dataesr/scientific-paragraphs-categorization huggingface dataset:
3%. The effect of ANN hyperparameters (dataset, number of epochs, data augmentation, etc. ) on the performance circuit synthesis approaches is explored in  [46]. The ANN models have fully connected layers without weight sharing. To eval- uate the ANN model, 80%-90% of the datasets are allocated for training. The remaining data is kept for model validation; meanwhile, a small portion is used to verify the practical application of the model. The number of nodes is kept high at initial layers and decreased through the further layers to have high performance in the training data, at the cost of overfitting, which is then addressed using L2 weight regularization. Then, a grid search is applied over the hyperparameters (number of layers, number of nodes per layer, nonlinearity and regularization factor) to fine tune the model.
During the ANN data sampling for p predictions, an acceptance coefficient of γ=0. 15 is used to expand the model validity beyond the dataset limits. The selection of solutions from these P predictions is performed by simulating the predicted circuit sizing, and, and either using of the FOMs or using the Pareto dominance sorting. To demonstrate the proposed approach, and a single stage amplifier using voltage combiners for gain enhancement is selected. A dataset consists of 16,600 different design points (before data augmentation) is used. DC Gain, bias current (IDD), gain-bandwidth product (GBW), and phase margin (PM) are determined as performance metrics. There different ANNs were trained in this study: ANN-1 is trained by original dataset with 5000 epochs, ANN-2 is trained by augmented (40 times) datasets with 5000, and ANN-3 is trained by the augmented dataset with 500 epochs, whose weights are initialized with the ANN-1.
Agent Environment Action (At) Reward (Rt) State (St) Figure  11 : The agent-environment interaction in reinforcement learning loop  [49]. As discussed in previous section, generation of training dataset for supervised learning to model circuits is difficult. This is due to the fact that circuit simulation is slow, thus rendering generation of a large-scale dataset as time-consuming and that most circuit designs are proprietary IPs within individual IC companies, making it expensive to collect largescale datasets. As a result, RL engine that know nothing about analog design is proposed in  [49]. The RL agent first learns to meet hard constraints, and then learns to optimize the targets. Compared with grid search-aided human design, L2DC can achieve 250× higher sample efficiency with comparable performance.
To classify the objectives, positive and negative weights are assigned for each objective, where a positive coefficient intends to maximize the objective while the negative one aims to minimize it. Before the simulator, a rough pre-evaluation is performed by a symbolic filter, where the small signal parameters of devices are extrapolated by mapping of circuit variables, which then turns into performance estimations. If the candidate is verified by the symbolic analysis, it is simulated through SPICE and rewarded by considering with the design objectives and constraints. Otherwise, the reward is set to zero. To demonstrate the proposed approach, a folded cascode amplifier is optimized, where dc gain, the bandwidth, the phase and the gain margins are chosen as the design specifications. According to the reported results, the proposed tool is able to optimize the circuit satisfying all the design specifications.
Reference Application Method(s) Contributions  [17]  Fault Dianosis MLP Haar wavelet followed by kPCA to reduce dimensionality of features  [72] ,  [73]  Fault Dianosis SVM A defect filter identifies hard and soft faults, and, for the soft faults inverse regression is used to locate the fault cause  [74]  Fault Dianosis ANN Wavelet and PCA to reduce dimensionality of features  [75]  Fault Dianosis ANN Dictionary and PCA reduce dimensionality of features  [76]  Fault Dianosis Fisher DT LDA to improve class separability while compressing the feature space  [77]  Fault Dianosis Naive Bayes Wavelet followed by kLDA  [78]  Fault Dianosis DBN End-to-end learning simplifies the feature engineering  [79]  Fault Dianosis DBN End-to-end with integrated random sampling for data gathering  [80]  Remaining Useful Performance Kernel RVM PSO is used to train a RVM that predict the trajectories of the circuits health and predict the remaining useful performance  [81]  Test Set Compression ONN NSGA optimization to select the smallest set of features sufficient to diagnose the CUT, resulting in a cheaper test procedure  [25]  One-Shot Calibration ANN Post-fabrication calibration to counter performance deviation due to fabrication in a single calibration step  [82]  Post-Layout Modeling BMF Uses cheap pre-silicon simulation data, together with a small dataset of fabricated circuits for efficient post silicon modeling from 1 for non-fault circuits as the circuit's elements degrade.
