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Recent work showing the existence of conflict-free almost-perfect hypergraph matchings has found many applications. We show that, assuming certain simple degree and codegree conditions on the hypergraph $ \mathcal{H} $ and the conflicts to be avoided, a conflict-free almost-perfect matching can be extended to one covering all of the vertices in a particular subset of $ V(\mathcal{H}) $, by using an additional set of edges; in particular, we ensure that our matching avoids all of a further set of conflicts, which may consist of both old and new edges. This setup is useful for various applications, and our main theorem provides a black box which encapsulates many long and tedious calculations, massively simplifying the proofs of results in generalised Ramsey theory., Comment: 19 pages + 4 pages appendix

Query rewrite systems perform graph substitutions using rewrite rules to generate optimal SQL query plans. Rewriting logical and physical relational query plans is proven to be an NP-hard sequential decision-making problem with a search space exponential in the number of rewrite rules. In this paper, we address the query rewrite problem by interleaving Equality Saturation and Graph Reinforcement Learning (RL). The proposed system, Aurora, rewrites relational queries by guiding Equality Saturation, a method from compiler literature to perform non-destructive graph rewriting, with a novel RL agent that embeds both the spatial structure of the query graph as well as the temporal dimension associated with the sequential construction of query plans. Our results show Graph Reinforcement Learning for non-destructive graph rewriting yields SQL plans orders of magnitude faster than existing equality saturation solvers, while also achieving competitive results against mainstream query optimisers.

Fault-tolerant quantum computation using two-dimensional topological quantum error correcting codes can benefit from multi-qubit long-range operations. By using simple commutation rules, a quantum circuit can be transpiled into a sequence of solely non-Clifford multi-qubit gates. Prior work on fault-tolerant compilation avoids optimal scheduling of such gates since they reduce the parallelizability of the circuit. We observe that the reduced parallelization potential is outweighed by the significant reduction in the number of gates. We therefore devise a method for scheduling multi-qubit lattice surgery using an earliest-available-first policy, solving the associated forest packing problem using a representation of the multi-qubit gates as Steiner trees. Our extensive testing on random and application-inspired circuits demonstrates the method's scalability and performance. We show that the transpilation significantly reduces the circuit length on the set of circuits tested, and that the resulting circuit of multi-qubit gates has a further reduction in the expected circuit execution time compared to serial execution., Comment: 23 pages, 7 figures, 4 tables

Planetary systems in mean motion resonances hold a special place among the planetary population. They allow us to study planet formation in great detail as dissipative processes are thought to have played an important role in their existence. Additionally, planetary masses in bright resonant systems may be independently measured both by radial velocities (RVs) and transit timing variations (TTVs). In principle, they also allow us to quickly determine the inclination of all planets in the system, as for the system to be stable, they are likely all in coplanar orbits. To describe the full dynamical state of the system, we also need the stellar obliquity that provides the orbital alignment of a planet with respect to the spin of their host star and can be measured thanks to the Rossiter-McLaughlin effect. It was recently discovered that HD 110067 harbours a system of six sub-Neptunes in resonant chain orbits. We here analyze an ESPRESSO high-resolution spectroscopic time series of HD 110067 during the transit of planet c. We find the orbit of HD 110067 c to be well aligned with sky projected obliquity $\lambda =6^{+24}_{-26}$ deg. This result is indicative that the current architecture of the system has been reached through convergent migration without any major disruptive events. Finally, we report transit-timing variation in this system as we find a significant offset of 19 $\pm$ 4 minutes in the center of the transit compared to the published ephemeris., Comment: Accepted to A&A

