Miracle-dz/UniFAR-FTUs huggingface dataset:
THE IMPACT OF EXPOSING HUMAN BLOOD SAMPLES TO THE RADON GAS ON THE PLATELET COUNT: USING NEW EXPOSURE TECHNIQUE

In the present technique, an amount of radon gas (2210 ± 5.1 Bq/m3) is collected in a tight PVC chamber equipped with two sources of radium (5 μCi). Blood samples and the pieces of CR-39NTDs are exposed (together) to a known radon concentration for different periods of exposure. Complete blood test and the computer scanning for each piece of CR-39NTDs before and after exposure are carried out. The results show a 95% retention of radon gas in the present technique during the exposure. Thus, the present technique is considered a better technique in terms of conservation of gas, because of the low rate of loss of the gas (4.915%), and this is the advantage of the present technique: keeping the concentration of gas during periods of exposure as much as possible. Deposition of the alpha particles, which are emitted from radon daughters, has reduced the number of platelets in both gender, and this is dependent on the energies of the alpha particles. Exposure time and the energy loss of the alpha particles within the target (blood and CR-39) through the atomic displacements are considered important parameters in the changes of platelet count. At 20-minute exposure time, the rate of absorption dose is 2.255 ± 0.11 μSv and the platelet count reduces rapidly.
Estratégias de uso de água salobra na produção de alface em hidroponia NFT

Plants of crisphead lettuce cv. 'Veronica' were grown under hydroponic conditions aiming to evaluate three different strategies of brackish waters utilization: 1) brackish waters to replace the evapotranspiration loss (ETc) and fresh water to prepare nutrient solution (NS); 2) brackish waters to prepare NS and fresh water to replace the ETc; 3) brackish waters to prepare NS and replace ETc. The levels of water salinity were obtained by addition of NaCl to fresh water (0.27 dS m-1): 1.45; 2.51; 3.6; 5.41 and 7.5 dS m-1. The experiment was conducted in randomized blocks with four replications per treatment. A research structure was built with 72 experimental units which simulate the nutrient film technique (NFT). The use of brackish water only to replace the ETc did not affect the lettuce yield. On the other hand, the use of brackish waters to prepare NS, and fresh water to replace the ETc loss, reduce the lettuce yield (shoot fresh matter) by 4.99% per (dS m-1). The lettuce yield was reduced by 7% per dS m-1 as brackish water was used to prepare NS and replace ETc loss. Despite the linear decline of lettuce yield as the water salinity increased, depreciative symptoms for quality of hydroponic lettuce were not registered.
Sparse online variational Bayesian regression

This work considers variational Bayesian inference as an inexpensive and scalable alternative to a fully Bayesian approach in the context of sparsity-promoting priors. In particular, the priors considered arise from scale mixtures of Normal distributions with a generalized inverse Gaussian mixing distribution. This includes the variational Bayesian LASSO as an inexpensive and scalable alternative to the Bayesian LASSO introduced in [65]. It also includes a family of priors which more strongly promote sparsity. For linear models the method requires only the iterative solution of deterministic least squares problems. Furthermore, for p unknown covariates the method can be implemented exactly online with a cost of $O(p^3)$ in computation and $O(p^2)$ in memory per iteration -- in other words, the cost per iteration is independent of n, and in principle infinite data can be considered. For large $p$ an approximation is able to achieve promising results for a cost of $O(p)$ per iteration, in both computation and memory. Strategies for hyper-parameter tuning are also considered. The method is implemented for real and simulated data. It is shown that the performance in terms of variable selection and uncertainty quantification of the variational Bayesian LASSO can be comparable to the Bayesian LASSO for problems which are tractable with that method, and for a fraction of the cost. The present method comfortably handles $n = 65536$, $p = 131073$ on a laptop in less than 30 minutes, and $n = 10^5$, $p = 2.1 \times 10^6$ overnight.
The full renormalization horseshoe for multimodal maps

