dataesr/scientific-paragraphs-categorization huggingface dataset:
In the analysis we focused on atoms with atomic number smaller than 96. By gathering information from different datasets we could reduce the amount of missing data to about 4% of the total. The stoichiometric integer, i. e. , the number of times the selected atoms appears in the chemical formula, is also supplied as one additional entry of the input vector. The non linear function ϕ maps every input vector into a latent space, as evident in the scheme depicted in Fig. 6. The dimensionality (d) of the target latent space (the space where the processed information is embedded by the trained network) can be tuned at will. The embedding of the input is self-consistently driven by data, thus allowing to overcome intrinsic bias that might arise when shaping the latent space with direct human intervention.
Datasets employed for learning and testing Data are extracted from the SuperCon database, which consists into two separate subsets: Oxide and Metallic compounds (inorganic materials containing metals, alloys, cuprate hightemperature superconductors, etc. ) and Organic compounds (organic superconductors). Removing compounds with incompletely specified chemical compositions and accounting for multiple records yields a final collection of 16395 independent entries. Roughly 5700 compounds are cuprates and 1500 are iron-based materials. The remaining set includes conventional phonon-driven superconductors, known unconventional superconductors like the layered nitrides and heavy fermions, and many materials for which the mechanism of superconductivity is still to be elucidated (such as bismuthates and borocarbides).
For the regression problem (i. e. , determining the critical temperature T c ), we split the SuperCon database into training (80% of the total) and test (the residual 20%) sets. This partition follows a random implementation, thus leaving a mark of inherent stochasticity the reflects back in the obtained predictions. The analysis is repeated by 50 times, each realization being refereed to independent sampling of the available datasets into training and test sets. For classification purposes (i. e. , determining if a given, supposedly unknown compound is superconducting or not) and to access a larger set of materials that are presumably non Fig. 6  The Deep Set architecture. A schematic layout the Deep Set architecture here employed. The input of the Deep Set is the set of elements that compose the analyzed compound.
Each element is associated to 22 chemical/physical instances as described in the main body of the paper. The stoichiometric contribution of a given element defines an additional entry of the supplied vector. The input is processed by the non linear function ϕ, represented as a feed forward neural network. The result of the processing are gathered together and further transformed by the application of a non linear filter ρ. This is also defined in terms of a neural network. The produced output is confronted to the expected target, during training stages, and the parameters of the networks ϕ, ρ tuned so as to minimise the assigned loss function. superconductors, we adopted a technique pioneered in ref. 11  and termed garbage in. This latter is here applied to the Crystallography Open Database (COD).
Since superconductors are just a few percent of the total, we label all materials belonging to COD as non superconductors. Then, the full database was randomly sampled to extract a collection of entries, that we used to thicken the population of superconductors. Since superconductors are rare, the errors (the inclusion of superconductors in the reservoir of non superconducting materials) are, in all probability, small: the benefits of enlarging the examined pool of materials, overcome the disadvantages, which stem from having contaminated the dataset with a few erroneous entries (garbage that is put in). The performance of the classification are shown to steadily increase as a function of the amount of material, selected from the COD as putative non superconductors, up to about 50,000 (see SI).
This is the setting that we have assumed in the course of the analysis reported in this study. Another employed database is that produced by Hosono et al. 1. This latter paper examined a relatively short list of pre-selected materials, each of which chosen by experts with a background in solid state chemistry. Several superconductors were identified (3% of the total, as anticipated above) and thoroughly characterised. Importantly, the list of unsuccessful materials tested for superconductivity was also provided. In this study, we make use of a distilled version of the original database. This is discussed in ref. 11  , and it will hereafter referred to as Hosono database. It contains 207 materials, 39 superconductors and 168 non superconductors. The Hosono database allows for an independent and more stringent test (since materials were pre-selected as possible superconductors) of the performance of Deep Set classificator.
