31,539 results on '"Marquardt, A."'
Search Results
2. A time adaptive optimal control approach for 4D-var data assimilation problems governed by parabolic PDEs
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Gräßle, Carmen and Marquardt, Jannis
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Mathematics - Optimization and Control ,49J20, 49K20, 49M41, 65M50, 65M15 - Abstract
We interpret the 4D-var data assimilation problem for a parabolic partial differential equation (PDE) in the context of optimal control and revisit the process of deriving optimality conditions for an initial control problem. This is followed by a reformulation of the optimality conditions into an elliptic PDE, which is only dependent on the adjoint state and can therefore be solved directly without the need for e.g. gradient methods or related iterative procedures. Furthermore, we derive an a-posteriori error estimation for this system as well as its initial condition. We utilize this estimate to formulate a procedure for the creation of an adaptive grid in time for the adjoint state. This is used for 4D-var data assimilation in order to identify suitable time points to take measurements.
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- 2024
3. Observing the crack tip behaviour at the nanoscale during fracture of ceramics
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Gavalda-Diaz, Oriol, Emmanuel, Max, Marquardt, Katharina, Saiz, Eduardo, and Giuliani, Finn
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Condensed Matter - Materials Science - Abstract
Ultimately, brittle fracture involves breaking atomic bonds. However, we still lack a clear picture of what happens in the highly deformed region around a moving crack tip. Consequently, we still cannot link nano to atomic-scale phenomena with the macroscopic toughness of materials. The unsolved challenge is to observe the movement of the crack front at the nanoscale while extracting quantitative information. Here we address this challenge by monitoring stable crack growth inside a TEM. Our analysis demonstrates how phase transformation toughening, previously thought to be effective at the microscale and above, promotes crack deflection at the nano-level and increases the fracture resistance by ~50%. The work will help to connect the atomistic and continuous view of fracture in a way that can guide the design of the next generation of strong and tough materials demanded by technologies as diverse as healthcare, energy generation or transport.
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- 2024
4. Motion simulation of radio-labeled cells in whole-body positron emission tomography
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Marquardt, Nils, Hengsbach, Tobias, Mauritz, Marco, Wirth, Benedikt, and Schäfers, Klaus
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Physics - Medical Physics - Abstract
Cell tracking is a subject of active research gathering great interest in medicine and biology. Positron emission tomography (PET) is well suited for tracking radio-labeled cells in vivo due to its exceptional sensitivity and whole-body capability. For validation, ground-truth data is desirable that realistically mimics the flow of cells in a clinical situation. This study develops a workflow (CeFloPS) for simulating moving radio-labeled cells in a human phantom. From the XCAT phantom, the blood vessels are reduced to nodal networks along which cells can move and distribute to organs and tissues. The movement is directed by the blood flow which is calculated in each node using the Hagen-Poiseuille equation and Kirchhoffs laws assuming laminar flow. Organs are voxelized and movement of cells from artery entry to vein exit is generated via a biased 3D random walk. The probabilities of whether cells move or stay in tissues are derived from rate constants of physiologically based compartment modeling. PET listmode data is generated using the Monte-Carlo simulation framework GATE based on the definition of a large-body PET scanner with cell paths as moving radioactive sources and the XCAT phantom providing attenuation data. From the flow simulation of 10000 cells, 100 sample cells were further processed by GATE and listmode data was reconstructed into images for comparison. As demonstrated by comparisons of simulated and reconstructed cell distributions, CeFloPS can realistically simulate the cell behavior of whole-body PET providing valuable data for development and validation of cell tracking algorithms., Comment: 8 pages, 6 figures
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- 2024
5. Discovering Local Hidden-Variable Models for Arbitrary Multipartite Entangled States and Arbitrary Measurements
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von Selzam, Nick and Marquardt, Florian
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Quantum Physics ,Condensed Matter - Mesoscale and Nanoscale Physics - Abstract
Measurement correlations in quantum systems can exhibit non-local behavior, a fundamental aspect of quantum mechanics with applications such as device-independent quantum information processing. However, the explicit construction of local hidden-variable (LHV) models remains an outstanding challenge in the general setting. To address this, we develop an approach that employs gradient-descent algorithms from machine learning to find LHV models which reproduce the statistics of arbitrary measurements for quantum many-body states. In contrast to previous approaches, our method employs a general ansatz, enabling it to discover an LHV model in all cases where the state is local. Therefore, it provides actual estimates for the critical noise levels at which two-qubit Werner states and three-qubit GHZ and W states become non-local. Furthermore, we find evidence suggesting that two-spin subsystems in the ground states of translationally invariant Hamiltonians are local, while bigger subsystems are in general not. Our method now offers a quantitative tool for determining the regimes of non-locality in any given physical context, including scenarios involving non-equilibrium and decoherence., Comment: 6+12 pages, 4+5 figures, GitHub: https://github.com/Nick-von-Selzam/AutoLHVs
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- 2024
6. A quantum mechanical evaluation of the intermediate scattering function
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Bindech, Oussama, Marquardt, Roberto, Gatti, Fabien, Mandal, Souvik, and Tremblay, Jean Christophe
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Quantum Physics ,81, 82 - Abstract
The intermediate scattering function is interpreted as a correlation function of thermal wave packets of the scattering centers perturbed by the scattering particles at different times. A proof of concept is given at the example of ballistic moving centers. The ensuing numerical method is then illustrated at the example of CO adsorbed on Cu(100)., Comment: 8 pages, 2 figures
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- 2024
7. Wedge-type engineered analog SiO$_\mathrm{x}$/Cu/SiO$_\mathrm{x}$-Memristive Devices for Neuromorphic Applications
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Lamprecht, Rouven, Vialetto, Luca, Gergs, Tobias, Zahari, Finn, Marquardt, Richard, Trieschmann, Jan, and Kohlstedt, Hermann
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Condensed Matter - Mesoscale and Nanoscale Physics - Abstract
This study presents a comprehensive examination of the development of TiN/SiO$_\mathrm{x}$/Cu/SiO$_\mathrm{x}$/TiN memristive devices, engineered for neuromorphic applications using a wedge-type deposition technique and Monte Carlo simulations. Identifying critical parameters for the desired device characteristics can be challenging with conventional trial-and-error approaches, which often obscure the effects of varying layer compositions. By employing an \textit{off-center} thermal evaporation method, we created a thickness gradient of SiO$_\mathrm{x}$ and Cu on a 4-inch wafer, facilitating detailed resistance map analysis through semiautomatic measurements. This allows to investigate in detail the influence of layer composition and thickness on single wafers, thus keeping every other process condition constant. Combining experimental data with simulations provides a precise understanding of the layer thickness distribution and its impact on device performance. Optimizing the SiO$_\mathrm{x}$ layers to be below 12.5 nm, coupled with a discontinuous Cu layer with a nominal thickness lower than 0.6 nm, exhibits analog switching properties with an R$_\mathrm{on}$/R$_\mathrm{off}$ ratio of $>$100, suitable for neuromorphic applications, whereas R $\times$ A analysis shows no clear signs of filamentary switching. Our findings highlight the significant role of carefully choosing the SiO$_\mathrm{x}$ and Cu thickness in determining the switching behavior and provide insights that could lead to the more systematic development of high-performance analog switching components for bio-inspired computing systems., Comment: Manuscript, 11 pages, 6 figures, not published
