Miracle-dz/UniFAR-FTUs huggingface dataset:
NCFET Based Logic for Energy Harvesting Systems

Negative Capacitance FETs (NCFETs) are emerging devices that could be utilized for designing powerefficient nonvolatile energy harvesting processors. The unique feature which makes these devices suitable for ultra-low power operation is the steep slope achieved by negative capacitance of the ferroelectric oxide based gate stack. This property is being actively explored to overcome the fundamental 60 mV/decade subthreshold swing limit in conventional MOSFET. NCFETs also show a hysteresis Ids-Vgs behavior, with which we propose to build nonvolatile circuits and noise immune logic. However, because large hysteresis may prevent further VDD scaling for NCFET based digital circuits, we analyze the circuit implementations and explore novel device and circuit techniques, to overcome the limitations and utilize the unique properties of the NCFETs for low-power nonvolatile noise immune systems. Keywords—NCFET, hysteresis, steep-slope devices, low power, tunability, nonvolatile processor, negative capacitance
Wavelet-based orientation of localizable Gaussian fields

In this work we give a sense to the notion of orientation for self-similar Gaussian fields with stationary increments, based on monogenic wavelet analysis of these fields, with isotropic Riesz wavelets. We compute the covariance of the random wavelet coefficients, which leads to a new formulation for the structure tensor and provides an intrinsic definition of the orientation vector as eigenvector of this tensor. We show that the orientation vector does not depend on the choice of the mother wavelet, nor the scale, but only on the anisotropy encoded in the spectral density of the field. Then we generalize this definition to a larger class of random fields called localizable Gaussian fields, whose orientation is derived from the orientation of their tangent fields. Two classes of Gaussian models with prescribed orientation are studied in the light of these new analysis tools.
Small-Format Digital Imaging for Informal Settlement Mapping

Recent advances in technology and wider commercial applications have promoted the remote sensing and airborne mapping communities to take a closer look at digital imaging and its advantages over conventional film-based, large-format aerial photography. In this paper, we focus on the potential of high-resolution, digital color imagery acquired from an airborne small-format digital camera for rapid mapping of complex urban informal settlements. We discuss the example of automated building extraction using a novel color edge extractor developed with similarity-based color morphology and report on investigations into the use of this low-cost digital imaging option for rapid spatial data acquisition of informal settlements under dynamic conditions. Results from our work suggest that the approach taking advantage of color information to generate attributed edges to hypothesize shack roof outlines by exploiting the fuzzy similarity measure and color morphology is a potential new avenue to automatically extract buildings from high-resolution color digital imagery.
Electric wheelchair control using head pose free eye-gaze tracker

Presented is a wheelchair control using a head pose free eye-gaze estimation. The direction of the wheelchair is computed from a user's eye-gaze pointing. The eye-gaze estimation system includes a 3D orientation sensor to take into account the head pose. Therefore, the user can change his or her pose with comfort during navigation. In experiments, its performance was evaluated in comparison with a manual keyboard control.
Osmotic shock toleranceandmembrane £ uidityof cold-adapted Cryptococcus £ avescens OH 182 . 9 , previously reportedas C . nodaensis , a biocontrol agentofFusarium head blight

Cryptococcus£avescens OH182.9, previously reportedas C.nodaensis , a biocontrol agentofFusarium head blight Christopher A. Dunlap, Kervin O. Evans, Bart Theelen, Teun Boekhout & David A. Schisler Crop Bioprotection Research Unit, National Center for Agricultural Utilization Research, Agricultural Research Service, United States Department of Agriculture, IL, USA; New Crops and Processing Technology Research Unit, National Center for Agricultural Utilization Research, Agricultural Research Service, United States Department of Agriculture, IL, USA; and Centraalbureau voor Schimmelcultures (CBS-KNAW), Uppsalalaan, Utrecht, The Netherlands
Dual-modality Talking-metrics: 3D Visual-Audio Integrated Behaviometric Cues from Speakers

Face-based behaviometrics focus on dynamic biological signatures generated from face behaviors, which are informative and subject-specific for identity recognition. Most existing face behaviometrics rely on 2D visual features and thus are sensitive to pose or intensity variations. This paper presents a dual-modality behaviometrics algorithm (talking-metrics) that integrates 3D video and audio cues from a human face speaking a passphrase. Static and dynamic 3D face features are extracted algorithmically and audio features are transformed through a few learning models. We concatenate the top 18 discriminative 3D visual-audio features to represent the bi-modality and utilize an linear discrimant analysis (LDA) classifier for identity recognition. The experiments were conducted on a new publicly released dataset (S3DFM). Both qualitative feature distributions and quantitative comparison results show the feasibility of the proposed pipeline and the superiority over using each modality independently. A 98.5% cross-validation recognition rate over 60 subjects and 10 trials was achieved. An anti-spoofing test also demonstrates the robustness of the proposed method.
libVNF: Building Virtual Network Functions Made Easy

