Search

The post-inflationary Peccei-Quinn (PQ) symmetry breaking scenario provides a unique opportunity to pinpoint the QCD axion dark matter mass, which is a crucial input for laboratory experiments that are designed for probing specific mass ranges. Predicting their mass requires a precise knowledge of how axions are produced from the decay of topological defects in the early Universe that are inevitably formed. In this contribution, we present recent results on the analysis of the spectrum of axions radiated from global strings based on large scale numerical simulations of the cosmological evolution of the PQ field on a static lattice. We highlight several systematic effects that have been overlooked in previous works, such as the dependence on the initial conditions, contaminations due to oscillations in the spectrum, and discretisation effects; some of which could explain the discrepancy in the current literature. Taking these uncertainties into account and performing the extrapolation to cosmologically relevant string tensions, we find that the dark matter mass is predicted to be in the range of $95\,\mu\text{eV} \lesssim m_a \lesssim 450 \, \mu\text{eV}$, which will be probed by some of the next generation direct detection experiments., Comment: 11 pages, 7 figures, contribution to the proceedings of the 2nd General Meeting and 2nd Training School of the COST Action COSMIC WISPers (COSMICWISPers)

We present an experimental method for the characterization of the kinetic energies of ions confined in a 22-pole radio frequency trap by inducing a small potential barrier using the surrounding ring electrodes, allowing the selective extraction of ions. Energy sampling experiments have been performed on buffer gas thermalized He$^+$ ions at trap temperatures between 10-180 K, resulting in distinct extraction curves as a function of the potential barrier, and a differentiated behavior depending on the escape time from the trap. The experiments are complemented by Monte Carlo simulations of the ion trajectories inside the calculated trap potential and allow us to investigate the properties of the sampling method, the role of ion motion coupling, and the impact of residual buffer gas collisions on the observed results. The technique has also been successfully applied to identify energetic H$_3^+$ ions produced in an exothermic reaction inside the trap. Upon calibration, this method can provide relative kinetic energy distributions or be used to filter the maximum desired kinetic energy of the ions inside the trap.

The eikonal limit of black hole quasinormal modes (the large multipole limit $\ell \gg 1$) can be realized geometrically as a next-to-leading order solution to the geometric optics approximation, and also as linear fluctuations about the Penrose limit plane wave adapted to the lightring. Extending this interpretation beyond the linear order in perturbation theory requires a robust understanding of quadratic quasinormal modes for large values of $\ell$. We analyze numerically the relative excitation of quadratic to linear quasinormal modes of Schwarzschild black holes, with two independent methods. Our results suggest that the ratio of quadratic to linear amplitudes for the $\ell \times \ell \to 2\ell$ channel converges towards a finite value for large $\ell$, in sharp contrast with a recent proposal inspired by the Penrose limit perspective. On the other hand, the $2 \times \ell \to \ell + 2$ channel seems to have a linearly growing ratio. Nevertheless, we show that there is no breakdown of black hole perturbation theory for physically realistic initial data., Comment: 5 pages, 2 figures. Comments are welcome

Exploration of the Universe requires communication with Earth, either on a direct path or through a cascade of proximate celestial bodies. Microwaves have traditionally been used for space communication, but electromagnetic waves of higher frequencies, such as in the optical domain, will enable probing farther in space due to their considerably lower diffraction loss. At a given data rate, the ultimate limit to point-to-point optical communication is determined by the received signal power and the photon information efficiency. The latter measures the number of information bits extracted per photon incident on a detector. As distances across space and, consequently, path loss increases, the system that can achieve the highest photon information efficiency will determine the longest distance at which communication is possible. We report here an experimental demonstration of optical detection at a record photon information efficiency of 14.5 bits per incident photon, or 17.8 bits per detected photon, after 87.5 dB of attenuation. Expressed in terms of energy per bit, this corresponds to 8.84 zeptojoules per bit, or 0.069 photons per bit at 1550 nm. To our knowledge, this is the highest photon information efficiency or lowest energy per bit detection system ever demonstrated at optical frequencies. Such a sensitive detection system holds promise for a wide range of applications.

