Search

Adaptive Mixed-Criticality (AMC) is a fixed-priority preemptive scheduling algorithm for mixed-criticality hard real-time systems. It dominates many other scheduling algorithms for mixed-criticality systems, but does so at the cost of occasionally dropping jobs of less important/critical tasks, when low-priority jobs overrun their time budgets. In this paper we enhance AMC with a deep reinforcement learning (DRL) approach based on a Deep-Q Network. The DRL agent is trained off-line, and at run-time adjusts the low-criticality budgets of tasks to avoid budget overruns, while ensuring that no job misses its deadline if it does not overrun its budget. We have implemented and evaluated this approach by simulating realistic workloads from the automotive domain. The results show that the agent is able to reduce budget overruns by at least up to 50%, even when the budget of each task is chosen based on sampling the distribution of its execution time. To the best of our knowledge, this is the first use of DRL in AMC reported in the literature., Comment: Version submitted to RTNS 2024, on 17/08/2024 (with some typos fixed)

Deep learning (DL) models in medical imaging face challenges in generalizability and robustness due to variations in image acquisition parameters (IAP). In this work, we introduce a novel method using conditional denoising diffusion generative models (cDDGMs) to generate counterfactual magnetic resonance (MR) images that simulate different IAP without altering patient anatomy. We demonstrate that using these counterfactual images for data augmentation can improve segmentation accuracy, particularly in out-of-distribution settings, enhancing the overall generalizability and robustness of DL models across diverse imaging conditions. Our approach shows promise in addressing domain and covariate shifts in medical imaging. The code is publicly available at https: //github.com/pedromorao/Counterfactual-MRI-Data-Augmentation

Recent spacecraft mission concepts propose larger payloads that have lighter, less rigid structures. For large lightweight structures, the natural frequencies of their vibration modes may fall within the attitude controller bandwidth, threatening the stability and settling time of the controller and compromising performance. This work tackles this issue by proposing an attitude control design paradigm of distributing momentum actuators throughout the structure to have more control authority over vibration modes. The issue of jitter disturbances introduced by these actuators is addressed by expanding the bandwidth of the attitude controller to suppress excess vibrations. Numerical simulation results show that, at the expense of more control action, a distributed configuration can achieve lower settling times and reduce structural deformation compared to a more standard centralized configuration., Comment: 10 pages, 9 figures

Data used by automated decision-making systems, such as Machine Learning models, often reflects discriminatory behavior that occurred in the past. These biases in the training data are sometimes related to label noise, such as in COMPAS, where more African-American offenders are wrongly labeled as having a higher risk of recidivism when compared to their White counterparts. Models trained on such biased data may perpetuate or even aggravate the biases with respect to sensitive information, such as gender, race, or age. However, while multiple label noise correction approaches are available in the literature, these focus on model performance exclusively. In this work, we propose Fair-OBNC, a label noise correction method with fairness considerations, to produce training datasets with measurable demographic parity. The presented method adapts Ordering-Based Noise Correction, with an adjusted criterion of ordering, based both on the margin of error of an ensemble, and the potential increase in the observed demographic parity of the dataset. We evaluate Fair-OBNC against other different pre-processing techniques, under different scenarios of controlled label noise. Our results show that the proposed method is the overall better alternative within the pool of label correction methods, being capable of attaining better reconstructions of the original labels. Models trained in the corrected data have an increase, on average, of 150% in demographic parity, when compared to models trained in data with noisy labels, across the considered levels of label noise.

Cracks are among the earliest indicators of deterioration in concrete structures. Early automatic detection of these cracks can significantly extend the lifespan of critical infrastructures, such as bridges, buildings, and tunnels, while simultaneously reducing maintenance costs and facilitating efficient structural health monitoring. This study investigates whether leveraging multi-temporal data for crack segmentation can enhance segmentation quality. Therefore, we compare a Swin UNETR trained on multi-temporal data with a U-Net trained on mono-temporal data to assess the effect of temporal information compared with conventional single-epoch approaches. To this end, a multi-temporal dataset comprising 1356 images, each with 32 sequential crack propagation images, was created. After training the models, experiments were conducted to analyze their generalization ability, temporal consistency, and segmentation quality. The multi-temporal approach consistently outperformed its mono-temporal counterpart, achieving an IoU of $82.72\%$ and a F1-score of $90.54\%$, representing a significant improvement over the mono-temporal model's IoU of $76.69\%$ and F1-score of $86.18\%$, despite requiring only half of the trainable parameters. The multi-temporal model also displayed a more consistent segmentation quality, with reduced noise and fewer errors. These results suggest that temporal information significantly enhances the performance of segmentation models, offering a promising solution for improved crack detection and the long-term monitoring of concrete structures, even with limited sequential data.

Belief Propagation (BP) decoders for quantum error correcting codes are not always precise. There is a growing interest in the application of tensor networks to quantum error correction in general and, in particular, in degenerate quantum maximum likelihood decoding and the tensor network decoder. We develop a unified view to make the generalized BP proposal by Kirkley et. al explicit on arbitrary graphical models. We derive BP schemes and provide inference equations for BP on loopy tensor networks and, more generally, loopy graphical models. In doing so we introduce a tree-equivalent approach which allows us to relate the tensor network BlockBP to a generalized BP for loopy networks. Moreover, we show that the tensor network message passing approach relies essentially on the same approximation as the method by Kirkley. This allows us to make tensor network message passing available for degenerate quantum maximum likelihood decoding. Our method and results are key to obtaining guidelines regarding how the exchange between complexity and decoding accuracy works between BP and tensor network decoders. Finally, we discuss how the tree-equivalent method and the method by Kirkley can justify why message scheduling improves the performance of BP.

