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We conducted a Ka-band (26.1--35 GHz) line survey towards Orion KL using the TianMa 65-m Radio Telescope (TMRT). It is the first blind line survey in the Ka band, and achieves a sensitivity of mK level (1--3 mK at a spectral resolution of $\sim$1 km s$^{-1}$). In total, 592 Gaussian features are extracted. Among them, 257 radio recombination lines (RRLs) are identified. The maximum $\Delta n$ of RRLs of H, He and C are 20, 15, and 5, respectively. Through stacking, we have detected the $\beta$ lines of ion RRLs (RRLs of C$^+$ with possible contribution of other ions like O$^+$) for the first time, and tentative signal of the $\gamma$ lines of ion RRLs can also be seen on the stacked spectrum. Besides, 318 other line features were assigned to 37 molecular species, and ten of these species were not detected in the Q-band survey of TMRT. The vibrationally excited states of nine species were also detected. Emission of most species can be modeled under LTE. A number of transitions of E-CH3OH ($J_2-J_1$) display maser effects, which are confirmed by our modeling, and besides the bumping peak at $J\sim 6$ there is another peak at $J\sim 13$. Methylcyanoacetylene (CH$_3$C$_3$N) is detected in Orion KL for the first time. This work emphasizes that the Ka band, which was long-ignored for spectral line surveys, is very useful for surveying RRLs and molecular lines simultaneously., Comment: accepted by ApJS

The occurrence of X-class solar flares and their potential impact on the space weather often receive great attention than other flares. But predicting when and where an X-class flare will occur is still a challenge. With the multi-wavelength observation from the Solar Dynamics Observatory and FengYun- 3E satellite, we investigate the triggering of a GOES X1.0 flare occurring in the NOAA active region (AR) 12887. Our results show that this unique X-class flare is bred in a relatively small but complex quadrupolar AR. Before the X-class flare, two filaments (F1 and F2) exist below a null-point topology of the quadrupolar AR. Magnetic field extrapolation and observation reveal that F1 and F2 correspond to two magnetic flux ropes with the same chirality and their adjacent feet rooted at nonconjugated opposite polarities, respectively. Interestingly, these two polarities collide rapidly, accompanied by photospheric magnetic flux emergence, cancellation and shear motion in the AR center. Above this site, F1 and F2 subsequently intersect and merge to a longer filament (F3) via a tether-cutting-like reconnection process. As a result, the F3 rises and erupts, involving the large-scale arcades overlying filament and the quadrupolar magnetic field above the AR, and eventually leads to the eruption of the X-class flare with a quasi-X-shaped flare ribbon and a coronal mass ejection. It suggests that the rapid collision of nonconjugated opposite polarities provides a key condition for the triggering of this X-class flare, and also provides a featured case for flare trigger mechanism and space weather forecasting., Comment: 24 pages, 7 figures, accepted for publication in ApJ

Surface-enhanced Raman spectroscopy (SERS) is enabled by local surface plasmon resonances (LSPRs) in metallic nanogaps. When SERS is excited by direct illumination of the nanogap, the background heating of lattice and electrons can prevent further manipulation of the molecules. To overcome this issue, we report SERS in electromigrated gold molecular junctions excited remotely: surface plasmon polaritons (SPPs) are excited at nearby gratings, propagate to the junction, and couple to the local nanogap plasmon modes. Like direct excitation, remote excitation of the nanogap can generate both SERS emission and an open-circuit photovoltage (OCPV). We compare SERS intensity and OCPV in both direct and remote illumination configurations. SERS spectra obtained by remote excitation are much more stable than those obtained through direct excitation when photon count rates are comparable. By statistical analysis of 33 devices, coupling efficiency of remote excitation is calculated to be around 10%, consistent with the simulated energy flow., Comment: 20 pages, 4 figures, plus 19 pages, 11 figures supporting information

