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Linking mathematics with reality is not new. It is also not new to use outdoor activities to learn mathematics. It seems to be new, to combine such mathematical outdoor activities with mobile technology, like the geocache community which makes use of GPS technology to guide their members to special places and points of interest. The use of mobile technologies to learn at any time and any location is known as “mobile learning”. This type of learning can be seen as an extension of eLearning. Considering the definition of O’Malley one notices that this definition does not exactly match with the idea of the MathCityMap-Project (MCM), because the learning environment in the MCM-Project is predetermined. Combined with the math trail method the project enables mobile learning within math trails with latest technology.In the MCM-Project students experience mathematics at real places and within real situations in out-of-school activities,with help of GPS-enabled smartphones and special math problems. In contrast to the paper versions of math trails we are able to give direct feedback on the solutions by using “mobile devices” such as smartphones or tablets. If the user has difficulties in solving the modeling task, stepped hints can be provided. The teacher is able to use the MCM-Portal to upload tasks developed by himself or by his students and he is also able to build a personal math trail for his students.
Jeden Tag werden 2,5 Trillionen Bytes an Daten generiert. Diese enorme Menge an Daten wird beispielsweise durch digitale Bilder, Videos, Beiträge in den sozialen Medien, intelligente Sensoren, Einzelhandels- und Finanztransaktionen und GPS-Signale von Handys erzeugt. Das ist Big Data. Es besteht kein Zweifel daran, dass Big Data und das, was wir damit tun, das Potential hat, ein signifikanter Treiber für Innovationen und Wertschöpfung zu werden.
Inspiration for artificial biologically inspired computing is often drawn from neural systems. This article shows how to analyze neural systems using information theory with the aim of obtaining constraints that help to identify the algorithms run by neural systems and the information they represent. Algorithms and representations identified this way may then guide the design of biologically inspired computing systems. The material covered includes the necessary introduction to information theory and to the estimation of information-theoretic quantities from neural recordings. We then show how to analyze the information encoded in a system about its environment, and also discuss recent methodological developments on the question of how much information each agent carries about the environment either uniquely or redundantly or synergistically together with others. Last, we introduce the framework of local information dynamics, where information processing is partitioned into component processes of information storage, transfer, and modification – locally in space and time. We close by discussing example applications of these measures to neural data and other complex systems.
The presented work inside this thesis aims to raise the degree of automation in analog circuit design. Therefore, a framework was developed to provide the necessary mechanisms in order to carry out a fully automated analog circuit synthesis, i.e., the construction of an analog circuit fulfilling all previously defined (electrical) specifications. Nowadays, analog circuit design in general is a very time consuming process compared to a digital design flow. Due to its discrete nature, the digital design process is highly automated and thus very efficient compared to analog circuit design. In modern Very-Large-Scale integration (VLSI) circuits the analog parts are mostly just a small portion of the overall chip area. Although this small portion is known to consume a major part of the needed workforce. Paired with product cycles which constantly get shorter, the time needed to develop the analog parts of an integrated circuit (IC) becomes a determinant factor. Apart from this, the ongoing progress in semiconductor processing technologies promises more speed with less power consumption on smaller areas, forcing the IC developers to keep track with the technology nodes in order to maintain competitiveness. Analog circuitry exhibits the inherent property of being hard to reuse, as porting from one technology node to another imposes critical changes for operating conditions (e.g., supply voltage) - mostly leading to a full redesign for most of the analog modules. This productivity gap between digital and analog design resembles the primary motivation for this thesis. Due to the availability of commercial sizing tools, this work deliberately focuses on the construction of circuit topologies in distinction to parameter synthesis, which can be obtained with a dedicated sizing tool. The focus on circuit construction allows the development of a framework which allows a full design space exploration. This thesis describes the needed concepts and methods to realize a deterministic, explorative analog synthesis framework. Despite this, a reference implementation is presented, which demonstrates the applicability in current analog design flows.
