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We describe the photometric data set assembled from the full six years of observations by the Dark Energy Survey (DES) in support of static-sky cosmology analyses. DES Y6 Gold is a curated data set derived from DES Data Release 2 (DR2) that incorporates improved measurement, photometric calibration, object classification and value added information. Y6 Gold comprises nearly $5000~{\rm deg}^2$ of $grizY$ imaging in the south Galactic cap and includes 669 million objects with a depth of $i_{AB} \sim 23.4$ mag at S/N $\sim 10$ for extended objects and a top-of-the-atmosphere photometric uniformity $< 2~{\rm mmag}$. Y6 Gold augments DES DR2 with simultaneous fits to multi-epoch photometry for more robust galaxy shapes, colors, and photometric redshift estimates. Y6 Gold features improved morphological star-galaxy classification with efficiency $98.6\%$ and contamination $0.8\%$ for galaxies with $17.5 < i_{AB} < 22.5$. Additionally, it includes per-object quality information, and accompanying maps of the footprint coverage, masked regions, imaging depth, survey conditions, and astrophysical foregrounds that are used for cosmology analyses. After quality selections, benchmark samples contain 448 million galaxies and 120 million stars. This paper will be complemented by online data access and documentation., Comment: Data products and documentation are publicly available at https://des.ncsa.illinois.edu/releases

We describe the fibrational structure of sets within the predicative variant $\mathbf{pEff}$ of Hyland's Effective Topos $\mathbf{Eff}$ previously introduced in Feferman's predicative theory of non-iterative fixpoints $\widehat{ID_1}$. Our structural analysis can be carried out in constructive and predicative variants of $\mathbf{Eff}$ within extensions of Aczel's Constructive Zermelo-Fraenkel Set Theory. All this shows that the full subcategory of discrete objects of Hyland's Effective topos $\mathbf{Eff}$ contains already a fibred predicative topos validating the formal Church's thesis, even when both are formalized in a constructive metatheory.

The impact of Large Language Models (LLMs) like GPT-3, GPT-4, and Bard in computer science (CS) education is expected to be profound. Students now have the power to generate code solutions for a wide array of programming assignments. For first-year students, this may be particularly problematic since the foundational skills are still in development and an over-reliance on generative AI tools can hinder their ability to grasp essential programming concepts. This paper analyzes the prompts used by 69 freshmen undergraduate students to solve a certain programming problem within a project assignment, without giving them prior prompt training. We also present the rules of the exercise that motivated the prompts, designed to foster critical thinking skills during the interaction. Despite using unsophisticated prompting techniques, our findings suggest that the majority of students successfully leveraged GPT, incorporating the suggested solutions into their projects. Additionally, half of the students demonstrated the ability to exercise judgment in selecting from multiple GPT-generated solutions, showcasing the development of their critical thinking skills in evaluating AI-generated code., Comment: This is the author's version of the work. It is posted here for your personal use. Not for redistribution

Scalable Vector Graphics (SVG) is a popular format on the web and in the design industry. However, despite the great strides made in generative modeling, SVG has remained underexplored due to the discrete and complex nature of such data. We introduce GRIMOIRE, a text-guided SVG generative model that is comprised of two modules: A Visual Shape Quantizer (VSQ) learns to map raster images onto a discrete codebook by reconstructing them as vector shapes, and an Auto-Regressive Transformer (ART) models the joint probability distribution over shape tokens, positions and textual descriptions, allowing us to generate vector graphics from natural language. Unlike existing models that require direct supervision from SVG data, GRIMOIRE learns shape image patches using only raster image supervision which opens up vector generative modeling to significantly more data. We demonstrate the effectiveness of our method by fitting GRIMOIRE for closed filled shapes on the MNIST and for outline strokes on icon and font data, surpassing previous image-supervised methods in generative quality and vector-supervised approach in flexibility.

We study a model describing the motion of the fluid in a lake, assuming inflow-outflow effects across the bottom, the porous coast and the inflows and outflows of rivers. We prove the global in time existence result for this model. The solvability is shown in the class of solutions with $L_{p}$-bounded vorticity for any given $p\in (1,\infty]$., Comment: arXiv admin note: text overlap with arXiv:2409.16166

This work is concerned with 2D-Navier Stokes equations in a multiply-connected bounded domain with permeable walls. The permeability is described by a Navier type condition. Our aim is to show that the inviscid limit is a solution of the Euler equations, satisfying the Navier type condition on the inflow zone of the walls.

