Miracle-dz/UniFAR-FTUs huggingface dataset:
Eficiencia Semifuerte Del Mercado Internacional Del Azúcar Entre Los Años 2001 Y 2011 (Semi-Strong Efficiency in the International Sugar Market During the Period 2001-2011)

Spanish Abstract: Este articulo tiene como objetivo comprobar la eficiencia en sentido semifuerte – de acuerdo a la definicion de Fama (1970) – del mercado internacional del azucar. Para lograr este objetivo, se seguira a Ferre y Hall (2002), quienes demuestran que la existencia de cointegracion es condicion necesaria pero no suficiente para afirmar que el mercado no es eficiente. En este orden de ideas, para probar esta hipotesis se determinara la existencia de cointegracion entre las dos series de precios del azucar (contrato no. 5 del mercado de Londres y el no. 11 de Nueva York). Nuestros resultados muestran que existe una relacion de largo plazo pero no una evidencia suficiente para rechazar la hipotesis de eficiencia en el sentido semifuerte para el mercado internacional del azucar.English Abstract: This work aims to verify the semi-strong efficient hypothesis described by Fama (1970) for the international sugar market. To achieve this goal, we follow Ferre and Hall (2002) who show that the existence of cointegration is a necessary but not sufficient condition to conclude that the market is not semi-strong efficient. To test this hypothesis we determined the existence of cointegration between the two sets of prices (sugar contract No. 5 in the London Market and contract No. 11 in the New York market). Our results show that there is a long term relationship, but there is not sufficient evidence to reject the null hypothesis of semi-strong efficiency in the international sugar market.
Spatial Correlation at the Boson Peak Frequency in Amorphous Materials

The Boson peak (BP), an excess of vibrational density of states, is ubiquitous for amorphous materials and is believed to hold the key to understanding the dynamics of glass and glass transition. Previous studies have established an energy scale for the BP, which is ~1-10 meV or ~THz in frequency. However, so far, little is known about the momentum dependence or spatial correlation of the BP. Here, we report the observation of the BP in model Zr-Cu-Al metallic glasses over a wide range of momentum transfer, using inelastic neutron scattering, heat capacity, Raman scattering measurements, and molecular dynamics (MD) simulations. The BP energy is largely dispersionless; however, the BP intensity was found to scale with the static structure factor. Additional MD simulations with a generic Lennard-Jones potential confirmed the same. Based on these results, an analytical expression for the dynamic structure factor was formulated for the BP excitation. Further analysis of the simulated disordered structures suggests that the BP is related to local structure fluctuations (e.g., in shear strain). Our results offered insights into the nature of the BP and provide guidance for the development of theories of amorphous materials.
A Fast Algorithm for Low Rank + Sparse column-wise Compressive Sensing

This paper focuses studies the following low rank + sparse (LR+S) column-wise compressive sensing problem. We aim to recover an n × q matrix, ${{\mathbf{X}}^{\ast}} = \left[ {x_1^{\ast},x_2^{\ast}, \cdots ,x_q^{\ast}} \right]$ from m independent linear projections of each of its q columns, given by ${{\mathbf{y}}_k}: = {{\mathbf{A}}_k}{\mathbf{x}}_k^{\ast},k \in [q]$. Here, yk is an m-length vector with m < n. We assume that the matrix X* can be decomposed as X* = L* + S*, where L* is a low rank matrix of rank r ≪ min(n, q) and S* is a sparse matrix. Each column of S contains ρ non-zero entries. The matrices Ak are known and mutually independent for different k. To address this recovery problem, we propose a novel fast GD-based solution called AltGDmin-LR+S, which is memory and communication efficient. We numerically evaluate its performance by conducting a detailed simulation-based study.
Harmonic analysis of Gaussian multiplicative chaos on the circle

