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Motivated by a recently discovered connection between the greybody factors of black holes and the ringdown signal, we investigate the greybody factors of ultracompact horizonless objects, also elucidating their connection to echoes. The greybody factor of ultracompact objects features both low-frequency resonances and high-frequency, quasi-reflectionless scattering modes, which become purely reflectionless in the presence of symmetric cavity potentials, as it might be the case for a wormhole. We show that it is these high-frequency (quasi-)reflectionless scattering modes, rather than low-frequency resonances, to be directly responsible for the echoes in the time-domain response of ultracompact objects or of black holes surrounded by matter fields localized at large distances.

Bayesian neural networks (BNNs) offer the potential for reliable uncertainty quantification and interpretability, which are critical for trustworthy AI in high-stakes domains. However, existing methods often struggle with issues such as overconfidence, hyperparameter sensitivity, and posterior collapse, leaving room for alternative approaches. In this work, we advance message passing (MP) for BNNs and present a novel framework that models the predictive posterior as a factor graph. To the best of our knowledge, our framework is the first MP method that handles convolutional neural networks and avoids double-counting training data, a limitation of previous MP methods that causes overconfidence. We evaluate our approach on CIFAR-10 with a convolutional neural network of roughly 890k parameters and find that it can compete with the SOTA baselines AdamW and IVON, even having an edge in terms of calibration. On synthetic data, we validate the uncertainty estimates and observe a strong correlation (0.9) between posterior credible intervals and its probability of covering the true data-generating function outside the training range. While our method scales to an MLP with 5.6 million parameters, further improvements are necessary to match the scale and performance of state-of-the-art variational inference methods., Comment: for code see https://github.com/christian-helms/mpbnns.git

We prove a fourth order analogue of the Saint-Venant inequality: the mean deflection of a clamped plate under uniform transverse load is maximal for the ball, among plates of prescribed volume in any dimension of space. The method works in Euclidean space, hyperbolic space, and the sphere. Similar results for clamped plates under small compression and for the compliance under non-uniform loads are proved to hold in two dimensional Euclidean space, with the higher dimensional and curved cases of those problems left open., Comment: 35 pages

A key challenge in Deep Reinforcement Learning is sample efficiency, especially in real-world applications where collecting environment interactions is expensive or risky. Recent off-policy algorithms improve sample efficiency by increasing the Update-To-Data (UTD) ratio and performing more gradient updates per environment interaction. While this improves sample efficiency, it significantly increases computational cost due to the higher number of gradient updates required. In this paper we propose a sample-efficient method to improve computational efficiency by separating training into distinct learning phases in order to exploit gradient updates more effectively. Our approach builds on top of the Dropout Q-Functions (DroQ) algorithm and alternates between an online, low UTD ratio training phase, and an offline stabilization phase. During the stabilization phase, we fine-tune the Q-functions without collecting new environment interactions. This process improves the effectiveness of the replay buffer and reduces computational overhead. Our experimental results on continuous control problems show that our method achieves results comparable to state-of-the-art, high UTD ratio algorithms while requiring 56\% fewer gradient updates and 50\% less training time than DroQ. Our approach offers an effective and computationally economical solution while maintaining the same sample efficiency as the more costly, high UTD ratio state-of-the-art.

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

The Parker Solar Probe (PSP) spacecraft observed a large coronal mass ejection (CME) on 5 September 2022, shortly before closest approach during the 13th PSP solar encounter. For several days following the CME, PSP detected a storm of Type III radio bursts. Stokes parameter analysis of the radio emission indicates that the Type III storm was highly circularly polarized. Left hand circularly polarized (LHC) emission dominated at the start of the storm, transitioning to right hand circularly polarized (RHC) emission at the crossing of the heliospheric current sheet on 6 September. We analyze the properties of this Type III storm. The drift rate of the Type IIIs indicates a constant beam speed of $\sim$0.1$c$, typical for Type III-producing electron beams. The sense of polarization is consistent with fundamental emission generated primarily in the $O$-mode. The stable and well organized post-CME magnetic field neatly separates the LHC- and RHC-dominated intervals of the storm, with minimal overlap between the senses of polarization. The proximity of PSP to the source region, both in radial distance and in heliographic longitude, makes this event an ideal case study to connect in situ plasma measurements with remote observations of radio emission., Comment: 14 pages, 3 figures

