@unpublished{50273, abstract = {{The Polynomial-Time Hierarchy ($\mathsf{PH}$) is a staple of classical complexity theory, with applications spanning randomized computation to circuit lower bounds to ''quantum advantage'' analyses for near-term quantum computers. Quantumly, however, despite the fact that at least \emph{four} definitions of quantum $\mathsf{PH}$ exist, it has been challenging to prove analogues for these of even basic facts from $\mathsf{PH}$. This work studies three quantum-verifier based generalizations of $\mathsf{PH}$, two of which are from [Gharibian, Santha, Sikora, Sundaram, Yirka, 2022] and use classical strings ($\mathsf{QCPH}$) and quantum mixed states ($\mathsf{QPH}$) as proofs, and one of which is new to this work, utilizing quantum pure states ($\mathsf{pureQPH}$) as proofs. We first resolve several open problems from [GSSSY22], including a collapse theorem and a Karp-Lipton theorem for $\mathsf{QCPH}$. Then, for our new class $\mathsf{pureQPH}$, we show one-sided error reduction for $\mathsf{pureQPH}$, as well as the first bounds relating these quantum variants of $\mathsf{PH}$, namely $\mathsf{QCPH}\subseteq \mathsf{pureQPH} \subseteq \mathsf{EXP}^{\mathsf{PP}}$.}}, author = {{Agarwal, Avantika and Gharibian, Sevag and Koppula, Venkata and Rudolph, Dorian}}, booktitle = {{arXiv:2401.01633}}, title = {{{Quantum Polynomial Hierarchies: Karp-Lipton, error reduction, and lower bounds}}}, year = {{2024}}, } @article{36466, author = {{Becker, Rieke}}, journal = {{Österreichische Zeitschrift für Geschichtswissenschaften. Themenband „New Diplomatic History“}}, keywords = {{New Diplomatic History, Neue Diplomatiegeschichte}}, number = {{1}}, title = {{{Hilfst du mir, so hilfst du dir. Diplomatische Überzeugungsstrategien der Regentin Christine Charlotte von Ostfriesland gegenüber Kaiser Leopold I. im 17. Jahrhundert (erscheint 2024)}}}, volume = {{35}}, year = {{2024}}, } @inproceedings{50296, author = {{Hemmrich, Simon and Schäfer, Jannika Marie and Hansmeier, Philipp and Beverungen, Daniel}}, booktitle = {{Proceedings of the 57th Hawaii International Conference on System Sciences}}, location = {{Honolulu}}, title = {{{The Value of Reputation Systems in Business Contexts – A Qualitative Study Taking the View of Buyers}}}, year = {{2024}}, } @inproceedings{50287, author = {{Kruse, Stephan and Schwabe, Tobias and Kneuper, Pascal and Kurz, Heiko G. and Meinecke, March-Michael and Scheytt, Christoph}}, booktitle = {{German Microwave Conference (GeMiC) }}, title = {{{Analysis and Simulation of a Photonic Multiband FMCW Radar Sensor System using Nyquist Pulses}}}, year = {{2024}}, } @article{50301, author = {{Schryen, Guido}}, journal = {{Journal of Parallel and Distributed Computing}}, title = {{{Speedup and efficiency of computational parallelization: A unifying approach and asymptotic analysis}}}, year = {{2024}}, } @article{46469, abstract = {{We show how to learn discrete field theories from observational data of fields on a space-time lattice. For this, we train a neural network model of a discrete Lagrangian density such that the discrete Euler--Lagrange equations are consistent with the given training data. We, thus, obtain a structure-preserving machine learning architecture. Lagrangian densities are not uniquely defined by the solutions of a field theory. We introduce a technique to derive regularisers for the training process which optimise numerical regularity of the discrete field theory. Minimisation of the regularisers guarantees that close to the training data the discrete field theory behaves robust and efficient when used in numerical simulations. Further, we show how to identify structurally simple solutions of the underlying continuous field theory such as travelling waves. This is possible even when travelling waves are not present in the training data. This is compared to data-driven model order reduction based approaches, which struggle to identify suitable latent spaces containing structurally simple solutions when these are not present in the training data. Ideas are demonstrated on examples based on the wave equation and the Schrödinger equation. }}, author = {{Offen, Christian and Ober-Blöbaum, Sina}}, issn = {{1054-1500}}, journal = {{Chaos}}, number = {{1}}, publisher = {{AIP Publishing}}, title = {{{Learning of discrete models of variational PDEs from data}}}, doi = {{10.1063/5.0172287}}, volume = {{34}}, year = {{2024}}, } @article{49652, abstract = {{Broadband coherent anti-Stokes Raman scattering (BCARS) is a powerful spectroscopy method combining high signal intensity with spectral sensitivity, enabling rapid imaging of heterogeneous samples in biomedical research and, more recently, in crystalline materials. However, BCARS encounters spectral distortion due to a setup-dependent non-resonant background (NRB). This study assesses BCARS reproducibility through a round robin experiment using two distinct BCARS setups and crystalline materials with varying structural complexity, including diamond, 6H-SiC, KDP, and KTP. The analysis compares setup-specific NRB correction procedures, detected and NRB-removed spectra, and mode assignment. We determine the influence of BCARS setup parameters like pump wavelength, pulse width, and detection geometry and provide a practical guide for optimizing BCARS setups for solid-state applications.