This first VELOCE data release comprises 18,225 high-precision RV measurements of 258 bona fide classical Cepheids on both hemispheres collected mainly between 2010 and 2022, alongside 1161 additional observations of 164 other stars. The median per-observation RV uncertainty is 0.037 km/s, and some reach 0.002 km/s. Non-variable standard stars characterize RV zero-point stability and provide a base for future cross-calibrations. We determined zero-point differences between VELOCE and 31 literature data sets using template fitting and measured linear period changes of 146 Cepheids. Seventy six spectroscopic binary Cepheids and 14 candidates are identified using VELOCE data alone and are investigated in detail in a companion paper (VELOCE II). Several new insights into Cepheid pulsations were obtained, including: a) the most detailed description of the Hertzsprung progression by RVs; b) the identification of double-peaked bumps in the RV curve; c) clear evidence that virtually all Cepheids feature spectroscopic variability signals that lead to modulated RV variability. We identified 36 such stars, of which 4 also exhibit orbital motion. Linear radius variations depend strongly on pulsation period and a steep increase in slope of the $\Delta$R/p versus logP-relation is found near 10d, challenging the existence of a tight relation between Baade-Wesselink projection factors and pulsation periods. We investigated the accuracy of RV time series measurements, v$_\gamma$, and RV amplitudes published in Gaia's DR3 and determined an average offset of 0.65 \pm 0.11 km/s relative to VELOCE. We recommend adopting a single set of template correlation parameters for distinct classes of large-amplitude variable stars to avoid systematic offsets in v$_\gamma$ among stars belonging to the same class. Peak-to-peak amplitudes of Gaia RVs exhibit significant (16%) dispersion compared to VELOCE. [abridged], Comment: A&A in press, 46 pages, 35 figures, 20 tables. Some data only available via the CDS at publication. VELOCE DR1 data will be made public in FITS format via zenodo.org at DOI: 10.5281/zenodo.10793507 upon publication of the paper in A&A

BOLD, the Barcode of Life Data System, supports the acquisition, storage, validation, analysis, and publication of DNA barcodes, activities requiring the integration of molecular, morphological, and distributional data. Its pivotal role in curating the reference library of DNA barcodes, coupled with its data management and analysis capabilities, make it a central resource for biodiversity science. It enables rapid, accurate identification of specimens and also reveals patterns of genetic diversity and evolutionary relationships among taxa. Launched in 2005, BOLD has become an increasingly powerful tool for advancing understanding of planetary biodiversity. It currently hosts 17 million specimen records and 14 million barcodes that provide coverage for more than a million species from every continent and ocean. The platform has the long-term goal of providing a consistent, accurate system for identifying all species of eukaryotes. BOLD's integrated analytical tools, full data lifecycle support, and secure collaboration framework distinguish it from other biodiversity platforms. BOLD v4 brought enhanced data management and analysis capabilities as well as novel functionality for data dissemination and publication. Its next version will include features to strengthen its utility to the research community, governments, industry, and society-at-large.

One can infer the orbital alignment of exoplanets with respect to the spin of their host stars using the Rossiter-McLaughlin effect, thereby giving us the chance to test planet formation and migration theories and improve our understanding of the currently observed population. We analyze archival HARPS and HARPS-N spectroscopic transit time series of six gas giant exoplanets on short orbits, namely WASP-77 Ab, WASP-101b, WASP-103b, WASP-105b, WASP-120b and WASP-131b. We find a moderately misaligned orbit for WASP-101b ($\lambda =34\degree\ \pm$ 3) and a highly misaligned orbit for WASP-131b ($\lambda =161\degree\ \pm$ 5), while the four remaining ones appear aligned: WASP-77 Ab ($\lambda =-8\degree\ ^{+19}_{-18}$), WASP-103b ($\lambda =2\degree\ ^{+35}_{-36}$), WASP-105b ($\lambda =-14\degree\ ^{+28}_{-24}$), and WASP-120b ($\lambda =-2\degree\ \pm$ 4). For WASP-77 Ab, we were able to infer its true orbital obliquity ($\Psi =48\degree\ ^{+22}_{-21}$). We additionally perform transmission spectroscopy of the targets in search of strong atomic absorbers in the exoatmospheres, but are unable to detect any features, most likely due to the presence of high-altitude clouds or Rayleigh scattering muting the strength of the features. Finally, we comment on future perspectives for studying these targets with the upcoming space missions to investigate the evolution and migration histories of these planets., Comment: Accepted to A&A

