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In this note we provide a quick proof that maximal truncations of oscillatory singular integrals are bounded from $L^1(\mathbb{R})$ to $L^{1,\infty}(\mathbb{R})$. The methods we use are entirely elementary, and rely only on pigeonholing and stationary phase considerations.

We present cosmological constraints from the Dark Energy Camera All Data Everywhere (DECADE) cosmic shear analysis. This work uses shape measurements for 107 million galaxies measured through Dark Energy Camera (DECam) imaging of $5,\!412$ deg$^2$ of sky that is outside the Dark Energy Survey (DES) footprint. We derive constraints on the cosmological parameters $S_8 = 0.791^{+0.027}_{-0.032}$ and $\Omega_{\rm m} =0.269^{+0.034}_{-0.050}$ for the $\Lambda$CDM model, which are consistent with those from other weak lensing surveys and from the cosmic microwave background. We combine our results with cosmic shear results from DES Y3 at the likelihood level, since the two datasets span independent areas on the sky. The combined measurements, which cover $\approx\! 10,\!000$ deg$^2$, prefer $S_8 = 0.791 \pm 0.023$ and $\Omega_{\rm m} = 0.277^{+0.034}_{-0.046}$ under the $\Lambda$CDM model. These results are the culmination of a series of rigorous studies that characterize and validate the DECADE dataset and the associated analysis methodologies (Anbajagane et. al 2025a,b,c). Overall, the DECADE project demonstrates that the cosmic shear analysis methods employed in Stage-III weak lensing surveys can provide robust cosmological constraints for fairly inhomogeneous datasets. This opens the possibility of using data that have been previously categorized as ``unusable'' for cosmic shear analyses, thereby increasing the statistical power of upcoming weak lensing surveys., Comment: 27 pages, 13 figures

We present the pipeline for the cosmic shear analysis of the Dark Energy Camera All Data Everywhere (DECADE) weak lensing dataset: a catalog consisting of 107 million galaxies observed by the Dark Energy Camera (DECam) in the northern Galactic cap. The catalog derives from a large number of disparate observing programs and is therefore more inhomogeneous across the sky compared to existing lensing surveys. First, we use simulated data-vectors to show the sensitivity of our constraints to different analysis choices in our inference pipeline, including sensitivity to residual systematics. Next we use simulations to validate our covariance modeling for inhomogeneous datasets. Finally, we show that our choices in the end-to-end cosmic shear pipeline are robust against inhomogeneities in the survey, by extracting relative shifts in the cosmology constraints across different subsets of the footprint/catalog and showing they are all consistent within $1\sigma$ to $2\sigma$. This is done for forty-six subsets of the data and is carried out in a fully consistent manner: for each subset of the data, we re-derive the photometric redshift estimates, shear calibrations, survey transfer functions, the data vector, measurement covariance, and finally, the cosmological constraints. Our results show that existing analysis methods for weak lensing cosmology can be fairly resilient towards inhomogeneous datasets. This also motivates exploring a wider range of image data for pursuing such cosmological constraints.

We present a universal framework for constructing confidence sets based on sequential likelihood mixing. Building upon classical results from sequential analysis, we provide a unifying perspective on several recent lines of work, and establish fundamental connections between sequential mixing, Bayesian inference and regret inequalities from online estimation. The framework applies to any realizable family of likelihood functions and allows for non-i.i.d. data and anytime validity. Moreover, the framework seamlessly integrates standard approximate inference techniques, such as variational inference and sampling-based methods, and extends to misspecified model classes, while preserving provable coverage guarantees. We illustrate the power of the framework by deriving tighter confidence sequences for classical settings, including sequential linear regression and sparse estimation, with simplified proofs.

Efficient solid-liquid separation is crucial in industries like mining, but traditional chamber filter presses depend heavily on manual monitoring, leading to inefficiencies, downtime, and resource wastage. This paper introduces a machine learning-powered digital twin framework to improve operational flexibility and predictive control. A key challenge addressed is the degradation of the filter medium due to repeated cycles and clogging, which reduces filtration efficiency. To solve this, a neural network-based predictive model was developed to forecast operational parameters, such as pressure and flow rates, under various conditions. This predictive capability allows for optimized filtration cycles, reduced downtime, and improved process efficiency. Additionally, the model predicts the filter mediums lifespan, aiding in maintenance planning and resource sustainability. The digital twin framework enables seamless data exchange between filter press sensors and the predictive model, ensuring continuous updates to the training data and enhancing accuracy over time. Two neural network architectures, feedforward and recurrent, were evaluated. The recurrent neural network outperformed the feedforward model, demonstrating superior generalization. It achieved a relative $L^2$-norm error of $5\%$ for pressure and $9.3\%$ for flow rate prediction on partially known data. For completely unknown data, the relative errors were $18.4\%$ and $15.4\%$, respectively. Qualitative analysis showed strong alignment between predicted and measured data, with deviations within a confidence band of $8.2\%$ for pressure and $4.8\%$ for flow rate predictions. This work contributes an accurate predictive model, a new approach to predicting filter medium cycle impacts, and a real-time interface for model updates, ensuring adaptability to changing operational conditions.

This work demonstrates the potential of fiber optic sensors for measuring thermal effects in lithium-ion batteries, using a fiber optic measurement method of Optical Frequency Domain Reflectometry (OFDR). The innovative application of fiber sensors allows for spatially resolved temperature measurement, particularly emphasizing the importance of monitoring not just the exterior but also the internal conditions within battery cells. Utilizing inert glass fibers as sensors, which exhibit minimal sensitivity to electric fields, opens up new pathways for their implementation in a wide range of applications, such as battery monitoring. The sensors used in this work provide real-time information along the entire length of the fiber, unlike commonly used Fiber Bragg Grating (FBG) sensors. It is shown that using the herein presented novel sensors in a temperature range of 0 to 80 degree celsius reveals a linear thermal dependency with high sensitivity and a local resolution of a few centimeters. Furthermore, this study presents preliminary findings on the potential application of fiber optic sensors in lithium-ion battery (LIB) cells, demonstrating that the steps required for battery integration do not impose any restrictive effects on thermal measurements.

