dataesr/scientific-paragraphs-categorization huggingface dataset:
We covered three seasons in Bamako, Ouagadougou, and Tamale, and two seasons in Bamenda. In Tamale, we captured peak and hot season data in two consecutive years. Each survey took place for 6-8 days on all access roads to the city. The number of days was based on the length of local market cycles, which historically differs in the cities and ranges from 3-to 8-day cycles  (Smith, 1971). The number of access roads varied by city and ranged from 5 in Tamale (4 major and 1 minor road) to 10 in Bamako (5 major and 5 minor roads). Data collection took place 24 h/day on major roads, and 12 h on minor roads. Data were recorded at existing check points operated by security forces (Tamale, Bamenda, Figure  3 ), or other governmental agents, including phytosanitary services (Bamako), as well as at toll gates (Ouagadougou).
The original list of commodities included unprocessed food products, livestock, firewood and charcoal, animal feed and slightly processed products, such as flour. For this study we used a subset of products (section Analysis). The data we present in this paper are mostly quantitative, summarizing flow data collected during the surveys. However, we also collected qualitative data to supplement the quantitative data collection. These included visits to various markets within and outside the cities, visits to primary production areas, and interviews with traders, transporters, farmers, and municipal authorities (assembly people, market inspectors). For instance, according to informants in the transport sector, overloading in Ghana is common and not reflected in official papers, for example in waybills required for cross-border trade (Supplementary Materials).
These types of qualitative information were necessary to understand the situation on the ground as accurately as possible. However, uncertainties remain (section Limitations). Each city has particular conditions for food distribution, which resulted in adjustments in data collection. For instance, in Bamenda, in-and outflows were also recorded at bus stations, because food was commonly transported on intercity buses, in small unit quantities, and by different passengers. Capturing these diverse products on the road would have been impossible; therefore, we recorded these products during loading and offloading at the city's bus stations. A similar adjustment was made in Tamale, where food from nearby rural market places, traded by a number of different traders was captured at these markets.
Processing and Preparation Data were entered and processed in a PostgreSQL database with a PostGIS extension. This allowed geocoding of locations (origin and destination of flows), for which only names were captured during data collection. Cleaning included removing duplicates and standardizing terms, such as for units, products, and metadata. Food was transported in a variety of units, including baskets, sacks, and vehicles, which all required calibration, that is the assignment of specific volumes, which was done through in situ volume measurements. Product densities were also determined through in situ measurements, or through secondary sources yielding final weights. Thereby, we distinguished between different processing levels, e. g. , dry and fresh fish, which, after quantification, were merged.
Tables containing calibration lists (Supplementary Materials), metadata, spatial characteristics of source and destination locations, and secondary data were joined with the primary data, and daily quantities per data row calculated. Source and destination locations were classified as rural or urban using a combination of distance to and size of the next urban settlement derived from the Africapolis database (OECD/SWAC, 2020). For instance, a location within 3 km to a small town's center with 10,000-30,000 inhabitants was classified urban, and rural beyond The subset of entries only contains selected commodities and no duplicates. The number of days per survey varied and the number of entries does not correspond to the number of trips as entries are commodity-specific and one vehicle can transport several commodities.
Analysis For analysis we used a subset of data with common products, which covered all food groups, except for leafy vegetables, and apart from non-processed food items, also included slightly processed food, such as flour and dried products (Supplementary Appendix 1). We only captured goods crossing the urban boundaries, and could not account for goods produced and consumed within the urban boundary. Therefore, we omitted leafy vegetables, such as amaranth (Amaranthus L. ) and lettuce (Latuca sativa L. ), which are produced almost exclusively in urban and peri-urban agriculture at the study sites and dominate urban crop production  (Karg et al. , 2016; Bellwood-Howard et al. , 2021). Throughout the paper, we used fresh weight (kilograms, grams) for the quantification of food flows; for per capita values, we relied on the Africapolis database for urban population size (OECD/SWAC, 2020).
