dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , (A • B) • C). Doing so requires multiple long-latency sensing operations in series, which become a new performance and energy efficiency bottleneck. In this work, we observe that NAND flash memory has inherent capability to perform bitwise operations on a large number (e. g. , tens) of operands at once (i. e. , with a single sensing operation) due to its unique cell-array structures that are similar to digital logic circuits for NAND and NOR gates. Second, ParaBit is applicable only to highly error-tolerant applications because it is not designed to take into account the highly error-prone nature of NAND flash memory. To ensure data reliability, modern NAND flash-based SSDs commonly use (i) error-correcting codes (ECC) and (ii) data randomization  [64] [65] [66] [67]. Unfortunately, ParaBit cannot leverage any of the widely-used ECC and data-randomization techniques, as it performs bitwise operations while sensing the cells that store the data.
Performing bitwise AND and OR operations on ECCencoded or randomized data using ParaBit can lead to incorrect results during ECC decoding and/or de-randomization. Although storing a smaller number of bits in a cell would reduce the raw bit error rate (RBER) of NAND flash memory, our characterization using 160 real 3D NAND flash chips shows that even storing a single bit per cell cannot provide sufficientlylow RBER for ParaBit to be adopted across a wide range of applications. Our goal is to improve both the performance and energy efficiency of in-flash bulk bitwise operations while ensuring high reliability (i. e. , zero bit errors) in computation results. To this end, we propose Flash-Cosmos (Flash Computation with One-Shot Multi-Operand Sensing), a novel in-flash processing technique for bulk bitwise operations that achieves our goal by exploiting two key ideas: (i) Multi-Wordline Sensing (MWS), which enables in-flash bulk bitwise operations on multiple (e.
Second, we relax the data location constraints of the basic MWS mechanism (e. g. , bitwise OR/NOR operations are possible only for wordlines in different NAND strings) by (i) storing each operand's inverse data and (ii) leveraging De Morgan's laws. For example, if the user stores the inverse of A, B, and C (i. e. , A, B, and C) in the same NAND string, Flash-Cosmos can perform bitwise OR of three wordlines by performing bitwise NAND of A, B, and C because (A OR B OR C)=NOT (A AND B AND C). Flash-Cosmos requires only small changes to the control logic of a NAND flash chip, but no changes to its cell array and sensing circuitry. For efficient post-fabrication tests and optimizations, most modern NAND flash chips are already capable of (i) simultaneously sensing multiple wordlines  [89]  and (ii) adjusting programming step and voltage at fine granularity  [67, 79, 81, 90].
In-Flash Bulk Bitwise Operations Many prior studies  [7-9, 20, 37, 38, 48]  investigate near-data processing (NDP) solutions for bulk bitwise operations due to two main reasons. First, bulk bitwise operations are used in a wide variety of important applications, including databases and web search  [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] , data analytics  [7, [14] [15] [16] [17] , graph processing  [9, [18] [19] [20] [21] , genome analysis  [22] [23] [24] [25] [26] [27] [28] [29] , cryptography  [30] [31] [32] , set operations  [7, 19] , and hyper-dimensional computing  [33] [34] [35] [36]. Second, bulk bitwise operations can significantly benefit from NDP. Due to the simple nature of bitwise operations, the performance and energy efficiency of bulk bitwise operations are bottlenecked by data movement between the computation units and the memory hierarchy in conventional systems  [7-9, 21, 37, 38].
However, when the operands are stored in the same block with their inverse data, it is possible to perform the same bitwise OR operation with a single intra-block MWS operation using inverse-mode read (Section 6. 1). Second, the host system needs to interact with the underlying SSD in order to efficiently store the data to maximize the benefits of Flash-Cosmos. Application Changes. In our design, the application program needs to decide how to store data in three aspects. First, the application determines the data that will be used for bulk bitwise operations so that it can inform the SSD to selectively use ESP for only such data (to minimize the storage overhead due to SLC mode). Second, depending on the computation that can benefit the most from Flash-Cosmos, the application decides whether or not to store the inverse of the original data.
