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The unique connectivity of kagome lattices gives rise to topological properties, such as flat bands and Dirac cones. When combined with ferromagnetism and a chemical potential near the 2D Dirac points, this structure offers the potential to realize the highly sought-after topological Chern magnetotransport. Recently, there was considerable excitement surrounding this possibility in the ferrimagnetic kagome metal TbMn$_\mathbf{6}$Sn$_\mathbf{6}$. However, density functional theory (DFT) calculations reveal that the 2D Chern gap lies well above the Fermi energy, challenging its relevance in the observed anomalous Hall conductivity. Here, we investigate YMn$_\mathbf{6}$Sn$_\mathbf{5.45}$Ga$_\mathbf{0.55}$, a compound with similar crystallographic, magnetic, and electronic properties to TbMn$_\mathbf{6}$Sn$_\mathbf{6}$. Our findings show that the intrinsic anomalous Hall conductivity in this material, while comparable in magnitude to that in TbMn$_\mathbf{6}$Sn$_\mathbf{6}$, is fully three-dimensional, thus providing experimental evidence that Hall conductivity in this class of materials does not originate from 2D Chern gaps. Additionally, we confirm that the newly proposed empirical scaling relation for extrinsic Hall conductivity is universally governed by spin fluctuations.

Recently, there has been growing interest in autonomous shipping due to its potential to improve maritime efficiency and safety. The use of advanced technologies, such as artificial intelligence, can address the current navigational and operational challenges in autonomous shipping. In particular, inland waterway transport (IWT) presents a unique set of challenges, such as crowded waterways and variable environmental conditions. In such dynamic settings, the reliability and robustness of autonomous shipping solutions are critical factors for ensuring safe operations. This paper examines the robustness of benchmark deep reinforcement learning (RL) algorithms, implemented for IWT within an autonomous shipping simulator, and their ability to generate effective motion planning policies. We demonstrate that a model-free approach can achieve an adequate policy in the simulator, successfully navigating port environments never encountered during training. We focus particularly on Soft-Actor Critic (SAC), which we show to be inherently more robust to environmental disturbances compared to MuZero, a state-of-the-art model-based RL algorithm. In this paper, we take a significant step towards developing robust, applied RL frameworks that can be generalized to various vessel types and navigate complex port- and inland environments and scenarios., Comment: 5 pages, 4 figures. Will be presented at IEEE RAAI 2024

The development of programmable quantum devices can be measured by the complexity of manybody states that they are able to prepare. Among the most significant are topologically ordered states of matter, which enable robust quantum information storage and processing. While topological orders are more readily accessible with qudits, experimental realisations have thus far been limited to lattice models of qubits. Here, we prepare a ground state of the Z3 toric code state on 24 qutrits in a trapped ion quantum processor with fidelity per qutrit exceeding 96.5(3)%. We manipulate two types of defects which go beyond the conventional qubit toric code: a parafermion, and its bound state which is related to charge conjugation symmetry. We further demonstrate defect fusion and the transfer of entanglement between anyons and defects, which we use to control topological qutrits. Our work opens up the space of long-range entangled states with qudit degrees of freedom for use in quantum simulation and universal error-correcting codes., Comment: 8+20 pages, 15 figures

Testis size is known to be one of the main predictors of male fertility, usually assessed in clinical workup via palpation or imaging. Despite its potential, population-level evaluation of testicular volume using imaging remains underexplored. Previous studies, limited by small and biased datasets, have demonstrated the feasibility of machine learning for testis volume segmentation. This paper presents an evaluation of segmentation methods for testicular volume using Magnet Resonance Imaging data from the UKBiobank. The best model achieves a median dice score of $0.87$, compared to median dice score of $0.83$ for human interrater reliability on the same dataset, enabling large-scale annotation on a population scale for the first time. Our overall aim is to provide a trained model, comparative baseline methods, and annotated training data to enhance accessibility and reproducibility in testis MRI segmentation research.

Existing theoretical stabilization results for linear, hyperbolic multi-dimensional problems are extended to the discretized multi-dimensional problems. In contrast to existing theoretical and numerical analysis in the spatially one-dimensional case the effect of the numerical dissipation is analyzed and explicitly quantified. Further, using dimensional splitting, the numerical analysis is extended to the multi-dimensional case. The findings are confirmed by numerical simulations for low-order and high-order DG schemes both in the one-dimensional and two-dimensional case.

We study the Kohmoto model including Sturmian Hamiltonians and the associated Kohmoto butterfly. We prove spectral estimates for the operators using Farey numbers. In addition, we determine the impurities at rational rotations leading to the spectral defects in the Kohmoto butterfly. Our results are similar to the ones obtained for the Almost-Mathieu operator and the associated Hofstadter butterfly.

