dataesr/scientific-paragraphs-categorization huggingface dataset:
We therefore examined expression in the PA, OFT + RV and LV + atria. We found 36 of the 39 genes show ). The types of interaction evidence for the network edges are indicated by the line color (text mining, experiments, database, co-expression, neighborhood, gene fusion and co-occurrence). The high confidence score of 0. 700 was used to create the network. Kmeans clustering was used to generate three clusters (cluster 1, red; cluster 2, blue; cluster 3, green). The nodes based on confidence; with line thickness indicates the strength of the support of the data in STRING. Six genes found by ORA are indicated (Italic blue font for gene names), chromatin genes found with de novo mutations in one or more cases with sporadic CHD (with asterisk next to the gene names). Constrained genes are indicated (Black circle surrounding nodes) and candidate sporadic CHD genes are shown (underscore for the gene names).
Principal component analysis (PCA) Details about PCA for this study have been described previously in ref. 10. Briefly, genome-wide diploid variants and the International HapMap project phase III release 3 data were used for PCA. Shared variants in our dataset and the HapMap dataset were extracted and combined into one dataset. Variants with A > T, T > A, G > C and C > G allele types were removed to avoid DNA strand-flip problems. Variants that had a minor allele frequency of <0. 05 and all variants on chromosome X and Y, were excluded. The autosomal common variants were filtered using the -indep function of PLINK to ensure only independent variants were used for PCA. Then PCA was conducted with the -pca function of PLINK1. 9 beta. A total of 879 (74. 4%) subjects were Caucasian, 184 (15. 6%) were of African descent or admixed and 119 (10.
Variant annotation and identification of the MDRVs Protein truncating variant (PTVs) i. e. , Loss of function (LoF) variants  49  including indel-frameshift, stop gain, splice donor, splice acceptor, stop loss, start loss were annotated in parallel by Variant Effect Predictor (VEP) plugin LOFTEE  50  , Bystro  51  and ANNOVAR software  52  and LoF variants were combined as shown in Supplementary Fig. 4. Damaging missense variants were annotated by ensemble score of MetaSVM  53. Damaging splicing variants were annotated by spliceAI  54  and two ensemble scores, ada  55  and random forest (rf) scores from the Ensemble database available from dbscSNV  56, 57. We are aware that the annotated functional variants may be enriched for false positives, for example, of sequence artifacts. Therefore, the putative functional variants were subjected to further filtering-based annotation to remove potential false positives and to obtain high-quality MDRVs.
The VCF files were split into individual VCF files for each sample, respectively. A total of 1151 samples had variants/VCF files from both pipelines. Next, each pair of VCF files were cross-compared using Bcftools (https://samtools. github. io/ bcftools/bcftools. html) as evaluated by concordance of chromosome, position, reference allele, alternate alleles and genotype i. e. in genotype mode or just the first four parameters (genotype mode =0). Indels were deemed as validated if > = 10% of the base pairs overlapped.
ACKNOWLEDGEMENTS We acknowledge support from the  Genomics and Epigenomics Core  facilities at Einstein. This work was supported by a  Leducq Foundation  grant and  NIH  grants  P01HD070454 ,  R01HL157157 ,  R01GM125757  and  U01MH101720. National Institute for Mental Health  (MBMvdB,  5U01MH101724 ). D. H. S. has been funded by  Instituto de Salud Carlos III  through the project " PI18/00847 " (co-funded by the  European Regional Development Fund/European Social Fund 's "A way to make Europe"/"  Investing in your future "). JASV is supported by  NIMH  grant  U01MH119741-01  and  SickKids Psychiatry Associates Chair in Developmental Psychopathology. JB is supported by a senior investigator fellowship of  FWO Flanders. MJO was supported by  Medical Research Council UK ,  Centre  Grant No. MR/L010305/1 ,  Program  Grant No.
DATA AVAILABILITY Data supporting the findings of this work are provided in Supplementary Data Tables. The RNA-seq data is available at the NCBI (National Center for Biotechnology Information) Sequence Read Archive (SRA) number: PRJNA885469. The raw WGS data for the 22q11. 2DS cohort was previously deposited on NIMH Data Archive (https:// nda. nih. gov/study. html?id=938). Data access requires Institutional approval and requests will be reviewed by the NIMH Data Archive Data Access Committee.
