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We explore electrical transport through a point contact in strained HgTe, a three-dimensional topological insulator. In the absence of a magnetic field $B$, there is no quantization. However, under higher magnetic fields, we observe distinct non-integer conductance steps. Based on numerical tight-binding calculations and a phenomenological Landauer-B\"uttiker approach, we attribute these atypical, non-integer quantized plateaus to significant scattering effects at the point contact., Comment: Submitted to Physical Review Research on 21st October 2024

Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows, extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Despite this, their use in commercial applications is often limited due to limited access to training data and restrictive licensing, which hinders open access. To address these limitations, we introduce BigDocs-7.5M, a high-quality, open-access dataset comprising 7.5 million multimodal documents across 30 tasks. We use an efficient data curation process to ensure our data is high-quality and license-permissive. Our process emphasizes accountability, responsibility, and transparency through filtering rules, traceable metadata, and careful content analysis. Additionally, we introduce BigDocs-Bench, a benchmark suite with 10 novel tasks where we create datasets that reflect real-world use cases involving reasoning over Graphical User Interfaces (GUI) and code generation from images. Our experiments show that training with BigDocs-Bench improves average performance up to 25.8% over closed-source GPT-4o in document reasoning and structured output tasks such as Screenshot2HTML or Image2Latex generation. Finally, human evaluations showed a preference for outputs from models trained on BigDocs over GPT-4o. This suggests that BigDocs can help both academics and the open-source community utilize and improve AI tools to enhance multimodal capabilities and document reasoning. The project is hosted at https://bigdocs.github.io ., Comment: The project is hosted at https://bigdocs.github.io

Accurate lifetime measurements of excited states of highly charged ions (HCIs) are essential for advancing diagnostics in both laboratory and astrophysical plasmas, especially in the X-ray regime. The Hanle effect, which utilizes external magnetic fields to modify photon scattering patterns, provides a powerful technique for these measurements. Previously, this method has been successfully employed for He-like ions. Here, we present a theoretical study of the prospects of the Hanle effect for lifetime determinations of Li-like ions. Our results highlight the potential for plasma diagnostics and X-ray spectral analysis.

Heterostructures made from 2D transition-metal dichalcogenides are known as ideal platforms to explore excitonic phenomena ranging from correlated moir\'e excitons to degenerate interlayer exciton ensembles. So far, it is assumed that the atomic reconstruction appearing in some of the heterostructures gives rise to a dominating localization of the exciton states. We demonstrate that excitonic states in reconstructed MoSe$_2$/WSe$_2$ heterostructures can extend well beyond the moir\'e periodicity of the investigated heterostructures. The results are based on real-space calculations yielding a lateral potential map for interlayer excitons within the strain-relaxed heterostructures and corresponding real-space excitonic wavefunctions. We combine the theoretical results with cryogenic photoluminescence experiments, which support the computed level structure and relaxation characteristics of the interlayer excitons.

Understanding and predicting the structure and evolution of coronal mass ejections (CMEs) in the heliosphere remains one of the most sought-after goals in heliophysics and space weather research. A powerful tool for improving current knowledge and capabilities consists of multi-spacecraft observations of the same event, which take place when two or more spacecraft fortuitously find themselves in the path of a single CME. Multi-probe events can not only supply useful data to evaluate the large-scale of CMEs from 1D in-situ trajectories, but also provide additional constraints and validation opportunities for CME propagation models. In this work, we analyse and simulate the coronal and heliospheric evolution of a slow, streamer-blowout CME that erupted on 23 September 2021 and was encountered in situ by four spacecraft approximately equally distributed in heliocentric distance between 0.4 and 1 au. We employ the Open Solar Physics Rapid Ensemble Information (OSPREI) modelling suite in ensemble mode to predict the CME arrival and structure in a hindcast fashion and to compute the "best-fit" solutions at the different spacecraft individually and together. We find that the spread in the predicted quantities increases with heliocentric distance, suggesting that there may be a maximum (angular and radial) separation between an inner and an outer probe beyond which estimates of the in-situ magnetic field orientation (parameterised by flux rope model geometry) increasingly diverge. We discuss the importance of these exceptional observations and the results of our investigation in the context of advancing our understanding of CME structure and evolution as well as improving space weather forecasts., Comment: 20 pages, 12 figures, 3 tables; accepted for publication in MNRAS

