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Mobile health has the potential to revolutionize health care delivery and patient engagement. In this work, we discuss how integrating Artificial Intelligence into digital health applications-focused on supply chain, patient management, and capacity building, among other use cases-can improve the health system and public health performance. We present an Artificial Intelligence and Reinforcement Learning platform that allows the delivery of adaptive interventions whose impact can be optimized through experimentation and real-time monitoring. The system can integrate multiple data sources and digital health applications. The flexibility of this platform to connect to various mobile health applications and digital devices and send personalized recommendations based on past data and predictions can significantly improve the impact of digital tools on health system outcomes. The potential for resource-poor settings, where the impact of this approach on health outcomes could be more decisive, is discussed specifically. This framework is, however, similarly applicable to improving efficiency in health systems where scarcity is not an issue., Comment: This article has been accepted for publication in Health Systems & Reform, published by Taylor & Francis

Positron Annihilation Lifetime Spectroscopy (PALS) is a well-established non-destructive technique used for nanostructural characterization of porous materials. It is based on the annihilation of a positron and an electron. Mean positron lifetime in the material depends on the free voids size and molecular environment, allowing the study of porosity and structural transitions in the nanometer scale. We have developed a novel method enabling spatially resolved PALS, thus providing tomography of nanostructural characterization of an extended object. Correlating space (position) and structural (lifetime) information brings new insight in materials studies, especially in the characterization of the purity and pore distribution. For the first time, a porosity image using stationary positron sources for the simultaneous measurement of the porous polymers XAD4, silica aerogel powder IC3100, and polyvinyl toluene scintillator PVT by the J-PET tomograph is demonstrated

Multi fidelity Bayesian optimization (MFBO) leverages experimental and or computational data of varying quality and resource cost to optimize towards desired maxima cost effectively. This approach is particularly attractive for chemical discovery due to MFBO's ability to integrate diverse data sources. Here, we investigate the application of MFBO to accelerate the identification of promising molecules or materials. We specifically analyze the conditions under which lower fidelity data can enhance performance compared to single-fidelity problem formulations. We address two key challenges, selecting the optimal acquisition function, understanding the impact of cost, and data fidelity correlation. We then discuss how to assess the effectiveness of MFBO for chemical discovery.

This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model development and optimization, comparing these results with previous manually developed models.

Common measures of accuracy used to assess the performance of automatic speech recognition (ASR) systems, as well as human transcribers, conflate multiple sources of error. Stylistic differences, such as verbatim vs non-verbatim, can play a significant role in ASR performance evaluation when differences exist between training and test datasets. The problem is compounded for speech from underrepresented varieties, where the speech to orthography mapping is not as standardized. We categorize the kinds of stylistic differences between 6 transcription versions, 4 human- and 2 ASR-produced, of 10 hours of African American English (AAE) speech. Focusing on verbatim features and AAE morphosyntactic features, we investigate the interactions of these categories with how well transcripts can be compared via word error rate (WER). The results, and overall analysis, help clarify how ASR outputs are a function of the decisions made by the training data's human transcribers., Comment: Published in Interspeech 2024 Proceedings, 5 pages excluding references, 5 figures

This work proposes a control-informed reinforcement learning (CIRL) framework that integrates proportional-integral-derivative (PID) control components into the architecture of deep reinforcement learning (RL) policies. The proposed approach augments deep RL agents with a PID controller layer, incorporating prior knowledge from control theory into the learning process. CIRL improves performance and robustness by combining the best of both worlds: the disturbance-rejection and setpoint-tracking capabilities of PID control and the nonlinear modeling capacity of deep RL. Simulation studies conducted on a continuously stirred tank reactor system demonstrate the improved performance of CIRL compared to both conventional model-free deep RL and static PID controllers. CIRL exhibits better setpoint-tracking ability, particularly when generalizing to trajectories outside the training distribution, suggesting enhanced generalization capabilities. Furthermore, the embedded prior control knowledge within the CIRL policy improves its robustness to unobserved system disturbances. The control-informed RL framework combines the strengths of classical control and reinforcement learning to develop sample-efficient and robust deep reinforcement learning algorithms, with potential applications in complex industrial systems.

We prove that any tensor product factorization with a commutative factor of a modular group algebra over a prime field comes from a direct product decomposition of the group basis. This extends previous work by Carlson and Kov\'acs for the commutative case and answers a question of them in some cases.

