@inproceedings{60680, abstract = {{Classical machine learning techniques often struggle with overfitting and unreliable predictions when exposed to novel conditions. Introducing causality into the modelling process offers a promising way to mitigate these challenges by enhancing predictive robustness. However, constructing an initial causal graph manually using domain knowledge is time-consuming, particularly in complex time series with numerous variables. To address this, causal discovery algorithms can provide a preliminary causal structure that domain experts can refine. This study investigates causal feature selection with domain knowledge using a data center system as an example. We use simulated time-series data to compare different causal feature selection with traditional machine-learning feature selection methods. Our results show that predictions based on causal features are more robust compared to those derived from traditional methods. These findings underscore the potential of combining causal discovery algorithms with human expertise to improve machine learning applications.}}, author = {{Zapata Gonzalez, David Ricardo and Meyer, Marcel and Müller, Oliver}}, keywords = {{Causal Machine Learning, Causality in Time Series, Causal Discovery, Human-Machine Collaboration}}, location = {{Amman, Jordan}}, title = {{{Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems}}}, year = {{2025}}, } @inproceedings{49109, abstract = {{We propose a diarization system, that estimates “who spoke when” based on spatial information, to be used as a front-end of a meeting transcription system running on the signals gathered from an acoustic sensor network (ASN). Although the spatial distribution of the microphones is advantageous, exploiting the spatial diversity for diarization and signal enhancement is challenging, because the microphones’ positions are typically unknown, and the recorded signals are initially unsynchronized in general. Here, we approach these issues by first blindly synchronizing the signals and then estimating time differences of arrival (TDOAs). The TDOA information is exploited to estimate the speakers’ activity, even in the presence of multiple speakers being simultaneously active. This speaker activity information serves as a guide for a spatial mixture model, on which basis the individual speaker’s signals are extracted via beamforming. Finally, the extracted signals are forwarded to a speech recognizer. Additionally, a novel initialization scheme for spatial mixture models based on the TDOA estimates is proposed. Experiments conducted on real recordings from the LibriWASN data set have shown that our proposed system is advantageous compared to a system using a spatial mixture model, which does not make use of external diarization information.}}, author = {{Gburrek, Tobias and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}}, booktitle = {{Proc. Asilomar Conference on Signals, Systems, and Computers}}, keywords = {{Diarization, time difference of arrival, ad-hoc acoustic sensor network, meeting transcription}}, title = {{{Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks}}}, year = {{2023}}, } @inproceedings{37312, abstract = {{Optimal decision making requires appropriate evaluation of advice. Recent literature reports that algorithm aversion reduces the effectiveness of predictive algorithms. However, it remains unclear how people recover from bad advice given by an otherwise good advisor. Previous work has focused on algorithm aversion at a single time point. We extend this work by examining successive decisions in a time series forecasting task using an online between-subjects experiment (N = 87). Our empirical results do not confirm algorithm aversion immediately after bad advice. The estimated effect suggests an increasing algorithm appreciation over time. Our work extends the current knowledge on algorithm aversion with insights into how weight on advice is adjusted over consecutive tasks. Since most forecasting tasks are not one-off decisions, this also has implications for practitioners.}}, author = {{Leffrang, Dirk and Bösch, Kevin and Müller, Oliver}}, booktitle = {{Hawaii International Conference on System Sciences}}, keywords = {{Algorithm aversion, Time series, Decision making, Advice taking, Forecasting}}, title = {{{Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time}}}, year = {{2023}}, } @inproceedings{50479, abstract = {{Verifying assertions is an essential part of creating and maintaining knowledge graphs. Most often, this task cannot be carried out manually due to the sheer size of modern knowledge graphs. Hence, automatic fact-checking approaches have been proposed over the last decade. These approaches aim to compute automatically whether a given assertion is correct or incorrect. However, most fact-checking