In this paper, we initiate the harmonic analysis of Gaussian multiplicative chaos (GMC) on the circle, i.e. the study of its Fourier coefficients. In particular, we show that almost surely GMC is a so-called Rajchman measure which means that its Fourier coefficients converge to $0$ when the frequency goes to infinity. We supplement this result with a convergence in law result for the rescaled Fourier coefficients.
STUDY THE QUALITY OF GROUNDWATER OF AL-ZOROUP AREA IN MAHDAH STATE, THE SULTANATE OF OMAN

The groundwater is the most important source of water in the Sultanate of Oman. Accordingly, the Oman Government has paid great attention commensurate with the importance of water resource in terms of their sources, quality, and ways to rationalize consumption to remain under the term of sustainability and validity of present and future generations. This study was conducted in Al Buraymi at Mahdah State in Al-Zoroub area targete a groundwater quality for 20 wells in private farms. The water samples were taken from these wells and subjected to analysis for microbial and geochemical aspects. For this analysis, the assorted devices and instruments were implemented. Additionally, to focus on the possible sources of pollution and the range of competence for the human user or livestock and agriculture of this water, the international guideline measures were used for compression. Based on investigations from a group of chemical and biological analysis; it turns out that this water contained varying proportions of salts and dissolved minerals. These results also build on the proportions of dissolved salts which showed different types of water in these wells which are mostly fresh (magnesium bicarbonate) in ten wells, brackish (magnesium chloride) in six wells and saline (sodium chloride) in four wells. The dominant water type (magnesium bicarbonate) resulted from the fresh water in the upper aquifer, which recharged from northeastern catchment areas of Mahadah, Wadi Al Jizi, and Wadi Hamad. Moreover, it was found that the abundance of the major cation ions in groundwater is in the following order: Na + > Mg 2+ > Ca 2+ > K + , where as the abundance of the major anion ions in groundwater is in the following order: Cl > HCO3 > SO4 > NO3. These distribution is ffected by salinization phenomena (ion exchange), mixing of saline water in the deep aquifer with fresh water and by anthropogenic sources. The building at the expense of sodium adsorption ratio (SAR) found that 65% of the wells are located within the category of hazardous water at least for sodium ion and
DETECCIÓN DEL DISCO ÓPTICO EN RETINOGRAFÍAS MEDIANTE UNA ESTRATEGIA EVOLUTIVA (μ+λ)

In this paper we show an optic disk (OD) detection approach based on evolution strategy (ES). The approach has two main steps: Coarse detection and contour edges refinement. Coarse detection estimates an approximated position using a ES in which each individual has a fitness function based on the bright pixels amount and the vasculature structure edge pixels quantity inside a circle. The contour edge refinement uses a geometrical approach to circle deformation in order to adjust the edge circle with OD edges. For this, the pixel with the largest intensity value variation along a normal line is considered. The proposed approach was evaluated using the STARED and DIAREDB
The Fairness Stitch: Unveiling the Potential of Model Stitching in Neural Network De-Biasing

The pursuit of fairness in machine learning models has emerged as a critical research challenge in different applications ranging from bank loan approval to face detection. Despite the widespread adoption of artificial intelligence algorithms across various domains, concerns persist regarding the presence of biases and discrimination within these models. To address this pressing issue, this study introduces a novel method called"The Fairness Stitch (TFS)"to enhance fairness in deep learning models. This method combines model stitching and training jointly, while incorporating fairness constraints. In this research, we assess the effectiveness of our proposed method by conducting a comprehensive evaluation of two well-known datasets, CelebA and UTKFace. We systematically compare the performance of our approach with the existing baseline method. Our findings reveal a notable improvement in achieving a balanced trade-off between fairness and performance, highlighting the promising potential of our method to address bias-related challenges and foster equitable outcomes in machine learning models. This paper poses a challenge to the conventional wisdom of the effectiveness of the last layer in deep learning models for de-biasing.
MFAAN: Unveiling Audio Deepfakes with a Multi-Feature Authenticity Network

