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Hair care robots can help address labor shortages in elderly care while enabling those with limited mobility to maintain their hair-related identity. We present MOE-Hair, a soft robot system that performs three hair-care tasks: head patting, finger combing, and hair grasping. The system features a tendon-driven soft robot end-effector (MOE) with a wrist-mounted RGBD camera, leveraging both mechanical compliance for safety and visual force sensing through deformation. In testing with a force-sensorized mannequin head, MOE achieved comparable hair-grasping effectiveness while applying significantly less force than rigid grippers. Our novel force estimation method combines visual deformation data and tendon tensions from actuators to infer applied forces, reducing sensing errors by up to 60.1% and 20.3% compared to actuator current load-only and depth image-only baselines, respectively. A user study with 12 participants demonstrated statistically significant preferences for MOE-Hair over a baseline system in terms of comfort, effectiveness, and appropriate force application. These results demonstrate the unique advantages of soft robots in contact-rich hair-care tasks, while highlighting the importance of precise force control despite the inherent compliance of the system.

We present Tensor-GaLore, a novel method for efficient training of neural networks with higher-order tensor weights. Many models, particularly those used in scientific computing, employ tensor-parameterized layers to capture complex, multidimensional relationships. When scaling these methods to high-resolution problems makes memory usage grow intractably, and matrix based optimization methods lead to suboptimal performance and compression. We propose to work directly in the high-order space of the complex tensor parameter space using a tensor factorization of the gradients during optimization. We showcase its effectiveness on Fourier Neural Operators (FNOs), a class of models crucial for solving partial differential equations (PDE) and prove the theory of it. Across various PDE tasks like the Navier Stokes and Darcy Flow equations, Tensor-GaLore achieves substantial memory savings, reducing optimizer memory usage by up to 75%. These substantial memory savings across AI for science demonstrate Tensor-GaLore's potential.

Spin Hall effect (SHE) in two-dimensional (2D) materials is promising to effectively manipulate spin angular momentum and identify topological properties. In this work, we implemented an automated Wannierization with spin-orbit coupling on 426 non-magnetic monolayers including 210 metal and 216 insulators. Intrinsic spin Hall conductivity (SHC) has been calculated to find candidates exhibiting novel properties. We discover that Y$_2$C$_2$I$_2$ has an unconventional SHE with canted spin due to low crystal symmetry, Ta$_4$Se$_2$ is a metallic monolayer with exceptionally high SHC, and the semi-metal Y$_2$Br$_2$ possesses efficient charge-to-spin conversion induced by anti-crossing in bands. Moreover, quantum spin Hall insulators are investigated for quantized SHC. The present work provides a high-quality Wannier Hamiltonian database of 2D materials, and paves the way for the integration of 2D materials into high-performance and low-power-consumption spintronic devices., Comment: 19 pages, 4 figures, 2 tables

In the corona, plasma is accelerated to hundreds of kilometers per second, and heated to temperatures hundreds of times hotter than the Sun's surface, before it escapes to form the solar wind. Decades of space-based experiments have shown that the energization process does not stop after it escapes. Instead, the solar wind continues to accelerate and it cools far more slowly than a freely-expanding adiabatic gas. Recent work suggests that fast solar wind requires additional momentum beyond what can be provided by the observed thermal pressure gradients alone whereas it is sufficient for the slowest wind. The additional acceleration for fast wind can be provided through an Alfv\'en wave pressure gradient. Beyond this fast-slow categorization, however, a subset of slow solar wind exhibits high Alfv\'enicity that suggest Alfv\'en waves could play a larger role in its acceleration compared to conventional slow wind outflows. Through a well-timed conjunction between Solar Orbiter and Parker Solar Probe, we trace the energetics of slow wind to compare with a neighboring Alfv\'enic slow solar wind stream. An analysis that integrates remote and heliospheric properties and modeling of the two distinct solar wind streams finds Alfv\'enic slow solar wind behaves like fast wind, where a wave pressure gradient is required to reconcile its full acceleration, while non-Alfv\'enic slow wind can be driven by its non-adiabatic electron and proton thermal pressure gradients. Derived coronal conditions of the source region indicate good model compatibility but extended coronal observations are required to effectively trace solar wind energetics below Parker's orbit., Comment: 20 pages, 9 figures, accepted for publication to ApJ on January 3, 2025

In this work, we explore the global existence of strong solutions for a class of partially diffusive hyperbolic systems within the framework of critical homogeneous Besov spaces. Our objective is twofold: first, to extend our recent findings on the local existence presented in J.-P. Adogbo and R. Danchin. Local well-posedness in the critical regularity setting for hyperbolic systems with partial diffusion. arXiv:2307.05981, 2024, and second, to refine and enhance the analysis of Kawashima (S. Kawashima. Systems of a hyperbolic parabolic type with applications to the equations of magnetohydrodynamics. PhD thesis, Kyoto University, 1983). To address the distinct behaviors of low and high frequency regimes, we employ a hybrid Besov norm approach that incorporates different regularity exponents for each regime. This allows us to meticulously analyze the interactions between these regimes, which exhibit fundamentally different dynamics. A significant part of our methodology is based on the study of a Lyapunov functional, inspired by the work of Beauchard and Zuazua (K. Beauchard and E. Zuazua. Large time asymptotics for partially dissipative hyperbolic system. Arch. Rational Mech. Anal, 199:177-227, 2011.) and recent contributions (T. Crin-Barat and R. Danchin. Partially dissipative hyperbolic systems in the critical regularity setting: the multi-dimensional case. J. Math. Pures Appl. (9), 165:1-41, 2022). To effectively handle the high-frequency components, we introduce a parabolic mode with better smoothing properties, which plays a central role in our analysis. Our results are particularly relevant for important physical systems, such as the magnetohydrodynamics (MHD) system and the Navier-Stokes-Fourier equations., Comment: 37 pages