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across various out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms on three test sets. In addition, we also evaluated pre-existing AI frameworks--which, differing from algorithms, are more flexible and can support different algorithms--including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain., Comment: Accepted to NeurIPS-2024

Automated knowledge extraction from scientific literature can potentially accelerate materials discovery. We have investigated an approach for extracting synthesis protocols for reticular materials from scientific literature using large language models (LLMs). To that end, we introduce a Knowledge Extraction Pipeline (KEP) that automatizes LLM-assisted paragraph classification and information extraction. By applying prompt engineering with in-context learning (ICL) to a set of open-source LLMs, we demonstrate that LLMs can retrieve chemical information from PDF documents, without the need for fine-tuning or training and at a reduced risk of hallucination. By comparing the performance of five open-source families of LLMs in both paragraph classification and information extraction tasks, we observe excellent model performance even if only few example paragraphs are included in the ICL prompts. The results show the potential of the KEP approach for reducing human annotations and data curation efforts in automated scientific knowledge extraction., Comment: 16 pages

Solving linear systems of equations plays a fundamental role in numerous computational problems from different fields of science. The widespread use of numerical methods to solve these systems motivates investigating the feasibility of solving linear systems problems using quantum computers. In this work, we provide a survey of the main advances in quantum linear systems algorithms, together with some applications. We summarize and analyze the main ideas behind some of the algorithms for the quantum linear systems problem in the literature. The analysis begins by examining the Harrow-Hassidim-Lloyd (HHL) solver. We note its limitations and reliance on computationally expensive quantum methods, then highlight subsequent research efforts which aimed to address these limitations and optimize runtime efficiency and precision via various paradigms. We focus in particular on the post-HHL enhancements which have paved the way towards optimal lower bounds with respect to error tolerance and condition number. By doing so, we propose a taxonomy that categorizes these studies. Furthermore, by contextualizing these developments within the broader landscape of quantum computing, we explore the foundational work that have inspired and informed their development, as well as subsequent refinements. Finally, we discuss the potential applications of these algorithms in differential equations, quantum machine learning, and many-body physics., Comment: 41 pages, 6 figures

As medical datasets rapidly expand, creating detailed annotations of different body structures becomes increasingly expensive and time-consuming. We consider that requesting radiologists to create detailed annotations is unnecessarily burdensome and that pre-existing AI models can largely automate this process. Following the spirit don't use a sledgehammer on a nut, we find that, rather than creating annotations from scratch, radiologists only have to review and edit errors if the Best-AI Labels have mistakes. To obtain the Best-AI Labels among multiple AI Labels, we developed an automatic tool, called Label Critic, that can assess label quality through tireless pairwise comparisons. Extensive experiments demonstrate that, when incorporated with our developed Image-Prompt pairs, pre-existing Large Vision-Language Models (LVLM), trained on natural images and texts, achieve 96.5% accuracy when choosing the best label in a pair-wise comparison, without extra fine-tuning. By transforming the manual annotation task (30-60 min/scan) into an automatic comparison task (15 sec/scan), we effectively reduce the manual efforts required from radiologists by an order of magnitude. When the Best-AI Labels are sufficiently accurate (81% depending on body structures), they will be directly adopted as the gold-standard annotations for the dataset, with lower-quality AI Labels automatically discarded. Label Critic can also check the label quality of a single AI Label with 71.8% accuracy when no alternatives are available for comparison, prompting radiologists to review and edit if the estimated quality is low (19% depending on body structures).

In this work, we study probability functions associated with Gaussian mixture models. Our primary focus is on extending the use of spherical radial decomposition for multivariate Gaussian random vectors to the context of Gaussian mixture models, which are not inherently spherical but only conditionally so. Specifically, the conditional probability distribution, given a random parameter of the random vector, follows a Gaussian distribution, allowing us to apply Bayesian analysis tools to the probability function. This assumption, together with spherical radial decomposition for Gaussian random vectors, enables us to represent the probability function as an integral over the Euclidean sphere. Using this representation, we establish sufficient conditions to ensure the differentiability of the probability function and provide and integral representation of its gradient. Furthermore, leveraging the Bayesian decomposition, we approximate the probability function using random sampling over the parameter space and the Euclidean sphere. Finally, we present numerical examples that illustrate the advantages of this approach over classical approximations based on random vector sampling.

The construction of large datasets for deep learning has raised concerns regarding unauthorized use of online data, leading to increased interest in protecting data from third-parties who want to use it for training. The Convolution-based Unlearnable DAtaset (CUDA) method aims to make data unlearnable by applying class-wise blurs to every image in the dataset so that neural networks learn relations between blur kernels and labels, as opposed to informative features for classifying clean data. In this work, we evaluate whether CUDA data remains unlearnable after image sharpening and frequency filtering, finding that this combination of simple transforms improves the utility of CUDA data for training. In particular, we observe a substantial increase in test accuracy over adversarial training for models trained with CUDA unlearnable data from CIFAR-10, CIFAR-100, and ImageNet-100. In training models to high accuracy using unlearnable data, we underscore the need for ongoing refinement in data poisoning techniques to ensure data privacy. Our method opens new avenues for enhancing the robustness of unlearnable datasets by highlighting that simple methods such as sharpening and frequency filtering are capable of breaking convolution-based unlearnable datasets.

We introduce a quadratic gradient type term for the Pucci extremal operators. Our analysis demonstrates that this proposed term extends the classical quadratic gradient term associated with the Laplace equation, and we investigate the impact of the Kazdan-Kramer transformation. As an application, we explore the existence, non-existence, uniqueness, Liouville-type results, and asymptotic behavior of solutions for the new class of Pucci equations under various conditions on both nonlinearity and domains.

