dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantum computers are anticipated to break modern public key cryptography due to Shor's algorithm  [10] , necessitating a transition to quantum-resistant algorithms  [11]. In 1996, Grover's algorithm was proposed, which could potentially search a database with square root time complexity  [12]. Then, in 2016, the application of Grover's algorithm to AES was examined, providing resource estimates for potential quantum attacks  [13]. In light of the rapidly advancing quantum technology capabilities, the urgency of transitioning to quantum-resistant algorithms is increasingly apparent. The Quantum Roadmap released by IBM underscores this urgency, highlighting the increase in the number of qubits in their quantum processors, from 27 qubits in 2019 to an anticipated 1121 qubits by 2023, and further scaling to 10,000-100,000 qubits by 2026 or later  [14].
This paper aims to study the feasibility of employing the longer AES keys, specifically AES-192 and AES-256, in the LoRaWAN protocol, considering their energy consumption, processing time, and security benefits compared to the currently used AES-128. Notably, prior to our work, there has been no research conducted in a real practical LoRaWAN environment addressing this topic. In addition to studying the impact on performance, our research highlights the fact that using larger keys with AES is a realistic solution for enhancing LoRaWAN security that can be deployed in the very short term, requiring only limited software updates to end devices and servers. The remainder of this paper is organized as follows: Section 2 provides a review of the related work in this field. In Section 3, we delve into the background information that is necessary for understanding the context of our study.
LoRaWAN Test Bed Our measurement campaign was carried out over a real-world LoRaWAN environment composed of three core elements: EDs, GWs, and LoRaWAN servers. Figure  5  provides a visual representation of our LoRaWAN setup. In the figure, the left-hand side highlights the measurement component of our experiment, while the center and right-hand sides show the LoRaWAN network. The ED deployed for the experiments was the B-L072Z-LRWAN1 Discovery Kit, an ARM-based development board equipped with a LoRa transceiver. It operates under the mbed-os platform, version 6. 16, developed using the C++ programming language. mbed-os provides the necessary operating environment to develop the ED application firmware. As LoRaWAN gateway, we used the LORANK8 model. It relies upon the Semtech UDP packet forwarder protocol to communicate with the LoRaWAN server over an IP network.
The server was deployed using the open-source ChirpStack project. It consists of a gateway bridge, an MQTT broker, a network server (NS), and an application server (AS). Chirpstack is developed using the Go programming language. The following settings were used for the LoRa communications. We utilized the LoRaWAN 1. 0. 2 protocol. The spreading factor was set to SF7, the bandwidth was set to 125 kHz, and the adaptive data rate (ADR) scheme was enabled. The specific frequency plan adopted in our experiment was EU863-870, following the standard for LoRaWAN operations in the European Union. For each run of our experiment, the over-the-air activation (OTAA) was used for the registration process of the ED. After OTAA completion, the ED sent consecutive data messages to the LoRaWAN server with increasing payload sizes.
Measurement Methodology Key to our experiment was the modification of the source codes of mbed-os  [35]  and specific components of ChirpStack, namely the network server  [36]  and application server  [37] , to support AES key sizes of 128, 192, and 256 bits. Specifically, the parts of the source code that were modified pertained to data encryption and MIC calculation. Both the ED and the LoRaWAN server were configured to use fixed keys for AES-192 and AES-256 to simplify implementation. Notably, these alterations required no hardware changes, thus preserving the physical configuration of our EDs. In our tests, we aimed to measure the impact of different AES key sizes (128, 192, and 256 bits) on the energy consumption and computational time duration during the operations of encrypt_payload() and compute_mic().
Concurrently, the software of both NS and AS should be modified to accommodate the longer AES encryption function and to update the database with the new longer AES key of ED. This coordinated approach promises a seamless transition and enhanced network security. On a broader note, in the current LoRaWAN system landscape, updating the root key requires a firmware modification for ED, which is a labor-intensive process and makes keeping the key up to date challenging. Recognizing this limitation, our future research efforts will explore the potential benefits of utilizing public key cryptography as a mechanism to facilitate more seamless and frequent updates to these longer root keys. By innovating ways to simplify and expedite root key renewals, we hope to significantly enhance the security resilience of the LoRaWAN communication system.