We provide a generalization of global hyperbolicity in pseudo-Riemannian spaces of signature $(p, q)$ for $p \ge q \ge 2$. We then prove the compactness of causal diamonds in globally hyperbolic spaces and deduce the existence of solutions to a Plateau problem for this class of spaces. Finally, we describe the topology of globally hyperbolic spaces in a result generalising Geroch's splitting theorem.

Generalized parameter estimation-based observers have proven very successful to deal with systems described in state-affine form. In this paper, we enlarge the domain of applicability of this method proposing an algebraic procedure to immerse} an $n$-dimensional general nonlinear system into and $n_z$-dimensional system in state affine form, with $n_z>n$. First, we recall the necessary and sufficient condition for the solution of the general problem, which requires the solution of a partial differential equation that, moreover, has to satisfy a restrictive injectivity condition. Given the complexity of this task we propose an alternative simple algebraic method to identify the required dynamic extension and coordinate transformation, a procedure that, as shown in the paper, is rather natural for physical systems. We illustrate the method with some academic benchmark examples from observer theory literature -- that, in spite of their apparent simplicity, are difficult to solve with the existing methods -- as well as several practically relevant physical examples.

We count the number of connected components in the space of triples of transverse flags in any flag manifold of $\mathrm{SO}_0(p,q)$. We compute the effect the involution of the unipotent radical has on those components and deduce that for certain parabolic subgroups $P_{\Theta}$, any $P_{\Theta}$-Anosov subgroup is virtually isomorphic to either a surface group of a free group. We give examples of Anosov subgroups which are neither free nor surface groups for some sets of roots which do not fall under the previous results. As a consequence of the methods developed here, we get an explicit algorithm based on computation of minors to check if a unipotent matrix in $\mathrm{SO}_0(p,q)$ belong to the $\Theta$-positive semigroup $U_\Theta^{>0}$ when $p\neq q$., Comment: 35 pages, 7 figures

This study is focused on enhancing the Haar Cascade Algorithm to decrease the false positive and false negative rate in face matching and face detection to increase the accuracy rate even under challenging conditions. The face recognition library was implemented with Haar Cascade Algorithm in which the 128-dimensional vectors representing the unique features of a face are encoded. A subprocess was applied where the grayscale image from Haar Cascade was converted to RGB to improve the face encoding. Logical process and face filtering are also used to decrease non-face detection. The Enhanced Haar Cascade Algorithm produced a 98.39% accuracy rate (21.39% increase), 63.59% precision rate, 98.30% recall rate, and 72.23% in F1 Score. In comparison, the Haar Cascade Algorithm achieved a 46.70% to 77.00% accuracy rate, 44.15% precision rate, 98.61% recall rate, and 47.01% in F1 Score. Both algorithms used the Confusion Matrix Test with 301,950 comparisons using the same dataset of 550 images. The 98.39% accuracy rate shows a significant decrease in false positive and false negative rates in facial recognition. Face matching and face detection are more accurate in images with complex backgrounds, lighting variations, and occlusions, or even those with similar attributes.

