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In a world designed for legs, quadrupeds, bipeds, and humanoids have the opportunity to impact emerging robotics applications from logistics, to agriculture, to home assistance. The goal of this survey is to cover the recent progress toward these applications that has been driven by model-based optimization for the real-time generation and control of movement. The majority of the research community has converged on the idea of generating locomotion control laws by solving an optimal control problem (OCP) in either a model-based or data-driven manner. However, solving the most general of these problems online remains intractable due to complexities from intermittent unidirectional contacts with the environment, and from the many degrees of freedom of legged robots. This survey covers methods that have been pursued to make these OCPs computationally tractable, with specific focus on how environmental contacts are treated, how the model can be simplified, and how these choices affect the numerical solution methods employed. The survey focuses on model-based optimization, covering its recent use in a stand alone fashion, and suggesting avenues for combination with learning-based formulations to further accelerate progress in this growing field., Comment: submitted for initial review; comments welcome

The prompt has become an effective linguistic tool for utilizing pre-trained language models. However, in few-shot scenarios, subtle changes in the prompt design always make the result widely different, and the prompt learning methods also make it easy to overfit the limited samples. To alleviate this, we explore utilizing suitable contrastive samples and multi-degree contrastive learning methods to improve the robustness of the prompt representation. Therefore, the proposed Consprompt combined with the prompt encoding network, contrastive sampling modules, and contrastive scoring modules, is introduced to realize differential contrastive learning. Our results exhibit state-of-the-art performance in different few-shot settings, and the ablation experiments also certify the effectiveness of utilizing multi-degree contrastive learning in the prompt-based fine-tuning process., Comment: 2 figures

Emotional Support Conversation (ESConv) aims to reduce help-seekers'emotional distress with the supportive strategy and response. It is essential for the supporter to select an appropriate strategy with the feedback of the help-seeker (e.g., emotion change during dialog turns, etc) in ESConv. However, previous methods mainly focus on the dialog history to select the strategy and ignore the help-seeker's feedback, leading to the wrong and user-irrelevant strategy prediction. In addition, these approaches only model the context-to-strategy flow and pay less attention to the strategy-to-context flow that can focus on the strategy-related context for generating the strategy-constrain response. In this paper, we propose a Feedback-Aware Double COntrolling Network (FADO) to make a strategy schedule and generate the supportive response. The core module in FADO consists of a dual-level feedback strategy selector and a double control reader. Specifically, the dual-level feedback strategy selector leverages the turn-level and conversation-level feedback to encourage or penalize strategies. The double control reader constructs the novel strategy-to-context flow for generating the strategy-constrain response. Furthermore, a strategy dictionary is designed to enrich the semantic information of the strategy and improve the quality of strategy-constrain response. Experimental results on ESConv show that the proposed FADO has achieved the state-of-the-art performance in terms of both strategy selection and response generation. Our code is available at https://github.com/Thedatababbler/FADO., Comment: Accepted on Knowl. Based Syst. (SCI I)

The effectiveness of prompt learning has been demonstrated in different pre-trained language models. By formulating suitable template and choosing representative label mapping, prompt learning can be used as an efficient knowledge probe. However, finding suitable prompt in existing methods requires multiple experimental attempts or appropriate vector initialization on formulating suitable template and choosing representative label mapping, which it is more common in few-shot learning tasks. Motivating by PLM working process, we try to construct the prompt from task semantic perspective and thus propose the STPrompt -Semantic-guided and Task-driven Prompt model. Specifically, two novel prompts generated from the semantic dependency tree (Dep-prompt) and task-specific metadata description (Meta-prompt), are firstly constructed in a prompt augmented pool, and the proposed model would automatically select a suitable semantic prompt to motivating the prompt learning process. Our results show that the proposed model achieves the state-of-the-art performance in five different datasets of few-shot text classification tasks, which prove that more semantic and significant prompts could assume as a better knowledge proving tool.

