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Assigning upper elementary grade teachers to teach their strongest subjects to multiple classes ("departmentalizing"), rather than teaching all subjects to a single class, ("self-contained instruction") could mean more specialized instructional expertise in the classroom or focus for teacher planning time and professional development. This study examined the experiences of 90 schools that either voluntarily switched to departmentalized instruction for up to two years or continued with self-contained teaching in 4th and 5th grade classes beginning in fall 2019. The findings show that schools that switched were able to implement the key steps needed to departmentalize instruction but struggled with aspects of the approach. Teachers' reported experiences were consistent with some of both the benefits and challenges that prior research had hypothesized. Although the pandemic significantly disrupted instruction and the study activities, school and teacher experiences during this challenging time may inform schools and districts considering adopting departmentalized instruction.

This appendix is a companion to the report "Evaluation of Departmentalized Instruction in Elementary Schools: Exploring Implementation Experiences." The appendix provides additional information about the study, which sought to explore schools' implementation of departmentalized instruction, teachers' experiences with the strategy, and how the strategy was related to key outcomes. This additional information includes details on departmentalized instruction (Section A), study design and execution (Section B), key findings presented in the report (Section C), and additional findings that are not included in the report but provide important context (Section D).

Estimating collision probabilities between robots and environmental obstacles or other moving agents is crucial to ensure safety during path planning. This is an important building block of modern planning algorithms in many application scenarios such as autonomous driving, where noisy sensors perceive obstacles. While many approaches exist, they either provide too conservative estimates of the collision probabilities or are computationally intensive due to their sampling-based nature. To deal with these issues, we introduce Deep Collision Probability Fields, a neural-based approach for computing collision probabilities of arbitrary objects with arbitrary unimodal uncertainty distributions. Our approach relegates the computationally intensive estimation of collision probabilities via sampling at the training step, allowing for fast neural network inference of the constraints during planning. In extensive experiments, we show that Deep Collision Probability Fields can produce reasonably accurate collision probabilities (up to 10^{-3}) for planning and that our approach can be easily plugged into standard path planning approaches to plan safe paths on 2-D maps containing uncertain static and dynamic obstacles. Additional material, code, and videos are available at https://sites.google.com/view/ral-dcpf., Comment: Preprint version of a paper accepted to the IEEE Robotics and Automation Letters

Open microcavities offer great potential for the exploration and utilization of efficient spin-photon interfaces with Purcell-enhanced quantum emitters thanks to their large spectral and spatial tunability combined with high versatility of sample integration. However, a major challenge for this platform is the sensitivity to cavity length fluctuations in the cryogenic environment, which leads to cavity resonance frequency variations and thereby a lowered averaged Purcell enhancement. This work presents a closed-cycle cryogenic fiber-based microcavity setup, which is in particular designed for a low passive vibration level, while still providing large tunability and flexibility in fiber and sample integration, and high photon collection efficiency from the cavity mode. At temperatures below 10 Kelvin, a stability level of around 25 picometer is reproducibly achieved in different setup configurations, including the extension with microwave control for manipulating the spin of cavity-coupled quantum emitters, enabling a bright photonic interface with optically active qubits., Comment: Authors Yanik Herrmann and Julius Fischer contributed equally, 14 pages, 9 figures

Bruns and Roddy constructed a $3$-generated modular ortholattice $L$ which cannot be embedded into any complete modular ortholattice. Motivated by their approach, we use shift operators to construct a $*$-regular $*$-ring $R$ of endomorphisms of an inner product space (which can be chosen as the Hilbert space $\ell^2$) such that direct finiteness fails for $R$., Comment: As observed by Wehrung, the shift has no quasi-inverse in the ring of row and column finite matrices. Thus, the claimed example does not work

Comparison studies in methodological research are intended to compare methods in an evidence-based manner, offering guidance to data analysts to select a suitable method for their application. To provide trustworthy evidence, they must be carefully designed, implemented, and reported, especially given the many decisions made in planning and running. A common challenge in comparison studies is to handle the ``failure'' of one or more methods to produce a result for some (real or simulated) data sets, such that their performances cannot be measured in those instances. Despite an increasing emphasis on this topic in recent literature (focusing on non-convergence as a common manifestation), there is little guidance on proper handling and interpretation, and reporting of the chosen approach is often neglected. This paper aims to fill this gap and provides practical guidance for handling method failure in comparison studies. In particular, we show that the popular approaches of discarding data sets yielding failure (either for all or the failing methods only) and imputing are inappropriate in most cases. We also discuss how method failure in published comparison studies -- in various contexts from classical statistics and predictive modeling -- may manifest differently, but is often caused by a complex interplay of several aspects. Building on this, we provide recommendations derived from realistic considerations on suitable fallbacks when encountering method failure, hence avoiding the need for discarding data sets or imputation. Finally, we illustrate our recommendations and the dangers of inadequate handling of method failure through two illustrative comparison studies.

