dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding: This research was funded by  SOLAB  project, grant number  ANR-18-CE32-0009 ;  University of Western Brittany (UBO)  through the " Ecole Doctorale des Sciences de la Mer et du Littoral" (EDSML) ;  IRD (Institut de Recherche pour le Développement)  for ARTS project; and  International Joint Laboratory ECLAIRS.
Function-as-a-Service (FaaS) is a popular programming model for building serverless applications, supported by all major cloud providers and many open-source software frameworks. One of the main challenges for FaaS providers is providing fault tolerance for the deployed applications, that is, providing the ability to mask failures of function invocations from clients. The basic fault tolerance approach in current FaaS platforms is automatically retrying function invocations. Although the retry approach is well suited for transient failures, it incurs delays in recovering from other types of failures, such as node crashes. This paper proposes the integration of a Request Replication mechanism in FaaS platforms and describes how this integration was implemented in Fission, a well-known, open-source platform.
Converting non-idempotent code to become idempotent may be challenging, especially when the code interacts with external, non-idempotent services, but FaaS providers give guidelines for performing this conversion (e. g. ,  [16, 17] ). Moreover, the proposed approach along with the Active-Standby approach maintain functions continuously running and thus using resources even if they receive no traffic. This resource cost is only paid for functions that require high availability. Current commercial FaaS platforms also include features that maintain functions continuously running, such as the provisioned concurrency feature in AWS Lambda  [18]. The motivation for that AWS feature, however, is to reduce start-up latency rather than provide fault-tolerance. This paper brings the following novel contributions: • Study of the integration of an active replication fault tolerance approach (called Request Replication) in a FaaS environment; • Implementation of the approach in the Fission FaaS platform (Fission Request Replication); • Comparative evaluation according to several metrics of Fission Request Replication, Fission Vanilla (native retry approach), and a new version of Fission Active-Standby (enhanced implementation of the fault tolerance approach proposed in  [15] ), using a computational application both in normal functioning and in various failure scenarios, including instance and node failures and network delays; • Insights on how to select a fault tolerance approach according to the application type and user requirements in terms of performance, resource consumption, and availability.
Our work considers fault tolerance mechanisms beyond automatic retry. Fault tolerance in serverless systems can also be realised through using additional services provided by cloud platforms. For instance, using Azure load-balancing and event ingestion services, developers can deploy functions in different regions to allow for disaster recovery. The functions can be deployed using an active-active or an active-passive configuration  [20]. Using serverless orchestration services (such as Google Workflows  [21] , AWS Step Functions  [22] , or Azure Durable Functions  [23] ), developers can define workflows that coordinate functions, automatically retry failed or timed-out invocations, and run custom code to handle different types of errors. For instance, using AWS Step Functions, developers can resume failed workflows from the state at which they failed  [24].
Similar capabilities are provided by open-source orchestration frameworks, such as Apache OpenWhisk Composer  [25]  or Faas-flow for OpenFaaS  [26]. Our work focuses on fault tolerance mechanisms implemented within FaaS platforms without involving external services. Recent research works investigate fault tolerance for stateful serverless applications, composed of multiple functions and interacting with storage services. Sreekanti et al. [27]  introduce a layer that lies between standard FaaS platforms and key-value databases to ensure atomic visibility of storage updates. The proposed system assures fault tolerance by enforcing the read atomic consistency guarantee. Zhang et al. [28]  describe a library and runtime for building transactional, fault-tolerant workflows on existing serverless platforms.
Error rate In this section, we present the error rate of Fission vanilla, AS, and RR with pod and node failures. This metric is a useful measure to evaluate the user perception when using the approaches under different failure scenarios. To measure the rate of requests that fail, we count the number of requests that return an HTTP status with a response code of 5xx (it means the request cannot be fulfilled due to a server error). If a request has been successfully handled, the HTTP status code returned in the response is from the 2xx class of status code. 1 Pod Failure Scenario As seen in Table  2  and Fig. 18 , in the pod failure scenario, Vanilla has a 0. 01% error rate (i. e. , some HTTP requests failed with code 503 service unavailable) which indicates that the router is unable to handle the request due to a temporary overload.
AS can be used when the requirement is for high availability as well as moderate resource usage. In the presented experiments, the three approaches were tested with a stateless application that does not retain state between requests (neither within the instance nor in external storage). In future efforts, we will investigate the behavior of these approaches with other application types, such as stateful FaaS applications. With these applications, state is typically maintained in external storage services, such as NoSQL databases  [28]. Using RR for such applications seems challenging. The reason is that concurrent accesses generate a high load on the storage service and introduce overhead for maintaining consistency. This may result in reduced performance in the case of normal, fault-free operation compared to using AS or retry.
