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The coefficients a and b of the Fundamental Plane relation R ~ Sigma^a I^b depend on whether one minimizes the scatter in the R direction or orthogonal to the Plane. We provide explicit expressions for a and b (and confidence limits) in terms of the covariances between logR, logSigma and logI. Our analysis is more generally applicable to any other correlations between three variables: e.g., the color-magnitude-Sigma relation, the L-Sigma-Mbh relation, or the relation between the X-ray luminosity, Sunyaev-Zeldovich decrement and optical richness of a cluster, so we provide IDL code which implements these ideas, and we show how our analysis generalizes further to correlations between more than three variables. We show how to account for correlated errors and selection effects, and quantify the difference between the direct, inverse and orthogonal fit coefficients. We show that the three vectors associated with the Fundamental Plane can all be written as simple combinations of a and b because the distribution of I is much broader than that of Sigma, and Sigma and I are only weakly correlated. Why this should be so for galaxies is a fundamental open question about the physics of early-type galaxy formation. If luminosity evolution is differential, and Rs and Sigmas do not evolve, then this is just an accident: Sigma and I must have been correlated in the past. On the other hand, if the (lack of) correlation is similar to that at the present time, then differential luminosity evolution must have been accompanied by structural evolution. A model in which the luminosities of low-L galaxies evolve more rapidly than do those of higher-L galaxies is able to produce the observed decrease in a (by a factor of 2 at z~1) while having b decrease by only about 20 percent. In such a model, the Mdyn/L ratio is a steeper function of Mdyn at higher z.
Matching plays a vital role in the rational allocation of resources in many areas, ranging from market operation to peoples daily lives. In economics, the term matching theory is coined for pairing two agents in a specific market to reach a stable or optimal state. In computer science, all branches of matching problems have emerged, such as the question-answer matching in information retrieval, user-item matching in a recommender system, and entity-relation matching in the knowledge graph. A preference list is the core element during a matching process, which can either be obtained directly from the agents or generated indirectly by prediction. Based on the preference list access, matching problems are divided into two categories, i.e., explicit matching and implicit matching. In this paper, we first introduce the matching theorys basic models and algorithms in explicit matching. The existing methods for coping with various matching problems in implicit matching are reviewed, such as retrieval matching, user-item matching, entity-relation matching, and image matching. Furthermore, we look into representative applications in these areas, including marriage and labor markets in explicit matching and several similarity-based matching problems in implicit matching. Finally, this survey paper concludes with a discussion of open issues and promising future directions in the field of matching.
Strong Balmer absorption lines and the lack of Ha and [OII] emission lines signify that E+As are post-starburst systems. Recent studies suggest that E+As may undergo the transition from the `blue cloud to the `red sequence and eventually migrate to red sequence ETGs. An observational validation of this scenario is to identify the intervening galaxy population between E+As and the red-sequence. Motivated by recent findings with GALEX that a large fraction of ETGs exhibit UV-excess as a sign of RSF, we investigate the possible connection of the UV-excess galaxies to E+As. In particular, we examine the FP scaling relations of the largest sample of ~1,000 E+As selected from the SDSS and ~20,000 morphologically-selected SDSS ETGs with GALEX UV data. The FP parameters, combined with stellar population indicators, reveal a certain group of UV-excess ETGs that bridges between E+As and quiescent red galaxies. The newly identified galaxies are the post-starburst systems characterized by UV-excess but no Ha emission. This is a conceptual generalisation of E+A, in that the Balmer absorption line in the E+A definition is replaced with UV-optical colours that are far more sensitive to RSF than the Balmer lines. We refer to these UV-excess galaxies as E+a galaxies, which stands for elliptical (E) galaxies with a minority of A-type (a) young stars. The species are either (1) galaxies that experienced starbursts weaker than those observed in E+As (1~10% of E+As, mild E+As) or (2) the products of passively evolved E+As after quenching star formation quite a while ago (~1 Gyr, old E+As). We suggest that the latter type of E+a galaxies represents the most recent arrival to the red sequence in the final phase of the E+A to red early-type transition. (Abridged)
Using new integral field observations of 106 galaxies in three nearby clusters we investigate how the intrinsic scatter of the Fundamental Plane depends on the way in which the velocity dispersion and effective radius are measured. Our spatially resolved spectroscopy, combined with a cluster sample with negligible relative distance errors allows us to derive a Fundamental Plane with minimal systematic uncertainties. From the apertures we tested, we find that velocity dispersions measured within a circular aperture with radius equal to one effective radius minimises the intrinsic scatter of the Fundamental Plane. Using simple yet powerful Jeans dynamical models we determine dynamical masses for our galaxies. Replacing luminosity in the Fundamental Plane with dynamical mass, we demonstrate that the resulting Mass Plane has further reduced scatter, consistent with zero intrinsic scatter. Using these dynamical models we also find evidence for a possibly non-linear relationship between dynamical mass-to-light ratio and velocity dispersion.
Cutting-plane methods are well-studied localization(and optimization) algorithms. We show that they provide a natural framework to perform machinelearning ---and not just to solve optimization problems posed by machinelearning--- in addition to their intended optimization use. In particular, theyallow one to learn sparse classifiers and provide good compression schemes.Moreover, we show that very little effort is required to turn them intoeffective active learning methods. This last property provides a generic way todesign a whole family of active learning algorithms from existing passivemethods. We present numerical simulations testifying of the relevance ofcutting-plane methods for passive and active learning tasks.
Tensor completion is a problem of filling the missing or unobserved entries of partially observed tensors. Due to the multidimensional character of tensors in describing complex datasets, tensor completion algorithms and their applications have received wide attention and achievement in areas like data mining, computer vision, signal processing, and neuroscience. In this survey, we provide a modern overview of recent advances in tensor completion algorithms from the perspective of big data analytics characterized by diverse variety, large volume, and high velocity. We characterize these advances from four perspectives: general tensor completion algorithms, tensor completion with auxiliary information (variety), scalable tensor completion algorithms (volume), and dynamic tensor completion algorithms (velocity). Further, we identify several tensor completion applications on real-world data-driven problems and present some common experimental frameworks popularized in the literature. Our goal is to summarize these popular methods and introduce them to researchers and practitioners for promoting future research and applications. We conclude with a discussion of key challenges and promising research directions in this community for future exploration.