dataesr/scientific-paragraphs-categorization huggingface dataset:
6. They allow us to use Iris ML resources when we are verifying C glue code in Iris C. Concretely, the rule ml-to-ffi allows us to turn ℓ ↦ → ML ì V (whenever we own the GC resource GC( )) into the runtime block ↦ → blk[0|mut] ì using Iris's resource updates  [Jung et al. 2018, §5. 4 ]. The runtime block location is tied to the ML location ℓ through a new assertion ℓ ∼ ML , which makes sure that always (uniquely) corresponds to ℓ. To relate the values of the runtime block ì and the reference ì V , the relation "∼ ML " is lifted to values as ì V ∼ ML ì (e. g. , by relating false ∼ ML 0). The rule ffi-to-ml reverses ml-to-ffi: we can take ownership of a runtime block and turn it (back) into ownership of a ML reference. The assertion ℓ ∼ ML appears after the update, because we can use this rule also to expose blocks that were freshly created in C (e.
Modeling garbage collection. In the semantics of ML+C , we need to model the observable actions of the garbage collector (GC): when viewed from the C side, the OCaml GC can move objects in memory, as well as free memory that was previously storing OCaml values. To account for this behavior, our model of garbage collection is built around the following three key principles: First, we decouple the identity of runtime values from their physical address in memory. In the semantics, we distinguish between blocks in the "runtime heap" and their concrete physical addresses. This allows us to disentangle the concept of a "runtime value" (stable across runs of the GC) from its physical representation (which can change across GC runs). This principle was used previously by  Hur and Dreyer [2011] , albeit in proving compiler correctness in the presence of a GC, not in defining a multilanguage semantics.
Unlike well-typed functions in OCaml, the function buf_upd is not safe in general: if it is not used according to its specification, then it can exhibit undefined behavior (e. g. , out-of-bounds accesses and iterator invalidation). Thus, OCaml code that uses the buffer library must carefully respect its specification. The function is_compressible, as we prove by verifying it, satisfies these requirements. Polymorphic equality. As another example, we have verified a polymorphic equality function that (deeply) compares the runtime representation of two OCaml values. What makes this function interesting is that it is not possible to implement it natively in OCaml (or in our case ML ), because there is no way to determine the shape of values (e. g. , whether they are sums or pairs). For example, a ML function designed to compare pairs would get stuck when we attempt to pass it a sum value.
(OCaml exposes an Obj module providing some escape hatches that expose the runtime representation of values, but it is undocumented and breaks type safety so we do not consider it here. ) As such, this example demonstrates that the FFI can be used to add new functionality to ML. Zig-Zag Lists. As another example, we have verified a "zig-zag list"-a linked list implementation whose cons-operation allocates cells on the C heap to store the head and tail of the list (OCaml values), and then exposes this "cons-cell" to OCaml by embedding it into a custom block. To make this work, the fields of the cons-cell are registered with the GC as global roots, ensuring that they are not accidentally deallocated by the GC. We provide C implementations of external functions to work with such lists in OCaml (e.
RELATED WORK Melocoton is, to our knowledge, the first program logic for programs spanning multiple languages with different memory models. (The only other multi-language program logic that we are aware of is Iris-Wasm discussed below. ) Here, we also compare with work that tackles the related problem of compiler verification in a multi-language setting (but is not based on a program logic). Iris-Wasm. Iris-Wasm  [Rao et al. 2023 ] provides an Iris-based program logic for reasoning about WebAssembly and its interaction with its host language, which in Iris-Wasm is a tiny subset of JavaScript. The memory model of the host is a minor extension of WebAssembly's memory model and hence, there is no view reconciliation challenge. In contrast, Melocoton shows how to scale verification to more complex FFIs that require integrating program logics with very different memory models and how to deal with the problems that arise such as view reconciliation ( §2.
