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Using a combination of HST, JWST, and ALMA data, we perform spatially resolved spectral energy distributions (SED) fitting of fourteen 4

We investigate the cosmological dynamics of a homogeneous scalar field non-minimally coupled to torsion gravity, which also interacts with cold dark matter through energy and momentum transfer. The matter and radiation perfect fluids are modeled using the Sorkin-Schutz formalism. We identify scaling regimes of the field during both the radiation and matter eras. Additionally, we discovered a field-dominated scaling attractor; however, it does not exhibit accelerated expansion, making it unsuitable for describing dark energy. Nevertheless, we find two attractor solutions that do exhibit accelerated expansion: one is a quintessence-like fixed point, and the other is a de Sitter fixed point., Comment: 16 pages, 8 figures

We investigate the spatial extent of the [CII] line emission in a sample of 34 galaxies at $z=4-6$ from the ALMA-CRISTAL Survey. By modeling the [CII] line emission in the visibility data directly, we derive the effective radius of [CII] line emission assuming exponential distribution. These measurements comprise not only isolated galaxies but also interacting systems, identified thanks to the high spatial resolution of the data. The [CII] line radius ranges from 0.5 to 3.5 kpc with an average value of 1.9 kpc. We compare the [CII] sizes with the sizes of UV and FIR continua, which were measured from the HST F160W and ALMA Band-7 continuum images, respectively. We confirm that the [CII] line emission is more spatially extended than the continuum emission, with average size ratios of $R_{e,[CII]}/R_{e,UV}=2.90$ and $R_{e,[CII]}/R_{e,FIR}=1.54$, although about half of the FIR-detected sample show comparable spatial extent between [CII] line and FIR continuum emission ($R_{e,[CII]}\approx R_{e, FIR}$). The residual visibility data of the best-fit model do not show evidence of flux excesses either individually or in stacking analysis. This indicates that the [CII] line emission in star-forming galaxies can be characterized by an extended exponential disk profile. Overall, our results suggest that the spatial extent of [CII] line emission can primarily be explained by photodissociation regions associated with star formation activity, while the contribution from diffuse neutral medium (atomic gas) and the effects of mergers may further expand the [CII] line distributions, causing their variations among our sample. We report the correlations between the [CII] line, dust, and Lya line properties, which may be in line with our scenario. Future 3D-analysis of Lya and Ha lines will shed light on the association of the extended [CII] line emission with atomic gas and outflows., Comment: Submitted to A&A, 21 pages, 14 figures, 4 tables

Extreme Mass Ratio Inspirals (EMRIs) are key observational targets for the Laser Interferometer Space Antenna (LISA) mission. Unresolvable EMRI signals contribute to forming a gravitational wave background (GWB). Characterizing the statistical features of the GWB from EMRIs is of great importance, as EMRIs will ubiquitously affect large segments of the inference scheme. In this work, we apply a frequentist test for GWB Gaussianity and stationarity, exploring three astrophysically-motivated EMRI populations. We construct the resulting signal by combining state-of-the-art EMRI waveforms and a detailed description of the LISA response with time-delay interferometric variables. Depending on the brightness of the GWB, our analysis demonstrates that the resultant EMRI foregrounds show varying degrees of departure from the usual statistical assumptions that the GWBs are both Gaussian and Stationary. If the GWB is non-stationary with non-Gaussian features, this will challenge the robustness of Gaussian-likelihood model, when applied to global inference results, e.g. foreground estimation, background detection, and individual-source parameters reconstruction., Comment: 12 pages, 5 figures, 3 tables

The metric dimension of a graph measures how uniquely vertices may be identified using a set of landmark vertices. This concept is frequently used in the study of network architecture, location-based problems and communication. Given a graph $G$, the metric dimension, denoted as $\dim(G)$, is the minimum size of a resolving set, a subset of vertices such that for every pair of vertices in $G$, there exists a vertex in the resolving set whose shortest path distance to the two vertices is different. This subset of vertices helps to uniquely determine the location of other vertices in the graph. A basis is a resolving set with a least cardinality. Finding a basis is a problem with practical applications in network design, where it is important to efficiently locate and identify nodes based on a limited set of reference points. The Cartesian product of $P_m$ and $P_n$ is the grid network in network science. In this paper, we investigate two novel types of grids in network science: the Villarceau grid Type I and Type II. For each of these grid types, we find the precise metric dimension.

In this manuscript, we deal with some particular type of homogeneous first order linear systems with variable coefficients, in which we provide qualitative properties of the solution. When the coefficients of the indeterminate functions are periodic with the same period, $T$, we obtain a simple method so as to obtain the Floquet coefficients. We give a new interpretation of the averages on the interval $(0,T)$ of the matrix of the coefficients. We discuss some examples and made some comparison with previous results., Comment: 10 pages

Few primitives are as intertwined with the foundations of cryptography as Oblivious Transfer (OT). Not surprisingly, with the advent of the use of quantum resources in information processing, OT played a central role in establishing new possibilities (and defining impossibilities) pertaining to the use of these novel assets. A major research path is minimizing the required assumptions to achieve OT, and studying their consequences. Regarding its computation, it is impossible to construct unconditionally-secure OT without extra assumptions; and, regarding communication complexity, achieving 1-shot (and even non-interactive) OT has proved to be an elusive task, widely known to be impossible classically. Moreover, this has strong consequencesfor realizing round-optimal secure computation, in particular 1-shot 2-Party Computation (2PC). In this work, three main contributions are evidenced by leveraging quantum resources: 1. Unconditionally-secure 2-message non-interactive OT protocol constructed in the Noisy-Quantum-Storage Model. 2. 1-shot OT in the Noisy-Quantum-Storage Model -- proving that this construction is possible assuming the existence of one-way functions and sequential functions. 3. 1-shot 2PC protocol compiled from a semi-honest 1-shot OT to semi-honest 1-shot Yao's Garbled Circuits protocol., Comment: 32 pages, 2 figures

