dataesr/scientific-paragraphs-categorization huggingface dataset:
FL builds machine learning models depending on multiple datasets located across multiple devices while hindering data leakage  [23]. FL was implemented for the first time to decentralized learning of mobile phone devices without taking privacy into consideration  [24]. Nishio and Yonetani addressed the issue of mobile-edge computing using FL where they proposed the FedCS protocol to diminish training process time  [25]. In  [26] , the authors proposed a federated transfer learning framework for wearable healthcare. In  [27] , the authors propose a federated learning framework, the federated transfer learning-enabled smart work packaging for protecting the personal image information of construction workers in occupational health and safety management. Although researchers have developed some privacy-preserving methods in the industrial IoT systems, they do not fully preserve the workers' privacy.
Problem definition The term ''branch'' is used all over the article to characterize entities in the industrial IoT environment. Entities might be an office or a factory, etc. Let  =  1 ,  2 , … ,   denotes the branch set. In the context of thermal comfort state prediction, we consider branches as the clients in the definition of FL. Each branch has a local database. We aim at predicting whether the workers are thermally comfortable having historical sensor data information from different branches without sharing raw data and without lack of privacy. We, therefore, design a secure weight parameter aggregation mechanism as follows. The thermal comfort data collected by each branch creates a database. The deep learning neural network model formulated in   uses local training data from   to adjust updated model parameters.
Methodology In a centralized learning process, three steps are followed: data processing, data fusion, and model creation. Traditional centralized data processing techniques consist of extracting data features and labels from original data before fusing. These techniques include outliers removal, data sampling, normalization, and combinations. Through data fusion, data is shared directly by the learning model and all parties to achieve a global aggregated database for training. This approach is a subject of debate due to information privacy issues as it violates the recommendations provided by the privacy regulations including the EU GDPR. In order to address this challenge, FL is introduced. Nevertheless, most of the existing FL techniques employ simple machine learning models such as XGBoost and Decision Tree rather than neural network models  [29].
These traditional models necessitate the upload of a huge number of parameters to the cloud in the federated learning process, which may cause a training failure for a local or global model due to expensive communication overhead  [30]  and limited network bandwidth. The communication overhead is measured by the number of bytes in each communication message sent. Accordingly, the FL framework needs to propose new aggregation mechanisms to deal with the problem of communication overhead. In this section, we propose the Fed-NN approach to predict the thermal comfort state, which is an improved version of the FedAvg algorithm as a way to reduce the communication overhead. In particular, the FL technique requires learning both single and global prediction models from multiple databases stored locally in separate dozens or hundreds of branches  [31].
Branch Selection Protocol: In an industrial IoT environment, the number of branches is small, including office places, factories, and other sites. In such cases, the FL problem is considered a small-scale problem. However, the number of branches might increase at any time. The increase is due to the presence of different factories all over the world. The industries wish to control the thermal comfort in all their factories. This will lead to the divergence of FedAvg algorithm due to the cost of communication overhead, and hence, decrease the accuracy of prediction in the FL framework. The communication overhead is the proportion of time the server spends communicating with the branches instead of aggregating the ML parameters. Moreover, in an FL framework, we are considering the presence of heterogeneous datasets, different computational capacities, and different channel conditions representing each branch, respectively.
For instance, a branch with a large dataset compared to other branches requires more time updating the model unless the branch possesses a better computational power. This will result in a delay of the new model update on the cloud. Furthermore, if a branch is under a poor channel condition, the upload time will become longer. The cloud can perform the Aggregation if and only if it receives all branches updates. Therefore, we designed a mechanism to select a set of branches that will participate in the th round training through a straight-forward approach. We set a deadline for random branches to accomplish the Training and Uploading steps. Submitted updates after the indicated deadlines are ignored. The participants in the branch selection protocol are branches and the server, which is defined as a cloud-based distribution  [23].
The late response of a branch might be linked to a severely poor network channel. • Training Phase: At first, the server loads the model already trained on server data (Fig. 3-2  ⃝). Then, the server sends the gradient weights to the selected branches in phase 1 (Fig. 3-3  ⃝). Locally, each branch will train the global model and send back the updated parameters as in Fig. 3 4 ⃝. The server sets a time  2 for branches to locally train their data and send the parameter updates. Some branches might fail in accomplishing training within this deadline due to large datasets. Moreover, some branches packets might be lost in the network and therefore, they will not be selected for the aggregation process. • Aggregation Phase: In the cloud, the uploaded parameters are aggregated to create the new global model through a secure framework (Fig.
