Search

Determining the internal structure of Uranus is a key objective for planetary science. Knowledge of Uranus's bulk composition and the distribution of elements is crucial to understanding its origin and evolutionary path. In addition, Uranus represents a poorly understood class of intermediate-mass planets (intermediate in size between the relatively well studied terrestrial and gas giant planets), which appear to be very common in the Galaxy. As a result, a better characterization of Uranus will also help us to better understand exoplanets in this mass and size regime. Recognizing the importance of Uranus, a Keck Institute for Space Studies (KISS) workshop was held in September 2023 to investigate how we can improve our knowledge of Uranus's internal structure in the context of a future Uranus mission that includes an orbiter and a probe. The scientific goals and objectives of the recently released Planetary Science and Astrobiology Decadal Survey were taken as our starting point. We reviewed our current knowledge of Uranus's interior and identified measurement and other mission requirements for a future Uranus spacecraft, providing more detail than was possible in the Decadal Survey's mission study and including new insights into the measurements to be made. We also identified important knowledge gaps to be closed with Earth-based efforts in the near term that will help guide the design of the mission and interpret the data returned., Comment: Study Report prepared for the W. M. Keck Institute for Space Studies (KISS). Study title: Determining the Interior Structure of Uranus: A Case Study for Leveraging Cross-Discipline Science to Answer Tough Questions. Study dates: September 11-15, 2023. Team Leads: Mark Hofstadter, Ravit Helled, and David Stevenson

The rapid advancements in artificial intelligence (AI) have presented new opportunities for enhancing efficiency and economic competitiveness across various industries, espcially in banking. Machine learning (ML), as a subset of artificial intelligence, enables systems to adapt and learn from vast datasets, revolutionizing decision-making processes, fraud detection, and customer service automation. However, these innovations also introduce new challenges, particularly in the realm of cybersecurity. Adversarial attacks, such as data poisoning and evasion attacks, represent critical threats to machine learning models, exploiting vulnerabilities to manipulate outcomes or compromise sensitive information. Furthermore, this study highlights the dual-use nature of AI tools, which can be used by malicious users. To address these challenges, the paper emphasizes the importance of developing machine learning models with key characteristics such as security, trust, resilience and robustness. These features are essential to mitigating risks and ensuring the secure deployment of AI technologies in banking sectors, where the protection of financial data is paramount. The findings underscore the urgent need for enhanced cybersecurity frameworks and continuous improvements in defensive mechanisms. By exploring both opportunities and risks, this paper aims to guide the responsible integration of AI in the banking sector, paving the way for innovation while safeguarding against emerging threats.

The sequential nature of decision-making in financial asset trading aligns naturally with the reinforcement learning (RL) framework, making RL a common approach in this domain. However, the low signal-to-noise ratio in financial markets results in noisy estimates of environment components, including the reward function, which hinders effective policy learning by RL agents. Given the critical importance of reward function design in RL problems, this paper introduces a novel and more robust reward function by leveraging imitation learning, where a trend labeling algorithm acts as an expert. We integrate imitation (expert's) feedback with reinforcement (agent's) feedback in a model-free RL algorithm, effectively embedding the imitation learning problem within the RL paradigm to handle the stochasticity of reward signals. Empirical results demonstrate that this novel approach improves financial performance metrics compared to traditional benchmarks and RL agents trained solely using reinforcement feedback.

The abilities of traditional perimeter-based security architectures are rapidly decreasing as more enterprise assets are moved toward the cloud environment. From a security viewpoint, the Zero Trust framework can better track and block external attackers while limiting security breaches resulting from insider attacks in the cloud paradigm. Furthermore, Zero Trust can better accomplish access privileges for users and devices across cloud environments to enable the secure sharing of resources. Moreover, the concept of zero trust architecture in cloud computing requires the integration of complex practices on multiple layers of system architecture, as well as a combination of a variety of existing technologies. This paper focuses on authentication mechanisms, calculation of trust score, and generation of policies in order to establish required access control to resources. The main objective is to incorporate an unbiased trust score as a part of policy expressions while preserving the configurability and adaptiveness of parameters of interest. Finally, the proof-of-concept is demonstrated on a micro-cloud plat-form solution., Comment: 10 pages, 2 figures