This paper introduces a reinforcement learning (RL) platform that enhances end-to-end user journeys in healthcare digital tools through personalization. We explore a case study with SwipeRx, the most popular all-in-one app for pharmacists in Southeast Asia, demonstrating how the platform can be used to personalize and adapt user experiences. Our RL framework is tested through a series of experiments with product recommendations tailored to each pharmacy based on real-time information on their purchasing history and in-app engagement, showing a significant increase in basket size. By integrating adaptive interventions into existing mobile health solutions and enriching user journeys, our platform offers a scalable solution to improve pharmaceutical supply chain management, health worker capacity building, and clinical decision and patient care, ultimately contributing to better healthcare outcomes., Comment: Presented at the Third Workshop on End-to-End Customer Journey Optimization at KDD 2024 (KDD CJ Workshop '24), August 26, Barcelona, Spain

Pharmacies are critical in healthcare systems, particularly in low- and middle-income countries. Procuring pharmacists with the right behavioral interventions or nudges can enhance their skills, public health awareness, and pharmacy inventory management, ensuring access to essential medicines that ultimately benefit their patients. We introduce a reinforcement learning operational system to deliver personalized behavioral interventions through mobile health applications. We illustrate its potential by discussing a series of initial experiments run with SwipeRx, an all-in-one app for pharmacists, including B2B e-commerce, in Indonesia. The proposed method has broader applications extending beyond pharmacy operations to optimize healthcare delivery., Comment: Presented at The First Workshop on AI Behavioral Science (AIBS'24) at KDD 2024, August 25, Barcelona, Spain

By providing evidence-based clinical decision support, digital tools and electronic health records can revolutionize patient management, especially in resource-poor settings where fewer health workers are available and often need more training. When these tools are integrated with AI, they can offer personalized support and adaptive interventions, effectively connecting community health workers (CHWs) and healthcare facilities. The CHARM (Community Health Access & Resource Management) app is an AI-native mobile app for CHWs. Developed through a joint partnership of Causal Foundry (CF) and mothers2mothers (m2m), CHARM empowers CHWs, mainly local women, by streamlining case management, enhancing learning, and improving communication. This paper details CHARM's development, integration, and upcoming reinforcement learning-based adaptive interventions, all aimed at enhancing health worker engagement, efficiency, and patient outcomes, thereby enhancing CHWs' capabilities and community health., Comment: Presented at the 7th epiDAMIK ACM SIGKDD International Workshop on Epidemiology meets Data Mining and Knowledge Discovery, August 26, 2024, Barcelona, Spain

The growing interest in human-grade total body positron emission tomography (PET) systems has also application in small animal research. Due to the existing limitations in human-based studies involving drug development and novel treatment monitoring, animal-based research became a necessary step for testing and protocol preparation. In this simulation-based study two unconventional, cost-effective small animal total body PET scanners (for mouse and rat studies) have been investigated in order to inspect their feasibility for preclinical research. They were designed with the novel technology explored by the Jagiellonian-PET (J-PET) Collaboration. Two main PET characteristics: sensitivity and spatial resolution were mainly inspected to evaluate their performance. Moreover, the impact of the scintillator dimension and time-of-flight on the latter parameter was examined in order to design the most efficient tomographs. The presented results show that for mouse TB J-PET the achievable system sensitivity is equal to 2.35% and volumetric spatial resolution to 9.46 +- 0.54 mm3, while for rat TB J-PET they are equal to 2.6% and 14.11 +- 0.80 mm3, respectively. Furthermore, it was shown that the designed tomographs are almost parallax-free systems, hence, they resolve the problem of the acceptance criterion tradeoff between enhancing spatial resolution and reducing sensitivity.

The optimisation of the sensitive region of CMOS sensors with complex non-uniform electric fields requires precise simulations, and this can be achieved by a combination of electrostatic field simulations and Monte Carlo methods. This paper presents the guiding principles of such simulations, using a CMOS pixel sensor with a small collection electrode and a high-resistivity epitaxial layer as an example. The full simulation workflow is described, along with possible pitfalls and how to avoid them. For commercial CMOS processes, detailed doping profiles are confidential, but the presented method provides an optimisation tool that is sufficiently accurate to investigate sensor behaviour and trade-offs of different sensor designs without knowledge of proprietary information. The workflow starts with detailed electric field finite element method simulations in TCAD, using generic doping profiles. Examples of the effect of varying different parameters of the simulated sensor are shown, as well as the creation of weighting fields, and transient pulse simulations. The fields resulting from TCAD simulations can be imported into the Allpix Squared Monte Carlo simulation framework, which enables high-statistics simulations, including modelling of stochastic fluctuations from the underlying physics processes of particle interaction. Example Monte Carlo simulation setups are presented and the different parts of a simulation chain are described. Simulation studies from small collection electrode CMOS sensors are presented, and example results are shown for both single sensors and multiple sensors in a test beam telescope configuration. The studies shown are those typically performed on sensor prototypes in test beam campaigns, and a comparison is made to test beam data, showing a maximum deviation of 4% and demonstrating that the approach is viable for generating realistic results., Comment: 22 pages, 25 figures, submitted to Nuclear Instruments and Methods in Physics Research, Section A

