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Effective image post-processing algorithms are vital for the successful direct imaging of exoplanets. Standard PSF subtraction methods use techniques based on a low-rank approximation to separate the rotating planet signal from the quasi-static speckles, and rely on signal-to-noise ratio maps to detect the planet. These steps do not interact or feed each other, leading to potential limitations in the accuracy and efficiency of exoplanet detection. We aim to develop a novel approach that iteratively finds the flux of the planet and the low-rank approximation of quasi-static signals, in an attempt to improve upon current PSF subtraction techniques. In this study, we extend the standard L2 norm minimization paradigm to an L1 norm minimization framework to better account for noise statistics in the high contrast images. Then, we propose a new method, referred to as Alternating Minimization Algorithm with Trajectory, that makes a more advanced use of estimating the low-rank approximation of the speckle field and the planet flux by alternating between them and utilizing both L1 and L2 norms. For the L1 norm minimization, we propose using L1 norm low-rank approximation, a low-rank approximation computed using an exact block-cyclic coordinate descent method, while we use randomized singular value decomposition for the L2 norm minimization. Additionally, we enhance the visibility of the planet signal using a likelihood ratio as a postprocessing step. Numerical experiments performed on a VLT/SPHERE-IRDIS dataset show the potential of AMAT to improve upon the existing approaches in terms of higher S/N, sensitivity limits, and ROC curves. Moreover, for a systematic comparison, we used datasets from the exoplanet data challenge to compare our algorithm to other algorithms in the challenge, and AMAT with likelihood ratio map performs better than most algorithms tested on the exoplanet data challenge., Comment: Submitted to A&A, 20 pages, 20 figures

Optical injection locking of the repetition frequency of a quantum cascade laser frequency comb is demonstrated using an intensity modulated near-infrared light at 1.55 $\mu$m illuminating the front facet of the laser. Compared to the traditional electrical modulation approach, the introduced technique presents benefits from several perspectives such as the availability of mature and high bandwidth equipment in the near-infrared, circumvent the need of dedicated electronic components for the quantum cascade laser, and allows a direct link between the near and mid-infrared for amplitude to frequency modulation. We show that this stabilization scheme, used with a moderate near-infrared power of 5 mW, allows a tight lock to a radio-frequency generator with less than 1 mrad residual phase noise at 1 s integration time. We also perform a full characterization of the mechanism and evidence that the locking range follows Adler's law. A comparison with our recent characterization of the traditional method indicates that the optical approach could potentially lead to lower phase noise, which would benefit mid-infrared spectroscopy and metrological applications.

Numerous studies have recently focused on incorporating different variations of equivariance in Convolutional Neural Networks (CNNs). In particular, rotation-equivariance has gathered significant attention due to its relevance in many applications related to medical imaging, microscopic imaging, satellite imaging, industrial tasks, etc. While prior research has primarily focused on enhancing classification tasks with rotation equivariant CNNs, their impact on more complex architectures, such as U-Net for image segmentation, remains scarcely explored. Indeed, previous work interested in integrating rotation-equivariance into U-Net architecture have focused on solving specific applications with a limited scope. In contrast, this paper aims to provide a more exhaustive evaluation of rotation equivariant U-Net for image segmentation across a broader range of tasks. We benchmark their effectiveness against standard U-Net architectures, assessing improvements in terms of performance and sustainability (i.e., computational cost). Our evaluation focuses on datasets whose orientation of objects of interest is arbitrary in the image (e.g., Kvasir-SEG), but also on more standard segmentation datasets (such as COCO-Stuff) as to explore the wider applicability of rotation equivariance beyond tasks undoubtedly concerned by rotation equivariance. The main contribution of this work is to provide insights into the trade-offs and advantages of integrating rotation equivariance for segmentation tasks.

We report on neutron diffraction, magnetoresistance, magnetization, and magnetic torque measurements under high magnetic field in the helical antiferromagnet CeVGe$_3$. This compound exhibits Kondo lattice coherence and helical antiferromagnetic (AFM) ordering at ambient pressure, similar to the well-studied CeRhIn$_5$. Our measurements reveal that CeVGe$_3$ undergoes a magnetic transition from an incommensurate (ICM) AFM state to an up-up-down-down commensurate (CM) AFM structure, followed by a transition to a novel phase at higher fields. A quantum phase transition occurs around 21.3 T. This rich magnetic field phase diagram closely resembles that of CeRhIn$_5$. Furthermore, angle-dependent magnetoresistance measurements reveal that all transitions in CeVGe$_3$ occur from the field component along the $ab$ plane. These findings highlight the intricate interplay among exchange interactions, crystal field effects, ground state properties, and crystalline symmetries., Comment: 8 pages, 9 figures, accepted by Physics Review B

The computation of integrals is a fundamental task in the analysis of functional data, which are typically considered as random elements in a space of squared integrable functions. Borrowing ideas from recent advances in the Monte Carlo integration literature, we propose effective unbiased estimation and inference procedures for integrals of uni- and multivariate random functions. Several applications to key problems in functional data analysis (FDA) involving random design points are studied and illustrated. In the absence of noise, the proposed estimates converge faster than the sample mean and the usual algorithms for numerical integration. Moreover, the proposed estimator facilitates effective inference by generally providing better coverage with shorter confidence and prediction intervals, in both noisy and noiseless setups., Comment: 26 pages, Supplementary Material separately available upon request

