Winnerless competition in coupled Lotka-Volterra maps

Winnerless competition is analyzed in coupled maps with discrete temporal evolution of the Lotka-Volterra type of arbitrary dimension. Necessary and sufficient conditions for the appearance of structurally stable heteroclinic cycles as a function of the model parameters are deduced. It is shown that under such conditions winnerless competition dynamics is fully exhibited. Based on these conditions different cases characterizing low, intermediate, and high dimensions are therefore computationally recreated. An analytical expression for the residence times valid in the N-dimensional case is deduced and successfully compared with the simulations.J.L.C. and E.D.G. acknowledge support from IVIC-141, L.A.G.-D. acknowledges support from IVIC-1089 and P.V. acknowledges support from MINECO TIN2012-30883

Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection

We introduce Deep-HiTS, a rotation invariant convolutional neural network (CNN) model for classifying images of transients candidates into artifacts or real sources for the High cadence Transient Survey (HiTS). CNNs have the advantage of learning the features automatically from the data while achieving high performance. We compare our CNN model against a feature engineering approach using random forests (RF). We show that our CNN significantly outperforms the RF model reducing the error by almost half. Furthermore, for a fixed number of approximately 2,000 allowed false transient candidates per night we are able to reduce the miss-classified real transients by approximately 1/5. To the best of our knowledge, this is the first time CNNs have been used to detect astronomical transient events. Our approach will be very useful when processing images from next generation instruments such as the Large Synoptic Survey Telescope (LSST). We have made all our code and data available to the community for the sake of allowing further developments and comparisons at https://github.com/guille-c/Deep-HiTS

S-PRAC: Fast Partial Packet Recovery with Network Coding in Very Noisy Wireless Channels

Well-known error detection and correction solutions in wireless communications are slow or incur high transmission overhead. Recently, notable solutions like PRAC and DAPRAC, implementing partial packet recovery with network coding, could address these problems. However, they perform slowly when there are many errors. We propose S-PRAC, a fast scheme for partial packet recovery, particularly designed for very noisy wireless channels. S-PRAC improves on DAPRAC. It divides each packet into segments consisting of a fixed number of small RLNC encoded symbols and then attaches a CRC code to each segment and one to each coded packet. Extensive simulations show that S-PRAC can detect and correct errors quickly. It also outperforms DAPRAC significantly when the number of errors is high

Characterizing mixed mode oscillations shaped by noise and bifurcation structure

Many neuronal systems and models display a certain class of mixed mode oscillations (MMOs) consisting of periods of small amplitude oscillations interspersed with spikes. Various models with different underlying mechanisms have been proposed to generate this type of behavior. Stochastic versions of these models can produce similarly looking time series, often with noise-driven mechanisms different from those of the deterministic models. We present a suite of measures which, when applied to the time series, serves to distinguish models and classify routes to producing MMOs, such as noise-induced oscillations or delay bifurcation. By focusing on the subthreshold oscillations, we analyze the interspike interval density, trends in the amplitude and a coherence measure. We develop these measures on a biophysical model for stellate cells and a phenomenological FitzHugh-Nagumo-type model and apply them on related models. The analysis highlights the influence of model parameters and reset and return mechanisms in the context of a novel approach using noise level to distinguish model types and MMO mechanisms. Ultimately, we indicate how the suite of measures can be applied to experimental time series to reveal the underlying dynamical structure, while exploiting either the intrinsic noise of the system or tunable extrinsic noise.Comment: 22 page

Improving the signal detection accuracy of functional Magnetic Resonance Imaging

Available online 12 April 2018A major drawback of functional Magnetic Resonance Imaging (fMRI) concerns the lack of detection accuracy of the measured signal. Although this limitation stems in part from the neuro-vascular nature of the fMRI signal, it also reflects particular methodological decisions in the fMRI data analysis pathway. Here we show that the signal detection accuracy of fMRI is affected by the specific way in which whole-brain volumes are created from individually acquired brain slices, and by the method of statistically extracting signals from the sampled data. To address these limitations, we propose a new framework for fMRI data analysis. The new framework creates whole-brain volumes from individual brain slices that are all acquired at the same point in time relative to a presented stimulus. These whole-brain volumes contain minimal temporal distortions, and are available at a high temporal resolution. In addition, statistical signal extraction occurred on the basis of a non-standard time point-by-time point approach. We evaluated the detection accuracy of the extracted signal in the standard and new framework with simulated and real-world fMRI data. The new slice-based data-analytic framework yields greatly improved signal detection accuracy of fMRI signals.See https://github.com/iamnielsjanssen/slice-based for a full analysis script using the Slice-Based method. This work was supported by The Spanish Ministry of Economy and Competitiveness (RYC2011-08433 and PSI2013-46334 to NJ)

