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Editorial: Jens Weidmann : "Central Banks and Monetary Policy after the Crisis" Research Finance: Holger Kraft,Claus Munk : "Optimal Housing, Consumption, and Investment Decisions over the Life Cycle" Research Money/Macro: Ester Faia, Eleni Iliopulos : "Financial Globalization and Monetary Policy" Research Law: Andreas Cahn, D. Schöneberger : "Shareholder Governance in Europe" Policy Platform: Michael Haliassos, Dimitri Vayanos : "Getting Greece Back on Track: How?" Interview: Raimond Maurer, Ralph Rogalla : "Longevity Risk and Capital Markets Solutions"
A generalization of the compressed string pattern match that applies to terms with variables is investigated: Given terms s and t compressed by singleton tree grammars, the task is to find an instance of s that occurs as a subterm in t. We show that this problem is in NP and that the task can be performed in time O(ncjVar(s)j), including the construction of the compressed substitution, and a representation of all occurrences. We show that the special case where s is uncompressed can be performed in polynomial time. As a nice application we show that for an equational deduction of t to t0 by an equality axiom l = r (a rewrite) a single step can be performed in polynomial time in the size of compression of t and l; r if the number of variables is fixed in l. We also show that n rewriting steps can be performed in polynomial time, if the equational axioms are compressed and assumed to be constant for the rewriting sequence. Another potential application are querying mechanisms on compressed XML-data bases.
Correctness of program transformations in extended lambda calculi with a contextual semantics is usually based on reasoning about the operational semantics which is a rewrite semantics. A successful approach to proving correctness is the combination of a context lemma with the computation of overlaps between program transformations and the reduction rules.The method is similar to the computation of critical pairs for the completion of term rewriting systems. We describe an effective unification algorithm to determine all overlaps of transformations with reduction rules for the lambda calculus LR which comprises a recursive let-expressions, constructor applications, case expressions and a seq construct for strict evaluation. The unification algorithm employs many-sorted terms, the equational theory of left-commutativity modeling multi-sets, context variables of different kinds and a mechanism for compactly representing binding chains in recursive let-expressions. As a result the algorithm computes a finite set of overlappings for the reduction rules of the calculus LR that serve as a starting point to the automatization of the analysis of program transformations.
In this paper we analyze the semantics of a higher-order functional language with concurrent threads, monadic IO and synchronizing variables as in Concurrent Haskell. To assure declarativeness of concurrent programming we extend the language by implicit, monadic, and concurrent futures. As semantic model we introduce and analyze the process calculus CHF, which represents a typed core language of Concurrent Haskell extended by concurrent futures. Evaluation in CHF is defined by a small-step reduction relation. Using contextual equivalence based on may- and should-convergence as program equivalence, we show that various transformations preserve program equivalence. We establish a context lemma easing those correctness proofs. An important result is that call-by-need and call-by-name evaluation are equivalent in CHF, since they induce the same program equivalence. Finally we show that the monad laws hold in CHF under mild restrictions on Haskell’s seq-operator, which for instance justifies the use of the do-notation.
The well-known proof of termination of reduction in simply typed calculi is adapted to a monomorphically typed lambda-calculus with case and constructors and recursive data types. The proof differs at several places from the standard proof. Perhaps it is useful and can be extended also to more complex calculi.
Tracer measurements in the tropical tropopause layer during the AMMA/SCOUT-O3 aircraft campaign
(2010)
We present airborne in situ measurements made during the AMMA (African Monsoon Multidisciplinary Analysis)/SCOUT-O3 campaign between 31 July and 17 August 2006 on board the M55 Geophysica aircraft, based in Ouagadougou, Burkina Faso. CO<sub>2</sub> and N<sub>2</sub>O were measured with the High Altitude Gas Analyzer (HAGAR), CO was measured with the Cryogenically Operated Laser Diode (COLD) instrument, and O<sub>3</sub> with the Fast Ozone ANalyzer (FOZAN). We analyze the data obtained during five local flights to study the dominant transport processes controlling the tropical tropopause layer (TTL) above West-Africa: deep convection up to the level of main convective outflow, overshooting of deep convection, horizontal inmixing across the subtropical tropopause, and horizontal transport across the subtropical barrier. Except for the flight of 13 August, distinct minima in CO<sub>2</sub> indicate convective outflow of boundary layer air in the TTL. The CO<sub>2</sub> profiles show that the level of main convective outflow was mostly located between 350 and 360 K, and for 11 August reached up to 370 K. While the CO<sub>2</sub> minima indicate quite significant convective influence, the O<sub>3</sub> profiles suggest that the observed convective signatures were mostly not fresh, but of older origin. When compared with the mean O<sub>3</sub> profile measured during a previous campaign over Darwin in November 2005, the O<sub>3</sub> minimum at the main convective outflow level was less pronounced over Ouagadougou. Furthermore O<sub>3</sub> mixing ratios were much higher throughout the whole TTL and, unlike over Darwin, rarely showed low values observed in the regional boundary layer. Signatures of irreversible mixing following overshooting of convective air were scarce in the tracer data. Some small signatures indicative of this process were found in CO<sub>2</sub> profiles between 390 and 410 K during the flights of 4 and 8 August, and in CO data at 410 K on 7 August. However, the absence of expected corresponding signatures in other tracer data makes this evidence inconclusive, and overall there is little indication from the observations that overshooting convection has a profound impact on TTL composition during AMMA. We find the amount of photochemically aged air isentropically mixed into the TTL across the subtropical tropopause to be not significant. Using the N<sub>2</sub>O observations we estimate the fraction of aged extratropical stratospheric air in the TTL to be 0.0±0.1 up to 370 K during the local flights, increasing above this level to 0.2±0.15 at 390 K. The subtropical barrier, as indicated by the slope of the correlation between N<sub>2</sub>O and O<sub>3</sub> between 415 and 490 K, does not appear as a sharp border between the tropics and extratropics, but rather as a gradual transition region between 10 and 25° N latitude where isentropic mixing between these two regions may occur.