In training neural networks, it is common practice to use partial gradients computed over batches, mostly very small subsets of the training set. This approach is motivated by the argument that such a partial gradient is close to the true one, with precision growing only with the square root of the batch size. A theoretical justification is with the help of stochastic approximation theory. However, the conditions for the validity of this theory are not satisfied in the usual learning rate schedules. Batch processing is also difficult to combine with efficient second-order optimization methods. This proposal is based on another hypothesis: the loss minimum of the training set can be expected to be well-approximated by the minima of its subsets. Such subset minima can be computed in a fraction of the time necessary for optimizing over the whole training set. This hypothesis has been tested with the help of the MNIST, CIFAR-10, and CIFAR-100 image classification benchmarks, optionally extended by training data augmentation. The experiments have confirmed that results equivalent to conventional training can be reached. In summary, even small subsets are representative if the overdetermination ratio for the given model parameter set sufficiently exceeds unity. The computing expense can be reduced to a tenth or less., Comment: To appear in KDIR 2024

Gaia characterizes the stellar populations of nearby open clusters with unprecedented precision. We investigate the B\"ohm-Vitense gap, which has been found as a prominent feature in the stellar sequence of open clusters. Using PARSEC isochrone fitting, we derive astrophysical parameters for more than 1100 stars in Praesepe, identify more than 1100 bona fide single stars in the alpha Persei (Melotte 20) open cluster, and confirm their approximate match in terms of age (approx. 710 Myr and 45 Myr) and metallicity ([M/H] approx. +0.15 dex and +0.13 dex) to the Hyades and Pleiades, respectively. By merging data of the cluster pairs, we improve number statistics. We do not find a clear gap in the combined observational G_abs vs. BP-RP color-magnitude diagram (CMD) in the stellar mass range corresponding to the location of the B\"ohm-Vitense gap. We reproduce gaps in simulated Hyades-type CMDs randomly drawn from an initial mass function. There is no strong evidence for a discontinuity originating in the transition from radiative to convective energy transport in the stellar photosphere. We conclude that the observed gaps in the stellar sequences of open clusters could be explained by small number statistics and the uneven mass-color relation at the transition from spectral type A to F., Comment: Accepted for publication in AJ, 10 pages, 6 figures, Tables 1, 2, and 6 will be published in their entirety in machine-readable format (mrt)

Transformers are a widespread and successful model architecture, particularly in Natural Language Processing (NLP) and Computer Vision (CV). The essential innovation of this architecture is the Attention Mechanism, which solves the problem of extracting relevant context information from long sequences in NLP and realistic scenes in CV. A classical neural network component, a Multi-Layer Perceptron (MLP), complements the attention mechanism. Its necessity is frequently justified by its capability of modeling nonlinear relationships. However, the attention mechanism itself is nonlinear through its internal use of similarity measures. A possible hypothesis is that this nonlinearity is sufficient for modeling typical application problems. As the MLPs usually contain the most trainable parameters of the whole model, their omission would substantially reduce the parameter set size. Further components can also be reorganized to reduce the number of parameters. Under some conditions, query and key matrices can be collapsed into a single matrix of the same size. The same is true about value and projection matrices, which can also be omitted without eliminating the substance of the attention mechanism. Initially, the similarity measure was defined asymmetrically, with peculiar properties such as that a token is possibly dissimilar to itself. A possible symmetric definition requires only half of the parameters. We have laid the groundwork by testing widespread CV benchmarks: MNIST and CIFAR-10. The tests have shown that simplified transformer architectures (a) without MLP, (b) with collapsed matrices, and (c) symmetric similarity matrices exhibit similar performance as the original architecture, saving up to 90% of parameters without hurting the classification performance., Comment: 8 pages, to appear in KDIR2024

Block-based programming environments like Scratch are widely used in introductory programming courses. They facilitate learning pivotal programming concepts by eliminating syntactical errors, but logical errors that break the desired program behaviour are nevertheless possible. Finding such errors requires testing, i.e., running the program and checking its behaviour. In many programming environments, this step can be automated by providing executable tests as code; in Scratch, testing can only be done manually by invoking events through user input and observing the rendered stage. While this is arguably sufficient for learners, the lack of automated testing may be inhibitive for teachers wishing to provide feedback on their students' solutions. In order to address this issue, we introduce a new category of blocks in Scratch that enables the creation of automated tests. With these blocks, students and teachers alike can create tests and receive feedback directly within the Scratch environment using familiar block-based programming logic. To facilitate the creation and to enable batch processing sets of student solutions, we extend the Scratch user interface with an accompanying test interface. We evaluated this testing framework with 28 teachers who created tests for a popular Scratch game and subsequently used these tests to assess and provide feedback on student implementations. An overall accuracy of 0.93 of teachers' tests compared to manually evaluating the functionality of 21 student solutions demonstrates that teachers are able to create and effectively use tests. A subsequent survey confirms that teachers consider the block-based test approach useful., Comment: To appear at the 24th Koli Calling International Conference on Computing Education Research

