Miracle-dz/UniFAR-FTUs huggingface dataset:
A Method for Accurate 3-D Gaze Estimation With a Single Camera and Two Collinear Light Sources

The simplest configuration for estimating 3-D line-of-sight (LoS) based on corneal reflection and pupil refraction uses a single camera and two noncollinear light sources. The corneal radius and 3-D corneal center can be determined by solving a system of six equations with six unknowns. However, when two light sources are collinear with the camera, the reflection planes of the two light sources are coplanar. In this case, the estimation of the 3-D corneal center is underdetermined in the absence of any preknown eyeball parameters (e.g., corneal radius). To solve this issue, this article proposes to calibrate the corneal radius with corneal reflections from two light sources combined with an iris imaging model so that the 3-D corneal center is estimated with a known corneal radius. Then, the pupil features or iris features can be selected to estimate the 3-D pupil center or 3-D iris center, which uses an optimization solution for only the center parameters to avoid unnecessary errors. Thus, the optical axis of the eyeball is constructed with two spatial centers, and the kappa angle calculated in real time is used to convert the optical axis of the eyeball to the 3-D LoS. We simulated this gaze estimation process using pupil and iris features, and performed error analysis on the proposed method. In practical experiments, the proposed method was verified using iris features, and the gaze accuracy was 1.22° and 1.13° in the $x$ - and $y$ -directions, respectively, which is comparable to that of more sophisticated systems. A single-camera-two-collinear-light-source system is the most typical and common system configuration, and the realization of 3-D gaze estimation under such a system configuration can effectively promote the wide application of eye-tracking interaction.
Canonical quantization of Plebanski gravity in diagonal variables

In this paper we have carried out a transformation from Ashtekar's theory of GR into a reduced theory where the physical degrees of freedom are explicit. We have performed the canonical analysis, computed the classical dynamics and have performed a quantization on this reduced space, constructing a Hilbert space of states for vanishing cosmological constant. Finally, we have clarified the canonical structure of the dual theory in relation to the original Ashtekar theory.
Systematic review on the prevalence and factors associated with childhood overweight and obesity in Malaysia

Background: Obesity in children is the world's most serious health problem, and it has been connected to death. Children who are obese are more prone to experience major health issues. Therefore, the purpose of this study is to systematically review the prevalence of childhood overweight and obesity in Malaysia and its associated factors. Materials and Methods: Three main databases were adopted in this review and study was done according to the PRISMA flow diagram. After being reviewed for stringent inclusion and exclusion criteria, the data were retrieved and compiled in Microsoft Excel. For quality evaluation, the articles included were appraised by using Newcastle-Ottawa scale. Result: The first search yielded 4121 articles for review and left with 21 articles after eliminating duplicates and applying inclusion criteria. All of data from the review were divided into five primary domains. Based on our data, we concluded that the prevalence of overweight and obesity in Malaysian children ranged from 23.8% to 58.0%. The ultimate risk for childhood overweight and obesity was due to genetics, followed by low education level of parents, physically inactive and others.
Effectiveness of foliar application of biostimulants and nanoparticles on growth, nitrogen assimilation and nutritional content in green bean

The use of biostimulants, such as salicylic acid (SA) and chitosan, are a sustainable strategy to solve stress problems in plants. Its use has been shown to have synergy with metallic microelements, which are very important for the development of crops under stress situations. An advance in the application of these nutrients is the use of nanoparticles, which emerge as a more precise alternative to achieve optimal plant development. The objective of this study was to evaluate the effect of foliar application of biostimulants, iron (Fe) and zinc (Zn) nanoparticles on growth, nitrogen assimilation, and nutritional content in green bean cv. ‘Strike’. Three treatments were used where complete nutrient solution was applied via foliar, the combination of chitosan (Q) plus SA and nanoparticles of Fe and Zn plus Q and SA. The application of nutrient solution favoured biomass content and carotene content. While the Q+SA treatment increased the nitrate reductase enzymatic activity, the mineral content in the root and the amino acid content, which places it as a viable alternative in situations where the supply of nutrients is limited or the plant cope with stressful situations. For its part, the application of nanoparticles of Fe and Zn plus biostimulants generated an increase in the mineral content of the aerial part, indicating that the application of this type of compound generates a greater mobility of nutrients within the plant.
Analysis on Norms of Word Embedding and Hidden Vectors in Neural Conversational Model Based on Encoder-Decoder RNN