In this paper, we consider the renormalization operator R for multimodal maps. We prove the renormalization operator R is a self-homeomorphism on any totally R-invariant set. As a corollary, we prove the existence of the full renormalization horseshoe for multimodal maps.
Facile synthesis of Au@TiO2 core–shell hollow spheres for dye-sensitized solar cells with remarkably improved efficiency

Au@TiO2 hollow submicrospheres with controllable sizes and shell thicknesses are easily synthesized by the hydrothermal method. When Au@TiO2 hollow submicrospheres are used as the photoanodes of DSSCs, a power conversion efficiency of 8.13% is achieved, which exhibits a 30% increase compared with the conventional DSSCs fabricated with P25 TiO2 photoanodes.
GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation

6D pose estimation from a single RGB image is a fundamental task in computer vision. The current top-performing deep learning-based methods rely on an indirect strategy, i.e., first establishing 2D-3D correspondences between the coordinates in the image plane and object coordinate system, and then applying a variant of the PnP/RANSAC algorithm. However, this two-stage pipeline is not end-to-end trainable, thus is hard to be employed for many tasks requiring differentiable poses. On the other hand, methods based on direct regression are currently inferior to geometry-based methods. In this work, we perform an in-depth investigation on both direct and indirect methods, and propose a simple yet effective Geometry-guided Direct Regression Network (GDR-Net) to learn the 6D pose in an end-to-end manner from dense correspondence-based intermediate geometric representations. Extensive experiments show that our approach remarkably outperforms state-of-the-art methods on LM, LM-O and YCB-V datasets. Code is available at https://git.io/GDR-Net.
Non-magnetic Pulsar Emission Mechanism

It is generally accepted that the pulsar magnetic field converts most of its rotational energy losses into radiation. In this paper, we propose an alternative emission mechanism, in which neither the pulsar’s rotational energy nor its magnetic field is involved. The emission mechanism proposed here is based on the hypothesis that the pulsar matter is stable only when moving with respect to the ambient medium at a velocity exceeding some threshold value. A decrease in velocity below this threshold value leads to the decay of matter with the emission of electromagnetic radiation. It is shown that decay regions on the pulsar surface in which the velocities of pulsar particles drops to arbitrarily small values are formed under simple driving condition. It is also shown that for the majority of pulsars having measured transverse velocities, such a condition is quite possible. Thus, the pulsar radiation carries away not the pulsar rotational energy, but its mass, while the magnitude of the rotational energy does not play any role. At the end of the paper, we consider the reason for the possible short-period precession of the pulsar.
AGN and star formation across cosmic time

We investigate the balance of power between stars and AGN across cosmic history, based on the comparison between the infrared (IR) galaxy luminosity function (LF) and the IR AGN LF. The former corresponds to emission from dust heated by stars and AGN, whereas the latter includes emission from AGN-heated dust only. We find that at all redshifts (at least up to z ∼ 2.5), the high-luminosity tails of the two LFs converge, indicating that the most IR-luminous galaxies are AGN-powered. Our results shed light to the decades-old conundrum regarding the flatter high-luminosity slope seen in the IR galaxy LF compared to that in the UV and optical. We attribute this difference to the increasing fraction of AGN-dominated galaxies with increasing total IR luminosity (LIR). We partition the LIR−z parameter space into a star formation-dominated and an AGN-dominated region, finding that the most luminous galaxies at all epochs lie in the AGN-dominated region. This sets a potential ‘limit’ to attainable star formation rates, casting doubt on the abundance of ‘extreme starbursts’: if AGN did not exist, LIR > 1013 L⊙ galaxies would be significantly rarer than they currently are in our observable Universe. We also find that AGN affect the average dust temperatures (Tdust) of galaxies and hence the shape of the well-known LIR−Tdust relation. We propose that the reason why local ULIRGs are hotter than their high-redshift counterparts is because of a higher fraction of AGN-dominated galaxies amongst the former group.
On the Thermal Anomaly of Lake Untersee