This paper showcases the use of a reinforcement learning-based Neural Architecture Search (NAS) agent to design a small neural network to perform active fire detection on multispectral satellite imagery. Specifically, we aim to design a neural network that can determine if a single multispectral pixel is a part of a fire, and do so within the constraints of a Low Earth Orbit (LEO) nanosatellite with a limited power budget, to facilitate on-board processing of sensor data. In order to use reinforcement learning, a reward function is needed. We supply this reward function in the shape of a regression model that predicts the F1 score obtained by a particular architecture, following quantization to INT8 precision, from purely architectural features. This model is trained by collecting a random sample of neural network architectures, training these architectures, and collecting their classification performance statistics. Besides the F1 score, we also include the total number of trainable parameters in our reward function to limit the size of the designed model and ensure it fits within the resource constraints imposed by nanosatellite platforms. Finally, we deployed the best neural network to the Google Coral Micro Dev Board and evaluated its inference latency and power consumption. This neural network consists of 1,716 trainable parameters, takes on average 984{\mu}s to inference, and consumes around 800mW to perform inference. These results show that our reinforcement learning-based NAS approach can be successfully applied to novel problems not tackled before., Comment: Added IEEE Submission Notice

In this publication, we assess the ability of a novel Reinforcement Learning-based solution to the problem of Neural Architecture Search, where a Reinforcement Learning (RL) agent learns to search for good architectures, rather than to return a single optimal architecture. We consider both the NAS-Bench-101 and NAS- Bench-301 settings, and compare against various known strong baselines, such as local search and random search. We conclude that our Reinforcement Learning agent displays strong scalability with regards to the size of the search space, but limited robustness to hyperparameter changes., Comment: 33 Pages, 19 Figures

We present a review of the work L. Raymond from 1995. The review aims at making this work more accessible and offers adaptations of some statements and proofs. In addition, this review forms an applicable framework for the complete solution of the Dry Ten Martini Problem for Sturmian Hamiltonians as appears in the work arXiv:2402.16703 by R. Band, S. Beckus and R. Loewy. A Sturmian Hamiltonian is a one-dimensional Schr\"odinger operator whose potential is a Sturmian sequence multiplied by a coupling constant, $V\in\mathbb{R}$. The spectrum of such an operator is commonly approximated by the spectra of designated periodic operators. If $V>4$, then the spectral bands of the periodic operators exhibit a particular combinatorial structure. This structure provides a formula for the integrated density of states. Employing this, it is shown that if $V>4$, then all the gaps, as predicted by the gap labelling theorem, are there., Comment: 63 pages, 5 figures

The utility of solving the Fermi-Hubbard model has been estimated in the billions of dollars. Digital quantum computers can in principle address this task, but have so far been limited to quasi one-dimensional models. This is because of exponential overheads caused by the interplay of noise and the non-locality of the mapping between fermions and qubits. Here, we show experimentally that a recently developed local encoding can overcome this problem. We develop a new compilation scheme, called "corner hopping", that reduces the cost of simulating fermionic hopping by 42% which allows us to conduct the largest digital quantum simulations of a fermionic model to date, using a trapped ion quantum computer to prepare adiabatically the ground state of a 6 x 6 spinless Fermi-Hubbard model encoded in 48 physical qubits. We also develop two new error mitigation schemes for systems with conserved quantities, one based on local postselection and one on extrapolation of local observables. Our results suggest that Fermi-Hubbard models beyond classical simulability can be addressed by digital quantum computers without large increases in gate fidelity., Comment: 26 pages, 21 figures

A recently developed coupling strategy for two nonconservative hyperbolic systems is employed to investigate a collapsing vapor bubble embedded in a liquid near a solid. For this purpose, an elastic solid modeled by a linear system of conservation laws is coupled to the two-phase Baer-Nunziato-type model for isothermal fluids, a nonlinear hyperbolic system with non-conservative products. For the coupling of the two systems the Jin-Xin relaxation concept is employed and embedded in a second order finite volume scheme. For a proof of concept simulations in one space dimension are performed., Comment: 14 pages, 7 figures