Plastic scintillator strips are considered as one of the promising solutions for the cost-effective construction of total-body positron emission tomography, (PET) system. The purpose of the performed measurements is to compare the transparency of long plastic scintillators with dimensions 6 mm x 24 mm x 1000 mm and with all surfaces polished. Six different types of commercial, general purpose, blue-emitting plastic scintillators with low attenuation of visible light were tested, namely: polyvinyl toluene-based BC-408, EJ-200, RP-408, and polystyrene-based Epic, SP32 and UPS-923A. For determination of the best type of plastic scintillator for totalbody Jagiellonian positron emission tomograph (TB-J-PET) construction, emission and transmission spectra, and technical attenuation length (TAL) of blue light-emitting by the scintillators were measured and compared. The TAL values were determined with the use of UV lamp as excitation source, and photodiode as light detector. Emission spectra of investigated scintillators have maxima in the range from 420 nm to 429 nm. The BC-408 and EJ-200 have the highest transmittance values of about 90% at the maximum emission wavelength measured through a 6 mm thick scintillator strip and the highest technical attenuation length reaching about 2000 mm, allowing assembly of long detection modules for time-of-flight (TOF) J-PET scanners. Influence of the 6 mm x 6 mm, 12 mm x 6 mm, 24 mm x 6 mm cross-sections of the 1000 mm long EJ-200 plastic scintillator on the TAL and signal intensity was measured. The highest TAL value was determined for samples with 24 mm x 6 mm cross-section., Comment: 10 pages, 5 figures

Humans and many animals show remarkably adaptive behavior and can respond differently to the same input depending on their internal goals. The brain not only represents the intermediate abstractions needed to perform a computation but also actively maintains a representation of the computation itself (task abstraction). Such separation of the computation and its abstraction is associated with faster learning, flexible decision-making, and broad generalization capacity. We investigate if such benefits might extend to neural networks trained with task abstractions. For such benefits to emerge, one needs a task inference mechanism that possesses two crucial abilities: First, the ability to infer abstract task representations when no longer explicitly provided (task inference), and second, manipulate task representations to adapt to novel problems (task recomposition). To tackle this, we cast task inference as an optimization problem from a variational inference perspective and ground our approach in an expectation-maximization framework. We show that gradients backpropagated through a neural network to a task representation layer are an efficient heuristic to infer current task demands, a process we refer to as gradient-based inference (GBI). Further iterative optimization of the task representation layer allows for recomposing abstractions to adapt to novel situations. Using a toy example, a novel image classifier, and a language model, we demonstrate that GBI provides higher learning efficiency and generalization to novel tasks and limits forgetting. Moreover, we show that GBI has unique advantages such as preserving information for uncertainty estimation and detecting out-of-distribution samples.

In the state-of-the-art Positron Emission Tomography (PET), information about the polarization of annihilation photons is not available. Current PET systems track molecules labeled with positron-emitting radioisotopes by detecting the propagation direction of two photons from positron-electron annihilation. However, annihilation photons carry more information than just the site where they originated. Here we present a novel J-PET scanner built from plastic scintillators, in which annihilation photons interact predominantly via the Compton effect, providing information about photon polarization in addition to information on photon direction of propagation. Theoretically, photons from the decay of positronium in a vacuum are maximally entangled in polarization. However, in matter, when the positron from positronium annihilates with the electron bound to the atom, the question arises whether the photons from such annihilation are maximally entangled. In this work, we determine the distribution of the relative angle between polarization orientations of two photons from positron-electron annihilation in a porous polymer. Contrary to prior results for positron annihilation in aluminum and copper, where the strength of observed correlations is as expected for maximally entangled photons, our results show a significant deviation. We demonstrate that in porous polymer, photon polarization correlation is weaker than for maximally entangled photons but stronger than for separable photons. The data indicate that more than 40% of annihilations in Amberlite resin lead to a non-maximally entangled state. Our result indicates the degree of correlation depends on the annihilation mechanism and the molecular arrangement. We anticipate that the introduced Compton interaction-based PET system opens a promising perspective for exploring polarization correlations in PET as a novel diagnostic indicator.

