Miracle-dz/UniFAR-FTUs huggingface dataset:
Model of electron and ion distributions in the plasma sheet boundary layer

Electron and ion velocity space distributions in the plasma sheet boundary layer have distinct features and exhibit a characteristic evolution with depth in the boundary layer. Near the lobeward edge of the layer, enhanced earthward and tailward directed electron flux is observed. Somewhat deeper in the boundary layer, earthward and tailward directed ion beams are observed. The electron and ion beams have low-speed cutoffs, and the earthward directed beams are consistently observed at lower speeds than the simultaneously observed tailward directed beams. The ion distributions evolve from magnetic-field-aligned beams, to “kidney bean” shaped distributions, to isotropic shells with increasing equatorward penetration into the boundary layer. A two-dimensional model based on quasi-steady reconnection occurring in the distant magnetotail is able to reproduce all of these observed features in the electron and ion distribution functions. The essential features of the model are the finite time-of-flight effect (velocity filter effect), conservation of energy, and conservation of magnetic moment as the particles stream from the low magnetic field region near the central plasma sheet to the higher field region in the boundary layer. The model can be used to estimate the central plasma sheet density, temperature, and bulk flow speed as functions of position earthward of a reconnection site from observed plasma sheet boundary layer distributions. These plasma distributions obtained from the model may be useful in determining the stability of the boundary layer plasma to various electrostatic and electromagnetic modes.
Discrete-Time Model Reference Adaptive Control for Nonminimum Phase Systems With Disturbances

This paper presents a new design method of discrete-time model reference adaptive control for nonminimum phase systems with disturbances. It is assumed that the disturbances are described by a polynomial function of time with known degree and unknown coefficients. The proposed scheme uses only input and output data and the existence of bounds for all signals is proved, which assures the output error convergence to zero. Finally, the results of computer simulation are presented to illustrate the effectiveness of the proposed method.
Enhancement of Composition and Oxidative Stability of Chia (Salvia hispanica L.) Seed Oil by Blending with Specialty Oils.

Chia seed (Salvia hispanica L.) oil is mainly composed of ω-3 fatty acids (61% to 70%). Despite being nutritionally favorable, higher amounts of polyunsaturated fatty acids result in poorer oxidative stability. Thus, the aim of this work was to produce edible vegetable oil blends rich in ω-3 fatty acids and with greater oxidative stability than pure chia oil. Blending of chia with other specialty oils (walnut, almond, virgin, and roasted sesame oils) was assessed in the following respective proportions: 20:80, 30:70, and 40:60 (v/v). An accelerated storage test was conducted (40 ± 1 °C, 12 days). Primary and secondary oxidation products, free fatty acid content, antioxidant compounds, fatty acid composition, and induction time were determined. The blends presented higher oxidative stability indices than chia oil. Sensory analysis showed that, given a pure oil, judges did not identify statistically significant differences among the blends. The results suggest that blending of chia oil is an adequate alternative to obtain ω-3-enriched oils with higher oxidative stability indices. PRACTICAL APPLICATION: Vegetable oil blending is a widely used practice in the edible oil industry to produce blended oils with enhanced stability and nutritional and sensory properties at affordable prices. The blends developed in this study from chia, sesame, walnut, and almond oils take advantage of the properties of each parent oil to yield products with improved oxidative stability, essential fatty acid presence, and sensory characteristics. To achieve a daily intake of 2.22 g/day of ω-3 fatty acids as recommended by the Intl. Society for the Study of Fatty Acids and Lipids (ISSFAL), it is necessary to consume approximately one spoonful of the formulated mixtures.
The Effects of Gestural Filler in Reducing Perceived Latency in Conversation with a Digital Human

The demand to utilize digital humans for conversation systems in multiple areas has increased. Time delays in a conversation with digital humans can be occurred because of network and natural language processing, which can deteriorate the users’ satisfaction and the availability of the digital humans. In this study, we designed a gestural filler that can be exhibited when delay occurs during a conversation with a digital human, and compared the effects of the gestural filler with conversational filler.
Sizes and Kinematics of Extended Narrow-line Regions in Luminous Obscured AGN Selected by Broadband Images