Database and phylogenetic inferences of M and M-like family of proteins The emm, and emm-like (enn, mrp and sph) genes from a genetically diverse collection of 1668 contiguous Mga regulons representing 130 different emm-types and 39 emm-clusters were extracted based on genetic probes, open reading frame predictions and sequence similarity to published gene sequences  [13, 16]. Sequences of emm and emm-like genes were translated and the signal peptide predicted using the SignalP-5. 0 server  [23]. Mature sequences were generated by in silico removal of the signal peptide up to the cleavage site and from the glycine residue of the LPXTG-sortase motif, used to attach the protein to the bacterial surface. The final database of M and M-like protein sequences analysed in this study contains 1054 unique proteins, comprising of 541 M, 228 Mrp, 275 Enn and ten Protein H sequences.
Co-occurrence network For co-occurrence analyses, genomes that did not contain an mrp or enn were removed from enn and mrp network analyses respectively. Where more than one isolate of an emm-type was available (n=110) the different combinations of alleles were used to generate co-occurrence networks using the igraph software package  [26]  with RStudio  [27]. The node sizes are proportionate to number of occurrences of each allele and the edge weight is proportionate to the number of occurrences of each unique combination of alleles. Community detection was performed using the label propagation algorithm, a clustering method which maximises the internal density of communities  [28].
M and M-like proteins form four distinct M-family-groups with high evolutionary plasticity In a recent analysis of global GAS genomes, we defined a database of 1688 Mga regulons  [13, 16]. In order to define the genetic relationship of M and M-like proteins within this database, we defined 1054 unique protein sequences comprising of 537 M-proteins and 493 M-like proteins (221 Mrp, 262 Enn, and 10 Protein H), and 24 outlier proteins. The splits network of all 1054 M and M-like proteins revealed several large protein groupings with multiple evolutionary histories as represented by multiple edges for most splits, leading to four 'M-family-groups' of protein sequences with a fifth grouping containing 24 outlier proteins (Fig. 1  and Table  S1 ). Such net-like splits structure is indicative of evolutionary incongruence, possibly driven by recombination.
Mrp proteins form four specific Mrp groups with ten Mrp sub-groups Analyses of Mrp proteins revealed the presence of four specific groups (Fig. 3 ) with evidence of a further ten sub-groups based on multidimensional scaling and partitioning around medoids grouping (Fig. 3 , Table  S3 ). Around half of the proteins (130/221) form groups of multiple Mrp sub-groups (SG) that are not congruent with network tree topology (Fig. 3 ; Mrp sub-group 1-5) whereas the other proteins (90/221) coincide with more structured branching patterns in the network, influenced by multiple edges in the splits network (Mrp sub-group 6-10). These differences may be attributed to the different weightings afforded to different variable or constant sites within MSAs between the SplitsTree and partitioning around medoids algorithms.
The overall structure of all ten sph alleles analysed in this study were similar even though high sequence variation existed within central domains of sph genes (Fig. 5 ). Alignment of two allelic forms of sph revealed high synteny (>98 %) with ~300 nucleotides of the cell wall associated C-terminal of emm1 (Clade Y), while the N-terminal of all sph in this dataset share >92 % synteny over ~120 nucleotides with the surface exposed N-terminal of enn205 (Fig. 5 ). The central portion of sph is genetically variable, as is common across M-family proteins. The known host protein interactions attributed to Protein H occur within the C-repeat and N-terminal regions  [30, 31]  meaning these functions most likely derive from the Enn precursor. These data indicate that Protein H is likely a chimeric descendent of an ancestral recombination event between the emm and enn gene families followed by diversification within the central repeat regions, leading to a new chimeric emm-like gene family that has been maintained within a subset of the GAS population.
