dataesr/scientific-paragraphs-categorization huggingface dataset:
The declaration on Lines 4-7 imports a clone of the generic module SmallCheck, instantiated with the characteristic predicate is_permut and the enumeration functions for permutations. This cloning provides the type verdict and the right instance of the generic function small_check to test properties for all permutations with a given size. For the size n = 6 the test program (on Lines 9-13) uses this instance and an anonymous oracle function working as follows: as required by small_check, its input l is a list of integers. The function to_array from Why3 standard library transforms it into an array a, then reversed in-place by application of the reverse function. Finally the Boolean function b_permut is applied to the resulting array. For efficiency and to get an explicit test result, the test code is executed in OCaml, after extraction of the test program and related modules.
Thanks to some additional lines of OCaml code, the test result is displayed as follows: Test passed. 720 data tested. meaning that the test was successful for the 6! = 720 permutations of size 6. This BET is executed in less than one second, in the environment used for the experimentation described in Section 4, where more efficiency results are provided. The current prototype does not allow to set a time limit for BET, but it can be completed with this feature. The approach is suitable for arrays containing integers in a small interval, as it is the case for permutations here. For larger integer ranges, random generation is preferable.
Certified Library of Enumeration Programs ENUM is a library of certified enumeration programs for BET, freely distributed at https://github. com/alaingiorgetti/enum. 1  Its first releases were composed of C programs specified in ACSL language and verified with Frama-C plugin WP for deductive verification  [10]. This section presents a new part of ENUM, composed of enumeration programs specified and implemented in WhyML. It is an almost complete adaptation in WhyML of the C/ACSL enumeration programs, completed by new generators. Its programs implement algorithms that enumerate combinatorial structures  [2]  and have various applications in combinatorics. Section 3. 1 introduces some expected properties of these generators and their formalization in WhyML. Section 3. 2 presents a simple way to define a generator, by filtering the output of another generator.
Contents of ENUM Library Metrics on the library and its contents are collected in Table  1. The first column assigns a name to each generator. The number of lines of code (resp. WhyML annotations) is recorded in the second (resp. third) column. The fourth (resp. fifth) column gives the number of transformations (resp. lemmas) needed for the proof of the soundness, progress and completeness properties. All of them have been proved automatically with Why3 1. 2. 0 and the SMT solvers Alt-Ergo 2. 2. 0, CVC4 1. 6 and Z3 4. 7. 1, except the completeness property for the generator of permutations, which required an interactive proof of two lemmas with Coq 8. 9. 0. The first block of lines in Table  1  concerns effective enumeration programs. The first four are adaptations of C++ programs proposed in  [2].
The program rgf (for "restricted growth function") enumerates the arrays a of length n such that a[0] = 0 and a[i] ≤ a[i-1]+1 for 1 ≤ i ≤ n-1. sorted generates all arrays from {0,. , n-1} to {0,. , k-1} sorted in increasing order. perm enumerates the permutations on {0,. , n -1}. barray (for "bounded array") (resp. endo) (for "endo-array") enumerates the arrays of length n whose values are in {0,. , k -1} (resp. {0,. , n -1}). fact enumerates the n! factorial arrays  [12]    The second block concerns enumeration programs obtained by filtering (Section 3. 2). We denote by Z ⊂ X an enumeration program of data Z by filtering among more general data X. For instance, sorted ⊂ barray enumerates increasing arrays filtered among bounded arrays. By filtering from barray we get generators for the following data families: arrays sorted in increasing order, injections from {0,.
Execution There are several ways to run an enumeration program: With Why3 as interpreter (command why3 execute), by executing code compiled from OCaml source code extracted from WhyML code, or by compiling and executing C code, either extracted from WhyML code or written by hand. Indeed, Rieu-Helft  [19]  has developed a method to extract in C language a subset of programs written in WhyML. The C code can be compiled with gcc or with the certified C compiler CompCert  [17]. Indeed, when you compile a program with an ordinary compiler like gcc, you have no assurance that the executed code has the same semantics as the source code. In contrast, the CompCert compiler is formally verified, using machine-assisted mathematical proofs, to be exempt from miscompilation issues.
