Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermal and mechanical analysis of lignocellulosic-polypropylene composites

Lignocellulosic-polypropylene composites have complex morphologies that influence their behavior. The interaction and adhesion between the fiber and matrix has a significant effect in determining the mechanical and physical behavior of fiber composites. Details such as whether the composite is above or below the glass transition, and the nature and degree to which crystallinity affects the stress transfer efficiency are imporSanadi: Research Scientist, Biological Systems Engineering, Univ. of Wisconsin, Madison, Wisconsin Caulfield: Research Chemist, USDA Forest Prod. Lab. , Madison, Wisconsin Stark, Clemens: Chemical Engineers, USDA Forest Prod. Lab. , Madison, Wisconsin The authors would like to acknowledge funding from USDA NRI/CSRS (No. 9701817). Mr. Sanadi would like to thank the USDA Forest Serv. , Forest Prod. Lab. for help during the course of the study. Experimental help from S. Prabava is appreciated. tant. Thermal analysis methods are useful in evaluating the effects of fiber content and fiber-matrix adhesion in the composite. Furthermore, information on the dynamic mechanical response of the material can be evaluated over a wide range of temperatures. This paper deals with the effects of the fiber content and the interphase on the dynamic and mechanical behavior of kenaf fiber composites. The use of coupling agents is imperative for achieving good stress transfer efficiency. The dynamic bending storage modulus of a 40 percent by weight coupled composite is higher than the storage modulus of a 50 percent by weight uncoupled composite, throughout the temperature range of -70°C to 160°C. Furthermore, the coupled composite has a slightly higher softening temperature than the uncoupled system. The improved properties are due to morphological and interphasial changes in the composite triggered by the coupling agent. Thermal analysis helps in gathering information on the changes and helps explain the reasons for the property changes. Sanadi, Caulfield, Stark, and Clemens ~ 67 The effects of particle size and interface on the dynamic and mechanical behavior of wood flour composites are presented. Particle size and interparticular distance can influence the morphology and therefore the mechanical response of the system. The thermomechanical responses of wood flour composites are compared with those of talcand calcium carbonate-filled polypropylene.
cplm : Compound Poisson Linear Models

The Tweedie compound Poisson distribution is a mixture of a degenerate distribution at the origin and a continuous distribution on the positive real line. It has been applied in a wide range of fields in which continuous data with exact zeros regularly arise. Nevertheless, statistical inference based on full likelihood and Bayesian methods is not available in most statistical software, largely because the distribution has an intractable density function and numerical methods that allow fast and accurate evaluation of the density did not appear until fairly recently. The cplm package provides likelihoodbased and Bayesian procedures for fitting common Tweedie compound Poisson linear models. In particular, models with hierarchical structures or penalized splines can be handled. Further, the package implements the Gini index based on an ordered version of the Lorenz curve as a robust model comparison tool involving zero-inflated and highly skewed distributions.
GROUPS AND HYPERGROUPS OF ARTIFICIAL NEURONS

When we study structure of the most used artiﬁcial neural network - multilayer perceptron and functionality of artiﬁcial n euron, t here i s p ossibility u sing s everal w ays to describe function and neural network properties on the basis of known algebraic structures, vector spaces and graphs theory or properties of relations. Using certain analogy with relations between descriptions of differential equations certain quality there is developed access to new view point on these subjects. In this paper some concepts of description and modelling systems of neurons are investigated.
Microstructure and tribology regarding precision studies of micro-sintered ceramic bearings for Ventricular Assist Devices

A novel third generation of Ventricular Assist Devices called Implantable Centrifugal Blood Pump is been developed in order to provide Mechanically Circulatory Support to Congestive Heart Failure patients. The design is based on conical impeller supported by ceramic pivot bearings in hydraulic levitation between 1400 and 2300 RPM. The proposed friction study consists in checking the mechanical wear occurred during the contact between impeller’s shaft and bearings. This paper presents the superficial characterization of the ceramic pivot bearings after Friction and Wear Tests performed in a workbench that simulates the operation of a centrifugal blood pump with those characteristics. Friction and wear tests were performed with alumina and zirconia ceramic axis in contact with the Ultra-high-molecular-weight Polyethylene at speed of 1800 RPM with 5.0N load during 10 hours. Lubrication was provided with water and surfaces were analysed microscopically before and after the induced wear. Results were compared and the superficial damages were checked out. The structural modification observed was considered moderate. Implantable centrifugal blood pump, alumina ceramic pivot bearings, ventricular assist devices, friction and wear, biotribology.
Higher-order finite elements for embedded simulation