Network Function Virtualization (NFV) aims to reduce costs and increase flexibility of networks by moving functionality traditionally implemented in custom hardware into software packet processing applications, or virtual network functions (VNFs), running on commodity servers in a cloud. This paper describes the design and implementation of libVNF, a library to build high performance, horizontally scalable VNFs. Unlike existing frameworks for VNF development, our library (i) can be used for the development of L2/L3 middleboxes as well as VNFs that are transport layer endpoints; (ii) seamlessly supports multiple network stacks in the backend; and (iii) enables distributed implementation of VNFs via functions for distributed state and replica management. We have implemented a variety of VNFs using our library to demonstrate the expressiveness of our API. Our evaluation shows that building VNFs using libVNF can reduce the number of lines of code in the VNF by up to 50%. Further, optimizations in our library ensure that the performance of VNFs built with our library scales well with increasing number of CPU cores and distributed replicas.
Replication Data for: Burden Sharing: Income, Inequality, and Willingness to Fight

What explains citizens’ willingness to fight for their country in times of war? Using six waves of the World Values Survey, this study finds that individual willingness to fight is negatively related with country-level income inequality. When income inequality is high, the rich are less willing to fight than the poor. When inequality is low, the poor and rich differ little in their willingness to fight. This change in the willingness to fight between low and high inequality countries is greater among the rich than among the poor. This article explores several explanations for these findings. The data are consistent with the argument that high inequality makes it more attractive for the rich to buy themselves out of military service.
Lions, leopards and muskoxen: a (very) light-hearted look at the ups, downs, ins and outs of a postdoctoral career through the eyes of two zoologists

This paper arose out of a seminar series for the Royal Zoological Society of New South Wales' 2006 student forum and workshop, “From the Horse's Mouth: Career Insights from Working Zoologists”. The paper is aimed mainly at honours and postgraduate zoologists, and describes our experiences as postdoctoral researchers, including what postdoctoral research is, how we got to be there (a PhD is essential), whether it was what we hoped it would be (a resounding yes), a brief summary of some postdoctoral highlights (MWH researched lions in Africa and AM muskoxen in Alaska), and of course some lowlights (low income, short-term positions and a lack of employment security). We follow up with a few recommendations on how to attain the lofty heights of a postdoctoral research scientist.
Signalling of Coherence Relations in Discourse, Beyond Discourse Markers

ABSTRACT We argue that coherence relations (relations between propositions, such as Concession or Purpose) are signalled more frequently and by more means than is generally believed. We examine how coherence relations in text are indicated by all possible textual signals, and whether every relation is signalled. To that end, we conducted a corpus study on the RST Discourse Treebank, a corpus of newspaper articles annotated for rhetorical (or coherence) relations. Results from our corpus study show that most relations in text (over 90%) are signalled and also that most signalled relations (over 80%) are indicated not only by discourse markers (and, but, if, since), but also by a wide variety of signals other than discourse markers, such as reference, lexical, semantic, syntactic and graphical features. These findings suggest that signalling of coherence relations is much more sophisticated than previously thought.
Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey

In the modern era, workflows are adopted as a powerful and attractive paradigm for expressing/solving a variety of applications like scientific, data intensive computing, and big data applications such as MapReduce and Hadoop. These complex applications are described using high-level representations in workflow methods. With the emerging model of cloud computing technology, scheduling in the cloud becomes the important research topic. Consequently, workflow scheduling problem has been studied extensively over the past few years, from homogeneous clusters, grids to the most recent paradigm, cloud computing. The challenges that need to be addressed lies in task-resource mapping, QoS requirements, resource provisioning, performance fluctuation, failure handling, resource scheduling, and data storage. This work focuses on the complete study of the resource provisioning and scheduling algorithms in cloud environment focusing on Infrastructure as a service (IaaS). We provided a comprehensive understanding of existing scheduling techniques and provided an insight into research challenges that will be a possible future direction to the researchers.
Adaptive Suppression of Inter-Area Oscillation Using Multiple Wind Power Systems in a Distributed Parameter Control Methodology

Abstract This paper presents a preliminary study of using adaptive control of grid connected wind farms to damp the inter-area oscillations in wind integrated power systems. The power system is modeled as a distributed parameter systems using a forced first order hyperbolic wave equation, which represents an aggregate model for the system of coupled swing equations subject to wind farm power injections. A direct adaptive controller is used to stabilize the power swing in the face of disturbances using power injected from the wind farms at either a single or multiple locations throughout the power system.
Three studies using the KTH speech tracking procedure

