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We construct a noncommutative (NC) AdS$_4$-charged black hole with a planar horizon topology. The NC effects of this geometry are captured by a Gaussian distribution of black hole mass codified in a dust-like energy-momentum tensor. A natural bound in radial coordinate is established, below which the scalar curvature changes its sign and defines a NC cutoff that embeds the point singularity. We study in detail the thermodynamic structure of this scenario, finding a well-defined black hole mass and an analytic criterion for its stability. Focusing on the AdS$_{2}$ structure near the horizon, we find a novel effective curvature radius with dependency on the NC cutoff. These results motivate us to explore the holographic superconducting system in terms of the nearness from the cutoff. The behavior of the magnetic field in the deep IR geometry is studied and we found semi-analytical novel expressions for the upper critical magnetic fields of a dual type-II superconductor in the canonical and grand canonical ensembles. The condensation in the form of hair is studied in terms of the bound states of the associated Schr\"odinger potential of the scalar field, interpreted as the dual to the density of Cooper pairs. The NC effects increase the hair formation due to a steeper AdS$_2$ throat comparable to the commutative case. Finally, we obtain the effective IR scalar field equation on the near horizon and near extremal NC Schwarzschild AdS$_2$ geometry and confirm that NC effects promote bound states that the commutative version forbids., Comment: 28 pages, 12 figures

Quantifying the myelin sheath radius of myelinated axons in vivo is important for understanding, diagnosing, and monitoring various neurological disorders. Despite advancements in diffusion MRI (dMRI) microstructure techniques, models specifically designed to estimate myelin sheath radii remain unavailable. In this proof-of-concept theoretical study, we present two novel dMRI models that characterize the signal from water diffusion confined to cylindrical surfaces, approximating myelin water diffusion. We derive their spherical mean signals, which conveniently eliminate fiber orientation and dispersion effects. These models are further extended to account for multiple concentric cylinders, mimicking the layered structure of myelin. Additionally, we introduce a method to convert histological distributions of axonal inner radii from the literature into myelin sheath radius distributions and derive analytical expressions to estimate the effective myelin sheath radius expected from these distributions. Monte Carlo (MC) simulations conducted in cylindrical and spiral geometries validate the models, demonstrating agreement with analytical predictions across various diffusion regimes and significant correlations between the effective radii estimated from the histological distributions and the effective radius obtained by fitting the resulting dMRI signal to a single-cylinder model. These models may be integrated into existing multi-compartment dMRI techniques, opening the door to non-invasive, in vivo assessments of myelin sheath radii in MRI scanners equipped with strong diffusion gradients that enable measurements with short echo times. Further work is required to validate the technique with real dMRI data., Comment: 55 pages, 7 figures, 1 table, manuscript under review

We investigate the dynamic properties of elastic lattices defined by tessellations of a hyperbolic strip domain. These strip lattices are generated by a conformal map of tessellations of the hyperbolic disk. Their vibrational modes are organized into three distinct classes: boundary-localized, interior-localized, and global. This mode classification is governed by a localization index quantifying the spatial localization of each mode along the strip's width. We show that, like hyperbolic lattices in the disk, hyperbolic strip lattices exhibit dynamic spectra populated primarily by localized modes. This finding is supported by numerical studies of the dynamics of a strip lattice whose hyperbolically distributed sites are coupled by structural beams. The integrated density of states computation for boundary, interior, and global modes reveals the predominance of localized modes and allows for the identification of spectral bands dominated by particular mode classes. This analysis informs time domain simulations of the lattice response to bandlimited inputs dominated by each mode class. The results illustrate distinctive wave propagation behavior when the excited frequency band is dominated by boundary-localized, interior-localized, or global modes. We experimentally confirm these observations in the frequency and time domains. In the frequency domain, the measured response confirms that the spectral neighborhoods of each excitation are indeed populated by the numerically predicted mode class. We further show that the time-averaged responses are consistent with simulations. Through this work, elastic hyperbolic strips emerge as a new class of lattices with characteristic truss-core architectures and novel nodal arrangements. The considered configuration shows promising capabilities to confine and guide elastic waves along varying spatial regions depending on the excited frequency content., Comment: 10 pages, 7 figures

