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Demand response is a concept that has been around since the very first electric power systems. However, we have seen an explosion of research on demand response and demand-side technologies in the past 30 years, coinciding with the shift towards liberalized/deregulated electricity markets and efforts to decarbonize the power sector. Now we are also seeing a shift towards more distributed/decentralized electric systems; we have entered the era of "distributed energy resources," which require new grid management, operational, and control strategies. Given this paradigm shift, we argue that the concept of demand response needs to be revisited, and more carefully/consistently defined to enable us to better utilize this massive resource for economic, technical, environmental, and societal aims. In this paper, we survey existing demand response definitions, highlight their shortcomings, propose a new definition, and describe how this new definition enables us to more effectively harness the value of demand response in modern power systems. We conclude with a demand response research agenda informed by a discussion of demand response barriers and enablers., Comment: 12 pages

NGC 2506 is a rich, intermediate-age (2.0 Gyr), metal-poor ([Fe/H] $\sim$ -0.2) open cluster. This work presents the results of 12,157 spectroscopic radial-velocity measurements of 2,442 stars in the NGC 2506 field over 41 years, made as part of the WIYN Open Cluster Study. Radial-velocity measurements are complete for the population of proper-motion member stars brighter than a Gaia G magnitude of 15.5, in which 320 proper-motion and radial-velocity cluster members were identified. Within the observation limit of G $<$ 16.5, 469 proper-motion and radial-velocity members were identified. This work reports on the characteristics of NGC 2506, including projected spatial distribution, radial-velocity dispersion, and virial mass. This work also presents orbital solutions for 49 binary members with periods between 1 and 7,580 days. NGC 2506 has an incompleteness-corrected binary frequency for binaries with periods less than $10^4$ days of $35 \pm 5$%. This work also discusses in detail the 14 blue stragglers stars of NGC 2506$\unicode{x2014}$finding at least 64 $\pm$ 21% to be in binaries, 5 yellow straggler stars, and several other stars of note., Comment: 41 pages, 13 figures, open access publication and machine-readable tables available at https://doi.org/10.3847/1538-3881/ad6b1a

Despite significant advancements in neural text-to-audio generation, challenges persist in controllability and evaluation. This paper addresses these issues through the Sound Scene Synthesis challenge held as part of the Detection and Classification of Acoustic Scenes and Events 2024. We present an evaluation protocol combining objective metric, namely Fr\'echet Audio Distance, with perceptual assessments, utilizing a structured prompt format to enable diverse captions and effective evaluation. Our analysis reveals varying performance across sound categories and model architectures, with larger models generally excelling but innovative lightweight approaches also showing promise. The strong correlation between objective metrics and human ratings validates our evaluation approach. We discuss outcomes in terms of audio quality, controllability, and architectural considerations for text-to-audio synthesizers, providing direction for future research., Comment: accepted to NeurIPS 2024 Workshop: Audio Imagination

Modelling partial differential equations (PDEs) is of crucial importance in science and engineering, and it includes tasks ranging from forecasting to inverse problems, such as data assimilation. However, most previous numerical and machine learning approaches that target forecasting cannot be applied out-of-the-box for data assimilation. Recently, diffusion models have emerged as a powerful tool for conditional generation, being able to flexibly incorporate observations without retraining. In this work, we perform a comparative study of score-based diffusion models for forecasting and assimilation of sparse observations. In particular, we focus on diffusion models that are either trained in a conditional manner, or conditioned after unconditional training. We address the shortcomings of existing models by proposing 1) an autoregressive sampling approach that significantly improves performance in forecasting, 2) a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths, and 3) a hybrid model which employs flexible pre-training conditioning on initial conditions and flexible post-training conditioning to handle data assimilation. We empirically show that these modifications are crucial for successfully tackling the combination of forecasting and data assimilation, a task commonly encountered in real-world scenarios., Comment: Accepted at NeurIPS 2024

The rise of large language models (LLMs) for visually rich document understanding (VRDU) has kindled a need for prompt-response, document-based datasets. As annotating new datasets from scratch is labor-intensive, the existing literature has generated prompt-response datasets from available resources using simple templates. For the case of key information extraction (KIE), one of the most common VRDU tasks, past work has typically employed the template "What is the value for the {key}?". However, given the variety of questions encountered in the wild, simple and uniform templates are insufficient for creating robust models in research and industrial contexts. In this work, we present K2Q, a diverse collection of five datasets converted from KIE to a prompt-response format using a plethora of bespoke templates. The questions in K2Q can span multiple entities and be extractive or boolean. We empirically compare the performance of seven baseline generative models on K2Q with zero-shot prompting. We further compare three of these models when training on K2Q versus training on simpler templates to motivate the need of our work. We find that creating diverse and intricate KIE questions enhances the performance and robustness of VRDU models. We hope this work encourages future studies on data quality for generative model training., Comment: Accepted to EMNLP Findings 2024

Artificial Neural Networks (ANNs), including fully-connected networks and transformers, are highly flexible and powerful function approximators, widely applied in fields like computer vision and natural language processing. However, their inability to inherently respect causal structures can limit their robustness, making them vulnerable to covariate shift and difficult to interpret/explain. This poses significant challenges for their reliability in real-world applications. In this paper, we introduce Causal Fully-Connected Neural Networks (CFCNs) and Causal Transformers (CaTs), two general model families designed to operate under predefined causal constraints, as specified by a Directed Acyclic Graph (DAG). These models retain the powerful function approximation abilities of traditional neural networks while adhering to the underlying structural constraints, improving robustness, reliability, and interpretability at inference time. This approach opens new avenues for deploying neural networks in more demanding, real-world scenarios where robustness and explainability is critical.

