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Juan Li,1,* Ziqing Gao,1,* Ning Li,1 Ling Yao,1 Chao Liu,1 Che Xu,1 Xiaohui Ren,1 Aiqin Wang,1 Siqi Gao,2 Miao Wang,2 Xiang Gao,2 Kun Li,3 Jianfeng Wang1 1Department of Ophthalmology, First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, 233004, People’s Republic of China; 2School of Clinical Medicine, Bengbu Medical University, Bengbu, Anhui, 233004, People’s Republic of China; 3School of Life and Health Science, Anhui Science and Technology University, Fengyang, Anhui, 233100, People’s Republic of China*These authors contributed equally to this workCorrespondence: Kun Li; Jianfeng Wang, Email liangliang2419@126.com; wangjianfeng1969@163.comBackground: Drug therapy for eye diseases has been limited by multiple protective mechanisms of the eye, which can be improved using well-designed drug delivery systems. Mesoporous silica nanoparticles (MSNs) had been used in many studies as carriers of therapeutic agents for ocular diseases treatment. However, no studies have focused on ocular biosafety. Considering that MSNs containing tetrasulfur bonds have unique advantages and have drawn increasing attention in drug delivery systems, it is necessary to explore the ocular biosafety of tetrasulfur bonds before their widespread application as ophthalmic drug carriers.Methods: In this study, hollow mesoporous silica nanoparticles (HMSNs) with different tetrasulfur bond contents were prepared and characterized. The ocular biosafety of HMSN-E was evaluated in vitro on the three selected ocular cell lines, including corneal epithelial cells, lens epithelial cells and retinal endothelial cells (HREC), and in vivo by using topical eye drops and intravitreal injections.Results: In cellular experiments, HMSNs caused obvious S content-dependent cytotoxic effect. HMSNs with the highest tetrasulfur bond content (HMSN-E), showed the highest cytotoxicity among all the HMSNs, and HREC was the most vulnerable cell to HMSN-E. It was shown that HM

Yang J, Li K, Chen J, Hu X, Wang H, Zhu X. Onco Targets Ther. 2020;13:1979–1991. We, the Editor and Publisher of the journal OncoTargets and Therapy are retracting the published article. Following publication of the article, the authors raised concerns about the duplication of images from Figure 5. Specifically, The images for Figure 5G, HepG2 and Huh 7, LINC00460+miR-342-3p have been duplicated. The corresponding author was cooperative and responded to our queries and explained how the images came to be duplicated but was unable to provide satisfactory original data for the study. As verifying the validity of published work is core to the integrity of the scholarly record, the Publisher and Editor requested to retract the article and the authors agreed with this decision. We have been informed in our decision-making by our editorial policies and COPE guidelines. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”.

Kun Li,1 Ahmad Alhaskawi,2 Haiyin Zhou,2 Yanzhao Dong,2 QingFang Zhao,3 Chenxi Wang,4 Hui Lu2 1Department of Critical Care Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, People’s Republic of China; 2Department of Orthopaedics, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, People’s Republic of China; 3Department of Plastic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, People’s Republic of China; 4Medical Department, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, People’s Republic of ChinaCorrespondence: Kun Li, Department of Critical Care Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, No. 79, Qingchun Road, Shangcheng District, Hangzhou, Zhejiang, People’s Republic of China, Email Likicu@zju.edu.cn Hui Lu, Department of Orthopaedics, The First Affiliated Hospital, Zhejiang University School of Medicine, No.79, Qingchun Road, Shangcheng District, Hangzhou, Zhejiang Province, People’s Republic of China, Email HuiLu@zju.edu.cnObjective: This retrospective study examines risk factors and electromyographic (EMG) characteristics associated with acquired weakness in critically ill patients and assesses their impact on patient prognosis.Methods: Ninety-seven critically ill patients, ventilated for over 48 hours, were included. Patient data, encompassing general condition, medical history, Medical Research Council (MRC) scores, serum markers (c-reactive protein, calcitonin gene, albumin, brain natriuretic peptide, urea nitrogen, creatinine), EMG characteristics, respiratory treatment modalities, and parameters, were recorded. Mechanical ventilation duration, ICU stay duration, hospitalization duration, and patient prognosis were documented. Based on MRC scores, patients were categorized into the ICU-ac

2D RGB images and 3D LIDAR point clouds provide complementary knowledge for the perception system of autonomous vehicles. Several 2D and 3D fusion methods have been explored for the LIDAR semantic segmentation task, but they suffer from different problems. 2D-to-3D fusion methods require strictly paired data during inference, which may not be available in real-world scenarios, while 3D-to-2D fusion methods cannot explicitly make full use of the 2D information. Therefore, we propose a Bidirectional Fusion Network with Cross-Modality Knowledge Distillation (CMDFusion) in this work. Our method has two contributions. First, our bidirectional fusion scheme explicitly and implicitly enhances the 3D feature via 2D-to-3D fusion and 3D-to-2D fusion, respectively, which surpasses either one of the single fusion schemes. Second, we distillate the 2D knowledge from a 2D network (Camera branch) to a 3D network (2D knowledge branch) so that the 3D network can generate 2D information even for those points not in the FOV (field of view) of the camera. In this way, RGB images are not required during inference anymore since the 2D knowledge branch provides 2D information according to the 3D LIDAR input. We show that our CMDFusion achieves the best performance among all fusion-based methods on SemanticKITTI and nuScenes datasets. © 2016 IEEE.

Inspired by the activity-silent and persistent activity mechanisms in human visual perception biology, we design a Unified Static and Dynamic Network (UniSDNet), to learn the semantic association between the video and text/audio queries in a cross-modal environment for efficient video grounding. For static modeling, we devise a novel residual structure (ResMLP) to boost the global comprehensive interaction between the video segments and queries, achieving more effective semantic enhancement/supplement. For dynamic modeling, we effectively exploit three characteristics of the persistent activity mechanism in our network design for a better video context comprehension. Specifically, we construct a diffusely connected video clip graph on the basis of 2D sparse temporal masking to reflect the "short-term effect" relationship. We innovatively consider the temporal distance and relevance as the joint "auxiliary evidence clues" and design a multi-kernel Temporal Gaussian Filter to expand the context clue into high-dimensional space, simulating the "complex visual perception", and then conduct element level filtering convolution operations on neighbour clip nodes in message passing stage for finally generating and ranking the candidate proposals. Our UniSDNet is applicable to both Natural Language Video Grounding (NLVG) and Spoken Language Video Grounding (SLVG) tasks. Our UniSDNet achieves SOTA performance on three widely used datasets for NLVG, as well as three datasets for SLVG, e.g., reporting new records at 38.88% R@1,IoU@0.7 on ActivityNet Captions and 40.26% R@1,IoU@0.5 on TACoS. To facilitate this field, we collect two new datasets (Charades-STA Speech and TACoS Speech) for SLVG task. Meanwhile, the inference speed of our UniSDNet is 1.56$\times$ faster than the strong multi-query benchmark. Code is available at: https://github.com/xian-sh/UniSDNet.

