nils-herrmann/clean_scirepeval huggingface dataset:
Building upon the CALLA Framework: The Intersection of Novel Problem Solving and Applied Wisdom
Venue: Human Development

The major issue with cognitive training interventions for older adults is that they typically fail to result in enduring skills that elicit far transfer cognitive abilities that generalize to other independent functions [for exception, see Rebok et al. 2014] In their novel Cognitive Agility across the Lifespan via Learning and Attention (CALLA) framework, Wu, Rebok, and Lin [2016] tackle this issue by exploring the optimal bioecological factors in early life that are most conducive to broad learning and the development of skills that generalize. Next, they frame the six critical factors that promote broad learning (basic cognitive abilities and far transfer of skills) and examine how they change over the life course. These factors offer implications for interventions designed at fostering neuroplasticity in the aging brain. Our commentary offers some additional important contributions of the CALLA framework to existing models and poses challenges and opportunities in the design of cognitive interventions that facilitate skills that far transfer for older adults. Specifically, this framework makes additional contributions as we extend it here to the bioecological systems framework and to engagement interventions. From here, we also identify the following challenges of implementing the factors of CALLA into cognitive interventions for older adults, including motivational shifts away from learning goals (socioemotional selectivity theory) and disposition towards certainty compared to novelty. Lastly, we identify the opportunities to create conditions that leverage older adults to apply their experience, knowledge, and skills towards novel problem solving.
Max Margin DeepWalk: Discriminative Learning of Network Representation
Venue: IJCAI

DeepWalk is a typical representation learning method that learns low dimensional representations for vertices in social networks. Similar to other network representation learning (NRL) models, it encodes the network structure into vertex representations and is learnt in unsupervised form. However, the learnt representations usually lack the ability of discrimination when applied to machine learning tasks, such as vertex classification. In this paper, we overcome this challenge by proposing a novel semi supervised model, max margin Deep Walk (MMDW) MMDW is a unified NRL framework that jointly optimizes the max margin classifier and the aimed social representation learning model. Influenced by the max margin classifier, the learnt representations not only contain the network structure, but also have the characteristic of discrimination. The visualizations of learnt representations indicate that our model is more discriminative than unsupervised ones, and the experimental results on vertex classification demonstrate that our method achieves a significant improvement than other state of the art methods. The source code can be obtained from https:/github.com/thunlp/MMDW.
ScienceWISE: Topic Modeling over Scientific Literature Networks
Venue: ArXiv

We provide an up to date view on the knowledge management system ScienceWISE (SW) and address issues related to the automatic assignment of articles to research topics. So far, SW has been proven to be an effective platform for managing large volumes of technical articles by means of ontological concept based browsing. However, as the publication of research articles accelerates, the expressivity and the richness of the SW ontology turns into a double edged sword: a more fine grained characterization of articles is possible, but at the cost of introducing more spurious relations among them. In this context, the challenge of continuously recommending relevant articles to users lies in tackling a network partitioning problem, where nodes represent articles and co occurring concepts create edges between them. In this paper, we discuss the three research directions we have taken for solving this issue: i) the identification of generic concepts to reinforce inter article similarities; ii) the adoption of a bipartite network representation to improve scalability; iii) the design of a clustering algorithm to identify concepts for cross disciplinary articles and obtain fine grained topics for all articles.
A Systematic Literature Review

Due to the growing complexity of software systems, there has been a dramatic increase and industry demand for tools and techniques on software refactoring in the last ten years, defined traditionally as a set of program transformations intended to improve the system design while preserving the behavior. Refactoring studies are expanded beyond code level restructuring to be applied at different levels (architecture, model, requirements, etc. adopted in many domains beyond the object oriented paradigm (cloud computing, mobile, web, etc. used in industrial settings and considered objectives beyond improving the design to include other non functional requirements (e.g. improve performance, security, etc. Thus, challenges to be addressed by refactoring work are, nowadays, beyond code transformation to include, but not limited to, scheduling the opportune time to carry refactoring, recommendations of specific refactoring activities, detection of refactoring opportunities, and testing the correctness of applied refactorings. Therefore, the refactoring research efforts are fragmented over several research communities, various domains, and objectives. To structure the field and existing research results, this paper provides a systematic literature review and analyzes the results of 3183 research papers on refactoring covering the last three decades to offer the most scalable and comprehensive literature review of existing refactoring research studies. Based on this survey, we created a taxonomy to classify the existing research, identified research trends, and highlighted gaps in the literature and avenues for further research.
Epidemiological and molecular characterization of dengue viruses imported into Guangzhou during 2009 2013
Venue: SpringerPlus