The larger the value of ϵ th the more certain the Deep Set ought to be before classifying the material as superconducting. For training the Deep Set network, we made use of Adam optimiser  8  , by setting a learning rate of 0. 001. training proceeded for a maximum number of epochs equal to 400. We adopted an early stopping regularisation with patience settled to 40. During successive epochs the network was fed with batches of size 64. These hyperparameters follow the Hyperband algorithm, and are set so as to improve the performance of the model. The code employed, as well as a notebook to reproduce our results, can be found in the public repository of this project.
The initial phase identification was carried out by HighScore program  26. Subsequent quantitative phase analysis was performed by Rietveld method using Topas 5 program  27. The crystal structure models were taken from the Inorganic Crystal Structure Database 2021  28. The fundamental parameter approach for the peak shape description for all phases present in the samples was used. Only scale factors, unit-cell parameters and parameters describing profile (CrySize L and Strain G) were refined. No atomic coordinates were refined. Correction for preferred orientation (March-Dollase) of Pd 3 HgTe 3 along [001] was applied. The X-ray powder patterns with the assigned phases of the studied samples are given in the SI, as Supplementary Figs. 6, 7, 8, 9, 10, 11 and 12. The two synthesized Pd 3 HgTe 3 samples resulted to be mainly constituted by the P3m1 phase (temagamite).
Magnetic characterization Static (dc) and dynamic (ac) magnetic measurements were performed on a Quantum Design MPMS magnetometer by using ball-shaped samples of loose microcrystalline powders wrapped in PTFE. Measurements were corrected for diamagnetic contributions of both PTFE (blank measurement) and compound (calculated with Pascal constants). Low temperature measurements (≤1. 6 K) were performed using a Quantum Design  3  He insert (iHelium3) adapted to the MPMS magnetometer. For both configurations, careful degaussing of the magnet was performed and the small remaining field (2 Oe) was compensated to ensure a measurement in zero-field conditions (H dc = 0 Oe). Optimal data for dynamic magnetic measurements were obtained for H ac = 3 Oe and oscillating field frequency equal to 10 Hz.
ACKNOWLEDGEMENTS This research was supported by the  Grant Agency of the Czech Republic  (project No. 22-26485S ), by the  MIUR-PRIN2017  via project " TEOREM deciphering geological processes using Terrestrial and Extraterrestrial ORE Minerals ", prot. 2017AK8C32 and the  Italian Ministero dell'Istruzione, dell'Università e della Ricerca  through the  "Progetto Dipartimenti di Eccellenza 2018-2022 ".
In addition, it also supports multi-party extensions of certain schemes and related cryptography primitives, namely digital signature schemes, proxy reencryption, and program obfuscation. PALISADE can be found on github  [30]. To have an extensive comparison for CKKS encryption, apart from these three mentioned libraries, we also measure its running time in HEAAN library  [24] , developed in 2016 by its own authors. HEAAN (Homomorphic Encryption for Arithmetic of Approximate Numbers) is an open-source cross platform software library which implements the approximate HE scheme proposed by Cheon, Kim, Kim and Song (CKKS). HEAAN executes only CKKS schemes with its complete properties. Following its owners, the library allows additions and multiplications to be performed by fixed point arithmetics and approximate operations between rational numbers.
Experimental implementation The main focus of our work is to compare the performance of each available scheme in different libraries. For this reason, in each library, we build our own "simple" project as a regular end-user. Each project is corresponding to one scheme, which includes five main homomorphic operations: KeyGen, Enc, Dec, Add, and Mult. The execution time needed to perform each operation will be recorded and then compared to each other. Every program collecting the performance metrics is carried out on an average commodity computer equipped with an Intel(R) Core(TM) i7-10700 CPU running at 2. 90GHz under Ubuntu 20. 04. In the results presented in the next section, Table  4  lists some useful notations. To ensure the consistency in test results, every experiment is executed according to the strategy below: -The time unit is microseconds (µs); -Each operation was executed in 1000 iterations and the time presented is its average; -The parameters are chosen to ensure the 128-bit encryption security level; -The time measured of encryption operation includes the execution time of random values for message inputs, together with encoding and decoding timings for batching; -Bootstrapping is not applied.
