dataesr/scientific-paragraphs-categorization huggingface dataset:
Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of distinct siliceous networks can be generated by computer-aided searches, no new hypothetical framework has yet been synthesized. The needle-in-a-haystack problem of finding promising candidates among large databases of predicted structures has intrigued materials scientists for decades; most work to date on the zeolite problem has been limited to intuitive structural descriptors. Here, we tackle this problem through a rigorous data science scheme-the "zeolite sorting hat"-that exploits interatomic correlations to produce a 95% real versus theoretical zeolites classification accuracy. The hypothetical frameworks that are grouped together with known zeolites are promising candidates for synthesis, that can be further ranked by estimating their thermodynamic stability.
I. INTRODUCTION Zeolites are nanoporous crystalline materials with exceptionally high thermal and hydrothermal stabilities, making them excellent candidates for a range of present and future technologies based on shape selectivity. Because of their controlled nanoporosity and acidic properties, zeolites find application in myriad industrially-relevant processes, predominantly in separation and catalysis. [1]  To accelerate zeolite discovery, databases of hypothetical zeolites have been created  [2] [3] [4] [5]  containing millions of new framework structures. Even though these databases have been successfully screened identifying materials with desirable properties  [6] [7] [8] [9] , but to date, none of them has been synthesized in the lab, a phenomenon referred to as the "zeolite conundrum.
II. RESULTS The scale of the zeolite conundrum can be appreciated by comparing the number of hypothetical frameworks with that of "real" zeolites. Different studies have suggested over 2,600,000 distinct topologies  [5] , and even the subset we consider here, which only contains particularly stable, fully connected frameworks selected among a larger pool of candidates, contains more than 300,000 all-silica structures (Ref. 5 , by Deem and coworkers, henceforth denoted as DEEM). In contrast, only 255 framework topologies have been collected in the International Zeolite Association database (henceforth denoted IZA), which can be realized in different compositional variations. To ensure that our comparisons are made on an equal footing, we perform our study on all-silica models. The great imbalance between known and hypothetical frameworks calls for a balancing act when applying data-driven analyses: models and structural descriptors must be flexible and sensitive enough to detect structural differences among all of the DEEM frameworks, but sufficiently robust and concise to extract useful information from a few hundred IZA entries without overfitting the smaller dataset.
To this end, we describe framework structures using the Smooth Overlap of Atomic Postions (SOAP) method  [23] , which allows systematic convergence of structural information by increasing the SOAP length scale and the order of atomic correlations (distances, angles, dihedrals,. [24, 25] ). In previous work, we proved this convergence by applying SOAP to machine-learn framework density and lattice energy of DEEM frameworks  [19]. The heuristic fingerprints that have been used previously to distinguish real and hypothetical zeolites do not allow such convergence, and thus are almost certainly incomplete. Here we find that the DEEM-trained models accurately predict the same properties for IZA frameworks (see Table  S1 , energy error below 0. 20 kJ/mol-Si). The accuracy of DEEM-trained predictions on IZA indicates substantial structural overlap between the two datasets -underscoring the significant challenge of telling them apart.
e. , the chemical composition of the first entry listed in the IZA database for each topology. Our parsing method follows from the premise that the composition of the first instance of a given zeolite topology points to a convenient synthetic approach, that can also be applied to realize a structurally similar DEEM framework. tions involve IZA2, a house that contains a broad range of compositions from high-silica alumino-silicates to low-silicon silico-aluminophosphates, justifying the overlaps with IZA1 and IZA3 houses. It is intriguing to note that most of the IZA being misclassified as DEEM belong to the no-silicon IZA3 house, while the all-SiO 2 IZA1 entries are never mistaken for a hypothetical framework. The success of this four-fold sort opens the door to recommending synthesis compositions for IZA-like DEEM structures.
