7,229 research outputs found
Tris[2-(benzyliminomethyl)phenolato-κ2 N,O]iron(III)
In the title compound, [Fe(C14H12NO)3], the FeIII atom has a slightly distorted octahedral geometry and is coordinated by three Schiff base ligands, viz. 2-(benzyliminomethyl)phenolate. The crystal structure is stabilized by intramolecular C—H⋯O and C—H⋯N hydrogen bonds
Simulation and Detection of Photonic Chern Insulators in One-Dimensional Circuit Quantum Electrodynamics Lattice
We introduce a simple method to realize and detect photonic topological Chern
insulators with one-dimensional circiut quantum electrodynamics arrays. By
periodically modulating the couplings of the array, we show that this
one-dimensional model can be mapped into a two-dimensional Chern insulator
model. In addition to allowing the study of photonic Chern insulators, this
approach also provides a natural platform to realise experimentally Laughlin's
pumping argument. Based on scattering theory of topological insulators and
input-output formalism, we show that the photonic edge state can be probed
directly and the topological invariant can be detected from the winding number
of the reflection coefficient phase.Comment: 5 pages, 3 figure
Semi-supervised MIMO Detection Using Cycle-consistent Generative Adversarial Network
In this paper, a new semi-supervised deep multiple-input multiple-output
(MIMO) detection approach using a cycle-consistent generative adversarial
network (CycleGAN) is proposed for communication systems without any prior
knowledge of underlying channel distributions. Specifically, we propose the
CycleGAN detector by constructing a bidirectional loop of two modified least
squares generative adversarial networks (LS-GAN). The forward LS-GAN learns to
model the transmission process, while the backward LS-GAN learns to detect the
received signals. By optimizing the cycle-consistency of the transmitted and
received signals through this loop, the proposed method is trained online and
semi-supervisedly using both the pilots and the received payload data. As such,
the demand on labelled training dataset is considerably controlled, and thus
the overhead is effectively reduced. Numerical results show that the proposed
CycleGAN detector achieves better performance in terms of both bit error-rate
(BER) and achievable rate than existing semi-blind deep learning (DL) detection
methods as well as conventional linear detectors, especially when considering
signal distortion due to the nonlinearity of power amplifiers (PA) at the
transmitter
A case of advanced mycosis fungoides with comprehensive skin and visceral organs metastasis: sensitive to chemical and biological therapy
AbstractMycosis fungoides is a common cutaneous T-cell lymphoma, which is usually characterized by chronic, indolence progression, with absence of typical symptoms in early stage, metastasis to lymph nodes, bone marrow and visceral organs in later stage and ultimately progression to systemic lymphoma. It can result in secondary skin infection which is a frequent cause of death. At present, no curative therapy existed. Therapeutic purpose is to induce remission, reduce tumor burden and protect immune function of patients. A case of patient with advanced severe mycosis fungoides receiving CHOP plus interferon α-2a was reported here, with disease-free survival of 7 months and overall survival of over 17.0 months, and current status as well as developments of mycosis fungoides were briefly introduced
DREAM: Efficient Dataset Distillation by Representative Matching
Dataset distillation aims to synthesize small datasets with little
information loss from original large-scale ones for reducing storage and
training costs. Recent state-of-the-art methods mainly constrain the sample
synthesis process by matching synthetic images and the original ones regarding
gradients, embedding distributions, or training trajectories. Although there
are various matching objectives, currently the strategy for selecting original
images is limited to naive random sampling.
We argue that random sampling overlooks the evenness of the selected sample
distribution, which may result in noisy or biased matching targets.
Besides, the sample diversity is also not constrained by random sampling.
These factors together lead to optimization instability in the distilling
process and degrade the training efficiency. Accordingly, we propose a novel
matching strategy named as \textbf{D}ataset distillation by
\textbf{RE}present\textbf{A}tive \textbf{M}atching (DREAM), where only
representative original images are selected for matching. DREAM is able to be
easily plugged into popular dataset distillation frameworks and reduce the
distilling iterations by more than 8 times without performance drop. Given
sufficient training time, DREAM further provides significant improvements and
achieves state-of-the-art performances.Comment: Efficient matching for dataset distillatio
4-{(Z)-(sec-Butylamino)(phenyl)methylene}-3-methyl-1-phenyl-1H-pyrazol-5(4H)-one
In the title compound, C21H23N3O, the dihedral angles formed by the pyrazolone ring with two phenyl rings are 10.38 (8) and 76.94 (6)°. The sec-butylamino group is disordered over two positions, with refined site-occupancy factors of 0.730 (4) and 0.270 (4). The compound could potentially be ligand stabilized in the solid state in a keto–enamine tautomeric form. The amine functionality is involved in an intramolecular N—H⋯O hydrogen bond, while weak intermolecular C—H⋯O and C—H⋯N hydrogen bonds participate in the formation of the crystal structure
Model-Independent Determination of and using Time-Delay Galaxy Lenses and Gamma-Ray Bursts
Combining the `time-delay distance' () measurements from galaxy
lenses and other distance indicators provides model-independent determinations
of the Hubble constant () and spatial curvature (), only
based on the validity of the Friedmann-Lema\^itre-Robertson-Walker (FLRW)
metric and geometrical optics. To take the full merit of combining measurements in constraining , we use gamma-ray burst (GRB) distances
to extend the redshift coverage of lensing systems much higher than that of
Type Ia Supernovae (SNe Ia) and even higher than quasars, whilst the general
cosmography with a curvature component is implemented for the GRB distance
parametrizations. Combining Lensing+GRB yields ~km
sMpc and (1). A
flat-universe prior gives slightly an improved ~km
sMpc. When combining Lensing+GRB+SN Ia, the error bar falls by 25\%, whereas is not improved due to the
degeneracy between SN Ia absolute magnitude, , and along with the
mismatch between the SN Ia and GRB Hubble diagrams at . Future
increment of GRB observations can help to moderately eliminate the
degeneracy in SN Ia distances and ameliorate the restrictions on cosmographic
parameters along with when combining Lensing+SN Ia+GRB. We
conclude that there is no evidence of significant deviation from a (an) flat
(accelerating) universe and is currently determined at 3\% precision. The
measurements show great potential to arbitrate the tension between the
local distance ladder and cosmic microwave background measurements and provide
a relevant consistency test of the FLRW metric.Comment: Accepted for publication in MNRA
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