dataesr/scientific-paragraphs-categorization huggingface dataset:
We design the Stencil dialect to represent stencil programs consisting of stencil operations connected between them and with input/output data structures through data flow, with optional control flow (Section 4. 5), following the multi-level IR rewriting principles defined in Section 2. Our dialect is explicitly decomposed (P3) into the high level, where we model the data flow between operators, and the low level, where we model the parallel execution of individual operators. The former enables flow rerouting transformations where a stencil operator can be seen as a unit (as opposed to lower-level IRs where a stencil is a collection of arithmetic instructions), while the latter supports parallelism exploitation (P1). The level separation also participates in progressive lowering (P2). The dialect is not designed as a user-facing DSL (cf.
Stencil Programs A stencil program executes a sequence of dependent stencil operators. It loads the data from the input arrays, implements the stencil operators inline, and stores the results to the output arrays. The SSA def-use graph of the program thus specifies the high-level data flow between the stencil operators (P2). Having both the high-level data flow and the inlined stencil operators in a single function facilitates code transformations across multiple stencil operators, eliminating any need for complex interprocedural analysis (P1). Three additional operations enable the program definition. The stencil. assert operation specifies the index range for an input or output array. A valid stencil program needs to define the index range for all input and output arrays. The stencil. load operation returns a temporary value that contains all input array elements accessed by dependent stencils.
It is an industry-grade compiler that lowers a domain-specific data flow language to target specific code. While its data flow language is a good fit for plain stencil computations, it is not designed to express loop-carried dependencies as they appear in weather and climate (cf. Section 4. 5). In contrast to our compiler and the other domain-specific solutions, Halide performs aggressive floating-point optimizations that affect the precision of the results (e. g. , fused multiply-add, approximate divisions, and operation reordering). While these limitations prevent a broad adoption in weather and climate, we still compare to Halide, since it is a widely accepted reference for stencil compilers. Figure  15  compares for all benchmarks the best-performing variant generated by the Open Earth Compiler to their Stella (COSMO) and Dawn (FV3) counterparts as well as to Halide variants.
Lowering User-facing Code to the Stencil Dialect We design our Stencil dialect as a target for user-facing domain-specific languages. To study the feasibility of such a lowering, we integrate our compiler with GridTools for Python (GT4Py)  [5] , an embedded domain-specific language for weather and climate. In Figure  16 , we show a GT4Py version of the example stencil program introduced in Section 4. 1. GT4Py lowers the user code to an internal IR, consisting of a compute domain, data field descriptors, and a set of computations in the form of an abstract syntax tree (AST). We traverse the AST to emit MLIR, compile the resulting MLIR program to a binary, and produce Python bindings to link the generated binary to the calling program. The functional lowering demonstrates our compiler's applicability in the context of an end-toend solution for the weather and climate domain.
Lift  [25]  has also been shown effective for stencil codes. This diversity demonstrates the importance of a shared compiler infrastructure. Multiple projects work on solutions for weather and climate. The CLIMA  [1]  effort develops a novel earth system model using the Julia language. The LFRic  [8]  climate modeling system relies on the Python-based PSyclone compiler. The Dawn  [42]  compiler lowers a high-level IR that has sequential semantics. Stella  [24]  and GridTools  [4]  use C++ template metaprogramming to support CPU and GPU systems. CLAW  [14]  and Hybrid Fortran  [39]  extend Fortran to achieve performance portability. Despite their heterogeneity, all of them could benefit from a shared compiler infrastructure. Several frameworks support the development of domain-specific compilers.
AnyDSL  [32]  supports partial evaluation using minimal annotations in the Impala front end language. Lightweight modular staging  [49]  is a technique that uses Scala's type system to transform codes before their execution. It forms the basis of the Delite  [53]  compiler framework. Lua script similarly supports staging via the Terra  [15]  low-level language. Lift  [52]  finally combines a functional language and rewrite rules to generate performance portable code. MLIR  [30]  is the only full-fledged compiler infrastructure among these contenders, not limited in terms of optimizations, and not tied to a particular front-end language. Stencil optimizations for GPU targets are a well-researched topic. Tiling  [18, 19, 27, 33, 34, 40]  and fusion  [22, 48, 57]  are the core optimizations for bandwidth-limited low-order stencils as they appear in weather and climate.