The immense shift to cloud computing has brought changes in security and privacy requirements, impacting critical Identity Management services. Currently, many IdM systems and solutions are accessible as cloud services, delivering identity services for applications in closed domains and the public cloud. This research paper centres on identity management in distributed environments, emphasising the importance of robust up to date authorisation mechanisms. The paper concentrates on implementing robust security paradigms to minimise communication overhead among services while preserving privacy and access control. The key contribution focuses on solving the problem of restricted access to resources in cases when the authentication token is still valid, but permissions are updated. The proposed solution incorporates an Identity and Access Management server as a component that authenticates all external requests. The IAM server key responsibilities include maintaining user data, assigning privileges within the system, and authorisation. Furthermore, it empowers users by offering an Application Programming Interface for managing users and their rights within the same organisation, providing finer granularity in authorisation. The IAM server has been integrated with a configuration dissemination tool designed as a distributed cloud infrastructure to evaluate the solution., Comment: 10 pages, 2 figures

Owing to their cost-effectiveness and flexibility, cloud services have been the default choice for the deployment of innumerable software systems over the years. However, novel paradigms are beginning to emerge, as the cloud can't meet the requirements of increasingly many latency- and privacy-sensitive applications. The distributed cloud model, being one of the attempts to overcome these challenges, places a distributed cloud layer between device and cloud layers, intending to bring resources closer to data sources. As application code should be kept separate from its configuration, especially in highly dynamic cloud environments, there is a need to incorporate configuration primitives in future distributed cloud platforms. In this paper, we present the design and implementation of a configuration management subsystem for an open-source distributed cloud platform. Our solution spreads across the cloud and distributed cloud layers and supports configuration versioning, selective dissemination to nodes in the distributed cloud layer, and logical isolation via namespaces. Our work serves as a demonstration of the feasibility and usability of the new cloud-extending models and provides valuable insight into one of the possible implementations.

The BL Lac Object PKS 1717+177 has been identified as potential neutrino-emitting AGN in the point source stacking analysis of IceCube data. We explore peculiarities in the morphology and kinematics of the jet and examine multi-wavelength light curves for distinctive effects which might allow to pinpoint a likely neutrino generation mechanism. We re-modeled 34 high resolution radio interferometric Very Long Baseline Array (VLBA) observations obtained at 15 GHz (between 1999/12/27 and 2023/05/03). A correlation and periodicity analysis of optical KAIT and Tuorla data, as well as for Fermi-LAT $\gamma$-ray data has been performed. The nuclear jet appears deflected and bent at about 0.5 mas distance from the radio core by an encounter with a dark, unseen object. The deviation of the jet evolves over 23.5 years from a simple apparent bend into a significantly meandering structure with increasing amplitude: a zig-zag line. To our knowledge, this is the first time that the temporal evolution of a jet deviation can be traced. The turning point shifts with time and the jet seems to brighten up almost periodically at the point of deviation. The radio core as well as the jet contribute approximately equally to the total flux-density at 15 GHz. We discuss scenarios which could explain the complex jet bending and quasi-regular flaring. We propose that the jet could either be deflected by the magnetosphere of a second massive black hole, by the pressure gradient due to a circumnuclear dense cloud, or via gravitational lensing by an intervening black hole., Comment: 22 pages, 9 figures, in print in MNRAS

The increased brightness temperature of young rocky protoplanets during their magma ocean epoch makes them potentially amenable to atmospheric characterization to distances from the solar system far greater than thermally equilibrated terrestrial exoplanets, offering observational opportunities for unique insights into the origin of secondary atmospheres and the near surface conditions of prebiotic environments. The Large Interferometer For Exoplanets (LIFE) mission will employ a space-based mid-infrared nulling interferometer to directly measure the thermal emission of terrestrial exoplanets. Here, we seek to assess the capabilities of various instrumental design choices of the LIFE mission concept for the detection of cooling protoplanets with transient high-temperature magma ocean atmospheres, in young stellar associations in particular. Using the LIFE mission instrument simulator (LIFEsim) we assess how specific instrumental parameters and design choices, such as wavelength coverage, aperture diameter, and photon throughput, facilitate or disadvantage the detection of protoplanets. We focus on the observational sensitivities of distance to the observed planetary system, protoplanet brightness temperature using a blackbody assumption, and orbital distance of the potential protoplanets around both G- and M-dwarf stars. Our simulations suggest that LIFE will be able to detect (S/N $\geq$ 7) hot protoplanets in young stellar associations up to distances of $\approx$100 pc from the solar system for reasonable integration times (up to $\sim$hours). Detection of an Earth-sized protoplanet orbiting a solar-sized host star at 1 AU requires less than 30 minutes of integration time. M-dwarfs generally need shorter integration times. The contribution from wavelength regions $<$6 $\mu$m is important for decreasing the detection threshold and discriminating emission temperatures., Comment: 18 pages, 19 figures; accepted for publication in A&A