Tissues of living cells are a prime example of active fluids. There is experimental evidence that tissues generate extensile active stress even though their constituting cells are contractile. Fluctuating forces that could result from cell-substrate interactions have been proposed to be able to induce a transition from contractile to extensile active stress. Through analytic calculations and numerical computations, we show that in isotropic active fluids, nonlinearities and a coupling between fluctuating forces and fluid density are necessary for such a transition to occur. Here, both transitions from extensile to contractile and vice versa are possible., Comment: 10 pages, 2 figures

Reconstruction functions are pivotal in sample compression theory, a framework for deriving tight generalization bounds. From a small sample of the training set (the compression set) and an optional stream of information (the message), they recover a predictor previously learned from the whole training set. While usually fixed, we propose to learn reconstruction functions. To facilitate the optimization and increase the expressiveness of the message, we derive a new sample compression generalization bound for real-valued messages. From this theoretical analysis, we then present a new hypernetwork architecture that outputs predictors with tight generalization guarantees when trained using an original meta-learning framework. The results of promising preliminary experiments are then reported., Comment: Accepted at the NeurIPS 2024 workshop on Compression in Machine Learning

The problem of classifying graphs is ubiquitous in machine learning. While it is standard to apply graph neural networks for such tasks, Gaussian processes can also be used, by transforming graph features into the spectral domain, and using the resulting spectral features as input points. However, this approach only takes into account features on vertices, whereas some graph data also support features on edges. In this work, we present a Gaussian process-based classification algorithm that can utilise vertex and/or edges features to help classify graphs. Furthermore, we take advantage of the Hodge decomposition of vertex and edge features to increase the flexibility of the model, which can be beneficial on some tasks., Comment: NeurIPS 2024 Workshop on Bayesian Decision-Making and Uncertainty (oral presentation)

We demonstrate efficient in-plane optical fiber collection of single photon emission from quantum dots embedded in photonic crystal cavities. This was achieved via adiabatic coupling between a tapered optical fiber and a tapered on-chip photonic waveguide coupled to the photonic crystal cavity. The collection efficiency of a dot in a photonic crystal cavity was measured to be 5 times greater via the tapered optical fiber compared to collection by a microscope objective lens above the cavity. The single photon source was also characterized by second order photon correlations measurements giving g(2)(0)=0.17 under non-resonant excitation. Numerical calculations demonstrate that the collection efficiency could be further increased by improving the dot-cavity coupling and by increasing the overlap length of the tapered fiber with the on-chip waveguide. An adiabatic coupling of near unity is predicted for an overlap length of 5 microns., Comment: 22 pages, 12 figures

Optimal stopping is a fundamental problem in optimization that has found applications in risk management, finance, economics, and recently in the fields of computer science. We extend the standard framework to a multi-agent setting, named multi-agent optimal stopping (MAOS), where a group of agents cooperatively solves finite-space, discrete-time optimal stopping problems. Solving the finite-agent case is computationally prohibitive when the number of agents is very large, so this work studies the mean field optimal stopping (MFOS) problem, obtained as the number of agents approaches infinity. We prove that MFOS provides a good approximate solution to MAOS. We also prove a dynamic programming principle (DPP), based on the theory of mean field control. We then propose two deep learning methods: one simulates full trajectories to learn optimal decisions, whereas the other leverages DPP with backward induction; both methods train neural networks for the optimal stopping decisions. We demonstrate the effectiveness of these approaches through numerical experiments on 6 different problems in spatial dimension up to 300. To the best of our knowledge, this is the first work to study MFOS in finite space and discrete time, and to propose efficient and scalable computational methods for this type of problem.

In the algorithm Intersort, Chevalley et al. (2024) proposed a score-based method to discover the causal order of variables in a Directed Acyclic Graph (DAG) model, leveraging interventional data to outperform existing methods. However, as a score-based method over the permutahedron, Intersort is computationally expensive and non-differentiable, limiting its ability to be utilised in problems involving large-scale datasets, such as those in genomics and climate models, or to be integrated into end-to-end gradient-based learning frameworks. We address this limitation by reformulating Intersort using differentiable sorting and ranking techniques. Our approach enables scalable and differentiable optimization of causal orderings, allowing the continuous score function to be incorporated as a regularizer in downstream tasks. Empirical results demonstrate that causal discovery algorithms benefit significantly from regularizing on the causal order, underscoring the effectiveness of our method. Our work opens the door to efficiently incorporating regularization for causal order into the training of differentiable models and thereby addresses a long-standing limitation of purely associational supervised learning.

With the rise of SSL and ASR technologies, the Wav2Vec2 ASR-based model has been fine-tuned for automated speech disorder quality assessment tasks, yielding impressive results and setting a new baseline for Head and Neck Cancer speech contexts. This demonstrates that the ASR dimension from Wav2Vec2 closely aligns with assessment dimensions. Despite its effectiveness, this system remains a black box with no clear interpretation of the connection between the model ASR dimension and clinical assessments. This paper presents the first analysis of this baseline model for speech quality assessment, focusing on intelligibility and severity tasks. We conduct a layer-wise analysis to identify key layers and compare different SSL and ASR Wav2Vec2 models based on pre-trained data. Additionally, post-hoc XAI methods, including Canonical Correlation Analysis (CCA) and visualization techniques, are used to track model evolution and visualize embeddings for enhanced interpretability., Comment: Accepted at the Spoken Language Technology (SLT) Conference 2024

We present ZeroComp, an effective zero-shot 3D object compositing approach that does not require paired composite-scene images during training. Our method leverages ControlNet to condition from intrinsic images and combines it with a Stable Diffusion model to utilize its scene priors, together operating as an effective rendering engine. During training, ZeroComp uses intrinsic images based on geometry, albedo, and masked shading, all without the need for paired images of scenes with and without composite objects. Once trained, it seamlessly integrates virtual 3D objects into scenes, adjusting shading to create realistic composites. We developed a high-quality evaluation dataset and demonstrate that ZeroComp outperforms methods using explicit lighting estimations and generative techniques in quantitative and human perception benchmarks. Additionally, ZeroComp extends to real and outdoor image compositing, even when trained solely on synthetic indoor data, showcasing its effectiveness in image compositing.