A novel Decentralized Noisy Model Update Tracking Federated Learning algorithm (FedNMUT) is proposed that is tailored to function efficiently in the presence of noisy communication channels that reflect imperfect information exchange. This algorithm uses gradient tracking to minimize the impact of data heterogeneity while minimizing communication overhead. The proposed algorithm incorporates noise into its parameters to mimic the conditions of noisy communication channels, thereby enabling consensus among clients through a communication graph topology in such challenging environments. FedNMUT prioritizes parameter sharing and noise incorporation to increase the resilience of decentralized learning systems against noisy communications. Theoretical results for the smooth non-convex objective function are provided by us, and it is shown that the $\epsilon-$stationary solution is achieved by our algorithm at the rate of $\mathcal{O}\left(\frac{1}{\sqrt{T}}\right)$, where $T$ is the total number of communication rounds. Additionally, via empirical validation, we demonstrated that the performance of FedNMUT is superior to the existing state-of-the-art methods and conventional parameter-mixing approaches in dealing with imperfect information sharing. This proves the capability of the proposed algorithm to counteract the negative effects of communication noise in a decentralized learning framework., Comment: arXiv admin note: text overlap with arXiv:2303.10695

Motion control of droplets has generated much attention for its applications to microfluidics, where precise control of small fluid volumes is an imperative requirement. Mechanical vibrations have been shown to be effective at inducing controllable depinning, and activation of different drop motion regimes. However, existing vibration-based strategies involve establishing homogeneous rigid-body dynamics on the substrate, and therefore lack any form of spatial heterogeneity and tuning. Addressing this limitation, metamaterials provide an ideal platform to achieve spectrally and spatially selective drop motion control, which leverages their ability to attenuate vibrations in selected frequency bands and in selected regions of a substrate. In this work, we illustrate the potential of metamaterials-based drop control by experimentally demonstrating a variety of drop motion capabilities on the surface of metaplates endowed with locally resonant stubs. The experiments leverage the design versatility of a LEGO component-enabled reconfigurable design platform and laser vibrometry measurements with high spatial resolution.

Precise localization of coronary arteries in Computed Tomography (CT) scans is critical from the perspective of medical assessment of coronary artery disease. Although various methods exist that offer high-quality segmentation of coronary arteries in cardiac contrast-enhanced CT scans, the potential of less invasive, non-contrast CT in this area is still not fully exploited. Since such fine anatomical structures are hardly visible in this type of medical images, the existing methods are characterized by high recall and low precision, and are used mainly for filtering of atherosclerotic plaques in the context of calcium scoring. In this paper, we address this research gap and introduce a deep learning algorithm for segmenting coronary arteries in multi-vendor ECG-gated non-contrast cardiac CT images which benefits from a novel framework for semi-automatic generation of Ground Truth (GT) via image registration. We hypothesize that the proposed GT generation process is much more efficient in this case than manual segmentation, since it allows for a fast generation of large volumes of diverse data, which leads to well-generalizing models. To investigate and thoroughly evaluate the segmentation quality based on such an approach, we propose a novel method for manual mesh-to-image registration, which is used to create our test-GT. The experimental study shows that the trained model has significantly higher accuracy than the GT used for training, and leads to the Dice and clDice metrics close to the interrater variability., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

We introduce Brain-Artificial Intelligence Interfaces (BAIs) as a new class of Brain-Computer Interfaces (BCIs). Unlike conventional BCIs, which rely on intact cognitive capabilities, BAIs leverage the power of artificial intelligence to replace parts of the neuro-cognitive processing pipeline. BAIs allow users to accomplish complex tasks by providing high-level intentions, while a pre-trained AI agent determines low-level details. This approach enlarges the target audience of BCIs to individuals with cognitive impairments, a population often excluded from the benefits of conventional BCIs. We present the general concept of BAIs and illustrate the potential of this new approach with a Conversational BAI based on EEG. In particular, we show in an experiment with simulated phone conversations that the Conversational BAI enables complex communication without the need to generate language. Our work thus demonstrates, for the first time, the ability of a speech neuroprosthesis to enable fluent communication in realistic scenarios with non-invasive technologies., Comment: 16 pages (39 with supplementary meterial), 6 figures