Metacognition has been recognized as an essential skill for academic success and for performance in solving problems. During learning or problem-solving, metacognitive skills facilitate a range of cognitive and affective processes, leading collectively to improved performance. This study explores the predictive potential of metacognition in the second introductory programming course. A two-dimensional model has been proposed, consisting of metacognitive awareness and metacognitive behavior. To evaluate the predictive capacity of metacognition empirically, an exploratory case study with 194 participants from two institutions was conducted in the second introductory programming course. A latent approach was employed to examine the associations between metacognition and performance in object-oriented programming. Our findings indicate that both metacognitive dimensions have a positive effect on programming. Likewise, the results of the structural equation modeling show that 27% of variance in programming performance is explained by metacognitive behavior. Following the results, metacognition has the potential to be considered as one of the important predictors of performance in introductory programming., Comment: Preprint submitted to ACM Koli Calling 2024 conference

The local (z=0.0315) AGN Mrk 817, was monitored over more than 500 days with space-borne and ground-based instruments as part of a large international campaign AGN STORM 2. Here, we present a comprehensive analysis of the broad-band continuum variations using detailed modeling of the broad line region (BLR), several types of disk winds classified by their optical depth, and new numerical simulations. We find that diffuse continuum (DC) emission, with additional contributions from strong and broad emission lines, can explain the continuum lags observed in this source during high and low luminosity phases. Disk illumination by the variable X-ray corona contributes only a small fraction of the observed continuum lags. Our BLR models assume radiation pressure-confined clouds distributed over a distance of 2-122 light days. We present calculated mean-emissivity radii of many emission lines, and DC emission, and suggest a simple, transfer-function-dependent method that ties them to cross-correlation lag determinations. We do not find clear indications for large optical depth winds but identify the signature of lower column density winds. In particular, we associate the shortest observed continuum lags with a combination of tau(1 Ryd) approx. 2 wind and a partly shielded BLR. Even smaller optical depth winds may be associated with X-ray absorption features and with noticeable variations in the width and lags of several high ionization lines like HeII and CIV. Finally, we demonstrate the effect of torus dust emission on the observed lags in the i and z bands., Comment: 23 pages, 18 figures. Corrected typographical error in the title of the paper as it appeared in the Metadata

The extremes of Active Galactic Nuclei (AGN) variability offer valuable new insights into the drivers and physics of AGN. We discuss some of the most extreme cases of AGN variability; the highest amplitudes, deep minima states, extreme spectral states, Seyfert-type changes, and semi-periodic signals, including new X-ray observations. The properties of changing-look (CL) AGN are briefly reviewed and a classification scheme is proposed which encompasses the variety of CL phenomena; distinguishing slow and fast events, repeat events, and frozen-look AGN which do not show any emission-line response. Long-term light curves that are densely covered over multiple years, along with follow-up spectroscopy, are utilized to gain insight into the underlying variability mechanisms including accretion disk and broad-line region physics. Remarkable differences are seen, for instance, in the optical spectral response to extreme outbursts, implying distinct intrinsic variability mechanisms. Furthermore, we discuss methods for distinguishing between CL AGN and CL look-alike events (tidal disruption events or supernovae in dense media). Finally, semi-periodic light curve variability is addressed and the latest multiwavelength (MWL) light curve of the binary supermassive black hole (SMBH) candidate OJ 287 from the MOMO project is presented. Recent results from that project have clearly established the need for new binary SMBH modelling matching the tight new constraints from observations, including the measurement of a low (primary) SMBH mass of ~10^8 Msun which also implies that OJ 287 is no longer in the regime of near-future pulsar timing arrays., Comment: Submitted to Advances in Space Research on July 11 (Special Issue on 'Astrophysical Spectroscopy and Data in Investigation of Laboratory and Space Plasmas'). Based on an invited talk given at the VI. Conference on AGN and Gravitational Lensing

X-ray reverberation mapping is a powerful technique for probing the innermost accretion disk, whereas continuum reverberation mapping in the UV, optical, and infrared (UVOIR) reveals reprocessing by the rest of the accretion disk and broad-line region (BLR). We present the time lags of Mrk 817 as a function of temporal frequency measured from 14 months of high-cadence monitoring from Swift and ground-based telescopes, in addition to an XMM-Newton observation, as part of the AGN STORM 2 campaign. The XMM-Newton lags reveal the first detection of a soft lag in this source, consistent with reverberation from the innermost accretion flow. These results mark the first simultaneous measurement of X-ray reverberation and UVOIR disk reprocessing lags$\unicode{x2013}$effectively allowing us to map the entire accretion disk surrounding the black hole. Similar to previous continuum reverberation mapping campaigns, the UVOIR time lags arising at low temporal frequencies are longer than those expected from standard disk reprocessing by a factor of 2-3. The lags agree with the anticipated disk reverberation lags when isolating short-timescale variability, namely timescales shorter than the H$\beta$ lag. Modeling the lags requires additional reprocessing constrained at a radius consistent with the BLR size scale inferred from contemporaneous H$\beta$-lag measurements. When we divide the campaign light curves, the UVOIR lags show substantial variations, with longer lags measured when obscuration from an ionized outflow is greatest. We suggest that, when the obscurer is strongest, reprocessing by the BLR elongates the lags most significantly. As the wind weakens, the lags are dominated by shorter accretion disk lags., Comment: 28 pages, 11 figures, 3 tables. Accepted for publication in the Astrophysical Journal