This paper addresses the problem of long-context linear system identification, where the state $x_t$ of a dynamical system at time $t$ depends linearly on previous states $x_s$ over a fixed context window of length $p$. We establish a sample complexity bound that matches the i.i.d. parametric rate up to logarithmic factors for a broad class of systems, extending previous works that considered only first-order dependencies. Our findings reveal a learning-without-mixing phenomenon, indicating that learning long-context linear autoregressive models is not hindered by slow mixing properties potentially associated with extended context windows. Additionally, we extend these results to (i) shared low-rank representations, where rank-regularized estimators improve rates with respect to dimensionality, and (ii) misspecified context lengths in strictly stable systems, where shorter contexts offer statistical advantages., Comment: 30 pages, 4 figures

Domain Generalization (DG) aims to train models that perform well not only on the training (source) domains but also on novel, unseen target data distributions. A key challenge in DG is preventing overfitting to source domains, which can be mitigated by finding flatter minima in the loss landscape. In this work, we propose Quantization-aware Training for Domain Generalization (QT-DoG) and demonstrate that weight quantization effectively leads to flatter minima in the loss landscape, thereby enhancing domain generalization. Unlike traditional quantization methods focused on model compression, QT-DoG exploits quantization as an implicit regularizer by inducing noise in model weights, guiding the optimization process toward flatter minima that are less sensitive to perturbations and overfitting. We provide both theoretical insights and empirical evidence demonstrating that quantization inherently encourages flatter minima, leading to better generalization across domains. Moreover, with the benefit of reducing the model size through quantization, we demonstrate that an ensemble of multiple quantized models further yields superior accuracy than the state-of-the-art DG approaches with no computational or memory overheads. Our extensive experiments demonstrate that QT-DoG generalizes across various datasets, architectures, and quantization algorithms, and can be combined with other DG methods, establishing its versatility and robustness., Comment: Code will be released soon

We present experimental evidence of multiple blood flow configurations in a relatively simple microfluidic network under constant inlet conditions. We provide evidence of multistability and unsteady dynamics and find good agreement with a theoretical {one-dimensional advection} model for blood flow in microvascular networks{ that relies on the widely used laws for rheology and phase separation}. We discuss the ramifications for microfluidic experiments and measurements using blood and implications for in vivo microcirculation. Our findings suggest that further modeling in microvascular networks should discard the usual assumption of unique, steady-state flow solutions, with crucial consequences regarding gas, nutrient, and waste transport.

Interacting binary star systems play a critical role in many areas of astrophysics. One interesting example of a binary merger product are Thorne-\.Zytkow Objects (T\.ZOs), stars that look like red supergiants but contain neutron stars at their cores. T\.ZOs were theorized nearly five decades ago, and significant work has gone into understanding the physics of their formation, evolution, and stability. Several searches for T\.ZO candidates have also been carried out. Whether or not T\.ZOs could even exist or if they would be stable after formation has also been investigated. Understanding the existence and possible prevalence of T\.ZOs would have important effects on our understanding of binary evolution, stellar mergers, and inform binary population synthesis models. In this chapter, we review the formation channels, evolution and structure, final fates, and observable signatures of T\.ZOs, as well as candidates in the literature, from the inception of T\.ZO theory to recent progress in the field., Comment: This is a pre-print of a chapter for the Encyclopedia of Astrophysics (edited by I. Mandel, section editor J. Andrews) to be published by Elsevier as a Reference Module. 17 pages, 10 figures

Stacking the public Planck CMB temperature maps (NILC, SMICA, SEVEM, Commander) on galaxy clusters from Planck catalogues reveals substantial residual contamination from thermal Sunyaev-Zeldovich (tSZ) emission. Unexpectedly, stacking "tSZ-free" CMB maps, like the Planck SMICA-noSZ or Constrained ILC (CILC) maps, still shows noticeable residual contamination from galaxy clusters. We demonstrate that this persisting residual stems from neglected relativistic SZ (rSZ) corrections in the CMB map estimation. Employing the component separation method outlined in Remazeilles & Chluba (2020) on Planck data, we map the rSZ first-order moment field $y(T_{\rm e}-\bar{T}_{\rm e})$ over the sky for different pivot temperatures $\bar{T}_{\rm e}$ ranging from $2$ to $10$ keV. Stacking these $y(T_{\rm e}-\bar{T}_{\rm e})$-maps on Planck clusters exhibits either an intensity decrement or increment at the centre, contingent upon whether $\bar{T}_{\rm e}$ is above or below the ensemble-averaged cluster temperature $T_{\rm e}$. For the pivot value $\bar{T}_{\rm e}=5$ keV, a vanishing intensity is observed in the stacked Planck $y(T_{\rm e}-\bar{T}_{\rm e})$-map, enabling us to infer the average gas temperature of $T_{\rm e}\simeq 5$ keV for Planck clusters. Building upon this finding, we revisit the Planck tSZ-free CMB map by deprojecting the complete rSZ emission using CILC, assuming an rSZ spectrum with $T_{\rm e} = 5$ keV. Our new, rSZ-free Planck CMB map, when stacked on clusters, shows a clear cancellation of residual SZ contamination in contrast to prior (non-relativistic) tSZ-free Planck CMB maps. Our map-based approach provides compelling evidence for an average temperature of the Planck galaxy clusters of $T_{\rm e} = 4.9 \pm 2.6$ keV using the rSZ effect., Comment: 10 pages, 9 figures, 3 tables. Minor rewording and reduced abstract for MNRAS

The rising interest in leveraging higher-order interactions present in complex systems has led to a surge in more expressive models exploiting high-order structures in the data, especially in topological deep learning (TDL), which designs neural networks on high-order domains such as simplicial complexes. However, progress in this field is hindered by the scarcity of datasets for benchmarking these architectures. To address this gap, we introduce MANTRA, the first large-scale, diverse, and intrinsically high order dataset for benchmarking high-order models, comprising over 43,000 and 249,000 triangulations of surfaces and three-dimensional manifolds, respectively. With MANTRA, we assess several graph- and simplicial complex-based models on three topological classification tasks. We demonstrate that while simplicial complex-based neural networks generally outperform their graph-based counterparts in capturing simple topological invariants, they also struggle, suggesting a rethink of TDL. Thus, MANTRA serves as a benchmark for assessing and advancing topological methods, leading the way for more effective high-order models., Comment: 26 pages, 2 figures, 22 tables

