@inproceedings{49364, author = {{Scholtysik, Michel and Rohde, Malte and Koldewey, Christian and Dumitrescu, Roman}}, title = {{{Business strategy taxonomy and solution patterns for the circular economy}}}, year = {{2024}}, } @unpublished{51160, abstract = {{We rigorously derive novel and sharp finite-data error bounds for highly sample-efficient Extended Dynamic Mode Decomposition (EDMD) for both i.i.d. and ergodic sampling. In particular, we show all results in a very general setting removing most of the typically imposed assumptions such that, among others, discrete- and continuous-time stochastic processes as well as nonlinear partial differential equations are contained in the considered system class. Besides showing an exponential rate for i.i.d. sampling, we prove, to the best of our knowledge, the first superlinear convergence rates for ergodic sampling of deterministic systems. We verify sharpness of the derived error bounds by conducting numerical simulations for highly-complex applications from molecular dynamics and chaotic flame propagation.}}, author = {{Philipp, Friedrich M. and Schaller, Manuel and Boshoff, Septimus and Peitz, Sebastian and Nüske, Feliks and Worthmann, Karl}}, booktitle = {{arXiv:2402.02494}}, title = {{{Extended Dynamic Mode Decomposition: Sharp bounds on the sample efficiency}}}, year = {{2024}}, } @article{51208, abstract = {{AbstractApproximation of subdifferentials is one of the main tasks when computing descent directions for nonsmooth optimization problems. In this article, we propose a bisection method for weakly lower semismooth functions which is able to compute new subgradients that improve a given approximation in case a direction with insufficient descent was computed. Combined with a recently proposed deterministic gradient sampling approach, this yields a deterministic and provably convergent way to approximate subdifferentials for computing descent directions.}}, author = {{Gebken, Bennet}}, issn = {{0926-6003}}, journal = {{Computational Optimization and Applications}}, keywords = {{Applied Mathematics, Computational Mathematics, Control and Optimization}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{A note on the convergence of deterministic gradient sampling in nonsmooth optimization}}}, doi = {{10.1007/s10589-024-00552-0}}, year = {{2024}}, } @article{46019, abstract = {{We derive efficient algorithms to compute weakly Pareto optimal solutions for smooth, convex and unconstrained multiobjective optimization problems in general Hilbert spaces. To this end, we define a novel inertial gradient-like dynamical system in the multiobjective setting, which trajectories converge weakly to Pareto optimal solutions. Discretization of this system yields an inertial multiobjective algorithm which generates sequences that converge weakly to Pareto optimal solutions. We employ Nesterov acceleration to define an algorithm with an improved convergence rate compared to the plain multiobjective steepest descent method (Algorithm 1). A further improvement in terms of efficiency is achieved by avoiding the solution of a quadratic subproblem to compute a common step direction for all objective functions, which is usually required in first-order methods. Using a different discretization of our inertial gradient-like dynamical system, we obtain an accelerated multiobjective gradient method that does not require the solution of a subproblem in each step (Algorithm 2). While this algorithm does not converge in general, it yields good results on test problems while being faster than standard steepest descent.}}, author = {{Sonntag, Konstantin and Peitz, Sebastian}}, journal = {{Journal of Optimization Theory and Applications}}, publisher = {{Springer}}, title = {{{Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems}}}, doi = {{10.1007/s10957-024-02389-3}}, year = {{2024}}, } @unpublished{51334, abstract = {{The efficient optimization method for locally Lipschitz continuous multiobjective optimization problems from [1] is extended from finite-dimensional problems to general Hilbert spaces. The method iteratively computes Pareto critical points, where in each iteration, an approximation of the subdifferential is computed in an efficient manner and then used to compute a common descent direction for all objective functions. To prove convergence, we present some new optimality results for nonsmooth multiobjective optimization problems in Hilbert spaces. Using these, we can show that every accumulation point of the sequence generated by our algorithm is Pareto critical under common assumptions. Computational efficiency for finding Pareto critical points is numerically demonstrated for multiobjective optimal control of an obstacle problem.