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A robust segmentation method that can be used to perform measurements on toenails is presented. The proposed method is used as the first step in a clinical trial to objectively quantify the incidence of a particular pathology. For such an assessment, it is necessary to distinguish a nail, which locally appears to be similar to the skin. Many algorithms have been used, each of which leverages different aspects of toenail appearance. We used the Hough transform to locate the tip of the toe and estimate the nail location and size. Subsequently, we classified the super-pixels of the image based on their geometric and photometric information. Thereafter, the watershed transform delineated the border of the nail. The method was validated using a 348-image medical dataset, achieving an accuracy of 0.993 and an F-measure of 0.925. The proposed method is considerably robust across samples, with respect to factors such as nail shape, skin pigmentation, illumination conditions, and appearance of large regions affected by a medical condition

Nonograms are logic puzzles in which cells in a grid must be colored or left blank according to the numbers that are located in its headers. In this study, we analyze different techniques to solve this type of logical problem using an Heuristic Algorithm, Genetic Algorithm, and Heuristic Algorithm with Neural Network. Furthermore, we generate a public dataset to train the neural networks. We published this dataset and the code of the algorithms. Combination of the heuristic algorithm with a neural network obtained the best results. From state of the art review, no previous works used neural network to solve nonograms, nor combined a network with other algorithms to accelerate the resolution process.

Recent studies have suggested that large language models (LLMs) underperform on mathematical and computer science tasks when these problems are translated from Romanian into English, compared to their original Romanian format. Accurate translation is critical for applications ranging from automatic translations in programming competitions to the creation of high-quality educational materials, as well as minimizing errors or fraud in human translations. This study shows that robust large language models (LLMs) can maintain or even enhance their performance in translating less common languages when given well-structured prompts. Our findings suggest that LLMs, with appropriate supervision, can be reliably used for the automatic translation of IOI (International Olympiad in Informatics)-style tasks. We evaluate several translation methods across multiple LLMs, including OpenRoLLM, Llama 3.1 8B, Llama 3.2 3B and GPT-4o, assessing their translation accuracy and performance stability through repeated runs. Additionally, we augment the OJI (Romanian County-Level Informatics Olympiad) Romanian dataset with accurate English translations, enhancing its utility for future LLM training and evaluation. Through detailed syntactic and semantic analyses, we confirm that with human oversight, LLMs can serve as a viable solution for multilingual problem-solving. We also compare the translation quality of LLMs against human translators, as evaluated by a certified expert, underscoring the potential of LLMs in realworld scenarios., Comment: 12 pages

Recent advances in digital pathology have demonstrated the effectiveness of foundation models across diverse applications. In this report, we present Atlas, a novel vision foundation model based on the RudolfV approach. Our model was trained on a dataset comprising 1.2 million histopathology whole slide images, collected from two medical institutions: Mayo Clinic and Charit\'e - Universt\"atsmedizin Berlin. Comprehensive evaluations show that Atlas achieves state-of-the-art performance across twenty-one public benchmark datasets, even though it is neither the largest model by parameter count nor by training dataset size.

We introduce an adaptive finite element scheme for the efficient approximation of a (large) collection of eigenpairs of selfadjoint elliptic operators in which the adaptive refinement is driven by the solution of a single source problem -- the so-called landscape problem for the operator -- instead of refining based on the computed eigenpairs. Some theoretical justification for the approach is provided, and extensive empirical results indicate that it can provide an attractive alternative to standard adaptive schemes, particularly in the hp-adaptive environment.

We consider logics derived from Euclidean spaces $\mathbb{R}^n$. Each Euclidean space carries relations consisting of those pairs that are, respectively, distance more than 1 apart, distance less than 1 apart, and distance 1 apart. Each relation gives a uni-modal logic of $\mathbb{R}^n$ called the farness, nearness, and constant distance logics, respectively. These modalities are expressive enough to capture various aspects of the geometry of $\mathbb{R}^n$ related to bodies of constant width and packing problems. This allows us to show that the farness logics of the spaces $\mathbb{R}^n$ are all distinct, as are the nearness logics, and the constant distance logics. The farness and nearness logics of $\mathbb{R}$ are shown to strictly contain those of $\mathbb{Q}$, while their constant distance logics agree. It is shown that the farness logic of the reals is not finitely axiomatizable and does not have the finite model property.