We explain the algorithm employed in SHADOW4 to generate bending magnet sources. We provide an overview of the fundamental equations used to calculate the spectral and angular distributions of synchrotron emission from bending magnets. We outline the procedures for ray sampling. We present examples for intensity and polarization of the bending magnet at the old ESRF1 storage ring, and phase spaces for the bending magnet of the upcoming ALSU ring. Finally, we discuss the calculation of the effective source size and compare it with ray tracing., Comment: 6 pages, 9 figures

The Takagi-Taupin equations are solved in their simplest form (zero deformation) to obtain the Bragg-diffracted and transmitted complex amplitudes. The case of plane-parallel crystal plates is discussed using a matrix model. The equations are implemented in an open-source python library crystalpy adapted for numerical applications such as crystal reflectivity calculations and ray tracing., Comment: 11 pages, 6 figures

Optical surfaces represented by second-degree polynomials (quadratic or conics) are ubiquitous in optics. We revisit the equations of the conic shapes in the context of grazing incidence optics, gathering together the curves commonly used in x-ray instruments and synchrotron beamlines. We present the equations for paraboloids, ellipsoids, and hyperboloids in a common and consistent notation. We develop the transformations from centered systems that are commonly used to describe conics and their axes of symmetry, to local coordinate systems centered on the off-axis mirror surfaces. The equations presented are directly applicable to ray tracing, fabrication, and metrology calculations. They can also be used to study misalignments, movement tolerances, and aberrations of optical surfaces., Comment: 16 pages, 6 figures, 5 Tables

How decisions are being made is of utmost importance within organizations. The explicit representation of business logic facilitates identifying and adopting the criteria needed to make a particular decision and drives initiatives to automate repetitive decisions. The last decade has seen a surge in both the adoption of decision modeling standards such as DMN and the use of software tools such as chatbots, which seek to automate parts of the process by interacting with users to guide them in executing tasks or providing information. However, building a chatbot is not a trivial task, as it requires extensive knowledge of the business domain as well as technical knowledge for implementing the tool. In this paper, we build on these two requirements to propose an approach for the automatic generation of fully functional, ready-to-use decisions-support chatbots based on a DNM decision model. With the aim of reducing chatbots development time and to allowing non-technical users the possibility of developing chatbots specific to their domain, all necessary phases for the generation of the chatbot were implemented in the Demabot tool. The evaluation was conducted with potential developers and end users. The results showed that Demabot generates chatbots that are correct and allow for acceptably smooth communication with the user. Furthermore, Demabots's help and customization options are considered useful and correct, while the tool can also help to reduce development time and potential errors., Comment: 40 pages, 14 figures. arXiv admin note: text overlap with arXiv:2109.00822

Transformers are vital assets for the reliable and efficient operation of power and energy systems. They support the integration of renewables to the grid through improved grid stability and operation efficiency. Monitoring the health of transformers is essential to ensure grid reliability and efficiency. Thermal insulation ageing is a key transformer failure mode, which is generally tracked by monitoring the hotspot temperature (HST). However, HST measurement is complex and expensive and often estimated from indirect measurements. Existing computationally-efficient HST models focus on space-agnostic thermal models, providing worst-case HST estimates. This article introduces an efficient spatio-temporal model for transformer winding temperature and ageing estimation, which leverages physics-based partial differential equations (PDEs) with data-driven Neural Networks (NN) in a Physics Informed Neural Networks (PINNs) configuration to improve prediction accuracy and acquire spatio-temporal resolution. The computational efficiency of the PINN model is improved through the implementation of the Residual-Based Attention scheme that accelerates the PINN model convergence. PINN based oil temperature predictions are used to estimate spatio-temporal transformer winding temperature values, which are validated through PDE resolution models and fiber optic sensor measurements, respectively. Furthermore, the spatio-temporal transformer ageing model is inferred, aiding transformer health management decision-making and providing insights into localized thermal ageing phenomena in the transformer insulation. Results are validated with a distribution transformer operated on a floating photovoltaic power plant., Comment: 18 pages, 16 figures

Despite decades of research, the Mpemba Effect challenges scientists, prompting further investigation and refinement of existing hypotheses. This work uses optical tools such as thermography to analyze and study the Mpemba effect on drops. We analyze times and contact angle changes with temperature with an easily controlled experiment. This work contributes to the ongoing discourse surrounding the Mpemba Effect, emphasizing the need for interdisciplinary collaboration and experimental rigor to unravel the complexities of this intriguing phenomenon. A deeper understanding of the Mpemba Effect enhances our knowledge of thermodynamics and fluid dynamics and opens avenues for practical applications in fields such as cryopreservation, meteorology, and materials science., Comment: 14 pages, color figures

