Static Context Research Articles

Support for full life cycle cloud-native application management: Dynamic TOSCA and SWITCH IDE

Motivated by the complexity and difficulty of engineering time-critical cloud applications, we investigated the methodology and software workbench to provide full application life cycle support to the software engineer. For this purpose we present a novel high-level concept that is concentrated on exchanging the complex, dynamic data using OASIS TOSCA standard, mainly used in static contexts. This methodology enables specifying an application logic, provisioning, deploying, monitoring, and dynamical adaptation to changing conditions. It can be applied effectively to the interchange of dynamically changing data in order to maintain Quality of Service. By extending TOSCA with dynamic mapping of Qualify of Service and runtime attributes related to the application and underlying architecture we show that TOSCA can be used to exchange data and reconfigure on-demand compute resources whilst the application is in the process of design, infrastructure provisioning, runtime or adaptation. To demonstrate that our novel research contributions can be realised, we implemented SWITCH IDE, which showcases this concept through its development workflow which offers application and underlying infrastructure description, manipulation and reconfiguration of QoS and runtime attributes. Our approach differs in that it supports the concept of dynamic TOSCA by directly enabling application runtime to be reconfigured on the fly.

The Home Market Effect, Economic Growth, and the Dynamic Trade Patterns

ABSTRACTIn spite of increasing globalization, the effects of international trade on economic growth remain unclear. I consider an endogenous economic growth model in an open economy with the home market effect and non-homothetic preferences to identify the determinants of the various results in this relationship. The model shows how trade between similar countries leads to convergence in economic growth when knowledge spillovers are present, while trade between highly asymmetric countries produces divergence and may become trade in a poverty or growth trap. Besides, international trade does not necessarily imply greater welfare, as is usual in a static context under CES preferences.

Learning longitudinal classification-regression model for infant hippocampus segmentation

Hippocampus plays an important role in the memory and spatial navigation function of human brain. Study on its growth and change during first year of life would assist the investigation of early brain development as well as the biomarker for neurological disorders. With the help of Magnetic Resonance (MR) imaging techniques, infant brain at different development stage can be acquired with multiple imaging modalities. In this situation, the longitudinal segmentation of infant hippocampus is highly demanded and feasible for the clinical studies regarding to the hippocampal volume changes. However, since the brain structures undergo dynamic appearance, structural changes and various tissue contrast during the first year of life, substantial challenges will be imposed for ensuring the robustness and accuracy of automatic hippocampus segmentation algorithms. In addition, most of the existing hippocampus segmentation methods generally handle each brain development stage independently without considering the potential longitudinal consistency among different stages. In view of the above factors, we propose a longitudinal classification-regression model for segmenting hippocampus in infant brain MRIs. Generally, our model proceeds on a per-timepoint basis, guided by the output of latter timepoint towards the infant hippocampus in the previous timepoint. The key ingredient of our method is a combination of longitudinal context, static context and appearance learning strategies under the classification-regression forest architecture. Specifically, the longitudinal context is borrowed from the mask of prior-timepoint estimation and the static context is from the current-timepoint estimation. Furthermore, we implement the proposed model in a multi-scale and iterative manner to improve the efficiency and effectiveness. The proposed method is evaluated on segmenting infant hippocampi from T1-weighted brain MR images acquired at the age of 2 weeks, 3 months, 6 months, 9 months, and 12 months. Experimental results demonstrate that our method achieves better performance in segmentation accuracy over the state-of-the-art classification and regression random forest model.

Life cycle of a star: Carl Sagan and the circulation of reputation.

It is a commonplace in the history of science that reputations of scientists play important roles in the stories of scientific knowledge. I argue that to fully understand these roles we should see reputations as produced by communicative acts, consider how reputations become known about, and study the factors influencing such processes. I reapply James Secord's 'knowledge-in-transit' approach; in addition to scientific knowledge, I also examine how 'biographical knowledge' of individuals is constructed through communications and shaped by communicative contexts. My case study is Carl Sagan, widely discussed - amongst scientists, media professionals and publics - for his skill as a charismatic popularizer, his perceived arrogance, his political activism, and his debated merit as a researcher. By examining how aspects of Sagan's reputation circulated alongside his scientific work - rather than existing as a static context for his scientific work - I show how different forms of knowledge (biographical and scientific) influence each other as they circulate.