A nonlinear phase-field model is developed to simulate corrosion damage. The motion of the electrode$-$ electrolyte interface follows the usual kinetic rate theory for chemical reactions based on the Butler-Volmer equation. The model links the surface polarization variation associated with the charging kinetics of an electric double layer (EDL) to the mesoscale transport. The effects of the EDL are integrated as a boundary condition on the solution potential equation. The boundary condition controls the magnitude of the solution potential at the electrode-electrolyte interface. The ion concentration field outside the EDL is obtained by solving the electro-diffusion equation and Ohm's law for the solution potential. The model is validated against the classic benchmark pencil electrode test. The framework developed reproduces experimental measurements of both pit kinetics and transient current density response. The model enables more accurate information on corrosion damage, current density, and environmental response in terms of the distribution of electric potential and charged species. The sensitivity analysis for different properties of the EDL is performed to investigate their role in the electrochemical response of the system. Simulation results show that the properties of the EDL significantly influence the transport of ionic species in the electrolyte.

In this paper, both edge and mixed metric dimensions of Johnson graphs $J_{n,k}$ are considered. A new tight lower bound for $\beta_E(J_{n,k})$ based on hitting sets has been obtained. Using this bound, exact values for $\beta_E(J_{n,2})$ and $\beta_M(J_{n,2})$ have been derived, and it is proved that $\beta_E(J_{n,2}) = \beta_M(J_{n,2})$., Comment: 14 pages

Causal structure learning (CSL) refers to the task of learning causal relationships from data. Advances in CSL now allow learning of causal graphs in diverse application domains, which has the potential to facilitate data-driven causal decision-making. Real-world CSL performance depends on a number of $\textit{context-specific}$ factors, including context-specific data distributions and non-linear dependencies, that are important in practical use-cases. However, our understanding of how to assess and select CSL methods in specific contexts remains limited. To address this gap, we present $\textit{CausalRegNet}$, a multiplicative effect structural causal model that allows for generating observational and interventional data incorporating context-specific properties, with a focus on the setting of gene perturbation experiments. Using real-world gene perturbation data, we show that CausalRegNet generates accurate distributions and scales far better than current simulation frameworks. We illustrate the use of CausalRegNet in assessing CSL methods in the context of interventional experiments in biology., Comment: 16 pages, 8 figures, 4 tables

We observed the Seyfert 1 galaxy Mrk817 during an intensive multi-wavelength reverberation mapping campaign for 16 months. Here, we examine the behavior of narrow UV absorption lines seen in HST/COS spectra, both during the campaign and in other epochs extending over 14 years. We conclude that while the narrow absorption outflow system (at -3750 km/s with FWHM=177 km/s) responds to the variations of the UV continuum as modified by the X-ray obscurer, its total column density (logNH =19.5 cm-2) did not change across all epochs. The adjusted ionization parameter (scaled with respect to the variations in the Hydrogen ionizing continuum flux) is log UH =-1.0. The outflow is located at a distance smaller than 38 parsecs from the central source, which implies a hydrogen density of nH > 3000 cm-3. The absorption outflow system only covers the continuum emission source and not the broad emission line region, which suggests that its transverse size is small (< 1e16 cm), with potential cloud geometries ranging from spherical to elongated along the line of sight., Comment: 18 pages, 12 Figures, Accepted for publication in ApJ

In this paper the recently introduced concept of equidistant dimension $eqdim(G)$ of graph $G$ is considered. Useful property of distance-equalizer set of arbitrary graph $G$ has been established. For Johnson graphs $J_{n,2}$ and Kneser graphs $K_{n,2}$ exact values for $eqdim(J_{n,2})$ and $eqdim(K_{n,2})$ have been derived, while for Johnson graphs $J_{n,3}$ it is proved that $eqdim(J_{n,3}) \le n-2$. Finally, exact value of $eqdim(J_{2k,k})$ for odd $k$ has been presented.