The extent of recombination within and between the different families of emm and emm-like genes remains unknown, and would require longitudinal rather than cross-sectional analysis. However, there were identifiable recombination events leading to the establishment of known chimeric M proteins (M4, M9, M44, M58, M73, M82)  [16]  and Protein H within the global population of GAS bacteria. Interestingly, the chimeric M proteins grouped within the Enn protein division (M-family-group 3) in the SplitsTree network, and Protein H appear as a sub-population between the Enn and M protein divisions (M-family-group II and III). Protein H is found in the majority of M1 isolates in certain geographical populations  [18]  however in this collection, Protein H was restricted to emm19. 4, emm238 and emm57 isolates  [16]  which represents a limitation of the data used in this study.
Chimeric M proteins were present less frequently, though in multiple lineages. Therefore, the epidemiological impact of these successful recombination events remain unclear. The mosaic evolutionary structure observed within M and M-like protein families may also be driven by selective pressures and convergent evolution that select for functional attributes. There is support for the existence of sub-populations of Mrp and Enn proteins on the basis of distance-based grouping approaches as applied in this study; however, these group designations are not iterative, with groupings dependent on database and distance matrices. Whether these sub-groups relate to functionally or biologically constrained populations requires future investigations, yet this approach provides a robust framework to facilitate targeted studies.
Body Federated multi-partner machine learning can be an appealing and efficient method to increase the effective training data volume and thereby the predictivity of models, particularly when the generation of training data is resource intensive. In the landmark MELLODDY project, each of ten pharmaceutical companies realized aggregated improvements on its own classification and/or regression models through federated learning. To this end, they leveraged a novel implementation extending multi-task learning across partners, on a platform audited for privacy and security. The experiments involved an unprecedented cross-pharma dataset of 2. 6+ billion confidential experimental activity data points, documenting 21+ million physical small molecules and 40+ thousand assays in on-target and secondary pharmacodynamics and pharmacokinetics.
13 derated learning enables machine learning across distributed datasets. 14, 15 Federated learning goes beyond applying conventional machine learning to federated data, i. e. distributed data that is somehow made accessible as a consolidated dataset. In federated learning, the training process itself is distributed, often accommodating additional constraints, for example, a requirement to protect the confidentiality of datasets. Distributed QSAR datasets can be compound-wise or endpoint-wise partitioned or show a mixed partition pattern. In the QSAR case, compound-wise partitioned datasets would document different sets of compounds with activity labels for the same assays and endpoint-wise partitioned datasets would document a compound space with activity labels for different sets of endpoints or tasks (Figure  1 ).
Existing federated learning solutions for QSAR modelling (and corresponding ones in other applications fields) have largely focused on the cross-compound federation of compound-wise partitioned datasets. 16 Crosscompound federation can pose challenges. The identification of endpoints from different sources that are similar enough to be matched is non-trivial and requires endpoint disclosure; endpoint-specific standardization may rely on data samples. From a usage right perspective, equal entitlement to resulting common task models may be hard to reconcile with asymmetrical data contribution, in other words owners of bigger data volumes may be discouraged from maximal data commitment. Here, we report the implementation and industrial application of a new and alternative approach to federated learning: one that in essence extends multi-task learning across multiple parties, while protecting the confidentiality of the underlying data.
Conceptually, this approach proposes cross-endpoint federation across endpoint-wise partitioned datasets. Beyond generic standardized formatting, there is no attempt to endpoint or task matching, so the approach does not require the disclosure of endpoints. It also imparts more symmetry to usage rights: the party contributing data for some task becomes exclusively entitled to the model components specific to that task, encouraging maximal commitment of confidential datasets. Like in other multi-task settings, a joint representation acts as the conduit of information. This joint representation is shared among the data participants, but not with the operator of the system. While, conceptually, cross-endpoint federation thus avoids some of the challenges of more established cross-compound federation, it may well pose questions of its own.