Magnetic nanoparticle-based hyperthermia has emerged as a promising therapeutic modality for treating malignant solid tumors that exhibit resistance to conventional cancer treatments, including chemotherapy and radiation. Despite the clinical approval of superparamagnetic iron oxide nanoparticles (SPIONs) for the adjunct treatment of recurrent glioblastoma, their therapeutic potential is undercut by chemical synthesis-inherent limitations such as low saturation magnetization, superparamagnetic characteristics, and a wide nanoparticle size distribution. Here, we introduce an micromagnetic modelling-based SAF-MDP design with in-plane magnetization, optimized through specific uniaxial anisotropy adjustments to avert the spin-flop phenomenon and eliminate hysteresis-free hard-axis magnetization loops, paired with a mechanofluidic modeling approach to assess the alignment of the SAF-MDP to the applied alternating magnetic field (AMF). Magnetic Force Microscopy characterization provides unprecedented insights into the particle switching behaviour on a single particle scale. This comprehensive strategy spanning micromagnetics and advanced magnetic characterization enables the design of particles with heating efficiencies to approach the theoretical maximum, dictated by the saturation magnetization of the utilized materials and limited solely by the biologically acceptable frequencies and amplitudes of the oscillating magnetic field. Our work not only addresses the limitations encountered by previous methodologies but also sets the stage for the development of advanced SAF-MDP designs and alignment techniques. This opens a new avenue to hyperthermia-based cancer therapy, delineated only by the boundaries of physical laws and biological safety standards., Comment: 9 pages, 5 figures, 3 pages Methods

The combination of the global Pad\'e approximation of the Mittag-Leffler function with its addition formula for the case $\alpha<1$ yields significantly higher accuracy results for a given arbitrary order $n$. We present a solution in terms of a Mathematica notebook to determine the general structure of the system of linear equations to be solved, followed by an implementation as a {\tt{C++}} program using the {\tt{Eigen}} template library for linear algebra. For a comparison with contour integral solutions we present an implementation as a {\tt{C++}} program using the {\tt{boost}} library's quadrature package employing the Gauss-Kronrod-method., Comment: 23 pages

We introduce a constructive method for defining a global loop-integrand basis for scattering amplitudes, encompassing both planar and nonplanar contributions. Our approach utilizes a graph-based framework to establish a well-defined, non-redundant basis of integrands. This basis, constructed from a chosen set of non-redundant graphs together with a selection of irreducible scalar products, provides clear insights into various physical properties of scattering amplitudes and proves useful in multiple contexts, such as on-shell Ward identities and manifesting gauge-choice independence. A key advantage of our integrand basis is its ability to streamline the generalized unitarity method. Specifically, we can directly read off the coefficients of basis elements without resorting to ansatze or solving linear equations. This novel approach allows us to lift generalized unitarity cuts -- expressed as products of tree amplitudes -- to loop-level integrands, facilitating the use of the tree-level double copy to generate complete gravitational integrands at any loop order. This method circumvents the difficulties in identifying complete higher-loop-order gauge-theory integrands that adhere to the color-kinematics duality. Additionally, our cut-based organization is well-suited for expansion in hard or soft limits, aiding in the exploration of ultraviolet or classical limits of scattering amplitudes., Comment: 56 pages, 15 figures

Full waveform inversion (FWI) is a powerful tool for reconstructing material fields based on sparsely measured data obtained by wave propagation. For specific problems, discretizing the material field with a neural network (NN) improves the robustness and reconstruction quality of the corresponding optimization problem. We call this method NN-based FWI. Starting from an initial guess, the weights of the NN are iteratively updated to fit the simulated wave signals to the sparsely measured data set. For gradient-based optimization, a suitable choice of the initial guess, i.e., a suitable NN weight initialization, is crucial for fast and robust convergence. In this paper, we introduce a novel transfer learning approach to further improve NN-based FWI. This approach leverages supervised pretraining to provide a better NN weight initialization, leading to faster convergence of the subsequent optimization problem. Moreover, the inversions yield physically more meaningful local minima. The network is pretrained to predict the unknown material field using the gradient information from the first iteration of conventional FWI. In our computational experiments on two-dimensional domains, the training data set consists of reference simulations with arbitrarily positioned elliptical voids of different shapes and orientations. We compare the performance of the proposed transfer learning NN-based FWI with three other methods: conventional FWI, NN-based FWI without pretraining and conventional FWI with an initial guess predicted from the pretrained NN. Our results show that transfer learning NN-based FWI outperforms the other methods in terms of convergence speed and reconstruction quality.

The Blatter radical has been suggested as a building block in future molecular spintronic devices due to its radical character and expected long-spin lifetime. However, whether and how the radical character manifests itself in the charge transport and magnetotransport properties seems to depend on the environment or has not yet been studied. Here, we investigate single-molecule junctions of the Blatter radical molecule in a mechanically controlled break junction device at low temperature. Differential conductance spectroscopy on individual junctions shows two types of zero-bias anomalies attributed to Kondo resonances revealing the ability to retain the open-shell nature of the radical molecule in a two-terminal device. Additionally, a high negative magnetoresistance is also observed in junctions without showing a zero-bias peak. We posit that the high negative magnetoresistance is due to the effect of a singlet-triplet Kondo effect under magnetic field originating from a double-quantum-dot system consisting of a Blatter radical molecule with a strong correlation to a second side-coupled molecule. Our findings not only provide the possibility of using the Blatter radical in a two-terminal system under cryogenic conditions but also reveal the magnetotransport properties emerging from different configurations of the molecule inside a junction., Comment: 17 pages, 5 figures

We explore the optical dipole orientation of single photon emitters in monolayer MoS2 as produced by a focused helium ion beam. The single photon emitters can be understood as single sulfur vacancies. The corresponding far-field luminescence spectra reveal several photoluminescence lines below the dominating luminescence of the exciton in MoS2. These sub-bandgap emission lines were predicted by ab initio theory, but they have never been resolved in luminescence experiments because of their small amplitude. We reveal the lines by their dependence as a function of the photon energy and momentum as measured in the back focal plane of the optical circuitry. The agreement between theory and experiment suggests that the defect states interact strongly within the Brillouin zone.

