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This study explores the underrepresentation of Black and Latinx students in California's community college Dual Enrollment (DE) programs. The study investigates how DE staff describe an understanding and commitment towards equity for Black and Latinx students in DE programs and how staff engage in equitably aimed praxis to serve Black and Latinx students through practices and collaborations between feeder high schools. Using a Critical Policy Analysis lens, the research highlights how Black and Latinx students are prioritized through equitable practices focused in advising and outreach. However, not all DE staff prioritize Black and Latinx through practices. Despite this, scant instances reveal that collaborative efforts between DE programs, high schools, and districts improve DE services and outcomes, though majority of K-12 partners are absent from collaborative efforts led by DE programs. The study emphasizes the need for increased collaboration between K-12 partners and integrating equitable approaches to DE outreach and advising to engage and recruit Black and Latinx students. This research advances the conversation of equity in DE programs and offers insights for addressing participation gaps among Black and Latinx students.

Microquasars are laboratories for the study of jets of relativistic particles produced by accretion onto a spinning black hole. Microquasars are near enough to allow detailed imaging of spatial features across the multiwavelength spectrum. The recent extension of the spatial morphology of a microquasar, SS 433, to TeV gamma rays \cite{abeysekara2018very} localizes the acceleration of electrons at shocks in the jet far from the black hole \cite{hess2024ss433}. Here we report TeV gamma-ray emission from another microquasar, V4641~Sgr, which reveals particle acceleration at similar distances from the black hole as SS~433. Additionally, the gamma-ray spectrum of V4641 is among the hardest TeV spectra observed from any known gamma-ray source and is detected up to 200 TeV. Gamma rays are produced by particles, either electrons or hadrons, of higher energies. Because electrons lose energy more quickly the higher their energy, such a spectrum either very strongly constrains the electron production mechanism or points to the acceleration of high-energy hadrons. This observation suggests that large-scale jets from microquasars could be more common than previously expected and that microquasars could be a significant source of Galactic cosmic rays. high energy gamma-rays also provide unique constraints on the acceleration mechanisms of extra-Galactic cosmic rays postulated to be produced by the supermassive black holes and relativistic jets of quasars. The distance to quasars limits imaging studies due to insufficient angular resolution of gamma-rays and due to attenuation of the highest energy gamma-rays by the extragalactic background light.

Very-high-energy (0.1-100 TeV) gamma-ray emission was observed in HAWC data from the lobes of the microquasar SS 433, making them the first set of astrophysical jets that were resolved at TeV energies. In this work, we update the analysis of SS 433 using 2,565 days of data from the High Altitude Water Cherenkov (HAWC) observatory. Our analysis reports the detection of a point-like source in the east lobe at a significance of $6.6\,\sigma$ and in the west lobe at a significance of $8.2\,\sigma$. For each jet lobe, we localize the gamma-ray emission and identify a best-fit position. The locations are close to the X-ray emission sites "e1" and "w1" for the east and west lobes, respectively. We analyze the spectral energy distributions and find that the energy spectra of the lobes are consistent with a simple power-law $\text{d}N/\text{d}E\propto E^{\alpha}$ with $\alpha = -2.44^{+0.13+0.04}_{-0.12-0.04}$ and $\alpha = -2.35^{+0.12+0.03}_{-0.11-0.03}$ for the east and west lobes, respectively. The maximum energy of photons from the east and west lobes reaches 56 TeV and 123 TeV, respectively. We compare our observations to various models and conclude that the very-high-energy gamma-ray emission can be produced by a population of electrons that were efficiently accelerated.

Machine learning models are increasingly used in critical areas such as loan approvals and hiring, yet they often function as black boxes, obscuring their decision-making processes. Transparency is crucial, as individuals need explanations to understand decisions, primarily if the decisions result in an undesired outcome. Our work introduces CoGS (Counterfactual Generation with s(CASP)), a model-agnostic framework capable of generating counterfactual explanations for classification models. CoGS leverages the goal-directed Answer Set Programming system s(CASP) to compute realistic and causally consistent modifications to feature values, accounting for causal dependencies between them. By using rule-based machine learning algorithms (RBML), notably the FOLD-SE algorithm, CoGS extracts the underlying logic of a statistical model to generate counterfactual solutions. By tracing a step-by-step path from an undesired outcome to a desired one, CoGS offers interpretable and actionable explanations of the changes required to achieve the desired outcome. We present details of the CoGS framework along with its evaluation., Comment: arXiv admin note: substantial text overlap with arXiv:2407.08179

Autoregressive (AR) Transformer-based sequence models are known to have difficulty generalizing to sequences longer than those seen during training. When applied to text-to-speech (TTS), these models tend to drop or repeat words or produce erratic output, especially for longer utterances. In this paper, we introduce enhancements aimed at AR Transformer-based encoder-decoder TTS systems that address these robustness and length generalization issues. Our approach uses an alignment mechanism to provide cross-attention operations with relative location information. The associated alignment position is learned as a latent property of the model via backprop and requires no external alignment information during training. While the approach is tailored to the monotonic nature of TTS input-output alignment, it is still able to benefit from the flexible modeling power of interleaved multi-head self- and cross-attention operations. A system incorporating these improvements, which we call Very Attentive Tacotron, matches the naturalness and expressiveness of a baseline T5-based TTS system, while eliminating problems with repeated or dropped words and enabling generalization to any practical utterance length., Comment: Submitted to NAACL

