Miracle-dz/UniFAR-FTUs huggingface dataset:
Time evolution of the line emission from the inner circumstellar ring of SN 1987A and its hot spots

Supernovae are some of the most energetic phenomena in the Universe and they have throughout history fascinated people as they appeared as new stars in the sky. Supernova (SN) 1987A exploded in the nearby satellite galaxy, the Large Magellanic Cloud (LMC), at a distance of only 168,000 light years. The proximity of SN 1987A offers a unique opportunity to study the medium surrounding the supernova in great detail. Powered by the dynamical interaction of the ejecta with the inner circumstellar ring, SN 1987A is dramatically evolving at all wavelengths on time scales less than a year. This makes SN 1987A a great ``laboratory'' for studies of shock physics. Repeated observations of the ejecta-ring collision have been carried out using the UVES echelle spectrograph at VLT. This thesis covers seven epochs of high resolution spectra taken between October 1999 and November 2007. Three different emission line components are identified from the spectra. A narrow (~10 km/s) velocity component emerges from the unshocked ring. An intermediate (~250 km/s) component arises in the shocked ring, and a broad component extending to ~15,000 km/s comes from the reverse shock. Thanks to the high spectral resolution of UVES, it has been possible to separate the shocked from the unshocked ring emission. For the unshocked gas, ionization stages from neutral up to Ne V and Fe VII were found. The line fluxes of the low-ionization lines decline during the period of the observations. However, the fluxes of the [O III] and [Ne III] lines appear to increase and this is found to be consistent with the heating of the pre-shock gas by X-rays from the shock interactions. The line emission from the ejecta-ring collision increases rapidly as more gas is swept up by the shocks. This emission comes from ions with a range of ionization stages (e.g., Fe II-XIV). The low-ionization lines show an increase in their line widths which is consistent with that these lines originate from radiative shocks. The high-ionization line profiles (Fe X-XIV) initially show larger spectral widths, which indicates that at least a fraction of the emission comes from non-radiative shocks.
CHAPTER 4 Generative Probabilistic Modeling : Understanding Causal Sensorimotor Integration

In this chapter, we argue that many aspects of human perception are best explained by adopting a modeling approach in which experimental subjects are assumed to possess a full generative probabilistic model of the task they are faced with, and that they use this model to make inferences about their environment and act optimally given the information available to them. We apply this generative modeling framework in two diverse settings—concurrent sensory and motor adaptation, and multisensory oddity detection—and show, in both cases, that the data are best described by a full generative modeling approach. Bayesian ideal-observer modeling is an elegant and successful normative approach to understanding human perception. One particular domain in which it has seen much success recently is that of understanding multisensory integration in human perception (see Chapter 1). Existing applications of this modeling approach have frequently focused on a simple special case where the ideal observer’s estimate of an unknown quantity in the environment is a reliability-weighted mean of the individual observed cues. This is all that is needed to understand a wide variety of interesting perceptual phenomena. We argue, however, that the Bayesian-observer approach can be more powerfully and generally applied by clear generative modeling of the perceptual task for each experiment. In other words, this assumes that people have access to a full generative model of their observations and that they use this model to make optimal decisions in performing the task. This systematic approach effectively provides a “model for modeling” that has some key advantages: (1) It provides the modeler with a clear framework for modeling new tasks beyond simply applying common normative models— such as linear combination—which may not apply for a new scenario and may fail to explain important aspects of human behavior; (2) Human performance can be measured against these clear “optimal” models such that we can draw conclusions about optimality of human perception or reveal architectural limitations of the human perceptual system, which cause it to deviate from optimality. For a particular perceptual task, the optimal solution requires inference in the true generative model of the task. Here, optimal is defined in the sense that the posterior probability over relevant unknowns in the environment is calculated. Any actions or decisions to be made can then be taken
Income Mobility of Individuals in China and the United States

Though much has been written about annual income inequality in China, little research has been conducted on longer run measures of income inequality and on income mobility. This paper compares income mobility of urban individuals in China and the United States in the 1990s. The following questions are taken up. To what extent are measures of annual income inequality misleading indicators of long-run income inequality? How much income mobility was there in China in the first half of the 1990s and how did this compare with mobility in other countries? Have real income increases been greater for the poor or the rich? How important is the variation in permanent incomes in China and how has this changed?
Spatial Statistical Modeling for Heterogeneous Cellular Networks - An Empirical Study

Modeling the spatial distribution of multi-tier base stations (BSs) is an important issue for understanding and validating the analysis and design of heterogeneous cellular networks (HCNs). In this paper, we use different spatial statistical models to describe the spatial distribution of BSs, by fitting real data sets of BS locations in HCNs. Classical statistics like the L function, and cellular network performance metrics like the coverage probability are used to evaluate the goodness-of-fit. The results reveal that notable distinctions exist between modeling HCNs and single-tier networks. Although each tier in a HCN may be most accurately fitted by statistical models other than the Poisson spatial distribution, it is surprising that multiple tiers of independent Poisson spatial distributions provide an accurate description of the overall HCN. The impacts of different network parameters and the dependency between tiers on the modeling accuracy are extensively investigated.
Constitutive Activation of Gli2 Impairs Bone Formation in Postnatal Growing Mice