Entanglement-based networks (EBNs) enable general-purpose quantum communication by combining entanglement and its swapping in a sequence that addresses the challenges of achieving long distance communication with high fidelity associated with quantum technologies. In this context, entanglement distribution refers to the process by which two nodes in a quantum network share an entangled state, serving as a fundamental resource for communication. In this paper, we study the performance of entanglement distribution mechanisms over a physical topology comprising end nodes and quantum switches, which are crucial for constructing large-scale links. To this end, we implemented a switch-based topology in NetSquid and conducted a series of simulation experiments to gain insight into practical and realistic quantum network engineering challenges. These challenges include, on the one hand, aspects related to quantum technology, such as memory technology, gate durations, and noise; and, on the other hand, factors associated with the distribution process, such as the number of switches, distances, purification, and error correction. All these factors significantly impact the end-to-end fidelity across a path, which supports communication between two quantum nodes. We use these experiments to derive some guidelines towards the design and configuration of future EBNs.

Coherent technologies have revolutionized optical communications, driving the capacity per fiber to multi-terabit per second (Tb/s) in combination with wavelength division multiplexing (WDM). With an ever-increasing deployment density of coherent systems, the demand for highly integrated WDM coherent transceivers has been rising. While tremendous progress has been made on silicon photonics compatible high-speed modulation and photodetection on chip, a solution for monolithically integrable amplifier with high gain and output power remains a challenge. Recently, an erbium doped waveguide amplifier based on ultra-low loss silicon nitride waveguides has demonstrated gain and output power levels potentially suitable for Terabit class coherent communications. Here, we demonstrate a WDM coherent system enabled by this integrated photonic amplification solution. The system uses the waveguide amplifier as a booster amplifier of 16 WDM signals each carrying a net data rate of 1.6 Tb/s, achieving 25.6-Tb/s net capacity over 81-km fiber transmission. Our results highlight a fully integrated solution for highly parallel coherent transceivers including amplification, that has the potential to transform future optical communications., Comment: Added acknowledgements

The rate coefficients of various isotopic variations of the H2+ + H2 and H3+ + H2 reactions in the 10-250 K temperature range were measured using a cryogenic 22 pole radio frequency ion trap. The processes involving diatomic ions were found to behave close to the Langevin rate, whereas temperature-dependent rate coefficients were obtained for the four isotopic exchange processes with triatomic ions. Fitting the experimental data using a chemical code allowed us in specific cases to constrain rate coefficients that were not directly measured in the ion trap. The reported rate coefficients suggest a more efficient hydrogenation of deuterated H3+ forms than usually assumed in astrochemical models, which might affect deuteration rates in warmer environments.

We study the dynamical response of viscous materials to gravitational waves, in the context of a fully relativistic theory of fluid dynamics. For the first time, we calculate oscillation modes and scattering properties of viscous stars. Viscous stars absorb high frequency radiation, following a dispersion relation introduced by Press. In the extremely large viscosity regime, stars would become reflectors of waves, but this regime appears to be forbidden by causality bounds. In the context of black hole mimickers, we show how maximally viscous stars on the threshold of stability mimic the absorption of a black hole with the same mass. Our results suggest that rotating viscous stars will amplify incoming radiation., Comment: 5+5 pages, 2+8 figures

Viscous fluids can dissipate and alter the propagation of gravitational waves, as well as modify the relaxation and stability properties of self-gravitating fluids. This is particularly relevant in order to understand the relaxation to equilibrium of neutron stars, and their gravitational wave emission. Here we study the linearized theory of perturbations of spherically symmetric self-gravitating fluids. Dissipative effects are included through the hydrodynamics theory of Bemfica, Disconzi, Noronha, and Kovtun (BDNK). This theory has been shown to be causal and stable, despite involving only first order gradients. We show how the problem reduces to two coupled wave equations in the axial sector, one of them associated to a novel viscous mode, and including explicitly dissipative terms. In the polar sector, we reduce the problem to five coupled wave equations and one additional constraint. We comment on their causal structure, and recover the causality bounds of the BDNK theory., Comment: 23+3 pages, no figures