The objective is to obtain a latent space that enlarges the interclass distance for different fault classes and reduces the intraclass distance among each fault, improving the classifier's ability to identify them. This approach was experimentally validated on two typical analog filter circuits, learning more discriminative features than traditional feature extraction, and thus, producing better diagnostic results with a smaller dataset. [79]  adaptively extracts features from Monte Carlo sampling and then uses them to train a general-purpose DBN classifier, while simultaneously embedding dimensionality reduction into it. The sampling is embedded into the methodology flow, discarding most of the human involvement in feature preparation. The complete flow is composed by the following steps:  nominal parameters, and their tolerances; (2) for each potential fault mode obtain via simulation the raw time-series signals, from time-domain transient, and Monte Carlo analysis; (3) build the dataset using the raw time-series signals, where each time-series signal of each Monte Carlo sample corresponds to one input instance; (4) data engineering for ML model construction and separation of the dataset into training and test sets; (5) used the data to train the DBN, which is accomplished by a layer-by-layer unsupervised pre-training stage and a fine-tuning stage using backpropagation; and (6) the DBN is then used to for fault classification.
Including some RF testing allowed the method to detect all failing devices with less than 10% of the cost. In  [25] , the authors propose a One-Shot calibration mechanism, similar to the one in Fig. 15  that relies on an ANN predictor trained to estimate the performance given the test measurements and the settings of the tuning knobs. The dataset is built of 67 fixed combinations of turning knobs for each measured circuit. Statistical sampling mechanisms  [87]  can also be used to build the training set. Once the system is in place, the CUT is measured, if performance is not within specifications, it used the trained regressor to search for the best settings of the tuning knobs. The method was applied to an RF power amplifier and was able to recover 96% of devices failing specifications. How-.
On top of it, the models can also be based on previous legacy knowledge. Future research directions in this field will most likely be focused on how to reuse those design patterns learned and generalize them well beyond the training data. These generalization capabilities are also expected to be achieved for newer circuit topologies. Still, an actual problem is how to generate a dataset robust enough for that purpose. While acquiring robust sizing data for several topologies is still quite feasible, acquiring validated legacy layouts is not straightforward. The approach taken recently was to use other EDA tools to generate synthetic data. Nonetheless, some (error-prone) mechanism or human inspection is still necessary to consider them "legacy-proved". For automatic routing, the application of ML is still taking its first steps, yet promising.
Before working on the proper deep models, the most urgent matter to be researched is data engineering, how to concisely and accurately represent routing data on a dataset, which data is relevant to be fed to the model, and, how far it could be generalized. The problems of acquiring robust legacy data previously found on placement, are only further aggravated here. While a handful of robust manually routed designs can still be acquired and used, the necessity of tens of thousands will ultimately rely on previous EDA tools, e. g. , template-based, heuristic-based of ILP routing procedures. The use of ML for fault diagnosis is well established in the research community. Several classification methods appear in the literature. There is no particular method standing out, but supervised learning is the most common approach.
However, large-scale effectiveness data of TCVs in many African settings are still limited. Effectiveness data provide critical input to decision making by national and regional stakeholders to drive country and regional uptake in Africa, where the epidemiology of typhoid fever differs from South Asia. Nonetheless, there are no cluster-randomized trials currently ongoing on the continent and populationlevel protection data are largely lacking. Herein, we present the design of the first clusterrandomized trial assessing the real-world vaccine effectiveness of TCVs in preventing typhoid fever on the African continent. The data generated will be used in estimating the budgetary impact and in understanding the value of the money invested in the potential introduction of TCV in Ghana.
