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High-precision surface defect detection in manufacturing is essential for ensuring quality control. Laser triangulation profilometric sensors are key to this process, providing detailed and accurate surface measurements over a line. To achieve a complete and precise surface scan, accurate relative motion between the sensor and the workpiece is required. It is crucial to control the sensor pose to maintain optimal distance and relative orientation to the surface. It is also important to ensure uniform profile distribution throughout the scanning process. This paper presents a novel Reinforcement Learning (RL) based approach to optimize robot inspection trajectories for profilometric sensors. Building upon the Boustrophedon scanning method, our technique dynamically adjusts the sensor position and tilt to maintain optimal orientation and distance from the surface, while also ensuring a consistent profile distance for uniform and high-quality scanning. Utilizing a simulated environment based on the CAD model of the part, we replicate real-world scanning conditions, including sensor noise and surface irregularities. This simulation-based approach enables offline trajectory planning based on CAD models. Key contributions include the modeling of the state space, action space, and reward function, specifically designed for inspection applications using profilometric sensors. We use Proximal Policy Optimization (PPO) algorithm to efficiently train the RL agent, demonstrating its capability to optimize inspection trajectories with profilometric sensors. To validate our approach, we conducted several experiments where a model trained on a specific training piece was tested on various parts in simulation. Also, we conducted a real-world experiment by executing the optimized trajectory, generated offline from a CAD model, to inspect a part using a UR3e robotic arm model.

The Jaynes-Cummings quantum optics model allows us to understand the dialogue between light and matter at its most fundamental level, which is crucial for advancements in quantum science and technology. Several generalizations of the model have long been proposed, emphasizing their dynamic behavior but paying less attention to their spectroscopy. Here, we obtain analytical expressions of the time-dependent spectral response of a nonlinear Jaynes-Cummings model based on deformed field operators. We show that the long-time response of the resulting nonlinear cavity field resembles the one experimentally obtained in the strong-dispersive regime of circuit quantum electrodynamics. The spectrum is intrinsically asymmetric with the nonlinear coupling, a signature of the impossibility of getting resonant conditions for finite field excitations., Comment: 14 pages, 7 figures

We propose and characterize a novel two-dimensional material, 2D-CRO, derived from bulk calcium-based ruthenates (CROs) of the Ruddlesden-Popper family, Ca$_{n+1}$Ru$_n$O$_{3n+1}$ ($n = 1$ and $2$). Using density functional theory, we demonstrate that 2D-CRO maintains structural stability down to the monolayer limit, exhibiting a tight interplay between structural and electronic properties. Notably, 2D-CRO displays altermagnetic behavior, characterized by zero net magnetization and strong spin-dependent phenomena, stabilized through dimensionality reduction. This stability is achieved by breaking inversion symmetry along the z-axis, favoring altermagnetic properties even in the absence of van der Waals interactions. Through theoretical models and computational analysis, we explore the altermagnetic behavior of 2D-CRO, both with and without spin-orbit coupling. We identify the spin components that contribute to the altermagnetic character and highlight the potential of 2D-CRO as a promising material for investigating altermagnetic phenomena and topological features., Comment: 18 pages, 21 figures

Natural hazard risk management is a demanding interdisciplinary task. It requires domain knowledge, integration of robust computational methods, and effective use of complex datasets. However, existing solutions tend to focus on specific aspects, data, or methods, limiting their impact and applicability. Here, we present a general data-driven framework to support risk assessment and policy making illustrating its usage in the context of fire hazard by integrating three unique datasets of fire behavior, street network, and census data for the whole state of California. We show that integrating spatial complexity by including a fire behavior layer and a socio-demographic layer changes the universal function observed in previous optimization frameworks that only work with the accessibility of facilities. These results open avenues for the future development of flexible interdisciplinary frameworks in natural hazards management using complex large-scale data., Comment: 17 pages, 6 figures

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015-2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14,000 deg^2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance., Comment: Accepted for publication in the A&A special issue`Euclid on Sky'

