Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Model for Mapping Relative Differences in Belowground Biomass and Root: Shoot Ratios Using Spectral Reflectance, Foliar N and Plant Biophysical Data within Coastal Marsh

Broad-scale estimates of belowground biomass are needed to understand wetland resiliency and C and N cycling, but these estimates are difficult to obtain because root:shoot ratios vary considerably both within and between species. We used remotely-sensed estimates of two aboveground plant characteristics, aboveground biomass and % foliar N to explore biomass allocation in low diversity freshwater impounded peatlands (Sacramento-San Joaquin River Delta, CA, USA). We developed a hybrid modeling approach to relate remotely-sensed estimates of % foliar N (a surrogate for environmental N and plant available nutrients) and aboveground biomass to field-measured belowground biomass for species specific and mixed species models. We estimated up to 90% of variation in foliar N concentration using partial least squares (PLS) regression of full-spectrum field spectrometer reflectance data. Landsat 7 reflectance data explained up to 70% of % foliar N and 67% of aboveground biomass. Spectrally estimated foliar N or aboveground biomass had negative relationships with belowground biomass and root:shoot ratio in both Schoenoplectus acutus and Typha, consistent with a balanced growth model, which suggests plants only allocate growth belowground when additional nutrients are necessary to support shoot development. Hybrid models explained up to 76% of variation in belowground biomass and 86% of variation in root:shoot ratio. Our modeling approach provides a method for developing maps of spatial variation in wetland belowground biomass.
UNIVERSAL OPERATIONS IN

We provide a general method for finding all natural operations on the Hochschild complex of E-algebras, where E is any algebraic structure encoded in a PROP with multiplication, as for example the PROP of Frobenius, commutative or A∞-algebras. We show that the chain complex of all such natural operations is approximated by a certain chain complex of formal operations, for which we provide an explicit model that we can calculate in a number of cases. When E encodes the structure of open topological conformal field theories, we identify this last chain complex, up quasi-isomorphism, with the moduli space of Riemann surfaces with boundaries, thus establishing that the operations constructed by Costello and Kontsevich-Soibelman via different methods identify with all formal operations. When E encodes open topological quantum field theories (or symmetric Frobenius algebras) our chain complex identifies with Sullivan diagrams, thus showing that operations constructed by TradlerZeinalian, again by different methods, account for all formal operations. As an illustration of the last result we exhibit two infinite families of non-trivial operations and use these to produce non-trivial higher string topology operations, which had so far been elusive.
Neuro Semantic Thresholding for High Precision OCR Applications

This paper shows a novel approach to binarization techniques. It presents a way to obtain an optimum threshold using a semantic description of the histogram and a neural network. The intended applications of this technique are high precision OCR algorithms over a limited number of document types. The histogram of the input image is smoothed and its derivative is found. Using a polygonal version of the derivative and the smoothed histogram, a new description of the histogram is calculated. Using this description a general regression neural network is capable of obtaining an optimum threshold for our application.
Runtime Optimizations for Prediction with Tree-Based Models

Tree-based models have proven to be an effective solution for web ranking as well as other problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, given an already-trained model. Although exceedingly simple conceptually, most implementations of tree-based models do not efficiently utilize modern superscalar processor architectures. By laying out data structures in memory in a more cache-conscious fashion, removing branches from the execution flow using a technique called predication, and micro-batching predictions using a technique called vectorization, we are able to better exploit modern processor architectures and significantly improve the speed of tree-based models over hard-coded if-else blocks. Our work contributes to the exploration of architecture-conscious runtime implementations of machine learning algorithms.
Cellular unfolded protein response against viruses used in gene therapy

Viruses are excellent vehicles for gene therapy due to their natural ability to infect and deliver the cargo to specific tissues with high efficiency. Although such vectors are usually “gutted” and are replication defective, they are subjected to clearance by the host cells by immune recognition and destruction. Unfolded protein response (UPR) is a naturally evolved cyto-protective signaling pathway which is triggered due to endoplasmic reticulum (ER) stress caused by accumulation of unfolded/misfolded proteins in its lumen. The UPR signaling consists of three signaling pathways, namely PKR-like ER kinase, activating transcription factor 6, and inositol-requiring protein-1. Once activated, UPR triggers the production of ER molecular chaperones and stress response proteins to help reduce the protein load within the ER. This occurs by degradation of the misfolded proteins and ensues in the arrest of protein translation machinery. If the burden of protein load in ER is beyond its processing capacity, UPR can activate pro-apoptotic pathways or autophagy leading to cell death. Viruses are naturally evolved in hijacking the host cellular translation machinery to generate a large amount of proteins. This phenomenon disrupts ER homeostasis and leads to ER stress. Alternatively, in the case of gutted vectors used in gene therapy, the excess load of recombinant vectors administered and encountered by the cell can trigger UPR. Thus, in the context of gene therapy, UPR becomes a major roadblock that can potentially trigger inflammatory responses against the vectors and reduce the efficiency of gene transfer.
A simple direct-torque neuro-fuzzy control of PWM-inverter-fed induction motor drive

