Identifying the fragment structure of the organic compounds by deeply learning the original NMR data

We preprocess the raw NMR spectrum and extract key characteristic features by using two different methodologies, called equidistant sampling and peak sampling for subsequent substructure pattern recognition; meanwhile may provide the alternative strategy to address the imbalance issue of the NMR dataset frequently encountered in dataset collection of statistical modeling and establish two conventional SVM and KNN models to assess the capability of two feature selection, respectively. Our results in this study show that the models using the selected features of peak sampling outperform the ones using the other. Then we build the Recurrent Neural Network (RNN) model trained by Data B collected from peak sampling. Furthermore, we illustrate the easier optimization of hyper parameters and the better generalization ability of the RNN deep learning model by comparison with traditional machine learning SVM and KNN models in detail.

OoD-Bench: Benchmarking and Understanding Out-of-Distribution Generalization Datasets and Algorithms

Deep learning has achieved tremendous success with independent and identically distributed (i.i.d.) data. However, the performance of neural networks often degenerates drastically when encountering out-of-distribution (OoD) data, i.e., training and test data are sampled from different distributions. While a plethora of algorithms has been proposed to deal with OoD generalization, our understanding of the data used to train and evaluate these algorithms remains stagnant. In this work, we position existing datasets and algorithms from various research areas (e.g., domain generalization, stable learning, invariant risk minimization) seemingly unconnected into the same coherent picture. First, we identify and measure two distinct kinds of distribution shifts that are ubiquitous in various datasets. Next, we compare various OoD generalization algorithms with a new benchmark dominated by the two distribution shifts. Through extensive experiments, we show that existing OoD algorithms that outperform empirical risk minimization on one distribution shift usually have limitations on the other distribution shift. The new benchmark may serve as a strong foothold that can be resorted to by future OoD generalization research.

Drone-based AI and 3D Reconstruction for Digital Twin Augmentation

Digital Twin is an emerging technology at the forefront of Industry 4.0, with the ultimate goal of combining the physical space and the virtual space. To date, the Digital Twin concept has been applied in many engineering fields, providing useful insights in the areas of engineering design, manufacturing, automation, and construction industry. While the nexus of various technologies opens up new opportunities with Digital Twin, the technology requires a framework to integrate the different technologies, such as the Building Information Model used in the Building and Construction industry. In this work, an Information Fusion framework is proposed to seamlessly fuse heterogeneous components in a Digital Twin framework from the variety of technologies involved. This study aims to augment Digital Twin in buildings with the use of AI and 3D reconstruction empowered by unmanned aviation vehicles. We proposed a drone-based Digital Twin augmentation framework with reusable and customisable components. A proof of concept is also developed, and extensive evaluation is conducted for 3D reconstruction and applications of AI for defect detection.

VSR: A Unified Framework for Document Layout Analysis combining Vision, Semantics and Relations

Document layout analysis is crucial for understanding document structures. On this task, vision and semantics of documents, and relations between layout components contribute to the understanding process. Though many works have been proposed to exploit the above information, they show unsatisfactory results. NLP-based methods model layout analysis as a sequence labeling task and show insufficient capabilities in layout modeling. CV-based methods model layout analysis as a detection or segmentation task, but bear limitations of inefficient modality fusion and lack of relation modeling between layout components. To address the above limitations, we propose a unified framework VSR for document layout analysis, combining vision, semantics and relations. VSR supports both NLP-based and CV-based methods. Specifically, we first introduce vision through document image and semantics through text embedding maps. Then, modality-specific visual and semantic features are extracted using a two-stream network, which are adaptively fused to make full use of complementary information. Finally, given component candidates, a relation module based on graph neural network is incorported to model relations between components and output final results. On three popular benchmarks, VSR outperforms previous models by large margins. Code will be released soon.

The Acceleration of Charged Particles and Formation of Power-law Energy Spectra in Nonrelativistic Magnetic Reconnection

Magnetic reconnection is a primary driver of particle acceleration processes in space and astrophysical plasmas. Understanding how particles are accelerated and the resulting particle energy spectra is among the central topics in reconnection studies. We review recent advances in addressing this problem in nonrelativistic reconnection that is relevant to space and solar plasmas and beyond. We focus on particle acceleration mechanisms, particle transport due to 3D reconnection physics, and their roles in forming power-law particle energy spectra. We conclude by pointing out the challenges in studying particle acceleration and transport in a large-scale reconnection layer and the relevant issues to be addressed in the future.

The Effect of Thermal Pressure on Collisionless Magnetic Reconnection Rate

Modeling collisionless magnetic reconnection rate is an outstanding challenge in basic plasma physics research. While the seemingly universal rate of an order $mathcal{O}(0.1)$ is often reported in the low-$beta$ regime, it is not clear how reconnection rate scales with a higher plasma $beta$. Due to the complexity of the pressure tensor, the available reconnection rate model is limited to the low plasma-$beta$ regime, where the thermal pressure is arguably negligible. However, the thermal pressure effect becomes important when $beta gtrsim mathcal{O}(1)$. Using first-principle kinetic simulations, we show that both the reconnection rate and outflow speed drop as $beta$ gets larger. A simple analytical framework is derived to take account of the self-generated pressure anisotropy and pressure gradient in the force-balance around the diffusion region, explaining the varying trend of key quantities and reconnection rates in these simulations with different $beta$. The predicted scaling of the normalized reconnection rate is $simeq mathcal{O}(0.1/sqrt{beta_{i0}})$ in the high $beta$ limit, where $beta_{i0}$ is the ion $beta$ of the inflow plasma.

