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6,059 results for “Journale”
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 3. Basic "Components" of Artificial and Biological (Brain-Controlled) Automation Systems
<p>Although basing on different concepts concerning their details, artificial and biological (brain-controlled) automation systems show common points concerning their principal components (see Figure 3).</p>
BRAIN Journal-Artificial Neuron Modelling Based on Wave Shape-Figure 1. Typical feedforward artificial neural network processing unit
<p>In Figure 1, each input X is weighted by a separate weight value w. These are then summed together to give a total input value for the neuron. This total can then be passed through a function, to transform it into the desired output value. This is then compared to the actual output value d; where the error or differences between the two sets is measured and used to correct the weight<br> values, to bring the two sets of values closer. One way to update the weights is after each individual pattern is presented and processed. Another option is to process the error after the presentation of the whole dataset, as part of a batch update.</p>
BRAIN Journal-Artificial Neuron Modelling Based on Wave Shape-Figure 2. New neuronal model, based on matching a wave-like representation of the output.
<p>For the new model, shown in Figure 2, the ‘differences’ between the outputs is measured and combined, to produce a kind of wave-like shape. If output point 1 has a value of 10, and output point 2 has a value of 5, for example, then this results in the shape moving down 5 points on the wave shape. This shape will, of course, change depending on the order that the dataset values are presented in. Also, if new data is presented, then that will produce a different shape. However, there is still an association between the input values and the output values and it is still this relation that is being learnt. The neural network needs to be able to learn a function that can generalise over the presented datasets, so that it can recognise the relation in previously unseen data as well. Generalising over trying to learn a wave-like shape, or trying to match the output values exactly looks quite similar, suggesting that the process is at least valid. In Figure 2, note that a transposition to move the learned shape up or down first is possible, before a weight value would try to scale it. If the initial match of the combined inputs can be as close as possible, then these adjustments will become less and will be more for fine tuning.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 4: Pseudo code for applying different axonal conduction delay between presynaptic neurons and postsynaptic neurons
<p>In each time step, axonal conduction delays between presynaptic neurons and postsynaptic neurons are examined whether they are equal to the elements of array I_S, to apply the respective spikes.<br> The pseudo code for applying different axonal conduction delay between presynaptic neurons and postsynaptic neurons are shown in Figure 4.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 15. Movements a typical successful creature to find food
<p>Figure 15 (a, b, c, d, e, f, g, h) illustrates movements of a typical successful creature for finding one food object.</p> <p>We believe that this study can be a step forward in understanding the morphology of artificial creatures. Also this paper suggests more complex artificial life examination by adding different part to these networks similar to different segments of the brain such as: vision, locomotion, hippocampus and communication in a future work. Because of the different axonal conduction delay between every two neurons in the neural network of artificial creatures in this paper, our next study is to enhance the artificial lives by STDP learning.</p>
Raw images and processed datasets related to the journal article Robust Assessment of Post-Localisation Hardening Behaviour in Eurofer97 using Inverse Finite Element Methods
Open the record for dataset details and reuse information.