Natural light-harvesting antenna complexes efficiently capture solar energy using chlorophyll, i.e., magnesium porphyrin pigments, embedded in a protein matrix. Inspired by this natural configuration, artificial clay-porphyrin antenna structures have been experimentally synthesized and have demonstrated remarkable excitation energy transfer properties. The study presents the computational design and simulation of a synthetic light-harvesting system that emulates natural mechanisms by arranging cationic free-base porphyrin molecules on an anionic clay surface. We investigated the transfer of excitation energy among the porphyrin dyes using a multiscale quantum mechanics/molecular mechanics (QM/MM) approach based on the semi-empirical density functional-based tight-binding (DFTB) theory for the ground state dynamics. To improve the accuracy of our results, we incorporated an innovative multifidelity machine learning (MFML) approach, which allows the prediction of excitation energies at the numerically demanding time-dependent density functional theory level with the Def2-SVP basis set. This approach was applied to an extensive dataset of 640K geometries for the 90-atom porphyrin structures, facilitating a thorough analysis of the excitation energy diffusion among the porphyrin molecules adsorbed to the clay surface. The insights gained from this study, inspired by natural light-harvesting complexes, demonstrate the potential of porphyrin-clay systems as effective energy transfer systems.

Identifying nonlinear dynamics and characterizing noise from data is critical across science and engineering for understanding and modeling the behavior of the systems accurately. The modified sparse identification of nonlinear dynamics (mSINDy) has emerged as an effective framework for identifying systems embedded in heavy noise; however, further improvements can expand its capabilities and robustness. By integrating the weak SINDy (WSINDy) into mSINDy, we introduce the weak mSINDy (WmSINDy) to improve the system identification and noise modeling by harnessing the strengths of both approaches. The proposed algorithm simultaneously identifies parsimonious nonlinear dynamics and extracts noise probability distributions using automatic differentiation. We evaluate WmSINDy using several nonlinear systems and it demonstrates improved accuracy and noise characterization over baselines for systems embedded in relatively strong noise., Comment: Submitted to Journal of Computational Physics

In this paper, pointwise convergence, uniform convergence and compact convergence of sequences of holomorphic functions on an open subset of the complex plane are compared from a linear point of view. In fact, it is proved the existence of large linear algebras consisting, except for zero, of sequences of holomorphic functions tending to zero compactly but not uniformly on the open set or of sequences of holomorphic functions tending pointwisely to zero but not compactly. Also dense linear subspaces in an appropriate Fr\'echet space as well as infinite dimensional Banach spaces of sequences converging to zero in the mentioned modes are shown to exist.

This paper presents a framework to estimate the environmental impact of solar electric vehicles, accounting for the emissions caused by photovoltaic system production as well as vehicle use. We leverage a cradle-to-gate life cycle assessment to estimate the greenhouse gas emissions of the vehicle-integrated photovoltaic system, from the raw material extraction to the final panel assembly, including the effect of the electricity mix both at the factory location and in the country of use. %the vehicle's life cycle, considering both Furthermore, we modify an existing optimization framework for battery electric vehicles to optimally design a solar electric vehicle and estimate its energy consumption. We showcase our framework by analyzing a case study where the mono-crystalline silicon extraction and refinement processes occur in China, while the final assembly of the panel is in The Netherlands, generating 118 kg of CO2 equivalents per square meter of solar panel. The results suggest that it is generally beneficial to operate solar electric vehicles in countries with a high irradiation index. However, when the local electricity mix already displays a low carbon intensity, the additional emissions introduced by the panel are unnecessary, requiring a longer vehicle lifetime to reach an advantageous emission balance., Comment: 6 pages, 8 figures, 2024 IEEE Vehicle Power and Propulsion Conference, Best Paper Award

The magnetocaloric effect enables magnetic refrigeration and plays an important role for cooling at cryogenic temperatures, which is essential for emergent technologies such as hydrogen liquefaction and quantum computing. Here, we study the magnetocaloric effect in multiferroic hexagonal manganites by conducting direct adiabatic temperature measurements in pulsed magnetic fields exceeding 20 T. Data gained on polycrystalline HoMnO3, ErMnO3, TmMnO3, and YMnO3 demonstrate a direct correlation between the magnetic 4f-moments and the measured adiabatic temperature change. In HoMnO3, i.e., the system with the largest magnetic 4f-moments, significant temperature changes, {\Delta}Tad, of up to 20.1 K are observed, whereas the effect is largely suppressed in YMnO3. Our systematic investigations show the importance of the rare-earth magnetism for the magnetocaloric effect in multiferroic hexagonal manganites at cryogenic temperatures, reaching about 64% of the adiabatic temperature changes reported for gadolinium at room temperature.

Model predictive control (MPC) is a powerful control technique for online optimization using system model-based predictions over a finite time horizon. However, the computational cost MPC requires can be prohibitive in resource-constrained computer systems. This paper presents a fuzzy controller synthesis framework guided by MPC. In the proposed framework, training data is obtained from MPC closed-loop simulations and is used to optimize a low computational complexity controller to emulate the response of MPC. In particular, autoregressive moving average (ARMA) controllers are trained using data obtained from MPC closed-loop simulations, such that each ARMA controller emulates the response of the MPC controller under particular desired conditions. Using a Takagi-Sugeno (T-S) fuzzy system, the responses of all the trained ARMA controllers are then weighted depending on the measured system conditions, resulting in the Fuzzy-Autoregressive Moving Average (F-ARMA) controller. The effectiveness of the trained F-ARMA controllers is illustrated via numerical examples., Comment: 8 pages, 8 figures, submitted to the American Control Conference 2025

Group fairness in machine learning is a critical area of research focused on achieving equitable outcomes across different groups defined by sensitive attributes such as race or gender. Federated learning, a decentralized approach to training machine learning models across multiple devices or organizations without sharing raw data, amplifies the need for fairness due to the heterogeneous data distributions across clients, which can exacerbate biases. The intersection of federated learning and group fairness has attracted significant interest, with 47 research works specifically dedicated to addressing this issue. However, no dedicated survey has focused comprehensively on group fairness in federated learning. In this work, we present an in-depth survey on this topic, addressing the critical challenges and reviewing related works in the field. We create a novel taxonomy of these approaches based on key criteria such as data partitioning, location, and applied strategies. Additionally, we explore broader concerns related to this problem and investigate how different approaches handle the complexities of various sensitive groups and their intersections. Finally, we review the datasets and applications commonly used in current research. We conclude by highlighting key areas for future research, emphasizing the need for more methods to address the complexities of achieving group fairness in federated systems.