Bayesian optimization has been successfully applied throughout Chemical Engineering for the optimization of functions that are expensive-to-evaluate, or where gradients are not easily obtainable. However, domain experts often possess valuable physical insights that are overlooked in fully automated decision-making approaches, necessitating the inclusion of human input. In this article we re-introduce the human back into the data-driven decision making loop by outlining an approach for collaborative Bayesian optimization. Our methodology exploits the hypothesis that humans are more efficient at making discrete choices rather than continuous ones and enables experts to influence critical early decisions. We apply high-throughput (batch) Bayesian optimization alongside discrete decision theory to enable domain experts to influence the selection of experiments. At every iteration we apply a multi-objective approach that results in a set of alternate solutions that have both high utility and are reasonably distinct. The expert then selects the desired solution for evaluation from this set, allowing for the inclusion of expert knowledge and improving accountability, whilst maintaining the advantages of Bayesian optimization. We demonstrate our approach across a number of applied and numerical case studies including bioprocess optimization and reactor geometry design, demonstrating that even in the case of an uninformed practitioner our algorithm recovers the regret of standard Bayesian optimization. Through the inclusion of continuous expert opinion, our approach enables faster convergence, and improved accountability for Bayesian optimization in engineering systems., Comment: 31 pages with appendix and references

The ability to collect statistics about the execution of a program within a CPU is of the utmost importance across all fields of computing since it allows characterizing the timing performance of a program. This capability is even more relevant in safety-critical software systems, where it is mandatory to analyze software timing requirements to ensure the correct operation of the programs. Moreover, in order to properly evaluate and verify the extra-functional properties of these systems, besides timing performance, there are many other statistics available on a CPU, such as those associated with resource utilization. In this paper, we showcase a Performance Measurement Unit, also known as Hardware Performance Monitor, integrated into a RISC-V On-Board Computer designed for space applications by our research group. The monitoring technique features a novel approach whereby the events triggered are not counted immediately but instead are propagated through the pipeline so that their annotation is synchronized with the executed instruction. Additionally, we demonstrate the use of this PMU in a process to characterize the execution model of the processor. Finally, as an example of the statistics provided by the PMU, the results obtained running the CoreMark and Dhrystone benchmarks on the RISC-V OBC are shown.

Analogue test structures were fabricated using the Tower Partners Semiconductor Co. CMOS 65 nm ISC process. The purpose was to characterise and qualify this process and to optimise the sensor for the next generation of Monolithic Active Pixels Sensors for high-energy physics. The technology was explored in several variants which differed by: doping levels, pixel geometries and pixel pitches (10-25 $\mu$m). These variants have been tested following exposure to varying levels of irradiation up to 3 MGy and $10^{16}$ 1 MeV n$_\text{eq}$ cm$^{-2}$. Here the results from prototypes that feature direct analogue output of a 4$\times$4 pixel matrix are reported, allowing the systematic and detailed study of charge collection properties. Measurements were taken both using $^{55}$Fe X-ray sources and in beam tests using minimum ionizing particles. The results not only demonstrate the feasibility of using this technology for particle detection but also serve as a reference for future applications and optimisations.

Monolithic active pixel sensors (MAPS) produced in a 65 nm CMOS imaging technology are being investigated for applications in particle physics. The MAPS design has a small collection electrode characterized by an input capacitance of ~fF, granting a high signal-to-noise ratio and low power consumption. Additionally, the 65 nm CMOS imaging technology brings a reduction in material budget and improved logic density of the readout circuitry, compared to previously studied technologies. Given these features, this technology was chosen by the TANGERINE project to develop the next generation of silicon pixel sensors. The sensor design targets temporal and spatial resolutions compatible with the requirements for a vertex detector at future lepton colliders. Simulations and test-beam characterization of technology demonstrators have been carried out in close collaboration with the CERN EP R&D program and the ALICE ITS3 upgrade. TCAD device simulations using generic doping profiles and Monte Carlo simulations have been used to build an understanding of the technology and predict the performance parameters of the sensor. Technology demonstrators of a 65 nm CMOS MAPS with a small collection electrode have been characterized in laboratory and test-beam facilities by studying performance parameters such as cluster size, charge collection, and efficiency. This work compares simulation results to test-beam data. The experimental results establish this technology as a promising candidate for a vertex detector at future lepton colliders and give valuable information for improving the simulation approach., Comment: 6 pages, 5 figures, submitted to Nuclear Instruments and Methods in Physics Research, Section A as a conference proceeding for the 13th International "Hiroshima" Symposium on the Development and Application of Semiconductor Tracking Detectors