The decision functions are then obtained by summing the values of these weight functions over all pairs and triplets of atoms in a structure. For a purely radial model, the decision process can then be interpreted as the incremental construction of a "decision trace" d(r) (see Methods) that, for r → ∞, gives the value used for classification. The length scales at which d(r) undergoes large changes are those that control the classification. We thus conclude that second-neighbor Si-O distances are the most clearly discernable structural feature that differentiate IZA and DEEM frameworks. More subtle structural correlations involving angular information and third-neighbor distances are needed to achieve classification accuracies above 90%, making an automatic data analysis preferable to ad hoc heuristics.
In conclusion, synthesizing new zeolites is both an exciting intellectual challenge, and a technological quest with high potential rewards. The huge databases of computationallyproposed zeolites stand in striking contrast to the 255 known framework topologies (IZA), configuring a highly asymmetric problem that hinders the solution of this zeolite conundrum by brute-force applications of data science. We developed and applied a multi-pronged strategy for sifting through the hypothetical zeolites in search of the most promising candidates for synthesis. This "Zeolite Sorting Hat" tackles data scarcity by using flexible and unbiased SOAP structural descriptors as inputs, and relatively simple and robust linear classification As it is the cases for many synthetic tasks, making zeolites is a form of art, guided by experience, chemical intuition and serendipity.
III. METHODS Zeolite structures were obtained as cif files from the IZA (http://www. iza-structure. org/databases/) and the hypothetical zeolites websites (http://www. hypotheticalzeolites. net/DATABASE/DEEM/DEEM_PCOD/index. php). IZA zeolites energies were calculated with GULP  [34]  following the procedure previously considered for the DEEM zeolites in Pophale et al. [5]  with a modified version of the Sanders-Leslie-Catlow (SLC) potential  [31]  to overcome the negative energy divergence due to the Buckingham contributions for r → 0 (see SI). Since DEEM frameworks are already relaxed with the SLC forcefield, we computed their energies though a relaxation of the atom shells only, keeping the cell and cores fixed. Our computed energies were in good agreement with those obtained by Deem and coworkers, except for five frameworks which were thus discarded from all of our analyses.
PCovR models were constructed using the same feature data and decision values as the corresponding SVM model. The optimization target was the PCovR loss (sum of regression and projection losses) based on a three-component latent space. The convex hull was constructed in the space defined by the framework energies and the first two PCovR components for the PCovR model based on the full 6. 0 Å SOAP power spectrum feature vectors and the corresponding four-class decision functions for the classification exercise on these same feature vectors. The train-test split of the databases was slightly modified based on the ultimate goal of the machine learning exercise (see details in the SI). To account for the imbalance in the class populations, we employed class-specific misclassification penalties for the SVM models: the penalty for a given class is weighted inversely proportional to the true class proportion in the train set.
The Altiplano-Puna Volcanic Complex (APVC) of the Central Andes is an arid region with extensive volcanism, possessing various geological features comparable to those of other solar system objects. The unique features of the APVC, e. g. , hydrothermal fields and evaporite salars, have been used as planetary analogs before, but the complexity of the APVC presents a wealth of opportunities for more analog studies that have not been exploited previously. Motivated by the potential of using the APVC as an analog of the volcanic terrains of solar system objects, we mapped the mineralogy and silica content of the APVC up to ~100,000 km 2 in northern Chile based on a combination of remote sensing data resembling those of the Moon and Mars. The band ratio indices of Landsat 8 Operational Land Imager multispectral images and mineral classifications based on spectral hourglass approach using Earth Observing-1 Hyperion hyperspectral images (both in the visible to shortwave infrared wavelengths) were used to map iron-bearing and alteration minerals.
