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To improve the utilization of public transportation systems (PTSs) during off-peak hours, we present an algorithmic framework that designs PTSs with hybrid transportation units (HTUs), which can transport passengers or freight by leveraging a flexible interior. Against this background, we study a capacitated network design problem to enable cargo-hitching in existing PTSs. Specifically, we study a setting with fixed vehicle routes and timetables in which vehicles can be equipped with HTUs to enable cargo-hitching. We optimize the network design from a total cost perspective to account for normalized network design costs tied to the investment in HTUs and freight routing costs. We present an algorithmic framework that encodes some of the problem's constraints in a spatially and temporally expanded, layered graph, and solves the resulting network design problem with a price-and-branch algorithm. We apply this framework to a case study based on the subway network in the city of Munich. Our algorithm outscales commercial solvers by a factor of six and yields integer feasible solutions with a median integrality gap of less than 1.02% for all instances. We show that cargo-hitching with HTUs increases the utilization of PTSs, especially during off-peak hours, without cannibalizing passenger service level and quality. Moreover, we present a sensitivity analysis that indicates that cargo-hitching is worthwhile if truck-based transport occurs at an externality cost of more than EUR 1.6 per vehicle and kilometer and loading and unloading costs of less than EUR 2 per passenger equivalent., Comment: 34 pages, 11 figures

Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters that match observed data and align with prior knowledge. Unlike traditional Bayesian inference, SBI only needs access to simulations from the model and does not require evaluations of the likelihood-function. In addition, SBI algorithms do not require gradients through the simulator, allow for massive parallelization of simulations, and can perform inference for different observations without further simulations or training, thereby amortizing inference. Over the past years, we have developed, maintained, and extended $\texttt{sbi}$, a PyTorch-based package that implements Bayesian SBI algorithms based on neural networks. The $\texttt{sbi}$ toolkit implements a wide range of inference methods, neural network architectures, sampling methods, and diagnostic tools. In addition, it provides well-tested default settings but also offers flexibility to fully customize every step of the simulation-based inference workflow. Taken together, the $\texttt{sbi}$ toolkit enables scientists and engineers to apply state-of-the-art SBI methods to black-box simulators, opening up new possibilities for aligning simulations with empirically observed data.

Millimeter-wave refracting telescopes targeting the degree-scale structure of the cosmic microwave background (CMB) have recently grown to diffraction-limited apertures of over 0.5 meters. These instruments are entirely housed in vacuum cryostats to support their sub-kelvin bolometric detectors and to minimize radiative loading from thermal emission due to absorption loss in their transmissive optical elements. The large vacuum window is the only optical element in the system at ambient temperature, and therefore minimizing loss in the window is crucial for maximizing detector sensitivity. This motivates the use of low-loss polymer materials and a window as thin as practicable. However, the window must simultaneously meet the requirement to keep sufficient vacuum, and therefore must limit gas permeation and remain mechanically robust against catastrophic failure under pressure. We report on the development of extremely thin composite polyethylene window technology that meets these goals. Two windows have been deployed for two full observing seasons on the BICEP3 and BA150 CMB telescopes at the South Pole. On BICEP3, the window has demonstrated a 6% improvement in detector sensitivity., Comment: 20 pages, 12 figures, 4 tables

Uncertainty Quantification (UQ) is crucial for reliable image segmentation. Yet, while the field sees continual development of novel methods, a lack of agreed-upon benchmarks limits their systematic comparison and evaluation: Current UQ methods are typically tested either on overly simplistic toy datasets or on complex real-world datasets that do not allow to discern true uncertainty. To unify both controllability and complexity, we introduce Arctique, a procedurally generated dataset modeled after histopathological colon images. We chose histopathological images for two reasons: 1) their complexity in terms of intricate object structures and highly variable appearance, which yields challenging segmentation problems, and 2) their broad prevalence for medical diagnosis and respective relevance of high-quality UQ. To generate Arctique, we established a Blender-based framework for 3D scene creation with intrinsic noise manipulation. Arctique contains 50,000 rendered images with precise masks as well as noisy label simulations. We show that by independently controlling the uncertainty in both images and labels, we can effectively study the performance of several commonly used UQ methods. Hence, Arctique serves as a critical resource for benchmarking and advancing UQ techniques and other methodologies in complex, multi-object environments, bridging the gap between realism and controllability. All code is publicly available, allowing re-creation and controlled manipulations of our shipped images as well as creation and rendering of new scenes., Comment: 13 pages, 4 figures