A critical milestone for quantum computers is to demonstrate fault-tolerant computation that outperforms computation on physical qubits. The tesseract subsystem color code protects four logical qubits in 16 physical qubits, to distance four. Using the tesseract code on Quantinuum's trapped-ion quantum computers, we prepare high-fidelity encoded graph states on up to 12 logical qubits, beneficially combining for the first time fault-tolerant error correction and computation. We also protect encoded states through up to five rounds of error correction. Using performant quantum software and hardware together allows moderate-depth logical quantum circuits to have an order of magnitude less error than the equivalent unencoded circuits., Comment: 12 pages, 13 figures

The inference and training stages of Graph Neural Networks (GNNs) are often dominated by the time required to compute a long sequence of matrix multiplications between the sparse graph adjacency matrix and its embedding. To accelerate these stages, we first propose the Compressed Binary Matrix (CBM) storage format to succinctly represent the binary adjacency matrix of an unweighted graph. Then, we show how to generalize this representation to normalized adjacency matrices of unweighted graphs which arise in the context of GNNs. Finally, we develop efficient matrix multiplication kernels based on this compressed representation. The matrix multiplication kernels proposed in this work never require more scalar operations than classic sparse matrix multiplication algorithms. Experimental evaluation shows that the matrix multiplication strategies proposed outperform the current state-of-the-art implementations provided by Intel MKL, achieving speedups close to 5$\times$. Furthermore, our optimized matrix-multiplication strategies accelerated the inference time of a GNN by up to $3\times$.

This Birds-of-a-Feather (BOF) session on 6 November 2023 was organized by leaders and members of SatHub at the International Astronomical Union Centre for the Protection of the Dark and Quiet Sky from Satellite Constellation Interference (IAU CPS). SatHub is dedicated to observations, data analysis, software, and related activities. The session opened with a talk on the current state of affairs with regards to satellite constellation mitigation, with a focus on optical astronomy, and moved to focused discussion around the top-voted topics. These included tools and techniques for forecasting satellite positions and brightnesses as well as streak detection and masking., Comment: 4 pages, 1 figure, conference proceeding from ADASS 2023

Satellite constellation interference occurs across astronomical disciplines. We present examples of interference from radio and $\gamma$-Ray astronomy to optical and spectroscopic interference in ground-based and space-borne facilities. In particular, we discuss the impact of artificial satellites on the Hubble Space Telescope (HST), the High Energy Stereoscopic System (H.E.S.S.), an Imaging Atmospheric Cherenkov Telescope, as well as possible mitigation strategies for the European Southern Observatory 4-metre Multi-Object Spectrograph Telescope (ESO 4MOST). Furthermore, we shed light on how ground-based optical telescopes such as the Oukaimeden Observatory contribute to IAU Centre for the Protection of the Dark and Quiet Sky from Satellite Constellation Interference (IAU CPS) efforts that quantify satellite brightness., Comment: 5 pages, accepted for publication in "Astronomy and Satellite Constellations: Pathways Forward", proceedings of IAU Symposium 385

The IAU Centre for the Protection of the Dark and Quiet Sky from Satellite Constellation Interference (CPS), established in early 2022 and co-hosted by NSF's NOIRLab and the SKA Observatory, was created to unify efforts to work towards mitigating some of the effects of satellite constellations on astronomy. SatHub, one of the four sub-groups of CPS, focuses on software and related tools to aid observers and industry partners in addressing some of the issues caused by commercial satellite constellations., Comment: 3 pages, 2 figures, conference proceeding from ADASS 2023

We simulate and analyse the contribution of the Rubin Observatory Legacy Survey of Space and Time (LSST) to the rate of discovery of Near Earth Object (NEO) candidates, their submission rates to the NEO Confirmation page (NEOCP), and the resulting demands on the worldwide NEO follow-up observation system. We find that, when using current NEOCP listing criteria, Rubin will typically contribute ~129 new objects to the NEOCP each night in the first year, an increase of ~8x relative to present day. Only 8.3% of the objects listed for follow-up will be NEOs, with the primary contaminant being a background of yet undiscovered, faint, main belt asteroids (MBAs). We consider follow-up prioritisation strategies to lessen the impact on the NEO follow-up system. We develop an algorithm that predicts (with 68% accuracy) whether Rubin itself will self recover any given tracklet; external follow-up of such candidates can be de-prioritised. With this algorithm enabled, the follow-up list would be reduced to 64 NEO candidates per night (with ~8.4% purity). We propose additional criteria based on trailing, apparent magnitude, and ecliptic latitude to further prioritise follow-up. We hope observation planners and brokers will adopt some of these open-source algorithms, enabling the follow-up community to effectively keep up with the NEOCP in the early years of LSST., Comment: 16 pages, 14 figures. In review in AJ, comments welcome!