The Calar Alto Void Integral-field Treasury surveY (CAVITY) is a legacy project aimed at characterising the population of galaxies inhabiting voids, which are the most under-dense regions of the cosmic web, located in the Local Universe. This paper describes the first public data release (DR1) of CAVITY, comprising science-grade optical data cubes for the initial 100 out of a total of $\sim$300 galaxies in the Local Universe ($0.005 < z < 0.050$). These data were acquired using the integral-field spectrograph PMAS/PPak mounted on the 3.5m telescope at the Calar Alto observatory. The DR1 galaxy sample encompasses diverse characteristics in the color-magnitude space, morphological type, stellar mass, and gas ionisation conditions, providing a rich resource for addressing key questions in galaxy evolution through spatially resolved spectroscopy. The galaxies in this study were observed with the low-resolution V500 set-up, spanning the wavelength range 3745-7500 \AA, with a spectral resolution of 6.0 \AA (FWHM). Here, we describe the data reduction and characteristics and data structure of the CAVITY datasets essential for their scientific utilisation, highlighting such concerns as vignetting effects, as well as the identification of bad pixels and management of spatially correlated noise. We also provide instructions for accessing the CAVITY datasets and associated ancillary data through the project's dedicated database., Comment: 17 pages, 9 figures, 5 tables. Accepted for publication in A&A

Large language models have demonstrated impressive performance when integrated with vision models even enabling video understanding. However, evaluating these video models presents its own unique challenges, for which several benchmarks have been proposed. In this paper, we show that the currently most used video-language benchmarks can be solved without requiring much temporal reasoning. We identified three main issues in existing datasets: (i) static information from single frames is often sufficient to solve the tasks (ii) the text of the questions and candidate answers is overly informative, allowing models to answer correctly without relying on any visual input (iii) world knowledge alone can answer many of the questions, making the benchmarks a test of knowledge replication rather than visual reasoning. In addition, we found that open-ended question-answering benchmarks for video understanding suffer from similar issues while the automatic evaluation process with LLMs is unreliable, making it an unsuitable alternative. As a solution, we propose TVBench, a novel open-source video multiple-choice question-answering benchmark, and demonstrate through extensive evaluations that it requires a high level of temporal understanding. Surprisingly, we find that most recent state-of-the-art video-language models perform similarly to random performance on TVBench, with only Gemini-Pro and Tarsier clearly surpassing this baseline.

In this paper we introduce two novel models derived from the discrete optimal transport problem. The first model extends the traditional transport problem by adding a quadratic congestion factor directly into the cost function, while the second model replaces conventional constraints with weighted penalization terms. We present theoretical contributions, including the study and characterization of interior and corner solution for some specific cases, convergence to the optimal solutions, as well as smooth comparative statics analysis. Finally, we propose an $O((N+L)(NL)^2)$ algorithm for computing the optimal plan for the penalized model assuming interior solutions.

Representation learning is a pivotal area in the field of machine learning, focusing on the development of methods to automatically discover the representations or features needed for a given task from raw data. Unlike traditional feature engineering, which requires manual crafting of features, representation learning aims to learn features that are more useful and relevant for tasks such as classification, prediction, and clustering. We introduce Principal Orthogonal Latent Components Analysis Network (POLCA Net), an approach to mimic and extend PCA and LDA capabilities to non-linear domains. POLCA Net combines an autoencoder framework with a set of specialized loss functions to achieve effective dimensionality reduction, orthogonality, variance-based feature sorting, high-fidelity reconstructions, and additionally, when used with classification labels, a latent representation well suited for linear classifiers and low dimensional visualization of class distribution as well.

We investigate path-wise observables in experiments on driven colloids in a periodic optical field to dissect selected intricate transport features, kinetics, and transition-path time statistics out of thermodynamic equilibrium. These observables directly reflect the properties of individual paths in contrast to the properties of an ensemble of particles, such as radial distribution functions or mean-squared displacements. In particular, we present two distinct albeit equivalent formulations of the underlying stochastic equation of motion, highlight their respective practical relevance, and show how to interchange between them. We discuss conceptually different notions of local velocities and interrogate one- and two-sided first-passage and transition-path-time statistics in and out of equilibrium. We emphasize how path-wise observables may be employed to systematically assess the quality of experimental data and demonstrate that, given sufficient control and sampling, one may quantitatively verify subtle theoretical predictions.

Autonomous systems, like vehicles or robots, require reliable, accurate, fast, resource-efficient, scalable, and low-latency trajectory predictions to get initial knowledge about future locations and movements of surrounding objects for safe human-machine interaction. Furthermore, they need to know the uncertainty of the predictions for risk assessment to provide safe path planning. This paper presents a lightweight method to address these requirements, combining Long Short-Term Memory and Mixture Density Networks. Our method predicts probability distributions, including confidence level estimations for positional uncertainty to support subsequent risk management applications and runs on a low-power embedded platform. We discuss essential requirements for human trajectory prediction in autonomous vehicle applications and demonstrate our method's performance using multiple traffic-related datasets. Furthermore, we explain reliability and sharpness metrics and show how important they are to guarantee the correctness and robustness of a model's predictions and uncertainty assessments. These essential evaluations have so far received little attention for no good reason. Our approach focuses entirely on real-world applicability. Verifying prediction uncertainties and a model's reliability are central to autonomous real-world applications. Our framework and code are available at: https://github.com/kav-institute/mdn_trajectory_forecasting.

