Unified representation of monitoring information across federated cloud infrastructures

Nowadays one of the issues hindering the potential of federating cloud-based infrastructures to reach much larger scales is their standard management and monitoring. In particular, this is true in cases where these federated infrastructures provide emerging Future Internet and Smart Cities-oriented services, such as the Internet of Things (IoT), that benefit from cloud services. The contribution of this paper is the introduction of a unified monitoring architecture for federated cloud infrastructures accompanied by the adoption of a uniform representation of measurement data. The presented solution is capable of providing multi-domain compatibility, scalability, as well as the ability to analyze large amounts of monitoring data, collected from datacenters and offered through open and standardized APIs. The solution described herein has been deployed and is currently running on a community of 5 infrastructures within the framework of the European Project XIFI, to be extended to 12 more infrastructures.

SMART Protocols: seMAntic represenTation for experimental protocols

Two important characteristics of science are the ?reproducibility? and ?clarity?. By rigorous practices, scientists explore aspects of the world that they can reproduce under carefully controlled experimental conditions. The clarity, complementing reproducibility, provides unambiguous descriptions of results in a mechanical or mathematical form. Both pillars depend on well-structured and accurate descriptions of scientific practices, which are normally recorded in experimental protocols, scientific workflows, etc. Here we present SMART Protocols (SP), our ontology-based approach for representing experimental protocols and our contribution to clarity and reproducibility. SP delivers an unambiguous description of processes by means of which data is produced; by doing so, we argue, it facilitates reproducibility. Moreover, SP is thought to be part of e-science infrastructures. SP results from the analysis of 175 protocols; from this dataset, we extracted common elements. From our analysis, we identified document, workflow and domain-specific aspects in the representation of experimental protocols. The ontology is available at http://purl.org/net/SMARTprotocol

Onyx: A Linked Data approach to emotion representation

Extracting opinions and emotions from text is becoming increasingly important, especially since the advent of micro-blogging and social networking. Opinion mining is particularly popular and now gathers many public services, datasets and lexical resources. Unfortunately, there are few available lexical and semantic resources for emotion recognition that could foster the development of new emotion aware services and applications. The diversity of theories of emotion and the absence of a common vocabulary are two of the main barriers to the development of such resources. This situation motivated the creation of Onyx, a semantic vocabulary of emotions with a focus on lexical resources and emotion analysis services. It follows a linguistic Linked Data approach, it is aligned with the Provenance Ontology, and it has been integrated with the Lexicon Model for Ontologies (lemon), a popular RDF model for representing lexical entries. This approach also means a new and interesting way to work with different theories of emotion. As part of this work, Onyx has been aligned with EmotionML and WordNet-Affect.

MedVir: an interactive representation system of multidimensional medical data applied to Traumatic Brain Injury's rehabilitation prediction

Clinicians could model the brain injury of a patient through his brain activity. However, how this model is defined and how it changes when the patient is recovering are questions yet unanswered. In this paper, the use of MedVir framework is proposed with the aim of answering these questions. Based on complex data mining techniques, this provides not only the differentiation between TBI patients and control subjects (with a 72% of accuracy using 0.632 Bootstrap validation), but also the ability to detect whether a patient may recover or not, and all of that in a quick and easy way through a visualization technique which allows interaction.

Multiple feature points representation in target localization of wireless visual sensor networks

This paper discusses the target localization problem in wireless visual sensor networks. Additive noises and measurement errors will affect the accuracy of target localization when the visual nodes are equipped with low-resolution cameras. In the goal of improving the accuracy of target localization without prior knowledge of the target, each node extracts multiple feature points from images to represent the target at the sensor node level. A statistical method is presented to match the most correlated feature point pair for merging the position information of different sensor nodes at the base station. Besides, in the case that more than one target exists in the field of interest, a scheme for locating multiple targets is provided. Simulation results show that, our proposed method has desirable performance in improving the accuracy of locating single target or multiple targets. Results also show that the proposed method has a better trade-off between camera node usage and localization accuracy.

Neural systems underlying learning and representation of global motion

We demonstrate performance-related changes in cortical and cerebellar activity. The largest learning-dependent changes were observed in the anterior lateral cerebellum, where the extent and intensity of activation correlated inversely with psychophysical performance. After learning had occurred (a few minutes), the cerebellar activation almost disappeared; however, it was restored when the subjects were presented with a novel, untrained direction of motion for which psychophysical performance also reverted to chance level. Similar reductions in the extent and intensity of brain activations in relation to learning occurred in the superior colliculus, anterior cingulate, and parts of the extrastriate cortex. The motion direction-sensitive middle temporal visual complex was a notable exception, where there was an expansion of the cortical territory activated by the trained stimulus. Together, these results indicate that the learning and representation of visual motion discrimination are mediated by different, but probably interacting, neuronal subsystems.