This paper describes three studies of speech tracking using the KTH computer-based procedure. The first study investigated whether tracking was a suitable substitute for other forms of training in the use of tactile aids. One subject was trained extensively using speech tracking with five different tactile aids. The results obtained indicated that other forms of training were necessary if optimal use of tactile aids was to be achieved. This appeared to be especially true for the Tactaid VII, a more complex multi-channel vibrotactile aid. This finding was supported by the second study where a training program for the Tactaid VII, combining the use of tracking and analytic training exercises, was provided to one subject. Over the course of the study the subject's performance for lipreading plus the tactile aid was clearly superior to that obtained with lipreading alone. The final study compared two different presentation rates clear and conversational speech and examined its efSects on tracking performance. Although the clear speech condition yielded a lower tracking rate overall the number of blockages was greatly reduced. The implications of the studies are discussed and suggestions made for further research.
Bayesian hidden Markov tree models for clustering genes with shared evolutionary history

Determination of functions for poorly characterized genes is crucial for understanding biological processes and studying human diseases. Functionally associated genes are often gained and lost together through evolution. Therefore identifying co-evolution of genes can predict functional gene-gene associations. We describe here the full statistical model and computational strategies underlying the original algorithm, CLustering by Inferred Models of Evolution (CLIME 1.0) recently reported by us [Li et al., 2014]. CLIME 1.0 employs a mixture of tree-structured hidden Markov models for gene evolution process, and a Bayesian model-based clustering algorithm to detect gene modules with shared evolutionary histories (termed evolutionary conserved modules, or ECMs). A Dirichlet process prior was adopted for estimating the number of gene clusters and a Gibbs sampler was developed for posterior sampling. We further developed an extended version, CLIME 1.1, to incorporate the uncertainty on the evolutionary tree structure. By simulation studies and benchmarks on real data sets, we show that CLIME 1.0 and CLIME 1.1 outperform traditional methods that use simple metrics (e.g., the Hamming distance or Pearson correlation) to measure co-evolution between pairs of genes.
A Framework for Conceiving of Job-related Affective Wellbeing

Beginning with the nature of happiness, the philosophical roots of job-related affect are explored to inform contemporary understandings of the phenomenon. Various disciplines, theories, models and schools of thought are contextualised and related to job-related affective wellbeing. Seminal constructs of affective wellbeing are identified, along with issues involved in developing an integrated model of affective wellbeing in the workplace. A synthesis of the literature is given using a tripartite heuristic framework comprising categories of Dispositional, Activity and Telic. Each category explains the tenets of the most influential theories and debates their veracity. Finally, the key theoretical developments are integrated to provide linkages between the conceptual bases of these constructs.
Weak Dependence on Initialization in Mixture of Linear Regressions Ryohei Nakano

This paper examined how initial values of a learning algorithm influence the performance of mixture of linear regressions (MoLR). MoLR employs the EM algorithm as a learning algorithm. Our experiments used two kinds of artificial data and one real dataset. Experiments using artificial data showed MoLR discovered the original lines from data containing Gaussian or t-distribution noise. Moreover, almost all our experiments showed the best solution can be easily found with any initialization method. This suggests MoLR may have rather weak dependence on initialization.
Modeling consensus emergence in groups using longitudinal multilevel methods

1GhentUniversity 2University of SouthCarolina 3DrewUniversity Correspondence JonasW.B. Lang,DepartmentofPersonnelManagement,WorkandOrganizational Psychology, GhentUniversity,HenriDunantlaan2, 9000 Ghent, Belgium. Email: jonas.lang@ugent.be Funding information FondsWetenschappelijkOnderzoek, Grant/AwardNumber:G019217N Abstract Organizational researchers have long been interested in studying bottom-up multilevel processes where lower level units (e.g., employees) in organizations interact to jointly create characteristics of higher level units (e.g., work groups). This article contributes to the literature on bottom-up processes by detailing a statistical approach—the consensus emergence model (CEM)—that allows researchers to study emergence of shared perceptions and feelings or climates in groups over time. The described methodological approach extends standard multilevel methodology by examining residual variances within a growthmodel to account for dynamic change in group consensus. The CEM provides a formal test for consensus emergence. The approach also allows researchers to test explanatory models of consensus emergence by including personlevel, group-level, and observation-level predictors.We illustrate the CEM by applying the method to data from two longitudinal studies of work units. The first study investigated job satisfaction in military companies. Our second study examined professional archeologists working in groups on a field excavation mission and focused on fatigue at the end of the work day. Our analyses demonstrate the CEM's ability to detect and study emergence, and suggest that the CEMmaybe a valuable tool to help extend the study of emergence in organizational research.
Co-worker dialogue – a tool for health, personal development, and an empowering development culture in the workplace

