Miracle-dz/UniFAR-FTUs huggingface dataset:
La capacitación agraria desde la visión del agricultor, en el municipio Boyeros, La Habana, Cuba

At present, Credit and Service Cooperatives (CSC) are the most productive and numerous in the country, with the greatest amount of partners and usufructuaries, which have promoted training needs of information on scientific and technical advances; therefore, this work was conducted with the objective of analyzing agrarian training from farmers’ view. The investigation was performed in 20 CSCs from Boyeros municipality, Havana, Cuba, by applying a survey to 256 producers, which had two general questions aimed to know producers’ working experience on the agrarian activity and three specific questions responding to three variables: training significance, the way producers receive training information and how producers demand training. The survey was processed through SPSS statistical package version 19 for complex designs. The main results showed that the development of training activities is very important to producers; besides, a high percentage of them refer that their training attendance obeys decision-makers’ indication and, finally, most of them point out that they have rarely or never participated in a training demand inquiry.
Relative field theories via relative dualizability

We investigate relative versions of dualizability designed for relative versions of topological field theories (TFTs), also called twisted TFTs, or quiche TFTs in the context of symmetries. In even dimensions we show an equivalence between lax and oplax fully extended framed relative topological field theories valued in an $(\infty , N)$-category in terms of adjunctibility. Motivated by this, we systematically investigate higher adjunctibility conditions and their implications for relative TFTs. Summarizing we arrive at the conclusion that oplax relative TFTs is the notion of choice. Finally, for fun we explore a tree version of adjunctibility and compute the number of equivalence classes thereof.
Efficient evaluation of joint pdf of a L\'evy process, its extremum, and hitting time of the extremum

For L\'evy processes with exponentially decaying tails of the L\'evy density, we derive integral representations for the joint cpdf $V$ of $(X_T, \bar X_T,\tau_T)$ (the process, its supremum evaluated at $T<+\infty$, and the first time at which $X$ attains its supremum). The first representation is a Riemann-Stieltjes integral in terms of the (cumulative) probability distribution of the supremum process and joint probability distribution function of the process and its supremum process. The integral is evaluated using a combination an analog of the trapezoid rule. The second representation is amenable to more accurate albeit slower calculations. We calculate explicitly the Laplace-Fourier transform of $V$ w.r. t. all arguments, apply the inverse transforms, and reduce the problem to evaluation of the sum of 5D integrals. The integrals can be evaluated using the summation by parts in the infinite trapezoid rule and simplified trapezoid rule; the inverse Laplace transforms can be calculated using the Gaver-Wynn-Rho algorithm. Under additional conditions on the domain of analyticity of the characteristic exponent, the speed of calculations is greatly increased using the conformal deformation technique. For processes of infinite variation, the program in Matlab running on a Mac with moderate characteristics achieves the precision better than E-05 in a fraction of a second; the precision better than E-10 is achievable in dozens of seconds. As the order of the process (the analog of the Blumenthal-Getoor index) decreases, the CPU time increases, and the best accuracy achievable with double precision arithmetic decreases.
Video-Based Rendering Techniques: A Survey

Three-dimensional reconstruction of events recorded on images has been a common challenge between computer vision and computer graphics for a long time. Estimating the real position of objects and surfaces using vision as an input is no trivial task and has been approached in several different ways. Although huge progress has been made so far, there are several open issues to which an answer is needed. The use of videos as an input for a rendering process (video-based rendering, VBR) is something that recently has been started to be looked upon and has added many other challenges and also solutions to the classical image-based rendering issue (IBR). This article presents the state of art on video-based rendering and image-based techniques that can be applied on this scenario, evaluating the open issues yet to be solved, indicating where future work should be focused.
Second- and third-order properties of multidimensional Langevin equations

