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There is a growing interest in enhancing compiler optimizations with ML models, yet interactions between compilers and ML frameworks remain challenging. Some optimizations require tightly coupled models and compiler internals,raising issues with modularity, performance and framework independence. Practical deployment and transparency for the end-user are also important concerns. We propose ML-Compiler-Bridge to enable ML model development within a traditional Python framework while making end-to-end integration with an optimizing compiler possible and efficient. We evaluate it on both research and production use cases, for training and inference, over several optimization problems, multiple compilers and its versions, and gym infrastructures.

Anomalous topological phases, where edge states coexist with topologically trivial Chern bands, can only appear in periodically driven lattices. When the driving is smooth and continuous, the bulk-edge correspondence is guaranteed by the existence of a bulk invariant known as the winding number. However, in lattices subject to periodic time-step walks the existence of edge states does not only depend on bulk invariants but also on the geometry of the boundary. This is a consequence of the absence of an intrinsic time-dependence or micromotion in discrete-step walks. We report the observation of edge states and a simultaneous measurement of the bulk invariants in anomalous topological phases in a two-dimensional discrete-step walk in a synthetic photonic lattice made of two coupled fibre rings. The presence of edge states is inherent to the periodic driving and depends on the geometry of the boundary in the implemented two-band model with zero Chern number. We provide a suitable expression for the topological invariants whose calculation does not rely on micromotion dynamics., Comment: 12 pages main text plus 7 pages of apppendix (19 pages total)

The current density-voltage characteristic (JV) is a critical tool for understanding the behaviour of solar cells. In this article, we present an overview of the key aspects of JV analysis and introduce a user-friendly flowchart that facilitates the swift identification of the most probable limiting process in a solar cell, based mainly on the outcomes of light-intensity-dependent JV measurements. The flowchart was developed through extensive drift-diffusion simulations and a rigorous review of the literature, with a specific focus on perovskite and organic solar cells. Moreover, the flowchart proposes supplementary experiments that can be conducted to obtain a more precise prediction of the primary performance losses. It therefore serves as an optimal starting point to analyse performance losses of solar cells., Comment: 50 pages (14 manuscript, 36 supporting information), 37 figures (5 main, 32 supporting information)

This position paper outlines a new approach to adapting 3D user interface (UI) layouts given the complex nature of end-user preferences. Current optimization techniques, which mainly rely on weighted sum methods, can be inflexible and result in unsatisfactory adaptations. We propose using multi-objective optimization and interactive preference elicitation to provide semi-automated, flexible, and effective adaptations of 3D UIs. Our approach is demonstrated using an example of single-element 3D layout adaptation with ergonomic objectives. Future work is needed to address questions around the presentation and selection of optimal solutions, the impact on cognitive load, and the integration of preference learning. We conclude that, to make adaptive 3D UIs truly effective, we must acknowledge the limitations of our optimization objectives and techniques and emphasize the importance of user control., Comment: Published in CHI 2023 Workshop on Future of Computational Approaches for Understanding and Adapting User Interfaces: ACM CHI Conference on Human Factors in Computing Systems, April 23, 2023, Hamburg, Germany, 6 pages

As an emerging non-volatile memory technology, magnetic random access memory (MRAM) has key features and advantages including non-volatility, high speed, endurance, low power consumption and radiation tolerance. Conventional MRAM utilizes magnetic tunnel junctions (MTJs), which consist of two ferromagnetic layers separated by an insulating tunnel barrier. The orientation of the magnetic layers represents the binary data (0 or 1), and electrical resistance changes depending on the relative orientation of these magnetic layers. Despite these advancements, the quest for a swifter, more stable magneto-resistive random-access memory paradigm persists. In this vein, we present a groundbreaking development: room-temperature antiferromagnetic tunnel junctions devoid of any net magnetic moment. Over 200% tunneling altermagnetic resistance (TAR) ratio was measured at RuO2 (110)/MgO/RuO2 (110)/W structure, which is achieved by changing the antiferromagnetic Neel vector of RuO2 with an ultralow current density 2 MA*cm-2., Comment: Modification required