Channel knowledge map (CKM), which aims to directly reflect the intrinsic channel properties of the local wireless environment, is a novel technique for achieving environmentaware communication. In this paper, to alleviate the large training overhead in millimeter wave (mmWave) beam alignment, an environment-aware and training-free beam alignment prototype is established based on a typical CKM, termed beam index map (BIM). To this end, a general CKM construction method is first presented, and an indoor BIM is constructed offline to learn the candidate transmit and receive beam index pairs for each grid in the experimental area. Furthermore, based on the location information of the receiver (or the dynamic obstacles) from the ultra-wide band (UWB) positioning system, the established BIM is used to achieve training-free beam alignment by directly providing the beam indexes for the transmitter and receiver. Three typical scenarios are considered in the experiment, including quasi-static environment with line-of-sight (LoS) link, quasistatic environment without LoS link and dynamic environment. Besides, the receiver orientation measured from the gyroscope is also used to help CKM predict more accurate beam indexes. The experiment results show that compared with the benchmark location-based beam alignment strategy, the CKM-based beam alignment strategy can achieve much higher received power, which is close to that achieved by exhaustive beam search, but with significantly reduced training overhead.

Video Motion Magnification (VMM) aims to reveal subtle and imperceptible motion information of objects in the macroscopic world. Prior methods directly model the motion field from the Eulerian perspective by Representation Learning that separates shape and texture or Multi-domain Learning from phase fluctuations. Inspired by the frequency spectrum, we observe that the low-frequency components with stable energy always possess spatial structure and less noise, making them suitable for modeling the subtle motion field. To this end, we present FD4MM, a new paradigm of Frequency Decoupling for Motion Magnification with a Multi-level Isomorphic Architecture to capture multi-level high-frequency details and a stable low-frequency structure (motion field) in video space. Since high-frequency details and subtle motions are susceptible to information degradation due to their inherent subtlety and unavoidable external interference from noise, we carefully design Sparse High/Low-pass Filters to enhance the integrity of details and motion structures, and a Sparse Frequency Mixer to promote seamless recoupling. Besides, we innovatively design a contrastive regularization for this task to strengthen the model's ability to discriminate irrelevant features, reducing undesired motion magnification. Extensive experiments on both Real-world and Synthetic Datasets show that our FD4MM outperforms SOTA methods. Meanwhile, FD4MM reduces FLOPs by 1.63$\times$ and boosts inference speed by 1.68$\times$ than the latest method. Our code is available at https://github.com/Jiafei127/FD4MM., Comment: Accepted by CVPR2024

Micro-action is an imperceptible non-verbal behaviour characterised by low-intensity movement. It offers insights into the feelings and intentions of individuals and is important for human-oriented applications such as emotion recognition and psychological assessment. However, the identification, differentiation, and understanding of micro-actions pose challenges due to the imperceptible and inaccessible nature of these subtle human behaviors in everyday life. In this study, we innovatively collect a new micro-action dataset designated as Micro-action-52 (MA-52), and propose a benchmark named micro-action network (MANet) for micro-action recognition (MAR) task. Uniquely, MA-52 provides the whole-body perspective including gestures, upper- and lower-limb movements, attempting to reveal comprehensive micro-action cues. In detail, MA-52 contains 52 micro-action categories along with seven body part labels, and encompasses a full array of realistic and natural micro-actions, accounting for 205 participants and 22,422 video instances collated from the psychological interviews. Based on the proposed dataset, we assess MANet and other nine prevalent action recognition methods. MANet incorporates squeeze-and excitation (SE) and temporal shift module (TSM) into the ResNet architecture for modeling the spatiotemporal characteristics of micro-actions. Then a joint-embedding loss is designed for semantic matching between video and action labels; the loss is used to better distinguish between visually similar yet distinct micro-action categories. The extended application in emotion recognition has demonstrated one of the important values of our proposed dataset and method. In the future, further exploration of human behaviour, emotion, and psychological assessment will be conducted in depth. The dataset and source code are released at https://github.com/VUT-HFUT/Micro-Action., Comment: Accepted by IEEE Transactions on Circuits and Systems for Video Technology

Large language models (LLMs) are complex artificial intelligence systems capable of understanding, generating and translating human language. They learn language patterns by analyzing large amounts of text data, allowing them to perform writing, conversation, summarizing and other language tasks. When LLMs process and generate large amounts of data, there is a risk of leaking sensitive information, which may threaten data privacy. This paper concentrates on elucidating the data privacy concerns associated with LLMs to foster a comprehensive understanding. Specifically, a thorough investigation is undertaken to delineate the spectrum of data privacy threats, encompassing both passive privacy leakage and active privacy attacks within LLMs. Subsequently, we conduct an assessment of the privacy protection mechanisms employed by LLMs at various stages, followed by a detailed examination of their efficacy and constraints. Finally, the discourse extends to delineate the challenges encountered and outline prospective directions for advancement in the realm of LLM privacy protection., Comment: 18 pages, 4 figures

Visual Question Answering (VQA) is a challenging task of predicting the answer to a question about the content of an image. It requires deep understanding of both the textual question and visual image. Prior works directly evaluate the answering models by simply calculating the accuracy of the predicted answers. However, the inner reasoning behind the prediction is disregarded in such a "black box" system, and we do not even know if one can trust the predictions. In some cases, the models still get the correct answers even when they focus on irrelevant visual regions or textual tokens, which makes the models unreliable and illogical. To generate both visual and textual rationales next to the predicted answer to the given image/question pair, we propose Convincing Rationales for VQA, CRVQA. Considering the extra annotations brought by the new outputs, {CRVQA} is trained and evaluated by samples converted from some existing VQA datasets and their visual labels. The extensive experiments demonstrate that the visual and textual rationales support the prediction of the answers, and further improve the accuracy. Furthermore, {CRVQA} achieves competitive performance on generic VQA datatsets in the zero-shot evaluation setting. The dataset and source code will be released under https://github.com/lik1996/CRVQA2024., Comment: under review

As Large Language Models (LLMs) scale up and gain powerful Chain-of-Thoughts (CoTs) reasoning abilities, practical resource constraints drive efforts to distill these capabilities into more compact Smaller Language Models (SLMs). We find that CoTs consist mainly of simple reasoning forms, with a small proportion ($\approx 4.7\%$) of key reasoning steps that truly impact conclusions. However, previous distillation methods typically involve supervised fine-tuning student SLMs only on correct CoTs data produced by teacher LLMs, resulting in students struggling to learn the key reasoning steps, instead imitating the teacher's reasoning forms and making errors or omissions on these steps. To address these issues, drawing an analogy to human learning, where analyzing mistakes according to correct solutions often reveals the crucial steps leading to successes or failures, we propose mistak\textbf{E}-\textbf{D}riven key reason\textbf{I}ng step distilla\textbf{T}ion (\textbf{EDIT}), a novel method that further aids SLMs learning key reasoning steps rather than mere simple fine-tuning. Firstly, to expose these crucial steps in CoTs, we design specific prompts to generate dual CoTs data with similar reasoning paths but divergent conclusions. Then, we apply the minimum edit distance algorithm on the dual CoTs data to locate these key steps and optimize the likelihood of these steps. Extensive experiments validate the effectiveness of EDIT across both in-domain and out-of-domain benchmark reasoning datasets. Further analysis shows that EDIT can generate high-quality CoTs with more correct key reasoning steps. Notably, we also explore how different mistake patterns affect performance and find that EDIT benefits more from logical errors than from knowledge or mathematical calculation errors in dual CoTs\footnote{Code can be found at \url{https://github.com/C-W-D/EDIT}}.