Research Questions We gather experimental data in order to answer the following research questions. In a nutshell, RQ1 is about certification only, RQ2 about efficiency only and RQ3 about how to find a good compromise between both quality criteria. RQ1: What is the most convenient approach to certify the enumeration programs? Since we have two versions, one in C/ACSL and another one in WhyML, we want to compare the effort required to prove their properties with Frama-C/WP and Why3. We quantify this proof effort with the number of lines of specification. These numbers for WhyML version are in Table  1. RQ2: What is the most efficient way to run our programs? The efficiency of our generators is estimated by computing their speed, i. e. , the number of data generated per second, for all the ways to run our programs presented in Section 4.
This convinces us that Why3 is more convenient than Frama-C/WP for the certification of ENUM. To answer RQ2 we compare the speed of data generation of various interpretations or compilations of implementations and extractions in WhyML, OCaml and C of the same enumeration algorithm. We consider an algorithm to enumerate permutations [2, page 243], and assume that speeds would be classified in the same order for other generators. The first column of Table  3  gives the size of the generated permutations. The other columns display the number of millions of data generated per second, for four implementations and execution scenarios. A dash (-) indicates that generation exceeds the 6 hour time limit. The interpretation of WhyML code is the least efficient enumeration method. It is not surprising since the other methods include a compilation, usually more efficient than an interpretation.
Next comes the execution of its extraction in OCaml. For instance, the OCaml program enumerates 5. 58 × 10 6 permutations of size 12 in 1 second. This may be appropriate in some applications, but is well below the speeds of the C programs. Indeed, C is a low-level imperative programming language. It has been designed to provide low-level memory access, which allows it to reduce the memory allocation required and optimize performance, particularly through the use of pointers. Although the extracted C code is behind the handwritten one, its speed is much higher than that of the OCaml code. Its performance allows us to continue our efficiency study only for the C code extracted from the WhyML code. Figure  1  shows data generation speeds for this C code compiled with gcc (without and with -O3 optimization option) and CompCert compilers.
This experiment confirms the claim that code compiled with CompCert is about twice as fast as that compiled by gcc without optimization, and quantifies the claim that it is a bit slower than that compiled by gcc with higher levels of optimization 2  : the code compiled by gcc with its third level of optimization is about 40% faster than the one compiled by CompCert. To answer RQ3 we first draw some conclusions from the former answers to RQ1 and RQ2. Firstly, thanks to its elementary theory of arrays, Why3 makes it possible to prove more challenging properties -such as completeness -than Frama-C and its WP plugin. Moreover, C code automatically extracted from WhyML code is almost as fast as handwritten C code, for a much lower implementation effort. If a higher speed (resp. more confidence) is expected, the C code can be compiled with gcc -O3 (resp.
CompCert). It remains to evaluate the additional effort required to specify and implement in WhyML enumeration programs suitable for C extraction. For the pointeradapted permutation generator, we had to write 49 lines of code (only 7 lines more than for the original program), and 107 lines of specifications, so 21 lines more than the original code. The number of specification lines is mainly related to the fact that we control the memory manually. To download the proofs, we need 56. 31 seconds, 3. 44 times more than the original code. In addition, in the case of this program, completeness is not proved. Other generators (barray and fact) were also adapted for extraction. All properties were proven for these programs, but the specification effort was also greater than for their original codes.
Results Depletion of IRE1α by shRNAs Attenuates 3D Growth of MM Cell Lines. Interrogation of the cancer cell line encyclopedia (CCLE) RNA sequencing (RNAseq) dataset (Broad Institute) demonstrated that MM cell lines express higher messenger RNA (mRNA) levels of IRE1α than all other cancer types (SI Appendix, Fig. S1A ). Further analysis of 12 human MM cell lines by immunoblot (IB) revealed abundant IRE1α protein, often in conjunction with detectable XBP1s protein (Fig. 1A ), suggesting frequent IRE1α-XBP1s pathway activation in MM cells. To investigate the importance of IRE1α for MM cell growth, we first used a doxycycline (Dox)inducible shRNA-based knockdown approach. As expected, Doxdriven anti-IRE1α shRNA induction markedly decreased IRE1α and XBP1s protein levels in KMS-11, OPM-2, and RPMI-8226 MM cells (Fig.