Star formation is a complex process that typically occurs in dense regions of molecular clouds mainly regulated by magnetic fields, magnetohydrodynamic (MHD) turbulence, and self-gravity. However, it remains a challenging endeavor to trace the magnetic field and determine regions of gravitational collapse where the star is forming. Based on the anisotropic properties of MHD turbulence, a new technique termed Velocity Gradient Technique (VGT) has been proposed to address these challenges. In this study, we apply the VGT to two regions of the giant California Molecular Cloud (CMC), namely, L1478 and L1482, and analyze the difference in their physical properties. We use the $^{12}$CO (J = 2 - 1), $^{13}$CO (J = 2 - 1), and C$^{18}$O (J = 2 - 1) emission lines observed with the Heinrich Hertz Submillimeter Telescope. We compare VGT results calculated in the resolutions of $3.3'$ and $10'$ to Planck polarization at 353 GHz and $10'$ to determine areas of MHD turbulence dominance and self-gravity dominance. We show that the resolution difference can introduce misalignment between the two measurements. We find the VGT-measured magnetic fields globally agree with that from Planck in L1478 suggesting self-gravity's effect is insignificant. The best agreement appears in VGT-$^{12}$CO. As for L1482, the VGT measurements are statistically perpendicular to the Planck polarization indicating the dominance of self-gravity. This perpendicular alignment is more significant in VGT-$^{13}$CO and VGT-C$^{18}$O., Comment: 14 pages, 16 figures, accepted for publication in MNRAS

Interaction of three-dimensional magnetic fields, turbulence, and self-gravity in the molecular cloud is crucial in understanding star formation but has not been addressed so far. In this work, we target the low-mass star-forming region L1688 and use the spectral emissions of $^{12}$CO, $^{13}$CO, C$^{18}$O, and H I, as well as polarized dust emissions. To obtain the 3D direction of the magnetic field, we employ the novel polarization fraction analysis. In combining with the plane-of-the-sky (POS) magnetic field strength derived from the Davis-Chandrasekhar-Fermi (DCF) method and the new Differential Measure Analysis (DMA) technique, we present the first measurement of L1688's three-dimensional magnetic field, including its orientation and strength. We find that L1688's magnetic field has two statistically different inclination angles. The low-intensity tail has an inclination angle $\approx55^\circ$ on average, while that of the central dense clump is $\approx30^\circ$. We find the global mean value of total magnetic field strength is $B_{\rm tot}\approx135$ uG from DCF and $B_{\rm tot}\approx75$ uG from DMA. We use the velocity gradient technique (VGT) to separate the magnetic fields' POS orientation associated with L1688 and its foreground/background. The magnetic fields' orientations are statistically coherent. The probability density function of H$_2$ column density and VGT reveal that L1688 is potentially undergoing gravitational contraction at large scale $\approx1.0$ pc and gravitational collapse at small scale $\approx0.2$ pc. The gravitational contraction mainly along the magnetic field results in an approximate power-law relation $B_{\rm tot}\propto n_{\rm H}^{1/2}$ when volume density $n_{\rm H}$ is less than approximately $6.0\times10^3$ cm$^{-3}$., Comment: 17 pages, 20 figures, accepted for publication in MNRAS

3D point cloud semantic segmentation is one of the fundamental tasks for 3D scene understanding and has been widely used in the metaverse applications. Many recent 3D semantic segmentation methods learn a single prototype (classifier weights) for each semantic class, and classify 3D points according to their nearest prototype. However, learning only one prototype for each class limits the model's ability to describe the high variance patterns within a class. Instead of learning a single prototype for each class, in this paper, we propose to use an adaptive number of prototypes to dynamically describe the different point patterns within a semantic class. With the powerful capability of vision transformer, we design a Number-Adaptive Prototype Learning (NAPL) model for point cloud semantic segmentation. To train our NAPL model, we propose a simple yet effective prototype dropout training strategy, which enables our model to adaptively produce prototypes for each class. The experimental results on SemanticKITTI dataset demonstrate that our method achieves 2.3% mIoU improvement over the baseline model based on the point-wise classification paradigm.

Controllable story generation is a challenging task in the field of NLP, which has attracted increasing research interest in recent years. However, most existing works generate a whole story conditioned on the appointed keywords or emotions, ignoring the psychological changes of the protagonist. Inspired by psychology theories, we introduce global psychological state chains, which include the needs and emotions of the protagonists, to help a story generation system create more controllable and well-planned stories. In this paper, we propose a Psychology-guIded Controllable Story Generation System (PICS) to generate stories that adhere to the given leading context and desired psychological state chains for the protagonist. Specifically, psychological state trackers are employed to memorize the protagonist's local psychological states to capture their inner temporal relationships. In addition, psychological state planners are adopted to gain the protagonist's global psychological states for story planning. Eventually, a psychology controller is designed to integrate the local and global psychological states into the story context representation for composing psychology-guided stories. Automatic and manual evaluations demonstrate that PICS outperforms baselines, and each part of PICS shows effectiveness for writing stories with more consistent psychological changes., Comment: Accepted by COLING 2022

