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We present the results of direct-method metallicity measurements in the disk and outflow of the low-metallicity starburst galaxy NGC 1569. We use Keck Cosmic Web Imager observations to map the galaxy across 54$\arcsec$ (800 pc) along the major axis and 48$\arcsec$ (700 pc) along the minor axis with a spatial resolution of 1$\arcsec$ ($\sim$15 pc). We detect common strong emission lines ([\ion{O}{III}] $\lambda$5007, H$\beta$, [\ion{O}{II}] $\lambda$3727) and the fainter [\ion{O}{III}] $\lambda$4363 auroral line, which allows us to measure electron temperature ($T_e$) and metallicity. Theory suggests that outflows drive metals out of the disk driving observed trends between stellar mass and gas-phase metallicity. Our main result is that the metallicity in the outflow is similar to that of the disk, $Z_{\rm out} / Z_{\rm ISM} \approx 1$. This is consistent with previous absorption line studies in higher mass galaxies. Assumption of a mass-loading factor of $\dot{M}_{\rm out}/{\rm SFR}\sim3$ makes the metal-loading of NGC 1569 consistent with expectations derived from the mass-metallicity relationship. Our high spatial resolution metallicity maps reveal a region around a supermassive star cluster (SSC-B) with distinctly higher metallicity and higher electron density, compared to the disk. Given the known properties of SSC-B the higher metallicity and density of this region are likely the result of star formation-driven feedback acting on the local scale. Overall, our results are consistent with the picture in which metal-enriched winds pollute the circumgalactic medium surrounding galaxies, and thus connect the small-scale feedback processes to large-scale properties of galaxy halos., Comment: 15 pages, 11 figures, accepted by MNRAS

We measure resolved (kiloparsec-scale) outflow properties in a sample of 10 starburst galaxies from the DUVET sample, using Keck/KCWI observations of H$\beta$ and [OIII]~$\lambda$5007. We measure $\sim450$ lines-of-sight that contain outflows, and use these to study scaling relationships of outflow velocity ($v_{\rm out}$), mass-loading factor ($\eta$; mass outflow rate per SFR) and mass flux ($\dot{\Sigma}_{\rm out}$; mass outflow rate per area) with co-located SFR surface density ($\Sigma_{\rm SFR}$) and stellar mass surface density ($\Sigma_{\ast}$). We find strong, positive correlations of $\dot{\Sigma}_{\rm out} \propto \Sigma_{\rm SFR}^{1.2}$ and $\dot{\Sigma}_{\rm out} \propto \Sigma_{\ast}^{1.7}$. We also find shallow correlations between $v_{\rm out}$ and both $\Sigma_{\rm SFR}$ and $\Sigma_{\ast}$. Our resolved observations do not suggest a threshold in outflows with $\Sigma_{\rm SFR}$, but rather we find that the local specific SFR ($\Sigma_{\rm SFR}/\Sigma_\ast$) is a better predictor of where outflows are detected. We find that outflows are very common above $\Sigma_{\rm SFR}/\Sigma_\ast\gtrsim 0.1$~Gyr$^{-1}$ and rare below this value. We argue that our results are consistent with a picture in which outflows are driven by supernovae, and require more significant injected energy in higher mass surface density environments to overcome local gravity. The correlations we present here provide a statistically robust, direct comparison for simulations and higher redshift results from JWST., Comment: 14 pages, 7 figures, plus 4 figures in appendix, submitted to MNRAS

We present NewsBench, a novel evaluation framework to systematically assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism. Our constructed benchmark dataset is focused on four facets of writing proficiency and six facets of safety adherence, and it comprises manually and carefully designed 1,267 test samples in the types of multiple choice questions and short answer questions for five editorial tasks in 24 news domains. To measure performances, we propose different GPT-4 based automatic evaluation protocols to assess LLM generations for short answer questions in terms of writing proficiency and safety adherence, and both are validated by the high correlations with human evaluations. Based on the systematic evaluation framework, we conduct a comprehensive analysis of ten popular LLMs which can handle Chinese. The experimental results highlight GPT-4 and ERNIE Bot as top performers, yet reveal a relative deficiency in journalistic safety adherence in creative writing tasks. Our findings also underscore the need for enhanced ethical guidance in machine-generated journalistic content, marking a step forward in aligning LLMs with journalistic standards and safety considerations., Comment: Long paper, ACL 2024 Main