Quantum computing is a rapidly advancing field facing a significant shortage of qualified experts, creating an urgent demand for skilled professionals. In Latin America, quantum education remains in its early stages, further exacerbating the regional talent gap due to deficiencies in educational infrastructure and limited financial support. This work presents the efforts of the Quantum Computing and Artificial Intelligence research group at Universidad Nacional de Colombia to integrate quantum computing into higher education using the European Competence Framework for Quantum Technologies, which offers a standardized approach to defining competency requirements and assessing essential skills in the field. We propose introductory courses aligned with the frameworks guidelines, designed to meet industry standards and reach a broad audience. Additionally, we introduce supplementary resources, including team dynamics and evaluation methodologies, to enhance the educational ecosystem. These initiatives aim to create a sustainable and comprehensive quantum education model across Latin America, to close the regional skills gap, foster inclusivity, and prepare a diverse workforce to contribute innovative solutions on the global stage., Comment: 9 pages, 6 figures

The existence of multiple superconducting phases induced by either pressure or magnetic field is one of the most striking features of superconductivity of UTe$_2$, among the many unusual superconducting properties of this system. Here we report thermodynamic measurements of the superconducting phase diagram combining pressure and magnetic fields up to 30 T. We show that the ambient pressure, high-field, superconducting phase evolves continuously with pressure to join the high-pressure, zero-field superconducting phase. This proves that these two phases are one and the same, and must have the same order parameter., Comment: 5 pages, 5 figure + supplementary material

The flat spectrum radio quasar PKS 1510-089 is one of the most active blazars in $\gamma$-rays, exhibiting phases of very high activity. This study investigates its variability over a decade across a wide range of wavelengths, from radio to $\gamma$-rays. Utilizing the non-thermal dominance parameter, we analyze the H$\beta$, H$\gamma$, and $\lambda5100\text{ \AA}$ continuum light curves to discern the primary source of continuum emission, either from the accretion disk or the jet, during different activity phases. Our findings underscore the dominance of jet emission in the continuum during flare-like events. We observed an approximately 80-day delay between the H$\beta$ and continuum emissions, which we attribute to the spatial separation between the optical emission zone and the broad-line region. Near-zero delays between optical and near-infrared emissions suggest that the emitting regions within the jet are co-spatial. Synchrotron self-Compton was identified as the primary mechanism for $\gamma$-ray emission during flares, supported by the minimal delay observed between optical/near-infrared emissions and $\gamma$-rays. Additionally, we found a delay of about 60 days between the leading optical/near-infrared emissions and X-rays, indicating that inverse Compton scattering within the jet predominantly drives X-ray emission. However, distinguishing between synchrotron self-Compton and external inverse Compton mechanism was not feasible. Shifts in the spectral index across the 15-230 GHz range corresponded with ejections from the radio core, suggesting changes in the physical conditions of the jet., Comment: 22 pages, 13 figures, 4 tables. Accepted for publication in ApJ

There is increasing interest in ''decision-focused'' machine learning methods which train models to account for how their predictions are used in downstream optimization problems. Doing so can often improve performance on subsequent decision problems. However, current methods for uncertainty quantification do not incorporate any information at all about downstream decisions. We develop a framework based on conformal prediction to produce prediction sets that account for a downstream decision loss function, making them more appropriate to inform high-stakes decision-making. Our approach harnesses the strengths of conformal methods--modularity, model-agnosticism, and statistical coverage guarantees--while incorporating downstream decisions and user-specified utility functions. We prove that our methods retain standard coverage guarantees. Empirical evaluation across a range of datasets and utility metrics demonstrates that our methods achieve significantly lower decision loss compared to standard conformal methods. Additionally, we present a real-world use case in healthcare diagnosis, where our method effectively incorporates the hierarchical structure of dermatological diseases. It successfully generates sets with coherent diagnostic meaning, aiding the triage process during dermatology diagnosis and illustrating how our method can ground high-stakes decision-making on external domain knowledge.