Multi-agent collaborative perception could significantly upgrade the perception performance by enabling agents to share complementary information with each other through communication. It inevitably results in a fundamental trade-off between perception performance and communication bandwidth. To tackle this bottleneck issue, we propose a spatial confidence map, which reflects the spatial heterogeneity of perceptual information. It empowers agents to only share spatially sparse, yet perceptually critical information, contributing to where to communicate. Based on this novel spatial confidence map, we propose Where2comm, a communication-efficient collaborative perception framework. Where2comm has two distinct advantages: i) it considers pragmatic compression and uses less communication to achieve higher perception performance by focusing on perceptually critical areas; and ii) it can handle varying communication bandwidth by dynamically adjusting spatial areas involved in communication. To evaluate Where2comm, we consider 3D object detection in both real-world and simulation scenarios with two modalities (camera/LiDAR) and two agent types (cars/drones) on four datasets: OPV2V, V2X-Sim, DAIR-V2X, and our original CoPerception-UAVs. Where2comm consistently outperforms previous methods; for example, it achieves more than $100,000 \times$ lower communication volume and still outperforms DiscoNet and V2X-ViT on OPV2V. Our code is available at https://github.com/MediaBrain-SJTU/where2comm., Comment: 24 pages, 19 figures, Accepted by Neurips 2022

We introduce the first learning-based reconstructability predictor to improve view and path planning for large-scale 3D urban scene acquisition using unmanned drones. In contrast to previous heuristic approaches, our method learns a model that explicitly predicts how well a 3D urban scene will be reconstructed from a set of viewpoints. To make such a model trainable and simultaneously applicable to drone path planning, we simulate the proxy-based 3D scene reconstruction during training to set up the prediction. Specifically, the neural network we design is trained to predict the scene reconstructability as a function of the proxy geometry, a set of viewpoints, and optionally a series of scene images acquired in flight. To reconstruct a new urban scene, we first build the 3D scene proxy, then rely on the predicted reconstruction quality and uncertainty measures by our network, based off of the proxy geometry, to guide the drone path planning. We demonstrate that our data-driven reconstructability predictions are more closely correlated to the true reconstruction quality than prior heuristic measures. Further, our learned predictor can be easily integrated into existing path planners to yield improvements. Finally, we devise a new iterative view planning framework, based on the learned reconstructability, and show superior performance of the new planner when reconstructing both synthetic and real scenes., Comment: Accepted by SIGGRAPH Asia 2022

Motivations, emotions, and actions are inter-related essential factors in human activities. While motivations and emotions have long been considered at the core of exploring how people take actions in human activities, there has been relatively little research supporting analyzing the relationship between human mental states and actions. We present the first study that investigates the viability of modeling motivations, emotions, and actions in language-based human activities, named COMMA (Cognitive Framework of Human Activities). Guided by COMMA, we define three natural language processing tasks (emotion understanding, motivation understanding and conditioned action generation), and build a challenging dataset Hail through automatically extracting samples from Story Commonsense. Experimental results on NLP applications prove the effectiveness of modeling the relationship. Furthermore, our models inspired by COMMA can better reveal the essential relationship among motivations, emotions and actions than existing methods., Comment: Accepted to COLING 2022

While the methods exploiting the tensor low-rank prior are booming in high-dimensional data processing and have obtained satisfying performance, their applications in dynamic magnetic resonance (MR) image reconstruction are limited. In this paper, we concentrate on the tensor singular value decomposition (t-SVD), which is based on the Fast Fourier Transform (FFT) and only provides the definite and limited tensor low-rank prior in the FFT domain, heavily reliant upon how closely the data and the FFT domain match up. By generalizing the FFT into an arbitrary unitary transformation of the transformed t-SVD and proposing the transformed tensor nuclear norm (TTNN), we introduce a flexible model based on TTNN with the ability to exploit the tensor low-rank prior of a transformed domain in a larger transformation space and elaborately design an iterative optimization algorithm based on the alternating direction method of multipliers (ADMM), which is further unrolled into a model-based deep unrolling reconstruction network to learn the transformed tensor low-rank prior (T$^2$LR-Net). The convolutional neural network (CNN) is incorporated within the T$^2$LR-Net to learn the best-matched transform from the dynamic MR image dataset. The unrolling reconstruction network also provides a new perspective on the low-rank prior utilization by exploiting the low-rank prior in the CNN-extracted feature domain. Experimental results on two cardiac cine MR datasets demonstrate that the proposed framework can provide improved recovery results compared with the state-of-the-art optimization-based and unrolling network-based methods., Comment: 17 pages, 9 figures, will submit to IEEE journal