Representation of sound localization cues in the auditory thalamus of the barn owl

Barn owls can localize a sound source using either the map of auditory space contained in the optic tectum or the auditory forebrain. The auditory thalamus, nucleus ovoidalis (N.Ov), is situated between these two auditory areas, and its inactivation precludes the use of the auditory forebrain for sound localization. We examined the sources of inputs to the N.Ov as well as their patterns of termination within the nucleus. We also examined the response of single neurons within the N.Ov to tonal stimuli and sound localization cues. Afferents to the N.Ov originated with a diffuse population of neurons located bilaterally within the lateral shell, core, and medial shell subdivisions of the central nucleus of the inferior colliculus. Additional afferent input originated from the ipsilateral ventral nucleus of the lateral lemniscus. No afferent input was provided to the N.Ov from the external nucleus of the inferior colliculus or the optic tectum. The N.Ov was tonotopically organized with high frequencies represented dorsally and low frequencies ventrally. Although neurons in the N.Ov responded to localization cues, there was no apparent topographic mapping of these cues within the nucleus, in contrast to the tectal pathway. However, nearly all possible types of binaural response to sound localization cues were represented. These findings suggest that in the thalamo-telencephalic auditory pathway, sound localization is subserved by a nontopographic representation of auditory space.

Identification of differentially expressed mRNA in prokaryotic organisms by customized amplification libraries (DECAL): The effect of isoniazid on gene expression in Mycobacterium tuberculosis

Understanding the effects of the external environment on bacterial gene expression can provide valuable insights into an array of cellular mechanisms including pathogenesis, drug resistance, and, in the case of Mycobacterium tuberculosis, latency. Because of the absence of poly(A)+ mRNA in prokaryotic organisms, studies of differential gene expression currently must be performed either with large amounts of total RNA or rely on amplification techniques that can alter the proportional representation of individual mRNA sequences. We have developed an approach to study differences in bacterial mRNA expression that enables amplification by the PCR of a complex mixture of cDNA sequences in a reproducible manner that obviates the confounding effects of selected highly expressed sequences, e.g., ribosomal RNA. Differential expression using customized amplification libraries (DECAL) uses a library of amplifiable genomic sequences to convert total cellular RNA into an amplified probe for gene expression screens. DECAL can detect 4-fold differences in the mRNA levels of rare sequences and can be performed on as little as 10 ng of total RNA. DECAL was used to investigate the in vitro effect of the antibiotic isoniazid on M. tuberculosis, and three previously uncharacterized isoniazid-induced genes, iniA, iniB, and iniC, were identified. The iniB gene has homology to cell wall proteins, and iniA contains a phosphopantetheine attachment site motif suggestive of an acyl carrier protein. The iniA gene is also induced by the antibiotic ethambutol, an agent that inhibits cell wall biosynthesis by a mechanism that is distinct from isoniazid. The DECAL method offers a powerful new tool for the study of differential gene expression.

Syntax processing by auditory cortical neurons in the FM–FM area of the mustached bat Pteronotus parnellii

Syntax denotes a rule system that allows one to predict the sequencing of communication signals. Despite its significance for both human speech processing and animal acoustic communication, the representation of syntactic structure in the mammalian brain has not been studied electrophysiologically at the single-unit level. In the search for a neuronal correlate for syntax, we used playback of natural and temporally destructured complex species-specific communication calls—so-called composites—while recording extracellularly from neurons in a physiologically well defined area (the FM–FM area) of the mustached bat’s auditory cortex. Even though this area is known to be involved in the processing of target distance information for echolocation, we found that units in the FM–FM area were highly responsive to composites. The finding that neuronal responses were strongly affected by manipulation in the time domain of the natural composite structure lends support to the hypothesis that syntax processing in mammals occurs at least at the level of the nonprimary auditory cortex.

Quantitative analysis of mRNA amplification by in vitro transcription

Effective transcript profiling in animal systems requires isolation of homogenous tissue or cells followed by faithful mRNA amplification. Linear amplification based on cDNA synthesis and in vitro transcription is reported to maintain representation of mRNA levels, however, quantitative data demonstrating this as well as a description of inherent limitations is lacking. We show that published protocols produce a template-independent product in addition to amplifying real target mRNA thus reducing the specific activity of the final product. We describe a modified amplification protocol that minimizes the generation of template-independent product and can therefore generate the desired microgram quantities of message-derived material from 100 ng of total RNA. Application of a second, nested round of cDNA synthesis and in vitro transcription reduces the required starting material to 2 ng of total RNA. Quantitative analysis of these products on Caenorhabditis elegans Affymetrix GeneChips shows that this amplification does not reduce overall sensitivity and has only minor effects on fidelity.