Recent work has addressed the problem of inferring Langevin dynamics from data. In this work, we address the problem of relating terms in the Langevin equation to statistical properties, such as moments of the probability density function and of the probability current density, as well as covariance functions. We first review the case of linear Gaussian dynamics, and then consider extensions beyond this simple case. We address the question of quantitative significance of effects. We also analyze underdamped (second-order) processes, specifically in the limit where dynamics in state space is almost Markovian.
Neural Concatenative Singing Voice Conversion: Rethinking Concatenation-Based Approach for One-Shot Singing Voice Conversion

Any-to-any singing voice conversion (SVC) is confronted with the challenge of ``timbre leakage'' issue caused by inadequate disentanglement between the content and the speaker timbre. To address this issue, this study introduces NeuCoSVC, a novel neural concatenative SVC framework. It consists of a self-supervised learning (SSL) representation extractor, a neural harmonic signal generator, and a waveform synthesizer. The SSL extractor condenses audio into fixed-dimensional SSL features, while the harmonic signal generator leverages linear time-varying filters to produce both raw and filtered harmonic signals for pitch information. The synthesizer reconstructs waveforms using SSL features, harmonic signals, and loudness information. During inference, voice conversion is performed by substituting source SSL features with their nearest counterparts from a matching pool which comprises SSL features extracted from the reference audio, while preserving raw harmonic signals and loudness from the source audio. By directly utilizing SSL features from the reference audio, the proposed framework effectively resolves the ``timbre leakage"issue caused by previous disentanglement-based approaches. Experimental results demonstrate that the proposed NeuCoSVC system outperforms the disentanglement-based speaker embedding approach in one-shot SVC across intra-language, cross-language, and cross-domain evaluations.
Embedding theory in ML toward real-time tracking of structural dynamics through hyperspectral datasets

In-situ Electron Energy Loss Spectroscopy (EELS) is an instrumental technique that has traditionally been used to understand how the choice of materials processing has the ability to change local structure and composition. However, more recent advances to observe and react to transient changes occurring at the ultrafast timescales that are now possible with EELS and Transmission Electron Microscopy (TEM) will require new frameworks for characterization and analysis. We describe a machine learning (ML) framework for the rapid assessment and characterization of in operando EELS Spectrum Images (EELS-SI) without the need for many labeled training datapoints as typically required for deep learning classification methods. By embedding computationally generated structures and experimental datasets into an equivalent latent space through Variational Autoencoders (VAE), we effectively predict the structural changes at latency scales relevant to closed-loop processing within the TEM. The framework described in this study is a critical step in enabling automated, on-the-fly synthesis and characterization which will greatly advance capabilities for materials discovery and precision engineering of functional materials at the atomic scale.
Sediment Loss Patterns in the Sub‐Humid Ethiopian Highlands

Controlling soil erosion is important for maintaining land productivity and reducing sedimentation of reservoirs in the Ethiopian highlands. To gain insights on sediment loss patterns, magnitude of peak sediment events, and their contribution to annual loads, hydrometric and sediment concentration data were collected for five years (2010 – 2014) from the 95‐ha Debre Mawi and four nested catchments (located 30 km south of Lake Tana). Soil and water conservation practices (SWCPs) consisting of soil bunds with 50‐cm‐deep furrows were implemented in the third year, which made it possible to examine the effects of SWCPs on peak sediment loads. The results show that a 10‐min event causes soil loss of up to 11·4 Mg ha−1, which is 22% of the annual sediment yield. Thirty to seventy‐five percent (up to 30 Mg ha−1day−1) of the sediment yield was contributed by the greatest daily flow in each year. The contribution increases to 86% for the two largest daily flows. SWCP interventions reduced sediment loss by half but did not affect the relative contribution of peak events to annual loads. Because of gully erosion, peak sediment loads at the outlet of the entire catchment were greater (up to 30 Mg ha−1day−1) as compared to the nested catchments without gullies (0·5 to 8 Mg ha−1day−1). Consequently, to reduce sediment loss, conservation measures should be designed to decrease runoff during large storms. This can be attained by deepening furrows on unsaturated hillsides and reducing the entrainment of unconsolidated sediment from failed gully banks. Copyright © 2016 John Wiley & Sons, Ltd.
Delocalization in a partially disordered interacting many-body system