S3A )  (11, 24). We compared the kinase selectivity of these compounds by testing 220 kinases via KinomeScan. Compound 18 displayed significantly better selectivity than 16, with >70% inhibition of only 1 off-target kinase (JNK2), compared with 7 for 16; another published IRE1α kinase inhibitor called KIRA6  (42)  was poorly selective, with >70% attenuation of 64/220 kinases (Fig. 3B  and SI Appendix, Fig. S3  A and B). Of note, whereas compound 18 significantly attenuated spheroid growth of KMS-11 cells on Matrigel, two different JNK-specific inhibitors, JNK-IN-8 and SP600125, had only minor impact (SI Appendix, Fig. S3C ). Furthermore, mRNA expression of JNK2 in RPMI-8226 and OPM-2 cells was relatively low compared with most other cell lines in the CCLE dataset (SI Appendix, Fig. S3D ). Therefore, any off-target inhibition of JNK2 by IRE1α kinase inhibitors is unlikely to be functionally significant in these MM cell lines.
The Lys-Glu salt bridge typically seen in the active state of kinases is absent in this structure, as K599 and E612 are separated by 5. 4 Å. The combined effects of back-pocket binding and salt-bridge disruption may induce critical structural changes throughout the cytoplasmic region that ultimately afford allosteric inhibition of the RNase. This ligand-binding mode is reminiscent of the interaction of 16 with IRE1α (Protein Data Bank [PDB] ID code 4U6R)  (35). However, the 1,4 substituted naphthyl linker of 18 pulls back from the kinase N-lobe by ∼1. 0 Å compared with the 3) were transiently transfected with expression plasmids encoding wild-type IRE1α (WT R IRE1α), a "kinase-dead" D688N mutant of IRE1α (KD R IRE1α), or an "autophosphorylation-deficient" S724A, S726A, S729A triple mutant of IRE1α (PD R IRE1α).
Cells were then incubated in the absence or presence of thapsigargin (Tg, 100 nM) for 3 h and analyzed either by IB for levels of the indicated proteins or by RT-qPCR for mRNA levels of XBP1s. (B) Chemical structures of compounds 18 and 16  (35)  and KIRA6  (42). (C) A close-up view of the crystal structure of 18 in complex with the kinase-RNase portion of IRE1α. The protein is rendered in ribbons with key residues in the ligand-binding pocket shown as sticks. Water molecules near the ligand are shown in red spheres. Black dashed lines indicate hydrogen bonding interactions. (D) Comparison between the cocrystal structures of 18 (colored in wheat and orange versus 16 (PDB ID code 4U6R, colored cyan and blue) bound to IRE1α. The C-terminal end of the αC-helix displays significant conformational changes between the two structures.
Orthometastatic Xenograft Efficacy Studies. For the orthometastatic xenograft model, 1 × 10 6 RPMI-8226-mCherry-Luc cells were injected i. v. via the tail vein of nonirradiated 8-wk-old female NOD/SCID/IL2rγ -/-mice (NSG; The Jackson Laboratory). The animals were imaged weekly under isoflurane anesthesia 5 min after i. p. luciferin injection with 200 μL of 25 mg•mL -1 D-luciferin (Invitrogen) and imaged on a Photon Imager (BioSpace Laboratory). During image acquisition, animals continued to receive anesthesia from a nose-cone delivery system, while their body temperatures were maintained on a thermostatically controlled platform. Photon counts per min per square centimeter of observational area were calculated and compared using M3 Vision software (BioSpace Laboratory). After 6 wk mice were grouped out into the following treatment groups: 1) vehicle control (100 μL total, IP, BID) or 2) compound 18 (30 mg/kg, 100 μL total, IP, BID).
After 14 d, mice were killed by cervical dislocation and bones harvested for fluorescence imaging using a Kodak In-Vivo FX system (Carestream Health Molecular Imaging) and Carestream Molecular Imaging (MI) Software. Excitation and emission wavelengths were fixed at 550 nm and 600 nm, respectively. Fluorescence images were coregistered with X-ray images using the open-source software Image J (https://imagej. nih. gov/ij/). Statistics. All values are represented as arithmetic mean ± SD if not otherwise indicated in the figure legends. Statistical analysis of the results was performed by unpaired, two-tailed t test or ANOVA followed by an appropriate post hoc analysis, including Bonferroni correction to compensate for multiple comparisons. A P value <0. 05 was considered significant. All statistical analyses were performed using GraphPad Prism 6 (GraphPad Software, Inc.