BabyIAXO is the intermediate stage of the International Axion Observatory (IAXO) to be hosted at DESY. Its primary goal is the detection of solar axions following the axion helioscope technique. Axions are converted into photons in a large magnet that is pointing to the sun. The resulting X-rays are focused by appropriate X-ray optics and detected by sensitive low-background detectors placed at the focal spot. The aim of this article is to provide an accurate quantitative description of the different components (such as the magnet, optics, and X-ray detectors) involved in the detection of axions. Our efforts have focused on developing robust and integrated software tools to model these helioscope components, enabling future assessments of modifications or upgrades to any part of the IAXO axion helioscope and evaluating the potential impact on the experiment's sensitivity. In this manuscript, we demonstrate the application of these tools by presenting a precise signal calculation and response analysis of BabyIAXO's sensitivity to the axion-photon coupling. Though focusing on the Primakoff solar flux component, our virtual helioscope model can be used to test different production mechanisms, allowing for direct comparisons within a unified framework., Comment: 36 pages, 18 figures, 4 tables, Submitted to JHEP

We assessed the validity of one of the most frequently used methods to estimate cancer incidence, on the basis of cancer mortality data and the incidence-to-mortality ratio IMR, the IMR method. Using the previous 15 year cancer mortality time series, we derived the expected yearly number of cancer cases in the period 2004 to 2013 for six cancer sites for each sex. Generalized linear mixed models, including a polynomial function for the year of death and smoothing splines for age, were adjusted. Models were fitted under a Bayesian framework based on Markov chain Monte Carlo methods. The IMR method was applied to five scenarios reflecting different assumptions regarding the behavior of the IMR. We compared incident cases estimated with the IMR method to observed cases diagnosed in 2004 to 2013 in Granada. A goodness-of-fit GOF indicator was formulated to determine the best estimation scenario. The relative differences between the observed and predicted numbers of cancer cases were less than 10 percent for most cancer sites. The constant assumption for the IMR trend provided the best GOF for colon, rectal, lung, bladder, and stomach cancers in men and colon, rectum, breast, and corpus uteri in women.

In prototype-based federated learning, the exchange of model parameters between clients and the master server is replaced by transmission of prototypes or quantized versions of the data samples to the aggregation server. A fully decentralized deployment of prototype-based learning, without a central agregartor of prototypes, is more robust upon network failures and reacts faster to changes in the statistical distribution of the data, suggesting potential advantages and quick adaptation in dynamic learning tasks, e.g., when the data sources are IoT devices or when data is non-iid. In this paper, we consider the problem of designing a communication-efficient decentralized learning system based on prototypes. We address the challenge of prototype redundancy by leveraging on a twofold data compression technique, i.e., sending only update messages if the prototypes are informationtheoretically useful (via the Jensen-Shannon distance), and using clustering on the prototypes to compress the update messages used in the gossip protocol. We also use parallel instead of sequential gossiping, and present an analysis of its age-of-information (AoI). Our experimental results show that, with these improvements, the communications load can be substantially reduced without decreasing the convergence rate of the learning algorithm., Comment: 15 pages, 2 tables, 7 figures, 6 algorithms

Understanding the interplay of non-Hermiticity and topology is crucial given the intrinsic openness of most natural and engineered systems and it has important ramifications in topological lasers and sensors. Intense efforts have been devoted to unveiling how non-Hermiticity may impact the most significant features of topological systems, but only recently it has been theoretically proposed that topological features could originate solely from the system's non-Hermiticity in photonic systems. In this work, we experimentally demonstrate the appearance of non-Hermitian topology exclusively from loss modulation in a photonic system that is topologically trivial in the absence of loss. We do this by implementing a non-Hermitian generalization of an Aubry-Andre-Harper model with purely imaginary potential in a programmable integrated photonics platform, which allows us to investigate different periodic and quasi-periodic configurations of the model. In both cases, we show the emergence of topological edge modes and explore their resilience to different kinds of disorder. Our work highlights loss engineering as a mechanism to generate topological properties.