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

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

Consider first a memoryless population model described by the usual branching process with a given mean reproduction matrix on a finite space of types. Motivated by the consequences of atavism in Evolutionary Biology, we are interested in a modification of the dynamics where individuals keep full memory of their forebears and procreation involves the reactivation of a gene picked at random on the ancestral lineage. By comparing the spectral radii of the two mean reproduction matrices (with and without memory), we observe that, on average, the model with memory always grows at least as fast as the model without memory. The proof relies on analyzing a biased Markov chain on the space of memories, and the existence of a unique ergodic law is demonstrated through asymptotic coupling.

The methoxy radical, CH$_3$O, has long been studied experimentally and theoretically by spectroscopists because it displays a weak Jahn-Teller effect in its electronic ground state, combined with a strong spin-orbit interaction. In this work, we report an extension of the measurement of the pure rotational spectrum of the radical in its vibrational ground state in the submillimeter-wave region (350$-$860 GHz). CH$_3$O was produced by H-abstraction from methanol using F-atoms, and its spectrum was probed in absorption using an association of source-frequency modulation and Zeeman modulation spectroscopy. All the observed transitions together with available literature data in $v = 0$ were combined and fit using an effective Hamiltonian allowing to reproduce the data at their experimental accuracy. The newly measured transitions involve significantly higher frequencies and rotational quantum numbers than those reported in the literature ($f < 860$ GHz and $N \leq 15$ instead of 272 GHz and 7, respectively) which results in significant improvements in the spectroscopic parameters determination. The present model is well constrained and allows a reliable calculation of the rotational spectrum of the radical over the entire microwave to submillimeter-wave domain. It can be used with confidence for future searches of CH$_3$O in the laboratory and the interstellar medium., Comment: 11 pages including references, figures, and tables. Accepted by The Journal of Physical Chemistry A

Transparent objects are ubiquitous in daily life, making their perception and robotics manipulation important. However, they present a major challenge due to their distinct refractive and reflective properties when it comes to accurately estimating the 6D pose. To solve this, we present ReFlow6D, a novel method for transparent object 6D pose estimation that harnesses the refractive-intermediate representation. Unlike conventional approaches, our method leverages a feature space impervious to changes in RGB image space and independent of depth information. Drawing inspiration from image matting, we model the deformation of the light path through transparent objects, yielding a unique object-specific intermediate representation guided by light refraction that is independent of the environment in which objects are observed. By integrating these intermediate features into the pose estimation network, we show that ReFlow6D achieves precise 6D pose estimation of transparent objects, using only RGB images as input. Our method further introduces a novel transparent object compositing loss, fostering the generation of superior refractive-intermediate features. Empirical evaluations show that our approach significantly outperforms state-of-the-art methods on TOD and Trans32K-6D datasets. Robot grasping experiments further demonstrate that ReFlow6D's pose estimation accuracy effectively translates to real-world robotics task. The source code is available at: https://github.com/StoicGilgamesh/ReFlow6D and https://github.com/StoicGilgamesh/matting_rendering.

The Laser Interferometer Space Antenna (LISA), an ESA L-class mission, is designed to detect gravitational waves in the millihertz frequency band, with operations expected to begin in the next decade. LISA will enable studies of astrophysical phenomena such as massive black hole mergers, extreme mass ratio inspirals, and compact binary systems. A key challenge in analyzing LISA's data is the significant laser frequency noise, which must be suppressed using time-delay interferometry (TDI). Classical TDI mitigates this noise by algebraically combining phase measurements taken at different times and spacecraft. However, data gaps caused by instrumental issues or operational interruptions complicate the process. These gaps affect multiple TDI samples due to the time delays inherent to the algorithm, rendering surrounding measurements unusable for parameter inference. In this paper, we apply the recently proposed variant of TDI known as TDI-$\infty$ to astrophysical parameter inference, focusing on the challenge posed by data gaps. TDI-$\infty$ frames the LISA likelihood numerically in terms of raw measurements, marginalizing over laser phase noises under the assumption of infinite noise variance. Additionally, TDI-$\infty$ is set up to incorporate and cancel other noise sources beyond laser noise, including optical bench motion, clock noise, and modulation noise, establishing it as an all-in-one TDI solution. The method gracefully handles measurement interruptions, removing the need to explicitly address discontinuities during template matching. We integrate TDI-$\infty$ into a Bayesian framework, demonstrating its superior performance in scenarios involving gaps. Compared to classical TDI, the method preserves signal integrity more effectively and is particularly interesting for low-latency applications, where the limited amount of available data makes data gaps particularly disruptive., Comment: 47 pages, 15 figures