Semantic 3D mapping, the process of fusing depth and image segmentation information between multiple views to build 3D maps annotated with object classes in real-time, is a recent topic of interest. This paper highlights the fusion overconfidence problem, in which conventional mapping methods assign high confidence to the entire map even when they are incorrect, leading to miscalibrated outputs. Several methods to improve uncertainty calibration at different stages in the fusion pipeline are presented and compared on the ScanNet dataset. We show that the most widely used Bayesian fusion strategy is among the worst calibrated, and propose a learned pipeline that combines fusion and calibration, GLFS, which achieves simultaneously higher accuracy and 3D map calibration while retaining real-time capability. We further illustrate the importance of map calibration on a downstream task by showing that incorporating proper semantic fusion on a modular ObjectNav agent improves its success rates. Our code will be provided on Github for reproducibility upon acceptance., Comment: This is a preprint for the work submitted to the ICRA 2024 conference

Federated learning is a privacy-preserving training method which consists of training from a plurality of clients but without sharing their confidential data. However, previous work on federated learning do not explore suitable neural network architectures for clients with different input images sizes and different numbers of output categories. In this paper, we propose an effective federated learning method named ScalableFL, where the depths and widths of the local models for each client are adjusted according to the clients' input image size and the numbers of output categories. In addition, we provide a new bound for the generalization gap of federated learning. In particular, this bound helps to explain the effectiveness of our scalable neural network approach. We demonstrate the effectiveness of ScalableFL in several heterogeneous client settings for both image classification and object detection tasks., Comment: 15 pages, 1 figure, 2023 22nd International Conference on Machine Learning and Applications (ICMLA)

We show how starting with one-string space of states in BRST formalism one can construct a large class of physical quantities containing, in particular, scattering amplitudes for bosonic string and superstring. The same techniques work for heterotic string., Comment: 20 pages

The analysis of gradient descent-type methods typically relies on the Lipschitz continuity of the objective gradient. This generally requires an expensive hyperparameter tuning process to appropriately calibrate a stepsize for a given problem. In this work we introduce a local first-order smoothness oracle (LFSO) which generalizes the Lipschitz continuous gradients smoothness condition and is applicable to any twice-differentiable function. We show that this oracle can encode all relevant problem information for tuning stepsizes for a suitably modified gradient descent method and give global and local convergence results. We also show that LFSOs in this modified first-order method can yield global linear convergence rates for non-strongly convex problems with extremely flat minima, and thus improve over the lower bound on rates achievable by general (accelerated) first-order methods.

Electrical transport in non-centrosymmetric materials departs from the well-established phenomenological Ohm's law. Instead of a linear relation between current and electric field, a non-linear conductivity emerges along specific crystallographic directions. This non-linear transport is fundamentally related to the lack of spatial inversion symmetry. However, the experimental implications of an inversion symmetry operation on the non-linear conductivity remain to be explored. Here, we report on a large, non-linear conductivity in chiral Tellurium. By measuring samples with opposite handedness, we demonstrate that the non-linear transport is odd under spatial inversion. Furthermore, by applying an electrostatic gate, we modulate the non-linear output by a factor of 300, reaching the highest reported value excluding engineered heterostructures. Our results establish chiral Te as an ideal compound not just to study the fundamental interplay between crystal structure, symmetry operations and non-linear transport, but also to develop wireless rectifiers and energy-harvesting chiral devices., Comment: 7 pages, 4 figures, Supplemental Material

