Electron energy-loss spectroscopy and ab initio electronic structure of the LaOFeP superconductor

The electronic band structures of the LaOFeP superconductor have been calculated theoretically by the first principles method and measured experimentally by electron energy loss spectroscopy. The calculations indicate that the Fe atom in LaOFeP crystal shows a weak magnetic moment and does not form a long-range magnetic ordering. Band structure, Fermi surfaces and fluorine-doping effects are also analyzed based on the data of the density functional theory. The fine structures of the EELS data have been carefully examined in both the low loss energy region and the core losses region (O K, Fe L2,3, and La M4,5). A slight bump edge at 44 eV shows notable orientation-dependence: it can be observed in the low loss EELS spectra with q parallel to c, but becomes almost invisible in the q vertical to c spectra. Annealing experiments indicate that low oxygen pressure favors the appearance of superconductivity in LaOFeP, this fact is also confirmed by the changes of Fe L2,3 and O K excitation edges in the experimental EELS data

Quantum multipartite maskers vs quantum error-correcting codes

Since masking of quantum information was introduced by Modi et al. in [PRL 120, 230501 (2018)], many discussions on this topic have been published. In this paper, we consider relationship between quantum multipartite maskers (QMMs) and quantum error-correcting codes (QECCs). We say that a subset $Q$ of pure states of a system $K$ can be masked by an operator $S$ into a multipartite system \H^{(n)} if all of the image states $S|\psi\>$ of states $|\psi\>$ in $Q$ have the same marginal states on each subsystem. We call such an $S$ a QMM of $Q$. By establishing an expression of a QMM, we obtain a relationship between QMMs and QECCs, which reads that an isometry is a QMM of all pure states of a system if and only if its range is a QECC of any one-erasure channel. As an application, we prove that there is no an isometric universal masker from \C^2 into \C^2\otimes\C^2\otimes\C^2 and then the states of \C^3 can not be masked isometrically into \C^2\otimes\C^2\otimes\C^2. This gives a consummation to a main result and leads to a negative answer to an open question in [PRA 98, 062306 (2018)]. Another application is that arbitrary quantum states of \C^d can be completely hidden in correlations between any two subsystems of the tripartite system \C^{d+1}\otimes\C^{d+1}\otimes\C^{d+1}, while arbitrary quantum states cannot be completely hidden in the correlations between subsystems of a bipartite system [PRL 98, 080502 (2007)].Comment: This is a revision about arXiv:2004.14540. In the present version, $k$ and $j$ old Eq. (2.2) have been exchanged and the followed three equations have been correcte

Transformer-based Multimodal Change Detection with Multitask Consistency Constraints

Change detection plays a fundamental role in Earth observation for analyzing temporal iterations over time. However, recent studies have largely neglected the utilization of multimodal data that presents significant practical and technical advantages compared to single-modal approaches. This research focuses on leveraging digital surface model (DSM) data and aerial images captured at different times for detecting change beyond 2D. We observe that the current change detection methods struggle with the multitask conflicts between semantic and height change detection tasks. To address this challenge, we propose an efficient Transformer-based network that learns shared representation between cross-dimensional inputs through cross-attention. It adopts a consistency constraint to establish the multimodal relationship, which involves obtaining pseudo change through height change thresholding and minimizing the difference between semantic and pseudo change within their overlapping regions. A DSM-to-image multimodal dataset encompassing three cities in the Netherlands was constructed. It lays a new foundation for beyond-2D change detection from cross-dimensional inputs. Compared to five state-of-the-art change detection methods, our model demonstrates consistent multitask superiority in terms of semantic and height change detection. Furthermore, the consistency strategy can be seamlessly adapted to the other methods, yielding promising improvements