The integration of distributed energy resources (DER) has escalated the challenge of voltage magnitude regulation in distribution networks. Traditional model-based approaches, which rely on complex sequential mathematical formulations, struggle to meet real-time operational demands. Deep reinforcement learning (DRL) offers a promising alternative by enabling offline training with distribution network simulators, followed by real-time execution. However, DRL algorithms tend to converge to local optima due to limited exploration efficiency. Additionally, DRL algorithms can not enforce voltage magnitude constraints, leading to potential operational violations when implemented in the distribution network operation. This study addresses these challenges by proposing a novel safe imitation reinforcement learning (IRL) framework that combines IRL and a designed safety layer, aiming to optimize the operation of Energy Storage Systems (ESSs) in active distribution networks. The proposed safe IRL framework comprises two phases: offline training and online execution. During the offline phase, optimal state-action pairs are collected using an NLP solver, guiding the IRL policy iteration. In the online phase, the trained IRL policy's decisions are adjusted by the safety layer to maintain safety and constraint compliance. Simulation results demonstrate the efficacy of Safe IRL in balancing operational efficiency and safety, eliminating voltage violations, and maintaining low operation cost errors across various network sizes, while meeting real-time execution requirements.

Deep Learning methods are notorious for relying on extensive labeled datasets to train and assess their performance. This can cause difficulties in practical situations where models should be trained for new applications for which very little data is available. While few-shot learning algorithms can address the first problem, they still lack sufficient explanations for the results. This research presents a workflow that tackles both challenges by proposing an explainable few-shot learning workflow for detecting invasive and exotic tree species in the Atlantic Forest of Brazil using Unmanned Aerial Vehicle (UAV) images. By integrating a Siamese network with explainable AI (XAI), the workflow enables the classification of tree species with minimal labeled data while providing visual, case-based explanations for the predictions. Results demonstrate the effectiveness of the proposed workflow in identifying new tree species, even in data-scarce conditions. With a lightweight backbone, e.g., MobileNet, it achieves a F1-score of 0.86 in 3-shot learning, outperforming a shallow CNN. A set of explanation metrics, i.e., correctness, continuity, and contrastivity, accompanied by visual cases, provide further insights about the prediction results. This approach opens new avenues for using AI and UAVs in forest management and biodiversity conservation, particularly concerning rare or under-studied species.

We investigate the nature of star formation in gas-rich galaxies at $z > 7$ forming in a markedly overdense region, in the whereabouts of a massive virialized halo already exceeding $10^{12}$ M$_{\odot}$. We find that not only the primary galaxy, but also the lower-mass companion galaxies rapidly develop massive self-gravitating compact gas disks, less than 500~pc in size, which undergo fragmentation by gravitational instability into very massive bound clumps. Star formation proceeds fast in the clumps, which quickly turn into compact star clusters with masses in the range $10^5$-$10^8$ M$_{\odot}$ and typical half-mass radii of a few pc, reaching characteristic densities above $10^5$ M$_{\odot}$ pc$^{-2}$. The properties of the clusters in the lowest-mass galaxy bear a striking resemblance to those recently discovered by the James Webb Space Telescope (JWST) in the lensed Cosmic Gems arc system at $z = 10.2$. We argue that, due to their extremely high stellar densities, intermediate-mass black holes (IMBHs) would form rapidly inside the clusters, which would then swiftly sink and merge on their way to the galactic nucleus, easily growing a $10^7$~M$_{\odot}$ supermassive black hole (SMBH). Due to the high fractional mass contribution of clusters to the stellar mass of the galaxies, in the range $20$-$40\%$, the central SMBH would comprise more than $10\%$ of the mass of its host galaxy, naturally explaining the overmassive SMBHs discovered by JWST at $z > 6$., Comment: 9 pages, 5 figures, to be submitted to ApJ Letters

Inverse Optimization (IO) is a framework for learning the unknown objective function of an expert decision-maker from a past dataset. In this paper, we extend the hypothesis class of IO objective functions to a reproducing kernel Hilbert space (RKHS), thereby enhancing feature representation to an infinite-dimensional space. We demonstrate that a variant of the representer theorem holds for a specific training loss, allowing the reformulation of the problem as a finite-dimensional convex optimization program. To address scalability issues commonly associated with kernel methods, we propose the Sequential Selection Optimization (SSO) algorithm to efficiently train the proposed Kernel Inverse Optimization (KIO) model. Finally, we validate the generalization capabilities of the proposed KIO model and the effectiveness of the SSO algorithm through learning-from-demonstration tasks on the MuJoCo benchmark., Comment: NeurIPS 2024

By defining an analogous stochastic quantity called Stochastic Fisher Information (SFI), we uncover two fluctuation relations with an inherent geometric nature, as the SFI acts as a single nonequilibrium trajectory metric. The geometric nature of these relations is expressed through a stochastic length derived from the system entropy associated with a nonequilibrium trajectory. We also explore the possibility of trajectory-dependent uncertainty relations linked to the time SFI. Finally, we test the newly discovered relations using two simple stochastic models., Comment: Main text w/ 4.5 pages, Supplemental Material w/ 2 pages, 80 references/footnotes and No figures

The topology of a pure state of two entangled photons is leveraged to provide a discretization of quantum information. Since discrete signals are inherently more resilient to the effects of perturbations, this discrete class of entanglement observables may offer an advantage against noise. Establishing this is the primary objective of this paper. We develop a noise model that exploits the specific form of such topological wave functions - an entangled state of two photons with one in an orbital angular momentum state and the other in a polarization state. We show that noise affecting both photons can be recast as a position-dependent perturbation affecting only the photon in the polarization state. This approach allows us to utilize both the language and concepts used in studying noisy qubits, as well as recent advances in quantum polarimetry. By adding noise to a finite-dimensional Hilbert space of polarization states, we can describe the noise using quantum operations expressed through appropriate Krauss operators, whose structure is determined by quantum polarimetry. For non-depolarizing noise, we provide an argument based on homotopic maps that demonstrates the topology's resilience to noise. For depolarizing noise, numerical studies using the quantum channel description show that the discrete entanglement signal remains completely resilient. Finally, we identify sources of local noise that can destabilize the topology. This foundational work establishes a framework for understanding how topology enhances the resilience of quantum information, directly impacting the distribution of information through entanglement in noisy environments, such as quantum computers and quantum networks., Comment: 24 pages, 15 figures