Neural networks have recently been employed as material discretizations within adjoint optimization frameworks for inverse problems and topology optimization. While advantageous regularization effects and better optima have been found for some inverse problems, the benefit for topology optimization has been limited -- where the focus of investigations has been the compliance problem. We demonstrate how neural network material discretizations can, under certain conditions, find better local optima in more challenging optimization problems, where we here specifically consider acoustic topology optimization. The chances of identifying a better optimum can significantly be improved by running multiple partial optimizations with different neural network initializations. Furthermore, we show that the neural network material discretization's advantage comes from the interplay with the Adam optimizer and emphasize its current limitations when competing with constrained and higher-order optimization techniques. At the moment, this discretization has only been shown to be beneficial for unconstrained first-order optimization.

Traditional cosmic ray filtering algorithms used in X-ray imaging detectors aboard space telescopes perform event reconstruction based on the properties of activated pixels above a certain energy threshold, within 3x3 or 5x5 pixel sliding windows. This approach can reject up to 98% of the cosmic ray background. However, the remaining unrejected background constitutes a significant impediment to studies of low surface brightness objects, which are especially prevalent in the high-redshift universe. The main limitation of the traditional filtering algorithms is their ignorance of the long-range contextual information present in image frames. This becomes particularly problematic when analyzing signals created by secondary particles produced during interactions of cosmic rays with body of the detector. Such signals may look identical to the energy deposition left by X-ray photons, when one considers only the properties within the small sliding window. Additional information is present, however, in the spatial and energy correlations between signals in different parts of the frame, which can be accessed by modern machine learning (ML) techniques. In this work, we continue the development of an ML-based pipeline for cosmic ray background mitigation. Our latest method consist of two stages: first, a frame classification neural network is used to create class activation maps (CAM), localizing all events within the frame; second, after event reconstruction, a random forest classifier, using features obtained from CAMs, is used to separate X-ray and cosmic ray features. The method delivers >40% relative improvement over traditional filtering in background rejection in standard 0.3-10keV energy range, at the expense of only a small (<2%) level of lost X-ray signal. Our method also provides a convenient way to tune the cosmic ray rejection threshold to adapt to a user's specific scientific needs., Comment: To appear in SPIE Astronomical Telescopes + Instrumentation proceedings 2024

Future strategic X-ray satellite telescopes, such as the probe-class Advanced X-ray Imaging Satellite (AXIS), will require excellent soft energy response in their imaging detectors to enable maximum discovery potential. In order to characterize Charge-Coupled Device (CCD) and Single Electron Sensitive Read Output (SiSeRO) detectors in the soft X-ray region, the X-ray Astronomy and Observational Cosmology (XOC) group at Stanford has developed, assembled, and commissioned a 2.5-meter-long X-ray beamline test system. The beamline is designed to efficiently produce monoenergetic X-ray fluorescence lines in the 0.3-10 keV energy range and achieve detector temperatures as low as 173 K. We present design and simulation details of the beamline, and discuss the vacuum, cooling, and X-ray fluorescence performance achieved. As a workhorse for future detector characterization at Stanford, the XOC beamline will support detector development for a broad range of X-ray astronomy instruments., Comment: To appear in SPIE Astronomical Telescopes and Instrumentation 2024 proceedings. 17 pages, 13 figures

The Advanced X-ray Imaging Satellite (AXIS) is a NASA probe class mission concept designed to deliver arcsecond resolution with an effective area ten times that of Chandra (at launch). The AXIS focal plane features an MIT Lincoln Laboratory (MIT-LL) X-ray charge-coupled device (CCD) detector working in conjunction with an application specific integrated circuit (ASIC), denoted the Multi-Channel Readout Chip (MCRC). While this readout ASIC targets the AXIS mission, it is applicable to a range of potential X-ray missions with comparable readout requirements. Designed by the X-ray astronomy and Observational Cosmology (XOC) group at Stanford University, the MCRC ASIC prototype (MCRC-V1.0) uses a 350 nm technology node and provides 8 channels of high speed, low noise, low power consumption readout electronics. Each channel implements a current source to bias the detector output driver, a preamplifier to provide gain, and an output buffer to interface directly to an analog-to-digital (ADC) converter. The MCRC-V1 ASIC exhibits comparable performance to our best discrete electronics implementations, but with ten times less power consumption and a fraction of the footprint area. In a total ionizing dose (TID) test, the chip demonstrated a radiation hardness equal or greater to 25 krad, confirming the suitability of the process technology and layout techniques used in its design. The next iteration of the ASIC (MCRC-V2) will expand the channel count and extend the interfaces to external circuits, advancing its readiness as a readout-on-a-chip solution for next generation X-ray CCD-like detectors. This paper summarizes our most recent characterization efforts, including the TID radiation campaign and results from the first operation of the MCRC ASIC in combination with a representative MIT-LL CCD., Comment: To appear in SPIE Astronomical Telescopes + Instrumentation proceedings 2024