The dissociative photoionization of \textit{tert}-butyl methyl methacrylate, a monomer unit found in many ESCAP resists, was investigated in a gas phase photoelectron photoion coincidence experiment employing extreme ultraviolet (EUV) synchrotron radiation at 13.5 nm. It was found that the interaction of EUV photons with the molecules leads almost exclusively to dissociation. However, the ionization can also directly deprotect the ester function, thus inducing the solubility switch wanted in a resist film. These results serve as a building block to reconstruct the full picture of the mechanism in widely used chemically amplified resist thin films, provide a knob to tailor more performant resist materials, and will aid interpreting advanced ultrafast time-resolved experiments.

We show the late time limit of the spectral form factor (SFF) in unorientable JT gravity agrees with universal random matrix theory up to genus one in the topological expansion, establishing a key signature of quantum chaos for the time-reversal symmetric case. The loop equations for an orthogonal matrix model with spectral curve $y(z) \propto \sin(2\pi z)$ are used to compute the moduli space volume of unorientable surfaces. The divergences of the unorientable volumes are regularized by first regularizing the resolvents of the orthogonal matrix model. Using properties of the volumes, we streamline the loop equations to allow computation of the volumes that were previously inaccessible. The method can efficiently extract the part of the volume that contributes in the late time limit of the SFF. In this limit, the SFF becomes finite and independent of regularization.

The consecutive steps of H to tau tau cascade can be useful for the measurement of Higgs couplings and parity. The analysis methos of ATLAS and CMS Collaborations was to fit a one-dimensional distribution of the phi* angle, phi*, which is sensitive to transverse spin correlations and, hence, to the CP mixing angle, phi^CP. Machine Learning techniques (ML) offer opportunities to manage complex multidimensional signatures. The 4-momenta of the tau decay products can be used as input to the machine learning and to predict the CP-sensitive pseudo-observables and/or provide discrimination between different CP hypotheses. We show that the classification or regression methods can be used to train an ML model to predict the spin weight sensitive to the CP state of the decaying Higgs boson, parameters of the functional form of the spin weight, or the most preferred CP mixing angle of the analysed sample. The one-dimensional distribution of the predicted spin weight or the most preferred CP mixing angle of the experimental data can be examined further, with the statistical methods, to derive the measurement of the CP mixing state of the Higgs signal events. This paper extends our previous studies, with more details for the features and how proposed pseudo-observables can be used in the measurement., Comment: 19 pages 12 figures

Giannopoulos, Hartzoulaki and Paouris asked in \cite{GHP} whether the best ratio between volume and surface area of convex bodies sharing a given orthogonal projection onto a fixed hyperplane is attained in the limit by a cylinder over the given projection. The answer to the question is known to be negative. In this paper, we prove a characterization of the positive answer in dimension $3$, using the Cheeger set of the common projection. A partial characterization is given in higher dimensions. We also prove that certain canal classes of convex bodies provide families of convex bodies satisfying a closely related inequality for a similar ratio.

We analyze data from multiple flybys by the Solar Orbiter, BepiColombo, and Parker Solar Probe (PSP) missions to study the interaction between Venus' plasma environment and the solar wind forming the induced magnetosphere. Through examination of magnetic field and plasma density signatures we characterize the spatial extent and dynamics of Venus' magnetotail, focusing mainly on boundary crossings. Notably, we observe significant differences in boundary crossing location and appearance between flybys, highlighting the dynamic nature of Venus' magnetotail. In particular, during Solar Orbiter's third flyby, extreme solar wind conditions led to significant variations in the magnetosheath plasma density and magnetic field properties, but the increased dynamic pressure did not compress the magnetotail. Instead, it is possible that the increased EUV flux at this time rather caused it to expand in size. Key findings also include the identification of several far downstream bow shock (BS), or bow wave, crossings to at least 60 Rv (1 Rv = 6,052 km is the radius of Venus), and the induced magnetospheric boundary to at least 20 Rv. These crossings provide insight into the extent of the induced magnetosphere. Pre-existing models from Venus Express were only constrained to within ~5 Rv of the planet, and we provide modifications to better fit the far-downstream crossings. The new model BS is now significantly closer to the central tail than previously suggested, by about 10 Rv at 60 Rv downstream.

