What makes student entrepreneurs? On the relevance (and irrelevance) of the university and the regional context for student start-ups

Student start-ups are a significant part of overall university entrepreneurship. Yet, we know little about the determinants of this type of start-ups and, specifically, the relevance of context effects. Drawing on organizational and regional context literature, we develop and test a model that aims to explain student entrepreneurship in a contextual perspective. Based on unique micro-data and using multi-level techniques, we analyse nascent and new entrepreneurial activities of business and economics students at 41 European universities. Our analysis reveals that individual and contextual determinants influence students’ propensity to start a business. While peoples’ individual characteristics are most important, the organizational and regional contexts also play a role and have a differentiated effect, depending on the source of the venture idea and the stage of its development. Organizational characteristics, like the prevalence of fellow students who have attended entrepreneurship education, influence whether students take action to start a new firm (nascent entrepreneurship) but do not seem to support the actual establishment of a new firm. In contrast, the latter is less dependent on the university context but more strongly influenced by regional characteristics. Overall, our study contributes to our understanding of the emergence of start-ups in the organizational context of universities and has implications for initiatives and programs that aim at encouraging students to become entrepreneurs. The final publication is available at Springer via http://dx.doi.org/10.1007/s11187-016-9700-6

Hierarchically structured determinants and phase-related patterns of economic resilience – An empirical case study for European regions

Our paper seeks to provide empirical evidence for a spatial-temporal system of (short-term) regional resilience determinants. Based on groundwork from Martin (2012) and Martin and Sunley (2015), we employ a nested hierarchy of regional and national determinants to constitute the spatial dimension, while we model the temporal dimension through a resistance and a recovery phase. Utilising hierarchical panel data models for a sample of 22 European countries, we can confirm the presence of a spatial-temporal system as we find significant determinants at both spatial levels that are connected via cross-level interactions and reveal varying, if not opposing directions of influences across the sensitivity and recovery phase

Eurokrise, Energiewende, Konjunkturabkühlung: Ende des deutschen Jobwunders?

Welche Determinanten sind für das »deutsche Jobwunder« verantwortlich? Dieter Hundt, Bundesvereinigung der Deutschen Arbeitgeberverbände, sieht hinter dem Erfolg das verantwortliche Handeln der Tarifpartner, die Nutzung von Arbeitszeitkonten und richtige politische Entscheidungen etwa beim Kurzarbeitergeld. Dies zeige, dass die Tarifautonomie in Deutschland selbst unter extremen Bedingungen gut funktioniert. Frank-Jürgen Weise, Bundesagentur für Arbeit, Nürnberg, geht davon aus, dass es auch 2012 noch eine positive Entwicklung am Arbeitsmarkt geben wird und die Arbeitslosenzahl unter der Marke von 3 Millionen bleibt. Die gute Arbeitsmarktentwicklung lässt sich nach Ansicht von Hagen Lesch, Institut der deutschen Wirtschaft Köln, auf drei Einflüsse zurückführen: auf das Horten von Arbeitskräften während der Krise, auf eine moderate Lohnpolitik und auf die Arbeitsmarktreformen der Jahre 2003 bis 2005. Wolfgang Lechthaler, Institut für Weltwirtschaft, und Christian Merkl, Universität Erlangen-Nürnberg, erwarten, unter der Bedingung einer glaubwürdigen Haushaltskonsolidierung in Südeuropa bei gleichzeitiger Reduktion der Leistungsbilanzdefizite, nur moderate rezessive Schocks für Deutschland. Der deutsche Arbeitsmarkt verfüge über die Voraussetzungen, um einen sanften Abschwung zu absorbieren.Arbeitsmarkt, Arbeitsmarktflexibilität, Deutschland

Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere

Fourier Neural Operators (FNOs) have proven to be an efficient and effective method for resolution-independent operator learning in a broad variety of application areas across scientific machine learning. A key reason for their success is their ability to accurately model long-range dependencies in spatio-temporal data by learning global convolutions in a computationally efficient manner. To this end, FNOs rely on the discrete Fourier transform (DFT), however, DFTs cause visual and spectral artifacts as well as pronounced dissipation when learning operators in spherical coordinates since they incorrectly assume a flat geometry. To overcome this limitation, we generalize FNOs on the sphere, introducing Spherical FNOs (SFNOs) for learning operators on spherical geometries. We apply SFNOs to forecasting atmospheric dynamics, and demonstrate stable auto\-regressive rollouts for a year of simulated time (1,460 steps), while retaining physically plausible dynamics. The SFNO has important implications for machine learning-based simulation of climate dynamics that could eventually help accelerate our response to climate change

