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13 results for “shortcuts”
Data from: taking a shortcut: what mechanisms do fish use?
<p>Path integration is a powerful navigational mechanism whereby individuals continuously update their distance and angular vector of movement to calculate their position in relation to their departure location, allowing them to return along the most direct route even across unfamiliar terrain. While path integration has been investigated in several terrestrial animals, it has never been demonstrated in aquatic vertebrates, where movement occurs through volumetric space and sensory cues available for navigation are likely to differ substantially from those in terrestrial environments. By performing displacement experiments with <em>Lamprologus ocellatus</em>, we show evidence consistent with fish using path integration to navigate alongside other mechanisms (allothetic place cues and route recapitulation). These results indicate that the use of path integration is likely to be deeply rooted within the vertebrate phylogeny irrespective of the environment, and suggests that fish may possess a spatial encoding system that parallels that of mammals.</p>
A Benchmark Suite for Systematically Evaluating Reasoning Shortcuts
<p><strong>Codebase</strong> [<a href="https://unitn-sml.github.io/rsbench/">Github</a>] | <strong>Dataset</strong> [<a href="doi.org/10.5281/zenodo.11612556">Zenodo</a>]</p> <p> </p> <p><strong>Abstract</strong></p> <p>The advent of powerful neural classifiers has increased interest in problems that require both learning and reasoning. These problems are critical for understanding important properties of models, such as trustworthiness, generalization, interpretability, and compliance to safety and structural constraints. However, recent research observed that tasks requiring both learning and reasoning on background knowledge often suffer from <em>reasoning shortcuts</em> (RSs): predictors can solve the downstream reasoning task without associating the correct concepts to the high-dimensional data. To address this issue, we introduce <strong>rsbench</strong>, a comprehensive benchmark suite designed to systematically evaluate the impact of RSs on models by providing easy access to highly customizable tasks affected by RSs. Furthermore, rsbench implements common metrics for evaluating concept quality and introduces novel formal verification procedures for assessing the presence of RSs in learning tasks. Using rsbench, we highlight that obtaining high quality concepts in both purely neural and neuro-symbolic models is a far-from-solved problem. rsbench is available on <a href="https://unitn-sml.github.io/rsbench">Github</a>.</p> <p> </p> <p><strong>Usage</strong></p> <p>We recommend visiting the official code <a href="https://unitn-sml.github.io/rsbench/">website</a> for instructions on how to use the dataset and accompaying software code.</p> <p> </p> <p><strong>License</strong></p> <p>All ready-made data sets and generated datasets are distributed under the <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA 4.0</a> license, with the exception of <code>Kand-Logic</code>, which is derived from <code>Kandinsky-patterns</code> and as such is distributed under the <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GPL-3.0</a> license.</p> <p> </p> <p><strong>Datasets Overview</strong></p> <ul> <li><strong>CLIP-embeddings. </strong>This folder contains the saved activations from a pretrained CLIP model applied to the tested dataset. It includes embeddings that represent the dataset in a format suitable for further analysis and experimentation.</li> <li><strong>BDD_OIA-original-dataset</strong>. This directory holds the original files from the X-OIA project by Xu et al. [1]. These datasets have been made publicly available for ease of access and further research. If you are going to use it, please consider citing the original authors.</li> <li><strong>kand-logic-3k</strong>. This folder contains all images generated for the Kand-Logic project. Each image is accompanied by annotations for both concepts and labels.</li> <li><strong>bbox-kand-logic-3k</strong>. In this directory, you will find images from the Kand-Logic project that have undergone a preprocessing step. These images are extracted based on bounding boxes, rescaled, and include annotations for concepts and labels.</li> <li><strong>sdd-oia</strong>. This folder includes all images and labels generated using rsbench.</li> <li><strong>sdd-oia-embeddings</strong>. This directory contains 512-dimensional embeddings extracted from a pretrained ResNet18 model on ImageNet. The embeddings are derived from the sdd-oia`dataset.</li> <li><strong>BDD-OIA-preprocessed</strong>. Here you will find preprocessed data that follow the methodology outlined by Sawada and Nakamura [2]. The folder contains 2048-dimensional embeddings extracted from a pretrained Faster-RCNN model on the BDD-100k dataset.</li> </ul> <p>The original BDD datasets can be downloaded from the following Google Drive link: [<a href="https://drive.google.com/file/d/1WFiwRi_sMA_McZnkbEjh8Rnl-Im7_9Mk/view">Download BDD Dataset</a>].</p> <p> </p> <p><strong>References</strong></p> <p>[1] Xu et al., *Explainable Object-Induced Action Decision for Autonomous Vehicles*, CVPR 2020.</p> <p>[2] Sawada and Nakamura, *Concept Bottleneck Model With Additional Unsupervised Concepts*, IEEE 2022.</p> <p> </p>