The ultimate aim is to self-consistently assign the weights of the links that connect pair of nodes belonging to contiguous layers, so that the trained network can cope with the specific task for which it was assembled. At the same time, the network should perform equally well when confronted with data non included in the reservoir that defines the training set, although belonging to the same category of pertinence. A first attempt to apply ML technology to a large superconducting database was reported in ref. 9  , where variants of random forest methods  10  have been used to predict the critical temperature T c (restricted to the range T c > 10 K). Importantly, several chemical characteristics-as e. g. , atomic mass, band gap, atomic configuration, melting temperature-are provided in ref.
Although universally shared, this is just one out of several other viable strategies to display the data in two dimensions. Starting from these premises, we here propose an approach to supervised classification and regression of superconductive materials which builds on the Deep Sets technology  12  , a family of deep learning algorithms that is specifically designed to operate on sets defined by the finite enumeration of distinct members (or elements). The modeling of sets is a subfield of paramount importance at the frontier of machine learning research. The computational architecture of Deep Set can be rationalized from an encoder-decoder perspective. The encoder embeds datasets into an appropriate vector space of variable size. Each input element of the set if transformed by a mapping function that is parameterized by a neural network.
Then, the extracted features are combined linearly at the pooling stage. This makes it possible to gauge the contribution of each individual atom to the onset of the superconductive transition: the whole is here the sum of its parts, a remarkable outcome of the automated feature selection strategy that is settled upon training. Interestingly, the encoding space can be successfully squeezed, even compactified to yield just one scalar output, so providing a handy and immediate proxy of the chemical propensity to superconductivity. The performance of the fully trained Deep Set is challenged against the so called Hosono dataset  1, 11  : a significant portion of the materials certified as superconductors are correctly spotted by the network, and the associated transition temperatures predicted with a high degree of confidence.
Training and application of the machine learning technology In the following, we will begin by illustrating the results of the analysis for what concerns regression and classification. The specific aspects that pertain the implementation of the Deep Set algorithm are discussed in the Method section. In Fig. 1 , we report the predicted T c against the corresponding values, as measured in direct experiments. Blue circles refer to materials of the SuperCon dataset  11  that were not presented to the Deep Set during training stages. The error bar is computed by gathering data from 50 independent realisations of the trained device. Hence, for any given material, the number of averaged entries is at most 50 (if the material has never been selected for a training session). Only materials that have been selected at least 10 times are considered.
However, the associated root mean square error is relatively large, quantified in about 80 K in ref. 15  (an for a subset of materials smaller than the one here considered). In our analysis, the root mean square error drops significantly to reach 9. 5 K, while the r 2 stays comparable to that claimed in ref. 15. The proposed scheme can be further improved by employing ensemble techniques that enable us to reduce the root mean square error to 9 K, with r 2 = 0. 93 (see SI). To the best of our knowledge, the root mean square error reported in our paper is indeed the smallest claimed in the literature with a correct handling of the (large) analyzed dataset (i. e. , by removing duplicate samples). The diamonds depicted in orange in Fig. 1  stand for the entries of the Hosono database  1, 11. By visual inspection, one can immediately appreciate the correspondence between the predicted and measured T c.
In this case, the root mean squared error is found to be 7 K (r 2 = 0. 84). Summing up, the results displayed in Fig. 1  testify on the inherent ability of the Deep Set network to satisfactorily reproduce the critical temperature of a superconducting material, starting from its description in terms of constitutive elements. The results here presented refer to a latent space of dimension (size of the vector where information are stored) d = 300. In the annexed Supplementary Information (SI), we show that the recorded predictions are remarkably accurate also when the dimension of the latent space gets dramatically reduced. Assuming d = 1, which amounts to impose a scalar Fig. 1  On the predicted critical temperature. Predicted vs. measured critical temperature, T c (K). The blue circles refer to materials from the SuperCon database.
A fraction of 20% of the total is randomly selected as part of the testset, while the remaining 80% is used for training. The operation is repeated 50 times and the collected temperatures from each independent run stored for further processing. The displayed data refer to the 328 materials selected to be part of the testset at least 10 times, out of 50 trials. The orange diamonds refer to the 39 superconducting materials (all tested 50 times by independent DeepSet realizations) that are present in the Hosono database. This latter contains a total of 207 (superconducting and non-superconducting) compounds. Symbols stand for the average temperature, while the error bars are the computed standard deviation. embedding in the latent space, yields a modest drop in the regression performance as quantified by the associated root mean squared error.
