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El texto completo de este trabajo no se encuentra disponible por no haber sido facilitado aún por su autor, por restricciones de copyright, o por no existir una versión digital, Neutral green-emitting four-coordinate Cu(I) complexes with general formula POPCu(NN), where POP = bis[2-(diphenylphosphino)phenyl]ether and NN = substituted 2-pyridine-1,2,4-triazole ligands, were synthesized. The crystal structures of (POPCuMeCN)+(PF6)− (1), POPCuPhPtp (2a, PhPtp = 2-(5-phenyl-2H-[1,2,4]triazol-3-yl)-pyridine), and POPCu(3,5-2FPhPtp) (2d, 3,5-2FPhPtp = 2-(5-(3,5-difluorophenyl)-2H-1,2,4-triazol-3-yl)pyridine) were determined by single-crystal X-ray diffraction analysis. The electronic and photophysical properties of the complexes were examined by UV–vis, steady-state, and time-resolved spectroscopy. At room temperature, weak emission was observed in solution, while in the solid state, all complexes exhibit intense green emission with quantum yield up to 0.54. The electronic and photophysical properties were further supported by calculation performed at the (time-dependent) density functional theory level. One of the complexes was also tested as dopant in electroluminescent devices., National Basic Research Program of China, National Natural Science Foundation of China (NSFC), Jiangsu Fund for Distinguished Young Scientist, Jiangsu Specially-Appointed Professor Program, Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Jiangsu Scientist and Technological Innovation Team, Natural Science Foundation of Jiangsu Province, China Postdoctoral Science Foundation, Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP), Region Wallonne (OPTI2MAT excellence program), European Community, Depto. de Física de Materiales, Fac. de Ciencias Físicas, TRUE, pub

Medical landmark detection is crucial in various medical imaging modalities and procedures. Although deep learning-based methods have achieve promising performance, they are mostly designed for specific anatomical regions or tasks. In this work, we propose a universal model for multi-domain landmark detection by leveraging transformer architecture and developing a prompting component, named as Adaptive Query Prompting (AQP). Instead of embedding additional modules in the backbone network, we design a separate module to generate prompts that can be effectively extended to any other transformer network. In our proposed AQP, prompts are learnable parameters maintained in a memory space called prompt pool. The central idea is to keep the backbone frozen and then optimize prompts to instruct the model inference process. Furthermore, we employ a lightweight decoder to decode landmarks from the extracted features, namely Light-MLD. Thanks to the lightweight nature of the decoder and AQP, we can handle multiple datasets by sharing the backbone encoder and then only perform partial parameter tuning without incurring much additional cost. It has the potential to be extended to more landmark detection tasks. We conduct experiments on three widely used X-ray datasets for different medical landmark detection tasks. Our proposed Light-MLD coupled with AQP achieves SOTA performance on many metrics even without the use of elaborate structural designs or complex frameworks.

Compressed sensing shows that a sparse signal can stably be recovered from incomplete linear measurements. But, in practical applications, some signals have additional structure, where the nonzero elements arise in some blocks. We call such signals as block-sparse signals. In this paper, the $\ell_2/\ell_1-\alpha\ell_2$ minimization method for the stable recovery of block-sparse signals is investigated. Sufficient conditions based on block mutual coherence property and associating upper bound estimations of error are established to ensure that block-sparse signals can be stably recovered in the presence of noise via the $\ell_2/\ell_1-\alpha\ell_2$ minimization method. For all we know, it is the first block mutual coherence property condition of stably reconstructing block-sparse signals by the $\ell_2/\ell_1-\alpha\ell_2$ minimization method. Additionally, the numerical experiments implemented verify the performance of the $\ell_2/\ell_1-\alpha\ell_2$ minimization., Comment: The content and structure of the article have been greatly adjusted

This paper concentrates on the recovery of block-sparse signals, which is not only sparse but also nonzero elements are arrayed into some blocks (clusters) rather than being arbitrary distributed all over the vector, from linear measurements. We establish high-order sufficient conditions based on block RIP to ensure the exact recovery of every block $s$-sparse signal in the noiseless case via mixed $l_2/l_p$ minimization method, and the stable and robust recovery in the case that signals are not accurately block-sparse in the presence of noise. Additionally, a lower bound on necessary number of random Gaussian measurements is gained for the condition to be true with overwhelming probability. Furthermore, the numerical experiments conducted demonstrate the performance of the proposed algorithm.

In this paper, we bring forward a completely perturbed nonconvex Schatten $p$-minimization to address a model of completely perturbed low-rank matrix recovery. The paper that based on the restricted isometry property generalizes the investigation to a complete perturbation model thinking over not only noise but also perturbation, gives the restricted isometry property condition that guarantees the recovery of low-rank matrix and the corresponding reconstruction error bound. In particular, the analysis of the result reveals that in the case that $p$ decreases $0$ and $a>1$ for the complete perturbation and low-rank matrix, the condition is the optimal sufficient condition $\delta_{2r}<1$ \cite{Recht et al 2010}. The numerical experiments are conducted to show better performance, and provides outperformance of the nonconvex Schatten $p$-minimization method comparing with the convex nuclear norm minimization approach in the completely perturbed scenario.

