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Many ethical issues in machine learning are connected to the training data. Online data markets are an important source of training data, facilitating both production and distribution. Recently, a trend has emerged of for-profit "ethical" participants in online data markets. This trend raises a fascinating question: Can online data markets sustainably and efficiently address ethical issues in the broader machine-learning economy? In this work, we study this question in a stylized model of an online data market. We investigate the effects of intervening in the data market to achieve balanced training-data production. The model reveals the crucial role of market conditions. In small and emerging markets, an intervention can drive the data producers out of the market, so that the cost of fairness is maximal. Yet, in large and established markets, the cost of fairness can vanish (as a fraction of overall welfare) as the market grows. Our results suggest that "ethical" online data markets can be economically feasible under favorable market conditions, and motivate more models to consider the role of data production and distribution in mediating the impacts of ethical interventions.

The stability of water against cavitation under negative pressures is a phenomenon known for considerable discrepancies between theoretical predictions and experimental observations. Using a combination of molecular dynamics simulations and classical nucleation theory, we explore how nanoscopic hydrocarbon droplets influence cavitation in water. Our findings reveal that while a macroscopic volume of absolutely pure water withstands up to -120 MPa of tension, introducing a single nanoscopic oil droplet, merely a few nanometers in radius, brings this cavitation threshold to around -30 MPa, closely matching the values typically observed in highly controlled experiments. The unavoidable presence of nanoscopic hydrophobic impurities, even in highly purified water used in experiments, imposes a practical limit on achieving the theoretical tensile strength in realistic settings. More broadly, our study highlights the profound impact of nonpolar residues on nucleation phenomena and enhances our understanding of metastability in real-world systems., Comment: 8 pages, 5 figures

We prove Hawking's singularity theorem for spacetime metrics of local Lipschitz regularity. The proof rests on (1) new estimates for the Ricci curvature of regularising smooth metrics that are based upon a quite general Friedrichs-type lemma and (2) the replacement of the usual focusing techniques for timelike geodesics -- which in the absence of a classical ODE-theory for the initial value problem are no longer available -- by a worldvolume estimate based on a segment-type inequality that allows one to control the volume of the set of points in a spacelike surface that possess long maximisers., Comment: 28 pages

Spot spraying represents an efficient and sustainable method for reducing the amount of pesticides, particularly herbicides, used in agricultural fields. To achieve this, it is of utmost importance to reliably differentiate between crops and weeds, and even between individual weed species in situ and under real-time conditions. To assess suitability for real-time application, different object detection models that are currently state-of-the-art are compared. All available models of YOLOv8, YOLOv9, YOLOv10, and RT-DETR are trained and evaluated with images from a real field situation. The images are separated into two distinct datasets: In the initial data set, each species of plants is trained individually; in the subsequent dataset, a distinction is made between monocotyledonous weeds, dicotyledonous weeds, and three chosen crops. The results demonstrate that while all models perform equally well in the metrics evaluated, the YOLOv9 models, particularly the YOLOv9s and YOLOv9e, stand out in terms of their strong recall scores (66.58 % and 72.36 %), as well as mAP50 (73.52 % and 79.86 %), and mAP50-95 (43.82 % and 47.00 %) in dataset 2. However, the RT-DETR models, especially RT-DETR-l, excel in precision with reaching 82.44 \% on dataset 1 and 81.46 % in dataset 2, making them particularly suitable for scenarios where minimizing false positives is critical. In particular, the smallest variants of the YOLO models (YOLOv8n, YOLOv9t, and YOLOv10n) achieve substantially faster inference times down to 7.58 ms for dataset 2 on the NVIDIA GeForce RTX 4090 GPU for analyzing one frame, while maintaining competitive accuracy, highlighting their potential for deployment in resource-constrained embedded computing devices as typically used in productive setups.

