Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of Hardware Transactional Memory

In the last two decades, Transactional Memories have been researched as a way of simplifying the implementation of concurrent programs, while at the same time guaranteeing that the resulting code will execute efficiently. In this paper, we discuss the landmark Hardware Transactional Memory implementations that have been proposed by the industry, as well as their intrinsic limitations. We also review related software-based tools that have been developed to deal with some of these limitations. Finally, we present the Syncchar performance tuning infrastructure and comment on possible future investigation of auto-tunable characteristics in transactional code.
Towards Describing Crouzon Syndrome via a Craniofacial Atlas

ho@imm.dtu. dk 2 3D-Laboratory, School of Dentistry, University of Copenhagen; Copenhagen University Hospital 3 Computational Imaging Lab, Department of Technology D.326, Pompeu Fabra University, Barcelona 4 Department of Pediatric Dentistry and Clinical Genetics, School of Dentistry, Faculty of Health Sciences, University of Copenhagen 5 Division of Plastic Surgery, Washington University School of Medicine
Influence of risk indicators on different Universal Newborn Hearing Screening steps

Purpose: To determine risk indicators for hearing loss affecting different evaluation steps of a universal newborn hearing screening program. Methods: Longitudinal, retrospective study of newborn hearing screening in a tertiary public hospital, including 832 newborns born between January and December 2012. Transient evoked otoacoustic emissions were measured at the first hearing evaluation in all newborns and retested in cases of “failure”. When a “failure” result persisted, auditory brainstem responses (ABR) was performed. All newborns with risk indicator for hearing loss were evaluated with ABR screening regardless of the outcome of the otoacoustic emissions test. Results: The presence of at least one risk indicator for hearing loss, associated or not with craniofacial malformations, genetic syndromes and birth weight below 1500 g significantly increased the chances of “failure” in the otoacoustic emissions test. Meningitis and craniofacial malformations significantly increased the odds of an abnormal ABR. Two newborns with normal otoacoustic emissions were diagnosed with auditory neuropathy. Conclusion: Craniofacial malformation was an indicator strongly associated with a diagnosis of deafness, regardless of the hearing screening being performed by otoacoustic emissions or ABR at different steps of a universal newborn hearing screening program. This finding justifies continuous and systematic monitoring of the screening service seeking quality improvement of the newborn health hearing program.
Privacy-Utility Trades in Wireless Data via Optimization and Learning

The multi-objective optimization problem which considers the competing goals of privacy and utility, e.g. users want location-based services but wish to keep their location secret, frames the design of data obfuscation functions. A theoretical treatment of this optimization over functional spaces is possible under certain assumptions, e.g. considering the convex privacy metric of mutual information between the true state and an adversary’s estimate of that state, and treating obfuscation functions as conditional probabilities specifying obfuscated data given true data. When this analytical treatment of the problem is not feasible or desirable, data-driven approaches like adversarial learning can be used to design obfuscation functions. Generative adversarial privacy, which leverages recent advancements in generative adversarial networks, considers two players in a minimax game: a privatizer that obfuscates data to minimize the risk of inference attacks while preserving the utility of the true data, and an adversary that infers sensitive information from the obfuscated data. In this work we study both theoretical and data-driven methods of approaching privacy-utility trades and apply these methods in the context of spectrum sharing and signal mapping.
Regional Research Institute Working Paper Series A J-test for Panel Models with Fixed Effects , Spatial and Time Dependence

In this paper we suggest a J-test in a spatial panel framework of a null model against one or more alternatives. The null model we consider has fixed effects, along with spatial and time dependence. The alternatives can have either fixed or random effects. We implement our procedure to test the specifications of a demand for cigarette model. We find that the most appropriate specification is one that contains the average price of cigarettes in neighboring states, as well as the spatial lag of the dependent variable. Along with formal large sample results, we also give small sample Monte Carlo results. Our large sample results are based on the assumption N → ∞ and T is fixed. Our Monte Carlo results suggest that our proposed J-test has good power, and proper size even for small to moderately sized samples. JEL classification: C01, C12
Fast On-Board 3D Torso Pose Recovery and Forecasting*

