Search

Amdahl's law implies that even small sequential bottlenecks can seriously limit the scalability of multi-threaded programs. To achieve scalability, developers must painstakingly identify sequential bottlenecks in their program and attempt to eliminate these bottlenecks by either changing synchronization strategies or rearchitecting and rewriting code with sequential bottlenecks. This can require significant effort by the developer to understand the best approach to address sequential bottlenecks.We bring a new tool, information flow, to the problem of understanding sequential bottlenecks. Information flow is powerful in that it helps developers understand whether a bottleneck is fundamental to the computation, or merely an artifact of the implementation. We combine information flow with field conflict analysis to help developers understand the limits and potential benefits of eliminating sequential bottlenecks through redesigning the locking strategy. Finally, we develop schedule impact analysis to help understand the potential impacts of optimizations before implementing them. We develop an implementation of this in FlowProf. We have evaluated FlowProf on a set of multi-threaded Java applications and manually examined the code to validate FlowProf's findings.

Opacity is an information flow property characterizing whether a system reveals its secret to a passive observer. Several notions of opacity have been introduced in the literature. We study the notions of language-based opacity, current-state opacity, initial-state opacity, initial-and-final-state opacity, K-step opacity, and infinite-step opacity. Comparing the notions is a natural question that has been investigated and summarized by Wu and Lafortune, who provided transformations among current-state opacity, initial-and-final-state opacity, and language-based opacity, and, for prefix-closed languages, also between language-based opacity and initial-state opacity. We extend these results by showing that all the discussed notions of opacity are transformable to each other. Besides a deeper insight into the differences among the notions, the transformations have applications in complexity results. In particular, the transformations are computable in polynomial time and preserve the number of observable events and determinism, and hence the computational complexities of the verification of the notions coincide. We provide a complete and improved complexity picture of the verification of the discussed notions of opacity, and improve the algorithmic complexity of deciding language-based opacity, infinite-step opacity, and K-step opacity.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Federated cloud systems increase the reliability and reduce the cost of the computational support. The resulting combination of secure private clouds and less secure public clouds, together with the fact that resources need to be located within different clouds, strongly affects the information flow security of the entire system. In this paper, the clouds as well as entities of a federated cloud system are assigned security levels, and a probabilistic flow sensitive security model for a federated cloud system is proposed. Then the notion of opacity --- a notion capturing the security of information flow --- of a cloud computing systems is introduced, and different variants of quantitative analysis of opacity are presented. As a result, one can track the information flow in a cloud system, and analyze the impact of different resource allocation strategies by quantifying the corresponding opacity characteristics.

The creators of technical infrastructure are under social and legal pressure to comply with expectations that can be difficult to translate into computational and business logics. This dissertation bridges this gap through three projects that focus on privacy engineering, information security, and data economics, respectively. These projects culminate in a new formal method for evaluating the strategic and tactical value of data: data games. This method relies on a core theoretical contribution building on the work of Shannon, Dretske, Pearl, Koller, and Nissenbaum: a definition of situated information flow as causal flow in the context of other causal relations and strategic choices. The first project studies privacy engineering's use of Contextual Integrity theory (CI), which defines privacy as appropriate information flow according to norms specific to social contexts or spheres. Computer scientists using CI have innovated as they have implemented the theory and blended it with other traditions, such as context-aware computing. This survey examines computer science literature using Contextual Integrity and discovers, among other results, that technical and social platforms that span social contexts challenge CI's current commitment to normative social spheres. Sociotechnical situations can and do defy social expectations with cross-context clashes, and privacy engineering needs its normative theories to acknowledge and address this fact. This concern inspires the second project, which addresses the problem of building computational systems that comply with data flow and security restrictions such as those required by law. Many privacy and data protection policies stipulate restrictions on the flow of information based on that information's original source. We formalize this concept of privacy as Origin Privacy. This formalization shows how information flow security can be represented using causal modeling. Causal modeling of information security leads to general theorems about the limits of privacy by design as well as a shared language for representing specific privacy concepts such as noninterference, differential privacy, and authorized disclosure. The third project uses the causal modeling of information flow to address gaps in current theory of data economics. Like CI, privacy economics has focused on individual economic contexts and so has been unable to comprehend an information economy that relies on the flow of information across contexts. Data games, an adaptation of Multi-Agent Influence Diagrams for mechanism design, are used to model the well known economic contexts of principal-agent contracts and price differentiation as well as new contexts such as personalized expert services and data reuse. This work reveals that information flows are not goods but rather strategic resources, and that trade in information therefore involves market externalities.

Background The key challenge to constructing functional corticomuscular coupling (FCMC) is to accurately identify the direction and strength of the information flow between scalp electroencephalography (EEG) and surface electromyography (SEMG). Traditional TE and TDMI methods have difficulty in identifying the information interaction for short time series as they tend to rely on long and stable data, so we propose a time-delayed maximal information coefficient (TDMIC) method. With this method, we aim to investigate the directional specificity of bidirectional total and nonlinear information flow on FCMC, and to explore the neural mechanisms underlying motor dysfunction in stroke patients. Methods We introduced a time-delayed parameter in the maximal information coefficient to capture the direction of information interaction between two time series. We employed the linear and non-linear system model based on short data to verify the validity of our algorithm. We then used the TDMIC method to study the characteristics of total and nonlinear information flow in FCMC during a dorsiflexion task for healthy controls and stroke patients. Results The simulation results showed that the TDMIC method can better detect the direction of information interaction compared with TE and TDMI methods. For healthy controls, the beta band (14–30 Hz) had higher information flow in FCMC than the gamma band (31–45 Hz). Furthermore, the beta-band total and nonlinear information flow in the descending direction (EEG to EMG) was significantly higher than that in the ascending direction (EMG to EEG), whereas in the gamma band the ascending direction had significantly higher information flow than the descending direction. Additionally, we found that the strong bidirectional information flow mainly acted on Cz, C3, CP3, P3 and CPz. Compared to controls, both the beta-and gamma-band bidirectional total and nonlinear information flows of the stroke group were significantly weaker. There is no significant difference in the direction of beta- and gamma-band information flow in stroke group. Conclusions The proposed method could effectively identify the information interaction between short time series. According to our experiment, the beta band mainly passes downward motor control information while the gamma band features upward sensory feedback information delivery. Our observation demonstrate that the center and contralateral sensorimotor cortex play a major role in lower limb motor control. The study further demonstrates that brain damage caused by stroke disrupts the bidirectional information interaction between cortex and effector muscles in the sensorimotor system, leading to motor dysfunction.

