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This study explores the dynamic relationship between corruption and economic growth through an approach based on a system of stochastic equations. In the context of globalization and economic interdependencies, corruption not only affects investment and distorts markets, but it can also, under certain conditions, temporarily boost economic activity. Using data from the Gross Domestic Product (GDP) and the Corruption Perception Index (CPI), we implement a time-series-based model to capture the interactions between these two variables. Through a coupled vector autoregressive equations system, our model identifies patterns of interdependence between economic fluctuations and perceptions of corruption at a global level. Employing graph theory and Granger causality, we build a network of interconnections that illustrates how corruption dynamics in one country can influence economic growth and corruption perception in others. The results provide a robust tool for analyzing international political-economic relationships and can serve as a basis for designing policies that promote transparency and sustainable development.

We investigate the cosmic evolution of the Universe across different cosmological epochs in exponential Weyl-type $f(Q, T)$ gravity model. The theoretical analysis involves a detailed dynamical system approach, where we define dimensionless variables and derive a system of linear differential equations to identify critical points corresponding to the radiation, matter and de Siter phase. The findings show the transition from deceleration to acceleration phase, with stable and unstable critical points characterizing different phases of the evolution. In the second approach, we validate the theoretical predictions by using observational data from Cosmic Chronometers ($CC$) and $Pantheon^+$ datasets. We constrain the Hubble parameter and subsequently analysed the other cosmological and geometrical parameters. In this approach also, the transition from deceleration to acceleration has been confirmed, with the equation of state (EoS) parameter approaching $\Lambda$CDM at late times. The further validate this, we present the behaviour of state finder pair. We obtain the age of the Universe $13.81$ Gyr according to $CC$ data and $13.96$ Gyr with the $Pantheon^+$ dataset. The model behaviour in both the approaches shows strong agreement in the late-time behavior of the Universe. The evolutionary behaviour of Hubble parameter and distance modulus, reinforcing the reliability of the Weyl-type $f(Q, T)$ gravity model in describing the expansion history of Universe., Comment: 17 pages, 18 figures

Despite the recent success of end-to-end models for automatic speech recognition, recognizing special rare and out-of-vocabulary words, as well as fast domain adaptation with text, are still challenging. It often happens that biasing to the special entities leads to a degradation in the overall performance. We propose a light on-the-fly method to improve automatic speech recognition performance by combining a bias list of named entities with a word-level n-gram language model with the shallow fusion approach based on the Aho-Corasick string matching algorithm. The Aho-Corasick algorithm has proved to be more efficient than other methods and allows fast context adaptation. An n-gram language model is introduced as a graph with fail and output arcs, where the arc weights are adapted from the n-gram probabilities. The language model is used as an additional support to keyword biasing when the language model is combined with bias entities in a single context graph to take care of the overall performance. We demonstrate our findings on 4 languages, 2 public and 1 private datasets including performance on named entities and out-of-vocabulary entities. We achieve up to 21.6% relative improvement in the general word error rate with no practical difference in the inverse real-time factor., Comment: Submitted to ICASSP2025

The training of automatic speech recognition (ASR) with little to no supervised data remains an open question. In this work, we demonstrate that streaming Transformer-Transducer (TT) models can be trained from scratch in consumer and accessible GPUs in their entirety with pseudo-labeled (PL) speech from foundational speech models (FSM). This allows training a robust ASR model just in one stage and does not require large data and computational budget compared to the two-step scenario with pre-training and fine-tuning. We perform a comprehensive ablation on different aspects of PL-based streaming TT models such as the impact of (1) shallow fusion of n-gram LMs, (2) contextual biasing with named entities, (3) chunk-wise decoding for low-latency streaming applications, and (4) TT overall performance as the function of the FSM size. Our results demonstrate that TT can be trained from scratch without supervised data, even with very noisy PLs. We validate the proposed framework on 6 languages from CommonVoice and propose multiple heuristics to filter out hallucinated PLs., Comment: Accepted to EMNLP Findings 2024

