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Quantum metrology has emerged as a promising avenue for surpassing the limitations of classical mechanics in high-precision measurements. However, the practical implementation of quantum metrology is hindered by the challenges of manipulating exotic quantum states in large systems. Here, we propose and demonstrate a hardware-efficient approach to achieve Heisenberg-limited quantum metrology using large photon-number Fock states. We have developed a programmable photon number filter that efficiently generates Fock states with up to 100 photons in a high-quality superconducting microwave cavity. Using these highly nontrivial states in displacement and phase measurements, we demonstrate a precision scaling close to the Heisenberg limit and achieve a maximum metrological gain of up to 14.8 dB. Our hardware-efficient quantum metrology can be extended to mechanical and optical systems and provides a practical solution for high metrological gain in bosonic quantum systems, promising potential applications in radiometry and the search for new particles., Main text: 10 pages, 4 figures; Supplement: 16 pages, 9 figures, 1 table

Energy spectroscopy is a powerful tool with diverse applications across various disciplines. The advent of programmable digital quantum simulators opens new possibilities for conducting spectroscopy on various models using a single device. Variational quantum-classical algorithms have emerged as a promising approach for achieving such tasks on near-term quantum simulators, despite facing significant quantum and classical resource overheads. Here, we experimentally demonstrate multi-level variational spectroscopy for fundamental many-body Hamiltonians using a superconducting programmable digital quantum simulator. By exploiting symmetries, we effectively reduce circuit depth and optimization parameters allowing us to go beyond the ground state. Combined with the subspace search method, we achieve full spectroscopy for a 4-qubit Heisenberg spin chain, yielding an average deviation of 0.13 between experimental and theoretical energies, assuming unity coupling strength. Our method, when extended to 8-qubit Heisenberg and transverse-field Ising Hamiltonians, successfully determines the three lowest energy levels. In achieving the above, we introduce a circuit-agnostic waveform compilation method that enhances the robustness of our simulator against signal crosstalk. Our study highlights symmetry-assisted resource efficiency in variational quantum algorithms and lays the foundation for practical spectroscopy on near-term quantum simulators, with potential applications in quantum chemistry and condensed matter physics.

Current arbitrary style transfer models are limited to either image or video domains. In order to achieve satisfying image and video style transfers, two different models are inevitably required with separate training processes on image and video domains, respectively. In this paper, we show that this can be precluded by introducing UniST, a Unified Style Transfer framework for both images and videos. At the core of UniST is a domain interaction transformer (DIT), which first explores context information within the specific domain and then interacts contextualized domain information for joint learning. In particular, DIT enables exploration of temporal information from videos for the image style transfer task and meanwhile allows rich appearance texture from images for video style transfer, thus leading to mutual benefits. Considering heavy computation of traditional multi-head self-attention, we present a simple yet effective axial multi-head self-attention (AMSA) for DIT, which improves computational efficiency while maintains style transfer performance. To verify the effectiveness of UniST, we conduct extensive experiments on both image and video style transfer tasks and show that UniST performs favorably against state-of-the-art approaches on both tasks. Our code and results will be released., 10 pages,10 figures

Video captioning aims to describe the content of videos using natural language. Although significant progress has been made, there is still much room to improve the performance for real-world applications, mainly due to the long-tail words challenge. In this paper, we propose a text with knowledge graph augmented transformer (TextKG) for video captioning. Notably, TextKG is a two-stream transformer, formed by the external stream and internal stream. The external stream is designed to absorb additional knowledge, which models the interactions between the additional knowledge, e.g., pre-built knowledge graph, and the built-in information of videos, e.g., the salient object regions, speech transcripts, and video captions, to mitigate the long-tail words challenge. Meanwhile, the internal stream is designed to exploit the multi-modality information in videos (e.g., the appearance of video frames, speech transcripts, and video captions) to ensure the quality of caption results. In addition, the cross attention mechanism is also used in between the two streams for sharing information. In this way, the two streams can help each other for more accurate results. Extensive experiments conducted on four challenging video captioning datasets, i.e., YouCookII, ActivityNet Captions, MSRVTT, and MSVD, demonstrate that the proposed method performs favorably against the state-of-the-art methods. Specifically, the proposed TextKG method outperforms the best published results by improving 18.7% absolute CIDEr scores on the YouCookII dataset., Accepted by CVPR2023

In this paper, we develop a new method, termed SDF-3DGAN, for 3D object generation and 3D-Aware image synthesis tasks, which introduce implicit Signed Distance Function (SDF) as the 3D object representation method in the generative field. We apply SDF for higher quality representation of 3D object in space and design a new SDF neural renderer, which has higher efficiency and higher accuracy. To train only on 2D images, we first generate the objects, which are represented by SDF, from Gaussian distribution. Then we render them to 2D images and use them to apply GAN training method together with 2D images in the dataset. In the new rendering method, we relieve all the potential of SDF mathematical property to alleviate computation pressure in the previous SDF neural renderer. In specific, our new SDF neural renderer can solve the problem of sampling ambiguity when the number of sampling point is not enough, \ie use the less points to finish higher quality sampling task in the rendering pipeline. And in this rendering pipeline, we can locate the surface easily. Therefore, we apply normal loss on it to control the smoothness of generated object surface, which can make our method enjoy the much higher generation quality. Quantitative and qualitative experiments conducted on public benchmarks demonstrate favorable performance against the state-of-the-art methods in 3D object generation task and 3D-Aware image synthesis task. Our codes will be released at https://github.com/lutao2021/SDF-3DGAN.

As the basis for generating multi-particle quantum correlations, inter-particle interaction plays a crucial role in collective quantum phenomena, quantum phase transitions, and quantum information processing. It can profoundly alter the band structure of quantum many-body systems and give rise to exotic topological phenomena. Conventional topological pumping, which has been well demonstrated in driven linear or noninteracting systems, may break down in the presence of strong interaction. However, the interplay between band topology and interaction could also induce emergent topological pumping of interacting particles, but its experimental realization has proven challenging. Here we demonstrate interaction-induced topological pumping in a solid-state quantum system comprising an array of 36 superconducting qubits. With strong interaction inherent in the qubits and site-resolved controllability of the lattice potential and hopping strength, we realize the topological Thouless pumping of single and two bounded particles. Beyond these topological phenomena with linear or noninteracting counterparts, we also observe topologically resonant tunneling and asymmetric edge-state transport of interacting particles. Our work creates a paradigm for multi-particle topological effects, and provides a new pathway to the study of exotic topological phenomena, many-body quantum transport, and quantum information transfer., 8+29 pages, 4+24 figures