As demands for high-fidelity physics-based animations increase, the need for accurate methods for simulating deformable solids grows. While higherorder finite elements are commonplace in engineering due to their superior approximation properties for many problems, they have gained little traction in the computer graphics community. This may partially be explained by the need for finite element meshes to approximate the highly complex geometry of models used in graphics applications. Due to the additional perelement computational expense of higher-order elements, larger elements are needed, and the error incurred due to the geometry mismatch eradicates the benefits of higher-order discretizations. One solution to this problem is the embedding of the geometry into a coarser finite element mesh. However, to date there is no adequate, practical computational framework that permits the accurate embedding into higher-order elements. We develop a novel, robust quadrature generation method that generates theoretically guaranteed high-quality sub-cell integration rules of arbitrary polynomial accuracy. The number of quadrature points generated is bounded only by the desired degree of the polynomial, independent of the embedded geometry. Additionally, we build on recent work in the Finite Cell Method (FCM) community so as to tackle the severe ill-conditioning caused by partially filled elements by adapting an Additive-Schwarz-based preconditioner so that it is suitable for use with state-of-the-art non-linear material models from the graphics literature. Together these two contributions constitute a general-purpose framework for embedded simulation with higher-order finite elements. We finally demonstrate the benefits of our framework in several scenarios, in which second-order hexahedra and tetrahedra clearly outperform their first-order counterparts.
Derivation of new chemotaxis models by asymptotic analysis of kinetic models for large binary cell mixtures

Abstract. This paper deals with the derivation of macroscopic tissue models from the underlying description delivered by a class of equations modeling, by methods of the kinetic theory, binary mixtures of multi-cellular systems. The analysis refers to a suitable parabolic approximation to show how the macroscopic tissue behavior can be described from the underlying cellular description. The approach is specifically focused on the modeling of classical chemotaxis phenomena, and the chemotaxis–fluid.
Diptychs of human and machine perceptions

We propose visual creations that put differences in algorithms and humans \emph{perceptions} into perspective. We exploit saliency maps of neural networks and visual focus of humans to create diptychs that are reinterpretations of an original image according to both machine and human attentions. Using those diptychs as a qualitative evaluation of perception, we discuss some crucial issues of current \textit{task-oriented} artificial intelligence.
A modular approach to integrating multiple data sources into real-time clinical prediction for pediatric diarrhea

Traditional clinical prediction models focus on parameters of the individual patient. For infectious diseases, sources external to the patient, including characteristics of prior patients and seasonal factors, may improve predictive performance. We describe the development of a predictive model that integrates multiple sources of data in a principled statistical framework using a post-test odds formulation. Our method enables electronic real-time updating and flexibility, such that components can be included or excluded according to data availability. We apply this method to the prediction of etiology of pediatric diarrhea, where "pre-test" epidemiologic data may be highly informative. Diarrhea has a high burden in low-resource settings, and antibiotics are often over-prescribed. We demonstrate that our integrative method outperforms traditional prediction in accurately identifying cases with a viral etiology, and show that its clinical application, especially when used with an additional diagnostic test, could result in a 61% reduction in inappropriately prescribed antibiotics.
Bytecover: Cover Song Identification Via Multi-Loss Training

We present in this paper ByteCover, which is a new feature learning method for cover song identification (CSI). Byte-Cover is built based on the classical ResNet model, and two major improvements are designed to further enhance the capability of the model for CSI. In the first improvement, we introduce the integration of instance normalization (IN) and batch normalization (BN) to build IBN blocks, which are major components of our ResNet-IBN model. With the help of the IBN blocks, our CSI model can learn features that are invariant to the changes of musical attributes such as key, tempo, timbre and genre, while preserving the version information. In the second improvement, we employ the BN-Neck method to allow a multi-loss training and encourage our method to jointly optimize a classification loss and a triplet loss, and by this means, the inter-class discrimination and intra-class compactness of cover songs, can be ensured at the same time. A set of experiments demonstrated the effectiveness and efficiency of ByteCover on multiple datasets, and in the Da-TACOS dataset, ByteCover outperformed the best competitive system by 18.0%.
LightSeq: A High Performance Inference Library for Sequence Processing and Generation

LightSeq is a high performance inference library for sequence processing and generation implemented in CUDA. To our best knowledge, this is the first open-source inference library which fully supports highly efficient computation of modern NLP models such as BERT, GPT, Transformer, etc. This library is efficient, functional and convenient. A demo usage can be found here: this https URL.
Predicting Gas-Particle Partitioning Coefficients of Atmospheric
Molecules with Machine Learning