Reported here is the outcome from a student internship undertaken with Dr. Chris McKay at the NASA Ames Research Center. The project for this internship focuses on Lake Untersee, an Earth analog for icy moons. The anoxic hole of Lake Untersee has a thermal bump that was first observed by Wand et al., 1997 and has been confirmed several times (Wand et al., 2006; Andersen 2011). The expected thermal profile of the hole is linear from 0 C at the thermocline to approximately 4 C, the ground temperature in Antarctica, at the bottom. Instead, there is an increase from 0 C near the thermocline to 5 C which is maintained for 7 m, then a linear profile to approximately 4 C near the bottom. Thermal modeling was conducted to quantify the energy input required to maintain the bump. The results revealed 2 sources. Chemical reactions and radiative energy were analyzed as possible explanation. The chemical analysis revealed a peak in Chlorophyll a at the same depth as the shallower source and several interesting reactions with maximum rates at the same location as the lower depth source. However, the energy released from these reactions was orders of magnitude smaller than required source. The radiation analysis revealed a profile with two peaks in similar locations to the sources and a total energy input within a factor of 1.5 of the required sources. The conclusion from this work is that photosynthesis and the chemical reactions support microbial life in the water column which in turn acts as an opacity to convert radiative energy into thermal energy. Recommendations for future work are aimed at quantifying the quantity and types of microbes present in the water column. Beyond the work of the project, two field trips are described and a discussion on benefits to the student of the internship is given.
The Chemical link between stars and their rocky planets

Instituto de Astrofísica e Ciências do Espaço, Universidade do Porto, CAUP, Rua das Estrelas, 4150-762 Porto, Portugal Departamento de Física e Astronomia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal University of Zurich, Institut of Computational Sciences, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland Max-Planck-Institut für Astronomie, Königstuhl 17, 69117, Heidelberg, Germany Instituto de Astrofísica de Canarias, E-38205 La Laguna, Tenerife, Spain Departamento de Astrofìsica, Universidad de La Laguna, E-38206 La Laguna, Tenerife, Spain Physikalisches Institut, University of Bern, Gesellschaftsstrasse 6, 3012, Bern, Switzerland Center for Cosmology and Astrophysics, Alikhanian National Science Laboratory, 2 Alikhanian Brothers Str. , 0036 Yerevan, Armenia European Southern Observatory, Alonso de Córdova 3107, Vitacura, Región Metropolitana, Chile Department of Physics, School of Medicine, Kurume University, 67 Asahimachi, Kurume, Fukuoka 830-0011, Japan National Astronomical Observatory, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan SOKENDAI, The Graduate University for Advanced Studies, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan Instituto de Astronomía, Universidad Nacional Autónoma de México, Ciudad Universitaria, CDMX, C.P. 04510, México Universidad Nacional de Córdoba (OAC), Laprida 854, X5000BGR, Córdoba, Argentina Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina Institut d’Astrophysique de Paris, UMR7095 CNRS, Université Pierre & Marie Curie, 98 bis Boulevard Arago, 75014 Paris, France Laboratório Nacional de Astrofísica, Rua Estados Unidos 154, Itajubá, MG 37504-364, Brazil ∗E-mail: vadibekyan@astro.up. pt
Spontaneous double alpha decay: First experimental limit and prospects of investigation

Nuclear decays with simultaneous emission of two alpha particles are energetically possible for a number of nuclides. Prospects of searching for such kind of decay for nuclides present in the natural isotopic composition of elements are discussed here. The first experimental limit on half-life for 2α decay is set for 209Bi as T1/2 > 2.9·1020 y at 90 % C.L. , using the data of work [P. de Marcillac et al. Nature 422 (2003) 876]. Theoretical T1/2 estimations for the process are also given. Using these values, which are on the level of 1033 y or more, one can conclude that the prospects of experimental observation of 2α decay are very pessimistic.
M-regularity of $\mathbb{Q}$-twisted sheaves and its application to linear systems on abelian varieties

G. Pareschi and M. Popa give criterions for global generations and surjectivity of multiplication maps of global sections of coherent sheaves on abelian varieties in the theory of M-regularity. In this paper, we generalize some of their criterions via the M-regularity of Q-twisted sheaves. As an application, we show that the M-regularity of a suitable Q-twisted sheaf implies property (Np) and jet-ampleness for ample line bundles on abelian varieties.
Dissecting subcycle interference in photoelectron holography