Scanning Electron Microscopy (SEM) is a widely used tool for nanoparticle characterization, but long-term directional drift can compromise image quality. We present a novel algorithm for post-imaging drift correction in SEM nanoparticle imaging. Our approach combines multiple rapidly acquired, noisy images to produce a single high-quality overlay through redundant cross-correlation, preventing drift-induced distortions. The preservation of critical geometrical properties and accurate imaging of surface features were verified using Atomic Force Microscopy. On platinum nanoparticles with diameters of 300 to 1000 nm, significant improvements in the mean-based signal-to-noise ratio (SNR) were achieved, increasing from 4.4 dB in single images to 11.3 dB when overlaying five images. This method offers a valuable tool for enhancing SEM image quality in nanoparticle research and metrology, particularly in settings without specialized hardware-based drift correction.

We use a custom-made calibrator to measure individual detectors' polarization angles of BICEP3, a small aperture telescope observing the cosmic microwave background (CMB) at 95GHz from the South Pole. We describe our calibration strategy and the statistical and systematic uncertainties associated with the measurement. We reach an unprecedented precision for such measurement on a CMB experiment, with a repeatability for each detector pair of $0.02\deg$. We show that the relative angles measured using this method are in excellent agreement with those extracted from CMB data. Because the absolute measurement is currently limited by a systematic uncertainty, we do not derive cosmic birefringence constraints from BICEP3 data in this work. Rather, we forecast the sensitivity of BICEP3 sky maps for such analysis. We investigate the relative contributions of instrument noise, lensing, and dust, as well as astrophysical and instrumental systematics. We also explore the constraining power of different angle estimators, depending on analysis choices. We establish that the BICEP3 2-year dataset (2017--2018) has an on-sky sensitivity to the cosmic birefringence angle of $\sigma = 0.078\deg$, which could be improved to $\sigma = 0.055\deg$ by adding all of the existing BICEP3 data (through 2023). Furthermore, we emphasize the possibility of using the BICEP3 sky patch as a polarization calibration source for CMB experiments, which with the present data could reach a precision of $0.035\deg$. Finally, in the context of inflation searches, we investigate the impact of detector-to-detector variations in polarization angles as they may bias the tensor-to-scalar ratio r. We show that while the effect is expected to remain subdominant to other sources of systematic uncertainty, it can be reliably calibrated using polarization angle measurements such as the ones we present in this paper., Comment: 29 Pages, 17 Figures, 6 Tables, as submitted to PRD

We extend an existing thermophysical activity model of comet 67P/Churyumov-Gerasimenko to include pressure buildup inside the pebbles making up the nucleus. We test various quantities of H$_{2}$O and CO$_{2}$, in order to simulate the material inside and outside of proposed water enriched bodies (WEBs). We find that WEBs can reproduce the peak water flux observed by Rosetta, but that the addition of a time-resolved heat-flow reduces the water fluxes away from perihelion as compared to the previously assumed equilibrium model. Our modelled WEBs eject dust continuously but with a rate that is much higher than the observed erosion and mass-loss, thus requiring an active area smaller than the total comet surface area or very large quantities of dust fallback. When simulating the CO$_{2}$-rich non-WEB material, we only find the ejection of large chunks under specific conditions (e.g.~low diffusivities between the pebbles or intense insolation at southern summer), whilst we also find CO$_{2}$ outgassing rates that are much greater than observed. This is a general problem in models where CO$_{2}$ drives erosion, alongside difficulties in simultaneously ejecting chunks from deep whilst eroding the surface layer. We therefore conclude that ejection of chunks by CO$_{2}$ must be a localised phenomenon, occurring separately in space or time from surface erosion and water emission. Simulating the global production rates of gas, dust, and chunks from a comet thus remains challenging, while the activity mechanism is shown to be very sensitive to the material structure (i.e.~porosity and diffusivity) at various scales., Comment: Accepted for publication in MNRAS, 14 pages, 9 figures