Living organisms create, copy, and make use of information, the content depending on the level of organization. In cells, a network of signal chain proteins regulates gene expression and other cell functions. Incoming information is encoded through signal reception, processed by the network, and decoded by the synthesis of new gene products and other biological functions. Signaling proteins represent nodes, and signal transmission proceeds via allosteric binding, chemical and structural modifications, synthesis, sequestering, and degradation. The induction of the gene caudal type homeobox 2 (CDX2) in the mammalian preimplantation embryo is outlined as a demonstration of this concept. CDX2 is involved in the decision of cells to enter the trophoblast lineage. Two signal chains are coordinated into an information processing model with the help of logic gates. The model introduces a formal structure that incorporates experimental and morphological data. Above the cell level, information flow relates to tissue formation and functioning, and whole cells play the role of network nodes. This is described for the anatomical patterning of bone with implications for bone formation and homeostasis. The information usage in cells and tissues is set into a context of the nervous system and the interaction of human individuals in societies, both established scenes of information processing., This article illustrates the informational context of a gene regulation event that is initiated by cell communication. The ramifications of such events into the development of anatomical structures and other biological systems of information processing are outlined. A comparison with the elements of electronic information technology has helped gain insight into biological function, although living cells and tissues follow different and mostly unknown data processing mechanisms.

Low trust levels among project parties are generally believed to harm construction project performance, but the role of trust in the context of information flow and solving design quality problems (DQP) remains understudied. Whereas lean design management research highlights the importance of managing information flow, few studies have investigated the association between trust, information flow and DQP (or any combination thereof). This exploratory study investigates the association between trust and information flow in design projects and how these factors influence design quality. The paper presents a conceptual framework based on existing research on building design management and trust in a project context. The framework is elaborated on and validated using a single case study approach with multiple sites. The results indicate that even though the trust trend in the project is somewhat negatively connected to the overall amount of work and work in progress of DQP, increasing trust is not a prerequisite for solving DQP and that owners and contractors can have opposite opinions on the trust level. This study contributes to ongoing discussions about trust, information flow and design management and suggests that teams should focus on improving communication and collaboration to compensate for low trust levels.

Over the past decade, unmanned aerial vehicles (UAVs) have received increasing attention and are being used in the areas of harvesting, videotaping, and the military industry. In this article, the consideration is focused on areas where video recording is required for ground inspections. This paper describes modern communication technologies and systems that enable interaction and data exchange between UAVs and a ground control station (GCS). This article focuses on different architectures of communication systems, establishing the characteristics of each to identify the preferred architecture that does not require a significant consumption of resources and whose data transmission is reliable. A coherent architecture that includes multiple UAVs, wireless sensor networks, cellular networks, GCSs, and satellite network to duplicate communications for enhanced system security has been offered. Some reliability problems have been discussed, the solution of which was suggested to be a backup connection via satellite, i.e., a second connection. This study focused not only on the communication channels but also on the data exchanged between system components, indicating the purpose of their application. Some of the communication problems and shortcomings of various systems, as well as further focus areas and improvement recommendations were discussed.

Panel data, which consist of observations on many individual units over two or more instances of time, have gradually become an important type of scientific data. Subsequently causal inference for panel data has attracted enormous interest from many fields as well as statistics. In this study, the rigorously formulated information flow analysis for time series, which is very concise in form and has been successfully applied in different disciplines, is generalized to identify the causality from homogeneous and independent identically distributed panel data. The resulting formula bears the same form as that for the former, though the meanings of the symbols differ. An algorithm is then proposed for panel data causality analysis, which has been validated with both linear and nonlinear problems. It has also been put to application to examine the causal relations among economic growth, energy consumption, trade openness, and energy price based on 15 Asian countries. Clearly identified are a strong bidirectional causality between economic growth and energy consumption, and a strong causality from import and export trade to economic growth. Energy price has no direct impact on energy consumption; it, instead, exerts a weak effect on the latter through influencing economic growth.

Information exchange in the human brain is crucial for vital tasks and to drive diseases. Neuroimaging techniques allow for the indirect measurement of information flows among brain areas and, consequently, for reconstructing connectomes analyzed through the lens of network science. However, standard analyses usually focus on a small set of network indicators and their joint probability distribution. Here, we propose an information-theoretic approach for the analysis of synthetic brain networks (based on generative models) and empirical brain networks, and to assess connectome’s information capacity at different stages of dementia. Remarkably, our framework accounts for the whole network state, overcoming limitations due to limited sets of descriptors, and is used to probe human connectomes at different scales. We find that the spectral entropy of empirical data lies between two generative models, indicating an interpolation between modular and geometry-driven structural features. In fact, we show that the mesoscale is suitable for characterizing the differences between brain networks and their generative models. Finally, from the analysis of connectomes obtained from healthy and unhealthy subjects, we demonstrate that significant differences between healthy individuals and the ones affected by Alzheimer’s disease arise at the microscale (max. posterior probability smaller than 1%) and at the mesoscale (max. posterior probability smaller than 10%)., Author Summary Capitalizing on network information theory we propose a new framework for the analysis and comparison of empirical and synthetic human connectomes, in health and disease. We quantitatively assess the ability of our approach in identifying differences and similarities in the structural connectivity of the human brain, ranging between microscopic and macroscopic scales. Relying on two different diffusion processes, namely classic and maximal entropy random walks, we show that the mesoscale is suitable for capturing differences between empirical connectomes and their synthetic models, while differences between healthy subjects and patients affected by Alzheimer’s disease are mostly significant at the microscopic scale.