Digital twin technology, traditionally used in industry, is increasingly recognized for its potential to enhance educational experiences. This study investigates the application of industrial digital twins (DTs) in education, focusing on how DT models of varying fidelity can support different stages of Bloom's taxonomy in the cognitive domain. We align Bloom's six cognitive stages with educational levels: undergraduate studies for "Remember" and "Understand," master's level for "Apply" and "Analyze," and doctoral level for "Evaluate" and "Create." Low-fidelity DTs aid essential knowledge acquisition and skill training, providing a low-risk environment for grasping fundamental concepts. Medium-fidelity DTs offer more detailed and dynamic simulations, enhancing application skills and problem-solving. High-fidelity DTs support advanced learners by replicating physical phenomena, allowing for innovative design and complex experiments. Within this framework, large language models (LLMs) serve as mentors, assessing progress, filling knowledge gaps, and assisting with DT interactions, parameter setting, and debugging. We evaluate the educational impact using the Kirkpatrick Model, examining how each DT model's fidelity influences learning outcomes. This framework helps educators make informed decisions on integrating DTs and LLMs to meet specific learning objectives., Comment: 8 pages, 7 figures

We implement a simulation environment on top of NetSquid that is specifically designed for estimating the end-to-end fidelity across a path of quantum repeaters or quantum switches. The switch model includes several generalizations which are not currently available in other tools, and are useful for gaining insight into practical and realistic quantum network engineering problems: an arbitrary number of memory registers at the switches, simplicity in including entanglement distillation mechanisms, arbitrary switching topologies, and more accurate models for the depolarization noise. An illustrative case study is presented, namely a comparison in terms of performance between a repeater chain where repeaters can only swap sequentially, and a single switch equipped with multiple memory registers, able to handle multiple swapping requests.

Federated Learning (FL) emerges as a distributed machine learning approach that addresses privacy concerns by training AI models locally on devices. Decentralized Federated Learning (DFL) extends the FL paradigm by eliminating the central server, thereby enhancing scalability and robustness through the avoidance of a single point of failure. However, DFL faces significant challenges in optimizing security, as most Byzantine-robust algorithms proposed in the literature are designed for centralized scenarios. In this paper, we present a novel Byzantine-robust aggregation algorithm to enhance the security of Decentralized Federated Learning environments, coined WFAgg. This proposal handles the adverse conditions and strength robustness of dynamic decentralized topologies at the same time by employing multiple filters to identify and mitigate Byzantine attacks. Experimental results demonstrate the effectiveness of the proposed algorithm in maintaining model accuracy and convergence in the presence of various Byzantine attack scenarios, outperforming state-of-the-art centralized Byzantine-robust aggregation schemes (such as Multi-Krum or Clustering). These algorithms are evaluated on an IID image classification problem in both centralized and decentralized scenarios., Comment: 18 pages, 7 figures, 1 table

Nowadays, many machine learning (ML) solutions to improve the wireless standard IEEE802.11p for Vehicular Adhoc Network (VANET) are commonly evaluated in the simulated world. At the same time, this approach could be cost-effective compared to real-world testing due to the high cost of vehicles. There is a risk of unexpected outcomes when these solutions are implemented in the real world, potentially leading to wasted resources. To mitigate this challenge, the hardware-in-the-loop is the way to move forward as it enables the opportunity to test in the real world and simulated worlds together. Therefore, we have developed what we believe is the pioneering hardware-in-the-loop for testing artificial intelligence, multiple services, and HD map data (LiDAR), in both simulated and real-world settings.