Future strategic X-ray astronomy missions will require unprecedentedly sensitive wide-field imagers providing high frame rates, low readout noise and excellent soft energy response. To meet these needs, our team is employing a multi-pronged approach to advance several key areas of technology. Our first focus is on advanced readout electronics, specifically integrated electronics, where we are collaborating on the VERITAS readout chip for the Athena Wide Field Imager, and have developed the Multi-Channel Readout Chip (MCRC), which enables fast readout and high frame rates for MIT-LL JFET (junction field effect transistor) CCDs. Second, we are contributing to novel detector development, specifically the SiSeRO (Single electron Sensitive Read Out) devices fabricated at MIT Lincoln Laboratory, and their advanced readout, to achieve sub-electron noise performance. Hardware components set the stage for performance, but their efficient utilization relies on software and algorithms for signal and event processing. Our group is developing digital waveform filtering and AI methods to augment detector performance, including enhanced particle background screening and improved event characterization. All of these efforts make use of an efficient, new X-ray beamline facility at Stanford, where components and concepts can be tested and characterized., Comment: To appear in SPIE Astronomical Telescopes + Instrumentation proceedings 2024

Bringing artificial intelligence (AI) alongside next-generation X-ray imaging detectors, including CCDs and DEPFET sensors, enhances their sensitivity to achieve many of the flagship science cases targeted by future X-ray observatories, based upon low surface brightness and high redshift sources. Machine learning algorithms operating on the raw frame-level data provide enhanced identification of background vs. astrophysical X-ray events, by considering all of the signals in the context within which they appear within each frame. We have developed prototype machine learning algorithms to identify valid X-ray and cosmic-ray induced background events, trained and tested upon a suite of realistic end-to-end simulations that trace the interaction of cosmic ray particles and their secondaries through the spacecraft and detector. These algorithms demonstrate that AI can reduce the unrejected instrumental background by up to 41.5 per cent compared with traditional filtering methods. Alongside AI algorithms to reduce the instrumental background, next-generation event reconstruction methods, based upon fitting physically-motivated Gaussian models of the charge clouds produced by events within the detector, promise increased accuracy and spectral resolution of the lowest energy photon events., Comment: Proceedings of the SPIE, Astronomical Telescopes and Instrumentation, Space Telescopes and Instrumentation 2024: Ultraviolet to Gamma Ray

Single electron Sensitive Read Out (SiSeRO) is a novel on-chip charge detection technology that can, in principle, provide significantly greater responsivity and improved noise performance than traditional charge coupled device (CCD) readout circuitry. The SiSeRO, developed by MIT Lincoln Laboratory, uses a p-MOSFET transistor with a depleted back-gate region under the transistor channel; as charge is transferred into the back gate region, the transistor current is modulated. With our first generation SiSeRO devices, we previously achieved a responsivity of around 800 pA per electron, an equivalent noise charge (ENC) of 4.5 electrons root mean square (RMS), and a full width at half maximum (FWHM) spectral resolution of 130 eV at 5.9 keV, at a readout speed of 625 Kpixel/s and for a detector temperature of 250 K. Importantly, since the charge signal remains unaffected by the SiSeRO readout process, we have also been able to implement Repetitive Non-Destructive Readout (RNDR), achieving an improved ENC performance. In this paper, we demonstrate sub-electron noise sensitivity with these devices, utilizing an enhanced test setup optimized for RNDR measurements, with excellent temperature control, improved readout circuitry, and advanced digital filtering techniques. We are currently fabricating new SiSeRO detectors with more sensitive and RNDR-optimized amplifier designs, which will help mature the SiSeRO technology in the future and eventually lead to the pathway to develop active pixel sensor (APS) arrays using sensitive SiSeRO amplifiers on each pixel. Active pixel devices with sub-electron sensitivity and fast readout present an exciting option for next generation, large area astronomical X-ray telescopes requiring fast, low-noise megapixel imagers., Comment: To appear in SPIE Astronomical Telescopes + Instrumentation proceedings 2024

We present a characterization of the forcing and the sub-filter scale terms produced in the volume-filtering immersed boundary (VF-IB) method by Dave et al, JCP, 2023. The process of volume-filtering produces bodyforces in the form of surface integrals to describe the boundary conditions at the interface. Furthermore, the approach also produces unclosed subfilter scale (SFS) terms. The level of contribution from SFS terms on the numerical solution depends on the filter width. In order to understand these terms better, we take a 2 dimensional, varying coefficient hyperbolic equation shown by Brady & Liverscu, JCP, 2021. This case is chosen for two reasons. First, the case involves 2 distinct regions seperated by an interface, making it an ideal case for the VF-IB method. Second, an existing analytical solution allows us to properly investigate the contribution from SFS term for varying filter sizes. The latter controls how well resolved the interface is. The smaller the filter size, the more resolved the interface will be. A thorough numerical analysis of the method is presented, as well as the effect of the SFS term on the numerical solution. In order to perform a direct comparison, the numerical solution is compared to the filtered analytical solution. Through this, we highlight three important points. First, we present a methodical approach to volume filtering a hyperbolic PDE. Second, we show that the VF-IB method exhibits second order convergence with respect to decreasing filter size (i.e. making the interface sharper). Finally, we show that the SFS term scales with square the filter size. Large filter sizes require modeling the SFS term. However, for sufficiently finer filters, the SFS term can be ignored without any significant reduction in the accuracy of solution.

