Recent self-supervised representation learning techniques have largely closed the gap between supervised and unsupervised on ImageNet classification. While particulars of pretraining are now relatively well understood, field still lacks widely accepted best practices for replicating this success other datasets. As a first step in direction, we study contrastive four diverse large-scale By looking through lenses data quantity, domain, quality, task granularity, provide new insights into...

Recent progress in self-supervised learning has resulted models that are capable of extracting rich representations from image collections without requiring any explicit label supervision. However, to date the vast majority these approaches have restricted themselves training on standard benchmark datasets such as ImageNet. We argue fine-grained visual categorization problems, plant and animal species classification, provide an informative testbed for learning. In order facilitate this area...

Appearance information alone is often not sufficient to accurately differentiate between fine-grained visual categories. Human experts make use of additional cues such as where, and when, a given image was taken in order inform their final decision. This contextual readily available many online collections but has been underutilized by existing classifiers that focus solely on making predictions based the contents. We propose an efficient spatio-temporal prior, when conditioned geographical...

Predicting all applicable labels for a given image is known as multi-label classification. Compared to the standard multi-class case (where each has only one label), it considerably more challenging annotate training data When number of potential large, human annotators find difficult mention image. Furthermore, in some settings detection intrinsically e.g. finding small object instances high resolution images. As result, often plagued by false negatives. We consider hardest version this...

Journal Article Practices for Social Interaction in the Language-Learning Classroom: Disengagements from Dyadic Task Get access John Hellermann, Hellermann Portland State University Search other works by this author on: Oxford Academic Google Scholar Elizabeth Cole Applied Linguistics, Volume 30, Issue 2, June 2009, Pages 186–215, https://doi.org/10.1093/applin/amn032 Published: 21 October 2008 history Received: 01 May

The peripheral retina of the human eye offers a unique opportunity for assessment and monitoring ocular diseases. We have developed novel wide-field (>70°) optical coherence tomography system (WF-OCT) equipped with wavefront sensorless adaptive optics (WSAO) enhancing visualization smaller (<25°) targeted regions in retina. iterated WSAO algorithm at speed individual OCT B-scans (~20 ms) by using raw spectral interferograms to calculate optimization metric. Our approach 3 mm beam diameter...

Camera traps enable the automatic collection of large quantities image data. Biologists all over world use camera to monitor animal populations. We have recently been making strides towards species classification in trap images. However, as we try expand geographic scope these models are faced with an interesting question: how do train that perform well on new (unseen during training) locations? Can leverage data from other modalities, such citizen science and remote sensing data? In order...

Spatial transcriptomics aims to elucidate cell coordination within biological tissues by linking the state of with its local tissue microenvironment. Imaging-based assays are particularly promising for exploring such interdependencies, as they can resolve molecular and cellular features subcellular resolution in three dimensions. Quantification analysis data, however, ultimately depends on ability recognize which molecules belong each cell. Despite computational experimental progress, this...

Optical coherence tomography (OCT) has revolutionized diagnosis and prognosis of ophthalmic diseases by visualization measurement retinal layers. To speed up the quantitative analysis disease biomarkers, an increasing number automatic segmentation algorithms have been proposed to estimate boundary locations While performance these significantly improved in recent years, a critical question ask is how far we are from theoretical limit OCT performance. In this paper, present Cramèr-Rao lower...

Optical coherence tomography angiography (OCTA) is a promising technique for non-invasive visualization of vessel networks in the human eye. We debut system capable acquiring wide field-of-view (>70°) OCT angiograms without mosaicking. Additionally, we report on enhancing peripheral microvasculature using wavefront sensorless adaptive optics (WSAO). employed fast WSAO algorithm that enabled correction <2 s by iterating mirror shape at speed B-scans rather than volumes. Also, contrasted ∼7°...

The current standard for large-volume (thousands of cubic meters) zooplankton sampling in the deep sea is MOCNESS, a system multiple opening–closing nets, typically lowered to within 50 m seabed and towed obliquely surface obtain low-spatial-resolution samples that integrate across 10 s meters water depth. SyPRID (Sentry Precision Robotic Impeller Driven) sampler an innovative, deep-rated (6000 m) plankton partners with Sentry Autonomous Underwater Vehicle (AUV) paired, at specified depths...

Camera traps enable the automatic collection of large quantities image data. Ecologists use camera to monitor animal populations all over world. In order estimate abundance a species from trap data, ecologists need know not just which were seen, but also how many individuals each seen. Object detection techniques can be used find number in image. However, since collect images motion-triggered bursts, simply adding up detections frames is likely lead an incorrect estimate. Overcoming these...

Computational sprinting is a class of mechanisms that boost performance but dissipate additional power. We describe architecture in which many, independent chip multiprocessors share power supply and sprints are constrained by the chips’ thermal limits rack’s limits. Moreover, we present computational game, multi-agent perspective on managing sprints. Strategic agents decide whether to sprint based application phases system conditions. The game produces an equilibrium improves task...

Estimating the geographical range of a species from sparse observations is challenging and important geospatial prediction problem. Given set locations where has been observed, goal to build model predict whether present or absent at any location. This problem long history in ecology, but traditional methods struggle take advantage emerging large-scale crowdsourced datasets which can include tens millions records for hundreds thousands species. In this work, we use Spatial Implicit Neural...

Predicting all applicable labels for a given image is known as multi-label classification. Compared to the standard multi-class case (where each has only one label), it considerably more challenging annotate training data When number of potential large, human annotators find difficult mention image. Furthermore, in some settings detection intrinsically e.g. finding small object instances high resolution images. As result, often plagued by false negatives. We consider hardest version this...

The goal of habitat suitability mapping is to predict the lo-cations in which a given species could be present. This typically accomplished by statistical models use envi-ronmental variables observation data. relationship between environmental characteristics location and that live there likely quite complex, so deep learning would seem natural use. In practice, are biases training data present obstacles standard approaches. First, large-scale collections consist presence-only data, means we...

Species range maps (SRMs) are essential tools for research and policy-making in ecology, conservation, environmental management. However, traditional SRMs rely on the availability of covariates high-quality species location observation data, both which can be challenging to obtain due geographic inaccessibility resource constraints. We propose a novel approach combining millions citizen science observations with textual descriptions from Wikipedia, covering habitat preferences tens thousands...