We introduce and study a random non-compact space called the bigeodesic Brownian plane, and prove that it is the tangent plane in distribution of the Brownian sphere at a point of its simple geodesic from the root (for the local Gromov-Hausdorff-Prokhorov-Uniform topology). We also show that it is the local limit of the Brownian plane rerooted further and further on its unique infinite geodesic. Furthermore, we discuss various properties of this space, such as its topology, the behavior of its geodesic rays, and its invariance in distribution under several natural transformations., Comment: 55 pages, 6 figures

The non-Hermitian skin effect is an intriguing physical phenomenon, in which all eigen-modes of a non-Hermitian lattice become localized at boundary regions. While such an exotic behavior has been demonstrated in various physical platforms, these realizations have been so far restricted to the linear regime. Here, we explore the interplay between nonlinearity and the non-Hermitian skin effect which can lead to extraordinary amplitude-driven skin localization dynamics that are unfeasible in linear systems. We show that nonlinear non-Hermitian systems can exhibit new kinds of non-Hermitian skin modes, capable of concentrating the energy of a source at any point of space, depending on the power level. This allows the realization of an energy guide that adapts spontaneously to the amount of energy previously stored in the system: it transfers the energy stored in bulk modes to a point only after a sufficient level of energy has been accumulated. Our theoretical, numerical and experimental findings open up exciting paths for a new generation of non-reciprocal systems, in which nonlinearities serve as a strategic tuning knob to manipulate the guiding process., Comment: 5 pages, 3 figures

By directly probing the initial conditions of our universe, cosmological surveys offer us a unique observational handle on quantum field theory in curved spacetime with dynamical gravity and might even allow us to glean information about a full theory of quantum gravity. Here we report on recent progress to study the natural observables in the problem, namely cosmological correlators. After setting the stage, we review results from three different approaches. First, we present the in-out formalism as an interesting alternative to the well-known in-in formalism and stress some of its advantages, such as the derivation of recursion relations, correlators cutting rules and a proposal for a de Sitter scattering matrix. Second, we tackle the important open problem of constructing effective theories in curved spacetime, which generally requires an open quantum system approach. Third, we provide an executive summary of general properties of the field-theoretic wavefunction that follow from symmetries, unitarity, causality and locality. We describe how these properties can be leveraged to bootstrap all tree-level results and we discuss loop contributions. These notes are based on a series of lectures held during the S-Matrix Marathon workshop at the Institute for Advanced Study on 11-22 March 2024., Comment: A chapter to be published by Springer Lecture Notes in Physics

Blue straggler stars in stellar clusters are a subset of stars that are bluer and appear younger than other cluster members, seemingly straggling behind in their evolution. They offer a unique opportunity to understand the stellar dynamics and populations within their hosts. In the collisional formation scenario, a persistent challenge is the excessive angular momentum in the collision product. The consequent significant mass loss during transition to a stable state leads to a star with too low a mass to be a blue straggler, unless it spins down efficiently. While many proposed spin-down mechanisms involve boosted angular momentum loss via magnetic braking within the collision product, the existence or strength of these magnetic fields has not been confirmed. Here, we report three-dimensional magnetohydrodynamical simulations of collisions between two low-mass main-sequence stars and investigate magnetic field amplification. Magnetic field energy is amplified by a factor of $10^{8}-10^{10}$, resulting in the magnetic field strength of $10^{7}-10^{8}$G at the core of the collision product, independent of collision parameters. The surface magnetic field strengths have increased up to $10-10^{4}$ G. In addition, a distinctly flattened, rotating gas structure appears around the collision products in off-axis collisions, which may be a hint of possible disk formation. Such significant magnetic amplification and potential disk formation suggest the possibility of efficient spin-down of collision products via magnetic braking and magnetic disk locking, which can result in their appearance as blue stragglers., Comment: 27 pages, 10 figures, 1 table. Comments welcome! Videos: https://www.youtube.com/watch?v=lkpTEJTI1Ew&list=PLxLK3qI02cQef6wX5wi-QMSFyxBGK4tGL

In the third APOKASC catalog, we present data for the complete sample of 15,808 evolved stars with APOGEE spectroscopic parameters and Kepler asteroseismology. We used ten independent asteroseismic analysis techniques and anchor our system on fundamental radii derived from Gaia $L$ and spectroscopic $T_{\rm eff}$. We provide evolutionary state, asteroseismic surface gravity, mass, radius, age, and the spectroscopic and asteroseismic measurements used to derive them for 12,418 stars. This includes 10,036 exceptionally precise measurements, with median fractional uncertainties in \nmax, \dnu, mass, radius and age of 0.6\%, 0.6\%, 3.8\%, 1.8\%, and 11.1\% respectively. We provide more limited data for 1,624 additional stars which either have lower quality data or are outside of our primary calibration domain. Using lower red giant branch (RGB) stars, we find a median age for the chemical thick disk of $9.14 \pm 0.05 ({\rm ran}) \pm 0.9 ({\rm sys})$ Gyr with an age dispersion of 1.1 Gyr, consistent with our error model. We calibrate our red clump (RC) mass loss to derive an age consistent with the lower RGB and provide asymptotic GB and RGB ages for luminous stars. We also find a sharp upper age boundary in the chemical thin disk. We find that scaling relations are precise and accurate on the lower RGB and RC, but they become more model dependent for more luminous giants and break down at the tip of the RGB. We recommend the usage of multiple methods, calibration to a fundamental scale, and the usage of stellar models to interpret frequency spacings., Comment: 43 pages, 25 figures, submitted ApJSupp. Comments welcome. Data tables available on request from pinsonneault.1@osu.edu

The sample compression theory provides generalization guarantees for predictors that can be fully defined using a subset of the training dataset and a (short) message string, generally defined as a binary sequence. Previous works provided generalization bounds for the zero-one loss, which is restrictive notably when applied to deep learning approaches. In this paper, we present a general framework for deriving new sample compression bounds that hold for real-valued unbounded losses. Using the Pick-To-Learn (P2L) meta-algorithm, which transforms the training method of any machine-learning predictor to yield sample-compressed predictors, we empirically demonstrate the tightness of the bounds and their versatility by evaluating them on random forests and multiple types of neural networks.