Digital twin (DT) technology is increasingly used in urban planning, leveraging real-time data integration for environmental monitoring. This paper presents an urban-focused DT that combines computational fluid dynamics simulations with live meteorological data to analyze pollution dispersion. Addressing the health impacts of pollutants like particulate matter and nitrogen dioxide, the DT provides real-time updates on air quality, wind speed, and direction. Using OpenStreetMaps XML-based data, the model distinguishes between porous elements like trees and solid structures, enhancing urban flow analysis. The framework employs the lattice Boltzmann method (LBM) within the open-source software OpenLB to simulate pollution transport. Nitrogen dioxide and particulate matter concentrations are estimated based on traffic and building emissions, enabling hot-spot identification. The DT was used from November 7 to 23, 2024, with hourly updates, capturing pollution trends influenced by wind patterns. Results show that alternating east-west winds during this period create a dynamic pollution distribution, identifying critical residential exposure areas. This work contributes a novel DT framework that integrates real-time meteorological data, OpenStreetMap-based geometry, and high-fidelity LBM simulations for urban wind and pollution modeling. Unlike existing DTs, which focus on structural monitoring or large-scale environmental factors, this approach enables fine-grained, dynamic analyses of urban airflow and pollution dispersion. By allowing interactive modifications to urban geometry and continuous data updates, the DT serves as a powerful tool for adaptive urban planning, supporting evidence-based policy making to improve air quality and public health.

We establish essentially optimal $L^2(\mathbb{R})$-estimates for variational variants of the maximal Fourier multiplier operators considered by Bourgain in his work on pointwise convergence of polynomial ergodic averages. We do so by addressing a simpler operator and then applying cheap interpolation methods. While elementary, these methods are strong enough to address the issue of pointwise convergence along fractional parts of real-variable polynomials in the regime $\frac{2d}{d+1} < p \leq \infty$, namely the pointwise convergence of \[ \frac{1}{N} \sum_{n \leq N} T^{\lfloor P(n) \rfloor} f, \; \; \; f \in L^p(X,\mu), \ P \in \mathbb{R}[\cdot], \ \text{deg}(P) = d \] for any $\sigma$-finite measure space equipped with a measure-preserving transformation, $T:X \to X$.

We develop a quantitive analytical treatment of two-point velocity correlations for two important classes of superfluid excitation in compressible quantum fluids: vortices, and rarefaction pulses. We achieve this using two approaches. First, we introduce a new ansatz for describing vortex cores in planar quantum fluids with improved analytic integrability that provides analytic results for power spectra and velocity correlations for general vortex distributions, in good agreement with numerical results using the exact vortex shape. The results show signatures of short and long range correlations associated with vortex dipoles and vortex pairs respectively. Second, for the fast rarefaction pulse regime of the Jones-Roberts soliton the asymptotic high velocity wavefunction provides analytical results for the velocity power spectrum and correlation function, capturing the main length scale of the soliton. We compare our analytical treatment of the homogeneous system with numerical results for a trapped system, finding good quantitative agreement. Our results are relevant to experimental work to characterize quantum vortices and solitons in quantum fluids of atoms and light, and for studies of quantum turbulence., Comment: 11 pages, 5 figures, comments welcome

Responsible disclosure limitation is an iterative exercise in risk assessment and mitigation. From time to time, as disclosure risks grow and evolve and as data users' needs change, agencies must consider redesigning the disclosure avoidance system(s) they use. Discussions about candidate systems often conflate inherent features of those systems with implementation decisions independent of those systems. For example, a system's ability to calibrate the strength of protection to suit the underlying disclosure risk of the data (e.g., by varying suppression thresholds), is a worthwhile feature regardless of the independent decision about how much protection is actually necessary. Having a principled discussion of candidate disclosure avoidance systems requires a framework for distinguishing these inherent features of the systems from the implementation decisions that need to be made independent of the system selected. For statistical agencies, this framework must also reflect the applied nature of these systems, acknowledging that candidate systems need to be adaptable to requirements stemming from the legal, scientific, resource, and stakeholder environments within which they would be operating. This paper proposes such a framework. No approach will be perfectly adaptable to every potential system requirement. Because the selection of some methodologies over others may constrain the resulting systems' efficiency and flexibility to adapt to particular statistical product specifications, data user needs, or disclosure risks, agencies may approach these choices in an iterative fashion, adapting system requirements, product specifications, and implementation parameters as necessary to ensure the resulting quality of the statistical product.

Artificial intelligence commonly refers to the science and engineering of artificial systems that can carry out tasks generally associated with requiring aspects of human intelligence, such as playing games, translating languages, and driving cars. In recent years, there have been exciting advances in learning-based, data-driven approaches towards AI, and machine learning and deep learning have enabled computer systems to perceive the world in unprecedented ways. Reinforcement learning has enabled breakthroughs in complex games such as Go and challenging robotics tasks such as quadrupedal locomotion. A key aspect of intelligence is to not only make predictions, but reason about the uncertainty in these predictions, and to consider this uncertainty when making decisions. This is what this manuscript on "Probabilistic Artificial Intelligence" is about. The first part covers probabilistic approaches to machine learning. We discuss the differentiation between "epistemic" uncertainty due to lack of data and "aleatoric" uncertainty, which is irreducible and stems, e.g., from noisy observations and outcomes. We discuss concrete approaches towards probabilistic inference and modern approaches to efficient approximate inference. The second part of the manuscript is about taking uncertainty into account in sequential decision tasks. We consider active learning and Bayesian optimization -- approaches that collect data by proposing experiments that are informative for reducing the epistemic uncertainty. We then consider reinforcement learning and modern deep RL approaches that use neural network function approximation. We close by discussing modern approaches in model-based RL, which harness epistemic and aleatoric uncertainty to guide exploration, while also reasoning about safety.