In this paper we study the existence of solutions of a parabolic-elliptic system of partial differential equations describing the behaviour of a biological species $u$ and a chemical stimulus $v$ in a bounded and regular domain $\Omega$ of $\mathbb{R}^N$. The equation for $u$ is a parabolic equation with a nonlinear second order term of chemotaxis type with flux limitation as $ -\chi div (u |\nabla \psi|^{p-2} \nabla v)$, for $p>1$. The chemical substance distribution $v$ satisfies the elliptic equation $-\Delta v+v=u$. The evolution of $u$ is also determined by a logistic type growth term $\mu u(1-u)$. The system is studied under homogeneous Neumann boundary conditions. The main result of the article is the existence of uniformly bounded solutions for $p<3/2$ and any $N\ge 2$.

This paper introduces novel deep reinforcement learning (Deep-RL) techniques using parallel distributional actor-critic networks for navigating terrestrial mobile robots. Our approaches use laser range findings, relative distance, and angle to the target to guide the robot. We trained agents in the Gazebo simulator and deployed them in real scenarios. Results show that parallel distributional Deep-RL algorithms enhance decision-making and outperform non-distributional and behavior-based approaches in navigation and spatial generalization., Comment: Paper accepted at the 24th International Conference on Control, Automation and Systems (ICCAS)

The parallel simulation of Spiking Neural P systems is mainly based on a matrix representation, where the graph inherent to the neural model is encoded in an adjacency matrix. The simulation algorithm is based on a matrix-vector multiplication, which is an operation efficiently implemented on parallel devices. However, when the graph of a Spiking Neural P system is not fully connected, the adjacency matrix is sparse and hence, lots of computing resources are wasted in both time and memory domains. For this reason, two compression methods for the matrix representation were proposed in a previous work, but they were not implemented nor parallelized on a simulator. In this paper, they are implemented and parallelized on GPUs as part of a new Spiking Neural P system with delays simulator. Extensive experiments are conducted on high-end GPUs (RTX2080 and A100 80GB), and it is concluded that they outperform other solutions based on state-of-the-art GPU libraries when simulating Spiking Neural P systems.

We present a catalog of optical and infrared identifications (ID) of X-ray sources in the AKARI North Ecliptic Pole (NEP) Deep field detected with Chandra covering $\sim 0.34\,{\rm deg^{2}}$ with 0.5-2 keV flux limits ranging $\sim 2 \mathrm{-} 20\times 10^{-16}\,{\rm erg\,s^{-1}\,cm^{-2}}$. The optical/near-infrared counterparts of the X-ray sources are taken from our Hyper Suprime Cam (HSC)/Subaru and Wide-Field InfraRed Camera (WIRCam)/Canada-France-Hawaii Telescope (CFHT) data because these have much more accurate source positions due to their spatial resolution than that of {Chandra} and longer wavelength infrared data. We concentrate our identifications in the HSC $g$ band and WIRCam $K_{\rm s}$ band-based catalogs. To select the best counterpart, we utilize a novel extension of the likelihood-ratio (LR) analysis, where we use the X-ray flux as well as $g - K_{\rm s}$ colors to calculate the likelihood ratio. Spectroscopic and photometric redshifts of the counterparts are summarized. Also, simple X-ray spectroscopy is made on the sources with sufficient source counts. We present the resulting catalog in an electronic form. The main ID catalog contains 403 X-ray sources and includes X-ray fluxes, luminosities, $g$ and $K_{\rm s}$ band magnitudes, redshifts, and their sources, optical spectroscopic properties, as well as intrinsic absorption column densities and power-law indices from simple X-ray spectroscopy. The identified X-ray sources include 27 Milky-Way objects, 57 type I AGNs, 131 other AGNs, and 15 galaxies. The catalog serves as a basis for further investigations of the properties of the X-ray and near-infrared sources in this field. (Abridged), Comment: 16 pages, 9 figures, Three electronic (fits) tables are included in src. Accepted to Astronomy and Astrophysics