This article is concerned with the existence of solution to the stochastic Degasperis-Procesi equation on $\mathbb{R}$ with an infinite dimensional multiplicative noise and integrable initial data. Writing the equation as a system composed of a stochastic nonlinear conservation law and an elliptic equation, we are able to develop a method based on the conjugation of kinetic theory with stochastic compactness arguments. More precisely, we first apply the stochastic Jakubowski-Skorokhod representation theorem to show the existence of a weak kinetic martingale solution. Next, we prove the pathwise uniqueness and invoke the Yamada-Watanabe-Engelbert theorem to conclude that the solution is strong in the probabilistic sense. In this framework, the solution is a stochastic process with sample paths in Lebesgue spaces, which are compatible with peakons and wave breaking physical phenomenon.

This article studies the Stochastic Degasperis-Procesi (SDP) equation on $\mathbb{R}$ with an additive noise. Applying the kinetic theory, and considering the initial conditions in $L^2(\mathbb{R})\cap L^{2+\delta}(\mathbb{R})$, for arbitrary small $\delta>0$, we establish the existence of a global pathwise solution. Restricting to the particular case of zero noise, our result improves the deterministic solvability results that exist in the literature.

We study an optimal control problem with a quadratic cost functional for non-Newtonian fluids of differential type. More precisely, we consider the system governing the evolution of a second grade fluid filling a two-dimensional bounded domain, supplemented with a Navier slip boundary condition, and under certain assumptions on the size of the initial data and parameters of the model, we prove the second-order sufficient optimality conditions. Furthermore, we establish a global uniqueness result for the solutions of the first-order optimality system.

We consider a velocity tracking problem for stochastic Navier-Stokes equations in a 2D-bounded domain. The control acts on the boundary through an injection-suction device with uncertainty, which acts in accordance with the non-homogeneous Navier-slip boundary conditions. After establishing a suitable stability result for the solution of the stochastic state equation, we prove the well-posedness of the stochastic linearized state equation and show that the G\^ateaux derivative of the control-to-state mapping corresponds to the unique solution of the linearized equation. Next, we study the stochastic backward adjoint equation and establish a duality relation between the solutions of the forward linearized equation and the backward adjoint equation. Finally, we derive the first-order optimality conditions.

In this work, we present a review of Energy-Momentum Squared Gravity (EMSG) -- more specifically, $f(R,T_{\mu\nu}T^{\mu\nu})$ gravity, where $R$ represents the Ricci scalar and $T_{\mu\nu}$ denotes the energy-momentum tensor. The inclusion of quadratic contributions from the energy-momentum components has intriguing cosmological implications, particularly during the Universe's early epochs. These effects dominate under high-energy conditions, enabling EMSG to potentially address unresolved issues in General Relativity (GR), such as the initial singularity and aspects of big-bang nucleosynthesis in certain models. The theory's explicit non-minimal coupling between matter and geometry leads to the non-conservation of the energy-momentum tensor, which prompts the investigation of cosmological scenarios through the framework of irreversible thermodynamics of open systems. By employing this formalism, we interpret the energy-balance equations within EMSG from a thermodynamic perspective, viewing them as descriptions of irreversible matter creation processes. Since EMSG converges to GR in a vacuum and differences emerge only in the presence of an energy-momentum distribution, these distinctions become significant in high-curvature regions. Therefore, deviations from GR are expected to be pronounced in the dense cores of compact objects. This review delves into these facets of EMSG, highlighting its potential to shed light on some of the fundamental questions in modern cosmology and gravitational theory., Comment: 40 pages, 4 figures, published version in Universe

This paper presents a comprehensive study and implementations onto FPGA device of an Expectation Propagation (EP)-based receiver for QPSK, 8-PSK, and 16-QAM. To the best of our knowledge, this is the first for this kind of receiver. The receiver implements a Frequency Domain (FD) Self-Iterated Linear Equalizer (SILE), where EP is used to approximate the true posterior distribution of the transmitted symbols with a simpler distribution. Analytical approximations for the EP feedback generation process and the three constellations are applied to lessen the complexity of the soft mapper/demapper architectures. The simulation results demonstrate that the fixed-point version performs comparably to the floating-point. Moreover, implementation results show the efficiency in terms of FPGA resource usage of the proposed architecture.