Parallel training methods are increasingly relevant in machine learning (ML) due to the continuing growth in model and dataset sizes. We propose a variant of the Additively Preconditioned Trust-Region Strategy (APTS) for training deep neural networks (DNNs). The proposed APTS method utilizes a data-parallel approach to construct a nonlinear preconditioner employed in the nonlinear optimization strategy. In contrast to the common employment of Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (Adam), which are both variants of gradient descent (GD) algorithms, the APTS method implicitly adjusts the step sizes in each iteration, thereby removing the need for costly hyperparameter tuning. We demonstrate the performance of the proposed APTS variant using the MNIST and CIFAR-10 datasets. The results obtained indicate that the APTS variant proposed here achieves comparable validation accuracy to SGD and Adam, all while allowing for parallel training and obviating the need for expensive hyperparameter tuning., Comment: 8 pages, 6 figures

Neural networks are fragile when confronted with data that significantly deviates from their training distribution. This is true in particular for simulation-based inference methods, such as neural amortized Bayesian inference (ABI), where models trained on simulated data are deployed on noisy real-world observations. Recent robust approaches employ unsupervised domain adaptation (UDA) to match the embedding spaces of simulated and observed data. However, the lack of comprehensive evaluations across different domain mismatches raises concerns about the reliability in high-stakes applications. We address this gap by systematically testing UDA approaches across a wide range of misspecification scenarios in both a controlled and a high-dimensional benchmark. We demonstrate that aligning summary spaces between domains effectively mitigates the impact of unmodeled phenomena or noise. However, the same alignment mechanism can lead to failures under prior misspecifications - a critical finding with practical consequences. Our results underscore the need for careful consideration of misspecification types when using UDA techniques to increase the robustness of ABI in practice.

In reinforcement learning (RL), agents often struggle to perform well on tasks that differ from those encountered during training. This limitation presents a challenge to the broader deployment of RL in diverse and dynamic task settings. In this work, we introduce memory augmentation, a memory-based RL approach to improve task generalization. Our approach leverages task-structured augmentations to simulate plausible out-of-distribution scenarios and incorporates memory mechanisms to enable context-aware policy adaptation. Trained on a predefined set of tasks, our policy demonstrates the ability to generalize to unseen tasks through memory augmentation without requiring additional interactions with the environment. Through extensive simulation experiments and real-world hardware evaluations on legged locomotion tasks, we demonstrate that our approach achieves zero-shot generalization to unseen tasks while maintaining robust in-distribution performance and high sample efficiency.

Let $M_0$ be a compact and orientable 3-manifold. After capping off spherical boundaries with balls and removing any torus boundaries, we prove that the resulting manifold $M$ contains handlebodies of arbitrary genus such that the closure of their complement is hyperbolic. We then extend the octahedral decomposition to obtain bounds on volume for some of these handlebody complements., Comment: 20 pages, 23 figures

The first International AI Safety Report comprehensively synthesizes the current evidence on the capabilities, risks, and safety of advanced AI systems. The report was mandated by the nations attending the AI Safety Summit in Bletchley, UK. Thirty nations, the UN, the OECD, and the EU each nominated a representative to the report's Expert Advisory Panel. A total of 100 AI experts contributed, representing diverse perspectives and disciplines. Led by the report's Chair, these independent experts collectively had full discretion over the report's content.

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 2,700 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 27 pages, 6 figures

We consider the problem of computing the probability of maximality (PoM) of a Gaussian random vector, i.e., the probability for each dimension to be maximal. This is a key challenge in applications ranging from Bayesian optimization to reinforcement learning, where the PoM not only helps with finding an optimal action, but yields a fine-grained analysis of the action domain, crucial in tasks such as drug discovery. Existing techniques are costly, scaling polynomially in computation and memory with the vector size. We introduce LITE, the first approach for estimating Gaussian PoM with almost-linear time and memory complexity. LITE achieves SOTA accuracy on a number of tasks, while being in practice several orders of magnitude faster than the baselines. This also translates to a better performance on downstream tasks such as entropy estimation and optimal control of bandits. Theoretically, we cast LITE as entropy-regularized UCB and connect it to prior PoM estimators., Comment: accepted in AISTATS 2025

Canada's research productivity in Library and Information Science (LIS) is significant: studies have found that Canada ranks third globally in terms of output. As the LIS field continues to grow, the pace of output accelerates, and the scope of this work expands. The recently launched Canadian Publications in Library and Information Science Database compiles all Canadian scientific publications, including those authored by faculty members and academic librarians. This database offers the advantage of encompassing articles and librarian publications that may not be typically included in traditional bibliometric surveys, such as those conducted using databases like Web of Science, Scopus, and Library and Information Science Abstracts (LISA). Using this data, this study maps the scholarly contributions of Canadian LIS scholars and academic librarians to the field of LIS and examines whether Canadian LIS research is characterized by silos. This paper examines the similarities and differences in research output, impact, topics, and publication venues between academic librarians and scholars in Canada, as well as the extent to which academics and practitioners engage in research collaborations or reference each other's work. We find that while there is some degree of overlap in research topics and publication venues between LIS academics and academic librarians, the two groups appear to act as distinct research communities with distinct topical foci and publishing habits. The two groups also do not appear to engage with each other strongly, either through collaboration or citing each other's work.