Splay trees are a simple and efficient dynamic data structure, invented by Sleator and Tarjan. The basic primitive for transforming a binary tree in this scheme is a rotation. Sleator, Tarjan, and Thurston proved that the maximum rotation distance between trees with n internal nodes is exactly 2n-6 for trees with n internal nodes (where n is larger than some constant). The proof of the upper bound is easy but the proof of the lower bound, remarkably, uses sophisticated arguments based on calculating hyperbolic volumes. We give an elementary proof of the same result. The main interest of the paper lies in the method, which is new. It basically relies on a potential function argument, similar to many amortized analyses. However, the potential of a tree is not defined explicitly, but by constructing an instance of a flow problem and using the max-flow min-cut theorem.

We demonstrate an external cavity laser with intrinsic linewidth below 100 Hz around an operating wavelength of 852 nm, selected for its relevance to laser cooling and manipulation of cesium atoms. This system achieves a maximum CW output power of 24 mW, wavelength tunability over 15 nm, and a side-mode suppression ratio exceeding 50 dB. This performance level is facilitated by careful design of a low-loss integrated silicon nitride photonic circuit serving as the external cavity combined with commercially available semiconductor gain chips. This approach demonstrates the feasibility of compact integrated lasers with sub-kHz linewidth centering on the needs of emerging sensor concepts based on ultracold atoms and can be further extended to shorter wavelengths via selection of suitable semiconductor gain media.

We introduce a general detection-based approach to text line recognition, be it printed (OCR) or handwritten (HTR), with Latin, Chinese, or ciphered characters. Detection-based approaches have until now been largely discarded for HTR because reading characters separately is often challenging, and character-level annotation is difficult and expensive. We overcome these challenges thanks to three main insights: (i) synthetic pre-training with sufficiently diverse data enables learning reasonable character localization for any script; (ii) modern transformer-based detectors can jointly detect a large number of instances, and, if trained with an adequate masking strategy, leverage consistency between the different detections; (iii) once a pre-trained detection model with approximate character localization is available, it is possible to fine-tune it with line-level annotation on real data, even with a different alphabet. Our approach, dubbed DTLR, builds on a completely different paradigm than state-of-the-art HTR methods, which rely on autoregressive decoding, predicting character values one by one, while we treat a complete line in parallel. Remarkably, we demonstrate good performance on a large range of scripts, usually tackled with specialized approaches. In particular, we improve state-of-the-art performances for Chinese script recognition on the CASIA v2 dataset, and for cipher recognition on the Borg and Copiale datasets. Our code and models are available at https://github.com/raphael-baena/DTLR.

Mean field type games (MFTGs) describe Nash equilibria between large coalitions: each coalition consists of a continuum of cooperative agents who maximize the average reward of their coalition while interacting non-cooperatively with a finite number of other coalitions. Although the theory has been extensively developed, we are still lacking efficient and scalable computational methods. Here, we develop reinforcement learning methods for such games in a finite space setting with general dynamics and reward functions. We start by proving that MFTG solution yields approximate Nash equilibria in finite-size coalition games. We then propose two algorithms. The first is based on quantization of the mean-field spaces and Nash Q-learning. We provide convergence and stability analysis. We then propose an deep reinforcement learning algorithm, which can scale to larger spaces. Numerical examples on 5 environments show the scalability and the efficiency of the proposed method.

Context: Over the last decade, the forestry sector has undergone substantial changes, evolving from a post-2008 financial crisis landscape to incorporating policies favoring sustainable and green alternatives, especially after the 2015 Paris agreement. This evolution was drastically disrupted with the advent of the COVID-19 pandemic in 2020, causing unprecedented interruptions in supply chains, product markets, and data collection. Grasping the aftermath of the COVID-19, regional instances of the forest supply chain sector need synthetic pictures of their present state and future opportunities for emerging wood products and better regional-scale carbon balance. But given the impact of COVID-19 lock-down on data collection, the production of such synthetic pictures has become more complex, yet essential. This was the case for the regional supply chain of the Grand-Est region in France that we studied. Aims: For this study, our aim was to demonstrate that an integrated methodology could provide such synthetic picture even though we sued heterogenous sources of data and different analytical objectives: i.e. (1) retrospectively evaluate the aftermath of COVID-19 pandemic on the supply chain outcomes within the forestry sector; and then (2) retrospectively explore possible options of structural change of regional supply chain that would be required to simultaneously recover from COVID-19 and transit to new objectives in line with the extraction of new bio-molecules from wood biomass, and with the reduction of the regional scale carbon footprint (in line with the IPCC Paris Agreement) Methods: To achieve this, our methodological approach was decomposed into three steps. We first used a Material Flow Analysis (MFA) recently conducted on the forestry sector in the Grand Est region to establish a Sankey diagram (i.e. a schematic representation of industrial sectors and biomass flows along the supply chain) for the pre-Covid-19 period (2014-2018). Then we compared pre-Covid-19 Sankey diagram to the only source of data we could access from the post-Covid-19 period (2020-2021) in order to estimate the impact of Covid-19. Finally, we used as input the reconciled supply chain model into a consequential Wood Product Model (WPMs), called CAT (carbon accounting tool) in order to compare three prospective scenarios: (1) a scenario that projected 2020-2021 Covid-19 conditions and assumed pre-Covid-19 business as usual practices, (2) a scenario illustrating the consequence or rerouting some of the biomass to satisfy the expected increase in pulp and paper production to satisfy the needs of the industry after Covid-19, and (3) a scenario that explored new opportunities in term of extraction of novel bio-molecules by the emerging biochemical wood industry. For every scenario we also evaluated the regional carbon gains and losses that these changes implied. Results: Our study conducted a detailed analysis of the impacts of the COVID-19 pandemic on the forestry sector's supply chain in the Grand Est region, using a dynamic and integrated Wood Product Model. We found significant disruptions during the pandemic period, with notable declines in industrial wood chips and timber hardwood production by 41.8% and 40%, respectively. Conversely, there were substantial increases in fuelwood, timber sawdust, and timber softwood, rising by 14.15%, 44.23%, and 15.29% respectively. These fluctuations underscore the resilience and vulnerabilities within the regional wood supply chain. Our findings also emphasize the potential for strategic rerouting of biomass flows to meet changing industry demands, which could play a crucial role in supporting the sector's recovery and adaptation to post-pandemic conditions. Discussion and conclusion: In addition, our study recognizes the limitations of the current approach combining MFA and WPM and suggests potential areas of enhancement. Ultimately, our findings shed light on the need to develop more integrated analytical methods to provide useful synthetic pictures of regional scale supply chains, when there is a need to adapt it to evolving situations and complex data landscapes.