The equilibrium rotation rate of a planet is determined by the sum of torques acting on its solid body. For planets with atmospheres, the dominant torques are usually the gravitational tide, which acts to slow the planet's rotation rate, and the atmospheric thermal tide, which acts to spin up the planet. Previous work demonstrated that rocky planets with thick atmospheres may produce strong enough thermal tides to avoid tidal locking, but a study of how the strength of the thermal tide depends on atmospheric properties has not been done. In this work, we use a combination of simulations from a global climate model and analytic theory to explore how the thermal tide depends on the shortwave and longwave optical depth of the atmosphere, the surface pressure, and the absorbed stellar radiation. We find that for planets in the habitable zones of M stars only high-pressure but low-opacity atmospheres permit asynchronous rotation owing to the weakening of the thermal tide at high longwave and shortwave optical depths. We conclude that asynchronous rotation may be very unlikely around low-mass stars, which may limit the potential habitability of planets around M stars.

We study correlators of insertions along 1/2 BPS line defects in the holographic dual to type IIB string theory in $AdS_3 \times S^3 \times T^4$ with mixed Ramond-Ramond and Neveu Schwarz-Neveu Schwarz three-form flux. These defects break the symmetries of the bulk CFT$_2$ as $PSU(1,1|2)^2\times SO(4) \rightarrow PSU(1,1|2)\times SU(2)$, defining displacement and tilt supermultiplets. We focus on the two-, three- and four-point functions of these supermultiplets, which we compute using analytic conformal bootstrap up to next-to-leading order in their strong coupling expansion. We obtain a bootstrap result that only depends on two OPE coefficients. We perform a Witten diagram check of the bootstrap result, obtaining an holographic interpretation of the two OPE coefficients that are not constrained by the bootstrap procedure., Comment: 39 pages, 3 figures

We describe some dynamical properties of one parameter families of billiards on convex curves (ovals) which are deformed by the curvature (curve-shortening) flow. We obtain the bifurcations of the period two orbits and some special non-Birkhoff orbits, the normal periodic orbits. We prove the destruction of non-convex caustics of the ellipse by deforming it through the curvature flow.

The Electron-Ion Collider, a next generation electron-hadron and electron-nuclei scattering facility, will be built at Brookhaven National Laboratory. The wealth of new data will shape research in hadron physics, from nonperturbative QCD techniques to perturbative QCD improvements and global QCD analyses, for the decades to come. With the present proposal, Latin America based physicists, whose expertise lies on the theory and phenomenology side, make the case for the past and future efforts of a growing community, working hand-in-hand towards developing theoretical tools and predictions to analyze, interpret and optimize the results that will be obtained at the EIC, unveiling the role of the glue that binds us all. This effort is along the lines of various initiatives taken in the U.S., and supported by colleagues worldwide, such as the ones by the EIC User Group which were highlighted during the Snowmass Process and the Particle Physics Project Prioritization Panel (P5)., Comment: White Paper contribution to the Latin American Strategy Forum for Research Infrastructure (III LASF4RI

We compute the differential cross section for direct quarkonium production in high-energy electron-nucleus collisions at small $x$. Our computation is performed within the nonrelativistic QCD factorization formalism that separates the calculation into short distance coefficients and long distance matrix elements that depend on the color and spin of the state. We obtain the short distance coefficients of the production of the heavy quark pair within the framework of the Color Glass Condensate effective field theory, which resums coherent multiple interactions of the heavy quark pair with the nucleus to all orders. Our results are expressed as the convolution of perturbatively calculable perturbative functions with multi-point light-like Wilson line correlators. In the correlation limit, we establish the correspondence between our CGC formulation with calculations employing the transverse momentum dependent (TMD) framework. We extend this correspondence by resumming kinematic power corrections within the improved TMD framework, which interpolates between the TMD formalism and $k_\perp$ factorization formalism. We present a detailed numerical analysis, focusing on $J/\psi$ production in the kinematics accessible at the future Electron-Ion Collider, highlighting the importance of genuine higher-order saturation contributions when the electron collides with a large nucleus., Comment: 31 pages, 8 figures

In this paper, we present geometric scaling models for axial flux motors (AFMs) to be used for in-wheel powertrain design optimization purposes. We first present a vehicle and powertrain model, with emphasis on the electric motor model. We construct the latter by formulating the analytical scaling laws for AFMs, based on the scaling concept of RFMs from the literature, specifically deriving the model of the main loss component in electric motors: the copper losses. We further present separate scaling models of motor parameters, losses and thermal models, as well as the torque limits and cost, as a function of the design variables. Second, we validate these scaling laws with several experiments leveraging high-fidelity finite-element simulations. Finally, we define an optimization problem that minimizes the energy consumption over a drive cycle, optimizing the motor size and transmission ratio for a wide range of electric vehicle powertrain topologies. In our study, we observe that the all-wheel drive topology equipped with in-wheel AFMs is the most efficient, but also generates the highest material cost., Comment: 5 pages, 6 figures, 4 tables, 2024 IEEE Vehicle Power and Propulsion Conference, Washington DC, USA