Рис. 2. Laternula elliptica: А – раковина вЗрослого моллюска иЗ морЯ Дейвиса, L=87 мм, вид сбоку; Б – вид с дорсальной стороны (по: Егорова [1982]); В – расположение пустых раковин Laternula elliptica в осыпаюЩемсЯ песчаном грунте на склоне подводного холма (по рисунку иЗ полевого дневника Б.И. Сиренко, ЗИН РАН); Г – наружные отверстиЯ вводного и выводного сифонов Laternula elliptica (King, 1832) на поверхности грунта. Fig. 2. Laternula elliptica: А – shell of adult mollusc from the Davis Sea, L=87 mm, lateral view; Б – dorsal view (after: Егорова [1982]); В – empty shells of Laternula elliptica in friable sand on a slope of underwater hill (after sketch in the field journal of Dr. B.I. Sirenko, Zool. Inst. RAS); Г – external openings of inhalant and exhalant siphons of Laternula elliptica on surface of bottom deposits. in Species of warm-water origin Laternula elliptica (King, 1832) (Mollusca: Bivalvia: Laternulidae), a widespread mollusk in recent Antarctica
Рис. 2. Laternula elliptica: А – раковина вЗрослого моллюска иЗ морЯ Дейвиса, L=87 мм, вид сбоку; Б – вид с дорсальной стороны (по: Егорова [1982]); В – расположение пустых раковин Laternula elliptica в осыпаюЩемсЯ песчаном грунте на склоне подводного холма (по рисунку иЗ полевого дневника Б.И. Сиренко, ЗИН РАН); Г – наружные отверстиЯ вводного и выводного сифонов Laternula elliptica (King, 1832) на поверхности грунта. Fig. 2. Laternula elliptica: А – shell of adult mollusc from the Davis Sea, L=87 mm, lateral view; Б – dorsal view (after: Егорова [1982]); В – empty shells of Laternula elliptica in friable sand on a slope of underwater hill (after sketch in the field journal of Dr. B.I. Sirenko, Zool. Inst. RAS); Г – external openings of inhalant and exhalant siphons of Laternula elliptica on surface of bottom deposits.
Code4Lib Journal, Issue 55
The lastest issue of Code4Lib Journal came out yesterday, and I wanted to see how quickly I could garner insights regarding the issue's themes, topics, and questions addressed. I was able to satisfy my curiosity about these self-imposed challenges, but ironically, it took me longer to write this blog posting than it did for me to do the analysis.
A Year of Journal of Open Humanities Data
I asked myself, "What can I learn by applying distant reading computing techniques against a single year of content from the Journal of Open Humanities Data?" In a sentence, I learned a great deal about the Journal, and it very much lives up to is name.
Reading Journal of eScience Librarianship: Responsible AI in Libraries and Archives
A special issue of Journal of eScience Librarianship was brought to my attention. The issue was on the topic of responsible AI in libraries and archives. I did a bit of distant reading against the issue, and outlined here are some of my take-aways. In short, AI is something to consider in Library Land, but not without some forethought.
Data Set for the Journal Article "Heron: Visualizing and Controlling Chemical Reaction Explorations and Networks"
<p>This data archive contains all data newly created in the following publication:</p> <p>Charlotte H. Müller, Miguel Steiner, Jan P. Unsleber, Thomas Weymuth, Moritz Bensberg, Katja-<br>Sophia Csizi, Maximilian Mörchen, Paul L. Türtscher, and Markus Reiher, "Heron: Visualizing and<br>Controlling Chemical Reaction Explorations and Networks", in preparation.</p> <p>The directory contents are as follows:</p> <ul> <li>steered_eschenmoser.tar.xz: Dump of the database created during the steered exploration</li> <li>steered_exploration_protocol_chemoton_3.1.json: Protocol used for the steered exploration</li> </ul>
Dataset: Daily Journal Corporation (DJCO) 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.
