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Information Directed Sampling and Bandits with Heteroscedastic NoiseJan 29 2018Apr 19 2018In the stochastic bandit problem, the goal is to maximize an unknown function via a sequence of noisy evaluations. Typically, the observation noise is assumed to be independent of the evaluation point and to satisfy a tail bound uniformly on the domain; ... More

Safe Exploration in Finite Markov Decision Processes with Gaussian ProcessesJun 15 2016Nov 15 2016In classical reinforcement learning, when exploring an environment, agents accept arbitrary short term loss for long term gain. This is infeasible for safety critical applications, such as robotics, where even a single unsafe action may cause system failure. ... More

Unsupervised Imitation LearningJun 19 2018We introduce a novel method to learn a policy from unsupervised demonstrations of a process. Given a model of the system and a set of sequences of outputs, we find a policy that has a comparable performance to the original policy, without requiring access ... More

Differentiable Submodular MaximizationMar 05 2018Jun 14 2018We consider learning of submodular functions from data. These functions are important in machine learning and have a wide range of applications, e.g. data summarization, feature selection and active learning. Despite their combinatorial nature, submodular ... More

Evaluating the performance of adapting trading strategies with different memory lengthsJan 05 2009We propose a prediction model based on the minority game in which traders continuously evaluate a complete set of trading strategies with different memory lengths using the strategies' past performance. Based on the chosen trading strategy they determine ... More

Galactic winds - How to launch galactic outflows in typical Lyman-break galaxiesJun 13 2013We perform hydrodynamical simulations of a young galactic disc embedded in a hot gaseous halo using parameters typical for Lyman break galaxies (LBGs). We take into account the (static) gravitational potentials due to a dark matter halo, a stellar bulge ... More

Microstructure Effects on Daily Return Volatility in Financial MarketsNov 17 2000We simulate a series of daily returns from intraday price movements initiated by microstructure elements. Significant evidence is found that daily returns and daily return volatility exhibit first order autocorrelation, but trading volume and daily return ... More

Learning Sparse Additive Models with Interactions in High DimensionsApr 18 2016A function $f: \mathbb{R}^d \rightarrow \mathbb{R}$ is referred to as a Sparse Additive Model (SPAM), if it is of the form $f(\mathbf{x}) = \sum_{l \in \mathcal{S}}\phi_{l}(x_l)$, where $\mathcal{S} \subset [d]$, $|\mathcal{S}| \ll d$. Assuming $\phi_l$'s ... More

Time parallel gravitational collapse simulationSep 04 2015Apr 24 2016This article demonstrates the applicability of the parallel-in-time method Parareal to the numerical solution of the Einstein gravity equations for the spherical collapse of a massless scalar field. To account for the shrinking of the spatial domain in ... More

Tradeoffs for Space, Time, Data and Risk in Unsupervised LearningMay 02 2016Faced with massive data, is it possible to trade off (statistical) risk, and (computational) space and time? This challenge lies at the heart of large-scale machine learning. Using k-means clustering as a prototypical unsupervised learning problem, we ... More

A Moral Framework for Understanding of Fair ML through Economic Models of Equality of OpportunitySep 10 2018Nov 27 2018We map the recently proposed notions of algorithmic fairness to economic models of Equality of opportunity (EOP)---an extensively studied ideal of fairness in political philosophy. We formally show that through our conceptual mapping, many existing definition ... More

Dimensions of triangulated categories via Koszul objectsFeb 07 2008Apr 15 2009Lower bounds for the dimension of a triangulated category are provided. These bounds are applied to stable derived categories of Artin algebras and of commutative complete intersection local rings. As a consequence, one obtains bounds for the representation ... More

Algorithms for Learning Sparse Additive Models with Interactions in High DimensionsMay 02 2016May 08 2017A function $f: \mathbb{R}^d \rightarrow \mathbb{R}$ is a Sparse Additive Model (SPAM), if it is of the form $f(\mathbf{x}) = \sum_{l \in \mathcal{S}}\phi_{l}(x_l)$ where $\mathcal{S} \subset [d]$, $|\mathcal{S}| \ll d$. Assuming $\phi$'s, $\mathcal{S}$ ... More