We study periodic approximations of aperiodic Schr\"odinger operators on lattices in Lie groups with dilation structure. The potentials arise through symbolic substitution systems that have been recently introduced in this setting. We characterize convergence of spectra of associated Schr\"odinger operators in the Hausdorff distance via properties of finite graphs. As a consequence, new examples of periodic approximations are obtained. We further prove that there are substitution systems that do not admit periodic approximations in higher dimensions, in contrast to the one-dimensional case. On the other hand, if the spectra converge, then we show that the rate of convergence is necessarily exponentially fast. These results are new even for substitutions over $\mathbb{Z}^d$., Comment: 33 pages, 5 figures

Single-shot quantum error correction has the potential to speed up quantum computations by removing the need for multiple rounds of syndrome extraction in order to be fault-tolerant. Using Quantinuum's H2 trapped-ion quantum computer, we implement the [[33,1,4]] 4D surface code and perform the first experimental demonstration of single-shot quantum error correction with bare ancilla qubits. We conduct memory experiments comparing the 2D and 4D surface codes and find that despite differences in qubit use and syndrome extraction circuit depth, the 4D surface code matches or outperforms the 2D surface code in both the fault-tolerant and single-shot regimes.

In ground-based astronomy, the ability to couple light into single-mode fibers (SMFs) is limited by atmospheric turbulence, which prohibits the use of many astrophotonic instruments. We propose a silicon-on-insulator photonic chip capable of coherently coupling the out-of-phase beamlets from the subapertures of a telescope pupil into an SMF. The photonic integrated circuit (PIC) consists of an array of grating couplers that are used to inject light from free space into single-mode waveguides on the chip. Metallic heaters modulate the refractive index of a coiled section of the waveguides, facilitating the co-phasing of the propagating modes. The phased beamlets can then be coherently combined to efficiently deliver the light to an output SMF. In an adaptive optics (AO) system, the phase corrector acts as a deformable mirror (DM) commanded by a controller that takes phase measurements from a wavefront sensor (WFS). We present experimental results for the PIC tested on an AO testbed and compare the performance to simulations., Comment: 13 pages, 11 figures, 2 tables, submitted to SPIE Adaptive Optics Systems IX

Long baseline optical interferometry and aperture synthesis using ground-based telescopes can enable unprecedented angular resolution astronomy in the optical domain. However, atmospheric turbulence leads to large, dynamic phase errors between participating apertures that limit fringe visibility using telescopes arrays or subaperture configurations in a single large telescope. Diffraction limited optics or adaptive optics can be used to ensure coherence at each aperture, but correlating the phase between apertures requires high speed, high stroke phase correction and recombination that is extremely challenging and costly. As a solution, we show an alternative phase correction and beam combination method using a centimeter-scale silicon astrophotonic chip optimized for H-band operation. The 4.7x10mm silicon photonic chip is fabricated using electron beam lithography with devices with 2 up to 32 independent channels. Light is coupled into the chip using single mode fiber ribbons. An array of microheaters is used to individually tune the effective index of each spiral delay waveguides. Narrowband spectral splitters at each spatial channel divert a modulated digital reference signal from an artificial guide star off-chip for phase measurement. Science light from other wavelengths is coherently combined using on-chip beam combiners and outputted to a single waveguide. We described the role, design, fabrication and characterization of the photonic chip. This photonic phase control scheme can be applied in astronomical interferometry or optical satellite communications., Comment: 12 pages, 13 figures, table 1, SPIE Astronomical Telescopes and Instrumentation

In an era where personalized technology is increasingly intertwined with daily life, traditional eye-tracking systems and autofocal glasses face a significant challenge: the need for frequent, user-specific calibration, which impedes their practicality. This study introduces a groundbreaking calibration-free method for estimating focal depth, leveraging machine learning techniques to analyze eye movement features within short sequences. Our approach, distinguished by its innovative use of LSTM networks and domain-specific feature engineering, achieves a mean absolute error (MAE) of less than 10 cm, setting a new focal depth estimation accuracy standard. This advancement promises to enhance the usability of autofocal glasses and pave the way for their seamless integration into extended reality environments, marking a significant leap forward in personalized visual technology.