In the contemporary digital age, the proliferation of deepfakes presents a formidable challenge to the sanctity of information dissemination. Audio deepfakes, in particular, can be deceptively realistic, posing significant risks in misinformation campaigns. To address this threat, we introduce the Multi-Feature Audio Authenticity Network (MFAAN)—an advanced architecture tailored for the detection of fabricated audio content. MFAAN incorporates multiple parallel paths designed to harness the strengths of different audio representations, including Mel-frequency cepstral coefficients (MFCC) [1], linear-frequency cepstral coefficients (LFCC) [1], and Chroma Short Time Fourier Transform (Chroma-STFT). By synergistically fusing these features, MFAAN achieves a nuanced understanding of audio content, facilitating robust differentiation between genuine and manipulated recordings. Preliminary evaluations of MFAAN on two benchmark datasets, ‘In-the-Wild’ Audio Deepfake Data [2] and The Fake-or-Real Dataset [3], demonstrate its superior performance, achieving accuracies of 98.93% and 94.47% respectively. Such results not only underscore the efficacy of MFAAN but also highlight its potential as a pivotal tool in the ongoing battle against deepfake audio content.
Multiband description of the upper critical field of bulk FeSe

The upper critical field of multi-band superconductors can be an essential quantity to unravel the nature of superconducting pairing and its interplay with the electronic structure. Here we experimentally map out the complete upper critical field phase diagram of FeSe for different magnetic field orientations at temperatures down to 0.3 K using both resistivity and torque measurements. The temperature dependence of the upper critical field reflects that of a multi-band superconductor and requires a two-band description in the clean limit with band coupling parameters favouring interband over intraband interactions. Despite the relatively small Maki parameter in FeSe of $\alpha \sim 1.6$, the multi-band description of the upper critical field is consistent with the stabilization of a FFLO state below $T/T_{\rm c}\sim 0.3$. We find that the anomalous behaviour of the upper critical field is linked to a departure from the single-band picture, and FeSe provides a clear example where multi-band effects and the strong anisotropy of the superconducting gap need to be taken into account.
Structural Transition and Magnetic Anisotropy in $\alpha$-RuCl$_{3}$

We report X-ray diffraction and magnetic susceptibility studies of the structural phase transition in $\alpha$-RuCl$_{3}$. By utilizing a single crystal sample with predominantly single twin domain, we show that $\alpha$-RuCl$_{3}$ goes from high-temperature C2/m structure to a rhombohedral structure with R$\bar{3}$ symmetry at low temperature. While the defining feature of the structural transition is changing the stacking direction from the monoclinic a-axis to the b-axis, bond-anisotropy disappears when the structural change occurs, indicating that the local $C_3$ symmetry is restored within the honeycomb layer. The symmetry change is corroborated by the vanishing magnetic anisotropy in the low-temperature structure. Our study demonstrates that magnetic interaction is extremely sensitive to structural details in $\alpha$-RuCl$_{3}$, which could explain the sample dependence found in this material.
Selective Visual Representations Improve Convergence and Generalization for Embodied AI

Embodied AI models often employ off the shelf vision backbones like CLIP to encode their visual observations. Although such general purpose representations encode rich syntactic and semantic information about the scene, much of this information is often irrelevant to the specific task at hand. This introduces noise within the learning process and distracts the agent's focus from task-relevant visual cues. Inspired by selective attention in humans-the process through which people filter their perception based on their experiences, knowledge, and the task at hand-we introduce a parameter-efficient approach to filter visual stimuli for embodied AI. Our approach induces a task-conditioned bottleneck using a small learnable codebook module. This codebook is trained jointly to optimize task reward and acts as a task-conditioned selective filter over the visual observation. Our experiments showcase state-of-the-art performance for object goal navigation and object displacement across 5 benchmarks, ProcTHOR, ArchitecTHOR, RoboTHOR, AI2-iTHOR, and ManipulaTHOR. The filtered representations produced by the codebook are also able generalize better and converge faster when adapted to other simulation environments such as Habitat. Our qualitative analyses show that agents explore their environments more effectively and their representations retain task-relevant information like target object recognition while ignoring superfluous information about other objects. Code and pretrained models are available at our project website: https://embodied-codebook.github. io.
The Magnetic Anomaly of 3d Sources Having Arbitrary Geometry and Magnetization

