Miracle-dz/UniFAR-FTUs huggingface dataset:
Subjective and objective quality assessment of omnidirectional video

Omnidirectional video, also known as 360° video, is becoming quite popularsince it provides a more immersive and natural representation of the real world. However, to fulfill the expectation of an high quality-of-experience (QoE), the video content delivered to the end users must also have high quality. To automatically evaluate the video quality, objective quality assessment metrics are then required. This paper starts by presenting the results of a subjective assessment campaign that was conducted to evaluate the impact, on quality, of HEVC compression and/or spatial/temporal subsampling, when the videos are displayed in a head mounted device (HDM). The subjective assessment results are then used as ground-truth to evaluate conventional quality assessment metrics developed for 2D video, as well as some of the recently proposed metrics for omnidirectional video, namely, spherical peak-signal to noise ratio (S-PSNR), weighted to spherically uniform PSNR (WS-PSNR), and viewport PSNR (VP-PSNR); in the context of this study, the adaptation of two SSIM based metrics, to omnidirectional contents, are also proposed and evaluated.
Analysis and Design of Single-to-Balanced Combline Bandpass Filters With Two Independently Controllable Transmission Zeros in LTCC Technology

In this paper, the analysis and design of single-to-balanced combline bandpass filter are presented. The cross-coupled effect is introduced to create two transmission zeros that can be located independently in either the upper or lower stopband. Notably, the effect of nonadjacent line coupling on the filter response is properly addressed, and an efficient way to compensate it is proposed. Also, the issue of a frequency-dependent J-inverter in bandpass filter design is also well treated. The proposed filter can be implemented using the low-temperature co-fired ceramic (LTCC) process to achieve very compact circuit size, in which the combline line lengths are as small as . Specifically, two third-order single-to-balanced combline bandpass filters in LTCC are implemented. Good balance performance, low loss, good selectivity, and compact size are achieved.
Terminating Sequential Delphi Survey Data Collection.

The Delphi survey technique is an iterative mail or electronic (e-mail or web-based) survey method used to obtain agreement or consensus among a group of experts in a specific field on a particular issue through a well-designed and systematic multiple sequential rounds of survey administrations. Each of the multiple rounds of the Delphi survey administration is augmented with continuous summary feedback of aggregated responses from the same group of experts. Statistical methods to analyze data from the Delphi surveys to make decisions for terminating subsequent Delphi data collection are needed to ensure that (a) stability of the responses of the panel of experts is reached; and (b) termination of the rounds of the Delphi survey administration is based on sound statistical results. The present study presents an overview of the parametric and nonparametric statistical methods that can be used to analyze the structured Delphi survey data to make decisions about terminating the sequential Delphi survey data collection. The Delphi technique is an and sequential
Sheaf quotients of algebraic supergroups are superschemes

To generalize some fundamental results on group schemes to the super context, we study the sheaf quotient G/H of an algebraic supergroup G by its closed supersubgroup H , in arbitrary characteristic 6= 2. Our main theorem states that G/H is a Noetherian superscheme. This together with derived results give positive answers to interesting questions posed by J. Brundan.
A 0.2 V 0.44 µW 20 kHz Analog to Digital Σ∆ Modulator with 57 fJ/conversion FoM

— This paper presents a 90 nm CMOS Σ∆ A/D modulator operating with a supply voltage of 0.2 V, well below the threshold voltage of the transistors. The modulator is an open-loop ﬁrst-order architecture based on a frequency-modulated intermediate signal, generated in a ring voltage-controlled oscillator. The linearity of the modulator is greatly improved by the adoption of a so-called soft-rail in the oscillator. Measurements show a dynamic range of 52 dB over a 20 kHz signal bandwidth with a sampling frequency of 3.4 MHz, for a total power consumption as low as 0.44 µ W. The corresponding peak SNDR is 44.2dB, while the peak SNR is 47.4dB.
Identifying Reward Functions using Anchor Actions

Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, and Karol Hausman. Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning. arXiv preprint arXiv:1910.11956, 2019. Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, and Sergey Levine. Reinforcement learning with deep energy-based policies. In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pages 1352–1361. JMLR. org, 2017.
An analytic theory of shallow networks dynamics for hinge loss classification — Supplementary Material

and then in differentiating RHS and LHS with respect to time, see e.g. [1]. The important point here (and later) is that the density ρ(θ, t), which depends on the random initial conditions, concentrates in the large M limit due to the nature of the interaction between parameters, which is only through the function f , and the type of distributions considered for the initial conditions.1. The derivative of the RHS leads to ∫ dθH(θ)∂tρ(θ, t) (4)
Association of human telomerase reverse transcriptase gene polymorphisms, serum levels, and telomere length with renal cell carcinoma risk and pathology

