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We study the dynamics of a first-order phase transition in a strongly coupled gauge theory at non-zero temperature and chemical potential, computing nucleation rates and wall speeds from first principles. The gauge theory is the four-dimensional superconformal SU(N)xSU(N) Klebanov-Witten theory, which at low temperatures displays an instability to forming scalar condensates that higgses the theory. The computation is made possible by utilizing the gravity dual, type IIB string theory on asymptotically AdS_5xT^{1,1} spacetimes. The instability is detected through the nucleation and subsequent localization of D-branes in the bulk, which in the probe limit is amenable to calculations. The nucleation rates suggest a preference for greater asymmetry between the two gauge groups as the temperature is lowered beyond its critical value. The wall speed stays below the speed of sound of the conformal plasma across all parameter values and exhibits nonlinear dependence on the energy difference between the two phases., Comment: 20 pages, 7 figures

Most of current computer vision-based advanced driver assistance systems (ADAS) perform detection and tracking of objects quite successfully under regular conditions. However, under adverse weather and changing lighting conditions, and in complex situations with many overlapping objects, these systems are not completely reliable. The spectral reflectance of the different objects in a driving scene beyond the visible spectrum can offer additional information to increase the reliability of these systems, especially under challenging driving conditions. Furthermore, this information may be significant enough to develop vision systems that allow for a better understanding and interpretation of the whole driving scene. In this work we explore the use of snapshot, video-rate hyperspectral imaging (HSI) cameras in ADAS on the assumption that the near infrared (NIR) spectral reflectance of different materials can help to better segment the objects in real driving scenarios. To do this, we have used the HSI-Drive 1.1 dataset to perform various experiments on spectral classification algorithms. However, the information retrieval of hyperspectral recordings in natural outdoor scenarios is challenging, mainly because of deficient colour constancy and other inherent shortcomings of current snapshot HSI technology, which poses some limitations to the development of pure spectral classifiers. In consequence, in this work we analyze to what extent the spatial features codified by standard, tiny fully convolutional network (FCN) models can improve the performance of HSI segmentation systems for ADAS applications. The abstract above is truncated due to submission limits. For the full abstract, please refer to the published article.

Semantic mapping is a key component of robots operating in and interacting with objects in structured environments. Traditionally, geometric and knowledge representations within a semantic map have only been loosely integrated. However, recent advances in deep learning now allow full integration of prior knowledge, represented as knowledge graphs or language concepts, into sensor data processing and semantic mapping pipelines. Semantic scene graphs and language models enable modern semantic mapping approaches to incorporate graph-based prior knowledge or to leverage the rich information in human language both during and after the mapping process. This has sparked substantial advances in semantic mapping, leading to previously impossible novel applications. This survey reviews these recent developments comprehensively, with a focus on online integration of knowledge into semantic mapping. We specifically focus on methods using semantic scene graphs for integrating symbolic prior knowledge and language models for respective capture of implicit common-sense knowledge and natural language concepts, Comment: Submitted to Robotics and Autonomous Systems

The financial sector is increasingly concerned with the physical risks of climate change, but economic and financial impact representations are still developing, particularly for chronic risks. Mexico's Central Bank conducted a comprehensive assessment using a suite of global models to evaluate both physical and transition risks. We present the analysis concerning with chronic physical risks, underlining innovations such as the use of a recent integrated assessment model that enables grid-cell level analysis and differentiates urban and non-urban areas, capturing the local effects of climate change more accurately. The model includes multiple damage functions and a probabilistic climate model for encompassing analyses and detailed economic impact insights. Under the Current Policies scenario, economic losses could exceed 35 percent of Mexico's GDP by 2100. Accounting for the urban heat island effect, losses could surpass 20 trillion (USD) in present value, over ten times Mexico's 2024 GDP. However, implementing a scenario aligned with the Paris Agreement significantly reduces these losses, showcasing the benefits of international mitigation efforts, though substantial residual impacts persist. This study emphasizes integrating chronic physical risks into financial evaluations, proposing new approaches, metrics, and methods that exploit detailed, spatially explicit measures to improve risk and loss estimation and facilitate communication., Comment: 19 pages, 7 figures

Using a mix of analytical and numerical methods, we construct new rotating, charged "hairy" black hole solutions of $D=5$, ${\cal N}=8$ gauged supergravity that are dual, via the AdS/CFT correspondence, to thermal states in $D=4$, ${\cal N}=4$ SYM at finite chemical and angular potential, thereby complementing and extending the results of [arXiv:1005.1287, arXiv:1806.01849, arXiv:1809.04084]. These solutions uplift to asymptotically AdS$_5 \times S^5$ solutions of Type IIB supergravity with equal angular momenta along AdS$_5$ ($J=J_1=J_2$) and $S^5$ ($Q=Q_1=Q_2=Q_3$). As we lower the mass $E$ at fixed $Q$ and $J$, the known Cveti\v{c}-L\"u-Pope (CLP) black holes are unstable to scalar condensation and the hairy black holes constructed here emerge as novel solutions associated to the instability. In the region of phase space where the CLP and hairy black holes coexist, the hairy black holes dominate the microcanonical ensemble and, therefore, describe a new thermodynamic phase of SYM. The hairy black holes extend beyond the CLP extremality surface all the way to the BPS surface, defined by $E = 3 Q + 2 J / L$. Through a combination of analytical and numerical techniques, we argue that the BPS limit of the hairy black holes is a singular, horizonless solution, and $not$ a new two-parameter family of BPS black holes that extend the known one-parameter Gutowski-Reall (GR) black hole solution, in contradiction with the conjectures of [arXiv:1005.1287, arXiv:1806.01849]. To further support our conclusions, we perform a near-horizon analysis of the BPS equations and argue that they do not admit any regular solutions with an horizon., Comment: 71 pages, 18 figures

