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Time-series anomaly detection plays an important role in engineering processes, like development, manufacturing and other operations involving dynamic systems. These processes can greatly benefit from advances in the field, as state-of-the-art approaches may aid in cases involving, for example, highly dimensional data. To provide the reader with understanding of the terminology, this survey introduces a novel taxonomy where a distinction between online and offline, and training and inference is made. Additionally, it presents the most popular data sets and evaluation metrics used in the literature, as well as a detailed analysis. Furthermore, this survey provides an extensive overview of the state-of-the-art model-based online semi- and unsupervised anomaly detection approaches for multivariate time-series data, categorising them into different model families and other properties. The biggest research challenge revolves around benchmarking, as currently there is no reliable way to compare different approaches against one another. This problem is two-fold: on the one hand, public data sets suffers from at least one fundamental flaw, while on the other hand, there is a lack of intuitive and representative evaluation metrics in the field. Moreover, the way most publications choose a detection threshold disregards real-world conditions, which hinders the application in the real world. To allow for tangible advances in the field, these issues must be addressed in future work.

Flow shops are widely studied machine environments in which all jobs must visit all machines in the same order. While conventional flow shops assume that each job traverses the shop only once, many industrial environments require jobs to loop through the shop multiple times before completion. This means that after traversing the shop and completing its processing on the last machine, a job must return to the first machine and traverse the shop again until it has completed all its required loops. Such a setting, referred to as a flow shop with reentry, has numerous applications in industry, e.g., semiconductor manufacturing. The planning problem is to schedule all loops of all jobs while minimizing the total weighted completion time. In this paper, we consider reentrant flow shops with unit processing times. We show that this problem is strongly NP-hard if the number of machines is part of the input. We propose the Least Remaining Loops First (LRL) priority rule and show that it minimizes the total unweighted completion time. Then, we analyze the Weighted Least Remaining Loops First (WLRL) priority rule and show that it has a worst-case performance ratio of $(1+\sqrt{2})/2$ (about 1.2). Additionally, we present a fully polynomial time approximation scheme (FPTAS) and a pseudo-polynomial time algorithm if the number of machines in the flow shop is fixed.

The very long-term evolution of the hierarchical restricted three-body problem with a massive perturber is analyzed analytically in the high eccentricity regime. Perturbations on the time scale of the outer orbit can accumulate over long timescales and be comparable to the effect of the octupole term. These perturbations are described by Brown's Hamiltonian - having different forms in the literature. We show that at the high eccentricity regime - the effect of Brown's Hamiltonian is an azimuthal precesssion of the eccentricity vector and can be solved analytically. In fact, the dynamics are equivalent to a simple pendulum model allowing an explicit flip criterion., Comment: 5 pages, 4 figures

Scientists generate petabytes of data daily to help uncover environmental trends or behaviors that are hard to predict. For example, understanding climate simulations based on the long-term average of temperature, precipitation, and other environmental variables is essential to predicting and establishing root causes of future undesirable scenarios and assessing possible mitigation strategies. While supercomputer centers provide a powerful infrastructure for generating petabytes of simulation output, accessing and analyzing these datasets interactively remains challenging on multiple fronts. This paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hiding the complexities of dealing with file systems or cloud services. We also optimize network utilization while streaming from petascale repositories through state-of-the-art progressive compression algorithms. Based on this abstraction, we provide customizable dashboards that can be accessed from any device with any internet connection, enabling interactive visual analysis of vast amounts of data to a wide range of users - from top scientists with access to leadership-class computing environments to undergraduate students of disadvantaged backgrounds from minority-serving institutions. We focus on NASA's use of petascale climate datasets as an example of particular societal impact and, therefore, a case where achieving equity in science participation is critical. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution., Comment: 11 pages, 12 figures, conference paper

ProtoDUNE Single-Phase (ProtoDUNE-SP) is a 770-ton liquid argon time projection chamber that operated in a hadron test beam at the CERN Neutrino Platform in 2018. We present a measurement of the total inelastic cross section of charged kaons on argon as a function of kaon energy using 6 and 7 GeV/$c$ beam momentum settings. The flux-weighted average of the extracted inelastic cross section at each beam momentum setting was measured to be 380$\pm$26 mbarns for the 6 GeV/$c$ setting and 379$\pm$35 mbarns for the 7 GeV/$c$ setting.

The task of indoor positioning is fundamental to several applications, including navigation, healthcare, location-based services, and security. An emerging field is inertial navigation for pedestrians, which relies only on inertial sensors for positioning. In this paper, we present inertial pedestrian navigation models and learning approaches. Among these, are methods and algorithms for shoe-mounted inertial sensors and pedestrian dead reckoning (PDR) with unconstrained inertial sensors. We also address three categories of data-driven PDR strategies: activity-assisted, hybrid approaches, and learning-based frameworks.

Plasma in the terrestrial magnetosheath is characterised by very weak particle-particle collisions, so kinetic microinstabilities are thought to be responsible for regulating the thermodynamics of the plasma. By exciting electromagnetic waves, these instabilities redistribute free energy in velocity space, moulding the velocity distribution function (VDF) into a lower energy state. In the high-beta magnetosheath, relatively small perturbations to the VDF can easily excite instabilities compared to in the low-beta inner heliosphere. Since magnetic fields cannot do work on the particles, electric fields mediate energy exchange between the electromagnetic field and the bulk fluid properties of the plasma. We investigate signatures of non-ideal energy conversion associated with turbulent fluctuations in the context of electron and ion temperature anisotropy-beta instabilities, utilising over 24 hours of data spread over 163 distinct intervals of in situ magnetosheath observations from Magnetospheric Multiscale (MMS). We find that average energy conversion into fluid flow is enhanced along instability boundaries, suggesting that turbulence is playing a role in how free energy is redistributed in the plasma. The work enables a quantification of the energetics which are associated with the role of kinetic microinstabilities in regulating collisionless plasma thermodynamics. This work provides insight into the open question of how specific plasma processes couple into the turbulent dynamics and ultimately lead to energy dissipation and particle energisation in collisionless plasmas., Comment: 10 pages, 3 figures, 1 table. Submitted for peer review at Astrophysical Journal Letters (ApJL), July 2024