For percentages, for example with respect to the share provided by different spatial units, we first computed the seasonal percentages, and then calculated their mean. In the case of Tamale, where we covered two peak and hot seasons, respectively, the seasonal mean values were computed first (Supplementary Materials). For section City Functions in Food Distribution, the mean unit quantity, i. e. , the mean quantity transported per product and vehicle, and the weighted mean distance were computed for incoming and outgoing flows, respectively. Secondary data on rail and air freight were included in the analysis of incoming and outgoing quantities but omitted for computing the average unit quantity (rail and air), and distance (air).
Limitations Limitations of the study refer to the study design and the complexity of the food provisioning system. Inherent to the study design is that inner-urban food production and distribution, both market-oriented and subsistence, are not captured because our check points were located outside the urban area. Another shortcoming relating to the study design is the limited survey period, so the balance between incoming and outgoing flows does not equate with food remaining in the city. For instance, outgoing quantities of maize from inner-urban warehouses, left unregistered, exceeded the incoming quantities. Related to this is the data gap in Tamale, where one road without a checkpoint was not covered. To compensate for this, flows were recorded at the main wholesale market in the city center, however, vehicles traveling along this road and targeting warehouses in the city were missed.
ACKNOWLEDGMENTS We are grateful to our local partners in the study cities for their support, in particular  Prof. George Nyarko  from the  University for Development Studies (UDS) in Tamale, Ghana ,  Dr. Zacharia Gnankambary  and  Dr. Désiré Jean-Pascal Lompo  from  Université de Dédougou, Dédougou ,  Ndzerem Stephen Njodzeka  and the team from  Strategic Humanitarian Services (Shumas) in Bamenda, Cameroon , and  Dr. Seydou Sidibé  from the  Institut d'Economie Rurale (IER) in Bamako, Mali. We thank  Mark Hoschek  and the  University of Freiburg  for supporting data management, the team of the Sahel and  West Africa Club (SWAC/OECD ).
FUNDING This study was funded by a  DFG research fellowship  (Grant Number  KA 5270/1-1 ). Data collection was carried out as part of the project UrbanFoodPlus, co-funded by the  German Federal Ministry of Education and Research (BMBF)  and the  German Federal Ministry for Economic Collaboration and Development (BMZ) , under the initiative  GlobE-Research for the Global Food Supply  (Grant Numbers  031A242-A  and  031A242-D ). The article processing charge was funded by the  University of Kassel  open access fund.
Observations and Data Reduction The classification spectrum of SN 2021ocs was obtained on 2021 June 13 using the SNIFS spectrograph  (Lantz et al. 2004 ) at the 2. 2 m University of Hawaii telescope on Maunakea as part of the Spectroscopic Classification of Astronomical Transients survey  (Tucker et al. 2022). Late-time spectroscopy of SN 2021ocs was conducted on the night of 2021 October 26 as part of the FORS+ Survey of Supernovae in Late Times program (FOSSIL; H. Kuncarayakti et al. 2022, in preparation)  using the FORS2 instrument  (Appenzeller et al. 1998 ) attached to the ESO Very Large Telescope at Cerro Paranal Observatory, Chile. FOSSIL targets all observable core-collapse SNe (CCSNe) brighter than ∼18. 5 mag in the photospheric phase, which are expected to be ∼21-22 mag or brighter when observed in the nebular phase, with the goal of obtaining nebular spectra for a large sample of objects in a magnitudelimited, unbiased way.
We used FORS2 with grism 300V and the 1 6 slit, achieving a wide wavelength coverage of 3500-9500 Å and a spectral resolution of R ∼ 400 measured from the narrow sky emission lines. The sky conditions were photometric with seeing around 0 5 during the length of the integration. The spectroscopic observations were obtained with 2 × 1300 s exposures accompanied by 20 s g-band imaging under 0 75 seeing conditions. Spectrophotometric standard stars were observed using the same grism setting. The data were reduced using the ESOReflex  (Freudling et al. 2013)  pipeline following standard procedures. The excellent seeing conditions allowed reasonable background subtraction during the spectrum extraction, evidenced by the absence of narrow host galaxy emission lines. The g-band photometry of FORS2 was measured with our own point-spread function photometry code.