Existing Open Vocabulary Detection (OVD) models exhibit a number of challenges. They often struggle with semantic consistency across diverse inputs, and are often sensitive to slight variations in input phrasing, leading to inconsistent performance. The calibration of their predictive confidence, especially in complex multi-label scenarios, remains suboptimal, frequently resulting in overconfident predictions that do not accurately reflect their context understanding. To understand these limitations, multi-label detection benchmarks are needed. A particularly challenging domain for such benchmarking is social activities. Due to the lack of multi-label benchmarks for social interactions, in this work we present ELSA: Evaluating Localization of Social Activities. ELSA draws on theoretical frameworks in urban sociology and design and uses in-the-wild street-level imagery, where the size of groups and the types of activities vary significantly. ELSA includes more than 900 manually annotated images with more than 4,300 multi-labeled bounding boxes for individual and group activities. We introduce a novel confidence score computation method NLSE and a novel Dynamic Box Aggregation (DBA) algorithm to assess semantic consistency in overlapping predictions. We report our results on the widely-used SOTA models Grounding DINO, Detic, OWL, and MDETR. Our evaluation protocol considers semantic stability and localization accuracy and further exposes the limitations of existing approaches.

To gain insight into the dynamics of the CN + C2H6 gas-phase reaction, quasi-classical trajectory (QCT) calculations were performed on a full-dimensional analytical potential energy surface. This reaction presents very high exothermicity, -22.20 kcal/mol, and it is practically barrierless, with a barrier height of 0.23 kcal/mol, being an early transition state reaction. The V-shape form of the excitation function is characteristic of non-threshold reactions. The pronounced increase observed at lower energies can be attributed to the substantial increase in the impact parameter within this energy regime. Vibrational excitations by one quantum of stretching and bending modes give rise to excitation functions that present a similar V-shaped profile.

Large Language Models (LLMs) like GPT and Bard are capable of producing code based on textual descriptions, with remarkable efficacy. Such technology will have profound implications for computing education, raising concerns about cheating, excessive dependence, and a decline in computational thinking skills, among others. There has been extensive research on how teachers should handle this challenge but it is also important to understand how students feel about this paradigm shift. In this research, 52 first-year CS students were surveyed in order to assess their views on technologies with code-generation capabilities, both from academic and professional perspectives. Our findings indicate that while students generally favor the academic use of GPT, they don't over rely on it, only mildly asking for its help. Although most students benefit from GPT, some struggle to use it effectively, urging the need for specific GPT training. Opinions on GPT's impact on their professional lives vary, but there is a consensus on its importance in academic practice., Comment: Author's version: this is a paper under revision

Much research has highlighted the impressive capabilities of large language models (LLMs), like GPT and Bard, for solving introductory programming exercises. Recent work has shown that LLMs can effectively solve a range of more complex object-oriented programming (OOP) exercises with text-based specifications. This raises concerns about academic integrity, as students might use these models to complete assignments unethically, neglecting the development of important skills such as program design, problem-solving, and computational thinking. To address this, we propose an innovative approach to formulating OOP tasks using diagrams and videos, as a way to foster problem-solving and deter students from a copy-and-prompt approach in OOP courses. We introduce a novel notation system for specifying OOP assignments, encompassing structural and behavioral requirements, and assess its use in a classroom setting over a semester. Student perceptions of this approach are explored through a survey (n=56). Generally, students responded positively to diagrams and videos, with video-based projects being better received than diagram-based exercises. This notation appears to have several benefits, with students investing more effort in understanding the diagrams and feeling more motivated to engage with the video-based projects. Furthermore, students reported being less inclined to rely on LLM-based code generation tools for these diagram and video-based exercises. Experiments with GPT-4 and Bard's vision abilities revealed that they currently fall short in interpreting these diagrams to generate accurate code solutions., Comment: This is the author's draft of this paper

Large Language Models (LLMs) have emerged as promising tools to assist students while solving programming assignments. However, object-oriented programming (OOP), with its inherent complexity involving the identification of entities, relationships, and responsibilities, is not yet mastered by these tools. Contrary to introductory programming exercises, there exists a research gap with regard to the behavior of LLMs in OOP contexts. In this study, we experimented with three prominent LLMs - GPT-3.5, GPT-4, and Bard - to solve real-world OOP exercises used in educational settings, subsequently validating their solutions using an Automatic Assessment Tool (AAT). The findings revealed that while the models frequently achieved mostly working solutions to the exercises, they often overlooked the best practices of OOP. GPT-4 stood out as the most proficient, followed by GPT-3.5, with Bard trailing last. We advocate for a renewed emphasis on code quality when employing these models and explore the potential of pairing LLMs with AATs in pedagogical settings. In conclusion, while GPT-4 showcases promise, the deployment of these models in OOP education still mandates supervision., Comment: This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in the Proceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training track (ICSE-SEET '24, Lisbon, Portugal)