The vanilla image completion approaches are sensitive to the large missing regions due to limited available reference information for plausible generation. To mitigate this, existing methods incorporate the extra cue as a guidance for image completion. Despite improvements, these approaches are often restricted to employing a single modality (e.g., segmentation or sketch maps), which lacks scalability in leveraging multi-modality for more plausible completion. In this paper, we propose a novel, simple yet effective method for Multi-modal Guided Image Completion, dubbed MaGIC, which not only supports a wide range of single modality as the guidance (e.g., text, canny edge, sketch, segmentation, reference image, depth, and pose), but also adapts to arbitrarily customized combination of these modalities (i.e., arbitrary multi-modality) for image completion. For building MaGIC, we first introduce a modality-specific conditional U-Net (MCU-Net) that injects single-modal signal into a U-Net denoiser for single-modal guided image completion. Then, we devise a consistent modality blending (CMB) method to leverage modality signals encoded in multiple learned MCU-Nets through gradient guidance in latent space. Our CMB is training-free, and hence avoids the cumbersome joint re-training of different modalities, which is the secret of MaGIC to achieve exceptional flexibility in accommodating new modalities for completion. Experiments show the superiority of MaGIC over state-of-arts and its generalization to various completion tasks including in/out-painting and local editing. Our project with code and models is available at yeates.github.io/MaGIC-Page/., Comment: 11 pages, 6 figures

Lexicographic Ranking SuperMartingale (LexRSM) is a generalization of Ranking SuperMartingale (RSM) that offers sound and efficient algorithms for verifying almost-sure termination of probabilistic programs. LexRSM is also a probabilistic counterpart of Lexicographic Ranking Function (LexRF), which verifies termination of non-probabilistic programs. Among the latter, Bradley-Manna-Sipma LexRF (BMS-LexRF) is known for its better applicability. In this paper, we introduce the first variant of LexRSM that instantiates BMS-LexRF. To carve out the LexRSM conditions we propose, an in-depth analysis about the treatment of non-negativity of LexRSM is performed. Our soundness result extends the existing one to a wider class of LexRSMs. A synthesis algorithm of the proposed LexRSM is also given; experiments show the algorithm's advantage on its applicability compared with existing ones.

Recent video inpainting methods have made remarkable progress by utilizing explicit guidance, such as optical flow, to propagate cross-frame pixels. However, there are cases where cross-frame recurrence of the masked video is not available, resulting in a deficiency. In such situation, instead of borrowing pixels from other frames, the focus of the model shifts towards addressing the inverse problem. In this paper, we introduce a dual-modality-compatible inpainting framework called Deficiency-aware Masked Transformer (DMT), which offers three key advantages. Firstly, we pretrain a image inpainting model DMT_img serve as a prior for distilling the video model DMT_vid, thereby benefiting the hallucination of deficiency cases. Secondly, the self-attention module selectively incorporates spatiotemporal tokens to accelerate inference and remove noise signals. Thirdly, a simple yet effective Receptive Field Contextualizer is integrated into DMT, further improving performance. Extensive experiments conducted on YouTube-VOS and DAVIS datasets demonstrate that DMT_vid significantly outperforms previous solutions. The code and video demonstrations can be found at github.com/yeates/DMT.

Planar object tracking is a critical computer vision problem and has drawn increasing interest owing to its key roles in robotics, augmented reality, etc. Despite rapid progress, its further development, especially in the deep learning era, is largely hindered due to the lack of large-scale challenging benchmarks. Addressing this, we introduce PlanarTrack, a large-scale challenging planar tracking benchmark. Specifically, PlanarTrack consists of 1,000 videos with more than 490K images. All these videos are collected in complex unconstrained scenarios from the wild, which makes PlanarTrack, compared with existing benchmarks, more challenging but realistic for real-world applications. To ensure the high-quality annotation, each frame in PlanarTrack is manually labeled using four corners with multiple-round careful inspection and refinement. To our best knowledge, PlanarTrack, to date, is the largest and most challenging dataset dedicated to planar object tracking. In order to analyze the proposed PlanarTrack, we evaluate 10 planar trackers and conduct comprehensive comparisons and in-depth analysis. Our results, not surprisingly, demonstrate that current top-performing planar trackers degenerate significantly on the challenging PlanarTrack and more efforts are needed to improve planar tracking in the future. In addition, we further derive a variant named PlanarTrack$_{\mathbf{BB}}$ for generic object tracking from PlanarTrack. Our evaluation of 10 excellent generic trackers on PlanarTrack$_{\mathrm{BB}}$ manifests that, surprisingly, PlanarTrack$_{\mathrm{BB}}$ is even more challenging than several popular generic tracking benchmarks and more attention should be paid to handle such planar objects, though they are rigid. All benchmarks and evaluations will be released at the project webpage., Comment: Tech. Report

Domain adaptive detection aims to improve the generalization of detectors on target domain. To reduce discrepancy in feature distributions between two domains, recent approaches achieve domain adaption through feature alignment in different granularities via adversarial learning. However, they neglect the relationship between multiple granularities and different features in alignment, degrading detection. Addressing this, we introduce a unified multi-granularity alignment (MGA)-based detection framework for domain-invariant feature learning. The key is to encode the dependencies across different granularities including pixel-, instance-, and category-levels simultaneously to align two domains. Specifically, based on pixel-level features, we first develop an omni-scale gated fusion (OSGF) module to aggregate discriminative representations of instances with scale-aware convolutions, leading to robust multi-scale detection. Besides, we introduce multi-granularity discriminators to identify where, either source or target domains, different granularities of samples come from. Note that, MGA not only leverages instance discriminability in different categories but also exploits category consistency between two domains for detection. Furthermore, we present an adaptive exponential moving average (AEMA) strategy that explores model assessments for model update to improve pseudo labels and alleviate local misalignment problem, boosting detection robustness. Extensive experiments on multiple domain adaption scenarios validate the superiority of MGA over other approaches on FCOS and Faster R-CNN detectors. Code will be released at https://github.com/tiankongzhang/MGA.

Supplementary Material 1

Correlated Equilibrium (CE) is a well-established solution concept that captures coordination among agents and enjoys good algorithmic properties. In real-world multi-agent systems, in addition to being in an equilibrium, agents' policies are often expected to meet requirements with respect to safety, and fairness. Such additional requirements can often be expressed in terms of the state density which measures the state-visitation frequencies during the course of a game. However, existing CE notions or CE-finding approaches cannot explicitly specify a CE with particular properties concerning state density; they do so implicitly by either modifying reward functions or using value functions as the selection criteria. The resulting CE may thus not fully fulfil the state-density requirements. In this paper, we propose Density-Based Correlated Equilibria (DBCE), a new notion of CE that explicitly takes state density as selection criterion. Concretely, we instantiate DBCE by specifying different state-density requirements motivated by real-world applications. To compute DBCE, we put forward the Density Based Correlated Policy Iteration algorithm for the underlying control problem. We perform experiments on various games where results demonstrate the advantage of our CE-finding approach over existing methods in scenarios with state-density concerns.