BFV Although BFV scheme is available in both SEAL and PALISADE as mentioned in Table  3 , they also have their differences in the implementation, indicated in Table  5. PALISADE allows users to change the parameters p, N, L as inputs, whereas SEAL-Python keeps L unchangeable from the user side. To accelerate the batching technique, the two libraries require that the chosen plaintext modulus p needs to be a prime number and congruent to 1 (mod 2n). This is the condition to operate on n packed integers in a SIMD (Single Instruction, Multiple Data) manner  [21]. In order to assess the relative practical efficiency of two libraries for BFV encryption, different implementations are done with the same input parameters and working environment given in Table  6. Unlike SEAL, PALISADE can calculate required N and Q based on chosen L and p to ensure a security level of 128 bits.
In Zreikat et al. [10] , a theoretical evaluation of 5G/WiFi-6 coexistence is performed to overcome limitations in coverage and capacity, studying performance parameters by modeling the system using MOSEL-2 simulation language. Mankar et al. [11]  explores the interplay between D2D communications and real-time monitoring systems in a cellular-based IoT network, characterizing D2D communication performance through average network throughput and quantifying real-time application performance using the Age of Information (AoI) metric. Analytical derivations and simulations are used to assess the mean success probability for D2D links, capturing the spatial disparity in AoI performance through moments of conditional success probability and conditional scheduling probability for status update links.
It gathers several measures that are recorded in a database for further analysis. In terms of its interface, AtomD has two particular fragments for managing experiments, shown in Fig. 3. 3  The first one, called ''Dashboard'', allows the user to define the roles that a node can play in the D2D communication and allows the user to observe the status and progress of the connection. The second one, called ''Experiments'', allows the user to set the different actions that the application can take during the D2D transfers. We detail the two fragments in the following.
During the transmission process, the transmitter and receiver will record in a database the information registered within the Chunks, as well as the number of bytes expected and the bytes transferred for each sent file. • Connectivity experiment (see Algorithm 3). This experiment allows the devices to perform a set of NDP. Among the configurations, the user can set the device to use either Bluetooth classic or BLE for the discovery and connectivity request procedures. In the course of this experiment, AtomD records the times of radio initiation, device discovery, request sending, and request reception in nanoseconds. In addition, it also records the GPS location of each device.
The Advertiser then chooses one of the Discoverers to perform the predefined ''File Experiment'', starting with the node in the top right corner and proceeding clockwise to the other nodes. It is worth underlying that the ''File Experiment'' process is bidirectional, with the Advertiser initially acting as the transmitter (data: Advertiser → Discoverer) and then as a receiver (data: Advertiser ← Discoverer). Once the device picked as chosen Advertiser has communicated with all the Discoverers, all nodes' databases are exported and reset. We choose then another device as the Advertiser and run the same process until all combinations have been tested. Similarly, for the POINT-TO-POINT strategy, we maintained the same node distribution as in the STAR strategy, with the Advertiser in the center.
The Advertiser then establishes a single connection to a Discoverer, one at a time, following the order explained in the STAR strategy, and then performs the execution of the experiment. Contrarily to the STAR strategy, once the Advertiser has completed all possible combinations, the databases of all nodes are exported and reset. Then, a new Advertiser is established, and the process is restarted. Through these tests, we evaluated the throughput during the transmission of a file to another node, providing insights into the performance that D2D communication can maintain under different strategies.