Sequential recommendation (SR) aims to predict the subsequent behaviors of users by understanding their successive historical behaviors. Recently, some methods for SR are devoted to alleviating the data sparsity problem (i.e., limited supervised signals for training), which take account of contrastive learning to incorporate self-supervised signals into SR. Despite their achievements, it is far from enough to learn informative user/item embeddings due to the inadequacy modeling of complex collaborative information and co-action information, such as user-item relation, user-user relation, and item-item relation. In this paper, we study the problem of SR and propose a novel multi-level contrastive learning framework for sequential recommendation, named MCLSR. Different from the previous contrastive learning-based methods for SR, MCLSR learns the representations of users and items through a cross-view contrastive learning paradigm from four specific views at two different levels (i.e., interest- and feature-level). Specifically, the interest-level contrastive mechanism jointly learns the collaborative information with the sequential transition patterns, and the feature-level contrastive mechanism re-observes the relation between users and items via capturing the co-action information (i.e., co-occurrence). Extensive experiments on four real-world datasets show that the proposed MCLSR outperforms the state-of-the-art methods consistently.

Magnetic fields play an important role in the formation and evolution of a galaxy, but it is challenging to measure them by observation. Here we study the Seyfert galaxy NGC 3627's magnetic field orientations measured from the synchrotron polarization observed with the Very Large Array (VLA) and from the Velocity Gradient Technique (VGT) using spectroscopic data. The latter employs the magnetohydrodynamical (MHD) turbulence's anisotropy to probe the magnetic fields. Being applied to the CO (2-1) and H$\alpha$ emission lines obtained from the PHANGS-ALMA and PHANGS-MUSE surveys, it reveals the magnetic field orientation globally consistent with the polarization. The agreement of the VGT-CO and polarization suggests that the magnetic fields associated with synchrotron emission also percolate through star-forming regions. The VGT-H$\alpha$ measurement reveals the magnetic fields in the warm ionized medium that permeates the disk and disk's vicinity so that it exhibits less agreement with polarization. We find prominent radial fields measured by synchrotron polarization appear in the transition regions from the spiral arms to the galactic bar, while such morphology is less apparent in the VGT-CO and VGT-H$\alpha$ measured magnetic fields. The radial fields suggest that the magnetic torque is important in removing orbiting gas' angular momentum. We notice that magnetic fields inferred from the dust polarization, VGT-CO, and synchrotron polarization are different in the east arm. We interpret this difference as arising from the fact that the three measurements are tracing the magnetic fields associated with pre-collision, the mixture of pre-collision and post-collision, and post-collision flows, respectively., Comment: 12 pages, 8 figures, accepted for publication in MNRAS

Probing magnetic fields in astrophysical environments is both important and challenging. The Gradient Technique (GT) is a new tool for tracing magnetic fields, rooted in the properties of magnetohydrodynamic (MHD) turbulence and turbulent magnetic reconnection. In this work, we examine the performance of GT when applied to synthetic synchrotron emission and spectroscopic data obtained from sub-Alfv\'enic and trans-Alfv\'enic MHD simulations. We demonstrate the improved accuracy of GT in tracing magnetic fields in the absence of low-spatial frequencies. Additionally, we apply a low-spatial frequency filter to a diffuse neutral hydrogen region selected from the GALFA-\ion{H}{1} survey. Our results show an increased alignment between the magnetic fields inferred from GT and the Planck 353 GHz polarization measurements., Comment: 24 pages, 14 figures, accepted for publication in ApJ

Visual relationship detection aims to detect the interactions between objects in an image; however, this task suffers from combinatorial explosion due to the variety of objects and interactions. Since the interactions associated with the same object are dependent, we explore the dependency of interactions to reduce the search space. We explicitly model objects and interactions by an interaction graph and then propose a message-passing-style algorithm to propagate the contextual information. We thus call the proposed method neural message passing (NMP). We further integrate language priors and spatial cues to rule out unrealistic interactions and capture spatial interactions. Experimental results on two benchmark datasets demonstrate the superiority of our proposed method. Our code is available at https://github.com/PhyllisH/NMP., Comment: Accepted by ICML LRG Workshop 2019