SUMMARY A neural conversational model (NCM) based on an encoder-decoder recurrent neural network (RNN) with an attention mechanism learns di ﬀ erent sequence-to-sequence mappings from what neural machine translation (NMT) learns even when based on the same technique. In the NCM, we conﬁrmed that target-word-to-source-word mappings captured by the attention mechanism are not as clear and stationary as those for NMT. Considering that vector norms indicate a magnitude of information in the processing, we analyzed the inner workings of an encoder-decoder GRU-based NCM focusing on the norms of word embedding vectors and hidden vectors. First, we conducted correlation analyses on the norms of word embedding vectors with frequencies in the training set and with conditional entropies of a bi-gram language model to understand what is correlated with the norms in the encoder and decoder. Second, we conducted correlation analyses on norms of change in the hidden vector of the recurrent layer with their input vectors for the encoder and decoder, respectively. These analyses were done to understand how the magnitude of information propagates through the network. The analytical results suggested that the norms of the word embedding vectors are associated with their semantic information in the encoder, while those are associated with the predictability as a language model in the decoder. The analytical results further revealed how the norms propagate through the recurrent layer in the encoder and decoder.
Searching For Loops And Sound Samples With Feature Learning

—In this paper, we evaluate feature learning in the problem of retrieving subjectively interesting sounds from electronic music tracks. We describe an active learning system designed to ﬁnd sounds categorized as samples or loops. These retrieval tasks originate from a broader R&D project, which concerns the use of machine learning for streamlining the creation of videogame content synchronized with soundtracks. The method is expected to function in the context of limited data availability, and as such cannot rely on supervised learning of what constitutes an "interesting sound". We apply an active learning procedure that allows us to ﬁnd sound samples without predeﬁned classes through user interaction, and evaluate the use of neural network feature extraction in the problem.
Polarized neutron scattering on HYSPEC: the HYbrid SPECtrometer at SNS

We describe some of the first polarized neutron scattering measurements performed at HYSPEC [1−4] spectrometer at the Spallation Neutron Source, Oak Ridge National Laboratory. We discuss details of the instrument setup and the experimental procedures in the mode with full polarization analysis. Examples of polarized neutron diffraction and polarized inelastic neutron data obtained on single crystal samples are presented.
Numerical Modeling of the Effect of Carbon Dioxide Sequestration on the Rate of Pressure Solution Creep in Limestone: Preliminary Results

When carbon dioxide (CO2 ) is injected into an aquifer or a depleted geological reservoir, its dissolution into solution results in acidification of the pore waters. As a consequence, the pore waters become more reactive, which leads to enhanced dissolution-precipitation processes and a modification of the mechanical and hydrological properties of the rock. This effect is especially important for limestones given that the solubility and reactivity of carbonates is strongly dependent on pH and the partial pressure of CO2 . The main mechanism that couples dissolution, precipitation and rock matrix deformation is commonly referred to as intergranular pressure solution creep (IPS) or pervasive pressure solution creep (PSC). This process involves dissolution at intergranular grain contacts subject to elevated stress, diffusion of dissolved material in an intergranular fluid, and precipitation in pore spaces subject to lower stress. This leads to an overall and pervasive reduction in porosity due to both grain indentation and precipitation in pore spaces. The percolation of CO2 -rich fluids may influence on-going compaction due to pressure solution and can therefore potentially affect the reservoir and its long-term CO2 storage capacity. We aim at quantifying this effect by using a 2D numerical model to study the coupling between dissolution-precipitation processes, local mass transfer, and deformation of the rock over long time scales. We show that high partial pressures of dissolved CO2 (up to 30 MPa) significantly increase the rates of compaction by a factor of ~50 to ~75, and also result in a concomitant decrease in the viscosity of the rock matrix.
Stellar Cycles from Photometric Data: CoRoT Stars

Until a few years ago, the amplitude variation in the photometric data had been limitedly explored mainly because of time resolution and photometric sensitivity limitations. This investigation is now possible thanks to the Kepler and CoRoT databases which provided a unique set of data for studying of the nature of stellar variability cycles. The present study characterizes the amplitude variation in a sample of main--sequence stars with light curves collected using CoRoT exo--field CCDs. We analyze potential stellar activity cycles by studying the variability amplitude over small boxes. The cycle periods and amplitudes were computed based on the Lomb-Scargle periodogram, harmonic fits, and visual inspection. As a first application of our approach we have considered the photometric data for 16 CoRoT FGK main sequence stars, revisited during the IRa01, LRa01 and LRa06 CoRoT runs. The 16 CoRoT stars appear to follow the empirical relations between activity cycle periods ($P_{cyc}$) and the rotation period ($P_{rot}$) found by previous works. In addition to the so-called A (active) and I (inactive) sequences previously identified, there is a possible third sequence, here named S (short-cycles) sequence. However, recovery fractions estimated from simulations suggest that only a half of our sample has confident cycle measurements. Therefore, more study is needed to verify our results and Kepler data shall be notably useful for such a study. Overall, our procedure provides a key tool for exploring the CoRoT and Kepler databases to identify and characterize stellar cycle variability.
Expression, Localization and Prognosis Association of MEP50 in Breast Cancer