The successful application of machine learning (ML) in catalyst design relies on high-quality and diverse data to ensure effective generalization to novel compositions, thereby aiding in catalyst discovery. However, due to complex interactions, catalyst design has long relied on trial-and-error, a costly and labor-intensive process leading to scarce data that is heavily biased towards undesired, low-yield catalysts. Despite the rise of ML in this field, most efforts have not focused on dealing with the challenges presented by such experimental data. To address these challenges, we introduce a robust machine learning and explainable AI (XAI) framework to accurately classify the catalytic yield of various compositions and identify the contributions of individual components. This framework combines a series of ML practices designed to handle the scarcity and imbalance of catalyst data. We apply the framework to classify the yield of various catalyst compositions in oxidative methane coupling, and use it to evaluate the performance of a range of ML models: tree-based models, logistic regression, support vector machines, and neural networks. These experiments demonstrate that the methods used in our framework lead to a significant improvement in the performance of all but one of the evaluated models. Additionally, the decision-making process of each ML model is analyzed by identifying the most important features for predicting catalyst performance using XAI methods. Our analysis found that XAI methods, providing class-aware explanations, such as Layer-wise Relevance Propagation, identified key components that contribute specifically to high-yield catalysts. These findings align with chemical intuition and existing literature, reinforcing their validity. We believe that such insights can assist chemists in the development and identification of novel catalysts with superior performance.

The Lunar Surface Electromagnetics Experiment at Night (LuSEE-Night) is a project designed to investigate the feasibility of observing the Cosmic Dark Ages using an instrument on the lunar far-side. LuSEE-Night will measure the redshifted 21 cm transition of neutral hydrogen over a frequency range of 0.1-50 MHz, covering the redshift range 27 < z < 1100. The LuSEE-Night instrument is a radio frequency spectrometer, consisting of four horizontal monopole antennas, arranged to give wide zenith-pointing beams with two orthogonal linear polarizations. This combination of polarization, spectral, and angular sensitivity will be necessary to separate the cosmological signal from significantly stronger foreground emissions. LuSEE-Night will observe in drift scan during lunar night while the moon shields it from radio frequency interference from both the Earth and sun, and will transmit science and telemetry data back to Earth via an orbital relay during the lunar day. LuSEE-Night will have to operate in a challenging environment: its electronics must operate under hard radiation, the instrument must be thermally isolated during the cold 100~K lunar night, and have a thermal rejection path to survive the 390~K daytime temperature, and its photovoltaic and battery systems must provide sufficient power to operate during two weeks of lunar night. Furthermore, the instrument spectrometer must be powered throughout the lunar night using only a 7~kWh battery, due to mass limitations. Here we describe the power generation, storage, and delivery subsystems of the LuSEE-Night instrument, and the simulations which were performed to design the power subsystems and ensure instrument survival and operation throughout the long lunar night. We also describe the Concept of Operations (ConOps) developed for the LuSEE-Night mission, which derives from the power management simulations., Comment: 17 pages, 13 figures, to be published in SPIE Astronomical Telescopes and Instrumentation

Pathological structures in medical images are typically deviations from the expected anatomy of a patient. While clinicians consider this interplay between anatomy and pathology, recent deep learning algorithms specialize in recognizing either one of the two, rarely considering the patient's body from such a joint perspective. In this paper, we develop a generalist segmentation model that combines anatomical and pathological information, aiming to enhance the segmentation accuracy of pathological features. Our Anatomy-Pathology Exchange (APEx) training utilizes a query-based segmentation transformer which decodes a joint feature space into query-representations for human anatomy and interleaves them via a mixing strategy into the pathology-decoder for anatomy-informed pathology predictions. In doing so, we are able to report the best results across the board on FDG-PET-CT and Chest X-Ray pathology segmentation tasks with a margin of up to 3.3% as compared to strong baseline methods. Code and models will be publicly available at github.com/alexanderjaus/APEx.

Interpreting machine learning model decisions is crucial for high-risk applications like healthcare. In digital pathology, large whole slide images (WSIs) are decomposed into smaller tiles and tile-derived features are processed by attention-based multiple instance learning (ABMIL) models to predict WSI-level labels. These networks generate tile-specific attention weights, which can be visualized as attention maps for interpretability. However, a standardized evaluation framework for these maps is lacking, questioning their reliability and ability to detect spurious correlations that can mislead models. We herein propose a framework to assess the ability of attention networks to attend to relevant features in digital pathology by creating artificial model confounders and using dedicated interpretability metrics. Models are trained and evaluated on data with tile modifications correlated with WSI labels, enabling the analysis of model sensitivity to artificial confounders and the accuracy of attention maps in highlighting them. Confounders are introduced either through synthetic tile modifications or through tile ablations based on their specific image-based features, with the latter being used to assess more clinically relevant scenarios. We also analyze the impact of varying confounder quantities at both the tile and WSI levels. Our results show that ABMIL models perform as desired within our framework. While attention maps generally highlight relevant regions, their robustness is affected by the type and number of confounders. Our versatile framework has the potential to be used in the evaluation of various methods and the exploration of image-based features driving model predictions, which could aid in biomarker discovery.