Cluster Randomization and Method of Blinding The TyVEGHA study population was recruited from a geographically defined catchment area in the Asante Akim District of Ghana (see Figure  1 , TyVEGHA catchment area). The baseline map of the area was developed in partnership with the Statistical Service office in Agogo, Ghana. Additional data from a baseline census were used to inform details on the map and to confirm the catchment area boundaries for the trial. The census population data were merged with the Geographic Information System mapping (GIS,) data for cluster formation (QGIS. org, 2021. QGIS Geographic Information System. QGIS Association. ). The study area was divided into 80 clusters. Each cluster consisted of a total population of 400-600 inhabitants at average, with an estimated proportion of 40% in the eligible age group for TyVEGHA.
The study card will also contain instructions for attending the study health care facilities or sentinel sites if the participant develops a fever at any time over the follow-up period of three years. A subset of 600 participants will be selected on a 2:1 ratio (Vi-TT vs. MCV-A) to have blood samples collected at baseline (Day (D) 0) and at D28, D365, D730 and D1095 postvaccination to assess vaccine immunogenicity. Safety assessments will include an active safety follow-up contact on day 7 to monitor adverse events following immunization in participants in the safety substudy (n = 3000, 1:1 assignment to intervention and control group) using study diary cards, and 3 years of passive hospital-level febrile and severe adverse event (SAE) surveillance. An independent Safety Monitoring Committee (SMC) was established to assess safety results and to correspond with the Data Safety Monitoring Board (DSMB).
The occurrence of any SAE within 6 months follow-up post-dose will be also summarized and listed. An immunogenicity analysis will be presented as the geometric mean titre (GMT) with 95% CI at baseline (D 0), D 28, D 365 D 730, and D 1095, and as a proportion of fourfold increase from baseline (seroconversion rate) with 95% CI at D 28, D 365, D 730, and D 1095. Data defined by the study protocol will be captured in a clinical study database using REDCap  [27]. All clinical and laboratory data were entered from paper forms into the electronic data capture (EDC) system. Data will be entered using tablets, laptops, or computers into a web-based version of the electronic case report forms (eCRFs). Edit checks programmed within the eCRFs will validate the data at the time of data entry. Performance qualification of the eCRFs will be performed, in particular on the edit checks.
In practice, treating the 'true' values of M and Ψ as missing data means that these are treated as free parameters which will also be updated in each iteration of the sampler. We refer the reader to K07 for further details, in particular section 6. 2. 1 which lists the steps performed in each iteration. We have employed the Python implementation of the K07 Gibbs sampler, as implemented by Josh Meyers. 5 As stated above, rather than having direct measurements of M and Ψ (which we referred to as x and y), we have beagle-derived joint M -Ψ posterior distributions that we do not want to assume to be described by a single (bi-variate) Gaussian. We therefore extend the K07 Gibbs sampler to accept Gaussian mixture models of the beagle-derived joint M -Ψ posterior probabilities. 6 Essentially, these models are given by the sum of K bi-variate Gaussians, each with weights given by π k , mean µ k = [ M , Ψ], and covariance matrix ζ k : P(M , Ψ | F, E) ≈ P(M , Ψ | γ) = K k=1 π k N2(µ k , ζ k ) (.
mixture. GaussianMixture from the Python package scikit-learn  Pedregosa et al. (2011). After some experimentation, we opted to fit 3 Gaussians to each posterior, finding that this prevented over-fitting to individual features, but was flexible enough to represent the range of mophologies in the output posterior distributions. An example GMM fit is shown in Fig. 3 , where we display samples from the joint Ψ -M posterior probability distribution from the beagle fit to a single object from the const-G scenario, which was fitted to with a constant SFH. Over-plotted are the samples from the GMM fit to this Ψ -M posterior. As constructed, the Gibbs sampler self-consistently propagates the uncertainties on Ψ and M estimates of individual objects onto the uncertainties on main sequence parameters α, β and σ.
