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In this article, we address the problem of determining a domain in $\mathbb{R}^N$ that minimizes the first eigenvalue of the Lam\'e system under a volume constraint. We begin by establishing the existence of such an optimal domain within the class of quasi-open sets, showing that in the physically relevant dimensions $N = 2$ and $3$, the optimal domain is indeed an open set. Additionally, we derive both first and second-order optimality conditions. Leveraging these conditions, we demonstrate that in two dimensions, the disk cannot be the optimal shape when the Poisson ratio is below a specific threshold, whereas above this value, it serves as a local minimizer. We also extend our analysis to show that the disk is nonoptimal for Poisson ratios $\nu$ satisfying $\nu \leq 0.4$.

1) Objectives: This technical report presents a synthetic summary and the principal outcomes of the project R-Vessel-X ("Robust vascular network extraction and understanding within hepatic biomedical images") funded by the French Agence Nationale de la Recherche, and developed between 2019 and 2023. 2) Material and methods: We used datasets and tools publicly available such as IRCAD, Bullitt or VascuSynth toobtain real or synthetic angiographic images. The main contributions lie in the field of 3D angiographic image analysis: filtering, segmentation, modeling and simulation, with a specific focus on the liver. 3) Results: We paid a particular attention to open-source software diffusion of the developed methods, by means of 3D Slicer plugins for the liver anatomy segmentation (SlicerRVXLiverSegmentation) and vesselness filtering (Slicer-RVXVesselnessFilters), and an online demo for the generation of synthetic and realistic vessels in 2D and 3D (OpenCCO). 4) Conclusion: The R-Vessel-X project provided extensive research outcomes, covering various topics related to 3D angiographic image analysis, such as filtering, segmentation, modeling and simulation. We also developed open-source and free softwares so that the research communities in biomedical engineering can use these results in their future research., Comment: Innovation and Research in BioMedical engineering, In press

We propose a method to compute expectation values in 1+1-dimensional massive Quantum Field Theories (QFTs) with line defects using Relativistic Continuous Matrix Product State (RCMPS). Exploiting Euclidean invariance, we use a quantization scheme where (imaginary) time runs perpendicularly to the defect. With this choice, correlation functions of local operators in the presence of the defect can be computed as expectation values of extended operators in the no-defect vacuum, which can be approximated by a homogeneous RCMPS. We demonstrate the effectiveness of this machinery by computing correlation functions of local bulk and defect operators in $\phi^4$ theory with a magnetic line defect, in perturbative, strong coupling, critical, and symmetry-broken regimes., Comment: 20 + 8 pages, 12 figures

The objective of BioCreative8 Track 3 is to extract phenotypic key medical findings embedded within EHR texts and subsequently normalize these findings to their Human Phenotype Ontology (HPO) terms. However, the presence of diverse surface forms in phenotypic findings makes it challenging to accurately normalize them to the correct HPO terms. To address this challenge, we explored various models for named entity recognition and implemented data augmentation techniques such as synonym marginalization to enhance the normalization step. Our pipeline resulted in an exact extraction and normalization F1 score 2.6\% higher than the mean score of all submissions received in response to the challenge. Furthermore, in terms of the normalization F1 score, our approach surpassed the average performance by 1.9\%. These findings contribute to the advancement of automated medical data extraction and normalization techniques, showcasing potential pathways for future research and application in the biomedical domain., Comment: This article is part of the Proceedings of the BioCreative VIII Challenge and Workshop: Curation and Evaluation in the era of Generative Models

This study explores the applications of the Prouhet-Thue-Morse (PTM) sequence in quantum computing, highlighting its mathematical elegance and practical relevance. We demonstrate the critical role of the PTM sequence in quantum error correction, in noise-resistant quantum memories, and in providing insights into quantum chaos. Notably, we demonstrate how the PTM sequence naturally appears in Ising X-X interacting systems, leading to a proposed robust encoding of quantum memories in such systems. Furthermore, connections to number theory, including the Riemann zeta function, bridge quantum computing with pure mathematics. Our findings emphasize the PTM sequence's importance in understanding the mathematical structure of quantum computing systems and the development of the full potential of quantum technologies and invite further interdisciplinary research.

Sparse optimization seeks an optimal solution with few nonzero entries. To achieve this, it is common to add to the criterion a penalty term proportional to the $\ell_1$-norm, which is recognized as the archetype of sparsity-inducing norms. In this approach, the number of nonzero entries is not controlled a priori. By contrast, in this paper, we focus on finding an optimal solution with at most~$k$ nonzero coordinates (or for short, $k$-sparse vectors), where $k$ is a given sparsity level (or ``sparsity budget''). For this purpose, we study the class of generalized $k$-support norms that arise from a given source norm. When added as a penalty term, we provide conditions under which such generalized $k$-support norms promote $k$-sparse solutions. The result follows from an analysis of the exposed faces of closed convex sets generated by $k$-sparse vectors, and of how primal support identification can be deduced from dual information. Finally, we study some of the geometric properties of the unit balls for the $k$-support norms and their dual norms when the source norm belongs to the family of $\ell_p$-norms.