We report a study of the hadronic transitions $\chi_{bJ}(2P)\to\omega\Upsilon(1S)$, with $\omega\to\pi^{+}\pi^{-}\pi^{0}$, using $28.2\times10^6~\Upsilon(3S)$ mesons recorded by the Belle detector. We present the first evidence for the near--threshold transition $\chi_{b0}(2P)\to\omega\Upsilon(1S)$, the analog of the charm sector decay $\chi_{c1}(3872)\to\omega J/\psi$, with a branching fraction of $B\big(\chi_{b0}(2P)\to\omega\Upsilon(1S)\big) = \big(0.55\pm0.19\pm0.07\big)\%$. We also obtain branching fractions of $B\big(\chi_{b1}(2P)\to\omega\Upsilon(1S)\big) = \big(2.39{}^{+0.20}_{-0.19}\pm0.24\big)\%$ and $B\big(\chi_{b2}(2P)\to\omega\Upsilon(1S)\big) = \big(0.47{}^{+0.13}_{-0.12}\pm0.06\big)\%$, confirming the measurement of the $\omega$ transitions of the $J=1,2~P$--wave states. The ratio for the $J=2$ to $J=1$ transitions is also measured and found to differ by 3.3 standard deviations from the expected value in the QCD multipole expansion., Comment: 6 pages, 2 figures

Liquid-liquid phase separation has recently emerged as an important topic in the context of cellular organization. Within this context, there are multiple poorly understood features; for instance hints of critical behavior in the plasma membrane, and how homeostasis maintains phase separation. In this paper, using statistical mechanics, we show that finite size effects in multicomponent mixtures can induce the system to behave as-if it were near a critical point, which we term apparent transitions. The apparent transition temperature is naturally driven towards the ambient temperature of the system.

We introduce global one-counter tree automata (GOCTA) which deviate from usual counter tree automata by working on only one counter which is passed through the tree in lexicographical order, rather than duplicating the counter at every branching position. We compare the capabilities of GOCTA to those of counter tree automata and obtain that their classes of recognizable tree languages are incomparable. Moreover, we show that the emptiness problem of GOCTA is undecidable while, in stark contrast, their membership problem is in P., Comment: This is an extended version of our CIAA 2024 paper

This paper presents the design of an autonomous race car that is self-designed, self-developed, and self-built by the Elefant Racing team at the University of Bayreuth. The system is created to compete in the Formula Student Driverless competition. Its primary focus is on the Acceleration track, a straight 75-meter-long course, and the Skidpad track, which comprises two circles forming an eight. Additionally, it is experimentally capable of competing in the Autocross and Trackdrive events, which feature tracks with previously unknown straights and curves. The paper details the hardware, software and sensor setup employed during the 2020/2021 season. Despite being developed by a small team with limited computer science expertise, the design won the Formula Student East Engineering Design award. Emphasizing simplicity and efficiency, the team employed streamlined techniques to achieve their success.

Soft tissue elasticity is directly related to different stages of diseases and can be used for tissue identification during minimally invasive procedures. By palpating a tissue with a robot in a minimally invasive fashion force-displacement curves can be acquired. However, force-displacement curves strongly depend on the tool geometry which is often complex in the case of medical tools. Hence, a tool calibration procedure is desired to directly map force-displacement curves to the corresponding tissue elasticity.We present an experimental setup for calibrating medical tools with a robot. First, we propose to estimate the elasticity of gelatin phantoms by spherical indentation with a state-of-the-art contact model. We estimate force-displacement curves for different gelatin elasticities and temperatures. Our experiments demonstrate that gelatin elasticity is highly dependent on temperature, which can lead to an elasticity offset if not considered. Second, we propose to use a more complex material model, e.g., a neural network, that can be trained with the determined elasticities. Considering the temperature of the gelatin sample we can represent different elasticities per phantom and thereby increase our training data.We report elasticity values ranging from 10 to 40 kPa for a 10% gelatin phantom, depending on temperature., Comment: Submitted to CURAC 2023

We present WonderWorld, a novel framework for interactive 3D scene extrapolation that enables users to explore and shape virtual environments based on a single input image and user-specified text. While significant improvements have been made to the visual quality of scene generation, existing methods are run offline, taking tens of minutes to hours to generate a scene. By leveraging Fast Gaussian Surfels and a guided diffusion-based depth estimation method, WonderWorld generates geometrically consistent extrapolation while significantly reducing computational time. Our framework generates connected and diverse 3D scenes in less than 10 seconds on a single A6000 GPU, enabling real-time user interaction and exploration. We demonstrate the potential of WonderWorld for applications in virtual reality, gaming, and creative design, where users can quickly generate and navigate immersive, potentially infinite virtual worlds from a single image. Our approach represents a significant advancement in interactive 3D scene generation, opening up new possibilities for user-driven content creation and exploration in virtual environments. We will release full code and software for reproducibility. Project website: https://WonderWorld-2024.github.io/, Comment: Project website: https://WonderWorld-2024.github.io/

It is shown that there is $N$ such that there is no algorithm to decide for identities in at most $N$ variables validity in the class of finite modular lattices. This is based on Slobodskoi's result that the Restricted Word Problem is unsolvable for the class of finite groups and relies on Freese's technique of capturing group presentations within free modular lattices.

We compute the potential-graviton contribution to the scattering amplitude, the radial action, and the scattering angle of two extremal black holes in N = 8 supergravity at the fifth post-Minkowskian order to next-to-leading order in a large mass expansion (first self-force order). Properties of classical unitarity cuts allow us to focus on the integration-by-parts reduction of planar integrals, while nonplanar integrals at this order are obtained from the planar ones by straightforward manipulations. We present all master integrals and solve their associated differential equations necessary to evaluate the classical scattering amplitudes of massive scalar particles at this order in all gravitational theories, in particular in N = 8 supergravity, and in general relativity. Despite the appearance of higher-weight generalized polylogarithms and elliptic functions in the solution to the differential equation for master integrals, the final supergravity answer is remarkably simple and contains only (harmonic) polylogarithmic functions up to weight 2. The systematic analysis of elliptic integrals discussed here, as well as the particular organization of boundary integrals in N = 8 observables are independent of supersymmetry and may have wider applications, including to aspects of collider physics., Comment: 50 pages, 6 figures