We investigate the experimental feasibility of detecting second-order double-magnetic dipole ($\gamma\gamma$-$M1M1$) decays from double isobaric analog states (DIAS), which have recently been found to be strongly correlated with the nuclear matrix elements of neutrinoless $\beta\beta$ decay. Using the nuclear shell model, we compute theoretical branching ratios for $\gamma\gamma$-$M1M1$ decays and compare them with other competing processes, such as single-$\gamma$ decay and proton emission, which represent the dominant decay channels. We also estimate the potential competition from internal conversion and internal pair creation, which can influence the decay dynamics. Additionally, we propose an experimental strategy based on using LaBr$_3$ scintillators to identify $\gamma\gamma$-$M1M1$ transitions from the DIAS amidst the background of the competing processes. Our approach emphasizes the challenges of isolating the rare $\gamma\gamma$-$M1M1$ decay and suggests ways to enhance the experimental detection sensitivity. Our simulations suggest that it may be possible to access experimentally $\gamma\gamma$-$M1M1$ decays from DIAS, shedding light on the neutrinoless $\beta\beta$ decay nuclear matrix elements., Comment: 8 pages, 4 figures, 5 tables

Touch is a crucial sensing modality that provides rich information about object properties and interactions with the physical environment. Humans and robots both benefit from using touch to perceive and interact with the surrounding environment (Johansson and Flanagan, 2009; Li et al., 2020; Calandra et al., 2017). However, no existing systems provide rich, multi-modal digital touch-sensing capabilities through a hemispherical compliant embodiment. Here, we describe several conceptual and technological innovations to improve the digitization of touch. These advances are embodied in an artificial finger-shaped sensor with advanced sensing capabilities. Significantly, this fingertip contains high-resolution sensors (~8.3 million taxels) that respond to omnidirectional touch, capture multi-modal signals, and use on-device artificial intelligence to process the data in real time. Evaluations show that the artificial fingertip can resolve spatial features as small as 7 um, sense normal and shear forces with a resolution of 1.01 mN and 1.27 mN, respectively, perceive vibrations up to 10 kHz, sense heat, and even sense odor. Furthermore, it embeds an on-device AI neural network accelerator that acts as a peripheral nervous system on a robot and mimics the reflex arc found in humans. These results demonstrate the possibility of digitizing touch with superhuman performance. The implications are profound, and we anticipate potential applications in robotics (industrial, medical, agricultural, and consumer-level), virtual reality and telepresence, prosthetics, and e-commerce. Toward digitizing touch at scale, we open-source a modular platform to facilitate future research on the nature of touch., Comment: 28 pages

Robotic manipulation of fresh fruits and vegetables, including the grasping of multiple loose items, has a strong industrial need but it still is a challenging task for robotic manipulation. This paper outlines the distinctive manipulation strategies used by humans to pick loose fruits and vegetables with the aim to better adopt them for robotic manipulation of diverse items. In this work we present a first version of a robotic setup designed to pick different single or multiple fresh items, featuring multi-fingered compliant robotic gripper. We analyse human grasping strategies from the perspective of industrial Key Performance Indicators (KPIs) used in the logistic sector. The robotic system was validated using the same KPIs, as well as taking into account human performance and strategies. This paper lays the foundation for future development of the robotic demonstrator for fresh fruit and vegetable intelligent manipulation, and outlines the need for generic approaches to handle the complexity of the task., Comment: *Authors contributed equally

In analogy with the 290-Theorem of Bhargava-Hanke, a criterion set is a finite subset $C$ of the totally positive integers in a given totally real number field such that if a quadratic form represents all elements of $C$, then it necessarily represents all totally positive integers, i.e., is universal. We use a novel characterization of minimal criterion sets to show that they always exist and are unique, and that they must contain certain explicit elements. We also extend the uniqueness result to the more general setting of representations of a given subset of the integers., Comment: 19 pages, comments are welcome!

Human exploration of the moon is expected to resume in the next decade, following the last such activities in the Apollo programme time. One of the major objectives of returning to the Moon is to continue retrieving geological samples, with a focus on collecting high-quality specimens to maximize scientific return. Tools that assist astronauts in making informed decisions about sample collection activities can maximize the scientific value of future lunar missions. A lunar rock classifier is a tool that can potentially provide the necessary information for astronauts to analyze lunar rock samples, allowing them to augment in-situ value identification of samples. Towards demonstrating the value of such a tool, in this paper, we introduce a framework for classifying rock types in thin sections of lunar rocks. We leverage the vast collection of petrographic thin-section images from the Apollo missions, captured under plane-polarized light (PPL), cross-polarised light (XPL), and reflected light at varying magnifications. Advanced machine learning methods, including contrastive learning, are applied to analyze these images and extract meaningful features. The contrastive learning approach fine-tunes a pre-trained Inception-Resnet-v2 network with the SimCLR loss function. The fine-tuned Inception-Resnet-v2 network can then extract essential features effectively from the thin-section images of Apollo rocks. A simple binary classifier is trained using transfer learning from the fine-tuned Inception-ResNet-v2 to 98.44\% ($\pm$1.47) accuracy in separating breccias from basalts.