Large language models (LLMs) exhibit enhanced reasoning at larger scales, driving efforts to distill these capabilities into smaller models via teacher-student learning. Previous works simply fine-tune student models on teachers' generated Chain-of-Thoughts (CoTs) data. Although these methods enhance in-domain (IND) reasoning performance, they struggle to generalize to out-of-domain (OOD) tasks. We believe that the widespread spurious correlations between questions and answers may lead the model to preset a specific answer which restricts the diversity and generalizability of its reasoning process. In this paper, we propose Cascading Decomposed CoTs Distillation (CasCoD) to address these issues by decomposing the traditional single-step learning process into two cascaded learning steps. Specifically, by restructuring the training objectives -- removing the answer from outputs and concatenating the question with the rationale as input -- CasCoD's two-step learning process ensures that students focus on learning rationales without interference from the preset answers, thus improving reasoning generalizability. Extensive experiments demonstrate the effectiveness of CasCoD on both IND and OOD benchmark reasoning datasets. Code can be found at https://github.com/C-W-D/CasCoD.

Digital watermarking methods are commonly used to safeguard digital media copyrights by confirming ownership and deterring unauthorized use. However, without reliable third-party oversight, these methods risk security vulnerabilities during watermark extraction. Furthermore, digital media lacks tangible ownership attributes, posing challenges for secure copyright transfer and tracing. This study introduces DataSafe, a copyright protection scheme that combines physical unclonable functions (PUFs) and blockchain technology. PUF devices use their unique fingerprints for blockchain registration. Subsequently, these devices incorporate invisible watermarking techniques to embed digital watermarks into media for copyright protection. The watermark verification process is confined within the devices, preserving confidentiality during extraction, validating identities during copyright exchanges, and facilitating blockchain-based traceability of copyright transfers. The implementation of a prototype system on the LPC55S69-EVK development board is detailed, illustrating the practicality and effectiveness of the proposed solution.

Drug response prediction (DRP) is a crucial phase in drug discovery, and the most important metric for its evaluation is the IC50 score. DRP results are heavily dependent on the quality of the generated molecules. Existing molecule generation methods typically employ classifier-based guidance, enabling sampling within the IC50 classification range. However, these methods fail to ensure the sampling space range's effectiveness, generating numerous ineffective molecules. Through experimental and theoretical study, we hypothesize that conditional generation based on the target IC50 score can obtain a more effective sampling space. As a result, we introduce regressor-free guidance molecule generation to ensure sampling within a more effective space and support DRP. Regressor-free guidance combines a diffusion model's score estimation with a regression controller model's gradient based on number labels. To effectively map regression labels between drugs and cell lines, we design a common-sense numerical knowledge graph that constrains the order of text representations. Experimental results on the real-world dataset for the DRP task demonstrate our method's effectiveness in drug discovery. The code is available at:https://anonymous.4open.science/r/RMCD-DBD1., Comment: 22 pages, 7 figures, 9 tables

StackOverflow, with its vast question repository and limited labeled examples, raise an annotation challenge for us. We address this gap by proposing RoBERTa+MAML, a few-shot named entity recognition (NER) method leveraging meta-learning. Our approach, evaluated on the StackOverflow NER corpus (27 entity types), achieves a 5% F1 score improvement over the baseline. We improved the results further domain-specific phrase processing enhance results., Comment: 5 pages

Scene graph generation (SGG) aims to understand the visual objects and their semantic relationships from one given image. Until now, lots of SGG datasets with the eyelevel view are released but the SGG dataset with the overhead view is scarcely studied. By contrast to the object occlusion problem in the eyelevel view, which impedes the SGG, the overhead view provides a new perspective that helps to promote the SGG by providing a clear perception of the spatial relationships of objects in the ground scene. To fill in the gap of the overhead view dataset, this paper constructs and releases an aerial image urban scene graph generation (AUG) dataset. Images from the AUG dataset are captured with the low-attitude overhead view. In the AUG dataset, 25,594 objects, 16,970 relationships, and 27,175 attributes are manually annotated. To avoid the local context being overwhelmed in the complex aerial urban scene, this paper proposes one new locality-preserving graph convolutional network (LPG). Different from the traditional graph convolutional network, which has the natural advantage of capturing the global context for SGG, the convolutional layer in the LPG integrates the non-destructive initial features of the objects with dynamically updated neighborhood information to preserve the local context under the premise of mining the global context. To address the problem that there exists an extra-large number of potential object relationship pairs but only a small part of them is meaningful in AUG, we propose the adaptive bounding box scaling factor for potential relationship detection (ABS-PRD) to intelligently prune the meaningless relationship pairs. Extensive experiments on the AUG dataset show that our LPG can significantly outperform the state-of-the-art methods and the effectiveness of the proposed locality-preserving strategy.

Human pose and shape (HPS) estimation with lensless imaging is not only beneficial to privacy protection but also can be used in covert surveillance scenarios due to the small size and simple structure of this device. However, this task presents significant challenges due to the inherent ambiguity of the captured measurements and lacks effective methods for directly estimating human pose and shape from lensless data. In this paper, we propose the first end-to-end framework to recover 3D human poses and shapes from lensless measurements to our knowledge. We specifically design a multi-scale lensless feature decoder to decode the lensless measurements through the optically encoded mask for efficient feature extraction. We also propose a double-head auxiliary supervision mechanism to improve the estimation accuracy of human limb ends. Besides, we establish a lensless imaging system and verify the effectiveness of our method on various datasets acquired by our lensless imaging system., Comment: Accepted to CVPR 2024. More results available at https://cic.tju.edu.cn/faculty/likun/projects/LPSNet

Named Entity Recognition (NER) is an essential steppingstone in the field of natural language processing. Although promising performance has been achieved by various distantly supervised models, we argue that distant supervision inevitably introduces incomplete and noisy annotations, which may mislead the model training process. To address this issue, we propose a robust NER model named BOND-MoE based on Mixture of Experts (MoE). Instead of relying on a single model for NER prediction, multiple models are trained and ensembled under the Expectation-Maximization (EM) framework, so that noisy supervision can be dramatically alleviated. In addition, we introduce a fair assignment module to balance the document-model assignment process. Extensive experiments on real-world datasets show that the proposed method achieves state-of-the-art performance compared with other distantly supervised NER.

Under low-potassium (K+) stress, a Ca2+ signaling network consisting of calcineurin B-like proteins (CBLs) and CBL-interacting kinases (CIPKs) play essential roles. Specifically, the plasma membrane CBL1/9-CIPK pathway and the tonoplast CBL2/3-CIPK pathway promotes K+ uptake and remobilization, respectively, by activating a series of K+ channels. While the dual CBL-CIPK pathways enable plants to cope with low-K+ stress, little is known about the early events that link external K+ levels to the CBL-CIPK proteins. Here we show that K+ status regulates the protein abundance and phosphorylation of the CBL-CIPK-channel modules. Further analysis revealed low K+-induced activation of VM-CBL2/3 happened earlier and was required for full activation of PM-CBL1/9 pathway. Moreover, we identified CIPK9/23 kinases to be responsible for phosphorylation of CBL1/9/2/3 in plant response to low-K+ stress and the HAB1/ABI1/ABI2/PP2CA phosphatases to be responsible for CBL2/3-CIPK9 dephosphorylation upon K+-repletion. Further genetic analysis showed that HAB1/ABI1/ABI2/PP2CA phosphatases are negative regulators for plant growth under low-K+, countering the CBL-CIPK network in plant response and adaptation to low-K+ stress.

