Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear Approaches to Intergenerational Income Mobility allowing for Measurement Error

This paper considers nonlinear measures of intergenerational income mobility such as (i) the effect of parents’ permanent income on the entire distribution of child’s permanent income, (ii) transition matrices, and (iii) rank-rank correlations when observed annual incomes are treated as measured-with-error versions of permanent incomes. We develop a new approach to identifying joint distributions in the presence of “two-sided” measurement error, and, hence, identify essentially all parameters of interest in the intergenerational income mobility literature. Using recent data from the 1997 National Longitudinal Study of Youth, we find that accounting for measurement error notably reduces various estimates of intergenerational mobility.
DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation

Unsupervised domain adaptation (UDA) for semantic segmentation aims to adapt a segmentation model trained on the labeled source domain to the unlabeled target domain. Existing methods try to learn domain invariant features while suffering from large domain gaps that make it difficult to correctly align discrepant features, especially in the initial training phase. To address this issue, we propose a novel Dual Soft-Paste (DSP) method in this paper. Specifically, DSP selects some classes from a source domain image using a long-tail class first sampling strategy and softly pastes the corresponding image patch on both the source and target training images with a fusion weight. Technically, we adopt the mean teacher framework for domain adaptation, where the pasted source and target images go through the student network while the original target image goes through the teacher network. Output-level alignment is carried out by aligning the probability maps of the target fused image from both networks using a weighted cross-entropy loss. In addition, feature-level alignment is carried out by aligning the feature maps of the source and target images from student network using a weighted maximum mean discrepancy loss. DSP facilitates the model learning domain-invariant features from the intermediate domains, leading to faster convergence and better performance. Experiments on two challenging benchmarks demonstrate the superiority of DSP over state-of-the-art methods. Code is available at https://github.com/GaoLii/DSP.
Hyperheavenly spaces theory in application to Walker and para-Kähler spaces. I

Spaces equipped with congruences of null strings are considered. A special attention is paid to the spaces which belong to the two-sided Walker class and para-Kähler class. Properties of an intersection of self-dual and anti-self-dual congruences of null strings are used as an additional criterion for a classification of such spaces. Finally, a few examples of para-Kähler and para-Kähler-Einstein spaces are presented.
A Bayesian Approach to Invariant Deep Neural Networks

We propose a novel Bayesian neural network architecture that can learn invariances from data alone by inferring a posterior distribution over different weight-sharing schemes. We show that our model outperforms other non-invariant architectures, when trained on datasets that contain specific invariances. The same holds true when no data augmentation is performed.
Revealing complex optical phenomena through vectorial metrics

Abstract. Advances in vectorial polarization-resolved imaging are bringing new capabilities to applications ranging from fundamental physics through to clinical diagnosis. Imaging polarimetry requires determination of the Mueller matrix (MM) at every point, providing a complete description of an object’s vectorial properties. Despite forming a comprehensive representation, the MM does not usually provide easily interpretable information about the object’s internal structure. Certain simpler vectorial metrics are derived from subsets of the MM elements. These metrics permit extraction of signatures that provide direct indicators of hidden optical properties of complex systems, while featuring an intriguing asymmetry about what information can or cannot be inferred via these metrics. We harness such characteristics to reveal the spin Hall effect of light, infer microscopic structure within laser-written photonic waveguides, and conduct rapid pathological diagnosis through analysis of healthy and cancerous tissue. This provides new insight for the broader usage of such asymmetric inferred vectorial information.
The Far Ultra-violet Background