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external document retrieval to provide domain-specific or up-to-date knowledge. The effectiveness of RAG depends on the relevance of retrieved documents, which is influenced by the semantic alignment of embeddings with the domain's specialized content. Although full fine-tuning can align language models to specific domains, it is computationally intensive and demands substantial data. This paper introduces Hierarchical Embedding Alignment Loss (HEAL), a novel method that leverages hierarchical fuzzy clustering with matrix factorization within contrastive learning to efficiently align LLM embeddings with domain-specific content. HEAL computes level/depth-wise contrastive losses and incorporates hierarchical penalties to align embeddings with the underlying relationships in label hierarchies. This approach enhances retrieval relevance and document classification, effectively reducing hallucinations in LLM outputs. In our experiments, we benchmark and evaluate HEAL across diverse domains, including Healthcare, Material Science, Cyber-security, and Applied Maths.

The coldest metal-poor population made of T and Y dwarfs are archaeological tracers of our Galaxy because they are very old and have kept the pristine material. The optical properties of these objects are important to characterise their atmospheric properties. We aim at characterising further the optical properties of ultracool metal-poor population with deep far-red optical images and parallax determinations. We solve trigonometric parallaxes of five metal-poor T dwarf candidates using 2-year monitoring with Calar-Alto 3.5-m telescope. We obtain $z'$-band photometry for the other 12 metal-poor T dwarf candidates using the 10.4-m GTC, the 8.2-m VLT, and the DES, increasing the sample of T subdwarfs with optical photometry from 12 to 24. We report a 3-$\sigma$ limit for the Accident in five optical bands using the 10.4-m GTC. We confirm four T subdwarfs and the Accident as a Y subdwarf, and propose two more Y subdwarf candidates. We emphasise that the $z_{PS1}-W1$ colour combining with the $W1-W2$ colour could break the metallicity-temperature degeneracy for T and possibly for Y dwarfs. The $z_{PS1}-W1$ colour shifts redward when metallicity decreases for a certain temperature, which is not predicted by state-of-the-art ultracool models. The Accident has the reddest $z_{PS1}-W1$ colour among our sample. The $z_{PS1}-W1$ colour will be useful to search for other examples of this cold and old population in upcoming and existing deep optical and infrared large-area surveys., Comment: 17 pages, 7 figures, 2 appendices, submitted to A&A, comments are welcome

This paper explores the integration of generative AI into the field of art therapy. Leveraging proven text-to-image models, we introduce a novel technical design to complement art therapy. The resulting AI-based tools shall enable patients to refine and customize their creative work, opening up new avenues of expression and accessibility. Using three illustrative examples, we demonstrate potential outputs of our solution and evaluate them qualitatively. Furthermore, we discuss the current limitations and ethical considerations associated with this integration and provide an outlook into future research efforts. Our implementations are publicly available at https://github.com/BFH-AMI/sds24.

High-quality material generation is key for virtual environment authoring and inverse rendering. We propose MaterialPicker, a multi-modal material generator leveraging a Diffusion Transformer (DiT) architecture, improving and simplifying the creation of high-quality materials from text prompts and/or photographs. Our method can generate a material based on an image crop of a material sample, even if the captured surface is distorted, viewed at an angle or partially occluded, as is often the case in photographs of natural scenes. We further allow the user to specify a text prompt to provide additional guidance for the generation. We finetune a pre-trained DiT-based video generator into a material generator, where each material map is treated as a frame in a video sequence. We evaluate our approach both quantitatively and qualitatively and show that it enables more diverse material generation and better distortion correction than previous work.

Ultraviolet absorption line spectroscopy is a sensitive diagnostic for the properties of interstellar and circumgalactic gas. Down-the-barrel observations, where the absorption is measured against the galaxy itself, are commonly used to study feedback from galactic outflows and to make predictions about the leakage of HI ionizing photons into the intergalactic medium. Nonetheless, the interpretation of these observations is challenging and observational compromises are often made in terms of signal-to-noise, spectral resolution, or the use of stacking analyses. In this paper, we present a novel quantitative assessment of UV absorption line measurement techniques by using mock observations of a hydrodynamical simulation. We use a simulated galaxy to create 22,500 spectra in the commonly used SiII lines while also modeling the signal-to-noise and spectral resolution of recent rest-frame UV galaxy surveys at both high and low redshifts. We show that the residual flux of absorption features is easily overestimated for single line measurements and for stacked spectra. Additionally, we explore the robustness of the partial covering model for estimating column densities from spectra and find under-predictions on average of 1.25 dex. We show that the under-prediction is likely caused by high-column-density sight-lines that are optically-thick to dust making them invisible in UV spectra., Comment: 27 pages, 18 figures, Accepted to ApJ

In modern deep neural networks, the learning dynamics of the individual neurons is often obscure, as the networks are trained via global optimization. Conversely, biological systems build on self-organized, local learning, achieving robustness and efficiency with limited global information. We here show how self-organization between individual artificial neurons can be achieved by designing abstract bio-inspired local learning goals. These goals are parameterized using a recent extension of information theory, Partial Information Decomposition (PID), which decomposes the information that a set of information sources holds about an outcome into unique, redundant and synergistic contributions. Our framework enables neurons to locally shape the integration of information from various input classes, i.e. feedforward, feedback, and lateral, by selecting which of the three inputs should contribute uniquely, redundantly or synergistically to the output. This selection is expressed as a weighted sum of PID terms, which, for a given problem, can be directly derived from intuitive reasoning or via numerical optimization, offering a window into understanding task-relevant local information processing. Achieving neuron-level interpretability while enabling strong performance using local learning, our work advances a principled information-theoretic foundation for local learning strategies., Comment: 24 pages, 11 figures