By operating in this setting, it paves the way for a straightforward interpretation of the obtained results (see "Discussion"). As concerns the classification task, the aim is to identify putative superconductors materials. To this end we train the Deep Set on a sub-portion of the SuperCon database, potentiated with additional data from the Crystallography Open Database (COD), as convened by the garbage in procedure discussed in the "Methods" section. The trained Deep Set is then used against the test-sets of interest-the remaining data within the SuperCon augmented with COD and the Hosono database. At the end of the assessment of each individual item, the Deep Set returns a scalar quantity ρ, ranging from zero (the material is deemed non superconducting) to unit (it is classified as superconductor).
e. , when the number of representative elements per class is not homogeneous). In Fig. 2a  the precision is plotted (green symbols) versus the recall for the SuperCon database (with the inclusion of the COD data). Different points stand for different thresholds ϵ th (by increasing ϵ th the precision increases smoothly and the curve is traveled from right to left). More specifically, at the exit node and for any supplied entry item, the DeeptSet returns a scalar quantity (termed ρ). If ρ > ϵ th , the material is deemed superconductor, viceversa otherwise. Symbols refer to the average precision/recall over the performed, statistically independent, runs. The computed standard deviation is smaller than the size of the employed symbols. The classifier threshold ϵ th can be chosen so as to return accurate results (high precision), with a rather modest quota of false negative (high recall).
The results for the Hosono database, as depicted with blue symbols in Fig. 2a , are instead less captivating. To improve on this, we propose a more stringent approach to the classification which consists in designation of the material as superconductor only if this is a shared conclusion by the majority of the performed independent runs, for a given choice of the threshold. Different points (as displayed in Fig. 2b ) refer to distinct choices of the threshold amount ϵ th which is now operated on an ensemble made of independently trained DeepSet models. By applying this strategy to the analysis of the Hosono dataset yields a much more satisfactory scenario as outlined in Fig. 2b. By assuming in particular ϵ th = 0. 85, we identified 29 out of 39 superconductors of the Hosono database, with 19 false positive.
This is a particularly satisfactory outcome given the fact that the Hosono database contains samples that are pre-filtered by experts in the field. Hence all materials are in principle reasonably good superconducting candidates. The performance of the method, as quantified above, are superior to those displayed by other competing approaches and so far reported in the literature  9, 11  , as we shall elaborate in the following. The algorithms developed in ref. 9  seek to separate materials (all superconducting a priori) into two distinct groups depending on whether T c is above or below a given threshold. Modulating the imposed threshold makes it in principle possible to implement a pipeline that integrates classification and regression, and so eventually decide on the nature of the examined material.
2  where the curve recall versus precision is plotted for same dataset (SuperCon, green symbols) as employed in ref. 9  : by properly tuning the threshold ϵ th , we can push both the precision and the recall well beyond 90%, for the considered classification. Again, and thanks to the garbage in procedure, in our case we deal with a genuine classification task between superconducting and nonsuperconducting materials (non-superconducting materials are in fact definitely seen by the DeepSet during training stages). Notice also that the scores obtained in ref. 9  for the limited subset of materials contained in both SuperCon and the Inorganic Crystal Structure Dataset (ICSD)-just 1500 compounds-are significantly smaller (0. 74 and 0. 66 are reported to be found for, respectively, precision and recall) than the homologous quantities here obtained for the closest benchmark possible, the SuperCon dataset complemented with COD (Fig.
2 ). Remarkably the score reported in ref. 9  for the intersection between SuperCon and ICSD is worse than that obtained by applying the DeepSet method to the Hosono database. As recalled above, this is a particularly challenging testbed as it contains pre-filtered samples and thus materials are in principle reasonably good superconducting candidates. By setting the recall in between 0. 6 and 0. 7 for the Hosono database, we can in fact still have precisions above 0. 8. More straightforward is the comparison with the work by Konno et al. 11  , where deep learning tools are operated to achieve a classification of the supplied materials. When it comes to classification, the scores for both precision and recall as displayed by our method are superior to those reported in ref. 11. This can be appreciated by inspection of Table  1  in ref.
11  for the case of the aforementioned Hosono database, as well as by looking at the results reported in Section XIV C in the supplementary material of ref. 11  (the top score precision 0. 52 reported in ref. 11  to be compared with 1, as obtained with our method, for suitable choices of the recall, against the same dataset). Also the F1 metric (the harmonic mean of precision and recall) for the same dataset (characterized by a baseline precision of 20%) is found to be 0. 71 to be compared with the 0. 63 reported in ref. 11. Following the above validation, we applied the trained Deep Set, in this latter version that implements the majority rule, to the analysis of the updated list (September 2021) of minerals accepted by the International Mineralogical Association. As for the above setting, we assumed ϵ th = 0.