The performance of software systems remains a persistent concern in the field of software engineering. While traditional metrics like binary size and execution time have long been focal points for developers, the power consumption concern has gained significant attention, adding a layer of complexity to performance evaluation. Configurable software systems, with their potential for numerous configurations, further complicate this evaluation process. In this experience paper, we examine the impact of configurations on performance, specifically focusing on the web stack generator JHipster. Our goal is to understand how configuration choices within JHipster influence the performance of the generated system. We undertake an exhaustive analysis of JHipster by examining its configurations and their effects on system performance. Additionally, we explore individual configuration options to gauge their specific influence on performance. Through this process, we develop a comprehensive performance model for JHipster, enabling us to automate the identification of configurations that optimize specific performance metrics. In particular, we identify configurations that demonstrate near-optimal performance across multiple indicators and report on significant correlations between configuration choices within JHipster and the performance of generated systems.

Observables in expanding universes are crucial to understand the physics of the early universe. In these lectures, we review some recent progress in understanding their mathematical structure and extract the physics encoded in them. After discussing the most salient features of an expanding background and their consequences for defining an observable, we focus on the so-called Bunch--Davies wavefunctional. We analyze its analytic properties on general grounds and introduce an integral representation for it in perturbation theory for a special class of scalar toy models. We discuss both the diagrammatics associated to the usual Feynman rules and combinatorial rules on the graphs, which generate a representation free of spurious poles. Such combinatorial rules find their origin in the combinatorics of the cosmological polytopes of which we provide a gentle introduction to its definition and its main features. Finally, the combinatorics of the cosmological polytopes turns out to determine the combinatorics of a special class of nestohedra that encode the asymptotic behaviour of the cosmological integrals. We provide a general description of such structures and behaviour, which is of crucial importance to understand the infrared divergences which plague observables in an expanding background. These notes are based on a series of lectures held during the S-Matrix Marathon workshop at the Institute for Advanced Study on 11--22 March 2024., Comment: 32 pages. A chapter to be published by Springer Lecture Notes in Physics

Understanding the topological characteristics of data is important to many areas of research. Recent work has demonstrated that synthetic 4D image-type data can be useful to train 4D convolutional neural network models to see topological features in these data. These models also appear to tolerate the use of image preprocessing techniques where existing topological data analysis techniques such as persistent homology do not. This paper investigates how methods from algebraic topology, combined with image processing techniques such as morphology, can be used to generate topologically sophisticated and diverse-looking 2-, 3-, and 4D image-type data with topological labels in simulation. These approaches are illustrated in 2D and 3D with the aim of providing a roadmap towards achieving this in 4D., Comment: 8 pages, DICTA 2024

The accuracy of atomistic simulations depends on the precision of force fields. Traditional numerical methods often struggle to optimize the empirical force field parameters for reproducing target properties. Recent approaches rely on training these force fields based on forces and energies from first-principle simulations. However, it is unclear whether these approaches will enable capturing complex material responses such as vibrational, or elastic properties. To this extent, we introduce a framework, employing inner loop simulations and outer loop optimization, that exploits automatic differentiation for both property prediction and force-field optimization by computing gradients of the simulation analytically. We demonstrate the approach by optimizing classical Stillinger-Weber and EDIP potentials for silicon systems to reproduce the elastic constants, vibrational density of states, and phonon dispersion. We also demonstrate how a machine-learned potential can be fine-tuned using automatic differentiation to reproduce any target property such as radial distribution functions. Interestingly, the resulting force field exhibits improved accuracy and generalizability to unseen temperatures than those fine-tuned on energies and forces. Finally, we demonstrate the extension of the approach to optimize the force fields towards multiple target properties. Altogether, differentiable simulations, through the analytical computation of their gradients, offer a powerful tool for both theoretical exploration and practical applications toward understanding physical systems and materials., Comment: 15 pages, 5 figures

Diabetic retinopathy and diabetic macular edema are significant complications of diabetes that can lead to vision loss. Early detection through ultra-widefield fundus imaging enhances patient outcomes but presents challenges in image quality and analysis scale. This paper introduces deep learning solutions for automated UWF image analysis within the framework of the MICCAI 2024 UWF4DR challenge. We detail methods and results across three tasks: image quality assessment, detection of referable DR, and identification of DME. Employing advanced convolutional neural network architectures such as EfficientNet and ResNet, along with preprocessing and augmentation strategies, our models demonstrate robust performance in these tasks. Results indicate that deep learning can significantly aid in the automated analysis of UWF images, potentially improving the efficiency and accuracy of DR and DME detection in clinical settings.

The intensity noise of a laser source represents one of the key factors limiting the ultimate sensitivity in laser-based systems for sensing and telecommunication. For advanced applications based on interferometry, the availability of a shot-noise-limited local oscillator is even more important for the effective feasibility of high-precision measurements. This is particularly crucial in quantum optics applications based on homodyne detection schemes to measure non-classical light states, such as squeezed states. This work deeply investigates and analyzes the intensity noise features of the most widely used mid-infrared semiconductor heterostructured lasers: quantum cascade and interband cascade lasers. For this purpose, a comprehensive comparison of three different continuous-wave lasers operating at room temperature around 4.5 {\mu}m wavelength is presented. First, a thorough electro-optical characterization is given, highlighting the differences and the shared common characteristics of the tested devices. Then, a detailed intensity noise analysis is reported, identifying their different noise operations with a particular reference to shot-noise-limited operations. Finally, some perspectives towards advanced applications are discussed., Comment: 17 pages, 5 figures

