Differential Equations and Finite Groups

AbstractThe classical solution of the Riemann–Hilbert problem attaches to a given representation of the fundamental group a regular singular linear differential equation. We present a method to compute this differential equation in the case of a representation with finite image. The approach uses Galois coverings of P1\{0,1,∞}, differential Galois theory, and a formula for the character of the Galois action of the space of holomorphic differentials. Examples are produced for the finite primitive unimodular groups of degree two and three

AutoML in Heavily Constrained Applications

Optimizing a machine learning pipeline for a task at hand requires careful configuration of various hyperparameters, typically supported by an AutoML system that optimizes the hyperparameters for the given training dataset. Yet, depending on the AutoML system's own second-order meta-configuration, the performance of the AutoML process can vary significantly. Current AutoML systems cannot automatically adapt their own configuration to a specific use case. Further, they cannot compile user-defined application constraints on the effectiveness and efficiency of the pipeline and its generation. In this paper, we propose Caml, which uses meta-learning to automatically adapt its own AutoML parameters, such as the search strategy, the validation strategy, and the search space, for a task at hand. The dynamic AutoML strategy of Caml takes user-defined constraints into account and obtains constraint-satisfying pipelines with high predictive performance

AutoML in heavily constrained applications

Optimizing a machine learning pipeline for a task at hand requires careful configuration of various hyperparameters, typically supported by an AutoML system that optimizes the hyperparameters for the given training dataset. Yet, depending on the AutoML system’s own second-order meta-configuration, the performance of the AutoML process can vary significantly. Current AutoML systems cannot automatically adapt their own configuration to a specific use case. Further, they cannot compile user-defined application constraints on the effectiveness and efficiency of the pipeline and its generation. In this paper, we propose Caml, which uses meta-learning to automatically adapt its own AutoML parameters, such as the search strategy, the validation strategy, and the search space, for a task at hand. The dynamic AutoML strategy of Caml takes user-defined constraints into account and obtains constraint-satisfying pipelines with high predictive performance

Die Rolle der AMPK beim cholangiozellulären Karzinom

Das Cholangiokarzinom (CC) ist ein seltener Tumor mit zunehmender Inzidenz. Die chirurgische Resektion stellt die einzig kurative Therapie dar, Gemcitabine/Cisplatin ist die Standard-Chemotherapie für Patienten mit inoperablem CC. Die 5-Jahres-Überlebensrate liegt lediglich bei 10%. Die AMP-aktivierte Proteinkinase (AMPK) besitzt eine katalytische α-Untereinheit, von welcher beim Menschen zwei Isoformen, α1 und α2, vorkom-men. AMPK spielt eine wichtige Rolle dabei, die Zelle im energetischen Gleichgewicht zu halten. In Tumoren führt eine Aktivierung der AMPK zu einer Verringerung des Tumorwachstums. Da die Auswirkungen der verschiedenen α-Untereinheiten sowie die Auswirkungen einer Suppression der AMPK auf das CC bislang unbekannt sind, wurden in der vorliegenden Arbeit mittels siRNA-Technologie Untersuchungen an humanen CC-(TFK-1) Zellen durchgeführt. Es zeigte sich, dass die Herunterregulierung der aktivierten p-AMPK durch die siRNA erfolgreich war. Im Proliferationsversuch stellte sich nach 48h für die kombinierte Suppression beider α-Untereinheiten eine signifikante Zunahme dar. Auch die Invasion war für diese Gruppe signifikant erhöht. Hingegen blieb die Migration ohne wesentliche Veränderungen. Ferner ergab sich ein signifi-kanter Anstieg der IL-6-Expression nach siRNA-Therapie. Die alleinige Supp-ression der AMPKα1 hatte, ebenso wie die alleinige Suppression der AMPKα2, keine Veränderung des Tumorzellwachstums zur Folge. Die siRNA-Behandlung ging in allen Gruppen mit einer Erhöhung der nfκb-Expression sowie einer Erhöhung von Markern der epithelialen-mesenchymalen Transition (E-Cadherin, N-Cadherin) einher. Zusammenfassend lässt sich festhalten, dass lediglich die kombinierte Supp-ression beider α-Untereinheiten zu einer signifikant verstärkten Malignität führte. Eine einzelne Suppression einer α-Untereinheit veränderte das Tumorwachstum nicht signifikant und auch zwischen den beiden α-Untereinheiten konnten keine deutlichen Unterschiede festgestellt werden. Eine Aktivierung beider AMPK-Untereinheiten könnte somit eine mögliche zu-künftige Therapieoption für das CC sein, jedoch bedarf es hierzu weiterer Un-tersuchungen, insbesondere in vivo Versuche könnten weitere Erkenntnisse liefern

The influence of radiotherapy techniques on the plan quality and on the risk of secondary tumors in patients with pituitary adenoma