Modern natural language generation systems with Large Language Models (LLMs) exhibit the capability to generate a plausible summary of multiple documents; however, it is uncertain if they truly possess the capability of information consolidation to generate summaries, especially on documents with opinionated information. We focus on meta-review generation, a form of sentiment summarisation for the scientific domain. To make scientific sentiment summarization more grounded, we hypothesize that human meta-reviewers follow a three-layer framework of sentiment consolidation to write meta-reviews. Based on the framework, we propose novel prompting methods for LLMs to generate meta-reviews and evaluation metrics to assess the quality of generated meta-reviews. Our framework is validated empirically as we find that prompting LLMs based on the framework -- compared with prompting them with simple instructions -- generates better meta-reviews., Comment: Long paper, ACL 2024 Main

Language models can learn sophisticated language understanding skills from fitting raw text. They also unselectively learn useless corpus statistics and biases, especially during finetuning on domain-specific corpora. In this paper, we propose a simple modification to causal language modeling called conditional finetuning, which performs language modeling conditioned on a context. We show that a context can "explain away" certain corpus statistics and make the model avoid learning them. In this fashion, conditional finetuning achieves selective learning from a corpus, learning knowledge useful for downstream tasks while avoiding learning useless corpus statistics like topic biases. This selective learning effect leads to less forgetting and better stability-plasticity tradeoff in domain finetuning, potentially benefitting lifelong learning with language models., Comment: To appear at the 41st International Conference on Machine Learning (ICML 2024)

We present TinyLLaVA Factory, an open-source modular codebase for small-scale large multimodal models (LMMs) with a focus on simplicity of code implementations, extensibility of new features, and reproducibility of training results. Following the design philosophy of the factory pattern in software engineering, TinyLLaVA Factory modularizes the entire system into interchangeable components, with each component integrating a suite of cutting-edge models and methods, meanwhile leaving room for extensions to more features. In addition to allowing users to customize their own LMMs, TinyLLaVA Factory provides popular training recipes to let users pretrain and finetune their models with less coding effort. Empirical experiments validate the effectiveness of our codebase. The goal of TinyLLaVA Factory is to assist researchers and practitioners in exploring the wide landscape of designing and training small-scale LMMs with affordable computational resources., Comment: Our codebase is made public at https://github.com/TinyLLaVA/TinyLLaVA_Factory with documentation available at https://tinyllava-factory.readthedocs.io/en/latest

Advances in cell-based therapy, particularly CAR-T cell therapy, have transformed the treatment of hematological malignancies. Although an important step forward for the field, autologous CAR-T therapies are hindered by high costs, manufacturing challenges, and limited efficacy against solid tumors. With ongoing progress in gene editing and culture techniques, engineered stem cells and their application in cell therapy are poised to address some of these challenges. Here, we review stem cell-based immunotherapy approaches, stem cell sources, gene engineering and manufacturing strategies, therapeutic platforms, and clinical trials, as well as challenges and future directions for the field.

Fang-Yi Yu,* Kua Zheng,* Yin-Fang Wu,* Shen-Wei Gao, Qing-Yu Weng, Chen Zhu, Yan-Ping Wu, Miao Li, Zhong-Nan Qin, Jia-Fei Lou, Zhi-Hua Chen, Song-Min Ying, Hua-Hao Shen, Wen Li Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wen Li, Tel +86-571-87783729, Fax +86-571-87068001, Email liwen@zju.edu.cnPurpose: This study aimed to explore the effect of Rapamycin (Rapa) in Staphylococcus aureus (S. aureus) pneumonia and clarify its possible mechanism.Methods: We investigated the effects of Rapa on S. aureus pneumonia in mouse models and in macrophages cultured in vitro. Two possible mechanisms were investigated: the mTOR-RPS6 pathway phosphorylation and phagocytosis. Furthermore, for the mechanism verification in vivo, mice with specific Mtor knockout in myeloid cells were constructed for pneumonia models.Results: Rapa exacerbated S. aureus pneumonia in mouse models, promoting chemokines secretion and inflammatory cells infiltration in lung. In vitro, Rapa upregulated the secretion of chemokines and cytokines in macrophages induced by S. aureus. Mechanistically, the mTOR-ribosomal protein S6 (RPS6) pathway in macrophages was phosphorylated in response to S. aureus infection, and the inhibition of RPS6 phosphorylation upregulated the inflammation level. However, Rapa did not increase the phagocytic activity. Accordingly, mice with specific Mtor knockout in myeloid cells experienced more severe S. aureus pneumonia.Conclusion: Rapa exacerbates S. aureus pneumonia by increasing the inflammatory levels of macrophages. Inhibition of mTOR-RPS6 pathway upregulates the expression of cytokines and chemokines in macrophages, thus increases inflammatory cells infiltration and exacerbates tissue damage.Keywords