Abstract. The formation, properties and lifetime of secondary organic aerosols in the atmosphere are largely determined by gas-particle partitioning coefficients of the participating organic vapours. Since these coefficients are often difficult to measure and to compute, we developed a machine learning model to predict them given molecular structure as input. Our data-driven approach is based on the dataset by Wang et al. (Atmos. Chem. Phys., 17, 7529 (2017)), who computed the partitioning coefficients and saturation vapour pressures of 3414 atmospheric oxidation products from the master chemical mechanism using the COSMOtherm program. We trained a kernel ridge regression (KRR) machine learning model on the saturation vapour pressure (Psat), and on two equilibrium partitioning coefficients: between a water-insoluble organic matter phase and the gas phase (KWIOM/G), and between an infinitely dilute solution with pure water and the gas phase (KW/G). For the input representation of the atomic structure of each organic molecule to the machine, we tested different descriptors. We find that the many-body tensor representation (MBTR) works best for our application, but the topological fingerprint (TopFP) approach is almost as good, and is significantly more cost effective. Our best machine learning model (KRR with a Gaussian kernel + MBTR) predicts Psat and KWIOM/G to within 0.3 logarithmic units and KW/G to within 0.4 logarithmic units of the original COSMOtherm calculations. This is equal or better than the typical accuracy of COSMOtherm predictions compared to experimental data (where available). We then applied our machine learning model to a dataset of 35,383 molecules that we generated based on a carbon 10 backbone functionalized with 0 to 6 carboxyl, carbonyl or hydroxyl groups to evaluate its performance for polyfunctional compounds with potentially low Psat. The resulting saturation vapor pressure and partitioning coefficient distributions were physico-chemically reasonable, and the volatility predictions for the most highly oxidized compounds were in qualitative agreement with experimentally inferred volatilities of atmospheric oxidation products with similar elemental composition.
Gill Memorial Medal Address 2014 Reviewing the Red Bishop: the bird that introduced me to ornithology

Ostrich is co-published by NISC (Pty) Ltd and Informa UK Limited (trading as Taylor & Francis Group) When I was a naïve postgraduate student at the University of Cape Town looking for a project in animal behaviour, Professor Gerry Broekhuysen suggested a study of the Southern Red Bishop Euplectes orix (hereafter ‘Red Bishop’). The polygynous mating system of a member of this genus had been described by David Lack in a paper on the Black-winged Bishop Euplectes hordeaceus in Tanzania (Lack 1935), and confirmed for the Red Bishop by Skead (1956). However, there had been no detailed study of Red Bishop behaviour or breeding biology in South Africa. My MSc thesis (Craig 1973) recorded the behaviour of male Red Bishops in the style of classical ethology. For this study I acquired my first pair of binoculars, trained as a bird-ringer with Clive Elliott and George Underhill among my mentors, and started learning to identify birds in the hand and in the field. A year as an exchange student at the University of Bielefeld in Germany, working with Professor Klaus Immelmann’s research team, greatly advanced my understanding of bird behaviour. I then returned to South Africa to start a PhD project under Professor Gordon Maclean at the University of Natal in Pietermaritzburg, a broader study of Euplectes biology with comparative information on the congeneric species which occurred in sympatry in that region (Craig 1977). Since then there have been other studies of the Red Bishop by several research groups, extensive research on other species in the genus both in southern Africa and elsewhere, and new techniques not yet available in the 1970s have been utilised. Here I provide a brief overview of my own findings on this species, re-assessed in the light of subsequent work over the period 1980–2013.
Hydrodynamical model of QED cascade expansion in an extremely strong laser pulse

Development of the self-sustained quantum-electrodynamical (QED) cascade in a single strong laser pulse is studied analytically and numerically. The hydrodynamical approach is used to construct the analytical model of the cascade evolution, which includes the key features of the cascade observed in 3D QED particle-in-cell (QED-PIC) simulations such as the magnetic field predominance in the cascade plasma and laser energy absorption. The equations of the model are derived in the closed form and are solved numerically. Direct comparison between the solutions of the model equations and 3D QED-PIC simulations shows that our model is able to describe the complex nonlinear process of the cascade development qualitatively well. The various regimes of the interaction based on the intensity of the laser pulse are revealed in both the solutions of the model equations and the results of the QED-PIC simulations.
Relative contrast estimation and inference for treatment recommendation

When there are resource constraints, it may be necessary to rank individualized treatment benefits to facilitate the prioritization of assigning different treatments. Most existing literature on individualized treatment rules targets absolute conditional treatment effect differences as a metric for the benefit. However, there can be settings where relative differences may better represent such benefit. In this paper, we consider modeling such relative differences formed as scale‐invariant contrasts between the conditional treatment effects. By showing that all scale‐invariant contrasts are monotonic transformations of each other, we posit a single index model for a particular relative contrast. We then characterize semiparametric estimating equations, including the efficient score, to estimate index parameters. To achieve semiparametric efficiency, we propose a two‐step approach that minimizes a doubly robust loss function for initial estimation and then performs a one‐step efficiency augmentation procedure. Careful theoretical and numerical studies are provided to show the superiority of our proposed approach.
Conservative Safety Critics for Exploration