In the near future, autonomous space systems will compose a large number of the spacecraft being deployed. Their tasks will involve autonomous rendezvous and proximity operations with large structures, such as inspections or assembly of orbiting space stations and maintenance and human-assistance tasks over shared workspaces. To promote replicable and reliable scientific results for autonomous control of spacecraft, we present the design of a space systems laboratory based on open-source and modular software and hardware. The simulation software provides a software-in-the-loop (SITL) architecture that seamlessly transfers simulated results to the ATMOS platforms, developed for testing of multi-agent autonomy schemes for microgravity. The manuscript presents the KTH space systems laboratory facilities and the ATMOS platform as open-source hardware and software contributions. Preliminary results showcase SITL and real testing., Comment: Preliminary release, to be submitted

QCD jets are considered important probes for quark gluon plasma created in collisions of nuclei at high energies. Their parton showers are significantly altered if they develop inside of a deconfined medium. Hadronization of jets is also thought to be affected by the presence of quarks and gluons. We present a systematic study of the effects of a thermal bath of partons on the hadronization of parton showers. We use the JETSCAPE framework to create parton showers both in vacuum and in a brick of quark gluon plasma. The brick setup allows important parameters, like the size of the plasma as well as the collective flow of partons, to be varied systematically. We hadronize the parton showers using Hybrid Hadronization, which permits shower partons to form strings with thermal partons, or to recombine directly with thermal partons as well as with each other. We find a sizeable amount of interaction of shower partons with thermal partons during hadronization, indicating a natural continuation of the interaction of jet and medium during this stage. The observed effects grow with the size of the medium. Collective flow easily transfers from the thermal partons onto the emerging jet hadrons. We also see a significant change in hadron chemistry as expected in the presence of quark recombination processes., Comment: 12 pages, 6 figures

This paper describes the complete sets of data of two different machines, a PMSM and an IM, that are made available to the public for modeling and simulation validation and benchmarking. For both machines, not only the complete sets of design parameters, i.e., motor geometry, electrical parameters, material properties, and winding schemes as well as the measured low-frequency equivalent circuit parameters are provided, but also comprehensive measurement results on six different drive cycles to allow for transient investigations. The data packages provide all the required information in terms of design parameters and measurement results that facilitate modeling and simulation validation and benchmarking results for verification of different modeling approaches. The paper serves as the key reference user manual for the extensive and comprehensive sets of data made available. It is therefore recommended to follow up on the reading of the paper by a study of the data packages themselves. To the authors' best knowledge, this is the first time that the complete sets of machine data of two different machines are published, allowing for benchmarking of modeling and simulation projects, and reproducibility and reusability following the FAIR data principle of analyses based on these data.

This article proposes an active-learning-based adaptive trajectory tracking control method for autonomous ground vehicles to compensate for modeling errors and unmodeled dynamics. The nominal vehicle model is decoupled into lateral and longitudinal subsystems, which are augmented with online Gaussian Processes (GPs), using measurement data. The estimated mean functions of the GPs are used to construct a feedback compensator, which, together with an LPV state feedback controller designed for the nominal system, gives the adaptive control structure. To assist exploration of the dynamics, the paper proposes a new, dynamic active learning method to collect the most informative samples to accelerate the training process. To analyze the performance of the overall learning tool-chain provided controller, a novel iterative, counterexample-based algorithm is proposed for calculating the induced L2 gain between the reference trajectory and the tracking error. The analysis can be executed for a set of possible realizations of the to-be-controlled system, giving robust performance certificate of the learning method under variation of the vehicle dynamics. The efficiency of the proposed control approach is shown on a high-fidelity physics simulator and in real experiments using a 1/10 scale F1TENTH electric car., Comment: Submitted to IEEE Transactions on Control Systems Technology

We present a model for the morphology evolution of printed organic solar cells which occurs during the drying of a mixture of polymer, the non-fullerene acceptor and the solvent. Our model uses a phase field approach coupled to a Navier-Stokes equation describing the macroscopic movement of the fluid. Additionally, we incorporate the evaporation process of the solvent using an Allen-Cahn equation. The model is discretized using a finite-element approach with a semi-implicit discretization in time. The resulting (non)linear systems are coupled and of large dimensionality. We present a preconditioned iterative scheme to solve them robustly with respect to changes in the discretization parameters. We illustrate that the preconditioned solver shows parameter-robust iteration numbers and that the model qualitatively captures the behavior of the film morphology during drying., Comment: 33 pages