Doping an antiferromagnetic Mott insulator is central to our understanding of a variety of phenomena in strongly-correlated electrons, including high-temperature superconductors. To describe the competition between tunneling $t$ of hole dopants and antiferromagnetic (AFM) spin interactions $J$, theoretical and numerical studies often focus on the paradigmatic $t$-$J$ model, and the direct analog quantum simulation of this model in the relevant regime of high-particle density has long been sought. Here, we realize a doped quantum antiferromagnet with next-nearest neighbour (NNN) tunnelings $t'$ and hard-core bosonic holes using a Rydberg tweezer platform. We utilize coherent dynamics between three Rydberg levels, encoding spins and holes, to implement a tunable bosonic $t$-$J$-$V$ model allowing us to study previously inaccessible parameter regimes. We observe dynamical phase separation between hole and spin domains for $|t/J|\ll 1$, and demonstrate the formation of repulsively bound hole pairs in a variety of spin backgrounds. The interference between NNN tunnelings $t'$ and perturbative pair tunneling gives rise to light and heavy pairs depending on the sign of $t$. Using the single-site control allows us to study the dynamics of a single hole in 2D square lattice (anti)ferromagnets. The model we implement extends the toolbox of Rydberg tweezer experiments beyond spin-1/2 models to a larger class of $t$-$J$ and spin-$1$ models., Comment: 8 pages, 5 figures

Quantum fluctuations can disrupt long-range order in one-dimensional systems, and replace it with the universal paradigm of the Tomonaga-Luttinger liquid (TLL), a critical phase of matter characterized by power-law decaying correlations and linearly dispersing excitations. Using a Rydberg quantum simulator, we study how TLL physics manifests in the low-energy properties of a spin chain, interacting under either the ferromagnetic or the antiferromagnetic dipolar XY Hamiltonian. Following quasi-adiabatic preparation, we directly observe the power-law decay of spin-spin correlations in real-space, allowing us to extract the Luttinger parameter. In the presence of an impurity, the chain exhibits tunable Friedel oscillations of the local magnetization. Moreover, by utilizing a quantum quench, we directly probe the propagation of correlations, which exhibit a light-cone structure related to the linear sound mode of the underlying TLL. Our measurements demonstrate the influence of the long-range dipolar interactions, renormalizing the parameters of TLL with respect to the case of nearest-neighbor interactions. Finally, comparison to numerical simulations exposes the high sensitivity of TLLs to doping and finite-size effects.

We introduce Uncommon Objects in 3D (uCO3D), a new object-centric dataset for 3D deep learning and 3D generative AI. uCO3D is the largest publicly-available collection of high-resolution videos of objects with 3D annotations that ensures full-360$^{\circ}$ coverage. uCO3D is significantly more diverse than MVImgNet and CO3Dv2, covering more than 1,000 object categories. It is also of higher quality, due to extensive quality checks of both the collected videos and the 3D annotations. Similar to analogous datasets, uCO3D contains annotations for 3D camera poses, depth maps and sparse point clouds. In addition, each object is equipped with a caption and a 3D Gaussian Splat reconstruction. We train several large 3D models on MVImgNet, CO3Dv2, and uCO3D and obtain superior results using the latter, showing that uCO3D is better for learning applications.

Collectively-encoded qubits, involving ensembles of atomic or solid-state emitters, present many practical advantages for quantum technologies. However, they suffer from uncontrolled inhomogeneous dephasing which couples them to a quasi-continuum of dark states. In most cases, this process cannot be encompassed in a standard master equation with time-independent coefficients, making its description either tedious or inaccurate. We show that it can be understood as a displacement in time-frequency phase space and accurately included in resource-efficient numerical simulations of the qubit's dynamics. This description unveils a regime where the qubit becomes protected from dephasing through a combination of strong driving and non-Markovianity. We experimentally investigate this regime using a Rydberg superatom and extend its coherent dynamics beyond the inhomogeneous-dephasing characteristic time by an order of magnitude.

White dwarf stars have attracted considerable attention in the past 15 years as hosts for potentially habitable planets, but their low luminosity and continuous cooling are major challenges for habitability. Recently, astronomers have found that about 6% of massive white dwarfs seem to have "paused" their cooling for up to ~10 Gyr. The leading explanation for this cooling delay is the distillation of neutron-rich isotopes such as $^{22}$Ne in the white dwarf's interior, which releases a considerable amount of gravitational energy as the star's internal structure rearranges. Here, we consider the impact of $^{22}$Ne distillation on the evolution of white dwarf habitable zones. We find that $^{22}$Ne distillation in the white dwarf host dramatically increases the time that a planet can continuously reside within the habitable zone (giving more time for life to arise) and that long-lasting habitable zones are located farther from the star (decreasing the impact of tidal forces). These properties may make white dwarfs undergoing $^{22}$Ne distillation more promising locations for habitability than white dwarfs undergoing standard cooling., Comment: 6 pages, 2 figures, 2 tables. To be submitted to OJA

Understanding the stability of exoplanet systems is crucial for constraining planetary formation and evolution theories. We use the machine-learning stability indicator, SPOCK, to characterize the stability of 126 high-multiplicity systems from the California Kepler Survey (CKS). We constrain the range of stable eccentricities for each system, adopting the value associated with a 50% chance of stability as the characteristic eccentricity. We confirm characteristic eccentricities via a small suite of N-body integrations. In studying correlations between characteristic eccentricity and various planet-pair and system-level metrics we find that minimum period ratio correlates most strongly with characteristic eccentricity. These characteristic eccentricities are approximately 20% of the eccentricities necessary for two-body mean-motion resonance overlap, suggesting three-body dynamics are needed to drive future instabilities. Systems in which the eccentricities would need to be high (> 0.15) to drive instability are likely dynamically relaxed and might be the fossils of a previous epoch of giant impacts that increased the typical planet-planet spacing., Comment: 39 pages, 4 figures, 3 tables, Submitted to AJ October 20 2024

The streaming service Deezer heavily relies on the search to help users navigate through its extensive music catalog. Nonetheless, it is primarily designed to find specific items and does not lead directly to a smooth listening experience. We present Text2Playlist, a stand-alone tool that addresses these limitations. Text2Playlist leverages generative AI, music information retrieval and recommendation systems to generate query-specific and personalized playlists, successfully deployed at scale.

