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Quantum Computing (QC) offers the potential to enhance traditional High-Performance Computing (HPC) workloads by leveraging the unique properties of quantum computers, leading to the emergence of a new paradigm: HPC-QC. While this integration presents new opportunities, it also brings novel challenges, particularly in ensuring the dependability of such hybrid systems. This paper aims to identify integration challenges, anticipate failures, and foster a diverse co-design for HPC-QC systems by bringing together QC, cloud computing, HPC, and network security. The focus of this emerging inter-disciplinary effort is to develop engineering principles that ensure the dependability of hybrid systems, aiming for a more prescriptive co-design cycle. Our framework will help to prevent design pitfalls and accelerate the maturation of the QC technology ecosystem. Key aspects include building resilient HPC-QC systems, analyzing the applicability of conventional techniques to the quantum domain, and exploring the complexity of scaling in such hybrid systems. This underscores the need for performance-reliability metrics specific to this new computational paradigm.

During relativistic magnetic reconnection, antiparallel magnetic fields undergo a rapid change in topology, releasing a large amount of energy in the form of non-thermal particle acceleration. This work explores the application of mesh refinement to 2D reconnection simulations to efficiently model the ineherent disparity in length-scales. We have systematically investigated the effects of mesh refinement and determined necessary modifications to the algorithm required to mitigate non-physical artifacts at the coarse-fine interface. We have used the ultrahigh-order Pseudo-Spectral Analytical Time-Domain (PSATD) Maxwell solver to analyze how its use can mitigate the numerical dispersion that occurs with the finite-difference time-domain (FDTD) (or ``Yee'') method. Absorbing layers are introduced at the coarse-fine interface to eliminate spurious effects that occur with mesh refinement. We also study how damping the electromagnetic fields and current density in the absorbing layer can help prevent the non-physical accumulation of charge and current density at the coarse-fine interface. Using a mesh refinement ratio of 8 for two-dimensional magnetic reconnection simulations, we obtained good agreement with the high resolution baseline simulation, using only 36% of the macroparticles and 71% of the node-hours needed for the baseline. The methods presented here are especially applicable to 3D systems where higher memory savings are expected than in 2D, enabling comprehensive, computationally efficient 3D reconnection studies in the future.

Background and Objective: Only about 14 % of eligible EU citizens finally participate in colorectal cancer (CRC) screening programs despite it being the third most common type of cancer worldwide. The development of CRC risk models can enable predictions to be embedded in decision-support tools facilitating CRC screening and treatment recommendations. This paper develops a predictive model that aids in characterizing CRC risk groups and assessing the influence of a variety of risk factors on the population. Methods: A CRC Bayesian Network is learnt by aggregating extensive expert knowledge and data from an observational study and making use of structure learning algorithms to model the relations between variables. The network is then parametrized to characterize these relations in terms of local probability distributions at each of the nodes. It is finally used to predict the risks of developing CRC together with the uncertainty around such predictions. Results: A graphical CRC risk mapping tool is developed from the model and used to segment the population into risk subgroups according to variables of interest. Furthermore, the network provides insights on the predictive influence of modifiable risk factors such as alcohol consumption and smoking, and medical conditions such as diabetes or hypertension linked to lifestyles that potentially have an impact on an increased risk of developing CRC. Conclusions: CRC is most commonly developed in older individuals. However, some modifiable behavioral factors seem to have a strong predictive influence on its potential risk of development. Modelling these effects facilitates identifying risk groups and targeting influential variables which are subsequently helpful in the design of screening and treatment programs.

Detecting exoplanet transits at X-ray wavelengths would provide a window into the effects of high energy irradiation on the upper atmospheres of planets. However, stars are relatively dim in the X-ray, making exoplanet transit detections difficult with current X-ray telescopes. To date, only one exoplanet (HD~189733~b) has an X-ray transit detection. In this study, we investigate the capability of future X-ray observatories to detect more exoplanet transits, focusing on both the NewAthena-WFI instrument and the proposed Advanced X-ray Imaging Satellite (AXIS), which provide more light-collecting power than current instruments. We examined all the transiting exoplanet systems in the NASA Exoplanet Archive and gathered X-ray flux measurements or estimates for each host star. We then predicted the stellar count rates for both AXIS and NewAthena and simulated light curves, using null-hypothesis testing to identify the top 15 transiting planets ranked by potential detection significance. We also evaluate transit detection probabilities when the apparent X-ray radius is enlarged due to atmospheric escape, finding that $\geq 5$ of these planetary systems may be detectable on the $>4\sigma$ level in this scenario. Finally, we note that the assumed host star coronal temperature, which affects the shape of an X-ray transit, can also significantly affect our ability to detect the planet., Comment: Accepted for publication in AAS Journals