Background This planning study compares different radiotherapy techniques for patients with pituitary adenoma, including flatness filter free mode (FFF), concerning plan quality and secondary malignancies for potentially young patients. The flatness filter has been described as main source of photon scatter. Material and methods Eleven patients with pituitary adenoma were included. An Elekta Synergy (TM) linac was used in the treatment planning system Oncentra (R) and for the measurements. 3D plans, IMRT, and VMAT plans and non-coplanar varieties were considered. The plan quality was evaluated regarding homogeneity, conformity, delivery time and dose to the organs at risk. The secondary malignancy risk was calculated from dose volume data and from measured dose to the periphery using different models for carcinoma and sarcoma risk. Results The homogeneity and conformity were nearly unchanged with and without flattening filter, neither was the delivery time found substantively different. VMAT plans were more homogenous, conformal and faster in delivery than IMRT plans. The secondary cancer risk was reduced with FFF both in the treated region and in the periphery. VMAT plans resulted in a higher secondary brain cancer risk than IMRT plans, but the risk for secondary peripheral cancer was reduced. Secondary sarcoma risk plays a minor role. No advantage was found for non-coplanar techniques. The FFF delivery times were not shortened due to additional monitor units needed and technical limitations. The risk for secondary brain cancer seems to depend on the irradiated volume. Secondary sarcoma risk is much smaller than carcinoma risk in accordance to the results of the atomic bomb survivors. The reduction of the peripheral dose and resulting secondary malignancy risk for FFF is statistically significant. However, it is negligible in comparison to the risk in the treated region. Conclusion Treatments with FFF can reduce secondary malignancy risk while retaining similar quality as with flattening filter and should be preferred. VMAT plans show the best plan quality combined with lowest peripheral secondary malignancy risk, but highest level of second brain cancer risk. Taking this into account VMAT FFF seems the most advantageous technique for the treatment of pituitary adenomas with the given equipment

Velocity Prediction Based on Map Data for Optimal Control of Electrified Vehicles Using Recurrent Neural Networks (LSTM)

In order to improve the efficiency of electrified vehicle drives, various predictive energy management strategies (driving strategies) have been developed. This article presents the extension of a generic prediction approach already proposed in a previous paper, which allows a robust forecasting of all traction torque-relevant variables for such strategies. The extension primarily includes the proper utilization of map data in the case of an a priori known route. Approaches from Artificial Intelligence (AI) have proven to be effective for such proposals. With regard to this, Recurrent Neural Networks (RNN) are to be preferred over Feed-Forward Neural Networks (FNN). First, preprocessing is described in detail including a wide overview of both calculating the relevant quantities from global navigation satellite system (GNSS) data in several steps and matching these with data from the chosen map provider. Next, an RNN including Long Short-Term Memory (LSTM) cells in an Encoder–Decoder configuration and a regular FNN are trained and applied. The models are used to forecast real driving profiles over different time horizons, both including and excluding map data in the model. Afterwards, a comparison is presented, including a quantitative and a qualitative analysis. The accuracy of the predictions is therefore assessed using Root Mean Square Error (RMSE) computations and analyses in the time domain. The results show a significant improvement in velocity prediction with LSTMs including map data

“Keep It Simple: Optimized Student Evaluations With Moodle”

Acquiring representative feedback from students is a common problem for universities. To address the often low response rates and participation bias, we focussed on a simplified evaluation process and improved user convenience. We developed and implemented a new tool for collecting feedback by sharing an accessible short survey on our Moodle-based e-learning platform. This new Moodle evaluation tool allows surveys to pop up visibly but non-invasively within every Moodle course offered by our university for the duration of the valuation period. After voting, the survey does not show up again. By condensing a questionnaire to three main queries using a 6-point Likert scale, we gathered data on overall satisfaction with the course, satisfaction with course structure and navigation, and satisfaction with course elements and content. Within two weeks, we collected 65,000 votes from over 1600 courses, with an average response rate of 30% among all active students using the Moodle platform. This paper describes the design and implementation of the short survey, provides an overview of the new evaluation tool and its features, and shares preliminary results and interpretations of the data. Based on these findings, we outline our plans for the continuation and extension of the short-survey approach

Automated salamander recognition using deep neural networks and feature extraction

This paper presents a study conducted to recognize salamanders by using their unique body markings based on images. The detection and matching of unique patterns in a salamander’s body can be complex due variability in individual animals size, shape, orientation and also influence from the external enviornment. While traditional methods require time intensive manual image corrections of the salamanders to achieve accurate recognition, in this work we propose a fully automatic techinque for straigthening. We also propose a matching technique based on the corrected images. The convolutional neural network ResNet50 and dense scale-invariant feature transform (DSIFT) are used for belly pattern localization, and matching for salamander recognition