Driven by artificial intelligence, data science, and high-resolution simulations, I/O workloads and hardware on high-performance computing (HPC) systems have become increasingly complex. This complexity can lead to large I/O overheads and overall performance degradation. These inefficiencies are often mitigated using tools and techniques for characterizing, analyzing, and optimizing the I/O behavior of HPC applications. That said, the myriad number of tools and techniques available makes it challenging to navigate to the best approach. In response, this paper surveys 131 papers from the ACM Digital Library, IEEE Xplore, and other reputable journals to provide a comprehensive analysis, synthesized in the form of a taxonomy, of the current landscape of parallel I/O characterization, analysis, and optimization of large-scale HPC systems. We anticipate that this taxonomy will serve as a valuable resource for enhancing I/O performance of HPC applications., Comment: 31 pages, 1 figure, 7 tables

This paper considers filtering, parameter estimation, and testing for potentially dynamically misspecified state-space models. When dynamics are misspecified, filtered values of state variables often do not satisfy model restrictions, making them hard to interpret, and parameter estimates may fail to characterize the dynamics of filtered variables. To address this, a sequential optimal transportation approach is used to generate a model-consistent sample by mapping observations from a flexible reduced-form to the structural conditional distribution iteratively. Filtered series from the generated sample are model-consistent. Specializing to linear processes, a closed-form Optimal Transport Filtering algorithm is derived. Minimizing the discrepancy between generated and actual observations defines an Optimal Transport Estimator. Its large sample properties are derived. A specification test determines if the model can reproduce the sample path, or if the discrepancy is statistically significant. Empirical applications to trend-cycle decomposition, DSGE models, and affine term structure models illustrate the methodology and the results.

Recently, F. Brenti put a preprint on the arXiv with several interesting open problems on Coxeter groups and unimodality. In this note, we refute one of these conjectures with a counterexample and provide supporting data related to it. This work serves as an initial step toward further exploration of the topic.

With today's multi-petawatt lasers, testing quantum electrodynamics (QED) in the strong field regime, where the electric field exceeds the Schwinger critical field in the rest frame of an electron, becomes within reach. Inverse Compton scattering of an intense laser pulse off a high-energy electron beam is the mainstream approach, resulting in the emission of high-energy photons that can decay into Breit-Wheeler electron-positron pairs. Here, we demonstrate experimentally that very high energy photons can be generated in a self-aligned single-laser Compton scattering setup, combining a laser-plasma accelerator and a plasma mirror. Reaching up to the GeV scale, photon emission via nonlinear Compton scattering exhibits a nonclassical scaling in the experiment that is consistent with electric fields reaching up to a fraction $\chi\simeq0.3$ of the Schwinger field in the electron rest frame. These foolproof collisions guaranteed by automatic laser-electron overlap provide a new approach for precise investigations of strong-field QED processes.

A one-loop Dyson-Schwinger-like resummation scheme is applied to scalar High-Electric-Charge compact Objects (HECOs), extending previous work on spin-1/2 case. The electromagnetic interactions of HECOs are considered within the framework of strongly coupled scalar Quantun Electrodynamics. The resummation amounts to determining non-trivial ultraviolet (UV) fixed points, at which the effective Lagrangian, which will lead to the pertinent predictions on the cross sections, is computed. In contrast to the fermionic HECO case, in which the fixed point structure was determined solely by the interactions of the HECOs with the photon field, in the scalar case the existence of non-trivial UV fixed points requires the presence of additional strong self interactions among the HECOs. Our resummation scheme, which is notably different from a lattice strong-coupling approach, makes the computation of the pertinent scalar-HECO-production cross sections reliable, thus allowing revisiting the mass bounds obtained from searches for such objects in current or future colliders. Our MadGraph implementation of the results leads to enhanced (up to ~30%) lower bounds on the mass of scalar HECOs, as compared to those extracted from the tree-level processes typically used in LHC collider searches by ATLAS and MoEDAL experiments., Comment: 18 pages, RevTex, 7 figures, 6 tables. arXiv admin note: text overlap with arXiv:2310.17452

We present two quaternion-based sliding variables for controlling the orientation of a manipulator's end-effector. Both sliding variables are free of singularities and represent global exponentially convergent error dynamics that do not exhibit unwinding when used in feedback. The choice of sliding variable is dictated by whether the end-effector's angular velocity vector is expressed in a local or global frame, and is a matter of convenience. Using quaternions allows the end-effector to move in its full operational envelope, which is not possible with other representations, e.g., Euler angles, that introduce representation-specific singularities. Further, the presented stability results are global rather than almost global, where the latter is often the best one can achieve when using rotation matrices to represent orientation.

Covariant integral quantization is implemented for systems whose phase space is $Z_{d} \times Z_{d}$, i.e., for systems moving on the discrete periodic set $Z_d= \{0,1,\dotsc d-1$ mod$ d\}$. The symmetry group of this phase space is the periodic discrete version of the Weyl-Heisenberg group, namely the central extension of the abelian group $Z_d \times Z_d$. In this regard, the phase space is viewed as the left coset of the group with its center. The non-trivial unitary irreducible representation of this group, as acting on $L^2(Z_{N})$, is square integrable on the phase phase. We derive the corresponding covariant integral quantizations from (weight) functions on the phase space, and display their phase space portrait., Comment: 27 pages, 3 figures