In altermagnets, time-reversal symmetry breaking spin-polarizes electronic states, while total magnetization remains zero. In addition, at altermagnetic surfaces Rashba-spin orbit coupling is activated due to broken inversion symmetry, introducing a competing spin-momentum locking interaction. Here we show that their interplay leads to the formation of complex, chiral spin textures that offer novel, non-linear spin-to-charge conversion properties. Whereas altermagnetic order suppresses the canonical linear in-plane Rashba-Edelstein response, we establish the presence of an anomalous transversal Edelstein effect for planar applied electric and magnetic field, or alternatively, an in-plane magnetization. Moreover the non-linear Edelstein response resulting purely from electric fields also triggers the anomalous out-of-plane magnetization. We determine the anomalous response with a model based on the ab-initio electronic structure of RuO$_2$ bilayers, ultimately opening experimental avenues to explore spin-charge conversion phenomena at altermagnetic interfaces., Comment: 11 pages, 15 figures

Continued fractions have been generalized over the field of $p$-adic numbers, where it is still not known an analogue of the famous Lagrange's Theorem. In general, the periodicity of $p$-adic continued fractions is well studied and addressed as a hard problem. In this paper, we show a strong connection between periodic $p$--adic continued fractions and the convergence to real quadratic irrationals. In particular, in the first part we prove that the convergence in $\mathbb{R}$ is a necessary condition for the periodicity of the continued fractions of a quadratic irrational in $\mathbb{Q}_p$. Moreover, we leave several conjectures on the converse, supported by experimental computations. In the second part of the paper, we exploit these results to develop a probabilistic argument for the non-periodicity of Browkin's $p$-adic continued fractions. The probabilistic results are conditioned under the assumption of uniform distribution of the $p$-adic digits of a quadratic irrational, that holds for almost all $p$-adic numbers.

Current star formation models are based on the local structure of the interstellar medium (ISM), yet the details on how the small-scale physics propagates up to global galactic-scale properties are still under debate. To investigate this we use {\small VINTERGATAN}, a high-resolution (20 pc) cosmological zoom-in simulation of a Milky Way-like galaxy. We study how the velocity dispersion and density structure of the ISM on 50-100 pc scales evolve with redshift, and quantify their impact on the star formation efficiency per free-fall timescale, $\epsilon_{\rm ff}$. During starbursts the ISM can reach velocity dispersions as high as $\sim 50$ km s$^{-1}$ for the densest and coldest gas, most noticeable during the last major merger event ($1.3 < z < 1.5$). After a merger-dominated phase ($11$ to 0.3\% at $z<1$. The mass-weighted average shows a universal $\langle \epsilon_{\rm ff} \rangle \approx 1\%$, caused by an almost invariant virial parameter distribution in star forming clouds. Changes in their density and turbulence levels are coupled so the kinetic-to-gravitational energy ratio remains close to constant. Finally, we show that a \textit{theoretically} motivated instantaneous $\epsilon_{\rm ff}$ is intrinsically different to its \textit{observational} estimates adopting tracers of star formation e.g. H$\alpha$. Since the physics underlying star formation can be lost on short ($\sim$ 10 Myr) timescales, caution must be taken when constraining star formation models from observational estimates of $\epsilon_{\rm ff}$., Comment: 14 pages, 6 figures, comments welcome