Goupil is a software library designed for the Monte Carlo transport of low-energy gamma-rays, such as those emitted from radioactive isotopes. The library is distributed as a Python module. It implements a dedicated backward sampling algorithm that is highly effective for transporting photons in low density media. In the air, computation time savings of five orders of magnitude are achieved while maintaining an accuracy comparable to that of conventional Monte Carlo methods, to the nearest percent., Comment: 50 pages, 8 figures. Submitted to CPC

The 3D reconstruction of faces gains wide attention in computer vision and is used in many fields of application, for example, animation, virtual reality, and even forensics. This work is motivated by monitoring patients in sleep laboratories. Due to their unique characteristics, sensors from the radar domain have advantages compared to optical sensors, namely penetration of electrically non-conductive materials and independence of light. These advantages of radar signals unlock new applications and require adaptation of 3D reconstruction frameworks. We propose a novel model-based method for 3D reconstruction from radar images. We generate a dataset of synthetic radar images with a physics-based but non-differentiable radar renderer. This dataset is used to train a CNN-based encoder to estimate the parameters of a 3D morphable face model. Whilst the encoder alone already leads to strong reconstructions of synthetic data, we extend our reconstruction in an Analysis-by-Synthesis fashion to a model-based autoencoder. This is enabled by learning the rendering process in the decoder, which acts as an object-specific differentiable radar renderer. Subsequently, the combination of both network parts is trained to minimize both, the loss of the parameters and the loss of the resulting reconstructed radar image. This leads to the additional benefit, that at test time the parameters can be further optimized by finetuning the autoencoder unsupervised on the image loss. We evaluated our framework on generated synthetic face images as well as on real radar images with 3D ground truth of four individuals.

This work presents Switti, a scale-wise transformer for text-to-image generation. Starting from existing next-scale prediction AR models, we first explore them for T2I generation and propose architectural modifications to improve their convergence and overall performance. We then argue that scale-wise transformers do not require causality and propose a non-causal counterpart facilitating ~11% faster sampling and lower memory usage while also achieving slightly better generation quality. Furthermore, we reveal that classifier-free guidance at high-resolution scales is often unnecessary and can even degrade performance. By disabling guidance at these scales, we achieve an additional sampling acceleration of ~20% and improve the generation of fine-grained details. Extensive human preference studies and automated evaluations show that Switti outperforms existing T2I AR models and competes with state-of-the-art T2I diffusion models while being up to 7 times faster., Comment: 20 pages, 22 figures

Decomposing 3D assets into material parts is a common task for artists and creators, yet remains a highly manual process. In this work, we introduce Select Any Material (SAMa), a material selection approach for various 3D representations. Building on the recently introduced SAM2 video selection model, we extend its capabilities to the material domain. We leverage the model's cross-view consistency to create a 3D-consistent intermediate material-similarity representation in the form of a point cloud from a sparse set of views. Nearest-neighbour lookups in this similarity cloud allow us to efficiently reconstruct accurate continuous selection masks over objects' surfaces that can be inspected from any view. Our method is multiview-consistent by design, alleviating the need for contrastive learning or feature-field pre-processing, and performs optimization-free selection in seconds. Our approach works on arbitrary 3D representations and outperforms several strong baselines in terms of selection accuracy and multiview consistency. It enables several compelling applications, such as replacing the diffuse-textured materials on a text-to-3D output, or selecting and editing materials on NeRFs and 3D-Gaussians., Comment: Project Page: https://mfischer-ucl.github.io/sama

This paper introduces a novel collaborative neurodynamic model for computing nonnegative Canonical Polyadic Decomposition (CPD). The model relies on a system of recurrent neural networks to solve the underlying nonconvex optimization problem associated with nonnegative CPD. Additionally, a discrete-time version of the continuous neural network is developed. To enhance the chances of reaching a potential global minimum, the recurrent neural networks are allowed to communicate and exchange information through particle swarm optimization (PSO). Convergence and stability analyses of both the continuous and discrete neurodynamic models are thoroughly examined. Experimental evaluations are conducted on random and real-world datasets to demonstrate the effectiveness of the proposed approach.

The classical McShane-Whitney extension theorem for Lipschitz functions is refined by showing that for a closed subset of the domain, it remains valid for any interval of the real line. This result is also extended to the setting of locally (pointwise) Lipschitz functions. In contrast to Lipschitz and pointwise Lipschitz extensions, the construction of locally Lipschitz extensions is based on Lipschitz partitions of unity of countable open covers of the domain. Such partitions of unity are a special case of a more general result obtained by Zden\v{e}k Frol\'{\i}k. To avoid the use of Stone's theorem (paracompactness of metrizable spaces), it is given a simple direct proof of this special case of Frol\'{\i}k's result. As an application, it is shown that the locally Lipschitz functions are precisely the locally finite sums of sequences of Lipschitz functions. Also, it is obtained a natural locally Lipschitz version of one of Michael's selection theorems.