This paper presents the first search for dark matter axions with mass in the ranges 76.56 to 76.82 $\mu$eV and 79.31 to 79.53 $\mu$eV using a prototype setup for the MAgnetized Disk and Mirror Axion eXperiment (MADMAX). The experimental setup employs a dielectric haloscope consisting of three sapphire disks and a mirror to resonantly enhance the axion-induced microwave signal within the magnetic dipole field provided by the 1.6 T Morpurgo magnet at CERN. Over 14.5 days of data collection, no axion signal was detected. A 95% CL upper limit on the axion-photon coupling strength down to $|g_{a\gamma}| \sim 2 \times 10^{-11} \mathrm{GeV}^{-1}$ is set in the targeted mass ranges, surpassing previous constraints, assuming a local axion dark matter density $\rho_{a}$ of $0.3~\mathrm{GeV}/\mathrm{cm}^3$. This study marks the first axion dark matter search using a dielectric haloscope.

The data collected by autonomous vehicle (AV) sensors such as LiDAR and cameras is crucial for creating high-definition (HD) maps to provide higher accuracy and enable a higher level of automation. Nevertheless, offloading this large volume of raw data to edge servers leads to increased latency due to network congestion in highly dense environments such as Vehicular Adhoc networks (VANET). To address this challenge, researchers have focused on the dynamic allocation of minimum contention window (CWmin) value. While this approach could be sufficient for fairness, it might not be adequate for prioritizing different services, as it also involves other parameters such as maximum contention window (CWmax) and infer-frame space number (IFSn). In response to this, we extend the scope of previous solutions to include the control of not only CWmin but also the adjustment of two other parameters in the standard IEEE802.11: CWmax and IFSn, alongside waiting transmission time. To achieve this, we introduced a methodology involving a cross-layer solution between the application and MAC layers. Additionally, we utilised multi-agent techniques, emphasising a hierarchical structure and independent learning (IL) to improve latency to efficiently handle map updates while interacting with multiple services. This approach demonstrated an improvement in latency against the standard IEEE802.11p EDCA by $31\%$, $49\%$, $87.3\%$, and $64\%$ for Voice, Video, HD Map, and Best-effort, respectively.

In recent years, the continued detection of complex organic molecules of prebiotic interest has refueled the interest on a panspermic origin of life. The prebiotic molecule glyceraldehyde is proposed to be formed from (Z)-1,2-ethenediol, a molecule recently detected towards the G+0.693-0.027 molecular cloud at the galactic center. In this work, we computationally simulate the formation of (Z)-1,2-ethenediol from vinyl alcohol on the surface of amorphous solid water in a two-step synthesis involving a OH addition and a H abstraction reaction. In total, we considered all reaction possibilities of the 1,1 and 1,2-OH addition to vinyl alcohol followed by H-abstraction or H-addition reactions on the resulting radicals. The combination of these reactions is capable of explaining the formation of (Z)-1,2-ethenediol provided a suprathermal diffusion of OH. We also conclude that our proposed formation pathway is not selective and also yields other abstraction and addition products. Key in our findings is the connection between the adsorption modes of the reactants and intermediates and the stereoselectivity of the reactions., Comment: This version is the unedited accepted version in the Astrophysical Journal

We report the first result from a dark photon dark matter search in the mass range from ${78.62}$ to $83.95~\mathrm{\mu eV}/c^2$ with a dielectric haloscope prototype for MADMAX (Magnetized Disc and Mirror Axion eXperiment). Putative dark photons would convert to observable photons within a stack consisting of three sapphire disks and a mirror. The emitted power of this system is received by an antenna and successively digitized using a low-noise receiver. No dark photon signal has been observed. Assuming unpolarized dark photon dark matter with a local density of $\rho_{\chi}=0.3~\mathrm{GeV/cm^3}$ we exclude a dark photon to photon mixing parameter $\chi > 3.0 \times 10^{-12}$ over the full mass range and $\chi > 1.2 \times 10^{-13}$ at a mass of $80.57~\mathrm{\mu eV}/c^2$ with a 95\% confidence level. This is the first physics result from a MADMAX prototype and exceeds previous constraints on $\chi$ in this mass range by up to almost three orders of magnitude., Comment: v1