Synthetic photometry was performed on the nebular spectrum to derive the g and r synthetic magnitudes; the obtained (gr) color was then applied to the g-band photometry from imaging to produce an r-band magnitude to be used in the light curve. In addition to the FORS2 gr imaging, photometry was obtained from a number of sources. Public ATLAS forced photometry in the o and c bands was obtained from the ATLAS Forced Photometry server,  16  and ZTF g-and r-band photometry was obtained through the ZTF forced photometry service 17   (Masci et al. 2019). Forced photometry data from the Pan-STARRS1 survey are used, yielding i-and w-band photometry.
The anonymous referee is thanked for the useful feedback that substantially improved the paper. Stephen Smartt, Masaomi Tanaka, and Lucy McNeill are thanked for the discussions and suggestions on the paper. H. K. and T. N. were funded by the Academy of Finland projects 324504 and 328898. The work is supported by the JSPS Open Partnership Bilateral Joint Research Project between Japan and Finland (JPJSBP120229923). K. M. acknowledges support from the Japan Society for the Promotion of Science (JSPS) KAKENHI grants JP18H05223 and JP20H00174. S. M. acknowledges support from the Academy of Finland project 350458. R. K. acknowledges support from the Academy of Finland project 340613. This work has made use of data from the Asteroid Terrestrial-impact Last Alert System (ATLAS) project. The ATLAS project is primarily funded to search for near-80NSSC18K1575; by-products of the NEO search include images and catalogs from the survey area.
made possible through contributions by the  Institute for Astronomy , the  University of Hawaii , the Pan- STARRS  Project Office, the  Max Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins University, Durham University , the  University of Edinburgh , the  Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under grant No. NNX08AR22G  issued through the  Planetary Science Division of the NASA Science Mission Directorate, National Science Foundation  grant No.
In addition, in the case of many problems in which there exists only one degree of freedom, angles can suffice. To be able to perform fast calculations with quaternions and at the same time analyze rotations using angles, it might be necessary to have an efficient method of converting the one set of parameters to the other. Calculating the corresponding quaternion (or rotation matrix) for a given set of Euler angles is trivial. Extracting the Euler angles is much harder, however. One of the following two methods has generally been used up to now. The first method consists in adopting a different set of formulas for each possible angle sequence  [4] , which is difficult to implement and debug. The second method is that described in  [5]. SciPy  [7] , for example, a widely used scientific library for the Python programming language, implements this method.
Results In this section, a performance comparison between our method and the Shuster method is presented. We adapted the SciPy library in order to compile the algorithm as described in Section 4. A real data set comprising the orientation of a spinning object with 3284 data points was used to compare the efficiency of the two algorithms. The full implementation and data set can be downloaded from  [9]. First we noted that both methods give the same results: adding the absolute value of the differences between the two methods in a whole data set gives an error of the order of 10 -12. The execution times required in our tests for each sequence (and their ratios) are presented in the Table  1. From this test, it can be clearly seen that the method presented here is about 30 times faster.
Understanding connectivity among these reefs and their resilience will be of critical importance for extending our understanding of this species, particularly in regions that are data poor. Network analysis was then used to integrate seasonal and annual dispersal events among coastal zones with reefs. We selected two network metrics to characterize the network structure, and two operations to evaluate changes in the network, e. g. , the fragmentation potential. We further compared scenarios of reef loss, whose selection criterion could be used as effective conservative strategies.
| The hydrodynamic ocean model The hydrodynamic conditions on the Atlantic and Channel coasts were simulated using the fully validated ocean model MARS3D (Model for Application at Regional Scale; https://mars3d. ifrem er. fr/docs/docum entat ion. html)  (Lazure et al. , 2009; Lazure & Dumas, 2008)  with the configuration MANGA2500. The model domain extends between 41°N and 55°N latitude and 5°E to 16°W longitude. The regular numerical grid has a 2. 5 km horizontal resolution and 40 sigma vertical layers. Open boundaries and initial conditions were provided by the larger-scale model Mercator with a resolution of 0. 0833°. Hourly atmospheric forcing was provided by the Meteo-France ARPEGE HR model with a 0. 1° spatial resolution. River discharge values, including Garonne, Vilaine and Loire rivers, were obtained from time series at daily frequencies provided by the French freshwater office database (http://www.