We report the first detection of radio recombination lines (RRLs) of ions heavier than helium. In a highly sensitive multi-band (12--50 GHz) line survey toward Orion KL with the TianMa 65-m Radio Telescope (TMRT), we successfully detected more than fifteen unblended $\alpha$ lines of RRLs of singly ionized species (XII) recombined from XIII. The Ka-band (35--50 GHz) spectrum also shows tentative signals of $\beta$ lines of ions. The detected lines can be successfully crossmatched with the the rest frequencies of RRLs of CII and/or OII. This finding greatly expands the connotation of ion RRLs, since before this work only two blended lines (105$\alpha$ and 121$\alpha$) of HeII had been reported. Our detected lines can be fitted simultaneously under assumption of local thermodynamic equilibrium (LTE). An abundance of CIII and OIII of 8.8$\times$10$^{-4}$ is obtained, avoiding the complexities of optical/infrared observations and the blending of RRLs of atoms. It is consistent with but approaches the upper bound of the value (10$^{-4}$--$10^{-3}$) estimated from optical/infrared observations. The effects of dielectronic recombination may contribute to enhancing the level populations even at large $n$. We expect future observations using radio interferometers could break the degeneracy between C and O, and help to reveal the ionization structure and dynamical evolution of various ionized regions., Comment: 3 figures, 2 tables, accepted by A&A Letter

We have conducted a line survey towards Orion KL using the Q-band receiver of Tianma 65 m radio telescope (TMRT), covering 34.8--50 GHz with a velocity resolution between 0.79 km s$^{-1}$ and 0.55 km s$^{-1}$ respectively. The observations reach a sensitivity on the level of 1-8 mK, proving that the TMRT is sensitive for conducting deep line surveys. In total, 597 Gaussian features are extracted. Among them, 177 radio recombination lines (RRLs) are identified, including 126, 40 and 11 RRLs of hydrogen, helium and carbon, with a maximum $\Delta n$ of 16, 7, and 3, respectively. The carbon RRLs are confirmed to originate from photodissociation regions with a $V_{\rm LSR}\sim$9 km s$^{-1}$. In addition, 371 molecular transitions of 53 molecular species are identified. Twenty-one molecular species of this survey were not firmly detected in the Q band by Rizzo et al. (2017), including species such as H$_2$CS, HCOOH, C$_2$H$_5$OH, H$_2^{13}$CO, H$_2$CCO, CH$_3$CHO, CH$_2$OCH$_2$, HCN $v_2=1$, and CH$_3$OCHO $v_t=1$. In particular, the vibrationally excited states of ethyl cyanide (C$_2$H$_5$CN $v$13/$v$21) are for the first time firmly detected in the Q band. NH$_3$ (15,15) and (16,16) are identified, and they are so far the highest transitions of the NH$_3$ inversion lines detected towards Orion KL. All the identified lines can be reproduced by a radiative transfer model., Comment: 51 pages, 18 figures, accepted by ApJS

High dynamic range (HDR) imaging is an important task in image processing that aims to generate well-exposed images in scenes with varying illumination. Although existing multi-exposure fusion methods have achieved impressive results, generating high-quality HDR images in dynamic scenes is still difficult. The primary challenges are ghosting artifacts caused by object motion between low dynamic range images and distorted content in under and overexposed regions. In this paper, we propose a deep progressive feature aggregation network for improving HDR imaging quality in dynamic scenes. To address the issues of object motion, our method implicitly samples high-correspondence features and aggregates them in a coarse-to-fine manner for alignment. In addition, our method adopts a densely connected network structure based on the discrete wavelet transform, which aims to decompose the input features into multiple frequency subbands and adaptively restore corrupted contents. Experiments show that our proposed method can achieve state-of-the-art performance under different scenes, compared to other promising HDR imaging methods. Specifically, the HDR images generated by our method contain cleaner and more detailed content, with fewer distortions, leading to better visual quality.