Learning robust and generalizable world models is crucial for enabling efficient and scalable robotic control in real-world environments. In this work, we introduce a novel framework for learning world models that accurately capture complex, partially observable, and stochastic dynamics. The proposed method employs a dual-autoregressive mechanism and self-supervised training to achieve reliable long-horizon predictions without relying on domain-specific inductive biases, ensuring adaptability across diverse robotic tasks. We further propose a policy optimization framework that leverages world models for efficient training in imagined environments and seamless deployment in real-world systems. Through extensive experiments, our approach consistently outperforms state-of-the-art methods, demonstrating superior autoregressive prediction accuracy, robustness to noise, and generalization across manipulation and locomotion tasks. Notably, policies trained with our method are successfully deployed on ANYmal D hardware in a zero-shot transfer, achieving robust performance with minimal sim-to-real performance loss. This work advances model-based reinforcement learning by addressing the challenges of long-horizon prediction, error accumulation, and sim-to-real transfer. By providing a scalable and robust framework, the introduced methods pave the way for adaptive and efficient robotic systems in real-world applications.

We investigate the formation and dynamics of Jones-Roberts solitons in a smoothly inhomogeneous quantum fluid. To do so, we create a superfluid of light using paraxial, near-resonant laser beam propagating through a hot rubidium vapor. We excite a bounded vortex-antivortex dipole in the superfluid and observe its transition to a rarefaction pulse and back, in agreement with the seminal predictions of Jones and Roberts. Employing an analogy with ray optics, we calculate the trajectory of the interacting vortices, deriving an effective refractive index from the inhomogeneous fluid density. Finally, we examine analytically and experimentally the superfluid velocity correlations, observing a transfer of coherence from incompressible to compressible velocity of the quantum fluid, a direct signature of the dynamical conversion between vortices and rarefaction pulse., Comment: Main: 8 pages, 4 figures SuppMat: 7 pages, 6 figures

We present a unified approach to extensions of Bourgain's Double Recurrence Theorem and Bourgain's Return Times Theorem to integer parts of the Kronecker sequence, emphasizing stopping times and metric entropy. Specifically, we prove the following two results for each $\alpha \in \mathbb{R}$: First, for each $\sigma$-finite measure-preserving system, $(X,\mu,T)$, and each $f,g \in L^{\infty}(X)$, for each $\gamma \in \mathbb{Q}$ the bilinear ergodic averages \[ \frac{1}{N} \sum_{n \leq N} T^{\lfloor \alpha n \rfloor } f \cdot T^{\lfloor \gamma n \rfloor} g \] converge $\mu$-a.e.; Second, for each aperiodic and countably generated measure-preserving system, $(Y,\nu,S)$, and each $g \in L^{\infty}(Y)$, there exists a subset $Y_{g} \subset Y$ with $\nu(Y_{g})= 1$ so that for all $\gamma \in \mathbb{Q}$ and $\omega \in Y_{g}$, for any auxiliary $\sigma$-finite measure-preserving system $(X,\mu,T)$, and any $f \in L^{\infty}(X)$, the ``return-times" averages \[ \frac{1}{N} \sum_{n \leq N} T^{\lfloor \alpha n \rfloor} f \cdot S^{\lfloor \gamma n \rfloor } g(\omega) \] converge $\mu$-a.e. Moreover, in both cases the sets of convergence are identical for all $\gamma \in \mathbb{Q}$.

We describe the photometric data set assembled from the full six years of observations by the Dark Energy Survey (DES) in support of static-sky cosmology analyses. DES Y6 Gold is a curated data set derived from DES Data Release 2 (DR2) that incorporates improved measurement, photometric calibration, object classification and value added information. Y6 Gold comprises nearly $5000~{\rm deg}^2$ of $grizY$ imaging in the south Galactic cap and includes 669 million objects with a depth of $i_{AB} \sim 23.4$ mag at S/N $\sim 10$ for extended objects and a top-of-the-atmosphere photometric uniformity $< 2~{\rm mmag}$. Y6 Gold augments DES DR2 with simultaneous fits to multi-epoch photometry for more robust galaxy shapes, colors, and photometric redshift estimates. Y6 Gold features improved morphological star-galaxy classification with efficiency $98.6\%$ and contamination $0.8\%$ for galaxies with $17.5 < i_{AB} < 22.5$. Additionally, it includes per-object quality information, and accompanying maps of the footprint coverage, masked regions, imaging depth, survey conditions, and astrophysical foregrounds that are used for cosmology analyses. After quality selections, benchmark samples contain 448 million galaxies and 120 million stars. This paper will be complemented by online data access and documentation., Comment: Data products and documentation are publicly available at https://des.ncsa.illinois.edu/releases

Synthetic source injection (SSI), the insertion of sources into pixel-level on-sky images, is a powerful method for characterizing object detection and measurement in wide-field, astronomical imaging surveys. Within the Dark Energy Survey (DES), SSI plays a critical role in characterizing all necessary algorithms used in converting images to catalogs, and in deriving quantities needed for the cosmology analysis, such as object detection rates, galaxy redshift estimation, galaxy magnification, star-galaxy classification, and photometric performance. We present here a source injection catalog of $146$ million injections spanning the entire 5000 deg$^2$ DES footprint, generated using the Balrog SSI pipeline. Through this sample, we demonstrate that the DES Year 6 (Y6) image processing pipeline provides accurate estimates of the object properties, for both galaxies and stars, at the percent-level, and we highlight specific regimes where the accuracy is reduced. We then show the consistency between SSI and data catalogs, for all galaxy samples developed within the weak lensing and galaxy clustering analyses of DES Y6. The consistency between the two catalogs also extends to their correlations with survey observing properties (seeing, airmass, depth, extinction, etc.). Finally, we highlight a number of applications of this catalog to the DES Y6 cosmology analysis. This dataset is the largest SSI catalog produced at this fidelity and will serve as a key testing ground for exploring the utility of SSI catalogs in upcoming surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time.