Even with increased computational efforts due to higher-resolution modelling, a delineation of smaller zones would not have been feasible for our study for the simple reason that we lack the information on reef status and occupation in smaller grid cells. In the most comprehensive S. alveolata database  (Curd et al. , 2020) , all known historical and contemporary records of the species presence and qualitative abundance were also polled into cells of 50 km (see figure at https://www. seanoe. org/data/00610/ 72164/) to provide an overview over the entire distribution domain of this species. Simulations were initialized with particle concentrations of 10 6 m -2 in the bottom layer of the model, assuming that homogenous densities of larvae are released from the seabed. Similar larval concentrations were estimated to be released by reefs in the Channel  (Ayata et al.
75, and three zones with the maximal scale of 1), nodes were randomly removed and bootstrap simulations (125 permutations) performed as per Scenario 1. Saved model outputs (netCDF files) from the ocean and transport model underwent post-processing in R, version 3. 5. 0 (R Core Team, 2020), specifically the particle concentrations in the coastal zones were extracted at the end of each run and connectivity matrices calculated. All network calculations and statistical analyses were undertaken using the igraph package  (Csardi & Nepusz, 2006). Zone mapping was performed using ArcGIS version 10. 6. 1 (ESRI).
0_ Break point_ Tables. pdf)  [9]. The reference strain P. aeruginosa ATCC 27,853 was used as quality control for all testing. Whole-genome sequencing (WGS) was performed for the four isolates with the ultimate goal to investigate the molecular mechanisms underlying such resistance pattern. The entire genome was sequenced using a MiSeq Illumina platform (Illumina, San Diego, CA, USA) using the Nextera sample preparation method with 2 × 150 bp paired end reads. Illumina short reads were assembled using the CLC Genomic Workbench (version 20. 0. 4; CLC Bio, Aarhus, Denmark), and contigs with a minimum contig length of 800 nucleotides (nt) were generated. The resulting assembled sequences were analyzed using ResFinder 4. 1 software (for antimicrobial resistance genes) and MLST 2. 0 software (for Multilocus sequence typing (MLST) analysis on the Center for Genomic Epidemiology server (http:// www.
75 to > 2 μg/ml), cefepime (8 to 48 μg/ml), aztreonam (8 to > 256 μg/ml), tetracycline (2 to > 256 μg/ml), and chloramphenicol (> 256 μg/ml). The MIC of colistin remained unchanged and was found to be 1-2 μg/ml for all those isolates. All isolates possessed identical intrinsic β-lactamase genes (bla OXA-847 [encoding a narrow-spectrum oxacillinase], bla PDC-1 [encoding an AmpC-type cephalosporinase]) but no acquired β-lactamase were identified. All four P. aeruginosa strains were found to belong to sequence-type ST244, one of the most widely distributed clones worldwide. WGS data of the four strains were analyzed compared to the reference strain P. aeruginosa PAO1 (GenBank no. AE004091. 2). Considering that no acquired β-lactamases were found in any of those isolates, efforts were directed towards examining P.
Discussion The siderophore cephalosporin cefiderocol is one of the most promising commercialized agents against carbapenem-resistant Gram negatives including carbapenemresistant P. aeruginosa. Although still uncommon, reduced susceptibility or resistance to FDC is being reported in that species. In this study, four sequential P. aeruginosa isolates were recovered from a single patient and analyzed using WGS. These isolates showed an increasing trend of resistance to broad-spectrum spectrum cephalosporins, carbapenems, and FDC over the time, without history of FDC exposure. Using the complete genome sequence data of the four P. aeruginosa isolates, all strains were shown to belong to the same sequence type, namely, ST244, a globally disseminated high-risk clone  [10, 11]. The same mutations were detected in various iron transporters (pirA, piuA, piuB, TonB3, fvpB, fecA and fiuA), responsible for the active transport of FDC, for the four strains.
