Intelligent optical performance monitor using multi-task learning based artificial neural network

An intelligent optical performance monitor using multi-task learning based artificial neural network (MTL-ANN) is designed for simultaneous OSNR monitoring and modulation format identification (MFI). Signals' amplitude histograms (AHs) after constant module algorithm are selected as the input features for MTL-ANN. The experimental results of 20-Gbaud NRZ-OOK, PAM4 and PAM8 signals demonstrate that MTL-ANN could achieve OSNR monitoring and MFI simultaneously with higher accuracy and stability compared with single-task learning based ANNs (STL-ANNs). The results show an MFI accuracy of 100% and OSNR monitoring root-mean-square error of 0.63 dB for the three modulation formats under consideration. Furthermore, the number of neuron needed for the single MTL-ANN is almost the half of STL-ANN, which enables reduced-complexity optical performance monitoring devices for real-time performance monitoring

Polybrominated Diphenyl Ethers (PBDEs) Emitted from Heating Machine for Waste Printed Wiring Boards Disassembling

AbstractPolybrominated diphenyl ethers (PBDEs) contained in waste printed wiring board (PWB) matrix and surface dust can be emitted into the air during thermal process, which is widely used to detach the electronic components from the base boards of waste PWB. In this study, PBDEs concentrations in air and dust samples were detected in a PWB-heating workshop. The results showed that the mean concentrations of ∑8PBDEs in PM10 and TSP were 479 and 1670 ng/m3, respectively. Compared with surface dust collected from waste PWB (15600 ng/g), PBDEs concentrations in dust from the workshop floor (31100 ng/g), heating machine inside (84700 ng/g), and the cyclone extractor (317000 ng/g), were condensed after thermal process. All the results showed that recycling of waste PWB was an important source of PBDEs emission

Dichloridobis(4-pyridylmethyl 1H-pyrrole-2-carboxyl­ate-κN)zinc

In the title mol­ecule, [ZnCl2(C11H10N2O2)2], the ZnII ion, situated on a twofold axis, is in a distorted tetra­hedral coordination environment formed by two chloride anions and two pyridine N atoms of the two organic ligands. In the pyrrole-2-carboxyl­ate unit, the pyrrole N—H group and the carbonyl group point approximately in the same direction. The dihedral angle between the two pyridine rings is 54.8 (3)°. The complex mol­ecules are connected into chains extending along [101] by N—H⋯Cl hydrogen bonds. The chains are further assembled into (-101) layers by C—H⋯O and C—H⋯Cl inter­actions

High Dynamic Range Imaging with Context-aware Transformer

Avoiding the introduction of ghosts when synthesising LDR images as high dynamic range (HDR) images is a challenging task. Convolutional neural networks (CNNs) are effective for HDR ghost removal in general, but are challenging to deal with the LDR images if there are large movements or oversaturation/undersaturation. Existing dual-branch methods combining CNN and Transformer omit part of the information from non-reference images, while the features extracted by the CNN-based branch are bound to the kernel size with small receptive field, which are detrimental to the deblurring and the recovery of oversaturated/undersaturated regions. In this paper, we propose a novel hierarchical dual Transformer method for ghost-free HDR (HDT-HDR) images generation, which extracts global features and local features simultaneously. First, we use a CNN-based head with spatial attention mechanisms to extract features from all the LDR images. Second, the LDR features are delivered to the Hierarchical Dual Transformer (HDT). In each Dual Transformer (DT), the global features are extracted by the window-based Transformer, while the local details are extracted using the channel attention mechanism with deformable CNNs. Finally, the ghost free HDR image is obtained by dimensional mapping on the HDT output. Abundant experiments demonstrate that our HDT-HDR achieves the state-of-the-art performance among existing HDR ghost removal methods.Comment: 8 pages, 5 figure

Spin-orbit-coupling-induced phase separation in trapped Bose gases

In a trapped spin-1/2 Bose-Einstein condensate with miscible interactions, a two-dimensional spin-orbit coupling can introduce an unconventional spatial separation between the two components. We reveal the physical mechanism of such a spin-orbit-coupling-induced phase separation. Detailed features of the phase separation are identified in a trapped Bose-Einstein condensate. We further analyze differences of phase separation in Rashba and anisotropic spin-orbit-coupled Bose gases. An adiabatic splitting dynamics is proposed as an application of the phase separation.Comment: 10 pages, 7 figure

meso-5,5′-Bis[(4-fluoro­phen­yl)diazen­yl]-2,2′-(pentane-3,3-di­yl)di-1H-pyrrole

There are two independent molecules in the asymmetric unit of the title compound, C25H24F2N6, in which the N=N bonds adopt a trans configuration with distances in the range 1.262 (2)–1.269 (3) Å. The dihedral angles between heterocycles are 86.7 (2) and 85.6 (2)° in the two molecules while the dihedral angles between the heterocylic rings and the adjacent benzene rings are 13.4 (2) and 13.4 (2)° in one molecule and 5.3 (2) and 6.5 (2)° in the other. In the crystal, pairs of independent mol­ecules are held together by four N—H⋯N hydrogen bonds, forming inter­locked dimers

On Minimum Spanning Subgraphs of Graphs With Proper Connection Number 2

An edge coloring of a connected graph G is a proper-path coloring if every two vertices of G are connected by a properly colored path. The minimum number of colors required of a proper-path coloring of G is called the proper connection number pc(G) of G. For a connected graph G with proper connection number 2, the minimum size of a connected spanning subgraph H of G with pc(H) = 2 is denoted by μ(G). It is shown that if s and t are integers such that t ≥ s + 2 ≥ 5, then μ(K_{s,t} ) = 2t − 2. We also determine μ(G) for several classes of complete multipartite graphs G. In particular, it is shown that if G = K_{n_1, n_2, ..., n_k} is a complete k-partite graph, where k ≥ 3, r = \sum^{k−1}_{i=1} n_i ≥ 3 and t = n_k ≥ r^2 + r, then μ(G) = 2t − 2r + 2

Visual Tactile Fusion Object Clustering

Object clustering, aiming at grouping similar objects into one cluster with an unsupervised strategy, has been extensivelystudied among various data-driven applications. However, most existing state-of-the-art object clustering methods (e.g., single-view or multi-view clustering methods) only explore visual information, while ignoring one of most important sensing modalities, i.e., tactile information which can help capture different object properties and further boost the performance of object clustering task. To effectively benefit both visual and tactile modalities for object clustering, in this paper, we propose a deep Auto-Encoder-like Non-negative Matrix Factorization framework for visual-tactile fusion clustering. Specifically, deep matrix factorization constrained by an under-complete Auto-Encoder-like architecture is employed to jointly learn hierarchical expression of visual-tactile fusion data, and preserve the local structure of data generating distribution of visual and tactile modalities. Meanwhile, a graph regularizer is introduced to capture the intrinsic relations of data samples within each modality. Furthermore, we propose a modality-level consensus regularizer to effectively align thevisual and tactile data in a common subspace in which the gap between visual and tactile data is mitigated. For the model optimization, we present an efficient alternating minimization strategy to solve our proposed model. Finally, we conduct extensive experiments on public datasets to verify the effectiveness of our framework.Comment: 8 pages, 5 figure