Dielectric capillaries are widely used to generate plasmas for plasma wakefield acceleration. When a relativistic drive bunch travels through a capillary with misaligned trajectory with respect to the capillary axis, it is deflected by the effect of the dielectric transverse wakefields it drives. We experimentally show that the deflection effect increases along the bunch and with larger misalignment, and we investigate the decay of dielectric wakefields by measuring the effect on the front of a trailing bunch. We discuss the implications for the design of a plasma wakefield accelerator based on dielectric capillaries., Comment: Proceedings of the 2024 Advanced Accelerator Concepts Workshop

In this paper we study the question of how to reconstruct the state of a power system using Phasor Measurement Units (PMUs). In our previous research we proved that this question has an affirmative answer imposing some rather strict structural assumptions: namely, neglecting the generator rotors saliency and assuming that the stator resistance of the synchronous generator is zero. It was shown in simulations that the performance of the proposed observer was sensitive to these assumptions, observing a transient quality degradation for realistic simulations not imposing these assumptions. Moreover, it was assumed in our previous work that the mechanical power and the field voltage are available for measurement, a scenario that it is not always realistic. In this paper we accomplish two ambitious objectives. First, we propose a new observer that does not impose the simplifying assumptions on the generator model. Secondly, we consider the more realistic scenario where only mechanical power is available for measurement. That is, we solve a problem of state reconstruction of a nonlinear system with partially known input measurements -- that is well-known to be a very challenging task. The design of the first observer relies on two recent developments proposed by the authors, a parameter estimation based approach to the problem of state estimation and the use of the Dynamic Regressor Extension and Mixing (DREM) technique to estimate these parameters. The use of DREM allows us to overcome the problem of lack of persistent excitation that stymies the application of standard parameter estimation designs. On the other hand, the observer for the partial input measurement scenario relies on the clever exploitation of the systems model. Simulation results illustrates the good performance of the proposed observers.

This paper works on Binary Neural Networks (BNNs), a promising avenue for efficient deep learning, offering significant reductions in computational overhead and memory footprint to full precision networks. However, the challenge of energy-intensive training and the drop in performance have been persistent issues. Tackling the challenge, prior works focus primarily on task-related inference optimization. Unlike prior works, this study offers an innovative methodology integrating BNNs with cyclic precision training, introducing the CycleBNN. This approach is designed to enhance training efficiency while minimizing the loss in performance. By dynamically adjusting precision in cycles, we achieve a convenient trade-off between training efficiency and model performance. This emphasizes the potential of our method in energy-constrained training scenarios, where data is collected onboard and paves the way for sustainable and efficient deep learning architectures. To gather insights on CycleBNN's efficiency, we conduct experiments on ImageNet, CIFAR-10, and PASCAL-VOC, obtaining competitive performances while using 96.09\% less operations during training on ImageNet, 88.88\% on CIFAR-10 and 96.09\% on PASCAL-VOC. Finally, CycleBNN offers a path towards faster, more accessible training of efficient networks, accelerating the development of practical applications. The PyTorch code is available at \url{https://github.com/fedeloper/CycleBNN/}, Comment: Published at Workshop CADL, ECCV-2024

Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecasting, downscaling, or nowcasting. While the parallel developments in the AI literature focus on foundation models -- models that can be effectively tuned to address multiple, different use cases -- the developments on the weather and climate side largely focus on single-use cases with particular emphasis on mid-range forecasting. We close this gap by introducing Prithvi WxC, a 2.3 billion parameter foundation model developed using 160 variables from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Prithvi WxC employs an encoder-decoder-based architecture, incorporating concepts from various recent transformer models to effectively capture both regional and global dependencies in the input data. The model has been designed to accommodate large token counts to model weather phenomena in different topologies at fine resolutions. Furthermore, it is trained with a mixed objective that combines the paradigms of masked reconstruction with forecasting. We test the model on a set of challenging downstream tasks namely: Autoregressive rollout forecasting, Downscaling, Gravity wave flux parameterization, and Extreme events estimation. The pretrained model with 2.3 billion parameters, along with the associated fine-tuning workflows, has been publicly released as an open-source contribution via Hugging Face.