Human telomerase reverse transcriptase (hTERT) is the catalytic subunit of the human telomerase and plays a key role in telomere restitution and gene regulation. Evidence suggests that hTERT is linked with the risk and progression of several malignancies, but there are no comprehensive data in renal cell carcinoma (RCC). In this case–control study, we assessed seven polymorphic hTERT gene variants (MNS16A, rs2736100, rs2736098, rs7726159, rs2853677, rs13172201, and rs10069690), hTERT serum levels, and the telomere length of 663 individuals, including 243 with clear cell RCC and 420 age‐ and gender‐matched healthy controls. The SL and SS genotypes of MNS16A were associated with a decreased risk for RCC on the multivariable logistic regression analysis (SL‐OR 0.72, SS‐OR 0.37, P < 0.001). The GG genotype of rs2736098 was associated with a decreased risk for RCC compared with AA (OR 0.18, P < 0.001). Both telomere length and hTERT serum levels increased with every G allele in rs2736098 (P = 0.008). Pretherapeutic hTERT serum levels were higher in patients with advanced tumor stages (P = 0.037) and distant metastases (P = 0.006). Rs2736100, rs7726159, rs2853677, rs13172201, and rs10069690 were not linked with RCC risk, and none of the polymorphisms was associated with RCC pathology. In conclusion, the polymorphic number of tandem repeats in hTERT (MNS16A) and rs2736098 may be linked with the risk for RCC. Rs2736098 may have an important role in telomere length restitution and serum hTERT levels may represent a novel biomarker for RCC. © 2015 Wiley Periodicals, Inc.
Event Detection via Tracking the Change in Community Structure and Communication Trends

Event detection is a popular research problem aiming to detect events from various data sources, such as news texts, social media postings or social interaction patterns. In this work, event detection is studied on social interaction and communication data via tracking changes in community structure and communication trends. With this aim, various community structure and communication trend based event detection methods are proposed. Additionally, a new strategy called community size range based change tracking is presented such that the proposed algorithms can focus on communities with different size ranges, and considerable time efficiency can be obtained. The event detection performance of the proposed methods is analyzed using a set of real world and benchmark data sets in comparison to previous solutions in the literature. The experiments show that the proposed methods have higher event detection accuracy than the baseline methods. Additionally, their scalability is presented through analysis by using high volume of communication data. Among the proposed methods, CN-NEW, which is a community structure based method, performs the best on the overall. The proposed communication trend based methods perform better mostly on communication data sets (such as CDR), whereas community structure based methods tend to perform better on social media-based data sets.
Contribution of the practice of resistance exercises and the type of delivery to the diastasis of the rectus abdominis muscles in primiparous women

ABSTRACT Maternal organism suffers physiological and biomechanical changes during pregnancy, including the separation of rectus abdominis muscles (RAM). This cross-sectional study aimed to compare the distance between the RAM among sedentary and active primiparous women who had vaginal childbirth and cesarean section, to correlate RAM separation with maternal and child variables and to compare these variables between primiparous women with and without RAM diastasis. In total, 56 women were evaluated in their ninth postpartum week, divided into four groups according to the mode of delivery and the practice of resistance exercises. RAM distance was calipered in the supraumbilical, umbilical, and infraumbilical regions. The values obtained were verified via analysis of variance (ANOVA), Pearson’s correlation, and independent t-test. We found no main effect between mode of delivery and practice of resistance exercises or interaction between mode of delivery and practice of resistance exercises (p≥0.118) for RAM distance. We found significant correlation between body weight before pregnancy and RAM distance (p<0.001). There was no association between body weight gain during pregnancy and the newborn’s weight with RAM distance (p≥0.132). We observed significant difference in body weight before pregnancy between primiparous women with and without RAM diastasis (p<0.005). We found no differences between groups regarding body weight gain during pregnancy and the newborn’s weight (p≥0.122). It was concluded that the practice of resistance exercises and the mode of delivery have no impact on the separation of supraumbilical, umbilical, and infraumbilical regions of RAM in primiparous women.
Con fi dence Bands for a Log-Concave Density

We present a new approach for inference about a univariate log-concave distribution: Instead of using the method of maximum likelihood, we propose to incorporate the log-concavity constraint in an appropriate nonparametric con fi dence set for the cdf F . This approach has the advantage that it automatically provides a measure of statistical uncertainty and it thus, overcomes a marked limitation of the maximum likelihood estimate. In particular, we show how to construct con fi dence bands for the density that have a fi nite sample guaranteedcon fi dencelevel.Thenonparametriccon fi dencesetfor F whichweintroduceherehasattractive computational and statistical properties: It allows to bring modern tools from optimization to bear on this problem via di ff erence of convex programming, and it results in optimal statistical inference. We show that the width of the resulting con fi dence bands converges at nearly the parametric n − 1 2 rate when the log density is k -a ffi ne. Supplementary materials for this article are available online.
Effectiveness of Cognitive Strategies on Short-Term Information Effectiveness of Cognitive Strategies on Short-Term Information Retention: An Experimental Study Retention: An Experimental Study