We present an exploratory study of the possibilities of the Deep Ritz Method (DRM) for the modeling of strain localization in solids as a sharp discontinuity in the displacement field. For this, we use a regularized strong discontinuity kinematics within a variational setting for elastoplastic solids. The corresponding mathematical model is discretized using Artificial Neural Networks (ANNs). The architecture takes care of the kinematics, while the variational statement of the boundary value problem is taken care of by the loss function. The main idea behind this approach is to solve both the equilibrium problem and the location of the localization band by means of trainable parameters in the ANN. As a proof of concept, we show through both 1D and 2D numerical examples that the computational modeling of strain localization for elastoplastic solids within the framework of DRM is feasible., Comment: The article has 22 pages including 14 figures and 26 references. The manuscript was prepared for submission to Archives of Computational Methods in Engineering

In the present work we introduce a holographic prescription to independently describe sea and valence quarks in the context of the gauge/gravity correspondence. We use such prescription to perform an initial calculation that permits us to compare our results with those obtained through lattice techniques when studding magnetic catalysis and its inverse. We find, in agreement with previous studies, that the elaborated behavior of the condensate is mostly attributable to the sea quarks, rather than the valence which show a quite featureless participation., Comment: 10 pages, 10 figures

We analyze gravitational lensing and their cast images from thin-disks in shadow observations of a family of spherically symmetric black hole solutions previously derived within the framework of Loop Quantum Gravity. Such black holes depend on two parameters (besides the mass of the black hole itself), $P$ and $a_0$, the latter imbuing the configurations with an interior wormhole structure. Using the bounds from the Event Horizon Telescope regarding the shadow's radius of Sgr A$^*$ that constrain the parameter $P \lesssim 0.08(2\sigma)$ (at $a_0=0$), we study the modifications to weak and strong gravitational lensing induced by these geometries as compared to the Schwarzschild black hole within this range. In particular, we discuss several observables in the strong field regime related to the luminosity decay, the angular separation, and the flux ratio between multiples images of the source. Furthermore, we consider the cast images of these black holes when illuminated by a geometrically and optically thin accretion disk according to several semi-analytic profiles for the disk's emission., Comment: 12 pages, 6 figures

In this letter, we explore different aspects of a $3+1$ strongly coupled superconductor in the presence of an intense magnetic field. In order to study this interesting and highly complicated system, we make use of the holographic correspondence. We construct a gravitational solution incorporating a fully backreacted Maxwell field while treating a charged scalar field as a perturbation propagating on this background. Our work focuses on the implications of intense magnetic fields on superconductivity in the strongly coupled regime, offering insights into the modifications that such a field induces on key aspects of the superconducting phase. Through rigorous numerical analysis of the holographic model, we uncover intriguing effects arising from the interplay between the magnetic field and the superconducting condensate., Comment: 19 pages, 4 figures

Constrained Spherical Deconvolution (CSD) is crucial for estimating white matter fiber orientations using diffusion MRI data. A relevant parameter in CSD is the maximum order $l_{max}$ used in the spherical harmonics series, influencing the angular resolution of the Fiber Orientation Distributions (FODs). Lower $l_{max}$ values produce smoother and more stable estimates, but result in reduced angular resolution. Conversely, higher $l_{max}$ values, as employed in the Super-Resolved CSD variant, are essential for resolving narrow inter-fiber angles but lead to spurious lobes due to increased noise sensitivity. To address this issue, we propose a novel Spatially Regularized Super-Resolved CSD (SR$^2$-CSD) approach, incorporating spatial priors into the CSD framework. This method leverages spatial information among adjacent voxels, enhancing the stability and noise robustness of FOD estimations. SR$^2$-CSD facilitates the practical use of Super-Resolved CSD by including a J-invariant auto-calibrated total variation FOD denoiser. We evaluated the performance of SR$^2$-CSD against standard CSD and Super-Resolved CSD using phantom numerical data and various real brain datasets, including a test-retest sample of six subjects scanned twice. In phantom data, SR$^2$-CSD outperformed both CSD and Super-Resolved CSD, reducing the angular error (AE) by approximately half and the peak number error (PNE) by a factor of three across all noise levels considered. In real data, SR$^2$-CSD produced more continuous FOD estimates with higher spatial-angular coherency. In the test-retest sample, SR$^2$-CSD consistently yielded more reproducible estimates, with reduced AE, PNE, mean squared error, and increased angular correlation coefficient between the FODs estimated from the two scans for each subject., Comment: 16 pages, 5 figures