We also used Hyperion imagery to detect feldspar spectral signatures and demonstrated that feldspar minerals can be detected on non-anorthosites, which may influence interpretations of feldspar spectral signatures on Mars. From the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Dataset, we derived the silica percentage of non-evaporite rocks within errors of approximately 2-3 wt. % SiO 2 for those in the 60-70 wt. % range (about 8 wt. % errors for the 50-60 wt. % range). Based on an integrated assessment of the three datasets, we highlighted three regions of particular interest worthy of further field investigation. We also evaluated the benefits and limitations of all three remote sensing methods for mapping key minerals and capturing rock diversity, based on available samples and existing geological maps.
From USGS EarthExplorer, we downloaded ASTER Global Emissivity Dataset (GED; files names available in Table  A1 ). ASTER GED was released by NASA Jet Propulsion Laboratory and is the most extensive and accurate emissivity product of the Earth's land surface to date  [40]. Emissivity is derived from the mean of cloud-free ASTER observations during the 2000-2008 period, and this dataset covers all land surfaces with spatial resolution of 100 m/pixel (coarser resolution data are also available  [40] ). First, we applied the DCS technique to obtain a general idea of the variabilities. Surfaces of water and water-bearing minerals (e. g. , gypsum) in salars appeared in green color with this DCS band combination (R:14 G:12 B:10; Figure  A1d ), and they were masked and removed from further analysis.
We used the technique of Hook et al. [45] , who determined a quantitative relationship between silica percentage and minimum position of reststrahlen bands from the ASTER spectral library  [49, 50] , to determine the silica percentage. Minima of reststrahlen bands were determined by fitting a four-term Gaussian function f(λ) = A 0 exp(-z 2 /2) + A 3  (1)  to emissivity spectra, where λ is the wavelength, A 0 is the amplitude, and A 3 is the offset. In the exponent, z is expressed as z = (λ -A 1 )/A 2 (2) where A 1 and A 2 are the wavelength position of the minimum value and the area enclosed by the function, respectively. For the ASTER dataset without ultramafics (which is appropriate for the APVC) the relationship between weight percent silica (y) and wavelength of minimum of the Gaussian function (A 1 ) is known as y = -16.
Results The main objective of this study was to conduct a mineralogical survey of the nonevaporite surfaces of the APVC, to identify potential areas of interest to use as planetary analogs, and to support geological field expeditions in subsequent works. We determined three regions of interest that showed potential and had at least some availability of Hyperion imagery. In this process, mineral and rock characterizations capitalize on the advantage of synthesizing multiple datasets and techniques that are different and complementary to one another (Section 2). The assessment of the utilization of multiple remote sensing datasets is itself noteworthy, and it is discussed in Section 4. 2.
Assessment of Different Datasets and Mapping Techniques First, the Landsat 8 OLI DCS map, which is not quantitative but is computationally fast and suited for the initial evaluation of a large area, captured the diversity of the volcanic features, namely, the relative differences between the ignimbrites, low-silica volcanic centers, silicic domes, alteration areas, and sedimentary deposits (Figure  A1c ). Compositional variations were also observed within volcanic centers (e. g. , Carcanal volcano versus the neighboring San Pedro and San Pablo volcanoes) or within ignimbrite bodies, which may appear in tones of yellow, orange, green, or purple in the Landsat DCS image.
Assessment of Different Datasets and Mapping Techniques First, the Landsat 8 OLI DCS map, which is not quantitative but is computationally fast and suited for the initial evaluation of a large area, captured the diversity of the volcanic features, namely, the relative differences between the ignimbrites, low-silica volcanic centers, silicic domes, alteration areas, and sedimentary deposits (Figure  A1c ). Compositional variations were also observed within volcanic centers (e. g. , Carcanal volcano versus the neighboring San Pedro and San Pablo volcanoes) or within ignimbrite bodies, which may appear in tones of yellow, orange, green, or purple in the Landsat DCS image (Figure  A1c ). Although the geological map did not distinguish them, possible differences may have arisen from the iron content, iron oxidation level, weathering, and compactness of the sediments.