We present a method to characterize star-formation driven outflows from edge-on galaxies and apply this method to the metal-poor starburst galaxy, Mrk 1486. Our method uses the distribution of emission line flux (from H$\beta$ and [OIII] 5007) to identify the location of the outflow and measure the extent above the disk, the opening angle, and the transverse kinematics. We show that this simple technique recovers a similar distribution of the outflow without requiring complex modelling of line-splitting or multi-Gaussian components, and is therefore applicable to lower spectral resolution data. In Mrk 1486 we observe an asymmetric outflow in both the location of the peak flux and total flux from each lobe. We estimate an opening angle of $17-37^{\circ}$ depending on the method and assumptions adopted. Within the minor axis outflows, we estimate a total mass outflow rate of $\sim2.5$ M$_{\odot}$ yr$^{-1}$, which corresponds to a mass loading factor of $\eta=0.7$. We observe a non-negligible amount of flux from ionized gas outflowing along the edge of the disk (perpendicular to the biconical components), with a mass outflow rate $\sim0.9$ M$_{\odot}$ yr$^{-1}$. Our results are intended to demonstrate a method that can be applied to high-throughput, low spectral resolution observations, such as narrow band filters or low spectral resolution IFS that may be more able to recover the faint emission from outflows., Comment: 12 Pages, 6 Figures

The Hot Universe Baryon Surveyor (HUBS) is a proposed space-based X-ray telescope for detecting X-ray emissions from the hot gas content in our universe. With its unprecedented spatially-resolved high-resolution spectroscopy and large field of view, the HUBS mission will be uniquely qualified to measure the physical and chemical properties of the hot gas in the interstellar medium, the circumgalactic medium, the intergalactic medium, and the intracluster medium. These measurements will be valuable for two key scientific goals of HUBS, namely to unravel the AGN and stellar feedback physics that governs the formation and evolution of galaxies, and to probe the baryon budget and multi-phase states from galactic to cosmological scales. In addition to these two goals, the HUBS mission will also help us solve some problems in the fields of galaxy clusters, AGNs, diffuse X-ray backgrounds, supernova remnants, and compact objects. This paper discusses the perspective of advancing these fields using the HUBS telescope., Comment: 52 pages, 22 figures. Accepted for publication in Science China: Physics, Mechanics and Astronomy

High-velocity atomic clouds in the Galactic center have attracted significant attention due to their enigmatic formation process, which is potentially linked to the starburst or supermassive black hole activities in the region. Further, the discovery of high-velocity molecular clouds (HVMCs) presents a greater puzzle, because they are much denser and more massive. If the HVMCs were accelerated by the strong activities in the Galactic center, they are expected to be destroyed before they reach such a high velocity. To shed light on this phenomenon, we perform three-dimensional numerical simulations to investigate the origin and hydrodynamic evolution of HVMCs during a starburst in the Galactic center. We find that the presence of a magnetic field provides effective protection and acceleration to molecular clouds (MCs) within the galactic winds. Consequently, the MCs can attain latitudes of approximately 1 kpc with velocities around 200 km/s, consistent with the observed characteristics of HVMCs. The consistency of our findings across a wide parameter space supports the conclusion that HVMCs can indeed withstand the starburst environment in the Galactic center, providing valuable insights into their survival mechanisms., Comment: 18 pages, 16 figures, submitted to MNRAS

Stars form in galaxies, from gas that has been accreted from the intergalactic medium. Simulations have shown that recycling of gas-the reaccretion of gas that was previously ejected from a galaxy-could sustain star formation in the early Universe. We observe the gas surrounding a massive galaxy at redshift 2.3 and detect emission lines from neutral hydrogen, helium, and ionized carbon that extend 100 kiloparsecs from the galaxy. The kinematics of this circumgalactic gas is consistent with an inspiraling stream. The carbon abundance indicates that the gas had already been enriched with elements heavier than helium, previously ejected from a galaxy. We interpret the results as evidence of gas recycling during high-redshift galaxy assembly., Comment: Published in Science, 5 May 2023 (accepted version), Main text 20 pages, four figures in the main text, and 13 figures and 4 tables in the supplementary materials