Adaptive Sequence SubmodularityFeb 15 2019In many machine learning applications, one needs to interactively select a sequence of items (e.g., recommending movies based on a user's feedback) or make sequential decisions in certain orders (e.g., guiding an agent through a series of states). Not ... More

Parareal convergence for 2D unsteady flow around a cylinderSep 14 2015In this technical report we study the convergence of Parareal for 2D incompressible flow around a cylinder for different viscosities. Two methods are used as fine integrator: backward Euler and a fractional step method. It is found that Parareal converges ... More

Efficient Minimization of Decomposable Submodular FunctionsOct 26 2010Many combinatorial problems arising in machine learning can be reduced to the problem of minimizing a submodular function. Submodular functions are a natural discrete analog of convex functions, and can be minimized in strongly polynomial time. Unfortunately, ... More

Optimal Value of Information in Graphical ModelsJan 15 2014Many real-world decision making tasks require us to choose among several expensive observations. In a sensor network, for example, it is important to select the subset of sensors that is expected to provide the strongest reduction in uncertainty. In medical ... More

Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic OptimizationMar 21 2010Oct 17 2012Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with uncertain outcomes, is a fundamental but notoriously difficult challenge. In this paper, we introduce the concept of adaptive submodularity, ... More

Adaptive Submodular Optimization under Matroid ConstraintsJan 24 2011Many important problems in discrete optimization require maximization of a monotonic submodular function subject to matroid constraints. For these problems, a simple greedy algorithm is guaranteed to obtain near-optimal solutions. In this article, we ... More

Crowd Access Path Optimization: Diversity MattersAug 08 2015Aug 11 2015Quality assurance is one the most important challenges in crowdsourcing. Assigning tasks to several workers to increase quality through redundant answers can be expensive if asking homogeneous sources. This limitation has been overlooked by current crowdsourcing ... More

Kinematics of massive star ejecta in the Milky Way as traced by 26AlSep 19 2013Massive stars form in groups and their winds and supernova explosions create superbubbles up to kpc in size. Their ejecta are important for the dynamics of the interstellar medium and chemical evolution models. However, ejecta kinematics and the characteristic ... More

Incentives for Privacy Tradeoff in Community SensingAug 19 2013Sep 14 2013Community sensing, fusing information from populations of privately-held sensors, presents a great opportunity to create efficient and cost-effective sensing applications. Yet, reasonable privacy concerns often limit the access to such data streams. How ... More

ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical SystemsFeb 17 2019Parameter inference in ordinary differential equations is an important problem in many applied sciences and in engineering, especially in a data-scarce setting. In this work, we introduce a novel generative modeling approach based on constrained Gaussian ... More

Scalable Variational Inference in Log-supermodular ModelsFeb 23 2015Feb 24 2015We consider the problem of approximate Bayesian inference in log-supermodular models. These models encompass regular pairwise MRFs with binary variables, but allow to capture high-order interactions, which are intractable for existing approximate inference ... More

A Utility-Theoretic Approach to Privacy in Online ServicesJan 16 2014Online offerings such as web search, news portals, and e-commerce applications face the challenge of providing high-quality service to a large, heterogeneous user base. Recent efforts have highlighted the potential to improve performance by introducing ... More

MOTS: Multi-Object Tracking and SegmentationFeb 10 2019This paper extends the popular task of multi-object tracking to multi-object tracking and segmentation (MOTS). Towards this goal, we create dense pixel-level annotations for two existing tracking datasets using a semi-automatic annotation procedure. Our ... More

Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEsApr 12 2018Parameter identification and comparison of dynamical systems is a challenging task in many fields. Bayesian approaches based on Gaussian process regression over time-series data have been successfully applied to infer the parameters of a dynamical system ... More

Auslander-Reiten duality for Grothendieck abelian categoriesApr 11 2016Auslander-Reiten duality for module categories is generalised to Grothendieck abelian categories that have a sufficient supply of finitely presented objects. It is shown that Auslander-Reiten duality amounts to the fact that the functor Ext^1(C,-) into ... More

Stellar feedback efficiencies: supernovae versus stellar windsNov 16 2015Nov 18 2015Stellar winds and supernova (SN) explosions of massive stars ("stellar feedback") create bubbles in the interstellar medium (ISM) and insert newly produced heavy elements and kinetic energy into their surroundings, possibly driving turbulence. Most of ... More