BackgroundDengue virus causes one of the most significant infectious diseases in tropical and subtropical regions, notable number of which is imported into China every year.ResultsIn this study, the molecular epidemiologic and phylogenetic analyses of dengue cases imported into Guangzhou in South China during 2009 2013 were conducted. During that period, 46 imported dengue cases were identified, including four serotypes. Most of the dengue patients were travelling from Southeast Asia, South Asia and Africa. The envelope (E) genes of 20 imported dengue viruses introduced from 13 countries and regions, were sequenced and used for phylogenetic analyses. The results indicated that the DENV 1 genotype I and DENV 2 Asian genotype I were the most predominant DENV strains, which were circulating in Southeast Asia and imported into South China. In addition, the new introduction of DENV 3 genotype III from West Asia was observed.ConclusionsThis study provided an overview on the genetic diversity of DENV strains imported into South China, and also gave information about the geographic distribution, dynamic transmission and molecular evolution of epidemic DENV strains.
ZIKA VIRUS INFECTION: AN OVERVIEW

Zika virus is a member of the virus family Flaviviridae and the genus Flavivirus is spread by daytime active Aedes mosquitoes, such as A. aegypti and A. albopictus. Zika virus is related to dengue, yellow fever, Japanese encephalitis, and West Nile viruses. Zika virus which was recently in news as it leads to microcephaly birth defect and often causes no or only mild symptoms, similar to a mild form of dengue fever. It is treated by rest and cannot be prevented by medications or vaccines. There has been total death of 152 as of 21 October 2015 in the world. The U.S. Centers for Disease Control and Prevention (CDC) and other governments or health agencies issued travel guidance on affected countries, including the use of enhanced precautions, and guidelines for pregnant women including considering postponing travel. This review focuses on understanding Zika virus, its transmission, structure, symptoms, diagnosis, treatment and prevention.
Situational Leadership Theory as a Foundation for a Blended Learning Framework.

Ultimately with the raise of computer technology, blended learning has found its way into teaching. The technology continues to evolve, challenging teachers and lecturers alike. Most studies on blended learning focus on the practical or applied side and use essentially pedagogical concepts. This study demonstrates that the leadership sciences can enrich pedagogy in building a framework for teaching in a blended learning setting. At the core, the study transforms situational leadership theory (Hersey, Blanchard and Johnson, 2008) into a framework for blended learning. The model presented helps in organizing a situationally correct employment of blended learning and crystallizes appropriate teaching methods for specific learning goals.  blended learning; situational leadership; teaching; adult education
Parenting Stress Among Low Income and Working Class Fathers
Venue: Journal of family issues

Contemporary norms of fatherhood emphasize the dual demands of breadwinning and daily involvement in child care. Recent qualitative research suggests that working class fathers find it difficult to meet these demands due to job instability and workplace inflexibility. Yet little quantitative research has examined how employment characteristics are related to fathers' parenting stress, in comparison with mothers' Analyses using data from the Fragile Families and Child Wellbeing Study (N 3,165) show that unemployment and workplace inflexibility, but not overwork, multiple jobs, odd jobs, and nonstandard hours, are related to more parenting stress for fathers. Although these two factors are also related to more parenting stress for mothers, nuanced gender differences emerged: these are better predictors than other parental or child characteristics for fathers only, and the effect size of workplace inflexibility is greater for fathers than mothers. In sum, securing a job with flexible schedule is central to reducing fathers' parenting stress.
Return of Frustratingly Easy Domain Adaptation
Venue: AAAI

Unlike human learning, machine learning often fails to handle changes between training (source) and test (target) input distributions. Such domain shifts, common in practical scenarios, severely damage the performance of conventional machine learning methods. Supervised domain adaptation methods have been proposed for the case when the target data have labels, including some that perform very well despite being "frustratingly easy" to implement. However, in practice, the target domain is often unlabeled, requiring unsupervised adaptation. We propose a simple, effective, and efficient method for unsupervised domain adaptation called CORrelation ALignment (CORAL) CORAL minimizes domain shift by aligning the second order statistics of source and target distributions, without requiring any target labels. Even though it is extraordinarily simple it can be implemented in four lines of Matlab code CORAL performs remarkably well in extensive evaluations on standard benchmark datasets.
Frustratingly Easy Neural Domain Adaptation
Venue: COLING

Popular techniques for domain adaptation such as the feature augmentation method of Daume III (2009) have mostly been considered for sparse binary valued features, but not for dense real valued features such as those used in neural networks. In this paper, we describe simple neural extensions of these techniques. First, we propose a natural generalization of the feature augmentation method that uses K 1 LSTMs where one model captures global patterns across all K domains and the remaining K models capture domain specific information. Second, we propose a novel application of the framework for learning shared structures by Ando and Zhang (2005) to domain adaptation, and also provide a neural extension of their approach. In experiments on slot tagging over 17 domains, our methods give clear performance improvement over Daume III (2009) applied on feature rich CRFs.
Seeking to Reproduce "Easy Domain Adaptation"

