Miracle-dz/UniFAR-FTUs huggingface dataset:
Resolved and Integrated Stellar Masses in the SDSS-IV/MaNGA Survey. II. Applications of PCA-based Stellar Mass Estimates

A galaxy’s stellar mass is one of its most fundamental properties, but it remains challenging to measure reliably. With the advent of very large optical spectroscopic surveys, efficient methods that can make use of low signal-to-noise spectra are needed. With this in mind, we created a new software package for estimating effective stellar mass-to-light ratios that uses a principal component analysis (PCA) basis set to optimize the comparison between observed spectra and a large library of stellar population synthesis models. In Paper I, we showed that with a set of six PCA basis vectors we could faithfully represent most optical spectra from the Mapping Nearby Galaxies at APO (MaNGA) survey, and we tested the accuracy of our M/L estimates using synthetic spectra. Here, we explore sources of systematic error in our mass measurements by comparing our new measurements to data from the literature. We compare our stellar mass surface density estimates to kinematics-derived dynamical mass surface density measurements from the DiskMass Survey and find some tension between the two that could be resolved if the disk scale heights used in the kinematic analysis were overestimated by a factor of ∼1.5. We formulate an aperture-corrected stellar mass catalog for the MaNGA survey, and compare to previous stellar mass estimates based on multiband optical photometry, finding typical discrepancies of 0.1 dex. Using the spatially resolved MaNGA data, we evaluate the impact of estimating total stellar masses from spatially unresolved spectra, and we explore how the biases that result from unresolved spectra depend upon the galaxy’s dust extinction and star formation rate. Finally, we describe an SDSS Value-Added Catalog that will include both spatially resolved and total (aperture-corrected) stellar masses for MaNGA galaxies.
Relative Afferent Pupillary Defect Screening Through Transfer Learning

Abnormalities in pupillary light reflex can indicate optic nerve disorders that may lead to permanent visual loss if not diagnosed in an early stage. In this study, we focus on relative afferent pupillary defect (RAPD), which is based on the difference between the reactions of the eyes when they are exposed to light stimuli. Incumbent RAPD assessment methods are based on subjective practices that can lead to unreliable measurements. To eliminate subjectivity and obtain reliable measurements, we introduced an automated framework to detect RAPD. For validation, we conducted a clinical study with lab-on-a-headset, which can perform automated light reflex test. In addition to benchmarking handcrafted algorithms, we proposed a transfer learning-based approach that transformed a deep learning-based generic object recognition algorithm into a pupil detector. Based on the conducted experiments, proposed algorithm RAPDNet can achieve a sensitivity and a specificity of 90.6% over 64 test cases in a balanced set, which corresponds to an AUC of 0.929 in ROC analysis. According to our benchmark with three handcrafted algorithms and nine performance metrics, RAPDNet outperforms all other algorithms in every performance category.
Bayesian Structure Learning in Graphical Models using Shrinkage priors

We consider the problem of learning the structure of a high dimensional precision matrix under sparsity assumptions. We propose to use a shrinkage prior, called the DL-graphical prior based on the Dirichlet-Laplace prior used for the Gaussian mean problem. A posterior sampling scheme based on Gibbs sampling is also provided along with theoretical guarantees of the method by obtaining the posterior convergence rate of the precision matrix.
Route identification in the National Football League

Abstract Tracking data in the National Football League (NFL) is a sequence of spatial-temporal measurements that varies in length depending on the duration of the play. In this paper, we demonstrate how model-based curve clustering of observed player trajectories can be used to identify the routes run by eligible receivers on offensive passing plays. We use a Bernstein polynomial basis function to represent cluster centers, and the Expectation Maximization algorithm to learn the route labels for each of the 33,967 routes run on the 6963 passing plays in the data set. With few assumptions and no pre-existing labels, we are able to closely recreate the standard route tree from our algorithm. We go on to suggest ideas for new potential receiver metrics that account for receiver deployment and movement common throughout the league. The resulting route labels can also be paired with film to enable streamlined queries of game film.
EC 22536-5304: SALT identifies a new lead-rich intermediate helium subdwarf★