In view of the classical visual servoing trajectory planning method which only considers the camera trajectory, this paper proposes one homography matrix based trajectory planning method for robot uncalibrated visual servoing. Taking the robot-end-effector frame as one generic case, eigenvalue decomposition is utilized to calculate the infinite homography matrix of the robot-end-effector trajectory, and then the image feature-point trajectories corresponding to the camera rotation is obtained, while the image feature-point trajectories corresponding to the camera translation is obtained by the homography matrix. According to the additional image corresponding to the robot-end-effector rotation, the relationship between the robot-end-effector rotation and the variation of the image feature-points is obtained, and then the expression of the image trajectories corresponding to the optimal robot-end-effector trajectories (the rotation trajectory of the minimum geodesic and the linear translation trajectory) are obtained. Finally, the optimal image trajectories of the uncalibrated visual servoing controller is modified to track the image trajectories. Simulation experiments show that, compared with the classical IBUVS method, the proposed trajectory planning method can obtain the shortest path of any frame and complete the robot visual servoing task with large initial pose deviation.

Multi-document summarization (MDS) aims to generate a summary for a number of related documents. We propose HGSUM, an MDS model that extends an encoder-decoder architecture, to incorporate a heterogeneous graph to represent different semantic units (e.g., words and sentences) of the documents. This contrasts with existing MDS models which do not consider different edge types of graphs and as such do not capture the diversity of relationships in the documents. To preserve only key information and relationships of the documents in the heterogeneous graph, HGSUM uses graph pooling to compress the input graph. And to guide HGSUM to learn compression, we introduce an additional objective that maximizes the similarity between the compressed graph and the graph constructed from the ground-truth summary during training. HGSUM is trained end-to-end with graph similarity and standard cross-entropy objectives. Experimental results over MULTI-NEWS, WCEP-100, and ARXIV show that HGSUM outperforms state-of-the-art MDS models. The code for our model and experiments is available at: https://github.com/oaimli/HGSum., Comment: AAAI 2023

The NLI4CT task aims to entail hypotheses based on Clinical Trial Reports (CTRs) and retrieve the corresponding evidence supporting the justification. This task poses a significant challenge, as verifying hypotheses in the NLI4CT task requires the integration of multiple pieces of evidence from one or two CTR(s) and the application of diverse levels of reasoning, including textual and numerical. To address these problems, we present a multi-granularity system for CTR-based textual entailment and evidence retrieval in this paper. Specifically, we construct a Multi-granularity Inference Network (MGNet) that exploits sentence-level and token-level encoding to handle both textual entailment and evidence retrieval tasks. Moreover, we enhance the numerical inference capability of the system by leveraging a T5-based model, SciFive, which is pre-trained on the medical corpus. Model ensembling and a joint inference method are further utilized in the system to increase the stability and consistency of inference. The system achieves f1-scores of 0.856 and 0.853 on textual entailment and evidence retrieval tasks, resulting in the best performance on both subtasks. The experimental results corroborate the effectiveness of our proposed method. Our code is publicly available at https://github.com/THUMLP/NLI4CT., Comment: Accepted by SemEval2023

We present PeerSum, a novel dataset for generating meta-reviews of scientific papers. The meta-reviews can be interpreted as abstractive summaries of reviews, multi-turn discussions and the paper abstract. These source documents have rich inter-document relationships with an explicit hierarchical conversational structure, cross-references and (occasionally) conflicting information. To introduce the structural inductive bias into pre-trained language models, we introduce Rammer ( Relationship-aware Multi-task Meta-review Generator), a model that uses sparse attention based on the conversational structure and a multi-task training objective that predicts metadata features (e.g., review ratings). Our experimental results show that Rammer outperforms other strong baseline models in terms of a suite of automatic evaluation metrics. Further analyses, however, reveal that RAMMER and other models struggle to handle conflicts in source documents of PeerSum, suggesting meta-review generation is a challenging task and a promising avenue for further research., Comment: Long paper; Accepted to EMNLP 2023; Soundness: 3, 3, 4; Excitement: 3, 4, 4