Quantifying axon and myelin properties (e.g., axon diameter, myelin thickness, g-ratio) in histology images can provide useful information about microstructural changes caused by neurodegenerative diseases. Automatic tissue segmentation is an important tool for these datasets, as a single stained section can contain up to thousands of axons. Advances in deep learning have made this task quick and reliable with minimal overhead, but a deep learning model trained by one research group will hardly ever be usable by other groups due to differences in their histology training data. This is partly due to subject diversity (different body parts, species, genetics, pathologies) and also to the range of modern microscopy imaging techniques resulting in a wide variability of image features (i.e., contrast, resolution). There is a pressing need to make AI accessible to neuroscience researchers to facilitate and accelerate their workflow, but publicly available models are scarce and poorly maintained. Our approach is to aggregate data from multiple imaging modalities (bright field, electron microscopy, Raman spectroscopy) and species (mouse, rat, rabbit, human), to create an open-source, durable tool for axon and myelin segmentation. Our generalist model makes it easier for researchers to process their data and can be fine-tuned for better performance on specific domains. We study the benefits of different aggregation schemes. This multi-domain segmentation model performs better than single-modality dedicated learners (p=0.03077), generalizes better on out-of-distribution data and is easier to use and maintain. Importantly, we package the segmentation tool into a well-maintained open-source software ecosystem (see https://github.com/axondeepseg/axondeepseg)., Comment: 12 pages, 8 figures

Enriching datasets with demographic information, such as gender, race, and age from names, is a critical task in fields like healthcare, public policy, and social sciences. Such demographic insights allow for more precise and effective engagement with target populations. Despite previous efforts employing hidden Markov models and recurrent neural networks to predict demographics from names, significant limitations persist: the lack of large-scale, well-curated, unbiased, publicly available datasets, and the lack of an approach robust across datasets. This scarcity has hindered the development of traditional supervised learning approaches. In this paper, we demonstrate that the zero-shot capabilities of Large Language Models (LLMs) can perform as well as, if not better than, bespoke models trained on specialized data. We apply these LLMs to a variety of datasets, including a real-life, unlabelled dataset of licensed financial professionals in Hong Kong, and critically assess the inherent demographic biases in these models. Our work not only advances the state-of-the-art in demographic enrichment but also opens avenues for future research in mitigating biases in LLMs., Comment: 8 pages, 7 Tables, 5 Figures

In this article, we focus on the communication costs of three symmetric matrix computations: i) multiplying a matrix with its transpose, known as a symmetric rank-k update (SYRK) ii) adding the result of the multiplication of a matrix with the transpose of another matrix and the transpose of that result, known as a symmetric rank-2k update (SYR2K) iii) performing matrix multiplication with a symmetric input matrix (SYMM). All three computations appear in the Level 3 Basic Linear Algebra Subroutines (BLAS) and have wide use in applications involving symmetric matrices. We establish communication lower bounds for these kernels using sequential and distributed-memory parallel computational models, and we show that our bounds are tight by presenting communication-optimal algorithms for each setting. Our lower bound proofs rely on applying a geometric inequality for symmetric computations and analytically solving constrained nonlinear optimization problems. The symmetric matrix and its corresponding computations are accessed and performed according to a triangular block partitioning scheme in the optimal algorithms., Comment: 43 pages, 6 figures. To be published in ACM Transactions on Parallel Computing

The $\mathrm{U}(1)$ Chern-Simons theory can be extended to a topological $\mathrm{U}(1)^n$ theory by taking a combination of Chern-Simons and BF actions, the mixing being achieved with the help of a collection of integer coupling constants. Based on the Deligne-Beilinson cohomology, a partition function can then be computed for such a $\mathrm{U}(1)^n$ Chern-Simons theory. This partition function is clearly a topological invariant of the closed oriented $3$-manifold on which the theory is defined. Then, by applying a reciprocity formula a new expression of this invariant is obtained which should be a Reshetikhin-Turaev invariant. Finally, a duality between $\mathrm{U}(1)^n$ Chern-Simons theories is demonstrated., Comment: 38 pages, 2 figures

Context: New spectroscopic surveys will increase the number of astronomical objects requiring characterization by over tenfold.. Machine learning tools are required to address this data deluge in a fast and accurate fashion. Most machine learning algorithms can not estimate error directly, making them unsuitable for reliable science. Aims: We aim to train a supervised deep-learning algorithm tailored for high-resolution observational stellar spectra. This algorithm accurately infer precise estimates while providing coherent estimates of uncertainties by leveraging information from both the neural network and the spectra. Methods: We train a conditional Invertible Neural Network (cINN) on observational spectroscopic data obtained from the GIRAFFE spectrograph (HR10 and HR21 setups) within the Gaia-ESO survey. A key features of cINN is its ability to produce the Bayesian posterior distribution of parameters for each spectrum. By analyzing this distribution, we inferred parameters and their uncertainties. Several tests have been applied to study how parameters and errors are estimated. Results: We achieved an accuracy of 28K in $T_{\text{eff}}$, 0.06 dex in $\log g$, 0.03 dex in $[\text{Fe/H}]$, and between 0.05 dex and 0.17 dex for the other abundances for high quality spectra. Accuracy remains stable with low signal-to-noise ratio spectra. The uncertainties obtained are well within the same order of magnitude. The network accurately reproduces astrophysical relationships both on the scale of the Milky Way and within smaller star clusters. We created a table containing the new parameters generated by our cINN. Conclusion: This neural network represents a compelling proposition for future astronomical surveys. These coherent derived uncertainties make it possible to reuse these estimates in other works as Bayesian priors and thus present a solid basis for future work.

Archaeological sites are the physical remains of past human activity and one of the main sources of information about past societies and cultures. However, they are also the target of malevolent human actions, especially in countries having experienced inner turmoil and conflicts. Because monitoring these sites from space is a key step towards their preservation, we introduce the DAFA Looted Sites dataset, \datasetname, a labeled multi-temporal remote sensing dataset containing 55,480 images acquired monthly over 8 years across 675 Afghan archaeological sites, including 135 sites looted during the acquisition period. \datasetname~is particularly challenging because of the limited number of training samples, the class imbalance, the weak binary annotations only available at the level of the time series, and the subtlety of relevant changes coupled with important irrelevant ones over a long time period. It is also an interesting playground to assess the performance of satellite image time series (SITS) classification methods on a real and important use case. We evaluate a large set of baselines, outline the substantial benefits of using foundation models and show the additional boost that can be provided by using complete time series instead of using a single image.