SALT spectra of the helium-rich hot subdwarf EC 22536-5304 show strong absorption lines of triply-ionized lead. Analysis of the HRS spectrum and a follow-up SALT/RSS spectrum show EC 22536-5304 to have surface properties (temperature, gravity, helium/hydrogen ratio) similar to other heavy-metal subdwarfs. With a lead overabundance of 4.8 dex relative to solar, EC 22536-5304 is the most lead-rich intermediate helium subdwarf discovered so far.
Black hole and cosmological particle production in Schwarzschild de Sitter

We compute the spectra and total fluxes of quantum mechanically produced particles crossing the black hole and cosmological horizons in Schwarzchild de Sitter (SdS). Particle states are defined with respect to well-behaved, Kruskal coordinates near the horizons, and as a consequence we find that these spectra are generally non-thermal. The non-thermal Bogoliubov coefficient for a vacuum fluctuation near the black hole horizon to produce a particle that crosses the cosmological horizon is shown to equal to the convolution of two thermal coefficients, one at the cosmological temperature and one at the black hole temperature, weighted by the transmission coefficient for wave propagation in static SdS coordinates. In this sense virtual thermal propagation underlies the production process. This representation leads to the useful result that the geometric optics approximation is reliable when used together with a low frequency cut-off determined by the transmission coefficient. The large black hole limit is a quasi-equilibruim situation as both temperatures approach the common value of zero, the particle spectra become equal, and both emissions are exponentially suppressed. Small black holes radiate as thermal bodies and absorb a tiny flux of cosmological particles. The behavior of the quantum fluctuations on the horizons is seen to be consistent with the Schottky anomaly behavior of classical gravitational fluctuations.
Towards closing the gap between the theory and practice of SVRG

Among the very first variance reduced stochastic methods for solving the empirical risk minimization problem was the SVRG method (Johnson & Zhang 2013). SVRG is an inner-outer loop based method, where in the outer loop a reference full gradient is evaluated, after which $m \in \mathbb{N}$ steps of an inner loop are executed where the reference gradient is used to build a variance reduced estimate of the current gradient. The simplicity of the SVRG method and its analysis has lead to multiple extensions and variants for even non-convex optimization. Yet there is a significant gap between the parameter settings that the analysis suggests and what is known to work well in practice. Our first contribution is that we take several steps towards closing this gap. In particular, the current analysis shows that $m$ should be of the order of the condition number so that the resulting method has a favorable complexity. Yet in practice $m =n$ works well irregardless of the condition number, where $n$ is the number of data points. Furthermore, the current analysis shows that the inner iterates have to be reset using averaging after every outer loop. Yet in practice SVRG works best when the inner iterates are updated continuously and not reset. We provide an analysis of these aforementioned practical settings and show that they achieve the same favorable complexity as the original analysis (with slightly better constants). Our second contribution is to provide a more general analysis than had been previously done by using arbitrary sampling, which allows us to analyse virtually all forms of mini-batching through a single theorem. Since our setup and analysis reflects what is done in practice, we are able to set the parameters such as the mini-batch size and step size using our theory in such a way that produces a more efficient algorithm in practice, as we show in extensive numerical experiments.
Obesity Definitions in Sarcopenic Obesity: Differences in Prevalence, Agreement and Association with Muscle Function