Nicholas Werby,1,2,* Andrew S. Maxwell,3,4,* Ruaridh Forbes,1,2,5 Philip H. Bucksbaum,1,2,6 and Carla Figueira de Morisson Faria4,† 1Stanford PULSE Institute, SLAC National Accelerator Laboratory 2575 Sand Hill Road, Menlo Park, California 94025, USA 2Department of Physics, Stanford University, Stanford, California 94305, USA 3Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels (Barcelona), Spain 4Department of Physics & Astronomy, University College London, Gower Street, London WC1E 6BT, United Kingdom 5Linac Coherent Light Source, SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA 6Department of Applied Physics, Stanford University, Stanford, California 94305, USA
Formation Process of CdS Nanorods via Solvothermal Route

The formation process of CdS nanorods prepared by the reaction of thiourea and cadmium nitrate in ethylenediamine was investigated in detail by X-ray powder diffraction, transmission electron microscopy, selected-area electron diffraction, and IR techniques. An accordion-like folding process was proposed to account for the formation of CdS nanorods. Further studies indicate that the dissociation of ethylenediamine molecule adsorbed on the surface of CdS results in the formation of CdS nanorods. The obtained CdS single-crystal nanorods showed an abnormal electron diffraction that was explained by the double diffraction of the incident electron inside the sample. The result from electron diffraction confirmed that CdS nanorods grew along c axis.
Set-valued classification - overview via a unified framework

Multi-class classification problem is among the most popular and well-studied statistical frameworks. Modern multi-class datasets can be extremely ambiguous and single-output predictions fail to deliver satisfactory performance. By allowing predictors to predict a set of label candidates, set-valued classification offers a natural way to deal with this ambiguity. Several formulations of set-valued classification are available in the literature and each of them leads to different prediction strategies. The present survey aims to review popular formulations using a unified statistical framework. The proposed framework encompasses previously considered and leads to new formulations as well as it allows to understand underlying trade-offs of each formulation. We provide infinite sample optimal set-valued classification strategies and review a general plug-in principle to construct data-driven algorithms. The exposition is supported by examples and pointers to both theoretical and practical contributions. Finally, we provide experiments on real-world datasets comparing these approaches in practice and providing general practical guidelines.
Understanding and Mitigating Accuracy Disparity in Regression

With the widespread deployment of large-scale prediction systems in high-stakes domains, e.g., face recognition, criminal justice, etc., disparity in prediction accuracy between different demographic subgroups has called for fundamental understanding on the source of such disparity and algorithmic intervention to mitigate it. In this paper, we study the accuracy disparity problem in regression. To begin with, we first propose an error decomposition theorem, which decomposes the accuracy disparity into the distance between marginal label distributions and the distance between conditional representations, to help explain why such accuracy disparity appears in practice. Motivated by this error decomposition and the general idea of distribution alignment with statistical distances, we then propose an algorithm to reduce this disparity, and analyze its game-theoretic optima of the proposed objective functions. To corroborate our theoretical findings, we also conduct experiments on five benchmark datasets. The experimental results suggest that our proposed algorithms can effectively mitigate accuracy disparity while maintaining the predictive power of the regression models.
Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks

Despite remarkable performance in producing realistic samples, Generative Adversarial Networks (GANs) often produce low-quality samples near low-density regions of the data manifold, e.g., samples of minor groups. Many techniques have been developed to improve the quality of generated samples, either by post-processing generated samples or by pre-processing the empirical data distribution, but at the cost of reduced diversity. To promote diversity in sample generation without degrading the overall quality, we propose a simple yet effective method to diagnose and emphasize underrepresented samples during training of a GAN. The main idea is to use the statistics of the discrepancy between the data distribution and the model distribution at each data instance. Based on the observation that the underrepresented samples have a high average discrepancy or high variability in discrepancy, we propose a method to emphasize those samples during training of a GAN. Our experimental results demonstrate that the proposed method improves GAN performance on various datasets, and it is especially effective in improving the quality and diversity of sample generation for minor groups.
On the Impact of Interpretability Methods in Active Image Augmentation Method