}}, author = {{Sonntag, Konstantin and Gebken, Bennet and Müller, Georg and Peitz, Sebastian and Volkwein, Stefan}}, booktitle = {{arXiv:2402.06376}}, title = {{{A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces}}}, year = {{2024}}, } @article{40171, abstract = {{We present a convolutional framework which significantly reduces the complexity and thus, the computational effort for distributed reinforcement learning control of dynamical systems governed by partial differential equations (PDEs). Exploiting translational equivariances, the high-dimensional distributed control problem can be transformed into a multi-agent control problem with many identical, uncoupled agents. Furthermore, using the fact that information is transported with finite velocity in many cases, the dimension of the agents’ environment can be drastically reduced using a convolution operation over the state space of the PDE, by which we effectively tackle the curse of dimensionality otherwise present in deep reinforcement learning. In this setting, the complexity can be flexibly adjusted via the kernel width or by using a stride greater than one (meaning that we do not place an actuator at each sensor location). Moreover, scaling from smaller to larger domains – or the transfer between different domains – becomes a straightforward task requiring little effort. We demonstrate the performance of the proposed framework using several PDE examples with increasing complexity, where stabilization is achieved by training a low-dimensional deep deterministic policy gradient agent using minimal computing resources.}}, author = {{Peitz, Sebastian and Stenner, Jan and Chidananda, Vikas and Wallscheid, Oliver and Brunton, Steven L. and Taira, Kunihiko}}, journal = {{Physica D: Nonlinear Phenomena}}, pages = {{134096}}, publisher = {{Elsevier}}, title = {{{Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning}}}, doi = {{10.1016/j.physd.2024.134096}}, volume = {{461}}, year = {{2024}}, } @misc{52318, author = {{Dorociak, Svitlana}}, title = {{{Implementierung eines Algorithmus zur motivbasierten Schnitt-Sparsifizierung}}}, year = {{2024}}, } @article{33461, abstract = {{Data-driven models for nonlinear dynamical systems based on approximating the underlying Koopman operator or generator have proven to be successful tools for forecasting, feature learning, state estimation, and control. It has become well known that the Koopman generators for control-affine systems also have affine dependence on the input, leading to convenient finite-dimensional bilinear approximations of the dynamics. Yet there are still two main obstacles that limit the scope of current approaches for approximating the Koopman generators of systems with actuation. First, the performance of existing methods depends heavily on the choice of basis functions over which the Koopman generator is to be approximated; and there is currently no universal way to choose them for systems that are not measure preserving. Secondly, if we do not observe the full state, we may not gain access to a sufficiently rich collection of such functions to describe the dynamics. This is because the commonly used method of forming time-delayed observables fails when there is actuation. To remedy these issues, we write the dynamics of observables governed by the Koopman generator as a bilinear hidden Markov model, and determine the model parameters using the expectation-maximization (EM) algorithm. The E-step involves a standard Kalman filter and smoother, while the M-step resembles control-affine dynamic mode decomposition for the generator. We demonstrate the performance of this method on three examples, including recovery of a finite-dimensional Koopman-invariant subspace for an actuated system with a slow manifold; estimation of Koopman eigenfunctions for the unforced Duffing equation; and model-predictive control of a fluidic pinball system based only on noisy observations of lift and drag.}}, author = {{Otto, Samuel E. and Peitz, Sebastian and Rowley, Clarence W.