Federated Neural Network Algorithm (Fed-NN) with branch selection: The Fed-NN addresses a real-time accurate prediction while maintaining privacy using FedAvg strategy, neural networks and the branch selection protocol. The pseudo-code of Fed-NN is presented in Algorithm 2 and it incorporates the following steps: • The server initializes the global learning model and sets a deadline  1 for a limited  number of branches to open a bi-directional stream.
Simulation setup Simulations in this section are all conducted using TensorFlow in Python 3. 6. We select the first 80% of the data to train our model and 20% for testing it. Input attributes are normalized. The work is conducted on a PC running 64-bit Windows 10 ProEducation on an Intel Core i7-8700T CPU and using 16 GB of memory. In order to precise the accuracy of our predictions, we adopt the following evaluation metrics: mean absolute error (MAE), mean squared error (MSE), accuracy, where   resembles the true thermal comfort state and ŷ is the predicted state. 𝑀𝐴𝐸 = 1 𝑁 𝑁 ∑ 𝑖=1 |𝑦 𝑖 -ŷ𝑝 | (4) 𝑀𝑆𝐸 = 1 𝑁 𝑁 ∑ 𝑖=1 (𝑦 𝑖 -ŷ𝑝 ) 2 (5)  =            (6)  Data are collected from a one-year longitudinal case study of workers' thermal comfort in an air-conditioned office building in Center City Philadelphia, USA  [32].
Thermal comfort data were collected every 15 min between the period of July 2012 and August 2013. There are 678 621 data samples in total related to four categories: Fig. 4  shows the variation in the value of the previously indicated input attributes. The comfort output state is divided equally between the two states. The outdoor temperature varies between -10 and 30 degrees. The outdoor humidity ranges between 20 and 100. The outdoor air velocity has values between 0 and 10. The metabolic rate has a small range related to the fact that workers in the office usually have three positions: sitting, standing, or walking. Fig. 5  reveals the correlation between each attribute and the other. Our focus is concentrated on the correlation between the comfort state and any other input parameter. It can be obvious that the thermal comfort state has a high correlation with the outdoor temperature as well as the clothing insulation.
Thermal comfort prediction accuracy In this section, we compare the performance of the Fed-NN algorithm with different centralized machine learning algorithms: the neural network (NN), the support vector machine (SVM), and multiple linear regression (MLR) of which are considered with good performance for prediction. The prediction evaluation metrics are presented in Table 2 where the same dataset is trained on all machine learning models. The results illustrate that the MAE of Fed-NN is higher than that of NN and lower than those of SVM and MLR. Precisely, the MAE of Fed-NN is 63. 8% lower than SVM and 38. 1% lower than MLR. This is because Fed-NN inherits the prediction performance of NN. That is, the crux structure of Fed-NN is NN. Hence, the MAE value proves the stability of  Fed-NN and the ability of Fed-NN to accurately predict thermal comfort state same as the NN model while preserving privacy.
Federated Averaging Algorithm (FedAvg) Input: Branches ,  is the fraction of branches on each round,  is the local mini-batch size. is the learning rate and  is the number of local epochs.   is the local training dataset. ∇(. ;. ) is the gradient optimization function and  the number of rounds. Output:  Server Executes: Initialize  0 ; for round  = 1, 2, … ,  do  ← max( ⋅ , 1); {  } ← random set of  branches to participate in the training; Send  0 to all branches in {  }; for each branch  ∈ {  } in parallel do Initialize    =  0 ;   +1 ← BranchUpdate(,    ); end  +1 ← 1 |{  }| ∑ ∈    +1 ; end Branch Executes: BranchUpdate (, ):  ← (split   into batches of size ); while local epoch i in  do while batch  ∈  do  ←  -∇(; ); end end return  to server; 4. 1. Federated learning and deep learning FL is a privacy-preserving machine learning paradigm where different branches in the industrial IoT environment can contribute to the overall model training while keeping their data locally.
datasets   of size   represents data from a set  of  set of branches. Therefore, the size of the local training datasets is  = ∑  =1. In deep learning, the typical settings are given by a set of input-output pairs {  ,   }   =1 , where   ∈ R  represents the input sample vector with  features and   ∈ {0, 1} indicates the labeled output vector. Through training, we need to identify the model parameter vector (weight)  ∈ R  that defines the output   with the loss function    (). The goal is to learn this model locally with secure aggregation of weights among branches. The loss function on the dataset of branch , therefore, is presented as follows:.