Cross-matching operation, which is to find corresponding data for the same celestial object or region from multiple catalogues,is indispensable to astronomical data analysis and research. Due to the large amount of astronomical catalogues generated by the ongoing and next-generation large-scale sky surveys, the time complexity of the cross-matching is increasing dramatically. Heterogeneous computing environments provide a theoretical possibility to accelerate the cross-matching, but the performance advantages of heterogeneous computing resources have not been fully utilized. To meet the challenge of cross-matching for substantial increasing amount of astronomical observation data, this paper proposes Heterogeneous-computing-enabled Large Catalogue Cross-matcher (HLC2), a high-performance cross-matching framework based on spherical position deviation on CPU-GPU heterogeneous computing platforms. It supports scalable and flexible cross-matching and can be directly applied to the fusion of large astronomical cataloguesfrom survey missions and astronomical data centres. A performance estimation model is proposed to locate the performance bottlenecks and guide the optimizations. A two-level partitioning strategy is designed to generate an optimized data placement according to the positions of celestial objects to increase throughput. To make HLC2 a more adaptive solution, the architecture-aware task splitting, thread parallelization, and concurrent scheduling strategies are designed and integrated. Moreover, a novel quad-direction strategy is proposed for the boundary problem to effectively balance performance and completeness. We have experimentally evaluated HLC2 using public released catalogue data. Experiments demonstrate that HLC2 scales well on different sizes of catalogues and the cross-matching speed is significantly improved compared to the state-of-the-art cross-matchers., Comment: Accepted for publication in Monthly Notices of the Royal Astronomical Society

Xiaomin Zhang,1,* Jun Huang,2,* Fan Weng,3 Yanting Wen,3 Xiaoxia Wang,2 Junrong Jiang,4 Yumei Xue,4 Kun Li1 1School of Nursing, Sun Yat-Sen University, Guangzhou, People’s Republic of China; 2Department of Geriatrics, Guangdong Provincial Geriatrics Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People’s Republic of China; 3Yuexiu District Center for Disease Control and Prevention, Guangzhou, People’s Republic of China; 4Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yumei Xue, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, No. 106 Zhong Shan Second Road, Guangzhou, 510080, People’s Republic of China, Tel +8613570082363, Email 1295439278@qq.com Kun Li, School of Nursing, Sun Yat-sen University, No. 74 Zhong Shan Second Road, Guangzhou, 510080, People’s Republic of China, Tel +8613822206519, Email likun22@mail.sysu.edu.cnBackground: Investigating adherence to the Atrial Fibrillation Better Care (ABC) pathway management and identifying gaps between the real world and established guidelines can lead to better integrated management of AF. Current data on adherence to ABC pathway management among community elderly patients with atrial fibrillation (AF) in Chinese communities is limited.Aim: To investigate the adherence to ABC pathway management among community elderly patients with AF in China.Methods: In this cross-sectional study, data were collected from the 2020 National Basic Public Health Service Program database that includes health examination information for all residents > 65 years of age in Yuexiu, Guangzhou, Guangdong Province. Demographic and clinical characteristics data from 197 community AF patients were obtained.Results: Among the 197 AF patients, 103 (52.3%) were male, 11

Key messageThe diploid wheat recessive stem rust resistance gene SrTm4 was fine-mapped to a 754-kb region on chromosome arm 2AmL and potential candidate genes were identified. Race Ug99 of Puccinia graminis f. sp. tritici (Pgt), the causal agent of wheat stem (or black) rust is one of the most serious threats to global wheat production. The identification, mapping, and deployment of effective stem rust resistance (Sr) genes are critical to reduce this threat. In this study, we generated SrTm4 monogenic lines and found that this gene confers resistance to North American and Chinese Pgt races. Using a large mapping population (9522 gametes), we mapped SrTm4 within a 0.06 cM interval flanked by marker loci CS4211 and 130K1519, which corresponds to a 1.0-Mb region in the Chinese Spring reference genome v2.1. A physical map of the SrTm4 region was constructed with 11 overlapping BACs from the resistant Triticum monococcum PI 306540. Comparison of the 754-kb physical map with the genomic sequence of Chinese Spring and a discontinuous BAC sequence of DV92 revealed a 593-kb chromosomal inversion in PI 306540. Within the candidate region, we identified an L-type lectin-domain containing receptor kinase (LLK1), which was disrupted by the proximal inversion breakpoint, as a potential candidate gene. Two diagnostic dominant markers were developed to detect the inversion breakpoints. In a survey of T. monococcum accessions, we identified 10 domesticated T. monococcum subsp. monococcum genotypes, mainly from the Balkans, carrying the inversion and showing similar mesothetic resistant infection types against Pgt races. The high-density map and tightly linked molecular markers developed in this study are useful tools to accelerate the deployment of SrTm4-mediated resistance in wheat breeding programs.

Kun Li,* Long Li,* Jie Wang* Department of Clinical Laboratory Medicine, Suining Central Hospital, Suining, Sichuan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Kun Li, Department of Clinical Laboratory Medicine, Suining Central Hospital, Suining, Sichuan, 629000, People’s Republic of China, Tel +86-13698355569, Email 286483463@qq.comPurpose: This study aimed to investigate the antibiotic resistance patterns and clinical distribution of blood culture-positive isolates at Suining Central Hospital between 2018 and 2021. The findings of this study can provide a basis for ensuring rational and effective use of antibiotic therapy in clinical settings.Methods: This retrospective study analyzed the data of 3660 non-repeating strains that tested positive for clinical blood culture, collected from the microbiology laboratory of Suining Central Hospital between January 2018 and December 2021. The identification of bacterial species and their antibiotic resistance patterns were analyzed.Results: The study found that 76.7% of the bacterial strains identified were Gram-negative bacteria, while 23.3% were Gram-positive bacteria. Escherichia coli (44.8%), Klebsiella spp. (19.2%), Staphylococcus aureus (9.2%), Enterococcus spp. (5.3%), and Enterobacter spp. were the top five bacterial ratios observed. These bacteria were detected most frequently in the Digestion Center, intensive care unit (ICU), Neurology Center, Urology Department, and Hematology Department. Among the Staphylococcus spp., methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-resistant coagulase-negative staphylococci (MRCNS) were detected at rates of 39.3% and 71.8%, respectively. However, no vancomycin- or linezolid-resistant staphylococci were identified. Enterococcus faecalis showed higher susceptibility to most antibiotic than Enterococcus faecium, except for tetracycline. The resistance rates of E. coli and Klebsiel

The role of formate species for CO2 hydrogenation is still under debate. Although formate has been frequently observed and commonly proposed as the possible intermediate, there is no definite evidence for the reaction of formate species for methanol production. Here, formate formation and conversion over the ZnZrOx solid solution catalyst are investigated by in situ/operando diffuse reflectance infrared Fourier transform spectroscopy-mass spectroscopy (DRIFTS-MS) coupled with density functional theory (DFT) calculations. Spectroscopic results show that bidentate carbonate formed from CO2 adsorption is hydrogenated to formate on Zn-O-Zr sites (asymmetric sites), where the Zn site is responsible for H2 activation and the Zr site is beneficial for the stabilization of reaction intermediates. The asymmetric Zn-O-Zr sites with adjacent and inequivalent features on the ZnZrOx catalyst promote not only formate formation but also its transformation. Both theoretical and experimental results demonstrate that the origin of the excellent performance of the ZnZrOx catalyst for methanol formation is associated with the H2 heterolytic cleavage promoted by the asymmetric Zn and Zr sites., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., ChemE/Inorganic Systems Engineering