Video games are a widespread leisure activity and essential for a substantial field of research. In several kinds of research, video games show positive effects on cognition. Video games’ ability to change the brain in a way that improves cognition is already evident in the research world. The underlying brain dynamics assessed by coherence (Coh) and partial-directed coherence (PDC) can shed light on the effect of video game playing. Here, resting-state brain dynamics have been analyzed before and after four weeks of video game playing. Fifteen participants took part in this article, which ran for one consecutive month. Participants played a gem-swapping game with a brain–computer interface (BCI) paradigm for five days per week for approximately 90 min for four continuous weeks. Significant ( $p ) changes in information flow and connectivity measures for Coh and PDC were found in the fronto-central, fronto-parietal, and centro-parietal networks due to extended interaction with BCI. The results suggest that BCI is a potential facilitator of such resting-state network changes and may help to develop new strategies for improving cognition, but we also cannot deny the possible effects of such an effort on the disruption of a player’s sense of engagement and increased mental fatigue.

The article deals with sentence-initial time adverbials in English declarative sentences. Theoretically, it is grounded in functionally-oriented and discourse-based studies, trying to combine the study of syntactic structure, information structure, information flow, and cognitive-pragmatic aspects of discourse construction and interpretation. The aim is to examine some regularities regarding the interplay of their syntactic and pragmatic characteristics and their textual and pragmatic discourse functions. The corpus-based research includes several parameters: (1) syntactic – the form and functional subtype of initial time adverbials, and the syntactic structure of the sentences containing them; (2) pragmatic – related to the information status of initial time adverbials; (3) textual – dealing with semantic, formal and functional similarities between the initial time adverbials and the sentences containing them, and other adverbials and structures in the relevant language segment, as well as with the position of the initial time adverbials in the text. The identified discourse functions (textual and pragmatic) of the initial placement of time adverbials include linking with the previous and following text, rearranging the functional elements within a sentence, temporal scene-setting, creating two especially prominent positions in a sentence, signaling segment boundaries of textual units, etc. It is suggested that particular discourse functions, rather than being associated with one of the parameters examined, actually depend on the interrelatedness of several of them.

Despite advances in deep neural networks for object recognition, the function of the mid-level stages of visual processing in the mammalian cortex evades a simple description. This review focuses on the status of cortical area V4 to present evidence for a diversity of roles for mid-level visual areas. Some properties of V4 neurons resemble the static nature of mid-layers of neural networks when training has been completed. However, V4 also gates information flow to higher cortical areas in a dynamic way, reflecting the influences of attention, context, and reward. The most recent evidence suggests a role for mid-level cortical areas in the selective generation of responses to specific sensory inputs. The finding of signals relating to response selection in cortical areas whose role was thought to be fundamentally concerned with sensory representations will require a different approach to evaluating the roles of these areas in cognition.

Introduction. The analysis of development and application of BIM technologies in the Russian construction industry has proven the absence of the system approach and uniform concepts of industry-specific information modelling technologies. Against this background, relevant issues include the strategic objective, or the digital transformation of the industry with the help of a uniform federal information platform, as well as development of long-term models designated for the organization and management of the whole life cycle of capital construction facilities. Materials and methods. The article covers features and opportunities provided by digital technologies, including cloud computing and big data, the industrial Internet of things and the blockсhain, in terms of practical prospects and information characteristics of the tasks that include organization of subsystems, participants and construction project stages. Results. The structure of an information model designated for the organization and management of a capital construction facility’s life cycle is presented as a set of interrelated information flows for consecutive stages, subsystems and project participants. It’s been proven that traditional principles of organization and management restrain the accumulated powerful potential of digital transformation; therefore, the development of the architecture of a unified BIM platform requires new production concepts for the organization and management of project information flows. The co-authors propose an implementation pattern for the management concept and model: it represents a virtual design company, which organizes interaction between participants of a capital construction project and manages its life cycle according to new information and communication principles. Conclusions. A virtual project company integrates the functions of digital integrated production concepts, including the BIM platform, the platform-based production chain and the virtual production corporation, which correspond to the features of transformations of successive stages and the composition of participants contributing to the facility’s life cycle. Any further development and introduction of the concept of a virtual design company can serve as the organizational, technological and structural management base for the whole life cycle of a project and a new pattern of the work process organization and interaction between project developers.

Cross-modal selective attention enhances the processing of sensory inputs that are most relevant to the task at hand. Such differential processing could be mediated by a swift network reconfiguration on the macroscopic level, but this remains a poorly understood process. To tackle this issue, we used a behavioral paradigm to introduce a shift of selective attention between the visual and auditory domains, and recorded scalp electroencephalographic signals from eight healthy participants. The changes in effective connectivity caused by the cross-modal attentional shift were delineated by analyzing spectral Granger Causality (GC), a metric of frequency-specific effective connectivity. Using data-driven methods of pattern-classification and feature-analysis, we found that a change in the α band (12 Hz–15 Hz) of GC is a stable feature across different individuals that can be used to decode the attentional shift. Specifically, auditory attention induces more pronounced information flow in the α band, especially from the parietal–occipital areas to the temporal–parietal areas, compared to the case of visual attention, reflecting a reconfiguration of interaction in the macroscopic brain network accompanying different processing. Our results support the role of α oscillation in organizing the information flow across spatially-separated brain areas and, thereby, mediating cross-modal selective attention.

In animal societies, individuals may take on different roles to fulfil their own needs and the needs of their groups. Ant colonies display high levels of organisational complexity, with ants fulfilling different roles at different timescales (what is known as task allocation). Factors affecting task allocation can be at the individual level (e.g., physiology), or at the group level (e.g., the network of interactions). We focus on group level processes by exploring the relationship between interaction networks, task allocation and task switching using a previously published dataset (Mersch et al., 2013) tracking the behaviour of six Camponotus fellah colonies over 41 days. In our new analyses, our goal was to better explain the noisy process of task switching beyond simple age polyethism. First, we investigated the architecture of interaction networks using node (individual) level network measures and their relation to the individual’s task – foraging, cleaning or nursing – and whether or not the ant switched tasks. We then explored how noisy information propagation was among ants, as a function of the colony composition (how many ants carried out which tasks), through the information-theoretic metric of Effective Information. Our results show that interaction history was tied to task allocation: ants who switched to a task are more likely to have interacted with other ants carrying out that task. The degree to which interactions related to task allocation, as well as the noise in those interactions, depended on which groups of ants were interacting. Overall, we showed that colony cohesion was stable even as ant-level network measures varied more for ants when they switched functional groups; thus, ant colonies maintained a high level of information flow as determined by network analysis, and ant functional groups played different roles in maintaining colony cohesion through varied information flows.HighlightsWe analysed the interaction networks of six Camponotus fellah coloniesWe tested how centrality and information flow were tied to task switchingNode-level network metrics and the information theoretic measure of Effective Information explained differences among functional groupsInteractions were correlated with task switching, but the strength of the correlation differed across functional groups