This chapter explores the essential role of Binding Corporate Rules (BCRs) in managing and facilitating secure health data transfers within corporate groups under the EU General Data Protection Regulation (GDPR). BCRs are tailored to ensure compliance with the GDPR and similar international data protection laws, presenting a flexible mechanism for transferring sensitive health and genomic data. The chapter situates BCRs within the broader spectrum of the GDPR international data transfer mechanisms, addressing the unique challenges posed by the sensitive nature of health data and the increased adoption of AI technologies. The European Data Protection Board (EDPB) Recommendations 1/2022 on BCRs, issued following the Schrems II decision, are critically analyzed, highlighting their stringent requirements and the need for a balanced approach that prioritizes data protection and an AI governance framework. The chapter outlines the BCR approval process, stressing the importance of streamlining this process to encourage broader adoption. It underscores the necessity of a multidisciplinary approach in developing BCRs, incorporating recently adopted international standards and frameworks, which offer valuable guidance for organizations to build trustworthy AI management systems. They guarantee the ethical development, deployment, and operation of AI, which is essential for its successful integration and the broader digital transformation. In conclusion, BCRs are positioned as essential tools for secure health data management, fostering transparency, accountability, and collaboration across international borders. The chapter calls for proactive measures to incentivize BCR adoption, streamline approval processes, and promote more innovative approaches, ensuring BCRs remain a robust mechanism for global data protection and compliance.

The General Data Protection Regulation contains a blanket prohibition on the transfer of personal data outside of the European Economic Area unless strict requirements are met. The rationale for this provision is to protect personal data and data subject rights by restricting data transfers to countries that may not have the same level of protection as the EEA. However, the ubiquitous and permeable character of new technologies such as cloud computing, and the increased inter connectivity between societies, has made international data transfers the norm and not the exception. The Schrems II case and subsequent regulatory developments have further raised the bar for companies to comply with complex and, often, opaque rules. Many firms are, therefore, pursuing technology-based solutions in order to mitigate this new legal risk. These emerging technological alternatives reduce the need for open-ended cross-border transfers and the practical challenges and legal risk that such transfers create after Schrems. This article examines one such alternative, namely a user-held data model. This approach takes advantage of personal data clouds that allows data subjects to store their data locally and in a more decentralised manner, thus decreasing the need for cross-border transfers and offering end-users the possibility of greater control over their data.

The sharing economy is sprawling across almost every sector and activity around the world. About a decade ago, there were only a handful of platform driven companies operating on the market. Zipcar, BlaBlaCar and Couchsurfing among them. Then Airbnb and Uber revolutionized the transportation and hospitality industries with a presence in virtually every major city. Access over ownership is the paradigm shift from the traditional business model that grants individuals the use of products or services without the necessity of buying them. Digital platforms, data and algorithm-driven companies as well as decentralized blockchain technologies have tremendous potential. But they are also changing the rules of the game. One of such technologies challenging the legal system are AI systems that will also reshape the current legal framework concerning the liability of operators, users and manufacturers. Therefore, this introductory chapter deals with explaining and describing the legal issues of some of these disruptive technologies. The chapter argues for a more forward-thinking and flexible regulatory structure.

This introductory chapter briefly outlines the main theme of this volume, namely, to review the new opportunities and risks of digital healthcare from various disciplinary perspectives. These perspectives include law, public policy, organisational studies, and applied ethics. Based on this interdisciplinary approach, we hope that effective strategies may arise to ensure that benefits of this on-going revolution are deployed in a responsible and sustainable manner. The second part of the chapter comprises a brief review of the four parts and fourteen substantive chapters that comprise this volume.

This chapter examines the challenges of the revised opt out system and the secondary use of health data in England. The analysis of this data could be very valuable for science and medical treatment as well as for the discovery of new drugs. For this reason, the UK government established the care.data program in 2013. The aim of the project was to build a central nationwide database for research and policy planning. However, the processing of personal data was planned without proper public engagement. Research has suggested that IT companies, such as in the Google DeepMind deal case, had access to other kinds of sensitive data and failed to comply with data protection law. Since May 2018, the government has launched the national data opt out system with the hope of regaining public trust. Nevertheless, there are no evidence of significant changes in the ND opt out, compared to the previous opt out system. Neither in the use of secondary data, nor in the choices that patients can make. The only notorious difference seems to be in the way that these options are communicated and framed to the patients. Most importantly, according to the new ND opt out, the type 1 opt out option, which is the only choice that truly stops data from being shared outside direct care, will be removed in 2020. According to the Behavioral Law and Economics literature (Nudge Theory), default rules, such as the revised opt out system in England, are very powerful, because people tend to stick to the default choices made readily available to them. The crucial question analyzed in this chapter is whether it is desirable for the UK government to stop promoting the type 1 opt outs, and whether this could be seen as a kind of hard paternalism.

Identifying and then implementing an effective response to disruptive new AI technologies is enormously challenging for any business looking to integrate AI into their operations, as well as regulators looking to leverage AI-related innovation as a mechanism for achieving regional economic growth. These business and regulatory challenges are particularly significant given the broad reach of AI, as well as the multiple uncertainties surrounding such technologies and their future development and effects. This article identifies two promising strategies for meeting the AI challenge, focusing on the example of Fintech. First, dynamic regulation, in the form of regulatory sandboxes and other regulatory approaches that aim to provide a space for responsible AI-related innovation. An empirical study provides preliminary evidence to suggest that jurisdictions that adopt a more proactive approach to Fintech regulation can attract greater investment. The second strategy relates to so-called innovation ecosystems. It is argued that such ecosystems are most effective when they afford opportunities for creative partnerships between well-established corporations and AI-focused startups and that this aspect of a successful innovation ecosystem is often overlooked in the existing discussion. The article suggests that these two strategies are interconnected, in that greater investment is an important element in both fostering and signaling a well-functioning innovation ecosystem and that a well-functioning ecosystem will, in turn, attract more funding. The resulting synergies between these strategies can, therefore, provide a jurisdiction with a competitive edge in becoming a regional hub for AI-related activity.

