55,549 research outputs found
Detection of Citrus psorosis virus in field trees by direct tissue blot immunoasay in comparison with ELISA, symptomatology, biological indexing and cross-protection tests.
Invertibility in groupoid C*-algebras
Given a second-countable, Hausdorff, \'etale, amenable groupoid G with
compact unit space, we show that an element a in C*(G) is invertible if and
only if \lambda_x(a) is invertible for every x in the unit space of G, where
\lambda_x refers to the "regular representation" of C*(G) on l_2(G_x). We also
prove that, for every a in C*(G), there exists some x in G^{(0)} such that
||a|| = ||\lambda_x(a)||.Comment: 8 page
Pneumothorax and Pneumomediastinum in a Sputum Positive Tuberculosis Patient: The Continuous Diaphragm Sign
Secondary pneumothorax is a very common medical emergency. At times it is associated with pneumomediastinum, which could be fatal at times if not identified. We present a case of a 11 years old sputum positive child who presented with both these conditions and was diagnosed on chest x ray
Parameterized Algorithms for Graph Partitioning Problems
We study a broad class of graph partitioning problems, where each problem is
specified by a graph , and parameters and . We seek a subset
of size , such that is at most
(or at least) , where are constants
defining the problem, and are the cardinalities of the edge sets
having both endpoints, and exactly one endpoint, in , respectively. This
class of fixed cardinality graph partitioning problems (FGPP) encompasses Max
-Cut, Min -Vertex Cover, -Densest Subgraph, and -Sparsest
Subgraph.
Our main result is an algorithm for any problem in
this class, where is the maximum degree in the input graph.
This resolves an open question posed by Bonnet et al. [IPEC 2013]. We obtain
faster algorithms for certain subclasses of FGPPs, parameterized by , or by
. In particular, we give an time algorithm for Max
-Cut, thus improving significantly the best known time
algorithm
Electro-Oxidation of Titanium Carbide Nanoparticles in Aqueous Acid Creates TiC@TiO2 Core-Shell Structures
Titanium carbide (TiC) is an attractive support material used in
electro-catalysis and sensing. We report the electrochemistry of
TiC nanoparticles (NPs, 35–50 nm in diameter) in different
electrolytes in the pH range of 0 to 8. The TiC NPs undergo
irreversible oxidation in acidic, basic, and neutral media,
attributed to the partial conversion into titanium dioxide (TiO2)
with the amount of oxidation highly dependent on the pH of
the solution. In H2SO4 (pH 0), multiple voltammetric scans
revealed the conversion to be partial but repeated scans
allowed a conversion approaching 100% to be obtained with
20 scans generating a ca 60% level of oxidation. The process is
inferred to lead to the formation of TiC@TiO2 core-shell nanoparticles (~12.5 nm core radius and ~5 nm shell width for a
60% conversion) and this value sharply decreases with an
increase of pH. Independent measurements were conducted at
a single NP level (via nano-impact experiments) to confirm the
oxidation of the NPs, showing consistent agreement with the
bulk measurements
AMK: An Interface For Object-oriented Newtonian Particle Mechanics
This article describes an object-oriented environment with an associated
user interface, AMK, for modelling simple Newtonian particle mechanics. It is
intended for educational use, and provides a framework for modelling which
generalises methodology. Physical objects are treated as logical objects, and
mathematical models are formulated by linking them. The implementation is
within the Windows environment using Mathematica and Visual Basic. Modelling
is done by constructing objects and linking them to produce new objects. The
aim is to produce an equation of motion object. The interface forces the user into
a modelling cycle of constructing and linking objects, and accessing their
methods. It constructs a Mathematica input automatically from information
supplied by the user, and communicates with Mathematica. The combination of
a generalised environment plus interface produces correct answers when modelling
many specific physical systems
Degree spectra for transcendence in fields
We show that for both the unary relation of transcendence and the finitary
relation of algebraic independence on a field, the degree spectra of these
relations may consist of any single computably enumerable Turing degree, or of
those c.e. degrees above an arbitrary fixed degree. In other
cases, these spectra may be characterized by the ability to enumerate an
arbitrary set. This is the first proof that a computable field can
fail to have a computable copy with a computable transcendence basis
Using Bars As Signposts of Galaxy Evolution at High and Low Redshifts
An analysis of the NICMOS Deep Field shows that there is no evidence of a
decline in the bar fraction beyond z~0.7, as previously claimed; both
bandshifting and spatial resolution must be taken into account when evaluating
the evolution of the bar fraction. Two main caveats of this study were a lack
of a proper comparison sample at low redshifts and a larger number of galaxies
at high redshifts. We address these caveats using two new studies. For a proper
local sample, we have analyzed 134 spirals in the near-infrared using 2MASS
(main results presented by Menendez-Delmestre in this volume) which serves as
an ideal anchor for the low-redshift Universe. In addition to measuring the
mean bar properties, we find that bar size is correlated with galaxy size and
brightness, but the bar ellipticity is not correlated with these galaxy
properties. The bar length is not correlated with the bar ellipticity. For
larger high redshift samples we analyze the bar fraction from the 2-square
degree COSMOS ACS survey. We find that the bar fraction at z~0.7 is ~50%,
consistent with our earlier finding of no decline in bar fraction at high
redshifts.Comment: In the proceedings of "Penetrating Bars through Masks of Cosmic Dust:
The Hubble Tuning Fork strikes a New Note
Joint Viewpoint and Keypoint Estimation with Real and Synthetic Data
The estimation of viewpoints and keypoints effectively enhance object
detection methods by extracting valuable traits of the object instances. While
the output of both processes differ, i.e., angles vs. list of characteristic
points, they indeed share the same focus on how the object is placed in the
scene, inducing that there is a certain level of correlation between them.
Therefore, we propose a convolutional neural network that jointly computes the
viewpoint and keypoints for different object categories. By training both tasks
together, each task improves the accuracy of the other. Since the labelling of
object keypoints is very time consuming for human annotators, we also introduce
a new synthetic dataset with automatically generated viewpoint and keypoints
annotations. Our proposed network can also be trained on datasets that contain
viewpoint and keypoints annotations or only one of them. The experiments show
that the proposed approach successfully exploits this implicit correlation
between the tasks and outperforms previous techniques that are trained
independently.Comment: 11 pages, 4 figure
- …