The proton radius puzzle remains a key challenge in modern physics, highlighting both the precision and limitations of current experimental and theoretical approaches. Recent studies, such as those by Xiong et al. and Bezginov et al., have consistently found a smaller proton radius of about 0.84 femtometers, in line with muonic hydrogen measurements, but discrepancies with earlier electron-proton scattering and atomic hydrogen spectroscopy persist. These unresolved differences reveal systematic errors or limitations in existing theoretical frameworks, as pointed out by Arrington and Sick. The divergence between results from muonic and electronic hydrogen measurements remains unexplained, with contributions from both experimental uncertainties and theoretical gaps. To address these challenges, a coordinated approach is needed-focused on reducing systematic uncertainties through advanced experimental setups, like improved scattering experiments at Jefferson Lab, and developing new theoretical models, such as those proposed by Alar\'{c}on et al. and Lin et al. Integrating experimental precision with theoretical rigor offers the best path toward resolving this enduring puzzle and providing deeper insights into the fundamental forces of nature.

Cavitation, the formation of vapor bubbles in metastable liquids, is highly sensitive to nanoscale surface defects. Using molecular dynamics simulations and classical nucleation theory, we show that pure water confined within defect-free walls can withstand extreme negative pressures, far beyond those observed experimentally. Hydrophobic surfaces trigger heterogeneous cavitation and lower the cavitation pressure magnitude, but not to experimental levels. Notably, a single nanoscopic surface defect capable of hosting a vapor bubble drastically reduces the tensile strength of water. We find that defects as small as 1-2 nm can act as effective cavitation nuclei, a scale smaller than predicted by simple mechanical stability arguments. This discrepancy arises from stochastic fluctuations of the vapor bubble, which can overcome the kinetic free-energy barrier for cavitation. Our findings show that cavitation is predominantly determined by the largest surface defect rather than the overall defect density, emphasizing the importance of eliminating the largest surface imperfections to enhance stability against cavitation., Comment: 10 pages, 7 figures

Structures in the magneto-ionic medium exist across a wide range of angular sizes due to large-scale magnetic fields coherent over the Galactic spiral arms combined with small-scale fluctuations in the magnetic field and electron density resulting from energy injection processes such as supernovae. For the first time, we produce diffuse Galactic synchrotron emission Faraday rotation maps covering all spatial scales down to $3'$ resolution for Galactic magnetic field studies. These maps complement standard total and polarized intensity maps combining single-antenna and interferometric data that have been produced, for example, for the Canadian Galactic Plane Survey (CGPS). Such combined maps have sensitivity to large scales from the single-antenna component, and the resolution from the interferometric component. We combine Global Magneto-Ionic Medium Survey High-Band-North (GMIMS-HBN) single-antenna and CGPS aperture-synthesis polarization data after spatial filtering, producing Stokes $Q$ and $U$ maps individually for the four CGPS frequency channels. We calculate Rotation Measures (RM) for all pixels using a linear fit to polarization angle versus wavelength squared. The RM maps show magnetic field structures on the full range of scales probed by this dataset. Regions of smooth polarized emission require the large-scale sensitivity of the single-antenna data to illuminate the Faraday depths, while the aperture-synthesis data reveal small-scale variability in the Faraday rotation. Despite the limitation of the 35 MHz bandwidth of the CGPS, we demonstrate that useful information on Faraday rotation structures can be obtained by combining single-antenna and aperture-synthesis data, highlighting the important synergy between future broadband interferometric and single-antenna polarization surveys., Comment: Submitted to ApJ

We show how (resource) bounded rationality can be understood as the interplay of two fundamental moral principles: deontology and utilitarianism. In particular, we interpret deontology as a regularisation function in an optimal control problem, coupled with a free parameter, the inverse temperature, to shield the individual from expected utility. We discuss the information geometry of bounded rationality and aspects of its relation to rate distortion theory. A central role is played by Markov kernels and regular conditional probability, which are also studied geometrically. A gradient equation is used to determine the utility expansion path. Finally, the framework is applied to the analysis of a disutility model of the restriction of constitutional rights that we derive from legal doctrine. The methods discussed here are also relevant to the theory of autonomous agents., Comment: 31 pages, 9 figures