On July 14th, 2022, the Danish Data Protection Authority issued a reprimand against Helsingor Municipality. It imposed a general ban on using Google Chromebooks and Google Workspace for education in primary schools in the Municipality. The Danish DPA banned such processing and suspended any related data transfers to the United States (U.S.) until it is brought in line with the General Data Protection Regulation (GDPR). The suspension took effect immediately, and the Municipality had until August 3rd, 2022, to withdraw and terminate the processing, as well as delete data already transferred. Finally, in a new decision on August 18th, 2022, the Danish DPA has ratified the ban to the use of Google Chromebooks and Workspace. In the eyes of the Danish DPA, the Municipality failed for example to document that they have assessed and reduced the relevant risks to the rights and freedoms of the pupils. This article is structured as follows: section II provides the background concerning the unfolding events after the Schrems II ruling. Section III discusses the origins and facts of the Danish DPA case. Section IV examines the reasoning and critical findings of the Danish DPA decision. Finally, section V concludes with some general recommendations the Danish municipalities must follow based on the ensuing effects stemming from this case.

The European Commission adequacy decision on the EU US Data Privacy Framework, adopted on July 10th, 2023, marks a crucial moment in transatlantic data protection. Following an Executive Order issued by President Biden in October 2022, this decision confirms that the United States meets European Union standards for personal data protection. The decision extends to all transfers from the European Economic Area to US entities participating in the framework, promoting privacy rights while facilitating data exchange. Key aspects include oversight of US public authorities access to transferred data, the introduction of a dual tier redress mechanism, and granting new rights to EU individuals, encompassing data access and rectification. However, the framework presents both promise and challenges in health data transfers. While streamlining exchange and aligning legal standards, it grapples with the complexities of divergent privacy laws. The recent bill for the introduction of a US federal privacy law emphasizes the urgent need for ongoing reform. Lingering concerns persist regarding the framework resilience, especially amid potential legal battles before the Court of Justice of the EU. The history of transatlantic data transfers between the EU and the US is riddled with vulnerabilities, reminiscent of the Ouroboros, an ancient symbol of a serpent or dragon eating its own tail, hinting at the looming possibility of the framework facing invalidation once again. This article delves into the main requirements of the framework and offers insights on how healthcare organizations can navigate it effectively.

The high-energy radiative output, from the X-ray to the ultraviolet, of exoplanet host stars drives photochemical reactions and mass loss in the upper regions of planetary atmospheres. In order to place constraints on the atmospheric properties of the three closest terrestrial exoplanets transiting M dwarfs, we observe the high-energy spectra of the host stars LTT1445A and GJ486 in the X-ray with XMM-Newton and Chandra and in the ultraviolet with HST/COS and STIS. We combine these observations with estimates of extreme ultraviolet flux, reconstructions of the Ly-a lines, and stellar models at optical and infrared wavelengths to produce panchromatic spectra from 1A--20um for each star. While LTT1445Ab, LTT1445Ac, and GJ486b do not possess primordial hydrogen-dominated atmospheres, we calculate that they are able to retain pure CO2 atmospheres if starting with 10, 15, and 50% of Earth's total CO2 budget, respectively, in the presence of their host stars' stellar wind. We use age-activity relationships to place lower limits of 2.2 and 6.6 Gyr on the ages of the host stars LTT1445A and GJ486. Despite both LTT1445A and GJ486 appearing inactive at optical wavelengths, we detect flares at ultraviolet and X-ray wavelengths for both stars. In particular, GJ486 exhibits two flares with absolute energies of 10^29.5 and 10^30.1 erg (equivalent durations of 4357+/-96 and 19724+/-169 s) occurring three hours apart, captured with HST/COS G130M. Based on the timing of the observations, we suggest that these high-energy flares are related and indicative of heightened flaring activity that lasts for a period of days, but our interpretations are limited by sparse time-sampling. Consistent high-energy monitoring is needed to determine the duration and extent of high-energy activity on individual M dwarfs, as well as the population as a whole., Comment: 21 pages, published in A&A

Hydrodynamic simulations of the stellar winds from Wolf-Rayet stars within the Galactic Center can provide predictions for the X-ray spectrum of supermassive black hole Sgr A*. Herein, we present results from updated smooth particle hydrodynamics simulations, building on the architecture of Cuadra et al. (2015); Russell et al. (2017), finding that a cold gas disk forms around Sgr A* with a simulation runtime of 3500 years. This result is consistent with previous grid-based simulations, demonstrating that a cold disk can form regardless of numerical method. We examine the plasma scenarios arising from an environment with and without this cold disk, by generating synthetic spectra for comparison to the quiescent Fe K alpha Sgr A* spectrum from Chandra HETG-S, taken through the Chandra X-ray Visionary Program. We find that current and future X-ray missions are unlikely to distinguish between the kinematic signatures in the plasma in these two scenarios. Nonetheless, the stellar wind plasma model presents a good fit to the dispersed Chandra spectra within 1.5" of Sgr A*. We compare our results to the Radiatively Inefficient Accretion Flow (RIAF) model fit to the HETG-S spectrum presented in Paper I and find that the Bayesian model evidence does not strongly favor either model. With 9" angular resolution and high spectral resolution of the X-IFU, NewAthena will offer a clearer differentiation between the RIAF plasma model and hydrodynamic simulations, but only a future X-ray mission with arcsecond resolution will significantly advance our understanding of Sgr A*'s accretion flow in X-rays., Comment: 14 pages, 6 figures. Accepted for publication in ApJ