ACKNOWLEDGMENTS. This work was supported in part by an  Irvington Postdoctoral Fellowship  of the  Cancer Research Institute  to D. A. A. and by a  Howard Hughes Collaborative Innovation Award  to P. W. P. W. is an Investigator of the  Howard Hughes Medical Institute. We thank  Rena Wang  and  Xiangnan Du  for help with cell line generation;  Brian Rabinovich  for mCherry lentiviral construct design;  Jing Peng  for help with mouse xenograft studies;  Christophe Langouët-Astrie  and  Shelby Bearrows  for technical assistance analyzing primary myeloma samples; members of the A. A. and P. W. laboratories; and  Marc Shuman ,  Stephen Gould ,  Matthew Wright ,  Shiva Malek ,  Daniel Sutherlin ,  Jessica Sims ,  Wendy Young , and  Ira Mellman  for helpful discussions.
Deep Hi imaging of AGC 203001 The GMRT 21 cm line observations of AGC 203001 were carried out on 23rd and 24th December 2017 for a total onsource time of ∼10 hours with the GMRT software backend. The total bandwidth was configured at 16. 66 MHz divided into 512 channels, thus corresponding to a velocity resolution of ∼7 km/s, and centered at the heliocentric redshift of AGC 203001. The data analysis was carried out using standard tasks in the Astronomical Imaging Processing System (aips ; Wells 1985) package. The initial flagging and calibration was done manually. Then a continuum image was made from the line free channels which was used for selfcalibration. After applying the self-calibration solution to the line visibilites, the continuum emission was subtracted using the task uvsub. The cube was then imaged using the task IMAGR, and residual continuum was subtracted out using the task imlin.
ACKNOWLEDGMENTS We thank the anonymous referee for several insightful comments that have improved both the content and presentation of this paper. OB would like to thank  Nissim Kanekar  for help with the GMRT data analysis. We thank the staff of the GMRT who have made these observations possible. GMRT is run by the  National Centre for Radio Astrophysics of the Tata Institute of Fundamental Research. Based on observations obtained with  MegaPrime/MegaCam , a joint project of CFHT and CEA/DAPNIA, at the  Canada-France-Hawaii Telescope (CFHT)  which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique of France , and the  University of Hawaii. PK's work at RUB is partly supported by  BMBF  project  05A17PC2  for  D-MeerKAT.
We address the following questions: (1) Do eddies in the Arabian Sea exhibit different vertical patterns depending on their geographic area ? (2) What is the average 3D shape of mesoscale eddies in the Arabian Sea ? (3) Which water masses of the Arabian Sea can be detected inside eddies ? In section 2, we present the dataset and the methods. Then, we use Argo floats to study the dynamical and hydrological properties of eddies in the different sub-basins of the Arabian Sea, and to compute a composite 3D structure of eddies in the northern Arabian Sea.
Argo floats Profiles collected by Argo floats in the period 2000-2015 in the region of interest (30 • E to 80 • E and 5 • N to 30 • N) are used to determine the vertical structure of the water column inside and outside mesoscale eddies. Only profiles containing both temperature (T) and salinity (S) data flagged as 'good' were downloaded from the Coriolis website (www. coriolis. eu. org). This dataset consists of 45,978 profiles (i. e T, S and pressure (P) data at a given time) non-uniformly distributed in the region (Fig. 2 (a)). The number of profiles increases with time such that about half of the available profiles  (21, 113) , were collected between 2012 and 2015. Contrary to this interannual trend, there is no seasonal bias in sampling: profiles are uniformly distributed in time for each year, with a median of 4,162 profiles per month for the 2000-2015 period.
Once the data were downloaded from the Argo repository we did additional processing, as follows. Firstly, any profile containing fewer than five measurment or sampling less than 10% of the water column was discarded. These two conditions discarded profiles concentrated near the surface and thus diminish biases in statistics. Secondly, profiles with suspicious variations of salinity and temperature at the bottom were removed. 29,516 profiles representing 64% of the initial dataset remained after these quality controls. Then, the depth (z), the density (ρ), the conservative temperature (CT) and the absolute salinity (SA) at each station were computed from the T, S and P profiles using the Gibbs SeaWater (GSW) Oceanographic Toolbox  (McDougall & Barker, 2011)  which uses the TEOS-10 equation of state for seawater (http://www.