In-context learning (ICL) is now a common method for teaching large language models (LLMs) new tasks: given labeled examples in the input context, the LLM learns to perform the task without weight updates. Do models guided via ICL infer the underlying structure of the task defined by the context, or do they rely on superficial heuristics that only generalize to identically distributed examples? We address this question using transformations tasks and an NLI task that assess sensitivity to syntax - a requirement for robust language understanding. We further investigate whether out-of-distribution generalization can be improved via chain-of-thought prompting, where the model is provided with a sequence of intermediate computation steps that illustrate how the task ought to be performed. In experiments with models from the GPT, PaLM, and Llama 2 families, we find large variance across LMs. The variance is explained more by the composition of the pre-training corpus and supervision methods than by model size; in particular, models pre-trained on code generalize better, and benefit more from chain-of-thought prompting., Comment: Accepted to NAACL 2024

We develop quantum information processing primitives for the planar rotor, the state space of a particle on a circle. By interpreting rotor wavefunctions as periodically identified wavefunctions of a harmonic oscillator, we determine the group of bosonic Gaussian operations inherited by the rotor. This $n$-rotor Clifford group, $\text{U}(1)^{n(n+1)/2} \rtimes \text{GL}_n(\mathbb{Z})$, is represented by continuous $\text{U}(1)$ gates generated by polynomials quadratic in angular momenta, as well as discrete $\text{GL}_n(\mathbb Z)$ momentum sign-flip and sum gates. We classify homological rotor error-correcting codes [arXiv:2303.13723] and various rotor states based on equivalence under Clifford operations. Reversing direction, we map homological rotor codes and rotor Clifford operations back into oscillators by interpreting occupation-number states as rotor states of non-negative angular momentum. This yields new multimode homological bosonic codes protecting against dephasing and changes in occupation number, along with their corresponding encoding and decoding circuits. In particular, we show how to non-destructively measure the oscillator phase using conditional occupation-number addition and post selection. We also outline several rotor and oscillator varieties of the GKP-stabilizer codes [arXiv:1903.12615]., Comment: 26 pages, 5 figures

We consider the well-posedness of the generalized surface quasi-geostrophic (gSQG) front equation. By using the null structure of the equation via a paradifferential normal form analysis, we obtain balanced energy estimates, which allow us to prove the local well-posedness of the non-periodic gSQG front equation at a low level of regularity (in the SQG case, at only one-half derivatives above scaling). In addition, we establish global well-posedness for small and localized rough initial data, as well as modified scattering, by using the testing by wave packet approach of Ifrim-Tataru., Comment: 47 pages. This paper is companion to arXiv:2310.20143 and arXiv:2212.00117, and contains structural overlap

State-of-the-art results in typical classification tasks are mostly achieved by unexplainable machine learning methods, like deep neural networks, for instance. Contrarily, in this paper, we investigate the application of rule learning methods in such a context. Thus, classifications become based on comprehensible (first-order) rules, explaining the predictions made. In general, however, rule-based classifications are less accurate than state-of-the-art results (often significantly). As main contribution, we introduce a voting approach combining both worlds, aiming to achieve comparable results as (unexplainable) state-of-the-art methods, while still providing explanations in the form of deterministic rules. Considering a variety of benchmark data sets including a use case of significant interest to insurance industries, we prove that our approach not only clearly outperforms ordinary rule learning methods, but also yields results on a par with state-of-the-art outcomes., Comment: 35 pages, 10 figures

With the ever-increasing popularity of pretrained Video-Language Models (VidLMs), there is a pressing need to develop robust evaluation methodologies that delve deeper into their visio-linguistic capabilities. To address this challenge, we present ViLMA (Video Language Model Assessment), a task-agnostic benchmark that places the assessment of fine-grained capabilities of these models on a firm footing. Task-based evaluations, while valuable, fail to capture the complexities and specific temporal aspects of moving images that VidLMs need to process. Through carefully curated counterfactuals, ViLMA offers a controlled evaluation suite that sheds light on the true potential of these models, as well as their performance gaps compared to human-level understanding. ViLMA also includes proficiency tests, which assess basic capabilities deemed essential to solving the main counterfactual tests. We show that current VidLMs' grounding abilities are no better than those of vision-language models which use static images. This is especially striking once the performance on proficiency tests is factored in. Our benchmark serves as a catalyst for future research on VidLMs, helping to highlight areas that still need to be explored., Comment: Preprint. 48 pages, 22 figures, 10 tables