Another limitation is that the mineral groups were not precise enough, particularly for the claysulfate-mica group. The low spectral resolution of the Landsat 8 OLI does not allow the distinction among different minerals within each group. On the other hand, hyperspectral images are able to capture narrow spectral features and are better suited for more precise mineral characterizations; thus, we utilized EO-1 Hyperion hyperspectral imagery to obtain more definitive mineral characterizations. The spectral hourglass technique was used to derive mineral identification and distribution from the scene, and it distinguished alunite, kaolinite, and montmorillonite spectra within the Landsat clay-sulfate-mica group. This is a complementary approach to the spectral indices used with the Landsat dataset, and it is a means to achieve more precise mineral characterizations and cross-check the results from the Landsat analysis.
In spite of that, precise distinctions were still not easy, since the minerals had overlapping or close spectral features, and spectral features were masked by other minerals. For this reason, we did not attempt to map alunite, kaolinite, and montmorillonite separately but rather distinguished only between alunite and a group of kaolinite and montmorillonite. Band-depth indices can also be computed from Hyperion. This was necessary for the feldspar identification, where the diagnostic spectral feature is weak, and the spectral feature range is not adequately captured by Landsat 8 OLI (or other multispectral datasets). For weak spectral features such as those of feldspars, masking and inadequate absorption depth are especially problematic, even for hyperspectral datasets. We detected high feldspar index values from Dacite Dome D, but the Chanka dome had much lower index values and the felsic ignimbrites did not have significant values at all, even though they were expected to have feldspars.
The spectral signature of feldspars is influenced by multiple parameters, including the mineral assemblage, the mineral's composition, its grain size, and the rock texture  [64, 66, 67]. Spectral unmixing methods could resolve at least some of these problems. Hyperion and other hyperspectral imaging techniques are usually spatially limited, only covering a fraction of Landsat 8 OLI imagery, with considerable spatial gaps among available images. For this reason, Hyperion is better suited to the study of small regions of interest already identified with Landsat or other datasets. The ASTER GED in the TIR was highly complementary to the mineral analysis performed with the Landsat and Hyperion dataset in the VNIR-SWIR. Initially, the ASTER DCS map (Figure  A1d ) qualitatively revealed the relative distribution of high silica content (red), low silica content (blue), and non-silicates (green).
The methodology adopted in this work is commonly used in Earth and planetary remote sensing studies. We first used Landsat and ASTER, which are multispectral instruments and have large spatial coverage but low spectral resolution, to identify smaller regions of interest. Next, the minerals within those smaller regions were further analyzed with Hyperion hyperspectral imagery. Although it is not new, we emphasize that the dataset and mapping approach adopted in this work resemble those available for the Moon and Mars, and thus, our work is transferable to the analysis of the analogous terrains of these planetary bodies. The three satellite imagery datasets of the Earth, Landsat 8 OLI, Terra ASTER, and EO-1 Hyperion, are comparable to the Mars Odyssey (MO) Thermal Emission Imaging System (THEMIS) VNIR bands  [78] , the Mars Reconnaissance Orbiter Compact Reconnaissance Imaging Spectrometer for Mars (CRISM)  [79] , and MO THEMIS TIR bands  [78].
Conclusions The remote sensing data from various instruments (ASTER, Hyperion, Landsat) were used to map the mineral groups of mainly volcanic and secondary volcanic surfaces of the Altiplano-Puna Volcanic Complex, which are sound analogs of these types of surface on the Moon and Mars. Although mineral maps covering 10,000 s to 100,000 km 2 scales of the APVC are some of the fundamental resources used to conduct geological field work and planetary analog studies, they have not been created before. Using a combination of Earth observation instruments with comparable characteristics to lunar and Martian orbital instruments, we characterized, with different techniques, the alteration minerals, iron-bearing minerals, feldspar minerals, and silica percentages of bulk rocks. Three areas of particular interest for planetary analog studies were highlighted in this work, although other areas not mentioned in detail were also noteworthy for further investigations.