DWE-Med

Recent advances in unsupervised language processing methods have created an opportunity to exploit massive text corpora for developing high-quality vector space representation (also known as word embeddings) of words. Towards this direction, practitioners have developed and applied several data driven embedding models with quite good rate of success. However, a drawback of these models lies in their premise of static context; wherein, the meaning of a word is assumed to remain the same over the period of time. This is limiting because it is known that the semantic meaning of a concept evolves over time. While such semantic drifts are routinely observed in almost all the domains; their effect is acute in domain such as biomedicine, where the semantic meaning of a concept changes relatively fast. To address this, in this study, we aim to learn temporally aware vector representation of medical concepts from the timestamped text data, and in doing so provide a systematic approach to formalize the problem. More specifically, a dynamic word embedding based model that jointly learns the temporal characteristics of medical concepts and performs across time-alignment is proposed. Apart from capturing the evolutionary characteristics in an optimal manner, the model also factors in the implicit medical properties useful for a variety of bio-medical applications. Empirical studies conducted on two important bio-medical use cases validates the effectiveness of the proposed approach and suggests that the model not only learns quality embeddings but also facilitates intuitive trajectory visualizations.

Dynamic pollution-adjusted inefficiency under the by-production of bad outputs

This article extends the by-production model to the dynamic context of adjustment costs associated with investment. The empirical application focuses on panel data of French suckler cow farms over the period 1978–2014, considering emissions of greenhouse gases as bad output. The paper estimates input and output-specific technical inefficiency scores in the dynamic context and compares them with efficiency measures from the conventional static context. Our results reveal significant differences between inefficiency scores derived from the static and the dynamic frameworks. For all variables except meat production (the good output), the inefficiency score is lower in the dynamic context than in the static context.

Polarities in structural analysis and design: n-dimensional graphic statics and structural transformations

This paper proposes a unified and entirely geometrical methodology for generating 2D and 3D force diagrams for given planar and spatial trusses in static equilibrium within the context of graphic statics. The trusses, regarded as form diagrams, are projections of higher dimensional simply-connected stress functions, can be either self—stressed or loaded with external forces, and need not be tension-only/ compression-only. First, we provide an in-depth overview of fundamental notions of graphic statics and projective geometry related to the construction of reciprocal form and force diagrams. Specifically, we describe a series of polar transformations and discuss them from a geometric and an algebraic standpoint. Moreover, we provide an exegesis and visualization of Maxwell's reciprocal methods while discussing them within the scientific framework of his time. We then develop a contemporary graphic statics framework grounded on projective geometry and higher-dimensional reciprocal stress functions. Within this framework, we provide a general methodology for deriving any type of reciprocal diagrams for the planar and the spatial case. Advantages of this method include: direct control on any of the four reciprocal objects (form diagram, force diagram, corresponding stress functions) and no need for iterative/ procedural reconstruction of any of the diagrams since it is based on direct geometrical transformations. Finally, we highlight how duality, and in particular polarity, can be used for a wide range of applications in structural analysis and design.

The effects of biofeedback‐based stimulated recall on self‐regulated online learning: A gender and cognitive taxonomy perspective

AbstractPrevious studies posited the effectiveness of stimulated recall. However, few studies explored how SR is implemented in a relatively static context, for example, online self‐directed learning, or took human factors, for example, cognitive style and gender, into consideration in such a context. To fill this gap, the current study, aims to introduce biofeedback as a stimulus for learners to engage in retrospection regarding their learning behavior. A quasi‐experimental design study was carried out over a 12‐week set of EFL self‐regulated online reading activities. Pretest and posttest on reading performance and their cognitive taxonomy were assessed through a developed scale instrument, whereas physiological signals (e.g., gazing duration, verbal fixation, and brain wave) were captured via eye‐tracking and electroencephalograph (EEG) technology. The results emphasized that (a) students' reading ability and cognitive hierarchy significantly improved through biofeedback stimulation. Moreover, (b) learners in single level‐one cognitive hierarchic groups had significant improvements in both cognitive abilities and reading comprehension, whereas learners in multilevel hierarchic groups had no significant enhancements. Finally, (c) the optical data results and EEG reports showed that males favor procedural feedback and females have a preference for a conclusive assessment.