This approach shifts reasoning to the target language. For example, they relate values from different languages using target-level conversion functions instead of Melocoton's source-level relations like V ∼ ML and ∼ C w. Verified compilers. Mates et al. [2019]  (building on the work of  Patterson et al. [2017] ) verify a compiler from a stateful language with closures to one without closures in a syntactic multilanguage  [Matthews and Findler 2007; Perconti and Ahmed 2014]  using a logical relation for contextual equivalence. Hur and Dreyer [2011]  verify a one-pass compiler from an ML-like language to an assembly-like language, via a cross-language logical relation where garbage collection is axiomatized. Their approach supports linking with manually verified assembly-level code, but only if that code can be proven behaviorally equivalent to some ML-level module.
Compositional CompCert  [Stewart et al. 2015]  and the line of work it inspired  [Gu et al. 2015; Koenig and Shao 2021; Song et al. 2020]  show how to extend the CompCert compiler with cross-language linking. The composition of languages based on external calls in Melocoton is inspired by Compositional CompCert's interaction semantics, but extends it with DimSum-style wrappers  [Sammler et al. 2023]  to handle linking of languages with different memory models. DimSum  [Sammler et al. 2023]  provides a "decentralized" approach for reasoning about multi-language programs via combinators for linking and language translation, which inspired the definition of the operational semantics in Melocoton (see §3). DimSum only considers the interaction of relatively low-level languages, none of which have such a rich FFI and runtime as OCaml.
Furthermore, we model the primitives for registering roots with a more elementary API than the one provided by the OCaml FFI through CAMLlocal, CAMLparam and CAMLreturn. Alternative APIs for rooting have also been proposed by  Munch-Maccagnoni and Scherer [2022] , and we believe that these APIs would be easy to specify on top of our primitives. Furr and Foster [2005]  build a type system for C glue code that uses the OCaml FFI to detect common misuses of the FFI. This type system is proven sound using a formal model of the C-side of the FFI. However, because their focus is on finding bugs in C glue code, they do not model the OCaml side of the FFI and do not target verification of mixed OCaml and C code like Melocoton. This also means that they do not handle advanced features such as callbacks and closures.
Aims. Surveys of the Milky Way at various wavelengths have changed our view of star formation in our Galaxy considerably in recent years. In this paper we give an overview of the GLOSTAR survey, a new survey covering large parts (145 square degrees) of the northern Galactic plane using the Karl G. Jansky Very Large Array in the frequency range 4-8 GHz and the Effelsberg 100-m telescope. This provides for the first time a radio survey covering all angular scales down to 1. 5 arcsecond, similar to complementary near-IR and mid-IR galactic plane surveys. We outline the main goals of the survey and give a detailed description of the observations and the data reduction strategy. Methods. In our observations we covered the radio continuum in full polarization, as well as the 6. 7 GHz methanol maser line, the 4.
Combined with the submillimeter and infrared surveys, and our ongoing work in the Bar and Spiral Structure Legacy (BeSSeL) Survey 4  to measure distances by trigonometric parallaxes to most of the dominant star-forming regions in the Galaxy, it offers a nearly complete census of the number, luminosities, and masses of massive star-forming clusters in a large range of evolutionary stages and provides a unique data set with true legacy value for a global perspective on star formation in our Galaxy. In addition to information on high-mass star formation, the GLOSTAR survey allows efficient identification and imaging of supernova remnants, planetary nebulae, and numerous extragalactic background sources.
3 GHz at the VLA site, and allows for an estimation of the spectral index between the lower and higher basebands at 4. 7 and 6. 9 GHz, respectively. Table  1  lists the details of the setup used, together with the expected sensitivities for two scans with a total integration time of 15 s for each pointing. We note that the final sensitivities are expected to be at least a factor of two better, since each field is also covered by six neighboring fields. The continuum data is recorded with 16 spectral windows with 64 channels each, resulting in a bandwidth of 2 MHz for each channel. We use a correlator dump time of 2 s for all configurations. The high spectral and time resolution avoids any significant time and bandwidth smearing even when imaging the full primary beam. This setup results in a total data rate of 17.
