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GaAs-based photocathodes are the only viable source capable of providing spin-polarized electrons for accelerator applications. This type of photocathode requires a thin surface layer, in order to achieve negative electron affinity (NEA) for efficient photo-emission. However, this layer is vulnerable to environmental and operational effects, leading to a decay of the quantum efficiency $\eta$ characterized by a decay constant or lifetime $\tau$. In order to increase $\tau$, additional agents can be introduced during the activation procedure to improve the chemical robustness of the surface layer. This paper presents the results of recent research on Li as enhancement agent for photocathode activation using Cs and O$_2$, forming Cs-O$_2$-Li as enhanced NEA layer. Measurements yielded an increase in lifetime by a factor of up to 19 $\pm$ 2 and an increase in extracted charge by a factor of up to 16.5 $\pm$ 2.4, without significant reduction of $\eta$. This performance is equal to or better than that reported for other enhanced NEA layers so far.

The ability to detect and engineer chirality plays a defining role in understanding nature, presenting itself in various phenomena ranging from the formation of DNA to enzymatic activities and digital encoding. Inducing a chiral response using nanophotonic practices typically entails lithographic efforts to create micro- and nano-structures that break mirror symmetry. Even with such configurations and despite the ubiquitousness of thermal radiation, generating chiral light at mid-infrared frequencies through thermal emission remains a challenge, since incandescence is by nature incoherent and unpolarized. In contrast to lithography-based approaches, we demonstrate that unpatterned twisted bilayers of in-plane anisotropic materials serve as a new material platform for intrinsic mid-infrared chirality engineering. Via absorption spectroscopy as well as direct thermal emission measurements, we report large circular dichroism and asymmetric circularly polarized thermal emission from exfoliated twisted bilayers of $\alpha$-molybdenum trioxide ($\alpha$-MoO$_3$). These findings highlight the potential of twisted layered van der Waals materials for polarization control and advanced thermal infrared technologies.

Data management is a critical component of modern experimental workflows. As data generation rates increase, transferring data from acquisition servers to processing servers via conventional file-based methods is becoming increasingly impractical. The 4D Camera at the National Center for Electron Microscopy (NCEM) generates data at a nominal rate of 480 Gbit/s (87,000 frames/s) producing a 700 GB dataset in fifteen seconds. To address the challenges associated with storing and processing such quantities of data, we developed a streaming workflow that utilizes a high-speed network to connect the 4D Camera's data acquisition (DAQ) system to supercomputing nodes at the National Energy Research Scientific Computing Center (NERSC), bypassing intermediate file storage entirely. In this work, we demonstrate the effectiveness of our streaming pipeline in a production setting through an hour-long experiment that generated over 10 TB of raw data, yielding high-quality datasets suitable for advanced analyses. Additionally, we compare the efficacy of this streaming workflow against the conventional file-transfer workflow by conducting a post-mortem analysis on historical data from experiments performed by real users. Our findings show that the streaming workflow significantly improves data turnaround time, enables real-time decision-making, and minimizes the potential for human error by eliminating manual user interactions.

We study a sequential decision-making problem for a profit-maximizing operator of an Autonomous Mobility-on-Demand system. Optimizing a central operator's vehicle-to-request dispatching policy requires efficient and effective fleet control strategies. To this end, we employ a multi-agent Soft Actor-Critic algorithm combined with weighted bipartite matching. We propose a novel vehicle-based algorithm architecture and adapt the critic's loss function to appropriately consider global actions. Furthermore, we extend our algorithm to incorporate rebalancing capabilities. Through numerical experiments, we show that our approach outperforms state-of-the-art benchmarks by up to 12.9% for dispatching and up to 38.9% with integrated rebalancing.

Recent advancements in detector technology have significantly increased the size and complexity of experimental data, and high-performance computing (HPC) provides a path towards more efficient and timely data processing. However, movement of large data sets from acquisition systems to HPC centers introduces bottlenecks owing to storage I/O at both ends. This manuscript introduces a streaming workflow designed for an high data rate electron detector that streams data directly to compute node memory at the National Energy Research Scientific Computing Center (NERSC), thereby avoiding storage I/O. The new workflow deploys ZeroMQ-based services for data production, aggregation, and distribution for on-the-fly processing, all coordinated through a distributed key-value store. The system is integrated with the detector's science gateway and utilizes the NERSC Superfacility API to initiate streaming jobs through a web-based frontend. Our approach achieves up to a 14-fold increase in data throughput and enhances predictability and reliability compared to a I/O-heavy file-based transfer workflow. Our work highlights the transformative potential of streaming workflows to expedite data analysis for time-sensitive experiments.

The (Local) Lifting Property ((L)LP) is introduced by Kirchberg and deals with lifting completely positive maps. We give a characterization of the (L)LP in terms of lifting $\ast$-homomorphisms. We use it to prove that if $A$ and $B$ have the LP and $F$ is their finite-dimensional C*-subalgebra, then $A\ast_F B$ has the LP. This answers a question of Ozawa. We prove that Exel's soft tori have the LP. As a consequence we obtain that $C^*(F_n\times F_n)$ is inductive limit of RFD C*-algebras with the LP. We prove that for a class of C*-algebras including $C^*(F_n\times F_n)$ and all contractible C*-algebras, the LLP is equivalent to Ext being a group. As byproduct of methods developed in the paper we generalize Kirchberg's theorem about extensions with the WEP, give short proofs of several, old and new, facts about soft tori, new unified proofs of Li and Shen's characterization of RFD property of free products amalgamated over a finite-dimensional subalgebra and Blackadar's characterization of semiprojectivity of them.