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- 2024
8. Quantum Equilibrium Propagation for efficient training of quantum systems based on Onsager reciprocity
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Wanjura, Clara C. and Marquardt, Florian
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Quantum Physics ,Condensed Matter - Disordered Systems and Neural Networks ,Computer Science - Emerging Technologies ,Computer Science - Machine Learning - Abstract
The widespread adoption of machine learning and artificial intelligence in all branches of science and technology has created a need for energy-efficient, alternative hardware platforms. While such neuromorphic approaches have been proposed and realised for a wide range of platforms, physically extracting the gradients required for training remains challenging as generic approaches only exist in certain cases. Equilibrium propagation (EP) is such a procedure that has been introduced and applied to classical energy-based models which relax to an equilibrium. Here, we show a direct connection between EP and Onsager reciprocity and exploit this to derive a quantum version of EP. This can be used to optimize loss functions that depend on the expectation values of observables of an arbitrary quantum system. Specifically, we illustrate this new concept with supervised and unsupervised learning examples in which the input or the solvable task is of quantum mechanical nature, e.g., the recognition of quantum many-body ground states, quantum phase exploration, sensing and phase boundary exploration. We propose that in the future quantum EP may be used to solve tasks such as quantum phase discovery with a quantum simulator even for Hamiltonians which are numerically hard to simulate or even partially unknown. Our scheme is relevant for a variety of quantum simulation platforms such as ion chains, superconducting qubit arrays, neutral atom Rydberg tweezer arrays and strongly interacting atoms in optical lattices., Comment: 10 pages, 3 figures; comments welcome!
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- 2024
9. Influence of Motion Restrictions in an Ankle Exoskeleton on Gait Kinematics and Stability in Straight Walking
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Dezman, Miha, Marquardt, Charlotte, Ugur, Adnan, and Asfour, Tamim
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Computer Science - Robotics - Abstract
Exoskeleton devices impose kinematic constraints on a user's motion and affect their stability due to added mass but also due to the simplified mechanical design. This paper investigates how these constraints resulting from simplified mechanical designs impact the gait kinematics and stability of users by wearing an ankle exoskeleton with changeable degree of freedom (DoF). The exoskeleton used in this paper allows one, two, or three DoF at the ankle, simulating different levels of mechanical complexity. This effect was evaluated in a pilot study consisting of six participants walking on a straight path. The results show that increasing the exoskeleton DoF results in an improvement of several metrics, including kinematics and gait parameters. The transition from 1 DoF to 2 DoF is shown to have a larger effect than the transition from 2 DoF to 3 DoF for an ankle exoskeleton. However, an exoskeleton with 3 DoF at the ankle featured the best results. Increasing the number of DoF resulted in stability values closer the values when walking without the exoskeleton, despite the added weight of the exoskeleton., Comment: This document is the non-revised version of the paper submitted to IEEE RAS EMBS BioRob 2024. The revised version has been submitted to IEEE Transactions on Medical Robotics and Bionics (IEEE T-MRB)
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- 2024
10. Training of Physical Neural Networks
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Momeni, Ali, Rahmani, Babak, Scellier, Benjamin, Wright, Logan G., McMahon, Peter L., Wanjura, Clara C., Li, Yuhang, Skalli, Anas, Berloff, Natalia G., Onodera, Tatsuhiro, Oguz, Ilker, Morichetti, Francesco, del Hougne, Philipp, Gallo, Manuel Le, Sebastian, Abu, Mirhoseini, Azalia, Zhang, Cheng, Marković, Danijela, Brunner, Daniel, Moser, Christophe, Gigan, Sylvain, Marquardt, Florian, Ozcan, Aydogan, Grollier, Julie, Liu, Andrea J., Psaltis, Demetri, Alù, Andrea, and Fleury, Romain
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Physics - Applied Physics ,Computer Science - Machine Learning - Abstract
Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern AI. Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "yes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained - primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models., Comment: 29 pages, 4 figures
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- 2024
11. HoloDevice: Holographic Cross-Device Interactions for Remote Collaboration
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Chulpongsatorn, Neil, Nguyen, Thien-Kim, Marquardt, Nicolai, and Suzuki, Ryo
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Computer Science - Human-Computer Interaction - Abstract
This paper introduces holographic cross-device interaction, a new class of remote cross-device interactions between local physical devices and holographically rendered remote devices. Cross-device interactions have enabled a rich set of interactions with device ecologies. Most existing research focuses on co-located settings (meaning when users and devices are in the same physical space) to achieve these rich interactions and affordances. In contrast, holographic cross-device interaction allows remote interactions between devices at distant locations by providing a rich visual affordance through real-time holographic rendering of the device's motion, content, and interactions on mixed reality head-mounted displays. This maintains the advantages of having a physical device, such as precise input through touch and pen interaction. Through holographic rendering, not only can remote devices interact as if they are co-located, but they can also be virtually augmented to further enrich interactions, going beyond what is possible with existing cross-device systems. To demonstrate this concept, we developed HoloDevice, a prototype system for holographic cross-device interaction using the Microsoft Hololens 2 augmented reality headset. Our contribution is threefold. First, we introduce the concept of holographic cross-device interaction. Second, we present a design space containing three unique benefits, which include: (1) spatial visualization of interaction and motion, (2) rich visual affordances for intermediate transition, and (3) dynamic and fluid configuration. Last we discuss a set of implementation demonstrations and use-case scenarios that further explore the space.
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- 2024
12. Transfer learning in predicting quantum many-body dynamics: from physical observables to entanglement entropy
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Schmidt, Philipp, Marquardt, Florian, and Mohseni, Naeimeh
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Quantum Physics - Abstract
Deep neural networks have demonstrated remarkable efficacy in extracting meaningful representations from complex datasets. This has propelled representation learning as a compelling area of research across diverse fields. One interesting open question is how beneficial representation learning can be for quantum many-body physics, with its notouriosly high-dimensional state space. In this work, we showcase the capacity of a neural network that was trained on a subset of physical observables of a many-body system to partially acquire an implicit representation of the wave function. We illustrate this by demonstrating the effectiveness of reusing the representation learned by the neural network to enhance the learning process of another quantity derived from the quantum state. In particular, we focus on how the pre-trained neural network can enhance the learning of entanglement entropy. This is of particular interest as directly measuring the entanglement in a many-body system is very challenging, while a subset of physical observables can be easily measured in experiments. We show the pre-trained neural network learns the dynamics of entropy with fewer resources and higher precision in comparison with direct training on the entanglement entropy.