For example, if the application performs bitwise OR more frequently than bitwise AND for certain data, it could be more beneficial to store the inverse data to leverage intra-block MWS for bitwise OR as well. Third, the application decides which operands to be stored in the same block to minimize the number of MWS operations required for the same bitwise operation. System Software Changes. In our design, the application program interacts with the SSD using the Flash-Cosmos library that includes two methods: (i) fc _ write, which writes the operand data for bitwise operations, and (ii) fc _ read, which reads the results of bitwise operations. Using fc _ write, the application informs the SSD of the context of the operation, such as the programming mode and the location (e. g. , logical block address), to ensure that the data is properly stored for in-flash computation.
Unless otherwise specified, we set all the evaluated systems to program (and thus read) the inputs and outputs of bitwise operations in SLC mode for fair performance comparison. Performance Modeling. We use two state-of-the-art simulators to analyze the performance of the evaluated systems. We model DRAM timing with the DDR4 interface [123] in Ramulator  [124, 125] , a widely-used cycle-accurate DRAM simulator. We model SSD performance using MQSim  [126, 127] , a stateof-the-art SSD simulator. We extend MQSim to faithfully model the performance of ISP, ParaBit, and Flash-Cosmos with the timing parameters we obtain from our real-device characterization (Section 5). We model the end-to-end throughput of the evaluated systems based on the throughput of each of two computation stages, SSD read (including in-storage processing in ISP, PB, and FC) and host computation (which we measure on a real host system).
1) Bitmap Index (BMI): Bitmap indices  [1]  are an alternative to traditional B-tree indices for databases, which can provide high space efficiency and high performance for many queries (e. g. , join and scan) compared to B-trees. We assume a database that tracks the log-in activities of u users for a website every day. For the i-th day, the database maintains a vector D i with u elements, each of which is a 1-bit flag to indicate each user's log-in activity on the day (0: not logged-in, 1: logged-in). Our BMI workload runs the following query: "How many users were active every day for the past m months?" Executing the query requires (i) bitwise AND operations on d vectors, where d is the number of days in the past m months, and (ii) a bitcount operation, i. e. , an operation that counts the number of '1' (logged-in) bits in a given result vector r.
We assume a database that tracks the log-in activities of 800 million users and evaluate the BMI workload while varying m from 1 to 36. For executing the BMI workload, PB and FC perform the bit-count operation using the host CPU concurrently with sending the result vector to main memory in batches. 2) Image Segmentation (IMS): Image segmentation  [135]  is an image processing kernel that aims to break an image into multiple regions depending on a given set of colors. To determine whether a pixel p belongs to a certain color C, our IMS workload uses the YUV color recognition and performs a bitwise AND operation of Y (p,C) • U(p,C) • V (p,C), where Y (p,C), U(p,C), and V (p,C) are binary values that can be obtained 13 We do not evaluate the other application evaluated in ParaBit  [21] , image encryption  [30] , as it relies only on bitwise XOR operations that commodity NAND flash chips already support (see Section 3), i.
Discussion Extensions to Other Applications. Flash-Cosmos can be used to accelerate not only bitwise operations but also any desired op-eration. This is because Flash-Cosmos supports a set of bitwise operations that are logically complete, like other processingusing-memory (PuM) substrates that use the operational principles of the memory cells for computation, such as Compute Caches  [37]  (SRAM-based PuM), Ambit  [7]  (DRAM-based PuM), and Pinatubo  [20]  (NVM-based PuM). Follow-up works (e. g. , DualityCache  [40] , SIMDRAM  [9] , and IMP  [200] ) propose frameworks that leverage these substrates and techniques to automate the creation of desired complex operations (e. g. , addition and multiplication) to accelerate a broad range of workloads, including graph processing, databases, neural networks and genome analysis.
Acknowledgments We thank the anonymous reviewers of ISCA 2022 and MI-CRO 2022 for feedback. We thank the  SAFARI group  members for feedback and the stimulating intellectual environment. We specifically thank  Nika Mansouri Ghiasi  and  Minesh Patel  who helped shape our arguments and strengthen our evaluation. We acknowledge the generous gifts and support provided by our industrial partners: Google,  Huawei ,  Intel ,  Microsoft ,  VMware , the  Semiconductor Research Corporation  and the  ETH Future Computing Laboratory. Jisung Park was in part supported by the  National Research Foundation (NRF) of Korea  ( NRF-2020R1A6A3A03040573 ). (Co-corresponding Authors:  Jisung Park ,  Myungsuk Kim , and  Onur Mutlu ).