Cells and other soft particles are often forced to flow in confined geometries in both laboratory and natural environments, where the elastic deformation induces an additional drag and pressure drop across the particle. In contrast with other multiphase flows, the physical parameters that determine this additional pressure are still not known. Here we start by measuring the pressure drop across a single spherical hydrogel particle as it flows in a microfluidic comparator. This pressure is found to depend on the amount of confinement, elastic modulus, fluid viscosity and velocity. A model for the force balance on the particle is then proposed, by incorporating the above ingredients and relying on simulations of bead geometry and lubrication flow considerations. The final model collapses the force measurements forces onto a single scaling law spanning several decades, while providing physical insights by recalling elements from classic multiphase flows and contact mechanics., Comment: Main Text 4 Pages, Supp text 5 pages. 4 main text figures, 4 supp text figures

Spin states in semiconductors provide exceptionally stable and noise-resistant environments for qubits, positioning them as optimal candidates for reliable quantum computing technologies. The proposal to use nuclear and electronic spins of donor atoms in silicon, introduced by Kane in 1998, sparked a new research field focused on the precise positioning of individual impurity atoms for quantum devices, utilising scanning tunnelling microscopy and ion implantation. This roadmap article reviews the advancements in the 25 years since Kane's proposal, the current challenges, and the future directions in atomic-scale semiconductor device fabrication and measurement. It covers the quest to create a silicon-based quantum computer and expands to include diverse material systems and fabrication techniques, highlighting the potential for a broad range of semiconductor quantum technological applications. Key developments include phosphorus in silicon devices such as single-atom transistors, arrayed few-donor devices, one- and two-qubit gates, three-dimensional architectures, and the development of a toolbox for future quantum integrated circuits. The roadmap also explores new impurity species like arsenic and antimony for enhanced scalability and higher-dimensional spin systems, new chemistry for dopant precursors and lithographic resists, and the potential for germanium-based devices. Emerging methods, such as photon-based lithography and electron beam manipulation, are discussed for their disruptive potential. This roadmap charts the path toward scalable quantum computing and advanced semiconductor quantum technologies, emphasising the critical intersections of experiment, technological development, and theory., Comment: 94 pages

Incorporating automatically predicted human feedback into the process of training generative models has attracted substantial recent interest, while feedback at inference time has received less attention. The typical feedback at training time, i.e., preferences of choice given two samples, does not naturally transfer to the inference phase. We introduce a novel type of feedback -- caption reformulations -- and train models to mimic reformulation feedback based on human annotations. Our method does not require training the image captioning model itself, thereby demanding substantially less computational effort. We experiment with two types of reformulation feedback: first, we collect a dataset of human reformulations that correct errors in the generated captions. We find that incorporating reformulation models trained on this data into the inference phase of existing image captioning models results in improved captions, especially when the original captions are of low quality. We apply our method to non-English image captioning, a domain where robust models are less prevalent, and gain substantial improvement. Second, we apply reformulations to style transfer. Quantitative evaluations reveal state-of-the-art performance on German image captioning and English style transfer, while human validation with a detailed comparative framework exposes the specific axes of improvement.