The magnetic orientation of coronal mass ejections (CMEs) is of great importance to understand their space weather effects. Although many evidences suggest that CMEs can undergo significant rotation during the early phases of evolution in the solar corona, there are few reports that CMEs rotate in the interplanetary space. In this work, we use multi-spacecraft observations and a numerical simulation starting from the lower corona close to the solar surface to understand the CME event on 2021 December 4, with an emphatic investigation of its rotation. This event is observed as a partial halo CME from the back side of the Sun by coronagraphs, and reaches the BepiColombo spacecraft and the MAVEN/Tianwen-1 as a magnetic flux rope-like structure. The simulation discloses that in the solar corona the CME is approximately a translational motion, while the interplanetary propagation process evidences a gradual change of axis orientation of the CME's flux rope-like structure. It is also found that the downside and the right flank of the CME moves with the fast solar wind, and the upside does in the slow-speed stream. The different parts of the CME with different speeds generate the nonidentical displacements of its magnetic structure, resulting in the rotation of the CME in the interplanetary space. Furthermore, at the right flank of the CME exists a corotating interaction region (CIR), which makes the orientation of the CME alter, and also deviates from its route due to the CME. These results provide new insight on interpreting CMEs' dynamics and structures during their travelling through the heliosphere., Comment: Accepted By ApJ, 22 pages, 6 figures

Energy consumption optimization of a two-link planar robotic arm is considered with the system's efficiency being the target for optimization. A new formulation of thermodynamic principles within the framework of dynamical systems is used. This approach is applied by considering cyclic motions for the robotic arm and analyzing the cyclic averaged energies while the robotic arm is tasked with going from point A to point B in the task space while resisting an external force. The energy transfer rate between the links is classified into positive and negative and the results combined with the averaged energy quantities, are used to address the optimization problem while adhering to the constraints imposed by the second law of thermodynamics in its new formulation.

As language models (LMs) approach human-level performance, a comprehensive understanding of their behavior becomes crucial. This includes evaluating capabilities, biases, task performance, and alignment with societal values. Extensive initial evaluations, including red teaming and diverse benchmarking, can establish a model's behavioral profile. However, subsequent fine-tuning or deployment modifications may alter these behaviors in unintended ways. We present a method for continual Behavioral Shift Auditing (BSA) in LMs. Building on recent work in hypothesis testing, our auditing test detects behavioral shifts solely through model generations. Our test compares model generations from a baseline model to those of the model under scrutiny and provides theoretical guarantees for change detection while controlling false positives. The test features a configurable tolerance parameter that adjusts sensitivity to behavioral changes for different use cases. We evaluate our approach using two case studies: monitoring changes in (a) toxicity and (b) translation performance. We find that the test is able to detect meaningful changes in behavior distributions using just hundreds of examples., Comment: 25 pages, 12 figures

We study the quantum Hall effect (QHE) in the three-dimensional topological insulator HgTe, which features topological Dirac-type surface states in a bulk gap opened by strain. Despite the co-existence of multiple carrier subsystems, the system exhibits perfectly quantized Hall plateaus at high magnetic fields. Here we study the system using three different experimental techniques: Transport experiments, capacitance measurements including the quantum capacitance, and current distribution measurements using electrostatically sensitive scanning probe microscopy. Our key finding is that at sufficiently high magnetic fields, the different electronic subsystems merge into one, and the current in a quantum Hall plateau is distributed across the entire width of the Hall bar device., Comment: 9 pages, 9 figures. Submitted to PRR

We analytically find quantitative, non-perturbative bounds to the validity of the rotating-wave approximation (RWA) for the multi-atom generalization of the quantum Rabi model: the Dicke model. Precisely, we bound the norm of the difference between the evolutions of states generated by the Dicke model and its rotating-wave approximated counterpart, that is, the Tavis-Cummings model. The intricate role of the parameters of the model in determining the bounds is discussed and compared with numerical results. Our bounds are intrinsically state-dependent and, in particular, are significantly different in the cases of entangled and non-entangled states; this behaviour also seems to be confirmed by the numerics., Comment: 26 pages, 3 figures

Diffusion models have found great success in generating high quality, natural samples of speech, but their potential for density estimation for speech has so far remained largely unexplored. In this work, we leverage an unconditional diffusion model trained only on clean speech for the assessment of speech quality. We show that the quality of a speech utterance can be assessed by estimating the likelihood of a corresponding sample in the terminating Gaussian distribution, obtained via a deterministic noising process. The resulting method is purely unsupervised, trained only on clean speech, and therefore does not rely on annotations. Our diffusion-based approach leverages clean speech priors to assess quality based on how the input relates to the learned distribution of clean data. Our proposed log-likelihoods show promising results, correlating well with intrusive speech quality metrics such as POLQA and SI-SDR.