Today's most advanced vision-language models (VLMs) remain proprietary. The strongest open-weight models rely heavily on synthetic data from proprietary VLMs to achieve good performance, effectively distilling these closed VLMs into open ones. As a result, the community has been missing foundational knowledge about how to build performant VLMs from scratch. We present Molmo, a new family of VLMs that are state-of-the-art in their class of openness. Our key contribution is a collection of new datasets called PixMo, including a dataset of highly detailed image captions for pre-training, a free-form image Q&A dataset for fine-tuning, and an innovative 2D pointing dataset, all collected without the use of external VLMs. The success of our approach relies on careful modeling choices, a well-tuned training pipeline, and, most critically, the quality of our newly collected datasets. Our best-in-class 72B model not only outperforms others in the class of open weight and data models, but also outperforms larger proprietary models including Claude 3.5 Sonnet, and Gemini 1.5 Pro and Flash, second only to GPT-4o based on both academic benchmarks and on a large human evaluation. Our model weights, new datasets, and source code are available at https://molmo.allenai.org/blog., Comment: Updated with ablations and more technical details

The BICEP3 and BICEP Array polarimeters are small-aperture refracting telescopes located at the South Pole designed to measure primordial gravitational wave signatures in the Cosmic Microwave Background (CMB) polarization, predicted by inflation. Constraining the inflationary signal requires not only excellent sensitivity, but also careful control of instrumental systematics. Both instruments use antenna-coupled orthogonally polarized detector pairs, and the polarized sky signal is reconstructed by taking the difference in each detector pair. As a result, the differential response between detectors within a pair becomes an important systematic effect we must control. Additionally, mapping the intensity and polarization response in regions away from the main beam can inform how sidelobe levels affect CMB measurements. Extensive calibration measurements are taken in situ every austral summer for control of instrumental systematics and instrument characterisation. In this work, we detail the set of beam calibration measurements that we conduct on the BICEP receivers, from deep measurements of main beam response to polarized beam response and sidelobe mapping. We discuss the impact of these measurements for instrumental systematics studies and design choices for future CMB receivers., Comment: 13 pages, 7 figures, 1 table, Proceedings paper SPIE 2024

Machine-learning interatomic potentials (MLPs) are fast, data-driven surrogate models of atomistic systems' potential energy surfaces that can accelerate ab-initio molecular dynamics (MD) simulations by several orders of magnitude. The performance of MLPs is commonly measured as the prediction error in energies and forces on data not used in their training. While low prediction errors on a test set are necessary, they do not guarantee good performance in MD simulations. The latter requires physically motivated performance measures obtained from running accelerated simulations. However, the adoption of such measures has been limited by the effort and domain knowledge required to calculate and interpret them. To overcome this limitation, we present a benchmark that automatically quantifies the performance of MLPs in MD simulations of a liquid-liquid phase transition in hydrogen under pressure, a challenging benchmark system. The benchmark's h-llpt-24 dataset provides reference geometries, energies, forces, and stresses from density functional theory MD simulations at different temperatures and mass densities. The benchmark's Python code automatically runs MLP-accelerated MD simulations and calculates, quantitatively compares and visualizes pressures, stable molecular fractions, diffusion coefficients, and radial distribution functions. Employing this benchmark, we show that several state-of-the-art MLPs fail to reproduce the liquid-liquid phase transition.