{  } ∈ , : the local mini-batch size, : the learning rate,  the number of rounds,   : the local training dataset and : the optimization function. • A number of branches are selected to participate in the training round for which a global model  0 is sent. • In each  selected branch, the model is trained based on local data. The    is updated for  local epochs of SGD to obtain the next   +1 based on the BranchUpdate function. • Using an aggregation method ( +1 ← 1.
Algorithm 1 : 1 Federated Averaging Algorithm (FedAvg) Input: Branches ,  is the fraction of branches on each round,  is the local mini-batch size. is the learning rate and  is the number of local epochs.   is the local training dataset. ∇(. ;. ) is the gradient optimization function and  the number of rounds. Output:  Server Executes: Initialize  0 ; for round  = 1, 2, … ,  do  ← max( ⋅ , 1); {  } ← random set of  branches to participate in the training; Send  0 to all branches in {  }; for each branch  ∈ {  } in parallel do Initialize    =  0 ;   +1 ← BranchUpdate(,    ); end  +1 ← 1 |{  }| ∑ ∈    +1 ; end Branch Executes: BranchUpdate (, ):  ← (split   into batches of size ); while local epoch i in  do while batch  ∈  do  ←  -∇(; ); end end return  to server; 4. 1. Federated learning and deep learning FL is a privacy-preserving machine learning paradigm where different branches in the industrial IoT environment can contribute to the overall model training while keeping their data locally.
{  } ∈ , : the local mini-batch size, : the learning rate,  the number of rounds,   : the local training dataset and : the optimization function. • A number of branches are selected to participate in the training round for which a global model  0 is sent. • In each  selected branch, the model is trained based on local data. The    is updated for  local epochs of SGD to obtain the next   +1 based on the BranchUpdate function. • Using an aggregation method ( +1 ← 1 Output: 𝜔 Server Executes: Initialize 𝜔 0 ; for round 𝑖 = 1, 2, … , 𝑚 do while 𝑡 𝑑1 > 0 do {𝐾 𝑛 } ← random set of 𝑛 branches to participate in the training; 𝑡 𝑑1 ← 𝑡 𝑑1 -1; end Send 𝜔 0 to all branches in {𝐾 𝑛 }; while 𝑡 𝑑2 > 0 do while ℎ 𝜔 has not convergence do for each branch 𝑘 ∈ {𝐾 𝑛 } in parallel do Initialize 𝜔 𝑘 𝑡 = 𝜔 0 ; Conduct a mini-batch input time step {𝑋 𝑖 𝑙 }; Conduct a mini-batch thermal comfort index series {𝑌 𝑖 𝑙 }; ℎ 𝜔 ← ∇ 𝜔 𝜔 𝑘 𝑡+1 ← BranchUpdate(𝑘, 𝜔 𝑘 1 ∑ 𝑙 𝑖=1 (𝑓 𝜔 (𝑋 𝑖 𝑙 ) -𝑌 𝑖 𝑙 ); 𝑙 𝑡 , ℎ 𝜔 ); end end 𝑡 𝑑2 ← 𝑡 𝑑2 -1; end 𝜔 𝑡+1 ← 1 |{𝐾 𝑛 }| ∑ 𝑘∈𝐾 𝑛 𝜔 𝑘 𝑡+1 ; end Branch Executes: BranchUpdate (𝑘, 𝜔, ℎ end end return 𝜔 to server; server, the received weights are aggregated based on the updates from all branches.
Algorithm 1 shows the detailed pseudo code of the FedAvg algorithm that can be summarized in the following steps: |{𝐾 𝑛 }| 1. Federated Averaging Algorithm (FedAvg): In the following algorithm, each branch performs gradient descent optimization on the server model based on the local dataset without sending informational data to the cloud. This will hinder the problem of network bandwidth limitations in cloud aggregation. On the Algorithm 2: Federated Neural Network Algorithm (Fed-NN) Input:  ):  ← (split   into batches of size ); while local epoch i in  do while batch  ∈  do  ←  -((; ));.