Markarian 817 is a bright and variable Seyfert-1.2 active galactic nucleus (AGN). X-ray monitoring of Mrk 817 with the Neil Gehrels Swift Observatory in 2022 revealed that the source flux had declined to a lower level than recorded at any prior point in the then-19-year mission. We present an analysis of deep XMM-Newton and NuSTAR observations obtained in this low flux state. The spectra reveal a complex X-ray wind consisting of neutral and ionized absorption zones. Three separate velocity components are detected as part of a structured ultra-fast outflow (UFO), with v/c = 0.043 (+0.007,-0.003), v/c = 0.079 (+0.003,-0.0008), and v/c = 0.074 (+0.004,-0.005). These projected velocities suggest that the wind likely arises at radii that are much smaller than the optical broad line region (BLR). In order for each component of the outflow to contribute significant feedback, the volume filling factors must be greater than f ~ 0.009, f ~ 0.003, and f ~ 0.3, respectively. For plausible, data-driven volume filling factors, these limits are passed, and the total outflow likely delivers the fierce feedback required to reshape its host environment, despite a modest radiative Eddington fraction of lambda ~ 0.008-0.016 (this range reflects plausible masses). UFOs are often detected at or above the Eddington limit; this result signals that black hole accretion has the potential to shape host galaxies even at modest Eddington fractions, and over a larger fraction of a typical AGN lifetime. We discuss our findings in terms of models for disk winds and black hole feedback in this and other AGN., Comment: Accepted for publication in ApJL

Classification, the computational process of categorizing an input into pre-existing classes, is now a cornerstone in modern computation in the era of machine learning. Here we propose a new type of quantum classifier, based on quantum transport of particles in a trained quantum network. The classifier is based on sending a quantum particle into a network and measuring the particle's exit point, which serves as a "class" and can be determined by changing the network parameters. Using this scheme, we demonstrate three examples of classification; in the first, wave functions are classified according to their overlap with predetermined (random) groups. In the second, we classify wave-functions according to their level of localization. Both examples use small training sets and achieve over 90\% precision and recall. The third classification scheme is a "real-world problem", concerning classification of catalytic aromatic-aldehyde substrates according to their reactivity. Using experimental data, the quantum classifier reaches an average 86\% classification accuracy. We show that the quantum classifier outperforms its classical counterpart for these examples, thus demonstrating quantum advantage, especially in the regime of "small data". These results pave the way for a novel classification scheme, which can be implemented as an algorithm, and potentially realized experimentally on quantum hardware such as photonic networks., Comment: 22 pages, 6 figs

Threshold selection is a fundamental problem in any threshold-based extreme value analysis. While models are asymptotically motivated, selecting an appropriate threshold for finite samples is difficult and highly subjective through standard methods. Inference for high quantiles can also be highly sensitive to the choice of threshold. Too low a threshold choice leads to bias in the fit of the extreme value model, while too high a choice leads to unnecessary additional uncertainty in the estimation of model parameters. We develop a novel methodology for automated threshold selection that directly tackles this bias-variance trade-off. We also develop a method to account for the uncertainty in the threshold estimation and propagate this uncertainty through to high quantile inference. Through a simulation study, we demonstrate the effectiveness of our method for threshold selection and subsequent extreme quantile estimation, relative to the leading existing methods, and show how the method's effectiveness is not sensitive to the tuning parameters. We apply our method to the well-known, troublesome example of the River Nidd dataset.

Multimodal federated learning (FL) aims to enrich model training in FL settings where devices are collecting measurements across multiple modalities (e.g., sensors measuring pressure, motion, and other types of data). However, key challenges to multimodal FL remain unaddressed, particularly in heterogeneous network settings: (i) the set of modalities collected by each device will be diverse, and (ii) communication limitations prevent devices from uploading all their locally trained modality models to the server. In this paper, we propose Federated Multimodal Fusion learning with Selective modality communication (FedMFS), a new multimodal fusion FL methodology that can tackle the above mentioned challenges. The key idea is the introduction of a modality selection criterion for each device, which weighs (i) the impact of the modality, gauged by Shapley value analysis, against (ii) the modality model size as a gauge for communication overhead. This enables FedMFS to flexibly balance performance against communication costs, depending on resource constraints and application requirements. Experiments on the real-world ActionSense dataset demonstrate the ability of FedMFS to achieve comparable accuracy to several baselines while reducing the communication overhead by over 4x., Comment: ICC 2024