We study a partially disordered one-dimensional system with interacting particles. Concretely, we impose a disorder potential to only every other site, followed by a clean site. Our numerical analysis of eigenstate properties is based on the entanglement entropy and density distributions. Most importantly, at large disorder, there exist eigenstates with large entanglement entropies and significant correlations between the clean sites. These states have volume-law scaling, embedded into a sea of area-law states, reminiscent of inverted quantum-scar states. These eigenstate features leave fingerprints in the nonequilibrium dynamics even in the large-disorder regime, with a strong initial-state dependence. We demonstrate that certain types of initial charge-density-wave states decay significantly, while others preserve their initial inhomogeneity, the latter being the typical behavior for many-body localized systems. This initial-condition dependent dynamics may give extra control over the delocalization dynamics at large disorder strength and should be experimentally feasible with ultracold atoms in optical lattices.
Unveiling Luminous Lyα Emitters at z ≈ 6 through JWST/NIRCam Imaging in the COSMOS Field

We study a sample of 14 spectroscopically confirmed Lyα emitters (LAEs) in the late era of reionization (at redshift z ≈ 6) based on the JWST/NIRCam imaging data set. These LAEs with high Lyα luminosity of L(Lyα) ∼1042.4–1043.4 erg s−1 have been covered by the (ongoing) COSMOS-Web survey over 0.28 deg2 in four NIRCam bands (F115W, F150W, F277W, and F444W). With JWST/NIRCam imaging, we determine the UV continua with M UV ranging from −20.5 to −18.5 mag. The UV slopes have a median value of β ≈ −2.35, and the steepest slopes can reach β < −3. Under the excellent spatial resolution of JWST, we identify three objects in the sample as potential merging/interacting systems. The 14 LAEs (and their components) are compact in morphology, residing substantially below the mass–size relation of high-z galaxies. We further investigate their physical properties, including the stellar mass (M *) and star formation rates (SFRs). Most of the LAEs lie on the SFR–M * main-sequence relation, while two of them, featured as “little red dots,” likely host active galactic nuclei (AGNs), implying a ∼10% AGN fraction. Moreover, we reveal that a new correlation may exist between Lyα equivalent width and the offset between Lyα and UV emission (Δd Lyα ), with a median Δd Lyα ∼ 1 kpc. This could be explained by the Lyα radiative transfer process in both the interstellar medium and circumgalactic medium. The results usher in a new era of detailed analysis on high-z LAEs with the JWST capability.
Pilot tone-guided focused navigation for free-breathing whole-liver fat-water and T2* quantification

Purpose To achieve whole-liver motion-corrected fat fraction (FF) and R2* quantification with a 3-minute free-breathing (FB) 3D radial isotropic acquisition, for increased organ coverage, ease-of-use, and patient comfort. Methods A FB 3D radial multiecho gradient-echo liver acquisition with integrated Pilot Tone (PT) navigation and NTE=8 echoes was reconstructed with a motion-correction algorithm based on focused navigation and guided by PT signals (PT-fNAV), with and without a denoising step. Fat fraction (FF) and R2* quantification using a graph cut algorithm was performed on the motion-corrected whole-liver multiecho volumes. Volunteer experiments (n=10) at 1.5T included reference 3D and 2D Cartesian breath-hold (BH) acquisitions. Image sharpness was assessed to evaluate the quality of motion correction with PT-fNAV, compared to a motion-resolved reconstruction. Fat-water images and parametric maps were compared to BH reference acquisitions following Cartesian trajectories, and to a routinely used clinical software (MRQuantiF). Results The image sharpness provided by PT-fNAV (with and without denoising) was similar in end-expiratory motion-resolved reconstructions. The 3D radial FB FF maps compared well with reference BH 3D Cartesian maps (bias +0.7%, limits of agreement (LOA) [-2.5; 4.0]%) and with 2D quantification with MRQuantiF (-0.2%, LOA [-1.1; 0.6]%). While expected visual deviations between proposed FB and reference BH R2* maps were observed, no significant differences were found in quantitative analyses. Conclusion A 3D radial technique with retrospective motion correction by PT-fNAV enabled FF and R2* quantification of the whole-liver at 1.5T. The FB whole-liver acquisition at isotropic spatial resolution compared in accuracy with BH techniques, enabling 3D assessment of steatosis in individuals with limited respiratory capabilities.
MoVQA: A Benchmark of Versatile Question-Answering for Long-Form Movie Understanding