In this paper, the concept and implementation of a new simple direct-torque neuro-fuzzy control (DTNFC) scheme for pulsewidth-modulation-inverter-fed induction motor drive are presented. An adaptive neuro-fuzzy inference system is applied to achieve high-performance decoupled flux and torque control. The theoretical principle and tuning procedure of this method are discussed. A 3 kW induction motor experimental system with digital signal processor TMS 320C31-based controller has been built to verify this approach. The simulation and laboratory experimental results, which illustrate the performance of the proposed scheme, are presented. Also, nomograms for controller design are given. It has been shown that the simple DTNFC is characterized by very fast torque and flux response, very-low-speed operation, and simple tuning capability.
Non-Fickian diffusion and tau approximation from numerical turbulence

Evidence for non-Fickian diffusion of a passive scalar is presented using direct simulations of homogeneous isotropic turbulence. The results compare favorably with an explicitly time-dependent closure model based on the tau approximation. In the numerical experiments three different cases are considered: (i) zero mean concentration with finite initial concentration flux, (ii) an initial top hat profile for the concentration, and (iii) an imposed background concentration gradient. All cases agree in the resulting relaxation time in the tau approximation relating the triple correlation to the concentration flux. The first-order smoothing approximation is shown to be inapplicable.
New mutations in the KVLQT1 potassium channel that cause long-QT syndrome.

BACKGROUND Long-QT syndrome (LQTS) is an inherited cardiac arrhythmia that causes sudden death in young, otherwise healthy people. Four genes for LQTS have been mapped to chromosome 11p15.5 (LQT1), 7q35-36 (LQT2), 3p21-24 (LQT3), and 4q25-27 (LQT4). Genes responsible for LQT1, LQT2, and LQT3 have been identified as cardiac potassium channel genes (KVLQT1, HERG) and the cardiac sodium channel gene (SCN5A). METHODS AND RESULTS After studying 115 families with LQTS, we used single-strand conformation polymorphism (SSCP) and DNA sequence analysis to identify mutations in the cardiac potassium channel gene, KVLQT1. Affected members of seven LQTS families were found to have new, previously unidentified mutations, including two identical missense mutations, four identical splicing mutations, and one 3-bp deletion. An identical splicing mutation was identified in affected members of four unrelated families (one Italian, one Irish, and two American), leading to an alternatively spliced form of KVLQT1. The 3-bp deletion arose de novo and occurs at an exon-intron boundary. This results in a single base deletion in the KVLQT1 cDNA sequence and alters splicing, leading to the truncation of KVLQT1 protein. CONCLUSIONS We have identified LQTS-causing mutations of KVLQT1 in seven families. Five KVLQT1 mutations cause the truncation of KVLQT1 protein. These data further confirm that KVLQT1 mutations cause LQTS. The location and character of these mutations expand the types of mutation, confirm a mutational hot spot, and suggest that they act through a loss-of-function mechanism or a dominant-negative mechanism.
Photonastic surface: pin matrix type display controlled with light

We present Photonastic Surface, a novel pin matrix type 3D form display. In Photonastic Surface, a photosensor is attached to each pin of the matrix, and the height of pins are independently controlled with light luminance or fast light flicker. It can express volume information effectively by overlapping projected images on the pin matrix and provide a new type of interaction between the pin matrix and human through light.
A cooking support system for people with higher brain dysfunction

This paper reports a cooking support system for persons with higher brain dysfunction. When a human injured its brain, various brain functions are damaged, such as memory and cognition. This symptom is known as higher brain dysfunction. An effective rehabilitation methods and support systems are essential factors for better Activity of Daily Life (ADL) of the persons with brain dysfunction. However, it is not easy to help the persons, because higher brain dysfunction is invisible and hard to understand. We aim to clarify helpful user interface design for such persons. Two aspects should be considered for the design. The aspects are an analysis of both ability and disability of patients, and customization of interface for a daily life. For the purpose, we focus on cooking, one of the most important ADL. Cooking is complex task because it includes many steps. We designed a multimodal interface of cooking-navigation to show instructions of such a complex task easily. We applied the system for rehabilitation training and a patient could use it nicely.
Recognizing facial expressions: Computational models and humans

This paper discusses various biologically plausible computational models that recognize human facial expression and analyze them. Identifying facial expressions is a non trivial task for a human and is a key part of social interactions. However, it is not as simple as that for a computational system. Here we analyze six different universally accepted facial expressions for analysis with the aid of six biologically plausible computational models. There have been a limited number of studies comparing the performance of human subjects with computational models for facial expression recognition. This paper does a genuine attempt in making this comparison.
Cluster analysis for overlapping clusters using genetic algorithm