We present ALMA 12-m, 7-m & Total Power (TP) Array observations of the FU Orionis outbursting system, covering spatial scales ranging from 160 to 25,000 au. The high-resolution interferometric data reveals an elongated $^{12}$CO(2-1) feature previously observed at lower resolution in $^{12}$CO(3-2). Kinematic modeling indicates that this feature can be interpreted as an accretion streamer feeding the binary system. The mass infall rate provided by the streamer is significantly lower than the typical stellar accretion rates (even in quiescent states), suggesting that this streamer alone is not massive enough to sustain the enhanced accretion rates characteristic of the outbursting class prototype. The observed streamer may not be directly linked to the current outburst but rather a remnant of a previous, more massive streamer that may have contributed enough to the disk mass to render it unstable and trigger FU Ori's outburst. The new data detects, for the first time, a vast, slow-moving carbon monoxide molecular outflow emerging from this object. To accurately assess the outflow properties (mass, momentum, kinetic energy), we employed $^{13}$CO(2-1) data to correct for optical depth effects. The analysis indicates that the outflow corresponds to swept-up material not associated with the current outburst, similar to slow-molecular outflows observed around other FUor and Class I protostellar objects.

Open-source simulation tools play a crucial role for neuromorphic application engineers and hardware architects to investigate performance bottlenecks and explore design optimizations before committing to silicon. Reconfigurable Architecture for Neuromorphic Computing (RANC) is one such tool that offers ability to execute pre-trained Spiking Neural Network (SNN) models within a unified ecosystem through both software-based simulation and FPGA-based emulation. RANC has been utilized by the community with its flexible and highly parameterized design to study implementation bottlenecks, tune architectural parameters or modify neuron behavior based on application insights and study the trade space on hardware performance and network accuracy. In designing architectures for use in neuromorphic computing, there are an incredibly large number of configuration parameters such as number and precision of weights per neuron, neuron and axon counts per core, network topology, and neuron behavior. To accelerate such studies and provide users with a streamlined productive design space exploration, in this paper we introduce the GPU-based implementation of RANC. We summarize our parallelization approach and quantify the speedup gains achieved with GPU-based tick-accurate simulations across various use cases. We demonstrate up to 780 times speedup compared to serial version of the RANC simulator based on a 512 neuromorphic core MNIST inference application. We believe that the RANC ecosystem now provides a much more feasible avenue in the research of exploring different optimizations for accelerating SNNs and performing richer studies by enabling rapid convergence to optimized neuromorphic architectures., Comment: Accepted for publication in Neuro-Inspired Computational Elements (NICE) Workshop 2024

Separating C6 cyclic hydrocarbons, specifically benzene and cyclohexane, presents a significant industrial challenge due to their similar physicochemical properties. We conducted Monte Carlo simulations in the Grand-Canonical ensemble to acquire adsorption properties and separation performance data for benzene and cyclohexane in three metal-organic frameworks featuring coordinatively unsaturated metal sites (Ni-MOF-74, Ni-ClBBTA, and Ni-ClBTDD). The separation performance of these MOFs was analyzed and compared with literature data for adsorbents of different natures, demonstrating superior performance. Additionally, we explored the molecular origins of this effective separation, examining the pore-filling mechanism, interaction of guest molecules with metal centers, and mutual interactions of each adsorbate. Our results highlight that the selected adsorbents, with remarkable loading capacity, can efficiently separate both compounds in a mixture with exceptional effectiveness.

Properly extracting patterns of individual mobility with high resolution data sources such as the one extracted from smartphone applications offers important opportunities. Potential opportunities not offered by call detailed records (CDRs), which offer resolutions triangulated from antennas, are route choices, travel modes detection and close encounters. Nowadays, there is not a standard and large scale data set collected over long periods that allows us to characterize these. In this work we thoroughly examine the use of data from smartphone applications, also referred to as location-based services (LBS) data, to extract and understand the vehicular route choice behavior. Taking the Dallas-Fort Worth metroplex as an example, we first extract the vehicular trips with simple rules and reconstruct the origin-destination matrix by coupling the extracted vehicular trips of the active LBS users and the United States census data. We then present a method to derive the commonly used routes by individuals from the LBS traces with varying sample rate intervals. We further inspect the relation between the number of routes and the trip characteristics, including the departure time, trip length and travel time. Specifically, we consider the travel time index and buffer index for the LBS users taking different number of routes. Empirical results demonstrate that during the peak hours, travelers tend to reduce the impact of traffic congestion by taking alternative routes. Overall, the proposed data analysis framework is cost-effective to treat sparse data generated from the use of smartphones to inform routing behavior. The potential in practice is to inform demand management strategies, by targeting individual users while generating large scale estimates of congestion mitigation., Comment: 24 pages, 7 Figures