Deep networks are frequently tuned to novel tasks and continue learning from ongoing data streams. Such sequential training requires consolidation of new and past information, a challenge predominantly addressed by retaining the most important data points - formally known as coresets. Traditionally, these coresets consist of entire samples, such as images or sentences. However, recent transformer architectures operate on tokens, leading to the famous assertion that an image is worth 16x16 words. Intuitively, not all of these tokens are equally informative or memorable. Going beyond coresets, we thus propose to construct a deeper-level data summary on the level of tokens. Our respectively named core tokensets both select the most informative data points and leverage feature attribution to store only their most relevant features. We demonstrate that core tokensets yield significant performance retention in incremental image classification, open-ended visual question answering, and continual image captioning with significantly reduced memory. In fact, we empirically find that a core tokenset of 1\% of the data performs comparably to at least a twice as large and up to 10 times larger coreset.

We introduce a novel method for extracting a fragmentation model directly from experimental data without requiring an explicit parametric form, called Histories and Observables for Monte-Carlo Event Reweighting (\homer), consisting of three steps: the training of a classifier between simulation and data, the inference of single fragmentation weights, and the calculation of the weight for the full hadronization chain. We illustrate the use of \homer on a simplified hadronization problem, a $q\bar{q}$ string fragmenting into pions, and extract a modified Lund string fragmentation function $f(z)$. We then demonstrate the use of \homer on three types of experimental data: (i) binned distributions of high level observables, (ii) unbinned event-by-event distributions of these observables, and (iii) full particle cloud information. After demonstrating that $f(z)$ can be extracted from data (the inverse of hadronization), we also show that, at least in this limited setup, the fidelity of the extracted $f(z)$ suffers only limited loss when moving from (i) to (ii) to (iii). Public code is available at https://gitlab.com/uchep/mlhad., Comment: 41 pages, 21 figures. Public code available

Future space observatories dedicated to direct imaging and spectroscopy of extra-solar planets will require ultra-low-noise detectors that are sensitive over a broad range of wavelengths. Silicon charge-coupled devices (CCDs), such as EMCCDs, Skipper CCDs, and Multi-Amplifier Sensing CCDs, have demonstrated the ability to detect and measure single photons from ultra-violet to near-infrared wavelengths, making them candidate technologies for this application. In this context, we study a relatively unexplored source of low-energy background coming from Cherenkov radiation produced by energetic charged particles traversing a silicon detector. In the intense radiation environment of space, energetic cosmic rays produce high-energy tracks and more extended halos of low-energy Cherenkov photons, which are detectable with ultra-low-noise detectors. We present a model of this effect that is calibrated to laboratory data, and we use this model to characterize the residual background rate for ultra-low noise silicon detectors in space. We find that the rate of cosmic-ray-induced Cherenkov photon production is comparable to other detector and astrophysical backgrounds that have previously been considered., Comment: SPIE Proceeding; 9 pages, 6 figures

There have been several attempts in recent years to extend the notions of symplectic and Poisson structures in order to create a suitable geometrical framework for classical field theories, trying to achieve a success similar to the use of these concepts in Hamiltonian mechanics. These notions always have a graded character, since the multisymplectic forms are of a higher degree than two. Another line of work has been to extend the concept of Dirac structures to these new scenarios. In the present paper we review all these notions, relate them and propose and study a generalization that (under some mild regularity conditions) includes them and is of graded nature. We expect this generalization to allow us to advance in the study of classical field theories, their integrability, reduction, numerical approximations and even their quantization.

Despite the advent of touchscreens, typing on physical keyboards remains most efficient for entering text, because users can leverage all fingers across a full-size keyboard for convenient typing. As users increasingly type on the go, text input on mobile and wearable devices has had to compromise on full-size typing. In this paper, we present TapType, a mobile text entry system for full-size typing on passive surfaces--without an actual keyboard. From the inertial sensors inside a band on either wrist, TapType decodes and relates surface taps to a traditional QWERTY keyboard layout. The key novelty of our method is to predict the most likely character sequences by fusing the finger probabilities from our Bayesian neural network classifier with the characters' prior probabilities from an n-gram language model. In our online evaluation, participants on average typed 19 words per minute with a character error rate of 0.6% after 30 minutes of training. Expert typists thereby consistently achieved more than 25 WPM at a similar error rate. We demonstrate applications of TapType in mobile use around smartphones and tablets, as a complement to interaction in situated Mixed Reality outside visual control, and as an eyes-free mobile text input method using an audio feedback-only interface., Comment: In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

The use of Autonomous Surface Vehicles, equipped with water quality sensors and artificial vision systems, allows for a smart and adaptive deployment in water resources environmental monitoring. This paper presents a real implementation of a vehicle prototype that to address the use of Artificial Intelligence algorithms and enhanced sensing techniques for water quality monitoring. The vehicle is fully equipped with high-quality sensors to measure water quality parameters and water depth. Furthermore, by means of a stereo-camera, it also can detect and locate macro-plastics in real environments by means of deep visual models, such as YOLOv5. In this paper, experimental results, carried out in Lago Mayor (Sevilla), has been presented as proof of the capabilities of the proposed architecture. The overall system, and the early results obtained, are expected to provide a solid example of a real platform useful for the water resource monitoring task, and to serve as a real case scenario for deploying Artificial Intelligence algorithms, such as path planning, artificial vision, etc.

