Class-Agnostic Counting

Nearly all existing counting methods are designed for a specific object class. Our work, however, aims to create a counting model able to count any class of object. To achieve this goal, we formulate counting as a matching problem, enabling us to exploit the image self-similarity property that naturally exists in object counting problems. We make the following three contributions: first, a Generic Matching Network (GMN) architecture that can potentially count any object in a class-agnostic manner; second, by reformulating the counting problem as one of matching objects, we can take advantage of the abundance of video data labeled for tracking, which contains natural repetitions suitable for training a counting model. Such data enables us to train the GMN. Third, to customize the GMN to different user requirements, an adapter module is used to specialize the model with minimal effort, i.e. using a few labeled examples, and adapting only a small fraction of the trained parameters. This is a form of few-shot learning, which is practical for domains where labels are limited due to requiring expert knowledge (e.g. microbiology). We demonstrate the flexibility of our method on a diverse set of existing counting benchmarks: specifically cells, cars, and human crowds. The model achieves competitive performance on cell and crowd counting datasets, and surpasses the state-of-the-art on the car dataset using only three training images. When training on the entire dataset, the proposed method outperforms all previous methods by a large margin.Comment: Asian Conference on Computer Vision (ACCV), 201

Detecting and Classifying Nuclei on a Budget

The benefits of deep neural networks can be hard to realise in medical imaging tasks because training sample sizes are often modest. Pre-training on large data sets and subsequent transfer learning to specific tasks with limited labelled training data has proved a successful strategy in other domains. Here, we implement and test this idea for detecting and classifying nuclei in histology, important tasks that enable quantifiable characterisation of prostate cancer. We pre-train a convolutional neural network for nucleus detection on a large colon histology dataset, and examine the effects of fine-tuning this network with different amounts of prostate histology data. Results show promise for clinical translation. However, we find that transfer learning is not always a viable option when training deep neural networks for nucleus classification. As such, we also demonstrate that semi-supervised ladder networks are a suitable alternative for learning a nucleus classifier with limited data

Representation of tropical deep convection in atmospheric models - Part 1 : Meteorology and comparison with satellite observations

Published under Creative Commons Licence 3.0. Original article can be found at : http://www.atmospheric-chemistry-and-physics.net/ "The author's copyright for this publication is transferred to University of Hertfordshire".Fast convective transport in the tropics can efficiently redistribute water vapour and pollutants up to the upper troposphere. In this study we compare tropical convection characteristics for the year 2005 in a range of atmospheric models, including numerical weather prediction (NWP) models, chemistry transport models (CTMs), and chemistry-climate models (CCMs). The model runs have been performed within the framework of the SCOUT-O3 (Stratospheric-Climate Links with Emphasis on the Upper Troposphere and Lower Stratosphere) project. The characteristics of tropical convection, such as seasonal cycle, land/sea contrast and vertical extent, are analysed using satellite observations as a benchmark for model simulations. The observational datasets used in this work comprise precipitation rates, outgoing longwave radiation, cloud-top pressure, and water vapour from a number of independent sources, including ERA-Interim analyses. Most models are generally able to reproduce the seasonal cycle and strength of precipitation for continental regions but show larger discrepancies with observations for the Maritime Continent region. The frequency distribution of high clouds from models and observations is calculated using highly temporally-resolved (up to 3-hourly) cloud top data. The percentage of clouds above 15 km varies significantly between the models. Vertical profiles of water vapour in the upper troposphere-lower stratosphere (UTLS) show large differences between the models which can only be partly attributed to temperature differences. If a convective plume reaches above the level of zero net radiative heating, which is estimated to be ~15 km in the tropics, the air detrained from it can be transported upwards by radiative heating into the lower stratosphere. In this context, we discuss the role of tropical convection as a precursor for the transport of short-lived species into the lower stratosphere.Peer reviewe

The objective selection as an alternative to reduce the negative impact of corruption on the activity of administrative contracting, in Colombia

