Miracle-dz/UniFAR-FTUs huggingface dataset:
Interoperabilidad semántica de la producción científica en repositorios académicos

(1) Departamento de Ciencias Básicas y Aplicadas.Universidad Nacional del Chaco Austral.Cdte. Fernández No 755.Pcia. Roque Sáenz Peña. Chaco – TE 0364-420137 Int 155 (2) Departamento de Informática, Facultad de Ciencias Exactas y Tecnologías, Universidad Nacional de Santiago del Estero. Avda. Belgrano No 1912.Santiago del Estero – TE 0385-4509560 Int 1838 ppz@uncaus.edu. ar, marielaag@hotmail.com, eduran@unse.edu. ar, alvarez@unse.edu. ar, jonemmanu@gmail.com. ar
Adequacy of Current Fertilizer Recommendations for Site-Specific Management

Variable rate fertilizer application (VRA) and site specific management (SSM) provide one of the greatest challenges and opportunities for improving fertilizer use efficiency since the wide scale adoption of soil testing in the 1950s and 1960s. The assumption ofVRA is that it will more closely match productivity, input efficiency and profitability compared with uniform application (Sawyer, 1994). The concept assumes that within-field variability exists, that it does influence crop yields, that the variation can be identified, measured, and mapped, that precise crop response models are available to determine inputs, and that data processing and application equipment are available that can effectively manage and apply the inputs. Site-specific management and VRA, however, raise many questions about current fertilizer recommendations. Are they adequate for this new technology? New methodology suggests that we reevaluate past research and assumptions. As the acreage of land under SSM increases, there is a need (i) to review past soil test correlation and calibration work and the resulting fertilizer recommendations and (ii) to determine what, if any, changes are required.
CONFERENCE PUBLICATIONS

RESEARCH EXPERIENCES Theoretical Machine Learning & Compressed Sensing: Recoverability of high dimensional structured information with heavy-tailed measurements Stein’s method and its application to compressed sensing with nonlinear transformations Robust covariance matrix estimation and U-statistics under weak moment assumptions Algorithm Design & Convergence Analysis: Convergence time analysis for online convex optimization and online MDPs. Near optimal algorithms for constrained Markov Decision Processes(MDPs) and bandit systems Convergence time analysis for constrained stochastic optimization and renewal optimization Applications in Data center server provision, wireless file transmission and sensor network scheduling
Online Submodular Maximization via Online Convex Optimization

We study monotone submodular maximization under general matroid constraints in the online setting. We prove that online optimization of a large class of submodular functions, namely, threshold potential functions, reduces to online convex optimization (OCO). This is precisely because functions in this class admit a concave relaxation; as a result, OCO policies, coupled with an appropriate rounding scheme, can be used to achieve sublinear regret in the combinatorial setting. We also show that our reduction extends to many different versions of the online learning problem, including the dynamic regret, bandit, and optimistic-learning settings.
Computable error bounds for quasi-Monte Carlo using points with non-negative local discrepancy

Let $f:[0,1]^d\to\mathbb{R}$ be a completely monotone integrand as defined by Aistleitner and Dick (2015) and let points $\boldsymbol{x}_0,\dots,\boldsymbol{x}_{n-1}\in[0,1]^d$ have a non-negative local discrepancy (NNLD) everywhere in $[0,1]^d$. We show how to use these properties to get a non-asymptotic and computable upper bound for the integral of $f$ over $[0,1]^d$. An analogous non-positive local discrepancy (NPLD) property provides a computable lower bound. It has been known since Gabai (1967) that the two dimensional Hammersley points in any base $b\ge2$ have non-negative local discrepancy. Using the probabilistic notion of associated random variables, we generalize Gabai's finding to digital nets in any base $b\ge2$ and any dimension $d\ge1$ when the generator matrices are permutation matrices. We show that permutation matrices cannot attain the best values of the digital net quality parameter when $d\ge3$. As a consequence the computable absolutely sure bounds we provide come with less accurate estimates than the usual digital net estimates do in high dimensions. We are also able to construct high dimensional rank one lattice rules that are NNLD. We show that those lattices do not have good discrepancy properties: any lattice rule with the NNLD property in dimension $d\ge2$ either fails to be projection regular or has all its points on the main diagonal.
Variations and Relaxations of Normalizing Flows

Normalizing Flows (NFs) describe a class of models that express a complex target distribution as the composition of a series of bijective transformations over a simpler base distribution. By limiting the space of candidate transformations to diffeomorphisms, NFs enjoy efficient, exact sampling and density evaluation, enabling NFs to flexibly behave as both discriminative and generative models. Their restriction to diffeomorphisms, however, enforces that input, output and all intermediary spaces share the same dimension, limiting their ability to effectively represent target distributions with complex topologies. Additionally, in cases where the prior and target distributions are not homeomorphic, Normalizing Flows can leak mass outside of the support of the target. This survey covers a selection of recent works that combine aspects of other generative model classes, such as VAEs and score-based diffusion, and in doing so loosen the strict bijectivity constraints of NFs to achieve a balance of expressivity, training speed, sample efficiency and likelihood tractability.
Two-Dimensional Dynamic Fusion for Continuous Authentication

