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 H0H_0 and ΩK,0\Omega_{K,0} using Time-Delay Galaxy Lenses and Gamma-Ray Bursts

Combining the `time-delay distance' ($D_{\Delta t}$) measurements from galaxy lenses and other distance indicators provides model-independent determinations of the Hubble constant ($H_0$) and spatial curvature ($\Omega_{K,0}$), only based on the validity of the Friedmann-Lema\^itre-Robertson-Walker (FLRW) metric and geometrical optics. To take the full merit of combining $D_{\Delta t}$ measurements in constraining $H_0$, 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 $H_0=71.5^{+4.4}_{-3.0}$~km s$^{-1}$Mpc$^{-1}$ and $\Omega_{K,0} = -0.07^{+0.13}_{-0.06}$ (1$\sigma$). A flat-universe prior gives slightly an improved $H_0 = 70.9^{+4.2}_{-2.9}$~km s$^{-1}$Mpc$^{-1}$. When combining Lensing+GRB+SN Ia, the error bar $\Delta H_0$ falls by 25\%, whereas $\Omega_{K,0}$ is not improved due to the degeneracy between SN Ia absolute magnitude, $M_B$, and $H_0$ along with the mismatch between the SN Ia and GRB Hubble diagrams at $z\gtrsim 1.4$. Future increment of GRB observations can help to moderately eliminate the $M_B-H_0$ degeneracy in SN Ia distances and ameliorate the restrictions on cosmographic parameters along with $\Omega_{K,0}$ when combining Lensing+SN Ia+GRB. We conclude that there is no evidence of significant deviation from a (an) flat (accelerating) universe and $H_0$ is currently determined at 3\% precision. The measurements show great potential to arbitrate the $H_0$ 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