An instability criterion in the MHD with the open boundary of magnetic field is proposed in this paper. We use a series of linear force-free extrapolation field, in which the normal part of magnetic field is fixed, to obtain the linear fitting coefficient called relative alpha by using the co-joined value of magnetic free energy and magnetic flux at the open boundary ($E_f \Phi ^2$) and the square of relative magnetic helicity ($H_R^2$). We calculate this coefficient of the magnetic field above active regions NOAA~8210 and NOAA~11429 obtained by the photospheric-data-driven magnetohydrodynamics (MHD) model. It is found that the fitting coefficient is a good proxy of the criterion to indicate the occurrence of instability after which the magnetic reconnection happens and caused the fast release of magnetic energy. We also applied this method to the continuous evolution of three-dimension magnetic field of NOAA~11158 based on the measurement of photospheric vector magnetic field of SDO/HMI by the Non-linear Force-Free (NLFF) extrapolation method. The calculated coefficient when the major flare happened based on the extrapolation data is very close to the expected ones, which perfectly reflects the occurrence of instability and the difference is even less than 7\%. This relative alpha is very helpful to evaluate how far it is from the instability in the MHD and quantitatively estimate the occurrence of solar eruption in the space weather forecast., Comment: ApJ,accepted

Since the discovery of heavy-fermion superconductivity in CeCu$_{2}$Si$_{2}$, the material has attracted great interest particularly with regard to the nature of the superconducting pairing and its mechanism. Consequently, it is essential to better understand the electronic Fermi surface topology and its role in strong antiferromagnetic fluctuations. The standard density functional theory method is insufficient to model the interplay of strong onsite Coulomb repulsion in localized 4{\it f}-electrons and their hybridization with itinerant ligand-orbital electrons. We have performed electronic ground state calculations on CeCu$_{2}$Si$_{2}$ using the Gutzwiller wavefunction approximation. The Gutzwiller approximation captures the quasiparticle band renormalization from the strong onsite Coulomb repulsion. We have performed an analysis of this effect on the electronic structure and the Fermi surface topology by varying the interaction strength and taking into account the crystal-field splitting. Using the de Haas van Alphen effect, the extremal Fermi surface cross-sectional areas were calculated to quantify the effects of quasiparticle mass renormalization on the Fermi surface. Our results confirm two Fermi surface sheets corresponding to the heavy (488m$_{e}$) and light (4.35m$_{e}$) quasiparticles, which is in close agreement with experimental measurements as well as the renormalized band method., Comment: 10 pages, 3 figures, 2 tables

There is growing interest in designing playful interactions with food, but food based tangible interactive narratives have received less attention. We introduce Gummy's Way Out, an interactive tangible narrative experience where interactors eat a gummy bear and help him find his way out of their bodies by eating various food items. By consuming different things, the interactor either helps or hinders the gummy bear's journey through an imagined Diegetic body that overlaps with their own. Interactors are endowed with the gummy bear's well-being and are also encouraged to reflect on how their actions can impact their Lived body. We present preliminary results of a user study and design considerations on how to design for the diegetic body in interactive food based narrative experiences. We recommend leveraging the sensory and emotional properties of food to create a visceral narrative experience., Comment: Conference: Submitted to Design of Interactive Systems (DIS) 20 Pages

As part of the BISTRO survey, we present JCMT 850 $\mu$m polarimetric observations towards the Orion Integral-Shaped Filament (ISF) that covers three portions known as OMC-1, OMC-2, and OMC-3. The magnetic field threading the ISF seen in the JCMT POL-2 map appears as a tale of three: pinched for OMC-1, twisted for OMC-2, and nearly uniform for OMC-3. A multi-scale analysis shows that the magnetic field structure in OMC-3 is very consistent at all the scales, whereas the field structure in OMC-2 shows no correlation across different scales. In OMC-1, the field retains its mean orientation from large to small scales, but shows some deviations at small scales. Histograms of relative orientations between the magnetic field and filaments reveal a bimodal distribution for OMC-1, a relatively random distribution for OMC-2, and a distribution with a predominant peak at 90$^\circ$ for OMC-3. Furthermore, the magnetic fields in OMC-1 and OMC-3 both appear to be aligned perpendicular to the fibers, which are denser structures within the filament, but the field in OMC-2 is aligned along with the fibers. All these suggest that gravity, turbulence, and magnetic field are each playing a leading role in OMC-1, 2, and 3, respectively. While OMC-2 and 3 have almost the same gas mass, density, and non-thermal velocity dispersion, there are on average younger and fewer young stellar objects in OMC-3, providing evidence that a stronger magnetic field will induce slower and less efficient star formation in molecular clouds., Comment: published in the ApJ Letters

The exponential increase in orbital debris and active satellites will lead to congested orbits, necessitating more frequent collision avoidance maneuvers by satellites. To minimize fuel consumption while ensuring the safety of satellites, enforcing a chance constraint, which poses an upper bound in collision probability with debris, can serve as an intuitive safety measure. However, accurately evaluating collision probability, which is critical for the effective implementation of chance constraints, remains a non-trivial task. This difficulty arises because uncertainty propagation in nonlinear orbit dynamics typically provides only limited information, such as finite samples or moment estimates about the underlying arbitrary non-Gaussian distributions. Furthermore, even if the full distribution were known, it remains unclear how to effectively compute chance constraints with such non-Gaussian distributions. To address these challenges, we propose a distributionally robust chance-constrained collision avoidance algorithm that provides a sufficient condition for collision probabilities under limited information about the underlying non-Gaussian distribution. Our distributionally robust approach satisfies the chance constraint for all debris position distributions sharing a given mean and covariance, thereby enabling the enforcement of chance constraints with limited distributional information. To achieve computational tractability, the chance constraint is approximated using a Conditional Value-at-Risk (CVaR) constraint, which gives a conservative and tractable approximation of the distributionally robust chance constraint. We validate our algorithm on a real-world inspired satellite-debris conjunction scenario with different uncertainty propagation methods and show that our controller can effectively avoid collisions., Comment: To be published in AIAA Scitech