Near-optimal Nonmyopic Value of Information in Graphical ModelsJul 04 2012A fundamental issue in real-world systems, such as sensor networks, is the selection of observations which most effectively reduce uncertainty. More specifically, we address the long standing problem of nonmyopically selecting the most informative subset ... More

Deriving Auslander's formulaSep 24 2014Jun 13 2015Auslander's formula shows that any abelian category C is equivalent to the category of coherent functors on C modulo the Serre subcategory of all effaceable functors. We establish a derived version of this equivalence. This amounts to showing that the ... More

Asymptotic Dynamics of Stochastic $p$-Laplace Equations on Unbounded DomainsAug 03 2014This thesis is concerned with the asymptotic behavior of solutions of stochastic $p$-Laplace equations driven by non-autonomous forcing on $\mathbb{R}^n$. Two cases are studied, with additive and multiplicative noise respectively. Estimates on the tails ... More

Towards Dark Energy from String-TheoryDec 31 2007Mar 12 2008We discuss vacuum energy in string and M-theory with a focus on heterotic M-theory. In the latter theory a mechanism is described for maintaining zero vacuum energy after supersymmetry breaking. Higher-order corrections can be expected to give a sufficiently ... More

Large Gravitational Waves and Lyth Bound in Multi Brane InflationAug 31 2007Sep 30 2008It is shown that multi M5-brane inflation in heterotic M-theory gives rise to a detectable gravitational wave power spectrum with tensor fraction $r$ typically larger than the projected experimental sensitivity, $r_{exp} = 0.01$. A measurable gravitational ... More

Supersymmetry Breaking with Zero Vacuum Energy in M-Theory Flux CompactificationsDec 31 2006Sep 13 2007An attractive mechanism to break supersymmetry in vacua with zero vacuum energy arose in E_8 x E_8 heterotic models with hidden sector gaugino condensate. An H-flux balances the exponentially small condensate on shell and fixes the complex structure moduli. ... More

Black Holes, Space-Filling Chains and Random WalksDec 30 2003Jul 30 2006Many approaches to a semiclassical description of gravity lead to an integer black hole entropy. In four dimensions this implies that the Schwarzschild radius obeys a formula which describes the distance covered by a Brownian random walk. For the higher-dimensional ... More

Schwarzschild Black Holes from Brane-Antibrane PairsApr 24 2002May 07 2002We show that D=4 Schwarzschild black holes can arise from a doublet of Euclidean D3-antiD3 pairs embedded in D=10 Lorentzian spacetime. By starting from a D=10 type IIB supergravity description for the D3-antiD3 pairs and wrapping one of them over an ... More

Dual Brane Pairs, Chains and the Bekenstein-Hawking EntropyJan 30 2002Oct 28 2005A proposal towards a microscopic understanding of the Bekenstein-Hawking entropy for D=4 spacetimes with event horizon is made. Since we will not rely on supersymmetry these spacetimes need not be supersymmetric. Euclidean D-branes which wrap the event ... More

A Small Cosmological Constant, Grand Unification and Warped GeometryJun 29 2000May 28 2006We explore a mechanism to obtain the observational small value for the 4-dimensional vacuum energy through an exponential warp-factor suppression. Intriguingly the required suppression scale relates directly to the GUT scale. We demonstrate the mechanism ... More

The stable derived category of a noetherian schemeMar 30 2004Sep 27 2004For a noetherian scheme, we introduce its unbounded stable derived category. This leads to a recollement which reflects the passage from the bounded derived category of coherent sheaves to the quotient modulo the subcategory of perfect complexes. Some ... More

Radiation feedback on dusty clouds during Seyfert activityMar 22 2011We investigate the evolution of dusty gas clouds falling into the centre of an active Seyfert nucleus. Two-dimensional high-resolution radiation hydrodynamics simulations are performed to study the fate of single clouds and the interaction between two ... More

Safe Controller Optimization for Quadrotors with Gaussian ProcessesSep 03 2015Apr 01 2016One of the most fundamental problems when designing controllers for dynamic systems is the tuning of the controller parameters. Typically, a model of the system is used to obtain an initial controller, but ultimately the controller parameters must be ... More