We analyze the impact of non-Markovian classical noise on single-qubit randomized benchmarking experiments, in a manner that explicitly models the realization of each gate via realistic finite-duration pulses. Our new framework exploits the random nature of each gate sequence to derive expressions for the full survival probability decay curve which are non-perturbative in the noise strength. In the presence of non-Markovian noise, our approach shows that the decay curve can exhibit a strong dependence on the implementation method, with regimes of both exponential and power law decays. We discuss how these effects can complicate the interpretation of a randomized-benchmarking experiment, but also how to leverage them to probe non-Markovianty.

A growing number of directly-imaged companions have been recently characterised, with robust constraints on carbon-to-oxygen ratios and even isotopic ratios. Many companions and isolated targets have also shown spectral variability. In this work we observed the super-Jupiter AB~Pictoris~b across four consecutive nights using VLT/CRIRES+ as part of the ESO SupJup survey, exploring how the constraints on chemical composition and temperature profile change over time using spectral line shape variations between nights. We performed atmospheric retrievals of the high-resolution observations and found broadly consistent results across all four nights, but there were differences for some parameters. We clearly detect H$_2$O, $^{12}$CO and $^{13}$CO in each night, but abundances varied by $\sim2\sigma$, which was correlated to the deep atmosphere temperature profiles. We also found differences in the $^{12}$C$/^{13}$C ratios in each night by up to $\sim3\sigma$, which seemed to be correlated with the cloud deck pressure. Our combined retrieval simultaneously analysing all nights together constrained broadly the average of each night individually, with the C/O$=0.59\pm0.01$, consistent with solar composition, and $^{12}$C$/^{13}$C~$ = 102\pm8$, slightly higher than the ISM and Solar System values. We also find a low projected rotational velocity, suggesting that AB~Pictoris~b is either intrinsically a slow rotator due to its young age or that the spin axis is observed pole-on with a $\sim90^\circ$ misalignment with its orbit inclination. Future observations will be able to further explore the variability and orbit of AB~Pictoris~b as well as for other companions., Comment: 22 pages, 17 figures, 4 tables, accepted for publication in MNRAS

Traditional thermoelectric materials rely on low thermal conductivity to enhance their efficiency but suffer from inherently limited power factors. Novel pathways to optimize electronic transport are thus crucial. Here, we achieve ultrahigh power factors in Ni$_3$Ge through a new materials design principle. When overlapping flat and dispersive bands are engineered to the Fermi level, charge carriers can undergo intense interband scattering, yielding an energy filtering effect similar to what has long been predicted in certain nanostructured materials. Via a multi-step DFT-based screening method developed herein, we discover a new family of L1$_2$-ordered binary compounds with ultrahigh power factors up to 11 mW m$^{-1}$ K$^{-2}$ near room temperature, which are driven by an intrinsic phonon-mediated energy filtering mechanism. Our comprehensive experimental and theoretical study of these new intriguing materials paves the way for understanding and designing high-performance scattering-tuned metallic thermoelectrics.

In recent years, the wood product industry has been facing a skilled labor shortage. The result is more frequent sudden failures, resulting in additional costs for these companies already operating in a very competitive market. Moreover, sawmills are challenging environments for machinery and sensors. Given that experienced machine operators may be able to diagnose defects or malfunctions, one possible way of assisting novice operators is through acoustic monitoring. As a step towards the automation of wood-processing equipment and decision support systems for machine operators, in this paper, we explore using a deep convolutional autoencoder for acoustic anomaly detection of wood planers on a new real-life dataset. Specifically, our convolutional autoencoder with skip connections (Skip-CAE) and our Skip-CAE transformer outperform the DCASE autoencoder baseline, one-class SVM, isolation forest and a published convolutional autoencoder architecture, respectively obtaining an area under the ROC curve of 0.846 and 0.875 on a dataset of real-factory planer sounds. Moreover, we show that adding skip connections and attention mechanism under the form of a transformer encoder-decoder helps to further improve the anomaly detection capabilities.

Large vision-language models (LVLMs) augment language models with visual understanding, enabling multimodal reasoning. However, due to the modality gap between textual and visual data, they often face significant challenges, such as over-reliance on text priors, hallucinations, and limited capacity for complex visual reasoning. Existing benchmarks to evaluate visual reasoning in LVLMs often rely on schematic or synthetic images and on imprecise machine-generated explanations. To bridge the modality gap, we present DrivingVQA, a new benchmark derived from driving theory tests to evaluate visual chain-of-thought reasoning in complex real-world scenarios. It offers 3,931 expert-crafted multiple-choice problems and interleaved explanations grounded with entities relevant to the reasoning process. We leverage this dataset to perform an extensive study of LVLMs' ability to reason about complex visual scenarios. Our experiments reveal that open-source and proprietary LVLMs struggle with visual chain-of-thought reasoning under zero-shot settings. We investigate training strategies that leverage relevant entities to improve visual reasoning. Notably, we observe a performance boost of up to 7\% when reasoning over image tokens of cropped regions tied to these entities.