The critical brain hypothesis posits that neural systems operate near a phase transition, optimising the transmission, storage, and processing of information. While scale invariance and non-Gaussian dynamics - hallmarks of criticality - have been observed in brain activity, a direct link between criticality and behavioural performance remains unexplored. Here, we use a phenomenological renormalisation group (PRG) approach to examine neuronal activity in the primary visual cortex of mice performing a visual recognition task. We show that non-trivial scaling in neuronal activity is associated with enhanced task performance, with pronounced scaling observed during successful task completion. When rewards were removed or non-natural stimuli presented, these scaling signatures diminished. These results suggest that critical dynamics in the brain may be crucial for optimising behavioural outcomes, offering new insights into the functional role of criticality in cortical processing.

The detection of static charge density waves (CDWs) in La$_{2-x}$Sr$_x$CuO$_4$ (LSCO) with x $\sim$ 0.12 at relatively high temperatures has raised the question of what lattice feature pins the CDWs. Some recent structural studies have concluded that some form of monoclinic distortion, indicated by the appearance of certain weak Bragg peaks (type M peaks) at otherwise forbidden positions, are responsible for CDW pinning. As a test of this idea, we present neutron diffraction results for a single crystal of La$_{2-x}$Ba$_x$CuO$_4$ (LBCO) with x = 1/8, which is known to undergo two structural transitions on cooling, from high-temperature tetragonal (HTT) to low-temperature orthorhombic (LTO) near 240 K, involving a collective tilt pattern of the corner-sharing CuO$_6$ octahedra, and from LTO to low temperature tetragonal (LTT) near 56 K, involving a new tilt pattern and the appearance of intensity at peaks of type T. We observe both type M and type T peaks in the LTT phase, while the type M peaks (but not type T) are still present in the LTO phase. Given that CDW order is observed only in the LTT phase of LBCO, it is apparent that the in-plane Cu-O bond anisotropy associated with the octahedral tilt pattern is responsible for charge pinning. We point out that evidence for a similar, but weaker, bond anisotropy has been observed previously in LSCO and should be responsible for CDW pinning there. In the case of LBCO, the monoclinic distortion may help to explain previously-reported magneto-optical evidence for gyrotropic order., Comment: 9 pages, 5 figures, 2 tables

Vertical federated learning trains models from feature-partitioned datasets across multiple clients, who collaborate without sharing their local data. Standard approaches assume that all feature partitions are available during both training and inference. Yet, in practice, this assumption rarely holds, as for many samples only a subset of the clients observe their partition. However, not utilizing incomplete samples during training harms generalization, and not supporting them during inference limits the utility of the model. Moreover, if any client leaves the federation after training, its partition becomes unavailable, rendering the learned model unusable. Missing feature blocks are therefore a key challenge limiting the applicability of vertical federated learning in real-world scenarios. To address this, we propose LASER-VFL, a vertical federated learning method for efficient training and inference of split neural network-based models that is capable of handling arbitrary sets of partitions. Our approach is simple yet effective, relying on the strategic sharing of model parameters and on task-sampling to train a family of predictors. We show that LASER-VFL achieves a $\mathcal{O}({1}/{\sqrt{T}})$ convergence rate for nonconvex objectives in general, $\mathcal{O}({1}/{T})$ for sufficiently large batch sizes, and linear convergence under the Polyak-{\L}ojasiewicz inequality. Numerical experiments show improved performance of LASER-VFL over the baselines. Remarkably, this is the case even in the absence of missing features. For example, for CIFAR-100, we see an improvement in accuracy of $21.4\%$ when each of four feature blocks is observed with a probability of 0.5 and of $12.2\%$ when all features are observed.

Scallop-shell stars, a recently discovered class of young M dwarfs, show complex optical light curves that are characterized by periodic dips as well as other features that are stable over tens to hundreds of rotation cycles. The origin of these features is not well-understood. 2MASS J05082729$-$2101444 is a $\sim$25 Myr old scallop-shell star that was identified using TESS data; it has a photometric period of 6.73h that has been attributed to rotation. Of the $\sim$50 recently confirmed scallop-shell stars, it is one of the few detected at radio frequencies between 1 and 8 GHz. We observed this rare system with the upgraded Giant Meterwave Radio Telescope at 575--720 MHz, covering 88% of the photometric period in each of the two observations scheduled almost a month apart in 2023. We detected $\sim$millijansky emission from the target in both epochs, with a significant circular polarization fraction: $|V/I|\sim$20--50%. The 3.5-min phase-folded light curves reveal unique variability in circular polarization, showing an $\sim$hour-long helicity reversal in both epochs, similar in amplitude, length, and (possibly) phase. These results suggest two emission components: The first is a persistent, moderately polarized component possibly ascribable to gyro-synchrotron emission driven by centrifugal breakout events. The second is a highly polarized, short burst-like component, likely due to an electron cyclotron maser (ECM), indicative of auroral emission and potentially responsible for the helicity reversal. To explain this, we discuss the different origins of the plasma responsible for the radio emission, including the possibility that the occulting material is acting as a plasma source. Future coordinated multifrequency radio and optical observations can further constrain the underlying scenario, as well as the magnetic geometry of the system, if we assume an ECM-like auroral emission., Comment: 9 pages, 5 figures, 1 table, Accepted for publication in Astronomy & Astrophysics Letters