Data and data preparation Combining pharmacological and toxicological assay data of 10 pharma partners (i. e. Amgen, Astellas, AstraZeneca, Bayer, Boehringer Ingelheim, GSK, Janssen, Merck KGaA, Novartis, and Servier) in the multipartner learning, the data volume amounted to 2. 6+ billion confidential experimental activity data points, documenting 21+ million chemical compounds and 40+ thousand assays. This corresponded to the vast majority of the partners data warehouses. The dataset included both alive assays (where data are currently being generated) and historical assays (which have been discontinued). In addition, data from publicly available sources  18  were included. Pharmacological and toxicological assay data can roughly be divided into three types: on-target activity, off-target activity, and ADME (Absorption, Distribution, Metabolism, and Excretion; describing the effect of the body on a drug).
Evaluation Metrics in labeled space Machine learning models are typically evaluated by comparing their predictions with the ground truth on a labeled test set not used for training. For classification models the area under the receiver operator characteristic curve (AUC-ROC) or precision recall curve (AUC-PR) is typically used. 27 AUC-ROC values range between 0 and 1 for systematically wrong and perfect predictors, respectively, with 0. 5 being the value of a random predictor. AUC-ROC is symmetric, meaning it is identical for the prediction of active compounds (typically the minority class) and inactive compounds. AUC-PR is considered more informative when the prediction of the minority class (the 'actives') in a highly unbalanced data set is of interest. 28 UC-PR has however the disadvantage that it depends on the class ratio, and therefore AUC-PR values cannot easily be compared across datasets or tasks in a multi-task setting.
Discussion The topic of federated learning is one that has attracted a lot of visionary discussion and theoretical exploration, but much less effort in concrete application on actual datasets at relevant scale. 9] [40]  In that setting, participants must disclose the assays to federate over, which rules out the involvement of more sensitive assays. Secondly, in principle participants (and often the operator) get access to the same resulting joint model. This may discourage participants to involve more than the minimally required data volume -why would bigger data owners contribute more than smaller ones for the same return, just because they can? Finally, more than one collaborative effort has been marred by underestimating the challenge of reconciling readouts of somewhat to very different assays even if those were designed to document the same mechanism or target.
Beyond material and equipment differences, various extents of divergence of protocols, modality (e. g. biochemical binding assays versus cellular functional assays) or activity direction (agonistic versus antagonistic activities on the same target) can be at play. 19 re we propose an alternative approach: cross-endpoint federation of endpoint-wise separated datasets. One that was inspired by the predictive benefits of multi-task learning extended across partners by  17, 41  and designed to incentivize maximal data involvement by all partners. Generally, a task is defined by the labels provided by a given task owner, who also becomes the exclusive owner of the head model for that task. This obviates a general need for task disclosure or reconciliation. The prospect of receiving more and better private head models encourages participants to involve tasks for many assays and maximal data volume per task.
This indicates that in practice the information transfer occurred generally and broadly across a vast spectrum of assays, many of which would not be amenable to cross-compound federation. Notably, a core cross-endpoint federation scheme can in principle be extended to enable cross-compound federation by mapping common assays to a shared head model. 25 o secure the benefits of cross-endpoint federation, such cross-compound extension may then best be reserved to a limited set of amenable assays, such as some safety panel assays that happen to be outsourced by multiple pharma partners to common contract research organizations. 19 Small off-line exercises proved promising (Figure  S6 ). However, this promise did not yet materialize in preliminary experiments at scale that extended cross-endpoint federation across the full dataset with crosscompound federation of a few dozen commonly outsourced safety panel assays.
GUN1 encodes a Carnitin-acetyltransferase (CAT) The gene mutated in GUN1 SG was identified through whole-genome sequencing. To that end, this mutant was backcrossed five times with the wild-type strain beforehand in order to eliminate most of the mutations generated during UV mutagenesis but not genetically linked to the mutation responsible for the mutant phenotype. The analysis of GUN1 SG whole-genome sequence revealed the presence of six silent mutations, and three missense mutations, one in Pa_1_13700, which encodes a putative protein of unknown function, another in Pa_5_7800, encoding a putative phosphoketolase and the last one in the Pa_6_1340 CDS, where an isoleucine was changed into an asparagine (I441N), caught our attention (Figure  3 ). RNA-seq data indicated that Pa_6_1340 was strongly induced (Fold Change = 56) during ascospore germination, emphasizing the involvement of this gene during ascospore germination  (Demoor, unpublished data).
