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Editorial: Reaching to Grasp Cognition: Analyzing Motor Behavior to Investigate Social Interactions
(2018)
Utilizing quad-trees for efficient design space exploration with partial assignment evaluation
(2018)
Recently, it has been shown that constraint-based symbolic solving techniques offer an efficient way for deciding binding and routing options in order to obtain a feasible system level implementation. In combination with various background theories, a feasibility analysis of the resulting system may already be performed on partial solutions. That is, infeasible subsets of mapping and routing options can be pruned early in the decision process, which fastens the solving accordingly. However, allowing a proper design space exploration including multi-objective optimization also requires an efficient structure for storing and managing non-dominated solutions. In this work, we propose and study the usage of the Quad-Tree data structure in the context of partial assignment evaluation during system synthesis. Out experiments show that unnecessary dominance checks can be avoided, which indicates a preference of Quad-Trees over a commonly used list-based implementation for large combinatorial optimization problems.
Imaginar la nación
(2018)
The problem of constructing and maintaining a tree topology in a distributed manner is a challenging task in WSNs. This is because the nodes have limited computational and memory resources and the network changes over time. We propose the Dynamic Gallager-Humblet-Spira (D-GHS) algorithm that builds and maintains a minimum spanning tree. To do so, we divide D-GHS into four phases, namely neighbor discovery, tree construction, data collection, and tree maintenance. In the neighbor discovery phase, the nodes collect information about their neighbors and the link quality. In the tree construction, D-GHS finds the minimum spanning tree by executing the Gallager-Humblet-Spira algorithm. In the data collection phase, the sink roots the minimum spanning tree at itself, and each node sends data packets. In the tree maintenance phase, the nodes repair the tree when communication failures occur. The emulation results show that D-GHS reduces the number of control messages and the energy consumption, at the cost of a slight increase in memory size and convergence time.
High storage density magnetic devices rely on the precise, reliable and ultrafast switching times of the magnetic states. Optical control of magnetization using femtosecond laser without applying any external magnetic field offers the advantage of switching magnetic states at ultrashort time scales, which has attracted a significant attention. Recently, it has been reported and demonstrated the,so-called, all-optical helicity-dependent switching (AO-HDS) in which a circularly polarized femtosecond laser pulse switches the magnetization of a ferromagnetic thin film as function of laser helicity [1]. Afterward, in more recent studies, it has been reported that AO-HDS is a general phenomenon existing in magnetic materials ranging from rare earth - transition metals ferrimagnetic (e.g. alloys, multilayers and hetero-structures system) to even ferromagnetic thin films. Among numerous studies in the literature which are discussing the microscopic origin of AO-HDS in ferromagnets or ferrimagnetic alloys, the most renowned concepts are momentum transfer via Inverse Faraday Effect (IFE) [1-3]and the concept of preferential thermal demagnetization for one magnetization direction by heating close to Tc (Curie temperature) in the presence of magnetic circular dichroism (MCD) [4-6]. In this study, we investigate all-optical magnetic switching using a stationary femtosecond laser spot (3-5 μm) in TbFe alloys via photoemission electron microscopy (PEEM) and x-ray magnetic circular dichroism (XMCD) with a spatial resolution of approximately 30 nm. We spatially characterize the effect of laser heating and local temperature profile created across the laser spot on AO-HDS in TbFe thin films. We find that AO-HDS occurs only in a `ring' shaped region surrounding the thermally demagnetized region formed by the laser spot and the formation of switched domains relies further on thermally induced domain wall motion. Our temperature dependent measurements highlight the importance of attainin...
Modern server systems with large NUMA architectures necessitate (i) data being distributed over the available computing nodes and (ii) NUMA-aware query processing to enable effective parallel processing in database systems. As these architectures incur significant latency and throughout penalties for accessing non-local data, queries should be executed as close as possible to the data. To further increase both performance and efficiency, data that is not relevant for the query result should be skipped as early as possible. One way to achieve this goal is horizontal partitioning to improve static partition pruning. As part of our ongoing work on workload-driven partitioning, we have implemented a recent approach called aggressive data skipping and extended it to handle both analytical as well as transactional access patterns. In this paper, we evaluate this approach with the workload and data of a production enterprise system of a Global 2000 company. The results show that over 80% of all tuples can be skipped in average while the resulting partitioning schemata are surprisingly stable over time.
Preface
(2018)
An Information System Supporting the Eliciting of Expert Knowledge for Successful IT Projects
(2018)
In order to guarantee the success of an IT project, it is necessary for a company to possess expert knowledge. The difficulty arises when experts no longer work for the company and it then becomes necessary to use their knowledge, in order to realise an IT project. In this paper, the ExKnowIT information system which supports the eliciting of expert knowledge for successful IT projects, is presented and consists of the following modules: (1) the identification of experts for successful IT projects, (2) the eliciting of expert knowledge on completed IT projects, (3) the expert knowledge base on completed IT projects, (4) the Group Method for Data Handling (GMDH) algorithm, (5) new knowledge in support of decisions regarding the selection of a manager for a new IT project. The added value of our system is that these three approaches, namely, the elicitation of expert knowledge, the success of an IT project and the discovery of new knowledge, gleaned from the expert knowledge base, otherwise known as the decision model, complement each other.