Disentangling meaning in the brain: Left temporal involvement in agreement processing

Published online 18 November 2016Sentence comprehension is successfully accomplished by means of a form-to-meaning mapping procedure that relies on the extraction of morphosyntactic information from the input and its mapping to higher-level semantic–discourse representations. In this study, we sought to determine whether neuroanatomically distinct brain regions are involved in the processing of different types of information contained in the propositional meaning of a sentence, namely person and number. While person information indexes the role that an individual has in discourse (i.e., the speaker, the addressee or the entity being talked about by speaker and addressee), number indicates its cardinality (i.e., a single entity vs a multitude of entities). An event-related functional magnetic resonance imaging (fMRI) experiment was run using agreement-Correct and Person- and Number-violated sentences in Spanish, to disentangle the processing mechanisms and neural substrates associated with the building of discourse and cardinality representations. The contrast between Person and Number Violations showed qualitative and quantitative differences. A greater response for person compared to number was found in the left middle temporal gyrus (LMTG). However, critically, a posterior-to-anterior functional gradient emerged within this region. While the posterior portion of the LMTG was sensitive to both Person and Number Violations, the anterior portion of this region showed selective response for Person Violations. These results confirm that the comprehension of the propositional meaning of a sentence results from a composite, feature-sensitive mechanism of form-to-meaning mapping in which the nodes of the language network are differentially involved.BCBL acknowledges funding from Ayuda Centro de Excelencia Severo Ochoa SEV-2015-0490. S.M. acknowledges funding from the Gipuzkoako Foru Aldundia Fellowship Program and from grant PI_2014_38 from the Basque Government. N.M. was funded by grant PSI2012- 32350 and PSI2015-65694-P from the Spanish Ministry of Economy and Competitiveness. M.C was funded by grant PSI2012-31448 from the Spanish Ministry of Science and Innovation and ERC-2011-ADG-295362 from the European Research Council

Nonsurgical Strategies for the Treatment of Temporomandibular Joint Disorders

Temporomandibular disorders are common maxillofacial disturbs of different etiologies (traumatic, inflammatory, degenerative, or congenital) that course with pain and dysfunctions of the temporomandibular joint. The treatment of these disorders includes systematically administered drugs (especially nonsteroid anti-inflammatory drugs and corticoids), physical therapies, and minimally invasive therapies that require intraarticular injections. These techniques are directed to clean or drain the articular cavity, to deliver intraarticularly drugs, biologically active compounds (as platelet-rich plasma), or to enhance lubrication (hyaluronic acid). Moreover, minimally invasive strategies are used in regenerative medicine for to deliver cells and stem cells, and nano- or micro-biomaterials. Surgery of temporomandibular disorders is only used in grave diseases that require arthrodesis or remotion of the temporomandibular joint. This review updates the nonsurgical therapeutic strategies to treat temporomandibular disorders, focusing the attention in the articular delivery or hyaluronic acid and platelet-rich plasma, two minimally invasive widely used at present

ERP and Economic Influence on the Development of Business

The use of software in business has become very significant, thanks to this business have access to a progressive technological development, as a result you get great benefits in optimizing processes and information. This research work emphasizes on the ERP and its economic influence in business, an unknown subject for many people. The realization of this research will help understand how it has contributed ERP largely to the development of enterprises, through the creation of systems that are responsible for optimizing most processes within companies to obtain a gradual enterprise-level development. Through documentary research it has been able to gather information from scientific papers, journals, academic papers, among others, which will help to better understand the problem to find a solution. Of enterprises or industries. The result of this research shows that ERP applied in companies have largely improved the process optimization and cost reduction and improved management practices efficiency, therefore a constant business development is produced