Simple Summary The arginine methyltransferase PRMT5 is an emerging therapeutic target for various cancers including breast cancer. In this study, we examine the expression and subcellular localization of its main cofactor, MEP50, in the different breast cancer subgroups. High levels of MEP50 are found in TNBC and associated with better recurrence-free survival. Abstract Breast cancer is composed of distinct subgroups, triple-negative breast cancer (TNBC), human epidermal growth factor receptor-2 (HER2), luminal A, and luminal B, which are associated with different prognosis. MEP50 is the main partner of the arginine methyltransferase PRMT5 required for its enzymatic activity. Here, we examined MEP50 expression in the different breast cancer subgroups from the transcriptomic data obtained on human breast cancer samples and on normal breast tissues in two cohorts (Curie, n = 141; The Cancer Genome Atlas—TCGA, n = 788). We observed higher levels of MEP50 mRNA in TNBC (Curie, n = 41; TCGA, n = 106) compared to the other breast cancer subgroups and normal breast tissues. Using an online KM-plotter database, which allows survival analyses in a larger number of breast cancer patients, we found that high MEP50 mRNA levels were associated with a more favorable recurrence-free survival (RFS) in TNBC (n = 953, p = 1.2 × 10−4) and luminal B (n = 1353, p = 0.013) tumors, whereas high PRMT5 mRNA levels were associated with worse RFS in these two subgroups (TNBC: n = 442, p = 1.0 × 10−4; luminal B: n = 566, p = 6.8 × 10−3). We next determined the expression and the subcellular localization of MEP50 protein by immunohistochemistry (IHC) in our Curie cohort of breast cancer (n = 94) and normal tissues (n = 7) using a validated MEP50 antibody. MEP50 was more expressed in breast tumors compared to normal breast tissues (p = 0.02). MEP50 was more localized to the cytosol in breast cancer cells compared to normal breast tissue (p = 4 × 10−4), and was more found at the plasma membrane in normal tissues compared to breast tumors (p = 0.01). We also evaluated PRMT5 activity by IHC in our Curie cohort using a validated antibody (H4R3me2s) detecting histone H4 symmetrically dimethylated on Arg3. High levels of H4R3me2s were found in normal breast tissues, whereas the lowest levels of H4R3me2s were observed in TNBC and HER2 breast cancer subgroups. Altogether, our study reports the expression of the PRMT5 cofactor (MEP50) and substrate (H4R3me2s) in breast cancer and highlights the association of PRMT5 and MEP50 mRNA with prognosis in luminal B and TNBC breast cancer subgroups and certain TNBC subtypes.
Transformational Leadership and Organizational Citizenship Behavior in the COVID-19 Era: Mediating Analysis

Human resources (HR) is an essential aspect of an organization and requires good governance. The COVID-19 outbreak that has affected the entire world has provided organizational learning in human resource management. This study examines the relationship between transformational leadership (TL) and organizational citizenship behavior (OCB) mediated by job satisfaction ( JS). A quantitative design was used to test models and hypotheses. The study sample included 116 professional-level employees representing team members from the marketing, accounting, human resources department, and operations departments of 35 wedding organizers operating in DIY. Data were analyzed using least squares structural equation modeling (PLS-SEM). The results show that TL has a positive effect on JS OCB. JS has no direct impact on OCB and is unable to mediate the relationship between TL and OCB. This study contributes to the literature by providing empirical findings that TL is a significant predictor of OCB, and also a predictor of JS. Keywords: transformational leadership, job satisfaction, organizational citizenship behavior
Decentralised and Predictive System for Efficient Agri-Transactions Through Blockchain Technology

: Agriculture is an art, a craftsmanship and a scientific way of cultivation, growth and maintenance of edible crops and livestock. Majority of the current farming communities do not have prior knowledge of predicting the suitable crop for their soil and climatic conditions. Difficulty in raising the initial investment for crop cultivation is also one of the serious concerns of these communities. The difficulties faced by the civic agriculture, the insecure monetary transactions, along with the concerns related to the financial process have been identified and listed. The paper proposes a feasible solution by predicting the appropriate crops that could be grown in a specific scenario or environmental conditions using the machine-learning model of Support Vector Classifier and provides data related to quality yields using Fuzzy Decision Merkle Tree (FDMT) Regressor. Additionally, a transparent and secure fund transfer mechanism is provided using Ethereum blockchain-based technology. The proposed model implements a secured, translucent and tamper-resistant digital platform for the farming communities to host their products. A fortified consensus can be formed between the farmer and the investor bounded with a rating mechanism to build the credibility of both the farmer and the investor 7 based on the prior knowledge obtained in the Agri-market.
Learning Tokenization in Private Federated Learning with Sub-Word Model Sampling