Learning from tabular data is of paramount importance, as it complements the conventional analysis of image and video data by providing a rich source of structured information that is often critical for comprehensive understanding and decision-making processes. We present Multi-task Contrastive Masked Tabular Modeling (MT-CMTM), a novel method aiming to enhance tabular models by leveraging the correlation between tabular data and corresponding images. MT-CMTM employs a dual strategy combining contrastive learning with masked tabular modeling, optimizing the synergy between these data modalities. Central to our approach is a 1D Convolutional Neural Network with residual connections and an attention mechanism (1D-ResNet-CBAM), designed to efficiently process tabular data without relying on images. This enables MT-CMTM to handle purely tabular data for downstream tasks, eliminating the need for potentially costly image acquisition and processing. We evaluated MT-CMTM on the DVM car dataset, which is uniquely suited for this particular scenario, and the newly developed HIPMP dataset, which connects membrane fabrication parameters with image data. Our MT-CMTM model outperforms the proposed tabular 1D-ResNet-CBAM, which is trained from scratch, achieving a relative 1.48% improvement in relative MSE on HIPMP and a 2.38% increase in absolute accuracy on DVM. These results demonstrate MT-CMTM's robustness and its potential to advance the field of multi-modal learning.

Many robotic tasks in real-world environments require physical interactions with an object such as pick up or push. For successful interactions, the robot needs to know the object's affordances, which are defined as the potential actions the robot can perform with the object. In order to learn a robot-specific affordance predictor, we propose an interactive exploration pipeline which allows the robot to collect interaction experiences while exploring an unknown environment. We integrate an object-level map in the exploration pipeline such that the robot can identify different object instances and track objects across diverse viewpoints. This results in denser and more accurate affordance annotations compared to state-of-the-art methods, which do not incorporate a map. We show that our affordance exploration approach makes exploration more efficient and results in more accurate affordance prediction models compared to baseline methods., Comment: International Symposium of Robotics Research (ISRR) 2024

Deep-learning-based nonlinear system identification has shown the ability to produce reliable and highly accurate models in practice. However, these black-box models lack physical interpretability, and often a considerable part of the learning effort is spent on capturing already expected/known behavior due to first-principles-based understanding of some aspects of the system. A potential solution is to integrate prior physical knowledge directly into the model structure, combining the strengths of physics-based modeling and deep-learning-based identification. The most common approach is to use an additive model augmentation structure, where the physics-based and the machine-learning (ML) components are connected in parallel. However, such models are overparametrized, training them is challenging, potentially causing the physics-based part to lose interpretability. To overcome this challenge, this paper proposes an orthogonal projection-based regularization technique to enhance parameter learning, convergence, and even model accuracy in learning-based augmentation of nonlinear baseline models., Comment: Submitted to L4DC 2025

This R\&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a demonstrator for real-time processing of high-rate data streams from sPHENIX experiment tracking detectors. The limitations of a 15 kHz maximum trigger rate imposed by the calorimeters can be negated by intelligent use of streaming technology in the tracking system. The approach efficiently identifies low momentum rare heavy flavor events in high-rate p+p collisions (3MHz), using Graph Neural Network (GNN) and High Level Synthesis for Machine Learning (hls4ml). Success at sPHENIX promises immediate benefits, minimizing resources and accelerating the heavy-flavor measurements. The approach is transferable to other fields. For the EIC, we develop a DIS-electron tagger using Artificial Intelligence - Machine Learning (AI-ML) algorithms for real-time identification, showcasing the transformative potential of AI and FPGA technologies in high-energy nuclear and particle experiments real-time data processing pipelines., Comment: proceedings for 42nd International Conference on High Energy Physics (ICHEP2024), 18-24 July 2024, Prague, Czech Republic

Defining low-dimensional reaction coordinates is crucial for analyzing the dynamics of complex systems and for comparison with experiments. The maximal value of the transition-path probability along the reaction coordinate $x$, $p(\mathrm{TP}|x)$, is a common estimator for reaction-coordinate quality by comparing to the theoretical maximal value of 1/2 in the overdamped Markovian limit. We show by analytical arguments and simulations that for non-Markovian dynamics $p(\mathrm{TP}|x)$ is non-monotonic as a function of the memory time and exceeds 1/2 for long memory time. This disqualifies $p(\mathrm{TP}|x)$ as a criterion for reaction coordinate quality.

