Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Model Averaging Estimation for Generalized Linear Models and Generalized Linear Mixed-Effects Models

ABSTRACT Considering model averaging estimation in generalized linear models, we propose a weight choice criterion based on the Kullback–Leibler (KL) loss with a penalty term. This criterion is different from that for continuous observations in principle, but reduces to the Mallows criterion in the situation. We prove that the corresponding model averaging estimator is asymptotically optimal under certain assumptions. We further extend our concern to the generalized linear mixed-effects model framework and establish associated theory. Numerical experiments illustrate that the proposed method is promising.
Benefit of Early Ruxolitinib Initiation Regardless of Fibrosis Grade in Patients with Primary Myelofibrosis: A Post Hoc Analysis of the Single-Arm Phase 3b JUMP Study

Simple Summary Bone marrow fibrosis (BMF) is a hallmark of myelofibrosis (a rare blood cancer), and BMF severity may be useful in predicting patient response to treatment, since high-severity BMF is associated with poor survival. In this study, we studied how BMF severity and early (within 2 years of diagnosis) versus late (more than 2 years after diagnosis) initiation of ruxolitinib treatment affected the response to treatment in patients with primary myelofibrosis. Our results showed that patients with low-severity BMF had a better response to treatment with ruxolitinib and longer survival; however, all patients treated with ruxolitinib showed improvements in spleen size and survival, regardless of their BMF severity. Our data also showed that initiation of early treatment with ruxolitinib resulted in better responses in all patients. This study showed that treatment with ruxolitinib can benefit patients with both low- and high-severity BMF, especially when it is started early. Abstract Bone marrow fibrosis (BMF) is an adverse prognostic factor for myelofibrosis (MF). The single-arm, open-label, phase 3b JUMP trial (NCT01493414) assessed the safety and efficacy of the JAK1/JAK2 inhibitor ruxolitinib in patients with symptomatic MF. This post hoc analysis investigated the impact of BMF grade on response and outcomes in patients with primary MF (PMF) from the JUMP study. BMF was assessed by biopsy and graded from 0 to 3; grades 0–1 were considered low-grade fibrosis (LGF) and grades 2–3 were considered high-grade fibrosis (HGF). Patients with LGF (n = 268) had lower rates of cytopenias at baseline but showed comparable disease burden vs. patients with HGF (n = 852). The proportion of patients achieving a spleen response was greater in the LGF group vs. the HGF group at Week 24 and at any time during the study, while overall survival estimates were improved in patients with LGF vs. patients with HGF. Early initiation of ruxolitinib therapy (within 2 years of diagnosis) was associated with increased response rates in all patients. These results highlight the efficacy of ruxolitinib in symptomatic patients with PMF, with the greatest clinical improvements observed in patients with LGF and in patients who received early treatment.
Classification of healthy and unhealthy banana leaves using deep learning approach: A comparative assessment

India is an agricultural nation where more than half of its population is dependent on agriculture for their livelihood. The lives of the farmers are disrupted by multiple constraints and challenges such as the low yield of crops and occurrence of diseases and pests, soil type, lack of HYV, inadequate irrigation facilities, to name a few. Plant disease is a significant constraint and forms a challenging task. Plant diseases could damage the leaves and fruit and thereby lead to significant economic loss in the agricultural industry. The diseases of the plants are mostly visible on the leaves of the plants and the stem. Banana is an important fruit crop grown in Assam, a state in the North-Eastern part of India. This paper deals with classification of the healthy and diseased banana leaves. The proposed method is based on a hybrid model, a self-constructed CNN model (SCCNN) infused with SVM. The features used for the classification have been extracted using the SCCNN and classified using the Support Vector Machine (SVM) classifier. To verify the robustness and accuracy of the proposed method in this paper, the dataset has been tested with the MobileNet model. The experiments, in this paper, have been performed on the on-field banana leaves images collected from different parts of the state of Assam. The experimental results show that the recognition accuracy of the proposed model is better, achieving a training accuracy of 99.1% and 92.8% testing accuracy, which can realize agricultural applications.
A Multifrequency Cross-Correlated Contrast Source Inversion Method Using Phaseless Measurements of the Total Fields