Autonomous driving promises transformative improvements to transportation, but building systems capable of safely navigating the unstructured complexity of real-world scenarios remains challenging. A critical problem lies in effectively predicting the various potential outcomes that may emerge in response to the vehicle's actions as the world evolves. To address this challenge, we introduce GAIA-1 ('Generative AI for Autonomy'), a generative world model that leverages video, text, and action inputs to generate realistic driving scenarios while offering fine-grained control over ego-vehicle behavior and scene features. Our approach casts world modeling as an unsupervised sequence modeling problem by mapping the inputs to discrete tokens, and predicting the next token in the sequence. Emerging properties from our model include learning high-level structures and scene dynamics, contextual awareness, generalization, and understanding of geometry. The power of GAIA-1's learned representation that captures expectations of future events, combined with its ability to generate realistic samples, provides new possibilities for innovation in the field of autonomy, enabling enhanced and accelerated training of autonomous driving technology., Comment: Technical Report

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control. However, its reliance on vehicular data for model training presents significant challenges related to in-vehicle user privacy and communication overhead generated by massive data volumes. Federated learning (FL) is a decentralized ML approach that enables multiple vehicles to collaboratively develop models, broadening learning from various driving environments, enhancing overall performance, and simultaneously securing local vehicle data privacy and security. This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV). First, centralized and decentralized frameworks of FL are analyzed, highlighting their key characteristics and methodologies. Second, diverse data sources, models, and data security techniques relevant to FL in CAVs are reviewed, emphasizing their significance in ensuring privacy and confidentiality. Third, specific applications of FL are explored, providing insight into the base models and datasets employed for each application. Finally, existing challenges for FL4CAV are listed and potential directions for future investigation to further enhance the effectiveness and efficiency of FL in the context of CAV are discussed., Comment: IEEE Transactions on Intelligent Vehicles

Object-centric representations enable autonomous driving algorithms to reason about interactions between many independent agents and scene features. Traditionally these representations have been obtained via supervised learning, but this decouples perception from the downstream driving task and could harm generalization. In this work we adapt a self-supervised object-centric vision model to perform object decomposition using only RGB video and the pose of the vehicle as inputs. We demonstrate that our method obtains promising results on the Waymo Open perception dataset. While object mask quality lags behind supervised methods or alternatives that use more privileged information, we find that our model is capable of learning a representation that fuses multiple camera viewpoints over time and successfully tracks many vehicles and pedestrians in the dataset. Code for our model is available at https://github.com/wayveai/SOCS., Comment: Presented at the CVPR 2023 Vision-Centric Autonomous Driving workshop

The quest for quiet or dormant black holes has been ongoing since several decades. Ellipsoidal variables possibly indicate the existence of a very high-mass invisible companion and are thought to be one of the best ways to find such dormant black holes. This, however, is not a panacea as we show here with one example. We indeed report the discovery of a new semi-detached interacting binary, V1315 Cas, discovered as an ellipsoidal variable. Using data from photometric surveys (ASAS-SN, TESS) and high-resolution spectroscopy, we derived a nearly circular orbit with an orbital period of $P_{\rm{orb}}$=34.54 d. The binary system consists of an evolved F-type star primary that is likely still filling its Roche lobe and a B-type star secondary. Using \textsc{phoebe}2, we derived the following masses and radii: for the primary, $M_p =0.84 \pm 0.03 \, M_\odot$ and $R_p =18.51^{+0.12}_{-0.07} \, R_\odot$; for the secondary, $M_s =7.3 \pm 0.3 \,M_\odot$ and $R_s =4.02^{+2.3}_{-2.0}\,R_\odot$. Modeling the evolution of the system with MESA, we found an age of $\sim$7.7e7 years. The system is at the end of a period of rapid non-conservative mass transfer that reversed its mass ratio, while significantly widening its orbit. The primary shows carbon depletion and nitrogen overabundance, indicative of CNO processed material being exposed due to mass transfer. An infrared excess as well as stationary H$\alpha$ emission suggest the presence of a circumstellar or circumbinary disc. V1315 Cas will likely become a detached stripped star binary., Comment: Accepted in MNRAS

DNA has been discussed as a potential medium for data storage. Potentially it could be denser, could consume less energy, and could be more durable than conventional storage media such as hard drives, solid-state storage, and optical media. However, computing on data stored in DNA is a largely unexplored challenge. This paper proposes an integrated circuit (IC) based on microfluidics that can perform complex operations such as artificial neural network (ANN) computation on data stored in DNA. It computes entirely in the molecular domain without converting data to electrical form, making it a form of in-memory computing on DNA. The computation is achieved by topologically modifying DNA strands through the use of enzymes called nickases. A novel scheme is proposed for representing data stochastically through the concentration of the DNA molecules that are nicked at specific sites. The paper provides details of the biochemical design, as well as the design, layout, and operation of the microfluidics device. Benchmarks are reported on the performance of neural network computation., Comment: 24 pages, 11 figures