When listening to spoken narratives, we must integrate information over multiple, concurrent timescales, building up from words to sentences to paragraphs to a coherent narrative. Recent evidence suggests that the brain relies on a chain of hierarchically organized areas with increasing temporal receptive windows to process naturalistic narratives. We hypothesized that the structure of this cortical processing hierarchy should result in an observable sequence of response lags between networks comprising the hierarchy during narrative comprehension. This study uses functional MRI to estimate the response lags between functional networks during narrative comprehension. We use inter-subject cross-correlation analysis to capture network connectivity driven by the shared stimulus. We found a fixed temporal sequence of response lags—on the scale of several seconds—starting in early auditory areas, followed by language areas, the attention network, and lastly the default mode network. This gradient is consistent across eight distinct stories but absent in data acquired during rest or using a scrambled story stimulus, supporting our hypothesis that narrative construction gives rise to inter-network lags. Finally, we build a simple computational model for the neural dynamics underlying the construction of nested narrative features. Our simulations illustrate how the gradual accumulation of information within the boundaries of nested linguistic events, accompanied by increased activity at each level of the processing hierarchy, can give rise to the observed lag gradient.Significance StatementOur findings reveal a consistent, stimulus-driven gradient of lags in connectivity along the cortical processing hierarchy—from early auditory cortex to the language network, then to the default mode network—during the comprehension of naturalistic, spoken narratives. We provide a simple computational model for the neural dynamics underlying the construction of nested narrative features, allowing us to systematically explore the conditions under which the lag gradient emerges and synthesize our results with previous findings based on simple well-controlled language stimuli. Our results illustrate the isomorphism between hierarchically structured neural dynamics and hierarchically structured, real-world narrative inputs.

With the growth of the world's population and depletion of limited resources, economic development is shifting from ‘take-make-waste’ linear model to a sustainable Circular Economic (CE) model. It is becoming essential to extend the useful life of products to avoid throwaway. In the manufacturing industry, this is especially relevant for long-life expensive capital items. Literature has presented many approaches to extend product life, such as refurbishment, upgrade, repair, and recycling. Depending on a capital item's condition, manufacturers perform a series of activities to extend its useful life, such as refurbishment activities followed by upgrade activities. These life extension activities may involve various stakeholders and systems and significantly impact the capital item's life cycle. It is a challenge to enable cross-collaboration between these different stakeholders and systems, and provide manufacturers a holistic view of the activities performed along the entire life cycle of a capital item. The Digital Thread (DT) concept, which has been applied in product lifecycle management and manufacturing, provides an opportunity to tackle these challenges. However, there is little research about developing and applying DT for extending the useful life of capital items in manufacturing companies. This paper presents ongoing research to develop a DT design tool for product life extension. We take the development of a DT sample for the Refurbishment protocol deployment as an example to illustrate the approach. This tool will pave the way to support manufacturers to extend their large capital items' useful life.

Multivoxel pattern analysis (MVPA) has become a standard tool for decoding mental states from brain activity patterns. Recent studies have demonstrated that MVPA can be applied to decode activity patterns of a certain region from those of the other regions. By applying a similar region-to-region decoding technique, we examined whether the information represented in the visual areas can be explained by those represented in the other visual areas. We first predicted the brain activity patterns of an area on the visual pathway from the others, then subtracted the predicted patterns from their originals. Subsequently, the visual features were derived from these residuals. During the visual perception task, the elimination of the top-down signals enhanced the simple visual features represented in the early visual cortices. By contrast, the elimination of the bottom-up signals enhanced the complex visual features represented in the higher visual cortices. The directions of such modulation effects varied across visual perception/imagery tasks, indicating that the information flow across the visual cortices is dynamically altered, reflecting the contents of visual processing. These results demonstrated that the distillation approach is a useful tool to estimate the hidden content of information conveyed across brain regions.

Countless informational proposals and models have explored the singular characteristics of biological systems: from the initial choice of information terms in the early days of molecular biology to the current bioinformatic avalanche in this “omic” era. However, this was conducted, most often, within partial, specialized scopes or just metaphorically. In this paper, we attempt a consistent informational discourse, initially based on the molecular recognition paradigm, which addresses the main stages of biological organization in a new way. It considers the interconnection between signaling systems and information flows, between informational architectures and biomolecular codes, between controlled cell cycles and multicellular complexity. It also addresses, in a new way, a central issue: how new evolutionary paths are opened by the cumulated action of multiple variation engines or mutational ‘vehicles’ evolved for the genomic exploration of DNA sequence space. Rather than discussing the possible replacement, extension, or maintenance of traditional neo-Darwinian tenets, a genuine informational approach to evolutionary phenomena is advocated, in which systemic variation in the informational architectures may induce differential survival (self-construction, self-maintenance, and reproduction) of biological agents within their open ended environment.

Pattern recognition is a major scientific topic. Strikingly, while machine learning algorithms are constantly refined, the human brain emerges as an ancestral biological example of such complex procedure. However, how it transforms sequences of single objects into meaningful temporal patterns remains elusive. Using magnetoencephalography (MEG) and magnetic resonance imaging (MRI), we discovered and mathematically modelled an inedited dual simultaneous processing responsible for pattern recognition in the brain. Indeed, while the objects of the temporal pattern were independently elaborated by a local, rapid brain processing, their combination into a meaningful superordinate pattern depended on a concurrent global, slower processing involving a widespread network of sequentially active brain areas. Expanding the established knowledge of neural information flow from low- to high-order brain areas, we revealed a novel brain mechanism based on simultaneous activity in different frequency bands within the same brain regions, highlighting its crucial role underlying complex cognitive functions.