Magnetic anomalies of complicated 3D sources can be calculated by using a combination of analytical and numerical integration. Two surfaces and the magnetization parameters (the amplitudes of the induced and remanent components and the direction cosines) of the source can be defined by arbitrary functions or by discrete data points in a plane. When combined with a polynomial magnetization function in the direction of the third axis, 3D magnetization distribution can also be modelled. The method gives very general equations for anomaly calculation. It can be used for direct modelling of sources interpreted by seismic or other methods and also for interactive interpretation with fast computers. It is possible to calculate anomalies of, for example, intrusives or folded sedimentary beds whose surfaces are functions of horizontal coordinates and which have polynomial magnetization variations in the vertical direction due to gravitational differentiation and arbitrarily varying magnetization in the horizontal direction due to regional metamorphosis. If the distribution of magnetization parameters in the vertical direction cannot be described satisfactorily by polynomials, models can be used whose surfaces are functions of the vertical coordinate and which can then have any arbitrary magnetization distribution in the vertical direction.
Clonal Variation in Rooting Responses of Juvenile Cuttings of Chir Pine ( Pinus roxburghii Sarg.)

The paper summarizes the results of rooting of juvenile cuttings of Chir Pine ( Pinus roxburghii Sarg.). Cuttings emanating from 9 clones of P. roxburghii . (UPTSB-14 (Bhasla), HPSNR-5 (Nurpur), UPBER-5 (Berinag), UPKAL-10 (Kaligarh), UPPAT-I094 (Patwadangar), UPTSS-3 (Sikanderdbarl, UPDNH-770 (Dhanari), UPBER-368 (Berinag) and UPDNH-81 (Dhanari) were treated with different concentrations of IBA viz. 1000 ppm, 2000 ppm, 3000 ppm and 4000 ppm and planted in vermiculite under mist condition. Significant differences in rooting percentage and other root parameters were observed between the clones. Among the four concentrations of IBA tried, 4000 ppm proved to be the best. 100% rooting was observed in three clones viz. UPTSB-14, UPKAL-10 and UPPAT-1094.
The role of sign indefinite invariants in shaping turbulent cascades

We highlight a non-canonical yet natural choice of variables for an efficient derivation of a kinetic equation for the energy density in non-isotropic systems, including internal gravity waves on a vertical plane, inertial and Rossby waves. The existence of a second quadratic invariant simplifies the kinetic equation and leads to extra conservation laws for resonant interactions. We analytically determine the scaling of the radial turbulent energy spectrum. Our findings suggest the existence of an inverse energy cascade of internal gravity waves, from small to large scales, in practically relevant scenarios.
Regional Low-Emission Pathways from Global Models

Governments worldwide have agreed that international climate policy should aim to limit the increase of global mean temperature to less than 2oC with respect to pre-industrial levels. The purpose of this paper is to analyse the emission reductions and related energy system changes in various countries in pathways consistent with the 2oC target. We synthesize and provide an overview of the national and regional information contained in different scenarios from various global models published over the last few years, as well as yet unpublished scenarios submitted by modelling teams participating in the MILES project (Modelling and Informing Low-Emission Strategies). We find that emissions in the mitigation scenarios are significantly reduced in all regions compared to the baseline without climate policies. The regional cumulative CO2 emissions show on average a 76% reduction between the baseline and 450 scenario. The 450 scenarios show a reduction of primary energy demand in all countries of roughly 30-40% compared to the baseline. In the baseline scenario, the contribution of low-carbon energy technology remains around 15%, i.e. similar as today. In the mitigation scenario, these numbers are scaled up rapidly towards 2050. Looking at air quality, sulphur dioxide and black carbon emissions are strongly reduced as a co-benefit of greenhouse gas emission reductions, in both developing and developed countries. However, black carbon emissions increase in countries that strongly rely on bioenergy to reach mitigation targets. Concerning energy security, energy importing countries generally experience a decrease in net-energy imports in mitigation scenarios compared to the baseline development, while energy exporters experience a loss of energy export revenues.
Joint modelling of recurrent and terminal events with discretely-distributed non-parametric frailty: application on re-hospitalizations and death in heart failure patients

