Benzene-1,4-dicarboxylic acid–N,N-dimethyl­acetamide (1/2)

The asymmetric unit of title compound, C8H6O4·2C4H9NO, contains one half-mol­ecule (an inversion centre in P21/n generates the other half of the molecule) of terephthalic acid (TA) and one mol­ecule of N,N-dimethyl­acetamide (DMAC). The DMAC mol­ecules are linked to TA by strong O—H⋯O hydrogen bonds

Naphthalene-2,6-dicarb­oxy­lic acid–1-methyl­pyrrolidin-2-one (1/2)

The asymmetric unit of the title compound, C12H8O4·2C5H9NO, contains one half-mol­ecule of naphthalene-2,6-dicarb­oxy­lic acid (NDA) and one mol­ecule of 1-methyl­pyrrolidin-2-one (NMP): the NDA molecules lie on the crystallographic twofold rotation axes. In the crystal, the components are linked by strong O—H⋯O hydrogen bonds and C—H⋯O inter­actions

A dual-branch weakly supervised learning based network for accurate mapping of woody vegetation from remote sensing images

Mapping woody vegetation from aerial images is an important task bluein environment monitoring and management. A few studies have shown that semantic segmentation methods involving deep learning achieve significantly better performance in mapping than methods involving field-based measurement and handcrafted features. However, current deep networks used for mapping vegetation require labour-intensive pixel-level annotations. Thus, this paper proposes the use of image-level annotations and a weakly supervised semantic segmentation (WSSS) network for mapping woody vegetation based on Unmanned Aerial Vehicle (UAV) imagery. The network comprises a Localization Branch (LB) and an Attention Relocation Branch (ARB). The LB is trained in stage 1 of the mapping to identify regions with the most discriminative vegetation, while the ARB is introduced to better mine semantic information, which enhances the ability of the class activation maps (CAMs) to represent useful information. The ARB inherits the weights from the LB in stage 2 and uses a Multi-layer Attention Refocus Structure (MARS) into the network to expand the receptive field to enable the model to process global features. Thus, same-category regions that are located farther apart are better captured. Finally, the region focused by the dual branches are integrated to more accurately cover the areas to be segmented. Using UAV imagery datasets, namely UOPNOA and MiniFrance, along with quantitative metrics and qualitative results, the network demonstrates performance better than existing state-of-the-art related methods. The effectiveness and generalization of each module of the network are validated by ablation experiments. The code for implementing the network will be accessible on https://github.com/Mr-catc/DWSLNet

Experiments on bright field and dark field high energy electron imaging with thick target material

Using a high energy electron beam for the imaging of high density matter with both high spatial-temporal and areal density resolution under extreme states of temperature and pressure is one of the critical challenges in high energy density physics . When a charged particle beam passes through an opaque target, the beam will be scattered with a distribution that depends on the thickness of the material. By collecting the scattered beam either near or off axis, so-called bright field or dark field images can be obtained. Here we report on an electron radiography experiment using 45 MeV electrons from an S-band photo-injector, where scattered electrons, after interacting with a sample, are collected and imaged by a quadrupole imaging system. We achieved a few micrometers (about 4 micrometers) spatial resolution and about 10 micrometers thickness resolution for a silicon target of 300-600 micron thickness. With addition of dark field images that are captured by selecting electrons with large scattering angle, we show that more useful information in determining external details such as outlines, boundaries and defects can be obtained.Comment: 7pages, 7 figure

Experimental Simulation of Larger Quantum Circuits with Fewer Superconducting Qubits

Although near-term quantum computing devices are still limited by the quantity and quality of qubits in the so-called NISQ era, quantum computational advantage has been experimentally demonstrated. Moreover, hybrid architectures of quantum and classical computing have become the main paradigm for exhibiting NISQ applications, where low-depth quantum circuits are repeatedly applied. In order to further scale up the problem size solvable by the NISQ devices, it is also possible to reduce the number of physical qubits by "cutting" the quantum circuit into different pieces. In this work, we experimentally demonstrated a circuit-cutting method for simulating quantum circuits involving many logical qubits, using only a few physical superconducting qubits. By exploiting the symmetry of linear-cluster states, we can estimate the effectiveness of circuit-cutting for simulating up to 33-qubit linear-cluster states, using at most 4 physical qubits for each subcircuit. Specifically, for the 12-qubit linear-cluster state, we found that the experimental fidelity bound can reach as much as 0.734, which is about 19\% higher than a direct simulation {on the same} 12-qubit superconducting processor. Our results indicate that circuit-cutting represents a feasible approach of simulating quantum circuits using much fewer qubits, while achieving a much higher circuit fidelity

Numerical Well Testing Interpretation Model and Applications in Crossflow Double-Layer Reservoirs by Polymer Flooding

This work presents numerical well testing interpretation model and analysis techniques to evaluate formation by using pressure transient data acquired with logging tools in crossflow double-layer reservoirs by polymer flooding. A well testing model is established based on rheology experiments and by considering shear, diffusion, convection, inaccessible pore volume (IPV), permeability reduction, wellbore storage effect, and skin factors. The type curves were then developed based on this model, and parameter sensitivity is analyzed. Our research shows that the type curves have five segments with different flow status: (I) wellbore storage section, (II) intermediate flow section (transient section), (III) mid-radial flow section, (IV) crossflow section (from low permeability layer to high permeability layer), and (V) systematic radial flow section. The polymer flooding field tests prove that our model can accurately determine formation parameters in crossflow double-layer reservoirs by polymer flooding. Moreover, formation damage caused by polymer flooding can also be evaluated by comparison of the interpreted permeability with initial layered permeability before polymer flooding. Comparison of the analysis of numerical solution based on flow mechanism with observed polymer flooding field test data highlights the potential for the application of this interpretation method in formation evaluation and enhanced oil recovery (EOR)