The results of our maps were consistent with existing geological maps and a previous study performed locally at the Azufre volcano, and they also showed consistency, to varying degrees, with the geochemical analysis of samples compiled from past studies and measurements performed at CRPG. The benefits and limitations of each dataset and method were evaluated, and we observed that synthetically analyzing remote sensing datasets with different characteristics (e. g. , wavelength range, spatial extent, spectral resolution) leads to more confident qualitative and quantitative interpretations of minerals and rocks over large areas. One immediate impact of this work on the current state of planetary analog studies is the detection of feldspar spectral signatures on non-anorthositic rocks. Our feldspar detections at Chanka dome and Dacite Dome D influenced on-going efforts to refine the spectral detection of feldspar minerals on rocks other than anorthosites through laboratory measurements  [31]  and Mars CRISM hyperspectral imagery  [32].
Acknowledgments: The authors thank the  NASA Hyperion team  for acquiring dedicated targets prior to the instrument decommissioning. We are also thankful to  Mélissa Martinot  and  Pascal Allemand  for fruitful discussions. The authors are also grateful to the  SARM Nancy , and in particular  Delphine Yeghicheyan  and  Christophe Cloquet , for performing sample analyses. This is CRPG contribution N • 2816.
Funding: This research was funded by  CNRS   Momentum grant , the  LUE future leader 'Viscous' , and the  ANR  ' Mars-Spec ' projects attributed to  J. Flahaut. Financial support by  FONDAP Folio 15090013  and  15200001  ( Centro de Excelencia and Geotermia de los Andes)  was attributed to B. Godoy.
Data Availability Statement: The remote sensing data listed in Table A1 are available on USGS EarthExplorer, https://earthexplorer. usgs. gov/, (accessed on 24 January 2022). The geochemistry analysis results are available in the Supplementary Material. The spectra of the samples are available on SSHADE, https://www. sshade. eu/db/mirabelle, (accessed on 24 January 2022).
Introduction A triangulation of a manifold M is a homeomorphism H : |A | → M, where A is a simplicial complex, and |A | is its underlying topological space. If such a homeomorphism exists, we say that A triangulates M. Editor in Charge: Kenneth Clarkson This work has been funded by the European Research Council under the European Union's ERC Grant Agreement number 339025 GUDHI (Algorithmic Foundations of Geometric Understanding in Higher Dimensions). Arijit Ghosh is supported by Ramanujan Fellowship (No. SB/S2/RJN-064/2015). Part of this work was done when Arijit Ghosh was a Researcher at Max-Planck-Institute for Informatics, Germany, supported by the IndoGerman Max Planck Center for Computer Science (IMPECS). Mathijs Wintraecken also received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No.
This approach has the advantage of not requiring any modification on the target programs or the Linux kernel while keeping a low overhead. Two different benchmark suites have been used to validate our algorithms: The NAS parallel benchmark, mainly devoted to computational routines, and the LevelDB database benchmark focused on read-write operations. These benchmarks allow us to illustrate the influence of our proposal in these two important types of codes. Note that those codes are state-of-the-art implementations of the routines, so few improvements could be initially expected. Experiments have been designed and conducted to emulate three different scenarios: a single program running in the system with full resources, an interactive server where multiple programs run concurrently varying the availability of resources, and a queue of tasks where granted resources are limited.
These quantities can be measured through hardware counters since it is well known that reading their information implies very low overheads. In this article, we propose a user space tool that, based on the 3DyRM performance model, implements several algorithms for the migration of threads and memory pages in NUMA systems. Using simple heuristics for keeping overhead low, the different migrations are considered and a score is given according to its likelihood to improve performance. Also, our tool is especially, but not exclusively, focused on multitasking environments, where several tasks are in execution concurrently and there are more chances to improve the performance and throughput of the system. Finally, it is important to note that the proposed migration tool does not require modifying the program or programs under inspection nor the Linux kernel, so any application might take immediate profit from it.