i. e. macroinvertebrates living on the bottom). Since we had no information about the presence/absence of the resource nodes, we assumed that the seven resource nodes were present in all the networks. We defined the set of fish node of the size-structured global network by splitting each fish species into nine size classes, each bounded by a minimum and maximum size matching lower and upper boundaries of nine empirical quantiles of the species' size distribution across the entire dataset.
Parameter α was considered as being not subjected to inter-specific variation  (Hart and Reynolds 2008) , and was therefore set to 0. 03 for all piscivorous fish species  (Claessen et al. 2002). Parameter β was either readily available from the literature (Mittelbach and Persson 1998) (8/15 species), or set to capture the maximum prey size observed in prey-predator size data (3/15 species), or set to the average of the values reported in other piscivores (β = 0. 455) (4/15 opportunistic piscivorous species, see the Supporting information for details). Fish-fish trophic interactions were hence established by considering sequentially all piscivorous size classes in size-structured or species-level networks and for each of them identifying all other size classes that fall in their predation window.
Network topology We then characterized the trophic structure of the 18 648 × 2 local networks by calculating five topological descriptors, portraying total trophic diversity (i. e. number of trophic species/nodes), vertical trophic diversity (i. e. the mean and maximum trophic level) and the vertical distribution of biomass in the network (i. e. the correlation between biomass and trophic level) (Fig. 1d ). Four descriptors were determined using the n_w program  (Gauzens et al. 2015) : 1) the total trophic diversity (S), which measures the overall diversity in the network (i. e. number of size groups/species depending on resolution), hence approximated by the total number of nodes; 2) the connectance (C), which measures link density, calculated as L/(S^2), where L is the total number of trophic links; the vertical trophic diversity, captured by 3) the mean (TLmean) and 4) maximum trophic level (TLmax) of nodes, given that the trophic level of a node is calculated recursively as the average of the trophic levels of its prey-nodes plus one (the trophic level of basal species being 1).
Statistical analysis We inferred the relationship between temperature and total and vertical trophic diversity by estimating effects of temperature on the topological descriptors, and by comparing effects between the size-structured and species-level networks. The final dataset for the statistical analysis featured 10 topological descriptors (five for each size-structured and species-level local networks) treated as response variables, as well as nine explanatory variables: 1) air temperature, 2) mean stream width, 3) mean stream slope, 4) month of the year and 5) year of sampling, 6) sampling site location and 7) hydrographic basin and 8) strategy and 9) technique of the sampling scheme, for each of the 18 648 fishing operation  (Edeline et al. 2013  for details). Variables (2) to (9) were included in the analysis to control for confounding environmental effects, such as seasonality, which may mask effects of temperature.
Alternatively, this may be explained by a strong inter-specific variation in the link between body size and trophic ecology, which would break the body size dependency in trophic level. Indeed, not all interactions in our system are solely size-dependent and specific to each species in our dataset  (Allan and Castillo 2007, Hart and Reynolds 2008) , which may prevent mapping size mediated effects of temperature at the population level onto the species level  (Vincent et al. 2020). Altogether, our study supports the idea that the top-down pathway might reliably predict changes in vertical biomass distribution and occurrence of top predators with warming, both at the population and species level, while the bottomup pathway might be driving an increase in the occurrence of intermediate trophic level predator species.
Context-dependence in trophic interactions We built our networks based on co-occurrence data and detailed literature evidence on the taxonomic-and sizedependence in trophic interactions. However, there is evidence that trophic interactions are context-dependent, and thus not always at play  (Barner et al. 2018 , Thurman et al. 2019). Size-dependence is one such source of contextdependence  (Gravel et al. 2013 ), which we have accounted for in depth in the construction of our networks by modelling ontogenetic diet shifts and size-dependence in piscivory. Furthermore, size-structured networks also account for fine variation in population size structure and thereby allow for nuanced interactions between the different populations present at a given location, even with binary interactions. Though body size is a strong determinant of trophic interactions, and there is strong support for size-dependent effects of temperature, temperature may further influence trophic interactions through size-independent effects.
Acknowledgements -We warmly thank  Nicolas Loeuille ,  Elisa Thébault  and  François Massol  for their comments and feedback on early versions of the work,  Gabrielle Ringot ,  David Rozen ,  Loïc Prosnier  and the  IEES lab  for their support during this project. Funding -This work was supported by the  ANR ARSENIC , grant number  ANR-14-CE02-0012.