In this work, we present compelling evidence that controlling model capacity during fine-tuning can effectively mitigate memorization in diffusion models. Specifically, we demonstrate that adopting Parameter-Efficient Fine-Tuning (PEFT) within the pre-train fine-tune paradigm significantly reduces memorization compared to traditional full fine-tuning approaches. Our experiments utilize the MIMIC dataset, which comprises image-text pairs of chest X-rays and their corresponding reports. The results, evaluated through a range of memorization and generation quality metrics, indicate that PEFT not only diminishes memorization but also enhances downstream generation quality. Additionally, PEFT methods can be seamlessly combined with existing memorization mitigation techniques for further improvement. The code for our experiments is available at: https://github.com/Raman1121/Diffusion_Memorization_HPO, Comment: Accepted at the GenLaw (Generative AI + Law) workshop at ICML'24

We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architectures, including Variational AutoEncoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, Diffusion models, and models based on Conditional Flow Matching. We compare all submissions in terms of quality of generated calorimeter showers, as well as shower generation time and model size. To assess the quality we use a broad range of different metrics including differences in 1-dimensional histograms of observables, KPD/FPD scores, AUCs of binary classifiers, and the log-posterior of a multiclass classifier. The results of the CaloChallenge provide the most complete and comprehensive survey of cutting-edge approaches to calorimeter fast simulation to date. In addition, our work provides a uniquely detailed perspective on the important problem of how to evaluate generative models. As such, the results presented here should be applicable for other domains that use generative AI and require fast and faithful generation of samples in a large phase space., Comment: 204 pages, 100+ figures, 30+ tables

This paper presents a new construction of non-Anosov Partially Hyperbolic Geodesic flows. Our construction is closely related to the construction made by Carneiro and Pujals, the novelty is the use of conformal deformations to produce the examples. Some of the necessary conditions appear more naturally and are easier to check. Besides that, we could enumerate the conditions required to produce partially hyperbolic geodesic flow examples. We show how to produce examples with metrics that are non-positively curved and with a finner analysis we can prove ergodicity for the Liouville measure and uniqueness of the measure of maximal entropy. These examples lie on the boundary of Anosov metrics, allowing us to also produce metrics with partially hyperbolic geodesic flows and conjugate points., Comment: 34 pages

We study stationary clouds of a gauged, complex scalar field on a magnetically (and possibly electrically as well) charged Kerr-Newman black hole (BH). The existence of a magnetic charge $Q_m$ promotes a north-south $\textit{asymmetry}$ of the scalar clouds. This breakdown of the clouds' $\mathbb{Z}_2$-symmetry carries through to the spacetime $\textit{geometry}$ for the non-linear continuation of the clouds: a family of magnetically charged (or dyonic) BHs with synchronized gauged scalar hair, which we construct. Their distinct phenomenology is illustrated by their imaging, exhibiting skewed shadows and lensing. Such hairy BHs could, in principle, result from the superradiant instability of magnetically charged Kerr-Newman BHs, unveiling a dynamical mechanism for creating north-south asymmetric BHs from standard $\mathbb{Z}_2$-symmetric electrovacuum BHs., Comment: 19 pages, 10 figures

Over the past decade, several millimeter interferometer programs have mapped the nearby star-forming galaxy M51 at a spatial resolution of ${\le}170$ pc. This study combines observations from three major programs: the PdBI Arcsecond Whirlpool Survey (PAWS), the SMA M51 large program (SMA-PAWS), and the Surveying the Whirlpool at Arcseconds with NOEMA (SWAN). The dataset includes the (1-0) and (2-1) rotational transitions of $^{12}$CO, $^{13}$CO, and C$^{18}$O isotopologues. The observations cover the $r{<}\rm 3\,kpc$ region including center and part of the disk, thereby ensuring strong detections of the weaker $^{13}$CO and C$^{18}$O lines. All observations are convolved in this analysis to an angular resolution of 4$''$, corresponding to a physical scale of ${\sim}$170 pc. We investigate empirical line ratio relations and quantitatively evaluate molecular gas conditions such as temperature, density, and the CO-to-H$_2$ conversion factor ($\alpha_{\rm CO}$). We employ two approaches to study the molecular gas conditions: (i) assuming local thermal equilibrium (LTE) to analytically determine the CO column density and $\alpha_{\rm CO}$, and (ii) using non-LTE modeling with RADEX to fit physical conditions to observed CO isotopologue intensities. We find that the $\alpha_{\rm CO}$ values {in the center and along the inner spiral arm} are $\sim$0.5 dex (LTE) and ${\sim}$0.1 dex (non-LTE) below the Milky Way inner disk value. The average non-LTE $\alpha_{\rm CO}$ is $2.4{\pm}0.5$ M$_\odot$ pc$^{-2}$ (K km s$^{-1}$)$^{-1}$. While both methods show dispersion due to underlying assumptions, the scatter is larger for LTE-derived values. This study underscores the necessity for robust CO line modeling to accurately constrain the molecular ISM's physical and chemical conditions in nearby galaxies., Comment: accepted for publication in AJ; 31 pages, 16 figures, 7 tables

Our work tackles the computational challenges of contrastive learning methods, particularly for the pretraining of Vision Transformers (ViTs). Despite the effectiveness of contrastive learning, the substantial computational resources required for training often hinder their practical application. To mitigate this issue, we propose an acceleration framework, leveraging ViT's unique ability to generalize across inputs of varying sequence lengths. Our method employs a mix of sequence compression strategies, including randomized token dropout and flexible patch scaling, to reduce the cost of gradient estimation and accelerate convergence. We further provide an in-depth analysis of the gradient estimation error of various acceleration strategies as well as their impact on downstream tasks, offering valuable insights into the trade-offs between acceleration and performance. We also propose a novel procedure to identify an optimal acceleration schedule to adjust the sequence compression ratios to the training progress, ensuring efficient training without sacrificing downstream performance. Our approach significantly reduces computational overhead across various self-supervised learning algorithms on large-scale datasets. In ImageNet, our method achieves speedups of 4$\times$ in MoCo, 3.3$\times$ in SimCLR, and 2.5$\times$ in DINO, demonstrating substantial efficiency gains.