The SNO+ detector operated initially as a water Cherenkov detector. The implementation of a sealed covergas system midway through water data taking resulted in a significant reduction in the activity of $^{222}$Rn daughters in the detector and allowed the lowest background to the solar electron scattering signal above 5 MeV achieved to date. This paper reports an updated SNO+ water phase $^8$B solar neutrino analysis with a total livetime of 282.4 days and an analysis threshold of 3.5 MeV. The $^8$B solar neutrino flux is found to be $\left(2.32^{+0.18}_{-0.17}\text{(stat.)}^{+0.07}_{-0.05}\text{(syst.)}\right)\times10^{6}$ cm$^{-2}$s$^{-1}$ assuming no neutrino oscillations, or $\left(5.36^{+0.41}_{-0.39}\text{(stat.)}^{+0.17}_{-0.16}\text{(syst.)} \right)\times10^{6}$ cm$^{-2}$s$^{-1}$ assuming standard neutrino oscillation parameters, in good agreement with both previous measurements and Standard Solar Model Calculations. The electron recoil spectrum is presented above 3.5 MeV.

Quantitative MRI (qMRI) offers significant advantages over weighted images by providing objective parameters related to tissue properties. Deep learning-based methods have demonstrated effectiveness in estimating quantitative maps from series of weighted images. In this study, we present qMRI Diffuser, a novel approach to qMRI utilising deep generative models. Specifically, we implemented denoising diffusion probabilistic models (DDPM) for T1 quantification in the brain, framing the estimation of quantitative maps as a conditional generation task. The proposed method is compared with the residual neural network (ResNet) and the recurrent inference machine (RIM) on both phantom and in vivo data. The results indicate that our method achieves improved accuracy and precision in parameter estimation, along with superior visual performance. Moreover, our method inherently incorporates stochasticity, enabling straightforward quantification of uncertainty. Hence, the proposed method holds significant promise for quantitative MR mapping., Comment: Accepted by Deep Generative Models workshop at MICCAI 2024

Understanding planet formation is important in the context of the origin of planetary systems in general and of the Solar System in particular, as well as to predict the likelihood of finding Jupiter, Neptune, and Earth analogues around other stars. We aim to precisely determine the radii and dynamical masses of transiting planets orbiting the young M star AU Mic using public photometric and spectroscopic datasets. We characterise the stellar activity and physical properties (radius, mass, density) of the transiting planets in the young AU Mic system through joint transit and radial velocity fits with Gaussian processes. We determine a radius of $R^{b}$= 4.79 +/- 0.29 R$_\oplus$, a mass of $M^{b}$= 9.0 +/- 2.7 M$_\oplus$, and a bulk density of $\rho^{b}$ = 0.49 +/- 0.16 g cm$^{-3}$ for the innermost transiting planet AU Mic b. For the second known transiting planet, AU Mic c, we infer a radius of $R^{c}$= 2.79 +/- 0.18 R$_\oplus$, a mass of $M^{c}$= 14.5 +/- 3.4 M$_\oplus$, and a bulk density of $\rho^{c}$ = 3.90 +/- 1.17 g cm$^{-3}$. According to theoretical models, AU Mic b may harbour an H2 envelope larger than 5\% by mass, with a fraction of rock and a fraction of water. AU Mic c could be made of rock and/or water and may have an H2 atmosphere comprising at most 5\% of its mass. AU Mic b has retained most of its atmosphere but might lose it over tens of millions of years due to the strong stellar radiation, while AU Mic c likely suffers much less photo-evaporation because it lies at a larger separation from its host. Using all the datasets in hand, we determine a 3$\sigma$ upper mass limit of $M^{[d]}\sin{i}$ = 8.6 M$_{\oplus}$ for the AU Mic 'd' TTV-candidate. In addition, we do not confirm the recently proposed existence of the planet candidate AU Mic 'e' with an orbital period of 33.4 days., Comment: Accepted in A&A. 28 pages, 15 figures

The interest in mobile platforms across a variety of applications has increased significantly in recent years. One of the reasons is the ability to achieve accurate navigation by using low-cost sensors. To this end, inertial sensors are fused with global navigation satellite systems (GNSS) signals. GNSS outages during platform operation can result in pure inertial navigation, causing the navigation solution to drift. In such situations, periodic trajectories with dedicated algorithms were suggested to mitigate the drift. With periodic dynamics, inertial deep learning approaches can capture the motion more accurately and provide accurate dead-reckoning for drones and mobile robots. In this paper, we propose approaches to extend deep learning-assisted inertial sensing and fusion capabilities during periodic motion. We begin by demonstrating that fusion between GNSS and inertial sensors in periodic trajectories achieves better accuracy compared to straight-line trajectories. Next, we propose an empowered network architecture to accurately regress the change in distance of the platform. Utilizing this network, we drive a hybrid approach for a neural-inertial fusion filter. Finally, we utilize this approach for situations when GNSS is available and show its benefits. A dataset of 337 minutes of data collected from inertial sensors mounted on a mobile robot and a quadrotor is used to evaluate our approaches.

While accurately detecting and correcting factual contradictions in language model outputs has become increasingly important as their capabilities improve, doing so is highly challenging. We propose a novel method, FACTTRACK, for tracking atomic facts and addressing factual contradictions. Crucially, FACTTRACK also maintains time-aware validity intervals for each fact, allowing for change over time. At a high level, FACTTRACK consists of a four-step pipeline to update a world state data structure for each new event: (1) decompose the event into directional atomic facts; (2) determine the validity interval of each atomic fact using the world state; (3) detect contradictions with existing facts in the world state; and finally (4) add new facts to the world state and update existing atomic facts. When we apply FACTTRACK to contradiction detection on structured story outlines, we find that FACTTRACK using LLaMA2-7B-Chat substantially outperforms a fair baseline using LLaMA2-7B-Chat, and achieves performance comparable to a GPT4 baseline. Moreover, when using GPT4, FACTTRACK significantly outperforms the GPT4 baseline., Comment: 22 pages

In this note, we are interested in the asymptotic efficiency of Sobol' indices esti-mators. After recalling the basis of asymptotic efficiency, we compute the efficientinfluence functions for Sobol' indices in two different contexts: the Pick-Freeze andthe given-data settings.