Accurate estimation of the relative pose between an object and a robot hand is critical for many manipulation tasks. However, most of the existing object-in-hand pose datasets use two-finger grippers and also assume that the object remains fixed in the hand without any relative movements, which is not representative of real-world scenarios. To address this issue, a 6D object-in-hand pose dataset is proposed using a teleoperation method with an anthropomorphic Shadow Dexterous hand. Our dataset comprises RGB-D images, proprioception and tactile data, covering diverse grasping poses, finger contact states, and object occlusions. To overcome the significant hand occlusion and limited tactile sensor contact in real-world scenarios, we propose PoseFusion, a hybrid multi-modal fusion approach that integrates the information from visual and tactile perception channels. PoseFusion generates three candidate object poses from three estimators (tactile only, visual only, and visuo-tactile fusion), which are then filtered by a SelectLSTM network to select the optimal pose, avoiding inferior fusion poses resulting from modality collapse. Extensive experiments demonstrate the robustness and advantages of our framework. All data and codes are available on the project website: https://elevenjiang1.github.io/ObjectInHand-Dataset

Millimeter wave radar can measure distances, directions, and Doppler velocity for objects in harsh conditions such as fog. The 4D imaging radar with both vertical and horizontal data resembling an image can also measure objects' height. Previous studies have used 3D radars for ego-motion estimation. But few methods leveraged the rich data of imaging radars, and they usually omitted the mapping aspect, thus leading to inferior odometry accuracy. This paper presents a real-time imaging radar inertial odometry and mapping method, iRIOM, based on the submap concept. To deal with moving objects and multipath reflections, we use the graduated non-convexity method to robustly and efficiently estimate ego-velocity from a single scan. To measure the agreement between sparse non-repetitive radar scan points and submap points, the distribution-to-multi-distribution distance for matches is adopted. The ego-velocity, scan-to-submap matches are fused with the 6D inertial data by an iterative extended Kalman filter to get the platform's 3D position and orientation. A loop closure module is also developed to curb the odometry module's drift. To our knowledge, iRIOM based on the two modules is the first 4D radar inertial SLAM system. On our and third-party data, we show iRIOM's favorable odometry accuracy and mapping consistency against the FastLIO-SLAM and the EKFRIO. Also, the ablation study reveal the benefit of inertial data versus the constant velocity model, and scan-to-submap matching versus scan-to-scan matching., Comment: 8 pages, 8 figures, 4 tables, the proofread version will appear on RA-L soon

Numerous evaluation metrics have been developed for natural language generation tasks, but their effectiveness in evaluating stories is limited as they are not specifically tailored to assess intricate aspects of storytelling, such as fluency and interestingness. In this paper, we introduce DELTASCORE, a novel methodology that employs perturbation techniques for the evaluation of nuanced story aspects. Our central proposition posits that the extent to which a story excels in a specific aspect (e.g., fluency) correlates with the magnitude of its susceptibility to particular perturbations (e.g., the introduction of typos). Given this, we measure the quality of an aspect by calculating the likelihood difference between pre- and post-perturbation states using pre-trained language models. We compare DELTASCORE with existing metrics on storytelling datasets from two domains in five fine-grained story aspects: fluency, coherence, relatedness, logicality, and interestingness. DELTASCORE demonstrates remarkable performance, revealing a surprising finding that a specific perturbation proves highly effective in capturing multiple aspects., Comment: 15 pages, 3 figures, 8 tables. Camera ready version for EMNLP 2023 findings

Miao Li,1,2 Jiazhao Zhang,3 Chenguang Jiang,2,4 Jun Wang,2 Ruhong Sun,5 Shayu Jin,5 Ning Zhang,1,* Zhenhe Zhou2,* 1Department of Psychology, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, People’s Republic of China; 2Department of Psychiatry, The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, People’s Republic of China; 3Grade 2019 Class 6, Basic Medicine College of Jinzhou Medical University, Jinzhou, People’s Republic of China; 4Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, People’s Republic of China; 5Department of Psychiatry, Nanjing Medical University Graduate School, Nanjing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ning Zhang, Department of Psychology, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, People’s Republic of China, Tel +86-13601588630, Email zn6360@126.com Zhenhe Zhou, Department of Psychiatry, The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, People’s Republic of China, Tel +86-13358118986, Email zhouzh@njmu.edu.cnPurpose: Deficits in facial emotional intensity recognition have been associated with social cognition in patients with major depression. The study examined multiple event-related potential (ERP) components in patients with major depression and investigated the relationships between ERPs, social cognition, and clinical features.Participants and Methods: Thirty-one patients met DSM-IV diagnosis of depression and 31 healthy participants completed the emotion intensity recognition task (EIRT), while ERPs were recorded. Data on ERP components (P100, N170, P200, and P300) were analyzed.Results: The behavioral results showed that patients with major depression performed worse on EIRT, including all six categories of emotions