Background Sarcopenic obesity (SO) is associated with poorer physical performance in the elderly and will increase in relevance with population ageing and the obesity epidemic. The lack of a consensus definition for SO has resulted in variability in its reported prevalence, poor inter-definitional agreement, and disagreement on its impact on physical performance, impeding further development in the field. While sarcopenia definitions have been compared, the impact of obesity definitions in SO has been less well-studied. Objectives To compare 3 widely-adopted definitions of obesity in terms of SO prevalence, inter-definitional agreement, and association with muscle function. Design Cross-sectional. Setting GERILABS study, Singapore Participants 200 community-dwelling, functionally-independent older adults. Measurements We utilized three commonly-used definitions of obesity: body mass index (BMI), waist circumference (WC) and DXA-derived fat mass percentage (FM%). Sarcopenia was defined using Asian Working Group for Sarcopenia criteria. For muscle function, we assessed handgrip strength, gait speed and Short Physical Performance Battery (SPPB). Subjects were classified into 4 body composition phenotypes (normal, obese, sarcopenic and SO), and outcomes were compared between groups. Results The prevalence rate for SO was lowest for BMI (0.5%) compared to FM% (10.0%) and WC (10.5%). Inter-definitional agreement was lowest between BMI and WC (κ=0.364), and at best moderate between FM% and WC (κ=0.583). SO performed the worst amongst body composition phenotypes in handgrip strength, gait speed and SPPB (all p<0.01) only when defined using WC. In regression analyses, SO was associated with decreased SPPB scores (β=−0.261, p=0.001) only for the WC definition. Conclusion There is large variation in the prevalence of SO across different obesity definitions, with low-to-moderate agreement between them. Our results corroborate recent evidence that WC, and thus central obesity, is best associated with poorer muscle function in SO. Thus, WC should be further explored in defining obesity for accurate and early characterization of SO among older adults in Asian populations.
Improving Deep Learning Image Recognition Performance Using Region of Interest Localization Networks

Deep Learning has been gaining momentum and achieving the state-of-the-art results on many visual recognition problems. The roots of this field can be traced back to the 1940s of the 20th century. The field has recently started delivering some interesting results on many image understanding problems. This is mainly due to the availability of powerful hardware that can accelerate the training process. In addition, the growth of the Internet and imaging devices such as mobile phones and cameras has contributed to the increase in the amount of data that can be used to train neural networks. All of these factors have contributed to the success of deep learning on large scale image understanding tasks. Many image understanding problems do not have large training data. This is especially true in many special purpose datasets such as medical images, astronomical images, and environmental images. These application do not have large training datasets because unlike natural images, users do not typically take these images and upload them to the web. In addition, some of these applications, such as medical imaging, have many restrictions on sharing the data in order to protect the privacy of the patients. Finally, the labeling process needed for training natural images can be done by any person, unlike special purpose datasets. For example, in medical imaging, the images must be labeled by medical or clinical experts in the field. This results in datasets that are normally much smaller than natural images datasets as these experts have limited time to invest in the creation of the training sets. Luckily, in many of these applications, the most discriminative features may be present in a small region of interest. In this work, we present a method of training deep learning models on problems with low number of training images. We will do that by localizing a region of interest in these images, which will help reduce the problem of overfitting. In this thesis, two localization architectures are introduced, namely: the standard localization network and the wide localization network (wide net). The latter has several advantages which we explain thoroughly. The first problem we will introduce is the Right whale recognition problem. The problem
Promiscuity of the catalytic Sec7 domain within the guanine nucleotide exchange factor GBF1 in ARF activation, Golgi homeostasis, and effector recruitment