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- 2024
13. Tackling Decision Processes with Non-Cumulative Objectives using Reinforcement Learning
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Nägele, Maximilian, Olle, Jan, Fösel, Thomas, Zen, Remmy, and Marquardt, Florian
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Computer Science - Machine Learning ,Quantitative Finance - Computational Finance ,Quantum Physics ,I.2.8 ,I.2.6 - Abstract
Markov decision processes (MDPs) are used to model a wide variety of applications ranging from game playing over robotics to finance. Their optimal policy typically maximizes the expected sum of rewards given at each step of the decision process. However, a large class of problems does not fit straightforwardly into this framework: Non-cumulative Markov decision processes (NCMDPs), where instead of the expected sum of rewards, the expected value of an arbitrary function of the rewards is maximized. Example functions include the maximum of the rewards or their mean divided by their standard deviation. In this work, we introduce a general mapping of NCMDPs to standard MDPs. This allows all techniques developed to find optimal policies for MDPs, such as reinforcement learning or dynamic programming, to be directly applied to the larger class of NCMDPs. Focusing on reinforcement learning, we show applications in a diverse set of tasks, including classical control, portfolio optimization in finance, and discrete optimization problems. Given our approach, we can improve both final performance and training time compared to relying on standard MDPs.
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- 2024
14. Human Factors in Model-Driven Engineering: Future Research Goals and Initiatives for MDE
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Liebel, Grischa, Klünder, Jil, Hebig, Regina, Lazik, Christopher, Nunes, Inês, Graßl, Isabella, Steghöfer, Jan-Philipp, Exelmans, Joeri, Oertel, Julian, Marquardt, Kai, Juhnke, Katharina, Schneider, Kurt, Gren, Lucas, Happe, Lucia, Herrmann, Marc, Wyrich, Marvin, Tichy, Matthias, Goulão, Miguel, Wohlrab, Rebekka, Kalantari, Reyhaneh, Heinrich, Robert, Greiner, Sandra, Rukmono, Satrio Adi, Chakraborty, Shalini, Abrahão, Silvia, and Amaral, Vasco
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Computer Science - Software Engineering - Abstract
Purpose: Software modelling and Model-Driven Engineering (MDE) is traditionally studied from a technical perspective. However, one of the core motivations behind the use of software models is inherently human-centred. Models aim to enable practitioners to communicate about software designs, make software understandable, or make software easier to write through domain-specific modelling languages. Several recent studies challenge the idea that these aims can always be reached and indicate that human factors play a role in the success of MDE. However, there is an under-representation of research focusing on human factors in modelling. Methods: During a GI-Dagstuhl seminar, topics related to human factors in modelling were discussed by 26 expert participants from research and industry. Results: In breakout groups, five topics were covered in depth, namely modelling human aspects, factors of modeller experience, diversity and inclusion in MDE, collaboration and MDE, and teaching human-aware MDE. Conclusion: We summarise our insights gained during the discussions on the five topics. We formulate research goals, questions, and propositions that support directing future initiatives towards an MDE community that is aware of and supportive of human factors and values.
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- 2024
15. Automated Discovery of Coupled Mode Setups
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Landgraf, Jonas, Peano, Vittorio, and Marquardt, Florian
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Physics - Optics ,Quantum Physics - Abstract
In optics and photonics, a small number of building blocks, like resonators, waveguides, arbitrary couplings, and parametric interactions, allow the design of a broad variety of devices and functionalities, distinguished by their scattering properties. These include transducers, amplifiers, and nonreciprocal devices, like isolators or circulators. Usually, the design of such a system is handcrafted by an experienced scientist in a time-consuming process where it remains uncertain whether the simplest possibility has indeed been found. In our work, we develop a discovery algorithm that automates this challenge. By optimizing the continuous and discrete system properties our automated search identifies the minimal resources required to realize the requested scattering behavior. In the spirit of artificial scientific discovery, it produces a complete list of interpretable solutions and leads to generalizable insights, as we illustrate in several examples. This now opens the door to rapid design in areas like photonic and microwave architectures or optomechanics., Comment: 18 pages, 4 figures, 2 figures in the appendix
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- 2024
16. Classical dynamics and semiclassical analysis of excitons in cuprous oxide
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Ertl, Jan, Marquardt, Michael, Schumacher, Moritz, Rommel, Patric, Main, Jörg, and Bayer, Manfred
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Condensed Matter - Mesoscale and Nanoscale Physics ,Nonlinear Sciences - Chaotic Dynamics ,Quantum Physics - Abstract
Excitons, as bound states of electrons and holes, embody the solid state analogue of the hydrogen atom, whose quantum spectrum is explained within a classical framework by the Bohr-Sommerfeld atomic model. In a first hydrogenlike approximation the spectra of excitons are also well described by a Rydberg series, however, due to the surrounding crystal environment deviations from this series can be observed. A theoretical treatment of excitons in cuprous oxide needs to include the band structure of the crystal, leading to a prominent fine-structure splitting in the quantum spectra. This is achieved by introducing additional spin degrees of freedom into the system, making the existence and meaningfulness of classical exciton orbits in the physical system a non-trivial question. Recently, we have uncovered the contributions of periodic exciton orbits directly in the quantum mechanical recurrence spectra of cuprous oxide [J. Ertl et al., Phys. Rev. Lett. 129, 067401 (2022)] by application of a scaling technique and fixing the energy of the classical dynamics to a value corresponding to a principle quantum number $n=5$ in the hydrogenlike case. Here, we present a comprehensive derivation of the classical and semiclassical theory of excitons in cuprous oxide. In particular, we investigate the energy dependence of the exciton dynamics. Both the semiclassical and quantum mechanical recurrence spectra exhibit stronger deviations from the hydrogenlike behavior with decreasing energy, which is related to a growing influence of the spin-orbit coupling and thus a higher velocity of the secular motion of the exciton orbits. The excellent agreement between semiclassical and quantum mechanical exciton recurrence spectra demonstrates the validity of the classical and semiclassical approach to excitons in cuprous oxide., Comment: 16 pages, 10 figures, accepted for publication in Phys. Rev. B
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- 2024
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17. BlendScape: Enabling Unified and Personalized Video-Conferencing Environments through Generative AI
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Rajaram, Shwetha, Numan, Nels, Kumaravel, Balasaravanan Thoravi, Marquardt, Nicolai, and Wilson, Andrew D.