Background : In medical education, particularly in the clinical environment, learning theories can enhance the education process. Cognitive theory, which focuses on how we receive, organize, and store incoming information and subsequently retrieve it, is an example. Based on this theory and the memory model, different strategies have been suggested to maintain learners ’ attention and improve their memory. For instance, changing the tone or position during lectures or beginning a lecture with a review or summary slide. Our study, using post-lecture quizzes, assessed the impact of cognitive strategies implemented in lectures delivered to the medical students of King Saud bin Abdulaziz University for Health Sciences. Method : We used an experimental pre-test-post-test control group design, and 36 medical students were randomly assigned to 2 groups, namely Interventional (received lectures that used cognitive strategies) and Traditional (received lectures that did not use cognitive strategies). Each group took a pre-lecture quiz, to determine pre-existing knowledge, and a post-lecture quiz. Results : The mean score of the pre-test showed no difference, but this difference was signi ﬁ cant post-test (p-value < 0.001). Both groups showed statistically signi ﬁ cant differences when the mean score was compared within each group. The Interventional group students scored signi ﬁ cantly higher in the post-lecture test compared to the Traditional group students. Conclusion : Using cognitive strategies and other learning modalities while teaching medical professionals would not only improve medical education, but also promote health care outcomes. Summarization also improved learning and enhanced information retention by helping the learners actively organize the received information. Mind-wandering and lack of attention during lectures negatively affected education and information retention
INITIAL DEVELOPMENT OF EUCALYPTUS CLONE I144 (Eucalyptus grandis x Eucalyptus urophylla) IN RESPONSE TO FOLIAR AND SOIL FERTILIZATION

The nutritional management of eucalyptus (Eucalyptus spp.) is one of the main factors responsible for the success of its cultivation. We aimed to evaluate the initial growth and nutritional status of eucalyptus clone I144 (Eucalyptus grandis x Eucalyptus urophylla) submitted to foliar fertilizers application containing boron-B, iron-Fe and bio-stimulant and via soil application of nitrogen-N and phosphorus-P doses. The experiment has been conducted in a rural private property of Tamarana, Parana (PR) in a subdivided plots experiment design with four replications (n=4). The plots treatments were boron, boron+iron, boron+iron+bio-stimulant applications under algae (Ecklonia maxima) presence and a control (water). The subplots treatments were composed by the application of 100, 150 and 250 g plant-1 rates of 10-20-00 (N-P2O5-K2O) and a control (0 g plant-1 of 10-20-00). It was evaluated the height and diameters of plants at 45, 78, 117, 150 and 235 days after planting (DAP) and the leaves nutrient content (N, potassium-K, calcium-Ca, magnesium-Mg, sulfur-S, copper-Cu, zinc-Zn, manganese-Mn and Fe) at the end of the cultivation. The fertilization via soil did not interfere on either growth or macronutrients contents. On the other hand, the combined application of foliar fertilizers has increased the macronutrients contents without interfering on growth parameters.
High Performance by Exploiting Information Locality through Reverse Computing

In this paper we present performance results for our register rematerialization technique based on reverse recomputing. Rematerialization adds instructions and we show on one specifically designed example that reverse computing alleviates the impact of these additional instructions on performance. We also show how thread parallelism may be optimized on GPUs by performing register allocation with reverse recomputing that increases the number of threads per Streaming Multiprocessor (SM). This is done on the main kernel of Lattice Quantum Chromo Dynamics (LQCD) simulation program where we gain a 10.84% speedup.
Nonthermal hot dark matter from inflaton or moduli decay: Momentum distribution and relaxation of the cosmological mass bound

Decay of the inflaton or moduli which dominated the energy density of the universe at early times leads to a matter to radiation transition epoch. We consider non-thermal sterile dark matter particles produced as decay product during such transitions. The particles have a characteristic energy distribution - that associated with decays taking place in a matter dominated universe evolving to radiation domination. We primarily focus on the case when the particles are hot dark matter, and study their effects on the Cosmic Microwave Background (CMB) and Large Scale Structure (LSS), explicitly taking into account their non-thermal momentum distribution. Our results for CMB angular power and linear matter power spectra reveal interesting features - such as an order of magnitude higher values of hot dark matter mass in comparison to the thermal case being consistent with the present data. We observe that this is related to the fact that $\Delta N_{\rm eff}$ and the hot DM energy density can be independent of each other unlike the case of thermal or non-resonantly produced sterile hot DM. We also find features in the CMB at low $\ell$ angular power potentially related to supersonic transmission of hot dark matter through the photon-baryon plasma.
Confidence intervals for the means of the selected populations