Reinforcement Learning (RL) algorithms have been used to address the challenging problems in the offloading process of vehicular ad hoc networks (VANET). More recently, they have been utilized to improve the dissemination of high-definition (HD) Maps. Nevertheless, implementing solutions such as deep Q-learning (DQN) and Actor-critic at the autonomous vehicle (AV) may lead to an increase in the computational load, causing a heavy burden on the computational devices and higher costs. Moreover, their implementation might raise compatibility issues between technologies due to the required modifications to the standards. Therefore, in this paper, we assess the scalability of an application utilizing a Q-learning single-agent solution in a distributed multi-agent environment. This application improves the network performance by taking advantage of a smaller state, and action space whilst using a multi-agent approach. The proposed solution is extensively evaluated with different test cases involving reward function considering individual or overall network performance, number of agents, and centralized and distributed learning comparison. The experimental results demonstrate that the time latencies of our proposed solution conducted in voice, video, HD Map, and best-effort cases have significant improvements, with 40.4%, 36%, 43%, and 12% respectively, compared to the performances with the single-agent approach.

One effective way to optimize the offloading process is by minimizing the transmission time. This is particularly true in a Vehicular Adhoc Network (VANET) where vehicles frequently download and upload High-definition (HD) map data which requires constant updates. This implies that latency and throughput requirements must be guaranteed by the wireless system. To achieve this, adjustable contention windows (CW) allocation strategies in the standard IEEE802.11p have been explored by numerous researchers. Nevertheless, their implementations demand alterations to the existing standard which is not always desirable. To address this issue, we proposed a Q-Learning algorithm that operates at the application layer. Moreover, it could be deployed in any wireless network thereby mitigating the compatibility issues. The solution has demonstrated a better network performance with relatively fewer optimization requirements as compared to the Deep Q Network (DQN) and Actor-Critic algorithms. The same is observed while evaluating the model in a multi-agent setup showing higher performance compared to the single-agent setup.

Many socio-economical critical domains (such as sustainability, public health, and disasters) are characterized by highly complex and dynamic systems, requiring data and model-driven simulations to support decision-making. Due to a large number of unknowns, decision-makers usually need to generate ensembles of stochastic scenarios, requiring hundreds or thousands of individual simulation instances, each with different parameter settings corresponding to distinct scenarios, As the number of model parameters increases, the number of potential timelines one can simulate increases exponentially. Consequently, simulation ensembles are inherently sparse, even when they are extremely large. This necessitates a platform for (a) deciding which simulation instances to execute and (b) given a large simulation ensemble, enabling decision-makers to explore the resulting alternative timelines, by extracting and visualizing consistent, yet diverse timelines from continuous-coupled simulation ensembles. In this article, we present DataStorm-EM platform for data- and model-driven simulation ensemble management, optimization, analysis, and exploration, describe underlying challenges and present our solution.

MADMAX, a future experiment to search for axion dark matter, is based on a novel detection concept called the dielectric haloscope. It consists of a booster composed of several dielectric disks positioned with $\mu$m precision. A prototype composed of one movable disk was built to demonstrate the mechanical feasibility of such a booster in the challenging environment of the experiment: high magnetic field to convert the axions into photons and cryogenic temperature to reduce the thermal noise. It was tested both inside a strong magnetic field up to 1.6 T and at cryogenic temperatures down to 35K. The measurements of the velocity and positioning accuracy of the disk are shown and are found to match the MADMAX requirements., Comment: 14 pages, 9 figures