The frustratingly easy domain adaptation technique proposed by Daume III (2007) is simple, easy to implement, and reported to be very successful in a range of NLP tasks (named entity recognition, part of speech tagging, and shallow parsing) giving us high hopes of successfully replicating and applying it to an English Portuguese hybrid machine translation system. While our hopes became 'frustration' in one translation direction as the results obtained with the domain adapted model do not improve upon the in domain baseline model our results are more encouraging in the opposite direction. This paper describes our replication of the technique and our application of it to machine translation, and offers a discussion on possible reasons for our mixed success in doing so.
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
Venue: ECCV Workshops

Deep neural networks are able to learn powerful representations from large quantities of labeled input data, however they cannot always generalize well across changes in input distributions. Domain adaptation algorithms have been proposed to compensate for the degradation in performance due to domain shift. In this paper, we address the case when the target domain is unlabeled, requiring unsupervised adaptation. CORAL is a "frustratingly easy" unsupervised domain adaptation method that aligns the second order statistics of the source and target distributions with a linear transformation. Here, we extend CORAL to learn a nonlinear transformation that aligns correlations of layer activations in deep neural networks (Deep CORAL) Experiments on standard benchmark datasets show state of the art performance.
Comparison of human and machine performance for copy move image forgery detection involving similar but genuine objects
Venue: 2016 IEEE Region 10 Conference (TENCON)

Copy move forgery (CMF) is considered easier to detect than general forgery mechanisms, but detecting it in the presence of multiple similar but genuine scene objects (SGOs) is non trivial. We study the efficacy of human visual perception for copy move image forgery detection (CMFD) involving SGOs, and compare the same with machine performance. Via an eye tracking study performed with 16 users where pairs of images (one real and the other tampered) were displayed in either parallel or serial fashion, we make the following observations: (1) Forgery detection is quicker and more accurate when images are spatially aligned and presented serially, so that the tampering is conspicuous. (2) Eye fixations focus on corresponding regions of the real and tampered images, with fewer and more localized fixations noted during serial comparison. (3) A gap is noted between CMFD performance of humans and machines, with each being more sensitive to different tampering factors. Overall, results reveal the need for systematic visual comparisons to distinguish SGOs from forged objects, as well as the promise of a human machine collaborative framework to this end.
High Frequency High Efficiency GaN Based Interleaved CRM Bidirectional Buck/Boost Converter with Inverse Coupled Inductor
Venue: IEEE Transactions on Power Electronics

This paper presents a high frequency high efficiency GaN device based interleaved critical current mode (CRM) bidirectional buck/boost converter with an inverse coupled inductor. The switching frequency is continually driven to megahertz range with GaN devices due to their small switching loss and driving loss, which greatly reduces the size of the passive components. The coupled inductor further reduces the core volume due to certain dc flux reductions. The equivalent inductance and the impact of the inverse coupled inductor on the CRM buck boost converter are analyzed in detail. The resonant period in CRM is less with an inverse coupled inductor than with a noncoupled inductor, which is beneficial for the high frequency operation. The soft switching range and the circulating energy are both improved using an inverse coupled inductor in CRM. Experimental results validate the theoretical analysis, and the coupled inductor prototype efficiency is 98.5% at 1 MHz, which is 0.3% higher than a prototype with a noncoupled inductor.
Genetic variants associated with subjective well being, depressive symptoms and neuroticism identified through genome wide analyses
Venue: Nature Genetics

Very few genetic variants have been associated with depression and neuroticism, likely because of limitations on sample size in previous studies. Subjective well being, a phenotype that is genetically correlated with both of these traits, has not yet been studied with genome wide data. We conducted genome wide association studies of three phenotypes: subjective well being (n 298,420) depressive symptoms (n 161,460) and neuroticism (n 170,911) We identify 3 variants associated with subjective well being, 2 variants associated with depressive symptoms, and 11 variants associated with neuroticism, including 2 inversion polymorphisms. The two loci associated with depressive symptoms replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes |r^ 0.8) strengthen the overall credibility of the findings and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal or pancreas tissues are strongly enriched for association.
The Nerves of the Adductor Canal and the Innervation of the Knee: An Anatomic Study
Venue: Regional Anesthesia Pain Medicine