—Anticipatory human intent modeling is important for robots operating alongside humans in dynamic or crowded environments, for quick and appropriate responses. Humans may telegraph intent through posture cues, such as torso or head cues. In this paper, we describe a multi-modal approach to human torso pose estimation and forecasting with a view towards lightweight computational and sensing needs for easy on-board deployment. Our end-to-end system combines RGB images and point cloud information to reason about 3D human pose. In addition to pose recovery, we use a simple ﬁlter-and-ﬁt method to forecast torso pose. While datasets suited to benchmark multi-person 3D pose prediction in real-world scenarios are scarce, we describe an easily replicable evaluation method for benchmarking in a near real-world setting. Further, we evaluate the pose estimation performance using the aforementioned evaluation procedure. Lastly, we evaluate the forecasting performance quantitatively on the Human3.6M motion capture dataset. Our simple forecasting algorithm outperforms complicated recurrent neural network methods, while being faster on the torso pose forecasting task.
Protea : A Framework for Building Flexible Storage Systems

Storage system design demands consideration of many different factors, including cost, throughput, durability, application workload, and availability. However, we claim that much of the complexity of storage system design can be eliminated by observing that most storage systems can be decomposed into functional modules. To this end, we present Protea, a framework for creating storage systems over heterogeneous storage hardware. We introduce the ProtoTree, a novel and powerful tree abstraction that can express a wide range of storage system designs in a simple, intuitive manner. We describe our implementation of Protea and demonstrate that it can trivially reimplement many existing storage systems with only a fraction of the time and effort. We additionally outline how Protea can be used to automate the discovery of novel storage systems over heterogeneous storage hardware.
International Conference on Alternative Energy in Developing Countries and Emerging Economies Heat Transfer Augmentation of Al 2 O 3 Nanofluid in 60 : 40 Water to Ethylene Glycol Mixture

Convectional coolant plays an important part in removing excess heat generated by automotive component to avoid damage and failure of the system. Through recent studies, nanoparticles suspended in base fluid or known as nanofluid have positive effect heat transfer performance. This paper presents the effect of increment of Alumina nanoparticles dispersed in 60:40 water to ethylene glycol based nanofluids towards heat transfer enhancement. For this purpose, nanofluids are prepared using Aluminium Oxide (Al2O3) with average diameter of 13 nm suspended in 60:40 of water to ethylene glycol by volume percentage. The nanofluid is synthesized using two step method and homogenized to lengthen the suspension for volume concentration of 0.2 %, 0.4 % and 0.6 %. The force convection investigation was conducted at a constant heat flux with Reynolds number of less than 20,000 at a constant working temperature of 50 C. The heat transfer coefficient of nanofluids is compared with the base fluid. It was observed that as the loading of nanoparticles suspended in the base fluid is increased, heat transfer coefficient is also higher. The heat transfer augmentation of Al2O3 nanofluid at 0.6 % volume concentration is higher than 0.2 % and 0.4 % concentrations. © 2015 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Organizing Committee of 2015 AEDCEE.
Effects of Majorana physics on the UHE ντ flux traversing the Earth

We study the effects produced by sterile Majorana neutrinos on the ντ flux traversing the Earth, considering the interaction between the Majorana neutrinos and the standard matter as modeled by an effective theory. The surviving tau-neutrino flux is calculated using transport equations including Majorana neutrino production and decay. We compare our results with the pure Standard Model interactions, computing the surviving flux for different values of the effective lagrangian couplings, considering the detected flux by IceCube for an operation time of 10 years, and Majorana neutrinos with mass mN ∼ mτ .
econstor Make Your Publications Visible . A Service of zbw