Maintaining roads is crucial to economic growth and citizen well-being because roads are a vital means of transportation. In various countries, the inspection of road surfaces is still done manually, however, to automate it, research interest is now focused on detecting the road surface defects via the visual data. While, previous research has been focused on deep learning methods which tend to process the entire image and leads to heavy computational cost. In this study, we focus our attention on improving the classification performance while keeping the computational cost of our solution low. Instead of processing the whole image, we introduce a segmentation model to only focus the downstream classification model to the road surface in the image. Furthermore, we employ contrastive learning during model training to improve the road surface condition classification. Our experiments on the public RTK dataset demonstrate a significant improvement in our proposed method when compared to previous works., Comment: 7 pages

As the popularity of autonomous vehicles has grown, many standards and regulators, such as ISO, NHTSA, and Euro NCAP, require safety validation to ensure a sufficient level of safety before deploying them in the real world. Manufacturers gather a large amount of public road data for this purpose. However, the majority of these validation activities are done manually by humans. Furthermore, the data used to validate each driving feature may differ. As a result, it is essential to have an efficient data selection method that can be used flexibly and dynamically for verification and validation while also accelerating the validation process. In this paper, we present a data selection method that is practical, flexible, and efficient for assessment of autonomous vehicles. Our idea is to optimize the similarity between the metadata distribution of the selected data and a predefined metadata distribution that is expected for validation. Our experiments on the large dataset BDD100K show that our method can perform data selection tasks efficiently. These results demonstrate that our methods are highly reliable and can be used to select appropriate data for the validation of various safety functions., Comment: 7 pages

Optical beams and starlight distorted by atmospheric turbulence can be corrected with adaptive optics systems to enable efficient coupling into single-mode fibers. Deformable mirrors, used to flatten the wavefront in astronomical telescopes, are costly, sensitive, and complex mechanical components that require careful calibration to enable high-quality imaging in astronomy, microscopy, and vision science. They are also impractical to deploy in large numbers for non-imaging applications like free-space optical communication. Here, we propose a photonic integrated c rcuit capable of spatially sampling the wavefront collected by the telescope and co-phasing the subapertures to maximize the flux delivered to an output single-mode fiber as the integrated photonic implementation of a deformable mirror. We present the results of end-to-end simulations to quantify the performance of the proposed photonic solution under varying atmospheric conditions toward realizing an adaptive optics system without a deformable mirror for free-space optical receivers., Comment: 14 pages, 8 figures, 1 table, to be published in Optics Express. Corresponding author: Momen Diab (momen.diab@utoronto.ca)

Roads are an essential mode of transportation, and maintaining them is critical to economic growth and citizen well-being. With the continued advancement of AI, road surface inspection based on camera images has recently been extensively researched and can be performed automatically. However, because almost all of the deep learning methods for detecting road surface defects were optimized for a specific dataset, they are difficult to apply to a new, previously unseen dataset. Furthermore, there is a lack of research on training an efficient model using multiple data sources. In this paper, we propose a method for classifying road surface defects using camera images. In our method, we propose a scheme for dealing with the invariance of multiple data sources while training a model on multiple data sources. Furthermore, we present a domain generalization training algorithm for developing a generalized model that can work with new, completely unseen data sources without requiring model updates. We validate our method using an experiment with six data sources corresponding to six countries from the RDD2022 dataset. The results show that our method can efficiently classify road surface defects on previously unseen data., Comment: 6 pages

Multi-component Baer-Nunziato-type models for isothermal and isentropic fluids are investigated. These are given by balance equations for volume fractions, density and momentum for each component accounting for the relaxation to equilibrium by means of relaxation terms. Mathematical properties of the models are derived such as hyperbolicity and symmetrization. The fields are characterized and corresponding Riemann invariants are determined. Appropriate entropy-entropy flux pairs are derived taking into account the phasic energy equations including the heat flux. Physically meaningful constraints are presented that ensure the entropy inequality to hold. Instantaneous relaxation to equilibrium is investigated and appropriate algorithms are presented. Numerical results for the isothermal Baer-Nunziato model are compared to an isothermal Euler model and to an isothermal phase-field model., Comment: 44 pages, 3 figures

This research dissects financial equity research reports (ERRs) by mapping their content into categories. There is insufficient empirical analysis of the questions answered in ERRs. In particular, it is not understood how frequently certain information appears, what information is considered essential, and what information requires human judgment to distill into an ERR. The study analyzes 72 ERRs sentence-by-sentence, classifying their 4940 sentences into 169 unique question archetypes. We did not predefine the questions but derived them solely from the statements in the ERRs. This approach provides an unbiased view of the content of the observed ERRs. Subsequently, we used public corporate reports to classify the questions' potential for automation. Answers were labeled "text-extractable" if the answers to the question were accessible in corporate reports. 78.7% of the questions in ERRs can be automated. Those automatable question consist of 48.2% text-extractable (suited to processing by large language models, LLMs) and 30.5% database-extractable questions. Only 21.3% of questions require human judgment to answer. We empirically validate using Llama-3-70B and GPT-4-turbo-2024-04-09 that recent advances in language generation and information extraction enable the automation of approximately 80% of the statements in ERRs. Surprisingly, the models complement each other's strengths and weaknesses well. The research confirms that the current writing process of ERRs can likely benefit from additional automation, improving quality and efficiency. The research thus allows us to quantify the potential impacts of introducing large language models in the ERR writing process. The full question list, including the archetypes and their frequency, will be made available online after peer review., Comment: JEL classes: C45; G11; G12; G14