The integrity of the Golgi and trans-Golgi network (TGN) is disrupted by brefeldin A (BFA), which inhibits the Golgi-localized BFA-sensitive factor (GBF1) and brefeldin A–inhibited guanine nucleotide-exchange factors (BIG1 and BIG2). Using a cellular replacement assay to assess GBF1 functionality without interference from the BIGs, we show that GBF1 alone maintains Golgi architecture; facilitates secretion; activates ADP-ribosylation factor (ARF)1, 3, 4, and 5; and recruits ARF effectors to Golgi membranes. Unexpectedly, GBF1 also supports TGN integrity and recruits numerous TGN-localized ARF effectors. The impact of the catalytic Sec7 domain (Sec7d) on GBF1 functionality was assessed by swapping it with the Sec7d from ARF nucleotide-binding site opener (ARNO)/cytohesin-2, a plasma membrane GEF reported to activate all ARFs. The resulting chimera (GBF1-ARNO-GBF1 [GARG]) targets like GBF1, supports Golgi/TGN architecture, and facilitates secretion. However, unlike GBF1, GARG activates all ARFs (including ARF6) at the Golgi/TGN and recruits additional ARF effectors to the Golgi/TGN. Our results have general implications: 1) GEF’s targeting is independent of Sec7d, but Sec7d influence the GEF substrate specificity and downstream effector events; 2) all ARFs have access to all membranes, but are restricted in their distribution by the localization of their activating GEFs; and 3) effector association with membranes requires the coincidental presence of activated ARFs and specific membrane identifiers.
Methods for assessment of cognitive workload in driving tasks

In this paper, we explain the concept of mental or cognitive workload and review the most common methods and procedures for its assessment. We focus primarily on driving tasks and interaction with various in-vehicle devices and systems. Safety is one of the most important human needs and consequentially also the primary concern of the automotive industry when introducing new in-vehicle information systems (IVIS), global positioning systems (GPS), interactive displays, etc. Since the human brain and its resources are limited, the primary task of driving can be seriously challenged when secondary tasks are performed simultaneously. Several different methods have been proposed for direct and indirect measurement of the driver’s cognitive workload and for the detection of its potential overload. We report briefly also on two user studies performed in our driving simulator, which illustrate the importance of correctly assessing cognitive workload in the process of evaluating new in-vehicle user interfaces.
Mitigating the Effects of GNSS Multipath with a Coded Filter

The Charles Stark Draper Laboratory has developed novel estimation algorithms that bring recent sparse signal reconstruction techniques from compressive sensing, a rapidly developing field in applied mathematics, to bear on traditional navigation problems. The new estimation methods are applied to the problem of detecting, estimating, and removing measurement and process errors that are poorly modeled as Gaussian noise. More specifically, the paper focuses on mitigating the effects of GNSS multipath errors. The two key insights that connect compressive sensing ideas to the problem of multipath mitigation are to recognize that multipath is sparse (or compressible) in some appropriately chosen basis—the canonical basis per time or the wavelet basis over time—and that a traditional Kalman filtering or smoothing problems can be cast as channel coding problems. In this framework, fast convex optimization solvers are employed to solve constrained 1-norm minimization problems to estimate and remove the sparse multipath signals. The results show that the new algorithms outperform standard algorithms by a significant margin in simulated and live-sky multipath scenarios.
Theory building: relating variation, uncertainty, buffering mechanisms and trade-offs

Operations management (OM) theories have started to emerge in an attempt to unify different OM perspectives now typified by the lean and agile paradigms. These have taken the form of laws (Hopp and Spearman, 1995), conceptual models (Fisher, 1997) and theories (Schmenner & Swink, 1998; Vastaag, 2000) to mention more prominent examples. They all address the need to conceptualise these developments in order to provide practical means of explaining and predicting OM phenomena and means of achieving improved performance. This paper aims to contribute to this process by further exploring the underlying significance of variation, uncertainty, buffering mechanisms and trade-offs. The paper reviews evidence of best practice and prior research before reporting on a multi-case study. The theory of variation and uncertainty buffering is proposed as a means of unifying and extending existing theory at a more abstract level. The paper finally evaluates the utility and parsimony of this theoretical proposal, acknowledging the need for further work in developing its practical application.
PARAMETRIC BLIND IMAGE DEBLURRING WITH GRADIENT BASED SPECTRAL KURTOSIS MAXIMIZATION