In science, we are often interested in obtaining a generative model of the underlying system dynamics from observed time series. While powerful methods for dynamical systems reconstruction (DSR) exist when data come from a single domain, how to best integrate data from multiple dynamical regimes and leverage it for generalization is still an open question. This becomes particularly important when individual time series are short, and group-level information may help to fill in for gaps in single-domain data. At the same time, averaging is not an option in DSR, as it will wipe out crucial dynamical properties (e.g., limit cycles in one domain vs. chaos in another). Hence, a framework is needed that enables to efficiently harvest group-level (multi-domain) information while retaining all single-domain dynamical characteristics. Here we provide such a hierarchical approach and showcase it on popular DSR benchmarks, as well as on neuroscientific and medical time series. In addition to faithful reconstruction of all individual dynamical regimes, our unsupervised methodology discovers common low-dimensional feature spaces in which datasets with similar dynamics cluster. The features spanning these spaces were further dynamically highly interpretable, surprisingly in often linear relation to control parameters that govern the dynamics of the underlying system. Finally, we illustrate transfer learning and generalization to new parameter regimes., Comment: Preprint

Quantum many-body systems provide a unique platform for exploring the rich interplay between chaos, randomness, and complexity. In a recently proposed paradigm known as deep thermalization, random quantum states of system A are generated by performing projective measurements on system B following chaotic Hamiltonian evolution acting jointly on AB. In this scheme, the randomness of the projected state ensemble arises from the intrinsic randomness of the outcomes when B is measured. Here we propose a modified scheme, in which classical randomness injected during the protocol is converted by quantum chaos into quantum randomness of the resulting state ensemble. We show that for generic chaotic systems this conversion is optimal in that each bit of injected classical entropy generates as much additional quantum randomness as adding an extra qubit to B. This significantly enhances the randomness of the projected ensemble without imposing additional demands on the quantum hardware. Our scheme can be easily implemented on typical analog quantum simulators, providing a more scalable route for generating quantum randomness valuable for many applications. In particular, we demonstrate that the accuracy of a shadow tomography protocol can be substantially improved., Comment: 6 pages, 3 figures

Given a commutative ring $R$, a $\pi_1$-$R$-equivalence is a continuous map of spaces inducing an isomorphism on fundamental groups and an $R$-homology equivalence between universal covers. When $R$ is an algebraically closed field, Raptis and Rivera described a full and faithful model for the homotopy theory of spaces up to $\pi_1$-$R$-equivalence by means of simplicial coalgebras considered up to a notion of weak equivalence created by a localized version of the Cobar functor. In this article, we prove a $G$-equivariant analog of this statement using a generalization of a celebrated theorem of Elmendorf. We also prove a more general result about modeling $G$-simplicial sets considered under a linearized version of quasi-categorical equivalence in terms of simplicial coalgebras., Comment: 15 pages

Graph generation is fundamental in diverse scientific applications, due to its ability to reveal the underlying distribution of complex data, and eventually generate new, realistic data points. Despite the success of diffusion models in this domain, those face limitations in sampling efficiency and flexibility, stemming from the tight coupling between the training and sampling stages. To address this, we propose DeFoG, a novel framework using discrete flow matching for graph generation. DeFoG employs a flow-based approach that features an efficient linear interpolation noising process and a flexible denoising process based on a continuous-time Markov chain formulation. We leverage an expressive graph transformer and ensure desirable node permutation properties to respect graph symmetry. Crucially, our framework enables a disentangled design of the training and sampling stages, enabling more effective and efficient optimization of model performance. We navigate this design space by introducing several algorithmic improvements that boost the model performance, consistently surpassing existing diffusion models. We also theoretically demonstrate that, for general discrete data, discrete flow models can faithfully replicate the ground truth distribution - a result that naturally extends to graph data and reinforces DeFoG's foundations. Extensive experiments show that DeFoG achieves state-of-the-art results on synthetic and molecular datasets, improving both training and sampling efficiency over diffusion models, and excels in conditional generation on a digital pathology dataset.

We show that Koszul duality between differential graded categories and pointed curved coalgebras interchanges smooth and proper (also known as left and right) Calabi-Yau structures. We discuss the following two applications. For a finite-dimensional Lie algebra a smooth Calabi-Yau structure on the universal enveloping algebra is equivalent to a proper Calabi-Yau structure on the Chevalley- Eilenberg chain coalgebra, which exists if and only if Poincare duality is satisfied. For any topological space X having the homotopy type of a finite complex we show an oriented Poincare duality structure (with local coefficients) on X is equivalent to a proper Calabi-Yau structure on the dg coalgebra of chains on X and to a smooth Calabi-Yau structure on the dg algebra of singular chains of the based loop space of X., Comment: 30 pages

We combine JWST/NIRCam and Subaru/MOIRCS dual Pa$\mathrm{\beta}$ + H$\mathrm{\alpha}$ narrow-band imaging to trace the dust attenuation and the star-formation activities of a sample of 43 H$\mathrm{\alpha}$ emitters at the core of one of the most massive and best-studied clusters in formation at the cosmic noon: the Spiderweb protocluster at $\mathrm{z=2.16}$. We find that most H$\mathrm{\alpha}$ emitters display Pa$\mathrm{\beta}$/H$\mathrm{\alpha}$ ratios compatible with Case B recombination conditions, which translates into nebular extinction values ranging at $\mathrm{A_V\approx0-3}$ magnitudes, and dust corrected $\mathrm{Pa\beta}$ star formation rates consistent with coeval main sequence field galaxies at fixed stellar mass ($\mathrm{9.4<\log M_*/M_\odot<11.0}$) during this cosmic epoch. Furthermore, we investigate possible environmental impacts on dust extinction across the protocluster large-scale structure and find no correlation between the dustiness of its members and environmental proxies such as phase-space position, clustercentric radius, or local density. These results support the scenario for which dust production within the main galaxy population of this protocluster is driven by secular star formation activities fueled by smooth gas accretion across its large-scale structure. This downplays the role of gravitational interactions in boosting star formation and dust production within the Spiderweb protocluster, in contrast with observations in higher redshift and less evolved protocluster cores., Comment: 21 pages, 8 figures. Accepted for publication in ApJ