Quantum teleportation~\cite{Bennett1993} is of both fundamental interest and great practical importance in quantum information science. To date, quantum teleportation has been implemented in various physical systems~\cite{Pirandola2015}, among which superconducting qubits are of particular practical significance as they emerge as a leading system to realize large-scale quantum computation~\cite{Arute2019,Wu2021}. Nevertheless, the number of superconducting qubits on the same chip is severely limited by the available chip size, the cooling power, and the wiring complexity. Realization of quantum teleportation and remote computation over qubits on distant superconducting chips is a key quantum communication technology to scaling up the system through a distributed quantum computational network~\cite{Gottesman1999,Eisert2000,Jiang2007,Kimble2008,Monroe2014}. However, this goal has not been realized yet in experiments due to the technical challenge of making a quantum interconnect between distant superconducting chips and the inefficient transfer of flying microwave photons over the lossy interconnects~\cite{Kurpiers2018,Axline2018,Campagne2018,Magnard2020}. Here we demonstrate deterministic teleportation of quantum states and entangling gates between distant superconducting chips connected by a 64-meter-long cable bus featuring an ultralow loss of 0.32~dB/km at cryogenic temperatures, where high fidelity remote entanglement is generated via flying microwave photons utilizing time-reversal-symmetry~\cite{Cirac1997,Korotkov2011}. Apart from the fundamental interest of teleporting macroscopic superconducting qubits over a long distance, our work lays a foundation to realization of large-scale superconducting quantum computation through a distributed computational network~\cite{Gottesman1999,Eisert2000,Jiang2007,Kimble2008,Monroe2014}.

Chengbing WangShanxi University, Taiyuan, ChinaMichael A. PetersBeijing Normal University, Beijing, ChinaContemporary Chinese Marxism is not only an important theory in the humanities and social sc...

Automatic security inspection relying on computer vision technology is a challenging task in real-world scenarios due to many factors, such as intra-class variance, class imbalance, and occlusion. Most previous methods rarely touch the cases where the prohibited items are deliberately hidden in messy objects because of the scarcity of large-scale datasets, hindering their applications. To address this issue and facilitate related research, we present a large-scale dataset, named PIDray, which covers various cases in real-world scenarios for prohibited item detection, especially for deliberately hidden items. In specific, PIDray collects 124,486 X-ray images for $12$ categories of prohibited items, and each image is manually annotated with careful inspection, which makes it, to our best knowledge, to largest prohibited items detection dataset to date. Meanwhile, we propose a general divide-and-conquer pipeline to develop baseline algorithms on PIDray. Specifically, we adopt the tree-like structure to suppress the influence of the long-tailed issue in the PIDray dataset, where the first course-grained node is tasked with the binary classification to alleviate the influence of head category, while the subsequent fine-grained node is dedicated to the specific tasks of the tail categories. Based on this simple yet effective scheme, we offer strong task-specific baselines across object detection, instance segmentation, and multi-label classification tasks and verify the generalization ability on common datasets (e.g., COCO and PASCAL VOC). Extensive experiments on PIDray demonstrate that the proposed method performs favorably against current state-of-the-art methods, especially for deliberately hidden items. Our benchmark and codes will be released at https://github.com/lutao2021/PIDray., Tech. report. arXiv admin note: text overlap with arXiv:2108.07020

Image inpainting is an ill-posed problem to recover missing or damaged image content based on incomplete images with masks. Previous works usually predict the auxiliary structures (e.g., edges, segmentation and contours) to help fill visually realistic patches in a multi-stage fashion. However, imprecise auxiliary priors may yield biased inpainted results. Besides, it is time-consuming for some methods to be implemented by multiple stages of complex neural networks. To solve this issue, we develop an end-to-end multi-modality guided transformer network, including one inpainting branch and two auxiliary branches for semantic segmentation and edge textures. Within each transformer block, the proposed multi-scale spatial-aware attention module can learn the multi-modal structural features efficiently via auxiliary denormalization. Different from previous methods relying on direct guidance from biased priors, our method enriches semantically consistent context in an image based on discriminative interplay information from multiple modalities. Comprehensive experiments on several challenging image inpainting datasets show that our method achieves state-of-the-art performance to deal with various regular/irregular masks efficiently., Accepted to ECCV 2022

This paper describes our champion solution for the CVPR2022 Generic Event Boundary Captioning (GEBC) competition. GEBC requires the captioning model to have a comprehension of instantaneous status changes around the given video boundary, which makes it much more challenging than conventional video captioning task. In this paper, a Dual-Stream Transformer with improvements on both video content encoding and captions generation is proposed: (1) We utilize three pre-trained models to extract the video features from different granularities. Moreover, we exploit the types of boundary as hints to help the model generate captions. (2) We particularly design an model, termed as Dual-Stream Transformer, to learn discriminative representations for boundary captioning. (3) Towards generating content-relevant and human-like captions, we improve the description quality by designing a word-level ensemble strategy. The promising results on the GEBC test split demonstrate the efficacy of our proposed model., Accepted to CVPR 2022 Workshop

Domain adaptive object detection is challenging due to distinctive data distribution between source domain and target domain. In this paper, we propose a unified multi-granularity alignment based object detection framework towards domain-invariant feature learning. To this end, we encode the dependencies across different granularity perspectives including pixel-, instance-, and category-levels simultaneously to align two domains. Based on pixel-level feature maps from the backbone network, we first develop the omni-scale gated fusion module to aggregate discriminative representations of instances by scale-aware convolutions, leading to robust multi-scale object detection. Meanwhile, the multi-granularity discriminators are proposed to identify which domain different granularities of samples(i.e., pixels, instances, and categories) come from. Notably, we leverage not only the instance discriminability in different categories but also the category consistency between two domains. Extensive experiments are carried out on multiple domain adaptation scenarios, demonstrating the effectiveness of our framework over state-of-the-art algorithms on top of anchor-free FCOS and anchor-based Faster RCNN detectors with different backbones., Accepted to CVPR 2022

Generic event boundary detection aims to localize the generic, taxonomy-free event boundaries that segment videos into chunks. Existing methods typically require video frames to be decoded before feeding into the network, which demands considerable computational power and storage space. To that end, we propose a new end-to-end compressed video representation learning for event boundary detection that leverages the rich information in the compressed domain, i.e., RGB, motion vectors, residuals, and the internal group of pictures (GOP) structure, without fully decoding the video. Specifically, we first use the ConvNets to extract features of the I-frames in the GOPs. After that, a light-weight spatial-channel compressed encoder is designed to compute the feature representations of the P-frames based on the motion vectors, residuals and representations of their dependent I-frames. A temporal contrastive module is proposed to determine the event boundaries of video sequences. To remedy the ambiguities of annotations and speed up the training process, we use the Gaussian kernel to preprocess the ground-truth event boundaries. Extensive experiments conducted on the Kinetics-GEBD dataset demonstrate that the proposed method achieves comparable results to the state-of-the-art methods with $4.5\times$ faster running speed.

