Miracle-dz/UniFAR-FTUs huggingface dataset:
Are 44 Ti-producing supernovae exceptional?

According to standard models supernovae produce radioactive 44 Ti, which should be visible in gamma-rays follow- ing decay to 44 Ca for a few centuries. 44 Ti production is believed to be the source of cosmic 44 Ca, whose abundance is well established. Yet, gamma-ray telescopes have not seen the expected young remnants of core collapse events. The 44 Ti mean life of ' 89 y and the Galactic supernova rate of' 3/100 y imply' several detectable 44 Ti gamma-ray sources, but only one is clearly seen, the 340-year-old Cas A SNR. Furthermore, supernovae which produce much 44 Ti are expected to occur primarily in the inner part of the Galaxy, where young massive stars are most abundant. Because the Galaxy is transparent to gamma-rays, this should be the dominant location of expected gamma-ray sources. Yet the Cas A SNR as the only one source is located far from the inner Galaxy (at longitude 112 ). We evaluate the surprising absence of detectable supernovae from the past three centuries. We discuss whether our understanding of SN explosions, their 44 Ti yields, their spatial distributions, and statistical arguments can be stretched so that this apparent disagreement may be accommodated within reasonable expectations, or if we have to revise some or all of the above aspects to bring expectations in agreement with the observations. We conclude that either core collapse supernovae have been improbably rare in the Galaxy during the past few centuries, or 44 Ti -producing supernovae are atypical supernovae. We also present a new argument based on 44 Ca/ 40 Ca ratios in mainstream SiC stardust grains that may cast doubt on massive-He-cap Type I supernovae as the source of most galactic 44 Ca.
ADHD in the Schools: Assessment and Intervention Strategies. Second Edition.

Overview of ADHD. Assessment of ADHD in School Settings. ADHD and Learning Difficulties: What Is the Connection? Early Screening, Identification, and Intervention. School-Based Intervention Strategies. Medication Therapy. Adjunctive Interventions for ADHD. Communication with Parents, Professionals, and Students. Conclusions and Future Directions.
Connectivity and functional profiling of abnormal brain structures in pedophilia

Despite its 0.5–1% lifetime prevalence in men and its general societal relevance, neuroimaging investigations in pedophilia are scarce. Preliminary findings indicate abnormal brain structure and function. However, no study has yet linked structural alterations in pedophiles to both connectional and functional properties of the aberrant hotspots. The relationship between morphological alterations and brain function in pedophilia as well as their contribution to its psychopathology thus remain unclear. First, we assessed bimodal connectivity of structurally altered candidate regions using meta‐analytic connectivity modeling (MACM) and resting‐state correlations employing openly accessible data. We compared the ensuing connectivity maps to the activation likelihood estimation (ALE) maps of a recent quantitative meta‐analysis of brain activity during processing of sexual stimuli. Second, we functionally characterized the structurally altered regions employing meta‐data of a large‐scale neuroimaging database. Candidate regions were functionally connected to key areas for processing of sexual stimuli. Moreover, we found that the functional role of structurally altered brain regions in pedophilia relates to nonsexual emotional as well as neurocognitive and executive functions, previously reported to be impaired in pedophiles. Our results suggest that structural brain alterations affect neural networks for sexual processing by way of disrupted functional connectivity, which may entail abnormal sexual arousal patterns. The findings moreover indicate that structural alterations account for common affective and neurocognitive impairments in pedophilia. The present multimodal integration of brain structure and function analyses links sexual and nonsexual psychopathology in pedophilia. Hum Brain Mapp 36:2374–2386, 2015. © 2015 Wiley Periodicals, Inc.
Complexity and regularity of maximal energy domains for the wave equation with fixed initial data

We consider the homogeneous wave equation on a bounded open connected subset $\Omega$ of $\R^n$. Some initial data being specified, we consider the problem of determining a measurable subset $\omega$ of $\Omega$ maximizing the $L^2$-norm of the restriction of the corresponding solution to $\omega$ over a time interval $[0,T]$, over all possible subsets of $\Omega$ having a certain prescribed measure. We prove that this problem always has at least one solution and that, if the initial data satisfy some analyticity assumptions, then the optimal set is unique and moreover has a finite number of connected components. In contrast, we construct smooth but not analytic initial conditions for which the optimal set is of Cantor type and in particular has an infinite number of connected components.
Codepourri: Creating visual coding tutorials using a volunteer crowd of learners