Ultrafast laser systems, those with a pulse duration on the order of picoseconds or less, have enabled advancements in a wide variety of fields. Of particular interest to this work, these laser systems are the key component to many High Energy Density (HED) physics experiments. Despite this, previous studies on the shape of the laser pulse within the HED community have focused primarily on pulse duration due to the relationship between pulse duration and peak intensity, while leaving the femtosecond scale structure of the pulse shape largely unstudied. To broaden the variety of potential pulses available for study, a method of reliably adjusting the pulse shape at the femtosecond scale using sub-nanometer resolution Direct Phase Control has been developed. This paper examines the capabilities of this new method compared to more commonplace dispersion-based pulse shaping methods. It also will detail the capabilities of the core algorithm driving this technique when used in conjunction with the WIZZLER and DAZZLER instruments that are common in high intensity laser labs. Finally, some discussion is given to possible applications on how the Direct Phase Control pulse shaping technique will be implemented in the future., Comment: 7 pages, 5 figures, 3 of these figures contain subfigures such that 11 image (png) files are used

The emergence of anomalous Hall effects (AHE) in antiferromagnets presents an intriguing phenomenon with potential spintronic device applications due to their ultrafast switching dynamics. Mn3NiN antiperovskite stands out as a promising candidate owing to its noncollinear antiferromagnetism. In this study, we report a significant AHE observed in epitaxial Mn3NiN thin films below the N\'eel temperature, comparable to those observed in typical ferromagnets. Through a combination of magnetometry, neutron diffraction and ab-initio data analyses, we propose that the substantial AHE can be attributed to the non-vanishing Berry curvature arising from the {\Gamma}4g-type spin structure, coupled with uncompensated magnetic domains featuring a weak canted moment. This coupling, due to robust antiferromagnetic domains, enables deterministic detection and switching of AHE via the application of a magnetic field.

The Suborbital Imaging Spectrograph for Transition region Irradiance from Nearby Exoplanet host stars (SISTINE) is a rocket-borne ultraviolet (UV) imaging spectrograph designed to probe the radiation environment of nearby stars. SISTINE operates over a bandpass of 98 -- 127 and 130 -- 158 nm, capturing a broad suite of emission lines tracing the full 10$^4$ -- 10$^5$ K formation temperature range critical for reconstructing the full UV radiation field incident on planets orbiting solar-type stars. SISTINE serves as a platform for key technology developments for future ultraviolet observatories. SISTINE operates at moderate resolving power ($R\sim$1500) while providing spectral imaging over an angular extent of $\sim$6', with $\sim$2" resolution at the slit center. The instrument is composed of an f/14 Cassegrain telescope that feeds a 2.1x magnifying spectrograph, utilizing a blazed holographically ruled diffraction grating and a powered fold mirror. Spectra are captured on a large format microchannel plate (MCP) detector consisting of two 113 x 42 mm segments each read out by a cross delay-line anode. Several novel technologies are employed in SISTINE to advance their technical maturity in support of future NASA UV/optical astronomy missions. These include enhanced aluminum lithium fluoride coatings (eLiF), atomic layer deposition (ALD) protective optical coatings, and ALD processed large format MCPs. SISTINE was launched a total of three times with two of the three launches successfully observing targets Procyon A and $\alpha$ Centauri A and B.

This paper presents a model-free, learning-based adaptive controller for the density tracking problem in a two-level Lindblad-Gorini-Kossakowski-Sudarshan (LGKS) quantum system. The adaptive controller is based on the continuous-time retrospective cost adaptive control. To preserve the geometric properties of the quantum system, an adaptive PID controller driven and optimized by Ulhmann's fidelity is used. The proposed controller is validated in simulation for a low and a high-entropy density-tracking problem.

Although the magnetic fields in the quiet Sun account for the majority of the magnetic energy in the solar photosphere, inferring their exact spatial distribution, origin, and evolution poses an important challenge because the signals lie at the limit of todays instrumental precision. This severely hinders and biases our interpretations, which are mostly made through nonlinear model-fitting approaches. Our goal is to directly compare simulated and observed polarization signals in the FeI 630.1 nm and 630.2 nm spectral lines in the solar internetwork. This way, we aim to constrain the mechanism responsible for the generation of the quiet Sun magnetism while avoiding the biases that plague other diagnostic methods. We used three different three-dimensional radiative magneto-hydrodynamic simulations representing different scenarios of magnetic field generation in the internetwork: small-scale dynamo, decay of active regions, and horizontal flux emergence. We synthesized Stokes profiles at different viewing angles and degraded them according to the instrumental specifications of the spectro-polarimeter on the Hinode satellite. Finally, we statistically compared the simulated spectra to the observations at the appropriate viewing angles. The small-scale dynamo simulation reproduced best the statistical properties of the observed polarization signals. This is especially prominent for the disk center viewing geometry, where the agreement is excellent. Moving toward more inclined lines of sight, the agreement worsens slightly. The agreement between the small-scale dynamo simulation and observations at the disk center suggests that small-scale dynamo action plays an important role in the generation of quiet Sun magnetism. However, the magnetic field around 50 km above the photosphere in this simulation does not reproduce observations as well as at the very base of the photosphere.