Eupatorium adenophorum (EA) has been spread widely in China and its increasing number has posed potential threats and damages to the ecosystem and environment. Therefore, it is urgent to develop an economical and efficient method to convert low-value biomass into high-value products. In this paper, pyrolysis of EA with non-catalytic and with sodium chloride (NaCl) catalyst and sodium carbonate (Na2CO3) catalyst was carried out in a conventional resistance furnace. The maximum yield of bio-oil was obtained with 3 wt% NaCl and 3 wt% Na2CO3, respectively. The composition of bio-oil indicated that NaCl and Na2CO3 promoted the formation of hydrocarbon, alcohol substitution leading to a decrease in the yield of phenol and acid. For bio-gas, the addition of NaCl and Na2CO3 could reduce the elimination of CO and CH4 and promote the formation of H2 and CO2. The results indicated that NaCl and Na2CO3 had the potential to be used as a pretreatment of pyrolysis of EA to generate more resources with low cost.

PurposeThe purpose of this study was to evaluate the accuracy of a lung stereotactic body radiotherapy (SBRT) treatment plan with the target of a newly predicted internal target volume (ITVpredict) and the feasibility of its clinical application. ITVpredict was automatically generated by our in-house deep learning model according to the cone-beam CT (CBCT) image database.MethodA retrospective study of 45 patients who underwent SBRT was involved, and Mask R-CNN based algorithm model helped to predict the internal target volume (ITV) using the CBCT image database. The geometric accuracy of ITVpredict was verified by the Dice Similarity Coefficient (DSC), 3D Motion Range (R3D), Relative Volume Index (RVI), and Hausdorff Distance (HD). The PTVpredict was generated by ITVpredict, which was registered and then projected on free-breath CT (FBCT) images. The PTVFBCT was margined from the GTV on FBCT images gross tumor volume on free-breath CT (GTVFBCT). Treatment plans with the target of Predict planning target volume on CBCT images (PTVpredict) and planning target volume on free-breath CT (PTVFBCT) were respectively re-established, and the dosimetric parameters included the ratio of the volume of patients receiving at least the prescribed dose to the volume of PTV (R100%), the ratio of the volume of patients receiving at least 50% of the prescribed dose to the volume of PTV in the Radiation Therapy Oncology Group (RTOG) 0813 Trial (R50%), Gradient Index (GI), and the maximum dose 2 cm from the PTV (D2cm), which were evaluated via Plan4DCT, plan which based on PTVpredict (Planpredict), and plan which based on PTVFBCT (PlanFBCT).ResultThe geometric results showed that there existed a good correlation between ITVpredict and ITV on the 4-dimensional CT [ITV4DCT; DSC= 0.83 ±0.18]. However, the average volume of ITVpredict was 10% less than that of ITV4DCT (p = 0.333). No significant difference in dose coverage was found in V100% for the ITV with 99.98 ± 0.04% in the ITV4DCT vs. 97.56 ± 4.71% in the ITVpredict (p = 0.162). Dosimetry parameters of PTV, including R100%, R50%, GI and D2cm showed no statistically significant difference between each plan (p > 0.05).ConclusionDosimetric parameters of Planpredict are clinically comparable to those of the original Plan4DCT. This study confirmed that the treatment plan based on ITVpredict produced by our model could automatically meet clinical requirements. Thus, for patients undergoing lung SBRT, the model has great potential for using CBCT images for ITV contouring which can be used in treatment planning.

Controlling the flow of quantum information is a fundamental task for quantum computers, which is unpractical to realize on classical devices. Coherent devices which can process quantum states are thus required to route the quantum states yielding the information. In this paper we demonstrate experimentally the smallest quantum transistor for superconducting processors, composed of collector and emitter qubits, and the coupler. The interaction strength between the collector and emitter is controlled by tuning the frequency and the state of the gate qubit, effectively implementing a quantum switch. From the truth-table measurement (open-gate fidelity 93.38%, closed-gate fidelity 98.77%), we verify the high performance of the quantum transistor. We also show that taking into account the third energy level of the qubits is critical to achieving a high-fidelity transistor. The presented device has a strong potential for quantum information processes in superconducting platforms., 10 pages and 5 figures + Supplemental Material

Now the OpenACC has become a popular programming interface for many-core application programming. Internationally, a lot of research have been done on OpenACC for CPU + GPU heterogeneous many-core architecture. Among them, the PGI OpenACC compiler developed by NVIDIA is the most advanced one. But there are few research on OpenACC related to the Home Grown Heterogeneous Many-Core (HGHM) Architecture that is different from GPU. This paper proposes an automatic mapping technique for OpenACC kernel code based on the OpenACC compiler to a heterogeneous and deeply fused many-core architecture. Our approach uses the static analysis and feedback dynamic analysis of the compiler to perform the automatic mapping of the program parallel kernel code to many-core devices, and it greatly improves the transformation quality of the compiler. Experimental results show that this technique can greatly improve the efficiency of using OpenACC to port applications to heterogeneous and fused many-core system without impacting program acceleration performance.

The recent mean field game (MFG) formalism has enabled the application of inverse reinforcement learning (IRL) methods in large-scale multi-agent systems, with the goal of inferring reward signals that can explain demonstrated behaviours of large populations. The existing IRL methods for MFGs are built upon reducing an MFG to a Markov decision process (MDP) defined on the collective behaviours and average rewards of the population. However, this paper reveals that the reduction from MFG to MDP holds only for the fully cooperative setting. This limitation invalidates existing IRL methods on MFGs with non-cooperative environments. To measure more general behaviours in large populations, we study the use of individual behaviours to infer ground-truth reward functions for MFGs. We propose Mean Field IRL (MFIRL), the first dedicated IRL framework for MFGs that can handle both cooperative and non-cooperative environments. Based on this theoretically justified framework, we develop a practical algorithm effective for MFGs with unknown dynamics. We evaluate MFIRL on both cooperative and mixed cooperative-competitive scenarios with many agents. Results demonstrate that MFIRL excels in reward recovery, sample efficiency and robustness in the face of changing dynamics., accepted to AAMAS 2022