Slow oscillations (SOs

This article addresses the question of how speakers manage information flow in specificationalit-clefts by balancing grammatical and prosodic choices in real time. We examine this in a qualitative and quantitative corpus study of both full and reducedit-clefts extracted from the firstLondon–Lund Corpus(LLC–1), whose prosody we studied combining auditory and instrumental analysis. Our empirical analysis resulted in the following findings about cleft usage in speech. Speakers have considerable freedom to choose what information to make prominent irrespective of the actual discourse-givenness of the constituents. Clefts allow speakers to highlight elements by means of two strategies, syntactic and prosodic, which may reinforce each other or create their own different types of prominence in sequence.It-clefts always have a high first pitch accent, which signals some form of reset of the expectations generated by preceding utterances. The choice of whether or not to produce a cleft relative clause is not purely informationally motivated. Rather, reduced clefts achieve specific unique rhetorical effects. All of this makes clefts a particularly useful device for speakers responding moment by moment to informational needs and shifting communicative goals.

Social interaction is of vital importance for human beings. While the hyperscanning approach has been extensively used to study interpersonal neural synchronization (INS) during social interactions, functional near-infrared spectroscopy (fNIRS) is one of the most popular techniques for hyperscanning naturalistic social interactions because of its relatively high spatial resolution, sound anatomical localization, and exceptionally high tolerance of motion artifacts. Previous fNIRS-based hyperscanning studies usually calculate a time-lagged INS using wavelet transform coherence (WTC) to describe the direction and temporal pattern of information flow between individuals. However, the results of this method might be confounded by the autocorrelation effect of the fNIRS signal of each individual. For addressing this issue, a method termed partial wavelet transform coherence (pWTC) was introduced, which aimed to remove the autocorrelation effect and maintain the high temporal-spectrum resolution of the fNIRS signal. In this study, a simulation experiment was performed first to show the effectiveness of the pWTC in removing the impact of autocorrelation on INS. Then, step-by-step guidance was offered on the operation of the pWTC based on the fNIRS dataset from a social interaction experiment. Additionally, a comparison between the pWTC method and the traditional WTC method and that between the pWTC method and the Granger causality (GC) method was drawn. The results showed that pWTC could be used to determine the INS difference between different experimental conditions and INS's directional and temporal pattern between individuals during naturalistic social interactions. Moreover, it provides better temporal and frequency resolution than the traditional WTC and better flexibility than the GC method. Thus, pWTC is a strong candidate for inferring the direction and temporal pattern of information flow between individuals during naturalistic social interactions.

Social networks are known for their decentralization and democracy. Each individual has a chance to participate and influence any discussion. Even with all the freedom, people’s behavior falls under patterns that are observed in numerous situations. In this paper, we propose a methodology that defines and searches for common communication patterns in topical networks on Twitter. We analyze clusters according to four traits: number of nodes the cluster has, their degree and betweenness centrality values, number of node types, and whether the cluster is open or closed. We find that cluster structures can be defined as (a) fixed, meaning that they are repeated across datasets/topics following uniform rules, or (b) variable if they follow an underlying rule regardless of their size. This approach allows us to classify 90% of all conversation clusters, with the number varying by topic. An increase in cluster size often results in difficulties finding topological shape rules, however, these types of clusters tend to exhibit rules regarding their node relationships in the form of centralization. Most individuals do not enter large-scale discussions on Twitter, meaning that the simplicity of communication clusters implies repetition. In general, power laws apply for the influencer connection distribution (degree centrality) even in topical networks.

In recent years, some studies brought the complex networks theory into neuroscience. Connectivity patterns are widely studied and vary from the data acquisition method: fMRI, EEG, and so on. Those connectivity data can be classified into three main types: anatomical, functional, and effective. The first one relies only on anatomical connections, the second relies on the correlations of signals recorded from different parts of the brain, and the latter on the direction of the information flow. In this work, it is proposed to study the variability of the parameters of complex networks, for two methods of effective connectivity (namely: Transfer Entropy and Phase Transfer Entropy) and EEG data, of 10 patients with absence epilepsy. It is known that the topology of the complex network of the epilepsy crisis varies substantially between patients. However, for the same patient, effective connectivity topologies may vary, but they should not. The variation of network parameters, for the same complex network (the same patient), can lead to misinterpretations of the analyzed phenomena. In this work, preliminary results indicate that such variability indeed occurs, for the same patient, even in strictly similar measures, such as Transfer Entropy and Phase Transfer Entropy.

In this paper, we evaluate the translation of negation both automatically and manually, in English--German (EN--DE) and English--Chinese (EN--ZH). We show that the ability of neural machine translation (NMT) models to translate negation has improved with deeper and more advanced networks, although the performance varies between language pairs and translation directions. The accuracy of manual evaluation in EN-DE, DE-EN, EN-ZH, and ZH-EN is 95.7%, 94.8%, 93.4%, and 91.7%, respectively. In addition, we show that under-translation is the most significant error type in NMT, which contrasts with the more diverse error profile previously observed for statistical machine translation. To better understand the root of the under-translation of negation, we study the model's information flow and training data. While our information flow analysis does not reveal any deficiencies that could be used to detect or fix the under-translation of negation, we find that negation is often rephrased during training, which could make it more difficult for the model to learn a reliable link between source and target negation. We finally conduct intrinsic analysis and extrinsic probing tasks on negation, showing that NMT models can distinguish negation and non-negation tokens very well and encode a lot of information about negation in hidden states but nevertheless leave room for improvement., To appear at TACL and to be presented at ACL 2021. Authors' final version

A cyber-physical system (CPS) represents the coupling network between a communication network and a power grid. Information flow (IF) as the medium of communication networks has an important influence on the performance assessment of this type of coupling network. However, traditional IF models cannot be applied to CPS directly because they do not take CPS distinctive coupling effect and associated operational mechanism into account. In this paper, we propose an information flow model (IFM) based on graph theory and traffic dynamics considering the functional architecture and transmission mode in realistic CPS to bridge the gap mentioned above. In the IFM, congestion rate and loss rate are employed to assess the performance of communication networks serving power grids under different transmission strengths and modes. Moreover, an auxiliary model about CPS topology is constructed, which can generate more realistic CPS topology. Case studies on an IEEE 39-bus system reveal the transmission performance of CPS in detail. The IFM is exploited to detect critical nodes and compare the effects of node enhancement on IF transmission of networks. In addition, IFM is also applied to compare the transmission performances of six network topologies, including three basic complex networks and three community-based networks. The results demonstrate the effectiveness of the proposed model, and some suggestions are provided on performance improvement of IF transmission for CPS.