While several long-form VideoQA datasets have been introduced, the length of both videos used to curate questions and sub-clips of clues leveraged to answer those questions have not yet reached the criteria for genuine long-form video understanding. Moreover, their QAs are unduly narrow and modality-biased, lacking a wider view of understanding long-term video content with rich dynamics and complex narratives. To remedy this, we introduce MoVQA, a long-form movie question-answering dataset, and benchmark to assess the diverse cognitive capabilities of multimodal systems rely on multi-level temporal lengths, with considering both video length and clue length. Additionally, to take a step towards human-level understanding in long-form video, versatile and multimodal question-answering is designed from the moviegoer-perspective to assess the model capabilities on various perceptual and cognitive axes.Through analysis involving various baselines reveals a consistent trend: the performance of all methods significantly deteriorate with increasing video and clue length. Meanwhile, our established baseline method has shown some improvements, but there is still ample scope for enhancement on our challenging MoVQA dataset. We expect our MoVQA to provide a new perspective and encourage inspiring works on long-form video understanding research.
Why neural functionals suit statistical mechanics.

We describe recent progress in the statistical mechanical description of many-body systems via machine learning combined with concepts from density functional theory and many-body simulations. We argue that the neural functional theory by Sammüller et al. [Proc. Natl. Acad. Sci. 120, e2312484120 (2023)] gives a functional representation of direct correlations and of thermodynamics that allows for thorough quality control and consistency checking of the involved methods of artiﬁcial intelligence. Addressing a prototypical system we here present a pedagogical application to hard core particle in one spatial dimension, where Percus' exact solution for the free energy functional provides an unambiguous reference. A corresponding standalone numerical tutorial that demonstrates the neural functional concepts together with the underlying fundamentals of Monte Carlo simulations, classical density functional theory, machine learning, and diﬀerential programming is available online at https://github.com/sfalmo/NeuralDFT-Tutorial.
Reconstructing Hands in 3D with Transformers

We present an approach that can reconstruct hands in 3D from monocular input. Our approach for Hand Mesh Recovery, HaMeR, follows a fully transformer-based architecture and can analyze hands with significantly increased accuracy and robustness compared to previous work. The key to HaMeR's success lies in scaling up both the data used for training and the capacity of the deep network for hand reconstruction. For training data, we combine multiple datasets that contain 2D or 3D hand annotations. For the deep model, we use a large scale Vision Transformer architecture. Our final model consistently outperforms the previous baselines on popular 3D hand pose benchmarks. To further evaluate the effect of our design in non-controlled settings, we annotate existing in-the-wild datasets with 2D hand keypoint annotations. On this newly collected dataset of annotations, HInt, we demonstrate significant improvements over existing baselines. We make our code, data and models available on the project website: https://geopavlakos.github. io/hamer/.
NeuSD: Surface Completion with Multi-View Text-to-Image Diffusion

