2,972 research outputs found

    Highly comparative feature-based time-series classification

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    A highly comparative, feature-based approach to time series classification is introduced that uses an extensive database of algorithms to extract thousands of interpretable features from time series. These features are derived from across the scientific time-series analysis literature, and include summaries of time series in terms of their correlation structure, distribution, entropy, stationarity, scaling properties, and fits to a range of time-series models. After computing thousands of features for each time series in a training set, those that are most informative of the class structure are selected using greedy forward feature selection with a linear classifier. The resulting feature-based classifiers automatically learn the differences between classes using a reduced number of time-series properties, and circumvent the need to calculate distances between time series. Representing time series in this way results in orders of magnitude of dimensionality reduction, allowing the method to perform well on very large datasets containing long time series or time series of different lengths. For many of the datasets studied, classification performance exceeded that of conventional instance-based classifiers, including one nearest neighbor classifiers using Euclidean distances and dynamic time warping and, most importantly, the features selected provide an understanding of the properties of the dataset, insight that can guide further scientific investigation

    Magnetism, FeS colloids, and Origins of Life

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    A number of features of living systems: reversible interactions and weak bonds underlying motor-dynamics; gel-sol transitions; cellular connected fractal organization; asymmetry in interactions and organization; quantum coherent phenomena; to name some, can have a natural accounting via physicalphysical interactions, which we therefore seek to incorporate by expanding the horizons of `chemistry-only' approaches to the origins of life. It is suggested that the magnetic 'face' of the minerals from the inorganic world, recognized to have played a pivotal role in initiating Life, may throw light on some of these issues. A magnetic environment in the form of rocks in the Hadean Ocean could have enabled the accretion and therefore an ordered confinement of super-paramagnetic colloids within a structured phase. A moderate H-field can help magnetic nano-particles to not only overcome thermal fluctuations but also harness them. Such controlled dynamics brings in the possibility of accessing quantum effects, which together with frustrations in magnetic ordering and hysteresis (a natural mechanism for a primitive memory) could throw light on the birth of biological information which, as Abel argues, requires a combination of order and complexity. This scenario gains strength from observations of scale-free framboidal forms of the greigite mineral, with a magnetic basis of assembly. And greigite's metabolic potential plays a key role in the mound scenario of Russell and coworkers-an expansion of which is suggested for including magnetism.Comment: 42 pages, 5 figures, to be published in A.R. Memorial volume, Ed Krishnaswami Alladi, Springer 201

    Values, attitudes, and goals of future Hungarian food engineers

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    Over the last few decades Hungarian higher education has been radically transformed, and this transformation was implemented to counter the backwardness that previously plagued the education system. Agricultural education in particular was part of this transformation process, which included the disciplines of food science and related technology. This attempt at transformation yielded only a partial success; student numbers shot up, but there was no subsequent general improvement in the efficiency of higher education. This article is based on two surveys carried out in 1997 and 2007. The students’ values can be characterised as pluralistic and heterogenous. Based on longitudinal research, a shift can be seen toward materialistic and hedonistic values. The motivation for choosing the Faculty of Food Science is varied in nature, mirroring the food industry’s often critical current situation. High schools’ professional orientation is weak. Although the Faculty’s Budapest location is attractive, in the long run this is not sufficient to replace carefully planned promotional work. By structural equation modelling a significant relationship can be proven between the students’ values, their types of knowledge, and their expectations for future types of work.higher education policy, human resource management, food science education, social psychology, empirical research, Agribusiness, Labor and Human Capital,

    PEALT: A reasoning tool for numerical aggregation of trust evidence

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    We present a tool that supports the understanding and validation of mechanisms that numerically aggregate trust evidence { which may stem from heterogenous sources such as geographical information, reputation, and threat levels. The tool is based on a policy com- position language Peal [3] and can declare Peal expressions and intended analyses of such expressions as input. The analyses include vacuity checking, sensitivity analysis of thresh- olds, and policy re nement. We develop and implement two methods for generating veri - cation conditions for analyses, using the SMT solver Z3 as backend. One method is explicit and space intense, the other one is symbolic and so linear in the analysis expressions. We experimentally investigate this space-time tradeo by observing the Z3 code generation and its running time on randomly generated analyses and on a non-random benchmark modeling majority voting. Our ndings suggest both methods have complementary value and may scale up su ciently for the analysis of most realistic case studies