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Computer Science - Human-Computer Interaction ,Computer Science - Artificial Intelligence - Abstract
Today's video-conferencing tools support a rich range of professional and social activities, but their generic, grid-based environments cannot be easily adapted to meet the varying needs of distributed collaborators. To enable end-user customization, we developed BlendScape, a system for meeting participants to compose video-conferencing environments tailored to their collaboration context by leveraging AI image generation techniques. BlendScape supports flexible representations of task spaces by blending users' physical or virtual backgrounds into unified environments and implements multimodal interaction techniques to steer the generation. Through an evaluation with 15 end-users, we investigated their customization preferences for work and social scenarios. Participants could rapidly express their design intentions with BlendScape and envisioned using the system to structure collaboration in future meetings, but experienced challenges with preventing distracting elements. We implement scenarios to demonstrate BlendScape's expressiveness in supporting distributed collaboration techniques from prior work and propose composition techniques to improve the quality of environments.
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- 2024
18. Nakayama algebras of small homological dimension and pattern avoiding permutations
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Klász, Viktória, Marczinzik, René, and Marquardt, Judith
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Mathematics - Representation Theory ,Mathematics - Combinatorics ,16G10, 16E10, 05E10 - Abstract
We give a combinatorial classification of Nakayama algebras of small homological dimension using the Krattenthaler bijection between Dyck paths and 132-avoiding permutations., Comment: 14 pages, 13 figures
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- 2024
19. Quantum Circuit Discovery for Fault-Tolerant Logical State Preparation with Reinforcement Learning
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Zen, Remmy, Olle, Jan, Colmenarez, Luis, Puviani, Matteo, Müller, Markus, and Marquardt, Florian
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Quantum Physics - Abstract
The realization of large-scale quantum computers requires not only quantum error correction (QEC) but also fault-tolerant operations to handle errors that propagate into harmful errors. Recently, flag-based protocols have been introduced that use ancillary qubits to flag harmful errors. However, there is no clear recipe for finding a fault-tolerant quantum circuit with flag-based protocols, especially when we consider hardware constraints, such as qubit connectivity and available gate set. In this work, we propose and explore reinforcement learning (RL) to automatically discover compact and hardware-adapted fault-tolerant quantum circuits. We show that in the task of fault-tolerant logical state preparation, RL discovers circuits with fewer gates and ancillary qubits than published results without and with hardware constraints of up to 15 physical qubits. Furthermore, RL allows for straightforward exploration of different qubit connectivities and the use of transfer learning to accelerate the discovery. More generally, our work opens the door towards the use of RL for the discovery of fault-tolerant quantum circuits for addressing tasks beyond state preparation, including magic state preparation, logical gate synthesis, or syndrome measurement., Comment: 34 pages, 20 figures
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- 2024
20. Training Coupled Phase Oscillators as a Neuromorphic Platform using Equilibrium Propagation
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Wang, Qingshan, Wanjura, Clara C., and Marquardt, Florian
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Computer Science - Emerging Technologies ,Condensed Matter - Disordered Systems and Neural Networks ,Condensed Matter - Mesoscale and Nanoscale Physics ,Computer Science - Neural and Evolutionary Computing ,Physics - Optics - Abstract
Given the rapidly growing scale and resource requirements of machine learning applications, the idea of building more efficient learning machines much closer to the laws of physics is an attractive proposition. One central question for identifying promising candidates for such neuromorphic platforms is whether not only inference but also training can exploit the physical dynamics. In this work, we show that it is possible to successfully train a system of coupled phase oscillators - one of the most widely investigated nonlinear dynamical systems with a multitude of physical implementations, comprising laser arrays, coupled mechanical limit cycles, superfluids, and exciton-polaritons. To this end, we apply the approach of equilibrium propagation, which permits to extract training gradients via a physical realization of backpropagation, based only on local interactions. The complex energy landscape of the XY/ Kuramoto model leads to multistability, and we show how to address this challenge. Our study identifies coupled phase oscillators as a new general-purpose neuromorphic platform and opens the door towards future experimental implementations., Comment: 12 pages, 4 figures, comments welcome
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- 2024
21. A novel model for direct numerical simulation of suspension dynamics with arbitrarily shaped convex particles
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Marquardt, J. E., Hafen, N., and Krause, M. J.
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Physics - Fluid Dynamics ,Physics - Computational Physics - Abstract
This study presents an innovative direct numerical simulation approach for complex particle systems with irregular shapes and large numbers. Using partially saturated methods, it accurately models arbitrary shapes, albeit at considerable computational cost when integrating a compatible contact model. The introduction of a novel parallelization strategy significantly improves the performance of the contact model, enabling efficient four-way coupled simulations. Through hindered settling studies, the criticality of the explicit contact model for maintaining simulation accuracy is highlighted, especially at high particle volume fractions and low Archimedes numbers. The feasibility of simulating thousands of arbitrarily shaped convex particles is demonstrated with up to 1934 surface-resolved particles. The study also confirms the grid independence and linear convergence of the method. It shows for the first time that cube swarms settle 13 to 26% slower than swarms of volume-equivalent spheres across different Archimedes numbers (500 to 2000) and particle volume fractions (10 to 30%). These findings emphasize the shape dependence of particle systems and suggest avenues for exploring their nuanced dynamics.
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- 2024
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22. Model-aware reinforcement learning for high-performance Bayesian experimental design in quantum metrology
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Belliardo, Federico, Zoratti, Fabio, Marquardt, Florian, and Giovannetti, Vittorio
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Quantum Physics - Abstract
Quantum sensors offer control flexibility during estimation by allowing manipulation by the experimenter across various parameters. For each sensing platform, pinpointing the optimal controls to enhance the sensor's precision remains a challenging task. While an analytical solution might be out of reach, machine learning offers a promising avenue for many systems of interest, especially given the capabilities of contemporary hardware. We have introduced a versatile procedure capable of optimizing a wide range of problems in quantum metrology, estimation, and hypothesis testing by combining model-aware reinforcement learning (RL) with Bayesian estimation based on particle filtering. To achieve this, we had to address the challenge of incorporating the many non-differentiable steps of the estimation in the training process, such as measurements and the resampling of the particle filter. Model-aware RL is a gradient-based method, where the derivatives of the sensor's precision are obtained through automatic differentiation (AD) in the simulation of the experiment. Our approach is suitable for optimizing both non-adaptive and adaptive strategies, using neural networks or other agents. We provide an implementation of this technique in the form of a Python library called qsensoropt, alongside several pre-made applications for relevant physical platforms, namely NV centers, photonic circuits, and optical cavities. This library will be released soon on PyPI. Leveraging our method, we've achieved results for many examples that surpass the current state-of-the-art in experimental design. In addition to Bayesian estimation, leveraging model-aware RL, it is also possible to find optimal controls for the minimization of the Cram\'er-Rao bound, based on Fisher information., Comment: 42 pages, 9 figures
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- 2023
23. A novel particle decomposition scheme to improve parallel performance of fully resolved particulate flow simulations
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Marquardt, J. E., Hafen, N., and Krause, M. J.