The extreme low-luminosity supermassive black hole Sgr A* provides a unique laboratory in which to test radiatively inefficient accretion flow (RIAF) models. Previous fits to the quiescent Chandra ACIS-S spectrum found a RIAF model with an equal inflow-outflow balance works well. In this work, we apply the RIAF model to the Chandra HETG-S spectrum obtained through the Chandra X-ray Visionary Program, which displays features suggestive of temperature and velocity structures within the plasma. A comprehensive forward model analysis accounting for the accretion flow geometry and HETG-S instrumental effects is required for a full interpretation of the quiescent Chandra HETG-S spectrum. We present a RIAF model that takes these effects into account. Our fits to the high-resolution gratings spectrum indicate an inflow balanced by an outflow ($s \sim 1$) alongside a temperature profile that appears shallower than what would be expected from a gravitational potential following $1/r$. The data require that the abundance of Iron relative to solar is $Z_{Fe} < 0.32 Z_\odot$ (90\% credible interval), much lower than the $2~Z_\odot$ metallicity measured in nearby late-type giants. While future missions like NewAthena will provide higher spectral resolution, source separation will continue to be a problem. Leveraging Chandra's unparalleled spatial resolution, which is not expected to be surpassed for decades, remains essential for detailed investigations of the densely populated Galactic Center in X-rays., Comment: 11 pages, 5 figures, 1 table. Accepted for publication in ApJ

Autonomous systems, such as self-driving cars, rely on reliable semantic environment perception for decision making. Despite great advances in video semantic segmentation, existing approaches ignore important inductive biases and lack structured and interpretable internal representations. In this work, we propose MCDS-VSS, a structured filter model that learns in a self-supervised manner to estimate scene geometry and ego-motion of the camera, while also estimating the motion of external objects. Our model leverages these representations to improve the temporal consistency of semantic segmentation without sacrificing segmentation accuracy. MCDS-VSS follows a prediction-fusion approach in which scene geometry and camera motion are first used to compensate for ego-motion, then residual flow is used to compensate motion of dynamic objects, and finally the predicted scene features are fused with the current features to obtain a temporally consistent scene segmentation. Our model parses automotive scenes into multiple decoupled interpretable representations such as scene geometry, ego-motion, and object motion. Quantitative evaluation shows that MCDS-VSS achieves superior temporal consistency on video sequences while retaining competitive segmentation performance.

Aeronomy, the study of Earth's upper atmosphere and its interaction with the local space environment, has long traced changes in the thermospheres of Earth and other solar system planets to solar variability in the X-ray and extreme ultraviolet (collectively, "XUV") bands. Extending comparative aeronomy to the short-period extrasolar planets may illuminate whether stellar XUV irradiation powers atmospheric outflows that change planetary radii on astronomical timescales. In recent years, near-infrared transit spectroscopy of metastable HeI has been a prolific tracer of high-altitude planetary gas. We present a case study of exoplanet aeronomy using metastable HeI transit observations from Palomar/WIRC and follow-up high-energy data from the Neil Gehrels Swift Observatory that were taken within one month of the WASP-69 system, a K-type main sequence star with a well-studied hot Jupiter companion. Supplemented by archival data, we find that WASP-69's X-ray flux in 2023 was less than 50% of what was recorded in 2016 and that the metastable HeI absorption from WASP-69b was lower in 2023 versus past epochs from 2017-2019. Via atmospheric modeling, we show that this time-variable metastable HeI signal is in the expected direction given the observed change in stellar XUV, possibly stemming from WASP-69's magnetic activity cycle. Our results underscore the ability of multi-epoch, multi-wavelength observations to paint a cohesive picture of the interaction between an exoplanet's atmosphere and its host star., Comment: 25 pages, 15 figures, 6 tables, accepted to The Astronomical Journal

Experience from seven decades of classical computing suggests that a sustainable computer industry depends on a community of software engineers writing programs to address a wide variety of specific end-user needs, achieving both performance and utility in the process. Quantum computing is an emerging technology, and we do not yet have the insight to understand what quantum software tools and practices will best support researchers, software engineers, or applications specialists. Developers for today's quantum computers are grappling with the low-level details of the hardware, and progress towards scalable devices does not yet suggest what higher-level abstractions may look like. In this paper, we analyze and reframe the current state of the quantum software stack using the language of programming models. We propose an abstraction hierarchy to support quantum software engineering and discuss the consequences of overlaps across the programming, execution, and hardware models found in current technologies. We exercise this hierarchy for solving the eigenvalue estimation problem in two ways (a variational algorithm with error mitigation, and phase estimation with error correction) and pinpoint key differences in these approaches in terms of these layered models and their overlaps. While our work points to concrete conceptual challenges and gaps in quantum programming and proposes some specific steps forward, our primary thesis is that progress hinges on thinking about the abstraction hierarchy holistically, and not just about its components., Comment: 11 pages, 3 figures. Submitted to IEEE QCE 24