Recovering the polarized cosmic microwave background (CMB) is crucial for shading light on Cosmic Inflation. Methods with different characteristics should be developed and optimized. We aim to use a neural network called CENN and train it for recovering the E and B modes of the CMB. We train the network with realistic simulations of 256x256 pixel squared patches at 100, 143 and 217 GHz Planck channels, which contain the CMB, thermal dust, synchrotron, PS and noise. We make several trainings sets: 30, 25 and 20 arcmin resolution patches at the same position in the sky. After being trained, CENN is able to recover the CMB signal at 143 GHz in Q and U patches. Then, we use NaMaster for estimating the EE and BB power spectrum for each input and output patches in the test dataset, as well as the difference between input and output power spectra and the residuals. We also test the methodology using a different foreground model at 5 arcmin resolution without noise. We recover the E-mode generally founding residuals bellow the input signal at all scales. In particular, we found a value of about 0.1 muK2 at l<200, decreasing below 0.01 muK2 at smaller scales. For the B-mode, we similarly recover the CMB with residuals between 0.01 and 0.001 muK2. We also train the network with 5 arcmin Planck simulations without noise, obtaining clearly better results with respect the previous cases. For a different foreground model, the recovery is similar, although B-mode residuals increase above the input signal. In general, we found that, the network performs better when training with the same resolution used for testing. Based on the results, CENN seems to be a promising for recovering both E and B modes at sub-degree scales in ground-base experiments such as POLARBEAR, SO and CMB-S4. Once extending its applicability at all sky, it could be an alternative component separation method for LiteBIRD satellite., Comment: 11 pages, 10 figures, Submitted to A&A

We present a theoretical model of multimode quantum correlations in bright frequency combs generated in continuous-wave regime by microresonators above threshold. Our analysis shows how these correlations emerge from cascading four-wave mixing processes fed by the input pump as well as the generated bright beams. Logarithmic negativity criterion is employed to quantify entanglement between partitions of modes, demonstrating the transition from a bipartite regime just above the oscillation threshold to the multipartite one at higher input pump powers. Due to its generality, our model can be safely used to describe other kinds of non-linear $\chi$ (3) cavities.

The building climate industry and its influence on energy consumption have consequences on the environment due to the emission of greenhouse gasses. Improving the efficiency of this sector is essential to reduce the effect on climate change. In recent years, the interest in porous materials in applications such as heat pumps has increased for their promising potential. To assess the performance of adsorption heat pumps and cooling systems, here we discuss a multistep approach based on the processing of adsorption data combined with a thermodynamic model. The process provides properties of interest, such as the coefficient of performance, the working capacity, the specific heat or cooling effect, or the released heat upon adsorption and desorption cycles, and it also has the advantage of identifying the optimal conditions for each adsorbent-fluid pair. To test this method, we select several metal-organic frameworks that differ in topology, chemical composition, and pore size, which we validate with available experiments. Adsorption equilibrium curves were calculated using molecular simulations to describe the adsorption mechanisms of methanol and ethanol as working fluids in the selected adsorbents. Then, using a thermodynamic model we calculate the energetic properties combined with iterative algorithms that simultaneously vary all the required working conditions. We discuss the strong influence of operating temperatures on the performance of heat pump devices. Our findings point to the highly hydrophobic metal azolate framework MAF-6 as a very good candidate for heating and cooling applications for its high working capacity and excellent energy efficiency.

This research foregrounds general practices in travel demand research, emphasizing the need to change our ways. A critical barrier preventing travel demand literature from effectively informing policy is the volume of publications without clear, consolidated benchmarks, making it difficult for researchers and policymakers to gather insights and use models to guide decision-making. By emphasizing reproducibility and open collaboration, we aim to enhance the reliability and policy relevance of travel demand research. We present a collaborative infrastructure for transit demand prediction models, focusing on their performance during highly dynamic conditions like the COVID-19 pandemic. Drawing from over 300 published papers, we develop an open-source infrastructure with five common methodologies and assess their performance under stable and dynamic conditions. We found that the prediction error for the LSTM deep learning approach stabilized at a mean arctangent absolute percentage error (MAAPE) of about 0.12 within 1.5 months, whereas other models continued to exhibit higher error rates even a year into the pandemic. If research practices had prioritized reproducibility before the COVID-19 pandemic, transit agencies would have had clearer guidance on the best forecasting methods and quickly identified those best suited for pandemic conditions to inform operations in response to changes in transit demand. The aim of this open-source codebase is to lower the barrier for other researchers to replicate, reproduce models and build upon findings. We encourage researchers to test their own modeling approaches on this benchmarking platform, challenge the analyses conducted in this paper, and develop model specifications that can outperform those evaluated here. Further, collaborative research approaches must be expanded across travel demand modeling if we wish to impact policy and planning., Comment: 18 pages, 8 figures