We present the results of the XMM-Newton and NuSTAR observations taken as part of the ongoing, intensive multi-wavelength monitoring program of the Seyfert 1 galaxy Mrk 817 by the AGN Space Telescope and Optical Reverberation Mapping 2 (AGN STORM 2) Project. The campaign revealed an unexpected and transient obscuring outflow, never before seen in this source. Of our four XMM-Newton/NuSTAR epochs, one fortuitously taken during a bright X-ray state has strong narrow absorption lines in the high-resolution grating spectra. From these absorption features, we determine that the obscurer is in fact a multi-phase ionized wind with an outflow velocity of $\sim$5200 km s$^{-1}$, and for the first time find evidence for a lower ionization component with the same velocity observed in absorption features in the contemporaneous HST spectra. This indicates that the UV absorption troughs may be due to dense clumps embedded in diffuse, higher ionization gas responsible for the X-ray absorption lines of the same velocity. We observe variability in the shape of the absorption lines on timescales of hours, placing the variable component at roughly 1000 $R_g$ if attributed to transverse motion along the line of sight. This estimate aligns with independent UV measurements of the distance to the obscurer suggesting an accretion disk wind at the inner broad line region. We estimate that it takes roughly 200 days for the outflow to travel from the disk to our line of sight, consistent with the timescale of the outflow's column density variations throughout the campaign.

Deep reinforcement learning (DRL) is a well-suited approach to financial decision-making, where an agent makes decisions based on its trading strategy developed from market observations. Existing DRL intraday trading strategies mainly use price-based features to construct the state space. They neglect the contextual information related to the position of the strategy, which is an important aspect given the sequential nature of intraday trading. In this study, we propose a novel DRL model for intraday trading that introduces positional features encapsulating the contextual information into its sparse state space. The model is evaluated over an extended period of almost a decade and across various assets including commodities and foreign exchange securities, taking transaction costs into account. The results show a notable performance in terms of profitability and risk-adjusted metrics. The feature importance results show that each feature incorporating contextual information contributes to the overall performance of the model. Additionally, through an exploration of the agent's intraday trading activity, we unveil patterns that substantiate the effectiveness of our proposed model.

Fault injection attacks are a potent threat against embedded implementations of neural network models. Several attack vectors have been proposed, such as misclassification, model extraction, and trojan/backdoor planting. Most of these attacks work by flipping bits in the memory where quantized model parameters are stored. In this paper, we introduce an encoding-based protection method against bit-flip attacks on neural networks, titled DeepNcode. We experimentally evaluate our proposal with several publicly available models and datasets, by using state-of-the-art bit-flip attacks: BFA, T-BFA, and TA-LBF. Our results show an increase in protection margin of up to $7.6\times$ for $4-$bit and $12.4\times$ for $8-$bit quantized networks. Memory overheads start at $50\%$ of the original network size, while the time overheads are negligible. Moreover, DeepNcode does not require retraining and does not change the original accuracy of the model.

To investigate the short-term variability and determine the size of the optical continuum emitting region of intermediate-mass black holes (IMBHs), we carried out high-cadence, multiband photometric monitoring of a Seyfert 1 galaxy J0249-0815 across two nights, together with a one-night single-band preliminary test. The presence of the broad Ha component in our target was confirmed by recent Paloma spectroscopic observations, 23 years after the Sloan Digital Sky Survey, ruling out the supernovae origin of the broad Ha line. The photometric experiment was primarily conducted utilizing four-channel imagers MuSCAT 3 and 4 mounted on 2 m telescopes within the Las Cumbres Observatory Global Telescope Network. Despite the expectation of variability, we observed no significant variation (<1.4%) on timescales of 6 to 10 hr. This nondetection is likely due to substantial host galaxy light diluting the subtle active galactic nucleus (AGN) variability. Additionally, we cannot rule out that the target was in a relatively quiescent state without intranight variability during our monitoring, owing to the stochastic nature of AGN variations. To enhance the possibility of detecting subtle variability signals and lag in future IMBH reverberation campaigns, it may be beneficial to select targets with a higher AGN-to-host flux ratio, and conduct dual-band preliminary tests and tailored simulations., Comment: 11 pages, 6 figures

We present a concise report on the '2DHybrid' method, an innovative extension of two-dimensional correlation spectroscopy (2D COS), tailored for quasar light curve analysis. Addressing the challenge of discerning periodic variations against the background of intrinsic "red" noise fluctuations, this method employs cross-correlation of wavelet transform matrices to reveal distinct correlation patterns between underlaying oscillations, offering new insights into quasar dynamics., Comment: published in Applied Spectroscopy, 2024 Apr 3:37028241241308. doi: 10.1177/00037028241241308