5 TeV muon beam energy. At 5 TeV muon beam energy, the beam line components in the arcs may have to be placed on movers to deform the ring periodically in small steps such that the muon beam direction would change over time. Studies must be performed to address the mechanical aspects of the solution and its impact on the beam operation. Similarly the flux densities arising from the straight sections at the interaction points are addressed in the study by optimising the location and orientation of the collider. In order to predict the environmental impact and to design suitable methods for demonstrating compliance, detailed studies of the expected neutrino and secondary-particle fluxes are being performed with the FLUKA Monte Carlo particle transport code  [141, 142]. The preliminary results confirm that the effective doses generated by the neutrinos are dominated by neutrons and electromagnetic showers produced in the material located close to the ground level.
The database of radiation effects on superconductors (HTS) and insulators also requires an extension to cover the projected conditions in the target area. Superconducting RF cavities Development of efficient superconducting RF with large accelerating gradient is essential for the high energy complex. Initially, the work will focus on cavity design; however eventually a high gradient prototype at an appropriate frequency will be required. In order to realise such a device, appropriate superconducting cavity production and test facilities will be required including surface preparation techniques and a capability for high power tests.
Characterisation of BIB Detailed BIB simulations were first performed in the context of the MAP studies, employing the MARS15 [154] Monte Carlo software. These are based on the accelerator lattice and interaction regions designed by MAP for a 1. 5 TeV MuC. The previously-mentioned optimised MDI design was based on these simulations. A Higgs-pole muon collider with 62. 5 GeV energy beams was also considered by MAP. The BIB simulation for the 1. 5 TeV collider have been repeated in  [155] , using the Monte Carlo multi-particle transport code FLUKA  [141, 142]. The complex FLUKA geometry was assembled by means of the LineBuilder program [156] using the optics file provided by the MAP collaboration. The accelerator elements have been defined in the FLUKA Elements Database following the information contained in this file and in MAP publications  [157, 158].
This stage is handled by standalone software, such as FLUKA or MARS15 for the BIB particles as previously described and Monte Carlo event generators for the μ + μ -scattering process. The input particles are then propagated through the detector material and their interactions with the passive and sensitive material of the detector are simulated with the Geant4 [160] software. The iLCSoft framework [161], previously used by CLIC [162] and now forked for developments of muon collider studies  [163] , is used for this and all further processing stages. The detector response and event reconstruction are handled inside the modular Marlin framework  [164]. The detector geometry is defined using the DD4hep detector description toolkit  [165] , which provides a consistent interface with both the Geant4 and Marlin environments.
1 Hz, and WW production via vector boson fusion will be at 1 Hz level. Storing full event content allows for later reprocessing of the data in order to improve the performance or reconstruct novel signatures. However, this approach would require about 4 PB of storage per day of running. It will produce a total dataset similar in size to that of ATLAS and CMS in HL-LHC, but dominated by BIB hits that are not interesting from the physics point of view. An alternative approach based on reducing event content by filtering out hits and clusters clearly unassociated with the hard scattering may be more prudent. Here one may choose to preserve the output rate of 750 kHz and reduce the total dataset size. Alternatively, one can aim for the same total dataset size but increase the rate of events, for example if on average 99% of BIB hits in each event can be filtered out, every produced event at 100 kHz can be sent to the storage.
4. 2. All simulations, unless otherwise stated, include overlaying on top of the desired μ + μ -collision the model of BIB described in Sect. 4. 1. The BIB sample used for detectorperformance studies is the one of the √ s = 1. 5 TeV muon collider produced with the MARS15 software, because the FLUKA-based workflow was not fully validated when most of the results were obtained.
It is possible that part of the computational improvements come from the code optimisation alone. For example, the ACTS Kalman Filter implementation is a factor 200 faster than the default iLCSoft implementation given the same inputs. This demonstrates the advantage of an experiment-independent track reconstruction library developed by a dedicated team with strong computing expertise. The expected performance is assessed using a sample of single muons originating from the interaction point. Two set of samples are used; one set is generated with the muon having a fixed momentum ( p) of 1, 10 and 100 GeV and a uniform distribution in θ. The second set is generated at discrete values of θ = 13 • , 30 • , 89 • and uniform transverse momentum distribution in the 1-100 GeV range. The chosen θ values correspond to particles expected to leave hits entirely in the endcap system, some in the barrel and some in the endcap, and entirely in the barrel region, respectively.