It is shown that black holes in a quark gluon plasma (QGP) obeying minimum viscosity bounds, exhibit a Schwarzschild radius in close match with the range of the strong force. For such black holes, an evaporation time of about 1016 secs is estimated, indicating that they would survive by far the quark-gluon plasma era, namely between 10^-10 and 10^-6 seconds after the big bang. On the assumption that the big-bang generated unequal amounts of quark and antiquarks, this suggests that such unbalance might have survived to this day in the form of excess antiquark nuggets hidden to all but gravitational interactions. A connection with the saturon picture, whereby minimum viscosity regimes would associate with the onset coherent quantum field structures with maximum storage properties, is also established, along with potential implication for quantum computing of classical systems., Comment: 10 Pages, 1 Figure

In traditional conversational intelligence from speech, a cascaded pipeline is used, involving tasks such as voice activity detection, diarization, transcription, and subsequent processing with different NLP models for tasks like semantic endpointing and named entity recognition (NER). Our paper introduces TokenVerse, a single Transducer-based model designed to handle multiple tasks. This is achieved by integrating task-specific tokens into the reference text during ASR model training, streamlining the inference and eliminating the need for separate NLP models. In addition to ASR, we conduct experiments on 3 different tasks: speaker change detection, endpointing, and NER. Our experiments on a public and a private dataset show that the proposed method improves ASR by up to 7.7% in relative WER while outperforming the cascaded pipeline approach in individual task performance. Our code is publicly available: https://github.com/idiap/tokenverse-unifying-speech-nlp, Comment: Accepted at EMNLP 2024 (Main Conference)

Self-supervised pretrained models exhibit competitive performance in automatic speech recognition on finetuning, even with limited in-domain supervised data. However, popular pretrained models are not suitable for streaming ASR because they are trained with full attention context. In this paper, we introduce XLSR-Transducer, where the XLSR-53 model is used as encoder in transducer setup. Our experiments on the AMI dataset reveal that the XLSR-Transducer achieves 4% absolute WER improvement over Whisper large-v2 and 8% over a Zipformer transducer model trained from scratch. To enable streaming capabilities, we investigate different attention masking patterns in the self-attention computation of transformer layers within the XLSR-53 model. We validate XLSR-Transducer on AMI and 5 languages from CommonVoice under low-resource scenarios. Finally, with the introduction of attention sinks, we reduce the left context by half while achieving a relative 12% improvement in WER., Comment: 5 pages, double column

The first systematic experimental study of the neutron-rich Br isotopes with two complementary state-of-the-art techniques is presented. These isotopes have been populated in the fission process at two different facilities, GANIL and ILL. New spectroscopic information has been obtained for odd-even $^{87-93}$Br isotopes and the experimental results have been compared with state-of-the-art Large-Scale Shell-Model and DNO Shell-Model calculations. As a result of such theoretical approaches, a transition from prolate ($^{87,89}$Br) to oblate ($^{91,93}$Br) shapes is obtained from the subtle balance between proton and neutron quadrupole deformations, as a clear signature of pseudo-SU3 quadrupole regime., Comment: 17 pages, 24 figures, 5 tables

Currently, the number of common benchmark datasets that researchers can use straight away for assessing data-driven deep learning approaches is very limited. Most studies provide data as configuration files. It is still up to each practitioner to follow a particular data generation method and run computationally intensive simulations to obtain usable data for model training and evaluation. In this work, we provide a collection of datasets that includes several small and medium size publicly available Water Distribution Networks (WDNs), including Anytown, Modena, Balerma, C-Town, D-Town, L-Town, Ky1, Ky6, Ky8, and Ky13. In total 1,394,400 hours of WDNs data operating under normal conditions is made available to the community., Comment: Presented at WDSA CCWI, Ferrara, Italy, July 2024