The article discusses the use of low cost System-On-Module (SOM) platforms for the implementation of efficient hyperspectral imaging (HSI) processors for application in autonomous driving. The work addresses the challenges of shaping and deploying multiple layer fully convolutional networks (FCN) for low-latency, on-board image semantic segmentation using resource- and power-constrained processing devices. The paper describes in detail the steps followed to redesign and customize a successfully trained HSI segmentation lightweight FCN that was previously tested on a high-end heterogeneous multiprocessing system-on-chip (MPSoC) to accommodate it to the constraints imposed by a low-cost SOM. This SOM features a lower-end but much cheaper MPSoC suitable for the deployment of automatic driving systems (ADS). In particular the article reports the data- and hardware-specific quantization techniques utilized to fit the FCN into a commercial fixed-point programmable AI coprocessor IP, and proposes a full customized post-training quantization scheme to reduce computation and storage costs without compromising segmentation accuracy.

Near-future experiments with Petawatt class lasers are expected to produce a high flux of gamma-ray photons and electron-positron pairs through Strong Field Quantum Electrodynamical processes. Simulations of the expected regime of laser-matter interaction are computationally intensive due to the disparity of the spatial and temporal scales and because quantum and classical descriptions need to be accounted for simultaneously (classical for collective effects and quantum for nearly-instantaneous events of hard photon emission and pair creation). A typical configuration for experiments is a scattering of an electron and a laser beam which can be mapped to an equivalent problem with constant magnetic field. We study the stochastic cooling of an electron beam in a strong constant uniform magnetic field, both its particle distribution functions and their energy momenta. We start by obtaining approximate closed-form analytical solutions to the relevant observables. Then, we apply the quantum-hybrid Variational Quantum Imaginary Time Evolution to the Fokker-Planck equation describing this process, and compare against theory and results from Particle-In-Cell simulations and classical Partial Differential Equation solvers, showing good agreement. This work will be useful as a first step towards quantum simulation of plasma physics scenarios where diffusion processes are important, in particular in strong electromagnetic fields.

As robots increasingly coexist with humans, they must navigate complex, dynamic environments rich in visual information and implicit social dynamics, like when to yield or move through crowds. Addressing these challenges requires significant advances in vision-based sensing and a deeper understanding of socio-dynamic factors, particularly in tasks like navigation. To facilitate this, robotics researchers need advanced simulation platforms offering dynamic, photorealistic environments with realistic actors. Unfortunately, most existing simulators fall short, prioritizing geometric accuracy over visual fidelity, and employing unrealistic agents with fixed trajectories and low-quality visuals. To overcome these limitations, we developed a simulator that incorporates three essential elements: (1) photorealistic neural rendering of environments, (2) neurally animated human entities with behavior management, and (3) an ego-centric robotic agent providing multi-sensor output. By utilizing advanced neural rendering techniques in a dual-NeRF simulator, our system produces high-fidelity, photorealistic renderings of both environments and human entities. Additionally, it integrates a state-of-the-art Social Force Model to model dynamic human-human and human-robot interactions, creating the first photorealistic and accessible human-robot simulation system powered by neural rendering., Comment: 8 pages, 5 figures

Recent advances in quantum computing have sparked excitement that this new computing paradigm could solve previously intractable problems. However, due to the faulty nature of current quantum hardware and quantum-intrinsic noise, the full potential of quantum computing is still years away. Hybrid quantum-classical computing has emerged as a possible compromise that achieves the best of both worlds. In this paper, we look at hybrid quantum-classical computing from a software engineering perspective and present the first empirical study focused on characterizing and evaluating recurrent issues faced by developers of hybrid quantum-classical applications. The study comprised a thorough analysis of 531 real-world issues faced by developers -- including software faults, hardware failures, quantum library errors, and developer mistakes -- documented in discussion threads from forums dedicated to quantum computing. By qualitatively analyzing such forum threads, we derive a comprehensive taxonomy of recurring issues in hybrid quantum-classical applications that can be used by both application and platform developers to improve the reliability of hybrid applications. The study considered how these recurring issues manifest and their causes, determining that hybrid applications are crash-dominant (74\% of studied issues) and that errors were predominantly introduced by application developers (70\% of issues). We conclude by identifying recurring obstacles for developers of hybrid applications and actionable recommendations to overcome them.

We present a new gauging of maximal supergravity in five spacetime dimensions with gauge group containing ISO(5), involving the local scaling symmetry of the metric, and admitting a supersymmetric anti-de Sitter vacuum. We show this maximal supergravity to arise by consistent truncation of M-theory on the (non-spherical, non-parallelisable) six-dimensional geometry associated to a stack of $N$ M5-branes wrapped on a smooth Riemann surface. The existence of this truncation allows us to holographically determine the complete, universal spectrum of light operators of the dual four-dimensional $\mathcal{N}=2$ theory of class $\mathcal{S}$. We then compute holographically the superconformal index of the dual field theory at large-$N$, finding perfect agreement with previously known field theory results in specific limits., Comment: 5 pages

Over the past seven years, full-field analyses of a wide range of classical as well as modern quasi-static fracture experiments on nominally elastic brittle materials -- ranging from hard ceramics to soft elastomers -- have repeatedly identified the material strength surface as one of the key material properties that governs not only the nucleation of cracks, but also their propagation. Central to these analyses are the results generated by the Griffith phase-field fracture theory with material strength introduced in [21,23,20]. The first of two objectives of this paper is to extend this theory to account for inertia, this for the basic case of isotropic linear elastic brittle materials. From an applications point of view, the theory amounts to solving an initial-boundary-value problem comprised of a hyperbolic PDE coupled with an elliptic PDE for the displacement field $\mathbf{u}(\mathbf{X},t)$ and the phase field $d(\mathbf{X},t)$. A robust scheme is presented to generate solutions for these equations that is based on an adaptive finite-element discretization of space and an implicit finite-difference discretization of time. %At every time increment $t_m$, the resulting discretized equations are solved separately in a staggered manner for $\mathbf{u}(\mathbf{X},t_m)$ and $d(\mathbf{X},t_m)$ by means of Newton-Raphson schemes. The second objective is to illustrate the descriptive and predictive capabilities of the proposed theory via simulations of benchmark problems and experiments. These include problems involving fracture nucleation from large pre-existing cracks, such as the classical Kalthoff-Winkler experiments, as well as problems involving fracture nucleation within the bulk, such as the dynamic Brazilian fracture experiments.