After the military communications network is attacked by the enemy, in order to ensure that the information flow in the command and control organization can continue to be transmitted smoothly, the damaged network topology needs to be repaired in a timely and effective manner. The problem of repairing military communication network under the circumstance of nodes are hit in the network is described. This paper propose a method to repair damaged network by adding edges to the damaged network topology, which aims at maximizing network invulnerability. Network topological connection cost is based on average connection degree. The repair model is established with the consideration of connect cost limit and network connectivity. The model solving method based on discrete artificial bee colony algorithm is designed. Finally, proposed repair method is tested and compared with other three edge addition repair methods with a specific case of network. The experimental results show that the proposed method could enhance damaged network's invulnerability and outperforms the compared methods.

The brain can be regarded as an information processing system in which neurons store and propagate information about external stimuli and internal processes. Therefore, estimating interactions between neural activity at the cellular scale has significant implications in understanding how neuronal circuits encode and communicate information across brain areas to generate behavior. While the number of simultaneously recorded neurons is growing exponentially, current methods relying only on pairwise statistical dependencies still suffer from a number of conceptual and technical challenges that preclude experimental breakthroughs describing neural information flows. In this review, we examine the evolution of the field over the years, starting from descriptive statistics to model-based and model-free approaches. Then, we discuss in detail the Granger Causality framework, which includes many popular state-of-the-art methods and we highlight some of its limitations from a conceptual and practical estimation perspective. Finally, we discuss directions for future research, including the development of theoretical information flow models and the use of dimensionality reduction techniques to extract relevant interactions from large-scale recording datasets.

The article deals with the use of digital forms in the manufacturing environment to reduce the amount of printed paper and streamline the flow of information of production and logistic processes. The aim of this article is to point out the possibility of using simple and commonly available solutions to collect the necessary data. The problem is the need for higher computer skills of ordinary users. It can be used in both logistics and production environments. In a medium-sized company, the amount of paper printed using a printer may be approximately 19000 pcs per year, which consider checklists, forms, diaries of processes or maintenance. This quantity amounts to approximately 1140 EUR. The article focuses on the tools, which are commonly available software to smaller and larger companies in the manufacturing industry, and of course in other industries.

Significance The rapid comprehension of speech is a remarkable but poorly understood human capacity. Central to this process is the integration of the meaning of each word, as it is heard, into the listener’s interpretation of the utterance. Here we focus on the real-time flow of neural activity that underpins this combinatorial process, using multivariate pattern analysis and computational semantic models to discover the contextual constraints that are constructed as each word is heard, and to determine how these constraints guide the interpretation of future words in the utterance. This combination of methods reveals a continuous information flow across the left-hemisphere language system, strongly constraining the immediate activation of word meanings and providing a neural substrate for seamless real-time speech comprehension., Human speech comprehension is remarkable for its immediacy and rapidity. The listener interprets an incrementally delivered auditory input, millisecond by millisecond as it is heard, in terms of complex multilevel representations of relevant linguistic and nonlinguistic knowledge. Central to this process are the neural computations involved in semantic combination, whereby the meanings of words are combined into more complex representations, as in the combination of a verb and its following direct object (DO) noun (e.g., “eat the apple”). These combinatorial processes form the backbone for incremental interpretation, enabling listeners to integrate the meaning of each word as it is heard into their dynamic interpretation of the current utterance. Focusing on the verb-DO noun relationship in simple spoken sentences, we applied multivariate pattern analysis and computational semantic modeling to source-localized electro/magnetoencephalographic data to map out the specific representational constraints that are constructed as each word is heard, and to determine how these constraints guide the interpretation of subsequent words in the utterance. Comparing context-independent semantic models of the DO noun with contextually constrained noun models reflecting the semantic properties of the preceding verb, we found that only the contextually constrained model showed a significant fit to the brain data. Pattern-based measures of directed connectivity across the left hemisphere language network revealed a continuous information flow among temporal, inferior frontal, and inferior parietal regions, underpinning the verb’s modification of the DO noun’s activated semantics. These results provide a plausible neural substrate for seamless real-time incremental interpretation on the observed millisecond time scales.

Purpose Although prior studies have noted the importance of trust for project performance, research remains scant on describing the role of trust when using lean design management (LDM) in projects. The purpose of this paper is to explore the connection between LDM and interpersonal trust in solving construction projects’ design management problems. Design/methodology/approach A qualitative study was conducted that included 29 trust- and LDM-themed semi-structured interviews in the USA (California), Brazil and Finland; 11 focus group discussions were also organized to validate the interview findings. Findings The study reveals how LDM contributes to solving design management problems through two distinct but interconnected mechanisms: improved information flow; and improved trust among project team members. A conceptual framework was crafted to illustrate the mechanisms in building trust by means of the social domain of LDM concepts. Research limitations/implications The conceptual framework requires testing through an international survey or through multiple case studies. Practical implications The results indicate that design management would benefit from trustful environments and that trust may be the catalyst for actors’ engagement with LDM. Managers in charge of design within projects can use the conceptual framework when selecting the appropriate LDM tools, which should include both the social and technical domains. Originality/value The study emphasizes the importance of the social domain of LDM concepts. Previous studies have focussed on information flow aspects of LDM but have overlooked the value of interpersonal trust in solving design management problems.

In this article, we show how speakers manage information flow in real time and signal their interactional expectations. Speech unfolds temporally, not only as a series of lexical items which are grouped into grammatical units, but also as a sequence of tone groups which have the potential to achieve an act of telling. While there is a general belief that speakers commence their discourse with information that is shared prior to telling information that updates the common ground, our analysis of a corpus of monologue and dialogue shows that matters are not so simple. Speakers’ informational needs are balanced moment by moment within and between increments which are themselves shaped by the interlocutors’ shifting apprehensions of communicative purpose and the extent of presumed shared information. In our analysis we combine (i) a speech functional analysis, (ii) a description of a hierarchy of informational foci and (iii) prosody in order to develop a detailed description of how speakers manage information flow in real time. This enables us to show how speakers simultaneously balance informational flow while signalling their interactional expectations. Our conclusion is that speakers manage information flow by balancing textual, interpersonal and ideational choices.