Modern science is dependent on imaging on the nanoscale, often achieved through processes that detect secondary electrons created by a highly focused incident charged particle beam. Multiple types of measurement noise limit the ultimate trade-off between the image quality and the incident particle dose, which can preclude useful imaging of dose-sensitive samples. Existing methods to improve image quality do not fundamentally mitigate the noise sources. Furthermore, barriers to assigning a physically meaningful scale make the images qualitative. Here we introduce ion count-aided microscopy (ICAM), which is a quantitative imaging technique that uses statistically principled estimation of the secondary electron yield. With a readily implemented change in data collection, ICAM substantially reduces source shot noise. In helium ion microscopy, we demonstrate 3x dose reduction and a good match between these empirical results and theoretical performance predictions. ICAM facilitates imaging of fragile samples and may make imaging with heavier particles more attractive., Comment: Updated Introduction, Updated Discussion and Outlook, Updated Methods. Main article: 17 pages, 4 figures

The vast high entropy alloy (HEA) composition space is promising for discovery of new material phases with unique properties. We explore the potential to achieve rare-earth-free high magnetic anisotropy materials in single-phase HEA thin films. Thin films of FeCoNiMnCu sputtered on thermally oxidized Si/SiO$_{2}$ substrates at room temperature are magnetically soft, with a coercivity on the order of 10 Oe. After post-deposition rapid thermal annealing (RTA), the films exhibit a single face-centered-cubic phase, with an almost 40-fold increase in coercivity. Inclusion of 50 at.% Pt in the film leads to ordering of a single L1$_{0}$ high entropy intermetallic phase after RTA, along with high magnetic anisotropy and 3 orders of magnitude coercivity increase. These results demonstrate a promising HEA approach to achieve high magnetic anisotropy materials using RTA., Comment: 28 pages, including 4 figures and 6 pages of supporting information (5 SI figures and 2 SI tables)

Multidimensional coherent spectroscopy is a powerful tool to characterize nonlinear optical response functions. Typically, multidimensional spectra are interpreted via a perturbative framework that straightforwardly provides intuition into the density matrix dynamics that give rise to specific spectral features. When the goal is to characterize system coupling to a thermal bath however, the perturbative formalism becomes unwieldy and yields less intuition. Here, we extend an approach developed by Vagov et al. to provide an exact expression for multidimensional spectra of a spin-boson Hamiltonian up to arbitrary order of electric field interaction. We demonstrate the utility of this expression by modeling polaron formation and coherent exciton-phonon coupling in quantum dots, which strongly agree with experiment.

Storing network traffic data is key to efficient network management; however, it is becoming more challenging and costly due to the ever-increasing data transmission rates, traffic volumes, and connected devices. In this paper, we explore the use of neural architectures for network traffic compression. Specifically, we consider a network scenario with multiple measurement points in a network topology. Such measurements can be interpreted as multiple time series that exhibit spatial and temporal correlations induced by network topology, routing, or user behavior. We present \textit{Atom}, a neural traffic compression method that leverages spatial and temporal correlations present in network traffic. \textit{Atom} implements a customized spatio-temporal graph neural network design that effectively exploits both types of correlations simultaneously. The experimental results show that \textit{Atom} can outperform GZIP's compression ratios by 50\%-65\% on three real-world networks., Comment: 7 pages, 6 figures, 2nd International Workshop on Graph Neural Networking (GNNet '23)