In the context of clinical and biomedical studies, joint frailty models have been developed to study the joint temporal evolution of recurrent and terminal events, capturing both the heterogeneous susceptibility to experiencing a new episode and the dependence between the two processes. While discretely-distributed frailty is usually more exploitable by clinicians and healthcare providers, existing literature on joint frailty models predominantly assumes continuous distributions for the random effects. In this article, we present a novel joint frailty model that assumes bivariate discretely-distributed non-parametric frailties, with an unknown finite number of mass points. This approach facilitates the identification of latent structures among subjects, grouping them into sub-populations defined by a shared frailty value. We propose an estimation routine via Expectation-Maximization algorithm, which not only estimates the number of subgroups but also serves as an unsupervised classification tool. This work is motivated by a study of patients with Heart Failure (HF) receiving ACE inhibitors treatment in the Lombardia region of Italy. Recurrent events of interest are hospitalizations due to HF and terminal event is death for any cause.
Testing RadiX-Nets: Advances in Viable Sparse Topologies

The exponential growth of data has sparked computational demands on ML research and industry use. Sparsification of hyper-parametrized deep neural networks (DNNs) creates simpler representations of complex data. Past research has shown that some sparse networks achieve similar performance as dense ones, reducing runtime and storage. RadiX-Nets, a subgroup of sparse DNNs, maintain uniformity which counteracts their lack of neural connections. Generation, independent of a dense network, yields faster asymptotic training and removes the need for costly pruning. However, little work has been done on RadiX-Nets, making testing challenging. This paper presents a testing suite for RadiX-Nets in TensorFlow. We test RadiX-Net performance to streamline processing in scalable models, revealing relationships between network topology, initialization, and training behavior. We also encounter"strange models"that train inconsistently and to lower accuracy while models of similar sparsity train well.
EFFECTS OF CONTROLLED DRAINAGE ON STORM EVENT HYDROLOGY IN A LOBLOLLY PINE PLANTATION 1

ABSTRACT: A paired watershed approach was utilized to study the effects of three water management regimes on storm event hydrology in three experimental watersheds in a drained loblolly pine (Pinus taeda L.) plantation in eastern North Carolina. The regimes were: (1) conventional drainage, (2) controlled drainage (CD) to reduce outflows during spring fish recruitment, and (3) controlled drainage to reduce outflows and conserve water during the growing season. Data from two pit‐treatment years and three years of CD treatment with raised weirs at the watershed outlet are presented. CD treatment resulted in rises in water table elevations during the summer. But the rises were small and short‐lived due to increased evapotranspiration (ET) rates as compared to the spring treatment with lower ET demands. CD treatment had no effect on water tables deeper than 1.3 m. CD treatments, however, significantly (α= 0.05) reduced the stoning outflows for all events, and peak outflow rates for most of the events depending upon the outlet weir level. In some events, flows did not occur at all in watersheds with CD. When event outflows occurred, duration of the event was sharply reduced because of reduced effective ditch depth. Water table depth at the start of an event influenced the effect of CD treatment on storm event hydrology.
Analytics, have some humility: a statistical view of fourth-down decision making

The standard mathematical approach to fourth-down decision making in American football is to make the decision that maximizes estimated win probability. Win probability estimates arise from machine learning models fit from historical data. These models, however, are overfit high-variance estimators, exacerbated by the highly correlated nature of football play-by-play data. Thus, it is imperative to knit uncertainty quantification into the fourth-down decision procedure; we do so using bootstrapping. We find that uncertainty in the estimated optimal fourth-down decision is far greater than that currently expressed by sports analysts in popular sports media. Our contribution is a major advance in fourth-down strategic decision making: far fewer fourth-down decisions are as obvious as analysts claim.