Given a set $A$ and an abelian group $B$ with operators in $A$, in the sense of Krull and Noether, we introduce the Ore group extension $B[x ; \delta_B , \sigma_B]$ as the additive group $B[x]$, with $A[x]$ as a set of operators. Here, the action of $A[x]$ on $B[x]$ is defined by mimicking the multiplication used in the classical case where $A$ and $B$ are the same ring. We derive generalizations of Vandermonde's and Leibniz's identities for this construction, and they are then used to establish associativity criteria. Additionally, we prove a version of Hilbert's basis theorem for this structure, under the assumption that the action of $A$ on $B$ is what we call weakly $s$-unital. Finally, we apply these results to the case where $B$ is a left module over a ring $A$, and specifically to the case where $A$ and $B$ coincide with a non-associative ring which is left distributive but not necessarily right distributive.

Recent advances in memory technologies, devices and materials have shown great potential for integration into neuromorphic electronic systems. However, a significant gap remains between the development of these materials and the realization of large-scale, fully functional systems. One key challenge is determining which devices and materials are best suited for specific functions and how they can be paired with CMOS circuitry. To address this, we introduce TEXEL, a mixed-signal neuromorphic architecture designed to explore the integration of on-chip learning circuits and novel two- and three-terminal devices. TEXEL serves as an accessible platform to bridge the gap between CMOS-based neuromorphic computation and the latest advancements in emerging devices. In this paper, we demonstrate the readiness of TEXEL for device integration through comprehensive chip measurements and simulations. TEXEL provides a practical system for testing bio-inspired learning algorithms alongside emerging devices, establishing a tangible link between brain-inspired computation and cutting-edge device research., Comment: 17 pages, 7 figures. Supplementary material: 8 pages, 4 figures

In this paper, we look at the nonlinear bending theory for thin prestrained elastic sheets with an oscillating periodic structure. We show that all limiting (in the sense of $\Gamma-$convergence) energy functionals which formally correspond to an infinite thickness-to-period ratio are equivalent. In contrast, there are several different limit models that formally correspond to a $0$ thickness-to-period ratio.

Continuous-variable (CV) quantum key distribution (QKD) allows for quantum secure communication with the benefit of being close to existing classical coherent communication. In recent years, CV QKD protocols using a discrete number of displaced coherent states have been studied intensively, as the modulation can be directly implemented with real devices with a finite digital resolution. However, the experimental demonstrations until now only calculated key rates in the asymptotic regime. To be used in cryptographic applications, a QKD system has to generate keys with composable security in the finite-size regime. In this paper, we present a CV QKD system using discrete modulation that is especially designed for urban atmospheric channels. For this, we use polarization encoding to cope with the turbulent but non-birefringent atmosphere. This will allow to expand CV QKD networks beyond the existing fiber backbone. In a first laboratory demonstration, we implemented a novel type of security proof allowing to calculate composable finite-size key rates against i.i.d. collective attacks without any Gaussian assumptions. We applied the full QKD protocol including a QRNG, error correction and privacy amplification to extract secret keys. In particular, we studied the impact of frame errors on the actual key generation.