Continuum data The continuum data were calibrated with the Obit package  (Cotton 2008 ) using instrumental calibration signals and observations of celestial sources. The primary flux density and polarization calibrator was 3C 286 using the flux density scale of  Perley & Butler (2013a)  and the polarization of  Perley & Butler (2013b)  After the first calibration pass the calibration tables were deleted, the flag table was kept, and the calibration was repeated. This ensures that the calibration used well-edited data. 10. Cross-hand delay calibration. The polarized calibrator, 3C 286, was used to determine cross-hand delay residuals. 11. Instrumental polarization calibration. The polarization state of the secondary calibrator and the instrumental polarization were determined using observations over a range of parallactic angle.
Continuum data imaging The imaging of the continuum data was done with the wideband, wide-field imaging task MFImage in the software package Obit  (Cotton 2008). Instead of a joint deconvolution of all pointings into a single image, we image each pointing individually and then combine the images into one large mosaic. For each pointing, we imaged a field of view of 8. 4 , which corresponds to the full primary beam of the lower continuum band. Since often there are sources outside the primary beam, which are bright enough to affect the data, we also use outlier fields at the location of known sources. We place an outlier field at the location of sources that are within 24 of the pointing center and have a peak intensity of more than 10 mJy beam -1. It is possible to use the NVSS catalog  (Condon et al.
1998 ) to determine the locations of outlier fields. However, since the NVSS was observed at a lower frequency of 1. 4 GHz, there might be sources that are weak in the NVSS, but much stronger at our frequencies. Hence, we first did a very shallow imaging run of the entire survey using outlier fields from the NVSS. These images were then used to generate a new catalog of bright sources at 6 GHz that was subsequently used for the outlier fields in the final imaging. Using wide-band data (bandwidth synthesis) with adequate spectral resolution provides improved coverage of the u-v plane giving a higher dynamic range as long as the varying source brightness and instrumental response with frequency are adequately accounted for. Wide-band imaging in MFImage is accomplished by dividing the observed total band-pass into frequency bins that are narrow enough not to cause significant problems, which in this case correspond to a fractional bandwidth of 5%.
D configuration The complex and bright emission in the Galactic plane on various different scales is a challenge, in particular for snapshot observations. However, the use of two observations separated by a few hours and the large bandwidth of the observations greatly improve the uv-coverage, even if there are only ∼15 s of data per pointing. Since the D configuration observations are sensitive to scales up to 4 , we used multi-scale cleaning with three different scales: a delta function and scales of 20 and 40 times the cell size of the images, respectively. The cell sizes of the individual images were determined from the uv-coverage of the individual pointings and were typically about 2. Cleaning was done down to a threshold of 0. 25 mJy beam -1 and a maximum of 2000 iterations. The individual pointings were all imaged using their intrinsic beam corresponding to the uv-coverage and weighting used.
25 mJy beam -1 , and the cell sizes in the images were 0. 2. The individual pointings were again imaged using their intrinsic beam corresponding to the uv-coverage and weighting used. Hence, the beam sizes are slightly different for each pointing. Since the images of the B-configuration data have about 100 times more pixels than the D-configuration images, it was not practical to combine all the pointings of the pilot region into a single mosaic. As a compromise, we combined pointings into eight 2 • ×1 • strips across the Galactic plane (roughly corresponding to the individual observations, but also including pointings from the edges from the neighboring observations). Before combining the pointings they were convolved to a circular beam of 1. 5.
Dirty image Once the calibration, RFI removal, and continuum subtraction is complete, we move onto the imaging and source finding stage. This is done using the CASA software; one of the main reasons it is preferred over other software is due to its mosaicking task that can do joint deconvolutions of different fields. A85, page 6 of 18 We first produce what we call a "dirty" image of the D-configuration data, which is essentially the brightness distribution convolved with what is called the synthesized-dirty beam or point spread function (PSF). This image uses only the sampled visibilities and cannot recreate the entire sky brightness distribution without reconstruction techniques. These techniques are implemented via the CLEAN algorithm. Ideally, we would run the CLEAN algorithm over the entire data cube; however, for a 1 • × 1 • cube spanning at least 1000 channels, this can take around 3 weeks to complete.