We study how much the in-sample performance of trading strategies based on linear predictive models is reduced out-of-sample due to overfitting. More specifically, we compute the in- and out-of-sample means and variances of the corresponding PnLs and use these to derive a closed-form approximation for the corresponding Sharpe ratios. We find that the out-of-sample ``replication ratio'' diminishes for complex strategies with many assets and based on many weak rather than a few strong trading signals, and increases when more training data is used. The substantial quantitative importance of these effects is illustrated with an empirical case study for commodity futures following the methodology of Garleanu-Pedersen., Comment: 29 pages, 10 figures

Charge transport in materials has an impact on a wide range of devices based on semiconductor, battery or superconductor technology. Charge transport in sliding Charge Density Waves (CDW) differs from all others in that the atomic lattice is directly involved in the transport process. To obtain an overall picture of the structural changes associated to the collective transport, the large coherent X-ray beam generated by an X-ray free-electron laser (XFEL) source was used. The CDW phase can be retrieved over the entire sample from diffracted intensities using a genetic algorithm. For currents below threshold, increasing shear deformation is observed in the central part of the sample while longitudinal deformation appears above threshold when shear relaxes. Shear thus precedes longitudinal deformation, with relaxation of one leading to the appearance of the other. Moreover, strain accumulates on surface steps in the sliding regime, demonstrating the strong pinning character of these surface discontinuities. The sliding process of nanometric CDW is based on an impressive spatial coherence involving the macroscopic sample dimensions., Comment: 20 pages, 5 figures

Most observed exoplanets have high equilibrium temperatures. Understanding the chemistry of their atmospheres and interpreting their observations requires the use of chemical kinetic models including photochemistry. The thermal dependence of the vacuum ultraviolet (VUV) absorption cross sections of molecules used in these models is poorly known at high temperatures, leading to uncertainties in the resulting abundance profiles. The aim of our work is to study experimentally the thermal dependence of VUV absorption cross sections of molecules of interest for exoplanet atmospheres and provide accurate data for use in atmospheric models. This study focuses on acetylene (C2H2). We measured absorption cross sections of C2H2 at seven temperatures ranging from 296 to 773 K recorded in the 115-230 nm spectral domain using VUV spectroscopy and synchrotron radiation. These data were used in our 1D thermo-photochemical model, to assess their impact on the predicted composition of a generic hot Jupiter-like exoplanet atmosphere. The absolute absorption cross sections of C2H2 increase with temperature. This increase is relatively constant from 115 to 185 nm and rises sharply from 185 to 230 nm. The abundance profile of C2H2 calculated using the model shows a slight variation, with a maximum decrease of 40% near 5 x 10-5 bar, when using C2H2 absorption cross sections measured at 773 K compared to those at 296 K. This is explained by the absorption, higher in the atmosphere, of the actinic flux from 150 to 230 nm due to the increase in the C2H2 absorption in this spectral range. This change also impacts the abundance profiles of other by-products such as methane (CH4) and ethylene (C2H4). We present the first experimental measurements of the VUV absorption cross sections of C2H2 at high temperatures. Similar studies of other major species are needed to improve our understanding of exoplanet atmospheres., Comment: 12 pages, 1 table, 11 figures

Exerting forces on biomolecules inside living cells would allow us to probe their dynamic interactions in their native environment. Magnetic iron oxide nanoparticles represent a unique tool capable of pulling on biomolecules with the application of an external magnetic field gradient; however, their use has been restricted to biomolecules accessible from the extracellular medium. Targeting intracellular biomolecules represents an additional challenge due to potential nonspecific interactions with cytoplasmic or nuclear components. We present the synthesis of sulfobetaine-phosphonate block copolymer ligands, which provide magnetic nanoparticles which are stealthy and targetable in living cells. We demonstrate for the first time their efficient targeting in the nucleus and their use for magnetic micromanipulation of a specific genomic locus in living cells. We believe that these stable and furtive magnetic nanoprobes represent a promising tool to manipulate specific biomolecules in living cells and probe the mechanical properties of living matter at the molecular scale.

Star-Planet Magnetic Interactions (SPMI) have been proposed as a mechanism for generating stellar hot-spots with energy outputs on the order of $10^{19-21}$ watts. This interaction is primarily believed to be mediated by Alfv\'en waves propagating towards the star. The stellar atmosphere dictates where and how much of this incoming energy can actually be deposited as heat. The stellar transition region separating the chromosphere from the corona of cool stars gives rise to a significant variation of the Alfv\'en speed over a short distance, and therefore a reflection of the Alfv\'en waves at the transition region is naturally expected. We aim to characterize the efficiency of energy transfer due to SPMI by quantifying a frequency dependent reflection of the wave energy at the stellar transition region and its transmission to the stellar chromosphere. Magnetohydrodynamic simulations are employed to model the frequency-dependent propagation of Alfv\'en waves through a realistic background stellar wind profile. The transmission efficiency as a function of the wave frequency is quantified, and further analysis is conducted to characterize the overall energy transfer efficiency of SPMI in several candidate systems where chromospheric hotspots have been tentatively detected. Low-frequency waves experience greater reflection compared to high-frequency waves, resulting in reduced energy transfer efficiency for lower frequencies. Conversely, the parametric decay instability of Alfv\'en waves substantially diminishes the energy transfer efficiency at higher frequencies. As a result, there exists a frequency range where energy transfer is most efficient. A significant fraction of the Alfv\'en wave energy is reflected at the stellar transition region, and in most realistic scenarios, the transmission efficiency to the chromosphere is found to be approximately 10 percent., Comment: 17 pages, 14 figures, 3 tables. Accepted for publication in Astronomy and Astrophysics