We present the Metadetection weak lensing galaxy shape catalogue from the six-year Dark Energy Survey (DES Y6) imaging data. This dataset is the final release from DES, spanning 4422 deg$^2$ of the southern sky. We describe how the catalogue was constructed, including the two new major processing steps, cell-based image coaddition and shear measurements with Metadetection. The DES Y6 Metadetection weak lensing shape catalogue consists of 151,922,791 galaxies detected over riz bands, with an effective number density of $n_{\rm eff}$ =8.22 galaxies per arcmin$^2$ and shape noise of $\sigma_e$ = 0.29. We carry out a suite of validation tests on the catalogue, including testing for PSF leakage, testing for the impact of PSF modeling errors, and testing the correlation of the shear measurements with galaxy, PSF, and survey properties. In addition to demonstrating that our catalogue is robust for weak lensing science, we use the DES Y6 image simulation suite (Mau, Becker et al. 2025) to estimate the overall multiplicative shear bias of our shear measurement pipeline. We find no detectable multiplicative bias at the roughly half-percent level, with m = (3.4 $\pm$ 6.1) x $10^{-3}$, at 3$\sigma$ uncertainty. This is the first time both cell-based coaddition and Metadetection algorithms are applied to observational data, paving the way to the Stage-IV weak lensing surveys., Comment: 30 pages, 22 figures

Diffusion models have emerged as the mainstream approach for visual generation. However, these models usually suffer from sample inefficiency and high training costs. This issue is particularly pronounced in the standard diffusion transformer architecture due to its quadratic complexity relative to input length. Recent works have addressed this by reducing the number of tokens processed in the model, often through masking. In contrast, this work aims to improve the training efficiency of the diffusion backbone by using predefined routes that store this information until it is reintroduced to deeper layers of the model, rather than discarding these tokens entirely. Further, we combine multiple routes and introduce an adapted auxiliary loss that accounts for all applied routes. Our method is not limited to the common transformer-based model - it can also be applied to state-space models. Unlike most current approaches, TREAD achieves this without architectural modifications. Finally, we show that our method reduces the computational cost and simultaneously boosts model performance on the standard benchmark ImageNet-1K 256 x 256 in class-conditional synthesis. Both of these benefits multiply to a convergence speedup of 9.55x at 400K training iterations compared to DiT and 25.39x compared to the best benchmark performance of DiT at 7M training iterations.

We consider a simple but infinite class of staked links known as bongles. We provide necessary and sufficient conditions for these bongles to be hyperbolic. Then, we prove that all balanced hyperbolic $n$-bongles have the same volume and the corresponding volume is an upper bound on the volume of any hyperbolic $n$-bongle for $n$ even. Moreover, all hyperbolic $n$-bongles have volume strictly less than $5n(1.01494\dots)$. We also include explicit volume calculations for all hyperbolic 3-bongles through 6-bongles., Comment: 16 pages, 11 figures

We study the obstructions to coarse universality in separable dual Banach spaces. We prove an `asymptotic linearization' theorem for nonlinear maps into Banach spaces and use it to give streamlined proofs of several results in the literature. We also prove coarse non-universality of several classes of dual spaces, including those with conditional spreading bases, as well as generalized James and James tree spaces. Furthermore, we give quantitative counterparts of some of the results, clarifying the distinction between coarse non-universality and the non-equi-coarse embeddings of the Kalton graphs., Comment: 41 pages

Among the many perplexing results of quantum mechanics is one that contradicts a result from introductory physics: the possibility of finding a quantum particle in a region that would be forbidden classically by energy conservation. An especially interesting example of this phenomenon with practical applications is quantum tunneling. Here we investigate the reasons for this puzzling result by focusing on the difference between how quantities like kinetic and potential energy are represented mathematically in classical and quantum mechanics. In quantum mechanics, physical observables, like energy, are represented by operators rather than real numbers. The consequences of this difference will be illustrated explicitly using a toy model in which the kinetic and potential energy operators are represented by $2 \times 2$ matrices, which do not commute like their classical analogs. This model will then illustrate how classically perplexing results, like a quantum particle being found in the forbidden region, can arise., Comment: 4 pages, 3 figures

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications., Comment: CVPR 2024 MedSAM on Laptop Competition Summary: https://www.codabench.org/competitions/1847/

Reinforcement learning (RL) algorithms aim to balance exploiting the current best strategy with exploring new options that could lead to higher rewards. Most common RL algorithms use undirected exploration, i.e., select random sequences of actions. Exploration can also be directed using intrinsic rewards, such as curiosity or model epistemic uncertainty. However, effectively balancing task and intrinsic rewards is challenging and often task-dependent. In this work, we introduce a framework, MaxInfoRL, for balancing intrinsic and extrinsic exploration. MaxInfoRL steers exploration towards informative transitions, by maximizing intrinsic rewards such as the information gain about the underlying task. When combined with Boltzmann exploration, this approach naturally trades off maximization of the value function with that of the entropy over states, rewards, and actions. We show that our approach achieves sublinear regret in the simplified setting of multi-armed bandits. We then apply this general formulation to a variety of off-policy model-free RL methods for continuous state-action spaces, yielding novel algorithms that achieve superior performance across hard exploration problems and complex scenarios such as visual control tasks.