Indian hedgehog (Ihh) signaling is indispensable for osteoblast differentiation during endochondral bone development in the mouse embryo. We have previously shown that the Gli2 transcription activator critically mediates Ihh function in osteoblastogenesis. To explore the possibility that activation of Hedgehog (Hh) signaling may enhance bone formation, we generated mice that expressed a constitutively active form of Gli2 in the Osx-lineage cells. Unexpectedly, these mice exhibited severe osteopenia due to a marked decrease in osteoblast number and function, although bone resorption was not affected. Quantitative analyses of the molecular markers indicated that osteoblast differentiation was impaired in the mutant mouse. However, the osteoblast-lineage cells isolated from these mice exhibited more robust osteoblast differentiation than normal in vitro. Similarly, pharmacological stimulation of Hh signaling enhanced osteoblast differentiation from Osx-expressing cells isolated from the wild-type mouse. Thus, even though Hh signaling directly promotes osteoblast differentiation in vitro, constitutive activation of this pathway impairs bone formation in vivo, perhaps through an indirect mechanism.
Automatic Summarization of Twitter Topics

During recent years, socially generated content has become pervasive on the World Wide Web. The enormous amount of content generated in blog sites, social networking sites such as Facebook and Myspace, encyclopedic sites such as Wikipedia, has not only empowered ordinary users of the Web but also contributed to the vastness as well as richness of the Web’s contents. In this paper, we focus on a recent trend called microblogging, and in particular a site called Twitter that allows a huge number of users to contribute frequent short messages. The content of such a site is an extra-ordinarily large number of small textual messages, posted by millions of users, at random or in response to perceived events or situations. However, out of such random and massive disorganization of messages usually trends emerge as a large number of users post similar messages on similar topics. These trends can be discovered using statistical analysis of mass of posts. We have developed an algorithm that takes a trending phrase or any phrase specified by a user, collects a large number of posts containing the phrase, and provides an automatically created summary of the posts related to the term. We present examples of summaries we produce along with initial qualitative evaluation. It is possible to get a global view of the content of the text message repository in terms of a set of short summaries of trending terms during the course of a period of time such as an hour or a day.
tFast Network Oscillations in the Hippocampal CA1 Region of the Behaving Rat

This study examined intermittent, high-frequency (100-200 Hz) oscillatory patterns in the CA1 region of the hippocampus in the absence of theta activity, i.e., during and in between sharp wave (SPW) bursts. Pyramidal and interneuronal activity was phase-locked not only to large amplitude (>7 SD from baseline) oscillatory events, which are present mainly during SPWs, but to smaller amplitude (<4 SD) patterns, as well. Large-amplitude events were in the 140-200 Hz, "ripple" frequency range. Lower-amplitude events, however, contained slower, 100-130 Hz ("slow") oscillatory patterns. Fast ripple waves reversed just below the CA1 pyramidal layer, whereas slow oscillatory potentials reversed in the stratum radiatum and/or in the stratum oriens. Parallel CA1-CA3 recordings revealed correlated CA3 field and unit activity to the slow CA1 waves but not to fast ripple waves. These findings suggest that fast ripples emerge in the CA1 region, whereas slow (100-130 Hz) oscillatory patterns are generated in the CA3 region and transferred to the CA1 field.
Representing and Retrieving Video Shots in Human-Centric Brain Imaging Space

Meaningful representation and effective retrieval of video shots in a large-scale database has been a profound challenge for the image/video processing and computer vision communities. A great deal of effort has been devoted to the extraction of low-level visual features, such as color, shape, texture, and motion for characterizing and retrieving video shots. However, the accuracy of these feature descriptors is still far from satisfaction due to the well-known semantic gap. In order to alleviate the problem, this paper investigates a novel methodology of representing and retrieving video shots using human-centric high-level features derived in brain imaging space (BIS) where brain responses to natural stimulus of video watching can be explored and interpreted. At first, our recently developed dense individualized and common connectivity-based cortical landmarks (DICCCOL) system is employed to locate large-scale functional brain networks and their regions of interests (ROIs) that are involved in the comprehension of video stimulus. Then, functional connectivities between various functional ROI pairs are utilized as BIS features to characterize the brain's comprehension of video semantics. Then an effective feature selection procedure is applied to learn the most relevant features while removing redundancy, which results in the formation of the final BIS features. Afterwards, a mapping from low-level visual features to high-level semantic features in the BIS is built via the Gaussian process regression (GPR) algorithm, and a manifold structure is then inferred, in which video key frames are represented by the mapped feature vectors in the BIS. Finally, the manifold-ranking algorithm concerning the relationship among all data is applied to measure the similarity between key frames of video shots. Experimental results on the TRECVID 2005 dataset demonstrate the superiority of the proposed work in comparison with traditional methods.
Sigma-point kalman filters for probabilistic inference in dynamic state-space models