In this study, we apply 1D quantum convolution to address the task of time series forecasting. By encoding multiple points into the quantum circuit to predict subsequent data, each point becomes a feature, transforming the problem into a multidimensional one. Building on theoretical foundations from prior research, which demonstrated that Variational Quantum Circuits (VQCs) can be expressed as multidimensional Fourier series, we explore the capabilities of different architectures and ansatz. This analysis considers the concepts of circuit expressibility and the presence of barren plateaus. Analyzing the problem within the framework of the Fourier series enabled the design of an architecture that incorporates data reuploading, resulting in enhanced performance. Rather than a strict requirement for the number of free parameters to exceed the degrees of freedom of the Fourier series, our findings suggest that even a limited number of parameters can produce Fourier functions of higher degrees. This highlights the remarkable expressive power of quantum circuits. This observation is also significant in reducing training times. The ansatz with greater expressibility and number of non-zero Fourier coefficients consistently delivers favorable results across different scenarios, with performance metrics improving as the number of qubits increases.

Automatic depression detection from conversational data has gained significant interest in recent years. The DAIC-WOZ dataset, interviews conducted by a human-controlled virtual agent, has been widely used for this task. Recent studies have reported enhanced performance when incorporating interviewer's prompts into the model. In this work, we hypothesize that this improvement might be mainly due to a bias present in these prompts, rather than the proposed architectures and methods. Through ablation experiments and qualitative analysis, we discover that models using interviewer's prompts learn to focus on a specific region of the interviews, where questions about past experiences with mental health issues are asked, and use them as discriminative shortcuts to detect depressed participants. In contrast, models using participant responses gather evidence from across the entire interview. Finally, to highlight the magnitude of this bias, we achieve a 0.90 F1 score by intentionally exploiting it, the highest result reported to date on this dataset using only textual information. Our findings underline the need for caution when incorporating interviewers' prompts into models, as they may inadvertently learn to exploit targeted prompts, rather than learning to characterize the language and behavior that are genuinely indicative of the patient's mental health condition., Comment: Accepted to Clinical NLP workshop at NAACL 2024

Evaluating the reliability of news sources is a routine task for journalists and organizations committed to acquiring and disseminating accurate information. Recent research has shown that predicting sources' reliability represents an important first-prior step in addressing additional challenges such as fake news detection and fact-checking. In this paper, we introduce a novel approach for source reliability estimation that leverages reinforcement learning strategies for estimating the reliability degree of news sources. Contrary to previous research, our proposed approach models the problem as the estimation of a reliability degree, and not a reliability label, based on how all the news media sources interact with each other on the Web. We validated the effectiveness of our method on a news media reliability dataset that is an order of magnitude larger than comparable existing datasets. Results show that the estimated reliability degrees strongly correlates with journalists-provided scores (Spearman=0.80) and can effectively predict reliability labels (macro-avg. F$_1$ score=81.05). We release our implementation and dataset, aiming to provide a valuable resource for the NLP community working on information verification., Comment: Accepted to NAACL 2024 Main Conference

Recently, Nathan and Rudner derived a Gorini-Kossakowski-Sudarshan-Lindblad master equation from the Redfield equation. The claim is that the level of approximation is equal to that of the Redfield equation. Here we benchmark the Nathan-Rudner equation (NRE) against the exact solution of a damped harmonic oscillator and compare its performance to that of the time-dependent Redfield equation (RE). We find that which of the equations performs better depends on the regime considered. It turns out that the short-time dynamics is generally much better captured by the RE, whereas the NRE delivers results comparable to those of the rotating-wave approximation. For the steady state, in the high-temperature limit the RE again performs better and its solution approaches the exact result for ultrahigh temperatures. Nevertheless, here also the NR equation constitutes a good approximation. In the low-temperature limit, in turn, the NRE provides a better approximation than the RE. For too strong coupling, here the RE might even fail completely by predicting unphysical behaviour.