Supernova remnants (SNRs) are believed to be capable of accelerating cosmic rays (CRs) to PeV energies. SNR G106.3+2.7 is a prime PeVatron candidate. It is formed by a head region, where the pulsar J2229+6114 and its boomerang-shaped pulsar wind nebula are located, and a tail region containing SN ejecta. The lack of observed gamma ray emission from the two regions of this SNR has made it difficult to assess which region would be responsible for the PeV CRs. We aim to characterize the very-high-energy (VHE, 0.1-100 TeV) gamma ray emission from SNR G106.3+2.7 by determining the morphology and spectral energy distribution of the region. This is accomplished using 2565 days of data and improved reconstruction algorithms from the HAWC Observatory. We also explore possible gamma ray production mechanisms for different energy ranges. Using a multi-source fitting procedure based on a maximum-likelihood estimation method, we evaluate the complex nature of this region. We determine the morphology, spectrum, and energy range for the source found in the region. Molecular cloud information is also used to create a template and evaluate the HAWC gamma ray spectral properties at ultra-high-energies (UHE, >56 TeV). This will help probe the hadronic nature of the highest-energy emission from the region. We resolve one extended source coincident with all other gamma ray observations of the region. The emission reaches above 100~TeV and its preferred log-parabola shape in the spectrum shows a flux peak in the TeV range. The molecular cloud template fit on the higher energy data reveals that the SNR's energy budget is fully capable of producing a purely hadronic source for UHE gamma rays.

We report an observation of ultra-high energy (UHE) gamma rays from the Galactic Center region, using seven years of data collected by the High-Altitude Water Cherenkov (HAWC) Observatory. The HAWC data are best described as a point-like source (HAWC J1746-2856) with a power-law spectrum ($\mathrm{d}N/\mathrm{d}E=\phi(E/26 \,\text{TeV})^{\gamma}$), where $\gamma=-2.88 \pm 0.15_{\text{stat}} - 0.1_{\text{sys}} $ and $\phi=1.5 \times 10^{-15}$ (TeV cm$^{2}$s)$^{-1}$ $\pm\, 0.3_{\text{stat}}\,^{+0.08_{\text{sys}}}_{-0.13_{\text{sys}}}$ extending from 6 to 114 TeV. We find no evidence of a spectral cutoff up to $100$ TeV using HAWC data. Two known point-like gamma-ray sources are spatially coincident with the HAWC gamma-ray excess: Sgr A$^{*}$ (HESS J1745-290) and the Arc (HESS J1746-285). We subtract the known flux contribution of these point sources from the measured flux of HAWC J1746-2856 to exclude their contamination and show that the excess observed by HAWC remains significant ($>$5$\sigma$) with the spectrum extending to $>$100 TeV. Our result supports that these detected UHE gamma rays can originate via hadronic interaction of PeV cosmic-ray protons with the dense ambient gas and confirms the presence of a proton PeVatron at the Galactic Center.

The first TeV gamma-ray source with no lower energy counterparts, TeV J2032+4130, was discovered by HEGRA. It appears in the third HAWC catalog as 3HWC J2031+415 and it is a bright TeV gamma-ray source whose emission has previously been resolved as 2 sources: HAWC J2031+415 and HAWC J2030+409. While HAWC J2030+409 has since been associated with the \emph{Fermi-LAT} Cygnus Cocoon, no such association for HAWC J2031+415 has yet been found. In this work, we investigate the spectrum and energy-dependent morphology of HAWC J2031+415. We associate HAWC J2031+415 with the pulsar PSR J2032+4127 and perform a combined multi-wavelength analysis using radio, X-ray, and $\gamma$-ray emission. We conclude that HAWC J2031+415 and, by extension, TeV J2032+4130 are most probably a pulsar wind nebula (PWN) powered by PSR J2032+4127.

The continuous increase in computational power of GPUs, essential for advancements in areas like artificial intelligence and data processing, is driving the adoption of liquid cooling in data centers. Skived copper cold plates featuring parallel straight channels are a mature technology, but they lack design freedom due to manufacturing limitations. As chips become increasingly complex in their design with the transition towards heterogeneous integration, these parallel straight channels are not able to address critical areas of concentrated high heat flux (hotspots) on a chip. A single hotspot exceeding the upper temperature limit can cause the full chip to throttle and hence limit performance. In addition, this would require a reduction in coolant inlet temperature in the data center, causing an increase in electricity and water consumption. Ideally, areas of the cold plate in contact with hotspots of the chip need smaller channels to increase convective heat transfer, whereas areas with low heat flux may benefit from larger channels to compensate for the increased pressure drop. However, manual optimization of such a cooling design is challenging due to the nonlinearity of the problem. In this paper, we explore the usage of topology optimization as a method to tailor microfluidic cooling design to the power distribution of a chip to address the hotspot temperatures in high-power chips, using a platform called Glacierware. We compare the hotspot-aware, topology-optimized microfluidic design to straight channels of various widths to benchmark its performance. Evaluations of this optimized design show a 13% lower temperature rise or a 55% lower pressure than the best-performing straight channels, indicating highly competitive performance in industrial settings where both pressure and flow rate are constrained.