We present a navigation concept for solving the lost in space and time problem using optical observations of $\delta$ Scuti variable stars. Only a small number of techniques exist that allow a spacecraft to recover from being lost in both space and time, which can be caused by a failure of the onboard clock and navigation systems. Optical observations of $\delta$ Scuti stars, which can be collected onboard from star trackers or navigation cameras, may enable faster and/or more accurate position and time solutions compared to existing techniques without requiring additional equipment. Our results indicate that one day of observation by the OSIRIS-APEX MapCam may enable position and time determination accuracy within 0.05 au (3$\sigma$) and 15 s (3$\sigma$)., Comment: 28 pages, 9 figures, to be submitted to the Journal of the Astronautical Sciences

In this work, we present a comparison between color spaces namely YUV, LAB, RGB and their effect on learned image compression. For this we use the structure and color based learned image codec (SLIC) from our prior work, which consists of two branches - one for the luminance component (Y or L) and another for chrominance components (UV or AB). However, for the RGB variant we input all 3 channels in a single branch, similar to most learned image codecs operating in RGB. The models are trained for multiple bitrate configurations in each color space. We report the findings from our experiments by evaluating them on various datasets and compare the results to state-of-the-art image codecs. The YUV model performs better than the LAB variant in terms of MS-SSIM with a Bj{\o}ntegaard delta bitrate (BD-BR) gain of 7.5\% using VTM intra-coding mode as the baseline. Whereas the LAB variant has a better performance than YUV model in terms of CIEDE2000 having a BD-BR gain of 8\%. Overall, the RGB variant of SLIC achieves the best performance with a BD-BR gain of 13.14\% in terms of MS-SSIM and a gain of 17.96\% in CIEDE2000 at the cost of a higher model complexity., Comment: Accepter pre-print version for ICIP 2024

Reinforcement learning has demonstrated impressive performance in various challenging problems such as robotics, board games, and classical arcade games. However, its real-world applications can be hindered by the absence of robustness and safety in the learned policies. More specifically, an RL agent that trains in a certain Markov decision process (MDP) often struggles to perform well in nearly identical MDPs. To address this issue, we employ the framework of Robust MDPs (RMDPs) in a model-based setting and introduce a novel learned transition model. Our method specifically incorporates an auxiliary pessimistic model, updated adversarially, to estimate the worst-case MDP within a Kullback-Leibler uncertainty set. In comparison to several existing works, our work does not impose any additional conditions on the training environment, such as the need for a parametric simulator. To test the effectiveness of the proposed pessimistic model in enhancing policy robustness, we integrate it into a practical RL algorithm, called Robust Model-Based Policy Optimization (RMBPO). Our experimental results indicate a notable improvement in policy robustness on high-dimensional MuJoCo control tasks, with the auxiliary model enhancing the performance of the learned policy in distorted MDPs. We further explore the learned deviation between the proposed auxiliary world model and the nominal model, to examine how pessimism is achieved. By learning a pessimistic world model and demonstrating its role in improving policy robustness, our research contributes towards making (model-based) RL more robust., Comment: Will be presented at the RL Safety Workshop at RLC 2024

The yield stress anomaly of L12 intermetallics such as Ni3Al or Ni3Ga is controlled by the so-called Kear-Wilsdorf lock (KWL), of which the formation and unlocking are governed by dislocation cross-slip. Despite the importance of L12 intermetallics for strengthening Ni-based superalloys, microscopic understanding of the KWL is limited. Here, molecular dynamics simulations are conducted by employing a dedicated machine-learning interatomic potential derived via physically-informed active-learning. The potential facilitates modelling of the dislocation behavior in Ni3Al with near ab initio accuracy. KWL formation and unlocking are observed and analyzed. The unlocking stress demonstrates a pronounced temperature dependence, contradicting the assumptions of existing analytical models. A phenomenological model is proposed to effectively describe the atomistic unlocking stresses and extrapolate them to the macroscopic scale. The model is general and applicable to other L12 intermetallics. The acquired knowledge of KWLs provides a deeper understanding on the origin of the yield stress anomaly.

On the path towards high-gain inertial confinement fusion ignition, foams are being considered to tamp the hohlraum wall-motion, and mitigate laser backscattering from Stimulated Raman Scattering (SRS) and Stimulated Brillouin Scattering (SBS). Here we present the results from an experimental campaign on foam-filled hohlraums conducted at the OMEGA laser facility. SiO2 foam-fills, with densities as low as 1 mg/cm3, successfully reduce the gold wall expansion, with laser backscattering comparable to gas-fill.