Formation of temporal-feature maps by axonal propagation of synaptic learning

Computational maps are of central importance to a neuronal representation of the outside world. In a map, neighboring neurons respond to similar sensory features. A well studied example is the computational map of interaural time differences (ITDs), which is essential to sound localization in a variety of species and allows resolution of ITDs of the order of 10 μs. Nevertheless, it is unclear how such an orderly representation of temporal features arises. We address this problem by modeling the ontogenetic development of an ITD map in the laminar nucleus of the barn owl. We show how the owl's ITD map can emerge from a combined action of homosynaptic spike-based Hebbian learning and its propagation along the presynaptic axon. In spike-based Hebbian learning, synaptic strengths are modified according to the timing of pre- and postsynaptic action potentials. In unspecific axonal learning, a synapse's modification gives rise to a factor that propagates along the presynaptic axon and affects the properties of synapses at neighboring neurons. Our results indicate that both Hebbian learning and its presynaptic propagation are necessary for map formation in the laminar nucleus, but the latter can be orders of magnitude weaker than the former. We argue that the algorithm is important for the formation of computational maps, when, in particular, time plays a key role.

Forming classes by stimulus frequency: Behavior and theory

Visual classification is the way we relate to different images in our environment as if they were the same, while relating differently to other collections of stimuli (e.g., human vs. animal faces). It is still not clear, however, how the brain forms such classes, especially when introduced with new or changing environments. To isolate a perception-based mechanism underlying class representation, we studied unsupervised classification of an incoming stream of simple images. Classification patterns were clearly affected by stimulus frequency distribution, although subjects were unaware of this distribution. There was a common bias to locate class centers near the most frequent stimuli and their boundaries near the least frequent stimuli. Responses were also faster for more frequent stimuli. Using a minimal, biologically based neural-network model, we demonstrate that a simple, self-organizing representation mechanism based on overlapping tuning curves and slow Hebbian learning suffices to ensure classification. Combined behavioral and theoretical results predict large tuning overlap, implicating posterior infero-temporal cortex as a possible site of classification.

Neural components of topographical representation

Studies of patients with focal brain damage suggest that topographical representation is subserved by dissociable neural subcomponents. This article offers a condensed review of the literature of “topographical disorientation” and describes several functional MRI studies designed to test hypotheses generated by that review. Three hypotheses are considered: (i) The parahippocampal cortex is critically involved in the acquisition of exocentric spatial information in humans; (ii) separable, posterior, dorsal, and ventral cortical regions subserve the perception and long term representation of position and identity, respectively, of landmarks; and (iii) there is a distinct area of the ventral occipitotemporal cortex that responds maximally to building stimuli and may play a role in the perception of salient landmarks. We conclude with a discussion of the inferential limitations of neuroimaging and lesion studies. It is proposed that combining these two approaches allows for inferences regarding the computational involvement of a neuroanatomical substrate in a given cognitive process although neither method can strictly support this conclusion alone.

The spatial and temporal representation of a tone on the guinea pig basilar membrane

In the mammalian cochlea, the basilar membrane's (BM) mechanical responses are amplified, and frequency tuning is sharpened through active feedback from the electromotile outer hair cells (OHCs). To be effective, OHC feedback must be delivered to the correct region of the BM and introduced at the appropriate time in each cycle of BM displacement. To investigate when OHCs contribute to cochlear amplification, a laser-diode interferometer was used to measure tone-evoked BM displacements in the basal turn of the guinea pig cochlea. Measurements were made at multiple sites across the width of the BM, which are tuned to the same characteristic frequency (CF). In response to CF tones, the largest displacements occur in the OHC region and phase lead those measured beneath the outer pillar cells and adjacent to the spiral ligament by about 90°. Postmortem, responses beneath the OHCs are reduced by up to 65 dB, and all regions across the width of the BM move in unison. We suggest that OHCs amplify BM responses to CF tones when the BM is moving at maximum velocity. In regions of the BM where OHCs contribute to its motion, the responses are compressive and nonlinear. We measured the distribution of nonlinear compressive vibrations along the length of the BM in response to a single frequency tone and estimated that OHC amplification is restricted to a 1.25- to 1.40-mm length of BM centered on the CF place.

Faithful representation of similarities among three-dimensional shapes in human vision.

Efficient and reliable classification of visual stimuli requires that their representations reside a low-dimensional and, therefore, computationally manageable feature space. We investigated the ability of the human visual system to derive such representations from the sensory input-a highly nontrivial task, given the million or so dimensions of the visual signal at its entry point to the cortex. In a series of experiments, subjects were presented with sets of parametrically defined shapes; the points in the common high-dimensional parameter space corresponding to the individual shapes formed regular planar (two-dimensional) patterns such as a triangle, a square, etc. We then used multidimensional scaling to arrange the shapes in planar configurations, dictated by their experimentally determined perceived similarities. The resulting configurations closely resembled the original arrangements of the stimuli in the parameter space. This achievement of the human visual system was replicated by a computational model derived from a theory of object representation in the brain, according to which similarities between objects, and not the geometry of each object, need to be faithfully represented.