In this work we propose a novel method to ensure important entropy inequalities are satisfied semi-discretely when constructing reduced order models (ROMs) on nonlinear reduced manifolds. We are in particular interested in ROMs of systems of nonlinear hyperbolic conservation laws. The so-called entropy stability property endows the semi-discrete ROMs with physically admissible behaviour. The method generalizes earlier results on entropy-stable ROMs constructed on linear spaces. The ROM works by evaluating the projected system on a well-chosen approximation of the state that ensures entropy stability. To ensure accuracy of the ROM after this approximation we locally enrich the tangent space of the reduced manifold with important quantities. Using numerical experiments on some well-known equations (the inviscid Burgers equation, shallow water equations and compressible Euler equations) we show the improved structure-preserving properties of our ROM compared to standard approaches and that our approximations have minimal impact on the accuracy of the ROM. We additionally generalize the recently proposed polynomial reduced manifolds to rational polynomial manifolds and show that this leads to an increase in accuracy for our experiments.

We present a study of the weak lensing matter profiles of 698 South Pole Telescope (SPT) thermal Sunyaev-Zel'dovich effect (tSZE) selected galaxy clusters in the redshift range $0.25

The determination of the direction of a stellar core collapse via its neutrino emission is crucial for the identification of the progenitor for a multimessenger follow-up. A highly effective method of reconstructing supernova directions within the Deep Underground Neutrino Experiment (DUNE) is introduced. The supernova neutrino pointing resolution is studied by simulating and reconstructing electron-neutrino charged-current absorption on $^{40}$Ar and elastic scattering of neutrinos on electrons. Procedures to reconstruct individual interactions, including a newly developed technique called ``brems flipping'', as well as the burst direction from an ensemble of interactions are described. Performance of the burst direction reconstruction is evaluated for supernovae happening at a distance of 10 kpc for a specific supernova burst flux model. The pointing resolution is found to be 3.4 degrees at 68% coverage for a perfect interaction-channel classification and a fiducial mass of 40 kton, and 6.6 degrees for a 10 kton fiducial mass respectively. Assuming a 4% rate of charged-current interactions being misidentified as elastic scattering, DUNE's burst pointing resolution is found to be 4.3 degrees (8.7 degrees) at 68% coverage., Comment: 25 pages, 16 figures

Static analysis is sound in theory, but an implementation may unsoundly fail to analyze all of a program's code. Any such omission is a serious threat to the validity of the tool's output. Our work is the first to measure the prevalence of these omissions. Previously, researchers and analysts did not know what is missed by static analysis, what sort of code is missed, or the reasons behind these omissions. To address this gap, we ran 13 static analysis tools and a dynamic analysis on 1000 Android apps. Any method in the dynamic analysis but not in a static analysis is an unsoundness. Our findings include the following. (1) Apps built around external frameworks challenge static analyzers. On average, the 13 static analysis tools failed to capture 61% of the dynamically-executed methods. (2) A high level of precision in call graph construction is a synonym for a high level of unsoundness; (3) No existing approach significantly improves static analysis soundness. This includes those specifically tailored for a given mechanism, such as DroidRA to address reflection. It also includes systematic approaches, such as EdgeMiner, capturing all callbacks in the Android framework systematically. (4) Modeling entry point methods challenges call graph construction which jeopardizes soundness.

In recent years, various methods and benchmarks have been proposed to empirically evaluate the alignment of artificial neural networks to human neural and behavioral data. But how aligned are different alignment metrics? To answer this question, we analyze visual data from Brain-Score (Schrimpf et al., 2018), including metrics from the model-vs-human toolbox (Geirhos et al., 2021), together with human feature alignment (Linsley et al., 2018; Fel et al., 2022) and human similarity judgements (Muttenthaler et al., 2022). We find that pairwise correlations between neural scores and behavioral scores are quite low and sometimes even negative. For instance, the average correlation between those 80 models on Brain-Score that were fully evaluated on all 69 alignment metrics we considered is only 0.198. Assuming that all of the employed metrics are sound, this implies that alignment with human perception may best be thought of as a multidimensional concept, with different methods measuring fundamentally different aspects. Our results underline the importance of integrative benchmarking, but also raise questions about how to correctly combine and aggregate individual metrics. Aggregating by taking the arithmetic average, as done in Brain-Score, leads to the overall performance currently being dominated by behavior (95.25% explained variance) while the neural predictivity plays a less important role (only 33.33% explained variance). As a first step towards making sure that different alignment metrics all contribute fairly towards an integrative benchmark score, we therefore conclude by comparing three different aggregation options., Comment: submitted to the ICLR 2024 Workshop on Representational Alignment (Re-Align)

When it comes to authentication in speaker verification systems, not all utterances are created equal. It is essential to estimate the quality of test utterances in order to account for varying acoustic conditions. In addition to the net-speech duration of an utterance, it is observed in this paper that phonetic richness is also a key indicator of utterance quality, playing a significant role in accurate speaker verification. Several phonetic histogram based formulations of phonetic richness are explored using transcripts obtained from an automatic speaker recognition system. The proposed phonetic richness measure is found to be positively correlated with voice authentication scores across evaluation benchmarks. Additionally, the proposed measure in combination with net speech helps in calibrating the speaker verification scores, obtaining a relative EER improvement of 5.8% on the Voxceleb1 evaluation protocol. The proposed phonetic richness based calibration provides higher benefit for short utterances with repeated words., Comment: Accepted by EUSIPCO 2024

Recent progress in generative AI technology has made audio deepfakes remarkably more realistic. While current research on anti-spoofing systems primarily focuses on assessing whether a given audio sample is fake or genuine, there has been limited attention on discerning the specific techniques to create the audio deepfakes. Algorithms commonly used in audio deepfake generation, like text-to-speech (TTS) and voice conversion (VC), undergo distinct stages including input processing, acoustic modeling, and waveform generation. In this work, we introduce a system designed to classify various spoofing attributes, capturing the distinctive features of individual modules throughout the entire generation pipeline. We evaluate our system on two datasets: the ASVspoof 2019 Logical Access and the Multi-Language Audio Anti-Spoofing Dataset (MLAAD). Results from both experiments demonstrate the robustness of the system to identify the different spoofing attributes of deepfake generation systems., Comment: Accepted by INTERSPEECH 2024