Rubrics are a commonly used tool for labeling voice corpora in speech quality assessment, although their application in the context of pathological speech remains relatively limited. In this study, we introduce a comprehensive rubric based on various dimensions of speech quality, including phonetics, fluency, and prosody. The objective is to establish standardized criteria for identifying errors within the speech of individuals with Down syndrome, thereby enabling the development of automated assessment systems. To achieve this objective, we utilized the Prautocal corpus. To assess the quality of annotations using our rubric, two experiments were conducted, focusing on phonetics and fluency. For phonetic evaluation, we employed the Goodness of Pronunciation (GoP) metric, utilizing automatic segmentation systems and correlating the results with evaluations conducted by a specialized speech therapist. While the obtained correlation values were not notably high, a positive trend was observed. In terms of fluency assessment, deep learning models like wav2vec were used to extract audio features, and we employed an SVM classifier trained on a corpus focused on identifying fluency issues to categorize Prautocal corpus samples. The outcomes highlight the complexities of evaluating such phenomena, with variability depending on the specific type of disfluency detected., Comment: Submitted to LREC-Coling 2024

One of the leading mechanisms invoked to explain the existence of the radius valley is atmospheric mass loss driven by X-ray and extreme-ultraviolet irradiation, with this process stripping the primordial envelopes of young, small planets to produce the observed bimodal distribution. We present an investigation into the TOI-431 and $\nu^2$ Lupi planetary systems, both of which host planets either side of the radius valley, to determine if their architectures are consistent with evolution by the XUV mechanism. With $\textit{XMM-Newton}$, we measure the current X-ray flux of each star, and see evidence for a stellar flare in the TOI-431 observations. We then simulate the evolution of all of the transiting planets across the two systems in response to the high-energy irradiation over their lifetimes. We use the measured X-ray fluxes as an anchor point for the XUV time evolution in our simulations, and employ several different models of estimating mass loss rates. While the simulations for TOI-431b encountered a problem with the initial calculated radii, we estimate a likely short ($\sim$ Myr) timespan for primordial envelope removal using reasonable assumptions for the initial planet. $\nu^2$ Lupi b is likely harder to strip, but is achieved in a moderate fraction of our simulations. None of our simulations stripped any of the lower density planets of their envelope, in line with prediction. We conclude that both systems are consistent with expectations for generation of the radius valley through XUV photoevaporation., Comment: 14 pages, 12 figures, 5 tables; accepted for publication in MNRAS

In this study, we investigate interstellar absorption lines along the line of sight toward the galactic low-mass X-ray binary Cygnus X-2. We combine absorption line data obtained from high-resolution X-ray spectra collected with Chandra and XMM-Newton satellites, along with Far-UV absorption lines observed by the Hubble Space Telescope's (HST) Cosmic Origins Spectrograph (COS) Instrument. Our primary objective is to understand the abundance and depletion of oxygen, iron, sulfur, and carbon. To achieve this, we have developed an analysis pipeline that simultaneously fits both the UV and X-ray datasets. This novel approach takes into account the line spread function (LSF) of HST/COS, enhancing the precision of our results. We examine the absorption lines of FeII, SII, CII, and CI present in the FUV spectrum of Cygnus X-2, revealing the presence of at least two distinct absorbers characterized by different velocities. Additionally, we employ Cloudy simulations to compare our findings concerning the ionic ratios for the studied elements. We find that gaseous iron and sulfur exist in their singly ionized forms, Fe II and S II, respectively, while the abundances of CII and CI do not agree with the Cloudy simulations of the neutral ISM. Finally, we explore discrepancies in the X-ray atomic data of iron and discuss their impact on the overall abundance and depletion of iron., Comment: Accepted for publication in The Astronomical Journal, 19 pages, 8 figures

Understanding realistic complex systems requires confronting significant conceptual, theoretical and experimental limitations rooted in the persistence of views that originated in the mechanics of simple moving bodies. We define the category of complex multiscale stochastic systems as a useful device for capturing the minimally required complexity of many types of phenomena. In doing so, we provide evidence indicating that determinism, continuity and reversibility can lead to theoretical inadequacies that manifest as intractability, inaccuracies or non-representativeness when applied to complex systems. We take the view that interactions are fundamental and summarize their portrayal across many disciplines. Despite their centrality, interactions remain largely neglected as subjects of research interest of their own. We hypothesize that a generalized theory of interactions may help organize evidence from multiple scientific domains towards a more unified realistic view of systems.