This paper shows a systematic experimental and theoretical study on the temperature dependence of the infrared optical properties of pyrolytic boron nitride (pBN), from 390 to 1050 $^{\circ}$CC for wavelengths between 4 and 16 {\mu}m. The temperature dependence of these properties has never been analyzed before. The measured emissivity spectra were fitted to a dielectric function model and an effective medium approximation. The phonon frequencies and dielectric constants agreed well with room temperature experimental values from the literature, as well as with ab initio and first principles calculations. In addition, the phonon frequencies of the perpendicular mode and the dielectric constants of the parallel mode showed an appreciable parabolic temperature dependence, which justifies the interest of more theoretical efforts in order to explain this behavior. Finally, the results of this work demonstrate that thermal emission spectroscopy allows obtaining the values of the optical and dielectric parameters of impure ceramic materials in a simple manner as a function of temperature., Comment: 6 pages, 6 figures

This paper indicates that the sensitivity and accuracy of an infrared radiometer for emissivity measurements depends not only on its design and the measurement method, but also on the spurious radiation. This spurious radiation must be taken into account in the calibration processes since it can be of the same order of magnitude as that of the sample in highly reflective surfaces. Its presence may also be the cause of the inability to detect small surface emissivity changes induced by any surface or bulk properties (anomalous skin effect, phase transitions, etc.). In this paper, the analysis of the spurious radiation is performed for a T form radiometer and a measurement method where the surroundings are considered to emit as if they were black bodies. However, the results and conclusions that are obtained can be extended without difficulty to any type of radiometer and to all measurement methods. Our research shows that if the sample is placed normal to the emitted radiation optical path, two different spurious radiation sources are detected. However, for low emitting and highly specular materials, they can be eliminated by tilting the sample between 6 and 20{\deg} without modifying the spectral emissivity.

The thermal emissivity spectrum in the mid infrared range (3 to 21 $\mu$m) as well as its dependence on temperature between 225 and 320 $^{\circ}$C has been obtained for the Mg-51%Zn (weight %) eutectic alloy, a candidate for thermal storage. The spectral curves show the typical behaviour of metals and alloys, with emissivity values between 0.05 and 0.2. It was also found that the emissivity spectrum shows variations in each heating cycle during the first few cycles. These changes are associated with the presence of metastable phases in the solid solid phase transition, present in the alloy below the melting point. The absence of signs of oxidation in air is very favourable for the use of this alloy in thermal energy storage systems. Moreover, the total normal emissivity curves obtained from dynamic spectral measurements have allowed analysing the behaviour phase transition sequence present in this alloy. These experimental results indicate that accurate emissivity measurements can be sensitive enough to account for the structural phase transitions in metals and alloys.

The total emissivity of the absorbing surfaces is a critical parameter in the calculation of the radiative thermal losses in solar thermal collectors. This is because the radiative heat losses have a significant economic impact on the final cost of the electricity produced in a solar thermal plant. This paper demonstrates the need to calculate the total emissivity from spectral emissivity measurements at the working temperature of the solar thermal collector, instead of using extrapolated values from spectral emissivities measured at room temperature. Usual uncertainties produced by the estimation of the total emissivity, in which its temperature dependence is only introduced by the Planck function, are analyzed., Comment: 4 pages, 6 figures

A study on the radiative properties of two pure metals, copper and nickel, using a high accuracy radiometer is carried out. Their spectral emissivity between 3 and 21 {\mu}m and its dependence on emission angle and temperature between 250 and 900 $^{\circ}$C is measured. An evolution of the samples emissivity associated to the surface stress relaxation is observed, which is relieved after two or three heating cycles. Spectral emissivity of metals usually decreases as wavelength increases, but in the case of copper an irregular behaviour has been found. Its spectral emissivity shows a broad plateau around 10 {\mu}m, which can be due to the anomalous skin effect. On the other hand, the emissivity usually increases with temperature, but in the case of nickel the emissivity changes little and even slightly decreases for T > 700 $^{\circ}$C. The exper-imental directional emissivity of both metals shows the dependence on the emission angle predicted by the electromagnetic theory for metallic samples. By increasing the emission angle, the emissivity dependence on the wavelength strongly decreases. Furthermore, in the case of nickel, an emissivity increase with wavelength is observed for k > 20 {\mu}m. The electrical resistivity for both metals is obtained by fitting the experimental emissivity curves with the Hagen Rubens equation. The results agree fairly well with direct electrical resistivity measurements for copper but show a poor agreement in the case of nickel., Comment: 6 pages, 8 figures