A common way to learn is by studying written step-by-step tutorials such as worked examples. However, tutorials for computer programming can be tedious to create since a static text-based format cannot convey what happens as code executes. We created a system called Codepourri that enables people to easily create visual coding tutorials by annotating steps in an automatically-generated program visualization. Using Codepourri, we developed a novel crowdsourcing workflow where learners who are visiting an educational Web site (www. pythontutor.com) collectively create a tutorial by annotating execution steps in a piece of code and then voting on the best annotations. Since there are far more learners than experts, using learners as a crowd is a potentially more scalable way of creating tutorials. Our experiments with 4 expert judges and 101 learners adding 145 raw annotations to two pieces of textbook Python code show the learner crowd's annotations to be accurate, informative, and containing some insights that even experts missed.
High-density ear-EEG

Ear-EEG enables recording of EEG in real-life environments in an unprecedented discreet and minimal obtrusive way. As ear-EEG are recorded from electrodes placed in or around the ear, the spatial coverage of the potential field on the scalp is inherently limited. Despite the limited spatial coverage, the potential field in-the-ear can still be measured in multiple points and thereby provide spatial information. We present a method to perform and visualize high-density ear-EEG recordings, and illustrate the method through recordings of auditory and visually evoked steady-state responses, for a single subject. The auditory and visually evoked responses showed distinctive differences in the response field in the ear, reflecting the very different locations of the underlying cortical sources. In conclusion, high-density ear-EEG can be used to investigate how different cortical sources maps to the ear, and provides a way to select optimal electrode positions for given brain phenomena.
“I Couldn’t Wait to Leave the Toxic Environment”: A Mixed Methods Study of Women Faculty Satisfaction and Departure From One Research Institution

A mixed methods analysis of women faculty departure at one research institution was conducted using Hagedorn’s model of faculty job satisfaction. Findings from an institution-wide survey and interviews with women faculty who had left the institution resulted in several themes: (a) a lack of resources to support faculty work, (b) a lack of consistent and quality leadership, (c) a lack of work–life balance policies and an environment to support them, and (d) overall negative institutional and departmental environments. These findings are discussed in relation to Hagedorn’s model as are implications for policy, practice, and future research.
Reducing Java internet project risks: a case study of public measurement of client component functionality in the user community

A major risk for Internet software projects which have server and client components are decisions related to availability and features on client computers in the user community. Specifically, bioinformatics software developers intending to use Java face critical decisions about which Java version to implement, but few statistics are available about Java presence on user machines. To obtain this information, we implemented a measurement system to detect the presence, functionality and version of Java Virtual Machines on client computers of a large base of users from the biology community. We show that our system effectively collects the necessary information and provides decision-relevant statistics. Measurements performed on 1753 client computers showed that Java presence is high and dominated by the most recent Java versions. The proposed empirical approach can be used to reduce decision risks in any type of Internet software project with low level of control on client equipment and high demands on client interaction and performance. More details together with source code and measurement results can be obtained from the J-vestigator survey page (https://www.genevestigator.ethz.ch/index.php? page=jvestigator)
Kaluza-Klein dimensional reduction and Gauss-Codazzi-Ricci equations

In this paper we imitate the traditional method which is used customarily in the general relativity and some mathematical literatures to derive the Gauss–Codazzi–Ricci equations for dimensional reduction. It would be more distinct concerning geometric meaning than the vielbein method. Especially, if the lower-dimensional metric is independent of reduced dimensions the counterpart of the symmetric extrinsic curvature is proportional to the antisymmetric Kaluza–Klein gauge field strength. For isometry group of internal space, the SO(n) symmetry and SU(n) symmetry are discussed. And the Kaluza–Klein instanton is also enquired.
Temporal and Situational Context Modeling for Improved Dominance Recognition in Meetings

We present and evaluate a novel approach towards automatically detecting a speaker’s level of dominance in a meeting scenario. Since previous studies reveal that audio appears to be the most important modality for dominance recognition, we focus on the analysis of the speech signals recorded in multi-party meetings. Unlike recently published techniques which concentrate on frame-level hidden Markov modeling, we pro-pose a recognition framework operating on segmental data and investigate context modeling on three different levels to explore possible performance gains. First, we apply a set of statistical functionals to capture large-scale feature-level context within a speech segment. Second, we consider bidirectional Long Short-Term Memory recurrent neural networks for long-range temporal context modeling between segments. Finally, we evaluate the beneﬁt of situational context incorporation by simultane-ously modeling speech of all meeting participants. Overall, our approach leads to a remarkable increase of recognition accuracy when compared to hidden Markov modeling.
Separating true range measurements from multi-path and scattering interference in commercial range cameras