In the past, quasi coherent modes were reported for nearly all tokamaks. The general definition describes modes as quasi coherent when the magnitude squared coherence is in the range of \SIrange{0.3}{0.6}{}. Quasi coherent modes are observed in the plasma core as well as in the plasma edge and can have quite different physical origins. The one in the core are observed in plasmas with low collisionality, where the electron temperature exceeds the ion temperature in the plasma core. This is the case for electron cyclotron heating in general. The origin of these modes are electrons trapped within a magnetic mirror, as reported in the past from various fusion devices. The so-called trapped-electron modes (TEMs) belong to drift wave instabilities and can be destabilized by electron-temperature gradients in the plasma core. From the diagnostic point of view, quasi coherent modes appear as fluctuations in electron density and temperature. Therefore, the microwave reflectometer is very well suited to monitor these modes. This paper describes experiments, conducted at the Wendelstein 7-X stellarator (W7-X), which aim at detecting quasi coherent modes at low wave numbers. A Poloidal Correlation Reflectometer (PCR) installed at W7-X, is able to measure low wave numbers ($k_\perp\le 3.5$ cm$^{-1}$). For different magnetic configurations and plasma parameters, broad quasi-coherent structures are observed in the coherence spectra. From the analysis of the rotation and the poloidal structure, these quasi coherent (QC) modes show the properties of electron-temperature-gradient driven TEMs. A linear relation between the mode velocity and the rotation frequency is found. The relation is uniform and confirms the nature of QC-mode observation as TEM in tokamaks, too.

Density reconstruction from X-ray projections is an important problem in radiography with key applications in scientific and industrial X-ray computed tomography (CT). Often, such projections are corrupted by unknown sources of noise and scatter, which when not properly accounted for, can lead to significant errors in density reconstruction. In the setting of this problem, recent deep learning-based methods have shown promise in improving the accuracy of density reconstruction. In this article, we propose a deep learning-based encoder-decoder framework wherein the encoder extracts robust features from noisy/corrupted X-ray projections and the decoder reconstructs the density field from the features extracted by the encoder. We explore three options for the latent-space representation of features: physics-inspired supervision, self-supervision, and no supervision. We find that variants based on self-supervised and physicsinspired supervised features perform better over a range of unknown scatter and noise. In extreme noise settings, the variant with self-supervised features performs best. After investigating further details of the proposed deep-learning methods, we conclude by demonstrating that the newly proposed methods are able to achieve higher accuracy in density reconstruction when compared to a traditional iterative technique.

In this paper we study some algebraic properties of the rack structure as well as the representation theory of it, following the ideas given by M. Elhamdadi and E. M. Moutuou in \cite{Elhamdadi}. We establish a correspondence between the irreducible strong representations of a finite and connected rack with the irreducible representation of its finite enveloping group which allows to use techniques of the latter topic in the other setting.

Assurance cases offer a structured way to present arguments and evidence for certification of systems where safety and security are critical. However, creating and evaluating these assurance cases can be complex and challenging, even for systems of moderate complexity. Therefore, there is a growing need to develop new automation methods for these tasks. While most existing assurance case tools focus on automating structural aspects, they lack the ability to fully assess the semantic coherence and correctness of the assurance arguments. In prior work, we introduced the Assurance 2.0 framework that prioritizes the reasoning process, evidence utilization, and explicit delineation of counter-claims (defeaters) and counter-evidence. In this paper, we present our approach to enhancing Assurance 2.0 with semantic rule-based analysis capabilities using common-sense reasoning and answer set programming solvers, specifically s(CASP). By employing these analysis techniques, we examine the unique semantic aspects of assurance cases, such as logical consistency, adequacy, indefeasibility, etc. The application of these analyses provides both system developers and evaluators with increased confidence about the assurance case.

Prophet inequalities are a cornerstone in optimal stopping and online decision-making. Traditionally, they involve the sequential observation of $n$ non-negative independent random variables and face irrevocable accept-or-reject choices. The goal is to provide policies that provide a good approximation ratio against the optimal offline solution that can access all the values upfront -- the so-called prophet value. In the prophet inequality over time problem (POT), the decision-maker can commit to an accepted value for $\tau$ units of time, during which no new values can be accepted. This creates a trade-off between the duration of commitment and the opportunity to capture potentially higher future values. In this work, we provide best possible worst-case approximation ratios in the IID setting of POT for single-threshold algorithms and the optimal dynamic programming policy. We show a single-threshold algorithm that achieves an approximation ratio of $(1+e^{-2})/2\approx 0.567$, and we prove that no single-threshold algorithm can surpass this guarantee. With our techniques, we can analyze simple algorithms using $k$ thresholds and show that with $k=3$ it is possible to get an approximation ratio larger than $\approx 0.602$. Then, for each $n$, we prove it is possible to compute the tight worst-case approximation ratio of the optimal dynamic programming policy for instances with $n$ values by solving a convex optimization program. A limit analysis of the first-order optimality conditions yields a nonlinear differential equation showing that the optimal dynamic programming policy's asymptotic worst-case approximation ratio is $\approx 0.618$. Finally, we extend the discussion to adversarial settings and show an optimal worst-case approximation ratio of $\approx 0.162$ when the values are streamed in random order.

The intermittent nature of photovoltaic (PV) solar energy, driven by variable weather, leads to power losses of 10-70% and an average energy production decrease of 25%. Accurate loss characterization and fault detection are crucial for reliable PV system performance and efficiency, integrating this data into control signal monitoring systems. Computational modeling of PV systems supports technological, economic, and performance analyses, but current models are often rigid, limiting advanced performance optimization and innovation. Conventional fault detection strategies are costly and often yield unreliable results due to complex data signal profiles. Artificial intelligence (AI), especially machine learning algorithms, offers improved fault detection by analyzing relationships between input parameters (e.g., meteorological and electrical) and output metrics (e.g., production). Once trained, these models can effectively identify faults by detecting deviations from expected performance. This research presents a computational model using the PVlib library in Python, incorporating a dynamic loss quantification algorithm that processes meteorological, operational, and technical data. An artificial neural network (ANN) trained on synthetic datasets with a five-minute resolution simulates real-world PV system faults. A dynamic threshold definition for fault detection is based on historical data from a PV system at Universidad de los Andes. Key contributions include: (i) a PV system model with a mean absolute error of 6.0% in daily energy estimation; (ii) dynamic loss quantification without specialized equipment; (iii) an AI-based algorithm for technical parameter estimation, avoiding special monitoring devices; and (iv) a fault detection model achieving 82.2% mean accuracy and 92.6% maximum accuracy., Comment: 37 pages, 26 figures, 9 tables, 1 algorithm