Combinatorial Kalman filter The CKF algorithm seeded using hit triplets was implemented using the ACTS library v13. 0. 0. The implementation is a Marlin Processor that serves as a drop-in replacement for the existing tracking Processors. In addition to providing an alternate algorithm designed for large hit multiplicity, ACTS also provides a heavily optimised code for fast computation. The reconstruction strategy used in this section reconstructs  The seeds for the CKF algorithm are formed from hit triplets in the four layers of the Vertex Detector. Only hits in the outer half of doublet layer are considered. Several heuristics are use to determine if each triplet is compatible with a track and can be used as a seed. The seeding algorithm is configured using the ACTS default values, with the exception of: -the radial distance between hits is between 5 mm and 80 mm, -the minimum estimated p T is 500 MeV, -the maximum forward angle of 80 • , -the extrapolated collision region is within 1 mm of detector centre, -the average radiation length per seed is 0.
Acknowledgements This work was triggered by the  Snowmass 2021 Community Planning Exercise  [351]. It is based on -and in some cases significantly extends -the Snowmass white papers [352][353][ 354][355][356][357 ] that have been prepared under the coordination of the IMCC. This work was supported by the  EU   HORIZON Research and Innovation Actions  under the grant agreement number  101094300. Funded by the  European Union (EU). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the EU or  European Research Executive Agency  (REA). Neither the EU nor the REA can be held responsible for them. The work has been supported by the  Fermi Research Alliance, LLC  under Contract No. DE-AC02-07CH11359  with the  U. S. Department of Energy, Office of Science, Office of High Energy Physics.
DE-SC0012704  with the  U. S. Department of Energy. J. Zurita  is supported by the  Generalitat Valenciana (Spain)  through the  plan   GenT program  ( CIDEGENT/2019/068 ), by the  Spanish Government (Agencia Estatal de Investigación)  and  ERDF  funds from  European Commission  ( MCIN/AEI/10. 13039/501100011033 , Grant No. PID2020-114473GB-I00 ). M. Gallinaro  and  G. Da Molin  acknowledge the support from the  Fundação para a Ciência e a Tecnologia (FCT) ,  Portugal. J. Reuter  acknowledges the support by the  Deutsche Forschungsgemeinschaft (DFG, German Research Association)  under Germany's  Excellence Strategy-EXC 2121  " Quantum Universe "-3908333. The work of L. Reina has been supported by the  U. S. Department of Energy  under grant  DE-SC0010102. This work was supported by the  EU Horizon 2020 Research and Innovation Programmes: AIDAinnova  under Grant Agreement No  101004761 , I.
FAST  under Grant Agreement No  101004730 , and the  Marie Sklodowska-Curie  grant agreement number  101006726. The work of  N. Kumar  is supported by  Department of Science and Technology, Government of India  under the SRG grant, Grant Agreement Number  SRG/2022/000363. We acknowledge financial support for this research from the  United Kingdom Science and Technology Facilities Council  via the  John Adams Institute, University of Oxford. R. Ruiz  acknowledges the support of  Narodowe Centrum Nauki  under Grant No. 2019/34/E /ST2/00186. R. Ruiz  also acknowledges the support of the  Polska Akademia Nauk  (grant agreement PAN. BFD. S. BDN. 613. 022. 2021 -PASIFIC 1 ,  POPSICLE ). S. Trifinopoulos is supported by the  Swiss National Science Foundation  -project n. P500PT 203156  and by the  Center of Theoretical Physics at MIT  ( MIT-CTP/5538 ).
W. Su is supported by the  Junior Foundation of Sun Yat-sen University and Shenzhen Science and Technology Program  (Grant No. 202206193000001 ,  20220816094256002 ). W. Kilian  was supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under grant  396021762 -TRR 257. The work of  Y. Kahn  was supported in part by  U. S. Department of Energy  grant  DE-SC0015655. The work of  G. Chachamis  was supported by the  Fundação para a Ciência e a Tecnologia (Portugal)  under project  CERN/FIS-PAR/0024/ 2019 and contract 'Investigador FCT -Individual Call/03216/2017'. The work of J. de Blas has been supported by the FEDER/Junta de Andalucía project grant P18-FRJ-3735. This work is Supported in part by the NSF under Grant No. PHY-2210361 and by the Maryland Center for Fundamental Physics (MCFP).