Low-cost autonomous agents including autonomous driving vehicles chiefly adopt monocular 3D object detection to perceive surrounding environment. This paper studies 3D intermediate representation methods which generate intermediate 3D features for subsequent tasks. For example, the 3D features can be taken as input for not only detection, but also end-to-end prediction and/or planning that require a bird's-eye-view feature representation. In the study, we found that in generating 3D representation previous methods do not maintain the consistency between the objects' implicit poses in the latent space, especially orientations, and the explicitly observed poses in the Euclidean space, which can substantially hurt model performance. To tackle this problem, we present a novel monocular detection method, the first one being aware of the poses to purposefully guarantee that they are consistent between the implicit and explicit features. Additionally, we introduce a local ray attention mechanism to efficiently transform image features to voxels at accurate 3D locations. Thirdly, we propose a handcrafted Gaussian positional encoding function, which outperforms the sinusoidal encoding function while retaining the benefit of being continuous. Results show that our method improves the state-of-the-art 3D intermediate representation method by 3.15%. We are ranked 1st among all the reported monocular methods on both 3D and BEV detection benchmark on KITTI leaderboard as of th result's submission time., Comment: 10 pages, 5 figures

Reconstructing ghosting-free high dynamic range (HDR) images of dynamic scenes from a set of multi-exposure images is a challenging task, especially with large object motion and occlusions, leading to visible artifacts using existing methods. To address this problem, we propose a deep network that tries to learn multi-scale feature flow guided by the regularized loss. It first extracts multi-scale features and then aligns features from non-reference images. After alignment, we use residual channel attention blocks to merge the features from different images. Extensive qualitative and quantitative comparisons show that our approach achieves state-of-the-art performance and produces excellent results where color artifacts and geometric distortions are significantly reduced.

In this paper, we consider the problem in defocus image deblurring. Previous classical methods follow two-steps approaches, i.e., first defocus map estimation and then the non-blind deblurring. In the era of deep learning, some researchers have tried to address these two problems by CNN. However, the simple concatenation of defocus map, which represents the blur level, leads to suboptimal performance. Considering the spatial variant property of the defocus blur and the blur level indicated in the defocus map, we employ the defocus map as conditional guidance to adjust the features from the input blurring images instead of simple concatenation. Then we propose a simple but effective network with spatial modulation based on the defocus map. To achieve this, we design a network consisting of three sub-networks, including the defocus map estimation network, a condition network that encodes the defocus map into condition features, and the defocus deblurring network that performs spatially dynamic modulation based on the condition features. Moreover, the spatially dynamic modulation is based on an affine transform function to adjust the features from the input blurry images. Experimental results show that our method can achieve better quantitative and qualitative evaluation performance than the existing state-of-the-art methods on the commonly used public test datasets.

This paper describes our system, which placed third in the Multilingual Track (subtask 11), fourth in the Code-Mixed Track (subtask 12), and seventh in the Chinese Track (subtask 9) in the SemEval 2022 Task 11: MultiCoNER Multilingual Complex Named Entity Recognition. Our system's key contributions are as follows: 1) For multilingual NER tasks, we offer an unified framework with which one can easily execute single-language or multilingual NER tasks, 2) for low-resource code-mixed NER task, one can easily enhance his or her dataset through implementing several simple data augmentation methods and 3) for Chinese tasks, we propose a model that can capture Chinese lexical semantic, lexical border, and lexical graph structural information. Finally, our system achieves macro-f1 scores of 77.66, 84.35, and 74.00 on subtasks 11, 12, and 9, respectively, during the testing phase.

Unpaired data has shown to be beneficial for low-resource automatic speech recognition~(ASR), which can be involved in the design of hybrid models with multi-task training or language model dependent pre-training. In this work, we leverage unpaired data to train a general sequence-to-sequence model. Unpaired speech and text are used in the form of data pairs by generating the corresponding missing parts in prior to model training. Inspired by the complementarity of speech-PseudoLabel pair and SynthesizedAudio-text pair in both acoustic features and linguistic features, we propose a complementary joint training~(CJT) method that trains a model alternatively with two data pairs. Furthermore, label masking for pseudo-labels and gradient restriction for synthesized audio are proposed to further cope with the deviations from real data, termed as CJT++. Experimental results show that compared to speech-only training, the proposed basic CJT achieves great performance improvements on clean/other test sets, and the CJT++ re-training yields further performance enhancements. It is also apparent that the proposed method outperforms the wav2vec2.0 model with the same model size and beam size, particularly in extreme low-resource cases., Comment: 5 pages, 3 figures