These iteration values were found to optimize the automatic cleaning for many sources to maximize image fidelity while avoiding overcleaning. The weighting parameter was set to uniform since it gives a better resolution, thus giving better positional accuracy of the source. Additionally, B configuration images were also produced for the methanol maser data. With a higher angular resolution of ∼1 , to image and CLEAN an area of similar size to the D-configuration data would take significantly more time. However, thanks to the benefit of first studying the D-configuration data, we made smaller images (0. 1 • × 0. 1 • ) using the B-configuration data centered on detected features from the D-configuration data, thereby efficiently using our available computing resources. We used a pixel size of 0.
Data reduction was carried out with the NOD3 software package  (Müller et al. 2017 ). Strong RFI is present across the observed frequencies. Thanks to the newly installed SPECPOL, we were able to select specific frequencies while filtering out RFI-affected channels. This resulted in two continuum images centered at 4. 8 GHz and 7. 0 GHz, respectively. The remaining Notes. (a) Native velocity channel width. The actual spectral resolution corresponds to twice the channel width as the data is smoothed in the process of the LSR velocity correction. (b) Channel width and FWHM of the gridded data cubes. (c) RFI/Artifact contamination of the pilot region data without any removal: "•" indicates medium contamination, possibly removable by excision techniques, while "••" indicates ubiquitous, strong contamination.
We used a filtering beam size of 30 and 50 iterations to decompose the difference map until the compact and extended emission are well separated (see Figs. 3d-e ). Then the extended component of the difference map (Fig. 3d ) was used to correct the zero-level shift in the original Effelsberg 4. 8 GHz continuum data, which leads to the restored data set (see Fig. 3f ). Since there is no available public data that can be used to correct the zero-level shift at 7 GHz, we derived the extended emission at 7 GHz using the data from the Effelsberg 11 cm survey  (Reich et al. 1990 ) and the Urumqi 6 cm survey  (Sun et al. 2007 (Sun et al. , 2011)). These data were convolved to an angular resolution of 15 in order to derive the spectral index of the extended emission. The 7 GHz extended emission was then calculated by interpolation with the derived spectral index.
Combination of VLA and Effelsberg data Combining the Effelsberg data and the VLA D-configuration continuum data is not straightforward, due to the different frequency coverages. While the VLA observations cover two 1 GHz bands, the Effelsberg observations include the full range from 4 to 8 GHz. Furthermore, the RFI situation at the two locations is very different. Hence, it is not possible to compute images averaged over frequency and then combine the two averaged images. Instead, we used the two images obtained from the Effelsberg data with frequency ranges of 4-6 GHz and 6-8 GHz and average frequencies of 4. 8 GHz and 7 GHz, respectively. With the multi-frequency data from the VLA data, we generated two images corresponding to the same frequencies. Then we used the task Feather in Obit  (Cotton 2017)  to generate two combined images of VLA and Effelsberg at low and high frequencies.
Finally, these two images were combined pixel-by-pixel to create the final image and a spectral index map. The combination of the spectral line data from the VLA and Effelsberg will be done in the future. Comparing the flux scales between the Effelsberg and VLA observations is difficult due to the different spatial resolutions. The Effelsberg images obviously pick up more extended emission, which is filtered out by the VLA. However, the Effelsberg data and the Urumqi 6 cm data agree to within 2%, and the flux calibration of VLA observations is typically also good to at least 5%. Medina et al. (2019)  found good agreement between the GLOSTAR and CORNISH/MAGPIS fluxes. To compare the scales, we smoothed the VLA image at 4. 8 GHz to the beam size of the Effelsberg observations and then fitted Gaussians to several sources in the entire region that are relatively isolated (although there is always some diffuse emission in the Effelsberg images).