Online Learning of Assignments that Maximize Submodular FunctionsAug 05 2009Which ads should we display in sponsored search in order to maximize our revenue? How should we dynamically rank information sources to maximize value of information? These applications exhibit strong diminishing returns: Selection of redundant ads and ... More

No-regret Bayesian Optimization with Unknown HyperparametersJan 10 2019Bayesian optimization (BO) based on Gaussian process models is a powerful paradigm to optimize black-box functions that are expensive to evaluate. While several BO algorithms provably converge to the global optimum of the unknown function, they assume ... More

Safe Exploration in Finite Markov Decision Processes with Gaussian ProcessesJun 15 2016In classical reinforcement learning, when exploring an environment, agents accept arbitrary short term loss for long term gain. This is infeasible for safety critical applications, such as robotics, where even a single unsafe action may cause system failure. ... More

Online Submodular Maximization under a Matroid Constraint with Application to Learning AssignmentsJul 03 2014Which ads should we display in sponsored search in order to maximize our revenue? How should we dynamically rank information sources to maximize the value of the ranking? These applications exhibit strong diminishing returns: Redundancy decreases the ... More

Near-optimal Bayesian Active Learning with Correlated and Noisy TestsMay 24 2016Jul 11 2016We consider the Bayesian active learning and experimental design problem, where the goal is to learn the value of some unknown target variable through a sequence of informative, noisy tests. In contrast to prior work, we focus on the challenging, yet ... More

Linear-time Outlier Detection via SensitivityMay 02 2016Outliers are ubiquitous in modern data sets. Distance-based techniques are a popular non-parametric approach to outlier detection as they require no prior assumptions on the data generating distribution and are simple to implement. Scaling these techniques ... More

Observation of strontium segregation in LaAlO$_{3}$/SrTiO$_{3}$ and NdGaO$_{3}$/SrTiO$_{3}$ oxide heterostructures by X-ray photoemission spectroscopyJan 31 2014LaAlO$_{3}$ and NdGaO$_{3}$ thin films of different thickness have been grown by pulsed laser deposition on TiO$_2$-terminated SrTiO$_{3}$ single crystals and investigated by soft X-ray photoemission spectroscopy. The surface sensitivity of the measurements ... More

Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine LearningFeb 13 2019Fairness for Machine Learning has received considerable attention, recently. Various mathematical formulations of fairness have been proposed, and it has been shown that it is impossible to satisfy all of them simultaneously. The literature so far has ... More

Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit OptimizationJun 27 2012Can one parallelize complex exploration exploitation tradeoffs? As an example, consider the problem of optimal high-throughput experimental design, where we wish to sequentially design batches of experiments in order to simultaneously learn a surrogate ... More

Stability of Cloud Orbits in the Broad Line Region of Active Galactic NucleiJul 01 2010Oct 11 2010We investigate the global dynamic stability of spherical clouds in the Broad Line Region (BLR) of Active Galactic Nuclei (AGN), exposed to radial radiation pressure, gravity of the central black hole (BH), and centrifugal forces assuming the clouds adapt ... More

Joint Optimization and Variable Selection of High-dimensional Gaussian ProcessesJun 27 2012Maximizing high-dimensional, non-convex functions through noisy observations is a notoriously hard problem, but one that arises in many applications. In this paper, we tackle this challenge by modeling the unknown function as a sample from a high-dimensional ... More

Inferring Networks of Diffusion and InfluenceJun 01 2010Oct 23 2011Information diffusion and virus propagation are fundamental processes taking place in networks. While it is often possible to directly observe when nodes become infected with a virus or adopt the information, observing individual transmissions (i.e., ... More

Magnetohydrodynamic stability of broad line region cloudsJul 03 2012Aug 16 2012Hydrodynamic stability has been a longstanding issue for the cloud model of the broad line region in active galactic nuclei. We argue that the clouds may be gravitationally bound to the supermassive black hole. If true, stabilisation by thermal pressure ... More

Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection SummarizationNov 23 2015Dec 01 2015We address the problem of maximizing an unknown submodular function that can only be accessed via noisy evaluations. Our work is motivated by the task of summarizing content, e.g., image collections, by leveraging users' feedback in form of clicks or ... More