}}, journal = {{SIAM Journal on Applied Dynamical Systems}}, number = {{1}}, pages = {{885--923}}, publisher = {{SIAM}}, title = {{{Learning Bilinear Models of Actuated Koopman Generators from Partially-Observed Trajectories}}}, doi = {{10.1137/22M1523601}}, volume = {{23}}, year = {{2024}}, } @misc{52663, abstract = {{Context Static analyses are well-established to aid in understanding bugs or vulnerabilities during the development process or in large-scale studies. A low false-positive rate is essential for the adaption in practice and for precise results of empirical studies. Unfortunately, static analyses tend to report where a vulnerability manifests rather than the fix location. This can cause presumed false positives or imprecise results. Method To address this problem, we designed an adaption of an existing static analysis algorithm that can distinguish between a manifestation and fix location, and reports error chains. An error chain represents at least two interconnected errors that occur successively, thus building the connection between the fix and manifestation location. We used our tool CogniCryptSUBS for a case study on 471 GitHub repositories, a performance benchmark to compare different analysis configurations, and conducted an expert interview. Result We found that 50 % of the projects with a report had at least one error chain. Our runtime benchmark demonstrated that our improvement caused only a minimal runtime overhead of less than 4 %. The results of our expert interview indicate that with our adapted version participants require fewer executions of the analysis. Conclusion Our results indicate that error chains occur frequently in real-world projects, and ignoring them can lead to imprecise evaluation results. The runtime benchmark indicates that our tool is a feasible and efficient solution for detecting error chains in real-world projects. Further, our results gave a hint that the usability of static analyses may benefit from supporting error chains.}}, author = {{Wickert, Anna-Katharina and Schlichtig, Michael and Vogel, Marvin and Winter, Lukas and Mezini, Mira and Bodden, Eric}}, keywords = {{Static analysis, error chains, false positive re- duction, empirical studies}}, title = {{{Supporting Error Chains in Static Analysis for Precise Evaluation Results and Enhanced Usability}}}, year = {{2024}}, } @unpublished{52691, abstract = {{We prove Feynman-Kac formulas for the semigroups generated by selfadjoint operators in a class containing Fr\"ohlich Hamiltonians known from solid state physics. The latter model multi-polarons, i.e., a fixed number of quantum mechanical electrons moving in a polarizable crystal and interacting with the quantized phonon field generated by the crystal's vibrational modes. Both the electrons and phonons can be confined to suitable open subsets of Euclidean space. We also include possibly very singular magnetic vector potentials and electrostatic potentials. Our Feynman-Kac formulas comprise Fock space operator-valued multiplicative functionals and can be applied to every vector in the underlying Hilbert space. In comparison to the renormalized Nelson model, for which analogous Feynman-Kac formulas are known, the analysis of the creation and annihilation terms in the multiplicative functionals requires novel ideas to overcome difficulties caused by the phonon dispersion relation being constant. Getting these terms under control and generalizing other construction steps so as to cover confined systems are the main achievements of this article.}}, author = {{Hinrichs, Benjamin and Matte, Oliver}}, booktitle = {{arXiv:2403.12147}}, title = {{{Feynman-Kac formulas for semigroups generated by multi-polaron Hamiltonians in magnetic fields and on general domains}}}, year = {{2024}}, } @article{52686, author = {{Ahmed, Qazi Arbab and Wiersema, Tobias and Platzner, Marco}}, issn = {{2509-3428}}, journal = {{Journal of Hardware and Systems Security}}, keywords = {{General Engineering, Energy Engineering and Power Technology}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Post-configuration Activation of Hardware Trojans in FPGAs}}}, doi = {{10.1007/s41635-024-00147-5}}, year = {{2024}}, } @inproceedings{52379, author = {{Hüsing, Sven and Schulte, Carsten and Sparmann, Sören and Bolte, Mario}}, booktitle = {{Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1}}, publisher = {{ACM}}, title = {{{Using Worked Examples for