As attention to recorded data grows in the realm of automotive testing and manual evaluation reaches its limits, there is a growing need for automatic online anomaly detection. This real-world data is complex in many ways and requires the modelling of testee behaviour. To address this, we propose a temporal variational autoencoder (TeVAE) that can detect anomalies with minimal false positives when trained on unlabelled data. Our approach also avoids the bypass phenomenon and introduces a new method to remap individual windows to a continuous time series. Furthermore, we propose metrics to evaluate the detection delay and root-cause capability of our approach and present results from experiments on a real-world industrial data set. When properly configured, TeVAE flags anomalies only 6% of the time wrongly and detects 65% of anomalies present. It also has the potential to perform well with a smaller training and validation subset but requires a more sophisticated threshold estimation method., Comment: Submitted to Studies in Computational Intelligence Journal. arXiv admin note: substantial text overlap with arXiv:2309.02253

Plasma turbulence involves complex, nonlinear interactions of electromagnetic fields and charged particles across multiple scales. Studying these phenomena in space plasmas, like the solar wind, is facilitated by the intrinsic scale separations and the availability of in situ spacecraft observations. However, the single-point or single-scale configurations of current spacecraft limit our understanding of many properties of the turbulent solar wind. To overcome these limitations, multipoint measurements spanning a range of characteristic scales are essential. This paper prepares for the enhanced measurement capabilities of upcoming multispacecraft missions by demonstrating that higher-order statistics, specifically kurtosis, as a baseline for intermittency can be accurately measured. Using synthetic turbulent fields with adjustable intermittency levels, we achieve scale separations analogous to those in the solar wind and apply these techniques to the planned trajectories of the HelioSwarm mission. This approach promises significant advancements in our understanding of plasma turbulence.

When mobile meets LLMs, mobile app users deserve to have more intelligent usage experiences. For this to happen, we argue that there is a strong need to appl LLMs for the mobile ecosystem. We therefore provide a research roadmap for guiding our fellow researchers to achieve that as a whole. In this roadmap, we sum up six directions that we believe are urgently required for research to enable native intelligence in mobile devices. In each direction, we further summarize the current research progress and the gaps that still need to be filled by our fellow researchers.

The gradual evolution of the restricted hierarchical three body problem is analyzed analytically, focusing on conditions of Kozai-Lidov Cycles that may lead to orbital flips from prograde to retrograde motion due to the octupole (third order) term which are associated with extremely high eccentricities. We revisit the approach described by Katz, Dong and Malhotra (\href{https://doi.org/10.1103/PhysRevLett.107.181101}{Phys. Rev. Lett. 107, 181101 (2011)}) and show that for most initial conditions, to an excellent approximation, the analytic derivation can be greatly simplified and reduces to a simple pendulum model allowing an explicit flip criterion. The resulting flip criterion is much simpler than the previous one but the latter is still needed in a small fraction of phase space. We identify a logical error in the earlier derivation but clarify why it does not affect the final results., Comment: 4 pages, 4 figures

Deep learning has achieved impressive performance across various medical imaging tasks. However, its inherent bias against specific groups hinders its clinical applicability in equitable healthcare systems. A recently discovered phenomenon, Neural Collapse (NC), has shown potential in improving the generalization of state-of-the-art deep learning models. Nonetheless, its implications on bias in medical imaging remain unexplored. Our study investigates deep learning fairness through the lens of NC. We analyze the training dynamics of models as they approach NC when training using biased datasets, and examine the subsequent impact on test performance, specifically focusing on label bias. We find that biased training initially results in different NC configurations across subgroups, before converging to a final NC solution by memorizing all data samples. Through extensive experiments on three medical imaging datasets -- PAPILA, HAM10000, and CheXpert -- we find that in biased settings, NC can lead to a significant drop in F1 score across all subgroups. Our code is available at https://gitlab.com/radiology/neuro/neural-collapse-fairness

Circularly polarized waves consistent with parallel-propagating ion cyclotron waves (ICWs) and fast magnetosonic waves (FMWs) are often observed by Parker Solar Probe (PSP) at ion kinetic scales. Such waves damp energy via the cyclotron resonance, with such damping expected to play a significant role in the enhanced, anisotropic heating of the solar wind observed in the inner heliosphere. We employ a linear plasma dispersion solver, PLUME, to evaluate frequencies of ICWs and FMWs in the plasma rest frame and Doppler-shift them to the spacecraft frame, calculating their damping rates at frequencies where persistently high values of circular polarization are observed. We find such ion-scale waves are observed during $20.37\%$ of PSP Encounters 1 and 2 observations and their plasma frame frequencies are consistent with them being transient ICWs. We estimate significant ICW dissipation onto protons, consistent with previous empirical estimates for the total turbulent damping rates, indicating that ICW dissipation could account for the observed enhancements in the proton temperature and its anisotropy with respect to the mean magnetic field.