The diffuse far-ultraviolet (FUV) background has received considerable attention from astronomers since the 1970s The initial impetus came from the hope of detecting UV radiation from the hot intergalactic medium. The central importance of the FUV background to the physics (heating and ionization) of the diffuse atomic phases motivated the next generation of experiments. The consensus view is that the diffuse FUV emission at high latitudes has three components: stellar FUV reflected by dust grains (diffuse galactic light or DGL), FUV from other galaxies and the intergalactic medium (extra-galactic background light or EBL) and a component of unknown origin (and referred to as the “offset” component). During the 1980s, there was some discussion that decaying dark matter particles produced FUV radiation. In this paper I investigate production of FUV photons by conventional sources: line emission from Galactic Hot Ionized Medium, two-photon emission from the Galactic Warm Ionized Medium and low-velocity shocks, and Lyman-β fluorescence of hydrogen at several locales in the Solar System (the interplanetary medium, the exosphere and the thermosphere of Earth). I conclude that two thirds and arguably all of the offset component can be explained by the sum of the radiation from the processes listed above.
Eigenvector Centrality and Uniform Dominant Eigenvalue of Graph Components

Eigenvector centrality is one of the outstanding measures of central tendency in graph theory. In this paper we consider the problem of calculating eigenvector centrality of graph partitioned into components and how this partitioning can be used. Two cases are considered; first where the a single component in the graph has the dominant eigenvalue, secondly when there are at least two components that share the dominant eigenvalue for the graph. In the first case we implement and compare the method to the usual approach (power method) for calculating eigenvector centrality while in the second case with shared dominant eigenvalues we show some theoretical and numerical results. Keywords: Eigenvector centrality, power iteration, graph, strongly connected component.
Reconstruction of the Density Power Spectrum from Quasar Spectra using Machine Learning

We describe a novel end-to-end approach using Machine Learning to reconstruct the power spectrum of cosmological density perturbations at high redshift from observed quasar spectra. State-of-the-art cosmological simulations of structure formation are used to generate a large synthetic dataset of line-of-sight absorption spectra paired with 1-dimensional fluid quantities along the same line-of-sight, such as the total density of matter and the density of neutral atomic hydrogen. With this dataset, we build a series of data-driven models to predict the power spectrum of total matter density. We are able to produce models which yield reconstruction to accuracy of about 1% for wavelengths $k \leq 2 h Mpc^{-1}$, while the error increases at larger $k$. We show the size of data sample required to reach a particular error rate, giving a sense of how much data is necessary to reach a desired accuracy. This work provides a foundation for developing methods to analyse very large upcoming datasets with the next-generation observational facilities.
EEG-based Cross-Subject Driver Drowsiness Recognition with Interpretable CNN

—In the context of electroencephalogram (EEG)-based driver drowsiness recognition, it is still a challenging task to design a calibration-free system, since there exists a significant variability of EEG signals among different subjects and recording sessions. As deep learning has received much research attention in recent years, many efforts have been made to use deep learning methods for EEG signal recognition. However, existing works mostly treat deep learning models as blackbox classifiers, while what have been learned by the models and to which extent they are affected by the noise from EEG data are still underexplored. In this paper, we develop a novel convolutional neural network that can “explain” its decision by highlighting the local areas of the input sample that contain important information for the classification. The network has a compact structure for ease of interpretation and takes advantage of separable convolutions to process the EEG signals in a spatial-temporal sequence. Results show that the model achieves an average accuracy of 78.35% on 11 subjects for leave-one-out cross-subject drowsiness recognition, which is higher than the conventional baseline methods of 53.4%-72.68% and state-of-art deep learning methods of 63.90%-65.61%. Visualization results show that the model has learned to recognize biologically explainable features from EEG signals, e.g., Alpha spindles, as strong indicators of drowsiness across different subjects. In addition, we also explore reasons behind some wrongly classified samples and how the model is affected by artifacts and noise in the data. Our work illustrates a promising direction on using interpretable deep learning models to discover meaning patterns related to different mental states from complex EEG signals.
Active 3D Shape Reconstruction from Vision and Touch