, 2018). For protein quantification, we used the software Progenesis® (Nonlinear Dynamics). In this software we used MASCOT version 2. 2. 06 (Matrix Science) against the Musa V2 database of M. acuminata and M. balbisiana  (Martin et al. , 2016; Wang et al. , 2019) (157832 proteins). Tandem mass spectra were extracted by Progenesis. All MS/MS spectra were searched with a fragment ion mass tolerance of 0,020 Da and a parent ion tolerance of 10,0 PPM. Carbamidomethyl of cysteine was specified in Mascot as a fixed modification. Deamidation of asparagine and glutamine and oxidation of methionine were specified in Mascot as variable modifications and the results were reintroduced in Progenesis. Scaffold (version Scaffold_4. 11. 0, Proteome Software) was used to validate MS/MS based peptide and protein identifications.
Proteins sharing significant peptide evidence were grouped into clusters. A protein false discovery rate of 0. 8% and a spectral false discovery rate of 0. 04% was observed by searching the reverse concatenated decoy database (157832 proteins). Gene annotations were taken from the banana Hub  (Droc et al. , 2013)  and verified in Plant Metabolic Network  (Hawkins et al. , 2021)  and Prosite (Expasy SIB Bioinformatics Resource Portal). Subcellular prediction were analysed via the software DeepLoc 1. 0 (Almagro  Armenteros et al. , 2017).
Metabolic Analysis To complement our proteomics data and improve our insights about plantain fruit development, we analysed major metabolic traits in pulp. Metabolites were extracted from 10 mg aliquots of lyophilized ground samples via three successive extractions with ethanol-buffer mixtures successively composed of 80, 80 and 50% ethanol and 10 mM Hepes/KOH buffer (pH 6). The supernatants were collected and pooled in order to measure soluble metabolites. Glucose, fructose and sucrose were measured enzymatically  (Stitt et al. , 1989). Glucose 6-phosphate (G6P), fructose 6- A c c e p t e d M a n u s c r i p t phosphate and glucose 1-phosphate (G1P) were measured using an enzyme cycling assay  (Gibon et al. , 2002). Malate was measured enzymatically as in  (Mollering, 1985). Total free amino acids were measured using fluorescamine  (Bantan-Polak et al.
Statistical analyses For proteins, statistical analyses were made using the software Statistica 8 (TIBCO) based on the exported protein quantifications of Progenesis. We performed a principal component analysis (PCA, with NIPALS algorithm) to get an overview of the proteome data. We performed a partial least squares analysis (PLS) (NIPALS algorithm) to differentiate proteins with a significant correlation to the time points, the cultivar or metabolite using all protein quantifications as continuous predictors (x matrix) and the time points, cultivar and quantified metabolite as dependent variables (y matrix). We applied a two-way ANOVA (P<0. 05) to the selected proteins to verify their significance affected by the time point, cultivar or the interaction between both. All displayed regressions were made in Microsoft excel and based on the best fit R 2.
M a n u s c r i p t To integrate the different omics data, the protein inference and isoform redundancy issue was tackled by quantifying the proteins at protein cluster level and EMPAI quantification (Scaffold_4. 11. 0, Proteome Software). To find the protein clusters that correlated to the modelled fluxes, we performed a two-block sparse partial-least-squares discriminant analysis (sPLS-DA) with mixOmics package of R  (Rohart et al. , 2017)  using DIABLO application  (Singh et al. , 2019)  with default parameters. To the relationships between the proteins and fluxes were calculated with P<0. 001 (after false discovery rate [FDR] correction) threshold for Pearson correlations.
Only one peptide was found with low confidence. The reason for the low confidence is probably the low abundance of the enzyme. We did confidently identify sucrose phosphatase in our study of detached ripening fruits; it proved to be low abundant and was significantly upregulated in the very late ripening stages  (Bhuiyan et al. , 2020). The formed sucrose can be degraded by invertase (29) and/or SuSy  (1)  in the cytoplasm or can be transported to the vacuole for storage or further processing by invertase (31) (Table  3 , Figure  7 ). Invertase (29) Mba10_g13890. 1, is only encoded on the B genome and is predicted via the software DeepLoc 1. 0 (Almagro  Armenteros et al. , 2017)  to be localized in the cytoplasm. The cytoplasmatic homologue coded on the acuminata genome is most probably not or expressed in a very low level since we did not find a confident specific spectrum.