To study the impact of active galactic nuclei (AGN) feedback on their galactic ISM, we present Magellan long-slit spectroscopy of 12 luminous nearby obscured AGN ( , z ∼ 0.1). These objects are selected from a parent sample of spectroscopically identified AGN to have high [O iii]λ5007 and Wide-field Infrared Survey Explorer mid-IR luminosities and extended emission in the Sloan Digital Sky Survey r-band images, suggesting the presence of extended [O iii]λ5007 emission. We find spatially resolved [O iii] emission (2–35 kpc) in 8 out of 12 of these objects. Combined with samples of higher luminosity obscured AGN, we confirm that the size of the narrow-line region (RNLR) scales with the mid-IR luminosity until the relation flattens at RNLR ∼ 10 kpc. Nine out of 12 objects in our sample have regions with broad [O iii] line widths (w80 > 600 km s−1), indicating outflows. We define these regions as the kinematically disturbed region (KDR). The size of the KDR ( ) is typically smaller than RNLR by few kiloparsecs but also correlates strongly with the AGN mid-IR luminosity. Given the uncertain outflow mass, we derive a loose constraint on the outflow energy efficiency . We find no evidence for an AGN luminosity threshold below which outflows are not launched. To explain the sizes, velocity profiles, and high occurrence rates of the outflows in the most luminous AGN, we propose a scenario in which energy-conserving outflows are driven by AGN episodes with ∼108 year durations. Within each episode, the AGN is unlikely to be constantly luminous but could flicker on shorter timescales (≲107 yr) with a moderate duty cycle (∼10%).
Some Remarks on Wild Observations

Editor's Note: At the 1959 meetings of the American Statistical Association held in Washington D. C. , Messrs. F. J. Anscombe and C. Daniel presented papers on the detection and rejection of 'outliers', that is, observations thought to be maverick or unusual. These papers and their discussion will appear in the next issue of Technometrics. The following comments of Dr. Kruskal are another indication of the present interest of statisticians in this important problem. The purpose of these remarks is to set down some non-technical thoughts on apparently wild or outlying observations. These thoughts are by no means novel, but do not seem to have been gathered in one convenient place. 1. Whatever use is or is not made of apparently wild observations in a statistical analysis, it is very important to say something about such observations in any but the most summary report. At least a statement of how many observa
PLASMID CARRIAGE AND THE UNORTHODOX USE OF 1 FISHER’S THEOREM IN EVOLUTIONARY BIOLOGY 2

The link between fitness and reproduction rate is a central tenet in evolutionary biology: 7 mutants reproducing faster than the dominant wild-type are favoured by selection, oth- 8 erwise the mutation is lost. This link is given by Fisher’s theorem under the assump- 9 tion that fitness can only change through mutations. Here I show that fitness, as for- 10 malised by Fisher, changes through time without invoking new mutations—allowing the 11 co-maintenance of fast- and slow-growing genotypes. The theorem does not account for 12 changes in population growth that naturally occur due to resource depletion, but it is key 13 to this unforeseen co-maintenance of growth strategies. To demonstrate that fitness is 14 not constant, as Fisher’s theorem predicates, I co-maintained a construct of Escherichia 15 coli harbouring pGW155B, a non-transmissible plasmid that protects against tetracycline, 16 in competition assays without using antibiotics. Despite growing 40% slower than its 17 drug-sensitive counterpart, the construct with pGW155B persisted throughout the com- 18 petition without tetracycline—maintaining the plasmid. Thus, predicting the selection 19 of microbial genotypes may be more difficult than previously thought. 20
Social stress induces autoimmune responses against the brain to promote stress susceptibility

Clinical studies have revealed a high comorbidity between autoimmune and psychiatric disorders, including major depressive disorder (MDD). However, the mechanisms connecting autoimmunity and depression remain unclear. Here, we aim to identify the processes linking adaptive immune abnormalities and depression. To examine this relationship, we analyzed antibody responses and autoimmunity in the chronic social defeat stress (CSDS) model in mice, and in clinical samples from patients with MDD. We show that socially stressed mice have elevated serum antibody concentrations. Activation of social stress-induced antibody responses were confirmed by detecting expansion of specific T and B cell populations particularly in the cervical lymph nodes, where brain-derived antigens are preferentially delivered. IgG antibody concentrations in the brain were significantly higher in stress-susceptible mice than in unstressed mice, and positively correlated with social avoidance. IgG antibodies accumulated around the blood vessels in brain sections from stress-susceptible mice. Moreover, sera from stress-susceptible mice exhibited high reactivity against brain tissue, and brain-reactive IgG antibody levels positively correlated with depression-like behavior. Similarly, in humans, increased peripheral levels of brain-reactive IgG antibodies were associated with increased anhedonia. Furthermore, high stress-resilience was observed in B cell-depleted mice, confirming a causal link between antibody-producing cells and depression-like behavior. This study provides novel mechanistic insights connecting stress-induced autoimmune reactions against the brain and stress susceptibility. Therapeutic strategies targeting autoimmune responses can therefore be devised to treat patients with MDD featuring immune abnormalities.
A compact breast shape acquisition system for improving diffuse optical tomography image reconstructions