After discussing the significance of interactions to understand complex multiscale stochastic systems (CMSS), we turn our attention to the construction of a Generalised Theory of Interactions (GToI). We define interactions as discrete, localised events where the thermodynamically irreversible exchange of degrees of freedom involves signal propagation and relaxation phenomena. We proceed to define the mechanics of such space, as well as how these approximately map onto dynamical manifolds. By characterising various systems through the effect of transformations across interaction space, we develop a view of systems where features such as emergence and information representation become apparent. Our theory appears to capture structural and dynamical features of CMSS efficiently by making explicit the extent of the dynamical range of action in them; we identify the boundaries of such range with statistical limits corresponding to physical laws, and derive a generalised form of the Correspondence Principle.

We present a high-resolution spectral study of Fe L-shell extinction by the diffuse interstellar medium (ISM) in the direction of the X-ray binaries Cygnus X-1 and GX 339-4, using the XMM-Newton reflection grating spectrometer. The majority of interstellar Fe is suspected to condense into dust grains in the diffuse ISM, but the compounds formed from this process are unknown. Here, we use the laboratory cross sections from Kortright & Kim (2000) and Lee et al. (2009) to model the absorption and scattering profiles of metallic Fe, and the crystalline compounds fayalite (Fe$_2$SiO$_4$), ferrous sulfate (FeSO$_4$), hematite ($\alpha$-Fe$_2$O$_3$), and lepidocrocite ($\gamma$-FeOOH), which have oxidation states ranging from Fe$^{0}$ to Fe$^{3+}$. We find that the observed Fe L-shell features are systematically offset in energy from the laboratory measurements. An examination of over two dozen published measurements of Fe L-shell absorption finds a 1-2 eV scatter in energy positions of the L-shell features. Motivated by this, we fit for the best energy-scale shift simultaneously with the fine structure of the Fe L-shell extinction cross sections. Hematite and lepidocrocite provide the best fits ($\approx +1.1$ eV shift), followed by fayalite ($\approx +1.8$ eV shift). However, fayalite is disfavored, based on the implied abundances and knowledge of ISM silicates gained by infrared astronomical observations and meteoritic studies. We conclude that iron oxides in the Fe$^{3+}$ oxidation state are good candidates for Fe-bearing dust. To verify this, new absolute photoabsorption measurements are needed on an energy scale accurate to better than 0.2 eV., Comment: accepted to AAS Journals

This paper studies the phenomenon of conduction block in model neurons using high-frequency biphasic stimulation (HFBS). The focus is investigating the triggering of undesired onset action potentials when the HFBS is turned on. The approach analyzes the transient behavior of an averaged system corresponding to the FitzHugh-Nagumo neuron model using Lyapunov and quasi-static methods. The first result provides a more comprehensive understanding of the onset activation through a mathematical proof of how to avoid it using a ramp in the amplitude of the oscillatory source. The second result tests the response of the blocked system to a piecewise linear stimulus, providing a quantitative description of how the HFBS strength translates into conduction block robustness. The results of this work can provide insights for the design of electrical neurostimulation therapies., Comment: 32 pages, 3 figures

The RoboCup Humanoid League holds annual soccer robot world championships towards the long-term objective of winning against the FIFA world champions by 2050. The participating teams continuously improve their systems. This paper presents the upgrades to our humanoid soccer system, leading our team NimbRo to win the Soccer Tournament in the Humanoid AdultSize League at RoboCup 2023 in Bordeaux, France. The mentioned upgrades consist of: an updated model architecture for visual perception, extended fused angles feedback mechanisms and an additional COM-ZMP controller for walking robustness, and parametric in-walk kicks through waveforms., Comment: Accepted for: RoboCup 2023: Robot World Cup XXVI, LNCS, Springer, to appear 2024

The STATIS method, proposed by L'Hermier des Plantes and Escoufier, is used to analyze multiple data tables in which is very common that each of the tables have information concerning the same set of individuals. The differences and similitudes between said tables are analyzed by means of a structure called the \emph{compromise}. In this paper we present a new algorithm for applying the STATIS method when the input consists of interval data. This proposal is based on Moore's interval arithmetic and the Centers Method for Principal Component Analysis with interval data, proposed by Cazes el al. \cite{cazes1997}. In addition to presenting the INTERSTATIS method in an algorithmic way, an execution example is shown, alongside the interpretation of its results., Comment: 10 pages

Post-stack seismic inversion is a widely used technique to retrieve high-resolution acoustic impedance models from migrated seismic data. Its modelling operator assumes that a migrated seismic data can be generated from the convolution of a source wavelet and the time derivative of the acoustic impedance model. Given the band-limited nature of the seismic wavelet, the convolutional model acts as a filtering operator on the acoustic impedance model, thereby making the problem of retrieving acoustic impedances from seismic data ambiguous. In order to compensate for missing frequencies, post-stack seismic inversion is often regularized, meaning that prior information about the structure of the subsurface is included in the inversion process. Recently, the Plug-and-Play methodology has gained wide interest in the inverse problem community as a new form of implicit regularization, often outperforming state-of-the-art regularization. Plug-and-Play can be applied to any proximal algorithm by simply replacing the proximal operator of the regularizer with any denoiser of choice. We propose to use Plug-and-Play regularization with a 2D pre-trained, deep denoiser for 2D post-stack seismic inversion. Additionally, we show that a generalization of Plug-and-Play, called Multi-Agent Consensus Equilibrium, can be adopted to solve 3D post-stack inversion whilst leveraging the same 2D pre-trained denoiser used in the 2D case. More precisely, Multi-Agent Consensus Equilibrium combines the results of applying such 2D denoiser in the inline, crossline, and time directions in an optimal manner. We verify the proposed methods on a portion of the SEAM Phase 1 velocity model and the Sleipner field dataset. 1, Comment: 24 pages, 10 figures