The United States opioid crisis contributed to 80,411 fatalities in 2021. It has strained hospitals, treatment facilities, and law enforcement agencies due to the enormous resources and procedures needed to respond to the crisis. As a result, many individuals who use opioids never receive or finish the treatment they need and instead have many interactions with hospitals or the criminal justice system. This paper introduces a discrete event simulation model that evaluates three opioid use disorder treatment policies: arrest diversion, re-entry case management, and overdose diversion. Publicly available data from 2011 to 2019 in Dane County, Wisconsin, was used to forecast opioid-related outcomes through 2032. Through analyzing a variety of policy-mix implementations, the study offers a versatile framework for evaluating policies at various implementation levels. The results demonstrate that treatment policies that create new pathways and programming by utilizing treatment services and successfully divert at least 20% of eligible individuals can lead to more opioid-resilient communities. The benefits increase when more policies are enacted and/or are offered to more individuals. We assume communities invest in increasing treatment capacity to meet increased treatment demand. In policy-mixes where societal savings from decreased opioid use, hospital encounters, and opioid-related arrests outweigh the costs of opioid use disorder treatment, the 2032 total savings range from $7.04 to $29.73 million. To reverse the opioid crisis within a community, treatment policies may need to be combined with other strategies, such as harm reduction, supply reduction, and use prevention.

The PRobe far-Infrared Mission for Astrophysics (PRIMA) is working to develop kinetic inductance detectors (KIDs) that can meet the sensitivity targets of a far-infrared spectrometer on a cryogenically cooled space telescope. An important ingredient for achieving high sensitivity is increasing the fractional-frequency responsivity. Here we present a study of the responsivity of aluminum KIDs fabricated at the Jet Propulsion Laboratory. Specifically, we model the KID's temporal response to pair-breaking excitations in the framework of the Mattis-Bardeen theory, incorporating quasiparticle recombination dynamics and the pair-breaking efficiency. Using a near-IR laser, we measure time-resolved photon pulses and fit them to our model, extracting the time-resolved quasiparticle density and the quasiparticle recombination lifetime. Comparing the fit to the known energy of the laser provides a measurement of the pair-breaking efficiency. In addition to photon-sourced excitations, it is important to understand the KID's response to phonon-sourced excitations from cosmic rays. We measure the rate of secondary cosmic rays detected by our devices, and predict the dead time due to cosmic rays for an array in L2 orbit. This work provides confidence in KIDs' robustness to cosmic ray events in the space environment., Comment: 8 pages, 4 figures

The semantic description of music metadata is a key requirement for the creation of music datasets that can be aligned, integrated, and accessed for information retrieval and knowledge discovery. It is nonetheless an open challenge due to the complexity of musical concepts arising from different genres, styles, and periods -- standing to benefit from a lingua franca to accommodate various stakeholders (musicologists, librarians, data engineers, etc.). To initiate this transition, we introduce the Music Meta ontology, a rich and flexible semantic model to describe music metadata related to artists, compositions, performances, recordings, and links. We follow eXtreme Design methodologies and best practices for data engineering, to reflect the perspectives and the requirements of various stakeholders into the design of the model, while leveraging ontology design patterns and accounting for provenance at different levels (claims, links). After presenting the main features of Music Meta, we provide a first evaluation of the model, alignments to other schema (Music Ontology, DOREMUS, Wikidata), and support for data transformation.

The KM3NeT neutrino telescope is currently being deployed at two different sites in the Mediterranean Sea. First searches for astrophysical neutrinos have been performed using data taken with the partial detector configuration already in operation. The paper presents the results of two independent searches for neutrinos from compact binary mergers detected during the third observing run of the LIGO and Virgo gravitational wave interferometers. The first search looks for a global increase in the detector counting rates that could be associated with inverse beta decay events generated by MeV-scale electron anti-neutrinos. The second one focuses on upgoing track-like events mainly induced by muon (anti-)neutrinos in the GeV--TeV energy range. Both searches yield no significant excess for the sources in the gravitational wave catalogs. For each source, upper limits on the neutrino flux and on the total energy emitted in neutrinos in the respective energy ranges have been set. Stacking analyses of binary black hole mergers and neutron star-black hole mergers have also been performed to constrain the characteristic neutrino emission from these categories., Comment: 19 pages, 11 figures

