The Necessity and Importance of Incorporating Media and Information Literacy into Holistic Metaliteracy

Digitalization and the emergence of the Internet have resulted in escalating access to information and communication. Given the circumstances that soaring access to information amounts to the intensification of misinformation and disinformation, a set of critical skills to navigate and critically assess the information is necessary. This paper outlines the significance of these skills, and provides a perspective on metaliteracy as a supplement to media and information literacy, and argues that the ability to conceptualize, access, comprehend, analyze, and use information is crucial in achieving inclusive, pluralistic, and participatory knowledge societies

Flexual buckling of structural glass columns. Initial geometrical imperfection as a base for Monte Carlo simulation

In this paper Monte Carlo simulations of structural glass columns are presented. The simulation was performed according to the analytical second order theory of compressed elastic rods. A previous research on shape and size of initial geometrical imperfections is briefly summarized. An experimental analysis of glass columns that were performed for evaluation of equivalent geometrical imperfections is mentioned too

Hardness Amplification of Optimization Problems

In this paper, we prove a general hardness amplification scheme for optimization problems based on the technique of direct products. We say that an optimization problem ? is direct product feasible if it is possible to efficiently aggregate any k instances of ? and form one large instance of ? such that given an optimal feasible solution to the larger instance, we can efficiently find optimal feasible solutions to all the k smaller instances. Given a direct product feasible optimization problem ?, our hardness amplification theorem may be informally stated as follows: If there is a distribution D over instances of ? of size n such that every randomized algorithm running in time t(n) fails to solve ? on 1/?(n) fraction of inputs sampled from D, then, assuming some relationships on ?(n) and t(n), there is a distribution D\u27 over instances of ? of size O(n??(n)) such that every randomized algorithm running in time t(n)/poly(?(n)) fails to solve ? on 99/100 fraction of inputs sampled from D\u27. As a consequence of the above theorem, we show hardness amplification of problems in various classes such as NP-hard problems like Max-Clique, Knapsack, and Max-SAT, problems in P such as Longest Common Subsequence, Edit Distance, Matrix Multiplication, and even problems in TFNP such as Factoring and computing Nash equilibrium

Permanent and live load model for probabilistic structural fire analysis : a review

Probabilistic analysis is receiving increased attention from fire engineers, assessment bodies and researchers. It is however often unclear which probabilistic models are appropriate for the analysis. For example, in probabilistic structural fire engineering, the models used to describe the permanent and live load differ widely between studies. Through a literature review, it is observed that these diverging load models largely relate to the same underlying datasets and basic methodologies, while differences can be attributed (largely) to specific assumptions in different background papers which have become consolidated through repeated use in application studies by different researchers. Taking into account the uncovered background information, consolidated probabilistic load models are proposed

The necessity and importance of incorporating media and information literacy into holistic metaliteracy

Digitalization and the emergence of the Internet have resulted in escalating access to information and communication. Given the circumstances that soaring access to information amounts to the intensification of misinformation and disinformation, a set of critical skills to navigate and critically assess the information is necessary. This paper outlines the significance of these skills, and provides a perspective on metaliteracy as a supplement to media and information literacy, and argues that the ability to conceptualize, access, comprehend, analyze, and use information is crucial in achieving inclusive, pluralistic, and participatory knowledge societies. (DIPF/Orig.