The Magellanic Stream, a tidal tail of diffuse gas falling onto the Milky Way, formed by interactions between the Small and Large Magellanic Clouds, is primarily composed of neutral atomic hydrogen (HI). The deficiency of dust and the diffuse nature of the present gas make molecular formation rare and difficult, but if present, could lead to regions potentially suitable for star formation, thereby allowing us to probe conditions of star formation similar to those at high redshifts. We search for HCO$^+$, HCN, HNC, and C$_2$H using the highest sensitivity observations of molecular absorption data from the Atacama Large Millimeter Array to trace these regions, comparing with HI archival data to compare these environments in the Magellanic Stream to the HI column density threshold for molecular formation in the Milky Way. We also compare the line of sight locations with confirmed locations of stars, molecular hydrogen, and OI detections, though at higher sensitivities than the observations presented here. We find no detections to a 3$\sigma$ significance, despite four sightlines having column densities surpassing the threshold for molecular formation in the diffuse regions of the Milky Way. Here we present our calculations for the upper limits of the column densities of each of these molecular absorption lines, ranging from $3 \times 10^{10}$ to $1 \times 10^{13}$ cm$^{-2}$. The non-detection of HCO$^+$ suggests that at least one of the following is true: (i) $X_{HCO^+, \mathrm{MS}}$ is significantly lower than the Milky Way value; (ii) that the widespread diffuse molecular gas observed in the Milky Way's diffuse ISM does not have a direct analog in the MS; (iii) the HI-to-H$_2$ transition occurs in the MS at a higher surface density in the MS than in the LMC or SMC; or (iv) molecular gas exists in the MS, but only in small, dense clumps., Comment: 19 pages, 7 figures, 4 tables, Accepted for publication in PASA

Hydrogen is vital for sectors like chemicals and others, driven by the need to reduce carbon emissions. Proton Electrolyte Membrane Water Electrolysis (PEMWE) is a key technology for the production of green hydrogen under fluctuating conditions of renewable power sources. However, due to the scarcity of noble metal materials, the stability of the anode catalyst layer under dynamic operating conditions must be better understood. Model-aided investigation approaches are essential due to the back-box nature of the electrochemical system and the high costs of experimental long-term testing. In this work, a temporal multiscale method based on a Heterogeneous technique is applied to reduce the computational effort of simulating long-term degradation, focused on catalyst dissolution. Such an approach characterizes the problem in fast locally periodic processes, influenced by the dynamic operation and slow processes attributed to the gradual degradation of the catalyst layer. A mechanistic model that includes the oxygen evolution reaction, catalyst dissolution and hydrogen permeation from the cathode to the anode side is hypothesized and implemented. The multiscale approach notably reduces computational effort of simulation from hours to mere minutes. This efficiency gain is ascribed to the limited evolution of Slow-Scale variables during each period of time of the Fast-Scale variables. Consequently, simulation of the fast processes is required only until local periodicity is achieved within each Slow-Scale time step. Thus, the developed temporal multiscale approach proves to be highly effective in accelerating parameter estimation and predictive simulation steps, as could be verified through the results of this article. In this way, the method can support systematic model development to describe degradation in PEMWE under dynamic operating conditions., Comment: 29 pages, 9 figures, acknowledgements are provided

Based on periodic orbit theory we address the individual-system versus ensemble interpretation of quantum gravity from a quantum chaos perspective. To this end we show that the spectrum of geodesic motion on high-dimensional hyperbolic manifolds, described by the Selberg trace formula, displays a Schwarzian ($\sinh 2\pi\sqrt{E}$) mean level density. Due to its chaotic classical limit, this quantum system also shows all universal signatures of quantum chaos. These two properties imply a possible duality to Jackiw-Teitelboim-type quantum gravity at the level of a single system instead of an ensemble of systems like matrix theories and SYK models. Beyond the universal regime we show how the full wormhole geometry on the gravity side emerges from the discreteness of the set of periodic orbits. Thereby, we take initial steps towards a duality between gravitational and mesoscopic chaotic quantum systems through the topological, respectively, periodic orbit expansions of their correlators., Comment: 5 pages, 1 figure. Additional references, minor changes to wording, typos corrected

We present a foundation model for zero-shot metric monocular depth estimation. Our model, Depth Pro, synthesizes high-resolution depth maps with unparalleled sharpness and high-frequency details. The predictions are metric, with absolute scale, without relying on the availability of metadata such as camera intrinsics. And the model is fast, producing a 2.25-megapixel depth map in 0.3 seconds on a standard GPU. These characteristics are enabled by a number of technical contributions, including an efficient multi-scale vision transformer for dense prediction, a training protocol that combines real and synthetic datasets to achieve high metric accuracy alongside fine boundary tracing, dedicated evaluation metrics for boundary accuracy in estimated depth maps, and state-of-the-art focal length estimation from a single image. Extensive experiments analyze specific design choices and demonstrate that Depth Pro outperforms prior work along multiple dimensions. We release code and weights at https://github.com/apple/ml-depth-pro, Comment: Code and weights available at https://github.com/apple/ml-depth-pro