}}, author = {{Hempel, Franz and Vernuccio, Federico and König, Lukas and Buschbeck, Robin and Rüsing, Michael and Cerullo, Giulio and Polli, Dario and Eng, Lukas M.}}, issn = {{1559-128X}}, journal = {{Applied Optics}}, keywords = {{Atomic and Molecular Physics, and Optics, Engineering (miscellaneous), Electrical and Electronic Engineering}}, number = {{1}}, publisher = {{Optica Publishing Group}}, title = {{{Comparing transmission- and epi-BCARS: a round robin on solid-state materials}}}, doi = {{10.1364/ao.505374}}, volume = {{63}}, year = {{2024}}, } @article{50409, abstract = {{AbstractBackgroundReal‐world problems are important in math instruction, but they do not necessarily trigger students' task motivation. Personalizing real‐world problems by (1) matching problems to students' shared living environment (context personalization) and (2) asking students to pose their own problems (active personalization) might be two interventions to increase students' task motivation.AimIn the current study, we investigated the effects of context personalization and active personalization on students' self‐efficacy expectations, intrinsic value, attainment value, utility value, and cost.SampleThe participants were 28 fifth‐ and sixth‐grade students who voluntarily took part in a six‐month afterschool program in which they posed problems with the aim of creating a math walk in their hometown.MethodUsing a within‐subjects design, at the end of the afterschool program, the students rated their self‐efficacy expectations and task values for four self‐developed problems associated with their hometown, four peer‐developed problems associated with their hometown, and four instructor‐provided problems associated with unfamiliar locations.ResultsStudents reported higher self‐efficacy expectations, intrinsic value, attainment value, and utility value for active‐personalized than non‐personalized problems. To a lesser extent, context personalization promoted intrinsic value and attainment value. No effect was found for cost.ConclusionsActive personalization (i.e. asking students to pose their own real‐world problems) is suited to enhance students' task motivation, specifically their self‐efficacy expectations, intrinsic value, attainment value, and utility value. Context personalization still boosts students' intrinsic value and attainment value. Implementation in classroom instruction is discussed.}}, author = {{Schoenherr, Johanna}}, issn = {{0007-0998}}, journal = {{British Journal of Educational Psychology}}, keywords = {{Developmental and Educational Psychology, Education}}, publisher = {{Wiley}}, title = {{{Personalizing real‐world problems: Posing own problems increases self‐efficacy expectations, intrinsic value, attainment value, and utility value}}}, doi = {{10.1111/bjep.12653}}, year = {{2024}}, } @unpublished{50406, abstract = {{What is the power of polynomial-time quantum computation with access to an NP oracle? In this work, we focus on two fundamental tasks from the study of Boolean satisfiability (SAT) problems: search-to-decision reductions, and approximate counting. We first show that, in strong contrast to the classical setting where a poly-time Turing machine requires $\Theta(n)$ queries to an NP oracle to compute a witness to a given SAT formula, quantumly $\Theta(\log n)$ queries suffice. We then show this is tight in the black-box model - any quantum algorithm with "NP-like" query access to a formula requires $\Omega(\log n)$ queries to extract a solution with constant probability. Moving to approximate counting of SAT solutions, by exploiting a quantum link between search-to-decision reductions and approximate counting, we show that existing classical approximate counting algorithms are likely optimal. First, we give a lower bound in the "NP-like" black-box query setting: Approximate counting requires $\Omega(\log n)$ queries, even on a quantum computer. We then give a "white-box" lower bound (i.e. where the input formula is not hidden in the oracle) - if there exists a randomized poly-time classical or quantum algorithm for approximate counting making $o(log n)$ NP queries, then $\text{BPP}^{\text{NP}[o(n)]}$ contains a $\text{P}^{\text{NP}}$-complete problem if the algorithm is classical and $\text{FBQP}^{\text{NP}[o(n)]}$ contains an $\text{FP}^{\text{NP}}$-complete problem if the algorithm is quantum.}}, author = {{Gharibian, Sevag and Kamminga, Jonas}}, booktitle = {{arXiv:2401.03943}}, title = {{{BQP, meet NP: Search-to-decision reductions and approximate counting}}}, year = {{2024}}, } @article{50101, author = {{Domenik Ackermann}}, journal = {{Quick And Easy Journal Title}}, title = {{{New Quick And Easy Publication - Will be edited by LibreCat team}}}, year = {{2024}}, }