VERBUM – virtual enhanced reality for building modelling (virtual technical tour in digital twins for building conservation)

The digital transformation of the construction sector is also involving cultural and architectural heritage conservation management to solve criticalities of information exchange in refurbishment/restoration, from the preliminary steps until the execution and monitoring of interventions. Nevertheless, time and resources required to complete digital models (point clouds, 3D meshes and HBIM model) are extensive and this can cause interruption of knowledge communication among professionals. The VERBuM project (Virtual Enhanced Reality For Building Modelling) aims at investigating how a central Virtual Technical Tour (VTT), would guarantee a continuous stream of information when other disruptive technologies are integrated in the process and their related products are linked to the VTT. The use of a VTT, based on 360° photos, may fill time and resources gaps as it is a rapid up-to-date and high-fidelityto-reality tool. The fostering of the paradigmatic change in refurbishment/restoration process requires the development of all-in-one digital environments for digital twinning of cultural and architectural heritage and its assessment, aware of potentialities and criticalities to be overcame. The research moves from stakeholders’ information requirements to implement the VERBuM process supported by the central VTT, editable via cloud-based platform (VERBuM product) to exchange digital contents, uploaded in different file format, but consulted in VR by all the involved actors via web services, without any software product installation. The tool has been evaluated via SWOT analysis supported by Task-Technology Fit (TTF) model and users’ perceptions. The results provide mitigation measures of threats related to distrust in use of VTT within working groups and fruition of point clouds, meshes and BIM models, possible via WebGL-based libraries.

What Is Open Source Software (OSS) and What Is Big Data?

This chapter discusses what Open Source Software is and its relationship to Big Data and how it differs from other types of software and its software development cycle. Open source software (OSS) is a type of computer software in which source code is released under a license in which the copyright holder grants users the rights to study, change, and distribute the software to anyone and for any purpose. Big Data are data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data can be discrete or a continuous stream data and is accessible using many types of computing devices ranging from supercomputers and personal workstations to mobile devices and tablets. It is discussed how fog computing can be performed with cloud computing for visualization of Big Data. This chapter also presents a summary of additional web-based Big Data visualization software.

Parasomnia: Sleep against Capitalism

Parasomnia (2019), a site-specific participatory performance by Patrícia Portela (PT/BE), addresses sleep in its biological and cultural meanings while retrieving its historicity. Sleep is one of the last resistance gestures against capitalised lives, opening a gap for social change through the aesthetic dimension as an extension of arts in politics. Parasomnia raises awareness for empathy and unproductiveness by inviting spectators to take a massage and eating delicacies. Bodily senses are therefore a way to activate potentials and becomings. Often understood as weaknesses and vulnerabilities, the actions elicited—contemplating, caring, and resting—bring up a strength and a capacity to arouse the imagination and fabulation as political acts. It is also argued that dimensions such as fantasmatic, cyclicity, and subjectivity are key social outputs of Parasomnia. Allowing for a pause in a continuous stream of goals, of connectivity and consumption, and without commodification purposes, sleep may return us to a sense of our own interiority made of several layers: like a fall into the sleep that enables alterity to emerge inside the self.

Does morphological complexity affect word segmentation? Evidence from computational modeling

How can infants detect where words or morphemes start and end in the continuous stream of speech? Previous computational studies have investigated this question mainly for English, where morpheme and word boundaries are often isomorphic. Yet in many languages, words are often multimorphemic, such that word and morpheme boundaries do not align. Our study employed corpora of two languages that differ in the complexity of inflectional morphology, Chintang (Sino-Tibetan) and Japanese (in Experiment 1), as well as corpora of artificial languages ranging in morphological complexity, as measured by the ratio and distribution of morphemes per word (in Experiments 2 and 3). We used two baselines and three conceptually diverse word segmentation algorithms, two of which rely purely on sublexical information using distributional cues, and one that builds a lexicon. The algorithms’ performance was evaluated on both word- and morpheme-level representations of the corpora.Segmentation results were better for the morphologically simpler languages than for the morphologically more complex languages, in line with the hypothesis that languages with greater inflectional complexity could be more difficult to segment into words. We further show that the effect of morphological complexity is relatively small, compared to that of algorithm and evaluation level. We therefore recommend that infant researchers look for signatures of the different segmentation algorithms and strategies, before looking for differences in infant segmentation landmarks across languages varying in complexity.

Parallel functional subnetworks embedded in the macaque face patch system

During normal vision, our eyes provide the brain with a continuous stream of useful information about the world. How visually specialized areas of the cortex, such as face-selective patches, operate under natural modes of behavior is poorly understood. Here we report that, during the free viewing of videos, cohorts of face-selective neurons in the macaque cortex fractionate into distributed and parallel subnetworks that carry distinct information. We classified neurons into functional groups based on their video-driven coupling with fMRI time courses across the brain. Neurons from each group were distributed across multiple face patches but intermixed locally with other groups at each recording site. These findings challenge prevailing views about functional segregation in the cortex and underscore the importance of naturalistic paradigms for cognitive neuroscience.