The electric dipole strength distribution in $^{58}$Ni between 6 and 20 MeV has been determined from proton inelastic scattering experiments at very forward angles at RCNP, Osaka. The experimental data are rather well reproduced by quasiparticle random-phase approximation calculations including vibration coupling, despite a mild dependence on the adopted Skyrme interaction. They allow an estimate of the experimentally inaccessible high-energy contribution above 20 MeV, leading to an electric dipole polarizability $\alpha_\mathrm{D}(^{58}{\rm Ni}) = 3.48(31)$ fm$^3$. This serves as a test case for recent extensions of coupled-cluster calculations with chiral effective field theory interactions to nuclei with two nucleons on top of a closed-shell system., Comment: 8 pages, 4 figures

Let $D(\mu)$ denote a harmonically weighted Dirichlet space on the unit disc $\mathbb D$. We show that outer functions $f\in D(\mu)$ are cyclic in $D(\mu)$, whenever $\log f$ belongs to the Pick-Smirnov class $N^+(D(\mu))$. If $f$ has $H^\infty$-norm less than or equal to 1, then cyclicity can also be checked via iterated logarithms. For example, we show that such outer functions $f$ are cyclic, whenever $\log(1+ \log(1/f))\in N^+(D(\mu))$. This condition can be checked by verifying that $\log(1+ \log(1/f))\in D(\mu)$. If $f$ satisfies a mild extra condition, then the conditions also become necessary for cyclicity., Comment: 9 pages, 1 figure

Depth perception is essential for a robot's spatial and geometric understanding of its environment, with many tasks traditionally relying on hardware-based depth sensors like RGB-D or stereo cameras. However, these sensors face practical limitations, including issues with transparent and reflective objects, high costs, calibration complexity, spatial and energy constraints, and increased failure rates in compound systems. While monocular depth estimation methods offer a cost-effective and simpler alternative, their adoption in robotics is limited due to their output of relative rather than metric depth, which is crucial for robotics applications. In this paper, we propose a method that utilizes a single calibrated camera, enabling the robot to act as a ``measuring stick" to convert relative depth estimates into metric depth in real-time as tasks are performed. Our approach employs an LSTM-based metric depth regressor, trained online and refined through probabilistic filtering, to accurately restore the metric depth across the monocular depth map, particularly in areas proximal to the robot's motion. Experiments with real robots demonstrate that our method significantly outperforms current state-of-the-art monocular metric depth estimation techniques, achieving a 22.1% reduction in depth error and a 52% increase in success rate for a downstream task., Comment: 8 pages, 5 figures

We propose the generalization of the thin film equation (TFE) to arbitrarily many immiscible liquid layers. Then, we provide different pathways for deriving the hydrodynamic pressure within the individual layers, showing how to understand the equation as a Cahn-Hilliard-type conservation equation and providing an algorithm to derive the associated Onsager Matrix. Furthermore, we employ a numerical solver based on the multilayer shallow water-lattice Boltzmann method (LBM) for two and three liquid layers in pseudo two and three dimensions to gain insights into the dynamics of the system and to validate the model. We perform a linear stability analysis and assess droplet equilibrium shapes. Furthermore, we compare the dynamics of the proposed thin film equation to full Navier-Stokes simulations and show the possible equilibrium states of the multilayer liquid thin film system., Comment: 19 pages, 8 Figures, 1 supporting video, 1 supporting maple script for symbolic calcullations

Deep State-Space Models (SSM) demonstrate state-of-the art performance on long-range sequence modeling tasks. While the recurrent structure of SSMs can be efficiently implemented as a convolution or as a parallel scan during training, recurrent token-by-token processing cannot currently be implemented efficiently on GPUs. Here, we demonstrate efficient token-by-token inference of the SSM S4D on Intel's Loihi 2 state-of-the-art neuromorphic processor. We compare this first ever neuromorphic-hardware implementation of an SSM on sMNIST, psMNIST, and sCIFAR to a recurrent and a convolutional implementation of S4D on Jetson Orin Nano (Jetson). While we find Jetson to perform better in an offline sample-by-sample based batched processing mode, Loihi 2 outperforms during token-by-token based processing, where it consumes 1000 times less energy with a 75 times lower latency and a 75 times higher throughput compared to the recurrent implementation of S4D on Jetson. This opens up new avenues towards efficient real-time streaming applications of SSMs., Comment: 6 pages, 2 figures