Facilitating large-scale, cross-institutional collaboration in biomedical machine learning projects requires a trustworthy and resilient federated learning (FL) environment to ensure that sensitive information such as protected health information is kept confidential. In this work, we introduce APPFLx, a low-code FL framework that enables the easy setup, configuration, and running of FL experiments across organizational and administrative boundaries while providing secure end-to-end communication, privacy-preserving functionality, and identity management. APPFLx is completely agnostic to the underlying computational infrastructure of participating clients. We demonstrate the capability of APPFLx as an easy-to-use framework for accelerating biomedical studies across institutions and healthcare systems while maintaining the protection of private medical data in two case studies: (1) predicting participant age from electrocardiogram (ECG) waveforms, and (2) detecting COVID-19 disease from chest radiographs. These experiments were performed securely across heterogeneous compute resources, including a mixture of on-premise high-performance computing and cloud computing, and highlight the role of federated learning in improving model generalizability and performance when aggregating data from multiple healthcare systems. Finally, we demonstrate that APPFLx serves as a convenient and easy-to-use framework for accelerating biomedical studies across institutions and healthcare system while maintaining the protection of private medical data.

Generative models are a class of machine learning models that aim to learn the underlying probability distribution of data. Unlike discriminative models, generative models focus on capturing the data's inherent structure, allowing them to generate new samples that resemble the original data. To fully exploit the potential of modeling probability distributions using quantum physics, a quantum-inspired generative model known as the Born machines have shown great advancements in learning classical and quantum data over matrix product state(MPS) framework. The Born machines support tractable log-likelihood, autoregressive and mask sampling, and have shown outstanding performance in various unsupervised learning tasks. However, much of the current research has been centered on improving the expressive power of MPS, predominantly embedding each token directly by a corresponding tensor index. In this study, we generalize the embedding method into trainable quantum measurement operators that can be simultaneously honed with MPS. Our study indicated that combined with trainable embedding, Born machines can exhibit better performance and learn deeper correlations from the dataset., Comment: 5 pages, 4 figures

BACKGROUND & AIMS: Histological remission (HR) is advocated and considered as a new therapeutic target in ulcerative colitis (UC). Diagnosis of histologic remission currently relies on biopsy; during this process, patients are at risk for bleeding, infection, and post-biopsy fibrosis. In addition, histologic response scoring is complex and time-consuming, and there is heterogeneity among pathologists. Endocytoscopy (EC) is a novel ultra-high magnification endoscopic technique that can provide excellent in vivo assessment of glands. Based on the EC technique, we propose a neural network model that can assess histological disease activity in UC using EC images to address the above issues. The experiment results demonstrate that the proposed method can assist patients in precise treatment and prognostic assessment. METHODS: We construct a neural network model for UC evaluation. A total of 5105 images of 154 intestinal segments from 87 patients undergoing EC treatment at a center in China between March 2022 and March 2023 are scored according to the Geboes score. Subsequently, 103 intestinal segments are used as the training set, 16 intestinal segments are used as the validation set for neural network training, and the remaining 35 intestinal segments are used as the test set to measure the model performance together with the validation set. RESULTS: By treating HR as a negative category and histologic activity as a positive category, the proposed neural network model can achieve an accuracy of 0.9, a specificity of 0.95, a sensitivity of 0.75, and an area under the curve (AUC) of 0.81. CONCLUSION: We develop a specific neural network model that can distinguish histologic remission/activity in EC images of UC, which helps to accelerate clinical histological diagnosis. keywords: ulcerative colitis; Endocytoscopy; Geboes score; neural network.

Tactile sensing is of great importance during human hand usage such as object exploration, grasping and manipulation. Different types of tactile sensors have been designed during the past decades, which are mainly focused on either the fingertips for grasping or the upper-body for human-robot interaction. In this paper, a novel soft tactile sensor has been designed to mimic the functionality of human palm that can estimate the contact state of different objects. The tactile palm mainly consists of three parts including an electrode array, a soft cover skin and the conductive sponge. The design principle are described in details, with a number of experiments showcasing the effectiveness of the proposed design.

During the process of robot-assisted ultrasound(US) puncture, it is important to estimate the location of the puncture from the 2D US images. To this end, the calibration of the US image becomes an important issue. In this paper, we proposed a depth camera-based US calibration method, where an easy-to-deploy device is designed for the calibration. With this device, the coordinates of the puncture needle tip are collected respectively in US image and in the depth camera, upon which a correspondence matrix is built for calibration. Finally, a number of experiments are conducted to validate the effectiveness of our calibration method.