Feldmann (2019) Feldmann (  2019 ) introduces LEO-Py, a package to estimate likelihoods for data while modelling the correlation between variables in a flexible way. LEO-Py accounts for complex measurement uncertainties, censored and missing data, as well as arbitrary marginal distributions of the variables. This is in many respects an extension of K07, and can be used with a likelihood maximisation algorithm or as the likelihood estimate within a sampler to determine the posterior probabilities of the model parameters. Although non-Gaussian uncertainties are accepted, accounting for multiple peaks in the uncertainty structure is not accounted for, whereas our extension to the K07 sampler can deal with this scenario. For the current implementation, the accounting for multiple peaks in the joint Ψ-M posterior is of relatively minor importance.
ACKNOWLEDGMENTS The authors would like to thank the anonymous referee whose skepticism of the original draft helped us significantly improve our treatment of dust in H ii regions within beagle. The paper is significantly improved thanks to their rigor and attention during the referee process. ECL gratefully ac-knowledges the abundant and fruitful discussions with  David Stenning  and  Tom Charnock  as well as the invaluable support of  Olga Degtyareva  et al. ECL, JC, and SC acknowledge support from the  European Research Council (ERC)  via an  Advanced Grant  under grant agreement no. 321323-NEOGAL. ECL and LS additionally acknowledges support from the  ERC Advanced  Grant  695671  'QUENCH' as well as the  Science and Technology Facilities Council (STFC). This research made use of Astropy, a community-developed core PYTHON package for astronomy (Astropy Collaboration et al.
The objective of this study was to describe the care pathway of patients with amyotrophic lateral sclerosis (ALS) based on real-life textual data from a regional coordination network, the Ile-de-France ALS network. This coordination network provides care for 92% of patients diagnosed with ALS living in Ile-de-France. We developed a modular ontology (Onto-PaRON) for the automatic processing of these unstructured textual data. OntoPaRON has different modules: the core, medical, socio-environmental, coordination, and consolidation modules. Our approach was unique in its creation of fully defined concepts at different levels of the modular ontology to address specific topics relating to healthcare trajectories. We also created a semantic annotation tool specific to the French language and the specificities of our corpus, the Ontology-Based Semantic Annotation Module (OnBaSAM), using the OntoPaRON ontology as a reference.
However, our knowledge of care pathways and their components, including their interruptions and the difficulties encountered by patients and their families, remains partial, because information is split between various actors and is often untraceable. The identification of interruptions of patient care requires a prior knowledge of the elements of the care pathway. We addressed these issues, using a real-life database for patients with ALS managed by the regional coordination network of Ile-de-France. ALS is a neurodegenerative disease that affects the motor neurons, causing progressive weakening of the voluntary muscles. This disease has two forms: a familial form, accounting for 10% of cases, and a sporadic form accounting for the other 90%. Median survival after diagnosis is generally three years  [5].
The ALS-IDF network established a database to make it easier to track the requests, needs and coordination actions implemented to support patients. This database contains two types of patient data: real-life and sociodemographic data. We hypothesized that an analysis of these textual data would make it possible to identify the difficulties and needs of patients and their families at home, to understand the coordination actions implemented and to identify situations or types of patients confronted with multiple difficulties. The identification of such situations should improve patient support and the early detection of risk situations. We processed these textual data by semantic annotation using an ontology corresponding to this domain as a lexical resource. An ontology is defined as the formalization of a shared conceptualization  [11].
A modular ontology corresponds to a set of modules, where each module is a stand-alone component that maintains relationships with other ontology modules  [15]. A modular ontology seemed be the most appropriate model for taking all these aspects into account. As a means of focusing our research on specific themes and identifying the difficulties encountered by patients during their care pathways, we decided to create fully defined concepts  [16] , each encompassing several classes relating to the same theme but from different modules. We developed a semantic annotation tool based on the General Architecture for Text Engineering (GATE https://gate. ac. uk) open framework, which provides basic building blocks for the annotation of textual data. GATE's resources have been adapted to our needs and to the French language.