In this paper, we define vertex algebras and vertex coalgebras in the category of rational $G_\Gamma$-modules, where $G_\Gamma$ is the group scheme defined by the group algebra $\mathsf k \Gamma$ for an abelian group $\Gamma$. In this context, we introduce the notion of $C_2$-coalgebra for a vertex coalgebra. We prove that there exists a duality between vertex algebras and vertex coalgebras in the category of $G_\Gamma$-modules, and this duality establishes a connection between $C_2$-algebras and $C_2$-coalgebras. Moreover, we also investigate the relationship between their respective modules/comodules., Comment: 53 pages. Comments are welcome

Rapid advances in Natural Language Processing (NLP) have revolutionized many fields, including healthcare. However, these advances raise significant privacy concerns, especially when models specialized on sensitive data can memorize and then expose and regurgitate confidential information. This paper presents a privacy-by-design language modeling approach to address the problem of language models anonymization, and thus promote their sharing. Specifically, we propose both a Masking Language Modeling (MLM) methodology to specialize a BERT-like language model, and a Causal Language Modeling (CLM) methodology to specialize a GPT-like model that avoids the model from memorizing direct and indirect identifying information present in the training data. We have comprehensively evaluated our approaches using medical datasets and compared them against different baselines. Our results indicate that by avoiding memorizing both direct and indirect identifiers during model specialization, our masking and causal language modeling schemes offer the best tradeoff for maintaining high privacy while retaining high utility.

In this paper, we are interested in the possible values taken by the pair $(\lambda_1(\Omega), \mu_1(\Omega))$ the first eigenvalues of the Laplace operator with Dirichlet and Neumann boundary conditions respectively of a bounded plane domain $\Omega$. We prove that, without any particular assumption on the class of open sets $\Omega$, the two classical inequalities (the Faber-Krahn inequality and the Weinberger inequality) provide a complete system of inequalities. Then we consider the case of convex plane domains for which we give new inequalities for the product $\lambda_1 \mu_1$. We plot the so-called Blaschke--Santal\'o diagram and give some conjectures.

Balancing predictive power and interpretability has long been a challenging research area, particularly in powerful yet complex models like neural networks, where nonlinearity obstructs direct interpretation. This paper introduces a novel approach to constructing an explainable neural network that harmonizes predictiveness and explainability. Our model, termed SparXnet, is designed as a linear combination of a sparse set of jointly learned features, each derived from a different trainable function applied to a single 1-dimensional input feature. Leveraging the ability to learn arbitrarily complex relationships, our neural network architecture enables automatic selection of a sparse set of important features, with the final prediction being a linear combination of rescaled versions of these features. We demonstrate the ability to select significant features while maintaining comparable predictive performance and direct interpretability through extensive experiments on synthetic and real-world datasets. We also provide theoretical analysis on the generalization bounds of our framework, which is favorably linear in the number of selected features and only logarithmic in the number of input features. We further lift any dependence of sample complexity on the number of parameters or the architectural details under very mild conditions. Our research paves the way for further research on sparse and explainable neural networks with guarantee.

Post-alignment of large language models (LLMs) is critical in improving their utility, safety, and alignment with human intentions. Direct preference optimisation (DPO) has become one of the most widely used algorithms for achieving this alignment, given its ability to optimise models based on human feedback directly. However, the vast number of DPO variants in the literature has made it increasingly difficult for researchers to navigate and fully grasp the connections between these approaches. This paper introduces a unifying framework inspired by mutual information, which proposes a new loss function with flexible priors. By carefully specifying these priors, we demonstrate that many existing algorithms, such as SimPO, TDPO, SparsePO, and others, can be derived from our framework. This unification offers a clearer and more structured approach, allowing researchers to understand the relationships between different DPO variants better. We aim to simplify the landscape of DPO algorithms, making it easier for the research community to gain insights and foster further advancements in LLM alignment. Ultimately, we hope our framework can be a foundation for developing more robust and interpretable alignment techniques.

The Compact Muon Solenoid (CMS) detector at the Large Hadron Collider at CERN is used to study the production of new particles in proton-proton collisions at a center of mass energy of 13.6 TeV. The detector includes a magnet based on a 6 m diameter superconducting coil operating at a current of 18.164 kA. This current creates a central magnetic flux density of 3.8 T that allows for the high-precision measurement of the momenta of the produced charged particles using tracking and muon subdetectors. The CMS magnet contains a 10,000 ton flux-return yoke made from the construction steel blocks. These blocks are magnetized, with the coil returned magnetic flux and wrap the muons escaping the hadronic calorimeters. To describe the distribution of the magnetic flux in the magnet yoke layers, a three-dimensional computer model of the CMS magnet is used. To validate the calculations, special measurements are performed, with the flux loops wound in 22 cross-sections of the flux-return yoke blocks. The measured voltages induced in the flux loops during the CMS magnet current variations, are integrated over time to obtain the initial magnetic flux densities in the flux loop cross-sections. In the last time, three fast discharges occurred during the standard ramp-downs of the magnet. This allows us to single out the contributions of the eddy currents, induced in steel, to the flux loop voltages. Accounting for these contributions to the flux loop measurements during intentionally triggered fast discharges in 2006 allows us to perform the validation of the CMS magnet computer model with better precision. The technique for the flux loop measurements and the obtained results are presented and discussed. The method for measuring magnetic flux density in steel blocks described in this study is innovative., Comment: 14 pages, 12 figures, 2 equations, 18 references

A summary of recent contributions in the field of rough partial differential equations is given. For that purpose we rely on the formalism of ``unbounded rough driver''. We present applications to concrete models including Landau-Lifshitz-Gilbert, Navier-Stokes and Euler equations.

Partial differential equation (PDE) is an important math tool in science and engineering. This paper experimentally demonstrates an optical neural PDE solver by leveraging the back-propagation-free on-photonic-chip training of physics-informed neural networks.