Evolving a Deep Neural Network Training Time Estimator

We present a procedure for the design of a Deep Neural Net- work (DNN) that estimates the execution time for training a deep neural network per batch on GPU accelerators. The estimator is destined to be embedded in the scheduler of a shared GPU infrastructure, capable of providing estimated training times for a wide range of network architectures, when the user submits a training job. To this end, a very short and simple representation for a given DNN is chosen. In order to compensate for the limited degree of description of the basic network representation, a novel co-evolutionary approach is taken to fit the estimator. The training set for the estimator, i.e. DNNs, is evolved by an evolutionary algorithm that optimizes the accuracy of the estimator. In the process, the genetic algorithm evolves DNNs, generates Python-Keras programs and projects them onto the simple representation. The genetic operators are dynamic, they change with the estimator’s accuracy in order to balance accuracy with generalization. Results show that despite the low degree of information in the representation and the simple initial design for the predictor, co-evolving the training set performs better than near random generated population of DNNs

Cluster externalities, firm capabilities, and the recessionary shock: How the macro-to-micro-transition shapes firm performance during stable times and times of crisis

In this paper, we examine the macro-to-micro-transition of cluster externalities to firms and how it is affected by the macroeconomic instability caused by the recessionary shock of 2008/2009. Using data from 16,166 manufacturing and business services firms nested in 390 German regions, we employ within-firm regression techniques to estimate the impact of cross-level interactions between firm- and cluster-level determinants on phase-related differences in firm performance between a pre-crisis (2004-2007) and a crisis period (2009-2011). The empirical results validate the existence of a macro-to-micro-transition that evolves best in the case of broad firm-level capabilities and variety-driven externalities. Furthermore, the results indicate that the transition strongly depends on the macroeconomic cycle. While the transition particularly benefits from a stable macroeconomic environment (2004-2007), its mechanisms are interrupted when being exposed to economic turmoil (2009-2011). Yet, the crisis-induced interruption of the transition is mainly restricted to the national recession in 2009. As soon as the macroeconomic pressure diminishes (2010-2011), we observe a reversion of the transmission mechanisms to the pre-crisis level. Our study contributes to the existing literature by corroborating previous findings that the economic performance of firms depends on a working macro-to-micro transition of external resources, which presupposes sufficient cluster externalities and adequate firm-level combinative capabilities. In contrast to previous studies on this topic, the transition mechanism is not modeled as time-invariant. Instead, it is coupled to the prevailing macroeconomic regime

Cluster externalities, firm capabilities, and the recessionary shock: How the macro-to-micro-transition shapes firm performance during stable times and times of crisis

In this paper, we examine the macro-to-micro-transition of cluster externalities to firms and how it is affected by the macroeconomic instability caused by the recessionary shock of 2008/2009. Using data from 16,166 manufacturing and business services firms nested in 390 German regions, we employ within-firm regression techniques to estimate the impact of cross-level interactions between firm- and cluster-level determinants on phase-related differences in firm performance between a pre-crisis (2004-2007) and a crisis period (2009-2011). The empirical results validate the existence of a macro-to-micro-transition that evolves best in the case of broad firm-level capabilities and variety-driven externalities. Furthermore, the results indicate that the transition strongly depends on the macroeconomic cycle. While the transition particularly benefits from a stable macroeconomic environment (2004-2007), its mechanisms are interrupted when being exposed to economic turmoil (2009-2011). Yet, the crisis-induced interruption of the transition is mainly restricted to the national recession in 2009. As soon as the macroeconomic pressure diminishes (2010-2011), we observe a reversion of the transmission mechanisms to the pre-crisis level. Our study contributes to the existing literature by corroborating previous findings that the economic performance of firms depends on a working macro-to-micro transition of external resources, which presupposes sufficient cluster externalities and adequate firm-level combinative capabilities. In contrast to previous studies on this topic, the transition mechanism is not modeled as time-invariant. Instead, it is coupled to the prevailing macroeconomic regime

Weakly-Supervised Free Space Estimation through Stochastic Co-Teaching

Free space estimation is an important problem for autonomous robot navigation. Traditional camera-based approaches train a segmentation model using an annotated dataset. The training data needs to capture the wide variety of environments and weather conditions encountered at runtime, making the annotation cost prohibitively high. In this work, we propose a novel approach for obtaining free space estimates from images taken with a single road-facing camera. We rely on a technique that generates weak free space labels without any supervision, which are then used as ground truth to train a segmentation model for free space estimation. Our work differs from prior attempts by explicitly taking label noise into account through the use of Co-Teaching. Since Co-Teaching has traditionally been investigated in classification tasks, we adapt it for segmentation and examine how its parameters affect performances in our experiments. In addition, we propose Stochastic Co-Teaching, which is a novel method to select clean samples that leads to enhanced results. We achieve an IoU of 82.6%, a Precision of 90.9%, and a Recall of 90.3%. Our best model reaches 87% of the IoU, 93% of the Precision, and 93% of the Recall of the equivalent fully-supervised baseline while using no human annotations. To the best of our knowledge, this work is the first to use Co-Teaching to train a free space segmentation model under explicit label noise. Our implementation and trained models are freely available online