Safe exploration presents a major challenge in reinforcement learning (RL): when active data collection requires deploying partially trained policies, we must ensure that these policies avoid catastrophically unsafe regions, while still enabling trial and error learning. In this paper, we target the problem of safe exploration in RL by learning a conservative safety estimate of environment states through a critic, and provably upper bound the likelihood of catastrophic failures at every training iteration. We theoretically characterize the tradeoff between safety and policy improvement, show that the safety constraints are likely to be satisfied with high probability during training, derive provable convergence guarantees for our approach, which is no worse asymptotically than standard RL, and demonstrate the efficacy of the proposed approach on a suite of challenging navigation, manipulation, and locomotion tasks. Empirically, we show that the proposed approach can achieve competitive task performance while incurring significantly lower catastrophic failure rates during training than prior methods. Videos are at this url this https URL
EdgeBench: A Workflow-based Benchmark for Edge Computing

Edge computing has been developed to utilize multiple tiers of resources for privacy, cost and Quality of Service (QoS) reasons. Edge workloads have the characteristics of data-driven and latency-sensitive. Because of this, edge systems have developed to be both heterogeneous and distributed. The unique characteristics of edge workloads and edge systems have motivated EdgeBench, a workflow-based benchmark aims to provide the ability to explore the full design space of edge workloads and edge systems. EdgeBench is both customizable and representative. It allows users to customize the workflow logic of edge workloads, the data storage backends, and the distribution of the individual workflow stages to different computing tiers. To illustrate the usability of EdgeBench, we also implements two representative edge workflows, a video analytics workflow and an IoT hub workflow that represents two distinct but common edge workloads. Both workflows are evaluated using the workflow-level and function-level metrics reported by EdgeBench to illustrate both the performance bottlenecks of the edge systems and the edge workloads.
MeltNet: Predicting alloy melting temperature by machine learning.

Thermodynamics is fundamental for understanding and synthesizing multi-component materials, while efficient and accurate prediction of it still remain urgent and challenging. As a demonstration of the "Divide and conquer" strategy decomposing a phase diagram into different learnable features, quantitative prediction of melting temperature of binary alloys is made by constructing the machine learning (ML) model "MeltNet" in the present work. The influences of model hyperparameters on the prediction accuracy is systematically studied, and the optimal hyperparameters are obtained by Bayesian optimization. A comprehensive error analysis is made on various aspects including training duration, chemistry and input features. It is found that except a few discrepancies mainly caused by less satisfactory treatment of metalloid/semimetal elements and large melting point difference with poor liquid mixing ability between constituent elements, MeltNet achieves overall success in prediction, especially capturing subtle composition-dependent features in the unseen chemical systems for the first time. The reliability, robustness and accuracy of MeltNet is further largely boosted by introducing the ensemble method with uncertainty quantification. Based on the state-of-the-art underlying techniques, MeltNet achieves a prediction mean average error (MAE) as low as about 120 K, at a minimal computational cost. We believe the present work has a general value for significant acceleration of predicting thermodynamics of complicated multi-component systems.
Multi-Class Zero-Shot Learning for Artistic Material Recognition

Zero-Shot Learning (ZSL) is an extreme form of transfer learning, where no labelled examples of the data to be classified are provided during the training stage. Instead, ZSL uses additional information learned about the domain, and relies upon transfer learning algorithms to infer knowledge about the missing instances. ZSL approaches are an attractive solution for sparse datasets. Here we outline a model to identify the materials with which a work of art was created, by learning the relationship between English descriptions of the subject of a piece and its composite materials. After experimenting with a range of hyper-parameters, we produce a model which is capable of correctly identifying the materials used on pieces from an entirely distinct museum dataset. This model returned a classification accuracy of 48.42% on 5,000 artworks taken from the Tate collection, which is distinct from the Rijksmuseum network used to create and train our model.
Collective Movement with Signaling.

We consider a population of mobile agents able to make noisy observation of the environment and communicate their observation by production and comprehension of signals. Individuals try to align their movement direction with their neighbors. Besides, they try to collectively find and travel towards an environmental direction. We show that, when the fraction of informed individuals is small, by increasing the noise in communication, similarly to the Viscek model, the model shows a discontinuous order-disorder transition with strong finite size effects. In contrast, for large fraction of informed individuals, it is possible to go from the ordered phase to the disordered phase without passing any phase transition. The ordered phase is composed of two phases separated by a discontinuous transition. Informed collective motion, in which the population collectively infers the correct environmental direction, occurs for high fraction of informed individuals. When the fraction of informed individuals is low, misinformed collective motion, where the population fails to find the environmental direction becomes stable as well. Besides, we show that an amount of noise in the production of signals is more detrimental for the inference capability of the population, and increases the density fluctuations and the probability of group fragmentation, compared to the same amount of noise in the comprehension.