This paper describes RHP Friends, a social humanoid robot developed to enable assistive robotic deployments in human-coexisting environments. As a use-case application, we present its potential use in nursing by extending its capabilities to operate human devices and tools according to the task and by enabling remote assistance operations. To meet a wide variety of tasks and situations in environments designed by and for humans, we developed a system that seamlessly integrates the slim and lightweight robot and several technologies: locomanipulation, multi-contact motion, teleoperation, and object detection and tracking. We demonstrated the system's usage in a nursing application. The robot efficiently performed the daily task of patient transfer and a non-routine task, represented by a request to operate a circuit breaker. This demonstration, held at the 2023 International Robot Exhibition (IREX), conducted three times a day over three days., Comment: IEEE Robotics and Automation Magazine, In press

We introduce a canonical way of performing the joint lift of a Brownian motion $W$ and a low-regularity adapted stochastic rough path $\mathbf{X}$, extending [Diehl, Oberhauser and Riedel (2015). A L\'evy area between Brownian motion and rough paths with applications to robust nonlinear filtering and rough partial differential equations]. Applying this construction to the case where $\mathbf{X}$ is the canonical lift of a one-dimensional fractional Brownian motion (possibly correlated with $W$) completes the partial rough path of [Fukasawa and Takano (2024). A partial rough path space for rough volatility]. We use this to model rough volatility with the versatile toolkit of rough differential equations (RDEs), namely by taking the price and volatility processes to be the solution to a single RDE. We argue that our framework is already interesting when $W$ and $X$ are independent, as correlation between the price and volatility can be introduced in the dynamics. The lead-lag scheme of [Flint, Hambly, and Lyons (2016). Discretely sampled signals and the rough Hoff process] is extended to our fractional setting as an approximation theory for the rough path in the correlated case. Continuity of the solution map transforms this into a numerical scheme for RDEs. We numerically test this framework and use it to calibrate a simple new rough volatility model to market data.

Obtaining the classification of 3d $\mathcal{N}=4$ quivers whose Coulomb branches have an isolated singularity is an essential step in understanding moduli spaces of vacua of supersymmetric field theories with 8 supercharges in any dimension. In this work, we derive a full classification for such Abelian quivers with arbitrary charges, and identify all possible Coulomb branch geometries as quotients of $\mathbb{H}^n$ by $\mathrm{U}(1)$ or a finite cyclic group. We give two proofs, one which uses the decay and fission algorithm, and another one relying only on explicit computations involving 3d mirror symmetry. In the process, we put forward a method for computing the 3d mirror of any $\mathrm{U}(1)^r$ gauge theory, which is sensitive to discrete gauge factors in the mirror theory. This constitutes a confirmation for the decay and fission algorithm., Comment: 31 pages + appendices

This paper presents an extension of a recently introduced multistage stochastic integer model designed for optimizing the deployment of charging stations under uncertainty. A key contribution of this work is incorporating additional constraints accounting for congestion management at charging stations. The solution approach combines a greedy heuristic with a branch-and-price algorithm, enabling the efficient handling of medium instances. In the branch-and-price algorithm, when the solution to the restricted master problem is not integer, a greedy heuristic and a local search procedure are conducted to obtain feasible solutions. Computational experiments illustrate the effectiveness of the proposed framework., Comment: 13 pages

Network reconstruction consists in retrieving the -- hidden -- interaction structure of a system from empirical observations such as time series. Many reconstruction algorithms have been proposed, although less research has been devoted to describe their theoretical limitations. To this end, we adopt an information-theoretical point of view and define the reconstructability -- the fraction of structural information recoverable from data. The reconstructability depends on the true data generating model which is shown to set the reconstruction limit, i.e., the performance upper bound for all algorithms. We show that the reconstructability is related to various performance measures, such as the probability of error and the Jaccard similarity. In an empirical context where the true data generating model is unknown, we introduce the reconstruction index as an approximation of the reconstructability. We find that performing model selection is crucial for the validity of the reconstruction index as a proxy of the reconstructability, and illustrate how it assesses the reconstruction limit of empirical time series and networks.

In strongly correlated materials, interacting electrons are entangled and form collective quantum states, resulting in rich low-temperature phase diagrams. Notable examples include cuprate superconductors, in which superconductivity emerges at low doping out of an unusual ``pseudogap'' metallic state above the critical temperature. The Fermi-Hubbard model, describing a wide range of phenomena associated with strong electron correlations, still offers major computational challenges despite its simple formulation. In this context, ultracold atoms quantum simulators have provided invaluable insights into the microscopic nature of correlated quantum states. Here, we use a quantum gas microscope Fermi-Hubbard simulator to explore a wide range of doping levels and temperatures in a regime where a pseudogap is known to develop. By measuring multi-point correlation functions up to fifth order, we uncover a novel universal behaviour in magnetic and higher-order spin-charge correlations. This behaviour is characterized by a doping-dependent energy scale that governs the exponential growth of the magnetic correlation length upon cooling. Accurate comparisons with determinant Quantum Monte Carlo and Minimally Entangled Typical Thermal States simulations confirm that this energy scale agrees well with the pseudogap temperature $T^{*}$. Our findings establish a qualitative and quantitative understanding of the magnetic origin and physical nature of the pseudogap and pave the way towards the exploration of pairing and collective phenomena among charge carriers expected to emerge at lower temperatures.

