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427 results for “modularity”
Data from: Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning
<div> <div> <p>Pulse timing is an important topic in nuclear instrumentation, with far-reaching applications from high energy physics to radiation imaging. While high-speed analog-to-digital converters become more and more developed and accessible, their potential uses and merits in nuclear detector signal processing are still uncertain, partially due to associated timing algorithms which are not fully understood and utilized.</p> <p>In the paper "Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning", we propose a novel method based on deep learning for timing analysis of modularized detectors without explicit needs of labelling event data. By taking advantage of the intrinsic time correlations, a label-free loss function with a specially designed regularizer is formed to supervise the training of neural networks towards a meaningful and accurate mapping function. We mathematically demonstrate the existence of the optimal function desired by the method, and give a systematic algorithm for training and calibration of the model. The proposed method is validated on <strong>two experimental datasets</strong> based on silicon photomultipliers (SiPM) as main transducers:</p> <ol> <li>In the toy experiment, we collect data from a pair of SiPM sensors from a common laser source. The neural network model achieves the single-channel time resolution of 8.8 ps and exhibits robustness against concept drift in the dataset. </li> <li>In the electromagnetic calorimeter experiment, we collect data from an eight-channel calorimeter module. Several neural network models (Fully-Connected, Convolutional Neural Network and Long Short Term Memory) are tested to show their conformance to the underlying physical constraint and to judge their performance against traditional methods. </li> </ol> <p>In total, the proposed method works well in either ideal or noisy experimental condition and recovers the time information from waveform samples successfully and precisely. <strong>The dataset in this repository serves as a basis for similar researches on timing performance of SiPM-based nuclear detectors, and on application of neural networks to typical signals of nuclear radiation detectors.</strong></p> </div> </div>
Initial Simulation Results Using ModularBuildingPy on a High-Rise Modular Steel Building
<p>These are output files from 'ModularBuildingPy', a Python-based tool designed for numerical modeling and analysis of volumetric modular steel buildings. Refer to <a href="https://mbbatukan.github.io/ModularBuildingPy/">the documentation</a> for more information. </p>
UPF3A and UPF3B are redundant and modular activators of nonsense-mediated mRNA decay in human cells
<p>Source data for the publication: UPF3A and UPF3B are redundant and modular activators of nonsense-mediated mRNA decay in human cells.<br> Includes raw image data (e.g. agarose gels, western blots, northern blots), quantifications, qPCR raw Ct values and other supporting material.</p>
Raw data for "Modular Pulse Program Generation for NMR Supersequences"
<p>Raw data for the paper <em>Modular Pulse Program Generation for NMR Supersequences</em>, which accompanies the GENESIS website for automatic generation of NOAH pulse programmes.</p> <p>Please note that this contains data only, not any of the accompanying figures. The same datasets may alternatively be downloaded from GitHub, if preferred: https://github.com/yongrenjie/genesis-paper/releases/tag/final-revision</p> <ul> <li>For the figures, and the scripts used to generate them, please see https://github.com/yongrenjie/genesis-paper (the repository readme contains detailed instructions on reproducing the figures)</li> <li>For the GENESIS source code and a LaTeX version of the paper itself, please see https://github.com/yongrenjie/genesis</li> <li>For the GENESIS website itself, please see https://nmr-genesis.co.uk</li> </ul>
Modular Floor by CARBODIN
<p>This video presents the physical mock-up of the CARBODIN demonstrator for a modular floor design applied to railway transport, developed within WS8 of the CARBODIN project.</p>
Fig. 2 in Functional And Phylogenetic Aspect In Modularity Of Palearctic Mustelids (Carnivora, Mustelidae) Mandible
Fig. 2. Distribution of mustelid specimens in the scatterplot of the allometric shape component (Regression Score 1) vs log10-transformed mandible length. Mandible shape changes associated with allometry are shown for zero (average shape, grey outline), minimal and maximal values of the regression scores (with magniFIcation factor 1). Masseteric fossa (landmark 12) is linked with landmarks 6, 8, 13 for the ease of visualization. Species are abbreviated: E. lutris = E; G. gulo = G; M. meles = M; L. lutra = L; Martes: M. martes = 1; M. foina = 2; M. zibellina = 3; M. flavigula = 4; Mustela: M. eversmani = e; M. putorius = p; M. lutreola = l; M. sibirica = s; M. erminea = r; M. nivalis = n.