Scholars are often categorized into two types: hedgehogs (specialists), who focus on working within a specific research field, and foxes (generalists), who actively contribute to a variety of fields. Despite the familiar anecdotes and popularity of this distinction, its empirical foundation has remained largely unexamined. We examine whether the research style of being a fox or a hedgehog is a stable personal trait or an evolving strategy over a scientist's career. Analyzing 2.3 million scholars' publication records over a century, we find that research styles exhibit remarkable stability. Notably, the proportion of fox-like scientists has dramatically declined in the past century, a phenomenon we term "the death of Renaissance scientists." This decline is particularly significant as science shifts toward team collaboration. Teams of foxes consistently outperform teams of hedgehogs in generating new ideas and directions, as confirmed by two emerging innovation metrics for papers: atypicality and disruption. Our research is the first to quantify the process and consequences of the decline of Renaissance scientists. By doing so, we establish a universal link between research styles, demographic shifts, and innovative output.

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit., Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figures

Decentralized learning and optimization is a central problem in control that encompasses several existing and emerging applications, such as federated learning. While there exists a vast literature on this topic and most methods centered around the celebrated average-consensus paradigm, less attention has been devoted to scenarios where the communication between the agents may be imperfect. To this end, this paper presents three different algorithms of Decentralized Federated Learning (DFL) in the presence of imperfect information sharing modeled as noisy communication channels. The first algorithm, Federated Noisy Decentralized Learning (FedNDL1), comes from the literature, where the noise is added to their parameters to simulate the scenario of the presence of noisy communication channels. This algorithm shares parameters to form a consensus with the clients based on a communication graph topology through a noisy communication channel. The proposed second algorithm (FedNDL2) is similar to the first algorithm but with added noise to the parameters, and it performs the gossip averaging before the gradient optimization. The proposed third algorithm (FedNDL3), on the other hand, shares the gradients through noisy communication channels instead of the parameters. Theoretical and experimental results demonstrate that under imperfect information sharing, the third scheme that mixes gradients is more robust in the presence of a noisy channel compared with the algorithms from the literature that mix the parameters., Comment: 24 pages, 2 figures

Connected and Automated Vehicles (CAVs) are one of the emerging technologies in the automotive domain that has the potential to alleviate the issues of accidents, traffic congestion, and pollutant emissions, leading to a safe, efficient, and sustainable transportation system. Machine learning-based methods are widely used in CAVs for crucial tasks like perception, motion planning, and motion control, where machine learning models in CAVs are solely trained using the local vehicle data, and the performance is not certain when exposed to new environments or unseen conditions. Federated learning (FL) is an effective solution for CAVs that enables a collaborative model development with multiple vehicles in a distributed learning framework. FL enables CAVs to learn from a wide range of driving environments and improve their overall performance while ensuring the privacy and security of local vehicle data. In this paper, we review the progress accomplished by researchers in applying FL to CAVs. A broader view of the various data modalities and algorithms that have been implemented on CAVs is provided. Specific applications of FL are reviewed in detail, and an analysis of the challenges and future scope of research are presented., Comment: 8 pages, 1 figure

Rewrite systems [6, 10, 12] have been widely employing equality saturation [9], which is an optimisation methodology that uses a saturated e-graph to represent all possible sequences of rewrite simultaneously, and then extracts the optimal one. As such, optimal results can be achieved by avoiding the phase-ordering problem. However, we observe that when the e-graph is not saturated, it cannot represent all possible rewrite opportunities and therefore the phase-ordering problem is re-introduced during the construction phase of the e-graph. To address this problem, we propose MCTS-GEB, a domain-general rewrite system that applies reinforcement learning (RL) to e-graph construction. At its core, MCTS-GEB uses a Monte Carlo Tree Search (MCTS) [3] to efficiently plan for the optimal e-graph construction, and therefore it can effectively eliminate the phase-ordering problem at the construction phase and achieve better performance within a reasonable time. Evaluation in two different domains shows MCTS-GEB can outperform the state-of-the-art rewrite systems by up to 49x, while the optimisation can generally take less than an hour, indicating MCTS-GEB is a promising building block for the future generation of rewrite systems., Comment: Published in 3rd Workshop on Machine Learning and Systems (EuroMLSys 23), May 8, 2023, Rome, Italy