We present a novel method for 3D surface reconstruction from multiple images where only a part of the object of interest is captured. Our approach builds on two recent developments: surface reconstruction using neural radiance fields for the reconstruction of the visible parts of the surface, and guidance of pre-trained 2D diffusion models in the form of Score Distillation Sampling (SDS) to complete the shape in unobserved regions in a plausible manner. We introduce three components. First, we suggest employing normal maps as a pure geometric representation for SDS instead of color renderings which are entangled with the appearance information. Second, we introduce the freezing of the SDS noise during training which results in more coherent gradients and better convergence. Third, we propose Multi-View SDS as a way to condition the generation of the non-observable part of the surface without fine-tuning or making changes to the underlying 2D Stable Diffusion model. We evaluate our approach on the BlendedMVS dataset demonstrating significant qualitative and quantitative improvements over competing methods.
Ringdown of a dynamical spacetime

The gravitational waves emitted (some time) after two black holes merge are well described by the theory of linear perturbations on a spacetime characterized by the mass and spin of the remnant. However, in the very early stages right after merger, both the mass and spin are changing. In this work we explore, in a set up based on Vaidya's spacetime, the dynamical consequences of a change of mass in the spacetime due to the accretion of null matter (for example, gravitational waves). We show that accretion imprints time-dependent frequencies and amplitude to a ringdown waveform, and we show how to model accurately this effect in certain regimes. We also comment on the direct emission of gravitational waves due to perturbations in the in--falling matter, which is of relevance for black holes embedded in astrophysical environments.
Kernel Based Estimation of Spectral Risk Measures

Spectral risk measures (SRMs) belong to the family of coherent risk measures. A natural estimator for the class of SRMs has the form of L-statistics. Various authors have studied and derived the asymptotic properties of the empirical estimator of SRM. We propose a kernel based estimator of SRM. We investigate the large sample properties of general L-statistics based on i.i. d and dependent observations and apply them to our estimator. We prove that it is strongly consistent and asymptotically normal. We compare the ﬁnite sample performance of our proposed kernel estimator with that of several existing estimators for different SRMs using Monte Carlo simulation. We observe that our proposed kernel estimator outperforms all the estimators. Based on our simulation study we have estimated the exponential SRM of four future indices-that is Nikkei 225, Dax, FTSE 100 and Hang Seng. We also perform a backtesting exercise of SRM.
Out of Context: How important is Local Context in Neural Program Repair?

Deep learning source code models have been applied very successfully to the problem of automated program repair. One of the standing issues is the small input window of current models which often cannot fully fit the context code required for a bug fix (e.g., method or class declarations of a project). Instead, input is often restricted to the local context, that is, the lines below and above the bug location. In this work we study the importance of this local context on repair success: how much local context is needed? ; is context before or after the bug location more important? how is local context tied to the bug type? To answer these questions we train and evaluate Transformer models in many different local context configurations on three datasets and two programming languages. Our results indicate that overall repair success increases with the size of the local context (albeit not for all bug types) and confirm the common practice that roughly 50-60% of the input window should be used for context leading the bug. Our results are not only relevant for researchers working on Transformer-based APR tools but also for benchmark and dataset creators who must decide what and how much context to include in their datasets.
Bioluminescence-Based Determination of Cytosolic Accumulation of Antibiotics in Escherichia coli

Antibiotic resistance is an alarming public health concern that affects millions of individuals across the globe each year. A major challenge in the development of effective antibiotics lies in their limited ability to permeate into cells, noting that numerous susceptible antibiotic targets reside within the bacterial cytosol. Consequently, improving cellular permeability is often a key consideration during antibiotic development, underscoring the need for reliable methods to assess the permeability of molecules across cellular membranes. Currently, methods used to measure permeability often fail to discriminate between arrival within the cytoplasm and the overall association of molecules with the cell. Additionally, these techniques typically possess throughput limitations. In this work, we describe a luciferase-based assay designed for assessing the permeability of molecules into the cytosolic compartment of Gram-negative bacteria. Our findings demonstrate a robust system that can elucidate the kinetics of intracellular antibiotics accumulation in live bacterial cells in real time.