We prove that the doubly generalized conjugacy problem is decidable for rational subsets of finitely generated virtually free groups: there is an algorithm taking as input two rational subsets $K_1$ and $K_2$ of a virtually free group that decides whether there is one element of $K_1$ conjugate to an element of $K_2$. For free groups, we prove that the same problem is decidable with rational constraints on the set of conjugators., Comment: 16 pages, comments are welcome

Given graphs $G$ and $H$, we say that $G$ is $H$-$good$ if the Ramsey number $R(G,H)$ equals the trivial lower bound $(|G| - 1)(\chi(H) - 1) + \sigma(H)$, where $\chi(H)$ denotes the usual chromatic number of $H$, and $\sigma(H)$ denotes the minimum size of a color class in a $\chi(H)$-coloring of $H$. Pokrovskiy and Sudakov [Ramsey goodness of paths. Journal of Combinatorial Theory, Series B, 122:384-390, 2017.] proved that $P_n$ is $H$-good whenever $n\geq 4|H|$. In this paper, given $\varepsilon>0$, we show that if $H$ satisfy a special unbalance condition, then $P_n$ is $H$-good whenever $n \geq (2 + \varepsilon)|H|$. More specifically, we show that if $m_1,\ldots, m_k$ are such that $\varepsilon\cdot m_i \geq 2m_{i-1}^2$ for $2\leq i\leq k$, and $n \geq (2 + \varepsilon)(m_1 + \cdots + m_k)$, then $P_n$ is $K_{m_1,\ldots,m_k}$-good., Comment: 10 pages, 1 figure

We extend the classical theory of homotopical $\Sigma$-sets $\Sigma^n$ developed by Bieri, Neumann, Renz and Strebel for abstract groups, to $\Sigma$-sets $\Sigma_{\mathrm{top}}^n$ for locally compact Hausdorff groups. Given such a group $G$, our $\Sigma_{\mathrm{top}}^n(G)$ are sets of continuous homomorphisms $G \to \mathbb{R}$ ("characters"). They match the classical $\Sigma$-sets $\Sigma^n(G)$ if $G$ is discrete, and refine the homotopical compactness properties $\mathrm C_n$ of Abels and Tiemeyer. Moreover, our theory recovers the definition of $\Sigma_{\mathrm{top}}^1$ and $\Sigma_{\mathrm{top}}^2$ proposed by Kochloukova. Besides presenting various characterizations of $\Sigma_{\mathrm{top}}^n$ (particularly for $n\in \{1,2\}$), we show that characters in $\Sigma_{\mathrm{top}}^n(G)$ are also in $\Sigma_{\mathrm{top}}^n(H)$ if $H\le G$ is a closed cocompact subgroup, and we generalize several classical results. Namely, we prove that the set of nonzero elements of $\Sigma_{\mathrm{top}}^n(G)$ is open, we prove that characters in a group of type $\mathrm C_n$ that do not vanish on the center always lie in $\Sigma_{\mathrm{top}}^n(G)$, and we relate the $\Sigma$-sets of a group with those of its quotients by closed subgroups of type $\mathrm C_n$. Lastly, we describe how $\Sigma_{\mathrm{top}}^n(G)$ governs whether a closed normal subgroup with abelian quotient is of type $\mathrm C_n$, generalizing one of the highlights of the classical theory., Comment: 59 pages, 5 figures; this version corrects the incomplete abstract in the article's metadata

Neural Radiance Fields (NeRF) have revolutionized the field of 3D visual representation by enabling highly realistic and detailed scene reconstructions from a sparse set of images. NeRF uses a volumetric functional representation that maps 3D points to their corresponding colors and opacities, allowing for photorealistic view synthesis from arbitrary viewpoints. Despite its advancements, the efficient streaming of NeRF content remains a significant challenge due to the large amount of data involved. This paper investigates the rate-distortion performance of two NeRF streaming strategies: pixel-based and neural network (NN) parameter-based streaming. While in the former, images are coded and then transmitted throughout the network, in the latter, the respective NeRF model parameters are coded and transmitted instead. This work also highlights the trade-offs in complexity and performance, demonstrating that the NN parameter-based strategy generally offers superior efficiency, making it suitable for one-to-many streaming scenarios.

The electrorheological (ER) characterization of low-viscosity fluids is paramount for producing micro- and nanoscale products through electrohydrodynamic (EHD) techniques, such as EHD-jet printing, electrospray, and electrospinning. Key properties such as viscosity, surface tension, dielectric properties, electrical conductivity, and relaxation time significantly influence both the quality and properties of the final products and the efficiency of the industrial process. ER characterization is essential for studying the macroscopic effects of the interaction between these physicochemical properties under controlled flow kinematics. Researchers may face several technical challenges in performing rigorous ER characterization of low-viscosity fluids. This characterization is crucial for formulating inks compatible with the EHD process and for understanding fluid dynamics in EHD processes to ensure stable printing conditions and achieve high-resolution, accurate prints. This work highlights the inherent limitations of current ER cells and proposes methodologies to mitigate their impact on measurement accuracy. Furthermore, we explore the potential of microfluidic technologies to offer innovative solutions for the ER characterization of low-viscosity fluids.