Measurements of B-mode polarization in the CMB sourced from primordial gravitational waves would provide information on the energy scale of inflation and its potential form. To achieve these goals, one must carefully characterize the Galactic foregrounds, which can be distinguished from the CMB by conducting measurements at multiple frequencies. BICEP Array is the latest-generation multi-frequency instrument of the BICEP/Keck program, which specifically targets degree-scale primordial B-modes in the CMB. In its final configuration, this telescope will consist of four small-aperture receivers, spanning frequency bands from 30 to 270 GHz. The 220/270 GHz receiver designed to characterize Galactic dust is currently undergoing commissioning at Stanford University and is scheduled to deploy to the South Pole during the 2024--2025 austral summer. Here, we will provide an overview of this high-frequency receiver and discuss the integration status and test results as it is being commissioned.

This paper introduces the Bosch street dataset (BSD), a novel multi-modal large-scale dataset aimed at promoting highly automated driving (HAD) and advanced driver-assistance systems (ADAS) research. Unlike existing datasets, BSD offers a unique integration of high-resolution imaging radar, lidar, and camera sensors, providing unprecedented 360-degree coverage to bridge the current gap in high-resolution radar data availability. Spanning urban, rural, and highway environments, BSD enables detailed exploration into radar-based object detection and sensor fusion techniques. The dataset is aimed at facilitating academic and research collaborations between Bosch and current and future partners. This aims to foster joint efforts in developing cutting-edge HAD and ADAS technologies. The paper describes the dataset's key attributes, including its scalability, radar resolution, and labeling methodology. Key offerings also include initial benchmarks for sensor modalities and a development kit tailored for extensive data analysis and performance evaluation, underscoring our commitment to contributing valuable resources to the HAD and ADAS research community.

Let $L\subseteq \mathbb{R}^{n}$ an even lattice and $T_{L}=\mathbb{R}^{n}/L$ the associated torus. Associated with $L$ we construct $T_{L}$--kernel on a hyperfinite factor type $\mathcal{A}_{L}$, i.e. a monomorphism $T_{L}\to\mathsf{Out}(\mathcal{A}_{L})$, and compute Sutherland's obstruction class in $H^{3}_{\mathrm{Borel}}(T_{L},\mathbb{T})\cong H^{4}(BT_{L} ,\mathbb{Z})$, which is an invariant of the $T_{L}$--kernel and an obstruction to the existence of a twisted crossed product by $T_{L}$. As a Corollary, we obtain that for any $n$-torus $T$ any class in $H^{3}_{\mathrm{Borel}}(T,\mathbb{T})$ arises as an obstruction for a $T$-kernel on the hyperfinite type III${}_{1}$ factor $R$. The construction is an analogue of the construction of Vaughan Jones for finite groups on the hyperfinite type II${}_{1}$ factor but is also motivated by and has applications to conformal nets. Namely, there is an associated local extension $\mathcal{A}_{L}\supseteq \mathcal{A}_{\mathbb{R}^n}$ of conformal nets and the $T_{L}$--kernel corresponds to a family of $T_{L^\ast}$--twisted sectors representations whose anomaly (obstruction) can be identified with the inner product on $L$ seen as a class in $H^{4}(BT_{L},\mathbb{Z})\cong \operatorname{Sym}^{2}(L,\mathbb{Z})$., Comment: Comments are welcome!