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Physics - Fluid Dynamics ,Physics - Computational Physics - Abstract
This study addresses the challenge of simulating realistic particle systems by proposing a novel particle decomposition scheme that improves the parallel performance of surface resolved particle simulations. Realistic particle systems often involve large numbers of particles and complex particle shapes. The resulting need to account for shape factors requires the inclusion of even more particles to obtain statistically meaningful results. However, the computational cost increases with the number of particles, making efficient parallelization crucial. Therefore, the proposed scheme aims to improve the scalability by optimizing the communication and data management between processors. Through hindered settling experiments, the applicability and performance of the novel particle decomposition scheme are thoroughly investigated using the homogenized lattice Boltzmann method. The results show that the proposed method significantly improves the performance, especially in scenarios with a large number of particles, by reducing communication constraints and improving scalability. This research contributes to the advancement of computational methods for efficient study of complex particle systems and provides valuable insights for future developments in this field.
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- 2023
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24. Boosting the Gottesman-Kitaev-Preskill quantum error correction with non-Markovian feedback
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Puviani, Matteo, Borah, Sangkha, Zen, Remmy, Olle, Jan, and Marquardt, Florian
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Quantum Physics - Abstract
Bosonic codes allow the encoding of a logical qubit in a single component device, utilizing the infinitely large Hilbert space of a harmonic oscillator. In particular, the Gottesman-Kitaev-Preskill code has recently been demonstrated to be correctable well beyond the break-even point of the best passive encoding in the same system. Current approaches to quantum error correction (QEC) for this system are based on protocols that use feedback, but the response is based only on the latest measurement outcome. In our work, we use the recently proposed Feedback-GRAPE (Gradient Ascent Pulse Engineering with Feedback) method to train a recurrent neural network that provides a QEC scheme based on memory, responding in a non-Markovian way to the full history of previous measurement outcomes, optimizing all subsequent unitary operations. This approach significantly outperforms current strategies and paves the way for more powerful measurement-based QEC protocols., Comment: 15 pages, 16 figures
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- 2023
25. Pan-cancer profiling of tumor-infiltrating natural killer cells through transcriptional reference mapping
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Netskar, Herman, Pfefferle, Aline, Goodridge, Jodie P., Sohlberg, Ebba, Dufva, Olli, Teichmann, Sarah A., Brownlie, Demi, Michaëlsson, Jakob, Marquardt, Nicole, Clancy, Trevor, Horowitz, Amir, and Malmberg, Karl-Johan
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- 2024
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26. Ultrasound segmentation analysis via distinct and completed anatomical borders
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Duque, Vanessa Gonzalez, Marquardt, Alexandra, Velikova, Yordanka, Lacourpaille, Lilian, Nordez, Antoine, Crouzier, Marion, Lee, Hong Joo, Mateus, Diana, and Navab, Nassir
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- 2024
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27. Nutzen der partizipatorischen Mitwirkung von PatientInnen an der Entwicklung einer dermatologischen Therapie-App – ein Bericht aus der Praxis
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Koopmann, Anne, Pfeifer, Anna Maria, Schweickart, Lara, Biniaminov, Nathalie, Haas, Valentin, Marquardt, Philipp, Gößwein, Astrid, Czaban, Christopher, Biniaminov, Sergey, Blauth, Mara, Glatzel, Caroline, Zimmermann, Christoph, Stork, Wilhelm, Olsavszky, Victor, and Schmieder, Astrid
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- 2024
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28. Technostresserleben in der stationären medizinischen Versorgung in deutschen und schweizerischen Kliniken: aktueller Forschungsstand
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Bail, Clara, Marquardt, Berit, Harth, Volker, and Mache, Stefanie
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- 2024
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29. Preparing to Serve: Sensemaking, Sensegiving, and Diversity Learning in an Alternative Break Program and Connected Honors Course
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Chelsea Redger-Marquardt and Jean A. Patterson
- Abstract
Atypical in Alternative Break (AB) practice, an intentionally connected course was examined to understand students' perceptions of their semester-long experience. Using qualitative narrative analysis, authors analyze data from 20 AB participants to evaluate student perceptions and experiential outcomes before, during, and after their service trips (one involving the social issue of human environment impact and the other, hunger and homelessness). Pre- and post-trip instruction on the causes and issues related to privilege, oppression, dominant narratives, and potential pitfalls related to immersive service is presented. Reflective blogs are measured against two theoretical frameworks: diversity learning and sensemaking. Results indicate powerful sensemaking when service and learning combine in a synergistic relationship, with students articulating the importance of class learning coupled with informal interactional learning and hands-on experience for gaining knowledge of an issue and understanding for those involved. Authors suggest that AB practitioners/faculty be diligent in preparing students to serve; thoughtful in selecting strong on-trip service experiences and community partners; mindful of the importance of reflection; and dedicated to furthering post-trip learning through sensegiving.
- Published
- 2023
30. Overcoming resolution attenuation during tilted cryo-EM data collection.
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Aiyer, Sriram, Baldwin, Philip, Tan, Shi, Shan, Zelin, Oh, Juntaek, Mehrani, Atousa, Bowman, Marianne, Louie, Gordon, Passos, Dario, Đorđević-Marquardt, Selena, Mietzsch, Mario, Hull, Joshua, Hoshika, Shuichi, Barad, Benjamin, Grotjahn, Danielle, McKenna, Robert, Agbandje-McKenna, Mavis, Benner, Steven, Noel, Joseph, Wang, Dong, Tan, Yong, and Lyumkis, Dmitry
- Subjects
Cryoelectron Microscopy ,Anisotropy ,Benchmarking ,Computer Systems ,Data Collection - Abstract
Structural biology efforts using cryogenic electron microscopy are frequently stifled by specimens adopting preferred orientations on grids, leading to anisotropic map resolution and impeding structure determination. Tilting the specimen stage during data collection is a generalizable solution but has historically led to substantial resolution attenuation. Here, we develop updated data collection and image processing workflows and demonstrate, using multiple specimens, that resolution attenuation is negligible or significantly reduced across tilt angles. Reconstructions with and without the stage tilted as high as 60° are virtually indistinguishable. These strategies allowed the reconstruction to 3 Å resolution of a bacterial RNA polymerase with preferred orientation, containing an unnatural nucleotide for studying novel base pair recognition. Furthermore, we present a quantitative framework that allows cryo-EM practitioners to define an optimal tilt angle during data acquisition. These results reinforce the utility of employing stage tilt for data collection and provide quantitative metrics to obtain isotropic maps.