approaches are binary classifiers that fail to consider the volatility of some assertions, i.e., the fact that such assertions are only valid at certain times or for specific time intervals. Moreover, the few approaches able to predict when an assertion was valid (i.e., time-point prediction approaches) rely on manual feature engineering. This paper presents TEMPORALFC, a temporal fact-checking approach that uses multiple sources of background knowledge to assess the veracity and temporal validity of a given assertion. We evaluate TEMPORALFC on two datasets and compare it to the state of the art in fact-checking and time-point prediction. Our results suggest that TEMPORALFC outperforms the state of the art on the fact-checking task by 0.13 to 0.15 in terms of Area Under the Receiver Operating Characteristic curve and on the time-point prediction task by 0.25 to 0.27 in terms of Mean Reciprocal Rank. Our code is open-source and can be found at https://github.com/dice-group/TemporalFC.}}, author = {{Qudus, Umair and Röder, Michael and Kirrane, Sabrina and Ngomo, Axel-Cyrille Ngonga}}, booktitle = {{The Semantic Web – ISWC 2023}}, editor = {{R. Payne, Terry and Presutti, Valentina and Qi, Guilin and Poveda-Villalón, María and Stoilos, Giorgos and Hollink, Laura and Kaoudi, Zoi and Cheng, Gong and Li, Juanzi}}, isbn = {{9783031472398}}, issn = {{0302-9743}}, keywords = {{temporal fact checking · ensemble learning · transfer learning · time-point prediction · temporal knowledge graphs}}, location = {{Athens, Greece}}, pages = {{465–483}}, publisher = {{Springer, Cham}}, title = {{{TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs}}}, doi = {{10.1007/978-3-031-47240-4_25}}, volume = {{14265}}, year = {{2023}}, } @inbook{33849, abstract = {{Modern traffic control systems are key to cope with current and future traffic challenges. In this paper information obtained from a microscopic traffic estimation using various data sources is used to feed a new developed traffic control approach. The presented method can control a traffic area with multiple traffic light systems (TLS) reacting to individual road users and pedestrians. In contrast to widespread green time extension techniques, this control selects the best phase sequence by analyzing the current traffic state reconstructed in SUMO and its predicted progress. To achieve this, the key aspect of the control strategy is to use Model Predictive Control (MPC). In order to maintain realism for real world applications, among other things, the traffic phase transitions are modelled in detail and integrated within the prediction. For the efficiency, the approach incorporates a fuzzy logic preselection of all phases reducing the computational effort. The evaluation itself is able to be easily adjusted to focus on various objectives like low occupancies, reducing waiting times and emissions, few number of phase transitions etc. determining the best switching times for the selected phases. Exemplary traffic simulations demonstrate the functionality of the MPC-based control and, in addition, some aspects under development like the real-world communication network are also discussed.}}, author = {{Malena, Kevin and Link, Christopher and Bußemas, Leon and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{Communications in Computer and Information Science}}, editor = {{Klein, Cornel and Jarke, Mathias and Helfert, Markus and Berns, Karsten and Gusikhin, Oleg}}, isbn = {{9783031170973}}, issn = {{1865-0929}}, keywords = {{Traffic control, Traffic estimation, Real-time, MPC, Fuzzy, Isolated intersection, Networked intersection, Sensor fusion}}, pages = {{232–254}}, publisher = {{Springer International Publishing}}, title = {{{Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments}}}, doi = {{10.1007/978-3-031-17098-0_12}}, volume = {{1612}}, year = {{2022}}, } @article{48854, abstract = {{We contribute to the theoretical understanding of randomized search heuristics for dynamic problems. We consider the classical vertex coloring problem on graphs and investigate the dynamic setting where edges are added to the current graph. We then analyze the expected time for randomized search heuristics to recompute high quality solutions. The (1+1) Evolutionary Algorithm and RLS operate in a setting where the number of colors is bounded and we are minimizing the number of conflicts. Iterated local search algorithms use an unbounded color palette and aim to use the smallest colors and, consequently, the smallest number of colors. We identify classes of bipartite graphs where reoptimization is as hard as or even harder than optimization from scratch, i.e., starting with a random initialization. Even adding a single edge can lead to hard symmetry problems. However, graph classes that are hard for one algorithm turn out to be easy for others. In most cases our bounds show that reoptimization is faster than optimizing from scratch. We further show that tailoring mutation operators to parts of the graph where changes have occurred can significantly reduce the expected reoptimization time. In most settings the expected reoptimization time for such tailored algorithms is linear in the number of added edges. However, tailored algorithms cannot prevent exponential times in settings where the original algorithm is inefficient.