In this paper we investigate the reconstruction conditions of nuclear norm minimization for low-rank matrix recovery. We obtain sufficient conditions $\delta_{tr}

Previous work regarding low-rank matrix recovery has concentrated on the scenarios in which the matrix is noise-free and the measurements are corrupted by noise. However, in practical application, the matrix itself is usually perturbed by random noise preceding to measurement. This paper concisely investigates this scenario and evidences that, for most measurement schemes utilized in compressed sensing, the two models are equivalent with the central distinctness that the noise associated with (\ref{eq.3}) is larger by a factor to $mn/M$, where $m,~n$ are the dimension of the matrix and $M$ is the number of measurements. Additionally, this paper discusses the reconstruction of low-rank matrices in the setting, presents sufficient conditions based on the associating null space property to guarantee the robust recovery and obtains the number of measurements. Furthermore, for the non-Gaussian noise scenario, we further explore it and give the corresponding result. The simulation experiments conducted, on the one hand show effect of noise variance on recovery performance, on the other hand demonstrate the verifiability of the proposed model.

The essential task of tensor data analysis focuses on the tensor decomposition and the corresponding notion of rank. In this paper, by introducing the notion of tensor Singular Value Decomposition (t-SVD), we establish a Regularized Tensor Nuclear Norm Minimization (RTNNM) model for low-tubal-rank tensor recovery. As we know that many variants of the Restricted Isometry Property (RIP) have proven to be crucial analysis tools for sparse recovery. In the t-SVD framework, we initiatively define a novel tensor Restricted Isometry Property (t-RIP). Furthermore, we show that any third-order tensor $\boldsymbol{\mathcal{X}}$ can stably be recovered from few linear noise measurements under some certain t-RIP conditions via the RTNNM model. We note that, as far as the authors are aware, such kind of result has not previously been reported in the literature.

In previous work, theoretical analysis based on the tensor Restricted Isometry Property (t-RIP) established the robust recovery guarantees of a low-tubal-rank tensor. The obtained sufficient conditions depend strongly on the assumption that the linear measurement maps satisfy the t-RIP. In this paper, by exploiting the probabilistic arguments, we prove that such linear measurement maps exist under suitable conditions on the number of measurements in terms of the tubal rank r and the size of third-order tensor n1, n2, n3. And the obtained minimal possible number of linear measurements is nearly optimal compared with the degrees of freedom of a tensor with tubal rank r. Specially, we consider a random sub-Gaussian distribution that includes Gaussian, Bernoulli and all bounded distributions and construct a large class of linear maps that satisfy a t-RIP with high probability. Moreover, the validity of the required number of measurements is verified by numerical experiments.

El texto completo de este trabajo no se encuentra disponible por no haber sido facilitado aún por su autor, por restricciones de copyright, o por no existir una versión digital, Neutral green-emitting four-coordinate Cu(I) complexes with general formula POPCu(NN), where POP = bis[2-(diphenylphosphino)phenyl]ether and NN = substituted 2-pyridine-1,2,4-triazole ligands, were synthesized. The crystal structures of (POPCuMeCN)+(PF6)− (1), POPCuPhPtp (2a, PhPtp = 2-(5-phenyl-2H-[1,2,4]triazol-3-yl)-pyridine), and POPCu(3,5-2FPhPtp) (2d, 3,5-2FPhPtp = 2-(5-(3,5-difluorophenyl)-2H-1,2,4-triazol-3-yl)pyridine) were determined by single-crystal X-ray diffraction analysis. The electronic and photophysical properties of the complexes were examined by UV–vis, steady-state, and time-resolved spectroscopy. At room temperature, weak emission was observed in solution, while in the solid state, all complexes exhibit intense green emission with quantum yield up to 0.54. The electronic and photophysical properties were further supported by calculation performed at the (time-dependent) density functional theory level. One of the complexes was also tested as dopant in electroluminescent devices., National Basic Research Program of China, National Natural Science Foundation of China (NSFC), Jiangsu Fund for Distinguished Young Scientist, Jiangsu Specially-Appointed Professor Program, Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Jiangsu Scientist and Technological Innovation Team, Natural Science Foundation of Jiangsu Province, China Postdoctoral Science Foundation, Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP), Region Wallonne (OPTI2MAT excellence program), European Community, Depto. de Física de Materiales, Fac. de Ciencias Físicas, TRUE, pub

In this paper, we investigate the tail properties of the generalized Maxwell distribution and gain an asymptotic behavior of Mills-type ratio. Meanwhile, We show two applications. The first application thinks about the asymptotic property of the ratio of density functions and the ratio of the tails of the generalized Maxwell and classical Maxwell distributions. Another application obtains the asymptotic distribution of the partial maximum of an independent and identically distributed sequence from the distribution.

Let $\{X_n, n\geq1\}$ be a sequence of independent and identically distributed random variables with common distribution $F$ following the generalized Maxwell distribution. In this paper, we obtain the exact uniform convergence rate of the distribution of the maximum to its extreme value distribution.

In this paper, we investigate the tail properties of the generalized Maxwell distribution and gain an asymptotic behavior of Mills-type ratio. Meanwhile, We show two applications. The first application thinks about the asymptotic property of the ratio of density functions and the ratio of the tails of the generalized Maxwell and classical Maxwell distributions. Another application obtains the asymptotic distribution of the partial maximum of an independent and identically distributed sequence from the distribution.