Robustness is a significant constraint in machine learning models. The performance of the algorithms must not deteriorate when training and testing with slightly different data. Deep neural network models achieve awe-inspiring results in a wide range of applications of computer vision. Still, in the presence of noise or region occlusion, some models exhibit inaccurate performance even with data handled in training. Besides, some experiments suggest deep learning models sometimes use incorrect parts of the input information to perform inference. Active image augmentation (ADA) is an augmentation method that uses interpretability methods to augment the training data and improve its robustness to face the described problems. Although ADA presented interesting results, its original version only used the vanilla backpropagation interpretability to train the U-Net model. In this work, we propose an extensive experimental analysis of the interpretability method’s impact on ADA. We use five interpretability methods: vanilla backpropagation, guided backpropagation, gradient-weighted class activation mapping (GradCam), guided GradCam and InputXGradient. The results show that all methods achieve similar performance at the ending of training, but when combining ADA with GradCam, the U-Net model presented an impressive fast convergence.
The Promises and Pitfalls of Deep Kernel Learning

Deep kernel learning (DKL) and related techniques aim to combine the representational power of neural networks with the reliable uncertainty estimates of Gaussian processes. One crucial aspect of these models is an expectation that, because they are treated as Gaussian process models optimized using the marginal likelihood, they are protected from overfitting. However, we identify situations where this is not the case. We explore this behavior, explain its origins and consider how it applies to real datasets. Through careful experimentation on the UCI, CIFAR-10, and the UTKFace datasets, we find that the overfitting from overparameterized maximum marginal likelihood, in which the model is"somewhat Bayesian", can in certain scenarios be worse than that from not being Bayesian at all. We explain how and when DKL can still be successful by investigating optimization dynamics. We also find that failures of DKL can be rectified by a fully Bayesian treatment, which leads to the desired performance improvements over standard neural networks and Gaussian processes.
Extraction of Caprolactam with Toluene in a Pulsed Disc and Doughnut Column—Part II: Experimental Evaluation of the Hydraulic Characteristics

Abstract In Part I of this study a theoretical model was recommended describing the hydraulic characteristics, being Sauter drop diameter, hold‐up, operating regimes, and operational window, of caprolactam extraction in a pulsed disc and doughnut column. In order to confirm the theoretical model pilot plant experiments for the caprolactam forward and back‐extraction were performed to determine the hydraulic characteristics as a function of the operating conditions. The experimental conditions covered the industrial operating range. All hydraulic experiments were performed at equilibrium conditions in order to avoid the influence of mass transfer. In the determination of the operational window flooding because of too low pulsation was qualitatively observed, while it was found that at high pulsation phase inversion was limiting for the back‐extraction process and flooding for the forward extraction process. Application of the in Part I recommended theoretical model for the description of the obtained hydraulic data resulted in an accurate description after fitting the drop diameter, hold‐up, and phase inversion data.
Dual-Awareness Attention for Few-Shot Object Detection

While recent progress has significantly boosted few-shot classification (FSC) performance, few-shot object detection (FSOD) remains challenging for modern learning systems. Existing FSOD systems follow FSC approaches, ignoring critical issues such as spatial variability and uncertain representations, and consequently result in low performance. Observing this, we propose a novel Dual-Awareness Attention (DAnA) mechanism that enables networks to adaptively interpret the given support images. DAnA transforms support images into query-position-aware (QPA) features, guiding detection networks precisely by assigning customized support information to each local region of the query. In addition, the proposed DAnA component is flexible and adaptable to multiple existing object detection frameworks. By adopting DAnA, conventional object detection networks, Faster R-CNN and RetinaNet, which are not designed explicitly for few-shot learning, reach state-of-the-art performance in FSOD tasks. In comparison with previous methods, our model significantly increases the performance by 47% (+6.9 AP), showing remarkable ability under various evaluation settings.