Elastomers filled with liquid inclusions -- as opposed to conventional solid fillers -- are a recent trend in the soft matter community because of their unique range of mechanical and physical properties. Such properties stem, in part, from the very large deformations that the underlying liquid inclusions are capable of undergoing. With the objective of advancing the understanding of the mechanics of this emerging class of materials, this paper presents a combined experimental/theoretical study of the nonlinear elastic deformation of initially spherical liquid inclusions embedded in elastomers that are subjected to quasistatic mechanical loads. The focus is on two fundamental problems, both within the limit regime when elasto-capillarity effects are negligible: ($i$) the problem of an isolated inclusion and ($ii$) that of a pair of closely interacting inclusions. Experimentally, specimens made of a polydimethylsiloxane (PDMS) elastomer filled with either isolated or pairs of initially spherical liquid glycerol inclusions are subjected to uniaxial tension. For the specimens with pairs of inclusions, three orientations of the two inclusions with respect to the direction of the applied macroscopic tensile load are considered, $0^\circ$, $45^\circ$, and $90^\circ$. The liquid glycerol is stained with a fluorescent dye that permits to measure the local deformation of the inclusions \emph{in situ} via confocal laser scanning fluorescent microscopy. Theoretically, a recently developed framework -- wherein the elastomer is considered to be a nonlinear elastic solid, the liquid comprising the inclusions is considered to be a nonlinear elastic fluid, and the interfaces separating the elastomer from the liquid inclusions can feature their own nonlinear elastic behavior (e.g., surface tension) -- is utilized to carry out full-field simulations of the experiments.

Cross-view Geo-localisation is typically performed at a coarse granularity, because densely sampled satellite image patches overlap heavily. This heavy overlap would make disambiguating patches very challenging. However, by opting for sparsely sampled patches, prior work has placed an artificial upper bound on the localisation accuracy that is possible. Even a perfect oracle system cannot achieve accuracy greater than the average separation of the tiles. To solve this limitation, we propose combining cross-view geo-localisation and relative pose estimation to increase precision to a level practical for real-world application. We develop PEnG, a 2-stage system which first predicts the most likely edges from a city-scale graph representation upon which a query image lies. It then performs relative pose estimation within these edges to determine a precise position. PEnG presents the first technique to utilise both viewpoints available within cross-view geo-localisation datasets to enhance precision to a sub-metre level, with some examples achieving centimetre level accuracy. Our proposed ensemble achieves state-of-the-art precision - with relative Top-5m retrieval improvements on previous works of 213%. Decreasing the median euclidean distance error by 96.90% from the previous best of 734m down to 22.77m, when evaluating with 90 degree horizontal FOV images. Code will be made available: tavisshore.co.uk/PEnG, Comment: 8 pages, 6 figures

The photo-electric effect was a historic milestone in the development of quantum theory, revealing the first evidence of discrete energy of the electromagnetic field through hallmark signatures such as the threshold frequency, intensity-independent energy transfer, and the near instantaneous ejection of photo-electrons. In this work, we revisit the photo-electric effect through the lens of semi-classical, quantum, and neo-classical models to explore their implications on energy conservation and discrete photon exchange. We extend this analysis to include the gravito-phononic effect through modelling energy transfer between coupled harmonic oscillators, rather than traditional atom-field interactions. Our approach demonstrates that two coupled harmonic oscillators, under a beam-splitter interaction, can exhibit hallmark signatures analogous to the photo-electric effect, including resonance conditions and quantised energy absorption. We discuss the implications of this model for the recently proposed gravito-phononic detections of single gravitons. This further clarifies that a gravitational version of the photo-electric effect, modelled as discrete energy transfer between harmonic oscillators can provide the first evidence of the graviton., Comment: 5 + 1 pages, 1 figure. A short note answering common questions

We study the dynamics of a coherent state of closed type II string gravitons within the framework of the Steepest Entropy Ascent Quantum Thermodynamics, an effective model where the quantum evolution is driven by a maximal increase of entropy. We find that by perturbing the pure coherent state of gravitons by the presence of other coherent fields in the string spectrum, there exists conditions upon which the system undergoes decoherence by reaching thermodynamical equilibrium. Following the proposal by Dvali, et al., this suggests the instability of the classical dS space. We identify the time scale it takes the system to reach equilibrium consisting of a mixed state of fields in the string spectrum and compare it with the quantum-break time. Also we find that in such final state the quantum-break time seems to be larger than the classical break-time, in agreement with the Swampland conjectures about the dS solution in string theory., Comment: 19 pages

Hallucinations in large language models are a widespread problem, yet the mechanisms behind whether models will hallucinate are poorly understood, limiting our ability to solve this problem. Using sparse autoencoders as an interpretability tool, we discover that a key part of these mechanisms is entity recognition, where the model detects if an entity is one it can recall facts about. Sparse autoencoders uncover meaningful directions in the representation space, these detect whether the model recognizes an entity, e.g. detecting it doesn't know about an athlete or a movie. This suggests that models can have self-knowledge: internal representations about their own capabilities. These directions are causally relevant: capable of steering the model to refuse to answer questions about known entities, or to hallucinate attributes of unknown entities when it would otherwise refuse. We demonstrate that despite the sparse autoencoders being trained on the base model, these directions have a causal effect on the chat model's refusal behavior, suggesting that chat finetuning has repurposed this existing mechanism. Furthermore, we provide an initial exploration into the mechanistic role of these directions in the model, finding that they disrupt the attention of downstream heads that typically move entity attributes to the final token.