Without phase information of the measured field data, the design complexity of the observation equipment can be greatly simplified. In the meantime, the phaseless data inverse scattering problems counter more serious nonlinearity and ill-posedness compared to the full data ones. In this article, a multifrequency cross-correlated contrast source inversion (CSI) method using phaseless measurements of the total fields (referred to as PD-CC-CSI) is proposed. By introducing the cross-correlated error term to the cost functional, PD-CC-CSI shows better robustness against data noise and challenges in more complicated inversion scenarios. Inverted results with transversal magnetic (TM) polarized synthetic and experimental data demonstrate the advantages of PD-CC-CSI in comparison to the phaseless data CSI (PD-CSI) method and its variant PD-MR-CSI. In addition, an empirical approach has been proposed to effectively determine the optimal inverted results. Since a fast algorithm can be straightforwardly applied, phaseless inversion with multifrequency data can be done within a few minutes.
Broadband chirality and asymmetric transmission in ultrathin 90°-twisted Babinet-inverted metasurfaces

A broadband asymmetric transmission of linearly polarized waves with totally suppressed copolarization transmission is experimentally demonstrated in ultrathin 90\ifmmode^\circ\else\textdegree\fi{}-twisted Babinet-inverted metasurfaces constructed by an array of asymmetrically split ring apertures. The only accessible direction-dependent cross-polarization transmission is allowed in this anisotropic chiral metamaterial. Through full-wave simulation and experiment results, the bilayered Babinet-inverted metasurface reveals broadband artificial chirality and asymmetric transmission, with a transmission contrast that is better than 17.7 dB within a 50% relative bandwidth for two opposite directions. In particular, we can modify polarization conversion efficiency and the bandwidth of asymmetric transmission via parametric study.
Design of High-Performance Carry Select Adder using Multiplexer based Logic in 90nm Technology

To design a high-speed SqRt CSLA(Square Root Carry Select Look Ahead Adder) by using Multiplexer logic in order to reduce the number of gates. Performance comparison of proposed carry select adder with conventional CSLA is performed. SqRt CSLA architectures, including two hybrid designs, a high-speeddesign, and a design with the smallest area in comparison to prior CSLAs is proposed here. The first suggested architecture uses a new, quick add-one and multiplexing circuit to apply an optimized design of the BEC-based CSLA. The available CSLA, needs approximately the equal amount of area but uses significantly less energy and time. In this work a FAM CSLA using Transmission Gates (TG) based Full adder is proposed in 90nm Technology. Simulation results show that the delay of 11.50 ns is obtained which is less when compared with the conventional CSLA architectures.
Scenario-feature identification from online reviews based on BERT

Scenario endows a product with meanings. It has become the key to win the competition to design a product according to specific usage scene. Traditional scenario identification and product feature association methods have disadvantages such as subjectivity, high cost, coarse granularity, and limited scenario can be identified. In this regard, we propose a BERT-based scenario-feature identification model to effectively extract the information about users’ experience and usage scene from online reviews. First, the scenario-feature identification framework is proposed to depict the whole identification process. Then, the BERT-based scene-sentence recognition model is constructed. The Skip-gram and word vector similarity methods are used to construct the scene and feature lexicon. Finally, the triad is constructed through the analysis of scene-feature co-occurrence matrix, which realizes the association of scenario and product features. This proposed model is of great practical value for product developers to better understand customer’s requirements in specific scenarios. The experiments of scenario-feature identification from the reviews of Pacific Auto verifies the effectiveness of this method.
The effect of consumer lockdown on the relationship between environmental beliefs and pro-environmental behaviors