We investigate the distribution of modular inverses modulo positive integers $c$ in a large interval. We provide upper and lower bounds for their box, ball and isotropic discrepancy, thereby exhibiting some deviations from random point sets. The analysis is based, among other things, on a new bound for a triple sum of Kloosterman sums., Comment: 27 pages, 3 figures. Added a second moment estimate

The relationship between B-field orientation and density structure in molecular clouds is often assessed using the Histogram of Relative Orientations (HRO). We perform a plane-of-the-sky geometrical analysis of projected B-fields, by interpreting HROs in dense, spheroidal, prestellar and protostellar cores. We use James Clerk Maxwell Telescope (JCMT) POL-2 850 $\mu$m polarisation maps and Herschel column density maps to study dense cores in the Ophiuchus molecular cloud complex. We construct two-dimensional core models, assuming Plummer column density profiles and modelling both linear and hourglass B-fields. We find high-aspect-ratio ellipsoidal cores produce strong HRO signals, as measured using the shape parameter $\xi$. Cores with linear fields oriented $< 45^{\circ}$ from their minor axis produce constant HROs with $-1 < \xi < 0$, indicating fields are preferentially parallel to column density gradients. Fields parallel to the core minor axis produce the most negative value of $\xi$. For low-aspect-ratio cores, $\xi \approx 0$ for linear fields. Hourglass fields produce a minimum in $\xi$ at intermediate densities in all cases, converging to the minor-axis-parallel linear field value at high and low column densities. We create HROs for six dense cores in Ophiuchus. $\rho$ Oph A and IRAS 16293 have high aspect ratios and preferentially negative HROs, consistent with moderately strong-field behaviour. $\rho$ Oph C, L1689A and L1689B have low aspect ratios, and $\xi \approx 0$. $\rho$ Oph B is too complex to be modelled using a simple spheroidal field geometry. We see no signature of hourglass fields, agreeing with previous findings that dense cores generally exhibit linear fields on these size scales., Comment: 16 pages, 19 figures, 2 tables. Accepted for publication in MNRAS

In the paper we consider convex cones in infinite-dimensional real vector spaces which are endowed with no topology. The main purpose is to study an internal geometric structure of convex cones and to obtain an analytical description of those. To this end, we first introduce the notion of an open component of a convex cone and then prove that an arbitrary convex cone is the disjoint union of the partial ordered family of its open components and, moreover, as an ordered set this family is an upper semilattice. We identify the structure of this upper semilattice with the internal geometric structure of a convex cone. We demonstrate that the internal geometric structure of a convex cone is related to its facial structure but in the infinite-dimensional setting these two structures may differ each other. Further, we study the internal geometric structure of conical halfspaces (convex cones whose complements are also convex cones). We show that every conical halfspace is the disjoint union of the linear ordered family of its open components each of which is a conical halfspace in its linear hull. Using the internal geometric structure of conical halfspaces, each asymmetric conical halfspace is associated with a linearly ordered family of linear functions, which generates in turn a real-valued function, called a step-linear one, analytically describing this conical halfspace. At last, we establish that an arbitrary asymmetric convex cone admits an analytical representation by the family of step-linear functions., Comment: 26 pages. arXiv admin note: text overlap with arXiv:2312.07714

We study the dynamical response of viscous materials to gravitational waves, in the context of a fully relativistic theory of fluid dynamics. For the first time, we calculate oscillation modes and scattering properties of viscous stars. Viscous stars absorb high frequency radiation, following a dispersion relation introduced by Press. In the extremely large viscosity regime, stars would become reflectors of waves, but this regime appears to be forbidden by causality bounds. In the context of black hole mimickers, we show how maximally viscous stars on the threshold of stability mimic the absorption of a black hole with the same mass. Our results suggest that rotating viscous stars will amplify incoming radiation., Comment: 5+5 pages, 2+8 figures

This document is a replication of the original "Rank-N-Contrast" (arXiv:2210.01189v2) paper published in 2023. This evaluation is done for academic purposes. Deep regression models often fail to capture the continuous nature of sample orders, creating fragmented representations and suboptimal performance. To address this, we reproduced the Rank-N-Contrast (RNC) framework, which learns continuous representations by contrasting samples by their rankings in the target space. Our study validates RNC's theoretical and empirical benefits, including improved performance and robustness. We extended the evaluation to an additional regression dataset and conducted robustness tests using a holdout method, where a specific range of continuous data was excluded from the training set. This approach assessed the model's ability to generalise to unseen data and achieve state-of-the-art performance. This replication study validates the original findings and broadens the understanding of RNC's applicability and robustness.

Understanding deep models is crucial for deploying them in safety-critical applications. We introduce GIFT, a framework for deriving post-hoc, global, interpretable, and faithful textual explanations for vision classifiers. GIFT starts from local faithful visual counterfactual explanations and employs (vision) language models to translate those into global textual explanations. Crucially, GIFT provides a verification stage measuring the causal effect of the proposed explanations on the classifier decision. Through experiments across diverse datasets, including CLEVR, CelebA, and BDD, we demonstrate that GIFT effectively reveals meaningful insights, uncovering tasks, concepts, and biases used by deep vision classifiers. Our code, data, and models are released at https://github.com/valeoai/GIFT.