}}, author = {{Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}}, issn = {{0178-4617}}, journal = {{Algorithmica}}, keywords = {{Dynamic optimization, Evolutionary algorithms, Running time analysis}}, number = {{10}}, pages = {{3148–3179}}, title = {{{Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem}}}, doi = {{10.1007/s00453-021-00838-3}}, volume = {{83}}, year = {{2021}}, } @inproceedings{24547, abstract = {{Over the last years, several approaches for the data-driven estimation of expected possession value (EPV) in basketball and association football (soccer) have been proposed. In this paper, we develop and evaluate PIVOT: the first such framework for team handball. Accounting for the fast-paced, dynamic nature and relative data scarcity of hand- ball, we propose a parsimonious end-to-end deep learning architecture that relies solely on tracking data. This efficient approach is capable of predicting the probability that a team will score within the near future given the fine-grained spatio-temporal distribution of all players and the ball over the last seconds of the game. Our experiments indicate that PIVOT is able to produce accurate and calibrated probability estimates, even when trained on a relatively small dataset. We also showcase two interactive applications of PIVOT for valuing actual and counterfactual player decisions and actions in real-time.}}, author = {{Müller, Oliver and Caron, Matthew and Döring, Michael and Heuwinkel, Tim and Baumeister, Jochen}}, booktitle = {{8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)}}, keywords = {{expected possession value, handball, tracking data, time series classification, deep learning}}, location = {{Online}}, title = {{{PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data}}}, year = {{2021}}, } @inproceedings{48847, abstract = {{Dynamic optimization problems have gained significant attention in evolutionary computation as evolutionary algorithms (EAs) can easily adapt to changing environments. We show that EAs can solve the graph coloring problem for bipartite graphs more efficiently by using dynamic optimization. In our approach the graph instance is given incrementally such that the EA can reoptimize its coloring when a new edge introduces a conflict. We show that, when edges are inserted in a way that preserves graph connectivity, Randomized Local Search (RLS) efficiently finds a proper 2-coloring for all bipartite graphs. This includes graphs for which RLS and other EAs need exponential expected time in a static optimization scenario. We investigate different ways of building up the graph by popular graph traversals such as breadth-first-search and depth-first-search and analyse the resulting runtime behavior. We further show that offspring populations (e. g. a (1 + {$\lambda$}) RLS) lead to an exponential speedup in {$\lambda$}. Finally, an island model using 3 islands succeeds in an optimal time of {$\Theta$}(m) on every m-edge bipartite graph, outperforming offspring populations. This is the first example where an island model guarantees a speedup that is not bounded in the number of islands.}}, author = {{Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-7128-5}}, keywords = {{dynamic optimization, evolutionary algorithms, running time analysis, theory}}, pages = {{1277–1285}}, publisher = {{Association for Computing Machinery}}, title = {{{More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization}}}, doi = {{10.1145/3377930.3390174}}, year = {{2020}}, } @inproceedings{48851, abstract = {{Several important optimization problems in the area of vehicle routing can be seen as variants of the classical Traveling Salesperson Problem (TSP). In the area of evolutionary computation, the Traveling Thief Problem (TTP) has gained increasing interest over the last 5 years. In this paper, we investigate the effect of weights on such problems, in the sense that the cost of traveling increases with respect to the weights of nodes already visited during a tour. This provides abstractions of important TSP variants such as the Traveling Thief Problem and time dependent TSP variants, and allows to study precisely the increase in difficulty caused by weight dependence. We provide a 3.59-approximation for this weight dependent version of TSP with metric distances and bounded positive weights. Furthermore, we conduct experimental investigations for simple randomized local search with classical mutation operators and two variants of the state-of-the-art evolutionary algorithm EAX adapted to the weighted TSP. Our results show the impact of the node weights on the position of the nodes in the resulting tour.