Autoformalization is the task of translating natural language materials into machine-verifiable formalisations. Progress in autoformalization research is hindered by the lack of a sizeable dataset consisting of informal-formal pairs expressing the same essence. Existing methods tend to circumvent this challenge by manually curating small corpora or using few-shot learning with large language models. But these methods suffer from data scarcity and formal language acquisition difficulty. In this work, we create $\texttt{MMA}$, a large, flexible, multilingual, and multi-domain dataset of informal-formal pairs, by using a language model to translate in the reverse direction, that is, from formal mathematical statements into corresponding informal ones. Experiments show that language models fine-tuned on $\texttt{MMA}$ produce $16-18\%$ of statements acceptable with minimal corrections on the $\texttt{miniF2F}$ and $\texttt{ProofNet}$ benchmarks, up from $0\%$ with the base model. We demonstrate that fine-tuning on multilingual formal data results in more capable autoformalization models even when deployed on monolingual tasks.

The visibilities measured by radio astronomical interferometers include non-astronomical correlated signals that arise from the local environment of the array. These correlated signals are especially important in compact arrays such as those under development for 21\,cm intensity mapping. The amplitudes of the contaminated visibilities can exceed the expected 21\,cm signal and represent a significant systematic effect. We study the receiver noise radiated by antennas in compact arrays and develop a model for how it couples to other antennas. We apply the model to the Tianlai Dish Pathfinder Array (TDPA), a compact array of 16, 6-m dish antennas. The coupling model includes electromagnetic simulations, measurements with a network analyzer, and measurements of the noise of the receivers. We compare the model to drift-scan observations with the array and set requirements on the level of antenna cross-coupling for 21\,cm intensity mapping instruments. We find that for the TDPA, cross-coupling would have to be reduced by TBD orders of magnitude in order to contribute negligibly to the visibilities., Comment: 20 pages, 22 figures, accepted for publication by JAI

On August 25th 2013 Dana Patchick from the "Deep Sky Hunters" (DSH) amateur astronomer group discovered a diffuse nebulosity in the Wide-field Infrared Survey Explorer (WISE) mid-IR image archive that had no optical counterpart but appeared similar to many Planetary Nebulae (PNe) in WISE. As his 30th discovery he named it Pa 30 and it was added to the HASH PN database as a new PN candidate. Little did he know how important his discovery would become. 10 years later this object is the only known bound remnant of a violent double WD merger accompanied by a rare Type Iax SN, observed and recorded by the ancient Chinese and Japanese in 1181 AD. This makes Pa 30 and its central star IRAS 00500+6713 (WD J005311) the only SN Iax remnant in our Galaxy, the only known bound remnant of any SN, and based on the central star's spectrum the only Wolf-Rayet star known that neither has a massive progenitor nor is the central star of a Planetary Nebula. We cover this story and our key role in it., Comment: 6 pages, 5 figures, 1 table, submitted to IAU 384 conference proceedings

We report the results of polarization-dependent Raman spectroscopy of phonon states in single-crystalline quasi-one-dimensional NbTe$_4$ and TaTe$_4$ van der Waals materials. The measurements were conducted in the wide temperature range from 80 K to 560 K. Our results show that although both materials have identical crystal structures and symmetries, there is a drastic difference in the intensity of their Raman spectra. While TaTe4 exhibits well-defined peaks through the examined frequency and temperature ranges, NbTe4 reveals extremely weak Raman signatures. The measured spectral positions of the phonon peaks agree with the phonon band structure calculated using the density-functional theory. We offer possible reasons for the in-tensity differences between the two van der Waals materials. Our results provide insights into the phonon properties of NbTe$_4$ and TaTe$_4$ van der Waals materials and indicate the potential of Raman spectroscopy for studying charge-density-wave quantum condensate phases., Comment: 24 pages; 6 figures; 1 table

