102 research outputs found
Knot in Cen A: Stochastic Magnetic Field for Diffusive Synchrotron Radiation?
The emission of relativistic electrons moving in the random and small-scale
magnetic field is presented by diffusive synchrotron radiation (DSR). In this
Letter, we revisit the perturbative treatment of DSR. We propose that random
and small-scale magnetic field might be generated by the turbulence. As an
example, multi-band radiation of the knot in Cen A comes from the electrons
with energy in the magnetic field of . The
multi-band spectrum of DSR is well determined by the feature of stochastic
magnetic field. These results put strong constraint to the models of particle
acceleration.Comment: accepted by ApJL, comments are welcom
The new model of fitting the spectral energy distributions of Mkn 421 and Mkn 501
The spectral energy distribution (SED) of TeV blazars has a double-humped
shape that is usually interpreted as Synchrotron Self Compton (SSC) model. The
one zone SSC model is used broadly but cannot fit the high energy tail of SED
very well. It need bulk Lorentz factor which is conflict with the observation.
Furthermore one zone SSC model can not explain the entire spectrum. In the
paper, we propose a new model that the high energy emission is produced by the
accelerated protons in the blob with a small size and high magnetic field, the
low energy radiation comes from the electrons in the expanded blob. Because the
high and low energy photons are not produced at the same time, the requirement
of large Doppler factor from pair production is relaxed. We present the fitting
results of the SEDs for Mkn 501 during April 1997 and Mkn 421 during March 2001
respectively.Comment: 5 pages, 1 figures, 1table. accepted for publication in Sciences in
China --
A mathematical and computational review of Hartree-Fock SCF methods in Quantum Chemistry
We present here a review of the fundamental topics of Hartree-Fock theory in
Quantum Chemistry. From the molecular Hamiltonian, using and discussing the
Born-Oppenheimer approximation, we arrive to the Hartree and Hartree-Fock
equations for the electronic problem. Special emphasis is placed in the most
relevant mathematical aspects of the theoretical derivation of the final
equations, as well as in the results regarding the existence and uniqueness of
their solutions. All Hartree-Fock versions with different spin restrictions are
systematically extracted from the general case, thus providing a unifying
framework. Then, the discretization of the one-electron orbitals space is
reviewed and the Roothaan-Hall formalism introduced. This leads to a exposition
of the basic underlying concepts related to the construction and selection of
Gaussian basis sets, focusing in algorithmic efficiency issues. Finally, we
close the review with a section in which the most relevant modern developments
(specially those related to the design of linear-scaling methods) are commented
and linked to the issues discussed. The whole work is intentionally
introductory and rather self-contained, so that it may be useful for non
experts that aim to use quantum chemical methods in interdisciplinary
applications. Moreover, much material that is found scattered in the literature
has been put together here to facilitate comprehension and to serve as a handy
reference.Comment: 64 pages, 3 figures, tMPH2e.cls style file, doublesp, mathbbol and
subeqn package
Transcriptome and Network Changes in Climbers at Extreme Altitudes
Extreme altitude can induce a range of cellular and systemic responses. Although it is known that hypoxia underlies the major changes and that the physiological responses include hemodynamic changes and erythropoiesis, the molecular mechanisms and signaling pathways mediating such changes are largely unknown. To obtain a more complete picture of the transcriptional regulatory landscape and networks involved in extreme altitude response, we followed four climbers on an expedition up Mount Xixiabangma (8,012 m), and collected blood samples at four stages during the climb for mRNA and miRNA expression assays. By analyzing dynamic changes of gene networks in response to extreme altitudes, we uncovered a highly modular network with 7 modules of various functions that changed in response to extreme altitudes. The erythrocyte differentiation module is the most prominently up-regulated, reflecting increased erythrocyte differentiation from hematopoietic stem cells, probably at the expense of differentiation into other cell lineages. These changes are accompanied by coordinated down-regulation of general translation. Network topology and flow analyses also uncovered regulators known to modulate hypoxia responses and erythrocyte development, as well as unknown regulators, such as the OCT4 gene, an important regulator in stem cells and assumed to only function in stem cells. We predicted computationally and validated experimentally that increased OCT4 expression at extreme altitude can directly elevate the expression of hemoglobin genes. Our approach established a new framework for analyzing the transcriptional regulatory network from a very limited number of samples
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Prior to the deep learning era, shape was commonly used to describe the
objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are
predominantly diverging from computer vision, where voxel grids, meshes, point
clouds, and implicit surface models are used. This is seen from numerous
shape-related publications in premier vision conferences as well as the growing
popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915
models). For the medical domain, we present a large collection of anatomical
shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument,
called MedShapeNet, created to facilitate the translation of data-driven vision
algorithms to medical applications and to adapt SOTA vision algorithms to
medical problems. As a unique feature, we directly model the majority of shapes
on the imaging data of real patients. As of today, MedShapeNet includes 23
dataset with more than 100,000 shapes that are paired with annotations (ground
truth). Our data is freely accessible via a web interface and a Python
application programming interface (API) and can be used for discriminative,
reconstructive, and variational benchmarks as well as various applications in
virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present
use cases in the fields of classification of brain tumors, facial and skull
reconstructions, multi-class anatomy completion, education, and 3D printing. In
future, we will extend the data and improve the interfaces. The project pages
are: https://medshapenet.ikim.nrw/ and
https://github.com/Jianningli/medshapenet-feedbackComment: 16 page
- …