We study the robustness of deep reinforcement learning algorithms against distribution shifts within contextual multi-stage stochastic combinatorial optimization problems from the operations research domain. In this context, risk-sensitive algorithms promise to learn robust policies. While this field is of general interest to the reinforcement learning community, most studies up-to-date focus on theoretical results rather than real-world performance. With this work, we aim to bridge this gap by formally deriving a novel risk-sensitive deep reinforcement learning algorithm while providing numerical evidence for its efficacy. Specifically, we introduce discrete Soft Actor-Critic for the entropic risk measure by deriving a version of the Bellman equation for the respective Q-values. We establish a corresponding policy improvement result and infer a practical algorithm. We introduce an environment that represents typical contextual multi-stage stochastic combinatorial optimization problems and perform numerical experiments to empirically validate our algorithm's robustness against realistic distribution shifts, without compromising performance on the training distribution. We show that our algorithm is superior to risk-neutral Soft Actor-Critic as well as to two benchmark approaches for robust deep reinforcement learning. Thereby, we provide the first structured analysis on the robustness of reinforcement learning under distribution shifts in the realm of contextual multi-stage stochastic combinatorial optimization problems., Comment: 11 pages, 8 figures

As a broader set of applications from simulations to data analysis and machine learning require more parallel computational capability, the demand for interactive and urgent high performance computing (HPC) continues to increase. This paper overviews the progress made so far and elucidates the challenges and opportunities for greater integration of interactive and urgent HPC policies, techniques, and technologies into HPC ecosystems., Comment: 10 pages, submitted to IEEE CiSE journal

We study vehicle dispatching in autonomous mobility on demand (AMoD) systems, where a central operator assigns vehicles to customer requests or rejects these with the aim of maximizing its total profit. Recent approaches use multi-agent deep reinforcement learning (MADRL) to realize scalable yet performant algorithms, but train agents based on local rewards, which distorts the reward signal with respect to the system-wide profit, leading to lower performance. We therefore propose a novel global-rewards-based MADRL algorithm for vehicle dispatching in AMoD systems, which resolves so far existing goal conflicts between the trained agents and the operator by assigning rewards to agents leveraging a counterfactual baseline. Our algorithm shows statistically significant improvements across various settings on real-world data compared to state-of-the-art MADRL algorithms with local rewards. We further provide a structural analysis which shows that the utilization of global rewards can improve implicit vehicle balancing and demand forecasting abilities. Our code is available at https://github.com/tumBAIS/GR-MADRL-AMoD., Comment: 22 pages, 6 figures, extended version of paper accepted at the 6th Learning for Dynamics & Control Conference (L4DC 2024)

The Next Generation Very Large Array (ngVLA) is a planned radio interferometer providing unprecedented sensitivity at wavelengths between 21 cm and 3 mm. Its 263 antenna element array will be spatially distributed across North America to enable both superb low surface brightness recovery and sub-milliarcsecond angular resolution imaging. The project was developed by the international astronomy community under the lead of the National Radio Astronomy Observatory (NRAO), and is anticipated to be built between 2027 and 2037. Two workshops have been held in 2022 and 2023 with the goal to discuss and consolidate the scientific interests in the ngVLA within the German astronomical community. This community paper constitutes a collection of 48 science ideas which the German community aims to pursue with the ngVLA in the 2030s. This is not a complete list and the ideas are not developed at the level of a "Science Book", such that the present document is mainly meant provide a basis for further discussion within the community. As such, additional contributions are welcome, and will be considered for inclusion in future revisions., Comment: Version 2.0 (status June 18, 2024): 169 pages, comments and future contributions welcome [v2.0: 7 new science cases added, some minor revisions to other chapters]

Phase retardation is a cornerstone of modern optics, yet, at mid-infrared (mid-IR) frequencies, it remains a major challenge due to the scarcity of simultaneously transparent and birefringent crystals. Most materials resonantly absorb due to lattice vibrations occurring at mid-IR frequencies, and natural birefringence is weak, calling for hundreds of microns to millimeters-thick phase retarders for sufficient polarization rotation. We demonstrate mid-IR phase retardation with flakes of $\alpha$-molybdenum trioxide ($\alpha$-MoO$_3$) that are more than ten times thinner than the operational wavelength, achieving 90 degrees polarization rotation within one micrometer of material. We report conversion ratios above 50% in reflection and transmission mode, and wavelength tunability by several micrometers. Our results showcase that exfoliated flakes of low-dimensional crystals can serve as a platform for mid-IR miniaturized integrated polarization control., Comment: 8 pages, 5 figures

High-quality low-dimensional layered and van der Waals materials are typically exfoliated, with sample cross sectional areas on the order of tens to hundreds of microns. The small size of flakes makes the experimental characterization of their dielectric properties unsuitable with conventional spectroscopic ellipsometry, due to beam-sample size mismatch and non-uniformities of the crystal axes. Previously, the experimental measurement of the dielectrirc permittivity of such microcrystals was carried out with near-field tip-based scanning probes. These measurements are sensitive to external conditions like vibrations and temperature, and require non-deterministic numerical fitting to some a priori known model. We present an alternative method to extract the in-plane dielectric permittivity of van der Waals microcrystals, based on identifying reflectance minima in spectroscopic measurements. Our method does not require complex fitting algorithms nor near field tip-based measurements and accommodates for small-area samples. We demonstrate the robustness of our method using hexagonal boron nitride and {\alpha}-MoO3, and recover their dielectric permittivities that are close to literature values., Comment: 10 pages, 4 figure and 3 tables