Face recognition in the wild has gained a lot of focus in the last few years, and many face recognition models are designed to verify faces in medium-quality images. Especially due to the availability of large training datasets with similar conditions, deep face recognition models perform exceptionally well in such tasks. However, in other tasks where substantially less training data is available, such methods struggle, especially when required to compare high-quality enrollment images with low-quality probes. On the other hand, traditional RankList-based methods have been developed that compare faces indirectly by comparing to cohort faces with similar conditions. In this paper, we revisit these RankList methods and extend them to use the logits of the state-of-the-art DaliFace network, instead of an external cohort. We show that through a reasonable Logit-Cohort Selection (LoCoS) the performance of RankList-based functions can be improved drastically. Experiments on two challenging face recognition datasets not only demonstrate the enhanced performance of our proposed method but also set the stage for future advancements in handling diverse image qualities., Comment: Accepted for presentation at IJCB 2024

We present WOFRY (Wave Optics FRamework in pYthon), a specialized toolbox designed for wave optics modeling, with particular emphasis on partial coherence. This package is tailored to assist synchrotron scientists and engineers in the prototyping and modeling of X-ray sources and optics. WOFRY offers functionalities for generating and propagating 1D and 2D wavefronts, along with various tools for interacting with different optical elements. The software developed is available in the OASYS suite., Comment: 13 pages, 8 figures

Precision medicine offers the potential to tailor treatment decisions to individual patients, yet it faces significant challenges due to the complex biases in clinical observational data and the high-dimensional nature of biological data. This study models various types of treatment assignment biases using mutual information and investigates their impact on machine learning (ML) models for counterfactual prediction and biomarker identification. Unlike traditional counterfactual benchmarks that rely on fixed treatment policies, our work focuses on modeling different characteristics of the underlying observational treatment policy in distinct clinical settings. We validate our approach through experiments on toy datasets, semi-synthetic tumor cancer genome atlas (TCGA) data, and real-world biological outcomes from drug and CRISPR screens. By incorporating empirical biological mechanisms, we create a more realistic benchmark that reflects the complexities of real-world data. Our analysis reveals that different biases lead to varying model performances, with some biases, especially those unrelated to outcome mechanisms, having minimal effect on prediction accuracy. This highlights the crucial need to account for specific biases in clinical observational data in counterfactual ML model development, ultimately enhancing the personalization of treatment decisions in precision medicine., Comment: 9 pages, 5 figures, conference

Future far-infrared (IR) observatories require compact and cost efficient optical linear variable bandpass filters (LVBFs) to define their instrument spectral bands. We have designed novel far-IR LVBFs that consist of metal-mesh bandpass filters comprised of a gold film with cross-slots of varying sizes along a silicon (Si) substrate with anti-reflection (AR) coatings. We present our work on the simulated and measured transmission of non-AR coated and AR coated LVBFs for bandpass peaks from wavelengths of 24 to 36 $\mu$m with a resolving power ($R=\lambda_0/\Delta\lambda$) of R$\approx$6 for non-AR coated LVBFs and R$\approx$4 for AR coated LVBFs. We also present a method to decrease the effects of out-of-band high frequency transmission exhibited by metal-mesh filters by depositing a thin layer of hydrogenated amorphous silicon (a-Si:H) on the metal-mesh of the LVBF. We have fabricated and measured the LVBFs at room temperature and cryogenic temperatures (5 K). We measure a high peak transmission of $\sim$80-90 \% for the AR coated LVBF at 5 K and demonstrate that the a-Si:H LVBF is a promising method to address out-of-band high frequency transmission., Comment: 8 pages, 10 figures, accepted to Applied Optics

The infall of the LMC into the Milky Way's halo impacts the distribution of stars and dark matter in our Galaxy. Mapping the observational consequences of this encounter can inform us about the properties of both galaxies, details of their interaction, and possibly even distinguish between different dark matter models. N-body simulations predict large-scale density asymmetries in the Galactic halo both in baryonic and dark matter due to the passage of the LMC, with the overdensity directly trailing its current orbit through the Southern hemisphere known as the wake. Using the VIRCAM and DECam instruments, we collected wide-field deep near-infrared and optical photometry in four fields chosen to cover the region of the sky expected to span most of the predicted density contrast of the wake. We identify more than 400 stars comprising two different tracers, near main sequence turn-off stars and red giants, that map the distant halo between $\sim 60$ - $100$ kpc, and use them to derive stellar halo densities as a function of position in the sky and Galactocentric radius. We detect (1) a break in the radial density profile of halo stars at 70 kpc that has not been seen in halo studies done from the North, and (2) a clear halo overdensity that starts also at 70 kpc and exhibits a density contrast that increases steadily when moving across the sky into the predicted current location of the LMC wake. If identifying this overdensity with the LMC wake, the peak density contrast we measure is more pronounced than in all available models of the LMC infall, which would indicate the need for a more massive LMC and/or with a different orbit than presently favored. Alternatively, contamination from unidentified substructure may be biasing our detections, so wider-area surveys with similar depth would be needed for confirmation., Comment: 21 pages, 10 figures, submitted to ApJ, comments are welcome!

Distribution system operators (DSOs) must cope with new challenges such as the reconstruction of distribution grids along climate neutrality pathways or the ability to manage and control consumption and generation in the grid. In order to meet the challenges, measurements within the distribution grid often form the basis for DSOs. Hence, it is an urgent problem that measurement devices are not installed in many low-voltage (LV) grids. In order to overcome this problem, we present an approach to estimate pseudo-measurements for non-measured LV feeders based on the metadata of the respective feeder using regression models. The feeder metadata comprise information about the number of grid connection points, the installed power of consumers and producers, and billing data in the downstream LV grid. Additionally, we use weather data, calendar data and timestamp information as model features. The existing measurements are used as model target. We extensively evaluate the estimated pseudo-measurements on a large real-world dataset with 2,323 LV feeders characterized by both consumption and feed-in. For this purpose, we introduce peak metrics inspired by the BigDEAL challenge for the peak magnitude, timing and shape for both consumption and feed-in. As regression models, we use XGBoost, a multilayer perceptron (MLP) and a linear regression (LR). We observe that XGBoost and MLP outperform the LR. Furthermore, the results show that the approach adapts to different weather, calendar and timestamp conditions and produces realistic load curves based on the feeder metadata. In the future, the approach can be adapted to other grid levels like substation transformers and can supplement research fields like load modeling, state estimation and LV load forecasting., Comment: 17 pages, 9 figures, 8 tables