}}, author = {{Bossek, Jakob and Casel, Katrin and Kerschke, Pascal and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-7128-5}}, keywords = {{dynamic optimization, evolutionary algorithms, running time analysis, theory}}, pages = {{1286–1294}}, publisher = {{Association for Computing Machinery}}, title = {{{The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics}}}, doi = {{10.1145/3377930.3390243}}, year = {{2020}}, } @article{17156, abstract = {{Business Process Management is a boundary-spanning discipline that aligns operational capabilities and technology to design and manage business processes. The Digital Transformation has enabled human actors, information systems, and smart products to interact with each other via multiple digital channels. The emergence of this hyper-connected world greatly leverages the prospects of business processes – but also boosts their complexity to a new level. We need to discuss how the BPM discipline can find new ways for identifying, analyzing, designing, implementing, executing, and monitoring business processes. In this research note, selected transformative trends are explored and their impact on current theories and IT artifacts in the BPM discipline is discussed to stimulate transformative thinking and prospective research in this field.}}, author = {{Beverungen, Daniel and Buijs, Joos C. A. M. and Becker, Jörg and Di Ciccio, Claudio and van der Aalst, Wil M. P. and Bartelheimer, Christian and vom Brocke, Jan and Comuzzi, Marco and Kraume, Karsten and Leopold, Henrik and Matzner, Martin and Mendling, Jan and Ogonek, Nadine and Post, Till and Resinas, Manuel and Revoredo, Kate and del-Río-Ortega, Adela and La Rosa, Marcello and Santoro, Flávia Maria and Solti, Andreas and Song, Minseok and Stein, Armin and Stierle, Matthias and Wolf, Verena}}, issn = {{2363-7005}}, journal = {{Business & Information Systems Engineering}}, keywords = {{Business process management (BPM), Social computing, Smart devices, Big data analytics, Real-time computing, BPM life-cycle}}, pages = {{145--156}}, publisher = {{SpringerNature}}, title = {{{Seven Paradoxes of Business Process Management in a Hyper-Connected World}}}, doi = {{10.1007/s12599-020-00646-z}}, volume = {{63}}, year = {{2020}}, } @inproceedings{17810, abstract = {{In all fields, the significance of a reliable and accurate predictive model is almost unquantifiable. With deep domain knowledge, models derived from first principles typically outperforms other models in terms of reliability and accuracy. When it may become a cumbersome or an unachievable task to build or validate such models of complex (non-linear) systems, machine learning techniques are employed to build predictive models. However, the accuracy of such techniques is not only dependent on the hyper-parameters of the chosen algorithm, but also on the amount and quality of data. This paper investigates the application of classical time series forecasting approaches for the reliable prognostics of technical systems, where black box machine learning techniques might not successfully be employed given insufficient amount of data and where first principles models are infeasible due to lack of domain specific data. Forecasting by analogy, forecasting by analytical function fitting, an exponential smoothing forecasting method and the long short-term memory (LSTM) are evaluated and compared against the ground truth data. As a case study, the methods are applied to predict future crack lengths of riveted aluminium plates under cyclic loading. The performance of the predictive models is evaluated based on error metrics leading to a proposal of when to apply which forecasting approach.}}, author = {{Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Sextro, Walter}}, booktitle = {{PHM Society European Conference}}, keywords = {{PHM 2019, crack propagation, forecasting, unevenly spaced time series, step ahead prediction, short time series}}, number = {{1}}, title = {{{Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data}}}, volume = {{5}}, year = {{2020}}, } @inproceedings{15488, abstract = {{The continuous refinement of sensor technologies enables the manufacturing industry to capture increasing amounts of data during the production process. As processes take time to complete, sensors register large amounts of time-series-like data for each product. In order to make this data usable, a feature extraction is mandatory. In this work, we discuss and evaluate different network architectures, input pre-processing and cost functions regarding, among other aspects, their suitability for time series of different lengths.