Active Galactic Nuclei (AGN) are prime candidate sources of the high-energy, astrophysical neutrinos detected by IceCube. This is demonstrated by the real-time multi-messenger detection of the blazar TXS 0506+056 and the recent evidence of neutrino emission from NGC 1068 from a separate time-averaged study. However, the production mechanism of the astrophysical neutrinos in AGN is not well established which can be resolved via correlation studies with photon observations. For neutrinos produced due to photohadronic interactions in AGN, in addition to a correlation of neutrinos with high-energy photons, there would also be a correlation of neutrinos with photons emitted at radio wavelengths. In this work, we perform an in-depth stacking study of the correlation between 15 GHz radio observations of AGN reported in the MOJAVE XV catalog, and ten years of neutrino data from IceCube. We also use a time-dependent approach which improves the statistical power of the stacking analysis. No significant correlation was found for both analyses and upper limits are reported. When compared to the IceCube diffuse flux, at 100 TeV and for a spectral index of 2.5, the upper limits derived are $\sim3\%$ and $\sim9\%$ for the time-averaged and time-dependent case, respectively., Comment: 14 Pages 7 Figures

We present a search for an eV-scale sterile neutrino using 7.5 years of data from the IceCube DeepCore detector. The analysis uses a sample of 21,914 events with energies between 5 and 150 GeV to search for sterile neutrinos through atmospheric muon neutrino disappearance. Improvements in event selection and treatment of systematic uncertainties provide greater statistical power compared to previous DeepCore sterile neutrino searches. Our results are compatible with the absence of mixing between active and sterile neutrino states, and we place constraints on the mixing matrix elements $|U_{\mu 4}|^2 < 0.0534$ and $|U_{\tau 4}|^2 < 0.0574$ at 90% CL under the assumption that $\Delta m^2_{41}\geq 1\;\mathrm{eV^2}$. These null results add to the growing tension between anomalous appearance results and constraints from disappearance searches in the 3+1 sterile neutrino landscape., Comment: 11 pages, 5 figures. Version accepted by Physical Review D for publication

We present neural network-based constitutive models for hyperelastic geometrically exact beams. The proposed models are physics-augmented, i.e., formulated to fulfill important mechanical conditions by construction. Strains and curvatures of the beam are used as input for feed-forward neural networks that represent the effective hyperelastic beam potential. Forces and moments are then received as the gradients of the beam potential, ensuring thermodynamic consistency. Furthermore, normalization conditions are considered via additional projection terms. To include the symmetry of beams with point-symmetric cross-sections, a flip symmetry constraint is introduced. Additionally, parameterized models are proposed that can represent the beam's constitutive behavior for varying cross-sectional geometries. The physically motivated parameterization takes into account the influence of the beam radius on the beam potential. Formulating the beam potential as a neural network provides a highly flexible model. This enables efficient constitutive surrogate modeling for geometrically exact beams with nonlinear material behavior and cross-sectional deformation, which otherwise would require computationally much more expensive methods. The models are calibrated to data generated for beams with circular, deformable cross-sections and varying radii, showing excellent accuracy and generalization. The applicability of the proposed model is further demonstrated by applying it in beam simulations. In all studied cases, the proposed model shows excellent performance.

This paper aims to develop a mathematical foundation to model knitting with graphs. We provide a precise definition for knit objects with a knot theoretic component and propose a simple undirected graph, a simple directed graph, and a directed multigraph model for any arbitrary knit object. Using these models, we propose natural categories related to the complexity of knitting structures. We use these categories to explore the hardness of determining whether a knit object of each class exists for a given graph. We show that while this problem is NP-hard in general, under specific cases, there are linear and polynomial time algorithms which take advantage of unique properties of common knitting techniques. This work aims to bridge the gap between textile arts and graph theory, offering a useful and rigorous framework for analyzing knitting objects using their corresponding graphs and for generating knitting objects from graphs., Comment: 11 pages, 4 tables, 5 figures

Unit testing, crucial for ensuring the reliability of code modules, such as classes and methods, is often overlooked by developers due to time constraints. Automated test generation techniques have emerged to address this, but they frequently lack readability and require significant developer intervention. Large Language Models (LLMs), such as GPT and Mistral, have shown promise in software engineering tasks, including test generation, but their overall effectiveness remains unclear. This study presents an extensive investigation of LLMs, evaluating the effectiveness of four models and five prompt engineering techniques for unit test generation. We analyze 216 300 tests generated by the selected advanced instruct-tuned LLMs for 690 Java classes collected from diverse datasets. Our evaluation considers correctness, understandability, coverage, and test smell detection in the generated tests, comparing them to a widely used automated testing tool, EvoSuite. While LLMs demonstrate potential, improvements in test quality particularly in reducing common test smells are necessary. This study highlights the strengths and limitations of LLM-generated tests compared to traditional methods, paving the way for further research on LLMs in test automation.

This work focuses on the analysis of the mean longitudinal magnetic field as a stellar activity tracer in the context of small exoplanet detection and characterisation in radial-velocity (RV) surveys. We use SDO/HMI filtergrams to derive Sun-as-a-star magnetic field measurements, and show that the mean longitudinal magnetic field is an excellent rotational period detector and a useful tracer of the solar magnetic cycle. To put these results into context, we compare the mean longitudinal magnetic field to three common activity proxies derived from HARPS-N Sun-as-a-star data: the full-width at half-maximum, the bisector span and the S-index. The mean longitudinal magnetic field does not correlate with the RVs and therefore cannot be used as a one-to-one proxy. However, with high cadence and a long baseline, the mean longitudinal magnetic field outperforms all other considered proxies as a solar rotational period detector, and can be used to inform our understanding of the physical processes happening on the surface of the Sun. We also test the mean longitudinal magnetic field as a "stellar proxy" on a reduced solar dataset to simulate stellar-like observational sampling. With a Gaussian Process regression analysis, we confirm that the solar mean longitudinal magnetic field is the most effective of the considered indicators, and is the most efficient rotational period indicator over different levels of stellar activity. This work highlights the need for polarimetric time series observations of stars., Comment: 23 pages, 27 figures

A major open question in the theory of Gorenstein liaison is whether or not every arithmetically Cohen-Macaulay subscheme of $\mathbb{P}^n$ can be G-linked to a complete intersection. Migliore and Nagel showed that, if such a scheme is generically Gorenstein (e.g., reduced), then, after re-embedding so that it is viewed as a subscheme of $\mathbb{P}^{n+1}$, indeed it can be G-linked to a complete intersection. Motivated by this result, we consider techniques for constructing G-links on a scheme from G-links on a closely related reduced scheme. Polarization is a tool for producing a squarefree monomial ideal from an arbitrary monomial ideal. Basic double G-links on squarefree monomial ideals can be induced from vertex decompositions of their Stanley-Reisner complexes. Given a monomial ideal $I$ and a vertex decomposition of the Stanley-Reisner complex of its polarization $\mathcal{P}(I)$, we give conditions that allow for the lifting of an associated basic double G-link of $\mathcal{P}(I)$ to a basic double G-link of $I$ itself. We use the relationship we develop in the process to show that the Stanley-Reisner complexes of polarizations of artinian monomial ideals and of stable Cohen-Macaulay monomial ideals are vertex decomposable, recovering and strengthening the recent result of Fl{\o}ystad and Mafi that these complexes are shellable. We then introduce and study polarization of a Gr\"obner basis of an arbitrary homogeneous ideal and give a relationship between geometric vertex decomposition of a polarization and elementary G-biliaison that is analogous to our result on vertex decomposition and basic double G-linkage., Comment: 30 pages, comments welcome