Precessing black-hole mergers can produce gravitational waves with net circular polarization, understood as an imbalance between right- and left-handed amplitudes. According to the Cosmological Principle, such emission must average to zero across all binary mergers in our Universe to preserve mirror-reflection symmetry at very large scales. We present a new, independent gravitational-wave test of this hypothesis. Using a novel observable based on the Chern-Pontryagin pseudo-scalar, we measure the emission of net circular polarization across 47 black-hole mergers recently analyzed by Islam. et. al. with a state-of-the art model for precessing black-hole mergers. The average value obtained is consistent with zero. Remarkably, however, we find that at least $82\%$ of the analysed sources must have produced net circular polarization, which requires orbital precession. Of these, GW200129 shows strong evidence for mirror asymmetry, with a Bayes Factor of 12.6 or, equivalently, $93.1\%$ probability. We obtain consistent (although stronger) results of $97.5\%$ and $94.3\%$ respectively using public results on this event from Hannam et. al. and performing our own parameter inference. This finding further implies indirect evidence for spontaneous emission of circularly polarized photons out of the quantum vacuum. Forthcoming black-hole merger detections will enable stronger constraints on large-scale mirror asymmetry and the Cosmological Principle., Comment: 9 pages, 6 Figures, 3 Appendixes

We analyze data retrieved by the Imaging Science System onboard the Cassini spacecraft to study the horizontal velocity and vorticity fields of Saturn's Polar Regions (latitudes 60-90$^\circ$N in June-December 2013 and 60-90$^\circ$S in October 2006 and July-December 2008), including the Northern region where the hexagonal wave is prominent. With the aid of an automated two dimensional correlation algorithm we determine two-dimensional maps of zonal and meridional winds, and deduce vorticity maps. We extract zonal averages of zonal winds, providing wind profiles that reach latitudes as high 89.5$^\circ$ in the south and 89.9$^\circ$ in the north. Wind measurements cover the intense polar cyclonic vortices that reach similar peak velocities of 150 ms-1 at 88.5$^\circ$. The hexagonal wave lies in the core of an intense eastward jet at planetocentric latitude 75.8$^\circ$N with motions that become non-zonal at the hexagonal feature. In the south hemisphere the peak of the eastward jet is located at planetocentric latitude 70.4$^\circ$S. A large anticyclone (the South Polar Spot, SPS), similar to the North Polar Spot (NPS) observed at the Voyager times (1980-81), has been observed in images from April 2008 to January 2009 in the South Polar Region at latitude -66.1$^\circ$ close to the eastward jet. The SPS does not apparently excite a wave on the jet. We analyze the stability of the zonal jets, finding potential instabilities at the flanks of the eastward jets around 70$^\circ$ and we measure the eddy wind components, suggesting momentum transfer from eddy motion to the westward jets closer to the poles.

The European HiPER project aims to demonstrate commercial viability of inertial fusion energy within the following two decades. This goal requires an extensive Research & Development program on materials for different applications (e.g., first wall, structural components and final optics). In this paper we will discuss our activities in the framework of HiPER to develop materials studies for the different areas of interest. The chamber first wall will have to withstand explosions of at least 100 MJ at a repetition rate of 5-10 Hz. If direct drive targets are used, a dry wall chamber operated in vacuum is preferable. In this situation the major threat for the wall stems from ions. For reasonably low chamber radius (5-10 m) new materials based on W and C are being investigated, e.g., engineered surfaces and nanostructured materials. Structural materials will be subject to high fluxes of neutrons leading to deleterious effects, such as, swelling. Low activation advanced steels as well as new nanostructured materials are being investigated. The final optics lenses will not survive the extreme ion irradiation pulses originated in the explosions. Therefore, mitigation strategies are being investigated. In addition, efforts are being carried out in understanding optimized conditions to minimize the loss of optical properties by neutron and gamma irradiation.

We investigate the long-term motion of Saturn's North-Pole Hexagon and the structure of its associated eastward jet, using Cassini ISS and ground-based images from 2008 to 2014. We show that both are persistent features that have survived the long polar night, the jet profile remaining essentially unchanged. During those years the hexagon vertices showed a steady rotation period of 10 hr 39 min 23.01 $\pm$ 0.01 s. Analysis of Voyager 1 and 2 (1980-1981) and HST and ground-based (1990-91) images shows a period shorter by 3.5s, due to the presence at the time of a large anticyclone. We interpret the hexagon as a manifestation of a vertically trapped Rossby wave on the polar jet and, because of their survival and unchanged properties under the strong seasonal variations in insolation, we propose that both hexagon and jet are deep-rooted atmospheric features that could reveal the true rotation of the planet Saturn.

A (left) group code of length n is a linear code which is the image of a (left) ideal of a group algebra via an isomorphism from FG to Fn which maps G to the standard basis of Fn. Many classical linear codes have been shown to be group codes. In this paper we obtain a criterion to decide when a linear code is a group code in terms of its intrinsical properties in the ambient space Fn, which does not assume an a priori group algebra structure on Fn. As an application we provide a family of groups (including metacyclic groups) for which every two-sided group code is an abelian group code. It is well known that Reed-Solomon codes are cyclic and its parity check extensions are elementary abelian group codes. These two classes of codes are included in the class of Cauchy codes. Using our criterion we classify the Cauchy codes of some lengths which are left group codes and the possible group code structures on these codes.