Haoshaqiang Zhang,1,* Hua Shang,1,2,* Zhigang Wang,1 Kun Li1 1Department of Orthopedics Surgery, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, People’s Republic of China; 2Department of Human Resources, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhigang Wang, Department of Orthopedics Surgery, People’s Hospital of Xinjiang Uygur Autonomous Region, No. 91, Tianchi Road, Tianshan District, Urumqi, 830001, People’s Republic of China, Tel +86-9918563146, Email guanjie2023yundong@163.comPurpose: To analyze the independent associations of miRNA-146a and miRNA-223 with rheumatoid arthritis (RA) and evaluate their predictive values for RA.Patients and Methods: A total of 68 RA patients were selected as cases, and meanwhile 68 patients with a traumatic knee condition were selected as controls by matching to the cases according to sex and age at the ratio of 1:1. The independent associations of miRNA-146a and miRNA-223 with RA were identified by binary logistic regression analysis. Receiver operating characteristic (ROC) curve was used to evaluate their predictive values for RA.Results: MiRNA-146a and miRNA-223 expression levels in both synovial tissues and serums were statistically higher in cases than in controls, and their expression levels in serums were not statistically different from those in synovial tissues in both cases and controls. The expression levels of miRNA-146a and miRNA-223 in synovial tissues were independently associated with RA, as well as the expression levels of miRNA-146a and miRNA-223 in serums. The area under curve (AUC) of combination of miRNA-146a and miRNA-223 in synovial tissues for the prediction of RA was 0.910 [95% confidence interval (CI): 0.863– 0.962], and the AUC of combination of miRNA-146a and miRNA-223 in serums was 0.904 (95% CI: 0.851– 0.957). Their difference was not sta

Kun Li,1,2,* Bin Cao,1,2,* Jing Ke,1,2 Longyan Yang,1,2 Dong Zhao1,2 1Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, 101149, People’s Republic of China; 2Beijing Key Laboratory of Diabetes Research and Care, Beijing, 101149, People’s Republic of China*These authors contributed equally to this workCorrespondence: Dong Zhao; Longyan Yang, Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, 101149, People’s Republic of China, Tel +86-10-69543901, Email zhaodong@ccmu.edu.cn; lingbeiyangstudent@163.comBackground: Diabetic vascular complications are the leading cause of crippling and death of diabetic patients and seriously affect patients’ quality of life. It is essential to control the risk factors contributing to vascular complications in patients with Type 2 diabetes (T2DM). This study aimed to examine the association between hyper-triglyceridemic waist phenotype (HWP) and the risk of vascular complication index of diabetes in T2DM patients.Methods: The participants with type 2 diabetes mellitus in this study registered at the National Metabolic Management Center (MMC) of Beijing Luhe Hospital from June 2017 to June 2021. Data were collected by trained personnel according to the protocol. The questionnaire containing information on demographic characteristics and lifestyle factors (including alcohol drinking and cigarette smoking et al) was administered by trained interviewers. Logistic regression analysis assessing the associations between the hyper-triglyceridemic waist phenotype and vascular complication index of diabetes. In addition, the subgroup analysis was performed by age, sex, HbA1c, hypertension or not, and education level.Results: After data cleaning, a total of 3221 participants with T2DM were enrolled. The median (IQR) duration of diabetes was 47.00 (3.00, 125.00) months. Compared to the participan

Stencil computation is one of the most important kernels in various scientific computing. Nowadays, most Stencil-driven scientific computing still relies heavily on supercomputers, suffering from expensive access, poor scalability, and duplicated optimizations. This paper proposes Tetris, the first system for high-performance Stencil on heterogeneous CPU+GPU, towards democratizing Stencil-driven scientific computing on Cloud. In Tetris, polymorphic tiling tetrominoes are first proposed to bridge different hardware architectures and various application contexts with a perfect spatial and temporal tessellation automatically. Tetris is contributed by three main components: (1) Underlying hardware characteristics are first captured to achieve a sophisticated Pattern Mapping by register-level tetrominoes; (2) An efficient Locality Enhancer is first presented for data reuse on spatial and temporal dimensions simultaneously by cache/SMEM-level tetrominoes; (3) A novel Concurrent Scheduler is first designed to exploit the full potential of on-cloud memory and computing power by memory-level tetrominoes. Tetris is orthogonal to (and complements) the optimizations or deployments for a wide variety of emerging and legacy scientific computing applications. Results of thermal diffusion simulation demonstrate that the performance is improved by 29.6x, reducing time cost from day to hour, while preserving the original accuracy.

Angle-constrained formation control has attracted much attention from control community due to the advantage that inter-edge angles are invariant under uniform translations, rotations, and scalings of the whole formation. However, almost all the existing angle-constrained formation control methods are limited to undirected triangulated sensing graphs. In this paper, we propose an angle-constrained formation control approach under a Leader-First Follower sensing architecture, where the sensing graph is directed and non-triangulated. Both shape stabilization and maneuver control are achieved under arbitrary initial configurations of the formation. During the formation process, the control input of each agent is based on relative positions from its neighbors measured in the local reference frame and wireless communications among agents are not required. We show that the proposed distributed formation controller ensures global exponential stability of the desired formation for an nagent system. Furthermore, it is interesting to see that the convergence rate of the whole formation is solely determined by partial specific angles within the target formation. The effectiveness of the proposed control algorithms is illustrated by carrying out experiments both in simulation environments and on real robotic platforms., Comment: This paper is the extended version of our paper published in Automatica

In this manuscript (ms), we propose causal inference based single-branch ensemble trees for uplift modeling, namely CIET. Different from standard classification methods for predictive probability modeling, CIET aims to achieve the change in the predictive probability of outcome caused by an action or a treatment. According to our CIET, two partition criteria are specifically designed to maximize the difference in outcome distribution between the treatment and control groups. Next, a novel single-branch tree is built by taking a top-down node partition approach, and the remaining samples are censored since they are not covered by the upper node partition logic. Repeating the tree-building process on the censored data, single-branch ensemble trees with a set of inference rules are thus formed. Moreover, CIET is experimentally demonstrated to outperform previous approaches for uplift modeling in terms of both area under uplift curve (AUUC) and Qini coefficient significantly. At present, CIET has already been applied to online personal loans in a national financial holdings group in China. CIET will also be of use to analysts applying machine learning techniques to causal inference in broader business domains such as web advertising, medicine and economics.

Image-based multi-person reconstruction in wide-field large scenes is critical for crowd analysis and security alert. However, existing methods cannot deal with large scenes containing hundreds of people, which encounter the challenges of large number of people, large variations in human scale, and complex spatial distribution. In this paper, we propose Crowd3D, the first framework to reconstruct the 3D poses, shapes and locations of hundreds of people with global consistency from a single large-scene image. The core of our approach is to convert the problem of complex crowd localization into pixel localization with the help of our newly defined concept, Human-scene Virtual Interaction Point (HVIP). To reconstruct the crowd with global consistency, we propose a progressive reconstruction network based on HVIP by pre-estimating a scene-level camera and a ground plane. To deal with a large number of persons and various human sizes, we also design an adaptive human-centric cropping scheme. Besides, we contribute a benchmark dataset, LargeCrowd, for crowd reconstruction in a large scene. Experimental results demonstrate the effectiveness of the proposed method. The code and datasets will be made public., Comment: Accepted by CVPR 2023

Visual question answering (VQA) is an important and challenging multimodal task in computer vision. Recently, a few efforts have been made to bring VQA task to aerial images, due to its potential real-world applications in disaster monitoring, urban planning, and digital earth product generation. However, not only the huge variation in the appearance, scale and orientation of the concepts in aerial images, but also the scarcity of the well-annotated datasets restricts the development of VQA in this domain. In this paper, we introduce a new dataset, HRVQA, which provides collected 53512 aerial images of 1024*1024 pixels and semi-automatically generated 1070240 QA pairs. To benchmark the understanding capability of VQA models for aerial images, we evaluate the relevant methods on HRVQA. Moreover, we propose a novel model, GFTransformer, with gated attention modules and a mutual fusion module. The experiments show that the proposed dataset is quite challenging, especially the specific attribute related questions. Our method achieves superior performance in comparison to the previous state-of-the-art approaches. The dataset and the source code will be released at https://hrvqa.nl/.