Adductor canal block contributes to analgesia after total knee arthroplasty. However, controversy exists regarding the target nerves and the ideal site of local anesthetic administration. The aim of this cadaveric study was to identify the trajectory of all nerves that course in the adductor canal from their origin to their termination and describe their relative contributions to the innervation of the knee joint.  After research ethics board approval, 20 cadaveric lower limbs were examined using standard dissection technique. Branches of both the femoral and obturator nerves were explored along the adductor canal and all branches followed to their termination.  Both the saphenous nerve (SN) and the nerve to vastus medialis (NVM) were consistently identified, whereas branches of the anterior obturator nerve were inconsistently present. The NVM contributed significantly to the innervation of the knee capsule, through intramuscular, extramuscular, and deep genicular nerves. The SN had a relatively more modest contribution through superficial infrapatellar and posterior branches as well as contributing to the origin of the deep genicular nerves.  The results suggest that both the SN and NVM contribute to the innervation of the anteromedial knee joint and are therefore important targets of adductor canal block. Given the site of exit of both nerves in the distal third of the adductor canal, the midportion of the adductor canal is suggested as an optimal site of local anesthetic administration to block both target nerves while minimizing the possibility of proximal spread to the femoral triangle.
An Introduction to Agent Based Modeling: Modeling Natural, Social and Engineered Complex Systems with NetLogo

Computing Reviews, the leading online review service for computing literature. The NetLogo environment developed at Northwestern University  has become one of the most widely used tools for agentbased modeling. NetLogo's restricted dialect of LISP has proven much more accessible to users such as social scientists than a general purpose language such as Java, required to work with other agentbased environments: for example, recursive porous agent simulation toolkit (REPAST) or multiagent simulator of neighborhoods (MASON) The simplicity of the language is not surprising, since NetLogo is a direct descendant of the Logo language, originally developed to teach programming to children, and NetLogo is widely used as a teaching language. Remarkably, the architects of NetLogo have managed to provide the system with extensions that allow it to be used for real research as well, and NetLogo models are becoming more and more common in serious research papers, particularly in the social sciences. NetLogo's motto is " Low Threshold, No Ceiling. " The rich tutorial materials and examples available on the system's website are sufficient to enable most users to overcome the low threshold. This volume, written by the author of the NetLogo language and one of his associates, supplements those materials in two important ways. First, it clearly conveys the motivation for agentbased modeling and the constructivist philosophy that guides its practitioners. Second, it develops models incrementally, showing how a very simple initial model can grow to take advantage of sophisticated functions and extensions that justify the " no ceiling " part of the motto. Perhaps to reinforce the point that the authors are serious computer scientists, the book's chapters start with 0 rather than 1. Chapters 0 and 1 motivate agentbased modeling (ABM) in the context of complex systems that demonstrate emergent behavior. They present alternative modeling paradigms and discuss the tradeoffs of ABM and when it is preferable to other approaches by reviewing the behavior of some simple models that are included in NetLogo's library (predatorprey, forest fires, ant foraging) Chapters 2 to 4 show how to construct an ABM, illustrating the technique of starting with a very simple model and then extending it incrementally. Chapter 5 backs away from individual models to examine the components of an agent based model: the different kinds of agents one might use, the importance of their environment and interactions, and the user interface and scheduling mechanism. The last three chapters are especially
Multiagent control of computational systems on the basis of meta monitoring and imitational simulation

This paper describes the control of computations in a distributed computing environment (DCE) on the basis of its meta monitoring and simulation modeling. Computations are controlled by a multiagent system with a given organizational structure. Resource allocation is carried out by agents with the use of economic mechanisms for controlling their supply and demand. Controlling actions for agents are formed on the basis of the simulation modeling of functional processes of the DCE. Data about the DCE resources and processes are collected and emergency situations in the DCE nodes are detected and prevented by the meta monitoring system of this environment. The research results are the techniques for selecting control actions and the methods for intellectual processing and effective storage of data.
Sleep deprivation increases formation of false memory
Venue: Journal of sleep research

Retrieving false information can have serious consequences. Sleep is important for memory, but voluntary sleep curtailment is becoming more rampant. Here, the misinformation paradigm was used to investigate false memory formation after 1 night of total sleep deprivation in healthy young adults (N 58, mean age SD 22.10 1.60 years; 29 males) and 7 nights of partial sleep deprivation (5 h sleep opportunity) in these young adults and healthy adolescents (N 54, mean age SD 16.67 1.03 years; 25 males) In both age groups, sleep deprived individuals were more likely than well rested persons to incorporate misleading post event information into their responses during memory retrieval (P 0.050) These findings reiterate the importance of adequate sleep in optimal cognitive functioning, reveal the vulnerability of adolescents' memory during sleep curtailment, and suggest the need to assess eyewitnesses' sleep history after encountering misleading information.