In recent years, hyperspectral imaging technologies have been broadly applied to evaluate complex plant physiological features such as leaf moisture content, nutrient level and disease stress. A critical component of this technique is white referencing used to remove the effect of non-uniform lighting intensity in different wavelengths on raw hyperspectral images. Based on the literature, the leaf geometry (e.g., tilt angles) and its interaction with the illumination severely impact the plant reflectance spectra and vegetation indices such as the normalized difference vegetation index (NDVI). This thesis is aimed to address the issues caused by the tilt angles across the leaf surface. To achieve this, two methods based on the fusion of the hyperspectral images and 3D point clouds were proposed. The first method was to build a 3D white reference library in which a point with almost the same tilt angle, height and position with the pixel on the plant leaf can be found, and then the white reference spectrum at that point can be used to calibrate the raw spectrum of the leaf pixel. The second method was to observe and summarize how the plant spectra and NDVI values changed with the leaf angles. Using the changing trends, the original NDVI and spectra of leaf pixels at different angles can be calibrate to a same standard as if the leaf was imaged at a flat and horizontal surface. The approach was called 3D calibration. The results showed that the NDVI values significantly changed with leaf angles and the changing trends differed between the corn and soybean species. To evaluate the performance of 3D calibration, 180 soybean plants with different genotypes, nitrogen (N), phosphorus (P) and water treatments were grown in the greenhouse. Each plant was imaged in three systems: the high-throughput greenhouse hyperspectral imaging system, the indoor desktop imaging system with a visible-near infrared (VINIR) hyperspectral camera and an Intel RealSense depth camera and the handheld device hyperspectral imaging system. In the greenhouse system, the whole canopy was captured. In the indoor desktop system, the partial canopy was captured because of the space limitation and the top-matured leaf (the middle leaf of the uppermost matured trifoliate) was focused. The proposed 3D calibration was applied on the top-matured leaf to remove angle impacts. In the handheld device system, the flat top-matured leaf was captured. After done with imaging work, the plants were harvested to collect the ground truth data such as relative water content (RWC), N content and P content. Combined with the ground truth data, the NDVI values from three systems were used to discriminate different genotypes and biochemical treatments, whereas, the spectra from three systems were used to build partial least squares regression (PLSR) models for N, P and RWC. The results showed that the averaged tilt angles of top-matured leaves were impacted by different treatments. For instance, the low-nitrogen (LN) plants showed significantly higher leaf angles than high-nitrogen (HN) plants; the leaf angles on water-stressed (WS) plants were higher than those on well-watered (WW) plants. The leaf angles carried some signals that influenced not only the NDVI discrimination but also the PLSR modelling results. The signals were lost after 3D calibration. For the top-matured leaves, the discrimination and modelling results after 3D calibration in the indoor desktop system were close to those from the flat leaves in the handheld device system. The proposed 3D calibration approach has a potential to eliminate leaf angle impacts.

Generic Event Boundary Detection (GEBD) aims to detect moments where humans naturally perceive as event boundaries. In this paper, we present Structured Context Transformer (or SC-Transformer) to solve the GEBD task, which can be trained in an end-to-end fashion. Specifically, we use the backbone convolutional neural network (CNN) to extract the features of each video frame. To capture temporal context information of each frame, we design the structure context transformer (SC-Transformer) by re-partitioning input frame sequence. Note that, the overall computation complexity of SC-Transformer is linear to the video length. After that, the group similarities are computed to capture the differences between frames. Then, a lightweight fully convolutional network is used to determine the event boundaries based on the grouped similarity maps. To remedy the ambiguities of boundary annotations, the Gaussian kernel is adopted to preprocess the ground-truth event boundaries to further boost the accuracy. Extensive experiments conducted on the challenging Kinetics-GEBD and TAPOS datasets demonstrate the effectiveness of the proposed method compared to the state-of-the-art methods.

Image inpainting seeks a semantically consistent way to recover the corrupted image in the light of its unmasked content. Previous approaches usually reuse the well-trained GAN as effective prior to generate realistic patches for missing holes with GAN inversion. Nevertheless, the ignorance of a hard constraint in these algorithms may yield the gap between GAN inversion and image inpainting. Addressing this problem, in this paper, we devise a novel GAN inversion model for image inpainting, dubbed InvertFill, mainly consisting of an encoder with a pre-modulation module and a GAN generator with F&W+ latent space. Within the encoder, the pre-modulation network leverages multi-scale structures to encode more discriminative semantics into style vectors. In order to bridge the gap between GAN inversion and image inpainting, F&W+ latent space is proposed to eliminate glaring color discrepancy and semantic inconsistency. To reconstruct faithful and photorealistic images, a simple yet effective Soft-update Mean Latent module is designed to capture more diverse in-domain patterns that synthesize high-fidelity textures for large corruptions. Comprehensive experiments on four challenging datasets, including Places2, CelebA-HQ, MetFaces, and Scenery, demonstrate that our InvertFill outperforms the advanced approaches qualitatively and quantitatively and supports the completion of out-of-domain images well., Accepted to ECCV2022

Video transition effects are widely used in video editing to connect shots for creating cohesive and visually appealing videos. However, it is challenging for non-professionals to choose best transitions due to the lack of cinematographic knowledge and design skills. In this paper, we present the premier work on performing automatic video transitions recommendation (VTR): given a sequence of raw video shots and companion audio, recommend video transitions for each pair of neighboring shots. To solve this task, we collect a large-scale video transition dataset using publicly available video templates on editing softwares. Then we formulate VTR as a multi-modal retrieval problem from vision/audio to video transitions and propose a novel multi-modal matching framework which consists of two parts. First we learn the embedding of video transitions through a video transition classification task. Then we propose a model to learn the matching correspondence from vision/audio inputs to video transitions. Specifically, the proposed model employs a multi-modal transformer to fuse vision and audio information, as well as capture the context cues in sequential transition outputs. Through both quantitative and qualitative experiments, we clearly demonstrate the effectiveness of our method. Notably, in the comprehensive user study, our method receives comparable scores compared with professional editors while improving the video editing efficiency by \textbf{300\scalebox{1.25}{$\times$}}. We hope our work serves to inspire other researchers to work on this new task. The dataset and codes are public at \url{https://github.com/acherstyx/AutoTransition}., Comment: To appear at ECCV 2022

Y Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

Globally, NAFLD is one of the most common liver disorders, with an estimated prevalence rate of more than 30% in men and 15% in women and an even higher prevalence in people with type 2 diabetes mellitus. Optimal pharmacologic therapeutic approaches for NAFLD are an urgent necessity.In this study, we showed that compared with healthy controls, hepatic ACSL4 levels in patients with NAFLD were found to be elevated. Suppression of ACSL4 expression promoted mitochondrial respiration, thereby enhancing the capacity of hepatocytes to mediate β-oxidation of fatty acids and to minimize lipid accumulation by up-regulating peroxisome proliferator-activated receptor coactivator-1 alpha. Moreover, we found that abemaciclib is a potent and selective ACSL4 inhibitor, and low dose of abemaciclib significantly ameliorated most of the NAFLD symptoms in multiple NAFLD mice models.Therefore, inhibition of ACSL4 is a potential alternative therapeutic approach for NAFLD.