Time-of-flight range cameras acquire a three-dimensional image of a scene simultaneously for all pixels from a single viewing location. Attempts to use range cameras for metrology applications have been hampered by the multi-path problem, which causes range distortions when stray light interferes with the range measurement in a given pixel. Correcting multi-path distortions by post-processing the three-dimensional measurement data has been investigated, but enjoys limited success because the interference is highly scene dependent. An alternative approach based on separating the strongest and weaker sources of light returned to each pixel, prior to range decoding, is more successful, but has only been demonstrated on custom built range cameras, and has not been suitable for general metrology applications. In this paper we demonstrate an algorithm applied to both the Mesa Imaging SR-4000 and Canesta Inc. XZ-422 Demonstrator unmodified off-the-shelf range cameras. Additional raw images are acquired and processed using an optimization approach, rather than relying on the processing provided by the manufacturer, to determine the individual component returns in each pixel. Substantial improvements in accuracy are observed, especially in the darker regions of the scene.
Hybrid SRL with Optimization Modulo Theories

Generally speaking, the goal of constructive learning could be seen as, given an example set of structured objects, to generate novel objects with similar properties. From a statistical-relational learning (SRL) viewpoint, the task can be interpreted as a constraint satisfaction problem, i.e. the generated objects must obey a set of soft constraints, whose weights are estimated from the data. Traditional SRL approaches rely on (finite) First-Order Logic (FOL) as a description language, and on MAX-SAT solvers to perform inference. Alas, FOL is unsuited for con- structive problems where the objects contain a mixture of Boolean and numerical variables. It is in fact difficult to implement, e.g. linear arithmetic constraints within the language of FOL. In this paper we propose a novel class of hybrid SRL methods that rely on Satisfiability Modulo Theories, an alternative class of for- mal languages that allow to describe, and reason over, mixed Boolean-numerical objects and constraints. The resulting methods, which we call Learning Mod- ulo Theories, are formulated within the structured output SVM framework, and employ a weighted SMT solver as an optimization oracle to perform efficient in- ference and discriminative max margin weight learning. We also present a few examples of constructive learning applications enabled by our method.
Phenazopyridine induces and synchronizes neuronal differentiation of embryonic stem cells

Embryonic stem (ES) cells are powerful tools to understand mechanisms of neuronal differentiation and to engineer neurons for in vitro studies and cell therapy. We developed a screening approach to identify small organic molecules driving neuronal differentiation of ES cells. For this purpose, we used a lentivector carrying a dual luciferase reporter system to engineer an ES cell line which allowed us to screen for small organic molecules enhancing neuronal differentiation. One of them, phenazopyridine, was further analysed in human ES cells. Phenazopyridine: (i) enhanced neuronal differentiation, (ii) increased cell survival, (iii) decreased the amount of non‐neuronal and undifferentiated cells and (iv) synchronized the cellular differentiation state. Phenazopyridine allowed the development of a differentiation protocol compatible with the generation of clinical grade neural precursors, which were able differentiate into different neuronal subtypes, astrocytes and oligodendrocytes. In summary, we describe a powerful approach to identify small molecules directing stem cell differentiation. This led to the establishment of a new application for an old drug and the development of a novel clinical grade protocol for neuronal differentiation of ES cells.
The structure of unseen trigrams and its application to language models: A first investigation

In a series of preparatory experiments in 4 languages on subsets of the Europarl corpus, we show that a large number of unseen trigrams can be reconstructed by proportional analogy with trigrams having the lowest frequencies. We derive a very simple smoothing scheme from this empirical result and show that it outperforms Good-Turing and Kneser-Ney smoothing schemes on trigrams models in all 11 languages on the common multilingual part of the Europarl corpus, except Finnish.
Delft University at the TREC 2009 Entity Track: Ranking Wikipedia Entities

Abstract : Entity ranking is a novel TREC task introduced this year and posing challenges similar to those already well-known in web retrieval research community. We present a system which leverages a number of popular web retrieval techniques, utilizes existing knowledge bases and relies on various task-specific heuristics to produce high quality runs. Since we had no training queries with relevant web-pages contained in Category B part of ClueWeb09 collection, we focused on using various strategies rather than on using one kind of approach with different parameter settings. However, in three of four submitted runs we treated Wikipedia part of the collection as the main source of evidence about relevance of entities that can be found on the Web.
Health Information Technology in the Workplace: Findings From a 2010 National Survey of Registered Nurses