Use of large language models such as ChatGPT (GPT-4) for mental health support has grown rapidly, emerging as a promising route to assess and help people with mood disorders, like depression. However, we have a limited understanding of GPT-4's schema of mental disorders, that is, how it internally associates and interprets symptoms. In this work, we leveraged contemporary measurement theory to decode how GPT-4 interrelates depressive symptoms to inform both clinical utility and theoretical understanding. We found GPT-4's assessment of depression: (a) had high overall convergent validity (r = .71 with self-report on 955 samples, and r = .81 with experts judgments on 209 samples); (b) had moderately high internal consistency (symptom inter-correlates r = .23 to .78 ) that largely aligned with literature and self-report; except that GPT-4 (c) underemphasized suicidality's -- and overemphasized psychomotor's -- relationship with other symptoms, and (d) had symptom inference patterns that suggest nuanced hypotheses (e.g. sleep and fatigue are influenced by most other symptoms while feelings of worthlessness/guilt is mostly influenced by depressed mood)., Comment: 21 pages, 3 tables, 6 figures, 1 supplementary table, 83 references

JWST has revealed a large population of compact, red galaxies at $z>4$ known as Little Red Dots (LRDs). We analyze the spectral energy distributions (SEDs) of 95 LRDs from the JWST PRIMER survey with complete photometric coverage from $1-18\ \mu$m using NIRCam and MIRI imaging, representing the most extensive SED analysis on a large LRD sample with long-wavelength MIRI data. We examine SED models in which either galaxy or active galactic nucleus (AGN) emission dominates the rest-frame UV or optical continuum, extracting physical properties to explore each scenario's implications. In the galaxy-only model, we find massive, dusty stellar populations alongside unobscured, low-mass components, hinting at inhomogeneous obscuration. The AGN-only model indicates dusty, luminous AGNs with low hot dust fractions compared to typical quasars. A hybrid AGN and galaxy model suggests low-mass, unobscured galaxies in the UV, with stellar mass estimates spanning $\sim$2 dex across the different models, underscoring the need for caution in interpreting LRD stellar masses. With MIRI photometry, the galaxy-only model produces stellar masses within cosmological limits, but extremely high stellar mass densities are inferred. The hybrid model infers highly overmassive black holes exceeding those in recently reported high-redshift AGNs, hinting at a partial AGN contribution to the rest-optical continuum or widespread super-Eddington accretion. Our findings highlight the extreme conditions required for both AGN or galaxy dominated scenarios in LRDs, supporting a mixed contribution to the red continuum, or novel scenarios to explain the observed emission., Comment: 22 pages, 10 figures, submitted to ApJ. Machine-readable form of Table 2 available at: https://github.com/geneckleung/lrd_primer_miri

This work presents the basic elements and results of a Clifford algebra valued fractional slice monogenic functions theory defined from the null-solutions of a suitably fractional Cauchy-Riemann operator in the Riemann-Liouville and Caputo sense with respect to a pair of real valued functions on certain domains of Euclidean spaces.

Generative AI (GenAI) tools have already started to transform software development practices. Despite their utility in tasks such as writing code, the use of these tools raises important legal questions and potential risks, particularly those associated with copyright law. In the midst of this uncertainty, this paper presents a study jointly conducted by software engineering and legal researchers that surveyed 574 GitHub developers who use GenAI tools for development activities. The survey and follow-up interviews probed the developers' opinions on emerging legal issues as well as their perception of copyrightability, ownership of generated code, and related considerations. We also investigate potential developer misconceptions, the impact of GenAI on developers' work, and developers' awareness of licensing/copyright risks. Qualitative and quantitative analysis showed that developers' opinions on copyright issues vary broadly and that many developers are aware of the nuances these legal questions involve. We provide: (1) a survey of 574 developers on the licensing and copyright aspects of GenAI for coding, (2) a snapshot of practitioners' views at a time when GenAI and perceptions of it are rapidly evolving, and (3) an analysis of developers' views, yielding insights and recommendations that can inform future regulatory decisions in this evolving field.

The pseudogap state of high-$T_{\rm c}$ cuprates, known for its partial gapping of the Fermi surface above the superconducting transition temperature $T_{\rm c}$, is believed to hold the key to understanding the origin of Planckian relaxation and quantum criticality. However, the nature of the Fermi surface in the pseudogap state has remained a fundamental open question. Here, we report the observation of the Yamaji effect above $T_{\rm c}$ in the single layer cuprate HgBa$_2$CuO$_{4+\delta}$. This observation is direct evidence of closed Fermi surface pockets in the normal state of the pseudogap phase. The small size of the pockets determined from the Yamaji effect (occupying approximately $1.3\%$ of the Brillouin zone area) is all the more surprising given the absence of evidence for long-range broken translational symmetry that can reconstruct the Fermi-surface., Comment: 26 pages including supplements

We consider the Rydberg electron in a circularly polarized microwave field, whose dynamics is described by a 2 d.o.f. Hamiltonian, which is a perturbation of size $K>0$ of the standard rotating Kepler problem. In a rotating frame, the largest chaotic region of this system lies around a center-saddle equilibrium point $L_1$ and its associated invariant manifolds. We compute the distance between stable and unstable manifolds of $L_1$ by means of a semi-analytical method, which consists of combining normal form, Melnikov, and averaging methods with numerical methods. Also, we introduce a new family of Hamiltonians, which we call Toy CP systems, to be able to compare our numerical results with the existing theoretical results in the literature. It should be noted that the distance between these stable and unstable manifolds is exponentially small in the perturbation parameter $K$ (in analogy with the $L_3$ libration point of the R3BP).