85 and averaged out 60 independent runs. In the SI we report the list of minerals classified as superconductors, when setting ϵ th = 0. 85. Interestingly enough, about 44% of the minerals selected by the trained network were indeed already verified to be superconductors (this information is displayed in the the annexed SI, together with the indication of corresponding study). Notice that none of the samples contained in the mineral catalogue was supplied during training. In the current version of the implementation we do not keep track of additional annotations as, e. g. , high pressure or thin layers. This is a worthwhile direction of further investigation which however necessitates investing on the quality of the existing database. For the sake of completeness, we can recalculate the percentage of success of the DeepSet prediction by excluding from the pool superconducting materials at high pressure and thin films.
7 Ni 0. 3 Te), Goyal et al. did not detect any SC transition above 2 K 22 ; (ii) for lower Ni content the transition becomes very broad  22  while it is rather abrupt in our sample of Pd 2 NiTe 2. We can thus reasonably conclude that also for this sample the results suggest an intrinsic superconductivity, as predicted by our ML approach. Optimized synthetic processes calibrated so as to significantly reduce the presence of impurities could yield improved samples for further investigations. To improve on the quality of the existing database it is crucial to keep track of materials which have been shown not to display a superconducting phase. To contribute along this line, while at the same time testing the predictions of the proposed method from a different perspective, we have also measured the ac susceptibility of three compounds tagged as non-superconducting by DeepSet.
Particularly illuminating is the limiting setting when d = 1. Each element of the periodic table is connected to a characteristic scalar attribute, termed x 1 (see SI). To illustrate the main idea, focus on the regression task (determining the associated critical temperature). The parameter x 1 can be hence interpreted as the individual endowment of every single atom to the estimated critical temperature, by recalling that distinct contributions are combined linearly in the latent space as in the spirit of the Deep Set technology. The larger x 1 , the more significant the shift to the ensuing critical temperature as stemming from the considered element. The predicted value of x 1 can be used to re-plot the periodic table with an apt color code as shown in Fig. 5. Working in this limiting setting, the obtained predictions are indeed less accurate ad compared to the reference choice d = 300, though still remarkable.
Particularly relevant results are obtained when challenging the trained network against the database of Hosono and collaborators 1 : in this case the list of materials is in fact pre-filtered by experts in the field to yield a selection of candidates that are worth of investigation as potential superconducting candidates, for their structural characteristics. The Deep Set trained on a set of independent data, not including the entries of the Hosono database, is capable of identifying a large fraction of the materials certified as superconductors in refs. 1, 11 , following dedicated experiments. Furthermore, the critical temperature of the superconductors is nicely predicted. Motivated by this finding we applied the Deep Set methodology to the list of minerals provided by the International Mineralogical Association and obtained a sublist of materials that, according to our analysis, are plausible superconducting candidates.
Among these, we selected three different materials and carried out direct experiments to test the adequacy of our predictions. Superconductivity has been observed for two of the investigated mineral analogues of michenerite and monchetundraite at critical temperatures in very good agreement with the predictions. Though these results need to be confirmed by more detailed investigations on samples of improved purity, they should be regarded as an additional proof of the success of the proposed method. Monchetundraite is in particular the first certified superconducting material to be identified by articial intelligence methodologies. To take one step in the direction of improving the quality of the current database, we also report on the results of experiments carried out on three compounds-the synthetic analogues of polarite (PdHg), telluropalladinite (Pd 9 Te 4 ), and paolovite (Pd 2 Sn) -tagged as non-superconducting by DeepSet.
For the specific case at hand, the elemental composition of the examined crystal is supplied as an input and define the set of interest. More specifically, and as outlined below, each element is characterised by a list of 22 instances, stored in a vector. The stoichiometric integer of the selected atom is also provided, as an additional entry of the vector. In Fig. 6  a schematic outline of the applied Deep Set architecture is provided. From the set of elements that compose the material to the input set The Deep Set receives as an input the set of elements that compose the examined material. Each element is associated to a vector of 22 instances drawn from the Mendeleev software  23. This is a freely available Application Programming Interface (API), written in Python, for accessing various properties of elements from the periodic table.
The 22 instances considered are respectively the atomic number, the atomic volume, the block in periodic table, the density at 295 K, the dipole polarizability, the electron affinity, the evaporation heat, the fusion heat, the group in periodic table, the lattice constant, the lattice structure symbol, the melting temperature, the period in the periodic table, the specific heat at 293. 15 K (20 o C), the thermal conductivity at 293. 15 K (20 o C), the Van der Waals radius, the covalent radius, the Pauling's scale of electronegativity, the atomic weight, the atomic radius, the ionization energies (in eV), the valence. Missing data were recovered from atom datasets made available on Mathematica, and specifically those referred to density, electron affinity, fusion heat, lattice constant, specific heat and thermal conductivity.