Empirical evidence for a gap between the computational powers of classical and quantum computers has been provided by experiments that sample the output distributions of two-dimensional quantum circuits. Many attempts to close this gap have utilized classical simulations based on tensor network techniques, and their limitations shed light on the improvements to quantum hardware required to frustrate classical simulability. In particular, quantum computers having in excess of $\sim 50$ qubits are primarily vulnerable to classical simulation due to restrictions on their gate fidelity and their connectivity, the latter determining how many gates are required (and therefore how much infidelity is suffered) in generating highly-entangled states. Here, we describe recent hardware upgrades to Quantinuum's H2 quantum computer enabling it to operate on up to $56$ qubits with arbitrary connectivity and $99.843(5)\%$ two-qubit gate fidelity. Utilizing the flexible connectivity of H2, we present data from random circuit sampling in highly connected geometries, doing so at unprecedented fidelities and a scale that appears to be beyond the capabilities of state-of-the-art classical algorithms. The considerable difficulty of classically simulating H2 is likely limited only by qubit number, demonstrating the promise and scalability of the QCCD architecture as continued progress is made towards building larger machines., Comment: Includes minor updates to the text and an updated author list to include researchers who made technical contributions in upgrading the machine to 56 qubits but were left off the original version by mistake

Training large AI models such as deep learning recommendation systems and foundation language (or multi-modal) models costs massive GPUs and computing time. The high training cost has become only affordable to big tech companies, meanwhile also causing increasing concerns about the environmental impact. This paper presents CoMERA, a Computing- and Memory-Efficient training method via Rank-Adaptive tensor optimization. CoMERA achieves end-to-end rank-adaptive tensor-compressed training via a multi-objective optimization formulation, and improves the training to provide both a high compression ratio and excellent accuracy in the training process. Our optimized numerical computation (e.g., optimized tensorized embedding and tensor-vector contractions) and GPU implementation eliminate part of the run-time overhead in the tensorized training on GPU. This leads to, for the first time, $2-3\times$ speedup per training epoch compared with standard training. CoMERA also outperforms the recent GaLore in terms of both memory and computing efficiency. Specifically, CoMERA is $2\times$ faster per training epoch and $9\times$ more memory-efficient than GaLore on a tested six-encoder transformer with single-batch training. With further HPC optimization, CoMERA may significantly reduce the training cost of large language models.

We extend the scope of a recently introduced dependence coefficient between a scalar response $Y$ and a multivariate covariate $X$ to the case where $X$ takes values in a general metric space. Particular attention is paid to the case where $X$ is a curve. While on the population level, this extension is straight forward, the asymptotic behavior of the estimator we consider is delicate. It crucially depends on the nearest neighbor structure of the infinite-dimensional covariate sample, where deterministic bounds on the degrees of the nearest neighbor graphs available in multivariate settings do no longer exist. The main contribution of this paper is to give some insight into this matter and to advise a way how to overcome the problem for our purposes. As an important application of our results, we consider an independence test.

SatHub is one of the four hubs of the IAU Centre for the Protection of the Dark and Quiet Sky from Satellite Constellation Interference (CPS). It focuses on observations, data analysis, software, and training materials to improve our understanding of the impact of satellite constellations on astronomy and observers worldwide. As a preface to more in-depth IAUS385 sessions, we gave a summary of some recent work by SatHub members and the current status of satellite constellations, including optical and radio observations. We shared how the audience can join or get more involved, e.g., via the CPS Slack for asynchronous collaboration. We also touched on what a future with hundreds of thousands of constellation satellites might look like., Comment: 8 pages, 5 figures, accepted for publication in "Astronomy and Satellite Constellations: Pathways Forward", proceedings of IAU Symposium 385

Quantum state teleportation is commonly used in designs for large-scale fault-tolerant quantum computers. Using Quantinuum's H2 trapped-ion quantum processor, we implement the first demonstration of a fault-tolerant state teleportation circuit for a quantum error correction code - in particular, the planar topological [[7,1,3]] color code, or Steane code. The circuits use up to 30 trapped ions at the physical layer qubits and employ real-time quantum error correction - decoding mid-circuit measurement of syndromes and implementing corrections during the protocol. We conduct experiments on several variations of logical teleportation circuits using both transversal gates and lattice surgery protocols. Among the many measurements we report on, we measure the logical process fidelity of the transversal teleportation circuit to be 0.975(2) and the logical process fidelity of the lattice surgery teleportation circuit to be 0.851(9). Additionally, we run a teleportation circuit that is equivalent to Knill-style quantum error correction and measure the process fidelity to be 0.989(2).