The construction of general amplitudes satisfying symmetries and $S$-matrix constraints has been the primary tool in studying the spectrum of hadrons for over half a century. In this work, we present a new parameterization, which can fulfill many expectations of $S$-matrix and Regge theory and connects the essential physics of hadron scattering in the resonance region and in asymptotic limits. In this construction, dynamical information is entirely contained in Regge trajectories that generalize resonance poles in the complex energy plane to moving poles in the angular momentum plane. We highlight the salient features of the model, compare with existing literature on dispersive and dual amplitudes, and benchmark the formalism with an initial numerical application to the $\rho$ and $\sigma/f_0(500)$ mesons in $\pi\pi$ scattering., Comment: 27 pages, 10 figures

We consider a state-space model (SSM) parametrized by some parameter $\theta$ and aim at performing joint parameter and state inference. A popular idea to carry out this task is to replace $\theta$ by a Markov chain $(\theta_t)_{t\geq 0}$ and then to apply a filtering algorithm to the extended, or self-organized SSM (SO-SSM). However, the practical implementation of this idea in a theoretically justified way has remained an open problem. In this paper we fill this gap by introducing constructions of $(\theta_t)_{t\geq 0}$ that ensure the validity of the SO-SSM for joint parameter and state inference. Notably, we show that such SO-SSMs can be defined even if $\|\mathrm{Var}(\theta_{t}|\theta_{t-1})\|\rightarrow 0$ slowly as $t\rightarrow\infty$. This result is important since these models can be efficiently approximated using a particle filter. While SO-SSMs have been introduced for online inference, the development of iterated filtering (IF) has shown that they can also serve for computing the maximum likelihood estimator of an SSM. We also derive constructions of $(\theta_t)_{t\geq 0}$ and theoretical guarantees tailored to these specific applications of SO-SSMs and introduce new IF algorithms. From a practical point of view, the algorithms we develop are simple to implement and only require minimal tuning to perform well., Comment: 102 pages (28 pages for the paper, 6 for the appendix and 68 for the supplementary material), 4 figures

Semiconductor quantum dots are a versatile source of single photons with tunable properties to be used in quantum-cryptographic applications. A crucial figure of merit of the emitted photons is photon number coherence (PNC), which impacts the security of many quantum communication protocols. In the process of single-photon generation, the quantum dot as a solid-state object is subject to an interaction with phonons, which can therefore indirectly affect the PNC. In this paper, we elaborate on the origin of PNC in optically excited quantum dots and how it is affected by phonons. In contrast to the expectation that phonons always deteriorate coherence, PNC can be increased in a quantum dot-cavity system due to the electron-phonon interaction.

We provide the first counter-example showing that the ground state energy of electrons in an external Coulomb potential is not always a convex function of the number of electrons. This property had been conjectured to hold for decades and it plays an important role in quantum chemistry. Our counter-example involves an external potential generated by six nuclei of small fractional charges, placed far away from each other. The ground state energy of 3 electrons is proved to be higher than the average of the energies for 2 and 4 electrons. In addition, we show that the nuclei can bind 2 or 4 electrons, but not 3. Although the conjecture remains open for real nuclei (of integer charges), our work sets some doubt on the validity of the energy convexity for general atoms and molecules., Comment: This paper is dedicated to Trygve Helgaker on the occasion of his 70th birthday

In a regular mean field game (MFG), the agents are assumed to be insignificant, they do not realize their effect on the population level and this may result in a phenomenon coined as the Tragedy of the Commons by the economists. However, in real life this phenomenon is often avoided thanks to the underlying altruistic behavior of (all or some of the) agents. Motivated by this observation, we introduce and analyze two different mean field models to include altruism in the decision making of agents. In the first model, mixed individual MFGs, there are infinitely many agents who are partially altruistic (i.e., they behave partially cooperatively) and partially non-cooperative. In the second model, mixed population MFGs, one part of the population behaves cooperatively and the remaining agents behave non-cooperatively. Both models are introduced in a general linear quadratic framework for which we characterize the equilibrium via forward backward stochastic differential equations. Furthermore, we give explicit solutions in terms of ordinary differential equations, and prove the existence and uniqueness results., Comment: 7 pages, accepted at CDC 2024

The integration of renewable energy sources (RES) into power grids presents significant challenges due to their intrinsic stochasticity and uncertainty, necessitating the development of new techniques for reliable and efficient forecasting. This paper proposes a method combining probabilistic forecasting and Gaussian copula for day-ahead prediction and scenario generation of load, wind, and solar power in high-dimensional contexts. By incorporating weather covariates and restoring spatio-temporal correlations, the proposed method enhances the reliability of probabilistic forecasts in RES. Extensive numerical experiments compare the effectiveness of different time series models, with performance evaluated using comprehensive metrics on a real-world and high-dimensional dataset from Midcontinent Independent System Operator (MISO). The results highlight the importance of weather information and demonstrate the efficacy of the Gaussian copula in generating realistic scenarios, with the proposed weather-informed Temporal Fusion Transformer (WI-TFT) model showing superior performance.

Saliency prediction models are constrained by the limited diversity and quantity of labeled data. Standard data augmentation techniques such as rotating and cropping alter scene composition, affecting saliency. We propose a novel data augmentation method for deep saliency prediction that edits natural images while preserving the complexity and variability of real-world scenes. Since saliency depends on high-level and low-level features, our approach involves learning both by incorporating photometric and semantic attributes such as color, contrast, brightness, and class. To that end, we introduce a saliency-guided cross-attention mechanism that enables targeted edits on the photometric properties, thereby enhancing saliency within specific image regions. Experimental results show that our data augmentation method consistently improves the performance of various saliency models. Moreover, leveraging the augmentation features for saliency prediction yields superior performance on publicly available saliency benchmarks. Our predictions align closely with human visual attention patterns in the edited images, as validated by a user study., Comment: 18 pages, published in ECCV 2024