How persistent is disaggregate inflation? An analysis across EU 15 countries and HICP sub-indices

This paper analyses the degree of inflation persistence in the EU15, the euro area and each of its member states using disaggregate price indices from the Harmonised Index of Consumer Prices. Our results reveal substantial heterogeneity across countries and indices. The overall results, based on both parametric and non-parametric persistence measures, suggest a very moderate degree of median and mean inflation persistence. For most price indices we are able to reject the unit root hypothesis, as well as the notion of disaggregate inflation exhibiting a high degree of persistence. Durable goods and services tend to be relatively less persistent than other indices. Aggregation effects, both across indices and countries, tend to be present. We find structural breaks both owing to the change in the monetary regime and to the modified treatment of sales in the official HICP series. The latter tends to reduce the measured degree of inflation persistence. JEL Classification: E31, C21, C22, C14Aggregation effect, Inflation persistence, Mean reversion, structural breaks

Half-integrality, LP-branching and FPT Algorithms

A recent trend in parameterized algorithms is the application of polytope tools (specifically, LP-branching) to FPT algorithms (e.g., Cygan et al., 2011; Narayanaswamy et al., 2012). However, although interesting results have been achieved, the methods require the underlying polytope to have very restrictive properties (half-integrality and persistence), which are known only for few problems (essentially Vertex Cover (Nemhauser and Trotter, 1975) and Node Multiway Cut (Garg et al., 1994)). Taking a slightly different approach, we view half-integrality as a \emph{discrete} relaxation of a problem, e.g., a relaxation of the search space from $\{0,1\}^V$ to $\{0,1/2,1\}^V$ such that the new problem admits a polynomial-time exact solution. Using tools from CSP (in particular Thapper and \v{Z}ivn\'y, 2012) to study the existence of such relaxations, we provide a much broader class of half-integral polytopes with the required properties, unifying and extending previously known cases. In addition to the insight into problems with half-integral relaxations, our results yield a range of new and improved FPT algorithms, including an $O^*(|\Sigma|^{2k})$-time algorithm for node-deletion Unique Label Cover with label set $\Sigma$ and an $O^*(4^k)$-time algorithm for Group Feedback Vertex Set, including the setting where the group is only given by oracle access. All these significantly improve on previous results. The latter result also implies the first single-exponential time FPT algorithm for Subset Feedback Vertex Set, answering an open question of Cygan et al. (2012). Additionally, we propose a network flow-based approach to solve some cases of the relaxation problem. This gives the first linear-time FPT algorithm to edge-deletion Unique Label Cover.Comment: Added results on linear-time FPT algorithms (not present in SODA paper

Braided Structure in 4-dimensional conformal Quantum Field Theory

Higher dimensional conformal QFT possesses an interesting braided structure which, different from the d=1+1 models, is restricted to the timelike region and therefore easily escapes euclidean action methods. It lies behind the spectrum of anomalous dimensions which may be viewed as a kind of substitute for a missing particle interpretation in the presence of interactions.Comment: Dedicated to Gerhard Mack and Robert Schrader on the occasion of their 60th birthday, submitted to Phys. Lett. B, 10 pages, improvements of the formulation, shortening of the text, addition of a formula, removal of typo

Flammability behaviour of wood and a review of the methods for its reduction

Wood is one of the most sustainable, aesthetically pleasing and environmentally benign materials. Not only is wood often an integral part of structures, it is also the main source of furnishings found in homes, schools, and offices around the world. The often inevitable hazards of fire make wood a very desirable material for further investigation. As well as ignition resistance and a low heat release rate, timber products have long been required to resist burn-through and maintain structural integrity whilst continuing to provide protection when exposed to fire or heat. Various industry standard tests are thus required to ensure adequate protection from fire is provided. When heated, wood undergoes thermal degradation and combustion to produce gases, vapours, tars and char. In order to understand and alter the fire behaviour of wood, it is necessary to know in as much detail as possible about its processes of decomposition. Various thermal analysis and flammability assessment techniques are utilised for this purpose, including thermogravimetric analysis, cone calorimetry and the single burning item test. The results of such tests are often highly dependent on various parameters including changes to the gas composition, temperature, heating rate, and sample shape size. Potential approaches for fire retarding timber are reviewed, identifying two main approaches: char formation and isolating layers. Other potential approaches are recognised, including the use of inorganic minerals, such as sericrite, and metal foils in combination with intumescent products. Formulations containing silicon, nitrogen and phosphorus have been reported, and efforts to retain silicon in the wood have been successful using micro-layers of silicon dioxide. Nano-scale fire retardants, such as nanocomposite coatings, are considered to provide a new generation of fire retardants, and may have potential for wood. Expandable graphite is identified for use in polymers and has potential for wood provided coating applications are preferred