Lasers are the workhorse of quantum engineering in the atomic-molecular-optic community. However, phase noise of the laser, which can be especially large in popular semiconductor-based lasers, can limit fidelity of operation. Here, we present a fully-fiberized instrument detecting and correcting the fast, sub-microsecond, phase fluctuations of lasers. We demonstrate a measurement noise floor of less than 0.1 Hz$^{2}$/Hz, and a noise suppression of more than 20 dB for Fourier frequencies in the 1 to 10 MHz region (reaching up to 30 dB at 3 MHz), where noise is critical for Rydberg-based quantum gates. Finally, we observe the improvement offered by this fast phase noise eater on a Raman transition driven by two such stabilized lasers. These measurement and correction techniques are important tools for high-fidelity manipulation of the excited electronic states of atoms and molecules.

In this paper, we focus on fully connected deep neural networks utilizing the Rectified Linear Unit (ReLU) activation function for nonparametric estimation. We derive non-asymptotic bounds that lead to convergence rates, addressing both temporal and spatial dependence in the observed measurements. By accounting for dependencies across time and space, our models better reflect the complexities of real-world data, enhancing both predictive performance and theoretical robustness. We also tackle the curse of dimensionality by modeling the data on a manifold, exploring the intrinsic dimensionality of high-dimensional data. We broaden existing theoretical findings of temporal-spatial analysis by applying them to neural networks in more general contexts and demonstrate that our proof techniques are effective for models with short-range dependence. Our empirical simulations across various synthetic response functions underscore the superior performance of our method, outperforming established approaches in the existing literature. These findings provide valuable insights into the strong capabilities of dense neural networks for temporal-spatial modeling across a broad range of function classes.

Despite significant advancements, segmentation based on deep neural networks in medical and surgical imaging faces several challenges, two of which we aim to address in this work. First, acquiring complete pixel-level segmentation labels for medical images is time-consuming and requires domain expertise. Second, typical segmentation pipelines cannot detect out-of-distribution (OOD) pixels, leaving them prone to spurious outputs during deployment. In this work, we propose a novel segmentation approach exploiting OOD detection that learns only from sparsely annotated pixels from multiple positive-only classes. These multi-class positive annotations naturally fall within the in-distribution (ID) set. Unlabelled pixels may contain positive classes but also negative ones, including what is typically referred to as \emph{background} in standard segmentation formulations. Here, we forgo the need for background annotation and consider these together with any other unseen classes as part of the OOD set. Our framework can integrate, at a pixel-level, any OOD detection approaches designed for classification tasks. To address the lack of existing OOD datasets and established evaluation metric for medical image segmentation, we propose a cross-validation strategy that treats held-out labelled classes as OOD. Extensive experiments on both multi-class hyperspectral and RGB surgical imaging datasets demonstrate the robustness and generalisation capability of our proposed framework.

Superlattice formation dictates the physical properties of many materials, including the nature of the ground state in magnetic materials. Chemical composition is commonly considered to be the primary determinant of superlattice identity, especially in intercalation compounds. Here, we find that, contrary to this conventional wisdom, kinetic control of superlattice growth leads to the coexistence of disparate domains within a compositionally "perfect" single crystal. We demonstrate that Cr$_{1/4}$TaS$_2$ is a bulk noncollinear antiferromagnet in which scattering between bulk and minority superlattice domains engenders complex magnetotransport below the N\'{e}el temperature, including an anomalous Hall effect. We characterize the magnetic phases in different domains, image their nanoscale morphology, and propose a mechanism for nucleation and growth. These results provide a blueprint for the deliberate engineering of macroscopic transport responses via microscopic patterning of magnetic exchange interactions in superlattice domains., Comment: 50 pages, 19 figures, 6 tables

We prove that, for every set of $n$ points $\mathcal{P}$ in $\mathbb{R}^2$, a random plane graph drawn on $\mathcal{P}$ is expected to contain less than $n/10.18$ isolated vertices. In the other direction, we construct a point set where the expected number of isolated vertices in a random plane graph is about $n/23.32$. For $i\ge 1$, we prove that the expected number of vertices of degree $i$ is always less than $n/\sqrt{\pi i}$ Our analysis is based on cross-graph charging schemes. That is, we move charge between vertices from different plane graphs of the same point set. This leads to information about the expected behavior of a random plane graph.

Bitumen extraction for the production of synthetic crude oil in Canada's Athabasca Oil Sands industry has recently come under spotlight for being a significant source of greenhouse gas emission. A major cause of concern is methane, a greenhouse gas produced by the anaerobic biodegradation of hydrocarbons in oil sands residues, or tailings, stored in settle basins commonly known as oil sands tailing ponds. In order to determine the methane emitting potential of these tailing ponds and have future methane projections, we use real-time weather data, mechanistic models developed from laboratory controlled experiments, and industrial reports to train a physics constrained machine learning model. Our trained model can successfully identify the directions of active ponds and estimate their emission levels, which are generally hard to obtain due to data sampling restrictions. We found that each active oil sands tailing pond could emit between 950 to 1500 tonnes of methane per year, whose environmental impact is equivalent to carbon dioxide emissions from at least 6000 gasoline powered vehicles. Although abandoned ponds are often presumed to have insignificant emissions, our findings indicate that these ponds could become active over time and potentially emit up to 1000 tonnes of methane each year. Taking an average over all datasets that was used in model training, we estimate that emissions around major oil sands regions would need to be reduced by approximately 12% over a year, to reduce the average methane concentrations to 2005 levels., Comment: 19 pages, 8 figures, 2 tables