This report presents a short summary of the activities of the "Standard Model" working group for the "Physics at TeV Colliders" workshop (Les Houches, France, 12-30 June, 2023)., Comment: Proceedings of the Standard Model Working Group of the 2023 Les Houches Workshop, Physics at TeV Colliders, Les Houches 12-30 June 2023. 48 pages

As large language models (LLMs) continue to demonstrate remarkable abilities across various domains, computer scientists are developing methods to understand their cognitive processes, particularly concerning how (and if) LLMs internally represent their beliefs about the world. However, this field currently lacks a unified theoretical foundation to underpin the study of belief in LLMs. This article begins filling this gap by proposing adequacy conditions for a representation in an LLM to count as belief-like. We argue that, while the project of belief measurement in LLMs shares striking features with belief measurement as carried out in decision theory and formal epistemology, it also differs in ways that should change how we measure belief. Thus, drawing from insights in philosophy and contemporary practices of machine learning, we establish four criteria that balance theoretical considerations with practical constraints. Our proposed criteria include accuracy, coherence, uniformity, and use, which together help lay the groundwork for a comprehensive understanding of belief representation in LLMs. We draw on empirical work showing the limitations of using various criteria in isolation to identify belief representations.

We study the average long-time behavior of the binary opinions of a social group with peer-to-peer interactions under the influence of an external bias and a persuadable leader, a strongly-biased agent with a dynamic opinion with the intention of spreading it across the system. We use a generalized, fully-connected Ising model, with each spin representing the binary opinion of an agent at a given time and a single, super spin representing the opinion of the leader. External fields and interaction constants model the opinion bias and peer-to-peer interactions, respectively, while the temperature $T$ models an idealized social climate, representing an authoritarian regime if $T$ is low or a liberal one if $T$ is high. We derive a mean-field solution for the average magnetization $m$, the "social mood", and investigate how $m$ and the super spin magnetization vary as a function of $T$. We find that, depending on the initial conditions, due to the presence of metastable states, the sign of the average magnetization depends on the temperature. Finally, we verify that this effect is also present even if we consider only nearest-neighbor interactions within the social group.

Quantiles, expectiles and extremiles can be seen as concepts defined via an optimization problem, where this optimization problem is driven by two important ingredients: the loss function as well as a distributional weight function. This leads to the formulation of a general class of functionals that contains next to the above concepts many interesting quantities, including also a subclass of distortion risks. The focus of the paper is on developing estimators for such functionals and to establish asymptotic consistency and asymptotic normality of these estimators. The advantage of the general framework is that it allows application to a very broad range of concepts, providing as such estimation tools and tools for statistical inference (for example for construction of confidence intervals) for all involved concepts. After developing the theory for the general functional we apply it to various settings, illustrating the broad applicability. In a real data example the developed tools are used in an analysis of natural disasters.

Testing earthquake forecasts is essential to obtain scientific information on forecasting models and sufficient credibility for societal usage. We aim at enhancing the testing phase proposed by the Collaboratory for the Study of Earthquake Predictability (CSEP, Schorlemmer et al., 2018) with new statistical methods supported by mathematical theory. To demonstrate their applicability, we evaluate three short-term forecasting models that were submitted to the CSEP Italy experiment, and two ensemble models thereof. The models produce weekly overlapping forecasts for the expected number of M4+ earthquakes in a collection of grid cells. We compare the models' forecasts using consistent scoring functions for means or expectations, which are widely used and theoretically principled tools for forecast evaluation. We further discuss and demonstrate their connection to CSEP-style earthquake likelihood model testing. Then, using tools from isotonic regression, we investigate forecast reliability and apply score decompositions in terms of calibration and discrimination. Our results show where and how models outperform their competitors and reveal a substantial lack of calibration for various models. The proposed methods also apply to full-distribution (e.g., catalog-based) forecasts, without requiring Poisson distributions or making any other type of parametric assumption.

In this study, we introduce a novel approach to synthesizing subsurface velocity models using diffusion generative models. Conventional methods rely on extensive, high-quality datasets, which are often inaccessible in subsurface applications. Our method leverages incomplete well and seismic observations to produce high-fidelity velocity samples without requiring fully sampled training datasets. The results demonstrate that our generative model accurately captures long-range structures, aligns with ground-truth velocity models, achieves high Structural Similarity Index (SSIM) scores, and provides meaningful uncertainty estimations. This approach facilitates realistic subsurface velocity synthesis, offering valuable inputs for full-waveform inversion and enhancing seismic-based subsurface modeling.

The sparse identification of nonlinear dynamics (SINDy) has been established as an effective technique to produce interpretable models of dynamical systems from time-resolved state data via sparse regression. However, to model parameterized systems, SINDy requires data from transient trajectories for various parameter values over the range of interest, which are typically difficult to acquire experimentally. In this work, we extend SINDy to be able to leverage data on fixed points and/or limit cycles to reduce the number of transient trajectories needed for successful system identification. To achieve this, we incorporate the data on these attractors at various parameter values as constraints in the optimization problem. First, we show that enforcing these as hard constraints leads to an ill-conditioned regression problem due to the large number of constraints. Instead, we implement soft constraints by modifying the cost function to be minimized. This leads to the formulation of a multi-objective sparse regression problem where we simultaneously seek to minimize the error of the fit to the transients trajectories and to the data on attractors, while penalizing the number of terms in the model. Our extension, demonstrated on several numerical examples, is more robust to noisy measurements and requires substantially less training data than the original SINDy method to correctly identify a parameterized dynamical system.