}}, author = {{Thiel, Christian and Steidl, Carolin and Henning, Bernd}}, booktitle = {{20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019}}, isbn = {{978-3-9819376-0-2}}, keywords = {{Dynamic Time Warping, Feature Extraction, Masking, Neural Networks}}, title = {{{P2.9 Comparison of deep feature extraction techniques for varying-length time series from an industrial piercing press}}}, doi = {{10.5162/SENSOREN2019/P2.9}}, year = {{2019}}, } @inproceedings{48843, abstract = {{We contribute to the theoretical understanding of randomized search heuristics for dynamic problems. We consider the classical graph coloring problem and investigate the dynamic setting where edges are added to the current graph. We then analyze the expected time for randomized search heuristics to recompute high quality solutions. This includes the (1+1) EA and RLS in a setting where the number of colors is bounded and we are minimizing the number of conflicts as well as iterated local search algorithms that use an unbounded color palette and aim to use the smallest colors and - as a consequence - the smallest number of colors. We identify classes of bipartite graphs where reoptimization is as hard as or even harder than optimization from scratch, i. e. starting with a random initialization. Even adding a single edge can lead to hard symmetry problems. However, graph classes that are hard for one algorithm turn out to be easy for others. In most cases our bounds show that reoptimization is faster than optimizing from scratch. Furthermore, we show how to speed up computations by using problem specific operators concentrating on parts of the graph where changes have occurred.}}, author = {{Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-6111-8}}, keywords = {{dynamic optimization, evolutionary algorithms, running time analysis, theory}}, pages = {{1443–1451}}, publisher = {{Association for Computing Machinery}}, title = {{{Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring}}}, doi = {{10.1145/3321707.3321792}}, year = {{2019}}, } @techreport{46544, author = {{Bünnings, Christian and Schiele, Valentin}}, keywords = {{road accidents, light conditions, daylight saving time}}, publisher = {{RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen}}, title = {{{Spring forward, don’t fall back: The effect of daylight saving time on road safety}}}, volume = {{768}}, year = {{2018}}, } @inproceedings{1156, abstract = {{In this paper, we present an IoT architecture which handles stream sensor data of air pollution. Particle pollution is known as a serious threat to human health. Along with developments in the use of wireless sensors and the IoT, we propose an architecture that flexibly measures and processes stream data collected in real-time by movable and low-cost IoT sensors. Thus, it enables a wide-spread network of wireless sensors that can follow changes in human behavior. Apart from stating reasons for the need of such a development and its requirements, we provide a conceptual design as well as a technological design of such an architecture. The technological design consists of Kaa and Apache Storm which can collect air pollution information in real-time and solve various problems to process data such as missing data and synchronization. This enables us to add a simulation in which we provide issues that might come up when having our architecture in use. Together with these issues, we state r easons for choosing specific modules among candidates. Our architecture combines wireless sensors with the Kaa IoT framework, an Apache Kafka pipeline and an Apache Storm Data Stream Management System among others. We even provide open-government data sets that are freely available.}}, author = {{Kersting, Joschka and Geierhos, Michaela and Jung, Hanmin and Kim, Taehong}}, booktitle = {{Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security}}, editor = {{Ramachandran, Muthu and Méndez Muñoz, Víctor and Kantere, Verena and Wills, Gary and Walters, Robert and Chang, Victor}}, isbn = {{978-989-758-245-5}}, keywords = {{Wireless Sensor Network, Internet of Things, Stream Data, Air Pollution, DSMS, Real-time Data Processing}}, location = {{Porto, Portugal}}, pages = {{117--124}}, publisher = {{SCITEPRESS}}, title = {{{Internet of Things Architecture for Handling Stream Air Pollution Data}}}, doi = {{10.5220/0006354801170124}}, year = {{2017}}, } @inproceedings{20559, author = {{Do, Lisa Nguyen Quang and Ali, Karim and Livshits, Benjamin and Bodden, Eric and Smith, Justin and Murphy-Hill, Emerson}}, booktitle = {{Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis}}, isbn = {{978-1-4503-5076-1}}, keywords = {{Just-in-Time, Layered analysis, Static analysis}}, pages = {{307--317}}, publisher = {{ACM}}, title = {{{Just-in-time Static Analysis}}}, doi = {{10.1145/3092703.3092705}}, year = {{2017}}, } @inproceedings{10676, author = {{Ho, Nam and Kaufmann, Paul and Platzner, Marco}}, booktitle = {{2017 International Conference on Field Programmable Technology (ICFPT)}}, keywords = {{Linux, cache storage, microprocessor chips, multiprocessing systems, LEON3-Linux based multicore processor, MiBench suite, block sizes, cache adaptation, evolvable caches, memory-to-cache-index mapping function, processor caches, reconfigurable cache mapping optimization, reconfigurable hardware technology, replacement strategies, standard Linux OS, time a complete hardware implementation, Hardware, Indexes, Linux, Measurement, Multicore processing, Optimization, Training}}, pages = {{215--218}}, title = {{{Evolvable caches: Optimization of reconfigurable cache mappings for a LEON3/Linux-based multi-core processor}}}, doi = {{10.1109/FPT.2017.8280144}}, year = {{2017}}, } @article{4244, abstract = {{In this work we study the resonant and coherent properties of single InP-based InAs quantum dots, which show an optical emission in the telecom C-band and L-band. High-resolution resonant photocurrent spectroscopy on p–i–n devices reveals narrow linewidths and fully resolved fine structure splittings. We observe Lorentzian line shapes, which allow for the extraction of dephasing times as a function of the applied bias voltage. Coherent ps laser excitation results in pronounced Rabi rotations with increasing pulse area. For π-pulse excitation, we obtain more than 93 % of the theoretically expected photocurrent amplitude. Our results also demonstrate that such state-of-the-art InP-based quantum dots for the telecom band exhibit promising key parameters comparable to well-established InAs/GaAs counterparts.}}, author = {{Gordon, S. and Yacob, M. and Reithmaier, J. P. and Benyoucef, M. and Zrenner, Artur}}, issn = {{0946-2171}}, journal = {{Applied Physics B}}, keywords = {{Bias Voltage, Optical Parametric Oscillator, Molecular Beam Epitaxy Growth, Internal Electric Field, Dephasing Time}}, number = {{2}}, publisher = {{Springer Nature}}, title = {{{Coherent photocurrent spectroscopy of single InP-based quantum dots in the telecom band at 1.5 µm}}}, doi = {{10.1007/s00340-015-6279-6}}, volume = {{122}}, year = {{2016}}, } @article{4246, abstract = {{Spins in semiconductor quantum dots have been considered as prospective quantum bit excitations. Their coupling to the crystal environment manifests itself in a limitation of the spin coherence times to the microsecond range, both for electron and hole spins. This rather short-lived coherence compared to atomic states asks for manipulations on timescales as short as possible. Due to the huge dipole moment for transitions between the valence and conduction band, pulsed laser systems offer the possibility to perform manipulations within picoseconds or even faster. Here, we report on results that show the potential of optical spin manipulations with currently available pulsed laser systems. Using picosecond laser pulses, we demonstrate optically induced spin rotations of electron and hole spins. We further realize the optical decoupling of the hole spins from the nuclear surrounding at the nanosecond timescales and demonstrate an all-optical spin tomography for interacting electron spin sub-ensembles.}}, author = {{Varwig, S. and Evers, E. and Greilich, A. and Yakovlev, D. R. and Reuter, Dirk and Wieck, A. D. and Meier, Torsten and Zrenner, Artur and Bayer, M.}}, issn = {{0946-2171}}, journal = {{Applied Physics B}}, keywords = {{Spin Polarization, Pump Pulse, Trion, Spin Component, Coherence Time}}, number = {{1}}, publisher = {{Springer Nature}}, title = {{{Advanced optical manipulation of carrier spins in (In,Ga)As quantum dots}}}, doi = {{10.1007/s00340-015-6274-y}}, volume = {{122}}, year = {{2016}}, } @inproceedings{10779, author = {{Guettatfi, Zakarya and Kermia, Omar and Khouas, Abdelhakim}}, booktitle = {{25th International Conference on Field Programmable Logic and Applications (FPL)}}, issn = {{1946-147X}}, keywords = {{embedded systems, field programmable gate arrays, operating systems (computers), scheduling, μC/OS-II, FPGAs, OS foundation, SafeRTOS, Xenomai, chip utilization ration, complex time constraints, embedded systems, hard real-time hardware task allocation, hard real-time hardware task scheduling, hardware-software real-time operating systems, partially reconfigurable field-programmable gate arrays, resource constraints, safety-critical RTOS, Field programmable gate arrays, Hardware, Job shop scheduling, Real-time systems, Shape, Software}}, publisher = {{Imperial College}}, title = {{{Over effective hard real-time hardware tasks scheduling and allocation}}}, doi = {{10.1109/FPL.2015.7293994}}, year = {{2015}}, }