Our annotator, Ontology-Based Semantic Annotation Module (OnBaSAM) uses the OntoPaRON ontology. We used the annotations performed to create a specific module with annotation frequency as output. One of the advantages of this work is that it makes it possible to convert unstructured textual data into structured quantitative data. These quantitative data can be used for population-based statistical approaches in which the annotation data obtained are used to describe the elements of patient care trajectories. The work includes information extraction using ontology and specifically fully defined concepts, and data mining tasks by looking for relationships between the information extracted by semantic annotation and the socio-demographic data from the ALS-IDF network database  [17].
Materials and methods Computer tools are essential for exploitation of the textual data of the ALS-IDF network, which include too great a volume of information for manually processing. We decided to use knowledge engineering and automatic natural language processing (NLP) tools. We present here: (a) the use of an ontology to model domain knowledge; (b) the creation of a fully defined concept concerning specific themes relating to the care pathway; (c) the use of a semantic annotation tool that we developed, the OnBaSAM module. All these analyses were performed with JMP 14 Pro Statistical Discovery software (SAS, Cary NC).
Materials The ALS-IDF network database has two parts: a structured static part containing sociodemographic data for the patients (sex, date of diagnosis, date of inclusion in the network, living conditions, date of birth, place of residence, etc. ), and a dynamic part consisting of real-life data in a textual format. Textual data for "events" (unstructured part of the database) are entered as free text by the coordinators and may be of different types (e. g. transmission, hospitalization reports, minutes of care team meetings, medical transcriptions, or the transcription of oral exchanges with patients or their families). The Table  1  presents two examples of events present in the database. The processing of textual event data required initial spelling correction and pseudo-anonymization to comply with the General Data Protection Regulation (GRPD) rules.
Construction of the OntoPaRON ontology We use 'single quotes' to denote OntoPaRON classes and italic font to denote the relationships in our ontology. The specific features of our project relate to (a) the decision to model knowledge through a modular ontology and (b) the use of fully defined concepts specifically created as themes of interest in statistical analyses, to improve clinical understanding of the care trajectory. Our ontology was developed with a methodology combining a top-down approach involving the use of a top-level ontology with a bottom-up approach involving searches for candidate terms in the corpus of the text  [18]. The first step was the extraction of corpus terms with NLP tools in BIOTEX software  [19]. The corpus used consisted of 60,130 events extracted from the database of the ALS-IDF network, covering a ten-year period of network activity (2005 to 2015).
The OntoPaRON ontology is composed of five modules (one core module, three domain modules, one consolidation module): 1. The core module, which contains all the high-level concepts common to the three ontologies, such as 'ideal objects','agents', 'processes', and 'modes'. Some of the core ontology and high-level concepts are shown in Fig 1. This ontology was inspired by the Menelas top-level ontology  [25]  available from: https://bioportal. bioontology. org/ontologies/ TOP-MENELAS. This module also contains all object properties used, and the fully defined high-level concepts. 2. The medical module is the largest in terms of the number of classes. It is specific to the medical domain and includes: 'medical agents' ('doctor', 'neurologist', 'physiotherapist', etc. ), 'medical processes' ('consultation', 'hospitalization', etc.
The coordination module consists mostly of specific coordination missions ('coordination actions' ). There are several types of coordination action: 'communication actions', 'assessment of needs actions', and 'resource search actions'. We based our model partly on a previous study  [27]  describing the creation of the Nursing Care Coordination Ontology (https:// bioportal. bioontology. org/ontologies/NCCO). This is the module with the smallest number of concepts. 5. The last module is a consolidation module. This module does not model any concepts, but all the concepts of the ontology are imported into it. Following importation of the various modules, the HermiT reasoner (1. 3. 8. 413)  [28]  from Protegeis used to infer classes under the fully defined concept. A reasoned version of the ontology, with the concepts inferred under the fully defined concept, is exported and used by the two tools we developed: the semantic annotator Ontology-Based Semantic Annotation Module (OnBaSAM), and the annotation evaluation tool Pronto.