In this paper, we present PointmapDiffusion, a novel framework for single-image novel view synthesis (NVS) that utilizes pre-trained 2D diffusion models. Our method is the first to leverage pointmaps (i.e. rasterized 3D scene coordinates) as a conditioning signal, capturing geometric prior from the reference images to guide the diffusion process. By embedding reference attention blocks and a ControlNet for pointmap features, our model balances between generative capability and geometric consistency, enabling accurate view synthesis across varying viewpoints. Extensive experiments on diverse real-world datasets demonstrate that PointmapDiffusion achieves high-quality, multi-view consistent results with significantly fewer trainable parameters compared to other baselines for single-image NVS tasks.

In recent years, a plethora of database management systems have surfaced to meet the demands of various scenarios. Emerging database systems, such as time-series and streaming database systems, are tailored to specific use cases requiring enhanced functionality and performance. However, as they are typically less mature, there can be bugs that either cause incorrect results or errors impacting reliability. To tackle this, we propose enhanced differential testing to uncover various bugs in emerging SQL-like database systems. The challenge is how to deal with differences of these emerging databases. Our insight is that many emerging database systems are conceptually extensions of relational database systems, making it possible to reveal logic bugs leveraging existing relational, known-reliable database systems. However, due to inevitable syntax or semantics gaps, it remains challenging to scale differential testing to various emerging database systems. We enhance differential testing for emerging database systems with three steps: (i) identifying shared clauses; (ii) extending shared clauses via mapping new features back to existing clauses of relational database systems; and (iii) generating differential inputs using extended shared clauses. We implemented our approach in a tool called SQLxDiff and applied it to four popular emerging database systems. In total, we found 57 unknown bugs, of which 17 were logic bugs and 40 were internal errors. Overall, vendors fixed 50 bugs and confirmed 5. Our results demonstrate the practicality and effectiveness of SQLxDiff in detecting bugs in emerging database systems, which has the potential to improve the reliability of their applications., Comment: 10 pages, 6 figures

An investigation of high transverse momentum (high-$p_T$) photon triggered jets in proton-proton ($p$-$p$) and ion-ion ($A$-$A$) collisions at $\sqrt{s_{NN}} = 0.2$ and $5.02~\mathrm{TeV}$ is carried out, using the multistage description of in-medium jet evolution. Monte Carlo simulations of hard scattering and energy loss in heavy-ion collisions are performed using parameters tuned in a previous study of the nuclear modification factor ($R_{AA}$) for inclusive jets and high-$p_T$ hadrons. We obtain a good reproduction of the experimental data for photon triggered jet $R_{AA}$, as measured by the ATLAS detector, the distribution of the ratio of jet to photon $p_T$ ($X_{\rm J \gamma}$), measured by both CMS and ATLAS, and the photon-jet azimuthal correlation as measured by CMS. We obtain a moderate description of the photon triggered jet $I_{AA}$, as measured by STAR. A noticeable improvement in the comparison is observed when one goes beyond prompt photons and includes bremsstrahlung and decay photons, revealing their significance in certain kinematic regions, particularly at $X_{J\gamma} > 1$. Moreover, azimuthal angle correlations demonstrate a notable impact of non-prompt photons on the distribution, emphasizing their role in accurately describing experimental results. This work highlights the success of the multistage model of jet modification to straightforwardly predict (this set of) photon triggered jet observables. This comparison, along with the role played by non-prompt photons, has important consequences on the inclusion of such observables in a future Bayesian analysis., Comment: 16 pages, 10 figures

In this paper, we present data-driven representations of linear parameter-varying (LPV) systems that can be used for direct data-driven analysis and control of LPV systems. Specifically, we use the behavioral approach for LPV systems to develop a data-driven representation of the finite-horizon behavior of an LPV system that can be represented by a kernel representation with shifted-affine scheduling dependence. Moreover, we provide a necessary and sufficient rank-based test on the available data that concludes whether the data-driven representation fully represents the finite-horizon behavior. The results in this paper allow for direct data-driven analysis and control of LPV systems with stability and performance guarantees. We demonstrate this by also solving the LPV data-driven simulation problem. Moreover, through the use of LPV systems as surrogates for nonlinear systems, our results may serve as a stepping stone towards direct data-driven analysis and control of nonlinear systems., Comment: 12 pages. Submitted to IEEE-TAC