Be it in the case of opening a website, sending an email, or high-frequency trading, bits and bytes of information have to cross numerous nodes at which micro-decisions are made. These decisions concern the most efficient path through the network, the processing speed, or the priority of incoming data packets.
Despite their multifaceted nature, micro-decisions are a dimension of control and surveillance in the twenty-first century that has received little critical attention. They represent the smallest unit and the technical precondition of a contemporary network politics – and of our potential opposition to it. The current debates regarding net neutrality and Edward Snowden’s revelation of NSA surveillance are only the tip of the iceberg. What is at stake is nothing less than the future of the Internet as we know it.
Angaben aus der Verlagsmeldung:
Ob beim Aufrufen einer Webseite, beim Versenden einer E-Mail oder beim Hochfrequenzhandel an der Börse: Auf ihrem Weg durch die Weiten digitaler Netze durchqueren Bits zahlreiche Knoten, an denen eine Reihe von Mikroentscheidungen getroffen werden. Diese Entscheidungen betreffen den besten Pfad zum Ziel, die Verarbeitungsgeschwindigkeit oder die Priorität zwischen den ankommenden Paketen.
In ihrer vielschichtigen Gestalt bilden solche Mikroentscheidungen eine bislang nur marginal beachtete Dimension von Kontrolle und Überwachung im 21. Jahrhundert. Sie sind sowohl die kleinste Einheit als auch die technische Voraussetzung einer gegenwärtigen Politik digitaler Netzwerke – und des Widerstands gegen sie. Die aktuellen Debatten um Netzneutralität und Edward Snowdens Enthüllung der NSA-Überwachung bilden dabei lediglich die Spitze des Eisbergs. Auf dem Spiel steht nicht weniger als die Zukunft des Internets, wie wir es kennen.
This paper provides a theoretical assessment of gestures in the context of authoring image-related hypertexts by example of the museum information system WikiNect. To this end, a first implementation of gestural writing based on image schemata is provided (Lakoff in Women, fire, and dangerous things: what categories reveal about the mind. University of Chicago Press, Chicago, 1987). Gestural writing is defined as a sort of coding in which propositions are only expressed by means of gestures. In this respect, it is shown that image schemata allow for bridging between natural language predicates and gestural manifestations. Further, it is demonstrated that gestural writing primarily focuses on the perceptual level of image descriptions (Hollink et al. in Int J Hum Comput Stud 61(5):601–626, 2004). By exploring the metaphorical potential of image schemata, it is finally illustrated how to extend the expressiveness of gestural writing in order to reach the conceptual level of image descriptions. In this context, the paper paves the way for implementing museum information systems like WikiNect as systems of kinetic hypertext authoring based on full-fledged gestural writing.
Different types of altruistic behavior, namely help-giving, altruistic punishment, and moral courage, are identifiable and distinguishable in the literature, but little is known how they relate to each other. This is significant because understanding altruism and other-regarding behavior is important in the attempt to solve global problems of overpopulation and depletion of natural resources. Understanding the helping forms of altruism (help-giving and sharing) can be helpful, for example, when designing donation collections or recruiting volunteers. Understanding the punitive forms of altruism (moral courage and altruistic punishment) gives valuable insight on individuals participating in campaigns where there is a need to take action, for example, against polluters or governments that violate human rights. The empirical evidence presented in this dissertation demonstrate individual and cultural differences in help-giving, altruistic punishment, and morally courageous behavior. The willingness to engage in the three types of altruistic behaviors relate differently to subjectively preferred thinking style and to personality traits, both in real-life and on the Internet. The divergencies are observable between Eastern (Indian) and Western (German) cultures, where the overlap of oneself and other individuals is experienced in different levels.