Histopathology, the microscopic study of diseased tissue, is increasingly digitized, enabling improved visualization and streamlined workflows. An important task in histopathology is the segmentation of cells and glands, essential for determining shape and frequencies that can serve as indicators of disease. Deep learning tools are widely used in histopathology. However, variability in tissue appearance and cell morphology presents challenges for achieving reliable segmentation, often requiring manual correction to improve accuracy. This work introduces CellPilot, a framework that bridges the gap between automatic and interactive segmentation by providing initial automatic segmentation as well as guided interactive refinement. Our model was trained on over 675,000 masks of nine diverse cell and gland segmentation datasets, spanning 16 organs. CellPilot demonstrates superior performance compared to other interactive tools on three held-out histopathological datasets while enabling automatic segmentation. We make the model and a graphical user interface designed to assist practitioners in creating large-scale annotated datasets available as open-source, fostering the development of more robust and generalized diagnostic models.

Deep Learning models tend to learn correlations of patterns on huge datasets. The bigger these systems are, the more complex are the phenomena they can detect, and the more data they need for this. The use of Artificial Intelligence (AI) is becoming increasingly ubiquitous in our society, and its impact is growing everyday. The promises it holds strongly depend on their fair and universal use, such as access to information or education for all. In a world of inequalities, they can help to reach the most disadvantaged areas. However, such a universal systems must be able to represent society, without benefiting some at the expense of others. We must not reproduce the inequalities observed throughout the world, but educate these IAs to go beyond them. We have seen cases where these systems use gender, race, or even class information in ways that are not appropriate for resolving their tasks. Instead of real causal reasoning, they rely on spurious correlations, which is what we usually call a bias. In this paper, we first attempt to define what is a bias in general terms. It helps us to demystify the concept of bias, to understand why we can find them everywhere and why they are sometimes useful. Second, we focus over the notion of what is generally seen as negative bias, the one we want to avoid in machine learning, before presenting a general zoology containing the most common of these biases. We finally conclude by looking at classical methods to detect them, by means of specially crafted datasets of templates and specific algorithms, and also classical methods to mitigate them., Comment: Publication in Spanish in the Journal Bits de Ciencias: https://www.dcc.uchile.cl/media/bits/pdfs/bits26.2-sesgos-fantasticos.pdf

The probability current is a vital quantity in the Fokker-Planck description of stochastic processes. It characterizes nonequilibrium stationary states, appears in linear response calculation, and has been related to the entropy production and the heat flux. We recover and review the probability current in the Onsager-Machlup functional approach to Markov processes. We derive a self contained expression for the stationary probability current and the non-equilibrium fluctuation-dissipation theorem using field theoretical methods. The derived formulas are explicitly evaluated in the Ornstein-Uhlenbeck process of a harmonically bound particle in shear flow as exemplary analytic expressions. Our work closes a gap since it removes a missing link, i.e.~the probability current, in the supposed equivalence of the Fokker-Planck and the path-integral approach., Comment: 17 pages

Arrays of gate-defined semiconductor quantum dots are among the leading candidates for building scalable quantum processors. High-fidelity initialization, control, and readout of spin qubit registers require exquisite and targeted control over key Hamiltonian parameters that define the electrostatic environment. However, due to the tight gate pitch, capacitive crosstalk between gates hinders independent tuning of chemical potentials and interdot couplings. While virtual gates offer a practical solution, determining all the required cross-capacitance matrices accurately and efficiently in large quantum dot registers is an open challenge. Here, we establish a Modular Automated Virtualization System (MAViS) -- a general and modular framework for autonomously constructing a complete stack of multi-layer virtual gates in real time. Our method employs machine learning techniques to rapidly extract features from two-dimensional charge stability diagrams. We then utilize computer vision and regression models to self-consistently determine all relative capacitive couplings necessary for virtualizing plunger and barrier gates in both low- and high-tunnel-coupling regimes. Using MAViS, we successfully demonstrate accurate virtualization of a dense two-dimensional array comprising ten quantum dots defined in a high-quality Ge/SiGe heterostructure. Our work offers an elegant and practical solution for the efficient control of large-scale semiconductor quantum dot systems., Comment: 14 pages, 5 figures, 8 pages of supplemental material

In this paper we introduce several quantitative methods for the lambda-calculus based on partial metrics, a well-studied variant of standard metric spaces that have been used to metrize non-Hausdorff topologies, like those arising from Scott domains. First, we study quantitative variants, based on program distances, of sensible equational theories for the $\lambda$-calculus, like those arising from B\"ohm trees and from the contextual preorder. Then, we introduce applicative distances capturing higher-order Scott topologies, including reflexive objects like the $D_\infty$ model. Finally, we provide a quantitative insight on the well-known connection between the B\"ohm tree of a $\lambda$-term and its Taylor expansion, by showing that the latter can be presented as an isometric transformation.

Disorder in the heterogeneous material stack of semiconductor spin qubit systems introduces noise that compromises quantum information processing, posing a challenge to coherently control large-scale quantum devices. Here, we exploit low-disorder epitaxial strained quantum wells in Ge/SiGe heterostructures grown on Ge wafers to comprehensively probe the noise properties of complex micron-scale devices comprising of up to ten quantum dots and four rf-charge sensors arranged in a two-dimensional array. We demonstrate an average charge noise of $\sqrt{S_{0}}=0.3(1)$ $\mu\mathrm{eV}/\sqrt{\mathrm{Hz}}$ at 1 Hz across different locations on the wafer, providing a benchmark for quantum confined holes. We then establish hole-spin qubit control in these heterostructures and extend our investigation from electrical to magnetic noise through spin echo measurements. Exploiting dynamical decoupling sequences, we quantify the power spectral density components arising from the hyperfine interaction with $^{73}$Ge spinful isotopes and identify coherence modulations associated with the interaction with the $^{29}$Si nuclear spin bath near the Ge quantum well. We estimate an integrated hyperfine noise amplitude $\sigma_f$ of 180(8) kHz from $^{73}$Ge and of 47(5) kHz from $^{29}$Si, underscoring the need for full isotopic purification of the qubit host environment.