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- 2024
31. Deterministic or probabilistic: U.S. children's beliefs about genetic inheritance
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Menendez, David, Donovan, Andrea Marquardt, Mathiaparanam, Olympia N, Seitz, Vienne, Sabbagh, Nour F, Klapper, Rebecca E, Kalish, Charles W, Rosengren, Karl S, and Alibali, Martha W
- Subjects
Psychology ,Education ,Specialist Studies In Education ,Applied and Developmental Psychology ,Genetics ,Behavioral and Social Science ,Pediatric ,Eye Disease and Disorders of Vision ,Cognitive Sciences ,Developmental & Child Psychology ,Specialist studies in education ,Applied and developmental psychology - Abstract
Do children think of genetic inheritance as deterministic or probabilistic? In two novel tasks, children viewed the eye colors of animal parents and judged and selected possible phenotypes of offspring. Across three studies (N = 353, 162 girls, 172 boys, 2 non-binary; 17 did not report gender) with predominantly White U.S. participants collected in 2019-2021, 4- to 12-year-old children showed a probabilistic understanding of genetic inheritance, and they accepted and expected variability in the genetic inheritance of eye color. Children did not show a mother bias but they did show two novel biases: perceptual similarity and sex-matching. These results held for unfamiliar animals and several physical traits (e.g., eye color, ear size, and fin type), and persisted after a lesson.
- Published
- 2024
32. The Role of Visual Representations in Undergraduate Students’ Learning about Genetic Inheritance
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Menendez, David, Donovan, Andrea Marquardt, Mathiaparanam, Olympia N, Klapper, Rebecca E, Yoo, Seung Heon, Rosengren, Karl S, and Alibali, Martha W
- Subjects
Education ,Specialist Studies In Education ,Eye Disease and Disorders of Vision ,visualization ,abstractness ,genetics ,biology education ,perceptual richness ,Education Systems ,Curriculum and Pedagogy ,Specialist Studies in Education ,Curriculum and pedagogy ,Education policy ,sociology and philosophy ,Specialist studies in education - Abstract
Prior work has shown that many undergraduate students have misconceptions about genetic inheritance, even after they take genetics courses. Visual representations, such as pedigree diagrams, are commonly used in genetics instruction, and they help students quickly visualize the phenotypes of multiple generations. In Study 1, we examined whether presenting a pedigree diagram of a wolf’s eye color in a rich and realistic manner (i.e., with rich perceptual images that resemble real animals) or in an abstract manner (i.e., with circles and squares representing animals) would help undergraduates learn from a brief, online lesson on inheritance of the wolf’s eye color, and whether they would transfer what they learned when reasoning about eye color in other species (near transfer) and other traits in other species (mid- and far transfer). Counter to our hypothesis, students transferred more with the rich diagram. In Study 2, we compared the rich diagram from Study 1 to a perceptually bland diagram (i.e., with color and textural features removed). There were no differences in students’ learning or transfer between the diagrams. These results suggest that realistic elements that are attention grabbing and easily interpretable by students can be beneficial for transfer in online lessons.
- Published
- 2024
33. Optimizing ZX-Diagrams with Deep Reinforcement Learning
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Nägele, Maximilian and Marquardt, Florian
- Subjects
Quantum Physics ,Computer Science - Machine Learning - Abstract
ZX-diagrams are a powerful graphical language for the description of quantum processes with applications in fundamental quantum mechanics, quantum circuit optimization, tensor network simulation, and many more. The utility of ZX-diagrams relies on a set of local transformation rules that can be applied to them without changing the underlying quantum process they describe. These rules can be exploited to optimize the structure of ZX-diagrams for a range of applications. However, finding an optimal sequence of transformation rules is generally an open problem. In this work, we bring together ZX-diagrams with reinforcement learning, a machine learning technique designed to discover an optimal sequence of actions in a decision-making problem and show that a trained reinforcement learning agent can significantly outperform other optimization techniques like a greedy strategy or simulated annealing. The use of graph neural networks to encode the policy of the agent enables generalization to diagrams much bigger than seen during the training phase., Comment: 12 pages, 7 figures - Revision on 26.04.2024: Fixed bug in training algorithm to give quantitatively better results (qualitative results unchanged)
- Published
- 2023
34. Simultaneous Discovery of Quantum Error Correction Codes and Encoders with a Noise-Aware Reinforcement Learning Agent
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Olle, Jan, Zen, Remmy, Puviani, Matteo, and Marquardt, Florian
- Subjects
Quantum Physics - Abstract
In the ongoing race towards experimental implementations of quantum error correction (QEC), finding ways to automatically discover codes and encoding strategies tailored to the qubit hardware platform is emerging as a critical problem. Reinforcement learning (RL) has been identified as a promising approach, but so far it has been severely restricted in terms of scalability. In this work, we significantly expand the power of RL approaches to QEC code discovery. Explicitly, we train an RL agent that automatically discovers both QEC codes and their encoding circuits for a given gate set, qubit connectivity and error model, from scratch. This is enabled by a reward based on the Knill-Laflamme conditions and a vectorized Clifford simulator, allowing us to scale our results to 20 physical qubits and distance 5 codes. Moreover, we introduce the concept of a noise-aware meta-agent, which learns to produce encoding strategies simultaneously for a range of noise models, thus leveraging transfer of insights between different situations. Our approach opens the door towards hardware-adapted accelerated discovery of QEC approaches across the full spectrum of quantum hardware platforms of interest., Comment: 14 p.main + 9 p.appendix. Codes available on github
- Published
- 2023
35. Polarization-entangled photons from a whispering gallery resonator
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Huang, Sheng-Hsuan, Dirmeier, Thomas, Shafiee, Golnoush, Laiho, Kaisa, Strekalov, Dmitry V., Leuchs, Gerd, and Marquardt, Christoph
- Subjects
Physics - Optics ,Quantum Physics - Abstract
Crystalline Whispering Gallery Mode Resonators (WGMRs) have been shown to facilitate versatile sources of quantum states that can efficiently interact with atomic systems. These features make WGMRs an efficient platform for quantum information processing. Here, we experimentally show that it is possible to generate polarization entanglement from WGMRs by using an interferometric scheme. Our scheme gives us the flexibility to control the phase of the generated entangled state by changing the relative phase of the interferometer. The S value of the Clauser-Horne-Shimony-Holt's inequality in the system is $2.45 \pm 0.07$, which violates the inequality by more than 6 standard deviations.