Surgical automation has the capability to improve the consistency of patient outcomes and broaden access to advanced surgical care in underprivileged communities. Shared autonomy, where the robot automates routine subtasks while the surgeon retains partial teleoperative control, offers great potential to make an impact. In this paper we focus on one important skill within surgical shared autonomy: Automating robotic assistance to maximize visual exposure and apply tissue tension for dissection and cautery. Ensuring consistent exposure to visualize the surgical site is crucial for both efficiency and patient safety. However, achieving this is highly challenging due to the complexities of manipulating deformable volumetric tissues that are prevalent in surgery.To address these challenges we propose \methodname, a framework for autonomous surgical robotic assistance to \methodfullname. We integrate a differentiable physics model with perceptual feedback to achieve our two key objectives: 1) Maximizing tissue exposure and applying tension for a specified dissection site through visual-servoing conrol and 2) Selecting optimal control positions for a dissection target based on deformable Jacobian analysis. We quantitatively assess our method through repeated real robot experiments on a tissue phantom, and showcase its capabilities through dissection experiments using shared autonomy on real animal tissue., Comment: Submitted to ICRA 2025

Chronic wounds, including diabetic ulcers, pressure ulcers, and ulcers secondary to venous hypertension, affects more than 6.5 million patients and a yearly cost of more than $25 billion in the United States alone. Chronic wound treatment is currently a manual process, and we envision a future where robotics and automation will aid in this treatment to reduce cost and improve patient care. In this work, we present the development of the first robotic system for wound dressing removal which is reported to be the worst aspect of living with chronic wounds. Our method leverages differentiable physics-based simulation to perform gradient-based Model Predictive Control (MPC) for optimized trajectory planning. By integrating fracture mechanics of adhesion, we are able to model the peeling effect inherent to dressing adhesion. The system is further guided by carefully designed objective functions that promote both efficient and safe control, reducing the risk of tissue damage. We validated the efficacy of our approach through a series of experiments conducted on both synthetic skin phantoms and real human subjects. Our results demonstrate the system's ability to achieve precise and safe dressing removal trajectories, offering a promising solution for automating this essential healthcare procedure., Comment: Submitted to ICRA 2025

The generation of single photons using solid-state quantum emitters is pivotal for advancing photonic quantum technologies, particularly in quantum communication. As the field continuously advances towards practical use cases and beyond shielded laboratory environments, specific demands are placed on the robustness of quantum light sources during operation. In this context, the robustness of the quantum light generation process against intrinsic and extrinsic effects is a major challenge. Here, we present a robust scheme for the coherent generation of indistinguishable single-photon states with very low photon number coherence (PNC) using a three-level system in a semiconductor quantum dot. Our novel approach combines the advantages of adiabatic rapid passage (ARP) and stimulated two-photon excitation (sTPE). We demonstrate robust quantum light generation while maintaining the prime quantum-optical quality of the emitted light state. Moreover, we highlight the immediate advantages for the implementation of various quantum cryptographic protocols., Comment: 12 pages, 6 figures

Generative speech enhancement has recently shown promising advancements in improving speech quality in noisy environments. Multiple diffusion-based frameworks exist, each employing distinct training objectives and learning techniques. This paper aims at explaining the differences between these frameworks by focusing our investigation on score-based generative models and Schr\"odinger bridge. We conduct a series of comprehensive experiments to compare their performance and highlight differing training behaviors. Furthermore, we propose a novel perceptual loss function tailored for the Schr\"odinger bridge framework, demonstrating enhanced performance and improved perceptual quality of the enhanced speech signals. All experimental code and pre-trained models are publicly available to facilitate further research and development in this.