CONCLUSION We introduced multi-level IR rewriting, an approach to build reusable components for domainspecific compilers. This approach is illustrated through the design and implementation of the Open Earth Compiler, which provides a high-performance compilation flow for weather and climate modeling. We demonstrated that, thanks to multi-level IR rewriting, a small yet self-consistent set of high-level operations specifically designed for stencil computations is sufficient to achieve better performance than state-of-the-art DSL compilers. Contrary to the latter, the Open Earth Compiler relies on existing and new reusable compiler abstractions, including the GPU kernel abstraction we introduced, by decoupling domain-specific and target-specific code transformations. Our evaluation of 11 stencil programs relevant to existing climate models, COSMO and FV3, demonstrates that the Open Earth Compiler, compared to state-of-the-art solutions for weather and climate, generates up to 3.
, 2020; Stat et al. , 2017) , which is proving to be effective in marine environments  (Boulanger et al. , 2021; Holman et al. , 2021; Juhel et al. , 2020). eDNA-based methods rely on the detection of DNA fragments from various sources including feces, shed skin cells, organelles, or extruded waste of animals, which become suspended in seawater  (Collins et al. , 2018; Dejean et al. , 2012; Harrison et al. , 2019). Using filtered water and molecular analyses, eDNA metabarcoding can be used to estimate biodiversity across different taxonomic levels without isolating any target organisms  (Holman et al. , 2021; Valentini et al. , 2016)  and often without exhaustive genetic reference databases  (Flynn et al. , 2015; Juhel et al. , 2020; Marques et al. , 2020 Marques et al. , , 2021)). eDNA metabarcoding overcomes limitations of common sampling methods by targeting complete species assemblages, detecting rare  (Rees et al.
For example, 92 × 2 L seawater samples accurately predict (R 2 = 0. 92) the distribution of species richness for different fish families  (Juhel et al. , 2020). Spatial diversity gradients have been recovered from only 3 × 0. 5 L water samples in temperate  (Thomsen et al. , 2012)  and tropical systems  (West et al. , 2020). However,  West et al. (2020)  report that more replicates were necessary to avoid false negatives and better sample diversity in a given site (>8). Budget and time limitations constrain the number of sampling replicates available  (Ficetola et al. , 2015) which require optimization to take full advantage of eDNA-based surveys. Here, we compared biodiversity of replicated eDNA samples in terms of Molecular Operational Taxonomic Units (MOTUs) since genetic reference databases have many gaps for tropical fishes  (Marques et al.
To control for any potential biases linked to the differences in sequencing platforms, the samples were titrated before library preparation to achieve a theoretical sequencing depth of 1,000,000 per sample in each library and sequencing platform. Library preparation and sequencing were performed at Fasteris facilities. Fifteen negative extraction controls and six negative PCR controls (ultrapure water, 12 replicates per PCR control) were amplified per primer pair and sequenced in parallel to the samples to monitor possible contaminants. To provide accurate diversity estimation in the absence of a complete genetic reference database  (Marques et al. , 2021) , we used sequence clustering and stringent cleaning thresholds  (Marques et al. , 2020). This procedure has been validated in  Marques et al.
| MOTU compositional dissimilarity To understand the variability in MOTUs recovered between filtration replicates, we quantified the compositional similarity of MOTUs. We estimated the pairwise Jaccard's dissimilarity index (β jac ) between filtration replicates per transect using the R package vegan  (Oksanen et al. , 2019). The Jaccard index ranges from 0 (species composition between the replicates is identical, that is, complete similarity) to 1 (no species in common between the replicates, i. e. , complete dissimilarity). We partitioned the Jaccard index into turnover (β jtu ) and nestedness (β jne ) components using the R package betapart  (Baselga & Orme, 2012). Nestedness quantifies the extent to which replicates are subsets of each other. Turnover indicates the amount of species replacement among replicates, that is, the substitution of species in one replicate by different species in the other one  (Baselga & Orme, 2012; Legendre & De Cáceres, 2013).