As medical ultrasound is becoming a prevailing examination approach nowadays, robotic ultrasound systems can facilitate the scanning process and prevent professional sonographers from repetitive and tedious work. Despite the recent progress, it is still a challenge to enable robots to autonomously accomplish the ultrasound examination, which is largely due to the lack of a proper task representation method, and also an adaptation approach to generalize learned skills across different patients. To solve these problems, we propose the latent task representation and the robotic skills adaptation for autonomous ultrasound in this paper. During the offline stage, the multimodal ultrasound skills are merged and encapsulated into a low-dimensional probability model through a fully self-supervised framework, which takes clinically demonstrated ultrasound images, probe orientations, and contact forces into account. During the online stage, the probability model will select and evaluate the optimal prediction. For unstable singularities, the adaptive optimizer fine-tunes them to near and stable predictions in high-confidence regions. Experimental results show that the proposed approach can generate complex ultrasound strategies for diverse populations and achieve significantly better quantitative results than our previous method.

For the SLAM system in robotics and autonomous driving, the accuracy of front-end odometry and back-end loop-closure detection determine the whole intelligent system performance. But the LiDAR-SLAM could be disturbed by current scene moving objects, resulting in drift errors and even loop-closure failure. Thus, the ability to detect and segment moving objects is essential for high-precision positioning and building a consistent map. In this paper, we address the problem of moving object segmentation from 3D LiDAR scans to improve the odometry and loop-closure accuracy of SLAM. We propose a novel 3D Sequential Moving-Object-Segmentation (3D-SeqMOS) method that can accurately segment the scene into moving and static objects, such as moving and static cars. Different from the existing projected-image method, we process the raw 3D point cloud and build a 3D convolution neural network for MOS task. In addition, to make full use of the spatio-temporal information of point cloud, we propose a point cloud residual mechanism using the spatial features of current scan and the temporal features of previous residual scans. Besides, we build a complete SLAM framework to verify the effectiveness and accuracy of 3D-SeqMOS. Experiments on SemanticKITTI dataset show that our proposed 3D-SeqMOS method can effectively detect moving objects and improve the accuracy of LiDAR odometry and loop-closure detection. The test results show our 3D-SeqMOS outperforms the state-of-the-art method by 12.4%. We extend the proposed method to the SemanticKITTI: Moving Object Segmentation competition and achieve the 2nd in the leaderboard, showing its effectiveness.

The prominence of embodied Artificial Intelligence (AI), which empowers robots to navigate, perceive, and engage within virtual environments, has attracted significant attention, owing to the remarkable advancements in computer vision and large language models. Privacy emerges as a pivotal concern within the realm of embodied AI, as the robot accesses substantial personal information. However, the issue of privacy leakage in embodied AI tasks, particularly in relation to reinforcement learning algorithms, has not received adequate consideration in research. This paper aims to address this gap by proposing an attack on the value-based algorithm and the gradient-based algorithm, utilizing gradient inversion to reconstruct states, actions, and supervision signals. The choice of using gradients for the attack is motivated by the fact that commonly employed federated learning techniques solely utilize gradients computed based on private user data to optimize models, without storing or transmitting the data to public servers. Nevertheless, these gradients contain sufficient information to potentially expose private data. To validate our approach, we conduct experiments on the AI2THOR simulator and evaluate our algorithm on active perception, a prevalent task in embodied AI. The experimental results demonstrate the effectiveness of our method in successfully reconstructing all information from the data across 120 room layouts., Comment: 7 pages, 4 figures, 2 tables

This report is the third in a series of studies that aimed to compile physiological parameters related to develop physiologically based pharmacokinetic (PBPK) models for drugs and environmental chemicals in food-producing animals including swine and cattle (Part I), chickens and turkeys (Part II), and finally sheep and goats (the focus of this manuscript). Literature searches were conducted in multiple databases (PubMed, Google Scholar, ScienceDirect, and ProQuest), with data on relevant parameters including body weight, relative organ weight (% of body weight), cardiac output, relative organ blood flow (% of cardiac output), residual blood volume (% of organ weight), and hematocrit reviewed and statistically summarized. The mean and standard deviation of each parameter are presented in tables. Equations describing the growth curves of sheep and goats are presented in figures. When data are sufficient, parameter values are reported for different ages or production classes of sheep, including fetal sheep, lambs, and market-age sheep (mature sheep). These data provide a reference database for developing standardized PBPK models to predict drug withdrawal intervals in sheep and goats, and also provide a basis for extrapolating PBPK models from major species such as cattle to minor species such as sheep and goats.