The pepenaactivity is part of the informal sector, characterized by precariousness and social invisibility; however, it is a key element in the recycling production chain, providing an economic income for many people. In the city of Chihuahua, this activity is carried out on a significant scale. Therefore, this study analyzes and dignifies the recycling of urban solid waste from the perspective of scavengers, also explaining their relationship with other social actors at the city's final disposal site. The study starts from the premise that waste picking is, in the current era of global environmental crisis, an effective way to conserve resources and reduce environmental impacts. Methodologically, participatory action research was used as a strategy to raise awareness among scavengers about the importance of their work and to present their community organization as an example to other urban waste picking groups in different locations. Finally, we found issues in these groups such as informality, lack of legal support,specific health risks, and, more broadly, the absence of public policies to recognize this activity as valuable in addressing the global environmental crisis, Comment: in Spanish language

Hub-filament systems are considered as natural sites for high-mass star formation. Kinematic analysis of the surroundings of hub-filaments is essential to better understand high-mass star formation within such systems. In this work, we present a detailed study of the massive Galactic protocluster IRAS 15394$-$5358, using continuum and molecular line data from the ALMA Three-millimeter Observations of Massive Star-forming Regions (ATOMS) survey. The 3~mm dust continuum map reveals the fragmentation of the massive ($\rm M=843~M_{\odot}$) clump into six cores. The core C-1A is the largest (radius = 0.04~pc), the most massive ($\rm M=157~M_{\odot}$), and lies within the dense central region, along with two smaller cores ($\rm M=7~and~3~M_{\odot}$). The fragmentation process is consistent with the thermal Jeans fragmentation mechanism and virial analysis shows that all the cores have small virial parameter values ($\rm \alpha_{vir}<<2$), suggesting that the cores are gravitationally bound. The mass vs. radius relation indicates that three cores can potentially form at least a single massive star. The integrated intensity map of $\rm H^{13}CO^{+}$ shows that the massive clump is associated with a hub-filament system, where the central hub is linked with four filaments. A sharp velocity gradient is observed towards the hub, suggesting a global collapse where the filaments are actively feeding the hub. We discuss the role of global collapse and the possible driving mechanisms for the massive star formation activity in the protocluster., Comment: 23 pages, 19 figures, accepted for publication in MNRAS

Detections of fast X-ray transients (FXTs) have been accrued over the last few decades. However, their origin has remained mysterious. There is now rapid progress thanks to timely discoveries and localisations with the Einstein Probe mission. Early results indicate that FXTs may frequently, but not always, be associated with gamma-ray bursts (GRBs). Here, we report on the multi-wavelength counterpart of FXT EP240414a, which has no reported gamma-ray counterpart. The transient is located 25.7~kpc in projection from a massive galaxy at $z=0.40$. We perform comprehensive photometric and spectroscopic follow-up. The optical light curve shows at least three distinct emission episodes with timescales of $\sim 1, 4$ and 15 days and peak absolute magnitudes of $M_R \sim -20$, $-21$, and $-19.5$, respectively. The optical spectrum at early times is extremely blue, inconsistent with afterglow emission. It may arise from the interaction of both jet and supernova shock waves with the stellar envelope and a dense circumstellar medium, as has been suggested for some Fast Blue Optical Transients (LFBOTs). At late times, the spectrum evolves to a broad-lined~Type~Ic supernova, similar to those seen in collapsar long-GRBs. This implies that the progenitor of EP240414a is a massive star creating a jet-forming supernova inside a dense envelope, resulting in an X-ray outburst with a luminosity of $\sim 10^{48}$ erg s$^{-1}$, and the complex observed optical/IR light curves. If correct, this argues for a causal link between the progenitors of long-GRBs, FXTs and LFBOTs., Comment: 36 pages, 13 figures, submitted to ApJ

We propose a robust and efficient data-driven predictive control (eDDPC) scheme which is more sample efficient (requires less offline data) compared to existing schemes, and is also computationally efficient. This is done by leveraging an alternative data-based representation of the trajectories of linear time-invariant (LTI) systems. The proposed scheme relies only on using (short and potentially irregularly measured) noisy input-output data, the amount of which is independent of the prediction horizon. To account for measurement noise, we provide a novel result that quantifies the uncertainty between the true (unknown) restricted behavior of the system and the estimated one from noisy data. Furthermore, we show that the robust eDDPC scheme is recursively feasible and that the resulting closed-loop system is practically stable. Finally, we compare the performance of this scheme to existing ones on a case study of a four tank system., Comment: 17 pages, 2 figures, submitted for Automatica

Single-ion mass identification is important for atomic and nuclear physics experiments on ions produced with low yields. Cooling the ion to ultra-low temperatures by interacting with a laser-cooled ion will enhance the precision of the measurements. In this paper we present axial-common-mode frequency measurements of balanced and unbalanced Coulomb crystals from the Fourier transform of the fluorescence photons from a Doppler-cooling transition in calcium ions, after probing the ion/crystal with a 5-radiofrequency comb. A single ion non-destructively detected can be used for identification yielding a mass resolving power $m/\Delta m_\mathrm{FWHM}\approx 310$ from the axial common mode. This identification can be performed from a single measurement within times below one second., Comment: 6 pages, 4 figures

