Venture Capitalists' Evaluations of Start-up Teams: Trade-offs, Knock-out Criteria, and the Impact of VC Experience

The start-up team plays a key role in venture capitalists' evaluations of venture proposals. Our findings go beyond existing research, first by providing a detailed exploration of VCs' team evaluation criteria, and second by investigating the moderator variable of VC experience. Our results reveal utility trade-offs between team characteristics and thus provide answers to questions such as "What strength does it take to compensate for a weakness in characteristic A?" Moreover, our analysis reveals that novice VCs tend to focus on the qualifications of individual team members, while experienced VCs focus more on team cohesion. Data was obtained in a conjoint experiment with 51 professionals in VC firms and analyzed using discrete choice econometric models. (author's abstract

Modeling spatio-temporal enhancer expression in Drosophila segmentation

Thermodynamic models are a key tool to investigate transcription control in the segmentation of Drosophila. By modeling the binding of transcription factors to DNA sequences and their effect on transcription initiation, thermodynamic models predict expression patterns directly from the enhancer sequence, given the binding motifs and concentrations of all relevant transcription factors (TFs). However, many parameters of the model are impossible to measure, e.g. the interaction strength between the TFs and the core promoter. Hence, it is necessary to estimate these parameters by training the thermodynamic model on known data, i.e. to fit the model predictions to already measured expression patterns of known enhancers. The quality of the parameter training result, evaluated on independent test data, indicates how well the model recapitulates the biological measurements, which can help us to improve our understanding of the underlaying mechanisms of transcription control. Therefore, proper parameter training is a crucial step for the construction of thermodynamic models. In this thesis, I develop a thorough parameter training setup that uses the limited amount of available training data efficiently and reduces parameter overfitting significantly. This optimized training setup applies a global parameter training algorithm, a method to artificially increase the amount of training data, called data augmentation, and parameter penalties, which is a technique to limit overfitting. I apply the novel training setup to expand the scope of thermodynamic models of Drosophila segmentation considerably by incorporating additional TFs into the model, and to investigate many aspects of transcription control in greater detail than it was possible before. Among these topics are the specificity of TF binding motifs, the nature of TF cooperativity and DNA accessibility. With the help of the here developed impact score, I assess the influence of all relevant TFs in silico, delineate the cooperativity range of the key TF bcd, and determine the importance of weak binding sites. Finally, I develop and discuss two alternative models of transcription control that lack the prediction quality of thermodynamic models, but, nevertheless, give valuable insights into the architectural principles of enhancers. This project is part of a larger effort to advance our current understanding of transcription regulation by reconstructing the segmentation network of Drosophila in silico. The results of this thesis facilitate future modeling efforts by optimally leveraging the available data as well as by improving our understanding of thermodynamic models

Ranges of control in the transcriptional regulation of Escherichia coli

<p>Abstract</p> <p>Background</p> <p>The positioning of genes in the genome is an important evolutionary degree of freedom for organizing gene regulation. Statistical properties of these distributions have been studied particularly in relation to the transcriptional regulatory network. The systematics of gene-gene distances then become important sources of information on the control, which different biological mechanisms exert on gene expression.</p> <p>Results</p> <p>Here we study a set of categories, which has to our knowledge not been analyzed before. We distinguish between genes that do not participate in the transcriptional regulatory network (i.e. that are according to current knowledge not producing transcription factors and do not possess binding sites for transcription factors in their regulatory region), and genes that via transcription factors either are regulated by or regulate other genes. We find that the two types of genes ("isolated" and "regulatory" genes) show a clear statistical repulsion and have different ranges of correlations. In particular we find that isolated genes have a preference for shorter intergenic distances.</p> <p>Conclusions</p> <p>These findings support previous evidence from gene expression patterns for two distinct logical types of control, namely digital control (i.e. network-based control mediated by dedicated transcription factors) and analog control (i.e. control based on genome structure and mediated by neighborhood on the genome).</p

PSO Facing Non-Separable and Ill-Conditioned Problems

This report investigates the behavior of particle swarm optimization (PSO) on ill-conditioned functions. We find that PSO performs very well on separable, ill-conditioned functions. If the function is rotated such that it becomes non-separable, the performance declines dramatically. On non-separable, ill-conditioned functions we find the search costs (number of function evaluations) of PSO increasing roughly proportional with the condition number. We never observe premature convergence, but on non-separable, ill-conditioned problems PSO is outperformed by a contemporary evolution strategy by orders of magnitude. The strong dependency of PSO on rotations originates from random events that are only independent within the given coordinate system. We argue that invariance properties, like rotational invariance, are desirable, because they increase the predictive power of performance results

The impact of the AO foundation on fracture care : an evaluation of 60 years AO foundation