In high-risk environments where unlawful surveillance is prevalent, securing confidential communications is critical. This study introduces a novel steganographic game-theoretic model to analyze the strategic interactions between a defending company and an adversary. By framing the scenario as a non-cooperative game, there is systematic evaluation of optimal strategies for both parties, incorporating costs and benefits such as implementation expenses, potential data leaks, and operational advantages. The derived equilibrium probabilities enable the assessment of success rates, illustrating conditions under which the company benefits from hiding messages or faces increased risks when not implementing steganography. Sensitivity analysis explores how changes in key parameters impact these strategies, enhancing the understanding of decision-making in secure communications. Furthermore, the introduction of an adversary model that quantifies the adversary's advantage using conditional probabilities derived from success rates allows for a quantitative measure of the adversary's effectiveness based on the defender's strategies. By integrating the adversary's advantage into a novel risk analysis framework and employing Monte Carlo simulations, dynamic interactions are captured across advantage scenarios, considering factors like impact factor, steganography effectiveness, and equilibrium probabilities. This comprehensive framework offers practical insights into optimizing security strategies by quantifying potential risk reductions when the adversary is disadvantaged, providing a clear methodology for assessing and mitigating adversarial threats in complex security environments., Comment: 21 pages, 7 figures, 1 table, 2 algorithms. This version is a preprint uploaded to arXiv

We consider a neural network architecture designed to solve inverse problems where the degradation operator is linear and known. This architecture is constructed by unrolling a forward-backward algorithm derived from the minimization of an objective function that combines a data-fidelity term, a Tikhonov-type regularization term, and a potentially nonsmooth convex penalty. The robustness of this inversion method to input perturbations is analyzed theoretically. Ensuring robustness complies with the principles of inverse problem theory, as it ensures both the continuity of the inversion method and the resilience to small noise - a critical property given the known vulnerability of deep neural networks to adversarial perturbations. A key novelty of our work lies in examining the robustness of the proposed network to perturbations in its bias, which represents the observed data in the inverse problem. Additionally, we provide numerical illustrations of the analytical Lipschitz bounds derived in our analysis., Comment: arXiv admin note: substantial text overlap with arXiv:2105.15044

Transductive few-shot learning has recently triggered wide attention in computer vision. Yet, current methods introduce key hyper-parameters, which control the prediction statistics of the test batches, such as the level of class balance, affecting performances significantly. Such hyper-parameters are empirically grid-searched over validation data, and their configurations may vary substantially with the target dataset and pre-training model, making such empirical searches both sub-optimal and computationally intractable. In this work, we advocate and introduce the unrolling paradigm, also referred to as "learning to optimize", in the context of few-shot learning, thereby learning efficiently and effectively a set of optimized hyper-parameters. Specifically, we unroll a generalization of the ubiquitous Expectation-Maximization (EM) optimizer into a neural network architecture, mapping each of its iterates to a layer and learning a set of key hyper-parameters over validation data. Our unrolling approach covers various statistical feature distributions and pre-training paradigms, including recent foundational vision-language models and standard vision-only classifiers. We report comprehensive experiments, which cover a breadth of fine-grained downstream image classification tasks, showing significant gains brought by the proposed unrolled EM algorithm over iterative variants. The achieved improvements reach up to 10% and 7.5% on vision-only and vision-language benchmarks, respectively.

The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.

LLMs have transformed the execution of numerous tasks, including those in the medical domain. Among these, summarizing patient-reported outcomes (PROs) into concise natural language reports is of particular interest to clinicians, as it enables them to focus on critical patient concerns and spend more time in meaningful discussions. While existing work with LLMs like GPT-4 has shown impressive results, real breakthroughs could arise from leveraging SLMs as they offer the advantage of being deployable locally, ensuring patient data privacy and compliance with healthcare regulations. This study benchmarks several SLMs against LLMs for summarizing patient-reported Q\&A forms in the context of radiotherapy. Using various metrics, we evaluate their precision and reliability. The findings highlight both the promise and limitations of SLMs for high-stakes medical tasks, fostering more efficient and privacy-preserving AI-driven healthcare solutions., Comment: 10 pages

This paper describes the Inria team's integrated robotics system used in the 1st euROBIN coopetition, during which service robots performed voice-activated household tasks in a kitchen setting.The team developed a modified Tiago++ platform that leverages a whole-body control stack for autonomous and teleoperated modes, and an LLM-based pipeline for instruction understanding and task planning. The key contributions (opens-sourced) are the integration of these components and the design of custom teleoperation devices, addressing practical challenges in the deployment of service robots.