La contratación administrativa ha sido, desde hace muchas décadas, uno de los problemas más grandes que enfrenta el Estado, en la medida en que se ha ido convirtiendo en uno de los bastiones de la corrupción, ya que mediante la contratación los recursos del Estado han sido objeto de dilapidación y de acciones que riñen con la rectitud, sujeción a las normas y cumplimiento de lo contratado. En el presente artículo se aborda la problemática enunciada, en una exploración bibliográfica, cuyo objetivo es mostrar las bondades y defectos de la Selección Objetiva, como alternativa para luchar contra la corrupción inmersa en la figura de la contratación con el Estado. El grupo pudo establecer cómo la selección objetiva es realmente una solución, en la medida que se la complemente con una cultura de cumplimiento, de buena fe y deseos de servir, a través de la contratación que utiliza el Estado para cumplir sus funciones constitucionales y satisfacer las necesidades de los coasociados. Administrative contracting has been, for many decades, one of the biggest problems faced by the State, to the extent that it has become one of the bastions of corruption, since through contracting the State resources have been the object of dilapidation and actions that conflict with rectitude, subject to the rules and compliance with the contract. This article addresses the stated problem, in a bibliographic exploration, whose objective is to show the benefits and defects of Objective Selection, as an alternative to fight against corruption immersed in the figure of contracting with the State. The group was able to establish how objective selection is really a solution, to the extent that it is complemented by a culture of compliance, in good faith and a desire to serve, through the contracting that the State uses to fulfill its constitutional functions and satisfy the needs of partners

Subitizing with Variational Autoencoders

Numerosity, the number of objects in a set, is a basic property of a given visual scene. Many animals develop the perceptual ability to subitize: the near-instantaneous identification of the numerosity in small sets of visual items. In computer vision, it has been shown that numerosity emerges as a statistical property in neural networks during unsupervised learning from simple synthetic images. In this work, we focus on more complex natural images using unsupervised hierarchical neural networks. Specifically, we show that variational autoencoders are able to spontaneously perform subitizing after training without supervision on a large amount images from the Salient Object Subitizing dataset. While our method is unable to outperform supervised convolutional networks for subitizing, we observe that the networks learn to encode numerosity as basic visual property. Moreover, we find that the learned representations are likely invariant to object area; an observation in alignment with studies on biological neural networks in cognitive neuroscience

Concepto fotosíntesis en profesores desde el análisis de sus modelos mentales

El presente estudio es una aproximación a los modelos mentales sobre el concepto fotosíntesis en cuatro profesores que enseñan ciencias naturales, en 5º y 11° de Educación Básica y Media respectivamente, de escuelas estatales de Barranquilla, Colombia. Se desarrolló como diseño metodológico un estudio de casos múltiples. Los resultados se analizan desde una metodología de corte cualitativo, describiendo dos aspectos del modelo del profesor: el constituyente ontológico y el epistemológico, mediante la aplicación del modelo ONEPSI (Gutiérrez 2001). El modelo mental explicativo del profesor, se contrasta con el Modelo Científico del concepto fotosíntesis y finalmente se muestran sus alcances y limitaciones, al tiempo que se presenta una reflexión crítica respecto a la enseñanza de este concepto crucial frente a los retos ambientales de nuestro planeta

SpikeletFCN: Counting Spikelets from Infield Wheat Crop Images Using Fully Convolutional Networks

Currently, crop management through automatic monitoring is growing momentum, but presents various challenges. One key challenge is to quantify yield traits from images captured automatically. Wheat is one of the three major crops in the world with a total demand expected to exceed 850 million tons by 2050. In this paper we attempt estimation of wheat spikelets from high-definition RGB infield images using a fully convolutional model. We propose also the use of transfer learning and segmentation to improve the model. We report cross validated Mean Absolute Error (MAE) and Mean Square Error (MSE) of 53.0, 71.2 respectively on 15 real field images. We produce visualisations which show the good fit of our model to the task. We also concluded that both transfer learning and segmentation lead to a very positive impact for CNN-based models, reducing error by up to 89%, when extracting key traits such as wheat spikelet counts

A Comprehensive Pediatric Asthma Management Program Reduces Emergency Department Visits and Hospitalizations

We evaluated the impact of a comprehensive pediatric asthma management program (the Children's Asthma Wellness Program, CAWP) on the frequency of emergency department (ED) visits and hospital admissions. The CAWP generally consisted of 4 clinic sessions over a 1-year period, but some patients attended fewer clinic sessions, and some required additional clinic sessions due to incomplete asthma control. Patients were evaluated and treated by pediatric pulmonologists, nurse asthma care coordinator/educator, and social worker. We retrospectively reviewed program results over an 8-year period (2005?2013). We compared ED visits and hospital admissions before and after participation in the CAWP. There were 254 children referred to the CAWP; 172 children were enrolled. Fifty-four children (31%) received >6 sessions due to incomplete asthma control. On average, children requiring additional clinic sessions were older and more likely to be African American, hold Medicaid insurance, and have severe asthma. We obtained a minimum of 1-year preprogram and 1-year postprogram administrative data for 86 children (50%). Using each participating child as his/her own control, we found that taking part in the program decreased the risk of ED visits to 0.26 times the preprogram rate (P?Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140178/1/ped.2015.0561.pd