Fig. 3 in Functional And Phylogenetic Aspect In Modularity Of Palearctic Mustelids (Carnivora, Mustelidae) Mandible
Fig. 3. Distribution of mustelid specimens in the scatterplot of PLS1 (A): variation within mandibular corpus (Block 1) presented at x-axis, and variation within ramus (Block 2) is at y-axis. Mandible shape changes associated with standard (B and C) and evolutionary (D and E) PLS1 are shown with black outline for extreme values of PLS1 scores. The reference shape is shown as grey outline. MagniFIcation scale factor is 1.5. Species are abbreviated as in FIg. 2.
Fig. 1 in Functional And Phylogenetic Aspect In Modularity Of Palearctic Mustelids (Carnivora, Mustelidae) Mandible
Fig. 1. The position of landmarks on a mandible outline of Mustela lutreola. A — scheme of landmarks from Romaniuk (2018); the picture of mandible is adapted from Novikov (1956). And the subdivisions into two (B) and three modules (C) with the lowest RV coefficients.
Sex-specific tuning of modular muscle activation patterns for locomotion in young and older adults
<p>There is increasing evidence that including sex as a biological variable is of crucial importance to promote rigorous, repeatable and reproducible science. In spite of this, the body of literature that accounts for the sex of participants in human locomotion studies is small and often produces controversial results. Here, we investigated the modular organization of muscle activation patterns for human locomotion using the concept of muscle synergies with a double purpose: i) uncover possible sex-specific characteristics of motor control and ii) assess whether these are maintained in older age. We recorded electromyographic activities from 13 ipsilateral muscles of the lower limb in young and older adults of both sexes walking (young and old) and running (young) on a treadmill. The data set obtained from the 215 participants was elaborated through non-negative matrix factorization to extract the time-independent (i.e., motor modules) and time-dependent (i.e., motor primitives) coefficients of muscle synergies. We found sparse sex-specific modulations of motor control. Motor modules showed a different contribution of hip extensors, knee extensors and foot dorsiflexors in various synergies. Motor primitives were wider (i.e., lasted longer) in males in the propulsion synergy for walking (but only in young and not in older adults) and in the weight acceptance synergy for running. Moreover, the complexity of motor primitives was similar in younger adults of both sexes, but lower in older females as compared to older males. In essence, our results revealed the existence of small but defined sex-specific differences in the way humans control locomotion and that these strategies are not entirely maintained in older age.</p> <p>In this supplementary data set we made available: a) the metadata with anonymized participant information; b) the raw EMG, already concatenated for the overground trials; c) the touchdown and lift-off timings of the recorded limb, d) the code to process the data. In total, 520 trials from 215 participants are included in the supplementary data set.</p> <p>The file “metadata.dat” is available in ASCII format and contains:</p> <ul> <li>Code: the participant’s code</li> <li>Group: the participant's group (G1=young adults, walking; G2=old adults, walking; G3=young adults, running)</li> <li>Sex: the participant’s sex (M or F)</li> <li>Locomotion: the type of locomotion (walking or running)</li> <li>Speed: the speed at which the recordings were conducted in [m/s]</li> <li>Speed_type: the distinction between fixed (decided by the researchers) or preferred (selected by the participant) speed</li> <li>Age: the participant’s age in years</li> <li>Height: the participant’s height in [cm]</li> <li>Mass: the participant’s body mass in [kg].</li> </ul> <p>The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Trials are named like “ID0020_M_YOUNG_TW_01,” where the characters “ID0020” indicate the participant number (in this example the 20th), the character “M” indicates the sex, the characters “YOUNG” indicate the age group, the characters “TW” indicate the locomotion type and environment (T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number.</p> <p><strong>Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a></strong></p> <p>The files containing the gait cycle breakdown are available in RData format, in the file named “CYCLE_TIMES.RData”. The files are structured as data frames with one row for each gait cycle and two columns. The first column contains the touchdown incremental times in seconds. The second column contains the duration of each stance phase in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_ID0020_M_YOUNG_TW_01,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times, the characters “ID0020” indicate the participant number (in this example the 20th), the character “M” indicates the sex, the characters “YOUNG” indicate the age group, the characters “TW” indicate the locomotion type and environment (T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number.</p> <p>The files containing the raw, filtered, and the normalized EMG data are available in RData format, in the files named “RAW_EMG.RData” and “FILT_EMG.RData”. The raw EMG files are structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_ID0003_F_OLD_TW_01”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “ID0003” indicate the participant number (in this example the 3rd), the character “F” indicates the sex, the characters “OLD” indicate the age group, the characters “TW” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number.</p> <p>All the code used for the pre-processing of EMG data and the extraction of muscle synergies is available in R format. Explanatory comments are profusely present throughout the script “muscle_synergies.R”. The latest version of this code can be found at https://github.com/alesantuz/musclesyneRgies.</p>