A free-by-cyclic group can often be viewed as a mapping torus of a free group automorphism (monodromy) in multiple ways. What dynamical properties must these monodromies share, and to what extent are they invariant under quasi-isometries? We give a new proof using cyclic splittings that the growth type of a monodromy is a geometric invariant of the free-by-cyclic group; we also characterise the degree of polynomial growth using slender splittings. For exponential growth, we conjecture that the nesting of attracting laminations is a geometric invariant., Comment: 20 pages, comments demanded

Robust and fine localization algorithms are crucial for autonomous driving. For the production of such vehicles as a commodity, affordable sensing solutions and reliable localization algorithms must be designed. This work considers scenarios where the sensor data comes from images captured by an inexpensive camera mounted on the vehicle and where the vehicle contains a fine global map. Such localization algorithms typically involve finding the section in the global map that best matches the captured image. In harsh environments, both the global map and the captured image can be noisy. Because of physical constraints on camera placement, the image captured by the camera can be viewed as a noisy perspective transformed version of the road in the global map. Thus, an optimal algorithm should take into account the unequal noise power in various regions of the captured image, and the intrinsic uncertainty in the global map due to environmental variations. This article briefly reviews two matching methods: (i) standard inner product (SIP) and (ii) normalized mutual information (NMI). It then proposes novel and principled modifications to improve the performance of these algorithms significantly in noisy environments. These enhancements are inspired by the physical constraints associated with autonomous vehicles. They are grounded in statistical signal processing and, in some context, are provably better. Numerical simulations demonstrate the effectiveness of such modifications.

The electrocaloric effect refers to the temperature change in a material when an electric field is applied or removed. Significant breakthroughs revealed its potential for solid-state cooling technologies in past decades. These devices offer a sustainable alternative to traditional vapor compression refrigeration, with advantages such as compactness, silent operation, and the absence of moving parts or refrigerants. Electrocaloric effects are typically studied using indirect methods using polarization data, and which suffer from inaccuracies related to assumptions about heat capacity. Direct methods, although more precise, require device fabrication and face challenges in studying meso- or nanoscale systems, like 2D materials, and materials with non-uniform polarization textures where high spatial resolution is required. In this study, a novel technique, Scanning Electrocaloric Thermometry, is introduced for characterizing the local electrocaloric effect in nanomaterials. This approach achieves high spatial resolution by locally applying electric fields and by simultaneously measuring the resulting temperature change. By employing AC excitation, the measurement sensitivity is further enhanced and the electrocaloric effect is disentangled from other heating mechanisms such as Joule heating and dielectric losses. The effectiveness of the method is demonstrated by examining electrocaloric and heat dissipation phenomena in two-dimensional In${}_2$Se${}_3$ micrometer-sized flakes.

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

This memorial article for Mark Sapir provides a brief overview of his life and career. Among his many contributions we highlight two of his most celebrated achievements: his groundbreaking solutions to Burnside-type problems for semigroups and his innovative construction of S-machines. Additionally, reflections from his colleagues and friends offer a heartfelt tribute, blending professional insights with personal memories., Comment: With contributions by Victor Guba, Olga Kharlampovich, Simon M. Goberstein, Stuart W. Margolis, Lev Shneerson, John Meakin, Ilya Kapovich, Eugene Plotkin, Michael Mihalik, Samuel Corson, and Arman Darbinyan

We present Monte Carlo simulations of the two-dimensional one-component plasma (2D OCP) confined to a cylindrical geometry, focusing on density profiles, fluctuations, and their connection to bulk correlation functions. The cylindrical geometry eliminates geometric frustration, allowing for a precise study of boundary density oscillations, the dependence on boundary conditions, and their relationship to the melting transition and triangular lattice structure. By triangulating particle configurations, we quantify the exponential suppression of topological defects in the crystalline phase. Furthermore, we propose an oriented correlation function that better links boundary density profiles with bulk correlation functions, motivating anisotropic generalizations of the phase-field crystal (PFC) model. These results provide new insights into the interplay between boundary effects, bulk correlations, and phase transitions in the 2D OCP., Comment: 17 pages, 10 figures

To manage exceptions, software relies on a key architectural guarantee, precision: that exceptions appear to execute between instructions. However, this definition, dating back over 60 years, fundamentally assumes a sequential programmers model. Modern architectures such as Arm-A with programmer-observable relaxed behaviour make such a naive definition inadequate, and it is unclear exactly what guarantees programmers have on exception entry and exit. In this paper, we clarify the concepts needed to discuss exceptions in the relaxed-memory setting -- a key aspect of precisely specifying the architectural interface between hardware and software. We explore the basic relaxed behaviour across exception boundaries, and the semantics of external aborts, using Arm-A as a representative modern architecture. We identify an important problem, present yet unexplored for decades: pinning down what it means for exceptions to be precise in a relaxed setting. We describe key phenomena that any definition should account for. We develop an axiomatic model for Arm-A precise exceptions, tooling for axiomatic model execution, and a library of tests. Finally we explore the relaxed semantics of software-generated interrupts, as used in sophisticated programming patterns, and sketch how they too could be modelled.

We introduce the Velocity Jumps approach, denoted as JUMP, a new class of Molecular dynamics integrators, replacing the Langevin dynamics by a hybrid model combining a classical Langevin diffusion and a piecewise deterministic Markov process, where the expensive computation of long-range pairwise interactions is replaced by a resampling of the velocities at random times. This framework allows for an acceleration in the simulation speed while preserving sampling and dynamical properties such as the diffusion constant. It can also be integrated in classical multi-timestep methods, pushing further the computational speedup, while avoiding some of the resonance issues of the latter thanks to the random nature of jumps. The JUMP, JUMP-RESPA and JUMP-RESPA1 integrators have been implemented in the GPU-accelerated version of the Tinker-HP package and are shown to provide significantly enhanced performances compared to their BAOAB, BAOAB-RESPA and BAOAB-RESPA1 counterparts respectively.