Humans build 3D understandings of the world through active object exploration, using jointly their senses of vision and touch. However, in 3D shape reconstruction, most recent progress has relied on static datasets of limited sensory data such as RGB images, depth maps or haptic readings, leaving the active exploration of the shape largely unexplored. Inactive touch sensing for 3D reconstruction, the goal is to actively select the tactile readings that maximize the improvement in shape reconstruction accuracy. However, the development of deep learning-based active touch models is largely limited by the lack of frameworks for shape exploration. In this paper, we focus on this problem and introduce a system composed of: 1) a haptic simulator leveraging high spatial resolution vision-based tactile sensors for active touching of 3D objects; 2)a mesh-based 3D shape reconstruction model that relies on tactile or visuotactile signals; and 3) a set of data-driven solutions with either tactile or visuotactile priors to guide the shape exploration. Our framework enables the development of the first fully data-driven solutions to active touch on top of learned models for object understanding. Our experiments show the benefits of such solutions in the task of 3D shape understanding where our models consistently outperform natural baselines. We provide our framework as a tool to foster future research in this direction.
Confidence Aware Neural Networks for Skin Cancer Detection

Deep learning (DL) models have received particular attention in medical imaging due to their promising pattern recognition capabilities. However, Deep Neural Networks (DNNs) require a huge amount of data, and because of the lack of sufficient data in this field, transfer learning can be a great solution. DNNs used for disease diagnosis meticulously concentrate on improving the accuracy of predictions without providing a figure about their confidence of predictions. Knowing how much a DNN model is confident in a computer-aided diagnosis model is necessary for gaining clinicians' confidence and trust in DL-based solutions. To address this issue, this work presents three different methods for quantifying uncertainties for skin cancer detection from images. It also comprehensively evaluates and compares performance of these DNNs using novel uncertainty-related metrics. The obtained results reveal that the predictive uncertainty estimation methods are capable of flagging risky and erroneous predictions with a high uncertainty estimate. We also demonstrate that ensemble approaches are more reliable in capturing uncertainties through inference.
Self-regenerative and self-enhanced smart graphene/Ag3PO4 hydrogel adsorbent under visible light

A new self-regenerative and self-enhanced smart adsorbent was synthesized by hybridizing the photocatalyst Ag3PO4 nanospheres into a 3D reduced graphene oxide (rGO) hydrogel, which was then used to absorb and mineralize the artificially synthesized dyes methylene blue (MB) and methyl orange (MO). The results indicated that Ag3PO4 particles anchored on the rGO sheets densely and firmly with a ∼20 nm average diameter. The large specific surface area (SSA) (∼100.29 m2 g−1) of the samples could provide many adsorption sites for organic dyes to get good removal effectiveness. The results showed that the 3D graphene/Ag3PO4 composites exhibited excellent removal effectiveness of MB and MO as well as reusability. Especially, the adsorption ability of the adsorbent increased greatly after recycling one-time. The negatively charged functional groups of rGO sheets could attract silver ions (Ag+), which could help the Ag3PO4 particles anchor onto the rGO sheets. The rGO hydrogel has adsorption ability toward dyes, which can help increase the contact chance between dyes and nanoparticles Ag3PO4. The positive effects between Ag3PO4 and rGO could be attributed to the adsorption activity and photocatalytic properties. This study provides a green way to fabricate self-regenerative and self-enhanced smart hydrogel adsorbents under visible light.
Extract Genomic DNA from Lentinula edodes Using CTAB Method

Genomic DNA was extracted from mycelium of 27 Lentinula edodes strains tested using the cationic detergent cetyl-trimethylammonium bromide(CTAB) method described for extracting plant DNA and adapted to L edodes.The extracted DNA was assessed by ultraviolet photometer,agarose gel electrophoresis,and PCR technique,respectively.The results show that CTAB is a convenient genomic DNA extraction method for L edodes.
An Environment-Friendly Method to Catalytically Oxidate NO in Waste Water by Supported Manganese Oxide on Graphite Oxide