Zooplankton evaluation As noted above, this model was already validated against oxygen, nitrate and chlorophyll  (Jose et al. , 2017). As a complement, we here compare the large zooplankton compartment of the model, averaged from January to March, with observational data collected on RV Meteor cruise M93. The samples obtained during this cruise include day-and nighttime hauls with a Hydro-Bios multinet (nine nets, 333 µm mesh) between 10 February and 3 March 2013 on a transect off the Peruvian coast (≈ 12 • S; see Fig. 2f ), capturing the vertical and horizontal gradient in zooplankton concentration. Samples were size-fractionated by sieving and processed according to the ZooScan method  (Gorsky et al. , 2010). Observations included crustaceans, chaetognaths and annelids greater than 500 µm.
This is also the case when comparing against a different dataset of observations (see Supplement). Below 100 m, however, model estimates are consistently lower than the observations, which is in particular evident at the deep offshore stations (Appendix B, Fig. B1a  and b ). Zooplankton in our model does not consume detritus or bacteria; small zooplankton feeds on phytoplankton, and large zooplankton feeds on small zooplankton and on phytoplankton. Therefore, in contrast to observations, its presence is not expected in deep water. In summary, the model matches the observed spatial pattern of zooplankton distribution but tends to overestimate zooplankton concentration in the surface layer and to underestimate it in the mesopelagic depths. Possible reasons for this mismatch will be discussed in Sect.
Acknowledgements. We would like to thank  Tianfei Xue  for carrying out some of the model simulations. We also thank the three anonymous reviewers for their very helpful and constructive comments. Simulations were performed using the computing facilities of the  Norddeutscher Verbund zur Förderung des Hoch-und Höchstleistungsrechnens -HLRN. Financial support. This research has been supported by the  Bundesministerium für Bildung und Forschung projects CUSCO and Humboldt Tipping  (grant no. 03F0813A  and grant no. 01LC1823B ). Rainer Kiko  was further supported via a  Make Our Planet Great Again grant  of the  French National Research Agency  within the  Programme d'Investissements d'Avenir , reference  ANR-19-MPGA-0012. Further support for this project was provided by the  Deutsche Forschungsgemeinschaft (DFG) , under the  Sonderforschungsbereich 754  " Climate-Biogeochemistry Interactions in the Tropical Ocean " project (http://www.
Here, we analyze the genome of a female Great Dane named Zoey that we sequenced using PacBio longread technology. We integrated this long-read data with additional sequencing resources, including standard high-coverage short-read sequence data, as well as sequence data derived from mate-pair and pooled fosmid libraries, to generate a highquality assembly. Using this new assembly, we annotate gene structures and GC-rich sequences that are absent from CanFam3. 1 and underrepresented in existing Illumina canine short-read sequence datasets. We demonstrate that gaps in the CanFam3. 1 assembly are enriched with sequences that have an extremely high GC content and that overlap with transcription start sites and recombination hotspots. We identify thousands of mobile element insertions, including intact LINE-1 copies, and make use of our fosmid library to subclone an intact L1_Cf element.
We additionally annotated 7,049 long noncoding RNAs (51), including 84 with no or only partial alignment to Can-Fam3. 1. Using existing RNA sequencing (RNA-Seq) data (  5 ), we estimated expression values for each protein-coding gene across 11 tissues and report the results as tracks on a custom University of California, Santa Cruz (UCSC) Genome Browser assembly hub (52) (Fig. 2 ). The assembly hub illustrates correspondence between the CanFam3. 1 and Zoey assemblies and displays the annotation of additional features including structural variants, segmental duplications, common repeats, and BAC clone end sequences (SI Appendix, section 7). Resolved Assembly Gaps Include GC-Rich Segments Underrepresented in Illumina Libraries. Alignment indicates that 12,806 of the autosomal gaps in CanFam3.
1 are confidently localized to a unique location in the Zoey genome assembly. In total, 16. 8% (2,151) of the gap segments overlap with a transcription start site of a protein-coding gene, which makes it possible to better understand the importance of these previously missing sequences in canine biology  (5, 53). Surprisingly, analysis of unique k-mer sequences that map to the CanFam3. 1 gap sequences suggested that these DNA segments often are absent from existing Illumina short-read datasets, even though analysis of DNA from the same samples using a custom array comparative genomic hybridization platform indicates their presence. Interrogation of read-pair signatures also suggests that these sequences are systematically depleted in Illumina libraries, which is due to their extreme GC-rich sequence composition (SI Appendix, section 4).