This paper examines the challenges and advancements in recognizing seals within their natural habitats using conventional photography, underscored by the emergence of machine learning technologies. We used the leopard seal, \emph{Hydrurga leptonyx}, a key species within Antarctic ecosystems, to review the different available methods found. As apex predators, Leopard seals are characterized by their significant ecological role and elusive nature so studying them is crucial to understand the health of their ecosystem. Traditional methods of monitoring seal species are often constrained by the labor-intensive and time-consuming processes required for collecting data, compounded by the limited insights these methods provide. The advent of machine learning, particularly through the application of vision transformers, heralds a new era of efficiency and precision in species monitoring. By leveraging state-of-the-art approaches in detection, segmentation, and recognition within digital imaging, this paper presents a synthesis of the current landscape, highlighting both the cutting-edge methodologies and the predominant challenges faced in accurately identifying seals through photographic data., Comment: 28th International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV'24), Las Vegas, USA

Results are presented for the measurement of large-scale anisotropies in the arrival directions of ultra-high-energy cosmic rays detected at the Pierre Auger Observatory during 19 years of operation, prior to AugerPrime, the upgrade of the Observatory. The 3D dipole amplitude and direction are reconstructed above $4\,$EeV in four energy bins. Besides the established dipolar anisotropy in right ascension above $8\,$EeV, the Fourier amplitude of the $8$ to $16\,$EeV energy bin is now also above the $5\sigma$ discovery level. No time variation of the dipole moment above $8\,$EeV is found, setting an upper limit to the rate of change of such variations of $0.3\%$ per year at the $95\%$ confidence level. Additionally, the results for the angular power spectrum are shown, demonstrating no other statistically significant multipoles. The results for the equatorial dipole component down to $0.03\,$EeV are presented, using for the first time a data set obtained with a trigger that has been optimized for lower energies. Finally, model predictions are discussed and compared with observations, based on two source emission scenarios obtained in the combined fit of spectrum and composition above $0.6\,$EeV., Comment: Minor modifications, accepted for publication in Astrophysical Journal

Deep Reinforcement Learning (DRL) presents a promising avenue for optimizing Energy Storage Systems (ESSs) dispatch in distribution networks. This paper introduces RL-ADN, an innovative open-source library specifically designed for solving the optimal ESSs dispatch in active distribution networks. RL-ADN offers unparalleled flexibility in modeling distribution networks, and ESSs, accommodating a wide range of research goals. A standout feature of RL-ADN is its data augmentation module, based on Gaussian Mixture Model and Copula (GMC) functions, which elevates the performance ceiling of DRL agents. Additionally, RL-ADN incorporates the Laurent power flow solver, significantly reducing the computational burden of power flow calculations during training without sacrificing accuracy. The effectiveness of RL-ADN is demonstrated using in different sizes of distribution networks, showing marked performance improvements in the adaptability of DRL algorithms for ESS dispatch tasks. This enhancement is particularly beneficial from the increased diversity of training scenarios. Furthermore, RL-ADN achieves a tenfold increase in computational efficiency during training, making it highly suitable for large-scale network applications. The library sets a new benchmark in DRL-based ESSs dispatch in distribution networks and it is poised to advance DRL applications in distribution network operations significantly. RL-ADN is available at: https://github.com/ShengrenHou/RL-ADN and https://github.com/distributionnetworksTUDelft/RL-ADN.

The design and optimization of laser-Compton x-ray systems based on compact distributed charge accelerator structures can enable micron-scale imaging of disease and the concomitant production of beams of Very High Energy Electrons (VHEEs) capable of producing FLASH-relevant dose rates. The physics of laser-Compton x-ray scattering ensures that the scattered x-rays follow exactly the trajectory of the incident electrons, thus providing a route to image-guided, VHEE FLASH radiotherapy. The keys to a compact architecture capable of producing both laser-Compton x-rays and VHEEs are the use of X-band RF accelerator structures which have been demonstrated to operate with over 100 MeV/m acceleration gradients. The operation of these structures in a distributed charge mode in which each radiofrequency (RF) cycle of the drive RF pulse is filled with a low-charge, high-brightness electron bunch is enabled by the illumination of a high-brightness photogun with a train of UV laser pulses synchronized to the frequency of the underlying accelerator system. The UV pulse trains are created by a patented pulse synthesis approach which utilizes the RF clock of the accelerator to phase and amplitude modulate a narrow band continuous wave (CW) seed laser. In this way it is possible to produce up to 10 {\mu}A of average beam current from the accelerator. Such high current from a compact accelerator enables production of sufficient x-rays via laser-Compton scattering for clinical imaging and does so from a machine of "clinical" footprint. At the same time, the production of 1000 or greater individual micro-bunches per RF pulse enables > 10 nC of charge to be produced in a macrobunch of < 100 ns. The design, construction, and test of the 100-MeV class prototype system in Irvine, CA is also presented., Comment: 28 pages, 13 figures, 2 tables

It is proved that, if $(P_n)$ is a sequence of polynomials with complex coefficients having unbounded valences and tending to infinity at sufficiently many points, then there is an infinite dimensional closed subspace of entire functions, as well a dense $\mathfrak{c}$-dimensional subspace of entire functions, all of whose nonzero members are hypercyclic for the corresponding sequence $(P_n(D))$ of differential operators. In both cases, the subspace can be chosen so as to contain any prescribed hypercylic function.