We present the design of the mechanical structure of the miniBeBe detector, a subsystem of the Multi-Purpose Detector, soon to enter into operation at the Nuclotron based Ion Collider fAcility of the Joint Institute for Nuclear Research. The miniBeBe detector was designed and is currently being developed by the MexNICA Collaboration to contribute to the level-zero trigger of the Time of Flight. The mechanical structure meets the requirements of minimizing the material budget and be free of ferromagnetic materials, without compromising its robustness. The design also allows easy module replacement for maintenance and overall removal at the end of the first stage of the experiment, without affecting the rest of the subsystems, to leave room for the installation of the Inner Tracking System. In addition, a Finite Element Method analysis of the mechanical components under load was performed. Based on this analysis, it was determined that the design meets the space constraints within the Multi-Purpose Detector, as well as a deformation of less than 1 mm with overall stress of less than 2 MPa, such that no material used in the design is at risk of mechanical failure during operation. A cooling system heat transfer analysis was performed showing that the detector Silicon Photo-Multipliers can be kept within a temperature range of 19$^{\circ}$C to 23$^{\circ}$C, which is adequate for their optimal performance., Comment: 15 pages, 13 figures, 6 tables

Count outcomes in longitudinal studies are frequent in clinical and engineering studies. In frequentist and Bayesian statistical analysis, methods such as Mixed linear models allow the variability or correlation within individuals to be taken into account. However, in more straightforward scenarios, where only two stages of an experiment are observed (pre-treatment vs. post-treatment), there are only a few tools available, mainly for continuous outcomes. Thus, this work introduces a Bayesian statistical methodology for comparing paired samples in binary pretest-posttest scenarios. We establish a Bayesian probabilistic model for the inferential analysis of the unknown quantities, which is validated and refined through simulation analyses, and present an application to a dataset taken from the Television School and Family Smoking Prevention and Cessation Project (TVSFP) (Flay et al., 1995). The application of the Full Bayesian Significance Test (FBST) for precise hypothesis testing, along with the implementation of adaptive significance levels in the decision-making process, is included.

In a recent study [1], we demonstrated a multi-heterodyne differential absorption lidar (DIAL) for greenhouse gas monitoring utilizing solid targets. The multi-frequency absorption measurement was achieved using electro-optic dual-comb spectroscopy (DCS) with a minimal number of comb teeth to maximize the optical power per tooth. In this work, we examine the lidar's working principle and architecture. Additionally, we present measurements of atmospheric CO2 at 1572 nm over a 1.4 km optical path conducted in summer 2023. Furthermore, we show that, due to the high power per comb tooth, multi-frequency Doppler wind speed measurements can be performed using the DCS signal from aerosol backscattering. This enables simultaneous radial wind speed and path-average gas concentration measurements, offering promising prospects for novel concepts of tunable multi-frequency lidar systems capable of simultaneously monitoring wind speed and gas concentrations. [1] W. Pati\~no Rosas and N. C\'ezard, "Greenhouse gas monitoring using an IPDA lidar based on a dual-comb spectrometer," Opt. Express 32, 13614-13627 (2024)., Comment: Conference paper for the 22nd Coherent Laser Radar Conference (2024)

The Ground-based Observational Support of the Fermi Gamma-ray Space Telescope is conducted by the University of Arizona using the 2.3m Bok and 1.54m Kuiper telescopes operated by the Steward Observatory (SO). This program monitors blazar sources with spectroscopic (among others) observations. Yet, the instrumental broadenings for the different slit widths used in the spectra, are unavailable (the widths range from 2.0 to 12.7 arcsec). Using quasi-simultaneous spectroscopic observations of the blazar 3C 273 between the SO, Observatorio Astrof\'isico Guillermo Haro (OAGH), and Observatorio Astron\'omico Nacional San Pedro M\'artir (OAN-SPM), we can provide an estimation of the instrumental profile for two of the slit widths. Since the instrumental broadening and the slit width are directly proportional, we were able to estimate the instrumental broadening for all six slit widths used at the SO. We found significant variations between the instrumental profiles, emphasizing the need to correct the instrumental broadening for each aperture., Comment: 9 pages, 2 figures, 1 table. Accepted on Revista Mexicana de Astronom\'ia y Astrof\'isica, to appear on Vol. 60, N{\deg}2, October 2024