Actively Learning Hemimetrics with Applications to Eliciting User PreferencesMay 23 2016May 27 2016Motivated by an application of eliciting users' preferences, we investigate the problem of learning hemimetrics, i.e., pairwise distances among a set of $n$ items that satisfy triangle inequalities and non-negativity constraints. In our application, the ... More

Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in RoboticsFeb 14 2016Robotics algorithms typically depend on various parameters, the choice of which significantly affects the robot's performance. While an initial guess for the parameters may be obtained from dynamic models of the robot, parameters are usually tuned manually ... More

Observational constraints on the formation and evolution of the Milky Way nuclear star cluster with Keck and GeminiOct 10 2016Due to its proximity, the Milky Way nuclear star cluster provides us with a wealth of data not available in other galactic nuclei. In particular, with adaptive optics, we can observe the detailed properties of individual stars, which can offer insight ... More

Near-Optimal Bayesian Active Learning with Noisy ObservationsOct 15 2010Dec 16 2013We tackle the fundamental problem of Bayesian active learning with noise, where we need to adaptively select from a number of expensive tests in order to identify an unknown hypothesis sampled from a known prior distribution. In the case of noise-free ... More

Online Distributed Sensor SelectionFeb 09 2010May 13 2010A key problem in sensor networks is to decide which sensors to query when, in order to obtain the most useful information (e.g., for performing accurate prediction), subject to constraints (e.g., on power and bandwidth). In many applications the utility ... More

Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family MixturesAug 21 2015May 02 2016Coresets are efficient representations of data sets such that models trained on the coreset are provably competitive with models trained on the original data set. As such, they have been successfully used to scale up clustering models such as K-Means ... More

A stencil-based implementation of Parareal in the C++ domain specific embedded language STELLASep 30 2014Dec 03 2014In view of the rapid rise of the number of cores in modern supercomputers, time-parallel methods that introduce concurrency along the temporal axis are becoming increasingly popular. For the solution of time-dependent partial differential equations, these ... More

Linear versus set valued Kronecker representationsJun 06 2016A set valued representation of the Kronecker quiver is nothing but a quiver. We apply the forgetful functor from vector spaces to sets and compare linear with set valued representations of the Kronecker quiver.

Acyclicity versus total acyclicity for complexes over noetherian ringsJun 14 2005Jun 13 2006It is proved that for a commutative noetherian ring with dualizing complex the homotopy category of projective modules is equivalent, as a triangulated category, to the homotopy category of injective modules. Restricted to compact objects, this statement ... More

Safe Learning of Regions of Attraction for Uncertain, Nonlinear Systems with Gaussian ProcessesMar 15 2016Oct 05 2016Control theory can provide useful insights into the properties of controlled, dynamic systems. One important property of nonlinear systems is the region of attraction (ROA), a safe subset of the state space in which a given controller renders an equilibrium ... More

Time parallel gravitational collapse simulationSep 04 2015Dec 28 2016This article demonstrates the applicability of the parallel-in-time method Parareal to the numerical solution of the Einstein gravity equations for the spherical collapse of a massless scalar field. To account for the shrinking of the spatial domain in ... More

Information-Directed Exploration for Deep Reinforcement LearningDec 18 2018Efficient exploration remains a major challenge for reinforcement learning. One reason is that the variability of the returns often depends on the current state and action, and is therefore heteroscedastic. Classical exploration strategies such as upper ... More

Radiatively enhanced elasticity and turbulence in clumpy tori of Active Galactic NucleiNov 25 2009Jul 01 2010The paper assumes radiation forces proportional to distance between equal temperature clouds. However, we assume there are clouds in any direction. The forces then cancel almost entirely, besides small velocity effects. Therefore, the presented theory ... More

Near-Optimally Teaching the Crowd to ClassifyFeb 10 2014Mar 07 2014How should we present training examples to learners to teach them classification rules? This is a natural problem when training workers for crowdsourcing labeling tasks, and is also motivated by challenges in data-driven online education. We propose a ... More

Horizontally Scalable Submodular MaximizationMay 31 2016A variety of large-scale machine learning problems can be cast as instances of constrained submodular maximization. Existing approaches for distributed submodular maximization have a critical drawback: The capacity - number of instances that can fit in ... More

Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional SubspacesFeb 08 2019Bayesian optimization is known to be difficult to scale to high dimensions, because the acquisition step requires solving a non-convex optimization problem in the same search space. In order to scale the method and keep its benefits, we propose an algorithm ... More

Distributed Submodular MaximizationNov 03 2014Jun 27 2016Many large-scale machine learning problems--clustering, non-parametric learning, kernel machines, etc.--require selecting a small yet representative subset from a large dataset. Such problems can often be reduced to maximizing a submodular set function ... More

Learning to Hire TeamsAug 12 2015Crowdsourcing and human computation has been employed in increasingly sophisticated projects that require the solution of a heterogeneous set of tasks. We explore the challenge of building or hiring an effective team, for performing tasks required for ... More

Information Gathering in Networks via Active ExplorationApr 24 2015May 06 2015How should we gather information in a network, where each node's visibility is limited to its local neighborhood? This problem arises in numerous real-world applications, such as surveying and task routing in social networks, team formation in collaborative ... More

Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental DesignDec 21 2009Jun 09 2010Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multi-armed bandit problem, where the payoff function is either sampled from a Gaussian process (GP) or has low RKHS norm. We resolve ... More

Efficient Informative Sensing using Multiple RobotsJan 15 2014The need for efficient monitoring of spatio-temporal dynamics in large environmental applications, such as the water quality monitoring in rivers and lakes, motivates the use of robotic sensors in order to achieve sufficient spatial coverage. Typically, ... More

Guaranteed Non-convex Optimization: Submodular Maximization over Continuous DomainsJun 17 2016Sep 20 2016Submodular continuous functions are a category of (generally) non-convex/non-concave functions with a wide spectrum of applications. We characterize these functions and demonstrate that they can be maximized efficiently with approximation guarantees. ... More

Algorithms for Learning Sparse Additive Models with Interactions in High DimensionsMay 02 2016A function $f: \mathbb{R}^d \rightarrow \mathbb{R}$ is a Sparse Additive Model (SPAM), if it is of the form $f(\mathbf{x}) = \sum_{l \in \mathcal{S}}\phi_{l}(x_l)$ where $\mathcal{S} \subset [d]$, $|\mathcal{S}| \ll d$. Assuming $\phi$'s, $\mathcal{S}$ ... More

Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set EstimationOct 24 2016We present a new algorithm, truncated variance reduction (TruVaR), that treats Bayesian optimization (BO) and level-set estimation (LSE) with Gaussian processes in a unified fashion. The algorithm greedily shrinks a sum of truncated variances within a ... More

Lazier Than Lazy GreedySep 28 2014Nov 28 2014Is it possible to maximize a monotone submodular function faster than the widely used lazy greedy algorithm (also known as accelerated greedy), both in theory and practice? In this paper, we develop the first linear-time algorithm for maximizing a general ... More

Building Hierarchies of Concepts via CrowdsourcingApr 27 2015Aug 01 2015Hierarchies of concepts are useful in many applications from navigation to organization of objects. Usually, a hierarchy is created in a centralized manner by employing a group of domain experts, a time-consuming and expensive process. The experts often ... More

Bond and site color-avoiding percolation in scale free networksJul 23 2018Sep 12 2018Recently the problem of classes of vulnerable vertices (represented by colors) in complex networks has been discussed, where all vertices with the same vulnerability are prone to fail together. Utilizing redundant paths each avoiding one vulnerability ... More

A lower bound on the tree-width of graphs with irrelevant verticesJan 14 2019For their famous algorithm for the disjoint paths problem, Robertson and Seymour proved that there is a function $f$ such that if the tree-width of a graph $G$ with $k$ pairs of terminals is at least $f(k)$, then $G$ contains a solution-irrelevant vertex ... More

Discovering Valuable Items from Massive DataJun 02 2015Suppose there is a large collection of items, each with an associated cost and an inherent utility that is revealed only once we commit to selecting it. Given a budget on the cumulative cost of the selected items, how can we pick a subset of maximal value? ... More

Improved Calibration of Instruments for Small Direct CurrentsMar 23 2016We report on new calibration methods for picoammeters and low-current sources. The "Ultrastable Low-noise Current Amplifier" (ULCA) was used for the exemplary calibration of commercial state-of-the-art ammeter and current source instruments in the current ... More

Representations of finite groups: Local cohomology and supportJul 24 2011These are the notes from an Oberwolfach Seminar which we ran from 23--29 May 2010.