The RHIC interaction rate at sPHENIX will reach around 3 MHz in pp collisions and requires the detector readout to reject events by a factor of over 200 to fit the DAQ bandwidth of 15 kHz. Some critical measurements, such as heavy flavor production in pp collisions, often require the analysis of particles produced at low momentum. This prohibits adopting the traditional approach, where data rates are reduced through triggering on rare high momentum probes. We explore a new approach based on real-time AI technology, adopt an FPGA-based implementation using a custom designed FELIX-712 board with the Xilinx Kintex Ultrascale FPGA, and deploy the system in the detector readout electronics loop for real-time trigger decision., Comment: 7 pages, 5 figures, proceedings for TWEPP 2023 conference, v2: corrected Table 1 numbers

The PHENIX experiment has performed a systematic study of identified charged-hadron ($\pi^\pm$, $K^\pm$, $p$, $\bar{p}$) production at midrapidity in $p$$+$Al, $^3$He$+$Au, Cu$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV and U$+$U collisions at $\sqrt{s_{_{NN}}}=193$ GeV. Identified charged-hadron invariant transverse-momentum ($p_T$) and transverse-mass ($m_T$) spectra are presented and interpreted in terms of radially expanding thermalized systems. The particle ratios of $K/\pi$ and $p/\pi$ have been measured in different centrality ranges of large (Cu$+$Au, U$+$U) and small ($p$$+$Al, $^3$He$+$Au) collision systems. The values of $K/\pi$ ratios measured in all considered collision systems were found to be consistent with those measured in $p$$+$$p$ collisions. However the values of $p/\pi$ ratios measured in large collision systems reach the values of $\approx0.6$, which is $\approx2$ times larger than in $p$$+$$p$ collisions. These results can be qualitatively understood in terms of the baryon enhancement expected from hadronization by recombination. Identified charged-hadron nuclear-modification factors ($R_{AB}$) are also presented. Enhancement of proton $R_{AB}$ values over meson $R_{AB}$ values was observed in central $^3$He$+$Au, Cu$+$Au, and U$+$U collisions. The proton $R_{AB}$ values measured in $p$$+$Al collision system were found to be consistent with $R_{AB}$ values of $\phi$, $\pi^\pm$, $K^\pm$, and $\pi^0$ mesons, which may indicate that the size of the system produced in $p$$+$Al collisions is too small for recombination to cause a noticeable increase in proton production., Comment: 480 authors from 78 institutions, 18 pages, 6 tables, 16 figures. v2 is version accepted for publication in Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

This paper proposes a new control framework for manipulating soft objects. A Deep Reinforcement Learning (DRL) approach is used to make the shape of a deformable object reach a set of desired points by controlling a robotic arm which manipulates it. Our framework is more easily generalizable than existing ones: it can work directly with different initial and desired final shapes without need for relearning. We achieve this by using learning parallelization, i.e., executing multiple agents in parallel on various environment instances. We focus our study on deformable linear objects. These objects are interesting in industrial and agricultural domains, yet their manipulation with robots, especially in 3D workspaces, remains challenging. We simulate the entire environment, i.e., the soft object and the robot, for the training and the testing using PyBullet and OpenAI Gym. We use a combination of state-of-the-art DRL techniques, the main ingredient being a training approach for the learning agent (i.e., the robot) based on Deep Deterministic Policy Gradient (DDPG). Our simulation results support the usefulness and enhanced generality of the proposed approach., Comment: 7 pages, 5 figures, 1 table, Accepted for IEEE CASE 2022

Robotic manipulation of deformable linear objects (DLOs) has great potential for applications in diverse fields such as agriculture or industry. However, a major challenge lies in acquiring accurate deformation models that describe the relationship between robot motion and DLO deformations. Such models are difficult to calculate analytically and vary among DLOs. Consequently, manipulating DLOs poses significant challenges, particularly in achieving large deformations that require highly accurate global models. To address these challenges, this paper presents MultiAC6: a new multi Actor-Critic framework for robot action space decomposition to control large 3D deformations of DLOs. In our approach, two deep reinforcement learning (DRL) agents orient and position a robot gripper to deform a DLO into the desired shape. Unlike previous DRL-based studies, MultiAC6 is able to solve the sim-to-real gap, achieving large 3D deformations up to 40 cm in real-world settings. Experimental results also show that MultiAC6 has a 66\% higher success rate than a single-agent approach. Further experimental studies demonstrate that MultiAC6 generalizes well, without retraining, to DLOs with different lengths or materials., Comment: 9 pages, 7 figures, 5 tables, Accepted for IEEE Robotics and Automation Letters (RA-L)

