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Quilled flowers : a garden of 35 paper projects
\"Roll, mold, and shape colorful strips of paper into a bevy of petals, blooms, and bouquets for all occasions\"--P. [4] of cover.
Book
Clique-factors in graphs with sublinear $\\boldsymbol\\ell$ -independence number
Given a graph
$G$
and an integer
$\\ell \\ge 2$
, we denote by
$\\alpha _{\\ell }(G)$
the maximum size of a
$K_{\\ell }$
-free subset of vertices in
$V(G)$
. A recent question of Nenadov and Pehova asks for determining the best possible minimum degree conditions forcing clique-factors in
$n$
-vertex graphs
$G$
with
$\\alpha _{\\ell }(G) = o(n)$
, which can be seen as a Ramsey–Turán variant of the celebrated Hajnal–Szemerédi theorem. In this paper we find the asymptotical sharp minimum degree threshold for
$K_r$
-factors in
$n$
-vertex graphs
$G$
with
$\\alpha _\\ell (G)=n^{1-o(1)}$
for all
$r\\ge \\ell \\ge 2$
.
Journal Article
The Zwicky Transient Facility
The Zwicky Transient Facility (ZTF) Observing System (OS) is the data collector for the ZTF project to study astrophysical phenomena in the time domain. ZTF OS is based upon the 48 inch aperture Schmidt-type design Samuel Oschin Telescope at the Palomar Observatory in Southern California. It incorporates new telescope aspheric corrector optics, dome and telescope drives, a large-format exposure shutter, a flat-field illumination system, a robotic bandpass filter exchanger, and the key element: a new 47-square-degree, 600 megapixel cryogenic CCD mosaic science camera, along with supporting equipment. The OS collects and delivers digitized survey data to the ZTF Data System (DS). Here, we describe the ZTF OS design, optical implementation, delivered image quality, detector performance, and robotic survey efficiency.
Journal Article
Easy paper projects : 60 crafts you can wear, gift, use and admire
\"Learn just how versatile paper can be when creating fun, colorful crafts. Whether you have plain printer paper, a rainbow array of cardstock or just a few scraps of construction paper, you?ll be able to create inventive paper crafts that require only a few materials you already have, making them a thrifty and accessible alternative to more complicated projects ... With easy-to-follow step-by-step instructions and plenty of photos to guide you, you can be on your way to creating paper masterpieces in no time\"--Back cover.
Book
Normalization in the simply typed ${\\lambda \\mu \\mu '}\\rho \\theta \\varepsilon$ -calculus
In this paper, in connection with the program of extending the Curry–Howard isomorphism to classical logic, we study the
$\\lambda \\mu$
-calculus of Parigot emphasizing the difference between the original version of Parigot and the version of de Groote in terms of normalization properties. In order to talk about a satisfactory representation of the integers, besides the usual
$\\beta$
-,
$\\mu$
-, and
$\\mu '$
-reductions, we consider the
$\\lambda \\mu$
-calculus augmented with the reduction rules
$\\rho$
,
$\\theta$
and
$\\varepsilon$
. We show that we need all of these rules for this purpose. Then we prove that, with the syntax of Parigot, the calculus enjoys the strong normalization property even when we add the rules
$\\rho$
,
$\\theta$
, and
$\\epsilon$
, while the
$\\lambda \\mu$
-calculus presented with the more flexible de Groote-style syntax, in contrast, has only the weak normalization property. In particular, we present a normalization algorithm for the
$\\beta \\mu \\mu '\\rho \\theta \\varepsilon$
-reduction in the de Groote-style calculus.
Journal Article
A General Approach to Domain Adaptation with Applications in Astronomy
The ability to build a model on a source task and subsequently adapt this model to a new target task is a pervasive need in many astronomical applications. The problem is generally known in the machine learning field as transfer learning, where domain adaptation is a popular scenario. An example is to build a predictive model on spectroscopic data to identify Type Ia supernovae (SNe Ia), while subsequently trying to adapt such a model to photometric data. In this paper we propose a new general approach to domain adaptation which does not rely on the proximity of source and target distributions. Instead we simply assume a strong similarity in model complexity across domains, and use active learning to mitigate the dependence on source examples. Our work leads to a new formulation for the likelihood as a function of empirical error using a theoretical learning bound; the result is a novel mapping from generalization error to a likelihood estimation. Results using two real astronomical problems, SN Ia classification and identification of Mars landforms, show two main advantages of our approach: increased performance accuracy and substantial savings in computational cost.
Journal Article