Gauge anomalous quantum field theories are inconsistent as full UV theories since they lead to the breaking of Lorentz invariance or Unitarity, as well as non-renormalizability. It is well known, however, that they can be interpreted as effective field theories (EFT) with a cut-off. The latter cannot be made arbitrarily large and it is related to the energy scale at which additional fermions with suitable gauge charges enter, rendering the full model anomaly-free. A nondecoupling effect that remains in the EFT is the appearance of anomalous loop-induced triple-gauge couplings, encapsulating information from the full UV theory. In this work we take as an example an Abelian gauge symmetry $U(1)'_\mu$ under which $2^{nd}$-generation leptons are axially charged, leading to an EFT that consists of the Standard Model (SM) with an additional massive $Z'$ gauge boson. As a consequence, there are triple gauge couplings involving the $Z'$ and Electroweak SM gauge bosons via mixed gauge anomalies. We study the possibility of probing these loop suppressed anomalous couplings at hadron and lepton colliders, with $Z'$-lepton couplings allowed by current experimental bounds, finding that due to the large SM backgrounds and small signal, the HL-LHC is incapable of this task. The 100 TeV $pp$ collider at $\mathcal{L}=20~\mathrm {ab}^{-1}$ on the other hand could probe anomalous couplings for $m_{Z'}\in[150,800]~\mathrm{GeV}$ and obtain discovery significances for $m_{Z'}\in[230,330]~\mathrm{GeV}$. Lepton colliders are also well suited for probing these anomalous couplings. In particular we show that a muon collider running at the $Z'$-resonance and an electron-positron collider such as CLIC with $\sqrt{s}=3~{\rm TeV}$ can be complimentary in probing the anomalous couplings for $m_{Z'}\in[100,700]~{\rm GeV}$, with CLIC sensitive to discovery for $m_{Z'}\in[125,225]~{\rm GeV}$., Comment: 24 pages, 11 figures

This paper finishes the series of two papers that we started with [arXiv:2405.05377], where we analyzed the transverse expansion of the metric at a general null hypersurface. While [arXiv:2405.05377] focused on uniqueness results, here we show existence of ambient manifolds given the full asymptotic expansion at the null hypersurface. When such expansion fulfills a set of "constraint equations" we prove that the ambient manifold solves the Einstein equations to infinite order at the hypersurface. Our approach does not make any assumptions regarding the dimension or topology of the null hypersurface and is entirely covariant. Furthermore, when the hypersurface exhibits a product topology we find the minimum amount of data on a cross-section that ensures the existence of an ambient space solving the Einstein equations to infinite order on the hypersurface. As an application we recall the notion of abstract Killing horizon data (AKH) introduced in [arXiv:2405.05377], namely the minimal data needed to define a non-degenerate Killing horizon from a detached viewpoint, and we prove that every AKH of arbitrary dimension and topology gives rise to an ambient space solving the {\Lambda}-vacuum equations to infinite order and with the given data as Killing horizon. Our result also includes the possibility of the Killing vector having zeroes at the horizon., Comment: 36 pages

The virtual cohomological dimension of $\operatorname{Out}(F_n)$ is given precisely by the dimension of the spine of Culler--Vogtmann Outer space. However, the dimension of the spine of untwisted Outer space for a general right-angled Artin group $A_\Gamma$ does not necessarily match the virtual cohomological dimension of the untwisted subgroup $U(A_\Gamma) \leq \operatorname{Out}(A_\Gamma)$. Under certain graph-theoretic conditions, we perform an equivariant deformation retraction of this spine to produce a new contractible cube complex upon which $U(A_\Gamma)$ acts properly and cocompactly. Furthermore, we give conditions for when the dimension of this complex realises the virtual cohomological dimension of $U(A_\Gamma)$., Comment: 36 pages, 13 figures. Comments welcome!

Ion bombardment is currently an active area of research for patterning rare earth/transition metal ferrimagnetic thin films because the magnetic properties are extremely sensitive to changes in the constituent sublattices. It has previously been shown that ion bombardment can be used to deliberately reduce the contribution of the rare earth sublattice in rare earth/transition metal ferrimagnets by selective oxidation. However, the exact mechanism by which oxidation occurs remains an outstanding question. We show that the defects introduced by ion bombardment of Tb/Co multilayers using different ion species with projected range (i.e., 10 keV He + , 15 keV O + , and 30 keV Ga +) create easy diffusion paths for oxygen to penetrate the system. The choice of ion species and fluence enables the effective composition of the films to be tailored by reducing the amount of magnetically-active Tb.