Cournot-like Interaction with Cournot Outcomes

This article works out a Cournot duopoly where firms take price as given, but it is not the same price that is taken for given by each firm; it depends on individual expectations about opponent’s behaviour as are shaped by the business cycle. Yet, the result of this interaction is a Cournot equilibrium as if the calculations were based on the same price. It is not the price that would be assumed in the absence of expectations. Expectations make inverse-U-shaped reaction curves peak at higher output levels relative to peaks in their absence, implying a lower price vis-à-vis that which is taken for granted in the traditional analysis. And, output levels are higher because they reflect optimism for the course of the economy. In the static context of the analysis, a Cournot equilibrium comes about as if the business cycle is at each peak. Policy-wise, the message is that observed price variations in an oligopoly do not necessarily indicate Bertrand competition despite the possibility that dynamic interaction may lead to outcomes other than the Cournot one; but it will be about disequilibrium and unstable outcomes. JEL Classifications: D43, L13, E32

Speech Act in Arafat Nur’s Novel Tanah Surga Merah

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Contextual factors influencing decision making: Perceptions of professional soccer players

ObjectivesThis study sought to explore highly-skilled soccer players’ perceptions of how contextual factors influence their decision making during matches. DesignA qualitative design was used in which individual semi-structured interviews were conducted with eight professional male soccer players aged between 18 and 22 years. MethodAn interview schedule was designed to explore the perceived influence of a range of situational factors on decision making during matches. The interviews were recorded and transcribed verbatim. The data were analysed via an inductive thematic analysis. ResultsSeven themes were identified from the data. The four dynamic contextual themes were: (a) personal performance, (b) score status, (c) momentum, and (d) external/coach instructions. The three static contextual themes were: (a) match importance, (b) personal pressures, and (c) preparation. ConclusionsThe results highlight the importance of considering the dynamic and static context within which highly-skilled soccer players make decisions.

The dative in Croatian Štokavian and Kajkavian grammars up to 1860

This article deals with the approach to the dative case in Kajkavian and Štokavian grammars from 1604 to 1859. We concentrate on seventeen grammars, primarily focusing on their presentation of the meaning of the dative case. Remarks related to the meaning of the dative are mainly found in the parts of these grammars discussing syntax, and to a lesser extent in other parts. A significant feature of the majority of the grammars examined is their presentation of what modern grammars consider dative and locative (constructions) under the same umbrella of the dative case or the “third” case. The reason for this is a “morphology first” rule: the identical morphological form (in the singular) influenced grammarians’ conceptualization of what are considered typical dative constructions (e.g., those with the preposition k) and locative ones (e.g., those with the preposition u in static contexts) as dative constructions. Including the dative and locative prepositions in the scope of a single case in the singular led to an implicit assumption about the semantic profile of the dative: it occurs in static and dynamic constructions, and its semantic profile is different in the singular and plural.

Deep Learning for Link Prediction in Dynamic Networks Using Weak Estimators

Link prediction is the task of evaluating the probability that an edge exists in a network, and it has useful applications in many domains. Traditional approaches rely on measuring the similarity between two nodes in a static context. Recent research has focused on extending link prediction to a dynamic setting, predicting the creation and destruction of links in networks that evolve over time. Though a difficult task, the employment of deep learning techniques has shown to make notable improvements to the accuracy of predictions. To this end, we propose the novel application of weak estimators in addition to the utilization of traditional similarity metrics to inexpensively build an effective feature vector for a deep neural network. Weak estimators have been used in a variety of machine learning algorithms to improve model accuracy, owing to their capacity to estimate the changing probabilities in dynamic systems. Experiments indicate that our approach results in increased prediction accuracy on several real-world dynamic networks.

Joint travel problem in space\u2013time multi-state supernetworks

Joint travel problem (JTP) is an extension of the classic shortest path problem and relevant to shared mobility. A pioneering endeavor via supernetwork framework has been put forward to model two-person JTP. However, it was only addressed in the static context and with the assumption of zero waiting disutility, which resulted in no or weak synchronization among the travelers. This paper proposes a space–time multi-state supernetwork framework to address JTP for conducting one joint activity in the time-dependent context. Space–time synchronization and various choice facets related to joint travel are captured systematically. Two-person JTP is first discussed in a uni-modal transport network, and further extended to incorporate multi-modal and multi-person respectively. Stage-wise recursive formulations are proposed to find the optimal joint paths. It is found that JTP is a variant of Steiner tree problem by reduction and the number of meeting/departing points has no impact on the run-time complexity in space–time multi-state supernetworks.