Dataset of KO journal paper: Semantic analysis of archival concepts in CIDOC-CRM and in RiC-CM and RiC-O
<p>Semantic analysis of archival concepts (class, relations, atributtes, and relation attributes) presents in Records in Context family (conceptual model and ontology) and its possible equivalents in CIDOC-CRM.</p>
Associated data for "The Roasting Marshmallows Program with IGRINS on Gemini South II -- WASP-121 b has super-stellar C/O and refractory-to-volatile ratios" Published in The Astronomical Journal
<table> <tbody> <tr> <td>File Name</td> <td>Description</td> </tr> <tr> <td>w121_1DRC_H2O_ONLY.txt</td> <td>Self consistent, solar composition model spectrum with only H2O opacity.</td> </tr> <tr> <td>w121_1DRC_OH_ONLY.txt</td> <td>Self consistent, solar composition model spectrum with only OH opacity.</td> </tr> <tr> <td>w121_1DRC_CO_ONLY.txt</td> <td>Self consistent, solar composition model spectrum with only CO opacity.</td> </tr> <tr> <td>w121_1DRC_EVERYTHING.txt</td> <td>Self consistent, solar composition model spectrum with all sources of opacity.</td> </tr> <tr> <td>pre_.pic</td> <td>Pre-eclipse data in data cuboid of shape N_order, N_frame, N_pixel</td> </tr> <tr> <td>pre_variance.pic</td> <td>Associated per-pixel variance for the pre-eclipse data.</td> </tr> <tr> <td>post_cube.pic</td> <td>Post-eclipse data in data cuboid of shape N_order, N_frame, N_pixel</td> </tr> <tr> <td>pre_time_BJD.pic</td> <td>Average frame time in BJD for the pre-eclipse sequence.</td> </tr> <tr> <td>pre_rvel.pic</td> <td>Stellar radial velocity, including barycentric correction, per frame for the pre-eclipse sequence.</td> </tr> <tr> <td>post_ph.pic</td> <td>Orbital phase per frame for the post-eclipse sequence.</td> </tr> <tr> <td>post_time_BJD.pic</td> <td>Average frame time in BJD for the post-eclipse sequence.</td> </tr> <tr> <td>post_rvel.pic</td> <td>Stellar radial velocity, including barycentric correction, per frame for the post-eclipse sequence</td> </tr> </tbody> </table>
Fig. 3 in The correlations between certain features of the journal Neotropical Ichthyology and its impact factor: a comparative analysis at the thematic and national levels
Fig. 3. Correlation between average IF and uncitedness rate of journals on zoology in Sample 1 between 2006 and 2010. The highlighted represents the data for Neotropical Ichthyology.
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 4.Performance based on no. of tumor pixel & execution time
<p>In this paper we segmented the brain tumors in axial view of MR images with the help of<br> unsupervised clustering method i.e. K-means clustering. The unsupervised clustering methods gave<br> the better results than traditional method.<br> The performance analysis and comparison is done f on the basis of no. of tumor pixels in<br> segmented brain tumor and the execution time for the same. Regarding the no. of tumor pixels, Kmeans<br> clustering gave a better result than the other methods. The clustering algorithms were tested<br> with a data base of 20 MRI brain images. K-means clustering achieved almost 90%result</p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 3:(a) Input MR Image (b) Enhanced Image (c) Segmented Tumor (d) Located brain tumor
<p>Figure 3 shows three different original brain MR images, contrast enhancement of the<br> images, segmented images using K-means algorithm and finally located tumor. Fig 1.4 shows the<br> performance of the unsupervised clustering methods with the no. of tumor pixels and execution<br> time to locate the brain tumor.</p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 2. Stages of software implementation
<p>The algorithm has two stages, first is pre-processing of given MRI image and after that<br> segmentation and then perform morphological operations.</p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 1. Diagnosis Rate in different Countrie
<p>In MRI images, the amount of data is too much for manual segmentation. The procedure is<br> tedious, time, labor consuming, subjective and requires expertise. This gave way to methods that are<br> computer-aided with user interaction at varying levels. These methods are automatic and objective<br> and the results are highly reproducible. We designed software tool for locating brain tumor, based<br> on unsupervised clustering methods and analyzed its performance</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 20. Overview about which Binding Mechanisms Work at what Hierarchical Levels and Development Stages of the Brain
<p>As a result of our research, in [60], a solution to the binding problem for perception was suggested by<br> combining the already existing binding hypotheses in a conclusive way, supplementing them with<br> other insights about the perceptual system of the brain, and translating them into a technically<br> implementable model. It was demonstrated via computational simulations that different binding<br> mechanisms proposed in literature are not mutually inclusive. On the contrary! At different<br> hierarchical levels and in different development stages, different binding mechanisms are acting in<br> perception. An overview about these circumstances is given in Figure 20. A detailed description can<br> be found in.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.