Engaging in Epistemic Programming Projects}}}, doi = {{10.1145/3626252.3630961}}, year = {{2024}}, } @article{52726, abstract = {{Heteroclinic structures organize global features of dynamical systems. We analyse whether heteroclinic structures can arise in network dynamics with higher-order interactions which describe the nonlinear interactions between three or more units. We find that while commonly analysed model equations such as network dynamics on undirected hypergraphs may be useful to describe local dynamics such as cluster synchronization, they give rise to obstructions that allow to design of heteroclinic structures in phase space. By contrast, directed hypergraphs break the homogeneity and lead to vector fields that support heteroclinic structures.}}, author = {{Bick, Christian and von der Gracht, Sören}}, issn = {{2051-1329}}, journal = {{Journal of Complex Networks}}, keywords = {{Applied Mathematics, Computational Mathematics, Control and Optimization, Management Science and Operations Research, Computer Networks and Communications}}, number = {{2}}, publisher = {{Oxford University Press (OUP)}}, title = {{{Heteroclinic dynamics in network dynamical systems with higher-order interactions}}}, doi = {{10.1093/comnet/cnae009}}, volume = {{12}}, year = {{2024}}, } @article{49905, abstract = {{For 0 ≤ t ≤ r let m(t, r) be the maximum number s such that every t-edge-connected r-graph has s pairwise disjoint perfect matchings. There are only a few values of m(t, r) known, for instance m(3, 3) = m(4, r) = 1, and m(t, r) ≤ r − 2 for all t  = 5, and m(t, r) ≤ r − 3 if r is even. We prove that m(2l, r) ≤ 3l − 6 for every l ≥ 3 and r ≥ 2l.}}, author = {{Ma, Yulai and Mattiolo, Davide and Steffen, Eckhard and Wolf, Isaak Hieronymus}}, issn = {{0209-9683}}, journal = {{Combinatorica}}, keywords = {{Computational Mathematics, Discrete Mathematics and Combinatorics}}, pages = {{429--440}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Edge-Connectivity and Pairwise Disjoint Perfect Matchings in Regular Graphs}}}, doi = {{10.1007/s00493-023-00078-9}}, volume = {{44}}, year = {{2024}}, } @inproceedings{52744, author = {{Jafarzadeh, Hanieh and Klemme, Florian and Amrouch, Hussam and Hellebrand, Sybille and Wunderlich, Hans-Joachim}}, booktitle = {{European Test Symposium, The Hague, Netherlands, May 20-24, 2024}}, location = {{The Hague, NL}}, pages = {{6}}, publisher = {{IEEE}}, title = {{{Time and Space Optimized Storage-based BIST under Multiple Voltages and Variations}}}, year = {{2024}}, } @inproceedings{52742, author = {{Jafarzadeh, Hanieh and Klemme, Florian and Amrouch, Hussam and Hellebrand, Sybille and Wunderlich, Hans-Joachim}}, booktitle = {{IEEE Latin American Test Symposium (LATS), Maceió, Brazil, April 9-12, 2024}}, location = {{Maceió}}, pages = {{6}}, publisher = {{IEEE}}, title = {{{Vmin Testing under Variations: Defect vs. Fault Coverage}}}, year = {{2024}}, } @inproceedings{52743, author = {{Hellebrand, Sybille and Sadeghi-Kohan, Somayeh and Wunderlich, Hans-Joachim}}, booktitle = {{International Symposium of EDA (ISEDA), Xi'an, China, May 10-13, 2024}}, location = {{Xi'an, China}}, pages = {{1}}, title = {{{Functional Safety and Reliability of Interconnects throughout the Silicon Life Cycle}}}, year = {{2024}}, } @inproceedings{52745, author = {{Wunderlich, Hans-Joachim and Jafarzadeh, Hanieh and Hellebrand, Sybille}}, booktitle = {{International Symposium of EDA (ISEDA), Xi’an, China, May 10-13, 2024}}, location = {{Xi’an, China}}, pages = {{1}}, title = {{{Robust Test of Small Delay Faults under PVT-Variations}}}, year = {{2024}}, } @misc{50284, author = {{Stiballe, Alisa and Reimer, Jan Dennis and Sadeghi-Kohan, Somayeh and Hellebrand, Sybille}}, publisher = {{37. ITG / GMM / GI -Workshop "Testmethoden und Zuverlässigkeit von Schaltungen und Systemen" (TuZ'24), Feb. 2024}}, title = {{{Modeling Crosstalk-induced Interconnect Delay with Polynomial Regression}}}, year = {{2024}}, } @inproceedings{52827, author = {{Hu, Lijie and Habernal, Ivan and Shen, Lei and Wang, Di}}, booktitle = {{Findings of the Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta, March 17-22, 2024}}, editor = {{Graham, Yvette and Purver, Matthew}}, pages = {{478–499}}, publisher = {{Association for Computational Linguistics}}, title = {{{Differentially Private Natural Language Models: Recent Advances and Future Directions}}}, year = {{2024}}, }