A VEDA simulation on cement paste: using dynamic atomic force microscopy to characterize cellulose nanocrystal distribution

Abstract

Feralness: A Sibling of Wilderness

In North America, the classic voice of colonial peoples' connectedness to nature and a wellspring of distinct new identity has been the romantic individualist writing of affinity for wilderness. The truth is, however, that wilderness account makes North Americans, Australians, and New Zealanders culturally blind to an emerging split between wilderness as a land management concept and the state of the wild characteristic of the lands near the cities where 80 percent of us now live. To say land is wilderness, one has to imagine a static systemic context creating conditions that, if it were not for the colonizing project of land conversion, population implantation, mineral exploitation, the land would forever reflect. What about land that runs away from past colonial domestications? What about land that has hybridized with colonial escapee species? Thoroughly worked over lands are fallow on the edges of cities, and new kinds of wilds are emerging upon them. Let me propose that the time is ripe for the sibling of wilderness and for cultural forms exploring and reflecting its stories, for how can you preserve what you cannot name or the culture has never helped you to categorize?

Identification of acoustic moving sources in the context of a road vehicle at pass-by: A numerical comparison

The problem of the identification of acoustic sources has been widely treated in static or quasi-static source contexts, more recently for moving sources, in a wide range of applications. Different methods have been developed in this endeavor. In the context of identification of sources from a passing-by vehicle, the beamforming method is a reference that has well-known limitations: a poor resolution at low frequencies causes difficulties to discriminate close sources or with too different levels, and the convolution of the point spread function with the source-signal makes it difficult to evaluate the level precisely. Deconvolution methods are used to overcome those limitations. Mostly developed in static contexts at the beginning, extensions to moving sources have been proposed in the transportation field, mainly in air transportation and underwater acoustics. In the present investigation, these methods are numerically tested with parameters fitted to the road vehicle context. Their performance is assessed at different speeds, with varying additional noise levels. Beamforming—as the reference method—and several deconvolution methods (DAMAS, CLEAN, NNLS) extended to moving sources are compared using performance including: location of the maximum, source gravity center, maximum level and extended level. Illustrations involving an academic pendulum setup are given.

Analytic Decision Rules for Financial Stochastic Programs

Contemporary financial stochastic programs typically involve a trade-off between return and (downside)-risk. Using stochastic programming we characterize analytically (rather than numerically) the optimal decisions that follow from characteristic single-stage and multi-stage versions of such programs. The solutions are presented in the form of decision rules with a clear-cut economic interpretation. This facilitates transparency and ease of communication with decision makers. The optimal decision rules exhibit switching behavior in terms of relevant state variables like the assets to liabilities ratio. We find that the model can be tuned easily using Value-at-Risk (VaR) related benchmarks. In the multi-stage setting, we formally prove that the optimal solution consists of a sequence of myopic (single-stage) decisions with risk-aversion increasing over time. The optimal decision rules in the dynamic setting therefore exhibit identical features as in the static context.

Electing a Leader in Dynamic Networks using Mobile Agents and Local Computations

Abstract: In dynamic distributed systems, the topology of networks changes over time which makes difficult the design and much harder the proof of distributed algorithms. These unavoidable changes of the topology make the election and the maintenance of the elected leader a complex task. The maintaining problem is not considered in a static context. To encode distributed algorithms, we adopt the local computation model. Distributed algorithms are formally presented by transitions systems. Based on both, the mobile agent paradigm and the local computation model, we present in this paper a distributed algorithm that elects a leader in a tree. A set of topological events that may affect the structure of the tree: we focus on the appearance and the disappearance of places as well as the communication channels. Our goal is to maintain always a tree with a single leader or a forest of trees where each one has his own leader.

The PLETHORA Gene Regulatory Network Guides Growth and Cell Differentiation in Arabidopsis Roots.

Organ formation in animals and plants relies on precise control of cell state transitions to turn stem cell daughters into fully differentiated cells. In plants, cells cannot rearrange due to shared cell walls. Thus, differentiation progression and the accompanying cell expansion must be tightly coordinated across tissues. PLETHORA (PLT) transcription factor gradients are unique in their ability to guide the progression of cell differentiation at different positions in the growing Arabidopsis thaliana root, which contrasts with well-described transcription factor gradients in animals specifying distinct cell fates within an essentially static context. To understand the output of the PLT gradient, we studied the gene set transcriptionally controlled by PLTs. Our work reveals how the PLT gradient can regulate cell state by region-specific induction of cell proliferation genes and repression of differentiation. Moreover, PLT targets include major patterning genes and autoregulatory feedback components, enforcing their role as master regulators of organ development.