Absorptive capacity and the growth and investment effects of regional transfers : a regression discontinuity design with heterogeneous treatment effects

Researchers often estimate average treatment effects of programs without investigating heterogeneity across units. Yet, individuals, firms, regions, or countries vary in their ability, e.g., to utilize transfers. We analyze Objective 1 Structural Funds transfers of the European Commission to regions of EU member states below a certain income level by way of a regression discontinuity design with systematically heterogeneous treatment effects. Only about 30% and 21% of the regions - those with sufficient human capital and good-enough institutions - are able to turn transfers into faster per-capita income growth and per-capita investment. In general, the variance of the treatment effect is much bigger than its mean

exploreCOSMOS: Interactive Exploration of Conditional Statistical Shape Models in the Web-Browser

Statistical Shape Models of faces and various body parts are heavily used in medical image analysis, computer vision and visualization. Whilst the field is well explored with many existing tools, all of them aim at experts, which limits their applicability. We demonstrate the first tool that enables the convenient exploration of statistical shape models in the browser, with the capability to manipulate the faces in a targeted manner. This manipulation is performed via a posterior model given partial observations. We release our code and application on GitHub https://github.com/maximilian-hahn/exploreCOSMOSComment: Dies ist ein Vorabdruck des folgenden Beitrages, ver\"offentlicht in BVM 2024, herausgegeben von Maier, A. et al, 2024, Springer Nature, vervielf\"altigt mit Genehmigung von Springer Nature. Die finale authentifizierte Version ist online verf\"ugbar unter: https://doi.org/10.1007/978-3-658-44037-4_3

Migration and Trade

Theoretical and empirical research in economics suggests that bilateral migration triggers bilateral trade through a number of channels. This paper assesses the functional form of the impact of migration on trade flows in a quasi-experimental setting. We provide evidence that the relationship is not log-linear. In particular, at small levels of migration (stocks) the elasticity of trade to migration is quite high, and it declines to zero at about 4,000 immigrants. If migration stocks exceed such a level, the evidence suggests that trade will not increase anymore. This suggests that cross-country network and other effects flowing from migration materialize at relatively low levels of migration, but there appears to be satiation as immigrant numbers increase by much.migration, bilateral trade, quasi-randomized experiment, generalized propensity score estimation

Going NUTS: The Effect of EU Structural Funds on Regional Performance

The European Union (EU) provides grants to disadvantaged regions of member states to allow them to catch up with the EU average. Under the Objective 1 scheme, NUTS2 regions with a GDP per capita level below 75% of the EU average qualify for structural funds transfers from the central EU budget. This rule gives rise to a regression-discontinuity design that exploits the discrete jump in the probability of EU transfer receipt at the 75% threshold. Additional variability arises for smaller regional aggregates - so-called NUTS3 regions - which are nested in a NUTS2 mother region. Whereas some relatively rich NUTS3 regions may receive EU funds because their NUTS2 mother region qualifies, other relatively poor NUTS3 regions may not receive EU funds because their NUTS2 mother region does not qualify. We find positive growth effects of Objective 1 funds, but no employment effects. A simple cost-benefit calculation suggests that Objective 1 transfers are not only effective, but also cost-efficient.structural funds, regional growth, regression discontinuity design, quasi-randomized experiment

Future-proofing the through-life engineering service systems

Future-proofing through-life engineering service systems (TESS) is crucial for ensuring their reliable, long and economical whole lives. The TESS are typically composed of high value industrial products and engineering services organised around them. Future-proofing can broadly be achieved by enabling disruption and change management capabilities. However, understanding of TESS future-proofing is limited, which is also important due to the recent industry 4.0 advancements. This paper contributes by presenting (1) a concept of TESS future-proofing, (2) a framework of TESS future-proofing, and (3) examples of the framework application at: (i) management level via change prediction method (CPM), and (ii) operational level via industrial augmented reality (AR)

Approximating Intersections and Differences Between Linear Statistical Shape Models Using Markov Chain Monte Carlo