International audience; Quantitative information flow aims to assess and control the leakage of sensitive information by computer systems. A key insight in this area is that no single leakage measure is appropriate in all operational scenarios; as a result, many leakage measures have been proposed, with many different properties. To clarify this complex situation, this paper studies information leakage axiomatically, showing important dependencies among different axioms. It also establishes a completeness result about the g-leakage family, showing that any leakage measure satisfying certain intuitively-reasonable properties can be expressed as a g-leakage.

Construction projects are characterised by complexity in the technical, organisational and environmental sphere. The organisational complexity of such projects makes it necessary to manage relationships between actors who fulfil various functions. Formal organisational structures that have been developed for this purpose do not always reflect the actual relationships between construction project participants. In literature, scholars more and more often point to the need to identify and monitor such informal relationships and attempt to manage them in order to effectively carry out projects. Structural analysis of so-called self-organising networks of relationships between project participants is carried out on the basis of established structural measures by performing Social Network Analysis (SNA). In a situation when inappropriate communication between project participants relative to management staff expectations is detected, interventions meant to improve communication in such networks are possible. The goal of the article is proposing an optimisation-oriented approach to planning such interventions while taking various constraints, such as communication costs, into consideration. As a part of this optimisation, the authors proposed a method from the heuristic methods group. This solution will support decision-making in terms of intervening within an informal relationship structure. The method was presented on the example of an actual construction project involving the construction of a complex of housing buildings. the self-organising network structure was defined on the basis of a survey carried out among the project’s participants and concerned communication between them over a four-week period. As a result of the structural network analysis, abnormalities in communication between project participants were detected. The optimisation method developed by the authors pointed to possibilities of improving communication effectiveness within this network. The effects of the analysis confirmed the application potential of the method that was presented.

The purpose of secure multi-party computation is to enable protocol participants to compute a public function of their private inputs while keeping their inputs secret, without resorting to any trusted third party. However, opening the public output of such computations inevitably reveals some information about the private inputs. We propose a measure generalizing both Renyi entropy and $g$ -entropy so as to quantify this information leakage. In order to control and restrain such information flows, we introduce the notion of function substitution, which replaces the computation of a function that reveals sensitive information with that of an approximate function. We exhibit theoretical bounds for the privacy gains that this approach provides and experimentally show that this enhances the confidentiality of the inputs while controlling the distortion of computed output values. Finally, we investigate the inherent compromise between accuracy of computation and privacy of inputs and we demonstrate how to realize such optimal trade-offs.

Separation is the primary consideration in cloud computing security. A series of security and safety problems would arise if a separation mechanism is not deployed appropriately, thus affecting the confidence of cloud end-users. In this paper, together with characteristics of cloud computing, the separation issue in cloud computing has been analyzed from the perspective of information flow. The process of information flow in cloud computing systems is formalized to propose corresponding separation rules. These rules have been verified in this paper and it is shown that the rules conform to non-interference security, thus ensuring the security and practicability of the proposed rules.

How cognitive task behavior is generated by brain network interactions is a central question in neuroscience. Answering this question calls for the development of novel analysis tools that can firstly capture neural signatures of task information with high spatial and temporal precision (the “where and when”), and then allow for empirical testing of alternative network models of brain function that link information to behavior (the “how”). We outline a novel network modeling approach suited to this purpose that is applied to non-invasive functional neuroimaging data in humans. We first dynamically decoded the spatiotemporal signatures of task information in the human brain by combining MRI-individualized source electroencephalography with multivariate pattern analysis. A newly developed network modeling approach - dynamic activity flow modeling - then simulated the flow of task-evoked activity over more causally interpretable (relative to standard functional connectivity approaches) resting-state functional connections (dynamic, lagged, direct and directional). We demonstrate the utility of this modeling approach by applying it to elucidate network processes underlying sensory-motor information flow in the brain, revealing accurate predictions of empirical response information dynamics underlying behavior. Extending the model towards simulating network lesions suggested a role for the cognitive control networks (CCNs) as primary drivers of response information flow, transitioning from early dorsal attention network-dominated sensory-to-response transformation to later collaborative CCN engagement during response selection. These results demonstrate the utility of the dynamic activity flow modeling approach in identifying the generative network processes underlying neurocognitive phenomena.

Action selection refers to the decision regarding which action to perform in order to reach a desired goal, that is, the “what” component of intention. Whether the action is freely chosen or externally instructed involves different brain networks during the selection phase, but it is assumed that the way an action is selected should not influence the subsequent execution phase of the same movement. Here, we aim to test this hypothesis by investigating whether the modality of movement selection influences the brain networks involved during the execution phase of the movement. Twenty healthy volunteers performed a delayed response task in an event-related functional magnetic resonance imaging design to compare freely chosen and instructed unimanual or bimanual movements during the execution phase. Using activation analyses, we found that the pre-supplementary motor area (preSMA) and the parietal and cerebellar areas were more activated during the execution phase of freely chosen as compared to instructed movements. Connectivity analysis showed an increase of information flow between the right posterior parietal cortex and the cerebellum for freely chosen compared to instructed movements. We suggest that the parieto-cerebellar network is particularly engaged during freely chosen movement to monitor the congruence between the intentional content of our actions and their outcome.

Since its introduction, Building Information Modelling has evolved into a major technology in the construction industry, where information flows play a major role. However, the very presence of waste within these flows prevents the technology from reaching its full potential. This paper aims to develop a taxonomy focused on the sources of waste within information flows in BIM projects, as existing taxonomies focus primarily on work and material flows. Using a collaborative design science research approach, the study was divided into two phases: first, semi-structured interviews were used to collect data on BIM practices. The data was then used to identify similarities and contradictions in the information flows using process maps. Second, the ShareLab approach was used to validate the findings through a common agreement. The paper’s main contribution is the taxonomy of sources of waste in BIM project information flows, as it closes knowledge gaps in one of the main flows of construction projects. Another contribution is its use of a new approach to validate an artefact in a Design Science Research methodology named the ShareLab approach.