Large X-ray observatories such as Chandra and XMM-Newton have been delivering scientific breakthroughs in research fields as diverse as our Solar System, the astrophysics of stars, stellar explosions and compact objects, accreting super-massive black holes, and large-scale structures traced by the hot plasma permeating and surrounding galaxy groups and clusters. The recently launched observatory XRISM is opening in earnest the new observational window of non-dispersive high-resolution spectroscopy. However, several quests are left open, such as the effect of the stellar radiation field on the habitability of nearby planets, the Equation-of-State regulating matter in neutron stars, the origin and distribution of metals in the Universe, the processes driving the cosmological evolution of the baryons locked in the gravitational potential of Dark Matter and the impact of supermassive black hole growth on galaxy evolution, just to mention a few. Furthermore, X-ray astronomy is a key player in multi-messenger astrophysics. Addressing these quests experimentally requires an order-of-magnitude leap in sensitivity, spectroscopy and survey capabilities with respect to existing X-ray observatories. This paper succinctly summarizes the main areas where high-energy astrophysics is expected to contribute to our understanding of the Universe in the next decade and describes a new mission concept under study by the European Space Agency, the scientific community worldwide and two International Partners (JAXA and NASA), designed to enable transformational discoveries: NewAthena. This concept inherits its basic payload design from a previous study carried out until 2022, Athena., Comment: Published in Nature Astronomy. 9 pages, 4 figures

Precision spectroscopic measurements in atoms and molecules play an increasingly important role in chemistry and physics, e.g., to characterize structure and dynamics at long timescales, to determine physical constants, or to search for physics beyond the standard model of particle physics. In this article, we demonstrate the combination of Zeeman deceleration and transverse laser cooling to generate slow (mean velocity of 175 m/s) and transversely ultracold ($T_\perp \approx 135 \,\mu$K) supersonic beams of metastable $(1s)(2s)\,^3S_1$ He (He$^*$) for precision spectroscopy. The curved-wavefront laser-cooling approach is used to achieve large capture velocities and high He$^*$ number densities. The beam properties are characterized by imaging, time-of-flight and high-resolution spectroscopic methods, and the factors limiting the Doppler widths in single-photon spectroscopic measurements of the $(1 s)(40 p) \,^3 P_J \, \leftarrow (1 s)(2 s) \,^3 S_1$ transition at UV frequencies around $1.15\times 10^{15}$ Hz are analyzed. In particular, the use of skimmers to geometrically confine the beam in the transverse directions is examined and shown to not always lead to a reduction of the Doppler width. Linewidths as narrow as 5 MHz could be obtained, enabling the determination of line centers with a precision of $\Delta \nu/\nu$ of $4\times 10^{-11}$ limited by the signal-to-noise ratio. Numerical particle-trajectory simulations are used to interpret the experimental observations and validate the conclusions.

The James Webb Space Telescope (JWST) is unveiling the rest-frame near-IR structure of galaxies. We measure the evolution with redshift of the rest-frame optical and near-IR S\'ersic index ($n$), and examine the dependence on stellar mass and star-formation activity across the redshift range $0.5\leq z\leq2.5$. We infer rest-frame near-IR S\'ersic profiles for $\approx 15.000$ galaxies in publicly available NIRCam imaging mosaics from the COSMOS-Web and PRIMER surveys. We augment these with rest-frame optical S\'ersic indices, previously measured from HST imaging mosaics. The median S\'ersic index evolves slowly or not at all with redshift, except for very high-mass galaxies ($M_\star > 10^{11}~{\text{M}}_\odot$), which show an increase from $n\approx 2.5$ to $n\approx 4$ at $z<1$. High-mass galaxies have higher $n$ than lower-mass galaxies ($M_\star=10^{9.5}~{\text{M}}_\odot$) at all redshifts, with a stronger dependence in the rest-frame near-IR than in the rest-frame optical at $z>1$. This wavelength dependence is caused by star-forming galaxies that have lower optical than near-IR $n$ at z>1 (but not at z<1). Both at optical and near-IR wavelengths, star-forming galaxies have lower $n$ than quiescent galaxies, fortifying the connection between star-formation activity and radial stellar mass distribution. At $z>1$ the median near-IR $n$ varies strongly with star formation activity, but not with stellar mass. The scatter in near-IR $n$ is higher in the green valley (0.25 dex) than on the star-forming sequence and among quiescent galaxies (0.18 dex) -- this trend is not seen in the optical because dust and young stars contribute to the variety in optical light profiles. Our newly measured rest-frame near-IR radial light profiles motivate future comparisons with radial stellar mass profiles of simulated galaxies as a stringent constraint on processes that govern galaxy formation., Comment: Accepted for publication on A&A