Flammability behaviour of wood and a review of the methods for its reduction

Wood is one of the most sustainable, aesthetically pleasing and environmentally benign materials. Not only is wood often an integral part of structures, it is also the main source of furnishings found in homes, schools, and offices around the world. The often inevitable hazards of fire make wood a very desirable material for further investigation. As well as ignition resistance and a low heat release rate, timber products have long been required to resist burn-through and maintain structural integrity whilst continuing to provide protection when exposed to fire or heat. Various industry standard tests are thus required to ensure adequate protection from fire is provided. When heated, wood undergoes thermal degradation and combustion to produce gases, vapours, tars and char. In order to understand and alter the fire behaviour of wood, it is necessary to know in as much detail as possible about its processes of decomposition. Various thermal analysis and flammability assessment techniques are utilised for this purpose, including thermogravimetric analysis, cone calorimetry and the single burning item test. The results of such tests are often highly dependent on various parameters including changes to the gas composition, temperature, heating rate, and sample shape size. Potential approaches for fire retarding timber are reviewed, identifying two main approaches: char formation and isolating layers. Other potential approaches are recognised, including the use of inorganic minerals, such as sericrite, and metal foils in combination with intumescent products. Formulations containing silicon, nitrogen and phosphorus have been reported, and efforts to retain silicon in the wood have been successful using micro-layers of silicon dioxide. Nano-scale fire retardants, such as nanocomposite coatings, are considered to provide a new generation of fire retardants, and may have potential for wood. Expandable graphite is identified for use in polymers and has potential for wood provided coating applications are preferred

Robustness and stability in dynamic constraint satisfaction problems

Constraint programming is a paradigm wherein relations between variables are stated in the form of constraints. It is well-known that many real life problems can be modeled as Constraint Satisfaction Problems (CSPs). Much effort has been spent to increase the efficiency of algorithms for solving CSPs. However, many of these techniques assume that the set of variables, domains and constraints involved in the CSP are known and fixed when the problem is modeled. This is a strong limitation because many problems come from uncertain and dynamic environments, where both the original problem may evolve because of the environment, the user or other agents. In such situations, a solution that holds for the original problem can become invalid after changes. There are two main approaches for dealing with these situations: reactive and proactive approaches. Using reactive approaches entails re-solving the CSP after each solution loss, which is a time consuming. That is a clear disadvantage, especially when we deal with short-term changes, where solution loss is frequent. In addition, in many applications, such as on-line planning and scheduling, the delivery time of a new solution may be too long for actions to be taken on time, so a solution loss can produce several negative effects in the modeled problem. For a task assignment production system with several machines, it could cause the shutdown of the production system, the breakage of machines, the loss of the material/object in production, etc. In a transport timetabling problem, the solution loss, due to some disruption at a point, may produce a delay that propagates through the entire schedule. In addition, all the negative effects stated above will probably entail an economic loss. In this thesis we develop several proactive approaches. Proactive approaches use knowledge about possible future changes in order to avoid or minimize their effects. These approaches are applied before the changes occur. Thus, our approaches search for robust solutions, which have a high probability to remain valid after changes. Furthermore, some of our approaches also consider that the solutions can be easily adapted when they did not resist the changes in the original problem. Thus, these approaches search for stable solutions, which have an alternative solution that is similar to the previous one and therefore can be used in case of a value breakage. In this context, sometimes there exists knowledge about the uncertain and dynamic environment. However in many cases, this information is unknown or hard to obtain. For this reason, for the majority of our approaches (specifically 3 of the 4 developed approaches), the only assumptions made about changes are those inherent in the structure of problems with ordered domains. Given this framework and therefore the existence of a significant order over domain values, it is reasonable to assume that the original bounds of the solution space may undergo restrictive or relaxed modifications. Note that the possibility of solution loss only exists when changes over the original bounds of the solution space are restrictive. Therefore, the main objective for searching robust solutions in this framework is to find solutions located as far away as possible from the bounds of the solution space. In order to meet this criterion, we propose several approaches that can be divided in enumeration-based techniques and a search algorithm.Climent Aunés, LI. (2013). Robustness and stability in dynamic constraint satisfaction problems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34785TESI

Interior Noise

The generation and control of flight vehicle interior noise is discussed. Emphasis is placed on the mechanisms of transmission through airborne and structure-borne paths and the control of cabin noise by path modification. Techniques for identifying the relative contributions of the various source-path combinations are also discussed along with methods for the prediction of aircraft interior noise such as those based on the general modal theory and statistical energy analysis

Laplacian Mixture Modeling for Network Analysis and Unsupervised Learning on Graphs

Laplacian mixture models identify overlapping regions of influence in unlabeled graph and network data in a scalable and computationally efficient way, yielding useful low-dimensional representations. By combining Laplacian eigenspace and finite mixture modeling methods, they provide probabilistic or fuzzy dimensionality reductions or domain decompositions for a variety of input data types, including mixture distributions, feature vectors, and graphs or networks. Provable optimal recovery using the algorithm is analytically shown for a nontrivial class of cluster graphs. Heuristic approximations for scalable high-performance implementations are described and empirically tested. Connections to PageRank and community detection in network analysis demonstrate the wide applicability of this approach. The origins of fuzzy spectral methods, beginning with generalized heat or diffusion equations in physics, are reviewed and summarized. Comparisons to other dimensionality reduction and clustering methods for challenging unsupervised machine learning problems are also discussed.Comment: 13 figures, 35 reference