This paper introduces an agent-based simulation model aimed at understanding urban commuters mode choices and evaluating the impacts of transport policies to promote sustainable mobility. Crafted for developing countries, where utilitarian travel heavily relies on motorcycles, the model integrates sociocultural factors that influence transport behavior. Multinomial models and inferential statistics applied to survey data from Cali, Colombia, inform the model, revealing significant influences of sociodemographic factors and travel attributes on mode choice. Findings highlight the importance of cost, time, safety, comfort, and personal security, with disparities across socioeconomic groups. Policy simulations demonstrate positive responses to interventions like free public transportation, increased bus frequency, and enhanced security, yet with modest shifts in mode choice. Multifaceted policy approaches are deemed more effective, addressing diverse user preferences. Outputs can be extended to cities with similar sociocultural characteristics and transport dynamics. The methodology applied in this work can be replicated for other territories., Comment: 12 pages, 7 figures, to be published in Proceedings of the 2024 Winter Simulation Conference

We present an exhaustive analytical exploration of a variation of the eigenvector ensemble initially proposed by Deutsch for the foundations of the Eigenstate Thermalization Hypothesis (ETH) that drastically (but not fully) increases system-dependent information, and therefore serves as a model for the study of complex quantum systems beyond the universal Random Matrix Theory (RMT) regime. We especially focus on the remnants of system-specific information that modify the late-time behavior of correlation functions like the spectral form factor, or the inclusion of explicit system-dependent Hamiltonian corrections not captured by the RMT regime. The consistency of the predictions in the new ensemble with respect to the universal (Haar) one is made explicit by showing how that for arbitrary systems it defines an unitary 1-design and for strongly chaotic systems it becomes an approximated 2-design. While we are able to provide universal expressions for two- and four-point ensemble-averaged correlation functions and show how system-dependent information is spectrally decoupled, we also show that for small energy windows correlation functions defined by the new ensemble reduce to the predictions of the ETH., Comment: 50 pages + appendices

With the establishment of machine learning (ML) techniques in the scientific community, the construction of ML potential energy surfaces (ML-PES) has become a standard process in physics and chemistry. So far, improvements in the construction of ML-PES models have been conducted independently, creating an initial hurdle for new users to overcome and complicating the reproducibility of results. Aiming to reduce the bar for the extensive use of ML-PES, we introduce ${\it Asparagus}$, a software package encompassing the different parts into one coherent implementation that allows an autonomous, user-guided construction of ML-PES models. ${\it Asparagus}$ combines capabilities of initial data sampling with interfaces to ${\it ab initio}$ calculation programs, ML model training, as well as model evaluation and its application within other codes such as ASE or CHARMM. The functionalities of the code are illustrated in different examples, including the dynamics of small molecules, the representation of reactive potentials in organometallic compounds, and atom diffusion on periodic surface structures. The modular framework of ${\it Asparagus}$ is designed to allow simple implementations of further ML-related methods and models to provide constant user-friendly access to state-of-the-art ML techniques.

We calculate exclusive vector meson photoproduction within the Color Glass Condensate framework in high-energy photon-nucleus scattering probed experimentally in ultra peripheral heavy ion collisions at RHIC and at the LHC. When the free parameters are constrained by the $\gamma+p$ data from HERA, we predict significant nuclear suppression for both the coherent and incoherent photoproduction cross section in the TeV range. Our results indicate that the LHC data prefers even stronger saturation effects at the highest collision energies. Furthermore, we demonstrate how the linear polarization of photons in ulra peripheral collisions generates azimuthal modulations in the decay products of the exclusively produced vector meson. We show how these measurements can probe details of the nuclear geometry, specifically the deformed structure of the uranium nuclei., Comment: 5 pages, 6 figures. Talk by H.M at DIS2024

HESS J1809-193 is an unidentified TeV source, first detected by the High Energy Stereoscopic System (H.E.S.S.) Collaboration. The emission originates in a source-rich region that includes several Supernova Remnants (SNR) and Pulsars (PSR) including SNR G11.1+0.1, SNR G11.0-0.0, and the young radio pulsar J1809-1917. Originally classified as a pulsar wind nebula (PWN) candidate, recent studies show the peak of the TeV region overlapping with a system of molecular clouds. This resulted in the revision of the original leptonic scenario to look for alternate hadronic scenarios. Marked as a potential PeVatron candidate, this region has been studied extensively by H.E.S.S. due to its emission extending up-to several tens of TeV. In this work, we use 2398 days of data from the High Altitude Water Cherenkov (HAWC) observatory to carry out a systematic source search for the HESS J1809-193 region. We were able to resolve emission detected as an extended component (modelled as a Symmetric Gaussian with a 1 $\sigma$ radius of 0.21 $^\circ$) with no clear cutoff at high energies and emitting photons up-to 210 TeV. We model the multi-wavelength observations for the region HESS J1809-193 using a time-dependent leptonic model and a lepto-hadronic model. Our model indicates that both scenarios could explain the observed data within the region of HESS J1809-193.

Machine learning models are increasingly used in areas such as loan approvals and hiring, yet they often function as black boxes, obscuring their decision-making processes. Transparency is crucial, and individuals need explanations to understand decisions, especially for the ones not desired by the user. Ethical and legal considerations require informing individuals of changes in input attribute values (features) that could lead to a desired outcome for the user. Our work aims to generate counterfactual explanations by considering causal dependencies between features. We present the CoGS (Counterfactual Generation with s(CASP)) framework that utilizes the goal-directed Answer Set Programming system s(CASP) to generate counterfactuals from rule-based machine learning models, specifically the FOLD-SE algorithm. CoGS computes realistic and causally consistent changes to attribute values taking causal dependencies between them into account. It finds a path from an undesired outcome to a desired one using counterfactuals. We present details of the CoGS framework along with its evaluation.