This paper solves the irreversible investment decision problem under uncertainty by a new real options method. It yields a Shadow Net Present Value rule such that the investment is triggered only when the shadow revenue of the investment reaches the investment cost. This paper hence corrects and extends the conventional Net Present Value (NPV) rule by figuring out the proper NPV of the investment. Meanwhile, the investment policy is shown to be correct and consistent with the real options theory since the shadow revenue records the economic value of the investment by deducting the option premium of waiting from the real revenue. This method allows for a generalization of the conventional analysis to all semi-martingale processes with deterministic or stochastic discount rates. Especially, we provide an explicit characterization of the threshold value when the uncertainty is specified as an exponential Lévy process. To highlight the significance of risk preferences on the decision rule, this paper also addresses the utility-based extension of the standard real option model. The investment policy in such a model is identified in the same form as that in the profit-maximization problem but signalled by the shadow utility process. The joint effect of risk aversion and jumps on the trigger value is analyzed through examples. JEL Subject Classification: D81, G3
A REVIEW ON DECEPTION DETECTION FOCUSED ON INTENTIONS

ción del engaño sentaba sus bases en el hecho de que un mentiroso podía ser detectado gracias a la emisión de claves conductuales que ponían en evidencia la falsedad de su relato. Varias han sido las teorías que intentaban sustentar y explicar esta afirmación: la hipótesis del filtraje (Ekman & Friesen, 1969), la teoría del factor múltiple (Zuckerman, DePaulo, & Rosenthal, 1981), la teoría autopresentacional (DePaulo, 1992; DePaulo et al., 2003) y la teoría del engaño interpersonal (Buller & Burgoon, 1994). Dentro del ámbito forense se desarrollaron diferentes técnicas para ayudar a establecer la credibilidad de víctimas, testigos así como detectar el posible engaño en las declaraciones de los delincuentes y criminales. Un ejemplo es la SVA (Statement Validity Analysis), cuya parte central, el CBCA (Criteria Based Content Analysis; Steller & Köhnken, 1989), ha extendido su uso a campos diferentes al inicial, a pesar de las críticas efectuadas en este sentido (Köhnken, Manzanero, & Scott, 2015). Otros ejemplos lo constituyen el control de la realidad (RM, Reality Monitoring; Johnson & Raye, 1981), o la técnica Reid (Inbau, Reid, Buckley, & Jayne, 2011). Esta última se enseña de manera habitual en los cursos de formación para cuerpos policiales en todo el mundo, pese a carecer de base científica (para una revisión reciente; Masip & Herrero, 2015a). Los resultados hallados en metaanálisis recientes han puesto de manifiesto, por un lado, la debilidad de las claves conductuales y su escasa relación con el engaño, y por otro que las claves de naturaleza verbal se relacionan en mayor medida con el engaño que las de carácter no verbal (DePaulo et al., 2003). Además, no somos buenos clasificando sinceros y mentirosos ya que rondamos el azar (Aamodt & Custer, 2006; Bond & Depaulo, 2008). Tampoco son mejores aquellos pertenecientes a colectivos como policías, jueces, o médicos para quienes es fundamental la detección del engaño (Bond & DePaulo, 2006). Todo ello hizo que primase la búsqueda de nuevos métodos para maximizar la diferencia entre mentirosos y sinceros. Por ello, surgen técnicas basadas en el incremento de la carga cognitiva, donde además, el entrevisLA INTENCIÓN ES LO QUE CUENTA. UNA REVISIÓN SOBRE LA DETECCIÓN DEL ENGAÑO EN INTENCIONES IT’S THE INTENTION THAT COUNTS. A REVIEW ON DECEPTION DETECTION FOCUSED ON INTENTIONS
O C ] 3 1 A ug 2 01 9 Parabolic Regularity in Geometric Variational Analysis