We consider the optimal control of occupied processes which record all positions of the state process. Dynamic programming yields nonlinear equations on the space of positive measures. We develop the viscosity theory for this infinite dimensional parabolic $occupied$ PDE by proving a comparison result between sub and supersolutions, and thus provide a characterization of the value function as the unique viscosity solution. Toward this proof, an extension of the celebrated Crandall-Ishii-Lions (second order) Lemma to this setting, as well as finite-dimensional approximations, is established. Examples including the occupied heat equation, and pricing PDEs of financial derivatives contingent on the occupation measure are also discussed., Comment: 23 pages

We report the successful growth of CeCuSi single crystals using a metallic flux method and the physical properties using structural, magnetic, electrical transport, optical, and heat capacity measurements. CeCuSi crystallizes in a hexagonal-bar shape, and single crystal x-ray diffraction confirms the ZrBeSi-type structure (space group $P6_{3}/mmc$). CeCuSi orders ferromagnetically below $T_\textrm{C}=15.5$ K with easy magnetization direction within the basal plane. The Ce$^{3+}$ ions are situated within a triangular lattice with a point group of $D_{3d}$. We perform a detailed crystalline electric field (CEF) analysis of the anisotropic magnetic susceptibility, the Schottky anomaly in heat capacity, and the Raman-active excitations. The results indicate a ground state doublet with magnetic moment primarily in the basal plane, and a ferromagnetic interaction along both directions. The exponential behavior in resistivity and in heat capacity below $T_\textrm{C}$ can also be well explained by the ferromagnetic magnon model. We found that CeCuSi does not exhibit the CEF hard axis ordering observed in many ferromagnetic Kondo lattice (FM-KL) compounds. Our CEF analysis suggests that the exchange interactions along both axes are ferromagnetic, potentially explaining the absence of hard-axis ordering., Comment: 11 pages, 6 figures, Accepted by Physics Review B

The Tayler instability (TI) of toroidal magnetic fields is a candidate mechanism for driving turbulence, angular momentum transport, and dynamo action in stellar radiative zones. Recently Skoutnev & Beloborodov (2024) revisited the linear stability analysis of a toroidal magnetic field in a rotating and stably stratified fluid. In this paper, we extend the analysis to include both thermal and compositional stratification, allowing for general application to stars. We formulate an analytical instability criterion for use as a ``toggle switch" in stellar evolution codes. It determines when and where in a star the Tayler-Spruit dynamo may be responsible for angular momentum transport. We implement such a ``toggle switch" in the MESA stellar evolution code and map out the stability of each mode of the TI on a grid of stellar evolution models. In evolved lower mass stars, the TI becomes suppressed in compositionally stratified regions, undermining the Tayler-Spruit dynamo as an explanation of the core-envelope coupling. In higher mass stars, the TI is active throughout the radiative zones, but at different wavenumbers than previously expected.

We propose a novel algorithm for combined unit/filter and layer pruning of deep neural networks that functions during training and without requiring a pre-trained network to apply. Our algorithm optimally trades-off learning accuracy and pruning levels while balancing layer vs. unit/filter pruning and computational vs. parameter complexity using only three user-defined parameters, which are easy to interpret and tune. The optimal network structure is found as the solution of a stochastic optimization problem over the network weights and the parameters of variational Bernoulli distributions for 0/1 Random Variables scaling the units and layers of the network. Pruning occurs when a variational parameter converges to 0 rendering the corresponding structure permanently inactive, thus saving computations during training and prediction. A key contribution of our approach is to define a cost function that combines the objectives of prediction accuracy and network pruning in a computational/parameter complexity-aware manner and the automatic selection of the many regularization parameters. We show that the solutions of the optimization problem to which the algorithm converges are deterministic networks. We analyze the ODE system that underlies our stochastic optimization algorithm and establish domains of attraction around zero for the dynamics of the network parameters. These results provide theoretical support for safely pruning units/filters and/or layers during training and lead to practical pruning conditions. We evaluate our method on the CIFAR-10/100 and ImageNet datasets using ResNet architectures and demonstrate that our method improves upon layer only or unit only pruning and favorably competes with combined unit/filter and layer pruning algorithms requiring pre-trained networks with respect to pruning ratios and test accuracy., Comment: 28 pages, 4 figures, 5 tables

Although diffusion models can generate remarkably high-quality samples, they are intrinsically bottlenecked by their expensive iterative sampling procedure. Consistency models (CMs) have recently emerged as a promising diffusion model distillation method, reducing the cost of sampling by generating high-fidelity samples in just a few iterations. Consistency model distillation aims to solve the probability flow ordinary differential equation (ODE) defined by an existing diffusion model. CMs are not directly trained to minimize error against an ODE solver, rather they use a more computationally tractable objective. As a way to study how effectively CMs solve the probability flow ODE, and the effect that any induced error has on the quality of generated samples, we introduce Direct CMs, which \textit{directly} minimize this error. Intriguingly, we find that Direct CMs reduce the ODE solving error compared to CMs but also result in significantly worse sample quality, calling into question why exactly CMs work well in the first place. Full code is available at: https://github.com/layer6ai-labs/direct-cms., Comment: NeurIPS 2024 ATTRIB Workshop