In order to generate powerful radicals as oxidizing species for the complete oxidation of NO, homogeneous activation of peroxymonosulfate (Oxone: PMS) by the Mn3O4/GO catalysts was explored. The catalytic oxidation of NO from waste gas was investigated using advanced oxidation process based on sulfate radicals that produced. The manganese oxide immobilized on graphene oxide (GO) can activate PMS for the oxidation of NO in water. We not only took advantage of the high oxidation–reduction potential of produced sulfite radicals but also an opportunity to oxidize NO on less complex compounds with low dosages. The Mn3O4/GO catalysis system was characterized using X-ray diffraction (XRD) and scanning electron microscopy (SEM). The results showed that Mn3O4 was well-distributed on GO. The Mn3O4/GO catalyst system exhibited high efficiency for NO oxidation when the Mn3O4/GO catalyst has an optimum Mn3O4 loading. In addition, the best catalytic oxidation can be achieved within 30 min with pH 4 and 6 mM PMS at 25 °C. Therefore, the results indicate promising potential for a system utilizing Mn3O4/PMS to oxidize NO for offgas treatment.
The Random Walk Simulation of the Ions Diffusion in the Membrane Materials of Lithium-Ion Battery

Based on the self-developed large-scale random walk simulaton software,something has been studied the diffusion behavior of lithium cation in the membrane materials.Systematic studies on the factors that influence the lithium ions diffusion in the membrane materials such as directed migration rate,the average pore diameter of the membrane and the plastic collision between the membrane skeletons and ions were carried out.The results show that the directed migration rate and the average pore diameter of the membrane greatly influence the diffusion behavior of the lithium ions in membrane materials.It is provide the theoretical supports to the design of the lithium-ion battery.
Clinical Management of Drug Resistant Epilepsy: A Review on Current Strategies

Abstract Drug resistant epilepsy (DRE) is defined as the persistence of seizures despite at least two syndrome-adapted antiseizure drugs (ASD) used at efficacious daily dose. Despite the increasing number of available ASD, about a third of patients with epilepsy still suffer from drug resistance. Several factors are associated with the risk of evolution to DRE in patients with newly diagnosed epilepsy, including epilepsy onset in the infancy, intellectual disability, symptomatic epilepsy and abnormal neurological exam. Pharmacological management often consists in ASD polytherapy. However, because quality of life is driven by several factors in patients with DRE, including the tolerability of the treatment, ASD management should try to optimize efficacy while anticipating the risks of drug-related adverse events. All patients with DRE should be evaluated at least once in a tertiary epilepsy center, especially to discuss eligibility for non-pharmacological therapies. This is of paramount importance in patients with drug resistant focal epilepsy in whom epilepsy surgery can result in long-term seizure freedom. Vagus nerve stimulation, deep brain stimulation or cortical stimulation can also improve seizure control. Lastly, considering the effect of DRE on psychologic status and social integration, comprehensive care adaptations are always needed in order to improve patients’ quality of life.
Using Differential Scanning Calorimetry To Explore the Phase Behavior of Chocolate

Chocolate is certainly one of the most familiar and commercially successful products made from plant resources. This success has been built on many decades of careful optimization and innovation in the procedures used to transform the complex mixture of chemical substances extracted from cocoa beans into commercial products. The favorable public image of chocolate has led to it being used both as a captivating aid to teaching chemistry and a convenient source of “real-world” samples for analytical chemistry classes. The objective of this experiment is to explore the melting behavior of different types of chocolate using differential scanning calorimetry. Students change the distribution of cocoa butter polymorphs by subjecting samples to specific thermal treatments. Interpretation of the thermograms of these samples helps students to understand the complexity and importance that low-melting phases have in real-world applications in cosmetics, pharmaceutics, and the food industry.
Studies on solubility parameter of amoxycillin trihydrate : Influence on in vitro release and antibacterial activity