The exchange of information between an open quantum system and its environment, especially the backflow of information from the environment to the open system associated with quantum notions of non-Markovianity, is a widely discussed topic for years now. This information flow can be quantified by means of the trace distance of pairs of quantum states which provides a measure for the distinguishability of the states. The same idea can also be used to characterize the information flow in classical open systems through a suitable distance measure for their probability distributions on phase space. Here, we investigate the connection between the trace distance based quantum measure and the Kolmogorov distance for differently ordered quasi-probability distributions on phase space. In particular, we show that for any pair of quantum states one can find a unique quasi-probability distribution for which the Kolmogorov distance coincides with the trace distance. We further study the quantum-to-classical transition of the distance measures. Employing the Caldeira-Legget model of quantum Brownian motion as a prototypical example, numerical simulations indicate a particularly rapid convergence of the Kolmogorov distance of the Wigner functions to the trace distance in the classical uncertainty limit, which establishes the Wigner function distance as an optimal tool for measuring semi-classical information backflow and for quantifying non-Markovianity in open continuous variable quantum systems.

We use a combinatorial approach to obtain exact expressions for the many-body density of states of fermionic and bosonic gases with equally spaced single-particle spectra. We identify a mapping that reveals a remarkable property, namely, fermionic and bosonic gases have the same many-body density of states, up to a shift corresponding to ground state energy. Additionally, we show that there is a regime, comprising the validity range of the Bethe approximation, where the many-body density of states becomes independent of the number of particles.

We rigorously derive an effective bending model for elastoplastic rods starting from three-dimensional finite plasticity. For the derivation we lean on a framework of evolutionary $\Gamma$-convergence for rate-independent systems. The main contribution of this paper is an ansatz for the mutual recovery sequence in the bending regime. In difference to previous works that deal with infinitesimal deformations in the limit, in the bending regime we are concerned with finite deformations that admit infinitesimally small strain but large rotations. To overcome these difficulties we provide new ideas based on a multiplicative decomposition for the Cosserat rod ansatz. As regularizing terms, we introduce strain gradient terms into the energy that vanish as the thickness of the rod tends to zero.

Air-lasing from single ionized N$_2^+$ molecules induced by laser filamentation in air has been intensively investigated and the mechanisms responsible for lasing are currently highly debated. We use ultrafast nitrogen K-edge spectroscopy to follow the strong field ionization and fragmentation dynamics of N$_2$ upon interaction with an ultrashort 800 nm laser pulse. Using probe pulses generated by extreme high-order harmonic generation, we observe transitions indicative of the formation of the electronic ground X$^2\Sigma_{g}^{+}$, first excited A$^2\Pi_u$ and second excited B$^2\Sigma^+_u$ states of N$_2^+$ on femtosecond time scales, from which we can quantitatively determine the time-dependent electronic state population distribution dynamics of N$_2^+$. Our results show a remarkably low population of the A$^2\Pi_u$ state, and nearly equal populations of the X$^2\Sigma_{g}^{+}$ and B$^2\Sigma^+_u$ states. In addition, we observe fragmentation of N$_2^+$ into N and N$^+$ on a time scale of several tens of picoseconds that we assign to significant collisional dynamics in the plasma, resulting in dissociative excitation of N$_2^+$.

Despite advances in the development of quantum computers, the practical application of quantum algorithms remains outside the current range of so-called noisy intermediate-scale quantum devices. Now and beyond, quantum circuit compilation (QCC) is a crucial component of any quantum algorithm execution. Besides translating a circuit into hardware-specific gates, it can optimize circuit depth and adapt to noise. Variational quantum circuit compilation (VQCC) optimizes the parameters of an ansatz according to the goal of reproducing a given unitary transformation. In this work, we present a VQCC-objective function called the quantum Wasserstein compilation (QWC) cost function based on the quantum Wasserstein distance of order 1. We show that the QWC cost function is upper bound by the average infidelity of two circuits. An estimation method based on measurements of local Pauli-observable is utilized in a generative adversarial network to learn a given quantum circuit. We demonstrate the efficacy of the QWC cost function by compiling a single-layer hardware efficient ansatz (HEA) as both the target and the ansatz and comparing other cost functions such as the Loschmidt echo test (LET) and the Hilbert-Schmidt test (HST). Finally, our experiments demonstrate that QWC as a cost function can mitigate the barren plateaus for the particular problem we consider., Comment: 12 pages, 8 figures