A complete experimental study of temperature dependence of the total spectral emissivity has been performed, for the first time, for absorber reflector selective coatings used in concentrated solar power (CSP) systems for energy harvesting. The coating consist of double cermet layers of silicon oxide with different amounts of molybdenum over a silver infrared mirror layer. The experimental measurements were carried out by a high accurate radiometer (HAIRL) with controlled atmosphere in the mid-infrared and for temperatures between 150 and 600 C. The spectral emissivity is nearly constant in this temperature range. Therefore, the temperature dependence of the total emissivity is given by Planck function. These results were compared with those obtained with the usual calculus using room temperature reflectance spectrum. Finally, the performance of the coating was analyzed by comparison of coated respect to non-coated stainless steel., Comment: 6 pages, 10 figures

Spectral directional emissivity has been measured in copper between 3 and 24 {\mu}m above room temperature. The experimental spectrum shows a weak broad peak between 7 and 14 {\mu}m, which is much more acute for higher emission angles. However, the peak width and position are both independent of the emission angle. The experimental results are in very good agreement with the semiclassical theory of the optical properties of metals in the regime of the anomalous skin effect, in particular with the asymptotic approximation. This comparison suggests that this work shows an optical experimental evidence of the anomalous skin effect., Comment: 4 pages, 4 figures

A revisiting of the Lindemann criterion under the recently established minimal viscosity formulation is proposed which uncovers intriguing insights into the melting process. The approach suggests that melting involves competition between the electron rest energy and the thermal one caused by temperature increase when considering a defect-free crystal structure. However, when accounting for the role of vacancies in the melting process, the analysis suggests that the key competing factors are the thermal energy and the one needed for vacancy migration. The estimated value of the Lindemann constant, in this case, is close to the reported values and with recent simulations studying the correlation between Born and Lindemann melting criteria., Comment: 5 pages, 1 figure

This article is devoted to the study of the thermodynamics phase transitions and critical phenomena of an FLRW cosmological model under the so-called Kaniadakis's statistics. The equation of state is derived from the corrected Friedmann field equations and the thermodynamics unified first law. This reveals the existence of non-trivial critical points where a first-order phase transition takes place. The system behaves as an "inverted" van der Waals fluid in this concern. Interestingly, the numerical values of the critical exponents are the same as those of the van der Waals system. Besides, to obtain more insights into the thermodynamics description, the so-called Ruppeiner's geometry is studied through the normalized scalar curvature, disclosing this invariant zone where the system undergoes repulsive/attractive interactions. Near the critical point, this curvature provides again the same critical exponent and universal constant value as for van der Waals fluid., Comment: 11 pages, 4 figures. Accepted version at Physics Letter B

For an FLRW model, thermodynamic phase transitions are investigated in the Einstein-Gauss-Bonnet gravity framework. Using the work density, the equation of state is derived, and the criticality conditions are employed to determine the critical points where possible phase transitions occur. The appearance of phase transitions strongly depends upon the space-time dimension $n$. In this concern, for $n=5$, there is a first-order phase transition, a liquid-gas van der Waals-like phase transition, where the Gibbs free energy presents a swallowtail behavior. On the other hand, for $n=6$, the system does not exhibit a first-order van der Waals-like phase transition. In such a case, the Gibbs free energy presents a cusp with stable and unstable branches. For the present study, the mentioned phenomena are present for an expanding cosmology, where the matter distribution filling the Universe corresponds to a speculative matter distribution with an equation of state parameter greater than one. Interestingly, there are no phase transitions for dimensions higher than $n=6$, nor for expanding or contracting cosmological scenarios. To gain more insights into the system, the micro-structure is analyzed using thermodynamic geometry to quantify the normalized scalar curvature. This invariant shows that a repulsive interaction dominates the phase transition region, while the region where the system has a gas-like behavior attractive interaction prevails. Besides, the critical exponents have been obtained, coinciding their numerical values with the universal ones.