In the context of the holographic correspondence, we introduce a purely extrinsic renormalization prescription, exemplified with the case of a minimally-coupled scalar field in AdS space. The counterterms depend only on the field and its radial derivatives. This would seem to conflict with the Dirichlet variational principle, but we show that consistency follows from the fact that the asymptotic structure of asymptotically locally AdS spacetimes requires not only the leading, but also all of the subleading non-normalizable modes to be fixed as a boundary condition. Crucially, as seen from a path integral definition of the bulk partition function involved in the standard GKPW formula, this condition is valid away from the saddle. We find that the extrinsic renormalization prescription is maximally efficient when the scalar field is massless, which is suggestive of a connection with the Kounterterm method for renormalization of pure gravity., Comment: 47 pages. v3: minor adjustments to the discussion and references added

The Super-Kamiokande and T2K collaborations present a joint measurement of neutrino oscillation parameters from their atmospheric and beam neutrino data. It uses a common interaction model for events overlapping in neutrino energy and correlated detector systematic uncertainties between the two datasets, which are found to be compatible. Using 3244.4 days of atmospheric data and a beam exposure of $19.7(16.3) \times 10^{20}$ protons on target in (anti)neutrino mode, the analysis finds a 1.9$\sigma$ exclusion of CP-conservation (defined as $J_{CP}=0$) and a preference for the normal mass ordering., Comment: 12 pages, 4 figures

Artificial Intelligence (AI) approaches are increasingly being applied to more and more domains of Science, Engineering, Chemistry, and Industries to not only improve efficiencies and enhance productivity, but also enable new capabilities. The new opportunities range from automated molecule design and screening, properties prediction, gaining insights of chemical reactions, to computer-aided design, predictive maintenance of systems, robotics, and autonomous vehicles. This review focuses on the new applications of AI in manufacturing and healthcare. For the Manufacturing Industries, we focus on AI and algorithms for (1) Battery, (2) Flow Chemistry, (3) Additive Manufacturing, (4) Sensors, and (5) Machine Vision. For Healthcare applications, we focus on: (1) Medical Vision (2) Diagnosis, (3) Protein Design, and (4) Drug Discovery. In the end, related topics are discussed, including physics integrated machine learning, model explainability, security, and governance during model deployment.

Configurable synaptic delays are a basic feature in many neuromorphic neural network hardware accelerators. However, they have been rarely used in model implementations, despite their promising impact on performance and efficiency in tasks that exhibit complex (temporal) dynamics, as it has been unclear how to optimize them. In this work, we propose a framework to train and deploy, in digital neuromorphic hardware, highly performing spiking neural network models (SNNs) where apart from the synaptic weights, the per-synapse delays are also co-optimized. Leveraging spike-based back-propagation-through-time, the training accounts for both platform constraints, such as synaptic weight precision and the total number of parameters per core, as a function of the network size. In addition, a delay pruning technique is used to reduce memory footprint with a low cost in performance. We evaluate trained models in two neuromorphic digital hardware platforms: Intel Loihi and Imec Seneca. Loihi offers synaptic delay support using the so-called Ring-Buffer hardware structure. Seneca does not provide native hardware support for synaptic delays. A second contribution of this paper is therefore a novel area- and memory-efficient hardware structure for acceleration of synaptic delays, which we have integrated in Seneca. The evaluated benchmark involves several models for solving the SHD (Spiking Heidelberg Digits) classification task, where minimal accuracy degradation during the transition from software to hardware is demonstrated. To our knowledge, this is the first work showcasing how to train and deploy hardware-aware models parameterized with synaptic delays, on multicore neuromorphic hardware accelerators.