Continuous authentication has been widely studied to provide high security and usability for mobile devices by continuously monitoring and authenticating users. Recent studies adopt multibiometric fusion for continuous authentication to provide high accuracy even when some of captured biometric data are of a low quality. However, existing continuous fusion approaches are resource-heavy as they rely on all classifiers being activated all the time and may not be suitable for mobile devices.In this paper, we propose a new approach to multibiometric continuous authentication: two-dimensional dynamic fusion. Our key insight is that multibiometric continuous authentication calculates two-dimensional matching scores over classifiers and over time. Based on this, we dynamically select a set of classifiers based on the context in which authentication is taking place, and fuse matching scores by multi-classifier fusion and multi-sample fusion. Through experimental evaluation, we show that our approach provides a better balance between resource usage and accuracy than the existing fusion methods. In particular, we show that our approach provides higher accuracy than the existing methods with the same number of score calculations by adopting multi-sample fusion.
A comparative study of coherent and incoherent drives in four-level quantum dot based spaser

In this article, we theoretically investigate a spaser (surface plasmon amplification by stimulated emission of radiation), which consists of a spherical silver nanoparticle surrounded by four-level gain medium of quantum dots (QDs). The spaser system is pumped coherently and incoherently with the same excitation rate, and the characteristics of coherent localized surface plasmon (LSP) mode, thus produced, are compared for the two pumping scenarios. We provide a detailed analytical expression for the steady state and show that the incoherent pump is more suitable for the continuous spaser mode. The reason is better understood by studying the temporal evolution of number of LSP Nn, where the oscillation of LSP starts early for incoherent drive and relaxes to steady state with a large value of Nn. At a large pump rate, spaser curve shows saturation. In addition, we have found that the resonance peak of spaser field is independent of coherent as well as incoherent pumping, while the peak amplitude of field depends on the pump rate.
Revealing the preference for correcting separated aberrations in joint optic-image design

The joint design of the optical system and the downstream algorithm is a challenging and promising task. Due to the demand for balancing the global optimal of imaging systems and the computational cost of physical simulation, existing methods cannot achieve efficient joint design of complex systems such as smartphones and drones. In this work, starting from the perspective of the optical design, we characterize the optics with separated aberrations. Additionally, to bridge the hardware and software without gradients, an image simulation system is presented to reproduce the genuine imaging procedure of lenses with large field-of-views. As for aberration correction, we propose a network to perceive and correct the spatially varying aberrations and validate its superiority over state-of-the-art methods. Comprehensive experiments reveal that the preference for correcting separated aberrations in joint design is as follows: longitudinal chromatic aberration, lateral chromatic aberration, spherical aberration, field curvature, and coma, with astigmatism coming last. Drawing from the preference, a 10% reduction in the total track length of the consumer-level mobile phone lens module is accomplished. Moreover, this procedure spares more space for manufacturing deviations, realizing extreme-quality enhancement of computational photography. The optimization paradigm provides innovative insight into the practical joint design of sophisticated optical systems and post-processing algorithms.
Less Power for More Learning: Restricting OCaml Features for Effective Teaching

We present a framework for sandboxing and restricting features of the OCaml programming language to effectively automate the grading of programming exercises, scaling to hundreds of submissions. We describe how to disable language and library features that should not be used to solve a given exercise. We present an overview of an implementation of a mock IO system to allow testing of IO-related exercises in a controlled environment. Finally, we detail a number of security considerations to ensure submitted code remains sandboxed, allowing automatic grading to be trusted without manual verification. The source code of our implementation is publicly available.
A Grounded Conceptual Model for Ownership Types in Rust

Programmers learning Rust struggle to understand ownership types, Rust’s core mechanism for ensuring memory safety without garbage collection. This paper describes our attempt to systematically design a pedagogy for ownership types. First, we studied Rust developers’ misconceptions of ownership to create the Ownership Inventory, a new instrument for measuring a person’s knowledge of ownership. We found that Rust learners could not connect Rust’s static and dynamic semantics, such as determining why an ill-typed program would (or would not) exhibit undefined behavior. Second, we created a conceptual model of Rust’s semantics that explains borrow checking in terms of flow-sensitive permissions on paths into memory. Third, we implemented a Rust compiler plugin that visualizes programs under the model. Fourth, we integrated the permissions model and visualizations into a broader pedagogy of ownership by writing a new ownership chapter for The Rust Programming Language, a popular Rust textbook. Fifth, we evaluated an initial deployment of our pedagogy against the original version, using reader responses to the Ownership Inventory as a point of comparison. Thus far, the new pedagogy has improved learner scores on the Ownership Inventory by an average of 9
Comparative Study of Visual SLAM-Based Mobile Robot Localization Using Fiducial Markers