Cluster analysis is an important task almost in all fields including bioinformatics, social networks, agriculture, and so on. It basically explores the natural structure of the data without any prior knowledge about it. In many real data sets, the objects reside in many clusters with different membership values. Many clustering algorithms have been proposed for finding such overlapping clusters to analyze high volume of data. In the paper, genetic algorithm based cluster analysis technique is proposed for finding the optimal set of overlapping clusters. The usefulness of applying the genetic algorithm based optimization technique is to assign a membership value only to the objects which are the members of several clusters, instead of assigning membership values for all clusters like fuzzy clustering algorithm. If any object positively belongs to a cluster, its membership value for this cluster is `1' and `0' for all other clusters. The overall performance of the method is investigated on some popular UCI data sets and the optimality of the clusters is measured by related cluster validation indices. The experimental results show the effectiveness of the proposed method.
Effects of Global Communication system radio-frequency fields on Well Being and Cognitive Functions of human subjects with and without subjective complaints

All information which is classified according to Dutch regulations shall be treated by the recipient in the same way as classified information of corresponding value in his own country. No part of this information will be disclosed to any third party. The classification designation Ongerubriceerd is equivalent to Unclassified, Stg. Confidentieel is equivalent to Confidential and Stg. Geheim is equivalent to Secret. All rights reserved. No part of this report may be reproduced in any form by print, photoprint, microfilm or any other means without the previous written permission from TNO. In case this report was drafted on instructions from the Ministry of Defence the rights and obligations of the principal and TNO are subject to the standard conditions for research and development instructions, established by the Ministry of Defence and TNO, if these conditions are declared applicable, or the relevant agreement concluded between the contracting parties. TNO-report
The role of employee engagement in work-related outcomes

Engaging employees is one of the top five most important challenges for management, according to a survey of 656 chief executive officers (CEOs) from countries around the world (Wah 1999). Employee engagement has become a hot topic in recent years among consulting firms and in the popular business press. However, employee engagement has rarely been studied in the academic literature. In this study we investigated the antecedents and consequences of employee engagement in Jordanian Industry. A snowball sample of 310 respondents from the Jordanian hotel industry was interviewed using the research instrument. The sample comprised of employees from different levels of management. The results confirm the relationship between Employee Engagement and Perceived Organizational Support. The effect of Job Characteristics, Intrinsic and Extrinsic Rewards, Perceived Supervisor Support, Perceptions of Procedural Justice, Perceptions of Distributive Justice on Employee Engagement is also confirmed. The hypotheses considered in this study are supported by the evidence from data collected from a sample of respondents drawn from the hotelindustry in Jordan.
Test-Retest Repeatability of Human Neurochemical Profiles Measured at 3 Versus 7 T

Introduction: Increased precision of metabolite quantification by H MRS at 7T relative to lower field strengths has been demonstrated via lower Cramér-Rao lower bounds (CRLB). This project was designed to determine whether lower CRLB at 7T relative to 3T lead to greater inter-session test-retest repeatability, a critical factor when designing longitudinal clinical studies. The experimental approach was to use a large number of repeat scans, extensively optimized single-voxel MRS methodology and optimal hardware at both fields. This included commercially available components at 3T, while an in-house built coil together with B1 shimming was utilized at 7T to optimize available B1 and minimize chemical shift displacement errors. Methods: Spectra were measured using body excitation and a 32 channel receive array coil at 3T and a 16-channel transceiver array coil and B1 phase shimming at 7T. Four healthy participants were each scanned 4 times at 3T and 4 times at 7T on a weekly basis. Semi-LASER and STEAM with VAPOR water suppression and outer volume suppression were used to measure spectra from 2 clinically relevant brain regions, the cerebellar vermis and posterior cingulate cortex. B0 homogeneity and B1 amplitudes were optimized for each voxel. Metabolite concentrations were quantified using unsuppressed water spectra and LCModel with a simulated basis set. They were reported when measured reliably (CRLB<50%, cross correlation coefficients r>-0.5) from more than 1⁄2 of the spectra at a given field strength. Test-retest coefficients of variance (CV) of metabolite concentrations were determined in each individual, and the inter-subject mean was calculated per field strength. CRLB and CV were compared at 3T versus 7T using a paired t test.
A Practical Approach for Enhancing Security of EPCglobal RFID Gen2 Tag