What mechanisms underlie linguistic generalization in large language models (LLMs)? This question has attracted considerable attention, with most studies analyzing the extent to which the language skills of LLMs resemble rules. As of yet, it is not known whether linguistic generalization in LLMs could equally well be explained as the result of analogical processes, which can be formalized as similarity operations on stored exemplars. A key shortcoming of prior research is its focus on linguistic phenomena with a high degree of regularity, for which rule-based and analogical approaches make the same predictions. Here, we instead examine derivational morphology, specifically English adjective nominalization, which displays notable variability. We introduce a new method for investigating linguistic generalization in LLMs: focusing on GPT-J, we fit cognitive models that instantiate rule-based and analogical learning to the LLM training data and compare their predictions on a set of nonce adjectives with those of the LLM, allowing us to draw direct conclusions regarding underlying mechanisms. As expected, rule-based and analogical models explain the predictions of GPT-J equally well for adjectives with regular nominalization patterns. However, for adjectives with variable nominalization patterns, the analogical model provides a much better match. Furthermore, GPT-J's behavior is sensitive to the individual word frequencies, even for regular forms, a behavior that is consistent with an analogical account of regular forms but not a rule-based one. These findings refute the hypothesis that GPT-J's linguistic generalization on adjective nominalization involves rules, suggesting similarity operations on stored exemplars as the underlying mechanism. Overall, our study suggests that analogical processes play a bigger role in the linguistic generalization of LLMs than previously thought.

Behavior models form an integral component of Digital Twins. The specific characteristics of these models may vary depending on the use case. One of these key characteristics is the modeling depth. Behavior models with a lower modeling depth depict the behavior of the asset in an abstract way, while those with a higher modeling depth depict the behavior in detail. Even if very detailed behavior models are flexible and realistic, they also require a lot of resources such as computing power, simulation time and memory requirements. In some applications, however, only limited resources are available. The automated creation of Digital Twins is of crucial importance for their widespread use. Although there are methods for the automated creation of behavior models for Digital Twins with a specific modeling depth, there is currently no method for the automated creation of behavior models with varying modeling depths. This article presents such an approach and demonstrates its advantages using two industrial use cases. It is demonstrated that the automatically created behavior models of lower modeling depth yield results that are almost identical to those of models with a higher modeling depth, but with significantly reduced computing time and required memory. This enables the efficient use of behavior models in a variety of use cases, regardless of the availability of resources.

Two-dimensional materials offering ultrafast photoresponse suffer from low intrinsic absorbance, especially in the mid-infrared wavelength range. Challenges in 2d material doping further complicate the creation of light-sensitive $p-n$ junctions. Here, we experimentally demonstrate a graphene-based infrared detector with simultaneously enhanced absorption and strong structural asymmetry enabling zero-bias photocurrent. A key element for those properties is an asymmetric singular metasurface (ASMS) atop graphene with keen metal wedges providing singular enhancement of local absorbance. The ASMS geometry predefines extra device functionalities. The structures with connected metallic wedges demonstrate polarization ratios up to 200 in a broad range of carrier densities at a wavelength of 8.6 $\mu$m. The structures with isolated wedges display gate-controlled switching between polarization-discerning and polarization-stable photoresponse, a highly desirable yet scarce property for polarized imaging.

Among all materials, mono-crystalline diamond has one of the highest measured thermal conductivities, with values above 2000 W/m/K at room temperature. This stems from momentum-conserving `normal' phonon-phonon scattering processes dominating over momentum-dissipating `Umklapp' processes, a feature that also suggests diamond as an ideal platform to experimentally investigate phonon heat transport phenomena that violate Fourier's law. Here, we introduce dilute nitrogen-vacancy color centers as in-situ, highly precise spin defect thermometers to image temperature inhomogeneities in single-crystal diamond microstructures heated from ambient conditions. We analyze cantilevers with cross-sections in the range from about 0.2 to 2.6 $\mathrm{\mu m}^2$, observing a relation between cross-section and heat flux that departs from Fourier's law predictions. We rationalize such behavior relying on first-principles simulations based on the linearized phonon Boltzmann transport equation, also discussing how fabrication-induced impurities influence conduction. Our temperature-imaging method can be applied to diamond devices of arbitrary geometry, paving the way for the exploration of unconventional, non-diffusive heat transport phenomena., Comment: 8 pages, 4 figures + supplementary materials (8 pages, 7 figures)

We develop methods for performing quantum teleportation of the total spin variables of an unknown state, using quantum nondemolition measurements, spin projection measurements, and classical communication. While theoretically teleportation of high-dimensional states can be attained with the assumption of generalized Bell measurements, this is typically experimentally non-trivial to implement. We introduce two protocols and show that, on average, the teleportation succeeds in teleporting the spin variables of a spin coherent state with average zero angular error in the ideal case, beating classical strategies based on quantum state estimation. In a single run of the teleportation, there is an angular error at the level of ~ 0.1 radians for large ensembles. A potential physical implementation for the scheme is with atomic ensembles and quantum nondemolition measurements performed with light. We analyze the decoherence of the protocols and find that the protocol is robust even in the limit of large ensemble sizes., Comment: 18 pages, 10 figures