We test the potential of Bayesian synthesis of upcoming multi-instrument data to extract orbital parameters and individual light curves of close binary supermassive black holes (CB-SMBH) with subparsec separations. Next generation (ng) interferometers, will make possible the observation of astrometric wobbles in CB-SMBH. Combining them with periodic variable time-domain data from surveys like the Vera C. Rubin Legacy Survey of Space and Time (LSST), allows for a more information on CB-SMBH candidates compared to standalone observational methods. Our method reliably determines binary parameters and component fluxes from binary total flux across long-term, intermediate and short-term binary dynamics and observational configurations, assuming ten annual observations, even in short period "q-accrete" objects. Expected CB-SMBH astrometric wobbles constructed from binary dynamical parameters, might serve in refining observational strategies for CB-SMBH. Combination of inferred mass ratio, light curves of binary components, and observed photocenter wobbles can be a proxy for the activity states of CB-SMBH components., Comment: The article was accepted for publication in the ApJ

In this work, a hybrid physics-based data-driven surrogate model for the microscale analysis of heterogeneous material is investigated. The proposed model benefits from the physics-based knowledge contained in the constitutive models used in the full-order micromodel by embedding them in a neural network. Following previous developments, this paper extends the applicability of the physically recurrent neural network (PRNN) by introducing an architecture suitable for rate-dependent materials in a finite strain framework. In this model, the homogenized deformation gradient of the micromodel is encoded into a set of deformation gradients serving as input to the embedded constitutive models. These constitutive models compute stresses, which are combined in a decoder to predict the homogenized stress, such that the internal variables of the history-dependent constitutive models naturally provide physics-based memory for the network. To demonstrate the capabilities of the surrogate model, we consider a unidirectional composite micromodel with transversely isotropic elastic fibers and elasto-viscoplastic matrix material. The extrapolation properties of the surrogate model trained to replace such micromodel are tested on loading scenarios unseen during training, ranging from different strain-rates to cyclic loading and relaxation. Speed-ups of three orders of magnitude with respect to the runtime of the original micromodel are obtained., Comment: 28 pages, 26 figures

An electro-chemo-mechanical phase-field formulation is developed to simulate pitting and stress corrosion in polycrystalline materials. The formulation incorporates dependencies of mechanical properties and corrosion potential on crystallographic orientation. The model considers the formation and charging dynamics of an electric double layer through a new general boundary condition for the solution potential. The potential of the model is demonstrated by simulating corrosion in polycrystalline materials with various grain morphology distributions. The results show that incorporating the underlying microstructure yields more extensive defects, faster defect kinetics, and irregular pit and crack shapes relative to a microstructurally-insensitive homogeneous material scenario.

In this paper, we prove that the variety $C_m(L)$ of commuting $m$-tuples of elements of simple Lie algebra $L$ is often reducible. Explicitely, we prove it is reducible for all simple Lie algebra $L$ not isomorphic to $\mathfrak{sl}_2$ and $\mathfrak{sl})_3$, and all $m \geq 4$. We also prove it is reducible for $C_3(L)$ for $L$ of types $B_k,C_k,E_7,E_8,F_4,G_2$, $k \geq 2$, as well as for $D_l$ for $l \geq 10$. We do this by proving Theorem on Adding Diagonals, that says that if we can find a simple Lie subalgebra $L'$ whose Dynkin diagram is a subdiagram of the Dynkin diagram of $L$, then under mild conditions, from the fact that $C_m(L')$ is reducible, it follows that $C_m(L)$ is also reducible.

The Wide-field Spectroscopic Telescope (WST) is proposed as a new facility dedicated to the efficient delivery of spectroscopic surveys. This white paper summarises the initial concept as well as the corresponding science cases. WST will feature simultaneous operation of a large field-of-view (3 sq. degree), a high multiplex (20,000) multi-object spectrograph (MOS) and a giant 3x3 sq. arcmin integral field spectrograph (IFS). In scientific capability these requirements place WST far ahead of existing and planned facilities. Given the current investment in deep imaging surveys and noting the diagnostic power of spectroscopy, WST will fill a crucial gap in astronomical capability and work synergistically with future ground and space-based facilities. This white paper shows that WST can address outstanding scientific questions in the areas of cosmology; galaxy assembly, evolution, and enrichment, including our own Milky Way; origin of stars and planets; time domain and multi-messenger astrophysics. WST's uniquely rich dataset will deliver unforeseen discoveries in many of these areas. The WST Science Team (already including more than 500 scientists worldwide) is open to the all astronomical community. To register in the WST Science Team please visit https://www.wstelescope.com/for-scientists/participate, Comment: 194 pages, 66 figures. Comments are welcome (wstelescope@gmail.com)