Federated Learning (FL) emerges as a distributed machine learning approach that addresses privacy concerns by training AI models locally on devices. Decentralized Federated Learning (DFL) extends the FL paradigm by eliminating the central server, thereby enhancing scalability and robustness through the avoidance of a single point of failure. However, DFL faces significant challenges in optimizing security, as most Byzantine-robust algorithms proposed in the literature are designed for centralized scenarios. In this paper, we present a novel Byzantine-robust aggregation algorithm to enhance the security of Decentralized Federated Learning environments, coined WFAgg. This proposal handles the adverse conditions and strength robustness of dynamic decentralized topologies at the same time by employing multiple filters to identify and mitigate Byzantine attacks. Experimental results demonstrate the effectiveness of the proposed algorithm in maintaining model accuracy and convergence in the presence of various Byzantine attack scenarios, outperforming state-of-the-art centralized Byzantine-robust aggregation schemes (such as Multi-Krum or Clustering). These algorithms are evaluated on an IID image classification problem in both centralized and decentralized scenarios., Comment: 18 pages, 7 figures, 1 table

"Sure, I am happy to generate a story for you: Captain Lyra stood at the helm of her trusty ship, the Maelstrom's Fury, gazing out at the endless sea. [...] Lyra's eyes welled up with tears as she realized the bitter truth - she had sacrificed everything for fleeting riches, and lost the love of her crew, her family, and herself." Although this story, generated by a large language model, is captivating, one may wonder -- how would the story have unfolded if the model had chosen "Captain Maeve" as the protagonist instead? We cannot know. State-of-the-art large language models are stateless -- they maintain no internal memory or state. Given a prompt, they generate a sequence of tokens as an output using an autoregressive process. As a consequence, they cannot reason about counterfactual alternatives to tokens they have generated in the past. In this work, our goal is to enhance them with this functionality. To this end, we develop a causal model of token generation that builds upon the Gumbel-Max structural causal model. Our model allows any large language model to perform counterfactual token generation at almost no cost in comparison with vanilla token generation, it is embarrassingly simple to implement, and it does not require any fine-tuning nor prompt engineering. We implement our model on Llama 3 8B-instruct and conduct both qualitative and quantitative analyses of counterfactually generated text. We conclude with a demonstrative application of counterfactual token generation for bias detection, unveiling interesting insights about the model of the world constructed by large language models.

Parton distribution functions (PDFs) describe universal properties of bound states and allow us to calculate scattering amplitudes in processes with large momentum transfer. Calculating PDFs involves the evaluation of matrix elements with a Wilson line in a light-cone direction. In contrast to Monte Carlo methods in Euclidean spacetime, these matrix elements can be directly calculated in Minkowski-space using the Hamiltonian formalism. The necessary spatial- and time-evolution can be efficiently applied using established tensor network methods. We present PDFs in the Schwinger model calculated with matrix product states., Comment: Proceedings of the 41st International Symposium on Lattice Field Theory, LATTICE2024, 28th July - 3rd August 2024, University of Liverpool, United Kingdom, Speaker: Manuel Schneider

Positioning a mobile cell in a seaport environment presents unique challenges due to the high density of User Equipments (UEs) and obstacles causing shadowing effects. This paper addresses the problem of optimal positioning for a mobile cell within a defined area containing UEs, fixed cells, and obstacles. By formulating an optimisation problem, we consider variables including user associations and different types of scheduling for packet transmission. The mobile cell wireless backhaul is designed to meet the total capacity requirements of the UEs it serves, based on the optimal positioning determined by our solution approach. Using a Genetic Algorithm (GA) solver, we achieve significant gains, with objective capacity improvements of up to 200% for the 90th percentile. The proposed solution enhances network performance, especially in scenarios requiring increased capacity for emergency situations., Comment: 8 pages, 10 figures

In this paper, we investigate the conditions under which direct laser acceleration (DLA) of electrons in a laser-irradiated plasma can produce distinct photon emission profiles, focusing on the mechanisms responsible for single-lobed versus double-lobed angular distributions of emitted \(\gamma\)-rays. Through a combination of particle-in-cell simulations, test-electron simulations, and theoretical analysis, we demonstrate that the efficiency of DLA is a key determinant of the resulting emission pattern. Our results show that inefficient DLA, characterized by electrons rapidly gaining and losing energy within a single laser cycle, leads to a double-lobed emission profile heavily influenced by laser fields. In contrast, in the efficient DLA regime, where electrons steadily accumulate energy over multiple cycles, the emission is primarily governed by the quasi-static azimuthal magnetic fields generated by the laser in the plasma, resulting in a well-collimated single-lobed emission profile. Additionally, we identify that reducing the electron density in the target enhances the efficiency of DLA, thereby transforming the emission from a double-lobed to a single-lobed profile. These findings provide valuable insights into the optimization of laser-driven \(\gamma\)-ray sources for applications requiring high-intensity, well-collimated beams.

This work explores classical discrete multiple orthogonal polynomials, including Hahn, Meixner of the first and second kinds, Kravchuk, and Charlier polynomials, with an arbitrary number of weights. Explicit expressions for the recursion coefficients of Hahn multiple orthogonal polynomials are derived. By leveraging the multiple Askey scheme and the recently discovered explicit hypergeometric representation of type I multiple Hahn polynomials, corresponding explicit hypergeometric representations are provided for the type I polynomials and recursion coefficients of all the aforementioned descendants within the Askey scheme. Additionally, integral representations for these families within the Hahn class in the Askey scheme are presented. The multiple Askey scheme is further completed by providing the corresponding limits for the weights, polynomials, and recurrence coefficients., Comment: 37 pages, 5 figures