Celigo Image Cytometer and Image Analysis Software The Celigo Image Cytometer has been described previously, which utilizes one bright field (BF) and four fluorescence (FL) imaging channels: Blue (Ex/Em: 377/470 nm), Green (Ex/Em: 483/536 nm), Red (Ex/Em: 531/629 nm), and Far Red (Ex/Em: 628/688 nm) in combination with high-power light-emitting diodes  [21] [22] [23] [24]. The image cytometry system acquires whole well (96-well plate) images by utilizing a proprietary mirror system that rapidly captures 16 images in one well, where the software algorithm automatically stitches them together for visualization. The entire imaging system provides an optical resolution of ~ 1 μm 2 /pixel. The Celigo software application "Confluence 1" was used to measure the host cell confluence percentages using the acquired bright field images.
The preset ANALYZE parameters were optimized to automatically measure confluence percentages. The confluence percentages were calculated as the ratio of cell surface area coverage divided by total surface area in the well measured directly from the image cytometer. The results were used to optimize Calu3 seeding density. The Celigo software application "Target 1 + 2 + Mask" was used to identify the total number of Hoechst-positive host cells (Ex/Em: 352/461 nm) in the Blue channel and the number of mNeonGreen fluorescent infected cells (Ex/ Em: 506/517 nm) in the Green channel. The Celigo instrument was set up to acquire images in the Target 1 (BF), Target 2 (Green), and Mask (Blue) channels, where the exposure times for mNeonGreen and Hoechst were 60,000 and 145,000 µs, respectively. Image-based autofocus was used to focus in the BF channel, and the focus offsets were applied for the BF (-2 µm), Green (0 µm) and Blue (-15 µm) channels.
The preset ANALYZE parameters were optimized to identify the Hoechst-positive host cells above an intensity threshold of 2, then the gating feature was used to determine the mNeonGreen-positive infected cell number. The Celigo software application "Target 1 + 2 + 3 + 4" was used to identify the total number of Hoechst-positive cells in the Blue channel, the number of live/metabolically active Calcein AM-positive cells (Ex/Em: 488/520 nm) in the Green channel, and dead propidium iodide-positive cells (PI, Ex/Em: 496/636 nm) in the Red channel. The Celigo instrument was set up to acquire images in the Target 1 (BF), Target 2 (Blue), Target 3 (Green), and Target 4 (Red) channels, where the exposure times for Hoechst, Calcein AM, and PI were 23,000, 10,000, and 10,000 µs, respectively. Image-based autofocus was used to focus in the BF channel, and the focus offsets were applied for the BF (-8 µm), Blue (-13 µm), Green (+ 7 µm) and Red (0 µm) channels.
Results SSEgenerator: construction of the synthetic data set. We choose the Cascadia subduction zone as the target region because (1) a link between slow deformation and tremor activity has been assessed  16  and a high-quality tremor catalog is available 26 ; (2) a preliminary catalog of SSEs has recently been proposed during the period 2007-2017 by Michel et al. 12  based on time-series decomposition via independent-component analysis. This proposed catalog will be used for comparison and baseline for our results, which are expected to provide a more comprehensive catalog that will better show the link between slow deformation and tremors. To overcome the scarcity of cataloged SSEs, we train SSEdetector on synthetic data, consisting of simulated sets of GNSS time series for the full station network.
Each group of signals (60 days and 135 stations) is considered a single input unit. In order to be able to detect SSEs in real raw time series, several characteristics need to be present in these synthetics. First, they must contain a wide range of realistic background signals at the level of the GNSS network, i. e. , spatially and temporally correlated realistic noise time series. On the other hand, while a subset of the samples (negative samples) will only consist of background noise, another subset must also include an SSE signal. Here, we use a training database containing half positive and half negative samples. For this, we model SSE signals that are realistic enough compared to real transients of aseismic deformation. The modeled signals do not propagate over space. Finally, the synthetics should also carry realistic missing data recordings, as many GNSS stations have data gaps in practice.