Zero-knowledge (ZK) protocols have recently found numerous practical applications, such as in authentication, online-voting, and blockchain systems. These protocols are powered by highly complex pipelines that process deterministic programs, called circuits, written in one of many domain-specific programming languages, e.g., Circom, Noir, and others. Logic bugs in circuit-processing pipelines could have catastrophic consequences and cause significant financial and reputational damage. As an example, consider that a logic bug in a ZK pipeline could result in attackers stealing identities or assets. It is, therefore, critical to develop effective techniques for checking their correctness. In this paper, we present the first systematic fuzzing technique for ZK pipelines, which uses metamorphic test oracles to detect critical logic bugs. We have implemented our technique in an open-source tool called Circuzz. We used Circuzz to test four significantly different ZK pipelines and found a total of 16 logic bugs in all pipelines. Due to their critical nature, 15 of our bugs have already been fixed by the pipeline developers.

Aims. Our goal is twofold. First, to detect new clusters we apply the newest methods for the detection of clustering with the best available wide-field sky surveys in the mid-infrared because they are the least affected by extinction. Second, we address the question of cluster detection's completeness, for now limiting it to the most massive star clusters. Methods. This search is based on the mid-infrared Galactic Legacy Infrared Mid Plane Survey Extraordinaire (GLIMPSE), to minimize the effect of dust extinction. The search Ordering Points To Identify the Clustering Structure (OPTICS) clustering algorithm is applied to identify clusters, after excluding the bluest, presumably foreground sources, to improve the cluster-to-field contrast. The success rate for cluster identification is estimated with a semi-empirical simulation that adds clusters, based on the real objects, to the point source catalog, to be recovered later with the same search algorithm that was used in the search for new cluster candidates. As a first step, this is limited to the most massive star clusters with a total mass of 104 $M_\odot$. Results. Our automated search, combined with inspection of the color-magnitude diagrams and images yielded 659 cluster candidates; 106 of these appear to have been previously identified, suggesting that a large hidden population of star clusters still exists in the inner Milky Way. However, the search for the simulated supermassive clusters achieves a recovery rate of 70 to 95%, depending on the distance and extinction toward them. Conclusions. The new candidates, if confirmed, indicate that the Milky Way still harbors a sizeable population of still unknown clusters. However, they must be objects of modest richness, because our simulation indicates that there is no substantial hidden population of supermassive clusters in the central region of our Galaxy., Comment: Accepted for publication in Astronomy and Astrophysics, 12 pages, 10 figures

We present 850 $\mu$m polarization observations of the IC 348 star-forming region in the Perseus molecular cloud as part of the B-fields In STar-forming Region Observation (BISTRO) survey. We study the magnetic properties of two cores (HH 211 MMS and IC 348 MMS) and a filamentary structure of IC 348. We find that the overall field tends to be more perpendicular than parallel to the filamentary structure of the region. The polarization fraction decreases with intensity, and we estimate the trend by power-law and the mean of the Rice distribution fittings. The power indices for the cores are much smaller than 1, indicative of possible grain growth to micron size in the cores. We also measure the magnetic field strengths of the two cores and the filamentary area separately by applying the Davis-Chandrasekhar-Fermi method and its alternative version for compressed medium. The estimated mass-to-flux ratios are 0.45-2.20 and 0.63-2.76 for HH 211 MMS and IC 348 MMS, respectively, while the ratios for the filament is 0.33-1.50. This result may suggest that the transition from subcritical to supercritical conditions occurs at the core scale ($\sim$ 0.05 pc) in the region. In addition, we study the energy balance of the cores and find that the relative strength of turbulence to the magnetic field tends to be stronger for IC 348 MMS than HH 211 MMS. The result could potentially explain the different configurations inside the two cores: a single protostellar system in HH 211 MMS and multiple protostars in IC 348 MMS., Comment: Accepted for publication in ApJ. 21 pages, 12 figures

Compromise estimation entails using a weighted average of outputs from several candidate models, and is a viable alternative to model selection when the choice of model is not obvious. As such, it is a tool used by both frequentists and Bayesians, and in both cases, the literature is vast and includes studies of performance in simulations and applied examples. However, frequentist researchers often prove oracle properties, showing that a proposed average asymptotically performs at least as well as any other average comprising the same candidates. On the Bayesian side, such oracle properties are yet to be established. This paper considers Bayesian stacking estimators, and evaluates their performance using frequentist asymptotics. Oracle properties are derived for estimators stacking Bayesian linear and logistic regression models, and combined with Monte Carlo experiments that show Bayesian stacking may outperform the best candidate model included in the stack. Thus, the result is not only a frequentist motivation of a fundamentally Bayesian procedure, but also an extended range of methods available to frequentist practitioners.

Neural differential equations offer a powerful approach for learning dynamics from data. However, they do not impose known constraints that should be obeyed by the learned model. It is well-known that enforcing constraints in surrogate models can enhance their generalizability and numerical stability. In this paper, we introduce projected neural differential equations (PNDEs), a new method for constraining neural differential equations based on projection of the learned vector field to the tangent space of the constraint manifold. In tests on several challenging examples, including chaotic dynamical systems and state-of-the-art power grid models, PNDEs outperform existing methods while requiring fewer hyperparameters. The proposed approach demonstrates significant potential for enhancing the modeling of constrained dynamical systems, particularly in complex domains where accuracy and reliability are essential., Comment: 17 pages, 6 figures