Blind image deconvolution/deblurring (BID) is a challenging task due to lack of prior information about the blurring process and image. Noise and ringing artefacts resulted during the restoration process further deter fine restoration of the pristine image. These artefacts mainly arise from using a poorly estimated point spread function (PSF) combined with an ineffective restoration filter. This paper presents a BID scheme based on the steepest descent in kurtosis maximization. Assuming uniform blur, the PSF can be modelled by a parametric form. The scheme tries to estimate the blur parameters by maximizing kurtosis of the deblurred image. The scheme is devised to handle any type of blur that can be framed into a parametric form such as Gaussian, motion and out-of-focus. Gradients for the blur parameters are computed and optimized in the direction of increasing kurtosis value using a steepest descent scheme. The algorithms for several common blurs are derived and the effectiveness has been corroborated through a set of experiments. Validation has also been carried out on various real examples. It is shown that the scheme optimizes on the parameters in a close vicinity of the true parameters. Results of both benchmark and real images are presented. Both full-reference and non-reference image quality measures have been used in quantifying the deblurring performance. The results show that the proposed method offers marked improvements over the existing methods.
Local versus Global Models for Just-InTime Software Defect Prediction

Just-in-time software defect prediction (JIT-SDP) is an active topic in software defect prediction, which aims to identify defectinducing changes. Recently, some studies have found that the variability of defect data sets can affect the performance of defect predictors. By using local models, it can help improve the performance of prediction models. However, previous studies have focused on module-level defect prediction. Whether local models are still valid in the context of JIT-SDP is an important issue. To this end, we compare the performance of local and global models through a large-scale empirical study based on six open-source projects with 227417 changes. -e experiment considers three evaluation scenarios of cross-validation, cross-project-validation, and timewise-cross-validation. To build local models, the experiment uses the k-medoids to divide the training set into several homogeneous regions. In addition, logistic regression and effort-aware linear regression (EALR) are used to build classification models and effort-aware prediction models, respectively. -e empirical results show that local models perform worse than global models in the classification performance. However, local models have significantly better effort-aware prediction performance than global models in the cross-validation and cross-project-validation scenarios. Particularly, when the number of clusters k is set to 2, local models can obtain optimal effort-aware prediction performance. -erefore, local models are promising for effortaware JIT-SDP.
Teaching Theories of Victimology Heather Zaykowski and Lena Campagna

ISSN: 1051-1253 (Print) 1745-9117 (Online) Journal homepage: http://www.tandfonline. com/loi/rcje20 Teaching Theories of Victimology Heather Zaykowski & Lena Campagna To cite this article: Heather Zaykowski & Lena Campagna (2014) Teaching Theories of Victimology, Journal of Criminal Justice Education, 25:4, 452-467, DOI: 10.1080/10511253.2014. 965410 To link to this article: https://doi.org/10.1080/10511253.2014. 965410
VISUALIZATION OF CLUSTERS USING scoivat ALGORITHM

Assessment of cluster tendency is an important step in cluster analysis. Clustering is the problem of partitioning a set of unlabeled objects O = {o1,. , on} into self-similar groups. In conventional (object data) cluster analysis, the objects are separated into groups according to their features. Although clustering is typically thought of as only the act of separating objects into the proper groups, cluster analysis actually consists of three concise questions: Cluster tendency, Partitioning and Cluster validity. One tool for assessing cluster tendency is the Visual assessment of cluster tendency(VAT) algorithm. The VAT algorithm is a visual method for determining the possible number of clusters in, or the cluster tendency of a set of objects.VAT produces an image matrix that can be used for visual assessment cluster tendency in either relational or object data. An efficient formulation of the iVAT algorithm is used which significantly reduces its computational complexity. iVAT implementation begins by finding the VAT reordered dissimilarity matrix and then performs a distance transform on this matrix. The iVAT is performed only on the small datasets. To improve the clustering accuracy and effectiveness of the iVAT scoiVAT algorithm is used. scoiVAT algorithm improves the cluster tendency and it supports large datasets. scoiVAT clustering algorithm can be used in various applications such as malignant tumors, genes expressed in a microarray