Whole-body hemodynamics simulators, which model blood flow and pressure waveforms as functions of physiological parameters, are now essential tools for studying cardiovascular systems. However, solving the corresponding inverse problem of mapping observations (e.g., arterial pressure waveforms at specific locations in the arterial network) back to plausible physiological parameters remains challenging. Leveraging recent advances in simulation-based inference, we cast this problem as statistical inference by training an amortized neural posterior estimator on a newly built large dataset of cardiac simulations that we publicly release. To better align simulated data with real-world measurements, we incorporate stochastic elements modeling exogenous effects. The proposed framework can further integrate in-vivo data sources to refine its predictive capabilities on real-world data. In silico, we demonstrate that the proposed framework enables finely quantifying uncertainty associated with individual measurements, allowing trustworthy prediction of four biomarkers of clinical interest--namely Heart Rate, Cardiac Output, Systemic Vascular Resistance, and Left Ventricular Ejection Time--from arterial pressure waveforms and photoplethysmograms. Furthermore, we validate the framework in vivo, where our method accurately captures temporal trends in CO and SVR monitoring on the VitalDB dataset. Finally, the predictive error made by the model monotonically increases with the predicted uncertainty, thereby directly supporting the automatic rejection of unusable measurements.

Quantum $n$-qubit states that are totally symmetric under the permutation of qubits are essential ingredients of important algorithms and applications in quantum information. Consequently, there is significant interest in developing methods to prepare and manipulate Dicke states, which form a basis for the subspace of fully symmetric states. Two simple protocols for transforming Dicke states are considered. An algebraic characterization of the operations that these protocols induce is obtained in terms of the Weyl algebra $W(2)$ and $\mathfrak{su}(2)$. Fixed points under the application of the combination of both protocols are explicitly determined. Connections with the binary Hamming scheme, the Hadamard transform, and Krawtchouk polynomials are highlighted., Comment: 15 pages

In the era of exoplanet studies with JWST, the transiting, hot gas giant WASP-52 b provides an excellent target for atmospheric characterization through transit spectroscopy. WASP-52 b orbits an active K-type dwarf recognized for its surface heterogeneities, such as star-spots and faculae, which offers challenges to atmospheric characterization via transmission spectroscopy. Previous transit observations have detected active regions on WASP-52 through crossing events in transit light-curves and via the spectral imprint of unocculted magnetic regions on transmission spectra. Here, we present the first JWST observations of WASP-52 b. Our JWST NIRISS/SOSS transit observation, obtained through the GTO 1201 Program, detects two clear spot-crossing events that deform the 0.6-2.8 $\mu$m transit light-curves of WASP-52 b. We find that these two occulted spots combined cover about 2.4 % of the stellar surface and have temperatures about 400-500 K colder than the stellar photosphere. Our NIRISS/SOSS transmission spectrum is best-fit by an atmosphere with H$_2$O (10.8 $\sigma$), He (7.3 $\sigma$, with evidence of an escaping tail at $\sim$ 2.9 $\sigma$), hints of K (2.5 $\sigma$), and unocculted star-spots and faculae (3.6 $\sigma$). The retrieved H$_2$O abundance ($\log$ H$_2$O $\approx -4 \pm 1$) is consistent with a subsolar or solar atmospheric metallicity for two independent data reductions. Our results underscore the importance of simultaneously modelling planetary atmospheres and unocculted stellar heterogeneities when interpreting transmission spectra of planets orbiting active stars and demonstrate the necessity of considering different stellar contamination models that account for both cold and hot active regions., Comment: Submitted to MNRAS; 17 pages, 11 figures and 3 tables

This study investigates the behavior of model-integrated routers in Mixture of Experts (MoE) models, focusing on how tokens are routed based on their linguistic features, specifically Part-of-Speech (POS) tags. The goal is to explore across different MoE architectures whether experts specialize in processing tokens with similar linguistic traits. By analyzing token trajectories across experts and layers, we aim to uncover how MoE models handle linguistic information. Findings from six popular MoE models reveal expert specialization for specific POS categories, with routing paths showing high predictive accuracy for POS, highlighting the value of routing paths in characterizing tokens., Comment: Accepted at COLING 2025

There has been an ongoing race for the past couple of years to develop the best universal machine learning interatomic potential. This rapid growth has driven researchers to create increasingly accurate models for predicting energy, forces, and stresses, combining innovative architectures with big data. Here, these models are benchmarked for their performance in predicting phonon properties, which are critical for understanding the vibrational and thermal behavior of materials. Our analysis is based on around 10 000 ab initio phonon calculations, enabling us to assess performance across a range of phonon-related parameters while testing the universal applicability of these models. The results reveal that some models are already capable of predicting phonon properties with a high level of accuracy. However, others still exhibit substantial inaccuracies, even if they excel in the prediction of the energy and the forces for materials close to dynamical equilibrium. These findings highlight the importance of considering phonon-related properties in the development of universal machine learning interatomic potentials.