Collinear antiferromagnetic (AFM) materials have unique promise of no stray fields, display ultrafast dynamics, and being robust against perturbation filed which motivates the extensive research of antiferromagnetic spintronics. However, the manipulation and detection of antiferromagnetic order remain formidable challenges. Here, we report the electrical detection of colinear antiferromagnetism in all-epitaxial RuO2/MgO/RuO2 three-terminal tunnel junctions (TJ) using spin-flop tunnel anisotropy magnetoresistance (TAMR). We measured a TAMR ratio of around 60% at room temperature, which arises between the parallel and perpendicular configurations of the adjacent collinear AFM state. Furthermore, we carried out angular dependent measurements using this AFM-TJ and showed that the magnitude of anisotropic longitudinal magnetoresistance in the AFM-TJ can be controlled by the direction of magnetic field. We also theoretically found that the colinear antiferromagnetic MTJ may produce a substantially large TAMR ratio as a result of the time-reversal, strong spin orbit coupling (SOC) characteristic of antiferromagnetic RuO2. Our work not only propels antiferromagnetic materials to the forefront of spintronic device innovation but also unveils a novel paradigm for electrically governed antiferromagnetic spintronics, auguring transformative advancements in high-speed, low-energy information devices.

Far-infrared (far-IR) astrophysics missions featuring actively cooled telescopes will offer orders of magnitude observing speed improvement at wavelengths where galaxies and forming planetary systems emit most of their light. The PRobe far-Infrared Mission for Astrophysics (PRIMA), which is currently under study, emphasizes low and moderate resolution spectroscopy throughout the far-IR. Full utilization of PRIMA's cold telescope requires far-IR detector arrays with per-pixel noise equivalent powers (NEPs) at or below 1 x 10-19 W/rtHz. We are developing low-volume Aluminum kinetic inductance detector (KID) arrays to reach these sensitivities. We will present on the development of our long-wavelength (210 um) array approach, with a focus on multitone measurements of our 1,008-pixel arrays. We measure an NEP below 1 x 10-19 W/rtHz for 73 percent of our pixels., Comment: 9 pages, 5 figures, 20th International Workshop on Low Temperature Detectors, submitted to the Journal of Low Temperature Physics

In this, the third of a series of papers, we present well determined chemical abundances for 124 Planetary nebulae (PNe) in the Galactic bulge from deep, long-slit FORS2 spectra from the 8.2 m ESO Very Large telescope (VLT). Prior to this work there were only ~240 bulge PNe with chemical abundances previously determined over a ~50 year period and of highly variable quality. For 34 of these PNe we are presenting their abundances for the first time which adds ~14% to the available sample of bulge PNe abundances. The interstellar reddening, physical conditions (electron densities, $n_{\mathrm{e}}$, temperatures, $T_{\mathrm{e}}$), and chemical compositions are derived as single values for each PN but also using different line diagnostics. Selected comparisons with the best literature fluxes for 75 PNe in common reveals that these significant new data are robust, reliable and internally self-consistent forming the largest independent, high quality and well understood derivation of PNe abundances currently available for study., Comment: Resubmitted to MNRAS after moderate revision

For the successful development and implementation of novel concepts and technology, the acceptance of potential users is crucial. Therefore, within the project HorizonUAM, we investigated passengers' acceptance of air taxis. One challenge is that not many people have real experiences with urban air mobility (UAM) at the moment and thus requirements formulated by potential users refer to rather abstract concepts. To allow participants to gain realistic impressions of UAM concepts, a Mixed Reality Air Taxi Simulator was set up. It allows participants to experience an inner-city business shuttle flight. A study with 30 participants assessed the information needs and the influence of another person on board on wellbeing in nominal situations (experiment 1) as well as one non-nominal situation (experiment 2). For the latter, participants experienced a re-routing of the flight due to an unavailability of landing sites at the vertidrome. During and after the flights, participants answered questionnaires and extensive interviews were conducted. The study produced first empirical data on relevant factors regarding interaction, information needs and comfort within an air taxi. The findings show that passengers want to be informed about intentions of the vehicle. The presence of a steward on board is not necessary but can increase wellbeing especially during non-nominal situations., Comment: 16 pages, 12 figures, 8 tables