Automatic security inspection using computer vision technology is a challenging task in real-world scenarios due to various factors, including intra-class variance, class imbalance, and occlusion. Most of the previous methods rarely solve the cases that the prohibited items are deliberately hidden in messy objects due to the lack of large-scale datasets, restricted their applications in real-world scenarios. Towards real-world prohibited item detection, we collect a large-scale dataset, named as PIDray, which covers various cases in real-world scenarios for prohibited item detection, especially for deliberately hidden items. With an intensive amount of effort, our dataset contains $12$ categories of prohibited items in $47,677$ X-ray images with high-quality annotated segmentation masks and bounding boxes. To the best of our knowledge, it is the largest prohibited items detection dataset to date. Meanwhile, we design the selective dense attention network (SDANet) to construct a strong baseline, which consists of the dense attention module and the dependency refinement module. The dense attention module formed by the spatial and channel-wise dense attentions, is designed to learn the discriminative features to boost the performance. The dependency refinement module is used to exploit the dependencies of multi-scale features. Extensive experiments conducted on the collected PIDray dataset demonstrate that the proposed method performs favorably against the state-of-the-art methods, especially for detecting the deliberately hidden items., Comment: Accepted by ICCV 2021

Background Randomised control trials and genetic analyses have demonstrated that vitamin D or 25-hydroxyvitamin D (25[OH]D) levels may not play a causal role in the development of cardiovascular disease. However, it is unclear if 25(OH)D is causally associated with cause-specific vascular disease and lipids. Therefore, we examined the causal association of 25(OH)D with myocardial infarction, stroke, ischaemic heart disease, ischaemic stroke, subarachnoid haemorrhage, intracerebral haemorrhage, and lipid levels among both Chinese and Europeans. Methods We used a Mendelian randomisation (MR) design in the China Kadoorie Biobank, the Copenhagen City Heart Study, and the Copenhagen General Population Study. The 25(OH)D-related genetic variants in the CYP2R1 and DCHR7 genes were genotyped in 99,012 Chinese adults and 106,911 Danish adults. Results In Chinese adults, plasma 25(OH)D levels were not significantly associated with cause-specific vascular disease or mortality, with the exception of intracerebral haemorrhage (HR, 1.09 [95% CI, 1.01,1.18] per 25 nmol/L higher plasma 25(OH)D). In Europeans, plasma 25(OH)D levels were inversely associated with all types of vascular diseases and mortality. However, MR analysis did not demonstrate causal associations of genetically increased 25(OH)D levels with cause-specific vascular diseases, or mortality in both Chinese and European adults. In addition, each 25 nmol/L higher 25(OH)D was observationally associated with lower total cholesterol and low-density lipoprotein cholesterol levels, but higher high-density lipoprotein cholesterol levels. Likewise, MR analysis showed that 25(OH)D levels were not causally associated with lipids in both Chinese and European adults after Bonferroni correction. Conclusions We found no evidence to support that genetically increased 25(OH)D was associated with a lower risk of ischaemic stroke, intracerebral haemorrhage, subarachnoid haemorrhage, and lipid levels in both Chinese and European adults. These results suggest that the inverse associations of vitamin D with vascular disease could be the result of confounding. Electronic supplementary material The online version of this article (10.1186/s12916-019-1401-y) contains supplementary material, which is available to authorized users.

Effects of different drip irrigation on the growth, development and yield of spring soybean was carried out by measuring the growth index and soil physical characteristics index of spring soybean (Glycine max (L.) Merr.). On the basis of natural precipitation in the same year, 4 drip irrigation levels, namely W1 (0 mm), W2 (200 mm), W3 (400 mm) and W4 (600 mm) were established. From flowering to the pod stage of soybean, a significant increase in the soil moisture and a reduction in the soil hardness and bulk density were observed. Though the difference between W3 and W4 was not significant, drip irrigation affected soil physical properties followed W4 > W3 > W2 > W1. Improved growth index including plant height, above-ground biomass and leaf area of soybean was also observed, but excessive drip irrigation triggered the decline of leaf area index and above-ground biomass. Changes caused in the soil physical properties due to drip irrigation affected soybean growth, which resulted a positive action on yield. Compared with W1 treatments, soybean yield in the different irrigation in the W3, W4 and W2 in the year 2015 and 2016 were increased by 83.68 and 46.99%, 61.58 and 39.47%, 23.51 and 20.21%, respectively. Based on the results of the present experiment it was observed that W3 treatment (irrigation rate 400 mm) was the best one for the improved crop yield of soybean.

In this work, the properties of pyrolysis products that obtained from the pyrolysis of Crofton weed was systematically researched. The effect of the different operation factors such as pyrolysis temperature, pyrolysis time, nitrogen flow rate and heating rate on the yields of three products was studied. The results showed that pyrolysis temperature and pyrolysis time have important influence on bio-oil yield, and heating rate has a decisive effect on bio-oil quality. The optimum pyrolysis condition is pyrolysis temperature of the 600 °C, heating rate of 20 °C/min, pyrolysis time of 50 min and nitrogen flow rate of 200 ml/min. The components of bio-oil and bio-gas are characterized by GC–MS and GC under optimum pyrolysis condition, respectively. The main identified compounds of bio-oils were aldehyde, phenol and ketone, corresponding to the contents of the 28.09%, 13.04% and 15.39% of the total area, respectively. Therefore, it can be used as the potential chemical materials in the food, chemical, pharmaceutical and other industries. In addition, Bio-gas mainly contains combustible gases, which accounts for as high as 82.51%. The method of the pyrolysis realizes comprehensive utilization of the Crofton weed.

An author correction to this article published in December 2020 is available at https://doi.org/10.1038/s41588-020-00760-4. Rupture of an intracranial aneurysm leads to subarachnoid hemorrhage, a severe type of stroke. To discover new risk loci and the genetic architecture of intracranial aneurysms, we performed a cross-ancestry, genome-wide association study in 10,754 cases and 306,882 controls of European and East Asian ancestry. We discovered 17 risk loci, 11 of which are new. We reveal a polygenic architecture and explain over half of the disease heritability. We show a high genetic correlation between ruptured and unruptured intracranial aneurysms. We also find a suggestive role for endothelial cells by using gene mapping and heritability enrichment. Drug-target enrichment shows pleiotropy between intracranial aneurysms and antiepileptic and sex hormone drugs, providing insights into intracranial aneurysm pathophysiology. Finally, genetic risks for smoking and high blood pressure, the two main clinical risk factors, play important roles in intracranial aneurysm risk, and drive most of the genetic correlation between intracranial aneurysms and other cerebrovascular traits.