Contents:
Yuki Chiba, Santiago Escobar, Naoki Nishida, and David Sabel, and Manfred Schmidt-Schauß : Preface:
The Collection of all Abstracts of the Talks at WPTE 2015 xi
Brigitte Pientka : Mechanizing Meta-Theory in Beluga
Giulio Guerrieri : Head reduction and normalization in a call-by-value lambda-calculus
Adrián Palacios and Germán Vidal : Towards Modelling Actor-Based Concurrency in Term Rewriting
David Sabel and Manfred Schmidt-Schauß : Observing Success in the Pi-Calculus
Sjaak Smetsers, Ken Madlener, and Marko van Eekelen : Formalizing Bialgebraic Semantics in PVS 6.0
The calculus LRP is a polymorphically typed call-by-need lambda calculus extended by data constructors, case-expressions, seq-expressions and type abstraction and type application. This report is devoted to the extension LRPw of LRP by scoped sharing decorations. The extension cannot be properly encoded into LRP if improvements are defined w.r.t. the number of lbeta, case, and seq-reductions, which makes it necessary to reconsider the claims and proofs of properties. We show correctness of improvement properties of reduction and transformation rules and also of computation rules for decorations in the extended calculus LRPw. We conjecture that conservativity of the embedding of LRP in LRPw holds.
This report documents the extension LRPw of LRP by sharing decorations. We show correctness of improvement properties of reduction and transformation rules and also of computation rules for decorations in the extended calculus LRPw. We conjecture that conservativity of the embedding of LRP in LRPw holds.
An improvement is a correct program transformation that optimizes the program, where the criterion is that the number of computation steps until a value is obtained is decreased. This paper investigates improvements in both { an untyped and a polymorphically typed { call-by-need lambda-calculus with letrec, case, constructors and seq. Besides showing that several local optimizations are improvements, the main result of the paper is a proof that common subexpression elimination is correct and an improvement, which proves a conjecture and thus closes a gap in Moran and Sands' improvement theory. We also prove that several different length measures used for improvement in Moran and Sands' call-by-need calculus and our calculus are equivalent.
An improvement is a correct program transformation that optimizes the program, where the criterion is that the number of computation steps until a value is obtained is decreased. This paper investigates improvements in both { an untyped and a polymorphically typed { call-by-need lambda-calculus with letrec, case, constructors and seq. Besides showing that several local optimizations are improvements, the main result of the paper is a proof that common subexpression elimination is correct and an improvement, which proves a conjecture and thus closes a gap in Moran and Sands' improvement theory. We also prove that several different length measures used for improvement in Moran and Sands' call-by-need calculus and our calculus are equivalent.
Viruses rely completely on the hosts' machinery for translation of viral transcripts. However, for most viruses infecting humans, codon usage preferences (CUPrefs) do not match those of the host. Human papillomaviruses (HPVs) are a showcase to tackle this paradox: they present a large genotypic diversity and a broad range of phenotypic presentations, from asymptomatic infections to productive lesions and cancer. By applying phylogenetic inference and dimensionality reduction methods, we demonstrate first that genes in HPVs are poorly adapted to the average human CUPrefs, the only exception being capsid genes in viruses causing productive lesions. Phylogenetic relationships between HPVs explained only a small proportion of CUPrefs variation. Instead, the most important explanatory factor for viral CUPrefs was infection phenotype, as orthologous genes in viruses with similar clinical presentation displayed similar CUPrefs. Moreover, viral genes with similar spatiotemporal expression patterns also showed similar CUPrefs. Our results suggest that CUPrefs in HPVs reflect either variations in the mutation bias or differential selection pressures depending on the clinical presentation and expression timing. We propose that poor viral CUPrefs may be central to a trade-off between strong viral gene expression and the potential for eliciting protective immune response.
Modern experiments in heavy ion collisions operate with huge data rates that can not be fully stored on the currently available storage devices. Therefore the data flow should be reduced by selecting those collisions that potentially carry the information of the physics interest. The future CBM experiment will have no simple criteria for selecting such collisions and requires the full online reconstruction of the collision topology including reconstruction of short-lived particles.