This study investigates the disruptive effect of the COVID-19 lockdown on the relationship between environmental beliefs and pro-environmental behaviors. We used a survey conducted in the four major cities of Colombia before COVID-19 arrived in December 2019 and then repeated it in October 2020, after most of the major restrictions on mobility and economic activity had been lifted. The survey captured ecocentric and anthropocentric beliefs using the New Ecological Paradigm (NEP) and Pro Environmental Behaviors (PEBS) using 18 questions about energy and water savings, recycling, and sustainable purchasing. In the 2020 survey, we included a measure of the severity of the individual’s experience of disruption, including economic, family, and health-related aspects. Controlling for demographic variables, we analyzed (1) descriptive changes in NEP and PEBS, (2) the moderating effect of the disruption on the effect of NEP on PEBS, (3) the direct effect of disruption severity on PEBS, and (4) the moderating effect of severity on the effect of NEP on PEBS in 2020. We found that disruption caused by the lockdown crisis decreased anthropocentric beliefs and slightly increased ecocentric beliefs. This disruption also modified the effects of NEP on PEBS, thereby ameliorating the capacity of NEP to activate PEBS. The severity of experience moderated most of these effects. We discuss the implications of our results for the theory of environmental beliefs and pro-environmental behaviors and offer recommendations to reverse the potential negative effect of the COVID-19 crisis on the promotion of sustainable consumption.
Multiple General Anesthesia in Children: A Systematic Review of Its Effect on Neurodevelopment

The effect of multiple general anesthesia (mGA) procedures administered in early life is a critical theme and has led the Food and Drug Administration (FDA) to issue an alert. This systematic review seeks to explore the potential effects on neurodevelopment of mGA on patients under 4 years. The Medline, Embase and Web of Science databases were searched for publications up to 31 March 2021. The databases were searched for publications regarding “children multiple general anesthesia OR pediatric multiple general anesthesia”. Case reports, animal studies and expert opinions were excluded. Systematic reviews were not included, but they were screened to identify any possible additional information. A total of 3156 studies were identified. After removing the duplicates, screening the remaining records and analyzing the systematic reviews’ bibliography, 10 studies were considered suitable for inclusion. Comprehensively, a total cohort of 264.759 unexposed children and 11.027 exposed children were assessed for neurodevelopmental outcomes. Only one paper did not find any statistically significant difference between exposed and unexposed children in terms of neurodevelopmental alterations. Controlled studies on mGA administered before 4 years of age support that there might be a greater risk of neurodevelopmental delay in children receiving mGA, warranting the need for careful risk/benefit considerations.
Waterwave: A GPU Memory Flow Engine for Concurrent DNN Training

Training Deep Neural Networks (DNN) concurrently is becoming increasingly important for deep learning practitioners, e.g., <italic>hyperparameter optimization (HPO)</italic> and <italic>neural architecture search (NAS)</italic>. The GPU memory capacity is the impediment that prohibits multiple DNNs from being trained on the same GPU due to the large memory usage during training. In this paper, we propose <italic>Waterwave</italic>, a GPU memory flow engine for concurrent deep learning training. First, to address the memory explosion brought by the long time lag between memory allocation and deallocation time, we develop an allocator tailored for multi-streams. By making the allocator aware of the stream information, a <italic>prioritized allocation</italic> is conducted based on the chunk's <italic>synchronization</italic> attributes, allowing us to provide useable memory after scheduling rather than waiting it to be really released after GPU computation. Second, <italic>Waterwave</italic> partitions the compute graph to a set of continuous <italic>node groups</italic> and then performs finer-grained scheduling: <italic>NodeGroup pipeline execution</italic>, to guarantee a proper memory requests order. <italic>Waterwave</italic> can accomplish up to 96.8% of the maximum batch size of solo training. Additionally, in scenarios with high memory demand, <italic>Waterwave</italic> can outperform existing spatial sharing and temporal sharing by up to 12x and 1.49x, respectively.
Kinetics of Gastro-Intestinal Absorption