No other means of communication determines through its seemingly unrestricted possibilities our everyday life more than the internet. From the mid-90s onwards, more and more technical advancements in the field of communication appear on the market, which in turn call for new terminology. In the first place, it is the internet (essentially based on the interaction between users and experts), which requires effective nomenclature in order to mediate between lay users and their restricted knowledge on the one, and experts and their sophisticated terminology on the other hand. At the interface between the new and complex realities and the need for simple linguistic access, a huge quantity of metaphoric denominations is used, making abstract innovations more comprehensible. Metaphor in the internet discourse serves to "reduce verticality" (Stenschke 2006) between specialized terminology and common language. The paper deals with metaphors based on spatial concepts. Space and spatiality play a key role in cognitive theories of metaphor as these theories themselves (according to Lakoff/Johnson 1980) are often based on the application of spatial concepts to non-spatial relations. After describing spatial concepts in general (referring to the internet), the paper explores which kind of metaphor takes advantage of the complexity present in the internet and how the medial space is linguistically recaptured in terms of spatial perception.
The Aral Sea desiccation and related changes in hydroclimatic conditions on a regional level is a hot topic for past decades. The key problem of scientific research projects devoted to an investigation of modern Aral Sea basin hydrological regime is its discontinuous nature - the only limited amount of papers takes into account the complex runoff formation system entirely. Addressing this challenge we have developed a continuous prediction system for assessing freshwater inflow into the Small Aral Sea based on coupling stack of hydrological and data-driven models. Results show a good prediction skill and approve the possibility to develop a valuable water assessment tool which utilizes the power of classical physically based and modern machine learning models both for territories with complex water management system and strong water-related data scarcity. The source code and data of the proposed system is available on a Github page (https://github.com/SMASHIproject/IWRM2018).
The electret state stability in nonpolar semicrystalline polymers is largely determined by the traps located at crystalline/ amorphous phase interfaces. Thus, the thermal history of such polymers should considerably influence their electret properties. In the present work, we investigate how recrystallization influences charge stability in low-density polyethylene corona electrets. It has been found that electret charge stability in quenched samples is higher than in slowly-crystallized ones. Phenomenologicaly, this can be explained by the increased number of deeper traps in samples with smaller crystallite size.
Why choice matters
(2018)
Measures of democracy are in high demand. Scientific and public audiences use them to describe political realities and to substantiate causal claims about those realities. This introduction to the thematic issue reviews the history of democracy measurement since the 1950s. It identifies four development phases of the field, which are characterized by three recurrent topics of debate: (1) what is democracy, (2) what is a good measure of democracy, and (3) do our measurements of democracy register real-world developments? As the answers to those questions have been changing over time, the field of democracy measurement has adapted and reached higher levels of theoretical and methodological sophistication. In effect, the challenges facing contemporary social scientists are not only limited to the challenge of constructing a sound index of democracy. Today, they also need a profound understanding of the differences between various measures of democracy and their implications for empirical applications. The introduction outlines how the contributions to this thematic issue help scholars cope with the recurrent issues of conceptualization, measurement, and application, and concludes by identifying avenues for future research.
This introductory essay to the HSR Special Issue “Economists, Politics, and Society” argues for a strong field-theoretical programme inspired by Pierre Bourdieu to research economic life as an integral part of different social forms. Its main aim is threefold. First, we spell out the very distinct Durkheimian legacy in Bourdieu’s thinking and the way he applies it in researching economic phenomena. Without this background, much of what is actually part of how Bourdieu analysed economic aspects of social life would be overlooked or reduced to mere economic sociology. Second, we sketch the main theoretical concepts and heuristics used to analyse economic life from a field perspective. Third, we focus on practical methodological issues of field-analytical research into economic phenomena. We conclude with a short summary of the basic characteristics of this approach and discuss the main insights provided by the contributions to this special issue.
We compare the robustness of humans and current convolutional deep neural networks (DNNs) on object recognition under twelve different types of image degradations. First, using three well known DNNs (ResNet-152, VGG-19, GoogLeNet) we find the human visual system to be more robust to nearly all of the tested image manipulations, and we observe progressively diverging classification error-patterns between humans and DNNs when the signal gets weaker. Secondly, we show that DNNs trained directly on distorted images consistently surpass human performance on the exact distortion types they were trained on, yet they display extremely poor generalisation abilities when tested on other distortion types. For example, training on salt-and-pepper noise does not imply robustness on uniform white noise and vice versa. Thus, changes in the noise distribution between training and testing constitutes a crucial challenge to deep learning vision systems that can be systematically addressed in a lifelong machine learning approach. Our new dataset consisting of 83K carefully measured human psychophysical trials provide a useful reference for lifelong robustness against image degradations set by the human visual system.
Metamaterial Devices
(2018)
In our hands-on demonstration, we show several objects, the functionality of which is defined by the objects' internal micro-structure. Such metamaterial machines can (1) be mechanisms based on their microstructures, (2) employ simple mechanical computation, or (3) change their outside to interact with their environment. They are 3D printed from one piece and we support their creating by providing interactive software tools.