The recent quantum circuit cutting technique enables simulating large quantum circuits on distributed smaller devices, significantly extending the capabilities of current noisy intermediate-scale quantum (NISQ) hardware. However, this method incurs substantial classical postprocessing and additional quantum resource demands, as both postprocessing complexity and sampling overhead scale exponentially with the number of cuts introduced. In this work, we propose an enhanced circuit cutting framework ShotQC with effective sampling overhead reduction. It effectively reduces sampling overhead through two key optimizations: shot distribution and cut parameterization. The former employs an adaptive Monte Carlo method to dynamically allocate more quantum resources to subcircuit configurations that contribute more to variance in the final outcome. The latter leverages additional degrees of freedom in postprocessing to further suppress variance. By integrating these optimization methods, ShotQC achieves significant reductions in sampling overhead without increasing classical postprocessing complexity, as demonstrated on a range of benchmark circuits., Comment: 11 pages, 6 figures, submitted to the International Symposium on Computer Architecture (ISCA), 2025

Tilt-rotor aerial robots are more dynamic and versatile than their fixed-rotor counterparts, since the thrust vector and body orientation are decoupled. However, the coordination of servomotors and propellers (the allocation problem) is not trivial, especially accounting for overactuation and actuator dynamics. We present and compare different methods of actuator allocation for tilt-rotor platforms, evaluating them on a real aerial robot performing dynamic trajectories. We extend the state-of-the-art geometric allocation into a differential allocation, which uses the platform's redundancy and does not suffer from singularities typical of the geometric solution. We expand it by incorporating actuator dynamics and introducing propeller limit curves. These improve the modeling of propeller limits, automatically balancing their usage and allowing the platform to selectively activate and deactivate propellers during flight. We show that actuator dynamics and limits make the tuning of the allocation not only easier, but also allow it to track more dynamic oscillating trajectories with angular velocities up to 4 rad/s, compared to 2.8 rad/s of geometric methods.

We present new radio images of the supernova remnant (SNR) CTA 1 at 1420 and 408 MHz, and in the 21 cm line of H I observed with the Dominion Radio Astrophysical Observatory Synthesis Telescope and at 1420 MHz observed with the Effelsberg 100 m telescope. We confirm previously described continuum features and elaborate further on filamentary features identified using the high-resolution (1') maps from these new observations. We investigate the abrupt change in sign of rotation measure (RM) across the SNR, using the linear polarization observations in the four bands around 1420 MHz. Following X. H. Sun et al.'s (2011) investigation, we both confirm that the distribution of signs of the RMs for extragalactic sources in the area appears to match that of the shell, as well as combine the data from the four bands to estimate the relative depolarization and the intrinsic rotation measure of the SNR. We do not conclusively reject X. H. Sun et al.'s (2011) claim of a Faraday screen in the foreground causing the distribution of RMs that we observe; however, we do suggest an alternative explanation of a swept-up stellar wind from the progenitor star with a toroidal magnetic field. Finally, we expand on the analysis of the H I observations by applying the Rolling Hough Transform to isolate filamentary structure and better identify H I emission with the SNR. Further constraining the H I velocity channels associated with CTA 1, we use more recent Galactic rotation curves to calculate an updated kinematic distance of 1.09 +/- 0.2 kpc., Comment: 10 pages, 9 figures, 2 tables

Neural networks continue to struggle with compositional generalization, and this issue is exacerbated by a lack of massive pre-training. One successful approach for developing neural systems which exhibit human-like compositional generalization is \textit{hybrid} neurosymbolic techniques. However, these techniques run into the core issues that plague symbolic approaches to AI: scalability and flexibility. The reason for this failure is that at their core, hybrid neurosymbolic models perform symbolic computation and relegate the scalable and flexible neural computation to parameterizing a symbolic system. We investigate a \textit{unified} neurosymbolic system where transformations in the network can be interpreted simultaneously as both symbolic and neural computation. We extend a unified neurosymbolic architecture called the Differentiable Tree Machine in two central ways. First, we significantly increase the model's efficiency through the use of sparse vector representations of symbolic structures. Second, we enable its application beyond the restricted set of tree2tree problems to the more general class of seq2seq problems. The improved model retains its prior generalization capabilities and, since there is a fully neural path through the network, avoids the pitfalls of other neurosymbolic techniques that elevate symbolic computation over neural computation., Comment: NeurIPS 2024. Code available at https://github.com/psoulos/sdtm