The paper is mainly devoted to systematic developments and applications of geometric aspects of second-order variational analysis that are revolved around the concept of parabolic regularity of sets. This concept has been known in variational analysis for more than two decades while being largely underinvestigated. We discover here that parabolic regularity is the key to derive new calculus rules and computation formulas for major second-order generalized differential constructions of variational analysis in connection with some properties of sets that go back to classical differential geometry and geometric measure theory. The established results of second-order variational analysis and generalized differentiation, being married to the developed calculus of parabolic regularity, allow us to obtain novel applications to both qualitative and quantitative/numerical aspects of constrained optimization including second-order optimality conditions, augmented Lagrangians, etc. under weak constraint qualifications.
UvA-DARE ( Digital Academic Repository ) An alternative PII protein in the regulation of glutamine synthetase in Escherichia coli

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Towards Real-Time Analytics for Mobile Big Data using the Edge Computing

The extraordinary proliferation of digital devices has led to the production of enormous amounts of heterogeneous data called mobile big data (MBD). However, obtaining insights into the flood of MBD in a real-time manner has become a crucial issue. Coping with this issue, we propose a theoretical framework based on the edge computing, which envisions bringing computation closer to the user’s proximity. The proposed framework comprises three layers, where each layer is dependent upon the layers above it. The qualitative analysis results reveal the proposed framework can enable real-time analytics for the business environments.
A Framework to Assess the Critical Success Factors for Cloud Enterprise Resource Planning Adoption in Small and Medium-sized Enterprises

Enterprise resource planning (ERP) is a well-known business management system used for improving effectiveness in organisations. In the current digital era, cloud ERP systems have evolved which are taking precedence over the traditional ERP due to convenience of remote information access in real-time with benefits of cost saving, flexibility and scalability. These systems are especially helpful to SMEs which usually are constrained in resources. However, there have not been many studies that look at the critical success factors for cloud ERP adoption in SMEs. This paper develops an integrative framework using technology-organisation-environment (TOE) and unified theory of acceptance and use of technology (UTAUT) models to investigate the individual, environmental, technological and organisational levels of cloud ERP adoption in SMEs for identifying factors for success. The findings will provide new insights on cloud ERP adoption and will help both academia and practitioners increase understanding for future research and implementation.
Stylistic Author Attribution Methods for Identifying Parody Trump Tweets

Style-sensitive methods for authorial attribution have been proposed and developed in recent years in order to assess the authorship of documents with content unrelated to the rest of an author’s body of work. We aim to train a classifier with a style-sensitive feature space alongside one without style sensitivity to examine how vital stylistic analysis is to a similar task parody detection. The distinct style of Donald Trump’s tweets has spun off a litany of parody tweets and parody accounts: we will train the classifier on Trump tweets and use it to determine whether tweets by other users, including non-parody accounts and parody accounts of similar and different content to Trump, are, in fact, parody.
Title Incorporating web analysis into neural networks : An example inhopfield net searching

Neural networks have been used in various applications on the World Wide Web, but most of them only rely on the available input-output examples without incorporating Webspecific knowledge, such as Web link analysis, into the network design. In this paper, we propose a new approach in which the Web is modeled as an asymmetric Hopfield Net. Each neuron in the network represents a Web page, and the connections between neurons represent the hyperlinks between Web pages. Web content analysis and Web link analysis are also incorporated into the model by adding a page content score function and a link score function into the weights of the neurons and the synapses, respectively. A simulation study was conducted to compare the proposed model with traditional Web search algorithms, namely, a breadth-first search and a best-first search using PageRank as the heuristic. The results showed that the proposed model performed more efficiently and effectively in searching for domain-specific Web pages. We believe that the model can also be useful in other Web applications such as Web page clustering and search result ranking.
Sentiment Analysis of Movie Review Using Supervised Machine Learning Techniques