Ultra-high-energy cosmic rays are known to be mainly of extragalactic origin, and their propagation is limited by energy losses, so their arrival directions are expected to correlate with the large-scale structure of the local Universe. In this work, we investigate the possible presence of intermediate-scale excesses in the flux of the most energetic cosmic rays from the direction of the supergalactic plane region using events with energies above 20 EeV recorded with the surface detector array of the Pierre Auger Observatory up to 31 December 2022, with a total exposure of 135,000 km^2 sr yr. The strongest indication for an excess that we find, with a post-trial significance of 3.1{\sigma}, is in the Centaurus region, as in our previous reports, and it extends down to lower energies than previously studied. We do not find any strong hints of excesses from any other region of the supergalactic plane at the same angular scale. In particular, our results do not confirm the reports by the Telescope Array collaboration of excesses from two regions in the Northern Hemisphere at the edge of the field of view of the Pierre Auger Observatory. With a comparable exposure, our results in those regions are in good agreement with the expectations from an isotropic distribution., Comment: submitted to ApJ

Despite numerous efforts from numerical approaches to complement experimental measurements, several fundamental challenges have still hindered one's ability to truly provide an atomistic picture of the nucleation process in nanocrystals. Among them, our study resolves three obstacles: (1) Machine-learning force fields including long-range interactions able to capture the finesse of the underlying atomic interactions, (2) Data-driven characterization of the local ordering in a complex structural landscape associated with several crystal polymorphs and (3) Comparing results from a large range of temperatures using both brute-force and rare-event sampling. Altogether, our simulation strategy has allowed us to study zinc oxide crystallization from nano-droplet melt. Remarkably, our results show that different nucleation pathways compete depending on the investigated degree of supercooling.

We demonstrate a compact ion beam device capable of accelerating H$^+$ and D$^+$ ions up to 75keV energy, on to a solid target, with sufficient beam current to study fusion reactions. The ion beam system uses a microwave driven plasma source to generate ions that are accelerated to high energy with a direct current (DC) acceleration structure. The plasma source is driven by pulsed microwaves from a solid-state radiofrequency (RF) amplifier, which is impedance matched to the plasma source chamber at the ISM band frequency (2.4-2.5GHz). The plasma chamber is held at high positive DC potential and is isolated from the impedance matching structure (at ground potential) by a dielectric-filled gap. To facilitate the use of high-energy-particle detectors near the target, the plasma chamber is biased to a high positive voltage, while the target remains grounded. A target loaded with deuterium is used to study D-D fusion and a B$_4$C or LaB$_6$ target is used to study p-$^{11}$B fusion. Detectors include solid-state charged particle detector and a scintillation fast neutron detector. The complete ion beam system can fit on a laboratory table and is a useful tool for teaching undergraduate and graduate students about the physics of fusion., Comment: 21 pages, 13 figures, accepted manuscript in Physics Open (ISSN: 2666-0326)

Transition Metal Dichalcogenides (TMD) are layered materials obtained by stacking two-dimensional sheets weakly bonded by van der Waals interactions. In bulk TMD, band dispersions are observed in the direction normal to the sheet plane (z-direction) due to the hybridization of out-of-plane orbitals but no kz-dispersion is expected at the single-layer limit. Using angle-resolved photoemission spectroscopy, we precisely address the two-dimensional to three-dimensional crossover of the electronic band structure of epitaxial WSe$_2$ thin films. Increasing number of discrete electronic states appears in given kz-ranges while increasing the number of layers. The continuous bulk dispersion is nearly retrieved for 6-sheet films. These results are reproduced by calculations going from a relatively simple tight-binding model to a sophisticated KKR-Green's function calculation. This two-dimensional system is hence used as a benchmark to compare different theoretical approaches.

Active Galactic Nuclei (AGN) are prime candidate sources of the high-energy, astrophysical neutrinos detected by IceCube. This is demonstrated by the real-time multi-messenger detection of the blazar TXS 0506+056 and the recent evidence of neutrino emission from NGC 1068 from a separate time-averaged study. However, the production mechanism of the astrophysical neutrinos in AGN is not well established which can be resolved via correlation studies with photon observations. For neutrinos produced due to photohadronic interactions in AGN, in addition to a correlation of neutrinos with high-energy photons, there would also be a correlation of neutrinos with photons emitted at radio wavelengths. In this work, we perform an in-depth stacking study of the correlation between 15 GHz radio observations of AGN reported in the MOJAVE XV catalog, and ten years of neutrino data from IceCube. We also use a time-dependent approach which improves the statistical power of the stacking analysis. No significant correlation was found for both analyses and upper limits are reported. When compared to the IceCube diffuse flux, at 100 TeV and for a spectral index of 2.5, the upper limits derived are $\sim3\%$ and $\sim9\%$ for the time-averaged and time-dependent case, respectively., Comment: 14 Pages 7 Figures

We present a search for an eV-scale sterile neutrino using 7.5 years of data from the IceCube DeepCore detector. The analysis uses a sample of 21,914 events with energies between 5 and 150 GeV to search for sterile neutrinos through atmospheric muon neutrino disappearance. Improvements in event selection and treatment of systematic uncertainties provide greater statistical power compared to previous DeepCore sterile neutrino searches. Our results are compatible with the absence of mixing between active and sterile neutrino states, and we place constraints on the mixing matrix elements $|U_{\mu 4}|^2 < 0.0534$ and $|U_{\tau 4}|^2 < 0.0574$ at 90% CL under the assumption that $\Delta m^2_{41}\geq 1\;\mathrm{eV^2}$. These null results add to the growing tension between anomalous appearance results and constraints from disappearance searches in the 3+1 sterile neutrino landscape., Comment: 11 pages, 5 figures. Version accepted by Physical Review D for publication