I. INTRODUCTION A "Botnet" is a cyberattack that can spread Distributed Denial of Service (DDoS) attacks and malware  [1] ,  [2]  over thousands of devices  [3] , by targeting IoT devices or IP addresses, and installing malware on its victims, which in turn may become "bots" which generate malicious traffic and spread the malware further to yet other devices  [4]. As an example, in 2016, the massive MIRAI DDoS Botnet targeted Domain Name System (DNS) provider Dyn  [5]  and gained access to servers of several leading cybersecurity companies  [6]. Botnets increase network congestion through additional traffic that overwhelms the communication ports of the devices they attack, but they also exploit the physical and logical resources of victim devices, including their batteries, processors, memory, operating systems and network software  [7].
Earlier work  [32]  examined the performance of the classical Random Neural Network (RNN)  [33]  with offline gradientdescent learning  [34]  to detect SYN denial of service (DoS) attacks. On the other hand, whereas the work reviewed above focused on detecting cyberattacks and malicious traffic, in this paper, we develop an ARNN-based decision system that identifies the compromised IoT nodes. Successful identification of compromised nodes along with malicious traffic paves the way to fend off distributed attacks (e. g. Botnet and DDoS attacks) in their early stages  [35]. Compromised Device Identification. Whereas the majority of related papers identify compromised devices by detecting malware conveyed by Botnets, some work focuses on identifying compromised devices directly using a variety of techniques including: optimization  [36] , analyzing communication features  [37] , using language analysis  [38] , tracking device location  [39] , or monitoring the downlink channels of a gateway  [40].
Moreover, Reference  [41]  proposed an ML-based system that analyzes traffic flows and packet features in network layer to identify intrusions in an IoT system. In  [42] , a Botnet detection system, called BotStop, was developed based on extreme gradient boosting model that analyzes packet traffic. In  [43] , a Compromised Device Identification System (CDIS-DRNN) is developed based on the DRNN model  [44]  that analyzes the network nodes' incoming and outgoing traffic. The performance of different attack detection techniques can depend on which datasets are used for learning and testing, and prior to the current paper, the CDIS-DRNN offered the best available state-ofthe-art performance for compromised device identification when the publicly available Kitsune Botnet dataset  [45] ,  [46]  is used.
However, none of these works considered the interrelationships between IoT nodes and the propagation of a Botnet attack through these nodes. In recent work a method was proposed to evaluate a set of network or IoT nodes simultaneously in a single recurrent RNN architecture composed of two interconnected and associated neural networks  [47] , trained and tested with ground truth data. Therefore, in the sequel we will also compare the ARNN technique developed in this paper using the Kitsune dataset, against the performance offered by CDIS-DRNN.
Here, it is hypothesized that the ARNN successfully identifies compromised IoT nodes based on its ability to learn both the interrelationships between those nodes and the propagation of a cyberattack. Therefore, after detailing the ARNN learning algorithm's components: the error metric, the weight restrictions, the ARNN initialization, and its learning algorithm are discussed in the Appendix, we thoroughly evaluate its performance for Mirai Botnet attacks on an IoT network with 107 nodes using the Kitsune dataset  [45]. First, we train the ARNN on ground truth data from the initial part of the Kitsune dataset, and test its prediction capabilities with ground truth data from the disjoint latter part of the dataset. Next, we discuss an average normalized metric based on six relevant metrics  [43]  extracted from traffic data.
We then test the ARNN trained with ground truth data, using the average input metric over the testing period which is disjoint from the training period and is subsequent to it. In all cases we evaluate the Accuracy, True Positive Rate and True Negative Rate of the ARNN and observe very accurate attack detection for most of the 107 nodes that are contained in the Kitsune dataset. Finally, we compare the performance of ARNN against the state-of-the-art best-in-class CDIS-DRNN and four wellknown Machine Learning (ML) models for the same problem  [43]. In this case, we also train the ARNN without the ground truth but using the ARNN incrementally on successive short training cycles, followed by testing, and pursued for all of the available Kistune data set. The experimental results again indicate that the ARNN offers superior performanceachieving 100% median accuracy and above 92% accuracy for more than 75% of the network nodes in the dataset -with about 3.