Cylindrical Algebraic Decomposition (CAD) was the first practical means for doing real quantifier elimination (QE), and is still a major method, with many improvements since Collins' original method. Nevertheless, its complexity is inherently doubly exponential in the number of variables. Where applicable, virtual term substitution (VTS) is more effective, turning a QE problem in $n$ variables to one in $n-1$ variables in one application, and so on. Hence there is scope for hybrid methods: doing VTS where possible then using CAD. This paper describes such a poly-algorithmic implementation, based on the second author's Ph.D. thesis. The version of CAD used is based on a new implementation of Lazard's recently-justified method, with some improvements to handle equational constraints., Comment: To appear in Proceedings SYNASC 2023

In the following work, we described the problems of porosity analysis of cement materials using backscattered electron images. We noticed that despite its great utility, the overflow porosity segmentation method allows for the introduction of an additional error by the effect of the subjective researcher. For this purpose, two developed variants of this method - regression I and II - were completely algorithmized and compared with the literature methods of overflow, triangle, and method preserving to choose the fastest and most consistent measurement method. Based on the comparison of the two data sets, it was judged that the improved overflow-based methods are the best candidates for the automated porosity assessment, with particular emphasis on regression II. All the algorithms used are summarized as Python source code in Supplementary Material., Comment: 13 pages, 6 figures

We provide sharp cylindrical parametrizations of cylindrical cell decompositions by maps with bounded $C^{r}$ norm in the sharply o-minimal setting, thus generalizing and strengthening the Yomdin-Gromov Algebraic Lemma. We introduce forts, geometrical objects encoding the combinatorial structure of cylindrical cell decompositions in o-minimal geometry. Cylindical decompositions, refinements of such decompositions, and cylindrical parametrizations of such decomposition become morphisms in the category of forts. We formulate and prove the above results in the language of forts.

The method of assessing porosity using images from scanning electron microscopy is ineffective in situations where the substrate and the coating have a significantly different average atomic number, which results in a different contrast of backscattered electrons. For this reason, the analyzes carried out to date were usually based on the manual distinction of important microstructure elements, which was followed by selective thresholding. However, this method depends on the subjective action of the researcher, and may result in errors that are difficult to estimate. The presented method is based on the simultaneous collection of both the image of backscattered electrons and elemental maps obtained using an energy dispersive X-ray analyzer. Multivariate comparative analysis of the matrix was used to characterize a technique of distinguishing epoxy resin from a hydrated cement-based substrate. The proposed algorithm significantly facilitates the detection of individual interface components, and also makes comparative analyzes repeatable and reliable., Comment: 10 pages, 5 figures

We propose and computationally study a method for simultaneously orienting the angular momentum of asymmetric top molecules along: 1) a laboratory-fixed direction; 2) the molecular intermediate moment of inertia axis; 3) the laser field wavevector. For this purpose we utilize a coherent control scheme in which a tailored-pulse optical centrifuge populates rotational states with well defined projections of the total angular momentum onto molecular axes. Appropriately time-shaped optical centrifuge pulses can leave the rotational wavepacket in peculiar rotational coherences which lead to a good degree of 3-dimensional transient alignment, with an arbitrary molecular axis pointing along the laser pulse propagation direction. As an example, we demonstrate how to generate highly resilient rotational quantum states of D2S in which the molecule rotates mainly about its intermediate inertia axis, such that its electric dipole moment is permanently aligned along the propagation direction of the laser pulse. Applications might include accessing less obscured information in various photo-electron imaging experiments.