Probabilistic inference is the problem of estimating the hidden variables (states or parameters) of a system in an optimal and consistent fashion as a set of noisy or incomplete observations of the system becomes available online. The optimal solution to this problem is given by the recursive Bayesian estimation algorithm which recursively updates the posterior density of the system state as new observations arrive. This posterior density constitutes the complete solution to the probabilistic inference problem, and allows us to calculate any “optimal” estimate of the state. Unfortunately, for most real-world problems, the optimal Bayesian recursion is intractable and approximate solutions must be used. Within the space of approximate solutions, the extended Kalman filter (EKF) has become one of the most widely used algorithms with applications in state, parameter and dual estimation. Unfortunately, the EKF is based on a sub-optimal implementation of the recursive Bayesian estimation framework applied to Gaussian random variables. This can seriously affect the accuracy or even lead to divergence of any inference system that is based on the EKF or that uses the EKF as a component part. Recently a number of related novel, more accurate and theoretically better motivated algorithmic alternatives to the EKF have surfaced in the literature, with specific application to state estimation for automatic control. We have extended these algorithms, all based on derivativeless deterministic sampling based approximations of the relevant Gaussian statistics, to a family of algorithms called Sigma-Point Kalman Filters (SPKF). Furthermore, we successfully expanded the use of this group of algorithms (SPKFs) within the general field of probabilistic inference and machine learning, both as stand-alone filters and as subcomponents of more powerful sequential Monte Carlo methods (particle filters). We have consistently shown that there are large performance benefits to be gained by applying Sigma-Point Kalman filters to areas where EKFs have been used as the de facto standard in the past, as well as in new areas where the use of the EKF is impossible.
Dynamics of Scale-Free Semi-Synchronous Boolean Networks

Random Boolean Networks have been introduced by Kauffman more than thirty years ago as a highly simplified model of genetic regulatory networks. These models are interesting in their own as complex dynamical systems and have been throughly studied as such. We believe that the original view of Kauffman is still a valid one, provided that the model is updated to take into account present knowledge, without loosing its attractive simplicity. Thus, we will present how the Kauffman model could be modified in order to qualitatively agree with experimental observations that were not available at the time. In particular, we will present a method for generating networks with given degree distributions, together with a new semi-synchronous updating scheme. Simulations of statistical ensembles of networks behaving according to the new model will be presented and discussed.
Severe familial intrahepatic cholestasis

Five cases of intrahepatic cholestasis are reported in 4 families in which 7 other children have had the same disease. Cholestasis, beginning before the age of one year, is notable for the absence of lipid retention, evolving by attacks brought on by intercurrent infections with gradual development of fibrosis and fatal cirrhosis before the age of 15. Extrahepatic and interlobular bile ducts are undamaged. The disease is sometimes accompanied by biliary and pancreatic abnormalities, perhaps due to an abnormality in the biliary acid metabolism.
Dissolved organic matter composition of winter flow in the Yukon River basin: Implications of permafrost thaw and increased groundwater discharge

Groundwater discharge to rivers has increased in recent decades across the circumpolar region and has been attributed to thawing permafrost in arctic and subarctic watersheds. Permafrost‐driven changes in groundwater discharge will alter the flux of dissolved organic carbon (DOC) in rivers, yet little is known about the chemical composition and reactivity of dissolved organic matter (DOM) of groundwater in permafrost settings. Here, we characterize DOM composition of winter flow in 60 rivers and streams of the Yukon River basin to evaluate the biogeochemical consequences of enhanced groundwater discharge associated with permafrost thaw. DOC concentration of winter flow averaged 3.9 ± 0.5 mg C L−1, yet was highly variable across basins (ranging from <1 to >20 mg C L−1). In comparison to the summer‐autumn period, DOM composition of winter flow had lower aromaticity (as indicated by specific ultraviolet absorbance at 254 nm, or SUVA254), lower hydrophobic acid content, and a higher proportion of hydrophilic compounds (HPI). Fluorescence spectroscopy and parallel factor analysis indicated enrichment of protein‐like fluorophores in some, but not all, winter flow samples. The ratio of DOC to dissolved organic nitrogen, an indicator of DOM biodegradability, was positively correlated with SUVA254and negatively correlated with the percentage of protein‐like compounds. Using a simple two‐pool mixing model, we evaluate possible changes in DOM during the summer‐autumn period across a range of conditions reflecting possible increases in groundwater discharge. Across three watersheds, we consistently observed decreases in DOC concentration and SUVA254 and increases in HPI with increasing groundwater discharge. Spatial patterns in DOM composition of winter flow appear to reflect differences in the relative contributions of groundwater from suprapermafrost and subpermafrost aquifers across watersheds. Our findings call for more explicit consideration of DOC loss and stabilization pathways associated with changing subsurface hydrology in watersheds underlain by thawing permafrost.
Progress Monitoring Using Direct Behavior Rating Single Item Scales in a Multiple-Baseline Design Study of the Daily Report Card Intervention