Acknowledgements. We thank the anonymous referee for providing useful suggestions and comments, which helped to improve the manuscript. This research made use of Astropy (http://www. astropy. org), a communitydeveloped core Python package for Astronomy (Astropy Collaboration 2013, 2018), and related packages such as Spectral-Cube and Reproject, as well as of APLpy (Robitaille & Bressert 2012), an open-source plotting package for Python. Early work on the project described here was supported by the  European Research Council (ERC)  via the  ERC Advanced Investigator Grant   247078  ( GLOSTAR :  A Global View of Star Formation in the Milky Way ) awarded to K. M. M, and a  Marie Curie International Outgoing Fellowship  (No. 275596 ) awarded to A. B. , while H. B. acknowledges support from the  Horizon 2020 Framework Programme  via the  ERC Consolidator  Grant  CSF-648505.
Introduction The growing availability of large databases of protein sequence and protein structure  [1, 2]  is opening the way to data-driven modeling and design of proteins. When a sufficient number of sequences are available for a protein family, i. e. , proteins that share similar sequences, structure and functionality, it becomes possible to learn meaningful statistical features of the family of sequences. These include the position and nature of conserved residues, but also the co-variation of residues that typically occurs on evolutionary timescales when amino acids are in contact in the protein structure. Statistical models trained on multiple sequence alignments of protein families have therefore proven useful in the prediction of protein structure  [3] , the prediction of the effect of mutations  [4] , the clustering of proteins, and the design of new protein variants  [5, 6].
From the classification perspective, the RBM loss will act as a smart regularization, with unlabeled sequences structuring the RBM representations. We evaluate the SSL-RBM method by modeling the Cas9 PID (PF16595 PFAM family, 1071 sequences). As labels, we used a dataset of PAM recognition motifs predicted for 154 PIDs using genomics data  [41]. We then evaluated the predictive capabilities of our model on a held-out validation set. We used MMseqs2 to cluster the sequences with a threshold of 90% similarity, and randomly extracted a set of clusters corresponding to *15% of the sequences as our validation set. We trained 250 models with γ ranging from 10 -2 to 10 2. For labelled data, each of the first 5 positions in the PAM motif was modelled independently by using as a target a binary vector encoding the recognized motifs (one unit for each nucleotide on each position, see methods in section 4.
Sampling variants under constraint: Constrained Langevin Dynamics in the RBM representation space RBMs are powerful models that create an interesting representation space in which proteins with similar properties, structure, sequence, or functionality cluster together  [10]. They are also relatively light models, which makes them well suited to model small datasets. Sequence models of higher complexity are easily limited by the number of available sequences  [11]. Besides, physically grounded modeling frameworks can also provide useful information (protein stability, binding affinity, etc. ) without the need for large datasets. We explore here a novel strategy based on a Langevin dynamics process that enables direct sampling according to external inputs provided by a physics-grounded model (FoldX), while at the same time providing control over the region of the latent space being explored.
A combination of sequence and structural information best predicts sequence activity. While our RBM score is on its own a good predictor of Cas9 activity, we see that the FoldX score provides complementary information and that the combination of both features can be advantageously used to identify active Cas9 variants. We further attempted to compare the information obtained by FoldX with the more recent protein structure prediction tool AlphaFold2  [21]. For each experimentally tested variant, we predicted a model of the structure (using ColabFold  [44] ). This model was then aligned with the AlphaFold2-predicted model for the wild-type protein using the TMscore program  [45]. From this alignment, we derived the root-mean-square deviation (RMSD) between the models. RMSD measures the root of the mean squared distance between the backbone atoms of the two protein models.
We also collected from AlphaFold2 the average pLDDT score for each model, pLDDT is a per-residue confidence score indicating the reliability of the predicted position for each amino acid in the protein structure, which in the past has been shown to correlate well with variant activity. This score can be found in supplementary (Fig B in S3 File). We found that the RMSD (AUC = 0. 810) performed better than the pLDDT (AUC = 0. 668) to predict the activity of experimentally measured variants. Last, we also compared the experimental data with the classifier score for predicting how likely a PID sequence is to recognize a NGG motif. On Fig  8a , we show the experimental activity of the enzymes given their structural scores, RBM score and classifier score. We also computed the Area Under the ROC Curve (AUROC) for the detection of active samples.