The BICEP/$\textit{Keck}$ (BK) series of cosmic microwave background (CMB) polarization experiments has, over the past decade and a half, produced a series of field-leading constraints on cosmic inflation via measurements of the "B-mode" polarization of the CMB. Primordial B modes are directly tied to the amplitude of primordial gravitational waves (PGW), their strength parameterized by the tensor-to-scalar ratio, $r$, and thus the energy scale of inflation. Having set the most sensitive constraints to-date on $r$, $\sigma(r)=0.009$ ($r_{0.05}<0.036, 95\%$ C.L.) using data through the 2018 observing season ("BK18"), the BICEP/$\textit{Keck}$ program has continued to improve its dataset in the years since. We give a brief overview of the BK program and the "BK18" result before discussing the program's ongoing efforts, including the deployment and performance of the $\textit{Keck Array}$'s successor instrument, BICEP Array, improvements to data processing and internal consistency testing, new techniques such as delensing, and how those will ultimately serve to allow BK reach $\sigma(r) \lesssim 0.003$ using data through the 2027 observing season., Comment: 9 pages, 5 figures. Contribution to the 2024 Cosmology session of the 58th Rencontres de Moriond

Carbon implantation at the nanoscale is highly desired for the engineering of defect-based qubits in a variety of materials, including silicon, diamond, SiC and hBN. However, the lack of focused carbon ion beams does not allow for the full disclosure of their potential for application in quantum technologies. Here, we develop and use a carbon source for focused ion beams for the simultaneous creation of two types of quantum emitters in silicon, the W and G centers. Furthermore, we apply a multi-step implantation protocol for the programmable activation of the G centers with sub-100- nm resolution. This approach provides a route for significant enhancement of the creation yield of single G centers in carbon-free silicon wafers. Our experimental demonstration is an important step towards nanoscale engineering of telecom quantum emitters in silicon of high crystalline quality and isotope purity., Comment: 7 pages, 5 figures

Generative models are invaluable in many fields of science because of their ability to capture high-dimensional and complicated distributions, such as photo-realistic images, protein structures, and connectomes. How do we evaluate the samples these models generate? This work aims to provide an accessible entry point to understanding popular sample-based statistical distances, requiring only foundational knowledge in mathematics and statistics. We focus on four commonly used notions of statistical distances representing different methodologies: Using low-dimensional projections (Sliced-Wasserstein; SW), obtaining a distance using classifiers (Classifier Two-Sample Tests; C2ST), using embeddings through kernels (Maximum Mean Discrepancy; MMD), or neural networks (Fr\'echet Inception Distance; FID). We highlight the intuition behind each distance and explain their merits, scalability, complexity, and pitfalls. To demonstrate how these distances are used in practice, we evaluate generative models from different scientific domains, namely a model of decision-making and a model generating medical images. We showcase that distinct distances can give different results on similar data. Through this guide, we aim to help researchers to use, interpret, and evaluate statistical distances for generative models in science.

Stellar multiplicity is a key aspect of exoplanet diversity, as the presence of more than one star in a planetary system can have both devastating and positive effects on its formation and evolution. In this paper, we present the results of a Lucky Imaging survey of 212 exoplanet host stars performed with AstraLux at CAHA 2.2 m. The survey includes data from seven observing epochs between August 2015 and September 2020, and data for individual targets from four earlier observing epochs. The targets of this survey are nearby, bright, solar-like stars with high proper motions. In total, we detected 46 co-moving companions of 43 exoplanet host stars. Accordingly, this survey shows that the minimum multiplicity rate of exoplanet host stars is 20 $\pm$ 3 %. In total, 33 binary and ten hierarchical triple star systems with exoplanets have been identified. All companions were found to have a common proper motion with the observed exoplanet host stars, and with our astrometry we even find evidence of orbital motion for 28 companions. For all targets, we determined the detection limit and explore the detection space for possible additional companions of these stars. Based on the reached detection limit, additional co-moving companions beyond the detected ones can be excluded around all observed exoplanet host stars. The increasing number of exoplanets discovered in multiple stellar systems suggests that the formation of planets in such systems is by no means rare, but common. Therefore, our study highlights the need to consider stellar multiplicity in future studies of exoplanet habitability., Comment: 20 pages, 9 figures, 13 tables, accepted for publication in MNRAS