Decision response and feedback in gambling are interrelated. Different decisions lead to different ranges of feedback, which in turn influences subsequent decisions. However, the mechanism underlying the continuous decision-feedback process is still left unveiled. To fulfill this gap, we applied the hidden Markov model (HMM) to the gambling electroencephalogram (EEG) data to characterize the dynamics of this process. Furthermore, we explored the differences between distinct decision responses (i.e. choose large or small bets) or distinct feedback (i.e. win or loss outcomes) in corresponding phases. We demonstrated that the processing stages in decision-feedback process including strategy adjustment and visual information processing can be characterized by distinct brain networks. Moreover, time-varying networks showed, after decision response, large bet recruited more resources from right frontal and right center cortices while small bet was more related to the activation of the left frontal lobe. Concerning feedback, networks of win feedback showed a strong right frontal and right center pattern, while an information flow originating from the left frontal lobe to the middle frontal lobe was observed in loss feedback. Taken together, these findings shed light on general principles of natural decision-feedback and may contribute to the design of biologically inspired, participant-independent decision-feedback systems.

As the energy infrastructure of smart cities, smart grid upgrades traditional power grid systems with state-of-the-art information and communication technologies. In particular, as the full deployment of the Internet of Things in the power grid (a.k.a. power Internet of Things or PIoT), the newly introduced information flow together with inherent energy flow makes it more efficient for power generation, transmission, distribution, and consumption. To further exploit the precious energy and the latest 5G technologies, this article boosts to add a value flow in the smart grid, mainly including the value created by innovative services and market mechanisms and the value added by the information flow. Specifically, by integrating PIoT with cyber-physical systems, this article sketches a conceptual framework of the cyber-physical power system (CPPS). The CPPS carries out holistic perception and ubiquitous connection of distributed energy sources and electrical facilities and builds up a smarter power grid with global information interaction, intelligent decision-making, and real-time agile control. Finally, for illustration purposes, we conduct a case study regarding an intelligent home management system.

Detection and measurement of abrupt changes in a process can provide us with important tools for decision making in systems management. In particular, it can be utilised to predict the onset of a sudden event such as a rare, extreme event which causes the abrupt dynamical change in the system. Here, we investigate the prediction capability of information theory by focusing on how sensitive information-geometric theory (information length diagnostics) and entropy-based information theoretical method (information flow) are to abrupt changes. To this end, we utilise a non-autonomous Kramer equation by including a sudden perturbation to the system to mimic the onset of a sudden event and calculate time-dependent probability density functions (PDFs) and various statistical quantities with the help of numerical simulations. We show that information length diagnostics predict the onset of a sudden event better than the information flow. Furthermore, it is explicitly shown that the information flow like any other entropy-based measures has limitations in measuring perturbations which do not affect entropy.

The modeling of conversational context plays a vital role in emotion recognition from conversation (ERC). In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network, namely DAG-ERC, to implement this idea. In an attempt to combine the strengths of conventional graph-based neural models and recurrence-based neural models, DAG-ERC provides a more intuitive way to model the information flow between long-distance conversation background and nearby context. Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison. The empirical results demonstrate the superiority of this new model and confirm the motivation of the directed acyclic graph architecture for ERC., Accepted to ACL-IJCNLP 2021 main conference

Mind blanking (MB) is a waking state during which we do not report any mental content, challenging the view of a constantly thought-oriented brain. Here, we comprehensively characterize the MB’s neurobehavioral profile with the aim to delineate its role during ongoing mentation. Using fMRI experience-sampling, we show that MB is reported less frequently, faster, and with low transitional dynamics among other mental states, pointing to its role as a transient mental relay. Regarding its neural underpinnings, we observe higher global signal amplitude during MB reports, indicating a distinct physiological substrate. Using the time-varying functional connectome MB reports get classified with high accuracy, suggesting that MB has a unique neural composition. Indeed, a pattern of globally positive-phase coherence shows the highest similarity to the connectivity patterns associated with MB reports. We observe that this pattern’s rigid signal architecture hinders content reportability due to the brain’s inability to differentiate signals in an informative way. Collectively, we show that MB has a unique neurobehavioural profile, indicating that non-reportable mental events can happen during wakefulness. Our results add to the characterization of spontaneous mentation and pave the way for more mechanistic investigations of MB’s phenomenology.Significance StatementThe human mind is generally assumed to be thought-oriented. Mind blanking (MB) challenges this stance because it appears as if we are derived of any particular mental content to report. We here show that, during spontaneous thinking, MB is a mental state that happens by default, it has a unique behavioural profile, and it is of a rigid neural architecture that does not permit the formulation of reportable contents. Our work essentially proposes that non-reportable mental events can happen during wakefulness, and challenges the view of the mind as a constant thought-oriented operator.

Today's web applications remain vulnerable to cross-site scripting attacks that enable data theft. Information flow tracking in the JavaScript VM of a web engine can track data flows through the web application and prevent the communication of sensitive data to unintended recipients, thereby stopping data theft. Existing solutions have focused on the incorporating information flow into the JS interpreter, rather than the just-in-time compiler, rendering the resulting performance non-competitive.This thesis presents an implementation of information flow tracking that works with the just-in-time compiler, outperforming all previous interpreter-based information flow tracking engines by more than a factor of two. The JIT-based engine (i) has the same coverage as previous implementations, (ii) requires comparatively light implementation effort, and (iii) introduces new optimizations to remain performant. When evaluated against three industry standard benchmark suites, the tracking engine retains an average slowdown of 73% over engines that do not support information flow, remaining will within the range that many users will find an acceptable price for obtaining substantially increased security against data theft.

Due to individual differences, different subjects have different response to different emotions. Only using the electroencephalogram (EEG) data that can best describe emotions, can the constructed brain networks better reflect the brain connection patterns. This paper proposes a window selection method of time-varying sequence to select critical EEG data that are most relevant to emotions. The generalized orthogonalized partial directed coherence (gOPDC) method is used to construct effective brain networks based on critical EEG data, which can explore the statistical relationship and the information flow between nodes. At the same time, the false connections caused by volume conduction in EEG channels can also be overcome. We extract network features for brain effective connectivity analysis and emotion recognition. The results of the experiments indicate that emotion is mainly related to the frontal region of the brain, and the parietal lobe also plays an important role in low valence emotions. The connection of brain network is the densest in negative emotions and the least in calm and relaxed emotions. The average classification accuracy reaches 78.78% by using KNN classifier on critical EEG data, and the classification results are better than using all data and the intermediate data.