To date, the prospect of using real-time hybrid simulation (RTHS) to study the effects of long-term or 'wear-and-tear' loads, such as exposure to harmful environmental conditions or fatigue, has remained underexplored. This study presents a conceptual framework to assess the impact of long-term degradation on infrastructure systems. The framework integrates the capabilities of RTHS with accelerated degradation techniques to evaluate the behavior of a degrading system over time. Experimental results obtained in this way not only capture the complex interactions but also provide a reliability-based method to determine the expected time-to-failure of the evaluated system. The developed framework is demonstrated using a virtual RTHS platform designed to test fiber-reinforced elastomeric isolators., Comment: 5 pages, 4 figures

In this work we report the variations of the atmospheric muons flux in Mexico City. The measurements were performed from September to December 2023 at at the Institute of Nuclear Sciences of UNAM, located at coordinates 19.32$^{\circ}$N 99.18$^{\circ}$W, with an altitude of 2274 meters above see level and a cutoff rigidity of 8.24 GV. The experimental setup consists of a pair of scintillators, with dimensions of 50cm x 1m. To compensate for fluctuations in air pressure, we calculate the barometric coefficient using data, which is determined to be -0.21%/mb. The observed muon flux corresponds to $98.3 \pm 2.6$ m$^{-2}$ s$^{-1}$ sr$^{-1}$, and is consistent with other measurements at the same location. We have identified three variations in the pressure-corrected muon flux that exhibit a pattern similar to those observed by a neutron cosmic ray detector at UNAM. Through an extensive analysis of the publicly available geomagnetic data, we have correlated these variations with a phenomenon known as a Forbush decrease., Comment: 6 pages, Proceedings of the 45th Symposium on Nuclear Physics, January 2024

We investigate the dynamic properties of elastic lattices defined by tessellations of a curved hyperbolic space. The lattices are obtained by projecting nodes of a regular hyperbolic tessellation onto a flat disk and then connecting those sites with simple linkages. Numerical and experimental investigations illustrate how their vibrational spectral properties are characterized by a high density of modes that are localized at the domain boundaries. Such properties govern the propagation of waves induced by broadband inputs. This suggests the potential for applications seeking the protection of bulk media from boundary-incident perturbations. We uncover the boundary-dominated nature of an exemplary hyperbolic lattice through the evaluation and analysis of its integrated density of states and vibrational spectrum. The dynamics of the lattice are also contextualized by comparing them with those of continuous disks characterized by Euclidean and hyperbolic property distributions, which confirms that the lattice retains the boundary-dominated spectrum observed in the hyperbolic plane. We then numerically investigate the response of the lattice to transient pulses incident on the boundary and find that edge-confined wave propagation occurs. The modal and transient pulse propagation behavior of the lattice is experimentally validated in a milled aluminum sample. By leveraging hyperbolic geometry, our mechanical lattice ushers in a novel class of mechanical metamaterials with boundary-dominated wave phenomena reminiscent of topologically protected systems suitable for applications in advanced wave control., Comment: 27 pages, 8 figures

The ALICE collaboration is proposing a completely new detector, ALICE 3, for operation during the LHC Runs 5 and 6. One of the ALICE~3 subsystems is the Muon IDentifier detector (MID), which has to be optimised to be efficient for the reconstruction of $J/\psi$ at rest (muons down to $p_{\rm T}\approx1.5$ GeV/$c$) for $|\eta|<1.3$. Given the modest particle flux expected in the MID of a few Hz/cm$^2$, technologies like plastic scintillator bars ($\approx1$ m length) equipped with wavelength-shifting fibers and silicon photomultiplier readout, and lightweight Multi-Wire Proportional Chambers (MWPCs) are under investigation. To this end, different plastic scintillator paddles and MWPCs were studied at the CERN T10 test beam facility. This paper reports on the performance of the scintillator prototypes tested at different beam momenta (from 0.5 GeV/$c$ up to 6 GeV/$c$) and positions (horizontal, vertical, and angular scans). The MWPCs were tested at different momenta (from 0.5 GeV/$c$ to 10 GeV/$c$) and beam intensities, their efficiency and position resolutions were verified beyond the particle rates expected with the MID in ALICE 3., Comment: 11 pages, 8 figures, to appear in JINST