Radio Frequency Identification (RFID) has been considered as an key infrastructure for the ubiquitous society. However, due to the inherent drawbacks RFID causes various security threats like privacy problems, tag cloning, etc. To address these issues, lots of works had been proposed, but most are not conformed to the EPCglobal RFID Gen2 standard. Some Genl-conformed schemes have the obstacles in implementations since they require the modifications of the standard and additional hardware-level implementations of the tags and readers. This paper proposes a novel practical approach, which are fully conformed to EPCglobal RFID Gen2 standard, for enhancing security of currently used RFID Gen2 tags against the various security threats.
A Multilevel Context-Based System for Classification of Very High Spatial Resolution Images

This paper proposes a novel pixel-based system for the supervised classification of very high geometrical (spatial) resolution images. This system is aimed at obtaining accurate and reliable maps both by preserving the geometrical details in the images and by properly considering the spatial-context information. It is made up of two main blocks: 1) a novel feature-extraction block that, extending and developing some concepts previously presented in the literature, adaptively models the spatial context of each pixel according to a complete hierarchical multilevel representation of the scene and 2) a classifier, based on support vector machines (SVMs), capable of analyzing hyperdimensional feature spaces. The choice of adopting an SVM-based classification architecture is motivated by the potentially large number of parameters derived from the contextual feature-extraction stage. Experimental results and comparisons with a standard technique developed for the analysis of very high spatial resolution images confirm the effectiveness of the proposed system
Gravitational and magnetosonic waves in gamma-ray bursts

One of the possible sources of gamma-ray bursts (GRBs) are merging, compact neutronstar binaries. More than 90% of the binding energy of such a binary is released in the form of gravitational waves (GWs) in the last few seconds of the spiral-in phase before the formation of a black hole. In this article we investigate whether a fraction of this GW energy is transferred to magnetohydrodynamic waves in the magnetized plasma wind around the binary. Using the 3 + 1 orthonormal tedrad formalism, we study the propagation of a monochromatic, plane fronted, linearly polarized GW perpendicular to the ambient magnetic field in an ultra-relativistic wind, first in the comoving and then in the observer frame. A closed set of general relativistic magnetohydrodynamic (GRM) equations is derived in the form of conservation laws for electric charge, matter energy, momentum and magnetic energy densities. We linearize the GRM equations under the action of a monochromatic GW, which acts as a driver and find that fast magneto-acoustic waves grow, with amplitudes proportional to the GW amplitude and frequency and the strength of the background magnetic field.
Orientation Selectivity without Orientation Maps in Visual Cortex of a Highly Visual Mammal

In mammalian neocortex, the orderly arrangement of columns of neurons is thought to be a fundamental organizing principle. In primary visual cortex (V1), neurons respond preferentially to bars of a particular orientation, and, in many mammals, these orientation-selective cells are arranged in a semiregular, smoothly varying map across the cortical surface. Curiously, orientation maps have not been found in rodents or lagomorphs. To explore whether this lack of organization in previously studied rodents could be attributable to low visual acuity, poorly differentiated visual brain areas, or small absolute V1 size, we examined V1 organization of a larger, highly visual rodent, the gray squirrel. Using intrinsic signal optical imaging and single-cell recordings, we found no evidence of an orientation map, suggesting that formation of orientation maps depends on mechanisms not found in rodents. We did find robust orientation tuning of single cells, and this tuning was invariant to stimulus contrast. Therefore, it seems unlikely that orientation maps are important for orientation tuning or its contrast invariance in V1. In vertical electrode penetrations, we found little evidence for columnar organization of orientation-selective neurons and little evidence for local anisotropy of orientation preferences. We conclude that an orderly and columnar arrangement of functional response properties is not a universal characteristic of cortical architecture.
Prototype hybrid couplings of macroscopic deterministic models and microscopic stochastic lattice dynamics

We study a class of model prototype hybrid systems comprised of a microscopic Arrhenius surface process describing adsorption/desorption and/or surface diffusion of particles coupled to an ordinary differential equation displaying bifurcations triggered by the microscopic process. The models proposed here are caricatures of realistic systems arising in diverse applications ranging from surface processes and catalysis to atmospheric and oceanic sciences. Furthermore, we derive and study closures of this hybrid system by employing three different methods: deterministic closures through an averaging principle, mean field approximation and stochastic closures by employing a hierarchy of coarse-grained models. We focus on analyzing the impact of microscopic fluctuations and interactions on the overall system's transient and long-time dynamics. For example, fluctuation-driven rare events may effect regimes exhibiting metastability. This type of phenomena can occur in several parameter regimes and typically they cannot be accounted for by the deterministic closures. In contrast, the stochastic coarse-grained closure gives rise to computationally inexpensive reduced hybrid models that capture correctly the behavior of the full microscopic system. This is further demonstrated in several Monte Carlo simulations testing a variety of parameter regimes and displaying numerically the extent, limitations and validity of the theory. (Less)