In this paper we present and validate the galaxy sample used for the analysis of the baryon acoustic oscillation (BAO) signal in the Dark Energy Survey (DES) Y6 data. The definition is based on a color and redshift-dependent magnitude cut optimized to select galaxies at redshifts higher than 0.6, while ensuring a high-quality photo-$z$ determination. The optimization is performed using a Fisher forecast algorithm, finding the optimal $i$-magnitude cut to be given by $i$<19.64+2.894$z_{\rm ph}$. For the optimal sample, we forecast an increase in precision in the BAO measurement of $\sim$25% with respect to the Y3 analysis. Our BAO sample has a total of 15,937,556 galaxies in the redshift range 0.6<$z_{\rm ph}$<1.2, and its angular mask covers 4,273.42 deg${}^2$ to a depth of $i$=22.5. We validate its redshift distributions with three different methods: directional neighborhood fitting algorithm (DNF), which is our primary photo-$z$ estimation; direct calibration with spectroscopic redshifts from VIPERS; and clustering redshift using SDSS galaxies. The fiducial redshift distribution is a combination of these three techniques performed by modifying the mean and width of the DNF distributions to match those of VIPERS and clustering redshift. In this paper we also describe the methodology used to mitigate the effect of observational systematics, which is analogous to the one used in the Y3 analysis. This paper is one of the two dedicated to the analysis of the BAO signal in DES Y6. In its companion paper, we present the angular diameter distance constraints obtained through the fitting to the BAO scale., Comment: 23 pages, 10 figures. Submitted to PRD

We present the angular diameter distance measurement obtained with the Baryonic Acoustic Oscillation feature from galaxy clustering in the completed Dark Energy Survey, consisting of six years (Y6) of observations. We use the Y6 BAO galaxy sample, optimized for BAO science in the redshift range 0.6<$z$<1.2, with an effective redshift at $z_{\rm eff}$=0.85 and split into six tomographic bins. The sample has nearly 16 million galaxies over 4,273 square degrees. Our consensus measurement constrains the ratio of the angular distance to sound horizon scale to $D_M(z_{\rm eff})/r_d$ = 19.51$\pm$0.41 (at 68.3% confidence interval), resulting from comparing the BAO position in our data to that predicted by Planck $\Lambda$CDM via the BAO shift parameter $\alpha=(D_M/r_d)/(D_M/r_d)_{\rm Planck}$. To achieve this, the BAO shift is measured with three different methods, Angular Correlation Function (ACF), Angular Power Spectrum (APS), and Projected Correlation Function (PCF) obtaining $\alpha=$ 0.952$\pm$0.023, 0.962$\pm$0.022, and 0.955$\pm$0.020, respectively, which we combine to $\alpha=$ 0.957$\pm$0.020, including systematic errors. When compared with the $\Lambda$CDM model that best fits Planck data, this measurement is found to be 4.3% and 2.1$\sigma$ below the angular BAO scale predicted. To date, it represents the most precise angular BAO measurement at $z$>0.75 from any survey and the most precise measurement at any redshift from photometric surveys. The analysis was performed blinded to the BAO position and it is shown to be robust against analysis choices, data removal, redshift calibrations and observational systematics., Comment: Submitted to PRD, 39 pages, 12 figures

Artificial intelligence (AI) has driven many information and communication technology (ICT) breakthroughs. Nonetheless, the scope of ICT systems has expanded far beyond AI since the Turing test proposal. Critically, recent AI regulation proposals adopt AI definitions affecting ICT techniques, approaches, and systems that are not AI. In some cases, even works from mathematics, statistics, and engineering would be affected. Worryingly, AI misdefinitions are observed from Western societies to the Global South. In this paper, we propose a framework to score how validated as appropriately-defined for regulation (VADER) an AI definition is. Our online, publicly-available VADER framework scores the coverage of premises that should underlie AI definitions for regulation, which aim to (i) reproduce principles observed in other successful technology regulations, and (ii) include all AI techniques and approaches while excluding non-AI works. Regarding the latter, our score is based on a dataset of representative AI, non-AI ICT, and non-ICT examples. We demonstrate our contribution by reviewing the AI regulation proposals of key players, namely the United States, United Kingdom, European Union, and Brazil. Importantly, none of the proposals assessed achieve the appropriateness score, ranging from a revision need to a concrete risk to ICT systems and works from other fields.

In this technical report, a new formulation for embedding a neural network into an optimization model is described. This formulation does not require binary variables to properly compute the output of the neural network for specific types of problems. Preliminary experiments show that this reformulation resulted in faster computation times when solving a proposed showcase model, in which non-linearity is necessary to be computed. This is in comparison with the classic formulation and off-the-shelf tools of commercial solvers.