Population protocols are a model of distributed computation in which an arbitrary number of indistinguishable finite-state agents interact in pairs to decide some property of their initial configuration. We investigate the behaviour of population protocols under adversarial faults that cause agents to silently crash and no longer interact with other agents. As a starting point, we consider the property ``the number of agents exceeds a given threshold $t$'', represented by the predicate $x \geq t$, and show that the standard protocol for $x \geq t$ is very fragile: one single crash in a computation with $x:=2t-1$ agents can already cause the protocol to answer incorrectly that $x \geq t$ does not hold. However, a slightly less known protocol is robust: for any number $t' \geq t$ of agents, at least $t' - t+1$ crashes must occur for the protocol to answer that the property does not hold. We formally define robustness for arbitrary population protocols, and investigate the question whether every predicate computable by population protocols has a robust protocol. Angluin et al. proved in 2007 that population protocols decide exactly the Presburger predicates, which can be represented as Boolean combinations of threshold predicates of the form $\sum_{i=1}^n a_i \cdot x_i \geq t$ for $a_1,...,a_n, t \in \mathbb{Z}$ and modulo prdicates of the form $\sum_{i=1}^n a_i \cdot x_i \bmod m \geq t $ for $a_1, \ldots, a_n, m, t \in \mathbb{N}$. We design robust protocols for all threshold and modulo predicates. We also show that, unfortunately, the techniques in the literature that construct a protocol for a Boolean combination of predicates given protocols for the conjuncts do not preserve robustness. So the question remains open., Comment: Final version to appear in Principles of Verification: Cycling the Probabilistic Landscape: Essays Dedicated to Joost-Pieter Katoen on the Occasion of His 60th Birthday, Part III (2025)

New types of high-resolution animal movement data allow for increasingly comprehensive biological inference, but method development to meet the statistical challenges associated with such data is lagging behind. In this contribution, we extend the commonly applied hidden Markov models for step lengths and turning angles to address the specific requirements posed by high-resolution movement data, in particular the very strong within-state correlation induced by the momentum in the movement. The models feature autoregressive components of general order in both the step length and the turning angle variable, with the possibility to automate the selection of the autoregressive degree using a lasso approach. In a simulation study, we identify potential for improved inference when using the new model instead of the commonly applied basic hidden Markov model in cases where there is strong within-state autocorrelation. The practical use of the model is illustrated using high-resolution movement tracks of terns foraging near an anthropogenic structure causing turbulent water flow features.

We present a novel machine learning (ML) method to accelerate conservative-to-primitive inversion, focusing on hybrid piecewise polytropic and tabulated equations of state. Traditional root-finding techniques are computationally expensive, particularly for large-scale relativistic hydrodynamics simulations. To address this, we employ feedforward neural networks (NNC2PS and NNC2PL), trained in PyTorch and optimized for GPU inference using NVIDIA TensorRT, achieving significant speedups with minimal accuracy loss. The NNC2PS model achieves $ L_1 $ and $ L_\infty $ errors of $ 4.54 \times 10^{-7} $ and $ 3.44 \times 10^{-6} $, respectively, while the NNC2PL model exhibits even lower error values. TensorRT optimization with mixed-precision deployment substantially accelerates performance compared to traditional root-finding methods. Specifically, the mixed-precision TensorRT engine for NNC2PS achieves inference speeds approximately 400 times faster than a traditional single-threaded CPU implementation for a dataset size of 1,000,000 points. Ideal parallelization across an entire compute node in the Delta supercomputer (Dual AMD 64 core 2.45 GHz Milan processors; and 8 NVIDIA A100 GPUs with 40 GB HBM2 RAM and NVLink) predicts a 25-fold speedup for TensorRT over an optimally-parallelized numerical method when processing 8 million data points. Moreover, the ML method exhibits sub-linear scaling with increasing dataset sizes. We release the scientific software developed, enabling further validation and extension of our findings. This work underscores the potential of ML, combined with GPU optimization and model quantization, to accelerate conservative-to-primitive inversion in relativistic hydrodynamics simulations., Comment: 17 pages, 4 figures, 1 table New results added