    Media do not exist : performativity and mediating conjunctures

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    Collection : Theory on demand ; 31Media Do Not Exist: Performativity and Mediating Conjunctures by Jean-Marc Larrue and Marcello Vitali-Rosati offers a radically new approach to the phenomenon of mediation, proposing a new understanding that challenges the very notion of medium. It begins with a historical overview of recent developments in Western thought on mediation, especially since the mid 80s and the emergence of the disciplines of media archaeology and intermediality. While these developments are inseparable from the advent of digital technology, they have a long history. The authors trace the roots of this thought back to the dawn of philosophy. Humans interact with their environment – which includes other humans – not through media, but rather through a series of continually evolving mediations, which Larrue and Vitali-Rosati call ‘mediating conjunctures’. This observation leads them to the paradoxical argument that ‘media do not exist’. Existing theories of mediation processes remain largely influenced by a traditional understanding of media as relatively stable entities. Media Do Not Exist demonstrates the limits of this conception. The dynamics relating to mediation are the product not of a single medium, but rather of a series of mediating conjunctures. They are created by ceaselessly shifting events and interactions, blending the human and the non-human, energy, and matter

    Roadmap on Machine learning in electronic structure

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    AbstractIn recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century

    Implementing a Component Based Parallel Distributed Finite Element Solver

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    Parallel computing is becoming more and more important in modern Finite Element Software. As problems grow larger, computation on a single processor may not be fast enough. To overcome this problem, one can utilize parallel programming using e.g SMP-machines or clusters. Furthermore, when local compute resources are scarce, it can be quite convenient to take advantage of non-local resources for performing the calculations. This thesis is a collaboration between the Division of Structural Mechanics, LTH and StruSoft AB in Malm¨o/Budapest. The aim is to address the issues above, specifically looking at the structural analysis program FEM-Design developed by StruSoft. There are several ways to parallelize code, focus will be on OpenMP, PETSc and Intel MKL. These methods have been studied in order to conclude which one is the most suitable for existing Finite Element applications. In the end Intel MKL was chosen and implemented. Regarding the distributed computations, a realistic client/server application was developedusing the Internet Communications Engine (Ice). The parallel properties of the implementation was studied and also, during a visit to the StruSoft Budapest branch, the implementation was integrated into FEM-Design. The results were astonishing, reaching speedups of a factor up to 360 compared to the original solver. Also, the scaling was almost linear for the implemented solver

    Plausible Petri nets as self-adaptive expert systems: A tool for infrastructure asset monitoring

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    This article provides a computational framework to model self-adaptive expert systems using the Petri net (PN) formalism. Self-adaptive expert systems are understood here as expert systems with the ability to autonomously learn from external inputs, like monitoring data. To this end, the Bayesian learning principles are investigated and also combined with the Plausible PNs (PPNs) methodology. PPNs are a variant within the PN paradigm, which are efficient to jointly consider the dynamics of discrete events, like maintenance actions, together with multiple sources of uncertain information about a state variable. The manuscript shows the mathematical conditions and computational procedure where the Bayesian updating becomes a particular case of a more general basic operation within the PPN execution semantics, which enables the uncertain knowledge being updated from monitoring data. The approach is general, but here it is demonstrated in a novel computational model acting as expert system for railway track inspection management taken as a case study using published data from a laboratory simulation of train loading on ballast. The results reveal selfadaptability and uncertainty management as key enabling aspects to optimize inspection actions in railway track, only being adaptively and autonomously triggered based on the actual learnt state of track and other contextual issues, like resource availability, as opposed to scheduled periodic maintenance activities.Lloyd'sRegister Foundation, Grant/Award Number: RB4539; Engineering and Physical SciencesResearch Council, Grant/Award Number:EP/M023028/

    Towards a monetary policy evaluation framework

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    Advances in the development of Dynamic Stochastic General Equilibrium (DSGE) models towards medium-scale structural frameworks with satisfying data coherence have considerably enhanced the range of analytical tools well-suited for monetary policy evaluation. The present paper intends to make a step forward in this direction: using US data over the Volker-Greenspan sample, we perform a DGSE-VAR estimation of a medium-scale DSGE model very close to Smets and Wouters [2007] specification, where monetary policy is set according to a Ramsey-planner decision problem. Those results are then contrasted with the DSGE-VAR estimation of the same model featuring a Taylortype interest rate rule. Our results show in particular that the restrictions imposed by the welfare-maximizing Ramsey policy deteriorates the empirical performance with respect to a Taylor rule specification. However, it turns out that, along selected conditional dimensions, and notably for productivity shocks, the Ramsey policy and the estimated Taylor rule deliver similar economic propagation. JEL Classification: E4, E5, F4Bayesian estimation, DSGE Models, optimal monetary policy
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