Diffuse optical tomography (DOT) has been investigated for diagnosing malignant breast lesions but its accuracy relies on model-based image reconstructions which in turn depends on the accuracy of breast shape acquisition. In this work, we have developed a dual-camera structured light imaging (SLI) breast shape acquisition system tailored for a mammography-like compression setting. Illumination pattern intensity is dynamically adjusted to account for skin tone differences while thickness-informed pattern masking reduces artifacts due to specular reflections. This compact system is affixed to a rigid mount that can be installed into existing mammography or parallel-plate DOT systems without the need for camera-projector re-calibration. Our SLI system produces sub-millimeter resolution with a mean surface error of 0.26 mm. This breast shape acquisition system results in more accurate surface recovery, with an average 1.6-fold reduction in surface estimation errors over a reference method via contour extrusion. Such improvement translates to 25% to 50% reduction in mean squared error in the recovered absorption coefficient for a series of simulated tumors 1-2 cm below the skin.
Distinct super-enhancer elements differentially control Il2ra gene expression in a cell-type specific fashion

The IL-2 receptor α-chain (IL-2Rα/CD25) is constitutively expressed on DN2/DN3 thymocytes and Treg cells but induced by IL-2 on mature T and NK cells. Il2ra expression is regulated by a super-enhancer extensively bound by STAT5 in mature T cells. Here, we demonstrate that STAT5 cooperates with Notch to induce/maintain Il2ra/CD25 expression in DN2/DN3 cells. Moreover, we systematically investigated CD25 regulation using a series of mice with deletions spanning STAT5 binding elements. Deleting the upstream super-enhancer region mainly affected constitutive CD25 expression on DN2/DN3 thymocytes and Tregs, whereas deleting an intronic region primarily decreased IL-2-induced CD25 on peripheral T and NK cells. Thus, distinct elements preferentially control constitutive versus inducible expression in a cell-type-specific manner, with the MED1 coactivator co-localizing with specific STAT5 binding sites. Moreover, the intronic region was a dominant element whose deletion altered the structure throughout the super-enhancer in mature T cells. These results demonstrate differential functions for distinct super-enhancer elements, thereby indicating ways to manipulate CD25 expression in a cell-type specific fashion.
The Rubber Hand Illusion in Virtual Reality and the Real World - Comparable but Different

Feeling ownership of a virtual body is crucial for immersive experiences in VR. Knowledge about body ownership is mainly based on rubber hand illusion (RHI) experiments in the real world. Watching a rubber hand being stroked while one’s own hidden hand is synchronously stroked, humans experience the rubber hand as their own hand and underestimate the distance between the rubber hand and the real hand (proprioceptive drift). There is also evidence for a decrease in hand temperature. Although the RHI has been induced in VR, it is unknown whether effects in VR and the real world differ. We conducted a RHI experiment with 24 participants in the real world and in VR and found comparable effects in both environments. However, irrespective of the RHI, proprioceptive drift and temperature differences varied between settings. Our findings validate the utilization of the RHI in VR to increase our understanding of embodying virtual avatars.
The Analysis of Two-Factor Interactions in Fixed Effects Linear Models

This article considers two related issues concerning the analysis of interactions in complex linear models. The first issue concerns the omnibus test for interaction. Apparently, it is not well known that the usual F test for interaction can be replaced, in many applications, by a test that is more powerful against a certain class of alternatives. The competing test is based on the maximal product interaction contrast F statistic and achieves its power advantage by focusing solely on product contrasts. The maximal product interaction F test is reviewed and three new results are reported: (a) An extended table of exact critical values is computed, (b) a table of moment functions useful for approximating the p-value corresponding to an observed maximal F statistic is computed, and (c) a simulation study concerning the null distribution of the maximal F statistic when data are unbalanced or covariates are present is reported. It is conjectured that lack of balance or presence of covariates has no effect on the null distribution. The simulation results support the conjecture. The second issue concerns follow-up tests when the omnibus test is significant. It appears that researchers, in general, do not perform coherent follow-up tests on interactions. To make it easier for researchers to do so, an exposition on the use of product interaction contrasts and partial interactions in complex fixed-effects models is provided. The recommended omnibus and follow-up tests are illustrated on an educational data set analyzed using SAS (SAS Institute, 1988) andSPSS (1990).
Inverse problems for the Boussinesq system

We obtain two results on inverse problems for a 2D Boussinesq system. One is that we prove the Lipschitz stability for the inverse source problem of identifying a time-independent external force in the system with observation data in an arbitrary sub-domain over a time interval of the velocity and the data of velocity and temperature at a fixed positive time t0 > 0 over the whole spatial domain. The other one is that we prove a conditional stability estimate for an inverse problem of identifying the two initial conditions with a single observation on a sub-domain.
Formation of Low-Mass X-ray Binaries. I. Constraints on Hydrogen-Rich Donors at the Onset of the X-ray Phase