In this paper, we establish a priori log-concavity estimates for the first Dirichlet eigenfunction of convex domains of a Riemannian manifold. Specifically, we focus on cases where the principal eigenfunction $u$ is assumed to be log-concave and our primary goal is to obtain quantitative estimates for the Hessian of $\log u$., Comment: 13 pages

Throttling in graphs optimizes a sum or product of resources used, such as the number of vertices in an initial set, and time required, such as the propagation time, to complete a given task. We introduce a new technique to establish sharp upper bounds in terms of graph order for sum throttling and initial cost product throttling for power domination. Furthermore, we establish sharp bounds on possible changes of the product throttling number, both with and without initial cost, caused by certain graph operations for standard zero forcing, positive semidefinite forcing, and power domination.

Recent advancements in prompting techniques for Large Language Models (LLMs) have improved their reasoning, planning, and action abilities. This paper examines these prompting techniques through the lens of model predictive control (MPC). We show that LLMs act as implicit planning cost function minimizers when planning prompts are used. Under our framework we demonstrate that LLM planning performance can be improved further by incorporating real planning cost functions and evaluators.

In order to understand the effects of a space radiation environment on cross-strip germanium detectors, we investigated the effects of high-energy proton damage on a COSI detector and the capabilities of high-temperature annealing in repairing detector spectral resolution. We irradiated a COSI-balloon cross-strip high-purity germanium (HPGe) detector with a high-energy proton fluence corresponding to ~10 years in a space radiation environment. We repaired the resulting degradation in spectral resolution within 16% of its preradiation value through a series of high-temperature anneals. We characterize the repair of charge traps with time spent under high-temperature anneal to inform an annealing procedure for long-term maintenance of COSI's spectral resolution., Comment: 22 pages, 7 figures, 2 tables, to be published in NIM A

In this paper, we propose a novel method for learning reward functions directly from offline demonstrations. Unlike traditional inverse reinforcement learning (IRL), our approach decouples the reward function from the learner's policy, eliminating the adversarial interaction typically required between the two. This results in a more stable and efficient training process. Our reward function, called \textit{SR-Reward}, leverages successor representation (SR) to encode a state based on expected future states' visitation under the demonstration policy and transition dynamics. By utilizing the Bellman equation, SR-Reward can be learned concurrently with most reinforcement learning (RL) algorithms without altering the existing training pipeline. We also introduce a negative sampling strategy to mitigate overestimation errors by reducing rewards for out-of-distribution data, thereby enhancing robustness. This strategy inherently introduces a conservative bias into RL algorithms that employ the learned reward. We evaluate our method on the D4RL benchmark, achieving competitive results compared to offline RL algorithms with access to true rewards and imitation learning (IL) techniques like behavioral cloning. Moreover, our ablation studies on data size and quality reveal the advantages and limitations of SR-Reward as a proxy for true rewards.

Motivated by recent experiments, we study the coupling of quantum Hall (QH) hydrodynamics to quantum electrodynamics (QED) within a resonance cavity. In agreement with experimental observations, we find that the Hall conductivity remains unchanged. However, the coupling to the cavity induces a second-order quantum reactance effect, contributing distinctly to the longitudinal AC conductivity. This effect arises from the exchange of energy between the QH fluid and cavity photons. Beyond the topological response, we show that the cavity couples to collective excitations, resulting in a shift of the Kohn mode frequency. Our methods are broadly applicable to both integral and fractional QH liquids, and our results offer a universal perspective on the protection of topological properties against long-range interactions induced by electromagnetic cavity modes., Comment: 6+4 pages, 4 figures

This paper analyzes how writing style affects the dispersion of embedding vectors across multiple, state-of-the-art language models. While early transformer models primarily aligned with topic modeling, this study examines the role of writing style in shaping embedding spaces. Using a literary corpus that alternates between topics and styles, we compare the sensitivity of language models across French and English. By analyzing the particular impact of style on embedding dispersion, we aim to better understand how language models process stylistic information, contributing to their overall interpretability., Comment: To appear in the Proceedings of the 31st International Conference on Computational Linguistics (COLING 2025), Abu Dhabi