Drones equipped with cameras can significantly enhance human ability to perceive the world because of their remarkable maneuverability in 3D space. Ironically, object detection for drones has always been conducted in the 2D image space, which fundamentally limits their ability to understand 3D scenes. Furthermore, existing 3D object detection methods developed for autonomous driving cannot be directly applied to drones due to the lack of deformation modeling, which is essential for the distant aerial perspective with sensitive distortion and small objects. To fill the gap, this work proposes a dual-view detection system named DVDET to achieve aerial monocular object detection in both the 2D image space and the 3D physical space. To address the severe view deformation issue, we propose a novel trainable geo-deformable transformation module that can properly warp information from the drone's perspective to the BEV. Compared to the monocular methods for cars, our transformation includes a learnable deformable network for explicitly revising the severe deviation. To address the dataset challenge, we propose a new large-scale simulation dataset named AM3D-Sim, generated by the co-simulation of AirSIM and CARLA, and a new real-world aerial dataset named AM3D-Real, collected by DJI Matrice 300 RTK, in both datasets, high-quality annotations for 3D object detection are provided. Extensive experiments show that i) aerial monocular 3D object detection is feasible; ii) the model pre-trained on the simulation dataset benefits real-world performance, and iii) DVDET also benefits monocular 3D object detection for cars. To encourage more researchers to investigate this area, we will release the dataset and related code in https://sjtu-magic.github.io/dataset/AM3D/., Comment: 8 pages, 8 figures

Collaborative perception has recently shown great potential to improve perception capabilities over single-agent perception. Existing collaborative perception methods usually consider an ideal communication environment. However, in practice, the communication system inevitably suffers from latency issues, causing potential performance degradation and high risks in safety-critical applications, such as autonomous driving. To mitigate the effect caused by the inevitable latency, from a machine learning perspective, we present the first latency-aware collaborative perception system, which actively adapts asynchronous perceptual features from multiple agents to the same time stamp, promoting the robustness and effectiveness of collaboration. To achieve such a feature-level synchronization, we propose a novel latency compensation module, called SyncNet, which leverages feature-attention symbiotic estimation and time modulation techniques. Experiments results show that the proposed latency aware collaborative perception system with SyncNet can outperforms the state-of-the-art collaborative perception method by 15.6% in the communication latency scenario and keep collaborative perception being superior to single agent perception under severe latency., Comment: 14 pages, 11 figures, Accepted by European conference on computer vision, 2022

Magnetic fields on the order of 100 $\mu$G observed in young supernova remnants cannot be amplified by shock compression alone. To investigate the amplification caused by turbulent dynamo, we perform three-dimensional MHD simulations of the interaction between shock wave and inhomogeneous density distribution with a shallow spectrum in the preshock medium. The postshock turbulence is mainly driven by the strongest preshock density contrast and follows the Kolmogorov scaling. The resulting turbulence amplifies the postshock magnetic field. The time evolution of the magnetic fields agrees with the prediction of the nonlinear turbulent dynamo theory in Xu & Lazarian (2016). When the initial weak magnetic field is perpendicular to the shock normal, the maximum amplification of the field's strength achieves a factor of $\approx200$, which is twice larger than that for a parallel shock. We find that the perpendicular shock exhibits a smaller turbulent Alfv\'en Mach number in the vicinity of the shock front than the parallel shock. However, the strongest magnetic field has a low volume filling factor and is limited by the turbulent energy due to the reconnection diffusion taking place in a turbulent and magnetized fluid. The magnetic field strength averaged along the $z$-axis is reduced by a factor $\gtrsim10$. We decompose the turbulent velocity and magnetic field into solenoidal and compressive modes. The solenoidal mode is dominant and evolves to follow the Kolmogorov scaling, even though the preshock density distribution has a shallow spectrum. When the preshock density distribution has a Kolmogorov spectrum, the turbulent velocity's compressive component increases., Comment: 19 pages, 15 figures, accepted for publication in ApJ

We define a filtration of Feigin-Odesskii's shuffle algebras of type B_n and G_2 using specialization maps, generalizing the results in type A_n case given by Negut and Tsymbaliuk. These filtrations are compatible with a class of PBW type bases for the quantum current algebras of type B_n and G_2., Comment: The paper has been withdrawn by the author since it is replaced by a majorly corrected and upgraded version, see arXiv:2305.00810, where we corrected some errors and upgraded the results by establishing the shuffle algebra realization of integral forms of quantum loop algebras and Yangians in type B_n and G_2

