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59 results for “machine intelligence”
MICrONS: Machine Intelligence from Cortical Networks
<p>A ground truth dataset for 3D neuron segmentation from electron microscopy (EM) images of mouse visual cortex, created for <a href="https://www.iarpa.gov/research-programs/microns">IARPA's Machine Intelligence from Cortical Networks (MICrONS) program</a>. For more information, please visit <a href="https://www.microns-explorer.org/">our MICrONS explorer website</a>.</p> <p> </p> <p><strong>Citation</strong></p> <p><em>Functional connectomics spanning multiple areas of mouse visual cortex</em><br> MICrONs Consortium et al.<br> bioRxiv 2021.07.28.454025; doi: https://doi.org/10.1101/2021.07.28.454025</p> <p> </p> <p><strong>Data Format Specification</strong></p> <p>Volumes and annotations are stored in a single HDF5 file with the following datasets:</p> <ul> <li><strong>volumes</strong>: are stored in row-major format, i.e., with dimensions (z, y, x). Every volume has an attribute <strong>resolution</strong> that specifies the voxel size in <em>nm</em>. <ul> <li><strong>image</strong></li> <li><strong>segmentation</strong></li> <li><strong>mitochondria</strong> (optional)</li> <li><strong>synapse</strong> (optional): annotation for postsynaptic densities (PSDs)</li> <li><strong>fold</strong> (optional): annotation for folds (a kind of image defect) in images</li> <li><strong>mask</strong>: valid/invalid voxels are represented as 1s/0s respectively.</li> </ul> </li> <li><strong>annotations</strong>: are incomplete, meaning that each volume may or may not contain proper annotations. <ul> <li><strong>object_ids</strong> (optional): small unidentified objects or intracellular organelles that are oversegmented, which may require special handling.</li> <li><strong>myelin_ids</strong> (optional)</li> <li><strong>blood_vessel_ids</strong> (optional)</li> <li><strong>soma_ids</strong> (optional)</li> <li><strong>nucleus_ids</strong> (optional)</li> </ul> </li> </ul> <p> </p>
CASM: A long-term Consistent Artificial-intelligence based Soil Moisture dataset based on machine learning and remote sensing
<p>Paper to cite: Skulovich, O., Gentine, P. A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset. <em>Sci Data</em> 10, 154 (2023). https://doi.org/10.1038/s41597-023-02053-x</p> <p> </p> <p>The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset is a global, consistent, and long-term, remote sensing soil moisture (SM) dataset created using machine learning. It is based on the NASA Soil Moisture Active Passive (SMAP) satellite mission SM data as a target and is aimed at extrapolating SMAP-like quality SM data back in time with previous satellite microwave platforms. Machine learning approach, such as neural network (NN) has the advantage of being both nonlinear, and state-dependent, and naturally imposing a global distribution matching between the source and the target data. Utilizing this, the new CASM dataset was created using high-quality SMAP SM as a target and Soil Moisture and Ocean Salinity (SMOS) or Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E/2) brightness temperature as a source, which allowed extrapolating SM data 13 years back from before SMAP mission launch. CASM represents SM in the top soil layer, defined on a global 25 km EASE-2 grid and covers 2002-2020 with a 3-day temporal resolution. The resulting dataset exhibits excellent spatial and temporal homogeneity, without compromising the interannual variability, and is in excellent agreement with the SMAP data (with a mean correlation of 0.97 between the SMAP and CASM SM for the period when the two overlap). Moreover, the input and target datasets were divided into seasonal cycle and residuals, with the NN trained on the residuals. This approach ensures that the high performance does not mask a simple seasonal cycle matching but rather exemplifies the skill targeted at predicting extremes; with the NN achieving a correlation of 0.75 on the test data for the residuals. Comparison to 367 global in-situ SM monitoring sites shows a SMAP-like median correlation of 0.66 between station SM and CASM SM from the corresponding grid cell. Additionally, the SM product uncertainty was assessed, and both aleatoric and epistemic uncertainties were estimated and included in the dataset. Mean epistemic uncertainty, related to the NN model structure, ranges from 0.007 m<sup>3</sup>/m<sup>3</sup> to 0.014 m<sup>3</sup>/m<sup>3</sup> and on average is close to a desired SM product stability threshold of 0.01 m<sup>3</sup>/m<sup>3</sup> per year. Aleatoric uncertainty, defined as input noise propagated through the system, depends on the introduced level of noise. With 10% noise applied to the residuals, the resulting mean standard deviation of the model outputs rises from 0.005 to 0.007 m<sup>3</sup>/m<sup>3</sup>. </p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 6. Overview of Brain-Inspired Architecture for Machine Perception
<p>Figure 6 gives an overview about the developed architecture for human-like machine perception which bases on insights about the working mechanisms of the human perceptual system. The central element of the model is the so-called “neuro-symbolic network”, which processes data coming from different sensor sources and additionally considers information coming from<br> “higher-level” sources referred to as memory, knowledge, and focus of attention . Within the neuro-symbolic network, so called “neuro-symbolic information processing” takes place based on information exchange of “neuro-symbols”. The focus in this article will be on the description of the<br> functioning of neuro-symbols and the neuro-symbolic network. Details about the other modules and functional aspects of the model can amongst others be found in.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 2. Two Possible Progress Scenarios for How to Reach Towards Machines and Systems with Human- Level Cognitive Skills