Querying Schemas With Access Restrictions

We study verification of systems whose transitions consist of accesses to a Web-based data-source. An access is a lookup on a relation within a relational database, fixing values for a set of positions in the relation. For example, a transition can represent access to a Web form, where the user is restricted to filling in values for a particular set of fields. We look at verifying properties of a schema describing the possible accesses of such a system. We present a language where one can describe the properties of an access path, and also specify additional restrictions on accesses that are enforced by the schema. Our main property language, AccLTL, is based on a first-order extension of linear-time temporal logic, interpreting access paths as sequences of relational structures. We also present a lower-level automaton model, Aautomata, which AccLTL specifications can compile into. We show that AccLTL and A-automata can express static analysis problems related to "querying with limited access patterns" that have been studied in the database literature in the past, such as whether an access is relevant to answering a query, and whether two queries are equivalent in the accessible data they can return. We prove decidability and complexity results for several restrictions and variants of AccLTL, and explain which properties of paths can be expressed in each restriction.Comment: VLDB201

Computing a Nonnegative Matrix Factorization -- Provably

In the Nonnegative Matrix Factorization (NMF) problem we are given an $n \times m$ nonnegative matrix $M$ and an integer $r > 0$. Our goal is to express $M$ as $A W$ where $A$ and $W$ are nonnegative matrices of size $n \times r$ and $r \times m$ respectively. In some applications, it makes sense to ask instead for the product $AW$ to approximate $M$ -- i.e. (approximately) minimize \norm{M - AW}_F where \norm{}_F denotes the Frobenius norm; we refer to this as Approximate NMF. This problem has a rich history spanning quantum mechanics, probability theory, data analysis, polyhedral combinatorics, communication complexity, demography, chemometrics, etc. In the past decade NMF has become enormously popular in machine learning, where $A$ and $W$ are computed using a variety of local search heuristics. Vavasis proved that this problem is NP-complete. We initiate a study of when this problem is solvable in polynomial time: 1. We give a polynomial-time algorithm for exact and approximate NMF for every constant $r$. Indeed NMF is most interesting in applications precisely when $r$ is small. 2. We complement this with a hardness result, that if exact NMF can be solved in time $(nm)^{o(r)}$, 3-SAT has a sub-exponential time algorithm. This rules out substantial improvements to the above algorithm. 3. We give an algorithm that runs in time polynomial in $n$, $m$ and $r$ under the separablity condition identified by Donoho and Stodden in 2003. The algorithm may be practical since it is simple and noise tolerant (under benign assumptions). Separability is believed to hold in many practical settings. To the best of our knowledge, this last result is the first example of a polynomial-time algorithm that provably works under a non-trivial condition on the input and we believe that this will be an interesting and important direction for future work.Comment: 29 pages, 3 figure

Probabilistic methods for wind turbine blades

The European Energy Research Alliance (EERA) has as a key purpose to elevate cooperation between national research institutes to a new level, from ad-hoc participation in joint projects to collectively planning and implementing joint strategic research programmes. The RES directive and the SET Plan enforce a high rate of deployment of wind energy, on- and offshore for the European Union’s member states leading to a high challenge for research in the two priority areas: Integration and Offshore. Wind energy was therefore at an early stage identified as an area for a joint research programme where the key players are the national wind energy research institutes but open to and encouraging universities to participate in the activities

Samplers and Extractors for Unbounded Functions

Blasiok (SODA\u2718) recently introduced the notion of a subgaussian sampler, defined as an averaging sampler for approximating the mean of functions f from {0,1}^m to the real numbers such that f(U_m) has subgaussian tails, and asked for explicit constructions. In this work, we give the first explicit constructions of subgaussian samplers (and in fact averaging samplers for the broader class of subexponential functions) that match the best known constructions of averaging samplers for [0,1]-bounded functions in the regime of parameters where the approximation error epsilon and failure probability delta are subconstant. Our constructions are established via an extension of the standard notion of randomness extractor (Nisan and Zuckerman, JCSS\u2796) where the error is measured by an arbitrary divergence rather than total variation distance, and a generalization of Zuckerman\u27s equivalence (Random Struct. Alg.\u2797) between extractors and samplers. We believe that the framework we develop, and specifically the notion of an extractor for the Kullback-Leibler (KL) divergence, are of independent interest. In particular, KL-extractors are stronger than both standard extractors and subgaussian samplers, but we show that they exist with essentially the same parameters (constructively and non-constructively) as standard extractors