This work aims to investigate the well-posedness and the existence of ergodic invariant measures for a class of third grade fluid equations in bounded domain $D\subset\mathbb{R}^d,d=2,3,$ in the presence of a multiplicative noise. First, we show the existence of a martingale solution by coupling a stochastic compactness and monotonicity arguments. Then, we prove a stabilty result, which gives the pathwise uniqueness of the solution and therefore the existence of strong probabilistic solution. Secondly, we use the stability result to show that the associated semigroup is Feller and by using "Krylov-Bogoliubov Theorem" we get the existence of an invariant probability measure. Finally, we show that all the invariant measures are concentrated on a compact subset of $L^2$, which leads to the existence of an ergodic invariant measure.

Quasi-classical trajectory (QCT) calculations were conducted on the newly developed full-dimensional potential energy surface, PES-2023, to analyse two critical aspects: the influence of vibrational versus translational energy in promoting reactivity, and the impact of vibrational excitation within similar vibrational modes. The former relates to Polanyi's rules, while the latter concerns mode selectivity. Initially, the investigation revealed that independent vibrational excitation by a single quantum of ethane's symmetric and asymmetric stretching modes (differing by only 15 cm-1) yielded comparable dynamics, reaction cross-sections, HCN vibrational product distributions, and scattering distributions. This observation dismisses any significant mode selectivity. Moreover, providing an equivalent amount of energy as translational energy (at total energies of 9.6 and 20.0 kcal/mol) gave rise to slightly lower reactivity compared to putting the same amount of energy as vibrational energy. This effect is more evident at low energies, presenting a counterintuitive scenario in an "early transition state" reaction. Regarding CN vibrational excitation, our calculations revealed that the reaction cross-section remains nearly unaffected by this vibrational excitation, suggesting the CN stretching mode is a spectator mode. The results were rationalized by considering several factors: the strong coupling between different vibrational modes, between vibrational modes and the reaction coordinate, and a significant vibrational energy redistribution within the ethane reactant before collision. This redistribution creates an unphysical energy flow, resulting in the loss of adiabaticity and vibrational memory before the reactants' collision.

Context. The determination of accurate photometric redshifts (photo-zs) in large imaging galaxy surveys is key for cosmological studies. One of the most common approaches are machine learning techniques. These methods require a spectroscopic or reference sample to train the algorithms. Attention has to be paid to the quality and properties of these samples since they are key factors in the estimation of reliable photo-zs. Aims. The goal of this work is to calculate the photo-zs for the Y3 DES Deep Fields catalogue using the DNF machine learning algorithm. Moreover, we want to develop techniques to assess the incompleteness of the training sample and metrics to study how incompleteness affects the quality of photometric redshifts. Finally, we are interested in comparing the performance obtained with respect to the EAzY template fitting approach on Y3 DES Deep Fields catalogue. Methods. We have emulated -- at brighter magnitude -- the training incompleteness with a spectroscopic sample whose redshifts are known to have a measurable view of the problem. We have used a principal component analysis to graphically assess incompleteness and to relate it with the performance parameters provided by DNF. Finally, we have applied the results about the incompleteness to the photo-z computation on Y3 DES Deep Fields with DNF and estimated its performance. Results. The photo-zs for the galaxies on DES Deep Fields have been computed with the DNF algorithm and added to the Y3 DES Deep Fields catalogue. They are available at https://des.ncsa.illinois.edu/releases/y3a2/Y3deepfields. Some techniques have been developed to evaluate the performance in the absence of "true" redshift and to assess completeness. We have studied... (Partial abstract), Comment: 14 pages, 17 figures

We study a stochastic velocity tracking problem for the 2D-Navier-Stokes equations perturbed by a multiplicative Gaussian noise. From a physical point of view, the control acts through a boundary injection/suction device with uncertainty, modeled by stochastic non-homogeneous Navier-slip boundary conditions. We show the existence and uniqueness of the solution to the state equation, and prove the existence of an optimal solution to the control problem.

In the present work, we investigate stochastic third grade fluids equations in a $d$-dimensional setting, for $d = 2, 3$. More precisely, on a bounded and simply connected domain $\mathcal{D}$ of $\mathbb{R}^d$, $d = 2,3$, with a sufficiently regular boundary $\partial \mathcal{D},$ we consider incompressible third grade fluid equations perturbed by a multiplicative Wiener noise. Supplementing our equations by Dirichlet boundary conditions and taking initial data in the Sobolev space $H^1(\mathcal{D})$, we establish the existence of global stochastic weak solutions by performing a strategy based on the conjugation of stochastic compactness criteria and monotonicity techniques. Furthermore, we study the asymptotic behaviour of these solutions, as $t \to \infty$.