Three Li-rich K giants: IRAS 12327-6523, IRAS 13539-4153, and IRAS 17596-3952

We report on spectroscopic analyses of three K giants previously suggested to be Li-rich: IRAS 12327-6523, IRAS 13539-4153, and IRAS 17596-3952. High-resolution optical spectra and the LTE model atmospheres are used to derive the stellar parameters: ($T_{\rm eff}$, log $g$, [Fe/H]), elemental abundances, and the isotopic ratio $^{12}$C/$^{13}$C. IRAS 13539-4153 shows an extremely high Li abundance of $\log\epsilon$(Li) $\approx$ 4.2, a value ten times more than the present Li abundance in the local interstellar medium. This is the third highest Li abundance yet reported for a K giant. IRAS 12327-6523 shows a Li abundances of $\log\epsilon$(Li)$\approx$ 1.4. IRAS 17596-3952 is a rapidly rotating ($V{\sin i}$ $\approx$ 35 km s$^{-1}$) K giant with $\log\epsilon$(Li) $\approx$ 2.2. Infrared photometry which shows the presence of an IR excess suggesting mass-loss. A comparison is made between these three stars and previously recognized Li-rich giants.Comment: 17 pages, 6 figures, accepted for A

Quantifying Facial Age by Posterior of Age Comparisons

We introduce a novel approach for annotating large quantity of in-the-wild facial images with high-quality posterior age distribution as labels. Each posterior provides a probability distribution of estimated ages for a face. Our approach is motivated by observations that it is easier to distinguish who is the older of two people than to determine the person's actual age. Given a reference database with samples of known ages and a dataset to label, we can transfer reliable annotations from the former to the latter via human-in-the-loop comparisons. We show an effective way to transform such comparisons to posterior via fully-connected and SoftMax layers, so as to permit end-to-end training in a deep network. Thanks to the efficient and effective annotation approach, we collect a new large-scale facial age dataset, dubbed `MegaAge', which consists of 41,941 images. Data can be downloaded from our project page mmlab.ie.cuhk.edu.hk/projects/MegaAge and github.com/zyx2012/Age_estimation_BMVC2017. With the dataset, we train a network that jointly performs ordinal hyperplane classification and posterior distribution learning. Our approach achieves state-of-the-art results on popular benchmarks such as MORPH2, Adience, and the newly proposed MegaAge.Comment: To appear on BMVC 2017 (oral) revised versio

On transfer operators for continued fractions with restricted digits

For any non-empty subset I of the natural numbers, let {Lambda}I denote those numbers in the unit interval whose continued fraction digits all lie in I. Define the corresponding transfer operator Formula. for Formula, where Re (rß) = {theta}I is the abscissa of convergence of the series Formula. When acting on a certain Hilbert space HI, rß, we show that the operator LI, rß is conjugate to an integral operator KI, rß. If furthermore rß is real, then KI, rß is selfadjoint, so that LI, rß : HI, rß -> HI, rß has purely real spectrum. It is proved that LI, rß also has purely real spectrum when acting on various Hilbert or Banach spaces of holomorphic functions, on the nuclear space C{omega} [0, 1], and on the Fréchet space C{infty} [0, 1]. The analytic properties of the map rß ↦ LI, rß are investigated. For certain alphabets I of an arithmetic nature (for example, I = primes, I = squares, I an arithmetic progression, I the set of sums of two squares it is shown that rß ↦ LI, rß admits an analytic continuation beyond the half-plane Re rß > {theta}I

Hymn to the heroes of Malta

Ġabra ta’ poeżiji u proża li tinkludi: Alla fil-ħolqien ta’ Ġużè Agius Bonello – Is-sena u l-bniedem ta’ Ġużè Ellul-Mercer – Li tiżra’ taħsad ta’ Vic. Apap – Huwa ta’ Gino Muscat-Azzopardi – Żewġ friefet ta’ Vincent Caruana – Iċ-ċagħka ta’ Ġużè Borg – Warda midbiela ta’ C. Gauci – It-tfajla tas-sulfarini ta’ Albert M. Cassola – L-aħħar traduzzjoni ta’ May Butcher qabel ma mietet – Hymn to the heroes of Malta.N/

Holistic, Instance-Level Human Parsing

Object parsing -- the task of decomposing an object into its semantic parts -- has traditionally been formulated as a category-level segmentation problem. Consequently, when there are multiple objects in an image, current methods cannot count the number of objects in the scene, nor can they determine which part belongs to which object. We address this problem by segmenting the parts of objects at an instance-level, such that each pixel in the image is assigned a part label, as well as the identity of the object it belongs to. Moreover, we show how this approach benefits us in obtaining segmentations at coarser granularities as well. Our proposed network is trained end-to-end given detections, and begins with a category-level segmentation module. Thereafter, a differentiable Conditional Random Field, defined over a variable number of instances for every input image, reasons about the identity of each part by associating it with a human detection. In contrast to other approaches, our method can handle the varying number of people in each image and our holistic network produces state-of-the-art results in instance-level part and human segmentation, together with competitive results in category-level part segmentation, all achieved by a single forward-pass through our neural network.Comment: Poster at BMVC 201