Direct behavior rating (DBR) may be a viable assessment for documenting current areas of impaired functioning and progress monitoring students’ response to a behavioral intervention. Challenging behaviors are often addressed in general education settings using interventions such as the daily report card (DRC). To best implement and monitor such interventions, effective progress monitoring tools are needed. The present study reports on the results of a multiple-baseline design across three elementary school children who had a DRC implemented to address academic engagement and social behaviors. The DRC and systematic direct observation (SDO) were compared with teacher-completed DBR to investigate the correspondence between these two measures of progress monitoring. Relative to SDO, results indicated that DBR was generally an effective means of monitoring progress for most cases and most target behaviors. The results of the present study support using DBR to monitor progress following the use of a behavioral intervention such as the DRC.
Gray matter covariance networks in the mouse brain

The presence of networks of correlation between gray matter volumes of brain regions - as measured across subjects in a group of individuals - has been consistently described in several human studies, an approach termed structural covariance MRI (scMRI). Complementary to prevalent brain connectivity modalities like functional and diffusion-weighted imaging, this approach can provide valuable insight into the mutual influence of regional trophic and plastic processes occurring between brain regions. Previous investigations highlighted coordinated growth of these regions within specific structural networks in healthy populations and described their derangement in pathological states. However, a number of fundamental questions about the origin and significance of these couplings remains open and the mechanisms behind the formation of scMRI networks are still poorly understood. To investigate whether analogous scMRI networks are present in lower mammal species amenable to genetic and experimental manipulation such as the laboratory mouse, I coupled high resolution morpho-anatomical MRI with network-based approaches on a large cohort of genetically-homogeneous wild-type mice (C57Bl6/J). To this purpose, I first developed a semi-automated pipeline enabling reliable Voxel Based Morphometry (VBM) of gray matter volumes in the mouse. To validate this approach and its ability to detect plastic changes in brain structures, I applied it to a cohort of aged mice treated with omega-3 polyunsaturated fatty acids (n3-PUFA). This study revealed that treatment with n3PUFA, but not isocaloric olive oil preserved gray matter volume of the hippocampus and frontal cortices, an effect coincident with amelioration of hippocampal-based spatial memory functions. I next employed VBM to investigate scMRI networks in inbred mice using a seed-based approach. In striking resemblance with human findings, I observed the presence of homotopic (i.e. bilateral) architecture in several scMRI cortical and subcortical networks, a finding corroborated by Independent Component Analyses. Subcortical structures also showed highly symmetric inter-hemispheric correlations, with evidence of distributed antero-posterior networks in diencephalic regions of the thalamus and hypothalamus. Hierarchical cluster analysis revealed six identifiable clusters of cortical and sub-cortical regions corresponding to previously described neuroanatomical systems. This work documents for the first time the presence of homotopic cortical and subcortical scMRI networks in the mouse brain, and is poised to pave the way to translational use of this species to investigate the elusive biological and neuroanatomical underpinnings of scMRI network development and its derangement in neuropathological states.
Effect of Gender, Season, and Vitamin D Status on Bone Biochemical Markers in Saudi Diabetes Patients

Biochemical bone turnover markers (BTMs) provide important information on the diagnosis, therapy and monitoring of metabolic bone diseases. They are evident before measurable changes in bone mineral density (BMD) take place. A total of 35 adult Saudi patients (23 males; 12 females) with type 2 diabetes and diagnosed to be vitamin D deficient were recruited in this prospective study. Here we investigated the effects of gender, season, and vitamin D status on bone biochemical markers of bone remodeling. Anthropometry and blood samples were collected at different intervals. Metabolic parameters and bone biomarkers were measured routinely and by ELISA. Both males and females had a significant increase in their vitamin D status over time, but no significant changes in the bone biomarkers were observed in females. In males there was a significant increase in circulating levels of corrected calcium and OPN (p = 0.004 and 0.01 respectively) and a significant decrease in crosslaps (p = 0.005). In all subjects there was a modest but significant positive relationship between vitamin D status and OC (R = 0.34; p = 0.04). In conclusion, our study demonstrates that changes in bone remodeling markers are affected by season, gender, and possibly vitamin D status. This gender difference may well reflect the physiologic pathway responsible for the higher peak bone mass achieved in males compared to females.