| Local-scale MOTU accumulation curves To analyze the local-scale richness accumulation, we repeated circular transects multiple times in Malpelo and Santa Marta. We sampled two locations in Santa Marta filtrating 6 replicates at each within 20 hr and one location in Malpelo filtrating 10 replicates within 3 days. This sampling design defined three local MOTU accumulation "experiments. " We produced MOTU richness accumulation curves across filtration replicates from each location using the specaccum function from the R package vegan  (Oksanen et al. , 2019). The "random" method was used to generate 1,000 accumulation curves which were used to fit 14 models using the sar_average function in the R package sars  (Matthews et al. , 2019 ; models fitted: 'powerR', 'emp1', 'loga', 'koba', 'mmf', 'monod', 'negexpo', 'chapman', 'weibull', 'asymp', 'ratio', 'weibull4', 'betap', 'heleg').
, 2017). Our results imply that if sample variability is not accounted for, or survey designs are not well replicated, eDNA-derived time series could over-emphasize compositional turnover by containing many false negatives. This point will be exacerbated where incomplete reference databases recover a small portion of common species and falsely identify low species turnover among samples  (Schenekar et al. , 2020) , even though MOTU turnover identified here may be very high. We found MOTU compositional differences between replicates to be higher in the more speciose Western Indian Ocean (under similar sampling protocols), perhaps due to the larger species pool, further challenging eDNA applications in most diverse tropical systems  (Juhel et al. , 2020). Current protocols should be cautiously applied to biomonitoring if such limitations remain unresolved.
During these simulations, the pressure and temperature were maintained constant by using the Berendsen algorithm, and the SHAKE algorithm was used to restrain the movement of the C-H bonds. The parameters set for acrolein (equilibrium bond lengths and equilibrium angles) were obtained from a DFT geometry optimization with the B3LYP functional  81, 82  and the 6-311G(2d,p) basis set using the Gaussian 09 D. 01 program package. 83 The point charges of the parameters set for acrolein were taken from the Mulliken charges of the DFT optimization. The force constants were taken from the GAFF force field. 84 After the simulation, an ensemble of 100 structures was taken from the equilibrated dynamics uniformly separated by 20 ps for the absorption spectra determinations. Classical MD (Condensed Phase) Sampling.
AIMD (Gas Phase) Sampling. An AIMD simulation of 10 ps was carried out from one snapshot extracted (the lowest in energy) from the classical 2 ns MD simulation in the gas phase. The simulation was done using the Sander program of Amber14 (for the propagator algorithm) calling Gaussian 16  87  (for the computations of the energies). 88 The temperature was set to 300 K using the Langevin thermostat and the pressure to 1 bar. The time step was set to 1 fs. The acrolein atoms were specified to be treated at the B3LYP/6-31G level. Once the simulation was done, 100 snapshots were selected for the spectra determinations (one snapshot every 0. 1 ps from the AIMD simulation). QM/MM MD (Condensed Phase). A series of QM/MM MD simulations of 1 ps were performed from 10 snapshots selected uniformly separated (taken every 200 ps) from the classical 2 ns MD simulations.
As for the AIMD simulations in the gas phase, the Sander program of the Amber14 suite of programs was used coupled with Gaussian 16. The temperature was set to 300 K using the Langevin thermostat and the pressure to 1 bar. The time step set of 1 fs was used. The acrolein molecule was described at the B3LYP/6-31G level and the water molecules treated classically with the TIP3P force field. As the computational demand for QM/MM is higher than for classical MD, the dynamics were shorter. A total of 100 snapshots were selected for the spectra computations (one snapshot every 0. 1 ps from each of the 10 QM/MM MD simulations). WS (Gas Phase) Sampling. In the gas phase, a set of 100 geometries was obtained according to a Wigner distribution  70  for the optimized geometry and using the vibrational harmonic frequencies of the normal modes obtained at the DFT/ B3LYP/6-31G and DFT/B3LYP/6-311G(2d,p) levels, for acrolein, and at the DFT/M06-2X/6-31++G(d,p) level, for the U6OH radical (as benchmarked elsewhere),  68, 89  with Gaussian 09.