Persuasive strategy recognition task requires the system to recognize the adopted strategy of the persuader according to the conversation. However, previous methods mainly focus on the contextual information, little is known about incorporating the psychological feedback, i.e. emotion of the persuadee, to predict the strategy. In this paper, we propose a Cross-channel Feedback memOry Network (CFO-Net) to leverage the emotional feedback to iteratively measure the potential benefits of strategies and incorporate them into the contextual-aware dialogue information. Specifically, CFO-Net designs a feedback memory module, including strategy pool and feedback pool, to obtain emotion-aware strategy representation. The strategy pool aims to store historical strategies and the feedback pool is to obtain updated strategy weight based on feedback emotional information. Furthermore, a cross-channel fusion predictor is developed to make a mutual interaction between the emotion-aware strategy representation and the contextual-aware dialogue information for strategy recognition. Experimental results on \textsc{PersuasionForGood} confirm that the proposed model CFO-Net is effective to improve the performance on M-F1 from 61.74 to 65.41., Comment: Accepted by ECML-PKDD 2022

Federated Learning (FL) enables training a global model without sharing the decentralized raw data stored on multiple devices to protect data privacy. Due to the diverse capacity of the devices, FL frameworks struggle to tackle the problems of straggler effects and outdated models. In addition, the data heterogeneity incurs severe accuracy degradation of the global model in the FL training process. To address aforementioned issues, we propose a hierarchical synchronous FL framework, i.e., FedHiSyn. FedHiSyn first clusters all available devices into a small number of categories based on their computing capacity. After a certain interval of local training, the models trained in different categories are simultaneously uploaded to a central server. Within a single category, the devices communicate the local updated model weights to each other based on a ring topology. As the efficiency of training in the ring topology prefers devices with homogeneous resources, the classification based on the computing capacity mitigates the impact of straggler effects. Besides, the combination of the synchronous update of multiple categories and the device communication within a single category help address the data heterogeneity issue while achieving high accuracy. We evaluate the proposed framework based on MNIST, EMNIST, CIFAR10 and CIFAR100 datasets and diverse heterogeneous settings of devices. Experimental results show that FedHiSyn outperforms six baseline methods, e.g., FedAvg, SCAFFOLD, and FedAT, in terms of training accuracy and efficiency., Comment: 10 pages, to appear in ICPP'2022

Magnetic fields play an important role in the evolution of molecular clouds and star formation. Using the Velocity Gradient Technique (VGT) model, we measured the magnetic field in Orion A using the 12CO, 13CO, and C18O (1-0) emission lines at a scale of 0.07 pc. The measured B-field shows an east-west orientation that is perpendicular to the integral shaped filament of Orion A at large scale. The VGT magnetic fields obtained from 13CO and C18O are in agreement with the B-field that is measured from the Planck 353 GHz dust polarization at a scale of 0.55 pc. Removal of density effects by using a Velocity Decomposition Algorithm can significantly improve the accuracy of the VGT in tracing magnetic fields with the 12CO (1-0) line. The magnetic field strength of seven sub-clouds, OMC-1, OMC-2, OMC-3, OMC-4, OMC-5, L 1641-N, and NGC 1999 has also been estimated with the Davis-Chandrasekhar-Fermi (DCF) and MM2 technique, and these are found to be in agreement with previous results obtained from dust polarization at far-infrared and sub-millimeter wavelengths. At smaller scales, the VGT proves a good method to measure magnetic fields., Comment: 15 pages, 6 figures, accepted to ApJ

Molecular gas is believed to be the fuel for star formation and nuclear activity in Seyfert galaxies. To explore the role of magnetic fields in funneling molecular gas into the nuclear region, measurements of the magnetic fields embedded in molecular gas are needed. By applying the new velocity gradient technique (VGT) to ALMA and PAWS's CO isotopolog data, we obtain the first detection of CO-associated magnetic fields in several nearby Seyfert galaxies and their unprecedented high-resolution magnetic field maps. The VGT-measured magnetic fields in molecular gas globally agree with those inferred from existing HAWC+ dust polarization and VLA synchrotron polarization. An overall good alignment between the magnetic fields traced by VGT-CO and by synchrotron polarization may support the correlation between star formation and cosmic ray generation. We find that the magnetic fields traced by VGT-CO have a significant radial component in the central regions of most Seyferts in our sample, where efficient molecular gas inflows or outflows may happen. In particular, we find local misalignment between the magnetic fields traced by CO and dust polarization within the nuclear ring of NGC 1097, and the former aligns with the central bar's orientation. This misalignment reveals different magnetic field configurations in different gas phases and may provide an observational diagnostic for the ongoing multi-phase fueling of Seyfert activity., Comment: 24 pages, 14 figures