Advanced Discretisation and Visualisation Methods for Performance Profiling of Wind Turbines

Wind turbines are typically organised as a fleet in a wind park, subject to similar, but varying, environmental conditions. This makes it possible to assess and benchmark a turbine’s output performance by comparing it to the other assets in the fleet. However, such a comparison cannot be performed straightforwardly on time series production data since the performance of a wind turbine is affected by a diverse set of factors (e.g., weather conditions). All these factors also produce a continuous stream of data, which, if discretised in an appropriate fashion, might allow us to uncover relevant insights into the turbine’s operations and behaviour. In this paper, we exploit the outcome of two inherently different discretisation approaches by statistical and visual analytics. As the first discretisation method, a complex layered integration approach is used. The DNA-like outcome allows us to apply advanced visual analytics, facilitating insightful operating mode monitoring. The second discretisation approach is applying a novel circular binning approach, capitalising on the circular nature of the angular variables. The resulting bins are then used to construct circular power maps and extract prototypical profiles via non-negative matrix factorisation, enabling us to detect anomalies and perform production forecasts.

Breaking into language in a new modality: the role of input and of individual differences in recognising signs

A key challenge when learning language in naturalistic circumstances is to extract linguistic information from a continuous stream of speech. This study investigates the predictors of such implicit learning amongst adults exposed to a new language in a new modality. Sign-naïve participants (N=93; British-English speakers) were shown a 4-minute weather forecast in Swedish Sign Language. Subsequently, we tested their ability to recognise 22 target sign forms. The target items differed in their occurrence frequency in the forecast, and in their degree of iconicity. The results revealed that both frequency and iconicity facilitated recognition cumulatively. The adult mechanism for language learning thus operates similarly on sign and spoken languages as regards frequency, but also exploits modality-salient properties, e.g., iconicity for sign languages. Individual differences in cognitive skills did not predict recognition. The properties of the input thus influenced adults’ language learning abilities at first exposure more than individual differences.

Tracking transitional probabilities and segmenting auditory sequences are dissociable processes in adults and neonates

Since speech is a continuous stream with no systematic boundaries between words, how do pre-verbal infants manage to discover words? A proposed solution is that they might use the transitional probability between adjacent syllables, which drops at word boundaries. Here, we tested the limits of this mechanism by increasing the size of the word-unit to 4 syllables, and its automaticity by testing asleep neonates. Using markers of statistical learning in neonates' EEG, compared to adult' behavioral performances in the same task, we confirmed that statistical learning is automatic enough to be efficient even in sleeping neonates. But we also revealed that : 1) Successfully tracking transition probabilities in a sequence is not sufficient to segment it 2) Prosodic cues, as subtle as subliminal pauses, enable to recover segmenting capacities 3) Adults' and neonates' capacities are remarkably similar despite the difference of maturation and expertise. Finally, we observed that learning increased the similarity of neural responses across infants, providing a new neural marker to monitor learning. Thus, from birth, infants are equipped with adult-like tools, allowing to extract small coherent word-like units within auditory streams, based on the combination of statistical analyses and prosodic cues.

Young children remember specific transitions but not general groups from structured input

As early as infancy, humans extract patterns from structured input, and demonstrate the ability to distinguish between reliably experienced patterns and new ones. However, the nature of memories that support these behaviors—and how their structure might change across childhood—remains unknown. Here, we ask what children and adults remember after exposure to a continuous stream of shapes: the particular sequence in which the shapes occurred, their higher-level group structure, or both? We showed 5- to 9-year-olds and adults (N=211) a stream of shapes comprising three triplets (groups of three shapes) that always occurred in a fixed order, followed by an old-new memory test including lure sequences that matched the exposure stream on a particular dimension (e.g., group structure). Given the early emergence of simple associative memories that increase in complexity over development, we predicted that the youngest children in our sample would remember specific shape-shape sequences, while older children and adults would additionally represent groups. After accounting for developmental improvements in overall memory, we found all ages were sensitive to specific transitions: Participants responded “old” to lures with intact shape-shape transitions at above-baseline levels. In contrast, order-independent group memory—as measured by “old” responses to shuffled triplets—was only observed in older children and adults. Our results show that while young children form memories for specific aspects of a structured experience, memory for commonalities across events is refined later—underscoring that even after identical experiences, adults and young children form different memories for those events.

Long Memory in Exchange Rate Volatility

Considering rapidly evolving technology and effective markets, wherein information and news are quickly and effectively reflected in financial asset prices, the positions of investors trading in financial markets regarding financial asset prices vary according to the continuous stream of information coming to the market. However, markets are not fully efficient in terms of maintaining a long memory that enables future pricing estimates based on the past market price of the financial asset. Revealing the existence of a long memory structure is essential to the development of monetary policies since exchange rates that tend to return to average exert high resistance. In this study, the exchange rate’s long-range dependence is determined in the scope of the log-periodogram estimator and using a fractional model structure, the average model, and the variance model structure related to the exchange rate between February 22, 2001–March 16, 2020 are examined. In this context, the parameters in the model allow an examination of the long memory process. According to the fractionally integrated exponential generalized autoregressive conditional heteroskedasticity model, it is determined that the effects of shocks in the exchange rate market continue and persist for a long period. Policy suggestions within the scope of exchange rates are evaluated within model outputs.