Civil registration and vital statistics (CRVS) systems should be the primary source of mortality data for governments. Accurate and timely measurement of the completeness of death registration helps inform interventions to improve CRVS systems and to generate reliable mortality indicators. In this work we propose the use of hierarchical Bayesian linear mixed models with Global-Local (GL) priors to estimate the completeness of death registration at global, national and subnational levels. The use of GL priors in this paper is motivated for situations where demographic covariates can explain much of the observed completeness but where unexplained within-country (i.e. by year) and between-country variability also play an important role. The use of our approach can allow institutions improve model parameter estimates and more accurately predict completeness of death registration. Our models are based on a dataset which uses Global Burden of Disease (GBD) death estimates based on the GBD 2019 and comprises 120 countries and 2,748 country-years from 1970-2019 (Demographics Collaborators, 2020). To illustrate the effectiveness of our proposal we consider the completeness of death registration in the departments of Colombia in 2017.

The role of axonal synaptic delays in the efficacy and performance of artificial neural networks has been largely unexplored. In step-based analog-valued neural network models (ANNs), the concept is almost absent. In their spiking neuroscience-inspired counterparts, there is hardly a systematic account of their effects on model performance in terms of accuracy and number of synaptic operations.This paper proposes a methodology for accounting for axonal delays in the training loop of deep Spiking Neural Networks (SNNs), intending to efficiently solve machine learning tasks on data with rich temporal dependencies. We then conduct an empirical study of the effects of axonal delays on model performance during inference for the Adding task, a benchmark for sequential regression, and for the Spiking Heidelberg Digits dataset (SHD), commonly used for evaluating event-driven models. Quantitative results on the SHD show that SNNs incorporating axonal delays instead of explicit recurrent synapses achieve state-of-the-art, over 90% test accuracy while needing less than half trainable synapses. Additionally, we estimate the required memory in terms of total parameters and energy consumption of accomodating such delay-trained models on a modern neuromorphic accelerator. These estimations are based on the number of synaptic operations and the reference GF-22nm FDX CMOS technology. As a result, we demonstrate that a reduced parameterization, which incorporates axonal delays, leads to approximately 90% energy and memory reduction in digital hardware implementations for a similar performance in the aforementioned task.

We report an updated measurement of the $\nu_{\mu}$-induced, and the first measurement of the $\bar{\nu}_{\mu}$-induced coherent charged pion production cross section on $^{12}C$ nuclei in the T2K experiment. This is measured in a restricted region of the final-state phase space for which $p_{\mu,\pi} > 0.2$ GeV, $\cos(\theta_{\mu}) > 0.8$ and $\cos(\theta_{\pi}) > 0.6$, and at a mean (anti)neutrino energy of 0.85 GeV using the T2K near detector. The measured $\nu_{\mu}$ CC coherent pion production flux-averaged cross section on $^{12}C$ is $(2.98 \pm 0.37 (stat.) \pm 0.31 (syst.) \substack{ +0.49 \\ -0.00 } \mathrm{ (Q^2\,model)}) \times 10^{-40}~\mathrm{cm}^{2}$. The new measurement of the $\bar{\nu}_{\mu}$-induced cross section on $^{12}{C}$ is $(3.05 \pm 0.71 (stat.) \pm 0.39 (syst.) \substack{ +0.74 \\ -0.00 } \mathrm{(Q^2\,model)}) \times 10^{-40}~\mathrm{cm}^{2}$. The results are compatible with both the NEUT 5.4.0 Berger-Sehgal (2009) and GENIE 2.8.0 Rein-Sehgal (2007) model predictions.