We introduce BLIP3-KALE, a dataset of 218 million image-text pairs that bridges the gap between descriptive synthetic captions and factual web-scale alt-text. KALE augments synthetic dense image captions with web-scale alt-text to generate factually grounded image captions. Our two-stage approach leverages large vision-language models and language models to create knowledge-augmented captions, which are then used to train a specialized VLM for scaling up the dataset. We train vision-language models on KALE and demonstrate improvements on vision-language tasks. Our experiments show the utility of KALE for training more capable and knowledgeable multimodal models. We release the KALE dataset at https://huggingface.co/datasets/Salesforce/blip3-kale

We introduce MEGATRON, a new galaxy formation model for cosmological radiation hydrodynamics simulations of high-redshift galaxies. The model accounts for the non-equilibrium chemistry and heating/cooling processes of $\geq 80$ atoms, ions, and molecules, coupled to on-the-fly radiation transfer. We apply the model in a cosmological setting to the formation of a $10^9\ {\rm M_{\odot}}$ halo at $z=6$, and run 25 realizations at pc-scale resolution, varying numerous parameters associated with our state-of-the-art star formation, stellar feedback, and chemical enrichment models. We show that the overall budget of feedback energy is the key parameter that controls star formation regulation at high redshift, with other numerical parameters (e.g. supernova clustering, star formation conditions) having a more limited impact. As a similar feedback model has been shown to produce realistic $z=0$ galaxies, our work demonstrates that calibration at $z=0$ does not guarantee strong regulation of star formation at high-redshift. Interestingly, we find that subgrid model variations that have little impact on the final $z=6$ stellar mass can lead to substantial changes on the observable properties of high-redshift galaxies. For example, different star formation models based on, e.g. density thresholds or turbulence inspired criteria, lead to fundamentally distinct nebular emission line ratios across the interstellar medium (ISM). These results highlight the ISM as an important resource for constraining models of star formation, feedback, and galaxy formation in the JWST era, where emission line measurements for $>1,000$ high-redshift galaxies are now available., Comment: 25 pages, 16 Figures, submitted to the Open Journal of Astrophysics

We report NMR scattering circuit experiments that reveal causal structure. The scattering circuit involves interacting a probe qubit with the system of interest and finally measuring the probe qubit. The scattering circuit thereby implements a coarse-grained projective measurement. Causal structure refers to which events influence others and in the quantum case corresponds to different quantum circuit structures. In classical scenarios, intervention is commonly used to infer causal structure. In this quantum scenario of a bipartite system at two times, we demonstrate via scattering circuit experiments that coarse-grained measurements alone suffice for determining the causal structure. The experiment is undertaken by manipulating the nuclear spins of four Carbon-13 atoms in crotonic acid. The data analysis determines the compatibility of the data with given causal structures via representing the data as a pseudo density matrix (PDM) and analysing properties of the PDM. We demonstrate the successful identification of the causal structure for partial swap channels and fully decohering channels., Comment: 10 pages,10 figures, a companion paper appeared the same day, comments are welcome!

We probe the foundations of causal structure inference experimentally. The causal structure concerns which events influence other events. We probe whether causal structure can be determined without intervention in quantum systems. Intervention is commonly used to determine causal structure in classical scenarios, but in the more fundamental quantum theory, there is evidence that measurements alone, even coarse-grained measurements, can suffice. We demonstrate the experimental discrimination between several possible causal structures for a bipartite quantum system at two times, solely via coarse-grained projective measurements. The measurements are implemented by an approach known as scattering circuits in a nuclear magnetic resonance platform. Using recent analytical methods the data thus gathered is sufficient to determine the causal structure. Coarse-grained projective measurements disturb the quantum state less than fine-grained projective measurements and much less than interventions that reset the system to a fixed state., Comment: 6 pages, 4 figures, a companion paper appeared the same day. Comments are Welcome!

In view of recently demonstrated joint use of novel Fourier-transform techniques and effective high-accuracy frequency domain solvers related to the Method of Moments, it is argued that a set of transformative innovations could be developed for the effective, accurate and efficient simulation of problems of wave propagation and scattering of broadband, time-dependent wavefields. This contribution aims to convey the character of these methods and to highlight their applicability in computational modeling of electromagnetic configurations across various fields of science and engineering.

The extension of elastic optical networks (EON) to multi-band transmission (MB-EON) shows promise in enhancing spectral efficiency, throughput, and long-term cost-effectiveness for telecom operators. However, designing MB-EON networks introduces complex challenges, notably the optimization of physical parameters like optical power and quality of transmission (QoT). Frequency-dependent characteristics of fiber, such as loss, dispersion, and nonlinear effects, alongside inter-channel stimulated Raman scattering, pose significant hurdles when extending beyond the L+C (LC) band to a continuous spectrum over 100 nm. In this study, we propose a span-by-span methodology for optimal power allocation, introducing two hyper-accelerated power optimization (HPO) strategies: flat launch power (FLP) and flat received power (FRP). These approaches significantly expedite network power optimization while preserving the stability of running services. Our comparative analysis of FLP and FRP models reveals that while FRP has a minimal effect on capacity (increasing less than 10 Tbps for an L+C+S (LCS) system over 100 km), it improves flatness and GSNR/OSNR metrics in the S-band by approximately 2/0 dB and 2.5/6 dB, respectively. A network-wide analysis across various topologies shows that the FRP technique enhances minimum GSNR, contributing to a throughput increase of 12% to 75%, depending on network scale, at a 1% bandwidth blocking rate. Lastly, our application of HPO in MB-EON for both local and global power optimization demonstrates that while both approaches offer comparable performance, global optimization is simpler and more cost-effective for large-scale networks.

