PREFERENTIAL ACCESS TO OBJECT SEMANTICS VIA LEXICAL PROCESSING IN THE VENTRAL STREAM OF THE BRAIN

Converging evidence supports a distributed-plus-hub view of semantic processing in the brain, in which there are distributed modular semantic sub-systems (e.g., for shape, colour, and action) connected to an amodal semantic hub. Furthermore, object semantic processing of colour and shape, and lexical reading and identification, are processed mainly along the ventral stream, while action semantic processing occurs mainly along the dorsal stream. In Experiment 1, participants read a prime word that required imagining either the object or action referent, and then named a lexical word target. In Experiments 2 and 3, participants performed a lexical decision task (LDT) with the same targets as in Experiment 1, in the presence of foils that were legal nonwords (NWs; Experiment 2; allows orthography, phonology, and semantics to contribute to responding) or pseudohomophones (PHs; Experiment 3; allows only orthography to contribute to responding). Semantic priming was similar in effect size regardless of prime type for naming and the LDT with NW foils, but was greater for object primes than action primes for the LDT with PH foils, suggesting a shared-stream advantage when the task demands focus on orthographic lexical processing. Experiment 4 used functional magnetic resonance imaging (fMRI) and identified the potential loci of shared-stream processing to regions in the ventral stream anterior to colour sensitive visual area V4 cortex and anterior to lexical and shape sensitive regions in the left fusiform gyrus, as well as in cerebellar lobule VI. Action priming showed more activation than object priming in dorsal stream motion related regions of the right parietal occipital junction, right superior occipital gyrus, and bilateral visual area V3. Experiment 5 identified structural connectivity using diffusion tensor imaging (DTI), and implicated connections from the cerebellar lobule VI to the anterior temporal lobe (ATL) semantic hub via the thalamus, supporting that this cerebellar region may act as a visual object semantic sub-system of the semantic network. The behavioural experiments demonstrate that object semantic and lexical processing are temporally shared, and the fMRI activation supports the theory that spatially shared-stream activation occurs in the ventral stream during object (but not action) priming of lexical processing. The DTI connectivity analysis supports the theory that lobule VI may act as an additional object semantic sub-system. This research suggests that shared-stream processing occurs between lexical identification and object semantic processing in the ventral stream, providing preferential access to object semantics via lexical processing. This shared-stream processing has implications for models of reading and the semantic system, which currently do not delineate between different modalities of semantic processing. The shared-stream regions identified may prove useful for pre-surgical localization of important intersections between the reading and semantic networks. These results also provide predictions that pure alexia and surface dyslexia patients with comorbid semantic deficits may be disproportionately affected by object semantic deficits compared to action semantic deficits

Marrying Big Data with Smart Data in Sensor Stream Processing

Widespread deployments of spatially distributed sensors are continuously generating data that require advanced analytical processing and interpretation by machines. Devising machine-interpretable descriptions of sensor data is a key issue in building a semantic stream processing engine. This paper proposes a semantic sensor stream processing pipeline using Apache Kafka to publish and subscribe semantic data streams in a scalable way. We use the Kafka Consumer API to annotate the sensor data using the Semantic Sensor Network ontology, then store the annotated output in an RDF triplestore for further reasoning or semantic integration with legacy information systems. We follow a Design Science approach addressing a Smart Airport scenario with geolocated audio sensors to evaluate the viability of the proposed pipeline under various Kafka-based configurations. Our experimental evaluations show that the multi-broker Kafka cluster setup supports read scalability thus facilitating the parallelization of the semantic enrichment of the sensor data