Most quantum theorists are familiar with different ways of describing the effective quantum dynamics of a system coupled to external degrees of freedom, such as the Born-Markov master equation or the adiabatic elimination. Understanding the deep connection between these -- sometimes apparently unrelated -- methods can be a powerful tool, allowing us to derive effective dynamics in unconventional systems or regimes. This tutorial aims at providing quantum theorists across multiple fields (e.g., quantum and atom optics, optomechanics, or hybrid quantum systems) with a self-contained practical toolbox to derive effective quantum dynamics, applicable to systems ranging from N-level emitters to mechanical resonators. First, we summarize the projector approach to open quantum systems and the derivation of the fundamental Nakajima-Zwanzig equation. Then, we show how three common effective equations, namely the Brownian master equation, the Born-Markov master equation, and the adiabatic elimination used in atom and molecular optics, can be derived from different perturbative expansions of the Nakajima-Zwanzig equation. We also solve in detail four specific examples using this formalism, namely a harmonic oscillator subject to displacement noise, the effective equations of a mechanical resonator cooled by an optical cavity, the Purcell effect for a qubit coupled to an optical cavity, and the adiabatic elimination in a Lambda system., Comment: 30 pages, 5 figures

The urban spatial structure represents the distribution of public and private spaces in cities and how people move within them. While it usually evolves slowly, it can change fast during large-scale emergency events, as well as due to urban renewal in rapidly developing countries. This work presents an approach to delineate such urban dynamics in quasi-real-time through a human mobility metric, the mobility centrality index $\Delta KS$. As a case study, we tracked the urban dynamics of eleven Spanish cities during the COVID-19 pandemic. Results revealed that their structures became more monocentric during the lockdown in the first wave, but kept their regular spatial structures during the second wave. To provide a more comprehensive understanding of mobility from home, we also introduce a dimensionless metric, $KS_{HBT}$, which measures the extent of home-based travel and provides statistical insights into the transmission of COVID-19. By utilizing individual mobility data, our metrics enable the detection of changes in the urban spatial structure.

Magnetic two-dimensional (2D) semiconductors have attracted a lot of attention because modern preparation techniques are capable of providing single crystal films of these materials with precise control of thickness down to the single-layer limit. It opens up a way to study rich variety of electronic and magnetic phenomena with promising routes towards potential applications. We have investigated the initial stages of epitaxial growth of the magnetic van der Waals semiconductor FeBr\textsubscript{2} on a single-crystal Au(111) substrate by means of low-temperature scanning tunneling microscopy, low-energy electron diffraction, x-ray photoemission spectroscopy, low-energy electron emission microscopy and x-ray photoemission electron microscopy. Magnetic properties of the one- and two-layer thick films were measured via x-ray absorption spectroscopy/x-ray magnetic circular dichroism. Our findings show a striking difference in the magnetic behaviour of the single layer of FeBr\textsubscript{2} and its bulk counterpart, which can be attributed to the modifications in the crystal structure due to the interaction with the substrate., Comment: 20 pages, 17 figures

The rising adoption of plug-in electric vehicles (PEVs) leads to the alignment of their electricity and their mobility demands. Therefore, transportation and power infrastructures are becoming increasingly interdependent. In this work, we uncover patterns of PEV mobility by integrating for the first time two unique data sets: (i) mobile phone activity of 1.39 million Bay Area residents and (ii) charging activity of PEVs in 580,000 sessions obtained in the same region. We present a method to estimate individual mobility of PEV drivers at fine temporal and spatial resolution integrating survey data with mobile phone data and income information obtained from census. Thereupon, we recommend changes in PEVs charging times of commuters at their work stations that take into account individual travel needs and shave the pronounced peak in power demand. Informed by the tariff of electricity, we calculate the12 monetary gains to incentivize the adoption of the recommendations. These results open avenues for planning for the future of coupled transportation and electricity needs using personalized data.