<p>Having identified the need for novel methods for machine recognition, situation assessment, and decision making in order to advance further in different automation domains, an important question is by what means can we reach such sophisticated mechanisms. The long-term goal in<br> mind is to construct machines and systems showing performances comparable to or even beyond human skill levels. In a guest talk at the Vienna University of Technology in 2008, Prof. Etienne Barnard, an expert in the field of Artificial Intelligence, made an interesting “conceptual suggestion” for two possible progress scenarios to reach this goal which could be summarized as depicted in Figure 2.</p>
Review of Recent Trends in Measuring the Computing Systems Intelligence-Figure 5. What machine intelligence is (accessed 01.11.2017) http://www.ibmbigdatahub.com/blog/measuring-artificial-intelligence-quotient)
<p>There are many developed cooperative systems composed of very simple agents that at the system’s level are considered intelligent. Yang, Galis, Guo, and Liu (2003) presented an intelligent cooperative mobile multiagent system composed of simple reactive agents. The mobile agents are specialized in a computer network administration. They are endowed with knowledge retained as a set of rules which describe network administration tasks. The multiagent system could be considered intelligent based on the fact that it simulates the behavior of a human network administrator. In some cooperative systems, the member agents can organize themselves into cooperative coalitions/groups. Each coalition being able to solve cooperatively problems. Iantovics and Zamfirescu (2013) presented such an adaptive cooperative multiagent system, able to reorganize autonomously the coalitions in order to solve more intelligently problems. The biological and artificial intelligence are by a completely different type (Figure 5). Recently, the biological intelligence is the source of inspiration for the development of many intelligent artificial systems and different problem-solving algorithms.</p>
Sensing Performance of Artificially Intelligent Nanopores Developed by Integrating Solid-State Nanopores with Machine Learning Methods
<p>Ionic current-time data obtained from measuring nanoparticles with the diameters of 90, 100, 150, 200, 220, 270, and 300 nm, using nanopores with a diameter of 300nm.</p> <p>Test_100nm_1 means a test data of nanoparticles with a diameter of 100 nm.</p> <p>Train_100nm_1 means a training data of nanoparticles with a diameter of 100 nm.</p>
Artificial Intelligence and COVID-19 using chest CT scan and chest X-ray images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment
<p>We uploaded the Table of included articles in the systematic review "Artificial Intelligence and COVID-19 using chest CT scan and chest X-ray images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment"</p>
Figure 26 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 26. Responses to the Phidippus, high detail text prompt by the Huggingface Stable Diffusion 2.1 engine (#6).
Figure 30 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 30. Responses to the Springspinne, high detail text prompt by the Huggingface Stable Diffusion 2.1 engine (#6).
Figure 18 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 18. Responses to the jumping spider, high detail text prompt by the Starryai Argo 2 engine (#8).
Figure 15 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 15. Responses to the jumping spider, high detail text prompt by the Replicate Stable Diffusion engine (#7).
Figure 14 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 14. Responses to the jumping spider, high detail text prompt by the Huggingface Stable Diffusion 2.1 engine (#6).
Figure 8 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 8. Responses to the jumping spider, high detail text prompt by the Stable Diffusion Playground engine (#4).
Figure 6 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 6. Responses to the jumping spider, high detail text prompt by the Stable Diffusion Playground engine (#4).
Figure 4 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 4. Responses to the jumping spider, high detail text prompt by the NightCafe Stable Diffusion engine (#3).
Figure 25 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 25. Responses to the peacock jumping spider, high detail prompt by the Huggingface Stable Diffusion 2.1 engine (#6).
Figure 11 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 11. Responses to the jumping spider, high detail text prompt by the Huggingface Stable Diffusion 2.1 engine (#6).
Figure 2 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 2. Responses to the jumping spider, high detail text prompt by the Pixray vqgan engine (Table 1: #1). Although primitive, elements of the salticid archetype shown in Figure 1.1, including a horizontal row of eyes, a clypeus, and a fringe above the eyes, can be seen here.
Figure 1. 1 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 1. 1, Hypothetical archetype of a salticid spider, showing key features that may be used by another salticid to recognize this image as a salticid (after Hill 2022). 2, Example of a crambid moth that displays the key features of this archetype on its wings. 3, Image generated in response to the text input jumping spider, highly detailed by an intelligent machine (Replicate stable diffusion; see Table 1, #7). Can intelligent machines provide us with insight into the nature of the salticid archetype? Attribution and ©: 2, Arnold Wijker (https://www.inaturalist.org/observations/19305397), modified under a CC BY-NC 4.0 license.
Figure 24 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 24. Responses to the peacock spider, high detail text prompt by the Huggingface Stable Diffusion 2.1 engine (#6).
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.