Federated learning with differential privacy, 001 i.e. private federated learning (PFL), makes it 002 possible to train models on private data dis-003 tributed across users’ devices without harming 004 privacy. However, it is only known how to do 005 this for models, such as neural networks, that 006 have a ﬁxed number of parameters, and thus a 007 ﬁxed-dimensional gradient vector. Such mod-008 els include neural-net language models, but 009 not n-gram language models or, indeed, tok-010 enizers, the topic of this work. Training a to-011 kenizer normally requires access to the train-012 ing data. An alternative is to train the to-013 kenizer on publicly available data, but this, 014 we show, degrades accuracy for a next-word 015 prediction task by 10–20 % across different 016 datasets and models. We propose to take a tok-017 enizer built on public data, use it to train a lan-018 guage model with PFL, and sample from the 019 language model to ﬁnd a new tokenizer. Re-020 training with the new tokenizer brings perfor-021 mance to within 2 % of the oracle tokenizer, 022 without expending additional privacy budget. 023 Finally, we build a new federated pipeline to 024 update the tokenizer during model training by 025 modifying affected model embeddings. 026
Nonlinear electron scattering activated by surface plasmon excitation of Ag nanostructures

The discovery of many fascinating new phenomena associated with the surface plasmon polariton (SPP) has triggered the rapid development of nanophotonics and nanoelectronics. We report here the experimental observation of a fundamentally new physical process, nonlinear electron scattering, stimulated by the SPP excitation of Ag nanostructures on graphite surface in scanning probe electron energy loss spectroscopy. The observed intensity of SPP energy loss peak normalized to the elastic scattering intensity shows clearly a quadratic dependence on the external electric field strength generated by the tip-sample bias. The strong coherent nature of the SPP has made the observation possible and a two-step scattering process is proposed to explain this novel nonlinear effect. Our findings shed new light on the nature of SPP and pave the way to new spectroscopic applications.
Repeated Significance Tests on Accumulating Data

THE general effect of performing repeated significance tests at different stages during the accumulation of a body of data is well known. If the null hypothesis is true and if each significance test is performed at the same nominal level, the probability that at some stage or another the test criterion is significant may be substantially greater than the nominal value. Feller (1940) discussed the possibility that some of the more significant results in card-guessing experiments in extra-sensory perception might be attributed to "optional stopping" at particularly favourable stages during an investigation. The law of the iterated logarithm shows that a test criterion which takes the form of a standardized cumulative sum of deviations from expectation divided by its standard error will, with probability one, eventually reach any preassigned value. Thus, in many common situations a result as highly significant as one desires can be obtained by sufficiently extensive sampling. Robbins (1952) and Anscombe (1954) provide further discussion of this point. The desire to control the error of the first kind, as well as the power of a test procedure, was of course one of the motivations of sequential analysis (Wald, 1947). More recently the practical relevance of this phenomenon has been called into question. Anscombe (1954) had pointed out that inferences based on likelihoods or, through likelihoods, on posterior probabilities were unaffected by stopping rules. The contrast between this property and the extreme sensitivity of frequency-type inferences to the stopping rule explains why sequential analysis is a topic of such contention between adherents of different viewpoints (Birnbaum, 1964; Cornfield, 1966; Armitage, 1967). The exchanges of opinion on these matters have been remarkable for the lack of quantitative information about the optional stopping effect. It has not, for example, been possible to answer questions such as the following. (a) What is the probability of obtaining a result "significant" at a certain nominal level, within the first 50 tests? (b) Does the enhancement of the probability of obtaining a significant result reach a noticeably high level only after a very large number of tests? (c) What is the effect of repeated tests when the null hypothesis is not true? The purpose of the present paper is to repair gome of these gaps in our knowledge without indulging in further discussion of inferential problems. We consider sequential observations of three different distributional forms: binomial, normal and exponential. In the binomial case exact results are obtained by direct calculation of
Performance of numerical approximation on the calculation of two-center two-electron integrals over non-integer Slater-type orbitals using elliptical coordinates

The two-center two-electron Coulomb and hybrid integrals arising in relativistic and nonrelativistic ab-initio calculations of molecules are evaluated over the non-integer Slater-type orbitals via ellipsoidal coordinates. These integrals are expressed through new molecular auxiliary functions and calculated with numerical Global-adaptive method according to parameters of non-integer Slatertype orbitals. The convergence properties of new molecular auxiliary functions are investigated and the results obtained are compared with results found in the literature. The comparison for two-center twoelectron integrals is made with results obtained from one-center expansions by translation of wavefunction to same center with integer principal quantum number and results obtained from the Cuba numerical integration algorithm, respectively. The procedures discussed in this work are capable of yielding highly accurate two-center two-electron integrals for all ranges of orbital parameters.