This paper presents a comparative study of three modes for mobile robot localization based on visual SLAM using fiducial markers (i.e., square-shaped artificial landmarks with a black-and-white grid pattern): SLAM, SLAM with a prior map, and localization with a prior map. The reason for comparing the SLAM-based approaches leveraging fiducial markers is because previous work has shown their superior performance over feature-only methods, with less computational burden compared to methods that use both feature and marker detection without compromising the localization performance. The evaluation is conducted using indoor image sequences captured with a hand-held camera containing multiple fiducial markers in the environment. The performance metrics include absolute trajectory error and runtime for the optimization process per frame. In particular, for the last two modes (SLAM and localization with a prior map), we evaluate their performances by perturbing the quality of prior map to study the extent to which each mode is tolerant to such perturbations. Hardware experiments show consistent trajectory error levels across the three modes, with the localization mode exhibiting the shortest runtime among them. Yet, with map perturbations, SLAM with a prior map maintains performance, while localization mode degrades in both aspects.
PRISTA-Net: Deep Iterative Shrinkage Thresholding Network for Coded Diffraction Patterns Phase Retrieval

The problem of phase retrieval (PR) involves recovering an unknown image from limited amplitude measurement data and is a challenge nonlinear inverse problem in computational imaging and image processing. However, many of the PR methods are based on black-box network models that lack interpretability and plug-and-play (PnP) frameworks that are computationally complex and require careful parameter tuning. To address this, we have developed PRISTA-Net, a deep unfolding network (DUN) based on the first-order iterative shrinkage thresholding algorithm (ISTA). This network utilizes a learnable nonlinear transformation to address the proximal-point mapping sub-problem associated with the sparse priors, and an attention mechanism to focus on phase information containing image edges, textures, and structures. Additionally, the fast Fourier transform (FFT) is used to learn global features to enhance local information, and the designed logarithmic-based loss function leads to significant improvements when the noise level is low. All parameters in the proposed PRISTA-Net framework, including the nonlinear transformation, threshold parameters, and step size, are learned end-to-end instead of being manually set. This method combines the interpretability of traditional methods with the fast inference ability of deep learning and is able to handle noise at each iteration during the unfolding stage, thus improving recovery quality. Experiments on Coded Diffraction Patterns (CDPs) measurements demonstrate that our approach outperforms the existing state-of-the-art methods in terms of qualitative and quantitative evaluations. Our source codes are available at \emph{https://github.com/liuaxou/PRISTA-Net}.
SegmentAnything helps microscopy images based automatic and quantitative organoid detection and analysis

Organoids are self-organized 3D cell clusters that closely mimic the architecture and function of in vivo tissues and organs. Quantification of organoid morphology helps in studying organ development, drug discovery, and toxicity assessment. Recent microscopy techniques provide a potent tool to acquire organoid morphology features, but manual image analysis remains a labor and time-intensive process. Thus, this paper proposes a comprehensive pipeline for microscopy analysis that leverages the SegmentAnything to precisely demarcate individual organoids. Additionally, we introduce a set of morphological properties, including perimeter, area, radius, non-smoothness, and non-circularity, allowing researchers to analyze the organoid structures quantitatively and automatically. To validate the effectiveness of our approach, we conducted tests on bright-field images of human induced pluripotent stem cells (iPSCs) derived neural-epithelial (NE) organoids. The results obtained from our automatic pipeline closely align with manual organoid detection and measurement, showcasing the capability of our proposed method in accelerating organoids morphology analysis.
BluNF: Blueprint Neural Field

Neural Radiance Fields (NeRFs) have revolutionized scene novel view synthesis, offering visually realistic, precise, and robust implicit reconstructions. While recent approaches enable NeRF editing, such as object removal, 3D shape modification, or material property manipulation, the manual annotation prior to such edits makes the process tedious. Additionally, traditional 2D interaction tools lack an accurate sense of 3D space, preventing precise manipulation and editing of scenes. In this paper, we introduce a novel approach, called Blueprint Neural Field (BluNF), to address these editing issues. BluNF provides a robust and user-friendly 2D blueprint, enabling intuitive scene editing. By leveraging implicit neural representation, BluNF constructs a blueprint of a scene using prior semantic and depth information. The generated blueprint allows effortless editing and manipulation of NeRF representations. We demonstrate BluNF’s editability through an intuitive click-and-change mechanism, enabling 3D manipulations, such as masking, appearance modification, and object removal. Our approach significantly contributes to visual content creation, paving the way for further research in this area.
Cyclic Operator Precedence Grammars for Parallel Parsing