Global vegetation structure mapping is critical for understanding the global carbon cycle and maximizing the efficacy of nature-based carbon sequestration initiatives. Moreover, vegetation structure mapping can help reduce the impacts of climate change by, for example, guiding actions to improve water security, increase biodiversity and reduce flood risk. Global satellite measurements provide an important set of observations for monitoring and managing deforestation and degradation of existing forests, natural forest regeneration, reforestation, biodiversity restoration, and the implementation of sustainable agricultural practices. In this paper, we explore the effectiveness of fine-tuning of a geospatial foundation model to estimate above-ground biomass (AGB) using space-borne data collected across different eco-regions in Brazil. The fine-tuned model architecture consisted of a Swin-B transformer as the encoder (i.e., backbone) and a single convolutional layer for the decoder head. All results were compared to a U-Net which was trained as the baseline model Experimental results of this sparse-label prediction task demonstrate that the fine-tuned geospatial foundation model with a frozen encoder has comparable performance to a U-Net trained from scratch. This is despite the fine-tuned model having 13 times less parameters requiring optimization, which saves both time and compute resources. Further, we explore the transfer-learning capabilities of the geospatial foundation models by fine-tuning on satellite imagery with sparse labels from different eco-regions in Brazil.

Strip and pixels sensors, fabricated on high resistivity silicon substrate, normally of p-type, are used in detectors for High Energy Physics (HEP) typically in a hybrid detector assembly. Furthermore, and owing to their inherent advantages over hybrid sensors, Monolithic Active Pixel Sensors (MAPS) fabricated in CMOS technology have been increasingly implemented in HEP experiments. In all cases, their use in higher radiation areas (HL-LHC and beyond) will require options to improve their radiation hardness and time resolution. These aspects demand a deep understanding of their radiation damage and reliable models to predict their behaviours at high fluences. As a first step, we fabricated several Schottky and n-on-p diodes, to allow a comparison of results and provide a backup solution for test devices, on 6 or 4-inch p-type silicon wafers with 50 {\mu}m epitaxial thickness and of doping concentration as they are normally used in HEP detectors and CMOS MAPS devices. In this paper, details of the design and fabrication process, along with test results of the fabricated devices before irradiation, will be provided. Additional test results on irradiated devices will be provided in subsequent publications.

Game-playing agents like AlphaGo have achieved superhuman performance through self-play, which is theoretically guaranteed to yield optimal policies in competitive games. However, most language tasks are partially or fully cooperative, so it is an open question whether techniques like self-play can effectively be used to improve language models. We empirically investigate this question in a negotiation game setting known as Deal or No Deal (DoND). Crucially, the objective in DoND can be modified to produce a fully cooperative game, a strictly competitive one, or anything in between. We finetune language models in self-play over multiple rounds of filtered behavior cloning in DoND for each of these objectives. Contrary to expectations, we find that language model self-play leads to significant performance gains in both cooperation and competition with humans, suggesting that self-play and related techniques have promise despite a lack of theoretical guarantees.

Shifting to cycling in urban areas reduces greenhouse gas emissions and improves public health. Street-level bicycle volume information would aid cities in planning targeted infrastructure improvements to encourage cycling and provide civil society with evidence to advocate for cyclists' needs. Yet, the data currently available to cities and citizens often only comes from sparsely located counting stations. This paper extrapolates bicycle volume beyond these few locations to estimate bicycle volume for the entire city of Berlin. We predict daily and average annual daily street-level bicycle volumes using machine-learning techniques and various public data sources. These include app-based crowdsourced data, infrastructure, bike-sharing, motorized traffic, socioeconomic indicators, weather, and holiday data. Our analysis reveals that the best-performing model is XGBoost, and crowdsourced cycling and infrastructure data are most important for the prediction. We further simulate how collecting short-term counts at predicted locations improves performance. By providing ten days of such sample counts for each predicted location to the model, we are able to halve the error and greatly reduce the variability in performance among predicted locations.

Magnesium is an excellent material in terms of biocompatibility and its corrosion products can serve as an active source for new bone formation. However, localized corrosion and H2 generation limit the potential of Mg-based implants. Utilizing low-alloyed Mg-Zn wires can strongly reduce problems with large H2 bubbles and improve the mechanical properties considerably while maintaining excellent long-term biocompatibility. Acidic pickling and a polymer coating can be effectively used to lower the rate of in vivo degradation. In this work, microstructural, mechanical, and in vitro characterization of 250 um and 300 um extruded wires made from ultra-pure Mg, commercially pure Mg, Mg-0.15Zn, Mg-0.4Zn and Mg-1Zn was performed. Additionally, Mg-0.4Zn wires together with a variant coated with a copolymer of L-lactide and {\epsilon}-caprolactone were tested in vivo on artificially damaged Wistar rat femurs. Based on the observed Mg-induced osteogenesis, polymer-coated Mg wires with a small addition of Zn are a perspective material for bone-support applications, such as cerclage and fixation wires.