We establish robust relations between Transverse Momentum Dependent distributions (TMDs) and collinear distributions. We define weighted integrals of TMDs that we call Transverse Momentum Moments (TMMs) and prove that TMMs are equal to collinear distributions evaluated in some minimal subtraction scheme. The conversion to the $\bar{MS}$-scheme can be done by a calculable factor, which we derive up to three loops for some cases. We discuss in detail the zeroth, the first, and the second TMMs and provide phenomenological results for them based on the current extractions of TMDs. The results of this paper open new avenues for theoretical and phenomenological investigation of the three-dimensional and collinear hadron structures., Comment: 24 pages, 6 figures

Saturn has an intense and broad eastward equatorial jet with a complex three-dimensional structure mixed with time variability. The equatorial region experiences strong seasonal insolation variations enhanced by ring shadowing and three of the six known giant planetary-scale storms have developed in it. These factors make Saturn's equator a natural laboratory to test models of jets in giant planets. Here we report on a bright equatorial atmospheric feature imaged in 2015 that moved steadily at a high speed of 450 ms-1 not measured since 1980-81 with other equatorial clouds moving within an ample range of velocities. Radiative transfer models show that these motions occur at three altitude levels within the upper haze and clouds. We find that the peak of the jet (latitudes 10\degree N to 10\degree S) suffers intense vertical shears reaching +2.5 ms-1 km-1, two orders of magnitude higher than meridional shears, and temporal variability above 1 bar altitude level.

Time series and extreme value analyses are two statistical approaches usually applied to study hydrological data. Classical techniques, such as ARIMA models (in the case of mean flow predictions), and parametric generalised extreme value (GEV) fits and nonparametric extreme value methods (in the case of extreme value theory) have been usually employed in this context. In this paper, nonparametric functional data methods are used to perform mean monthly flow predictions and extreme value analysis, which are important for flood risk management. These are powerful tools that take advantage of both, the functional nature of the data under consideration and the flexibility of nonparametric methods, providing more reliable results. Therefore, they can be useful to prevent damage caused by floods and to reduce the likelihood and/or the impact of floods in a specific location. The nonparametric functional approaches are applied to flow samples of two rivers in the U.S. In this way, monthly mean flow is predicted and flow quantiles in the extreme value framework are estimated using the proposed methods. Results show that the nonparametric functional techniques work satisfactorily, generally outperforming the behaviour of classical parametric and nonparametric estimators in both settings., Comment: 25 pages, 10 figures

In June 2015, Cassini high-resolution images of Saturn's limb southwards of the planet's hexagonal wave revealed a system of at least six stacked haze layers above the upper cloud deck. Here, we characterize those haze layers and discuss their nature. Vertical thickness of layers ranged from 7 to 18 km, and they extended in altitude approx 130 km, from pressure level 0.5 bar to 0.01 bar. Above them, a thin but extended aerosol layer reached altitude approx 340 km (0.4 mbar). Radiative transfer modeling of spectral reflectivity shows that haze properties are consistent with particles of diameter 0.07- 1.4 \{mu}m and number density 100 - 500 cm -3. The nature of the hazes is compatible with their formation by condensation of hydrocarbon ices, including acetylene and benzene at higher altitudes. Their vertical distribution could be due to upward propagating gravity waves generated by dynamical forcing by the hexagon and its associated eastward jet.

Symbolic Computation algorithms and their implementation in computer algebra systems often contain choices which do not affect the correctness of the output but can significantly impact the resources required: such choices can benefit from having them made separately for each problem via a machine learning model. This study reports lessons on such use of machine learning in symbolic computation, in particular on the importance of analysing datasets prior to machine learning and on the different machine learning paradigms that may be utilised. We present results for a particular case study, the selection of variable ordering for cylindrical algebraic decomposition, but expect that the lessons learned are applicable to other decisions in symbolic computation. We utilise an existing dataset of examples derived from applications which was found to be imbalanced with respect to the variable ordering decision. We introduce an augmentation technique for polynomial systems problems that allows us to balance and further augment the dataset, improving the machine learning results by 28\% and 38\% on average, respectively. We then demonstrate how the existing machine learning methodology used for the problem $-$ classification $-$ might be recast into the regression paradigm. While this does not have a radical change on the performance, it does widen the scope in which the methodology can be applied to make choices.