Phase I clinical trials are essential to bringing novel therapies from chemical development to widespread use. Traditional approaches to dose-finding in Phase I trials, such as the '3+3' method and the Continual Reassessment Method (CRM), provide a principled approach for escalating across dose levels. However, these methods lack the ability to incorporate uncertainty regarding the dose-toxicity ordering as found in combination drug trials. Under this setting, dose-levels vary across multiple drugs simultaneously, leading to multiple possible dose-toxicity orderings. The Partial Ordering CRM (POCRM) extends to these settings by allowing for multiple dose-toxicity orderings. In this work, it is shown that the POCRM is vulnerable to 'estimation incoherency' whereby toxicity estimates shift in an illogical way, threatening patient safety and undermining clinician trust in dose-finding models. To this end, the Bayesian model averaged POCRM (BMA-POCRM) is proposed. BMA-POCRM uses Bayesian model averaging to take into account all possible orderings simultaneously, reducing the frequency of estimation incoherencies. The effectiveness of BMA-POCRM in drug combination settings is demonstrated through a specific instance of estimate incoherency of POCRM and simulation studies. The results highlight the improved safety, accuracy and reduced occurrence of estimate incoherency in trials applying the BMA-POCRM relative to the POCRM model., Comment: 41 pages, 10 figures

Analytic combinatorics in several variables refers to a suite of tools that provide sharp asymptotic estimates for certain combinatorial quantities. In this paper, we apply these tools to determine the Gilbert--Varshamov lower bound on the rate of optimal codes in $L_1$ metric. Several different code spaces are analyzed, including the simplex and the hypercube in $\mathbb{Z^n}$, all of which are inspired by concrete data storage and transmission models such as the sticky insertion channel, the permutation channel, the adjacent transposition (bit-shift) channel, the multilevel flash memory channel, etc., Comment: 33 pages, 3 figures, submitted to IEEE Transactions on Information Theory

The Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory is poised to observe thousands of quasars using the Deep Drilling Fields (DDF) across six broadband filters over a decade. Understanding quasar accretion disc (AD) time delays is pivotal for probing the physics of these distant objects. Pozo Nu\~nez et al. (2023) has recently demonstrated the feasibility of recovering AD time delays with accuracies ranging from 5\% to 20\%, depending on the quasar's redshift and time sampling intervals. Here we reassess the potential for measuring AD time delays under the current DDF observing cadence, which is placeholder until a final cadence is decided. We find that contrary to prior expectations, achieving reliable AD time delay measurements for quasars is significantly more challenging, if not unfeasible, due to the limitations imposed by the current observational strategies., Comment: Accepted as a research note in RNAAS; 3 pages, 1 figure

While maps provide upfront content, this might not always be the most effective way for users to remember information. With the proliferation of interactive displays for tourists and visitors in public spaces, we can create a more playful user experience with maps than just exploring them. Adding interactions with the map could also help users retain more information as they use them. In this paper, we investigated whether completing a jigsaw puzzle of a map supports users in retaining more information about a specific map. The results of a between-subject study with a sample of n=28 indicate that additional interaction helped improve mean scores of textual and spatial recall but not visual recall. However, the results are not statistically significant, and the topic is subject to further investigation. Our findings contribute to discussions on using interactive touchscreen displays in similar learning scenarios involving memory retention., Comment: HCI SI 2023: Human-Computer Interaction Slovenia 2023, January 26, 2024, Maribor, Slovenia, 2 tables, 4 figures, 10 pages

During the energy transition, the significance of collaborative management among institutions is rising, confronting challenges posed by data privacy concerns. Prevailing research on distributed approaches, as an alternative to centralized management, often lacks numerical convergence guarantees or is limited to single-machine numerical simulation. To address this, we present a distributed approach for solving AC Optimal Power Flow (OPF) problems within a geographically distributed environment. This involves integrating the energy system Co-Simulation (eCoSim) module in the eASiMOV framework with the convergence-guaranteed distributed optimization algorithm, i.e., the Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN). Comprehensive evaluations across multiple system scenarios reveal a marginal performance slowdown compared to the centralized approach and the distributed approach executed on single machines -- a justified trade-off for enhanced data privacy. This investigation serves as empirical validation of the successful execution of distributed AC OPF within a geographically distributed environment, highlighting potential directions for future research.