Recent learning-based approaches have achieved significant progress in light field (LF) image super-resolution (SR) by exploring convolution-based or transformer-based network structures. However, LF imaging has many intrinsic physical priors that have not been fully exploited. In this paper, we analyze the coordinate transformation of the LF imaging process to reveal the geometric relationship in the LF images. Based on such geometric priors, we introduce a new LF subspace of virtual-slit images (VSI) that provide sub-pixel information complementary to sub-aperture images. To leverage the abundant correlation across the four-dimensional data with manageable complexity, we propose learning ensemble representation of all $C_4^2$ LF subspaces for more effective feature extraction. To super-resolve image structures from undersampled LF data, we propose a geometry-aware decoder, named EPIXformer, which constrains the transformer's operational searching regions with a LF physical prior. Experimental results on both spatial and angular SR tasks demonstrate that the proposed method outperforms other state-of-the-art schemes, especially in handling various disparities.

Malware detection models based on deep learning have been widely used, but recent research shows that deep learning models are vulnerable to adversarial attacks. Adversarial attacks are to deceive the deep learning model by generating adversarial samples. When adversarial attacks are performed on the malware detection model, the attacker will generate adversarial malware with the same malicious functions as the malware, and make the detection model classify it as benign software. Studying adversarial malware generation can help model designers improve the robustness of malware detection models. At present, in the work on adversarial malware generation for byte-to-image malware detection models, there are mainly problems such as large amount of injection perturbation and low generation efficiency. Therefore, this paper proposes FGAM (Fast Generate Adversarial Malware), a method for fast generating adversarial malware, which iterates perturbed bytes according to the gradient sign to enhance adversarial capability of the perturbed bytes until the adversarial malware is successfully generated. It is experimentally verified that the success rate of the adversarial malware deception model generated by FGAM is increased by about 84\% compared with existing methods.

Building end-to-end task bots and maintaining their integration with new functionalities using minimal human efforts is a long-standing challenge in dialog research. Recently large language models (LLMs) have demonstrated exceptional proficiency in conversational engagement and adherence to instructions across various downstream tasks. In this work, we introduce SGP-TOD, Schema-Guided Prompting for building Task-Oriented Dialog systems effortlessly based on LLMs. Utilizing the symbolic knowledge -- task schema, we instruct fixed LLMs to generate appropriate responses on novel tasks, circumventing the need for training data. Specifically, SGP-TOD comprises three components: a LLM for engaging with users, a DST Prompter to aid the LLM with dialog state tracking, which is then used to retrieve database items, and a Policy Prompter to elicit proper responses adhering to the provided dialog policy. Experimental results on Multiwoz, RADDLE and STAR datasets show that our training-free strategy SGP-TOD, without any task-specific data, yields state-of-the-art (SOTA) zero-shot performance, greatly surpasses the few-shot approaches. In a domain-extension setting, SGP-TOD aptly adapts to new functionalities by merely adding supplementary schema rules. We make our code and data publicly available., Comment: 21 pages, 13 figures

The lack of freely available standardized datasets represents an aggravating factor during the development and testing the performance of novel computational techniques in exposure assessment and dosimetry research. This hinders progress as researchers are required to generate numerical data (field, power and temperature distribution) anew using simulation software for each exposure scenario. Other than being time consuming, this approach is highly susceptible to errors that occur during the configuration of the electromagnetic model. To address this issue, in this paper, the limited available data on the incident power density and resultant maximum temperature rise on the skin surface considering various steady-state exposure scenarios at 10$-$90 GHz have been statistically modeled. The synthetic data have been sampled from the fitted statistical multivariate distribution with respect to predetermined dosimetric constraints. We thus present a comprehensive and open-source dataset compiled of the high-fidelity numerical data considering various exposures to a realistic source. Furthermore, different surrogate models for predicting maximum temperature rise on the skin surface were fitted based on the synthetic dataset. All surrogate models were tested on the originally available data where satisfactory predictive performance has been demonstrated. A simple technique of combining quadratic polynomial and tensor-product spline surrogates, each operating on its own cluster of data, has achieved the lowest mean absolute error of 0.058 {\deg}C. Therefore, overall experimental results indicate the validity of the proposed synthetic dataset., Comment: 6 pages, 3 figures, in proceedings of BioEM2023

Recently, cross-source point cloud registration from different sensors has become a significant research focus. However, traditional methods confront challenges due to the varying density and structure of cross-source point clouds. In order to solve these problems, we propose a cross-source point cloud fusion algorithm called HybridFusion. It can register cross-source dense point clouds from different viewing angle in outdoor large scenes. The entire registration process is a coarse-to-fine procedure. First, the point cloud is divided into small patches, and a matching patch set is selected based on global descriptors and spatial distribution, which constitutes the coarse matching process. To achieve fine matching, 2D registration is performed by extracting 2D boundary points from patches, followed by 3D adjustment. Finally, the results of multiple patch pose estimates are clustered and fused to determine the final pose. The proposed approach is evaluated comprehensively through qualitative and quantitative experiments. In order to compare the robustness of cross-source point cloud registration, the proposed method and generalized iterative closest point method are compared. Furthermore, a metric for describing the degree of point cloud filling is proposed. The experimental results demonstrate that our approach achieves state-of-the-art performance in cross-source point cloud registration.

As robotics technology advances, dense point cloud maps are increasingly in demand. However, dense reconstruction using a single unmanned aerial vehicle (UAV) suffers from limitations in flight speed and battery power, resulting in slow reconstruction and low coverage. Cluster UAV systems offer greater flexibility and wider coverage for map building. Existing methods of cluster UAVs face challenges with accurate relative positioning, scale drift, and high-speed dense point cloud map generation. To address these issues, we propose a cluster framework for large-scale dense reconstruction and real-time collaborative localization. The front-end of the framework is an improved visual odometry which can effectively handle large-scale scenes. Collaborative localization between UAVs is enabled through a two-stage joint optimization algorithm and a relative pose optimization algorithm, effectively achieving accurate relative positioning of UAVs and mitigating scale drift. Estimated poses are used to achieve real-time dense reconstruction and fusion of point cloud maps. To evaluate the performance of our proposed method, we conduct qualitative and quantitative experiments on real-world data. The results demonstrate that our framework can effectively suppress scale drift and generate large-scale dense point cloud maps in real-time, with the reconstruction speed increasing as more UAVs are added to the system.