Direct-current resistivity is a key probe for the physical properties of materials. In metals, Fermi-liquid (FL) theory serves as the basis for understanding transport. A $T^2$ behavior of the resistivity is often taken as a signature of FL electron-electron scattering. However, the presence of impurity and phonon scattering as well as material-specific aspects such as Fermi surface geometry can complicate this interpretation. We demonstrate how density-functional theory combined with dynamical mean-field theory can be used to elucidate the FL regime. We take as examples SrVO$_{3}$ and SrMoO$_{3}$, two moderately correlated perovskite oxides, and establish a precise framework to analyze the FL behavior of the self-energy at low energy and temperature. Reviewing published low-temperature resistivity measurements, we find agreement between our calculations and experiments performed on samples with exceptionally low residual resistivity. This comparison emphasizes the need for further theoretical, synthesis, and characterization developments in these and other FL materials., Comment: 9 pages, 4 figures

The rise of the generative models quality during the past years enabled the generation of edited variations of images at an important scale. To counter the harmful effects of such technology, the Image Difference Captioning (IDC) task aims to describe the differences between two images. While this task is successfully handled for simple 3D rendered images, it struggles on real-world images. The reason is twofold: the training data-scarcity, and the difficulty to capture fine-grained differences between complex images. To address those issues, we propose in this paper a simple yet effective framework to both adapt existing image captioning models to the IDC task and augment IDC datasets. We introduce BLIP2IDC, an adaptation of BLIP2 to the IDC task at low computational cost, and show it outperforms two-streams approaches by a significant margin on real-world IDC datasets. We also propose to use synthetic augmentation to improve the performance of IDC models in an agnostic fashion. We show that our synthetic augmentation strategy provides high quality data, leading to a challenging new dataset well-suited for IDC named Syned1., Comment: This paper has been accepted for the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2025

The processes that generate rogue waves on the sea surface remain a mystery. Despite their different natures, the nonlinear bending waves generated in a thin elastic plate share some similarities with waves on the surface of the sea. For instance, both involve four-wave interactions during energy exchange, but in bending waves, the number of waves is not conserved. Here, we present an experimental study to investigate extreme event statistics in random elastic bending waves forced by an electromagnetic shaker on a thin stainless steel plate. In our setup, the standard statistical criterion used to define extreme events, such as rogue waves on the sea surface, is insufficiently restrictive. We therefore apply a new criterion to determine the occurrence frequency of rare events, similar to those observed in wave tanks. With this new criterion, we examine correlations between extreme events amplitude and wave slopes, energy, and periods, raising questions about the links between statistically rare events and rogue waves.

Achieving high conversion efficiencies in second-order nonlinear optical processes is a key challenge in integrated photonics for both classical and quantum applications. This paper presents the first demonstration of Transverse Orientation-Patterned gallium phosphide (TOP-GaP) waveguides showing high-efficiency second harmonic generation. In such devices, first order modal phase matching is unlocked and optimized through the inversion of the nonlinear susceptibility along the vertical direction. We discuss here the theory behind modal phase matching in TOP structures, describe the fabrication process, and present linear and nonlinear characterizations of the TOP-GaP waveguides.

We analyze the French housing market prices in the period 1970-2022, with high-resolution data from 2018 to 2022. The spatial correlation of the observed price field exhibits logarithmic decay characteristic of the two-dimensional random diffusion equation -- local interactions may create long-range correlations. We introduce a stylized model, used in the past to model spatial regularities in voting patterns, that accounts for both spatial and temporal correlations with reasonable values of parameters. Our analysis reveals that price shocks are persistent in time and their amplitude is strongly heterogeneous in space. Our study confirms and quantifies the diffusive nature of housing prices that was anticipated long ago (Clapp et al. 1994, Pollakowski et al. 1997), albeit on much restricted, local data sets., Comment: 14 pages, 9 figures

The sparse and spatio-temporally discontinuous nature of precipitation data presents significant challenges for simulation and statistical processing for bias correction and downscaling. These include incorrect representation of intermittency and extreme values (critical for hydrology applications), Gibbs phenomenon upon regridding, and lack of fine scales details. To address these challenges, a common approach is to transform the precipitation variable nonlinearly into one that is more malleable. In this work, we explore how deep learning can be used to generate a smooth, spatio-temporally continuous variable as a proxy for simulation of precipitation data. We develop a normally distributed field called pseudo-precipitation (PP) as an alternative for simulating precipitation. The practical applicability of this variable is investigated by applying it for downscaling precipitation from \(1\degree\) (\(\sim\) 100 km) to \(0.25\degree\) (\(\sim\) 25 km).

The acceleration of material property calculations while maintaining ab initio accuracy (1 meV/atom) is one of the major challenges in computational physics. In this paper, we introduce a Python package enhancing the computation of (finite temperature) material properties at the ab initio level using machine learning interatomic potentials (MLIP). The Machine-Learning Assisted Canonical Sampling (MLACS) method, grounded in a self-consistent variational approach, iteratively trains a MLIP using an active learning strategy in order to significantly reduce the computational cost of ab initio simulations. MLACS offers a modular and user-friendly interface that seamlessly integrates Density Functional Theory (DFT) codes, MLIP potentials, and molecular dynamics packages, enabling a wide range of applications, while maintaining a near-DFT accuracy. These include sampling the canonical ensemble of a system, performing free energy calculations, transition path sampling, and geometry optimization, all by utilizing surrogate MLIP potentials, in place of ab initio calculations. This paper provides a comprehensive overview of the theoretical foundations and implementation of the MLACS method. We also demonstrate its accuracy and efficiency through various examples, showcasing the capabilities of the MLACS package.

Dynamically maintaining the minimum cut in a graph $G$ under edge insertions and deletions is a fundamental problem in dynamic graph algorithms for which no conditional lower bound on the time per operation exists. In an $n$-node graph the best known $(1+o(1))$-approximate algorithm takes $\tilde O(\sqrt{n})$ update time [Thorup 2007]. If the minimum cut is guaranteed to be $(\log n)^{o(1)}$, a deterministic exact algorithm with $n^{o(1)}$ update time exists [Jin, Sun, Thorup 2024]. We present the first fully dynamic algorithm for $(1+o(1))$-approximate minimum cut with $n^{o(1)}$ update time. Our main technical contribution is to show that it suffices to consider small-volume cuts in suitably contracted graphs., Comment: To appear at SODA2025