P. anserina possess two CATs A BLASTP search on the P. anserina predicted CDS database (http://podospora. i2bc. paris-saclay. fr/) identified a second CAT encoded by the Pa_3_7660 putative CDS. Compared to GUN1, this putative enzyme did not harbor any localization signal. Similarly to other fungi, P. anserina may be endowed with two types of CAT, one located in peroxisomes and in mitochondria (GUN1) and one remaining in the cytoplasm (Pa_3_7660). To confirm this hypothesis, we undertook a phylogenetical analysis of CATs in Eumycetes and searched the homologs of GUN1 in the genome sequence of representative fungal species (see Mat. & Met. ). In these species, 2 CATs were always identified, except for S. cerevisiae in which three CATs were identified as previously shown  (Swiegers et al. , 2001).
Homokaryotic mat-and mat+ mutant strains were isolated from the progenies and homozygous mutant crosses were performed to check for the fertility/sterility of the mutants. For every isolated strain, microscopic observations were also performed to determine whether the ascospores germinated through the germination pore at the tip of the ascospore or through any other part of the ascospore (data not shown). Tetrad analysis in the GUN1 SG and in the ΔGUN1 strains Demonstration of the recessivity of the GUN1 SG allele: P. anserina produces mainly asci containing four heterokaryotic/dikaryotic ascospores, allowing non-ordered tetrad analysis of first division segregation (FDS) asci and second division segregation (SDS) asci. To determine dominance/recessivity of the GUN1 SG allele, we crossed the GUN1 SG mutant with the WT.
The PCR product was cloned blunt-end into the pBC-genR plasmid previously digested with EcoRV. The insert was then sequenced, digested with the restriction enzymes ApaI and XhoI and gel purified to be finally cloned to the mCherry CDS into the pBC-mCherry-hygR plasmid digested by ApaI and XhoI (Table  S2 ). This pBC-GUN1-mCherry-hygR (renamed pGUN1-mCherry for sake of simplicity) plasmid was sequenced and transformed into both Δmus52::genR and GUN1 SG Δmus52::genR and [hygR] transformants were selected. We obtained 1 [hygR] transformant for Δmus52::genR and 2  [hygR]  transformants for GUN1 SG Δmus52::genR. Every transformant showed red fluorescence under the microscope. Correct GUN1-mCherry and GUN1 SG -mCherry gene fusions were verified by sequencing (data not shown). One transformant of each genotype was selected and crossed with the S strain to purify [hygR, genS] GUN1-mCherry and GUN1 SG -mCherry homokaryotic mat-and mat+ strains in the progeny.
This GUN1-mCHerry-AKI strain showed red fluorescence under the microscope and correct GUN1-mCHerry-AKI gene fusion was verified by sequencing (data not shown). This GUN1-mCherry-AKI strain was then crossed with the GUN1 SG mutant to generate the GUN1 SG -mCherry-AKI strain by meiotic recombination between the GUN1 SG mutation and the mCherry-AKI-nouR insertion. In the progeny of this cross, we isolated 13 thalli from spontaneously germinating  [nouR]  ascospores on M2 medium supplemented with Nourseothricin. All these isolates were heterokaryotic auto-fertile (mat+ / mat-). Since the GUN1 SG mutation is recessive, we hypothesized that these isolates arose from GUN1 SG -mCherry-AKI/GUN1 SG -mCherry-AKI recombinated heterokaryotic ascospores. One of these isolates was fragmented and [NouR] homokaryotic GUN1 SG -mCherry-AKI ascospores of each mating type were purified establishing the GUN1 SG -mCherry-AKI strain.