In this article, we investigate how the entrywise application of a non-linear function to symmetric orthogonally invariant random matrix ensembles alters the spectral distribution. We treat also the multivariate case where we apply multivariate functions to entries of several orthogonally invariant matrices; where even correlations between the matrices are allowed. We find that in all those cases a Gaussian equivalence principle holds, that is, the asymptotic effect of the non-linear function is the same as taking a linear combination of the involved matrices and an additional independent GOE. The ReLU-function in the case of one matrix and the max-function in the case of two matrices provide illustrative examples.

We present a joint analysis of TTVs and Doppler data for the transiting exoplanet system TOI-4504. TOI-4504 c is a warm Jupiter-mass planet that exhibits the largest known transit timing variations (TTVs), with a peak-to-node amplitude of $\sim$ 2 days, the largest value ever observed, and a super-period of $\sim$ 930 d. TOI-4504 b and c were identified in public TESS data, while the TTVs observed in TOI-4504 c, together with radial velocity (RV) data collected with FEROS, allowed us to uncover a third, non-transiting planet in this system, TOI-4504 d. We were able to detect transits of TOI-4504 b in the TESS data with a period of 2.4261$\pm 0.0001$ days and derive a radius of 2.69$\pm 0.19$ R$_{\oplus}$. The RV scatter of TOI-4504 was too large to constrain the mass of TOI-4504 b, but the RV signals of TOI-4504 c \& d were sufficiently large to measure their masses. The TTV+RV dynamical model we apply confirms TOI-4504 c as a warm Jupiter planet with an osculating period of 82.54$\pm 0.02$ d, mass of 3.77$\pm 0.18$ M$_{\rm J}$ and a radius of 0.99$\pm 0.05$ R$_{\rm J}$, while the non-transiting planet TOI-4504 d, has an orbital period of 40.56$\pm 0.04$ days and mass of 1.42$_{-0.06}^{+0.07}$ M$_{\rm J}$. We present the discovery of a system with three exoplanets: a hot sub-Neptune and two warm Jupiter planets. The gas giant pair is stable and likely locked in a first-order 2:1 mean-motion resonance (MMR). The TOI-4504 system is an important addition to MMR pairs, whose increasing occurrence supports a smooth migration into a resonant configuration during the protoplanetary disk phase., Comment: 20 pages, 12 figures, accepted in ApJL

We introduce a family of dualities between certain non-supersymmetric self-dual gauge theories on a large class of $4d$ self-dual asymptotically flat backgrounds, and the large $N$ limit of an independently defined $2d$ chiral defect CFT. Our construction goes via twisted holography for the type I topological string on a Calabi-Yau five-fold which fibres over twistor space. In particular, we show that single-trace operators of the $2d$ defect CFT are in bijection with states of the celestial chiral algebra. We match the operator products of these states with the collinear splitting amplitudes of the self-dual gauge theory up to one-loop. Assigning vacuum expectations to central operators in the boundary theory computes bulk amplitudes on self-dual backgrounds. We are able to extract form factors from these amplitudes, which we use to give a simple closed formula for certain $n$-point two-loop all $+$ amplitudes in $\mathrm{SU}(K) \times \mathrm{SU}(R)$ gauge theory coupled to bifundamental massless fermions., Comment: 121 pages + 18 page appendix, 22 figures; v2: computational error pointed out by Anthony Morales corrected

This paper constitutes a presentation of work in progress at a discussion session of the conference Dense ionic fluids Faraday Discussion, 8-10 July 2024, London, and is published on pages 293-295 in Ref. [Faraday Discuss. 2024, volume 253, page 289]., Comment: This paper constitutes a presentation of work in progress at a discussion session of the conference Dense ionic fluids Faraday Discussion, 8-10 July 2024, London, and is published on pages 293-295 in Ref. [Faraday Discuss. 2024, volume 253, page 289]