A shortcut to calculate SPAM limb-darkening coefficients
<p>This repository contains data files and other supplementary material associated with the manuscript entitled "A shortcut to calculate SPAM limb-darkening coefficients", published on the Research Notes of the American Astronomical Society (RNAAS).</p>
Finding shortcuts through collective tunnel excavation in a subterranean termite
<p>Facilitating efficient resource transfer requires building an optimized transportation network that balances cost minimization with benefit maximization. For animals that forage for food located remotely, optimizing their transportation networks is critically related to survival. This process often involves finding and using the shortest route to save time and energy. Subterranean termites forage for wood resources by excavating underground foraging networks for search and transport. Because termites have no prior knowledge of food location during the food searching phase, establishment of a short tunnel between the nest and feeding site is difficult at the beginning of foraging. Thus, finding a short route should logically follow initial food discovery. However, it remains elusive as to how subterranean termites find the shortest route for food transportation. We simulated different scenarios using <em>Coptotermes formosanus</em> by providing different shapes and distances of pre-formed tunnels (straight, detour, and detour + twisting arenas) to food, where food items were located at a fixed distance from the arena entrance. Termites in the straight arena continuously used the pre-formed tunnel, showing negligible branching efforts. However, termites in the detour and detour + twisting arenas followed the pre-formed tunnel only for the initial few hours before excavating many branching tunnels. This branching activity ultimately resulted in termites finding shorter commuting routes than the pre-formed tunnels. In addition, the shortest established routes were widened over time. This study demonstrated that <em>C. formosanus</em> could actively alter tunnel networks to minimize the cost in food transportation by using short and wide tunnels. </p>
Data from: taking a shortcut: what mechanisms do fish use?
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Finding shortcuts through collective tunnel excavation in a subterranean termite
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Data from: Are landscape attributes a useful shortcut for classifying vegetation in the tropics? A case study of La Amistad International Park
Effective vegetation classification schemes identify the processes determining species assemblages and support the management of protected areas. They can also provide a framework for ecological research. In the tropics, elevation-based classifications dominate over alternatives such as river catchments. Given the existence of floristic data for many localities, we ask how useful floristic data are for developing classification schemes in species-rich tropical landscapes and whether floristic data provide support for classification by river catchment. We analyzed the distribution of vascular plant species within 141 plots across an elevation gradient of 130 to 3200 m asl within La Amistad National Park. We tested the hypothesis that river catchment, combined with elevation, explains much of the variation in species composition. We found that annual mean temperature, elevation, and river catchment variables best explained the variation within local species communities. However, only plots in high-elevation oak forest and Páramo were distinct from those in low- and mid-elevation zones. Beta diversity did not significantly differ in plots grouped by elevation zones, except for low-elevation forest, although it did differ between river catchments. None of the analyses identified discrete vegetation assemblages within mid-elevation (700–2600 m asl) plots. Our analysis supports the hypothesis that river catchment can be an alternative means for classifying tropical forest assemblages in conservation settings.
Data from: Are landscape attributes a useful shortcut for classifying vegetation in the tropics? A case study of La Amistad International Park
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Data from: Meat ants cut more trail shortcuts when facing long detours
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Leveraging Machine Learning for Size and Shape Analysis of Nanoparticles: A Shortcut to Electron Microscopy
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The ShortCut™ Continued Access Study Protocol
ClinicalTrials.gov study NCT06211296. IPD Sharing: NO. Countries: 1. Publications: 0.
The ShortCut™ Study Protocol
ClinicalTrials.gov study NCT04952909. IPD Sharing: NO. Countries: 5. Publications: 0.
Data from: Risk assessment and the use of novel shortcuts in spatial detouring tasks in jumping spiders
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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)
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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.