A Bottom-Up Approach to SUSY Analyses

This paper proposes a new way to do event generation and analysis in searches for new physics at the LHC. An abstract notation is used to describe the new particles on a level which better corresponds to detector resolution of LHC experiments. In this way the SUSY discovery space can be decomposed into a small number of eigenmodes each with only a few parameters, which allows to investigate the SUSY parameter space in a model-independent way. By focusing on the experimental observables for each process investigated the Bottom-Up Approach allows to systematically study the boarders of the experimental efficiencies and thus to extend the sensitivity for new physics.Comment: 12 page

The Sciences of Data – Moving Towards a Comprehensive Systems Perspective

Data science’s rapid development in a dynamically growing data environment endows it with unique characteristics among scientific disciplines, juxtaposing challenges typically encountered in theoretical as well as empirical sciences. This raises questions as to the identification of the most pressing problems for data science, as well as to what constitutes its theoretical foundations. In this contribution, we first describe data science from the perspective of philosophy of science. We argue that the current mode of development of data science is adequately described by what we term the differentiational-expansionist mode. This leads us to conclude that data science concerns the acquisition of scientific theories relating to the application of methods, workflows and algorithms that generate value for users – which we term the integrative view. This definition emphasizes the interdependent nature of human and algorithmic elements in complex data workflows. We then offer four challenges for the future of the field. We conclude that since full control of entire data workflows is unfeasible, attention should be redirected towards the creation of an infrastructure by which data workflows will self-organize in a useful manner

Efficient global optimization: Motivation, variations and applications

A popular optimization method of a black box objective function is Efficient Global Optimization (EGO), also known as Sequential Model Based Optimization, SMBO, with kriging and expected improvement. EGO is a sequential design of experiments aiming at gaining as much information as possible from as few experiments as feasible by a skillful choice of the factor settings in a sequential way. In this paper we will introduce the standard procedure and some of its variants. In particular, we will propose some new variants like regression as a modeling alternative to kriging and two simple methods for the handling of categorical variables, and we will discuss focus search for the optimization of the infill criterion. Finally, we will give relevant examples for the application of the method. Moreover, in our group, we implemented all the described methods in the publicly available R package mlrMBO

Industrial Data Science: Developing a Qualification Concept for Machine Learning in Industrial Production

The advent of Industry 4.0 and the availability of large data storage systems lead to an increasing demand for specially educated data-oriented professionals in industrial production. The education of these specialists should combine elements from three fields: Industrial engineering, data analysis and data administration. However, a comprehensive education program incorporating all three elements has not yet been established in Germany. The aim of the acquired research project, titled “Industrial Data Science” is to develop and implement a qualification concept for Machine Learning based on demands coming up in industrial environments. The concept is targeted at two groups: Advanced students from any of the three mentioned fields (Mechanical Engineering, Statistics, Computer Science) and industrial professionals. In the qualification concept the needs of industrial companies are considered. Therefore, a survey was created to inquire the use and potentials of Machine Learning and the requirements for future Data Scientists in industrial production. The evaluation of the survey and the resulting conclusions affecting the qualification concept are presented in this paper

Drone radio signal detection with multi-timescale deep neural networks

We develop a multi-timescale deep learning algorithm to detect drones from radio signals. While previous approaches focused on the analysis of high-frequency radio data alone we integrate signals from the higher timescale of the drone communication protocol in an end-to-end architecture. To this end, we develop a new meta-CNN layer, which generalizes the idea of the standard CNN (which slides a single, fully connected kernel along a higher-level input) towards arbitrarily complex kernel models. To detect higher timescale patterns our system uses an LSTM layer in the top layers. As a result, our model is able to extend drone identification abilities significantly toward very small SNRs

Modulation of BOLD and Arterial Spin Labeling (ASL-CBF) Response in Patients with Transient Visual Impairment after Posterior Circulation Stroke*

Background and Purpose:: Blood oxygenation level-dependent (BOLD) signal and arterial spin labeling cerebral blood flow (ASL-CBF) changes, as detected by functional magnetic resonance imaging (fMRI) are closely related to neural activity. The aim of this case series study was to investigate modulations of the BOLD and ASL-CBF response in the primary visual cortex after posterior circulation stroke with transient visual impairment. Methods:: BOLD activity, resting CBF and task-related ASL-CBF response have been investigated 24-48 h after onset of transient visual symptoms in two patients who were treated conservatively, two patients who received thrombolysis after posterior circulation stroke, and five healthy controls with checkerboard stimulation and visual evoked potentials (VEPs). Results:: After normalization of transient visual symptoms the BOLD response and VEPs showed no hemispheric differences between patients and controls. The relative blood flow in the posterior cerebral arteries and the relative ASL-CBF response to checkerboard stimulation were reduced in three patients, compared to controls. In one patient who received intraarterial thrombolytic therapy, improvement of the relative CBF and ASL-CBF responses was observed, indicating early reperfusion. Conclusion:: In this case series of four patients, different CBF responses to conservative and thrombolytic therapy were observed, and early reperfusion after intraarterial thrombolysis was detected. Functional imaging, which makes use of the ASL-CBF technique, is feasible to measure early poststroke vascular changes, which are hardly detectable with BOLD-fMR

A learned simulation environment to model student engagement and retention in automated online courses

We developed a simulator to quantify the effect of exercise ordering on both student engagement and retention. Our approach combines the construction of neural network representations for users and exercises using a dynamic matrix factorization method. We further created machine learning models of success and dropout prediction. As a result, our system is able to predict student engagement and retention based on a given sequence of exercises selected. This opens the door to the development of versatile reinforcement learning agents which can substitute the role of private tutoring in exam preparation

Robust drone detection and classification from radio frequency signals using convolutional neural networks

As the number of unmanned aerial vehicles (UAVs) in the sky increases, safety issues have become more pressing. In this paper, we compare the performance of convolutional neural networks (CNNs) using first, 1D in-phase and quadrature (IQ) data and second, 2D spectrogram data for detection and classification of UAVs based on their radio frequency (RF) signals. We focus on the robustness of the models to low signal-to-noise ratios (SNRs), as this is the most relevant aspect for a real-world application. Within an input type, either IQ or spectrogram, we found no significant difference in performance between models, even as model complexity increased. In addition, we found an advantage in favor of the 2D spectrogram representation of the data. While there is basically no performance difference at SNRs ≥ 0 dB, we observed a 100% improvement in balanced accuracy at −12 dB, i.e. 0.842 on the spectrogram data compared to 0.413 on the IQ data for the VGG11 model. Together with an easy-to-use benchmark dataset, our findings can be used to develop better models for robust UAV detection systems