Metallic strontium titanate (SrTiO$_3$) is known to have both normal-state and superconducting properties that vary strongly over a wide range of charge carrier densities. This indicates the importance of nonlinear dynamics, and has hindered the development of a clear qualitative description of the observed behaviour. A major challenge is to understand how the charge carriers themselves evolve with doping and temperature, with possible polaronic effects and evidence of an effective mass that strongly increases with temperature. Here we use $^{47,49}$Ti nuclear magnetic resonance (NMR) to perform a comprehensive study of the electronic spin susceptibility in the dilute metallic state of strontium titanate across the doping-temperature phase diagram. We find a temperature-dependent Knight shift that can be quantitatively understood within a non-degenerate Fermi gas model that fully takes into account the complex band structure of SrTiO$_3$. Our data are consistent with a temperature-independent effective mass, and we show that the behavior of the spin susceptibility is universal in a wide range of temperatures and carrier concentrations. These results provide a microscopic foundation for the understanding of the properties of the unconventional low-density metallic state in strontium titanate and related materials., Comment: 8 pages, 4 figures

This report summarizes the work of the EMMI Rapid Reaction Task Force on "Real and Virtual Photon Production at Ultra-Low Transverse Momentum and Low Mass at the LHC". We provide an overview of the soft-photon puzzle, i.e., of the long-standing discrepancy between experimental data and predictions based on Low's soft-photon theorem, also referred to as "anomalous" soft photon production, and we review the current theoretical understanding of soft radiation and soft theorems. We also focus on low-mass dileptons as a tool for determining the electrical conductivity of the medium produced in high-energy nucleus-nucleus collisions. We discuss how both topics can be addressed with the planned ALICE 3 detector at the LHC., Comment: 63 pages, 20 figures

Coordinating a high number of flexibility providing units (e.g. to provide ancillary services for the transmission system) across various grid layers requires new control concepts. A flexibility request at a point of common coupling can be met by utilizing a cascaded control structure based on online feedback optimization. In this paper the influence of the parameterization of the individual controllers on the performance of the hierarchical flexibility provision is studied on a three-level test system. The results show a high interdependency between the choice of control parameters of one controller and the behavior of other controllers as well as a significant impact on the accuracy and speed of flexibility provision. With a careful tuning, a cascaded structure based on online feedback optimization can achieve efficient vertical coordination of flexibility providing units.

The influence of a rotating magnetic field (in the $ab-$plane) on the density of states has been investigated in the superconducting state of the nematic FeSe superconductor using high sensitivity specific heat measurements. As expected for (quasi-)nodal superconductors, oscillations in the specific heat ($C$) associated to the Doppler energy shift of Cooper pairs with momenta close to the gap minima are observed. In the $T_c = 9$~K crystal, $C(\phi$) displays a twofold symmetry at low temperature and low magnetic field confirming the nematic character of FeSe from thermodynamical measurements. As expected, a $\pi/2$ phase shift is observed for increasing temperatures (at $H=1$~T) but the gap structure abruptly changes above $\sim 1$~K in this sample. At low temperature, the maxima observed for $H||a-$axis at low $H$ split into lobes at $\pm 45^\circ$ when the magnetic field is increased indicating an anomalous field dependence of the gaps.

Galaxy clusters are useful cosmological probes and interesting astrophysical laboratories. With growing cluster samples, a deeper understanding of the sample characteristics and improved control of systematics becomes more crucial. In this analysis we create a new and larger ACT-DR5-based thermal Sunyaev-Zel'dovich Effect (tSZE) selected galaxy cluster catalog with improved control over sample purity and completeness. We employ the red sequence based cluster redshift and confirmation tool MCMF together with optical imaging data from the Legacy Survey DR-10 and infrared data from the WISE satellite to systematicallyidentify true clusters from a new cluster candidate detection run on the ACT-DR5 dataset. The resulting ACT-DR5 MCMF sample contains 6,237 clusters with a residual contamination of 10.7%. This is an increase of 51% compared to the previous ACT-DR5 cluster catalog, making this catalog the largest tSZE-selected cluster catalog to date. The z>1 subsample contains 703 clusters, three times more than in the previous ACT-DR5 catalog. Matching the ACT-DR5 MCMF cluster catalog with a deeper tSZE sample from SPTpol 500d allows us to confirm the completeness and purity of the new ACT-DR5 MCMF sample. Cross-matching to the two largest X-ray selected cluster samples, the all-sky RASS MCMF and the half-sky eRASS1, confirms the sample purity of the RASS MCMF sample and in the case of eRASS1 reveals that 43% of the matched clusters are designated in eRASS1 as X-ray point sources rather than clusters. Cross-correlating the ACT-DR5 MCMF cluster catalog with ACT-DR6 lensing maps results in a 16.4\sigma detection of CMB lensing around the clusters, corresponding to the strongest signal found so far for a galaxy cluster sample. Repeating the measurement for the z>1 cluster subsample yields a significance of 4.3\sigma, which is the strongest CMB lensing detection in a z>1 cluster sample to date., Comment: 18 pages, 15 figures, submitted to A&A

Synthetic contact networks are useful for modeling epidemic spread and social transmission, but data to infer realistic contact patterns that take account of assortative connections at the geographic and economic levels is limited. We developed a method to generate synthetic contact networks for any region of the United States based on publicly available data. First, we generate a synthetic population of individuals within households from US census data using combinatorial optimization. Then, individuals are assigned to workplaces and schools using commute data, employment statistics, and school enrollment data. The resulting population is then connected into a realistic contact network using graph generation algorithms. We test the method on two census regions and show that the synthetic populations accurately reflect the source data. We further show that the contact networks have distinct properties compared to networks generated without a synthetic population, and that those differences affect the rate of disease transmission in an epidemiological simulation. We provide open-source software to generate a synthetic population and contact network for any area within the US., Comment: 12 pages,2 figures, 4 tables

The generation of equilibrium samples of molecular systems has been a long-standing problem in statistical physics. Boltzmann Generators are a generative machine learning method that addresses this issue by learning a transformation via a normalizing flow from a simple prior distribution to the target Boltzmann distribution of interest. Recently, flow matching has been employed to train Boltzmann Generators for small molecular systems in Cartesian coordinates. We extend this work and propose a first framework for Boltzmann Generators that are transferable across chemical space, such that they predict zero-shot Boltzmann distributions for test molecules without being retrained for these systems. These transferable Boltzmann Generators allow approximate sampling from the target distribution of unseen systems, as well as efficient reweighting to the target Boltzmann distribution. The transferability of the proposed framework is evaluated on dipeptides, where we show that it generalizes efficiently to unseen systems. Furthermore, we demonstrate that our proposed architecture enhances the efficiency of Boltzmann Generators trained on single molecular systems.