An optical flux lattice is a set of light beams that couple different internal states of an atom, thereby producing topological energy bands. Here we present a configuration in which the atoms exhibit a dark state, i.e. an internal state that is not coupled to the light. At large light intensity, the low-energy dynamics is restricted to the dark state, leading to an effective continuum model with a Landau-level-like structure. This structure is dramatically different from that of usual topological optical lattices, which lead to discrete models in the tight-binding limit. The proposed system is essentially immune to heating due to photon scattering, making it a highly promising way to emulate the integer or fractional quantum Hall effect., Comment: 8 pages, 7 figures

This summary of the second Terrestrial Very-Long-Baseline Atom Interferometry (TVLBAI) Workshop provides a comprehensive overview of our meeting held in London in April 2024, building on the initial discussions during the inaugural workshop held at CERN in March 2023. Like the summary of the first workshop, this document records a critical milestone for the international atom interferometry community. It documents our concerted efforts to evaluate progress, address emerging challenges, and refine strategic directions for future large-scale atom interferometry projects. Our commitment to collaboration is manifested by the integration of diverse expertise and the coordination of international resources, all aimed at advancing the frontiers of atom interferometry physics and technology, as set out in a Memorandum of Understanding signed by over 50 institutions., Comment: Summary of the second Terrestrial Very-Long-Baseline Atom Interferometry Workshop held at Imperial College London: https://indico.cern.ch/event/1369392/

Time-delay interferometry (TDI) suppresses laser frequency noise by forming linear combinations of time-shifted interferometric measurements. The time-shift operation is implemented by interpolating discretely sampled data. To enable in-band laser noise reduction by eight to nine orders of magnitude, interpolation has to be performed with high accuracy. Optimizing the design of those interpolation methods is the focus of this work. Previous research that studied constant time-shifts suggested Lagrange interpolation as the interpolation method for TDI. Its transfer function performs well at low frequency but requires a high number of coefficients. Furthermore, when applied in TDI we observed prominent time-domain features when a time-varying shift scanned over a pure integer sample shift. To limit this effect we identify an additional requirement for the interpolation kernel: when considering time-varying shifts the interpolation kernel must be sufficiently smooth to avoid unwanted time-domain transitions that produce glitch-like features in power spectral density estimates. The Lagrange interpolation kernel exhibits a discontinuous first derivative by construction, which is insufficient for the application to LISA or other space-based GW observatories. As a solution we propose a novel design method for interpolation kernels that respect a predefined requirement on in-band interpolation residuals and that possess continuous derivatives up to a prescribed order. Using this method we show that an interpolation kernel with 22 coefficients is sufficient to respect LISA's picometre-requirement and to allow for a continuous first derivative which suppresses the magnitude of the time-domain transition adequately. The reduction from 42 (Lagrange interpolation) to 22 coefficients enables us to save computational cost and increases robustness against artefacts in the data., Comment: 27 pages, 10 figures

Consider a graph $G$ with a long path $P$. When is it the case that $G$ also contains a long induced path? This question has been investigated in general as well as within a number of different graph classes since the 80s. We have recently observed in a companion paper (Long induced paths in sparse graphs and graphs with forbidden patterns, arXiv:2411.08685, 2024) that most existing results can recovered in a simple way by considering forbidden ordered patterns of edges along the path $P$. In particular we proved that if we forbid some fixed ordered matching along a path of order $n$ in a graph $G$, then $G$ must contain an induced path of order $(\log n)^{\Omega(1)}$. Moreover, we completely characterized the forbidden ordered patterns forcing the existence of an induced path of polynomial size. The purpose of the present paper is to completely characterize the ordered patterns $H$ such that forbidding $H$ along a path $P$ of order $n$ implies the existence of an induced path of order $(\log n)^{\Omega(1)}$. These patterns are star forests with some specific ordering, which we called constellations. As a direct consequence of our result, we show that if a graph $G$ has a path of length $n$ and does not contain $K_t$ as a topological minor, then $G$ contains an induced path of order $(\log n)^{\Omega(1/t \log^2 t)}$. The previously best known bound was $(\log n)^{f(t)}$ for some unspecified function $f$ depending on the Topological Minor Structure Theorem of Grohe and Marx (2015)., Comment: Some material from arXiv:2304.09679 has been reused (such as definitions and remarks), so text overlap is to be expected

Binary optimization is a fundamental area in computational science, with wide-ranging applications from logistics to cryptography, where the tasks are often formulated as Quadratic or Polynomial Unconstrained Binary Optimization problems (QUBO/PUBO). In this work, we propose to use a parametrized Gaussian Boson Sampler (GBS) with threshold detectors to address such problems. We map general PUBO instance onto a quantum Hamiltonian and optimize the Conditional Value-at-Risk of its energy with respect to the GBS ansatz. In particular, we observe that, when the algorithm reduces to standard Variational Quantum Eigensolver, the cost function is analytical. Therefore, it can be computed efficiently, along with its gradient, for low-degree polynomials using only classical computing resources. Numerical experiments on 3-SAT and Graph Partitioning problems show significant performance gains over random guessing, providing a first proof of concept for our proposed approach., Comment: 17 pages, 7 figures, extended version of arXiv:2312.07235

We analyzed mathematically a model describing flux jumps in superconductivity in a 1D configuration. Three effects occur from fastest to slowest: Joule heating, magnetic relaxation and temperature diffusion. Adimensionalising the equations showed that magnetic field fronts penetrate the material as inhomogeneous Burgers fronts. An additional nonlinear term affects the magnetic field and is responsible for flux jumps. We considered a medium temperature for which the heat capacity of a sample can be assumed constant and a low temperature where heat capacity depends on temperature causing a nonlinear temperature evolution. We found that flux jumps occur for pulses of duration close to the magnetic relaxation time and mostly at low temperature. Flux trapping is maximal for medium amplitude long duration pulses and low to medium temperatures.