Basic algebraic and combinatorial properties of finite vector spaces in which individual vectors are allowed to have multiplicities larger than $ 1 $ are derived. An application in coding theory is illustrated by showing that multispace codes that are introduced here may be used in random linear network coding scenarios, and that they generalize standard subspace codes (defined in the set of all subspaces of $ \mathbb{F}_q^n $) and extend them to an infinitely larger set of parameters. In particular, in contrast to subspace codes, multispace codes of arbitrarily large cardinality and minimum distance exist for any fixed $ n $ and $ q $.

As Large Language Models (LLMs) become increasingly integrated into everyday life, their capabilities to understand and emulate human cognition are under steady examination. This study investigates the ability of LLMs to comprehend and interpret linguistic pragmatics, an aspect of communication that considers context and implied meanings. Using Grice's communication principles, LLMs and human subjects (N=76) were evaluated based on their responses to various dialogue-based tasks. The findings revealed the superior performance and speed of LLMs, particularly GPT4, over human subjects in interpreting pragmatics. GPT4 also demonstrated accuracy in the pre-testing of human-written samples, indicating its potential in text analysis. In a comparative analysis of LLMs using human individual and average scores, the models exhibited significant chronological improvement. The models were ranked from lowest to highest score, with GPT2 positioned at 78th place, GPT3 ranking at 23rd, Bard at 10th, GPT3.5 placing 5th, Best Human scoring 2nd, and GPT4 achieving the top spot. The findings highlight the remarkable progress made in the development and performance of these LLMs. Future studies should consider diverse subjects, multiple languages, and other cognitive aspects to fully comprehend the capabilities of LLMs. This research holds significant implications for the development and application of AI-based models in communication-centered sectors., Comment: 19 pages, 1 figure, 2 tables

Background: Electronic health records (EHRs) are a valuable resource for data-driven medical research. However, the presence of protected health information (PHI) makes EHRs unsuitable to be shared for research purposes. De-identification, i.e. the process of removing PHI is a critical step in making EHR data accessible. Natural language processing has repeatedly demonstrated its feasibility in automating the de-identification process. Objectives: Our study aims to provide systematic evidence on how the de-identification of clinical free text has evolved in the last thirteen years, and to report on the performances and limitations of the current state-of-the-art systems. In addition, we aim to identify challenges and potential research opportunities in this field. Methods: A systematic search in PubMed, Web of Science and the DBLP was conducted for studies published between January 2010 and February 2023. Titles and abstracts were examined to identify the relevant studies. Selected studies were then analysed in-depth, and information was collected on de-identification methodologies, data sources, and measured performance. Results: A total of 2125 publications were identified for the title and abstract screening. 69 studies were found to be relevant. Machine learning (37 studies) and hybrid (26 studies) approaches are predominant, while six studies relied only on rules. Majority of the approaches were trained and evaluated on public corpora. The 2014 i2b2/UTHealth corpus is the most frequently used (36 studies), followed by the 2006 i2b2 (18 studies) and 2016 CEGS N-GRID (10 studies) corpora., Comment: Submitted to Artificial Intelligence in Medicine

In this paper a new graph invariant based on the minimal hitting set problem is introduced. It is shown that it represents a tight lower bound for the doubly metric dimension of a graph. Exact values of new invariant for paths, stars, complete graphs and complete bipartite graph are obtained. The paper analyzes some tight bounds for the new invariant in general case. Also several extremal differences between some related invariants are determined.

We fit the UV/optical lightcurves of the Seyfert 1 galaxy Mrk 817 to produce maps of the accretion disk temperature fluctuations $\delta T$ resolved in time and radius. The $\delta T$ maps are dominated by coherent radial structures that move slowly ($v \ll c$) inwards and outwards, which conflicts with the idea that disk variability is driven only by reverberation. Instead, these slow-moving temperature fluctuations are likely due to variability intrinsic to the disk. We test how modifying the input lightcurves by smoothing and subtracting them changes the resulting $\delta T$ maps and find that most of the temperature fluctuations exist over relatively long timescales ($\sim$100s of days). We show how detrending AGN lightcurves can be used to separate the flux variations driven by the slow-moving temperature fluctuations from those driven by reverberation. We also simulate contamination of the continuum emission from the disk by continuum emission from the broad line region (BLR), which is expected to have spectral features localized in wavelength, such as the Balmer break contaminating the $U$ band. We find that a disk with a smooth temperature profile cannot produce a signal localized in wavelength and that any BLR contamination should appear as residuals in our model lightcurves. Given the observed residuals, we estimate that only $\sim$20% of the variable flux in the $U$ and $u$ lightcurves can be due to BLR contamination. Finally, we discus how these maps not only describe the data, but can make predictions about other aspects of AGN variability., Comment: 23 pages, 18 figures, submitting to ApJ, comments welcome