Operator precedence languages (OPL) enjoy the local parsability property, which essentially means that a code fragment enclosed within a pair of markers -- playing the role of parentheses -- can be compiled with no knowledge of its external context. Such a property has been exploited to build parallel compilers for languages formalized as OPLs. It has been observed, however, that when the syntax trees of the sentences have a linear substructure, its parsing must necessarily proceed sequentially making it impossible to split such a subtree into chunks to be processed in parallel. Such an inconvenience is due to the fact that so far much literature on OPLs has assumed the hypothesis that equality precedence relation cannot be cyclic. This hypothesis was motivated by the need to keep the mathematical notation as simple as possible. We present an enriched version of operator precedence grammars, called cyclic, that allows to use a simplified version of regular expressions in the right hand sides of grammar's rules; for this class of operator precedence grammars the acyclicity hypothesis of the equality precedence relation is no more needed to guarantee the algebraic properties of the generated languages. The expressive power of the cyclic grammars is now fully equivalent to that of other formalisms defining OPLs such as operator precedence automata, monadic second order logic and operator precedence expressions. As a result cyclic operator precedence grammars now produce also unranked syntax trees and sentences with flat unbounded substructures that can be naturally partitioned into chunks suitable for parallel parsing.
Context-Aware Prompt Tuning for Vision-Language Model with Dual-Alignment

Large-scale vision-language models (VLMs), e.g., CLIP, learn broad visual concepts from tedious training data, showing superb generalization ability. Amount of prompt learning methods have been proposed to efficiently adapt the VLMs to downstream tasks with only a few training samples. We introduce a novel method to improve the prompt learning of vision-language models by incorporating pre-trained large language models (LLMs), called Dual-Aligned Prompt Tuning (DuAl-PT). Learnable prompts, like CoOp, implicitly model the context through end-to-end training, which are difficult to control and interpret. While explicit context descriptions generated by LLMs, like GPT-3, can be directly used for zero-shot classification, such prompts are overly relying on LLMs and still underexplored in few-shot domains. With DuAl-PT, we propose to learn more context-aware prompts, benefiting from both explicit and implicit context modeling. To achieve this, we introduce a pre-trained LLM to generate context descriptions, and we encourage the prompts to learn from the LLM's knowledge by alignment, as well as the alignment between prompts and local image features. Empirically, DuAl-PT achieves superior performance on 11 downstream datasets on few-shot recognition and base-to-new generalization. Hopefully, DuAl-PT can serve as a strong baseline. Code will be available.
Toward Sufficient Spatial-Frequency Interaction for Gradient-aware Underwater Image Enhancement

Underwater images suffer from complex and diverse degradation, which inevitably affects the performance of underwater visual tasks. However, most existing learning-based Underwater image enhancement (UIE) methods mainly restore such degradations in the spatial domain, and rarely pay attention to the fourier frequency information. In this paper, we develop a novel UIE framework based on spatial-frequency interaction and gradient maps, namely SFGNet, which consists of two stages. Specifically, in the first stage, we propose a dense spatial-frequency fusion network (DSFFNet), mainly including our designed dense fourier fusion block and dense spatial fusion block, achieving sufficient spatial-frequency interaction by cross connections between these two blocks. In the second stage, we propose a gradient-aware corrector (GAC) to further enhance perceptual details and geometric structures of images by gradient map. Experimental results on two real-world underwater image datasets show that our approach can successfully enhance underwater images, and achieves competitive performance in visual quality improvement. The code is available at https://github.com/zhihefang/SFGNet.
The impermanent fate of massive stars in AGN disks

Stars are likely to form or to be captured in AGN disks. Their mass reaches an equilibrium when their rate of accretion is balanced by that of wind. If the exchanged gas is well mixed with the stellar core, this metabolic process would indefinitely sustain an"immortal"state on the main sequence (MS) and pollute the disk with He byproducts. This theoretical extrapolation is inconsistent with the super-solar {\alpha} element and Fe abundances inferred from the broad emission lines in active AGNs with modest He concentration. We show this paradox can be resolved with a highly-efficient retention of the He ashes or the suppression of chemical blending. The latter mechanism is robust in the geometrically-thin, dense, sub-pc regions of the disk where the embedded-stars' mass is limited by the gap-formation condition. These stars contain a radiative zone between their mass-exchange stellar surface and the nuclear-burning core. Insulation of the core lead to the gradual decrease of its H fuel and the stars' equilibrium masses. These stars transition to their post-main-sequence (PostMS) tracks on a chemical evolution time scale of a few Myr. Subsequently, the triple-{\alpha} and {\alpha}-chain reactions generate {\alpha} and Fe byproducts which are released into their natal disks. These PostMS stars also undergo core collapse, set off type II supernova, and leave behind a few solar-mass residual black holes or neutron stars