Sentiment Analysis is a new subject in Research and is useful in many other fields. In Modern World, A huge amount of textual data is collected using surveys, comments, and reviews over the web. All of the collected data is used to improve products and services provided by both private organizations and governments around the world. This Paper includes sentiment analysis of movie reviews using feature-based opinion mining and supervised machine learning. In this paper, the main focus is to determine the polarity of reviews using nouns, verbs, and adjectives as opinion words. Reviews will be Classified into two different categories positive and negative. Reviews of Open Movie Database is used as source data set and Natural Language Processing Toolkit for Part of Speech Tagging. This paper also contains some facts about the classification of data on basis of polarity.
Influence of Wear on Leakage through Reed-Type Valves of Small Reciprocating Compressors

Leakage can significantly decrease the efficiency of small reciprocating compressors adopted in household refrigeration systems. Leakage occurs in the piston-cylinder clearance and through gaps in valves formed by geometric irregularities associated with the manufacturing process. This paper presents a procedure developed to analyze experimentally gas leakage in reed-type valves taking into account the wear effect. Accordingly, leakage is measured in the valves of a small-capacity reciprocating compressor before and after the valves being subjected to wear, with the roughness of the reed and seat being measured in both conditions. Measurements of leakages in the suction and discharge valves were carried out independently in an experimental setup based on the constant volume method. Finally, a simulation model for valve leakage and an optimization algorithm were used to estimate the edge gap between the reed and seat. The edge gap was found in the range between 0.13 and 0.94 µm before wear and between 0.11 and 0.47 µm after wear. It was also found that the reed and seat roughness were reduced by approximately half after wear.
SOCORRO MENDOZA READING STRATEGIES TO SUPPORT HOME-TO-SCHOOL CONNECTIONS USED BY TEACHERS OF ENGLISH LANGUAGE LEARNERS

This article contains the findings and interpretations of the participant's responses and includes recommendations for organizations and suggestions for future research. The research problem is the lack of resources and approaches identified by teachers to support the home-to-school connection regarding reading strategies of ELLs in kindergarten through third grade. The purpose of this study was to examine reading strategies, approaches, and resources used by teachers in K-3 with ELLs. The goal was to describe and explain the teacher's perspective regarding the reading strategies used in the classroom and the parental involvement in regard to their child's reading development. The researcher chose a particularistic case study to examine teachers' reading strategies, resources, and approaches used with ELLs to support the home-toschool connection. The main source of data collection was twenty-two face-to-face interviews. The objectivity of the information gathered was reflective of the participants' strategies to gain caregiver support to the population of ELL K-3 students in the district. The findings may be generalizable, but limited because of the unique population involved in the study.
Distribution of TrkB receptor in the mouse hippocampal formation depends on sex and estrous cycle stage

TrkB is a neurotrophin receptor important for the synaptic plasticity underlying hippocampaldependent learning and memory. Because this receptor is widely expressed in hippocampal neurons, the precise location of TrkB activation is likely important for its specific actions. The goal of this study was to identify the precise sites of TrkB activation in the mouse hippocampal formation, and to determine any changes in the distribution of activated TrkB under conditions of enhanced BDNF expression and hippocampal excitability. Using electron microscopy, we localized TrkB phosphorylated at tyrosine 816 in the hippocampal formation of male mice and female mice under conditions of naturally low circulating estradiol and naturally high circulating estradiol, when BDNF expression, TrkB signaling, and synaptic plasticity are enhanced. To compare relative amounts of pTrkB in each group, we counted profiles containing pTrkBimmunoreactivity (pTrkB-ir) in all hippocampal subregions. pTrkB-ir was in axons and axon terminals, dendrites and dendritic spines of neurons in the hippocampal formation, but the majority of pTrkB-ir localized to presynaptic profiles. pTrkB-ir also was abundant in glial profiles, which were further identified as microglia using immunofluorescence and confocal microscopy. Axonal and glial pTrkB-ir and pTrkB-ir in the CA1 stratum radiatum were more abundant in highestradiol states (proestrus females) than low-estradiol states (estrus and diestrus females and males). These findings suggest that presynaptic TrkB is positioned to modulate estradiol-mediated and BDNF-dependent synaptic plasticity. Furthermore, they suggest a novel role for TrkB in microglial function in the neuroimmune system.