Despite significant research on lightweight deep neural networks (DNNs) designed for edge devices, the current face detectors do not fully meet the requirements for "intelligent" CMOS image sensors (iCISs) integrated with embedded DNNs. These sensors are essential in various practical applications, such as energy-efficient mobile phones and surveillance systems with always-on capabilities. One noteworthy limitation is the absence of suitable face detectors for the always-on scenario, a crucial aspect of image sensor-level applications. These detectors must operate directly with sensor RAW data before the image signal processor (ISP) takes over. This gap poses a significant challenge in achieving optimal performance in such scenarios. Further research and development are necessary to bridge this gap and fully leverage the potential of iCIS applications. In this study, we aim to bridge the gap by exploring extremely low-bit lightweight face detectors, focusing on the always-on face detection scenario for mobile image sensor applications. To achieve this, our proposed model utilizes sensor-aware synthetic RAW inputs, simulating always-on face detection processed "before" the ISP chain. Our approach employs ternary (-1, 0, 1) weights for potential implementations in image sensors, resulting in a relatively simple network architecture with shallow layers and extremely low-bitwidth. Our method demonstrates reasonable face detection performance and excellent efficiency in simulation studies, offering promising possibilities for practical always-on face detectors in real-world applications., Comment: Accepted to ICCV 2023 Workshop on Low-Bit Quantized Neural Networks (LBQNN), Oral

Future space-based far-infrared astrophysical observatories will require exquis-itely sensitive detectors consistent with the low optical backgrounds. The PRobe far-Infrared Mission for Astrophysics (PRIMA) will deploy arrays of thousands of superconducting kinetic inductance detectors (KIDs) sensitive to radiation between 25 and 265 $\mu$m. Here, we present laboratory characterization of prototype, 25 -- 80 $\mu$m wavelength, low-volume, aluminum KIDs designed for the low-background environment expected with PRIMA. A compact parallel plate capacitor is used to minimize the detector footprint and suppress TLS noise. A novel resonant absorber is designed to enhance response in the band of interest. We present noise and optical efficiency measurements of these detectors taken with a low-background cryostat and a cryogenic blackbody. A microlens-hybridized KID array is found to be photon noise limited down to about 50 aW with a limiting detector NEP of about $6.5 \times 10^{-19}~\textrm{W/Hz}^{1/2}$. A fit to an NEP model shows that our optical system is well characterized and understood down to 50 aW. We discuss future plans for low-volume aluminum KID array development as well as the testbeds used for these measurements., Comment: 8 pages, 4 figures, 20th International Workshop on Low Temperature Detectors, submitted to the Journal of Low Temperature Physics

School finance debates frequently turn on two crucial questions: (1) How much do state and local governments spend on K-12 education?; and (2) How are education dollars distributed across jurisdictions? This focus makes sense because the answers to these questions determine how well states are able to provide an adequate, equal education for all students. This report, however, focuses on two different questions: (1) Where do school revenues come from?; and (2) How does revenue composition affect education policymaking? Put differently, this report focuses on the degree to which states rely on state versus local revenue, or on different types of taxes (e.g., those on income, sales, or property), and how such features matter for the adequacy and equity of education spending. Two states with identical finance systems and student populations, and which spend the same amount overall on K-12 schools, might have very different adequacy and equity outcomes depending on the composition of their revenue. In short, money matters; but where money comes from matters too.