We identify and quantify the set of constraints that neutron star-normal star binaries must satisfy in order to become observable LMXBs. These constraints are related to (i) the thermal and hydrostatic equilibrium of the donors, (ii) the degree to which the mass transfer process is conservative, and (iii) the age of the systems. They divide the parameter space of potential LMXBs in several distinct parts, of which those that actually become LMXBs at the onset of mass transfer occupy only a small part. Of the remainder, many become unstable to dynamical time scale mass transfer either at the onset or later in the course of mass transfer, and enter common envelope evolution. Others experience super-Eddington mass transfer but may eventually survive to become LMXBs. These survivors arguably include binary millisecond pulsars with orbital periods in excess of $100$\,d, ultrashort-period LMXBs with hydrogen-deficient donors, and long-period LMXBs with giant donors.
On the generalized fréchet distance and its applications

Measuring the similarity of spatio-temporal trajectories in a sensible fashion is an important building block for applications such as trajectory clustering or movement pattern analysis. However, typically employed similarity measures only take the spatial components of the trajectory into account, or are complicated combinations of different measures. In this paper we introduce the so called Generalized Fréchet distance, which extends the well-known Fréchet distance. For two polygonal curves of length n and m in d-dimensional space, the Generalized Fréchet distance enables an individual weighting of each dimension on the similarity value by using a convex function. This allows to integrate arbitrary data dimensions as e.g. temporal information in an elegant, flexible and application-aware manner. We study the Generalized Fréchet Distance for both the discrete and the continuous version of the problem, prove useful properties, and present efficient algorithms to compute the decision and optimization problem. In particular, we prove that for d ∈ O(1) the asymptotic running times of the optimization problem for the continuous version are O(nm log(nm)) under realistic assumptions, and O(nm) for the discrete version for arbitrary weight functions. Therefore the theoretical running times match those of the classical Fréchet distance. In our experimental evaluation, we demonstrate the usefulness of the Generalized Fréchet distance and study the practical behaviour of our algorithms. On sets of real-world trajectories, we confirm that the weighting of the spatial and temporal dimensions heavily impacts the relative similarity, and hence the ability to tailor the measure to the application is a useful tool.
Spatio-temporal graph convolutional network for stochastic traffic speed imputation

The rapid increase of traffic data generated by different sensing systems opens many opportunities to improve transportation services. An important opportunity is to enable stochastic routing that computes the arrival time probabilities for each suggested route instead of only the expected travel time. However, traffic datasets typically have many missing values, which prevents the construction of stochastic speeds. To address this limitation, we propose the Stochastic Spatio-Temporal Graph Convolutional Network (SST-GCN) architecture that accurately imputes missing speed distributions in a road network. SST-GCN combines Temporal Convolutional Networks and Graph Convolutional Networks into a single framework to capture both spatial and temporal correlations between road segments and time intervals. Moreover, to cope with datasets with many missing values, we propose a novel self-adaptive context-aware diffusion process that regulates the propagated information around the network, avoiding the spread of false information. We extensively evaluate the effectiveness of SST-GCN on real-world datasets, showing that it achieves from 4.6% to 50% higher accuracy than state-of-the-art baselines using three different evaluation metrics. Furthermore, multiple ablation studies confirm our design choices and scalability to large road networks.
Deep Learning Analysis in Development of Handwritten and Plain Text Classification API

Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR) are technologies that enable text recognition. The difference between OCR and HTR is one designed specifically for digital text and one designed for handwritten text. There are already various implementations of OCR and HTR online. However, such systems do not guarantee the systems are in premises. To solve this problem, the OCR and HTR system must be built from the scratch. The purpose of this research is to improve the recognition by separating the text whether it is a handwritten or a printed text, which will later be forwarded into the appropriate recognition system. An application program interface (API) was also created in order to finalize the classification system into real world usage. In this research, the classification system being developed using convolutional neural network (CNN) method. To be able to reach the highest accuracy of the classification system, the experimentation and improvement on hyperparameters, dataset format, data augmentation and analysis on 3 CNN architectures were conducted. In the end of this research, there are 2 architectures in a tight competition on ideal data testing, one is VGG-16 with 90.63% accuracy and one is AlexNet with 90.17% accuracy. However, AlexNet is chosen as the winner after real data testing.
Optimization of Language Models by Word Computing

Word computation is a type of sentiment analysis that requires the identification not only of linguistic features, but also of the correlations of these features. There has been a great deal of research in this area. In order to understand linguistic representations and their applications in various domains of analysis, various factors such as demographics, emotions, and gender are taken into account in a transactional context. In this paper, we focus on those factors that can be extracted from existing data using natural language processing. We find that the most successful personality trait prediction models rely heavily on NLP techniques. To automate this process, researchers around the world have used a variety of machine learning and deep learning techniques. Different combinations of factors have led to different research results. We have conducted a comparative analysis of these experiments in the hope of determining the future course of action.