In this work the KF Particle Finder package for online reconstruction and selection of short-lived particles is proposed and developed. It reconstructs more than 70 decays, covering signals from all the physics cases of the CBM experiment: strange particles, strange resonances, hypernuclei, low mass vector mesons, charmonium, and open-charm particles.
The package is based on the Kalman filter method providing a full set of the particle parameters together with their errors including position, momentum, mass, energy, lifetime, etc. It shows a high quality of the reconstructed particles, high efficiencies, and high signal to background ratios.
The KF Particle Finder is extremely fast for achieving the reconstruction speed of 1.5 ms per minimum-bias AuAu collision at 25 AGeV beam energy on single CPU core. It is fully vectorized and parallelized and shows a strong linear scalability on the many-core architectures of up to 80 cores. It also scales within the First Level Event Selection package on the many-core clusters up to 3200 cores.
The developed KF Particle Finder package is a universal platform for short- lived particle reconstruction, physics analysis and online selection.
We provide elementary algorithms for two preservation theorems for first-order sentences (FO) on the class ℭd of all finite structures of degree at most d: For each FO-sentence that is preserved under extensions (homomorphisms) on ℭd, a ℭd-equivalent existential (existential-positive) FO-sentence can be constructed in 5-fold (4-fold) exponential time. This is complemented by lower bounds showing that a 3-fold exponential blow-up of the computed existential (existential-positive) sentence is unavoidable. Both algorithms can be extended (while maintaining the upper and lower bounds on their time complexity) to input first-order sentences with modulo m counting quantifiers (FO+MODm). Furthermore, we show that for an input FO-formula, a ℭd-equivalent Feferman-Vaught decomposition can be computed in 3-fold exponential time. We also provide a matching lower bound
This paper shows equivalence of several versions of applicative similarity and contextual approximation, and hence also of applicative bisimilarity and contextual equivalence, in LR, the deterministic call-by-need lambda calculus with letrec extended by data constructors, case-expressions and Haskell's seq-operator. LR models an untyped version of the core language of Haskell. The use of bisimilarities simplifies equivalence proofs in calculi and opens a way for more convenient correctness proofs for program transformations. The proof is by a fully abstract and surjective transfer into a call-by-name calculus, which is an extension of Abramsky's lazy lambda calculus. In the latter calculus equivalence of our similarities and contextual approximation can be shown by Howe's method. Similarity is transferred back to LR on the basis of an inductively defined similarity. The translation from the call-by-need letrec calculus into the extended call-by-name lambda calculus is the composition of two translations. The first translation replaces the call-by-need strategy by a call-by-name strategy and its correctness is shown by exploiting infinite trees which emerge by unfolding the letrec expressions. The second translation encodes letrec-expressions by using multi-fixpoint combinators and its correctness is shown syntactically by comparing reductions of both calculi. A further result of this paper is an isomorphism between the mentioned calculi, which is also an identity on letrec-free expressions.
The Fisher information constitutes a natural measure for the sensitivity of a probability distribution with respect to a set of parameters. An implementation of the stationarity principle for synaptic learning in terms of the Fisher information results in a Hebbian self-limiting learning rule for synaptic plasticity. In the present work, we study the dependence of the solutions to this rule in terms of the moments of the input probability distribution and find a preference for non-Gaussian directions, making it a suitable candidate for independent component analysis (ICA). We confirm in a numerical experiment that a neuron trained under these rules is able to find the independent components in the non-linear bars problem. The specific form of the plasticity rule depends on the transfer function used, becoming a simple cubic polynomial of the membrane potential for the case of the rescaled error function. The cubic learning rule is also an excellent approximation for other transfer functions, as the standard sigmoidal, and can be used to show analytically that the proposed plasticity rules are selective for directions in the space of presynaptic neural activities characterized by a negative excess kurtosis.