Funding We acknowledge  USAID  for the project  AID-BFS-G-II-00002-11  Reviving the plantain breeding program at  IITA -International Institute for Tropical Agriculture. The authors would furthermore like to thank all donors who supported this work through their contributions to the  CGIAR Fund  (http://www. cgiar. org/who-we-are/cgiar-fund/fund-donors-2/),and in particular to the  CGIAR Research Program on Roots, Tubers and Bananas , and the  MetaboHUB  ( ANR-11-INBS-0010 ) and  PHENOME  (French  ANR-11-INBS-0012 ) projects for funding. The metabolite analyses were performed on  Bordeaux Metabolome facility.
Simulation code for toy-models The following Matlab functions are provided in a code repository accessible at [INCLUDE REFERENCE]: (i) functions to compute the state rate matrix (Laplacian matrix of the master equation) for each model as a function of input parameters; (ii) a generic function to compute the various regulatory phenotypes and other features (waiting time distributions, fluxes, entropy production, etc. ) from the state rate matrix; (iii) a function to generate stochastic realizations of the models; (iv) functions to generate the different figures.
Manuel curation with orthogonal evidence, including chromatin proximity (Hi-C) and Bionano optical maps, delineated error locations that were fixed, e. g. 260 and 63 missed joins in GRCg7b and GRC-g7w (online suppl. Material 1, Table  1 ; for all online suppl. material, see www. karger. com/doi/10. 1159/000529376). Depending on the descriptive context, we use the assembled GenBank versions (GRCg6a, GRCg7b, and GRCg7w) and their common names (RJF, broiler, and layer) interchangeably throughout the remainder of this report. While contig N50 length was comparable to GRCg6a (which also used PacBio long read data to fill gaps), phasing and revised mapping data led to a 4. 5-fold increase in N50 scaffold length and a 2-fold decrease in the number of unplaced sequences in broiler and layer assemblies (Table  1 ).
2 Mb in GRCg7b (Fig. 1 ). Other errors include the fusion of chr31 and chr29 in GRCg6a, which is likely due to repeat sequences identified on the Hi-C heat map (online suppl. Material 2, Fig. 2 ). Avian microchromosomes show more frequent recombination, and thus the positive correlation between recombination and interspecies divergence observed in mammals is not seen in birds, at least at the resolution of whole chromosomes  [International Chicken Genome Sequencing Consortium, 2004]. If the origin of the microchromosomes was initiated by a number of random fis-Fig. 1. Assembled structural errors detected in RJF compared to broiler for chromosome 27 using Hi-C mapped data to the scaffolds. Genetic linkage map markers (n = 125) displayed as green tic marks below the x axis for the chromosome 27 heat map were mapped to each assembly to validate sequence order and orientation.
WGS Mapping and SNV Analysis It is probable that the choice of broiler, layer or RJF as a reference for alignment of various resequenced chicken populations could contribute to SNV ascertainment bias as has been shown in human  [Schneider et al. , 2017]. We examined the mapping rates of WGS short-read data of 6 genetically diverse chicken samples (online suppl. Material 1, Table  6 ): a male and female for layer and broiler chicken, as well as an Ethiopian indigenous chicken breed. For various mapping metrics, regardless of the reference, we find no large differences, indicating that for measures of genetic diversity, all 3 references have comparable initial abilities to call SNVs or indels (online suppl. Material 1, Table  7 ). Despite similar WGS mapping rates across references, the optimal SNVs set for the experimental purpose intended is not certain.
RNAseq Mapping The mapping of RNAseq data to estimate transcriptome changes between samples for biological interpretation is a crucial reference usage. To address this application, we first analyzed a large number of diverse tissues where total percent mapping is available in the NCBI gene annotation report (https://www. ncbi. nlm. nih. gov/genome/annotation_euk/Gallus_gallus/106/) and found very little difference between haplotypes (GRCg7b and GRCg7w). However, since RNAseq alignment in this application is optimized for verifying gene model predictions, we also tested the STAR aligner, which is typically considered best practice for bulk RNAseq studies  [Dobin et al. , 2013]. Using a small number (n = 8) of RNAseq samples from diverse tissue origins, including ileum, bone-derived macrophages, and uterus, we observe a small average range of 0.