Pressure and flow estimation in Water Distribution Networks (WDN) allows water management companies to optimize their control operations. For many years, mathematical simulation tools have been the most common approach to reconstructing an estimate of the WDN hydraulics. However, pure physics-based simulations involve several challenges, e.g. partially observable data, high uncertainty, and extensive manual configuration. Thus, data-driven approaches have gained traction to overcome such limitations. In this work, we combine physics-based modeling and Graph Neural Networks (GNN), a data-driven approach, to address the pressure estimation problem. First, we propose a new data generation method using a mathematical simulation but not considering temporal patterns and including some control parameters that remain untouched in previous works; this contributes to a more diverse training data. Second, our training strategy relies on random sensor placement making our GNN-based estimation model robust to unexpected sensor location changes. Third, a realistic evaluation protocol considers real temporal patterns and additionally injects the uncertainties intrinsic to real-world scenarios. Finally, a multi-graph pre-training strategy allows the model to be reused for pressure estimation in unseen target WDNs. Our GNN-based model estimates the pressure of a large-scale WDN in The Netherlands with a MAE of 1.94mH$_2$O and a MAPE of 7%, surpassing the performance of previous studies. Likewise, it outperformed previous approaches on other WDN benchmarks, showing a reduction of absolute error up to approximately 52% in the best cases., Comment: submitted to Water Resources Research. Huy Truong and Andr\'es Tello contributed equally to this work

In this study, we present two distinct approaches within the realm of Deep Reinforcement Learning (Deep-RL) aimed at enhancing mapless navigation for a ground-based mobile robot. The research methodology primarily involves a comparative analysis between a Deep-RL strategy grounded in the foundational Deep Q-Network (DQN) algorithm, and an alternative approach based on the Double Deep Q-Network (DDQN) algorithm. The agents in these approaches leverage 24 measurements from laser range sampling, coupled with the agent's positional differentials and orientation relative to the target. This amalgamation of data influences the agents' determinations regarding navigation, ultimately dictating the robot's velocities. By embracing this parsimonious sensory framework as proposed, we successfully showcase the training of an agent for proficiently executing navigation tasks and adeptly circumventing obstacles. Notably, this accomplishment is attained without a dependency on intricate sensory inputs like those inherent to image-centric methodologies. The proposed methodology is evaluated in three different real environments, revealing that Double Deep structures significantly enhance the navigation capabilities of mobile robots compared to simple Q structures.

Today, there are standard and well established procedures within the Human Activity Recognition (HAR) pipeline. However, some of these conventional approaches lead to accuracy overestimation. In particular, sliding windows for data segmentation followed by standard random k-fold cross validation, produce biased results. An analysis of previous literature and present-day studies, surprisingly, shows that these are common approaches in state-of-the-art studies on HAR. It is important to raise awareness in the scientific community about this problem, whose negative effects are being overlooked. Otherwise, publications of biased results lead to papers that report lower accuracies, with correct unbiased methods, harder to publish. Several experiments with different types of datasets and different types of classification models allow us to exhibit the problem and show it persists independently of the method or dataset.

Acceleration of electrons in vacuum directly by intense laser fields, often termed vacuum laser acceleration (VLA), holds great promise for the creation of compact sources of high-charge, ultrashort, relativistic electron bunches. However, while the energy gain is expected to be higher with tighter focusing (i.e. stronger electric field), this does not account for the reduced acceleration range, which is limited by diffraction. Here, we present the results of an experimental investigation of VLA, using tungsten nanotips driven by relativistic-intensity few-cycle laser pulses. We demonstrate the acceleration of relativistic electron beams with typical charge of 100s pC to 15 MeV energies. Two different focusing geometries (tight and loose, with f-numbers one and three respectively) produced comparable results, despite a factor of ten difference in the peak intensities, which is evidence for the importance of post-injection acceleration mechanisms around the focus. Our results are in good agreement with the results of full-scale, three-dimensional particle-in-cell simulations.