WS+MD (Condensed Phase) Sampling. In water solution, first, QM computations at the DFT/B3LYP/6-31G and DFT/ M06-2X/6-31++G(d,p) levels of theory and using the polarizable implicit model (PCM)  24  with Gaussian 09 were performed to obtain the optimized geometry and frequencies of acrolein with two and nine explicit water molecules for acrolein and U6OH • , respectively (forming hydrogen bonds with the carbonyl, amino, and alcohol groups). Second, similarly as in the gas phase, a set of 100 structures was generated according to a Wigner distribution. Third, two independent MD simulations, one with Amber14 and the other with the Materials Studio 2019 package of software,  90  were run in acrolein to compare slightly different implementations; for U6OH • , only the latter was used. With Amber14, an MD simulation of 1 ns of acrolein in a box of water was performed for a fixed geometry of the solute using the DFT equilibrium structure and electrostatic potential fitted (ESP)  91, 92  charges for the atoms of acrolein computed at the same QM level of theory (removing the two explicit water molecules).
TIP3P parameters were used for the water molecules. To fix the geometry of acrolein, we tested two strategies either increasing the masses of the atoms by 10 7 amu or adding a restraint constant of 10 5 kcal mol -1 Å -2 to their Cartesian coordinates, with the same outcome. For Materials Studio 2019, an MD simulation of 1 ns with NVT conditions at 300 K and 1 bar with the Noséthermostat  [93] [94] [95]  and a time step of 1 fs was then performed with the Forcite module of the Materials Studio 2019 package of software  90  and the fixed acrolein or U6OH • geometry optimized with DFT. Cartesian coordinates were constrained here to be fixed, observing the same behavior as in Amber14. The Amorphous Cell module of the program was initially used, which uses MM computations to efficiently produce appropriate conditions for the subsequent dynamics.
The general Dreiding force field 96 with simple-point charges (SPC)  97, 98  was used for water and ESP charges for the acrolein and U6OH •. Fourth, 10 snapshots were selected from the MD simulations of Amber14 in the production region, and the acrolein geometries were replaced randomly with those obtained with the 100 Wigner distribution (10 per each, ratio of 100-in-10). For this replacement, a program was herein developed (lstrans program) with a modified mathematical approach to solve the problem of geometry alignments. 1] [102]  For the MD simulations of Materials Studio, 25 snapshots were taken (in acrolein and U6OH • ) and replaced similarly by the 100 Wigner geometries (four per each, ratio of 100-in-25). The water molecules were kept at the same positions in such procedures. Fifth, for each of the two MD simulations (with Amber14 and Materials Studio 2019), 100 snapshots were extracted, and for each one of the snapshots, classical MD simulations of 10 ps with Amber14 or of 5 ps with Materials Studio were performed while freezing the chromophore Wigner geometry as described above to allow the water molecules to adapt to the new geometry of the acrolein or U6OH •.
Finally, the last snapshot of these shorttime MD simulations was chosen leading to two ensembles of 100 snapshots, one from the Amber14 MD and one from the Materials Studio MD, for determining the respective absorption spectra. See a representative snapshot for the Materials Studio simulations in water solution in Figure  1a. Under the water surface conditions (only for acrolein), a 1 ns MD simulation of a water-gas interface was carried out (without acrolein) with Materials Studio 2019. Next, five snapshots uniformly separated were extracted, and a simulated annealing procedure using Monte Carlo with the Adsorption Locator module of Materials Studio was followed in each one to obtain the five most stable adsorption interactions between acrolein and the surface water molecules. This gave a total of 25 structures that were replaced by the 100 Wigner geometries (four per each, randomly, ratio of 100-in-25) with the lstrans program, similarly to the water solution computations described above.
Five picosecond classical MD simulations with the Materials Studio program were also performed for freezing the acrolein Wigner geometry, giving rise to a total amount of 100 sampled geometries taken from the last snapshot of the MD computations. See a representative snapshot for the Materials Studio simulations at the water-air interface in Figure  1b. Determination of E va 0→l and f 0→l. QM and QM/MM electrostatic embedding computations were carried out with the OpenMolcas program  103  for the gas phase and condensed phases, respectively, with the CASSCF/CASPT2 method for the chromophore and using the same field of charges for the solvent atoms as in the corresponding MD simulations performed in the previous step to generate the nuclear sampling. For acrolein, the active space was composed by six active electrons distributed in five active orbitals (hereafter, CAS-(6,5)), including the valence π, π* and oxygen lone pair (n) orbitals.