While low-rank matrix prior has been exploited in dynamic MR image reconstruction and has obtained satisfying performance, tensor low-rank models have recently emerged as powerful alternative representations for three-dimensional dynamic MR datasets. In this paper, we introduce a novel deep unrolling network for dynamic MRI, namely the learned transform-based tensor low-rank network (LT$^2$LR-Net). First, we generalize the tensor singular value decomposition (t-SVD) into an arbitrary unitary transform-based version and subsequently propose the novel transformed tensor nuclear norm (TTNN). Then, we design a novel TTNN-based iterative optimization algorithm based on the alternating direction method of multipliers (ADMM) to exploit the tensor low-rank prior in the transformed domain. The corresponding iterative steps are unrolled into the proposed LT$^2$LR-Net, where the convolutional neural network (CNN) is incorporated to adaptively learn the transformation from the dynamic MR dataset for more robust and accurate tensor low-rank representations. Experimental results on the cardiac cine MR dataset demonstrate that the proposed framework can provide improved recovery results compared with the state-of-the-art methods., Comment: 4 pages, 2 figures, 1 tabel, accepted by IEEE ISBI 2023 Conference

Low-rank tensor models have been applied in accelerating dynamic magnetic resonance imaging (dMRI). Recently, a new tensor nuclear norm based on t-SVD has been proposed and applied to tensor completion. Inspired by the different properties of the tensor nuclear norm (TNN) and the Casorati matrix nuclear norm (MNN), we introduce a combined TNN and Casorati MNN regularizations framework to reconstruct dMRI, which we term as TMNN. The proposed method simultaneously exploits the spatial structure and the temporal correlation of the dynamic MR data. The optimization problem can be efficiently solved by the alternating direction method of multipliers (ADMM). In order to further improve the computational efficiency, we develop a fast algorithm under the Cartesian sampling scenario. Numerical experiments based on cardiac cine MRI and perfusion MRI data demonstrate the performance improvement over the traditional Casorati nuclear norm regularization method., Comment: 4 pages, 3 figures, 1 table, accepted in IEEE ISBI 2022

The heart of the Large Magellanic Cloud, 30 Doradus, is a complex region with a clear core-halo structure. Feedback from the stellar cluster R$\,$136 has been shown to be the main source of energy creating multiple pc-scale expanding-shells in the outer region, and carving a nebula core in the proximity of the ionization source. We present the morphology and strength of the magnetic fields (B-fields) of 30 Doradus inferred from the far-infrared polarimetric observations by SOFIA/HAWC+ at 89, 154, and 214$\,\mu$m. The B-field morphology is complex, showing bending structures around R$\,$136. In addition, we use high spectral and angular resolution [\textsc{CII}] observations from SOFIA/GREAT and CO(2-1) from APEX. The kinematic structure of the region correlates with the B-field morphology and shows evidence of multiple expanding shells. Our B-field strength maps, estimated using the Davis-Chandrasekhar-Fermi method and structure-function, show variations across the cloud within a maximum of 600, 450, and 350$\,\mu$G at 89, 154, and 214$\,\mu$m, respectively. We estimated that the majority of the 30 Doradus clouds are sub-critical and sub-Alfv\'enic. The probability distribution function of the gas density shows that the turbulence is mainly compressively driven, while the plasma beta parameter indicates supersonic turbulence. We show that the B-field is sufficient to hold the cloud structure integrity under feedback from R$\,$136. We suggest that supersonic compressive turbulence enables the local gravitational collapse and triggers a new generation of stars to form. The velocity gradient technique (VGT) using [\textsc{CII}] and CO(2-1) is likely to confirm these results., Comment: 26 pages, 17 figures, 3 tables, accepted to ApJ

In this paper, we propose a new test for checking the parametric form of the conditional variance based on distance covariance in nonlinear and nonparametric regression models. Inherit from the nice properties of distance covariance, our test is very easy to implement in practice and less effected by the dimensionality of covariates. The asymptotic properties of the test statistic are investigated under the null and alternative hypotheses. We show that the proposed test is consistent against any alternative and can detect local alternatives converging to the null hypothesis at the parametric rate 1/root(n) in both the nonlinear and nonparametric settings. As the limiting null distribution of the test statistic is intractable, we propose a residual bootstrap to approximate the limiting null distribution. Simulation studies are presented to assess the finite sample performance of the proposed test. We also apply the proposed test to a real data set for illustration.