Bulk bitwise operations, i. e. , bitwise operations on large bit vectors, are prevalent in a wide range of important application domains, including databases, graph processing, genome analysis, cryptography, and hyper-dimensional computing. In conventional systems, the performance and energy efficiency of bulk bitwise operations are bottlenecked by data movement between the compute units (e. g. , CPUs and GPUs) and the memory hierarchy. In-flash processing (i. e. , processing data inside NAND flash chips) has a high potential to accelerate bulk bitwise operations by fundamentally reducing data movement through the entire memory hierarchy, especially when the processed data does not fit into main memory. We identify two key limitations of the state-of-the-art inflash processing technique for bulk bitwise operations; (i) it falls short of maximally exploiting the bit-level parallelism of bulk bitwise operations that could be enabled by leveraging the unique cell-array architecture and operating principles of NAND flash memory; (ii) it is unreliable because it is not designed to take into account the highly error-prone nature of NAND flash memory.
Introduction Many data-intensive applications rely on bulk bitwise operations, i. e. , bitwise operations on large bit vectors. As such, it is important for modern computing systems to support highperformance and energy-efficient bulk bitwise operations. In databases and web search, prior works (e. g. ,  [1] [2] [3] [4] [5] [6] [7] [8] [9] ) propose various techniques that heavily use bulk bitwise operations to accelerate queries. Bulk bitwise operations are also prevalent in various other important application domains, 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].
In conventional systems, the performance and energy efficiency of bulk bitwise operations are bottlenecked by data movement between the compute units (e. g. , CPUs or GPUs) and the memory hierarchy  [7, 8, 20, 21, [37] [38] [39]. To perform a bulk bitwise operation, a conventional system must first move every operand to the compute unit and eventually write the results back into the memory hierarchy. Due to the simple nature of bitwise operations, such data movement dominates the execution time and energy consumption in bulk bitwise operations. Processing data inside NAND flash chips, i. e. , in-flash processing (IFP), can fundamentally reduce the data movement that bottlenecks the execution of bulk bitwise operations. IFP is an instance of near-data processing (NDP), a computing paradigm that moves computation closer to where the data resides (e.
g. ,  [7-9, 20, 21, 37, 40-54] ). When processing large amounts of data that do not fit in main memory, IFP significantly reduces data movement across the entire memory hierarchy by performing computation within the underlying storage media (i. e. , NAND flash chips) and transferring only the result (when needed, to main memory and CPUs/GPUs). As we discuss in Section 3, IFP can significantly outperform in-storage processing (ISP) approaches that leverage hardware accelerators inside the NAND flash-based solid-state drive (SSD) (e. g. ,  [49-52, 55, 56] ), by reducing data movement to/from NAND flash chips. To our knowledge, only one recent work, ParaBit  [21] , proposes an in-flash processing technique for bulk bitwise operations. 1 We identify that ParaBit has two major limitations. First, ParaBit falls greatly short of exploiting the full potential of NAND flash memory to significantly improve the perfor- 1 There are many prior works (e.
g. ,  [57] [58] [59] [60] [61] [62] [63] ) that leverage analog current sensing to perform accumulative computation (e. g. , multiply-accumulate operation) inside NAND flash chips. However, these proposals 1) use NAND flash memory solely as an accelerator but not as a storage medium, and 2) require significant changes (e. g. , adding a precise analog-to-digital converter to each bitline) to commodity NAND flash chips, which increases cost. See Section 9 for more detail. mance and energy efficiency of bulk bitwise operations. To perform bulk bitwise operations for more than two operands (e. g. , A • B • C), which frequently happens in many data-intensive applications such as data analytics  [7, [14] [15] [16] [17] , databases  [10] [11] [12] [13]  and graph processing  [9, [19] [20] [21] , ParaBit must serially perform multiple two-operand bitwise operations (e.