There are abundant strategic rare-scattered metal resources in lead and zinc deposits in China, especially zinc resources in Yunnan province, which are associated with indium (In), selenium (Se), tellurium (Te), etc. Small amounts of these rare-scattered metal ions are dissolved into zinc acidic leaching solutions, and are difficult to remove by conventional zinc powder replacement. They are enriched in the ZnSO4 solution, which causes serious problems to the zinc electrowinning process such as hydrogen generation, zinc re-dissolution, plate burning, low current efficiency, etc. So, they need to be deeply purified from ZnSO4 solution. This article used ozone, a strong oxidizing agent, to make oxidative precipitation of the impurities from ZnSO4 solution. The effects of reaction temperature, ozone flow rate, and residence time on In, Se, and Te removal together with the associated Mn removal and Zn loss were investigated. The ozonation reaction kinetics was evaluated by plotting the negative logarithm of the metals in solution concentration as a function of reaction time. One precipitate prepared at optimized conditions was characterized by XRD, EDS, and SEM to identify the chemical compounds and morphology.

This report presents the approach used in the submission of Generic Event Boundary Detection (GEBD) Challenge at CVPR21. In this work, we design a Cascaded Temporal Attention Network (CASTANET) for GEBD, which is formed by three parts, the backbone network, the temporal attention module, and the classification module. Specifically, the Channel-Separated Convolutional Network (CSN) is used as the backbone network to extract features, and the temporal attention module is designed to enforce the network to focus on the discriminative features. After that, the cascaded architecture is used in the classification module to generate more accurate boundaries. In addition, the ensemble strategy is used to further improve the performance of the proposed method. The proposed method achieves 83.30% F1 score on Kinetics-GEBD test set, which improves 20.5% F1 score compared to the baseline method. Code is available at https://github.com/DexiangHong/Cascade-PC.

Mean field games (MFGs) provide a mathematically tractable framework for modelling large-scale multi-agent systems by leveraging mean field theory to simplify interactions among agents. It enables applying inverse reinforcement learning (IRL) to predict behaviours of large populations by recovering reward signals from demonstrated behaviours. However, existing IRL methods for MFGs are powerless to reason about uncertainties in demonstrated behaviours of individual agents. This paper proposes a novel framework, Mean-Field Adversarial IRL (MF-AIRL), which is capable of tackling uncertainties in demonstrations. We build MF-AIRL upon maximum entropy IRL and a new equilibrium concept. We evaluate our approach on simulated tasks with imperfect demonstrations. Experimental results demonstrate the superiority of MF-AIRL over existing methods in reward recovery., Comment: Accepted to AAMAS 2023

In this study, manganese dioxide was evenly distributed on the surface of activated carbon (AC), and the porous structure of AC and the surface functional groups of manganese dioxide were used to adsorb the heavy metal ion Pb(II). The advantages of microwave heating are fast heating and high selectivity. The mole ratio control of the AC and MnO2 in 1:0.1, microwave heating to 800 °C, heat preservation for 30 min. The maximum adsorption capacity of the MnO2-AC prepared by this method on Pb(II) can reach 664 mg/L at pH = 6. It can be observed by scanning electron microscope (SEM) that manganese dioxide particles are dispersed evenly on the surface and pore diameter of AC, and there is almost no agglomeration. The specific surface area was 752.8 m2/g, and the micropore area was 483.9 m2/g. The adsorption mechanism was explored through adsorption isotherm, adsorption kinetics, Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS). It is speculated that the adsorption mechanism includes electrostatic interaction and specific adsorption, indicating that lead ions enter into the void of manganese dioxide and form spherical complexes. The results showed that the adsorption behavior of Pb(II) by MnO2-AC was consistent with the Langmuir adsorption model, the quasi-second-order kinetic model, and the particle internal diffusion model.

In AC/DC systems, unit interaction factor (UIF) has been developed based on engineering experience to quantify the strength of interactions between generators and LCC-HVDC ties. However, there is no theoretical justification for UIF, which is deemed necessary for the application and further development of UIF. In this paper, we present theoretical proof for UIF by the small signal stability analysis of AC/DC systems. On this basis, critical UIF (CUIF) is further proposed to characterize the stability boundary of AC/DC systems so that UIF along with CUIF can be used to assess the interaction strength and analyze system stability. The theoretical results are verified by eigenvalue analysis and time-domain simulations.

Unsupervised domain adaptation is critical in various computer vision tasks, such as object detection, instance segmentation, and semantic segmentation, which aims to alleviate performance degradation caused by domain-shift. Most of previous methods rely on a single-mode distribution of source and target domains to align them with adversarial learning, leading to inferior results in various scenarios. To that end, in this paper, we design a new spatial attention pyramid network for unsupervised domain adaptation. Specifically, we first build the spatial pyramid representation to capture context information of objects at different scales. Guided by the task-specific information, we combine the dense global structure representation and local texture patterns at each spatial location effectively using the spatial attention mechanism. In this way, the network is enforced to focus on the discriminative regions with context information for domain adaption. We conduct extensive experiments on various challenging datasets for unsupervised domain adaptation on object detection, instance segmentation, and semantic segmentation, which demonstrates that our method performs favorably against the state-of-the-art methods by a large margin. Our source code is available at https://isrc.iscas.ac.cn/gitlab/research/domain-adaption., Accepted to ECCV 2020

In view of the carrying capacity of the nursing robot to pick up the patient, a dual-arm mobile nursing robot is designed. According to the characteristics of human body structure, the overall structure and some basic parameters of the nursing robot are determined. Combined with the current design characteristics of the mechanical arm and modular joint of the advanced dual-arm robot, the nursing robot scheme that can hold patients is proposed. A waist arm structure with 12 DOF (degrees of freedom) and an omni-directional mobile chassis with forward anti-collapse mechanism are designed by using SolidWorks. According to the D-H method, the link coordinate system of the nursing robot is established. Based on the parameters of the link coordinate system, the forward kinematics equation and the kinematics forward solution of the nursing robot are deduced. The forward kinematics of the nursing robot was verified by the simulation of MATLAB, and the results show that the design of the robot is effective.