Knowledge of the kinetics of gastrointestinal absorption is required for reliable dose estimates for ingested radionuclides. A method is described by which absorption rates as a function of time as well as the total fraction absorbed (f 1 value) can be determined by analysis of tracer concentrations in blood after oral and intravenous administration. The method was applied to study the absorption dynamics of Ca, Fe, and Mo in humans and is adapted to Ru, Zr, Sr and lanthanides. Radioactive or stable isotopes of the respective elements were used as tracers. The absorption kinetics and the total fractional absorption differ considerably for different elements. For a particular element, the absorption rates as well as the f 1 values vary considerably with respect to the chemical form and the amount administered. Absorption patterns are characteristically different for uptake from solutions or from whole meals. This information may be used to improve the dosimetric model for the gastrointestinal tract.
Optimal bucking of stems from terrestrial laser scanning data to maximize forest value

ABSTRACT An optimal bucking strategy that allocates cutting patterns to forest stands based on the individual characteristics of each stem is critical for maximizing value recovery. However, cutting patterns are usually excluded from bucking algorithms due to the difficulties associated with capturing tree quality features when collecting forest inventory data (e.g. branchiness and tree shape). This paper presents a non-destructive and fully automated methodology for the optimal bucking of stems based on terrestrial laser scanning (TLS) point clouds that aims to maximize the economic value of trees in a forest stand. It is based on the three-dimensional modelling of stems and includes the diameter and curvature of each log. The bucking algorithm also considers several timber products and calculates the most valuable log combination for each tree. The methodology was tested in a Pinus radiata plot with 120 trees, and the results were compared with those obtained with input data that do not take curvature into account: i.e. only diameters from TLS and taper equations. The analysis of the results suggests that not including curvature in the algorithm for optimal bucking results in an overestimation of the commercial value of timber products.
The Prognostic Value of Anti-PLA2R Antibodies Levels in Primary Membranous Nephropathy

Anti-PLA2R antibodies (Ab) are a diagnostic and prognostic biomarker in primary membranous nephropathy (PMN). We assessed the relationship between the levels of anti-PLA2R Ab at diagnosis and different variables related to disease activity and prognosis in a western population of PMN patients. Forty-one patients with positive anti-PLA2R Ab from three nephrology departments in Israel were enrolled. Clinical and laboratory data were collected at diagnosis and after one year of follow-up, including serum anti-PLA2R Ab levels (ELISA) and glomerular PLA2R deposits on biopsy. Univariable statistical analysis and permutation-based ANOVA and ANCOVA tests were performed. The median [(interquartile range (IQR)) age of the patients was 63 [50–71], with 28 (68%) males. At the time of diagnosis, 38 (93%) of the patients had nephrotic range proteinuria, and 19 (46%) had heavy proteinuria (≥8 gr/24 h). The median [IQR] level of anti-PLA2R at diagnosis was 78 [35–183] RU/mL. Anti-PLA2R levels at diagnosis were correlated with 24 h proteinuria, hypoalbuminemia and remission after one year (p = 0.017, p = 0.003 and p = 0.034, respectively). The correlations for 24 h proteinuria and hypoalbuminemia remained significant after adjustment for immunosuppressive treatment (p = 0.003 and p = 0.034, respectively). Higher levels of anti-PLA2R Ab at diagnosis in patients with active PMN from a western population are associated with higher proteinuria, lower serum albumin and remission one year after the diagnosis. This finding supports the prognostic value of anti-PLA2R Ab levels and their possible use in stratifying PMN patients.
Efficient implicit simulation of incremental sheet forming