One of the key research themes identified by the Astro2020 decadal survey is Worlds and Suns in Context. The Advanced X-ray Imaging Satellite (AXIS) is a proposed NASA APEX mission that will become the prime high-energy instrument for studying star-planet connections from birth to death. This work explores the major advances in this broad domain of research that will be enabled by the AXIS mission, through X-ray observations of stars in clusters spanning a broad range of ages, flaring M-dwarf stars known to host exoplanets, and young stars exhibiting accretion interactions with their protoplanetary disks. In addition, we explore the ability of AXIS to use planetary nebulae, white dwarfs, and the Solar System to constrain important physical processes from the microscopic (e.g., charge exchange) to the macroscopic (e.g., stellar wind interactions with the surrounding interstellar medium)., Comment: This White Paper is part of a series commissioned for the AXIS Probe Concept Mission

The Advanced X-ray Imaging Satellite (AXIS) is a Probe-class concept that will build on the legacy of the Chandra X-ray Observatory by providing low-background, arcsecond-resolution imaging in the 0.3-10 keV band across a 450 arcminute$^2$ field of view, with an order of magnitude improvement in sensitivity. AXIS utilizes breakthroughs in the construction of lightweight segmented X-ray optics using single-crystal silicon, and developments in the fabrication of large-format, small-pixel, high readout rate CCD detectors with good spectral resolution, allowing a robust and cost-effective design. Further, AXIS will be responsive to target-of-opportunity alerts and, with onboard transient detection, will be a powerful facility for studying the time-varying X-ray universe, following on from the legacy of the Neil Gehrels (Swift) X-ray observatory that revolutionized studies of the transient X-ray Universe. In this paper, we present an overview of AXIS, highlighting the prime science objectives driving the AXIS concept and how the observatory design will achieve these objectives., Comment: Published in Proceedings of SPIE Optics & Photonics 2023, San Diego

Impact crater experiments in granular media traditionally involve loosely packed sand targets. However, this study investigates granular impact craters on both loosely and more tightly packed sand targets. We report granular vs. granular experiments that consistently adhere to power-law scaling laws for diameter as a function of impacting energy, similar to those reported by other groups for their experiments utilizing both solid and granular projectiles. In contrast, we observe significant deviations in the depth vs. energy power-law predicted by previous models. To address this discrepancy, we introduce a physical model of uniaxial compression that explains how depth saturates in granular collisions. Furthermore, we present an energy balance alongside this model that aligns well with our crater volume measurements and describes the energy transfer mechanisms acting during crater formation. Central peak formation also plays an essential role in better transferring vertical momentum to horizontal degrees of freedom, resulting in shallow craters on compacted sandbox targets. Our results reveal a depth-to-diameter aspect ratio of approximately $\sim 1/5$, allowing us to interpret the shallowness of planetary craters in light of the uniaxial compression mechanism proposed in this work.

Laboratory made granular-granular impact craters have been used as model analogues of planetary impact craters. These kind of craters have been observed and studied using profilometry techniques that allow to retrieve important morphologic features from the impacted surface. In this work, we propose to use a Time-of-Flight camera (Microsoft Kinect One) for the acquisition of depth data. We show comparisons between the typically used technique and the analysis derived from the Time-of-Flight data. We also release craterslab, a Python library developed to automate most of the tasks from the process of studying impact craters produced by granular projectiles hitting on the surface of granular targets. The library is able to acquire, identify, and measure morphological features of impacted surfaces through the reconstruction of 3D topographic maps. Our results show that using a Time-of-Flight camera and automating the data processing with a software library for the systematic study of impact craters can produce very accurate results while reducing the time spent on different stages of the process.

Existing abstract models of quantum computation make reference to circuit elements, much in contrast to their classical counterparts. Circuits, as a model of computation, substantially limit algorithmic expression and obscure high-level connections between problems and quantum resources. It is argued here that new models are needed to achieve high-level algorithmic expressiveness that allow composable procedural abstractions to manifest, leading to the development of instructions in the sense usually understood in high-level programming languages. Doing so appears essential to the discovery of new quantum algorithms, and deeper understanding of how quantum resources compose into useful patterns, or \emph{quantum motifs}. To achieve this, stronger investment in the intersection between higher-algebra, mathematical physics and quantum science is required to cope with future challenges brought forth by \textit{very large quantum scale integration}., Comment: DoE ASCR Basic Research Needs in Quantum Computing and Networking, July 11-13 Gaithersburg MD

The inversion of petrophysical parameters from seismic data represents a fundamental step in the process of characterizing the subsurface. We propose a novel, data-driven approach named Seis2Rock that utilizes optimal basis functions learned from well log information to directly link band-limited petrophysical reflectivities to pre-stack seismic data. Seis2Rock is composed of two stages: training and inference. During training, a set of optimal basis functions are identified by performing singular value decomposition on one or more synthetic AVO gathers created from measured or rock-physics synthesized elastic well-logs. In inference, seismic pre-stack data are first projected into a set of band-limited petrophysical properties using the previously computed basis functions; this is followed by regularized post-stack seismic inversion of the individual properties. In this work, we apply the Seis2Rock methodology to a synthetic dataset based on the Smeaheia reservoir model and the open Volve field dataset. Numerical results reveal the ability of the proposed method in recovering accurate porosity, shale content, and water saturation models. Finally, the proposed methodology is applied in the context of reservoir monitoring to invert time-lapse, pre-stack seismic data for water saturation changes.