Naturally controllable human-scene interaction (HSI) generation has an important role in various fields, such as VR/AR content creation and human-centered AI. However, existing methods are unnatural and unintuitive in their controllability, which heavily limits their application in practice. Therefore, we focus on a challenging task of naturally and controllably generating realistic and diverse HSIs from textual descriptions. From human cognition, the ideal generative model should correctly reason about spatial relationships and interactive actions. To that end, we propose Narrator, a novel relationship reasoning-based generative approach using a conditional variation autoencoder for naturally controllable generation given a 3D scene and a textual description. Also, we model global and local spatial relationships in a 3D scene and a textual description respectively based on the scene graph, and introduce a partlevel action mechanism to represent interactions as atomic body part states. In particular, benefiting from our relationship reasoning, we further propose a simple yet effective multi-human generation strategy, which is the first exploration for controllable multi-human scene interaction generation. Our extensive experiments and perceptual studies show that Narrator can controllably generate diverse interactions and significantly outperform existing works. The code and dataset will be available for research purposes., Comment: Project page: http://cic.tju.edu.cn/faculty/likun/projects/Narrator

Jing Ke,1,2 Kun Li,1,2 Bin Cao1,2 1Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, 101149, People’s Republic of China; 2Beijing Key Laboratory of Diabetes Research and Care, Beijing, 101149, People’s Republic of ChinaCorrespondence: Bin Cao, Tel +86-10-6954-3901, Email dor_caobin666@163.comAim: This study aims to develop a nomogram for predicting vision-threatening diabetic retinopathy (VTDR) in type 2 diabetes mellitus (T2DM) with mild non-proliferative diabetic retinopathy (NPDR) patients.Materials and Methods: In case–control analysis, 440 patients with mild NPDR or VTDR were enrolled to identify predictors and develop a nomogram. In the prospective cohort, 120 T2DM patients with mild NPDR were enrolled for external validation. Sensitivity, specificity, and area under the receiver operating characteristic (AUC) were calculated to evaluate the predictive performance of the nomogram.Results: In case–control analysis, 2-h C-peptide (OR = 0.85, 95% CI: 0.75 to 0.95, p = 0.006), sural nerve conduction impaired (SNCI) (mildly: OR = 2.18, 95% CI: 1.10 to 4.33, p = 0.026; moderately/severely: 3.66, 95% CI: 1.74 to 7.70, p < 0.001) and UACR (microalbuminuria: OR = 2.37, 95% CI: 1.25 to 4.48, p = 0.008; macroalbuminuria: 4.02, 95% CI: 1.61 to 10.06, p = 0.003) were identified as independent predictors. The concordance index of the prediction nomogram was 0.76 in the training set. In the test set, sensitivity, specificity, and AUC were 84.8%, 60.6%, and 0.73, respectively. In the prospective cohort, median follow-up period was 42 months, and 15 patients (12.5%) developed VTDR. Sensitivity, specificity, and AUC of prediction were 66.7%, 89.5%, and 0.75, respectively.Conclusion: Introducing 2-h C-peptide, UACR, and SNCI, the nomogram demonstrated a good discriminatory power for predicting risk of VTDR in mild NPDR individuals.Keywords: nomogram, diabetic retinopathy, type 2 diabetes mellitus, predicti

Cross-matching operation, which is to find corresponding data for the same celestial object or region from multiple catalogues,is indispensable to astronomical data analysis and research. Due to the large amount of astronomical catalogues generated by the ongoing and next-generation large-scale sky surveys, the time complexity of the cross-matching is increasing dramatically. Heterogeneous computing environments provide a theoretical possibility to accelerate the cross-matching, but the performance advantages of heterogeneous computing resources have not been fully utilized. To meet the challenge of cross-matching for substantial increasing amount of astronomical observation data, this paper proposes Heterogeneous-computing-enabled Large Catalogue Cross-matcher (HLC2), a high-performance cross-matching framework based on spherical position deviation on CPU-GPU heterogeneous computing platforms. It supports scalable and flexible cross-matching and can be directly applied to the fusion of large astronomical cataloguesfrom survey missions and astronomical data centres. A performance estimation model is proposed to locate the performance bottlenecks and guide the optimizations. A two-level partitioning strategy is designed to generate an optimized data placement according to the positions of celestial objects to increase throughput. To make HLC2 a more adaptive solution, the architecture-aware task splitting, thread parallelization, and concurrent scheduling strategies are designed and integrated. Moreover, a novel quad-direction strategy is proposed for the boundary problem to effectively balance performance and completeness. We have experimentally evaluated HLC2 using public released catalogue data. Experiments demonstrate that HLC2 scales well on different sizes of catalogues and the cross-matching speed is significantly improved compared to the state-of-the-art cross-matchers., Comment: Accepted for publication in Monthly Notices of the Royal Astronomical Society

Dual-lens super-resolution (SR) is a practical scenario for reference (Ref) based SR by utilizing the telephoto image (Ref) to assist the super-resolution of the low-resolution wide-angle image (LR input). Different from general RefSR, the Ref in dual-lens SR only covers the overlapped field of view (FoV) area. However, current dual-lens SR methods rarely utilize these specific characteristics and directly perform dense matching between the LR input and Ref. Due to the resolution gap between LR and Ref, the matching may miss the best-matched candidate and destroy the consistent structures in the overlapped FoV area. Different from them, we propose to first align the Ref with the center region (namely the overlapped FoV area) of the LR input by combining global warping and local warping to make the aligned Ref be sharp and consistent. Then, we formulate the aligned Ref and LR center as value-key pairs, and the corner region of the LR is formulated as queries. In this way, we propose a kernel-free matching strategy by matching between the LR-corner (query) and LR-center (key) regions, and the corresponding aligned Ref (value) can be warped to the corner region of the target. Our kernel-free matching strategy avoids the resolution gap between LR and Ref, which makes our network have better generalization ability. In addition, we construct a DuSR-Real dataset with (LR, Ref, HR) triples, where the LR and HR are well aligned. Experiments on three datasets demonstrate that our method outperforms the second-best method by a large margin. Our code and dataset are available at https://github.com/ZifanCui/KeDuSR., Comment: 14 pages, 10 figures. Accepted by AAAI-2024

Deep learning-based drug response prediction (DRP) methods can accelerate the drug discovery process and reduce R\&D costs. Although the mainstream methods achieve high accuracy in predicting response regression values, the regression-aware representations of these methods are fragmented and fail to capture the continuity of the sample order. This phenomenon leads to models optimized to sub-optimal solution spaces, reducing generalization ability and may result in significant wasted costs in the drug discovery phase. In this paper, we propose \MN, a contrastive learning framework with natural language supervision for the DRP. The \MN~converts regression labels into text, which is merged with the captions text of the drug response as a second modality of the samples compared to the traditional modalities (graph, sequence). In each batch, two modalities of one sample are considered positive pairs and the other pairs are considered negative pairs. At the same time, in order to enhance the continuous representation capability of the numerical text, a common-sense numerical knowledge graph is introduced. We validated several hundred thousand samples from the Genomics of Drug Sensitivity in Cancer dataset, observing the average improvement of the DRP method ranges from 7.8\% to 31.4\% with the application of our framework. The experiments prove that the \MN~effectively constrains the samples to a continuous distribution in the representation space, and achieves impressive prediction performance with only a few epochs of fine-tuning after pre-training. The code is available at: \url{https://gitee.com/xiaoyibang/clipdrug.git}., Comment: 9 pages, 4 figures, 3 tables

3D human generation is increasingly significant in various applications. However, the direct use of 2D generative methods in 3D generation often results in losing local details, while methods that reconstruct geometry from generated images struggle with global view consistency. In this work, we introduce Joint2Human, a novel method that leverages 2D diffusion models to generate detailed 3D human geometry directly, ensuring both global structure and local details. To achieve this, we employ the Fourier occupancy field (FOF) representation, enabling the direct generation of 3D shapes as preliminary results with 2D generative models. With the proposed high-frequency enhancer and the multi-view recarving strategy, our method can seamlessly integrate the details from different views into a uniform global shape. To better utilize the 3D human prior and enhance control over the generated geometry, we introduce a compact spherical embedding of 3D joints. This allows for an effective guidance of pose during the generation process. Additionally, our method can generate 3D humans guided by textual inputs. Our experimental results demonstrate the capability of our method to ensure global structure, local details, high resolution, and low computational cost simultaneously. More results and the code can be found on our project page at http://cic.tju.edu.cn/faculty/likun/projects/Joint2Human.