From this point, the maternal and paternal haplotigs independently underwent the same steps. Separately, we assembled the mitochondrial (MT) genome with the mitoVGP pipeline (v2. 2)  [Formenti et al. , 2020]  and added it to the haplotigs to keep any raw MT reads from being mapped to nuclear sequences preventing conversion of possible mitochondrial nuclear integrations into MT sequence during the polishing steps. We used Arrow from smrtlink (v6. 0. 0. 47841) to improve base calling accuracy and purge_dups (v1. 0. 0)  [Guan et al. , 2020]  in an adapted trio mode to remove erroneous duplications. The median insert sizes of WGS libraries were approximately 400 bp and individual libraries were tagged with unique dual index DNA barcodes to allow pooling and minimize the impact of barcode hopping.
com/) using the Arima-Hi-C kit (P/N: A510008). From size selected fragments, Illumina-compatible libraries were generated using the KAPA Hyper Prep kit (P/N: KK8504). The resulting libraries were sequenced on an Illumina HiSeq X instrument to ∼70× coverage. With 10× long-linked reads, BioNano, and Hi-C maps in hand, the earlier polished and purged haplotigs were scaffolded in 3 stages according to  Rhie et al. [2021] : first, we used the 10× linked-reads in 2 rounds of scaff10x (v4. 1. 0) (https://github. com/wtsi-hpag/Scaff10X) to generate the primary scaffolds. Second, we generated BioNano cmaps and used BioNano Solve (v3. 2. 1_04122018)  [Lam et al. , 2012]  for hybrid scaffolding and to break misassemblies. Third, we used Salsa2 (v2. 2)  [Ghurye et al. , 2019]  to generate chromosomal-level scaffolds using the molecular contact information from Hi-C linked reads.
Assembly Statistics and Evaluation Following each stage of the assembly, we calculated various metrics of assembly quality, for example, N50 contig length, number of contigs, and quality value (QV) scores for each base call to assess progress. We used Merqury (v1. 0) for overall assembly evaluations (including k-mer completeness and spectra copy number analysis) as well as phasing assessment with hap-mers. We first generated 21-mer databases (dbs) from the raw F1 10× data and the parental Illumina data using meryl. We then built inherited hap-mer dbs by taking the difference between the maternal and paternal k-mer dbs, filtering according to the filter level used by TrioCanu for binning, intersecting both with the F1 dbs, and filtering again, as below (steps 1-4). For evaluation of genome completeness and protein-coding gene representation, we ran BUSCO v4.
Gene Annotation Both assemblies, GRCg7b, and GRCg7w were gene annotated using the standard NCBI pipeline  [Pruitt et al. , 2014] , including masking of repeats prior to ab initio gene predictions, for evidence-supported gene-model building. All annotation processes used publicly available RNA-seq and Iso-Seq data from diverse tissue sources. We relied on the NCBI gene annotation report release 106 to compare the outcomes for each assembly. GRCg6a gene annotation data were reported earlier in NCBI release 104 using the same process as above.
WGS and RNAseq Mapping WGS short-read data of 6 chicken samples; a male and female layer, broiler and Ethiopian indigenous chicken breed were used to compare the mapping rate across the 3 genome assemblies (online suppl. Material 1, Table  6 ). WGS were first checked for quality using Fastqc  [Andrews, 2010]. Trimmomatic  [Bolger et al. , 2014]  was used to remove the remaining Illumina adapter sequences and low-quality bases with default parameters. Clean reads were mapped to the 3 reference assemblies (GRCg6a, GRCg7b, and GRCg7w) using bwa-mem with default parameters  [Li and Durbin, 2009]. Picard (http://broadin- stitute. github. io/picard/) was used to sort the mapped files and merge files from multiple sequencing runs and to mark duplicate reads. Finally, SAMtools  [Li et al. , 2009]  was used to assess mapping quality.
RNAseq data from 8 chicken samples were used to compare the mapping rate across the 3 genome assemblies. We retrieved all sequence data from the NCBI Sequence Read Archive that included the diverse tissue sources of ileum, bone-derived macrophages, uterus and muscle from a male and female layer (online suppl. Material 1, Table  9 ). Sequencing quality was checked by FastQC software (v 0. 11. 7), qualifying reads were mapped using STAR software (v 2. 5. 3a) with default parameters to all assemblies (GRCg6a, GRCg7b, and GRCg7w), and the percentage of uniquely mapped reads, multiple mapped reads, and reads mapped to too many loci were taken for the comparison. Moreover, the resulting bam files were used for assessing the mapping rate for each sample with the Samtools (v 1. 9) "flagstat" command  [Li et al.