CT Dataset associated with the paper: (PLOSONE) Modular robotic platform for precision neurosurgery with a bio-inspired needle: system overview and first in-vivo deployment
<p>Imaging dataset associated with the work entitled "Modular robotic platform for precision neurosurgery with a bio-inspired needle: system overview and first in-vivo deployment.", published in the journal PLOS ONE</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 9. Modular Hierarchical Organization of Perceptual Neuro-Symbolic Networks
<p>In analogy to how it is reported for the brain by A. Luria, connections of the lowest levels of the architecture of Figure 9 are predefined. Higher-level connections are set via a learning process, concretely via a supervised learning process that was described in detail in . More recent research findings indicate that learning could also already take place at lower levels of<br> perception and that unsupervised learning could be crucial for setting these connections. In, first attempts have been made to develop an unsupervised learning strategy for the model.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 8. Modular Hierarchical Organization of the Human Perceptual System
<p>In order to perform complex tasks, neuro-symbols have to be connected to neuro-symbolic networks. For the structural organization of this neuro-symbolic network, the modular hierarchical organization of the human perceptual cortex as described by A. Luria [27] was taken as a blueprint (see Figure 8).</p>
Dataset: Modular Medical, Inc. (MODD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Data for "A replicable and modular benchmark for long-read transcript quantification methods"
<p>This archive contains the input necessary to run the inital (TranSigner-protocol and IsoQuant-protocol) benchmarks associated with the <a href="https://github.com/COMBINE-lab/lr_quant_benchmarks" target="_blank" rel="noopener"><code>lr_quant_benchmarks repository</code></a>. The archive can be decompressed with <code>tar</code> and <code>zstd</code> using the command <code>tar --use-compress-program=zstd -xf input.tar.zstd</code>.</p>
FilamentSensor 2.0: An open-source modular toolbox for 2D/3D cytoskeletal filament tracking
<p>This is the software described in our article 'FilamentSensor 2.0: An open-source modular toolbox for 2D/3D cytoskeletal filament tracking' and the used datasets for image analysis. It is intended as a easy to use software for tracking of cytoskeletal fibers offering both source code and GUI-based executable. Datasets are sorted according to figures in the article with folders containing raw images, analysis results and resulting figure files. The source folder also includes a tutorial and installation notes.</p> <p>For a system running Ubuntu 21.04 there is a slightly modified command line needed: java --module-path /usr/share/openjfx/lib –add-modules=javafx.base,javafx.controls,javafx.fxml,javafx.graphics,javafx.media,javafx.swing,javafx.web -jar GUIFocalAdhesionOnly.jar</p>
Early diversification of avian limb morphology and the role of modularity in the locomotor evolution of crown birds
<p>High disparity among avian forelimb and hind limb segments in crown birds relative to non-avialan theropod dinosaurs, potentially driven by the origin of separate forelimb and hind limb locomotor modules, has been linked to the evolution of diverse avian locomotor behaviors. However, this hypothesized relationship has not been quantitatively investigated in a phylogenetic framework. We assessed the relationship between the evolution of limb morphology and locomotor behavior by comparing a numerical proxy for locomotor diversity to morphospace sizes derived from a dataset of 1241 extant species. We then estimated how limb disparity accumulated during the crown avian radiation. Lastly, we tested whether limb segments evolved independently between each limb module using phylogenetically informed regressions. Disparity increased significantly with behavioral diversity after accounting for clade age and species richness. We found that forelimb disparity accumulated rapidly early in avian evolution, whereas hindlimb disparity accumulated later, in more recent divergences. We recovered little support for strong correlations between forelimb and hind limb morphology. We posit that these findings support independent evolution of locomotor modules that enabled the striking morphological and behavioral diversity of extant birds.</p>
Modular Synthesis of (Borylmethyl)silanes through Orthogonal Functionalization of a Carbon Atom
<p>Raw data for the characterization of the compounds in the research paper with the same name.</p>
AggreBots: configuring CiliaBots through guided, modular tissue aggregation
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Early diversification of avian limb morphology and the role of modularity in the locomotor evolution of crown birds
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Data from: Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.