Amoxycillin trihydrate, a broad spectrum antibiotic, is a poorly water-soluble drug and has low bioavailability on oral administration. Solubility parameter of the drug (I´2) was evaluated in blends of ethyl acetate-propylene glycol and water-propylene glycol in the ratios 100:0, 75:25, 50:50, 25:75 and 0:100. The results obtained were compared with the I´2, values obtained using Molar Volume method and Fedor's group substitution method. Ethyl acetate-propylene glycol (75:25) was found to give maximum solubility with an experimental value of 13.32 H in comparison to the theoretical values of 13.25 H by molar volume method and 15.46 H from Fedor's group substitution method. In vitro drug release studies and antibacterial effect were evaluated with an intention to study the effect of the solvent blends on the drug release and its activity. In the in vitro studies, the water-propylene glycol (100:0) and the ethyl acetate-propylene glycol (50:50) ratios gave maximum release of the drug in their respective blends. The results of the in vitro studies correlated with the antibiotic Study conducted on both binary blends.
Overlaps of multiple database retrieval and citation tracking in dementia care research: a methodological study

Objective: We aimed to determine overlaps and optimal combination of multiple database retrieval and citation tracking for evidence synthesis, based on a previously conducted scoping review on facilitators and barriers to implementing nurse-led interventions in dementia care. Methods: In our 2019 scoping review, we performed a comprehensive literature search in eight databases (CENTRAL, CINAHL, Embase, Emcare, MEDLINE, Ovid Nursing Database, PsycINFO, and Web of Science Core Collection) and used citation tracking. We retrospectively analyzed the coverage and overlap of 10,527 retrieved studies published between 2015 and 2019. To analyze database overlap, we used cross tables and multiple correspondence analysis (MCA). Results: Of the retrieved studies, 6,944 were duplicates and 3,583 were unique references. Using our search strategies, considerable overlaps can be found in some databases, such as between MEDLINE and Web of Science Core Collection or between CINAHL, Emcare, and PsycINFO. Searching MEDLINE, CINAHL, and Web of Science Core Collection and using citation tracking were necessary to retrieve all included studies of our scoping review. Conclusions: Our results can contribute to enhancing future search practice related to database selection in dementia care research. However, due to limited generalizability, researchers and librarians should carefully choose databases based on the research question. More research on optimal database retrieval in dementia care research is required for the development of methodological standards.
Evaluation of Intraocular Lens Rotational Stability in a Multicenter Clinical Trial

Purpose To evaluate the postoperative rotational stability of two prototype intraocular lens (IOL) designs (subsequently termed version 1 and version 2). Patients and Methods A prospective, multicenter, randomized, paired-eye, 6-month study evaluated the version 1 and version 2 IOLs. Results were compared with a control IOL (TECNIS® toric 1-piece monofocal IOL) evaluated in a separate, similarly designed study. Participants aged ≥22 years and scheduled to undergo bilateral cataract extraction were randomly assigned 1:1 to receive the version 1 or version 2 IOL in the first operative eye; the alternate test IOL was then implanted in the second operative eye. Results Mean absolute IOL rotation at postoperative week 1 was the primary effectiveness end point. Additional end points included the percentage of eyes with postoperative IOL rotation >5°/>10°, direction of lens rotation, surgeon-reported ease of IOL handling during implantation, and safety. At postoperative week 1, mean (±standard deviation) absolute IOL rotation was significantly lower for both version 1 and version 2 versus control (0.88° [±0.94] and 0.71° [±0.69] vs 2.24° [±3.21], respectively; both P < 0.001). For both study lenses, absolute rotation was <5° for all eyes at postoperative week 1, and no cases of rotation >10° were observed at any postoperative time point. From postoperative week 1 onward, version 2 had a statistically significant clockwise bias in the direction of rotation (P = 0.03); similar findings were observed for version 1. Surgeons reported acceptable ease of IOL handling during implantation for both version 1 and version 2. No device-related adverse events were reported. Conclusion Both the version 1 and version 2 IOLs, each with frosted, squared haptics, demonstrated improved postoperative rotational stability compared with a control lens without frosted haptics. Because version 2 had the same overall geometry as the current TECNIS toric IOL, this design was selected for commercialization. Trial Registration German Clinical Trials Register, DRKS00015287.