Visual perception has increasingly grown important during the last decades in the robotics domain. Mobile robots have to localize themselves in known environments and carry out complex navigation tasks. This thesis presents an appearance-based or view-based approach to robot self-localization and robot navigation using holistic, spherical views obtained by cameras with large fields of view. For view-based methods, it is crucial to have a compressed image representation where different views can be stored and compared efficiently. Our approach relies on the spherical Fourier transform, which transforms a signal defined on the sphere to a small set of coefficients, approximating the original signal by a weighted sum of orthonormal basis functions, the so-called spherical harmonics. The truncated low order expansion of the image signal allows to compare input images efficiently, and the mathematical properties of spherical harmonics also allow for estimating rotation between two views, even in 3D. Since no geometrical measurements need to be done, modest quality of the vision system is sufficient. All experiments shown in this thesis are purely based on visual information to show the applicability of the approach. The research presented on robot self localization was focused on demonstrating the usability of the compressed spherical harmonics representation to solve the well-known kidnapped robot problem. To address this problem, the basic idea is to compare the current view to a set of images from a known environment to obtain a likelihood of robot positions. To localize the robot, one could choose the most probable position from the likelihood map; however, it is more beneficial to apply standard methods to integrate information over time while the robot moves, that is, particle or Kalman filters. The first step was to design a fast expansion method to obtain coefficient vectors directly in image space. This was achieved by back-projecting basis functions on the input image. The next steps were to develop a dissimilarity measure, an estimator for rotations between coefficient vectors, and a rotation-invariant dissimilarity measure, all of them purely based on the compact signal representation. With all these techniques at hand, generating likelihood maps is straightforward, but first experiments indicated strong dependence on illumination conditions. This is obviously a challenge for all holistic methods, in particular for a spherical harmonics approach, since local changes usually affect each single element of the coefficient vector. To cope with illumination changes, we investigated preprocessing steps leading to feature images (e.g. edge images, depth images), which bring together our holistic approach and classical feature-based methods. Furthermore, we concentrated on building a statistical model for typical changes of the coefficient vectors in presence of changes in illumination. This task is more demanding but leads to even better results. The second major topic of this thesis is appearance-based robot navigation. I present a view-based approach called Optical Rails (ORails), which leads a robot along a prerecorded track. The robot navigates in a network of known locations which are denoted as waypoints. At each waypoint, we store a compressed view representation. A visual servoing method is used to reach a current target waypoint based on the appearance and the current camera image. Navigating in a network of views is achieved by reaching a sequence of stopover locations, one after another. The main contribution of this work is a model which allows to deduce the best driving direction of the robot based purely on the coefficient vectors of the current and the target image. It is based on image registration as the classical method by Lucas-Kanade, but has been transferred to the spectral domain, which allows for great speedup. ORails also includes a waypoint selection strategy and a module for steering our nonholonomic robot. As for our self-localization algorithm, dependance on illumination changes is also problematic in ORails. Furthermore, occlusions have to be handled for ORails to work properly. I present a solution based on the optimal expansion, which is able to deal with incomplete image signals. To handle dynamic occlusions, i.e. objects appearing in an arbitrary region of the image, we use the linearity of the expansion process and cut the image into segments. These segments can be treated separately, and finally we merge the results. At this point, we can decide to disregard certain segments. Slicing the view allows for local illumination compensation, which is inherently non-robust if applied to the whole view. In conclusion, this approach allows to handle the most important criticism to holistic view-based approaches, that is, occlusions and illumination changes, and consequently improves the performance of Optical Rails.
We make three points. First, the decade before the financial crisis in 2007 was characterized by a collapse in the yield on TIPS. Second, estimated VARs for the federal funds rate and the TIPS yield show that while monetary policy shocks had negligible effects on the TIPS yield, shocks to the latter had one-to-one effects on the federal funds rate. Third, these findings can be rationalized in a New Keynesian model.
There is ample empirical evidence documenting widespread financial illiteracy and limited pension knowledge. At the same time, the distribution of wealth is widely dispersed and many workers arrive on the verge of retirement with few or no personal assets. In this paper, we investigate the relationship between financial literacy and household net worth, relying on comprehensive measures of financial knowledge designed for a special module of the DNB (De Nederlandsche Bank) Household Survey. Our findings provide evidence of a strong positive association between financial literacy and net worth, even after controlling for many determinants of wealth. Moreover, we discuss two channels through which financial literacy might facilitate wealth accumulation. First, financial knowledge increases the likelihood of investing in the stock market, allowing individuals to benefit from the equity premium. Second, financial literacy is positively related to retirement planning, and the development of a savings plan has been shown to boost wealth. Overall, financial literacy, both directly and indirectly, is found to have a strong link to household wealth. JEL Classification: D91, D12, J26 Keywords: Financial Education, Savings and Wealth Accumulation, Retirement Preparation, Knowledge of Finance and Economics, Overconfidence, Stock Market Participation