B. THE ARNN EXTERNAL INPUTS ΛI AND λI The external inputs are obtained from data from packet statistics in the network, or from ground truth that is used for training the ARNN, regarding whether given packets are attack packets or normal packets, or other data used for training, or real operational data for testing. We therefore consider that Q i ∈ [0, 1] and q i ∈ [0, 1] are functions Q i (Λ i , λ i ) and q i (λ i , Λ i ). Noting that: Q i (1 -q i ) q i (1 -Q i ) = lim t→∞ P rob[K i (t) > k i (t)] , (7) we define the outputs of the ARNN for each network node i, as being the binary Z i variables: Z i = 1 if Q i (1 -q i ) q i (1 -Q i ) > γ > 0, Z i = 0 otherwise, (8) where Z i = 1 stands for node i being compromised, while Z i = 0 has the opposite meaning, and 0 < γ < 1 is a threshold.
C. THE LEARNING DATASET LD The Learning Dataset is a set of packets LD where, for each packet, we know in advance whether it is an attack or a benign, i. e. "normal", packet. Thus the LD is used to train the ARNN. The set of packets LD that we use to train the ARNN, as well as the dataset used for testing, contain the ground truth for each packet denoted pk(t, s, d, a), where: • t is the transmission instant of the packet from the source node s, and d is the packet's destination node, • a is a binary label so that a = 1 indicates that it is an "attack" packet and a = 0 that it is a "benign" packet, • The length of the packet in bytes, including the header, is denoted by |pk(t, s, d, a)|. • Packets are grouped into "slots" lasting τ = 10 seconds, so that the packet's slot number is l = t τ , i.
e. when (l -1)τ ≤ t < lτ , and M is the total number of slots in the dataset: 1 ≤ I ≤ M , In the dataset that we use, we observe that on average roughly 100 packets are contained in a 10 sec time slot. We now determine the successive ARNN inputs from the dataset LD, namely: Λ l Gi ∈ [0, 1] and λ l Gi = 1 -Λ l Gi , the corresponding output K l i , and the decision output Z l Gi which is a binary variable related to K l i. Let S l (i) and R l (i) be the set of packets that have been transmitted or received by node i from the first slot until the end of the l = t τ -th time slot: S l (i) = {pk(t, s, d, a) : 0 < t ≤ lτ, ∀d, a = 0, 1}, R l (i) = {pk(t, s, d, a) : 0 < t ≤ lτ, ∀s, a = 0, 1}, and: If |R l (i)| > 0, then Λ l Gi = |{pk(t, s, i, 1) : ∀s}| |R l (i)| , else Λ l Gi = 0. (9) Furthermore λ l Gi = 1 -Λ l Gi.
D. LEARNING THE ARNN WEIGHTS FROM THE LD To construct a balanced training dataset LD, the sequence of slots in the MIRAI dataset  [45]  was scanned from the first slot l = 1 up to and including the first slot where some node sends attack packets, which turns out to be slot l = 445, and the LD then included slot 433 up to and including slot 457 (a total of 25 slots). On the other hand, the test dataset T D contains all the subsequent slots, starting with slot 458. The ARNN is trained with the LD that uses the slots l of the dataset which are being used for training using the Gradient Descent Algorithm detailed in the Appendix, with the learning rate η = 0. 1. It adjusts the ARNN weights so as to minimize the following error function  (14)  for each successive bucket l within the LD: E l = 1 2 n i=1 Q i (Λ l i , 1 -Λ l i ) -K l i 2 + q l i (1 -Λ l i , Λ l i ) -(1 -K l i ) 2 , ( 14 ) where Q l i (.
III. TESTING THE ARNN WITH THE AVERAGE TRAFFIC METRIC Six representative traffic metrics were introduced in recent work  [43]  as being indicative of network attacks and were shown to be effective for MIRAI Botnet detection using available real datasets. Rather than using the full metrics, in this section their average normalized value will be used to test the ARNN attack detector. To define these metrics, let |p| be the size in bytes of some packet p, including its header and all the data it contains. Let P S,i l be the set of all packets sent by all network nodes to node i in slot l, and let the maximum length in bytes of packets sent by node s to i up to the end of slot l, be L l s = max{|p| : p ∈ P l (s)}. The six metrics from  [43] , all normalized to a value between 0 and 1, are as follows: • Average packet size of packets received by device i in slot l : x i,1 l = p∈P S,i l |p| s∈S L l s × |P S,i l |.