An accurate model of the environment and the dynamic agents acting in it offers great potential for improving motion planning. We present MILE: a Model-based Imitation LEarning approach to jointly learn a model of the world and a policy for autonomous driving. Our method leverages 3D geometry as an inductive bias and learns a highly compact latent space directly from high-resolution videos of expert demonstrations. Our model is trained on an offline corpus of urban driving data, without any online interaction with the environment. MILE improves upon prior state-of-the-art by 31% in driving score on the CARLA simulator when deployed in a completely new town and new weather conditions. Our model can predict diverse and plausible states and actions, that can be interpretably decoded to bird's-eye view semantic segmentation. Further, we demonstrate that it can execute complex driving manoeuvres from plans entirely predicted in imagination. Our approach is the first camera-only method that models static scene, dynamic scene, and ego-behaviour in an urban driving environment. The code and model weights are available at https://github.com/wayveai/mile., Comment: NeurIPS 2022

The diffuse, unresolved sky provides most of the photons that the Hubble Space Telescope (HST) receives, yet remains poorly understood. HST Archival Legacy program SKYSURF aims to measure the 0.2-1.6 $\mu$m sky surface brightness (sky-SB) from over 140,000 HST images. We describe a sky-SB measurement algorithm designed for SKYSURF that is able to recover the input sky-SB from simulated images to within 1% uncertainty. We present our sky-SB measurements estimated using this algorithm on the entire SKYSURF database. Comparing our sky-SB spectral energy distribution (SED) to measurements from the literature shows general agreements. Our SKYSURF SED also reveals a possible dependence on Sun angle, indicating either non-isotropic scattering of solar photons off interplanetary dust or an additional component to Zodiacal Light. Finally, we update Diffuse Light limits in the near-IR based on the methods from Carleton et al. (2022), with values of 0.009 MJy sr$^{-1}$ (22 nW m$^{-2}$ sr$^{-1}$) at 1.25 $\mu$m, 0.015 MJy sr$^{-1}$ (32 nW m$^{-2}$ sr$^{-1}$) at 1.4 $\mu$m, and 0.013 MJy sr$^{-1}$ (25 nW m$^{-2}$ sr$^{-1}$) at 1.6 $\mu$m. These estimates provide the most stringent all-sky constraints to date in this wavelength range. SKYSURF sky-SB measurements are made public on the official SKYSURF website and will be used to constrain Diffuse Light in future papers., Comment: Revised based on helpful comments from the reviewer, and accepted to AJ on April 12th, 2023. Main paper: 18 pages, 9 figures, 4 tables. Appendices: 16 pages, 10 figures, 1 table. Main results shown in Figure 7 and Table 4

We prove a few results about non-nilpotent graphs of symmetric groups $S_n$ -- namely that they have a Hamiltonian cycle and they satisfy a conjecture of Nongsiang and Saikia. The latter is likewise proven for alternating groups $A_n$. We also show that the class of non-nilpotent graphs does not have any ''local'' properties, ie. for every simple graph $X$ there is a group $G$, such that its non-nilpotent graph has $X$ as an induced subgraph.

We relate binary words with a given number of subsequences to continued fractions of rational numbers with a given denominator. We deduce that there are binary strings of length $O(\log n \log \log n)$ with exactly $n$ subsequences; this can be improved to $O(\log n)$ under assumption of Zaremba's conjecture.

We present a detailed study of the barium star at the heart of the planetary nebula Abell 70. Time-series photometry obtained over a period of more than ten years demonstrates that the barium-contaminated companion is a rapid rotator with temporal variability due to spots. The amplitude and phasing of the photometric variability changes abruptly, however there is no evidence for a change in the rotation period (P = 2.06~d) over the course of the observations. The co-addition of 17 high-resolution spectra obtained with VLT-UVES allow us to measure the physical and chemical properties of the companion, confirming it to be a chromospherically-active, late G-type sub-giant with more than +1~dex of barium enhancement. We find no evidence of radial velocity variability in the spectra, obtained over the course of approximately 130~d with a single additional point some 8 years later, with the radial velocities of all epochs approximately $-$10 \kms{} from the previously measured systemic velocity of the nebula. This is perhaps indicative that the binary has a relatively long period (P $\gtrsim$ 2~yr) and high eccentricity ($e\gtrsim$ 0.3), and that all the observations were taken around radial velocity minimum. However, unless the binary orbital plane is not aligned with the waist of the nebula or the systemic velocity of the binary is not equal to the literature value for the nebula, this would imply an unfeasibly large mass for the nebular progenitor., Comment: Accepted for publication in MNRAS