To date, the comparison of Statistical Shape Models (SSMs) is often solely performance-based, carried out by means of simplistic metrics such as compactness, generalization, or specificity. Any similarities or differences between the actual shape spaces can neither be visualized nor quantified. In this paper, we present a new method to qualitatively compare two linear SSMs in dense correspondence by computing approximate intersection spaces and set-theoretic differences between the (hyper-ellipsoidal) allowable shape domains spanned by the models. To this end, we approximate the distribution of shapes lying in the intersection space using Markov chain Monte Carlo and subsequently apply Principal Component Analysis (PCA) to the posterior samples, eventually yielding a new SSM of the intersection space. We estimate differences between linear SSMs in a similar manner; here, however, the resulting spaces are no longer convex and we do not apply PCA but instead use the posterior samples for visualization. We showcase the proposed algorithm qualitatively by computing and analyzing intersection spaces and differences between publicly available face models, focusing on gender-specific male and female as well as identity and expression models. Our quantitative evaluation based on SSMs built from synthetic and real-world data sets provides detailed evidence that the introduced method is able to recover ground-truth intersection spaces and differences accurately.Comment: Accepted to WACV'2

Fast and Straggler-Tolerant Distributed SGD with Reduced Computation Load

In distributed machine learning, a central node outsources computationally expensive calculations to external worker nodes. The properties of optimization procedures like stochastic gradient descent (SGD) can be leveraged to mitigate the effect of unresponsive or slow workers called stragglers, that otherwise degrade the benefit of outsourcing the computation. This can be done by only waiting for a subset of the workers to finish their computation at each iteration of the algorithm. Previous works proposed to adapt the number of workers to wait for as the algorithm evolves to optimize the speed of convergence. In contrast, we model the communication and computation times using independent random variables. Considering this model, we construct a novel scheme that adapts both the number of workers and the computation load throughout the run-time of the algorithm. Consequently, we improve the convergence speed of distributed SGD while significantly reducing the computation load, at the expense of a slight increase in communication load

Sparsity and Privacy in Secret Sharing: A Fundamental Trade-Off

This work investigates the design of sparse secret sharing schemes that encode a sparse private matrix into sparse shares. This investigation is motivated by distributed computing, where the multiplication of sparse and private matrices is moved from a computationally weak main node to untrusted worker machines. Classical secret-sharing schemes produce dense shares. However, sparsity can help speed up the computation. We show that, for matrices with i.i.d. entries, sparsity in the shares comes at a fundamental cost of weaker privacy. We derive a fundamental tradeoff between sparsity and privacy and construct optimal sparse secret sharing schemes that produce shares that leak the minimum amount of information for a desired sparsity of the shares. We apply our schemes to distributed sparse and private matrix multiplication schemes with no colluding workers while tolerating stragglers. For the setting of two non-communicating clusters of workers, we design a sparse one-time pad so that no private information is leaked to a cluster of untrusted and colluding workers, and the shares with bounded but non-zero leakage are assigned to a cluster of partially trusted workers. We conclude by discussing the necessity of using permutations for matrices with correlated entries

Sparse and Private Distributed Matrix Multiplication with Straggler Tolerance

This paper considers the problem of outsourcing the multiplication of two private and sparse matrices to untrusted workers. Secret sharing schemes can be used to tolerate stragglers and guarantee information-theoretic privacy of the matrices. However, traditional secret sharing schemes destroy all sparsity in the offloaded computational tasks. Since exploiting the sparse nature of matrices was shown to speed up the multiplication process, preserving the sparsity of the input matrices in the computational tasks sent to the workers is desirable. It was recently shown that sparsity can be guaranteed at the expense of a weaker privacy guarantee. Sparse secret sharing schemes with only two output shares were constructed. In this work, we construct sparse secret sharing schemes that generalize Shamir's secret sharing schemes for a fixed threshold $t=2$ and an arbitrarily large number of shares. We design our schemes to provide the strongest privacy guarantee given a desired sparsity of the shares under some mild assumptions. We show that increasing the number of shares, i.e., increasing straggler tolerance, incurs a degradation of the privacy guarantee. However, this degradation is negligible when the number of shares is comparably small to the cardinality of the input alphabet