Search results for "NEURAL NETWORK"
showing 10 items of 1385 documents
Adaptive design optimization: a mutual information-based approach to model discrimination in cognitive science.
2010
Discriminating among competing statistical models is a pressing issue for many experimentalists in the field of cognitive science. Resolving this issue begins with designing maximally informative experiments. To this end, the problem to be solved in adaptive design optimization is identifying experimental designs under which one can infer the underlying model in the fewest possible steps. When the models under consideration are nonlinear, as is often the case in cognitive science, this problem can be impossible to solve analytically without simplifying assumptions. However, as we show in this letter, a full solution can be found numerically with the help of a Bayesian computational trick d…
User bandwidth usage-driven HNN neuron excitation method for maximum resource utilization within packet-switched communication networks
2006
Mobile and wireless systems beyond 3G are being designed under the user-centric paradigm. Dynamic resource allocation (DRA) is a topic on intensive research to address efficiently such paradigm. Hopfield neural networks (HNN) have proved useful in the past to solve this kind of complex optimization problems. Recently, various approaches have been proposed to realize HNN-based user-centric DRA. However, the initial algorithms suffer from severe instability problems impacting the overall performance. This letter analyses the source of the existing limitations and proposes an enhanced formulation, ensuring maximum resource utilization while optimizing the convergence of the neural network. The…
A methodology for the semi-automatic generation of analytical models in manufacturing
2018
International audience; Advanced analytics can enable manufacturing engineers to improve product quality and achieve equipment and resource efficiency gains using large amounts of data collected during manufacturing. Manufacturing engineers, however, often lack the expertise to apply advanced analytics, relying instead on frequent consultations with data scientists. Furthermore, collaborations between manufacturing engineers and data scientists have resulted in highly specialized applications that are not relevant to broader use cases. The manufacturing industry can benefit from the techniques applied in these collaborations if they can be generalized for a wide range of manufacturing probl…
Testing for non-linearity in an artificial financial market: a recurrence quantification approach
2004
Abstract In this paper, earlier work on testing for non-linear dynamics on realizations from an artificial financial market is extended in two ways. On the one hand, Hinich’s bispectral test and White’s neural network test are computed. On the other hand, a recently developed methodology to test for hidden structures in data, inherited from Physics, is successfully applied on the realizations of the artificial market. Results among alternative tests are compared.
Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis
2016
Physically-based radiative transfer models (RTMs) help understand the interactions of radiation with vegetation and atmosphere. However, advanced RTMs can be computationally burdensome, which makes them impractical in many real applications, especially when many state conditions and model couplings need to be studied. To overcome this problem, it is proposed to substitute RTMs through surrogate meta-models also named emulators. Emulators approximate the functioning of RTMs through statistical learning regression methods, and can open many new applications because of their computational efficiency and outstanding accuracy. Emulators allow fast global sensitivity analysis (GSA) studies on adv…
Multilabel segmentation of cancer cell culture on vascular structures with deep neural networks
2020
New increasingly complex in vitro cancer cell models are being developed. These new models seem to represent the cell behavior in vivo more accurately and have better physiological relevance than prior models. An efficient testing method for selecting the most optimal drug treatment does not exist to date. One proposed solution to the problem involves isolation of cancer cells from the patients' cancer tissue, after which they are exposed to potential drugs alone or in combinations to find the most optimal medication. To achieve this goal, methods that can efficiently quantify and analyze changes in tested cell are needed. Our study aimed to detect and segment cells and structures from canc…
Prediction of bone mass gain by bone turnover parameters after parathyroidectomy for primary hyperparathyroidism: neural network software statistical…
2006
Background Primary hyperparathyroidism (pHPT) is the most frequent endocrine hypersecretion disease, and parathyroidectomy is the only curative option, since pharmacologic therapy reduces hypercalcemia but does not impede parathyroid hormone hypersecretion. According to guidelines from the National Institutes of Health, parathyroidectomy is associated with bone mass increase in some asymptomatic patients, while in others bone mass is not changed after surgery. Therefore, we performed the present study in an attempt to elucidate whether a preoperative biochemical bone parameter can be predictive of a significant vertebral bone mass increase in patients with pHPT. Methods For each patient we …
Emergency Analysis: Multitask Learning with Deep Convolutional Neural Networks for Fire Emergency Scene Parsing
2021
In this paper, we introduce a novel application of using scene semantic image segmentation for fire emergency situation analysis. To analyse a fire emergency scene, we propose to use deep convolutional image segmentation networks to identify and classify objects in a scene based on their build material and their vulnerability to catch fire. We introduce our own fire emergency scene segmentation dataset for this purpose. It consists of real world images with objects annotated on the basis of their build material. We use state-of-the-art segmentation models: DeepLabv3, DeepLabv3+, PSPNet, FCN, SegNet and UNet to compare and evaluate their performance on the fire emergency scene parsing task. …
Search for standard model Higgs bosons produced in association with W bosons.
2007
We report on the results of a search for standard model Higgs bosons produced in association with W bosons from p-pbar collisions at root s = 1.96 TeV. The search uses a data sample corresponding to approximately 1 fb-1 of integrated luminosity. Events consistent with the W to l-nu and H to b-bbar signature are selected by triggering on a high-pT electron or muon candidate and tagging one or two of the jet candidates as having originated from b quarks. A neural network filter rejects a fraction of tagged charm and light flavor jets, increasing the b-jet purity in the sample and thereby reducing the background to Higgs boson production. We observe no excess l-nu-b-bbar production beyond the …
Alterations in membrane and firing properties of layer 2/3 pyramidal neurons following focal laser lesions in rat visual cortex.
2013
Focal cortical injuries are well known to cause changes in function and excitability of the surviving cortical areas but the cellular correlates of these physiological alterations are not fully understood. In the present study we employed a well established ex vivo-in vitro model of focal laser lesions in the rat visual cortex and we studied membrane and firing properties of the surviving layer 2/3 pyramidal neurons. Patch-clamp recordings, performed in the first week post-injury, revealed an increased input resistance, a depolarized spike threshold as well as alterations in the firing pattern of neurons in the cortex ipsilateral to the lesion. Notably, the reported lesion-induced alteratio…