It is commonly expected that for quantum chaotic many body systems the statistical properties approach those of random matrices when increasing the system size. We demonstrate for various kicked spin-$1/2$ chain models that the average eigenstate entanglement indeed approaches the random matrix result. However, the distribution of the eigenstate entanglement differs significantly. While for autonomous systems such deviations are expected, they are surprising for the more scrambling kicked systems. We attribute the origin of the deviations to the local two-dimensional Hilbert spaces. This is also supported by similar deviations occurring in a local random matrix model with global diagonal coupling.

The ComPair gamma-ray telescope is a technology demonstrator for a future gamma-ray telescope called the All-sky Medium Energy Gamma-ray Observatory (AMEGO). The instrument is composed of four subsystems, a double-sided silicon strip detector, a virtual Frisch grid CdZnTe calorimeter, a CsI:Tl based calorimeter, and an anti-coincidence detector (ACD). The CsI calorimeter's goal is to measure the position and energy deposited from high-energy events. To demonstrate the technological readiness, the calorimeter has flown onboard a NASA scientific balloon as part of the GRAPE-ComPair mission and accumulated around 3 hours of float time at an altitude of 40 km. During the flight, the CsI calorimeter observed background radiation, Regener-Pfotzer Maximum, and several gamma-ray activation lines originating from aluminum.

Due to their uncertainty quantification, Bayesian solutions to inverse problems are the framework of choice in applications that are risk averse. These benefits come at the cost of computations that are in general, intractable. New advances in machine learning and variational inference (VI) have lowered the computational barrier by learning from examples. Two VI paradigms have emerged that represent different tradeoffs: amortized and non-amortized. Amortized VI can produce fast results but due to generalizing to many observed datasets it produces suboptimal inference results. Non-amortized VI is slower at inference but finds better posterior approximations since it is specialized towards a single observed dataset. Current amortized VI techniques run into a sub-optimality wall that can not be improved without more expressive neural networks or extra training data. We present a solution that enables iterative improvement of amortized posteriors that uses the same networks architectures and training data. The benefits of our method requires extra computations but these remain frugal since they are based on physics-hybrid methods and summary statistics. Importantly, these computations remain mostly offline thus our method maintains cheap and reusable online evaluation while bridging the approximation gap these two paradigms. We denote our proposed method ASPIRE - Amortized posteriors with Summaries that are Physics-based and Iteratively REfined. We first validate our method on a stylized problem with a known posterior then demonstrate its practical use on a high-dimensional and nonlinear transcranial medical imaging problem with ultrasound. Compared with the baseline and previous methods from the literature our method stands out as an computationally efficient and high-fidelity method for posterior inference.

Weak convergence of maxima of dependent sequences of identically distributed continuous random variables is studied under normalizing sequences arising as subsequences of the normalizing sequences from an associated iid sequence. This general framework allows one to derive several generalizations of the well-known Fisher-Tippett-Gnedenko theorem under conditions on the univariate marginal distribution and the dependence structure of the sequence. The limiting distributions are shown to be compositions of a generalized extreme value distribution and a distortion function which reflects the limiting behavior of the diagonal of the underlying copula. Uniform convergence rates for the weak convergence to the limiting distribution are also derived. Examples covering well-known dependence structures are provided. Several existing results, e.g. for exchangeable sequences or stationary time series, are embedded in the proposed framework.

We present a semi-amortized variational inference framework designed for computationally feasible uncertainty quantification in 2D full-waveform inversion to explore the multimodal posterior distribution without dimensionality reduction. The framework is called WISER, short for full-Waveform variational Inference via Subsurface Extensions with Refinements. WISER leverages the power of generative artificial intelligence to perform approximate amortized inference that is low-cost albeit showing an amortization gap. This gap is closed through non-amortized refinements that make frugal use of acoustic wave physics. Case studies illustrate that WISER is capable of full-resolution, computationally feasible, and reliable uncertainty estimates of velocity models and imaged reflectivities.

We warn against a common but incomplete understanding of empirical research in machine learning that leads to non-replicable results, makes findings unreliable, and threatens to undermine progress in the field. To overcome this alarming situation, we call for more awareness of the plurality of ways of gaining knowledge experimentally but also of some epistemic limitations. In particular, we argue most current empirical machine learning research is fashioned as confirmatory research while it should rather be considered exploratory., Comment: 20 pages, accepted for publication at ICML 2024, camera-ready version

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.

We present a novel computational method for direct numerical simulations of particle-laden flows with fully-resolved particles (PR-DNS). The method is based on the recently developed Volume-Filtering Immersed Boundary method [Dave et al, Journal of Computational Physics, 487:112136, 2023] derived by volume-filtering the transport equations. This approach is mathematically and physically rigorous, in contrast to other PR-DNS methods which rely on ad-hoc numerical schemes to impose no-slip boundary conditions on the surface of particles. With the present PR-DNS strategy, we show that the ratio of filter size to particle diameter acts as a parameter that controls the level of fidelity. In the limit where this ratio is very small, a well-resolved PR-DNS is obtained. Conversely, when the ratio of filter size to particle diameter is large, a classic point-particle method is obtained. The discretization of the filtered equations is discussed and compared to other PR-DNS strategies based on direct-forcing immersed boundary methods. Numerical examples with sedimenting resolved particles are discussed.