The onset of Industry 4.0 is rapidly transforming the manufacturing world through the integration of cloud computing, machine learning (ML), artificial intelligence (AI), and universal network connectivity, resulting in performance optimization and increase productivity. Digital Twins (DT) are one such transformational technology that leverages software systems to replicate physical process behavior, representing the physical process in a digital environment. This paper aims to explore the use of photogrammetry (which is the process of reconstructing physical objects into virtual 3D models using photographs) and 3D Scanning techniques to create accurate visual representation of the 'Physical Process', to interact with the ML/AI based behavior models. To achieve this, we have used a readily available consumer device, the iPhone 15 Pro, which features stereo vision capabilities, to capture the depth of an Industry 4.0 system. By processing these images using 3D scanning tools, we created a raw 3D model for 3D modeling and rendering software for the creation of a DT model. The paper highlights the reliability of this method by measuring the error rate in between the ground truth (measurements done manually using a tape measure) and the final 3D model created using this method. The overall mean error is 4.97\% and the overall standard deviation error is 5.54\% between the ground truth measurements and their photogrammetry counterparts. The results from this work indicate that photogrammetry using consumer-grade devices can be an efficient and cost-efficient approach to creating DTs for smart manufacturing, while the approaches flexibility allows for iterative improvements of the models over time.

Continual learning (CL) poses the important challenge of adapting to evolving data distributions without forgetting previously acquired knowledge while consolidating new knowledge. In this paper, we introduce a new methodology, coined as Tabular-data Rehearsal-based Incremental Lifelong Learning framework (TRIL3), designed to address the phenomenon of catastrophic forgetting in tabular data classification problems. TRIL3 uses the prototype-based incremental generative model XuILVQ to generate synthetic data to preserve old knowledge and the DNDF algorithm, which was modified to run in an incremental way, to learn classification tasks for tabular data, without storing old samples. After different tests to obtain the adequate percentage of synthetic data and to compare TRIL3 with other CL available proposals, we can conclude that the performance of TRIL3 outstands other options in the literature using only 50% of synthetic data., Comment: 11 pages, 4 tables, 3 figures

Deep generative models have demonstrated the ability to create realistic audiovisual content, sometimes driven by domains of different nature. However, smooth temporal dynamics in video generation is a challenging problem. This work focuses on generic sound-to-video generation and proposes three main features to enhance both image quality and temporal coherency in generative adversarial models: a triple sound routing scheme, a multi-scale residual and dilated recurrent network for extended sound analysis, and a novel recurrent and directional convolutional layer for video prediction. Each of the proposed features improves, in both quality and coherency, the baseline neural architecture typically used in the SoTA, with the video prediction layer providing an extra temporal refinement., Comment: Full paper of the homonym paper published in the ICCV 2023 workshop "AV4D: Visual Learning of Sounds in Spaces"

We present spectra of the first overtone vibration transition of $\text{C-H}$/$\text{O-H}$ stretch ($2\nu_1$) in $\text{HCO}^+$ and $\text{HOC}^+$, recorded using a laser induced reaction action scheme inside a cryogenic 22 pole radio frequency trap. Band origins have been located at 6078.68411(19) and 6360.17630(26) $\text{cm}^{-1}$, respectively. We introduce a technique based on mass selective ejection from the ion trap for recording background free action spectra. Varying the number density of the neutral action scheme reactant ($\text{CO}_2$ and Ar, respectively) and collisional partner reactant inside the ion trap, permitted us to estimate the radiative lifetime of the state to be 1.53(34) and 1.22(34) ms, respectively, and the collisional quenching rates of $\text{HCO}^+$ ($2\nu_1$) with He, H$_2$, and N$_2$., Comment: To appear in ChemPhysChem