- Published
- 2023
36. Reservoir Engineering for Classical Nonlinear Fields
- Author
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Tissot, Benedikt, Ribeiro, Hugo, and Marquardt, Florian
- Subjects
Quantum Physics ,Physics - Optics - Abstract
Reservoir engineering has become a prominent tool to control quantum systems. Recently, there have been first experiments applying it to many-body systems, especially with a view to engineer particle-conserving dissipation for quantum simulations using bosons. In this work, we explore the dissipative dynamics of these systems in the classical limit. We derive a general equation of motion capturing the effective nonlinear dissipation introduced by the bath and apply it to the special case of a Bose-Hubbard model, where it leads to an unconventional type of dissipative nonlinear Schr\"odinger equation. Building on that, we study the dynamics of one and two solitons in such a dissipative classical field theory., Comment: 14 pages, 6 figures
- Published
- 2023
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37. Fast quantum control of cavities using an improved protocol without coherent errors
- Author
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Landgraf, Jonas, Flühmann, Christa, Fösel, Thomas, Marquardt, Florian, and Schoelkopf, Robert J.
- Subjects
Quantum Physics - Abstract
The selective number-dependent arbitrary phase (SNAP) gates form a powerful class of quantum gates, imparting arbitrarily chosen phases to the Fock modes of a cavity. However, for short pulses, coherent errors limit the performance. Here we demonstrate in theory and experiment that such errors can be completely suppressed, provided that the pulse times exceed a specific limit. The resulting shorter gate times also reduce incoherent errors. Our approach needs only a small number of frequency components, the resulting pulses can be interpreted easily, and it is compatible with fault-tolerant schemes., Comment: 19 pages, 3 figures in the main text, 1 figure in the Appendix
- Published
- 2023
38. Merging automatic differentiation and the adjoint method for photonic inverse design
- Author
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Luce, Alexander, Alaee, Rasoul, Knorr, Fabian, and Marquardt, Florian
- Subjects
Physics - Computational Physics ,Physics - Optics - Abstract
Optimizing shapes and topology of physical devices is crucial for both scientific and technological advancements, given its wide-ranging implications across numerous industries and research areas. Innovations in shape and topology optimization have been seen across a wide range of fields, notably structural mechanics, fluid mechanics, and photonics. Gradient-based inverse design techniques have been particularly successful for photonic and optical problems, resulting in integrated, miniaturized hardware that has set new standards in device performance. To calculate the gradients, there are typically two approaches: implementing specialized solvers using automatic differentiation or deriving analytical solutions for gradient calculation and adjoint sources by hand. In this work, we propose a middle ground and present a hybrid approach that leverages and enables the benefits of automatic differentiation and machine learning frameworks for handling gradient derivation while using existing, proven solvers for numerical solutions. Utilizing the adjoint method, we turn existing numerical solvers differentiable and seamlessly integrate them into an automatic differentiation framework. Further, this enables users to integrate the optimization environment with machine learning applications which could lead to better photonic design workflows. We illustrate the approach through two distinct examples: optimizing the Purcell factor of a magnetic dipole in the vicinity of an optical nanocavity and enhancing the light extraction efficiency of a {\textmu}LED.
- Published
- 2023
39. Deterministic or Probabilistic: U.S. Children's Beliefs about Genetic Inheritance
- Author
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David Menendez, Andrea Marquardt Donovan, Olympia N. Mathiaparanam, Vienne Seitz, Nour F. Sabbagh, Rebecca E. Klapper, Charles W. Kalish, Karl S. Rosengren, and Martha W. Alibali
- Abstract
Do children think of genetic inheritance as deterministic or probabilistic? In two novel tasks, children viewed the eye colors of animal parents and judged and selected possible phenotypes of offspring. Across three studies (N = 353, 162 girls, 172 boys, 2 non-binary; 17 did not report gender) with predominantly White U.S. participants collected in 2019-2021, 4- to 12-year-old children showed a probabilistic understanding of genetic inheritance, and they accepted and expected variability in the genetic inheritance of eye color. Children did not show a mother bias but they did show two novel biases: perceptual similarity and sex-matching. These results held for unfamiliar animals and several physical traits (e.g., eye color, ear size, and fin type), and persisted after a lesson.
- Published
- 2024
- Full Text
- View/download PDF
40. Mindsets of Experienced Action Learning Coaches and Their Impact on the Practice of Coaching Action Learning Groups
- Author
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Michael Marquardt
- Abstract
Team coaching has become more utilized in organizations as they realize the importance of developing highly effective teams. There has been some research done on the skills needed by those who coach teams. However, very little has been done on the mindset needed for effectively coaching teams, and no research on the mindset for coaching action learning teams. This qualitative research is the study of 19 highly experienced master action learning coaches who have been certified by the World Institute for Action Learning (WIAL) and have over 500 h of action learning coaching experiences over a 10-year-plus time period. The Master Action Learning Coaches (MALCs) were asked to: (a) confirm if the 5 key mindsets identified by the researcher for coaching action learning teams were valid and (b) provide examples and questions that accrued from incorporating these mindsets in their coaching of action learning teams. The MALCs concurred on the 5 mindsets, and also provided a rich array of examples of how these mindsets affected their coaching. Two additional mindsets were offered as well. Implications for research and as well as the practice of action learning coaching are presented.
- Published
- 2024
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41. Liran Einav and Amy Finkelstein: We’ve got you covered: rebooting American health care
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Marquardt, Kelli
- Published
- 2024
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42. Some Must Watch While Some Must Sleep : An Impromptu Text Thread
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Marquardt, Tanya and Stephenson, Jenn
- Published
- 2024
43. The Politics of Youth Representation at Climate Change Conferences: Who Speaks, Who Is Spoken of, and Who Listens?
- Author
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Marquardt, Jens, Lövbrand, Eva, and Buhre, Frida
- Published
- 2024
44. Development of Smart Jute Composite with a Thermochromic Agent
- Author
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Hiller, Ana Paula, Marquardt, André Luiz, Bierhalz, Andrea Cristiane Krause, and Steffens, Fernanda
- Published
- 2024
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45. Fully Non-Linear Neuromorphic Computing with Linear Wave Scattering
- Author
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Wanjura, Clara C. and Marquardt, Florian
- Subjects
Physics - Optics ,Computer Science - Emerging Technologies ,Physics - Data Analysis, Statistics and Probability - Abstract
The increasing complexity of neural networks and the energy consumption associated with training and inference create a need for alternative neuromorphic approaches, e.g. using optics. Current proposals and implementations rely on physical non-linearities or opto-electronic conversion to realise the required non-linear activation function. However, there are significant challenges with these approaches related to power levels, control, energy-efficiency, and delays. Here, we present a scheme for a neuromorphic system that relies on linear wave scattering and yet achieves non-linear processing with a high expressivity. The key idea is to inject the input via physical parameters that affect the scattering processes. Moreover, we show that gradients needed for training can be directly measured in scattering experiments. We predict classification accuracies on par with results obtained by standard artificial neural networks. Our proposal can be readily implemented with existing state-of-the-art, scalable platforms, e.g. in optics, microwave and electrical circuits, and we propose an integrated-photonics implementation based on racetrack resonators that achieves high connectivity with a minimal number of waveguide crossings., Comment: 18 pages, 6 figures; comments welcome!