2017). In order to understand the regulatory logic downstream of Nodal, we searched for putative transcription factor binding sites (TFBS) in the differentially accessible genomic regions of "all neural (G5)" and "all epidermal (G5)" genes (fig. 1 ). We first used HOMER to find enriched motifs that were then compared with known TFBS in vertebrates. Although we used a vertebrate database, it has been shown that transcription factor (TF) binding specificities are highly conserved in animals, even between species as evolutionarily distant as human and Drosophila  (Nitta et al. 2015). We found that for the "all neural (G5)" genes, the more significantly enriched motifs, which are also the more abundant, correspond to putative binding sites for transcription factors of the SOX family (supplementary table  S3  and fig.
At G5, Zeb, whose expression is detected in the dorsal mesendoderm at G4, is expressed in both the dorsal mesendoderm and the presumptive neural plate. Zeb expression is then FIG. 4. Analysis of "all neural (G5)" genes expression in whole embryos. Mfuzz clustering graphs are presented on the left. The color code reflects "Membership" values calculated by Mfuzz (magenta for high values and green for low values of Membership score). Corresponding box plots (center line, mean; box limits, upper and lower quartiles; points, outliers; box in magenta = box with the highest mean expression), enrichment in TFBS of the ATAC-seq peaks with differential accessibility corresponding to the core genes of each cluster, and GO term enrichment of these core genes are presented. Neural and epidermal gene regulatory networks • https://doi.
Explants were then collected and either frozen for RNA extraction or directly processed for ATAC-seq. Staging is as proposed in  Bertrand et al. (2021)  and  Carvalho et al. (2021). RNA-seq and ATAC-seq RNA was extracted using the RNeasy Plus Micro Kit (Qiagen) following the manufacturer's instructions. For ATAC-seq library construction, 80-100 explants were transferred in a 1. 5 ml tube, in duplicates. We then followed the method described in  Magri et al. (2020). Sequencing of the RNA-seq and ATAC-seq libraries was undertaken at the Centre for Genomic Regulation (CRG, Barcelona) Genomics Unit platform. ATAC-seq data analysis was performed using the nf-core/atacseq pipeline (v1. 0. 0)  (Ewels et al. 2020) , which runs Nextflow (v19. 10. 0) (Di  Tommaso et al. 2017 ), for quality controls, read alignment against Bl71nemr assembly  (Marletaz et al.
TFBS Prediction Motif analysis on sequences corresponding to ATAC-seq peak regions was undertaken using three methods: HOMER (v4. 11, default parameters, vertebrate set of motifs)  (Heinz et al. 2010) ,  MEME-ChIP (classic mode and JASPAR CORE [2018]  VERTEBRATES as motifs database)  (Machanick and Bailey 2011) , and RSAT (peak-motifs tool, motifs compared with JASPAR CORE [2020] VERTEBRATES database)  (Nguyen et al. 2018).
2003). Clustering of Whole-Embryo RNA-seq Data RNA-seq data on whole embryos published in  (Marletaz et al. 2018 ) (available under Gene Expression Omnibus accession GSE106430) were normalized and clustered using Mfuzz  (Kumar and Futschik 2007) , implemented in R, choosing the fuzzy c-mean soft clustering method. The fuzzifier parameter m was directly estimated from the data. The α-threshold was set to the default value. The core genes for each cluster were used for GO-term enrichment analysis and for TFBS enrichment analysis of the corresponding differentially accessible ATAC-seq peak sequences using analysis of motif enrichment (McLeay and Bailey 2010).
In situ Hybridization of GRN Nodes Wild-type embryos were fixed at the eight-cell, morula, blastula, G0, G1, G2, G4, G5, N0, N2, N3, and N4 stages in PFA 4% in MOPS buffer (0. 1 M MOPS pH 7. 5; 2 mM MgSO 4 ; 1 mM EGTA; 0. 5 M NaCl). In situ hybridizations were performed as described previously  (Somorjai et al. 2008). The accession number/sequences used for probe synthesis are given in supplementary table S22, Supplementary Material online. Neural and epidermal gene regulatory networks • https://doi. org/10. 1093/molbev/msac055 MBE.
Acknowledgments We would like to dedicate this work to  Jose-Luis Gomez-Skarmeta , a friend and an incredible researcher without whom this work could never have been accomplished. This work benefited from access to the  Observatoire Oceanologique de Banyuls sur Mer (OOB) , an  EMBRC-France  and  EMBRC-ERIC  site. The work was supported by the  Centre Nationale de la Recherche Scientifique , by the  "Agence Nationale de la Recherche " (grants  ANR-19-CE13-0011-01  and  ANR-16-CE12-0008-01  to H. E. ) and by the  European project Assemble Plus  ( H2020-INFRAIA-1-2016-2017 ; grant no. 730984  to H. E. ). S. B. was supported by the  Institut Universitaire de France. The work was supported by the  European Research Council  (grant  ERC-StG-LS2-637591  to M. I. ) and by the  Spanish Ministerio de Ciencia  (grant  BFU2017-89201-P  to M.