Intention, emotion and action are important psychological factors in human activities, which play an important role in the interaction between individuals. How to model the interaction process between individuals by analyzing the relationship of their intentions, emotions, and actions at the cognitive level is challenging. In this paper, we propose a novel cognitive framework of individual interaction. The core of the framework is that individuals achieve interaction through external action driven by their inner intention. Based on this idea, the interactions between individuals can be constructed by establishing relationships between the intention, emotion and action. Furthermore, we conduct analysis on the interaction between individuals and give a reasonable explanation for the predicting results. To verify the effectiveness of the framework, we reconstruct a dataset and propose three tasks as well as the corresponding baseline models, including action abduction, emotion prediction and action generation. The novel framework shows an interesting perspective on mimicking the mental state of human beings in cognitive science., Comment: Accepted by IJCNN 2022

Emotional support conversation aims at reducing the emotional distress of the help-seeker, which is a new and challenging task. It requires the system to explore the cause of help-seeker's emotional distress and understand their psychological intention to provide supportive responses. However, existing methods mainly focus on the sequential contextual information, ignoring the hierarchical relationships with the global cause and local psychological intention behind conversations, thus leads to a weak ability of emotional support. In this paper, we propose a Global-to-Local Hierarchical Graph Network to capture the multi-source information (global cause, local intentions and dialog history) and model hierarchical relationships between them, which consists of a multi-source encoder, a hierarchical graph reasoner, and a global-guide decoder. Furthermore, a novel training objective is designed to monitor semantic information of the global cause. Experimental results on the emotional support conversation dataset, ESConv, confirm that the proposed GLHG has achieved the state-of-the-art performance on the automatic and human evaluations. The code will be released in here \footnote{\small{~https://github.com/pengwei-iie/GLHG}}., Comment: Accepted by IJCAI,2022

Inspection robots are widely used in the field of smart grid monitoring in substations, and partial discharge (PD) is an important sign of the insulation state of equipments. PD direction of arrival (DOA) algorithms using conventional beamforming and time difference of arrival (TDOA) require large-scale antenna arrays and high computational complexity, which make them difficult to implement on inspection robots. To address this problem, a novel directional multiple signal classification (Dir-MUSIC) algorithm for PD direction finding based on signal strength is proposed, and a miniaturized directional spiral antenna circular array is designed in this paper. First, the Dir-MUSIC algorithm is derived based on the array manifold characteristics. This method uses strength intensity information rather than the TDOA information, which could reduce the computational difficulty and the requirement of array size. Second, the effects of signal-to-noise ratio (SNR) and array manifold error on the performance of the algorithm are discussed through simulations in detail. Then according to the positioning requirements, the antenna array and its arrangement are developed, optimized, and simulation results suggested that the algorithm has reliable direction-finding performance in the form of 6 elements. Finally, the effectiveness of the algorithm is tested by using the designed spiral circular array in real scenarios. The experimental results show that the PD direction-finding error is 3.39{\deg}, which can meet the need for Partial discharge DOA estimation using inspection robots in substations., Comment: 8 pages,13 figures,24 references

The principal task in supervised neural machine translation (NMT) is to learn to generate target sentences conditioned on the source inputs from a set of parallel sentence pairs, and thus produce a model capable of generalizing to unseen instances. However, it is commonly observed that the generalization performance of the model is highly influenced by the amount of parallel data used in training. Although data augmentation is widely used to enrich the training data, conventional methods with discrete manipulations fail to generate diverse and faithful training samples. In this paper, we present a novel data augmentation paradigm termed Continuous Semantic Augmentation (CsaNMT), which augments each training instance with an adjacency semantic region that could cover adequate variants of literal expression under the same meaning. We conduct extensive experiments on both rich-resource and low-resource settings involving various language pairs, including WMT14 English-{German,French}, NIST Chinese-English and multiple low-resource IWSLT translation tasks. The provided empirical evidences show that CsaNMT sets a new level of performance among existing augmentation techniques, improving on the state-of-the-art by a large margin. The core codes are contained in Appendix E., Comment: Accepted by ACL 2022 (main conference)