An advantage of the Materials Studio program is that it has better optimized tools and a practical and efficient interface, which facilitates the preparation of the system and the application of the WS+MD strategy. Another aspect that is worth mentioning at this point is the anharmonicity description by the distinct sampling approaches. Thermal sampling used in MD and AIMD allow capturing it. Meanwhile, pure WS uses harmonic frequencies and therefore lacks an anharmonic treatment of the vibrations used to the sampling. 108 Nevertheless, anharmonicity becomes relevant for the low frequencies, which are especially significant in condensed phases and are mainly related to the degrees of freedom of the nearby solvent environment. In this sense, WS+MD captures that anharmonicity part, lacking only that related to the low frequencies of the chromophore.
Recently, new sequencing techniques were developed to map G4s in vitro (G4-seq)  [23] , and G4s in vivo (G4 ChIP-seq)  [24]  as regions of few hundred bases. Machine and deep learning methods were proposed to predict such G4 regions, i. e. regions comprising the G4(s) along with flanking sequences. For instance, Quadron-a machine learning approach-was proposed to predict G4s based on sequence features (such as k-mer occurrences) from a region of more than 100 bases, and trained using in vitro G4 regions with G4-seq  [25]. By combining with regular expressions, Quadron could predict if a region was found in vitro, but also the exact location and stability value of G4(s) within the different cell lines from ENCODE (http:// hgdownload. soe. ucsc. edu/goldenPath/hg19/ encodeDCC/), and processed ATAC-seq bigwig files for HaCaT cell line from GSE7668.
We downloaded processed ATAC-seq bigwig files from ICGC cancer cohorts from https://gdc. cancer. gov/ about-data/publications/ATACseq-AWG. We downloaded ChromHMM annotations for ENCODE cell lines from http://hgdownload. cse. ucsc. edu/goldenpath/hg19/encodeDCC/ wgEncodeBroadHmm/. We downloaded breast cancer processed mutation data from ICGC BRCA-US cohort from the portal https://dcc. icgc. org. We downloaded position weight matrices for transcription factor binding sites from the JASPAR 2018 database (http://jaspar. genereg. net). DeepG4 region. Other deep learning approaches had lower resolution for mapping G4s (around 200 bases), but they showed higher prediction performance. PENGUINN, a deep convolutional neural network (CNN), was trained to predict G4 regions in vitro  [26]. Another CNN, G4detector, was also designed to predict G4 regions forming in vitro  [27].
G4 data We downloaded G4 ChIP-seq data for HaCaT, K562 and HEKnp cell lines from Gene Expression Omnibus (GEO) accession numbers GSE76688, GSE99205 and GSE107690  [24, 28, 29]. For every cell line, replicates were mapped to hg19 and merged for peak calling using macs2 with default parameters (https://pypi. org/project/MACS2/). We downloaded G4P ChIP-seq (similar to G4 ChIP-seq) peaks already mapped to hg19 for A549, H1975, 293T and HeLa-S3 cell lines from GEO accession number GSE133379  [30]. We used peaks from both replicates (when there were two available replicates). We downloaded processed G4-seq peaks mapped to hg19 from GEO accession number GSE63874  [23]. We used G4-seq from the sodium (Na) and potassium (K) conditions. No filtering step was performed on peak selection.
Active G4 sequences We defined positive DNA sequences (active G4 region sequences) as forming both in vitro and in vivo G4s as follows. We only kept G4 ChIP-seq peaks overlapping with G4-seq peaks. We then used the 201-bp DNA sequences centered on the G4 ChIP-seq peak summits. As negative (control) sequences, we used sequences randomly drawn from the human genome with sizes, GC content (% GC), and repeat content (tandem repeat number from Tandem Repeat Finder mask from hg19 genome) similar to those of positive DNA sequences using genNullSeqs function from gkmSVM R package (https://cran. r-project. org/web/ packages/gkmSVM).
PWMs were next computed from PFMs assuming background probability of 0. 25 for each DNA letter as done in JASPAR. Because many PWMs from DeepG4 were redundant, we used the motif clustering program matrix-clustering from RSAT suite (http://rsat. sb-roscoff. fr/) with parameters: median, cor = 0. 6, ncor = 0. 6. We used PWM cluster centers as DNA motifs for further analyses.