We introduce an approach for analyzing the responses of dynamical systems to external perturbations that combines score-based generative modeling with the Generalized Fluctuation-Dissipation Theorem (GFDT). The methodology enables accurate estimation of system responses, including those with non-Gaussian statistics. We numerically validate our approach using time-series data from three different stochastic partial differential equations of increasing complexity: an Ornstein-Uhlenbeck process with spatially correlated noise, a modified stochastic Allen-Cahn equation, and the 2D Navier-Stokes equations. We demonstrate the improved accuracy of the methodology over conventional methods and discuss its potential as a versatile tool for predicting the statistical behavior of complex dynamical systems., Comment: In press. Includes supplementary material in the file supp_material.pdf

We introduce ensembles within score-based sampling methods to develop gradient-free approximate sampling techniques that leverage the collective dynamics of particle ensembles to compute approximate reverse diffusion drifts. We introduce the underlying methodology, emphasizing its relationship with generative diffusion models and the previously introduced F\"ollmer sampler. We demonstrate the efficacy of ensemble strategies through various examples, ranging from low- to medium-dimensionality sampling problems, including multi-modal and highly non-Gaussian probability distributions, and provide comparisons to traditional methods like NUTS. Our findings highlight the potential of ensemble strategies for modeling complex probability distributions in situations where gradients are unavailable. Finally, we showcase its application in the context of Bayesian inversion problems within the geophysical sciences.

RW Aurigae is a young stellar system containing a classical T Tauri star as the primary component. It shows deep, irregular dimmings, first detected in 2010. At the University Observatory Jena, we carried out optical follow-up observations. We performed multiband (BVRI) photometry of the system with the Cassegrain-Teleskop-Kamera II and the Schmidt-Teleskop-Kamera between September 2016 and April 2019, as well as spectroscopy, using the Fibre Linked \'ECHelle Astronomical Spectrograph between September 2016 and April 2018. We present the apparent photometry of RW Aur, which is consistent with and complementary to photometric data from the American Association of Variable Star Observers. The V-band magnitude of RW Aur changed by up to three magnitudes during the timespan of our monitoring campaign. For the observing epochs 2016/2017 and 2017/2018 we report a decreasing brightness, while in the epoch 2018/2019 the system remained in a relatively constant bright state. In the color-magnitude diagram, we see that RW Aur lies close to a track for grey extinction. The spectra show a decreasing equivalent width of the H$\alpha$ emission line for decreasing brightness, whereas the equivalent width of the [OI] line increases, indicating an increased outflow activity during the obscuration. Both gives further evidence for the favored theory that the obscurations are caused by a hot dusty wind emerging from the inner disk., Comment: 14 pages, 6 figures, 5 tables, accepted for publication in AN

Because of their high photon flux, X-ray free-electron lasers (FEL) allow to resolve the structure of individual nanoparticles via coherent diffractive imaging (CDI) within a single X-ray pulse. Since the inevitable rapid destruction of the sample limits the achievable resolution, a thorough understanding of the spatiotemporal evolution of matter on the nanoscale following the irradiation is crucial. We present a technique to track X-ray induced structural changes in time and space by recording two consecutive diffraction patterns of the same single, free-flying nanoparticle, acquired separately on two large-area detectors opposite to each other, thus examining both the initial and evolved particle structure. We demonstrate the method at the extreme ultraviolet (XUV) and soft X-ray Free-electron LASer in Hamburg (FLASH), investigating xenon clusters as model systems. By splitting a single XUV pulse, two diffraction patterns from the same particle can be obtained. For focus intensities of about $2\cdot10^{12}\,\text{W/cm}^2$ we observe still largely intact clusters even at the longest delays of up to 650 picoseconds of the second pulse, indicating that in the highly absorbing systems the damage remains confined to one side of the cluster. Instead, in case of five times higher flux, the diffraction patterns show clear signatures of disintegration, namely increased diameters and density fluctuations in the fragmenting clusters. Future improvements to the accessible range of dynamics and time resolution of the approach are discussed.