Dark matter (DM) can be composed of a collection of axions, or axion-like particles (ALPs), whose existence is due to the spontaneous breaking of the Peccei-Quinn $U(1)$ symmetry which is the most compelling solution of the strong $CP$-problem of Quantum Chromodynamics (QCD). Axions must be spin-$0$ particles with very small masses and extremely weak interactions with themselves as well as with the particles that constitute the Standard Model. In general, the physics of axions is detailed by a quantum field theory of a real scalar field, $\phi$. Nevertheless, it is more convenient to implement a non-relativistic effective field theory with a complex scalar field, $\psi$, to characterize the mentioned axions in the low-energy regime. A possible application of this equivalent description is to study the collapse of cold dark matter into more complex structures. There have been a few derivations of effective Lagrangians for the complex field $\psi$; resulting to be all equivalent after a nonlocal-space transformation between $\phi$ and $\psi$ was found, and some other corrections were introduced. Our contribution herein is to further provide higher order corrections, in particular, we compute the effective field theory Lagrangian up to order $(\psi^\ast\psi)^5$, incorporating also the fast-oscillating field fluctuations into the dominant slowly-varying non-relativistic field., Comment: 48 pages

Aerial operation in turbulent environments is a challenging problem due to the chaotic behavior of the flow. This problem is made even more complex when a team of aerial robots is trying to achieve coordinated motion in turbulent wind conditions. In this paper, we present a novel multi-robot controller to navigate in turbulent flows, decoupling the trajectory-tracking control from the turbulence compensation via a nested control architecture. Unlike previous works, our method does not learn to compensate for the air-flow at a specific time and space. Instead, our method learns to compensate for the flow based on its effect on the team. This is made possible via a deep reinforcement learning approach, implemented via a Graph Convolutional Neural Network (GCNN)-based architecture, which enables robots to achieve better wind compensation by processing the spatial-temporal correlation of wind flows across the team. Our approach scales well to large robot teams -- as each robot only uses information from its nearest neighbors -- , and generalizes well to robot teams larger than seen in training. Simulated experiments demonstrate how information sharing improves turbulence compensation in a team of aerial robots and demonstrate the flexibility of our method over different team configurations.

Large-scale datasets are increasingly being used to inform decision making. While this effort aims to ground policy in real-world evidence, challenges have arisen as selection bias and other forms of distribution shifts often plague observational data. Previous attempts to provide robust inference have given guarantees depending on a user-specified amount of possible distribution shift (e.g., the maximum KL divergence between the observed and target distributions). However, decision makers will often have additional knowledge about the target distribution which constrains the kind of possible shifts. To leverage such information, we propose a framework that enables statistical inference in the presence of selection bias which obeys user-specified constraints in the form of functions whose expectation is known under the target distribution. The output is high-probability bounds on the value of an estimand for the target distribution. Hence, our method leverages domain knowledge in order to partially identify a wide class of estimands. We analyze the computational and statistical properties of methods to estimate these bounds and show that our method can produce informative bounds on a variety of simulated and semisynthetic tasks, as well as in a real-world use case.

We analyze the broad H$\beta$ line profile variability of the "changing look" active galactic nucleus (CL-AGN) NGC 3516 over a long period of 25 years. The observed change in the broad line profile may indicate a change in the geometry of the broad line region (BLR). Using spectral line profiles, we aim to explore changes in the kinematics and dimensions of the BLR in NGC 3516. We consider two possible scenarios, i.e. changes in the broad-line emission are caused by a decrease of ionization continuum emission or by the BLR obscuration by outer dusty regions. With this investigation we aim to clarify the CL mechanism of this AGN. We analyze the spectral band around the H$\beta$ line as well as the broad H$\beta$ line parameters, and how they change in time. We model the broad-line profiles assuming that there is an emission from the accretion disc superposed with an emission from a surrounding region that is outside the disc. We find that in the Type 1 activity phase, the BLR is very complex. There is a clear disc-like BLR that contributes to the broad line wings and an additional intermediate line region (ILR) that contributes to the line core. In the high activity phase, the ILR emission is close to the center of the line (in some cases slightly shifted to the red), whereas in the low activity phase (i.e., Type 2 phase), the ILR component has a significant shift to the blue, indicating an outflow. We propose that the changing look mechanism in NGC 3516 is rather connected with the intrinsic effects than with an outer obscuring region. It may still be possible that the dust has an important role in the low activity phase when it is coming inside of the BLR, making a dusty BLR. In this way, it causes a decrease in the ionization and recombination rates., Comment: 16 pages, accepted in A&A