Precision therapy for liver cancer necessitates accurately delineating liver sub-regions to protect healthy tissue while targeting tumors, which is essential for reducing recurrence and improving survival rates. However, the segmentation of hepatic segments, known as Couinaud segmentation, is challenging due to indistinct sub-region boundaries and the need for extensive annotated datasets. This study introduces LiverFormer, a novel Couinaud segmentation model that effectively integrates global context with low-level local features based on a 3D hybrid CNN-Transformer architecture. Additionally, a registration-based data augmentation strategy is equipped to enhance the segmentation performance with limited labeled data. Evaluated on CT images from 123 patients, LiverFormer demonstrated high accuracy and strong concordance with expert annotations across various metrics, allowing for enhanced treatment planning for surgery and radiation therapy. It has great potential to reduces complications and minimizes potential damages to surrounding tissue, leading to improved outcomes for patients undergoing complex liver cancer treatments.

Stars have been recognized as optimal laboratories to probe axion properties. In the last decades there have been significant advances in this field due to a better modelling of stellar systems and accurate observational data. In this work we review the current status of constraints on axions from stellar physics. We focus in particular on the Sun, globular cluster stars, white dwarfs and (proto)-neutron stars., Comment: 127 pages, 59 figures, 9 tables. Prepared for the submission to Physics Reports. Comments are welcome!

Breast cancer presents the second largest cancer risk in the world to women. Early detection of cancer has been shown to be effective in reducing mortality. Population screening programs schedule regular mammography imaging for participants, promoting early detection. Currently, such screening programs require manual reading. False-positive errors in the reading process unnecessarily leads to costly follow-up and patient anxiety. Automated methods promise to provide more efficient, consistent and effective reading. To facilitate their development, a number of datasets have been created. With the aim of specifically targeting population screening programs, we introduce NL-Breast-Screening, a dataset from a Canadian provincial screening program. The dataset consists of 5997 mammography exams, each of which has four standard views and is biopsy-confirmed. Cases where radiologist reading was a false-positive are identified. NL-Breast is made publicly available as a new resource to promote advances in automation for population screening programs.

In this document, we will build a logical calculus that is a generalization of classical propositional logic but contains a finite multiplicity of negations weaker than classical negation. In this way, we can introduce contradictions in this new system without it becoming inconsistent., Comment: In Spanish language, In preparation for Journal Submission

We present InstantGeoAvatar, a method for efficient and effective learning from monocular video of detailed 3D geometry and appearance of animatable implicit human avatars. Our key observation is that the optimization of a hash grid encoding to represent a signed distance function (SDF) of the human subject is fraught with instabilities and bad local minima. We thus propose a principled geometry-aware SDF regularization scheme that seamlessly fits into the volume rendering pipeline and adds negligible computational overhead. Our regularization scheme significantly outperforms previous approaches for training SDFs on hash grids. We obtain competitive results in geometry reconstruction and novel view synthesis in as little as five minutes of training time, a significant reduction from the several hours required by previous work. InstantGeoAvatar represents a significant leap forward towards achieving interactive reconstruction of virtual avatars., Comment: Accepted as poster to Asian Conference on Computer Vison (ACCV 2024)

In this paper, we present XST-GCNN (eXplainable Spatio-Temporal Graph Convolutional Neural Network), a novel architecture for processing heterogeneous and irregular Multivariate Time Series (MTS) data. Our approach captures temporal and feature dependencies within a unified spatio-temporal pipeline by leveraging a GCNN that uses a spatio-temporal graph aimed at optimizing predictive accuracy and interoperability. For graph estimation, we introduce techniques, including one based on the (heterogeneous) Gower distance. Once estimated, we propose two methods for graph construction: one based on the Cartesian product, treating temporal instants homogeneously, and another spatio-temporal approach with distinct graphs per time step. We also propose two GCNN architectures: a standard GCNN with a normalized adjacency matrix and a higher-order polynomial GCNN. In addition to accuracy, we emphasize explainability by designing an inherently interpretable model and performing a thorough interpretability analysis, identifying key feature-time combinations that drive predictions. We evaluate XST-GCNN using real-world Electronic Health Record data from University Hospital of Fuenlabrada to predict Multidrug Resistance (MDR) in ICU patients, a critical healthcare challenge linked to high mortality and complex treatments. Our architecture outperforms traditional models, achieving a mean ROC-AUC score of 81.03 +- 2.43. Furthermore, the interpretability analysis provides actionable insights into clinical factors driving MDR predictions, enhancing model transparency. This work sets a benchmark for tackling complex inference tasks with heterogeneous MTS, offering a versatile, interpretable solution for real-world applications.

As a companion work to [1], this Note presents a series of simple formulae and explicit results that illustrate and highlight why classical variational phase-field models cannot possibly predict fracture nucleation in elastic brittle materials. The focus is on ``tension-dominated'' problems where all principal stresses are non-negative, that is, problems taking place entirely within the first octant in the space of principal stresses.

The demand for real-time Indoor Air Quality (IAQ) management has increased recently, since low-cost and modern sensors such as Particulate Matter (PM), Volatile Organic Compounds (VOCs), Carbon Monoxide (CO), Carbon Dioxide (CO2), Radon (Rn), among others, have been put forward with considerable accuracy. Although these low-cost sensors cannot be considered measurement instruments, they are very useful for a vast number of application domains, such as home automation, smart building management, IAQ management, risk exposure assessment, to name a few. This paper presents a literature review and a prospective analysis and discussion regarding Internet of Things (IoT) technologies adopted to deal with scenarios that present known indoor Radon gas problems. Specifically, the main requirements for developing IoT-enabled radon management solutions are reviewed. Thus, a traditional IoT architecture is described, its main components are analyzed and some of the most recent academic solutions are reviewed. Finally, novel approaches for deploying IoT radon management architectures are presented together with the most relevant open challenges. In this way, this article presents a holistic review of the past, present, and future of indoor radon management in order to provide guidelines for future designers and developers., Comment: Paper accepted in Edge-IoT 2022

Lifelong reinforcement learning (RL) has been developed as a paradigm for extending single-task RL to more realistic, dynamic settings. In lifelong RL, the "life" of an RL agent is modeled as a stream of tasks drawn from a task distribution. We propose EPIC (\underline{E}mpirical \underline{P}AC-Bayes that \underline{I}mproves \underline{C}ontinuously), a novel algorithm designed for lifelong RL using PAC-Bayes theory. EPIC learns a shared policy distribution, referred to as the \textit{world policy}, which enables rapid adaptation to new tasks while retaining valuable knowledge from previous experiences. Our theoretical analysis establishes a relationship between the algorithm's generalization performance and the number of prior tasks preserved in memory. We also derive the sample complexity of EPIC in terms of RL regret. Extensive experiments on a variety of environments demonstrate that EPIC significantly outperforms existing methods in lifelong RL, offering both theoretical guarantees and practical efficacy through the use of the world policy.