Properties of rock-cemented coal gangue-fly ash backfill (CGFB) bimaterials determine the effects of strip CGFB mining on controlling the surface subsidence in coal mines, which are affected by the coal gangue particle size in CGFB. In this paper, uniaxial compression tests were conducted on the coarse sandstone-CGFB composite samples with different coal gangue particle sizes, and their strength, acoustic emission (AE), and failure characteristics were investigated. The uniaxial compressive strength (UCS) and elastic modulus of the composite sample decreased with the coal gangue particle size. The strength of the composite sample is mainly dependent on that of CGFB in it, affected by interactions between CGFB and coarse sandstone. The deformation of the coarse sandstone weakened the damage accumulation within CGFB, resulting in the strength of the composite sample larger than that of CGFB. The average UCS values of composite samples with coal gangue particle sizes of 0∼5 mm, 5∼10 mm, and 10∼15 mm, increased by 10.78%, 14.98%, and 12.70% compared with CGFB in them, respectively. AE event signal regularity of the composite sample was divided into three stages: rising period, calm period, and active period. The intensity and frequency of AE event signals in three periods were strengthened with the coal gangue particle size. The calm period can be taken as the precursory information for the failure and instability of composite sample under loading, whose duration became shortly with the coal gangue particle size. The rebound deformation of coarse sandstone caused the fluctuations of AE event signals at the later stage of active period. The failures of the composite sample occurred within CGFB, and no obvious failures were found in the coarse sandstone. The CGFB mainly experienced the splitting failure accompanying by varying degrees of surface spalling failures. The broken degree of CGFB increased with the coal gangue particle size, and the largest weight ratio of CGFB fragments (chips) after failure was determined by the coal gangue particle size.

A new adsorbent was synthesized by functionalizing poly(glycidyl methacrylate) with 2,6-diaminopyridine for the selective adsorption of Au(III) from aqueous solution. The fourier transform infrared spectrometer, X-ray photoelectron spectroscopy and scanning electron microscope demonstrated that 2,6-diaminopyridine was successfully grafted onto poly(glycidyl methacrylate). The batch adsorption experiments were conducted to evaluate the adsorption abilities of the adsorbent. The results show that the adsorbent exhibits high adsorption capacity (459.29 mg/g) for Au(III) at pH 4. The adsorption process of Au(III) are well described by pseudo-second-order kinetic model. The adsorption isotherm is well-obeyed Langmuir and D–R models. The calculated thermodynamic parameters of the adsorption process are: ΔG0 = − 24.2 kJ/mol; ΔH0 = 37.0 kJ/mol; ΔS0 = 205.4 J/mol K. The adsorbent is interacted with Au(III) through chelation and ion exchange between the amines groups and Au(III). Moreover, the adsorbent shows good selectivity from the interfering ions and can be reused at least five times.

The present study investigates the use of magnesium sulfate (MgSO4) instead of (NH4)2SO4 as a lixiviant in the recovery of rare earth elements (REEs) from clays. Experiments were carried out to investigate the influence of leaching conditions such as leaching time, lixiviant concentration and liquid:solid ratio on the leaching efficiency. The optimum leaching conditions, leading to 75.48% of total REE leaching efficiency, required a stirring speed of 500 rpm, a leaching time of 30 min, a lixiviant concentration of 3 wt.% and a liquid:solid ratio of 3:1. After extension of the leaching process by a second stage, the leaching efficiency may reach up to 96.19%, which is slightly higher than that obtained by (NH4)2SO4. Leaching varies from element to element, with Ce presenting the lowest leaching efficiency, and the partition in leaching solution is in agreement with that in raw ore other than for Ce. Based on these findings, MgSO4 lixiviant is an excellent alternative leaching agent for a sustainable REE industry because it reduces or eliminates NH4+–N pollution.

Stimulated Raman adiabatic passage (STIRAP) is a widely-used technique of coherent state-to-state manipulation for many applications in physics, chemistry, and beyond. The adiabatic evolution of the state involved in STIRAP, called adiabatic passage, guarantees its robustness against control errors, but also leads to problems of low efficiency and decoherence. Here we propose and experimentally demonstrate an alternative approach, termed stimulated Raman "user-defined" passage (STIRUP), where a parameterized state is employed for constructing desired evolutions to replace the adiabatic passage in STIRAP. The user-defined passages can be flexibly designed for optimizing different objectives for different tasks, e.g. minimizing leakage error. To experimentally benchmark its performance, we apply STIRUP to the task of coherent state transfer in a superconducting Xmon qutrit. We found that STIRUP completed the transfer more then four times faster than STIRAP with enhanced robustness, and achieved a fidelity of 99.5%, which is the highest among all recent experiments based on STIRAP and its variants. In practice, STIRUP differs from STIRAP only in the design of driving pulses; therefore, most existing applications of STIRAP can be readily implemented with STIRUP., 5 pages, 4 figures

Xuejing Lu,1,2 William Forde Thompson,3,4 Libo Zhang,1,2 Li Hu1,2 1CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, People’s Republic of China; 2Department of Psychology, University of Chinese Academy of Sciences, Beijing, People’s Republic of China; 3Department of Psychology, Macquarie University, Sydney, New South Wales, Australia; 4ARC Centre of Excellence in Cognition and Its Disorders, Sydney, New South Wales, AustraliaCorrespondence: Li HuCAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, People’s Republic of ChinaTel +86 18084053555Fax +86-10-84249369Email huli@psych.ac.cnBackground: Music is sometimes used as an adjunct to pain management. However, there is limited understanding of by what means music modulates pain perception and how the brain responds to nociceptive inputs while listening to music, because clinical practice typically involves the coexistence of multiple therapeutic interventions. To address this challenge, laboratory studies with experimental and control conditions are needed.Methods: In the present investigation, we delivered nociceptive laser stimuli on 30 participants under three conditions – participants were sitting in silence, listening to their preferred music, or listening to white noise. Differences among conditions were quantified by self-reports of pain intensity and unpleasantness, and brain activity sampled by electroencephalography (EEG).Results: Compared with the noise and silence conditions, participants in the music condition reported lower ratings on pain unpleasantness, as reflected by reduced brain oscillations immediately prior to the nociceptive laser stimulus at frequencies of 4–15 Hz in EEG. In addition, participants showed smaller P2 amplitudes in laser-evoked potentials (LEPs) when they were listening to music or white noise in comparison to sitting in silence. These findings suggest that a general modulation effect of sounds on pain, with a specific reduction of pain unpleasantness induced by the positive emotional impact.Conclusion: Music may serve as a real-time regulator to modulate pain unpleasantness. Results are discussed in view of current understandings of music-induced analgesia.Keywords: preferred music, pain, analgesic effect, emotional modulation, EEG, brain oscillations

Copper modified activated carbon was explored by N2 adsorption, XRD, SEM, EDX and FT-IR. The parameters of the optimal conditions, including the loading temperature, heating time, heating rate, ionic concentration and dipping time, were 450 °C, 40 min, 25 °C/min, 0.1 mol/L and 12 h, respectively. Copper nanoparticles were uniformly arranged on the carbon surface according to SEM analysis. The aqueous solution temperature, initial dose and adsorbent dose of copper modified AC were the parameters of the simultaneous experiment. The Langmuir isotherm model was revealed according to the applicable experimental data, the kinetic data were fit with a pseudo-second-order equation model, and the thermodynamic parameters indicated that MB adsorption was an endothermic and spontaneous process.