Stream Fusion, to Completeness

Stream processing is mainstream (again): Widely-used stream libraries are now available for virtually all modern OO and functional languages, from Java to C# to Scala to OCaml to Haskell. Yet expressivity and performance are still lacking. For instance, the popular, well-optimized Java 8 streams do not support the zip operator and are still an order of magnitude slower than hand-written loops. We present the first approach that represents the full generality of stream processing and eliminates overheads, via the use of staging. It is based on an unusually rich semantic model of stream interaction. We support any combination of zipping, nesting (or flat-mapping), sub-ranging, filtering, mapping-of finite or infinite streams. Our model captures idiosyncrasies that a programmer uses in optimizing stream pipelines, such as rate differences and the choice of a "for" vs. "while" loops. Our approach delivers hand-written-like code, but automatically. It explicitly avoids the reliance on black-box optimizers and sufficiently-smart compilers, offering highest, guaranteed and portable performance. Our approach relies on high-level concepts that are then readily mapped into an implementation. Accordingly, we have two distinct implementations: an OCaml stream library, staged via MetaOCaml, and a Scala library for the JVM, staged via LMS. In both cases, we derive libraries richer and simultaneously many tens of times faster than past work. We greatly exceed in performance the standard stream libraries available in Java, Scala and OCaml, including the well-optimized Java 8 streams

A Neurocomputational Model of the N400 and the P600 in Language Processing

Ten years ago, researchers using event-related brain potentials (ERPs) to study language comprehension were puzzled by what looked like a Semantic Illusion: Semantically anomalous, but structurally well-formed sentences did not affect the N400 component—traditionally taken to reflect semantic integration—but instead produced a P600 effect, which is generally linked to syntactic processing. This finding led to a considerable amount of debate, and a number of complex processing models have been proposed as an explanation. What these models have in common is that they postulate two or more separate processing streams, in order to reconcile the Semantic Illusion and other semantically induced P600 effects with the traditional interpretations of the N400 and the P600. Recently, however, these multi-stream models have been called into question, and a simpler single-stream model has been proposed. According to this alternative model, the N400 component reflects the retrieval of word meaning from semantic memory, and the P600 component indexes the integration of this meaning into the unfolding utterance interpretation. In the present paper, we provide support for this “Retrieval–Integration (RI)” account by instantiating it as a neurocomputational model. This neurocomputational model is the first to successfully simulate the N400 and P600 amplitude in language comprehension, and simulations with this model provide a proof of concept of the single-stream RI account of semantically induced patterns of N400 and P600 modulations

Grasping with the eyes: The role of elongation in visual recognition of manipulable objects

Processing within the dorsal visual stream subserves object-directed action, whereas visual object recognition is mediated by the ventral visual stream. Recent findings suggest that the computations performed by the dorsal stream can nevertheless influence object recognition. Little is known, however, about the type of dorsal stream information that is available to assist in object recognition. Here, we present a series of experiments that explored different psychophysical manipulations known to bias the processing of a stimulus toward the dorsal visual stream in order to isolate its contribution to object recognition. We show that elongated-shaped stimuli, regardless of their semantic category and familiarity, when processed by the dorsal stream, elicit visuomotor grasp-related information that affects how we categorize manipulable objects. Elongated stimuli may reduce ambiguity during grasp preparation by providing a coarse cue to hand shaping and orientation that is sufficient to support action planning. We propose that this dorsal-stream-based analysis of elongation along a principal axis is the basis for how the dorsal visual object processing stream can affect categorization of manipulable objects

Une méthode de detection et modélisation d'événements des messages sur Twitter

IRSTEA PUB00045753International audienceThis paper introduces TEWS —Twitter Events on the Semantic Web, pronounced like " news " —a semantic web tool for detection and representation of events taking as an input the social stream Twitter. The tool assists the user throughout a complete processing chain, starting from the detection of events on Twitter, their modeling and representation following the semantic web principles, to their storing in an RDF knowledge base that can be further published on the Web of Data

Body-part-specific Representations of Semantic Noun Categories.

Word meaning processing in the brain involves ventrolateral temporal cortex, but a semantic contribution of the dorsal stream, especially frontocentral sensorimotor areas, has been controversial. We here examine brain activation during passive reading of object-related nouns from different semantic categories, notably animal, food, and tool words, matched for a range of psycholinguistic features. Results show ventral stream activation in temporal cortex along with category-specific activation patterns in both ventral and dorsal streams, including sensorimotor systems and adjacent pFC. Precentral activation reflected action-related semantic features of the word categories. Cortical regions implicated in mouth and face movements were sparked by food words, and hand area activation was seen for tool words, consistent with the actions implicated by the objects the words are used to speak about. Furthermore, tool words specifically activated the right cerebellum, and food words activated the left orbito-frontal and fusiform areas. We discuss our results in the context of category-specific semantic deficits in the processing of words and concepts, along with previous neuroimaging research, and conclude that specific dorsal and ventral areas in frontocentral and temporal cortex index visual and affective–emotional semantic attributes of object-related nouns and action-related affordances of their referent objects