Rendering 3D human appearance in different views is crucial for achieving holographic communication and immersive VR/AR. Existing methods either rely on multi-camera setups or have low-quality rendered images from a single image. In this paper, we propose R2Human, the first approach for real-time inference and rendering of photorealistic 3D human appearance from a single image. The core of our approach is to combine the strengths of implicit texture fields and explicit neural rendering with our novel representation, namely Z-map. Based on this, we present an end-to-end network that performs high-fidelity color reconstruction of visible areas and provides reliable color inference for occluded regions. To further enhance the 3D perception ability of our network, we leverage the Fourier occupancy field as a prior for generating the texture field and providing a sampling surface in the rendering stage. We also propose a consistency loss and a spatio-temporal fusion strategy to ensure the multi-view coherence. Experimental results show that our method outperforms the state-of-the-art methods on both synthetic data and challenging real-world images, in real time.

The generation of 3D clothed humans has attracted increasing attention in recent years. However, existing work cannot generate layered high-quality 3D humans with consistent body structures. As a result, these methods are unable to arbitrarily and separately change and edit the body and clothing of the human. In this paper, we propose a text-driven layered 3D human generation framework based on a novel physically-decoupled semantic-aware diffusion model. To keep the generated clothing consistent with the target text, we propose a semantic-confidence strategy for clothing that can eliminate the non-clothing content generated by the model. To match the clothing with different body shapes, we propose a SMPL-driven implicit field deformation network that enables the free transfer and reuse of clothing. Besides, we introduce uniform shape priors based on the SMPL model for body and clothing, respectively, which generates more diverse 3D content without being constrained by specific templates. The experimental results demonstrate that the proposed method not only generates 3D humans with consistent body structures but also allows free editing in a layered manner. The source code will be made public.

Video Motion Magnification (VMM) aims to break the resolution limit of human visual perception capability and reveal the imperceptible minor motion that contains valuable information in the macroscopic domain. However, challenges arise in this task due to photon noise inevitably introduced by photographic devices and spatial inconsistency in amplification, leading to flickering artifacts in static fields and motion blur and distortion in dynamic fields in the video. Existing methods focus on explicit motion modeling without emphasizing prioritized denoising during the motion magnification process. This paper proposes a novel dynamic filtering strategy to achieve static-dynamic field adaptive denoising. Specifically, based on Eulerian theory, we separate texture and shape to extract motion representation through inter-frame shape differences, expecting to leverage these subdivided features to solve this task finely. Then, we introduce a novel dynamic filter that eliminates noise cues and preserves critical features in the motion magnification and amplification generation phases. Overall, our unified framework, EulerMormer, is a pioneering effort to first equip with Transformer in learning-based VMM. The core of the dynamic filter lies in a global dynamic sparse cross-covariance attention mechanism that explicitly removes noise while preserving vital information, coupled with a multi-scale dual-path gating mechanism that selectively regulates the dependence on different frequency features to reduce spatial attenuation and complement motion boundaries. We demonstrate extensive experiments that EulerMormer achieves more robust video motion magnification from the Eulerian perspective, significantly outperforming state-of-the-art methods. The source code is available at https://github.com/VUT-HFUT/EulerMormer.

This paper addresses the problem of generating whole-body motion from speech. Despite great successes, prior methods still struggle to produce reasonable and diverse whole-body motions from speech. This is due to their reliance on suboptimal representations and a lack of strategies for generating diverse results. To address these challenges, we present a novel hybrid point representation to achieve accurate and continuous motion generation, e.g., avoiding foot skating, and this representation can be transformed into an easy-to-use representation, i.e., SMPL-X body mesh, for many applications. To generate whole-body motion from speech, for facial motion, closely tied to the audio signal, we introduce an encoder-decoder architecture to achieve deterministic outcomes. However, for the body and hands, which have weaker connections to the audio signal, we aim to generate diverse yet reasonable motions. To boost diversity in motion generation, we propose a contrastive motion learning method to encourage the model to produce more distinctive representations. Specifically, we design a robust VQ-VAE to learn a quantized motion codebook using our hybrid representation. Then, we regress the motion representation from the audio signal by a translation model employing our contrastive motion learning method. Experimental results validate the superior performance and the correctness of our model. The project page is available for research purposes at http://cic.tju.edu.cn/faculty/likun/projects/SpeechAct., Comment: the manuscript should be revised

Much progress has been made in reconstructing garments from an image or a video. However, none of existing works meet the expectations of digitizing high-quality animatable dynamic garments that can be adjusted to various unseen poses. In this paper, we propose the first method to recover high-quality animatable dynamic garments from monocular videos without depending on scanned data. To generate reasonable deformations for various unseen poses, we propose a learnable garment deformation network that formulates the garment reconstruction task as a pose-driven deformation problem. To alleviate the ambiguity estimating 3D garments from monocular videos, we design a multi-hypothesis deformation module that learns spatial representations of multiple plausible deformations. Experimental results on several public datasets demonstrate that our method can reconstruct high-quality dynamic garments with coherent surface details, which can be easily animated under unseen poses. The code will be provided for research purposes.

Group detection, especially for large-scale scenes, has many potential applications for public safety and smart cities. Existing methods fail to cope with frequent occlusions in large-scale scenes with multiple people, and are difficult to effectively utilize spatio-temporal information. In this paper, we propose an end-to-end framework,GroupTransformer, for group detection in large-scale scenes. To deal with the frequent occlusions caused by multiple people, we design an occlusion encoder to detect and suppress severely occluded person crops. To explore the potential spatio-temporal relationship, we propose spatio-temporal transformers to simultaneously extract trajectory information and fuse inter-person features in a hierarchical manner. Experimental results on both large-scale and small-scale scenes demonstrate that our method achieves better performance compared with state-of-the-art methods. On large-scale scenes, our method significantly boosts the performance in terms of precision and F1 score by more than 10%. On small-scale scenes, our method still improves the performance of F1 score by more than 5%. The project page with code can be found at http://cic.tju.edu.cn/faculty/likun/projects/GroupTrans., Comment: 11 pages, 5 figures

We study model extraction attacks in natural language processing (NLP) where attackers aim to steal victim models by repeatedly querying the open Application Programming Interfaces (APIs). Recent works focus on limited-query budget settings and adopt random sampling or active learning-based sampling strategies on publicly available, unannotated data sources. However, these methods often result in selected queries that lack task relevance and data diversity, leading to limited success in achieving satisfactory results with low query costs. In this paper, we propose MeaeQ (Model extraction attack with efficient Queries), a straightforward yet effective method to address these issues. Specifically, we initially utilize a zero-shot sequence inference classifier, combined with API service information, to filter task-relevant data from a public text corpus instead of a problem domain-specific dataset. Furthermore, we employ a clustering-based data reduction technique to obtain representative data as queries for the attack. Extensive experiments conducted on four benchmark datasets demonstrate that MeaeQ achieves higher functional similarity to the victim model than baselines while requiring fewer queries. Our code is available at https://github.com/C-W-D/MeaeQ., Comment: Accepted by EMNLP 2023 main conference