Image registration is essential for medical image applications where alignment of voxels across multiple images is needed for qualitative or quantitative analysis. With recent advancements in deep neural networks and parallel computing, deep learning-based medical image registration methods become competitive with their flexible modelling and fast inference capabilities. However, compared to traditional optimization-based registration methods, the speed advantage may come at the cost of registration performance at inference time. Besides, deep neural networks ideally demand large training datasets while optimization-based methods are training-free. To improve registration accuracy and data efficiency, we propose a novel image registration method, termed Recurrent Inference Image Registration (RIIR) network. RIIR is formulated as a meta-learning solver to the registration problem in an iterative manner. RIIR addresses the accuracy and data efficiency issues, by learning the update rule of optimization, with implicit regularization combined with explicit gradient input. We evaluated RIIR extensively on brain MRI and quantitative cardiac MRI datasets, in terms of both registration accuracy and training data efficiency. Our experiments showed that RIIR outperformed a range of deep learning-based methods, even with only $5\%$ of the training data, demonstrating high data efficiency. Key findings from our ablation studies highlighted the important added value of the hidden states introduced in the recurrent inference framework for meta-learning. Our proposed RIIR offers a highly data-efficient framework for deep learning-based medical image registration., Comment: Preprint

Program repair techniques offer cost-saving benefits for debugging within software development and programming education scenarios. With the proven effectiveness of Large Language Models (LLMs) in code-related tasks, researchers have explored their potential for program repair. However, it is crucial to recognize that existing repair benchmarks may have influenced LLM training data, potentially causing data leakage. To evaluate LLMs' realistic repair capabilities, (1) we introduce an extensive, non-crawled benchmark, referred to as TutorCode, comprising 1,239 C++ defect codes and associated information such as tutor guidance, solution description, failing test cases, and the corrected code. Our work assesses the repair performance of 12 LLMs on TutorCode, measuring repair correctness (TOP-5 and AVG-5) and patch precision (RPSR). (2) We then provide a comprehensive investigation into which types of extra information can help LLMs improve their performance in repairing defects. Among these types, tutor guidance was found to be the most effective information in enhancing LLM repair capabilities. To fully harness LLMs' conversational capabilities and the benefits of augmented information, (3) we introduce a novel conversational semi-automatic repair framework CREF assisting human tutor. It demonstrates a remarkable AVG-5 improvement of 17.2%-24.6% compared to the baseline, achieving an impressive AVG-5 of 76.6% when utilizing GPT-4. These results highlight the potential for enhancing LLMs' repair capabilities through interactions with tutors and historical conversations involving incorrect responses. The successful application of CREF in a real-world educational setting demonstrates its effectiveness in reducing tutors' workload and improving students' learning experience, while also showcasing its promise for facilitating other software engineering tasks, such as code review.

The fact that neutrinos carry a non-vanishing rest mass is evidence of physics beyond the Standard Model of elementary particles. Their absolute mass bears important relevance from particle physics to cosmology. In this work, we report on the search for the effective electron antineutrino mass with the KATRIN experiment. KATRIN performs precision spectroscopy of the tritium $\beta$-decay close to the kinematic endpoint. Based on the first five neutrino-mass measurement campaigns, we derive a best-fit value of $m_\nu^{2} = {-0.14^{+0.13}_{-0.15}}~\mathrm{eV^2}$, resulting in an upper limit of $m_\nu < {0.45}~\mathrm{eV}$ at 90 % confidence level. With six times the statistics of previous data sets, amounting to 36 million electrons collected in 259 measurement days, a substantial reduction of the background level and improved systematic uncertainties, this result tightens KATRIN's previous bound by a factor of almost two., Comment: 61 pages, 20 figures, 2 tables

Although repeatability and reproducibility are essential in science, failed attempts to replicate results across diverse fields made some scientists argue for a reproducibility crisis. In response, several high-profile venues within computing established artifact evaluation tracks, a systematic procedure for evaluating and badging research artifacts, with an increasing number of artifacts submitted. This study compiles recent artifact evaluation procedures and guidelines to show how artifact evaluation in distributed systems research lags behind other computing disciplines and/or is less unified and more complex. We further argue that current artifact assessment criteria are uncoordinated and insufficient for the unique challenges of distributed systems research. We examine the current state of the practice for artifacts and their evaluation to provide recommendations to assist artifact authors, reviewers, and track chairs. We summarize the recommendations and best practices as checklists for artifact authors and evaluation committees. Although our recommendations alone will not resolve the repeatability and reproducibility crisis, we want to start a discussion in our community to increase the number of submitted artifacts and their quality over time., Comment: 13 pages, 2 figures, 3 tables

This second paper presents an in-depth analysis of the composition of the planetary material that has been accreted onto seven white dwarfs with circumstellar dust and gas emission discs with abundances reported in Paper I. The white dwarfs are accreting planetary bodies with a wide range of oxygen, carbon, and sulfur volatile contents, including one white dwarf that shows the most enhanced sulfur abundance seen to date. Three white dwarfs show tentative evidence (2-3$\sigma$) of accreting oxygen-rich material, potentially from water-rich bodies, whilst two others are accreting dry, rocky material. One white dwarf is accreting a mantle-rich fragment of a larger differentiated body, whilst two white dwarfs show an enhancement in their iron abundance and could be accreting core-rich fragments. Whilst most planetary material accreted by white dwarfs display chondritic or bulk Earth-like compositions, these observations demonstrate that core-mantle differentiation, disruptive collisions, and the accretion of core-mantle differentiated material are important. Less than one percent of polluted white dwarfs host both observable circumstellar gas and dust. It is unknown whether these systems are experiencing an early phase in the disruption and accretion of planetary bodies, or alternatively if they are accreting larger planetary bodies. From this work there is no substantial evidence for significant differences in the accreted refractory abundance ratios for those white dwarfs with or without circumstellar gas, but there is tentative evidence for those with circumstellar gas discs to be accreting more water rich material which may suggest that volatiles accrete earlier in a gas-rich phase., Comment: Accepted for publication in MNRAS