The objective of this study was to examine RNs' experiences with health information technology (HIT) and their perceptions of the effect of this technology on quality of care and daily work. The adoption and use of HIT are expected to increase substantially over the next 5 years because of policy efforts at the federal and state levels. Given the size of the RN workforce and their critical role in healthcare delivery, their experiences with HIT could help adoption efforts. The method used was a nationally representative survey of 1500 nurses with a 56% response rate. Findings suggest wide variation in the availability of HIT functionality, with functions more likely available to hospital RNs. Overall, RNs perceived the effect of these technologies on quality of care and their daily work as positive. Ensuring that HIT systems are relevant to and usable for RNs will be a critical component in achieving the meaningful use of these systems.
Please Scroll down for Article Journal of Applied Statistics Clustering Time-course Microarray Data Using Functional Bayesian Infinite Mixture~model Clustering Time-course Microarray Data Using Functional Bayesian Infinite Mixture Model

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. This paper presents a new Bayesian, infinite mixture model based, clustering approach, specifically designed for time-course microarray data. The problem is to group together genes which have " similar " expression profiles, given the set of noisy measurements of their expression levels over a specific time interval. In order to capture temporal variations of each curve, a non-parametric regression approach is used. Each expression profile is expanded over a set of basis functions and the sets of coefficients of each curve are subsequently modeled through a Bayesian infinite mixture of Gaussian distributions. Therefore, the task of finding clusters of genes with similar expression profiles is then reduced to the problem of grouping together genes whose coefficients are sampled from the same distribution in the mixture. Dirich-let processes prior is naturally employed in such kinds of models, since it allows one to deal automatically with the uncertainty about the number of clusters. The posterior inference is carried out by a split and merge MCMC sampling scheme which integrates out parameters of the component distributions and updates only the latent vector of the cluster membership. The final configuration is obtained via the maximum a poste-riori estimator. The performance of the method is studied using synthetic and real microarray data and is compared with the performances of competitive techniques.
Language Ability and Motivation Among Foreigners in Survey Responding

With increasing migration and linguistic diversification in many countries, survey researchers and methodologists should consider whether data provided by individuals with variable levels of command of the survey language are of the same quality. This paper examines the question of whether answers from resident foreign respondents who do not master available survey languages may suffer from problems of comprehension of survey items, especially items that are more complicated in terms of content and/or form. In addition, it addresses the extent to which motivation may affect the response quality of resident foreigners. We analyzed data from two large-scale surveys conducted in Switzerland, a country with three national languages and a burgeoning foreign population, employing a set of dependent measures of response quality, including don't know responses, extreme responding, mid-5 responding, recency effects, and straight-lining. Results show overall poorer response quality among foreigners, and indicate that both reduced language mastery and motivation among foreigners are relevant factors. This is especially true for foreign groups from countries that do not share a common language with those spoken in Switzerland. A general conclusion is that the more distant respondents are culturally and linguistically from the majority mainstream within a country, the more their data may be negatively affected. We found that more complex types of questions do generally lead to poorer response quality, but to a much lesser extent than respondent characteristics, such as nationality, command of the survey language, level of education, and age.
Psychosocial and Cultural Correlates of Suicidal Ideation Among American Indian Early Adolescents on a Northern Plains Reservation

Although research has increased concerning the psychological and social correlates of American Indian adolescent suicidal ideation, little is known about the precursors to suicidal ideation with the early adolescent age group. In this study, we examined a number of cognitive, behavioral, and emotional correlates of suicidal ideation among 122 American Indian middle school youth who lived on a reservation in the Northern Plains area of the United States in February 2006. The participants were on average 12.4 years old, and about 20% reported that they had made a nonfatal suicide attempt at some time in their lives. When using a multivariate model (adjusted R 2 of .70), depression and substance use were the only significant predictors of suicidal ideation as measured by the Suicidal Ideation-JR scale (Reynolds, 1988). We suggest potential ways in which variables such as gender, friend and family member suicidal behavior, and engagement in traditional activities may affect suicidal ideation through their effects on depression and substance use. We recommend that suicide prevention programs with American Indian early adolescents draw on cognitive-behavioral interventions emphasizing the strengths of select cultural tenets in tandem with substance abuse prevention strategies that appear promising.