The rapid growth of end-user AI applications, such as computer vision and generative AI, has led to immense data and processing demands often exceeding user devices' capabilities. Edge AI addresses this by offloading computation to the network edge, crucial for future services in 6G networks. However, it faces challenges such as limited resources during simultaneous offloads and the unrealistic assumption of homogeneous system architecture. To address these, we propose a research roadmap focused on profiling AI models, capturing data about model types, hyperparameters, and underlying hardware to predict resource utilisation and task completion time. Initial experiments with over 3,000 runs show promise in optimising resource allocation and enhancing Edge AI performance.

Motivated by the contemporary advances in quantum implementation of non-unitary operations, we propose a new dilation method based on the biorthogonal representation of the non-unitary operator, mapping it to an isomorphic unitary matrix in the orthonormal computational basis. The proposed method excels in implementing non-unitary operators whose eigenvalues have absolute values exceeding one, when compared to other dilation and decomposition techniques. Unlike the Linear Combination of Unitaries (LCU) method, which becomes less efficient as the number of unitary summands grows, the proposed technique is optimal for small-dimensional non-unitary operators regardless of the number of unitary summands. Thus, it can complement the LCU method for implementing general non-unitary operators arising in positive only open quantum systems and pseudo-Hermitian systems., Comment: 8 Pages, 3 Figures

The peculiar Galactic globular cluster $\omega$ Centauri (NGC 5139) has drawn attention for its unique features - such as a high stellar mass and a broad distribution of chemical elements - that have led to the hypothesis that it might be the nuclear remnant of an ancient dwarf galaxy accreted by the Milky Way (MW), potentially bringing along its own globular cluster (GC) system. In this work, we adopt an innovative approach by examining the individual chemical abundances of Galactic GCs. Applying Gaussian Mixture Models to globular cluster stars, whose membership is based on Gaia EDR3, and whose chemical abundances are provided by APOGEE DR17, we depart from traditional kinematic-based procedures and search for GCs that are chemically compatible with $\omega$ Cen in a 8-dimensional space defined by [Fe/H], [Mg/Fe], [Si/Fe], [Ca/Fe], [C/Fe], [Al/Fe], [K/Fe], and [Mn/Fe]. Our analysis leads to the identification of six GCs - NGC 6752, NGC 6656, NGC 6809, NGC 6273, NGC 6205, and NGC 6254 - that exhibit strong chemical similarities with $\omega$ Cen, and which have metallicities that coincide with those of the two main peaks of $\omega$ Cen's metallicity distribution. The chemical patterns of these clusters lead to the exclusion that they formed in progenitor galaxies with chemical enrichment histories similar to those of the Large and Small Magellanic Clouds, Sagittarius, and Fornax. Once placed in kinematic spaces such as the energy - angular momentum plane, these GCs result scattered across an extended region, which is predicted by N-body simulations if their common progenitor was sufficiently massive compared to the MW. Our novel approach suggests a common origin for NGC 6752, NGC 6656, NGC 6809, NGC 6273, NGC 6205, NGC 6254 and $\omega$ Cen, indicating that Nephele, as we propose to call the progenitor in which these GCs formed, played a substantial role in the Galaxy's history., Comment: Accepted for publication on A&A

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

Deep neural networks have been very successful in applications ranging from computer vision and natural language processing to strategy optimization in games. Recently neural networks with sinusoidal activation functions (SinNN) were found to be ideally suited for representing complex natural signals and their fine spatial and temporal details, which makes them effective representations of images, sound, and video, and good solvers of differential equations. However, training SinNN via gradient descent often results in bad local minima, posing a significant challenge when optimizing their weights. Furthermore, when the weights are discretized for better memory and inference efficiency on small devices, we find that a vanishing gradient problem appears on the resulting discrete SinNN (DSinNN). Brute force search provides an alternative way to find the best weights for DSinNN but is intractable for a large number of parameters. We here provide a qualitatively different training method: an algorithm for quantum training of DSinNNs. The quantum training evolves an initially uniform superposition over weight values to one that is guaranteed to peak on the best weights. We demonstrate the algorithm on toy examples and show that it indeed outperforms gradient descent in optimizing the loss function and outperforms brute force search in the time required.

It is known that the Shannon capacity of the q-ary symmetric channel (qSC) is the same as the list-decoding capacity of an adversarial channel, raising the question of whether there is a formal (and black-box) connection between the two. We show that there is: Any linear code $C\subseteq \mathbb{F}_q^n$ that has minimum distance $d_{\min}=\omega(q^3)$ and achieves list-decoding capacity also achieves capacity on the qSC.

We show an analogue of the Klain-Schneider theorem for valuations that are invariant under rotations around a fixed axis, called zonal. Using this, we establish a new integral representation of zonal valuations involving mixed area measures with a disk. In our argument, we introduce an easy way to translate between this representation and the one involving area measures, yielding a shorter proof of a recent characterization by Knoerr. As applications, we obtain various zonal integral geometric formulas, extending results by Hug, Mussnig, and Ulivelli. Finally, we provide a simpler proof of the integral representation of the mean section operators by Goodey and Weil., Comment: 35 pages