- Published
- 2023
46. c axis electrical transport at the metamagnetic transition in the heavy-fermion superconductor UTe2 under pressure
- Author
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Knebel, G., Pourret, A., Rousseau, S., Marquardt, N., Braithwaite, D., Honda, F., Aoki, D., Lapertot, G., Knafo, W., Seyfarth, G., Brison, J-P., and Flouquet, J.
- Subjects
Condensed Matter - Strongly Correlated Electrons ,Condensed Matter - Superconductivity - Abstract
The electrical resistivity of the unconventional superconductor UTe$_2$ shows very anisotropic behavior in the normal state depending on the current direction. In the present paper we show that the maximum in the resistivity $\rho_c$ for current applied along the $c$ axis at $T^{\rm max}_{\rho_c} \approx 14.75$~K follows the minimum in the thermal expansion $T_\alpha^\star$ along $b$ axis. Under a magnetic field applied along the $b$ axis, $T^{\rm max}_{\rho_c}$ can be tracked up to the critical point of the first order metamagnetic transition, which is located near 6~K and 34.5~T. Surprisingly, at the metamagnetic field $H_m$ the resistivity $\rho_c$ shows a steplike decrease while the resistivities $\rho_a$ and $\rho_b$, for current along the $a$ and $b$ axis, respectively, show a steplike increase. Under hydrostatic pressure $T^{\rm max}_{\rho_c}$ and $H_m$ decrease significantly up to the critical pressure $p_c$ at which superconductivity is suppressed and a long range antiferromagnetic order appears. We show that the phase diagram at different pressures can be scaled by $T^{\rm max}_{\rho_c}$ in field and temperature suggesting that this temperature scale is governing the main interactions in the normal state., Comment: accepted in Phys. Rev. B
- Published
- 2023
47. Deep Bayesian Experimental Design for Quantum Many-Body Systems
- Author
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Sarra, Leopoldo and Marquardt, Florian
- Subjects
Quantum Physics ,Computer Science - Machine Learning - Abstract
Bayesian experimental design is a technique that allows to efficiently select measurements to characterize a physical system by maximizing the expected information gain. Recent developments in deep neural networks and normalizing flows allow for a more efficient approximation of the posterior and thus the extension of this technique to complex high-dimensional situations. In this paper, we show how this approach holds promise for adaptive measurement strategies to characterize present-day quantum technology platforms. In particular, we focus on arrays of coupled cavities and qubit arrays. Both represent model systems of high relevance for modern applications, like quantum simulations and computing, and both have been realized in platforms where measurement and control can be exploited to characterize and counteract unavoidable disorder. Thus, they represent ideal targets for applications of Bayesian experimental design.
- Published
- 2023
48. Eavesdropper localization for quantum and classical channels via nonlinear scattering
- Author
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Popp, Alexandra, Sedlmeir, Florian, Stiller, Birgit, and Marquardt, Christoph
- Subjects
Quantum Physics ,Physics - Optics - Abstract
Optical fiber networks are part of important critical infrastructure and known to be prone to eavesdropping attacks. Hence cryptographic methods have to be used to protect communication. Quantum key distribution (QKD), at its core, offers information theoretical security based on the laws of physics. In deployments one has to take into account practical security and resilience. The latter includes the localization of a possible eavesdropper after an anomaly has been detected by the QKD system to avoid denial-of-service. Here, we present a novel approach to eavesdropper location that can be employed in quantum as well as classical channels using stimulated Brillouin scattering. The tight localization of the acoustic wave inside the fiber channel using correlated pump and probe waves allows to discover the coordinates of a potential threat within centimeters. We demonstrate that our approach outperforms conventional OTDR in the task of localizing an evanescent outcoupling of 1% with cm precision inside standard optical fibers. The system is furthermore able to clearly distinguish commercially available standard SMF28 from different manufacturers, paving the way for fingerprinted fibers in high security environments.
- Published
- 2023
49. Quality of Service Based Radar Resource Management for Navigation and Positioning
- Author
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Müller, Tobias, Durst, Sebastian, Marquardt, Pascal, and Brüggenwirth, Stefan
- Subjects
Electrical Engineering and Systems Science - Signal Processing - Abstract
In hostile environments, GNSS is a potentially unreliable solution for self-localization and navigation. Many systems only use an IMU as a backup system, resulting in integration errors which can dramatically increase during mission execution. We suggest using a fighter radar to illuminate satellites with known trajectories to enhance the self-localization information. This technique is time-consuming and resource-demanding but necessary as other tasks depend on the self-localization accuracy. Therefore an adaption of classical resource management frameworks is required. We propose a quality of service based resource manager with capabilities to account for inter-task dependencies to optimize the self-localization update strategy. Our results show that this leads to adaptive navigation update strategies, mastering the trade-off between self-localization and the requirements of other tasks., Comment: 8 pages, 9 figures
- Published
- 2023
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50. Sociodemographic influences on private and professional contact behaviour during the COVID-19 pandemic in Germany: cross-sectional analysis based on a Regional Blood Donor Cohort
- Author
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Robert Pohl, Christoph Stallmann, Pauline Marquardt, Ute Bank, Jacqueline Färber, Lotte Scheibler, Hans-Gert Heuft, Achim J. Kaasch, and Christian Apfelbacher
- Subjects
COVID-19 ,Contact reduction ,Blood donors ,SeMaCo study ,Medicine ,Biology (General) ,QH301-705.5 ,Science (General) ,Q1-390 - Abstract
Abstract Objective The COVID-19 pandemic has had significant health and socioeconomic impacts worldwide. Extensive measures, including contact restrictions, were implemented to control the spread of the virus. This study aims to examine the factors that influenced private and professional contact behaviour during the COVID-19 pandemic. Results We used baseline data (January–April 2021) from the SeMaCo study (Serologische Untersuchungen bei Blutspendern des Großraums Magdeburg auf Antikörper gegen SARS-CoV-2), a longitudinal, regional cohort study assessing COVID-19 seroprevalence in blood donors from Magdeburg and surrounding areas in Germany. In the blood donor cohort (n = 2,195), there was a general reduction in private contacts (by 78.9%) and professional contacts (by 54.4%) after March 18, 2020. Individuals with higher education reduced both private (by 84.1%) and professional (by 70.1%) contacts more than those with lower education levels (private contacts 59.5%; professional contacts 37%). Younger age groups (18–30 years) reduced private contacts more frequently (by 85.4%) than older individuals (61–83 years, by 68.6%) and demonstrated a higher likelihood of private contact reduction compared to older age groups (51–60 years: odds ratio (OR) 0.45 [95% [CI] 0.32–0.65]; 61–83 years: OR 0.33 [95% [CI] 0.22–0.48]).
- Published
- 2024
- Full Text
- View/download PDF
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