Module categories for finite group algebrasFeb 13 2011This survey article is intended as an introduction to the recent categorical classification theorems of the three authors, restricting to the special case of the category of modules for a finite group.

A local-global principle for small triangulated categoriesMay 07 2013Jul 27 2014Local cohomology functors are constructed for the category of cohomological functors on an essentially small triangulated category T equipped with an action of a commutative noetherian ring. This is used to establish a local-global principle and to develop ... More

Parallel-in-Space-and-Time Simulation of the Three-Dimensional, Unsteady Navier-Stokes Equations for Incompressible FlowMay 17 2017In this paper we combine the Parareal parallel-in-time method together with spatial parallelization and investigate this space-time parallel scheme by means of solving the three-dimensional incompressible Navier-Stokes equations. Parallelization of time ... More

Quantum Logical Structures For Identical ParticlesMay 22 2013In this work we discuss logical structures related to indistinguishable particles. Most of the framework used to develop these structures was presented in [17, 28] and in [20, 14, 15, 16]. We use these structures and constructions to discuss possible ... More

Colocalizing subcategories and cosupportAug 22 2010Feb 12 2011The Hom closed colocalizing subcategories of the stable module category of a finite group are classified. Along the way, the colocalizing subcategories of the homotopy category of injectives over an exterior algebra, and the derived category of a formal ... More

Stratifying modular representations of finite groupsOct 08 2008Apr 14 2011We classify localising subcategories of the stable module category of a finite group that are closed under tensor product with simple (or, equivalently all) modules. One application is a proof of the telescope conjecture in this context. Others include ... More

On Convergence of Modulated Ergodic Hilbert TransformsOct 17 2016Let $p(t)$ be a Hardy field function which grows "super-linearly" and stays "sufficiently far" from polynomials. We show that for each measure-preserving system, $(X,\Sigma,\mu,\tau)$, with $\tau$ a measure-preserving $\mathbb{Z}$-action, the modulated ... More

An Iterative Approach for Time Integration Based on Discontinuous Galerkin MethodsOct 05 2016We present a new class of iterative schemes for solving initial value problems (IVP) based on discontinuous Galerkin (DG) methods. Starting from the weak DG formulation of an IVP, we derive a new iterative method based on a preconditioned Picard iteration. ... More

Service Choreography, SBVR, and TimeDec 24 2015We propose the use of structured natural language (English) in specifying service choreographies, focusing on the what rather than the how of the required coordination of participant services in realising a business application scenario. The declarative ... More

The impact of intrinsic alignment on current and future cosmic shear surveysJun 29 2015Intrinsic alignment (IA) of source galaxies is one of the major astrophysical systematics for ongoing and future weak lensing surveys. This paper presents the first forecasts of the impact of IA on cosmic shear measurements for current and future surveys ... More

Signals of the electroweak phase transition at colliders and gravitational wave observatoriesFeb 06 2018We explore new-physics setups that at low energy exhibit a Higgs potential with sizeable higher-dimensional operators. We focus on the parameter regions promoting a first-order electroweak phase transition (FOEWPT). For weakly-interacting setups, we find ... More

Dimension-Free $L^p$-Maximal Inequalities in $\mathbb{Z}_{m+1}^N$Jun 27 2014Nov 29 2014For $m \geq 2$, let $(\mathbb{Z}_{m+1}^N, |\cdot|)$ denote the group equipped with the so-called $l^0$ metric, \[ |y| = \left| \big( y(1), \dots, y(N) \big) \right| := | \{1 \leq i \leq N : y(i) \neq 0 \} |,\] and define the $L^1$-normalized indicator ... More

Magnetic interaction of jets and molecular clouds in NGC 4258Mar 13 2007NGC 4258 is a well known spiral galaxy with a peculiar large scale jet flow detected in the radio and in H-alpha. Due to the special geometry of the galaxy, the jets emerge from the nuclear region through the galactic disk. Also the distribution of molecular ... More