We investigate the first-order transport coefficients of a fluid made of quasiparticles with a temperature-dependent mass extracted from chiral models. We describe this system using an effective kinetic theory, given by the relativistic Boltzmann equation coupled to a temperature-dependent background field determined from the thermal masses. We then simplify the collision term using the relaxation time approximation and implement a Chapman-Enskog expansion to calculate all first-order transport coefficients. In particular, we compute the bulk and shear viscosities using thermal masses extracted from the linear sigma model coupled with constituent quarks and the NJL model., Comment: 11 pages, 17 figures

We investigate entanglement entropy in $3d$ $\mathcal{N}=2$ superconformal field theories from two different perspectives. We first confirm that the dependence of supersymmetric entanglement entropy (as defined in arXiv:1306.2958) on the entangling region is purely topological. We then compute entanglement entropy of $3d$ $\mathcal{N}=2$ superconformal gauge theories at arbitrary Yang-Mills coupling using the heat kernel method. This reveals a particular divergence originating in charge fluctuations in the vacuum, hence carrying information about internal symmetries of the theory. This computation leads to universal contributions to entanglement entropy from chiral and vector multiplets for unitary gauge groups of arbitrary rank, and has the advantage that the integral over the gauge moduli is simple to compute., Comment: 14 pages + appendix, 2 figures

Parallel real-time embedded applications can be modelled as directed acyclic graphs (DAGs) whose nodes model subtasks and whose edges model precedence constraints among subtasks. Efficiently scheduling such parallel tasks can be challenging in itself, particularly in hard real-time systems where it must be ensured offline that the deadlines of the parallel applications will be met at run time. In this paper, we tackle the problem of scheduling DAG tasks on identical multiprocessor systems efficiently, in terms of processor utilisation. We propose a new algorithm that attempts to use dedicated processor clusters for high-utilisation tasks, as in federated scheduling, but is also capable of reclaiming the processing capacity lost to fragmentation, by splitting the execution of parallel tasks over different existing clusters, in a manner inspired by semi-partitioned C=D scheduling (originally devised for non-parallel tasks). In the experiments with synthetic DAG task sets, our Segmented-Flattened-and-Split scheduling approach achieves a significantly higher scheduling success ratio than federated scheduling., Comment: Version submitted to RTNS 2024, on 16/08/2024 (with some typos fixed)

IRAS 22568+6141 has been classified as a low-ionisation planetary nebula (PN) and presents non-thermal radio continuum emission, which could be a signature of nascent PNe. We present intermediate-resolution long-slit spectra obtained in 2021 and 2023, high-resolution long-slit spectra taken in 2023, and a light curve at the $r$-filter between 1953 and 2019, that reveal changes in IRAS 22568+6141 with timescales of decades and a few years. The object underwent an energetic event around 1990 that suddenly increased its brightness which has been fading since then. A comparison with a published spectrum from 1988 shows an increase of the H$\beta$ flux in 2021 by factor of $\simeq$6 and the [O III] emission lines that were absent in 1988. Between 2021 and 2023 the H$\beta$ flux decreased by a factor of $\simeq$1.7, and the [O III] emission lines almost vanished. These results and the variability observed in other emission lines indicate that IRAS 22568+6141 is recombining and cooling down between 2021 and 2023, and probably since 2005, as suggested by archival radio continuum and mid-IR observations. The intermediate- and high-resolution spectra show that the excitation of the emission lines is dominated by shocks in 2021 and 2023, and, probably, also in 1988, which may be related to the non-thermal radio continuum emission from the object. Although the variability might be due to changes in the physical conditions in the shocks or in a nova-like eruption, it accommodates better to that expected from a late thermal pulse, which is further suggested by a comparison with other similar objects. New observations and monitoring in the coming years are crucial to corroborate the origin of the variability., Comment: 11 pages, 8 figures, 6 tables. Accepted for publication in A&A

We propose that a dark matter (DM) spike around the Galactic Center's (GC) supermassive black hole, Sgr A*, could account for most of the bulge's measured 511 keV line intensity while remaining cosmologically compatible. DM annihilation can be the primary source of the 511 keV line emission without violating constraints from disk emission observations and in-flight positron annihilation with the interstellar medium, provided the disk emission is dominated by an astrophysical source of low-energy positrons. We find that a DM mass up to approximately 20 MeV, either with a Gondolo-Silk spike or one softened by stellar heating, could explain the observed 511 keV bulge emission profile. Our proposal can be tested by future observations of the continuum diffuse emission close to the GC., Comment: 18 pages of main text containing 7 figures and one table. 3 appendices with other 7 figures

The growing volume of academic publications poses significant challenges for researchers conducting timely and accurate Systematic Literature Reviews, particularly in fast-evolving fields like artificial intelligence. This growth of academic literature also makes it increasingly difficult for lay people to access scientific knowledge effectively, meaning academic literature is often misrepresented in the popular press and, more broadly, in society. Traditional SLR methods are labor-intensive and error-prone, and they struggle to keep up with the rapid pace of new research. To address these issues, we developed \textit{PROMPTHEUS}: an AI-driven pipeline solution that automates the SLR process using Large Language Models. We aimed to enhance efficiency by reducing the manual workload while maintaining the precision and coherence required for comprehensive literature synthesis. PROMPTHEUS automates key stages of the SLR process, including systematic search, data extraction, topic modeling using BERTopic, and summarization with transformer models. Evaluations conducted across five research domains demonstrate that PROMPTHEUS reduces review time, achieves high precision, and provides coherent topic organization, offering a scalable and effective solution for conducting literature reviews in an increasingly crowded research landscape. In addition, such tools may reduce the increasing mistrust in science by making summarization more accessible to laypeople. The code for this project can be found on the GitHub repository at https://github.com/joaopftorres/PROMPTHEUS.git