ABSTRACT This study explores managers’ perspective on how co-worker dialogue [CWD] can foster co-worker health and personal development, and contribute to an empowering development culture in the workplace. The interview study was performed at a hospital in Sweden. Seventeen hospital managers participated. The managers, both men and women, worked in different areas, and a majority had 30–40 co-workers. To uncover underlying pattern phenomena in the interview data, a six-step inductive qualitative thematic analysis was conducted. The findings present three themes, each highlighting different prerequisites for CWD to function as a resource for the co-workers and the workplace: (1) Utility; (2) Content, and (3) Implementation. The study provides suggestions for and problems of practical implications from the findings. To make the findings useful in other organizations, practical implications are presented and discussed in the light of workplace health promotion [WHP]. The CWD is not focusing on performance the way traditional PA does. The CWD is therefore an important complement to PA in annual co-worker meetings, to also highlight the co-worker perspective. If managers realize the value of working with both PA and CWD, opportunities for health, personal development, and an empowering development culture are created.
Partially supervised hierarchical classification for urban features from lidar data with aerial imagery

Although spatial and spectral resolutions of remotely sensed data have been improved, the usage of multispectral imagery is not sufficient for urban feature classification. This article addresses the problem of automated classification by integrating airborne lidar range data and aerial imagery. In this study, the classification procedure is divided into three phases. We first use the lidar range data to obtain the coarse lidar-based classification results, by which a lidar-driven labelled image and a lidar-driven high-rise object mask are acquired in this phase. Then, at the image-based classification level, we train samples based on the lidar-driven labelled image and conduct maximum likelihood classification experience with the lidar-driven normalized digital surface model as a high-rise object mask. Finally, we propose a knowledge-based cross-validation (KBCV) for misclassification between the lidar-based classification results and the image-based classification results. Experimental results are presented to demonstrate the benefits of the training sample selection of the lidar-driven labelled image, using the lidar-driven high-rise object mask, and the greater classification accuracy of the KBCV.
Agricultural Academy MUCIN 2 GENE EXPRESSION IN THE CHICKEN INTESTINAL GOBLET CELLS ARE AFFECTED BY DIETARY ESSENTIAL OILS

Zeinali, S. , S. Ghazanfari and M. A. Ebrahimi, 2017. Mucin2 gene expression in the chicken intestinal goblet cells are affected by dietary essential oils. Bulg. J. Agric. Sci., 23 (1): 134–141 The mucous layer that covers the intestinal absorptive surface acts as a diffusive barrier between the intestinal lumen and absorptive cells. In this report, we studied the effect of changing the body weight, intestinal mucin2 gene expression and number of goblet cells by feeding 1-d-old chicks a control diet or a diet containing either an antibiotic growth promoter or dietary essential oils (clove, coriondrum, artemisia sieberi and myrtle) at three levels. On day 42 of age two birds from each replicate were randomly selected, weight and then slaughtered. Goblet cell number was analyzed from each preparation. Mucin2 mRNA expression in intestinal jejunum tissue was quantitated using real-time quantitative PCR. We found that body weights were increased in chicks fed antibiotic and essential oil treatments compared with those fed control diet at 42 days of age (P<0.01). Dietary essential oils increased the mucin2 gene expression in the jejunum compared with the control treatment (P<0.05). In dietary essential oils fed chicks, the number of goblet cells was decreased in the small intestine compared with control treatment (P<0.05). These results indicate that essential oils altered processes of mucin biosynthesis via changes in the intestinal mucin2 gene expression and number of goblet cells. These modifications in mucin dynamics influence gut function and health and may change nutrient uptake.
Deep Dictionary Learning v10

Abstract— Two popular representation learning paradigms are dictionary learning and deep learning. While dictionary learning focuses on learning “basis’’ and “features’’ by matrix factorization, deep learning focuses on extracting features via learning “weights’’ or “filter’’ in a greedy layer by layer fashion. This research focuses on combining the concepts of these two paradigms. In this paper, we propose deep dictionary learning and show how deeper architectures can be built using layers of dictionary learning. The proposed technique is compared with other deep learning approaches such as Stacked AutoEncoder, Deep Belief Network, and Convolutional Neural Network. Experiments on benchmark datasets show that the proposed technique achieves higher classification and clustering accuracies. On a real world problem of electrical appliance classification, we show that deep dictionary learning excels where others do not yield atpar performance. We postulate that the proposed formulation can pave the path for a new class of deep learning tools.