This paper introduces the Neural Transcoding Vision Transformer (\modelname), a generative model designed to estimate high-resolution functional Magnetic Resonance Imaging (fMRI) samples from simultaneous Electroencephalography (EEG) data. A key feature of \modelname is its Domain Matching (DM) sub-module which effectively aligns the latent EEG representations with those of fMRI volumes, enhancing the model's accuracy and reliability. Unlike previous methods that tend to struggle with fidelity and reproducibility of images, \modelname addresses these challenges by ensuring methodological integrity and higher-quality reconstructions which we showcase through extensive evaluation on two benchmark datasets; \modelname outperforms the current state-of-the-art by a significant margin in both cases, e.g. achieving a $10\times$ reduction in RMSE and a $3.14\times$ increase in SSIM on the Oddball dataset. An ablation study also provides insights into the contribution of each component to the model's overall effectiveness. This development is critical in offering a new approach to lessen the time and financial constraints typically linked with high-resolution brain imaging, thereby aiding in the swift and precise diagnosis of neurological disorders. Although it is not a replacement for actual fMRI but rather a step towards making such imaging more accessible, we believe that it represents a pivotal advancement in clinical practice and neuroscience research. Code is available at \url{https://github.com/rom42pla/ntvit}., Comment: ECCV24 Workshop on Synthetic Data for Computer Vision

We analyze the evolution of the radial profiles and the azimuthal variations of the stellar metallicities from the Vintergatan simulation of a Milky Way-like galaxy. We find that negative gradients exist as soon as the disk settles at high redshift, and are maintained throughout the long term evolution of the galaxy, including during major merger events. The inside-out growth of the disk and an overall outward radial migration tend to flatten these gradients in time. Major merger events only have a moderate and short-lived imprint on the [Fe/H] distributions with almost no radial dependence. The reason lies in the timescale for enrichment in Fe being significantly longer than the duration of the starbursts episodes, themselves slower than dynamical mixing during typical interactions. It results that signatures of major mergers become undetectable in [Fe/H] only a few Myr after pericenter passages. We note that considering other tracers like the warm interstellar medium, or monitoring the evolution of the metallicity gradient as a single value instead of a radial full profile could lead to different interpretations, and warn against an oversimplification of this complex problem., Comment: A&A accepted

Cross-connections of normal categories was introduced by K.S.S.Nambooripad while discussing the structure of regular semigroups and via this cross-connections he obtained a beautiful representetion of regualr semigroup called the cross-connection semigroup (see cf.[4]). Subsequently cross-connection representation of various other semigroups such as concordant semigroups, semigroup of endomorphisms of a vector space are also described (cf.[6][5]). In this paper we describe the semigroup amalgam of cross-connection semigroups of the fibers of a vector bundle., Comment: 11 pages 3 figures

Using Parker Solar Probe data from orbits 8 through 17, we examine fluctuation amplitudes throughout the critical region where the solar wind flow speed approaches and then exceeds the Alfv\'en wave speed, taking account of various exigencies of the plasma data. In contrast to WKB theory for non-interacting Alfv\'en waves streaming away from the Sun, the magnetic and kinetic fluctuation energies per unit volume are not monotonically decreasing. Instead, there is clear violation of conservation of standard WKB wave action, which is consistent with previous indications of strong in-situ fluctuation energy input in the solar wind near the Alfv\'en critical region. This points to strong violations of WKB theory due to nonlinearity (turbulence) and major energy input near the critical region, which we interpret as likely due to driving by large-scale coronal shear flows.