Convective storms (CS) on Saturn appear as bright clouds, irregular in shape and rapidly changing over time, accompanied by lightning phenomena. They cover a wide range of size scales, from 500 km or less and up to sizes that encircle the planet. In this chapter we focus on medium or synoptic-scale events (typical sizes 2,000 - 7,000 km) and planetary-scale major storms known as Great White Spots (GWS). In general, the storms emerge in westward jets and in the peak of the strong eastward equatorial jet. They have been observed preferentially in the northern hemisphere and seem to occur in the same latitude in intervals of 30 and 60 years coinciding with the times of maximum insolation within the seasonal cycle. Moist convection models locate the origin of the storms in the water clouds (at about 10 bars of pressure) and to some extent in ammonia clouds, and their thermodynamics explain the possible periodicity of the phenomena. Once triggered, the storms grow and expand, and their interaction with the zonal wind system gives rise to anticyclonic ovals and to disturbances that propagate zonally covering an entire latitude band in the case of the GWS., Comment: Peer reviewed and accepted for publication as Chapter 14 of a multi-volume work edited by Kevin Baines, Michael Flasar, Norbert Krupp, and Thomas Stallard, entitled Cassini at Saturn: The Grand Finale, to be published by Cambridge University Press. 31 pages, 15 figures

The pressure sensors on Mars rover Perseverance measure the pressure field in the Jezero crater on regular hourly basis starting in sol 15 after landing. The present study extends up to sol 460 encompassing the range of solar longitudes from Ls 13{\deg} - 241{\deg} (Martian Year (MY) 36). The data show the changing daily pressure cycle, the sol-to-sol seasonal evolution of the mean pressure field driven by the CO2 sublimation and deposition cycle at the poles, the characterization of up to six components of the atmospheric tides and their relationship to dust content in the atmosphere. They also show the presence of wave disturbances with periods 2-5 sols, exploring their baroclinic nature, short period oscillations (mainly at night-time) in the range 8-24 minutes that we interpret as internal gravity waves, transient pressure drops with duration 1-150 s produced by vortices, and rapid turbulent fluctuations. We also analyze the effects on pressure measurements produced by a regional dust storm over Jezero at Ls 155{\deg}.

This review describes the dynamic phenomena in the atmosphere of Mars that are visible in images taken in the visual range through cloud formation and dust lifting. We describe the properties of atmospheric features traced by aerosols covering a large range of spatial and temporal scales, including dynamical interpretations and modelling when available. We present the areographic distribution and the daily and seasonal cycles of those atmospheric phenomena. We rely primarily on images taken by cameras on Mars Express., Comment: Invited review

Discrete symmetries play an important role in particle physics with violation of CP connected to the matter-antimatter imbalance in the Universe. We report the most precise test of P, T and CP invariance in decays of ortho-positronium, performed with methodology involving polarization of photons from these decays. Positronium, the simplest bound state of an electron and positron, is of recent interest with discrepancies reported between measured hyperfine energy structure and theory at the level of $10^{-4}$ signaling a need for better understanding of the positronium system at this level. We test discrete symmetries using photon polarizations determined via Compton scattering in the dedicated J-PET tomograph on an event-by-event basis and without the need to control the spin of the positronium with an external magnetic field, in contrast to previous experiments. Our result is consistent with QED expectations at the level of 0.0007 and one standard deviation., Comment: 17 pages, 10 figures

We explore quantum corrections of electrically charged black holes subject to vacuum polarization effects of fermion fields in QED. Solving this problem exactly is challenging so we restrict to perturbative corrections that one can obtain using the heat kernel expansion in the one-loop effective action for electrons. Starting from the corrections originally computed by Drummond and Hathrell, we solve the full semiclassical Einstein-Maxwell system of coupled equations to leading order in Planck's constant, and find a new electrically charged, static black hole solution. To probe these quantum corrections, we study electromagnetic and gravitational (axial) perturbations on this background, and derive the coupled system of Regge-Wheeler master equations that govern the propagation of these waves. In the classical limit our results agree with previous findings in the literature. We finally compare these results with those that one can obtain by working out the Euler-Heisenberg effective action. We find again a new electrically charged static black hole spacetime, and derive the coupled system of Regge-Wheeler equations governing the propagation of axial electromagnetic and gravitational perturbations. Results are qualitatively similar in both cases. We briefly discuss some challenges found in the numerical computation of the quasinormal mode frequency spectra when quantum corrections are included., Comment: 22 pages, one column format, 2 figures. Revised version: extra references added, minor corrections, new appendix. Published in PRD

The dynamics of the interaction between an atom of three levels interacting with a quantized field of two modes in a cavity is studied within the rotating wave approximation, by taking into account experimental values of the accessible hyperfine levels of alkaline atoms. An equal detuning is considered to determine the matter-field entanglement, the statistical properties of the photons, and the occupation probabilities of the atom. For a large detuning or weak dipolar strength appear the Raman condition, that is, the suppression of one of his atomic transitions. Analytic expression for the time evolution operator allows to have also explicit closed expressions for the field and matter observables., Comment: 37 pages, 9 figures