Objectives Sixty years ago, the Association of Osteosynthesis (AO) was founded with the aim to improve fracture treatment and has since grown into one of the largest medical associations worldwide. Aim of this study was to evaluate AO's impact on science, education, patient care and the MedTech business. Design/methods Impact evaluations were conducted as appropriate for the individual domains: Impact on science was measured by analyzing citation frequencies of publications promoted by AO. Impact on education was evaluated by analyzing the evolution of number and location of AO courses. Impact on patient care was evaluated with a health economic model analyzing cost changes and years of life gained through the introduction of osteosynthesis in 17 high-income countries (HICs). Impact on MedTech business was evaluated by analyzing sales data of AO-associated products. Results Thirty-five AO papers and 2 major AO textbooks are cited at remarkable frequencies in high ranking journals with up to 2000 citations/year. The number of AO courses steadily increased with a total of 645'000 participants, 20‘000 teaching days and 2‘500 volunteer faculty members so far. The introduction of osteosynthesis saved at least 925 billion Swiss Francs [CHF] in the 17 HICs analyzed and had an impact on avoiding premature deaths comparable to the use of antihypertensive drugs. AO-associated products generated sales of 55 billion CHF. Conclusion AO's impact on science, education, patient care, and the MedTech business was significant because AO addressed hitherto unmet needs by combining activities that mutually enriched and reinforced each other

Modeling spatio-temporal enhancer expression in Drosophila segmentation

Thermodynamic models are a key tool to investigate transcription control in the segmentation of Drosophila. By modeling the binding of transcription factors to DNA sequences and their effect on transcription initiation, thermodynamic models predict expression patterns directly from the enhancer sequence, given the binding motifs and concentrations of all relevant transcription factors (TFs). However, many parameters of the model are impossible to measure, e.g. the interaction strength between the TFs and the core promoter. Hence, it is necessary to estimate these parameters by training the thermodynamic model on known data, i.e. to fit the model predictions to already measured expression patterns of known enhancers. The quality of the parameter training result, evaluated on independent test data, indicates how well the model recapitulates the biological measurements, which can help us to improve our understanding of the underlaying mechanisms of transcription control. Therefore, proper parameter training is a crucial step for the construction of thermodynamic models. In this thesis, I develop a thorough parameter training setup that uses the limited amount of available training data efficiently and reduces parameter overfitting significantly. This optimized training setup applies a global parameter training algorithm, a method to artificially increase the amount of training data, called data augmentation, and parameter penalties, which is a technique to limit overfitting. I apply the novel training setup to expand the scope of thermodynamic models of Drosophila segmentation considerably by incorporating additional TFs into the model, and to investigate many aspects of transcription control in greater detail than it was possible before. Among these topics are the specificity of TF binding motifs, the nature of TF cooperativity and DNA accessibility. With the help of the here developed impact score, I assess the influence of all relevant TFs in silico, delineate the cooperativity range of the key TF bcd, and determine the importance of weak binding sites. Finally, I develop and discuss two alternative models of transcription control that lack the prediction quality of thermodynamic models, but, nevertheless, give valuable insights into the architectural principles of enhancers. This project is part of a larger effort to advance our current understanding of transcription regulation by reconstructing the segmentation network of Drosophila in silico. The results of this thesis facilitate future modeling efforts by optimally leveraging the available data as well as by improving our understanding of thermodynamic models

Interacting with a gaze-aware virtual character

In this paper, we present the user’s attentive state interpreted through eye gaze while interacting with a virtual character. The underlying narrative in which the approach was tested is based on a classical XIX th century psychological novel: Madame Bovary, by Flaubert. We connected a remote eye tracker with a dynamic 3D world. An empirical study revealed individual user experiences and behavioral patterns. In particular, we identified two different groups of users: one that was showing natural eye gaze behaviors with rhythmic eye gaze shifts between the characters ’ eyes, face and the scene and another one permanently staring at the character. Interestingly, the group with more natural behaviors towards the character also rated the experience with the system more positively

Affective interactive narrative in the CALLAS Project

This full text version, available on TeesRep, is the post-print (final version prior to publication) of

Efficient and Robust Orientation Estimation of Strawberries for Fruit Picking Applications

Recent developments in agriculture have highlighted the potential of as well as the need for the use of robotics. Various processes in this field can benefit from the proper use of state of the art technology [1], in terms of efficiency as well as quality. One of these areas is the harvesting of ripe fruit. In order to be able to automate this process, a robotic harvester needs to be aware of the full poses of the crop/fruit to be collected in order to perform proper path- and collision planning. The current state of the art mainly considers problems of detection and segmentation of fruit with localisation limited to the 3D position only. The reliable and real-time estimation of the respective orientations remains a mostly unaddressed problem. In this paper, we present a compact and efficient network architecture for estimating the orientation of soft fruit such as strawberries from colour and, optionally, depth images. The proposed system can be automatically trained in a realistic simulation environment. We evaluate the system’s performance on simulated datasets and validate its operation on publicly available images of strawberries to demonstrate its practical use. Depending on the amount of training data used, coverage of state space, as well as the availability of RGB-D or RGB data only, mean errors of as low as 11° could be achieved