In order to combat the creation and spread of harmful content online, this paper defines and contextualizes the concept of inauthentic, societal-scale manipulation by malicious actors. We review the literature on societally harmful content and how it proliferates to analyze the manipulation strategies used by such actors and the vulnerabilities they target. We also provide an overview of three case studies of extensive manipulation campaigns to emphasize the severity of the problem. We then address the role that Artificial Intelligence plays in the development and dissemination of harmful content, and how its evolution presents new threats to societal cohesion for countries across the globe. Our survey aims to increase our understanding of not just particular aspects of these threats, but also the strategies underlying their deployment, so we can effectively prepare for the evolving cybersecurity landscape.

In this paper we deal with the quantum dynamics of hyperbolic linear automorphisms of the torus, such as "Arnold's cat map", and study its behavior after Ehrenfest time. We show that, in the basis of wave packets, the associated propagator is well approximed by Birkhoff sums of nilrotations on the torus., Comment: Master's thesis, in French, comments welcome, in any language

The Weyl-Heisenberg symmetries originate from translation invariances of various manifolds viewed as phase spaces, e.g. Euclidean plane, semi-discrete cylinder, torus, in the two-dimensional case, and higher-dimensional generalisations. In this review we describe, on an elementary level, how this symmetry emerges through displacement operators and standard Fourier analysis, and how their unitary representations are used both in Signal Analysis (time-frequency techniques, Gabor transform) and in quantum formalism (covariant integral quantizations and semi-classical portraits). An example of application of the formalism to the Majorana stellar constellation in the plane is presented., Comment: 33 pages, 6 figures

The measurement of energy is a fundamental tool used, for example, in exploring the early universe, characterizing particle decay processes, as well as in quantum technology and computing. Some of the most sensitive energy detectors are thermal, i.e., bolometers and calorimeters, which operate by absorbing incoming energy, converting it into heat, and reading out the resulting temperature change electrically using a thermometer. Extremely sensitive calorimeters, including transition edge sensors, magnetic microcalorimeters and devices based on 2D conductors such as graphene, have been shown to reach impressive energy resolutions of 17.6 zJ. Very recently superconductor--normal-conductor--superconductor (SNS) radiation sensors with metallic and graphene absorbers have resulted in predictions of full-width-at-half-maximum (FWHM) energy resolutions of 0.75 zJ and 0.05 zJ = 71 GHz$\times h$, respectively, where $h$ is the Planck constant. However, since these estimates are only mathematically extracted from steady-state noise and responsivity measurements, no calorimetry reaching single-zeptojoule energy resolution or beyond has been demonstrated. Here, we use a metallic SNS sensor to measure the energy of 1-$\mu$s-long 8.4-GHz microwave pulses with a FWHM energy resolution finer than (0.95 $\pm$ 0.02) zJ = (5.9 $\pm$ 0.12) meV, corresponding to 170 photons at 8.4 GHz. The techniques of this work, combined with graphene-based sensors, provide a promising path to real-time calorimetric detection of single photons in the 10 GHz range. Such a device has potential in operating as an accurate measurement device of quantum states such as those of superconducting qubits, or used in fundamental physics explorations including quantum thermodynamics, and the search for axions., Comment: 25 pages, 7 figures

Hypersonic flow conditions pose exceptional challenges for Reynolds-Averaged Navier-Stokes (RANS) turbulence modeling. Critical phenomena include compressibility effects, shock/turbulent boundary layer interactions, turbulence-chemistry interaction in thermo-chemical non-equilibrium, and ablation-induced surface roughness and blowing effects. This comprehensive review synthesizes recent developments in adapting turbulence models to hypersonic applications, examining approaches ranging from empirical modifications to physics-based reformulations and novel data-driven methodologies. We provide a systematic evaluation of current RANS-based turbulence modeling capabilities, comparing eddy viscosity and Reynolds stress transport formulations in their ability to predict engineering quantities of interest such as separation characteristics and wall heat transfer. Our analysis encompasses the latest experimental and direct numerical simulation datasets for validation, specifically addressing two- and three-dimensional equilibrium turbulent boundary layers and shock/turbulent boundary layer interactions across both smooth and rough surfaces. Key multi-physics considerations including catalysis and ablation phenomena along with the integration of conjugate heat transfer into a RANS solver for efficient design of a thermal protection system are also discussed. We conclude by identifying the critical gaps in the available validation databases and limitations of the existing turbulence models and suggest potential areas for future research to improve the fidelity of turbulence modeling in the hypersonic regime., Comment: 36 pages, 3 figures

In this paper, we establish a necessary and sufficient stability condition for a class of two coupled first-order linear hyperbolic partial differential equations. Through a backstepping transform, the problem is reformulated as a stability problem for an integral difference equation, that is, a difference equation with distributed delay. Building upon a St\'ep\'an--Hassard argument variation theorem originally designed for time-delay systems of retarded type, we then introduce a theorem that counts the number of unstable roots of our integral difference equation. This leads to the expected necessary and sufficient stability criterion for the system of first-order linear hyperbolic partial differential equations. Finally, we validate our theoretical findings through simulations.