IMOGEN The Integrated Model Of Global Effects of climatic aNomalies (IMOGEN) is an intermediate complexity climate model that can be fully coupled with any land surface model. In this case, IMOGEN supplies the meteorological data to drive JULES which simulates changes in the carbon stores of the land surface that feedback onto changes in the IMOGEN-simulated atmospheric CO 2 concentration  [31]. We denote this model JULES-IMOGEN. IMOGEN consists of a simple energy balance model (EBM;  [32] ) that relates changes in concentrations of different atmospheric greenhouse gases to the global mean land temperature response, via changes in their combined radiative forcing. IMOGEN uses a "pattern-scaling" approach  [32]  to then relate, linearly, the amount of annual global average warming over land (via the EBM) to monthly changes in local meteorology.
Funding: This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No  101003536  ( ESM2025-Earth   System Models for the Future ). E. B. was supported by the  Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ). S. C. was supported by a  Natural Environment Research Council  independent research fellowship (grant no. NE/R015791/1 ). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Data Availability Statement: Both the model code and the files for running it are available from the Met Office Science Repository Service: https://code. metoffice. gov. uk/ (accessed on 20 March 2022). Registration is required and code is freely available subject to completion of a software license. The results presented in this paper were obtained from running JULES branch: https://code. metoffice. gov. uk/ trac/jules/browser/main/branches/dev/eleanorburke/vn5. 1_permafrost (accessed on 20 March 2022) which is a branch of JULESv5. 1. The runs were completed with the Rose suite https://code. metoffice. gov. uk/trac/roses-u/browser/a/j/2/5/7/ (accessed on 20 March 2022) and branches there of. All data are available on request from the author.
Algorithm We now focus on the results of the DLNN runs combined using the Meta-learner, which gives the best redshift estimation. By comparing the two samples of galaxies with S/N > 10 and S/N > 3, the redshift estimation is impro v ed only when four filters are considered as input. This shows that, in the case with nine input filters, the impro v ement in data quality given by selecting only S/N > 10 is shadowed by a decrease of the number of objects in the training sample (see Section 4. 3 for further discussion). The inclusion of the additional five ground-based filters, i. e. u , g , r , i , and z, decreases the fraction of outliers from 0. 066-0. 088 to 0. 001-0. 002, depending on the S/N limit. The bias is al w ays very small, below 0. 001, while the NMAD decreases from ∼0. 05 to < 0.
The impro v ement of the machine-learning algorithms o v er SED fitting has also been shown by Euclid Collaboration: Humphrey et al. ( 2022a ) when selecting passive galaxies. These authors argued that this is due to the machine-learning networks' capability to optimally weight the different input data points; while, the SED fitting methods generally use a more direct weighting method, based on the S/N. More details on the SED fitting results are reported in Appendix C (available online). Overall, among the different cases tested here, the best network for redshift estimation consists of the DLNN with nine input filters and S/N > 3, combined using a Meta-learner.
acknowledge the support of the STFC Cosmic Vision funding. LB acknowledges the financial support of Agenzia Spaziale Italiana (ASI) under the research contract 2018-31-HH. 0. SvM acknowledges funding from the European Research Council through the award of the Consolidator Grant ID 681627-BUILDUP. HH is supported by a Heisenberg grant of the Deutsche Forschungsgemeinschaft (Hi 1495/5-1) as well as an ERC Consolidator Grant (No. 770935). MB acknowledges financial contributions from the agreement ASI/INAF 2018-23-HH. 0, Euclid ESA mission -Phase D.
In this paper, we adopted an extended complete version of Amazon dataset  [28] , which incorporate instances requests information concerning multimodal instances families. While the majority of previous techniques deployed greedy denoising techniques increasing power consumption and time consumption, we focus in this paper on a power efficient implementation of a Bidirectional Gated Recurrent Unit based Stacked denoising Autoencoders. Using the developed predictor, the virtual vCPU, memory, and storage time series utilization were forecasted with a great precision, according to the following different three AWS instances families: the compute-optimized, the memory-optimized and the -Computationally intensive and greater calculation time which affect the prediction performance. -Very dependent on Fruit fly optimization convergence.