In single point incremental forming (SPIF), the sheet is incrementally deformed by a small spherical tool following a lengthy tool path. The simulation by the ﬁnite element method of SPIF requires extremely long computing times that limit the application to simple academic cases. The main challenge is to perform thousands of load increments modelling the lengthy tool path with elements that are small enough to model the small contact area. Because of the localised deformation in the process, a strong nonlinearity is observed in the vicinity of the tool. The rest of the sheet experiences an elastic deformation that introduces only a weak nonlinearity because of the change of shape. The standard use of the implicit time integration scheme is ineﬃcient because it applies an iterative update (Newton–Raphson) strategy for the entire system of equations. The iterative update is recommended for the strong nonlinearity that is active in a small domain but is not required for the large part with only weak nonlinearities. It is proposed in this paper to split the ﬁnite element mesh into two domains. The ﬁrst domain models the plastically deforming zone that experiences the strong nonlinearity. It applies a full nonlinear update for the internal force vector and the stiﬀness matrix every iteration. The second domain models the large elastically deforming zone of the sheet. It applies a pseudolinear update strategy based on a linearization at the beginning of each increment. Within the increment, it reuses the stiﬀness matrix and linearly updates the internal force vector. The partly linearized update strategy is cheaper than the full nonlinear update strategy, resulting in a reduction of the overall computing. Furthermore, in this paper, adaptive reﬁnement is combined with the two domain method. It results in accelerating the standard SPIF implicit simulation of 3200 shell elements by a factor of 3.6. Copyright © 2011 John Wiley & Sons, Ltd.
Analysis of Stochasticity and Heterogeneity of Car-Following Behavior Based on Data-Driven Modeling

Traffic dynamics on freeways are stochastic in nature because of errors in perception and operation of drivers as well as the heterogeneity between and within drivers. This stochasticity is often represented in car-following models by a stochastic term, which is assumed to follow a normal distribution for the convenience of mathematical processing. However, the validity of this assumption has not been studied yet. In this study, we focused on the shape of the distribution of a stochastic term in the car-following model that predicts an acceleration after a time step. Based on vehicle trajectory data on a freeway in Japan, a car-following model is first developed by using data-driven methodology in which long short-term memory (LSTM) network is applied. In this LSTM network, the acceleration value is discretized and the model parameters are trained with the focal loss function. The relationship between the predicted distributions’ modality, standard deviation (SD), and I A with respect to traffic states is then examined. The findings demonstrate that: 1) the developed model can accurately predict the accelerations; 2) a probabilistic distribution tends to have a large SD and multimodality around a merging point and at the beginning of and along stop-and-go waves; and 3) driving behavior can be classed in one of four clusters based on the variation of the percentile value that a driver takes within the probability distribution. The proposed model and the insights are helpful for improving microscopic simulation models when considering new traffic management measures.
Touch Technology in Affective Human–, Robot–, and Virtual–Human Interactions: A Survey

Given the importance of affective touch in human interactions, technology designers are increasingly attempting to bring this modality to the core of interactive technology. Advances in haptics and touch-sensing technology have been critical to fostering interest in this area. In this survey, we review how affective touch is investigated to enhance and support the human experience with or through technology. We explore this question across three different research areas to highlight their epistemology, main findings, and the challenges that persist. First, we review affective touch technology through the human–computer interaction literature to understand how it has been applied to the mediation of human–human interaction and its roles in other human interactions particularly with oneself, augmented objects/media, and affect-aware devices. We further highlight the datasets and methods that have been investigated for automatic detection and interpretation of affective touch in this area. In addition, we discuss the modalities of affective touch expressions in both humans and technology in these interactions. Second, we separately review how affective touch has been explored in human–robot and real-human–virtual-human interactions where the technical challenges encountered and the types of experience aimed at are different. We conclude with a discussion of the gaps and challenges that emerge from the review to steer research in directions that are critical for advancing affective touch technology and recognition systems. In our discussion, we also raise ethical issues that should be considered for responsible innovation in this growing area.
Research on Building Scanning Point Cloud Registration Based on Geometric Feature Constraint

Aimming at the characteristic of three-dimensional laser scanning data,based on the theory of least squares adjustment,this paper presents a method for point cloud registration,based on point plane geometric feature.Firstly,according to superposition between two planes,model of point cloud registration and error equation are derived,based on geometric feature of point and plane.Secondly,combined error equation of point cloud registration based on feature points,error equation of point cloud registration is derived,based on geometric feature of point and plane.Finally,making a test,using the actual scan data form three-dimensional laser scanner,confirm that this method can improve the accuracy of point cloud registration.