Let $H$ be a monoid (written multiplicatively). We call $H$ Archimedean if, for all $a, b \in H$ such that $b$ is a non-unit, there is an integer $k \ge 1$ with $b^k \in HaH$; strongly Archimedean if, for each $a \in H$, there is an integer $k \ge 1$ such that $HaH$ contains any product of any $k$ non-units of $H$; and duo if $aH = Ha$ for all $a \in H$. We prove that the ideals of two strongly Archimedean, cancellative, duo monoids make up isomorphic semigroups under the induced operation of setwise multiplication if and only if the monoids themselves are isomorphic up to units; and the same holds upon restriction to finitely generated ideals in Archimedean, cancellative, duo monoids. Then we use the previous results to tackle a new case of a problem of Tamura and Shafer from the late 1960s., Comment: 12 pp., to appear in Proceedings of the Amer. Math. Soc

We derive lower bounds on the time needed for a quantum annealer to prepare the ground state of a target Hamiltonian. These bounds do not depend on the annealing schedule and can take the local structure of the Hamiltonian into account. Consequently, the bounds are computable without knowledge of the annealer's dynamics and, in certain cases, scale with the size of the system. We discuss spin systems where the bounds are polynomially tighter than existing bounds, qualitatively capturing the scaling of the exact annealing times as a function of the number of spins.

In this work we apply a procedure based on the quantum imaginary time evolution method to solve the unit-disk maximum independent set problem. Numerical simulations were performed for instances of 6, 8 and 10-qubits graphs. We have found that the failure probability of the procedure is relatively small and rapidly decreases with the number of shots. In addition, a theoretical upper bound for the failure probability of the procedure was obtained., Comment: 19 pages, 13 figures

We prove by counterexample that a large deviation principle established by Chen and Feng [{\em Comm. Statist. Theory Methods} {\bf 45} (2016), 400--412] in the framework of sublinear expectations is incorrect. That implies that the rate function cannot, in general, be obtained by computing the Fenchel transform of the cumulant generating function, as is the case for ordinary probabilities. We derive a corrected version of that result and show that the original presentation holds under a stronger independence assumption.

Current and future large scale structure surveys aim to constrain the neutrino mass and the equation of state of dark energy. We aim to construct accurate and interpretable symbolic approximations to the linear and nonlinear matter power spectra as a function of cosmological parameters in extended $\Lambda$CDM models which contain massive neutrinos and non-constant equations of state for dark energy. This constitutes an extension of the syren-halofit emulators to incorporate these two effects, which we call syren-new (SYmbolic-Regression-ENhanced power spectrum emulator with NEutrinos and $W_0-w_a$). We also obtain a simple approximation to the derived parameter $\sigma_8$ as a function of the cosmological parameters for these models. Our results for the linear power spectrum are designed to emulate CLASS, whereas for the nonlinear case we aim to match the results of EuclidEmulator2. We compare our results to existing emulators and $N$-body simulations. Our analytic emulators for $\sigma_8$, the linear and nonlinear power spectra achieve root mean squared errors of 0.1%, 0.3% and 1.3%, respectively, across a wide range of cosmological parameters, redshifts and wavenumbers. We verify that emulator-related discrepancies are subdominant compared to observational errors and other modelling uncertainties when computing shear power spectra for LSST-like surveys. Our expressions have similar accuracy to existing (numerical) emulators, but are at least an order of magnitude faster, both on a CPU and GPU. Our work greatly improves the accuracy, speed and range of applicability of current symbolic approximations to the linear and nonlinear matter power spectra. We provide publicly available code for all symbolic approximations found., Comment: 18 pages, 15 figures

This paper presents the first comprehensive interpretability analysis of a Transformer-based Sign Language Translation (SLT) model, focusing on the translation from video-based Greek Sign Language to glosses and text. Leveraging the Greek Sign Language Dataset, we examine the attention mechanisms within the model to understand how it processes and aligns visual input with sequential glosses. Our analysis reveals that the model pays attention to clusters of frames rather than individual ones, with a diagonal alignment pattern emerging between poses and glosses, which becomes less distinct as the number of glosses increases. We also explore the relative contributions of cross-attention and self-attention at each decoding step, finding that the model initially relies on video frames but shifts its focus to previously predicted tokens as the translation progresses. This work contributes to a deeper understanding of SLT models, paving the way for the development of more transparent and reliable translation systems essential for real-world applications., Comment: Accepted at IAI Workshop @ NeurIPS 2024

While behavioural equivalences among systems of the same type, such as Park/Milner bisimilarity of labelled transition systems, are an established notion, a systematic treatment of relationships between systems of different type is currently missing. We provide such a treatment in the framework of universal coalgebra, in which the type of a system (nondeterministic, probabilistic, weighted, game-based etc.) is abstracted as a set functor: We introduce relational connectors among set functors, which induce notions of heterogeneous (bi)simulation among coalgebras of the respective types. We give a number of constructions on relational connectors. In particular, we identify composition and converse operations on relational connectors; we construct corresponding identity relational connectors, showing that the latter generalize the standard Barr extension of weak-pullback-preserving functors; and we introduce a Kantorovich construction in which relational connectors are induced from relations between modalities. For Kantorovich relational connectors, one has a notion of dual-purpose modal logic interpreted over both system types, and we prove a corresponding Hennessy-Milner-type theorem stating that generalized (bi)similarity coincides with theory inclusion on finitely-branching systems. We apply these results to a number of example scenarios involving labelled transition systems with different label alphabets, probabilistic systems, and input/output conformances.