First, we thus generate realistic synthetic time series, that reproduce the spatial and temporal correlated noise of the data acquired by the GNSS network, based on the method developed by Costantino et al. 25. This database of 60,000 synthetic time series is derived from real GNSS time series (details in section SSEgenerator: Generation of realistic noise time series). We select data in the periods 2007-2014 and 2018-2022 as sources for the noise generation, while we keep data in the period 2014-2017 as an independent test data set (details in section SSEgenerator: data selection). In order to create the positive samples (time series containing an SSE), we model 30,000 dislocations (approximated as a point source) distributed along the Cascadia subduction interface (see Fig. 1b ) following the slab2 geometry  27  (detailed procedure in section SSEgenerator: Modeling of synthetic slow slip events).
Eventually, we compute a database of 30,000 synthetic SSE transients, where the SSE signal is added to the positive samples (placed in the middle of the 60-day window). The synthetic data set is thus made of 60,000 samples and labels, equally split into pure noise (labeled as 0) and signal (labeled as 1) with different nuances of signal-to-noise ratio, resulting both from different station noise levels and differences in magnitude and location so that the deep-learning method effectively learns to detect a variety of slow deformation transients from the background noise. The data set is further split into three independent training (60%), validation (20%), and test (20%) sets, with the latter being used after the training phase only. SSEdetector: high-level architecture. SSEdetector is a deep neural network made of a CNN  30  and a Transformer network  31  that are sequentially connected (detailed structure in section SSEdetector: Detailed architecture).
First, the disclosed outputs of the Transformer are averaged and passed through a sigmoid activation function. The output values are a detection probability lying in the (0,1) range and can be further interpreted as a confidence measure of the method. Second, we train the SSEdetector by minimizing the binary cross-entropy loss between the target and the predicted labels (details in section Training details). The combination of the two strategies allows SSEdetector to be successfully applied in a real context because (1) we can run our detector on 1-day-shift windows of real data and collect an output value for each day used to build a temporal probability curve, (2) thanks to the Transformer neural network, such a curve will be smooth and the value of probability will gradually increase in time as SSEdetector identifies slow deformation in the GNSS time series.
Continuous SSE detection in Cascadia from raw GNSS time series during 2007-2022 Overall characteristics of the detected events. In order to evaluate how SSEdetector performs on real continuous data, we applied it to non-post-processed daily GNSS position time series in Cascadia for the period 2007-2022. SSEdetector scans the data with a 60-day sliding window (1-day stride), providing a probability of detection for the central day in each window. Figure  3a  shows the probability of slow slip event detection (in blue) together with the tremor activity over the period 2007-2022 (in gray). We consider having a reliable detection when the probability value exceeds 0. 5. We find 78 slow slip events over the period 2007-2022, with durations ranging from 2 to 79 days. We find 55 slow slip events in the period 2007-2017, to be compared with the 40 detections of the catalog of Michel et al.
6 7. We observe that the results are similar, with an excellent alignment with tremors and similar correlation and lag values, although with this setting the detection power  slightly decreases, probably due to a larger number of missing data, suggesting that there is a trade-off between the number of GNSS stations and the number of missing data. We also test the ability of SSEdetector to identify SSEs in a sub-region only (even if it is trained with a large-scale network. For that, we test SSEdetector (trained on 135 stations), without re-training, on a subset of the GNSS network, situated in the northern part of Cascadia. To this end, we replace with zeros all the data associated with stations located at latitudes lower than 47 degrees (see Supplementary Fig. 8 ). Similarly, we find that SSEdetector retrieves all the events that were found by Michel et al.
Methods SSEgenerator: data selection. We consider the 550 stations in the Cascadia subduction zone, belonging to the MAGNET GNSS network, and we select data from 2007 to 2022. We train the SSEdetector with synthetic data whose source was affected by different noise and data-gap patterns. We the data into three periods: 2007-2014, 2014-2018, and 2018-2022. In order to create a more diverse training set, data in the period 2007-2014 and 2018-2022 has been chosen as a source for synthetic data generation. The period 2014-2018 was left aside and used as an independent validation set for performance assessment on real data. Nonetheless, since synthetic data is performed by applying a methodology based on randomization (SSEgenerator), a test on the whole sequence 2007-2022 is possible without overfitting.