In this work, a physics process known since quite long ago, double-gamma decay ($\gamma\gamma$), has been revisited from a new perspective: providing valuable insights into neutrinoless double-beta decay ($0\nu\beta\beta$) nuclear matrix elements. At the same time that an eager experimental search of $0\nu\beta\beta$ decay is underway, the nuclear and particle physics communities have made huge progress during the last years. The main goal of this thesis has been to investigate one of those approaches which is the computation of nuclear observables related to $0\nu\beta\beta$ decay as a way to help in determining and reducing the theoretical uncertainties in $0\nu\beta\beta$-decay NMEs. This way, we have proposed that the measurement of the double magnetic dipole $\gamma\gamma$ decay from the double isobaric analog state in the $\beta\beta$-decay final nucleus could establish the value and reduce the uncertainty in $0\nu\beta\beta$-decay NMEs, because the NMEs of the two processes are very well correlated. We have explored the validity of this approach to predict and quantify NMEs uncertainties in the case where data is available for the nuclear observable related to $0\nu\beta\beta$ decay, that is for the Standard Model allowed $2\nu\beta\beta$ decay. A further objective has been to start with the theoretical characterization of the first steps toward the measurement of the proposed $\gamma\gamma$ double magnetic dipole decay from the double isobaric analogue state. In particular, we have studied the main decaying modes that can compete with this process: single gamma decay and proton emission, vi and we have calculated the corresponding branching ratios. In addition, we have also given the first steps in the study of the relation between $\gamma\gamma$ and $0\nu\beta\beta$-decay NMEs in the ab initio valence space in-medium similarity renormalization group.

This paper addresses the challenge of probabilistic parameter estimation given measurement uncertainty in real-time. We provide a general formulation and apply this to pose estimation for an autonomous visual landing system. We present three probabilistic parameter estimators: a least-squares sampling approach, a linear approximation method, and a probabilistic programming estimator. To evaluate these estimators, we introduce novel closed-form expressions for measuring calibration and sharpness specifically for multivariate normal distributions. Our experimental study compares the three estimators under various noise conditions. We demonstrate that the linear approximation estimator can produce sharp and well-calibrated pose predictions significantly faster than the other methods but may yield overconfident predictions in certain scenarios. Additionally, we demonstrate that these estimators can be integrated with a Kalman filter for continuous pose estimation during a runway approach where we observe a 50\% improvement in sharpness while maintaining marginal calibration. This work contributes to the integration of data-driven computer vision models into complex safety-critical aircraft systems and provides a foundation for developing rigorous certification guidelines for such systems., Comment: Accepted at DASC '24. 9 pages, 4 figures

In this paper, we apply the recently developed generalized parameter estimation-based observer design technique for state-affine systems to the practically important case of linear time-varying descriptor systems with uncertain parameters. We give simulation results of benchmark examples that illustrate the performance of the proposed adaptive observer.

Liver zonation is a fundamental characteristic of hepatocyte spatial heterogeneity, which is challenging to recapitulate in traditional cell cultures. This study presents a novel microfluidic device designed to induce zonation in liver cell cultures by establishing an oxygen gradient using standard laboratory gases. The device consists of two layers; a bottom layer containing a gas channel network that delivers high and low oxygenated gases to create three distinct zones within the cell culture chamber in the layer above. Computational simulations and ratiometric oxygen sensing were employed to validate the oxygen gradient, demonstrating that stable oxygen levels were achieved within two hours. Liver zonation was confirmed using immunofluorescence staining, which showed zonated albumin production in HepG2 cells directly correlating with oxygen levels and mimicking in-vivo zonation behavior. This user-friendly device supports studies on liver zonation and related metabolic disease mechanisms in vitro. It can also be utilized for experiments that necessitate precise gas concentration gradients, such as hypoxia-related research areas focused on angiogenesis and cancer development.