The first JWST/MIRI photometric observations of TRAPPIST-1 b allowed for the detection of the thermal emission of the planet at 15 $\mu m$, suggesting that the planet could be a bare rock with a zero albedo and no redistribution of heat. These observations at 15 $\mu m$ were acquired as part of GTO time that included a twin program at 12.8 $\mu m$ in order to have a measurement in and outside the CO$_2$ absorption band. Here we present five new occultations of TRAPPIST-1 b observed with MIRI in an additional photometric band at 12.8 $\mu m$. We perform a global fit of the 10 eclipses and derive a planet-to-star flux ratio and 1-$\sigma$ error of 452 $\pm$ 86 ppm and 775 $\pm$ 90 ppm at 12.8 $\mu m$ and 15 $\mu m$, respectively. We find that two main scenarios emerge. An airless planet model with an unweathered (fresh) ultramafic surface, that could be indicative of relatively recent geological processes fits well the data. Alternatively, a thick, pure-CO2 atmosphere with photochemical hazes that create a temperature inversion and result in the CO2 feature being seen in emission also works, although with some caveats. Our results highlight the challenges in accurately determining a planet's atmospheric or surface nature solely from broadband filter measurements of its emission, but also point towards two very interesting scenarios that will be further investigated with the forthcoming phase curve of TRAPPIST-1 b., Comment: 49 pages, 3 main text figure, 2 extended figures, 10 supplementary figures, accepted for publication in Nature Astronomy on October 29, 2024

Constraining the helium enhancement in stars is critical for understanding the formation mechanisms of multiple populations in star clusters. However, measuring helium variations for many stars within a cluster remains observationally challenging. We use Hubble Space Telescope photometry combined with MUSE spectroscopic data for over 7,200 red-giant branch stars in \omc\ to measure helium differences between distinct groups of stars as a function of metallicity separating the impact of helium enhancements from other abundance variations on the pseudo-color (chromosome) diagrams. Our results show that stars at all metallicities have subpopulations with significant helium enhancement ($\Delta Y_{min} \gtrsim$ 0.11). We find a rapid increase in helium enhancement from low metallicities ($\rm{[Fe/H] \simeq -2.05}$ to $\rm{[Fe/H] \simeq -1.92})$, with this enhancement leveling out at \deltay\ $= 0.154$ at higher metallicities. The fraction of helium-enhanced stars steadily increases with metallicity ranging from 10\% at $\rm{[Fe/H] \simeq -2.04}$ to over $90\%$ at $\rm{[Fe/H] \simeq -1.04}$. This study is the first to examine helium enhancement across the full range of metallicities in \omc{}, providing new insight into its formation history and additional constraints on enrichment mechanisms.

This report documents the development, test, and application of Large Language Models (LLMs) for automated text analysis, with a specific focus on gambling-like elements in digital games, such as lootboxes. The project aimed not only to analyse user opinions and attitudes towards these mechanics, but also to advance methodological research in text analysis. By employing prompting techniques and iterative prompt refinement processes, the study sought to test and improve the accuracy of LLM-based text analysis. The findings indicate that while LLMs can effectively identify relevant patterns and themes on par with human coders, there are still challenges in handling more complex tasks, underscoring the need for ongoing refinement in methodologies. The methodological advancements achieved through this study significantly enhance the application of LLMs in real-world text analysis. The research provides valuable insights into how these models can be better utilized to analyze complex, user-generated content.

Cosmic shear, galaxy clustering, and the abundance of massive halos each probe the large-scale structure of the universe in complementary ways. We present cosmological constraints from the joint analysis of the three probes, building on the latest analyses of the lensing-informed abundance of clusters identified by the South Pole Telescope (SPT) and of the auto- and cross-correlation of galaxy position and weak lensing measurements (3$\times$2pt) in the Dark Energy Survey (DES). We consider the cosmological correlation between the different tracers and we account for the systematic uncertainties that are shared between the large-scale lensing correlation functions and the small-scale lensing-based cluster mass calibration. Marginalized over the remaining $\Lambda$CDM parameters (including the sum of neutrino masses) and 52 astrophysical modeling parameters, we measure $\Omega_\mathrm{m}=0.300\pm0.017$ and $\sigma_8=0.797\pm0.026$. Compared to constraints from Planck primary CMB anisotropies, our constraints are only 15% wider with a probability to exceed of 0.22 ($1.2\sigma$) for the two-parameter difference. We further obtain $S_8\equiv\sigma_8(\Omega_\mathrm{m}/0.3)^{0.5}=0.796\pm0.013$ which is lower than the Planck measurement at the $1.6\sigma$ level. The combined SPT cluster, DES 3$\times$2pt, and Planck datasets mildly prefer a non-zero positive neutrino mass, with a 95% upper limit $\sum m_\nu<0.25~\mathrm{eV}$ on the sum of neutrino masses. Assuming a $w$CDM model, we constrain the dark energy equation of state parameter $w=-1.15^{+0.23}_{-0.17}$ and when combining with Planck primary CMB anisotropies, we recover $w=-1.20^{+0.15}_{-0.09}$, a $1.7\sigma$ difference with a cosmological constant. The precision of our results highlights the benefits of multiwavelength multiprobe cosmology., Comment: Submitted to Phys. Rev. D

This paper presents the development and implementation of a versatile ad-hoc metropolitan-range Quantum Key Distribution (QKD) network. The approach presented integrates various types of physical channels and QKD protocols, and a mix of trusted and untrusted nodes. Unlike conventional QKD networks that predominantly depend on either fiber-based or free-space optical (FSO) links, the testbed presented amalgamates FSO and fiber-based links, thereby overcoming some inherent limitations. Various network deployment strategies have been considered, including permanent infrastructure and provisional ad-hoc links to eradicate coverage gaps. Furthermore, the ability to rapidly establish a network using portable FSO terminals and to investigate diverse link topologies is demonstrated. The study also showcases the successful establishment of a quantum-secured link to a cloud server.