Consider an experiment in which p independent populations πi with corresponding unknown means θi are available, and suppose that for every 1 ≤ i ≤ p, we can obtain a sample Xi1, . , Xin from πi. In this context, researchers are sometimes interested in selecting the populations that yield the largest sample means as a result of the experiment, and then estimate the corresponding population means θi. In this paper, we present a frequentist approach to the problem and discuss how to construct simultaneous confidence intervals for the means of the k selected populations, assuming that the populations πi are independent and normally distributed with a common variance σ2. The method, based on the minimization of the coverage probability, obtains confidence intervals that attain the nominal coverage probability for any p and k, taking into account the selection procedure.
The distances covered by basketball referees in a match increase throughout the competition phases , with no change in physiological demand

License. The full terms of the License are available at http://creativecommons.org/licenses/by-nc/3.0/. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Ltd, provided the work is properly attributed. Permissions beyond the scope of the License are administered by Dove Medical Press Ltd. Information on how to request permission may be found at: http://www.dovepress. com/permissions.php Open Access Journal of Sports Medicine 2013:4 193–198 Open Access Journal of Sports Medicine Dovepress
Shigui Ruan epidemiological model of influenza A drift Sustained oscillations in an evolutionary

References pa.2009. 0435.full. html#ref-list-1 http://rspa.royalsocietypublishing. org/content/early/2009/12/01/rs This article cites 38 articles, 9 of which can be accessed free P<P journal. Published online 3 December 2009 in advance of the print Subject collections (125 articles) applied mathematics  (34 articles) differential equations  (44 articles) mathematical modelling  Articles on similar topics can be found in the following collections
Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent

Figure 7: Learning rate dependence of the generalization performance. Nonlinear feedforward neural networks of different depths are trained on a simple task with varying learning rates. We see that, when the learning rate is vanishingly small so that the continuous-time approximation is good, the continuous neural tangent kernel (NTK) provides an accurate characterization of the result of training. However, as the learning rate becomes large, the learning deviates significantly and qualitatively from the NTK prediction, sometimes for the better, sometimes for the worse. Reproduced from Mori and Ueda (2020b). For other interesting experiments concerning large learning rate, see Lewkowycz et al. (2020).
A Survey on Text Classification: From Shallow to Deep Learning

Text classification is the most fundamental and essential task in natural language processing. The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the gap by reviewing the state of the art approaches from 1961 to 2020, focusing on models from shallow to deep learning. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification. We then discuss each of these categories in detail, dealing with both the technical developments and benchmark datasets that support tests of predictions. A comprehensive comparison between different techniques, as well as identifying the pros and cons of various evaluation metrics are also provided in this survey. Finally, we conclude by summarizing key implications, future research directions, and the challenges facing the research area.
Development of Regression Models by Closed–Loop Identification of Distillation Column - A Case Study

Objectives: Closed-loop identification is reported to provide better results for identification of systems for control applications. This study conducted closed-loop identification on an Aspen Plus® dynamic simulation based on a pilot-plant distillation column to develop discrete-time linear time-invariant models. Methods/Statistical Analysis: Identification data was generated using set-point perturbations in control variables under proportional-integral control. Identified models were compared with a model identified using open-loop data using 20-step ahead predictions. Findings: Results indicate that closed-loop identification provides more precise prediction models than open-loop identification in this case study. 20-step predictions for closed loop models exceeded 90% fit, whereas the open loop model predictions provided a 70% fit and missed the steady-state values. Application/Improvements: Thus closed-loop identification is more appropriate for applications in model-based controllers.
Role of NBCe1 and AE2 in Secretory Ameloblasts

The H+/base transport processes that control the pH of the microenvironment adjacent to ameloblasts are not currently well-understood. Mice null for the AE2 anion exchanger have abnormal enamel. In addition, persons with mutations in the electrogenic sodium bicarbonate co-transporter NBCe1 and mice lacking NBCe1 have enamel abnormalities. These observations suggest that AE2 and NBCe1 play important roles in amelogenesis. In the present study, we aimed to understand the roles of AE2 and NBCe1 in ameloblasts. Analysis of the data showed that NBCe1 is expressed at the basolateral membrane of secretory ameloblasts, whereas AE2 is expressed at the apical membrane. Transcripts for AE2a and NBCe1-B were detected in RNA isolated from cultured ameloblast-like LS8 cells. Our data are the first evidence that AE2 and NBCe1 are expressed in ameloblasts in vivo in a polarized fashion, thereby providing a mechanism for ameloblast transcellular bicarbonate secretion in the process of enamel formation and maturation.