First-Principle Prediction of X-ray Polarization from Magnetic Reconnection in High-Frequency BL Lacs

Relativistic magnetic reconnection is a potential particle acceleration mechanism for high-frequency BL Lacs (HBLs). The {it Imaging X-ray Polarimetry Explorer} ({it IXPE}) scheduled to launch in 2021 has the capability to probe the magnetic field evolution in HBLs, examining the magnetic reconnection scenario for the HBL flares. In this paper, we make the first attempt to self-consistently predict HBL X-ray polarization signatures arising from relativistic magnetic reconnection via combined particle-in-cell (PIC) and polarized radiation transfer simulations. We find that although the intrinsic optical and X-ray polarization degrees are similar on average, the X-ray polarization is much more variable in both polarization degree and angle (PD and PA). Given the sensitivity of the {it IXPE}, it may obtain one to a few polarization data points for one flaring event of nearby bright HBLs Mrk~421 and 501. However, it may not fully resolve the highly variable X-ray polarization. Due to the temporal depolarization, where the integration of photons with variable polarization states over a finite period of time can lower the detected PD, the measured X-ray PD can be considerably lower than the optical counterpart or even undetectable. The lower X-ray PD than the optical thus can be a characteristic signature of relativistic magnetic reconnection. For very bright flares where the X-ray polarization is well resolved, relativistic magnetic reconnection predicts smooth X-ray PA swings, which originate from large plasmoid mergers in the reconnection region.

Is a Green Screen Really Necessary for Real-Time Portrait Matting?

For portrait matting without the green screen, existing works either require auxiliary inputs that are costly to obtain or use multiple models that are computationally expensive. Consequently, they are unavailable in real-time applications. In contrast, we present a light-weight matting objective decomposition network (MODNet), which can process portrait matting from a single input image in real time. The design of MODNet benefits from optimizing a series of correlated sub-objectives simultaneously via explicit constraints. Moreover, since trimap-free methods usually suffer from the domain shift problem in practice, we introduce (1) a self-supervised strategy based on sub-objectives consistency to adapt MODNet to real-world data and (2) a one-frame delay trick to smooth the results when applying MODNet to portrait video sequence. MODNet is easy to be trained in an end-to-end style. It is much faster than contemporaneous matting methods and runs at 63 frames per second. On a carefully designed portrait matting benchmark newly proposed in this work, MODNet greatly outperforms prior trimap-free methods. More importantly, our method achieves remarkable results in daily photos and videos. Now, do you really need a green screen for real-time portrait matting?

Designer spin order in diradical nanographenes

The magnetic properties of carbon materials are at present the focus of an intense research effort in physics, chemistry and materials science due to their potential applications in spintronics and quantum computations. Although the presence of spins in open-shell nanographenes has been recently confirmed, the ability to control magnetic coupling sign has remained elusive, but the most desirable. Here, we demonstrate an effective approach of engineering magnetic ground states in atomically precise open-shell bipartite/nonbipartite nanographenes using combined scanning probe techniques and mean-field Hubbard model calculations. The magnetic coupling sign between two spins has been controlled via breaking bipartite lattice symmetry of nanographenes. In addition, the exchange-interaction strength between two spins has been widely tuned by finely tailoring their spin density overlap, realizing a large exchange-interaction strength of 42 meV. Our demonstrated method provides ample opportunities for designer above-room-temperature magnetic phases and functionalities in graphene nanomaterials.

Radiation and Polarization Signatures from Magnetic Reconnection in Relativistic Jets--I. A Systematic Study

Blazars are relativistic magnetized plasma outflows from supermassive black holes that point very close to our line of sight. Their emission is nonthermal dominated and highly variable across the entire electromagnetic spectrum. Relativistic magnetic reconnection has been proposed as the driver of particle acceleration during blazar flares. While recent particle-in-cell simulations have self-consistently studied the evolution of magnetic reconnection and particle acceleration therein, the resulting radiation signatures have not been systematically explored. In particular, the polarization signatures, which directly reflect the characteristic strongly dynamical magnetic field evolution during reconnection, have not been carefully investigated. In this paper, we present a systematic study of radiation and polarization signatures arising from magnetic reconnection in blazars, based on combined PIC and polarized radiation transfer simulations with various physical parameters. We identify a harder-when-brighter trend in the spectral evolution. Moreover, higher-frequency bands tend to flare earlier than lower-frequency bands in the synchrotron spectral component. Most importantly, polarization signatures appear more variable with higher frequencies. We find that the temporal polarization variations strongly depends on the guide field strength. Specifically, reconnection with significant guide field component leads to very high polarization degree that contradict to typical blazar observations, while large polarization angle rotations are unique signatures of magnetic reconnection between nearly anti-parallel magnetic field lines. These rotations are at least $90^o$ and can extend to $>180^o$, and they may rotate in both directions. These results imply that blazars that have shown large polarization angle rotations intrinsically have more nearly anti-parallel magnetic field morphology.