Offline Reinforcement Learning (ORL) offers a robust solution to training agents in applications where interactions with the environment must be strictly limited due to cost, safety, or lack of accurate simulation environments. Despite its potential to facilitate deployment of artificial agents in the real world, Offline Reinforcement Learning typically requires very many demonstrations annotated with ground-truth rewards. Consequently, state-of-the-art ORL algorithms can be difficult or impossible to apply in data-scarce scenarios. In this paper we propose a simple but effective Reward Model that can estimate the reward signal from a very limited sample of environment transitions annotated with rewards. Once the reward signal is modeled, we use the Reward Model to impute rewards for a large sample of reward-free transitions, thus enabling the application of ORL techniques. We demonstrate the potential of our approach on several D4RL continuous locomotion tasks. Our results show that, using only 1\% of reward-labeled transitions from the original datasets, our learned reward model is able to impute rewards for the remaining 99\% of the transitions, from which performant agents can be learned using Offline Reinforcement Learning., Comment: RLBRew Workshop @ RLC 2024

We theoretically discuss the emergence of fractional Shapiro steps in a Josephson junction created by confining a two-dimensional electron gas at an oxide interface. This phenomenon is induced by an alternating current of proper amplitude and frequency and can be tuned by a magnetic field applied perpendicular to the Rashba spin-orbit axis. The presence of fractional Shapiro steps can be associated with the creation of Majorana bound states at the boundaries of the superconducting leads. Our findings represent a route for the identification of topological superconductivity in non-centrosymmetric materials and confined systems in the presence of spin--orbit interaction, offering also new insights into recently explored frameworks., Comment: 8 pages, 4 figures

The space-charge field of a relativistic charged bunch propagating in plasma is screened due to the presence of mobile charge carriers. We experimentally investigate such screening by measuring the effect of dielectric wakefields driven by the bunch in a uncoated dielectric capillary where the plasma is confined. We show that the plasma screens the space-charge field and therefore suppresses the dielectric wakefields when the distance between the bunch and the dielectric surface is much larger than the plasma skin depth. Before full screening is reached, the effects of dielectric and plasma wakefields are present simultaneously.

We propose a novel neutron interferometry setup to explore the potential existence of mirror neutrons, a candidate for dark matter. Our work demonstrates that if mirror neutrons exist, neutrons will acquire an observable geometric phase due to mixing with these mirror counterparts. This geometric phase, detectable through our interferometric setup, could serve as a direct probe for the presence of mirror matter particles. Additionally, this investigation could shed light on unresolved issues in particle physics, such as the neutron lifetime puzzle. We discuss the setup's versatility and limitations, showing its capability to explore a wide range of parameters in neutron interferometry and potentially uncover new physics., Comment: 5 pages, 5 figures

We present a controller for a power electronic system composed of a fuel cell (FC) connected to a boost converter which feeds a resistive load. The controller aims to regulate the output voltage of the converter regardless of the uncertainty of the load. Leveraging the monotonicity feature of the fuel cell polarization curve we prove that the nonlinear system can be controlled by means of a passivity-based proportional-integral approach. We afterward extend the result to an adaptive version, allowing the controller to deal with parameter uncertainties, such as inductor parasitic resistance, load, and FC polarization curve parameters. This adaptive design is based on an indirect control approach with online parameter identification performed by a ``hybrid'' estimator which combines two techniques: the gradient-descent and immersion-and-invariance algorithms. The overall system is proved to be stable with the output voltage regulated to its reference. Experimental results validate our proposal under two real-life scenarios: pulsating load and output voltage reference changes., Comment: 11 pages, 8 Figs

Deepfake detection aims to contrast the spread of deep-generated media that undermines trust in online content. While existing methods focus on large and complex models, the need for real-time detection demands greater efficiency. With this in mind, unlike previous work, we introduce a novel deepfake detection approach on images using Binary Neural Networks (BNNs) for fast inference with minimal accuracy loss. Moreover, our method incorporates Fast Fourier Transform (FFT) and Local Binary Pattern (LBP) as additional channel features to uncover manipulation traces in frequency and texture domains. Evaluations on COCOFake, DFFD, and CIFAKE datasets demonstrate our method's state-of-the-art performance in most scenarios with a significant efficiency gain of up to a $20\times$ reduction in FLOPs during inference. Finally, by exploring BNNs in deepfake detection to balance accuracy and efficiency, this work paves the way for future research on efficient deepfake detection., Comment: Accepted at CVPR24 DFAD Workshop