Surface drifters To obtain an in situ estimate of surface velocities in eddies, we use the position and horizontal velocity data from 15-m drogued drifters of the Global Drifter Program (GDP). The dataset consists in 39,247 values of velocities distributed over the regions of interest for the 2000-2015 period. For further information on these data see  Lumpkin & Pazos (2007)  and  Laurindo et al. (2017). The density of drifters is directly related to the surface flow convergence and divergence, with a maximum number of drifters located where velocities are weak and the convergence is large  (L'Hégaret et al. , 2018). In our case, most drifters were located on the Eastern boundary of the Arabian basin and at latitudes lower than 20 • N (Fig. 2 (b)).
Eddy detection from altimetry A regional 1/8 • resolution altimetric product is used to detect mesoscale structures in the Arabian Sea for the 2000-2015 period. This specific altimetric series was processed by Collecte Localisation Satellites (CLS) and features interpolated absolute dynamical topography (ADT) and surface geostrophic velocities on a daily Mercator grid. It combines up-to-date datasets from different available missions (Topex/Poseidon, ERS-2, GFOO, Jason-1, Envisat, Jason-2, Cryosat-2, Altika and HY2A). A snapshot of ADT and surface velocity from this product in the region of interest is presented in Fig. 2(d ). As discussed in previous studies  (Le Traon et al. , 1998; Le Vu et al. , 2018) , nonphysical fluctuations or distortions in this 1/8 • gridded product could be induced by the spatiotemporal heterogeneity of the satellites' tracks.
Methodology of colocalization To characterize the 3D structure of eddies we implemented an algorithm of colocalization allowing to determine if either an Argo float or a surface drifter lies inside or outside an eddy detected by the AMEDA algorithm. The method is here illustrated with Argo profiles, but the algorithm works the same on surface drifters dataset. Every day, we seek all Argo profiles collected. Then, for each eddy detected on this day, we check if an Argo float has surfaced inside the streamline defining the eddy contour. To do so, the eddy contour is decomposed into a 20-vertex polygon. A 'point-in-polygon' algorithm is then used to determine if the float is inside the eddy. If this condition is met, the Argo profile is flagged as 'colocalized'. An example of such colocalization is presented in Fig.
3. We also collect the surface geostrophic velocity at the position of the Argo float from the altimetric dataset. All Argo profiles which have not been colocalized are flagged as 'out of eddies'; they will allow us to compute thermohaline climatologies out of eddies. This method could lead to errors due to the misevaluation of the eddy position at the moment of Argo float sampling, due to the time interpolation of the altimetric product. Thus, we seek Argo floats surfacing within ±12hours around the time of eddy detection by altimetry. The tolerance is set knowing that the typical time for an Argo float to achieve a profile is about 5 hours (2000 m of ascent at ∼ 10 cm s -1 for APEX and SOLO floats) and the transmission time varies between an hour and a dozen of hours (depending on the transmission system).
The average Rossby number of the eddies in this study being weak, R o ∼ 0. 05, no cyclogeostrophic correction is applied  (Ioannou et al. , 2017). R o is calculated using the velocity of the altimetric dataset at the location of the Argo profiles we considered to compute the composite. We also assume that density varies only radially, so that u r = 0. In the obtained sections, the density anomaly vanishes below the positive (resp. negative) anomalies for cyclones (resp. anticyclones). This results from density averaging at depth. We assumed a level of no motion at 1500 m depth (u θ (-1500 m) = 0). This is the maximum common dive depth for the Argo floats considered in the northern Arabian Sea. This assumption, used in previous studies  (Chaigneau et al. , 2011; Keppler et al. , 2018) , may underestimate the velocity since deep motion is assumed nil.
Since we used a 3D method, our estimation of the typical maximal velocity of AE in this region is thus more accurate than if we only considered a surface-only product (e. g an altimetric dataset). CE are surface-intensified, and their maximum velocity is at the surface. Nevertheless, the estimated error of these composites is important, respectively 31% and 56% for the CE and the AE. It indicates that these composites do not represent all eddies in the area, and that mesoscale eddies in the NAS can have significantly different properties than the composites we present here.