The aim of the Canadian publications in Library and Information Science (LIS) database is to help break down the silos in which the two main target audiences - LIS faculty members and academic librarians - conduct their research. As part of a larger project entitled "Breaking down research silos", we created a database of research contributions by Canadian LIS researchers (academics and practitioners). This was motivated by a desire to make research by Canadian LIS scholars and practitioners more visible and foster collaboration between these two groups. The aim of this paper is to introduce the database, describe the process through which it was created, provide descriptive statistics of the database content, and highlight areas for future development.

We investigate automated in situ optimization of the potential landscape in a quantum point contact device, using a $3 \times 3$ gate array patterned atop the constriction. Optimization is performed using the covariance matrix adaptation evolutionary strategy, for which we introduce a metric for how "step-like" the conductance is as the channel becomes constricted. We first perform the optimization of the gate voltages in a tight-binding simulation and show how such in situ tuning can be used to mitigate a random disorder potential. The optimization is then performed in a physical device in experiment, where we also observe a marked improvement in the quantization of the conductance resulting from the optimization procedure., Comment: 8 pages, 4 figures (supplement: 6 pages, 3 figures)

The Coronagraphic Instrument onboard the Nancy Grace Roman Space Telescope is an important stepping stone towards the characterization of habitable, rocky exoplanets. In a technology demonstration phase conducted during the first 18 months of the mission (expected to launch in late 2026), novel starlight suppression technology may enable direct imaging of a Jupiter analog in reflected light. Here we summarize the current activities of the Observation Planning working group formed as part of the Community Participation Program. This working group is responsible for target selection and observation planning of both science and calibration targets in the technology demonstration phase of the Roman Coronagraph. We will discuss the ongoing efforts to expand target and reference catalogs, and to model astrophysical targets (exoplanets and circumstellar disks) within the Coronagraph's expected sensitivity. We will also present preparatory observations of high priority targets., Comment: Proceedings Volume 13092, Space Telescopes and Instrumentation 2024: Optical, Infrared, and Millimeter Wave; 1309255 (2024)

The Covering Tour Location Routing Problem (CTLRP) unites the well-known location routing problem with the possibility of covering customers through intermediary facilities. The objective is to select a subset of greenfield or brownfield depots and a subset of facilities, assign customers to suitable facilities, and design routes for a fleet of vehicles such that each customer's demand of a homogenous good can be satisfied and a given objective function is minimized. We focus particularly on the case when customers can not be directly visited, i.e. they have to pick up their orders from intermediary facilities, and the objective function consists of the strategic and operational costs, i.e. total routing costs plus costs for establishing or operating the selected depots and facilities. We introduce a mixed-integer programming model derived from a 2-commodity flow formulation, which can be seen as a baseline model for various applications. As such models are often impractical to solve for realistically-sized scenarios, a local search-based matheuristic to solve the problem at hand is developed. Furthermore, we construct benchmark instances for the CTLRP by expanding existing LRP instances to include information on facilities and customers. Extensive computational experiments on said instances with the aim of analyzing different heuristic configurations show that the proposed heuristic, if designed carefully, is able to produce very good results within a short period of time, while exact solving methods using the proposed MIP formulation frequently return highly suboptimal solutions and exhibit slow convergence speeds.

The processes driving the formation and evolution of late-type galaxies (LTGs) continue to be a debated subject in extragalactic astronomy. Investigating stellar kinematics, especially when combined with age estimates, provides crucial insights into the formation and subsequent development of galactic discs. Post-processing of exceptionally high-quality Integral Field Spectroscopy (IFS) data of NGC 4030 acquired with the Multi Unit Spectroscopic Explorer (MUSE), clearly reveals a striking grand design spiral pattern in the velocity dispersion map not previously detected in other galaxies. This pattern spatially correlates with HII regions, suggesting that stars currently being born exhibit lower velocity dispersion as compared to surrounding areas where star formation (SF) is less active. We examine the age-velocity relation (AVR) and propose that its configuration might be shaped by a combination of heating mechanisms, seemingly consistent with findings from recent high-resolution cosmological zoom-in simulations. The complex structure of the uncovered AVR of NGC 4030 support the hypothesis that stellar populations initially inherit the velocity dispersion {\sigma} of the progenitor cold molecular gas, which depends on formation time and galactocentric distance, subsequently experiencing kinematic heating by cumulative gravitational interactions during their lifetime. While advancing our understanding of the AVR, these findings offer a new framework for investigating disk heating mechanisms, and their role in the evolution of galactic disks., Comment: accepted for publication in A&A letters

A notion of support for objects in any Grothendieck category is introduced. This is based on the spectral category of a Grothendieck category and uses its Boolean lattice of localising subcategories. The support provides a classification of all subcategories that are closed under arbitrary coproducts, subobjects, and essential extensions. There is also a notion of exact support which classifies certain thick subcategories. As an application, the coproduct decompositions of objects are described in terms of Boolean lattices. Also, for any ring Crawley-Boevey's correspondence between definable subcategories of modules and closed subsets of the Ziegler spectrum is extended., Comment: 26 pages. V2: Substantial revision, including a new section on coproduct decompositions

We consider the problem of predicting perturbation effects via causal models. In many applications, it is a priori unknown which mechanisms of a system are modified by an external perturbation, even though the features of the perturbation are available. For example, in genomics, some properties of a drug may be known, but not their causal effects on the regulatory pathways of cells. We propose a generative intervention model (GIM) that learns to map these perturbation features to distributions over atomic interventions in a jointly-estimated causal model. Contrary to prior approaches, this enables us to predict the distribution shifts of unseen perturbation features while gaining insights about their mechanistic effects in the underlying data-generating process. On synthetic data and scRNA-seq drug perturbation data, GIMs achieve robust out-of-distribution predictions on par with unstructured approaches, while effectively inferring the underlying perturbation mechanisms, often better than other causal inference methods.