A Neural Model of Multidigit Numerical Representation and Comparison

The Extended Spatial Number Network (ESpaN) is a neural model that simulates processing of high-level numerical stimuli such as multi-digit numbers. The ESpaN model targets the explanation of human psychophysical data, such as error rates and reaction times, about multi-digit (base 10) numerical stimuli, and describes how such a competence can develop through learning. The model suggests how the brain represents and processes an open-ended set of numbers and their regularities, such as the place-value structure, with finite resources in the brain. The model does that by showing how a multi-digit spatial number map forms through interactions with learned semantic categories that symbolize separate digits, as well as place markers like "tens," "hundreds," "thousands," etc. When number-stimuli are presented to the network, they trigger learning of associations between specific semantic categories and corresponding spatial locations of the spatial number map that together build a multi-digit spatial representation. Training of the network is aimed at portraying the process of development of human numerical competence during the first years of a child's life. The earlier SpaN model proposed a spatial number map, which both human and animal possess in their Where cortical processing stream, that can explain many data about analog numerical representation and comparison. The ESpaN model shows how learned cognitive categories in the What cortical processing stream can extend numerical competence to multi-digit numbers with a place-value structure. The ESpaN model hereby suggests how cortical cognitive and spatial processes can utilize a learned What-and-Where interstream interaction to control the development of multidigit numerical abilities.National Science Foundation (IRI-97-20333); Defense Advanced Research Projects Agency and the Office of Naval Research (NOOOI4-95-I-0409

On the road to the evaluation of RDF stream compression techniques

Proceedings of RDF Stream Processing Workshop in conjunction with the 12th Extended Semantic Web Conference (ESWC 2015), May 31st, 2015 in Portoroz, SloveniaThe popularization of data streaming applications, such as those related to social networks and the Internet of Things, has fostered the interest of the Semantic Web community for this kind of data. As a result of this interest, the W3C RDF Stream Processing (RSP) community group has recently been started with the goal of defining a common model “for producing, transmitting and continuously querying RDF Streams”. In this EOI we focus on the transmission model. As pointed out by recent research efforts (e.g. Ztreamy and CQELS Cloud), the efficient transmission of RDF streams is a necessary step to ensure higher throughput in RDF stream processors.This work is partially funded by Ministerio de Economía y Competitividad (Spain) under the projects “HERMES-SMARTDRIVER” (TIN2013-46801-C4-2-R) and “4V: Volumen, Velocidad, Variedad y Validez en la Gestión Innovadora de Datos” (TIN2013-46238-C4-2-R), and Austrian Science Fund (FWF): M1720-G1

Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams

Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems (CPS) present novel challenges to Big Data platforms for performing online analytics. Ubiquitous sensors from IoT deployments are able to generate data streams at high velocity, that include information from a variety of domains, and accumulate to large volumes on disk. Complex Event Processing (CEP) is recognized as an important real-time computing paradigm for analyzing continuous data streams. However, existing work on CEP is largely limited to relational query processing, exposing two distinctive gaps for query specification and execution: (1) infusing the relational query model with higher level knowledge semantics, and (2) seamless query evaluation across temporal spaces that span past, present and future events. These allow accessible analytics over data streams having properties from different disciplines, and help span the velocity (real-time) and volume (persistent) dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP) framework that provides domain-aware knowledge query constructs along with temporal operators that allow end-to-end queries to span across real-time and persistent streams. We translate this query model to efficient query execution over online and offline data streams, proposing several optimizations to mitigate the overheads introduced by evaluating semantic predicates and in accessing high-volume historic data streams. The proposed X-CEP query model and execution approaches are implemented in our prototype semantic CEP engine, SCEPter. We validate our query model using domain-aware CEP queries from a real-world Smart Power Grid application, and experimentally analyze the benefits of our optimizations for executing these queries, using event streams from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems, October 27, 201