The Microwave SQUID Multiplexer ({\mu}MUX) is the device of choice for the readout of a large number of Low-Temperature Detectors in a wide variety of experiments within the fields of astronomy and particle physics. While it offers large multiplexing factors, the system noise performance is highly dependent on the cold and warm-readout electronic systems used to read it out, as well as the demodulation domain and parameters chosen. In order to understand the impact of the readout systems in the overall detection system noise performance, first we extended the available {\mu}MUX simulation frameworks including additive and multiplicative noise sources in the probing tones (i.e. phase and amplitude noise), along with the capability of demodulating the scientific data, either in resonator's phase or scattering amplitude. Then, considering the additive noise as a dominant noise source, the optimum readout parameters to achieve minimum system noise were found for both open-loop and flux-ramp demodulation schemes in the aforementioned domains. Later, we evaluated the system noise sensitivity to multiplicative noise sources under the optimum readout parameters. Finally, as a case study, we evaluated the optimal demodulation domain and expected system noise level for a typical Software-Defined Radio (SDR) readout system. This work leads to an improved system performance prediction and noise engineering based on the available readout electronics and selected demodulation domain., Comment: This article has been submitted to the Journal of Applied Physics. This is the revised version (V1)

This paper presents a trajectory optimization and control approach for the guidance of an orbital four-arm robot in extravehicular activities. The robot operates near the target spacecraft, enabling its arm's end-effectors to reach the spacecraft's surface. Connections to the target spacecraft can be established by the arms through specific footholds (docking devices). The trajectory optimization allows the robot path planning by computing the docking positions on the target spacecraft surface, along with their timing, the arm trajectories, the six degrees of freedom body motion, and the necessary contact forces during docking. In addition, the paper introduces a controller designed to track the planned trajectories derived from the solution of the nonlinear programming problem. A weighted controller formulated as a convex optimization problem is proposed. The controller is defined as the optimization of an objective function that allows the system to perform a set of tasks simultaneously. Simulation results show the application of the trajectory optimization and control approaches to an on-orbit servicing scenario., Comment: 8 pages. To be presented at the 15th IEEE International Conference on Mechanical and Aerospace Engineering (ICMAE)

Bounding and predicting the generalization gap of overparameterized neural networks remains a central open problem in theoretical machine learning. There is a recent and growing body of literature that proposes the framework of fractals to model optimization trajectories of neural networks, motivating generalization bounds and measures based on the fractal dimension of the trajectory. Notably, the persistent homology dimension has been proposed to correlate with the generalization gap. This paper performs an empirical evaluation of these persistent homology-based generalization measures, with an in-depth statistical analysis. Our study reveals confounding effects in the observed correlation between generalization and topological measures due to the variation of hyperparameters. We also observe that fractal dimension fails to predict generalization of models trained from poor initializations. We lastly reveal the intriguing manifestation of model-wise double descent in these topological generalization measures. Our work forms a basis for a deeper investigation of the causal relationships between fractal geometry, topological data analysis, and neural network optimization.

Magnetic reconnection is a ubiquitous plasma process that transforms magnetic energy into particle energy during eruptive events throughout the universe. Reconnection not only converts energy during solar flares and geomagnetic substorms that drive space weather near Earth, but it may also play critical roles in the high energy emissions from the magnetospheres of neutron stars and black holes. In this review article, we focus on collisionless plasmas that are most relevant to reconnection in many space and astrophysical plasmas. Guided by first-principles kinetic simulations and spaceborne in-situ observations, we highlight the most recent progress in understanding this fundamental plasma process. We start by discussing the non-ideal electric field in the generalized Ohm's law that breaks the frozen-in flux condition in ideal magnetohydrodynamics and allows magnetic reconnection to occur. We point out that this same reconnection electric field also plays an important role in sustaining the current and pressure in the current sheet and then discuss the determination of its magnitude (i.e., the reconnection rate), based on force balance and energy conservation. This approach to determining the reconnection rate is applied to kinetic current sheets of a wide variety of magnetic geometries, parameters, and background conditions. We also briefly review the key diagnostics and modeling of energy conversion around the reconnection diffusion region, seeking insights from recently developed theories. Finally, future prospects and open questions are discussed., Comment: Submitted to Space Science Reviews. This is a review paper as an outcome of the 2022 Magnetic Reconnection Workshop in the International Space Science Institute