Brillouin Light Scattering (BLS) spectroscopy is a non-invasive, non-contact, label-free optical technique that can provide information on the mechanical properties of a material on the sub-micron scale. Over the last decade it has seen increased applications in the life sciences, driven by the observed significance of mechanical properties in biological processes, the realization of more sensitive BLS spectrometers and its extension to an imaging modality. As with other spectroscopic techniques, BLS measurements not only detect signals characteristic of the investigated sample, but also of the experimental apparatus, and can be significantly affected by measurement conditions. The aim of this consensus statement is to improve the comparability of BLS studies by providing reporting recommendations for the measured parameters and detailing common artifacts. Given that most BLS studies of biological matter are still at proof-of-concept stages and use different--often self-built--spectrometers, a consensus statement is particularly timely to assure unified advancement., Comment: Main Text & Supplementary Text: 56 pages, 3 Figures, 2 Supplementary Figures, 1 Supplementary Table

We explore synergies between the Nancy Grace Roman Space Telescope High Latitude Wide Area Survey (HLWAS) and CMB experiments, specifically Simons Observatory (SO) and CMB-Stage4 (S4). Our simulated analyses include weak lensing, photometric galaxy clustering, CMB lensing, thermal SZ, and cross-correlations between these probes. While we assume the nominal 16,500 square degree area for SO and S4, we consider multiple survey designs for Roman that overlap with Rubin Observatory's Legacy Survey of Space and Time (LSST): the 2000 square degree reference survey using four photometric bands, and two shallower single-band surveys that cover 10,000 and 18,000 square degree, respectively. We find a ~2x increase in the dark energy figure of merit when including CMB-S4 data for all Roman survey designs. We further find a strong increase in constraining power for the Roman wide survey scenario cases, despite the reduction in galaxy number density, and the increased systematic uncertainties assumed due to the single band coverage. Even when tripling the already worse systematic uncertainties in the Roman wide scenarios, which reduces the 10,000 square degree FoM from 269 to 178, we find that the larger survey area is still significantly preferred over the reference survey (FoM 64). We conclude that for the specific analysis choices and metrics of this paper, a Roman wide survey is unlikely to be systematics-limited (in the sense that one saturates the improvement that can be obtained by increasing survey area). We outline several specific implementations of a two-tier Roman survey (1000 square degree with 4 bands, and a second wide tier in one band) that can further mitigate the risk of systematics for Roman wide concepts., Comment: Comments welcome!

This is the interim publication of the first International Scientific Report on the Safety of Advanced AI. The report synthesises the scientific understanding of general-purpose AI -- AI that can perform a wide variety of tasks -- with a focus on understanding and managing its risks. A diverse group of 75 AI experts contributed to this report, including an international Expert Advisory Panel nominated by 30 countries, the EU, and the UN. Led by the Chair, these independent experts collectively had full discretion over the report's content., Comment: Available under the open government license at https://www.gov.uk/government/publications/international-scientific-report-on-the-safety-of-advanced-ai

We propose practical deep Gaussian process models on Riemannian manifolds, similar in spirit to residual neural networks. With manifold-to-manifold hidden layers and an arbitrary last layer, they can model manifold- and scalar-valued functions, as well as vector fields. We target data inherently supported on manifolds, which is too complex for shallow Gaussian processes thereon. For example, while the latter perform well on high-altitude wind data, they struggle with the more intricate, nonstationary patterns at low altitudes. Our models significantly improve performance in these settings, enhancing prediction quality and uncertainty calibration, and remain robust to overfitting, reverting to shallow models when additional complexity is unneeded. We further showcase our models on Bayesian optimisation problems on manifolds, using stylised examples motivated by robotics, and obtain substantial improvements in later stages of the optimisation process. Finally, we show our models to have potential for speeding up inference for non-manifold data, when, and if, it can be mapped to a proxy manifold well enough.

Robust modeling of non-linear scales is critical for accurate cosmological inference in Stage IV surveys. For weak lensing analyses in particular, a key challenge arises from the incomplete understanding of how non-gravitational processes, such as supernovae and active galactic nuclei - collectively known as baryonic feedback - affect the matter distribution. Several existing methods for modeling baryonic feedback treat it independently from the underlying cosmology, an assumption which has been found to be inaccurate by hydrodynamical simulations. In this work, we examine the impact of this coupling between baryonic feedback and cosmology on parameter inference at LSST Y1 precision. We build mock 3$\times$2pt data vectors using the Magneticum suite of hydrodynamical simulations, which span a wide range of cosmologies while keeping subgrid parameters fixed. We perform simulated likelihood analyses for two baryon mitigation techniques: (i) the Principal Component Analysis (PCA) method which identifies eigenmodes for capturing the effect baryonic feedback on the data vector and (ii) HMCode2020 (Mead et al. 2021) which analytically models the modification in the matter distribution using a halo model approach. Our results show that the PCA method is robust to the coupling between cosmology and baryonic feedback, whereas, when using HMCode2020 there can be up to $0.5\sigma$ bias in $\Omega_\text{m}$-$S_8$. For HMCode2020, the bias also correlates with the input cosmology while for PCA we find no such correlation., Comment: correction to acknowledgements