We study star formation driven outflows in a $z\sim0.02$ starbursting disk galaxy, IRAS08339+6517, using spatially resolved measurements from the Keck Cosmic Web Imager (KCWI). We develop a new method incorporating a multi-step process to determine whether an outflow should be fit in each spaxel, and then subsequently decompose the emission line into multiple components. We detect outflows ranging in velocity, $v_{\rm out}$, from $100-600$ km s$^{-1}$ across a range of star formation rate surface densities, $\Sigma_{\rm SFR}$, from $\sim$0.01-10 M$_\odot$ yr$^{-1}$ kpc$^{-2}$ in resolution elements of a few hundred parsec. Outflows are detected in $\sim100\%$ of all spaxels within the half-light radius, and $\sim70\%$ within $r_{90}$, suggestive of a high covering fraction for this starbursting disk galaxy. Around $2/3$ of the total outflowing mass originates from the star forming ring, which corresponds to $<10\%$ of the total area of the galaxy. We find that the relationship between $v_{\rm out}$ and the $\Sigma_{\rm SFR}$, as well as between the mass loading factor, $\eta$, and the $\Sigma_{\rm SFR}$, are consistent with trends expected from energy-driven feedback models. We study the resolution effects on this relationship and find stronger correlations above a re-binned size-scale of $\sim500$ pc. Conversely, we do not find statistically significant consistency with the prediction from momentum-driven winds., Comment: 15 pages, 9 Figures, accepted by MNRAS

We study the adiabatic accretion process of ordinary baryonic gas onto spherically symmetric black holes in Einstein-Maxwell-scalar theory, with two parameters $\alpha$ and $\beta$ in the coupling term. Especially, we demonstrate the range of the transonic points in terms of the charge-to-mass ratio squared and the dimensionless coordinate radius, in two important classes of black holes as examples. Further, we find that the two coupling parameters give modifications to the mass accretion rate at different orders of the sound speed at infinity. We also present their different effects on the temperature ratios of the accreted gas., Comment: 17 pages, 4 figures

When a gravitational wave or a graviton travels through an electric or magnetic background, it could convert into a photon with some probability. In this paper, a dipole magnetic field is considered as this kind of background in both the Minkowski spacetime and the curved spacetime in the near-zone of a neutron star. In the former case, we find that the graviton traveling vertically rather than parallel to the background magnetic field could be more effectively converted into an electromagnetic radiation field. In the latter case, we focus on the situation, in which the graviton travels along the radial direction near a neutron star. The radius of a neutron star is about ten kilometers, so the gravitational wave with long wavelength or low frequency may bypass neutron stars by diffraction. For high frequency gravitational wave, the conversion probability is proportional to the distance square as that in the static electric or magnetic background case. The smaller the inclination angle between the dipole field and the neutron star north pole is, the larger magnetic amplitude will be. The term that described curved spacetime will slightly enhance this kind of probability. We estimate that this value is about the order of $\sim 10^{-14}- 10^{-10}$. Therefore, it is expectable that this kind of conversion process may have a potential to open a window for observing high frequency gravitational waves., Comment: 17pages,2figures

The stiffness modulation mechanism for soft robotics has gained considerable attention to improve deformability, controllability, and stability. However, for the existing stiffness soft actuator, high lateral stiffness and a wide range of bending stiffness are hard to be provided at the same time. This paper presents a bioinspired bidirectional stiffening soft actuator (BISA) combining the air-tendon hybrid actuation (ATA) and a bone-like structure (BLS). The ATA is the main actuation of the BISA, and the bending stiffness can be modulated with a maximum stiffness of about 0.7 N/mm and a maximum magnification of 3 times when the bending angle is 45 deg. Inspired by the morphological structure of the phalanx, the lateral stiffness can be modulated by changing the pulling force of the BLS. The lateral stiffness can be modulated by changing the pulling force to it. The actuator with BLSs can improve the lateral stiffness about 3.9 times compared to the one without BLSs. The maximum lateral stiffness can reach 0.46 N/mm. And the lateral stiffness can be modulated decoupling about 1.3 times (e.g., from 0.35 N/mm to 0.46 when the bending angle is 45 deg). The test results show the influence of the rigid structures on bending is small with about 1.5 mm maximum position errors of the distal point of actuator bending in different pulling forces. The advantages brought by the proposed method enable a soft four-finger gripper to operate in three modes: normal grasping, inverse grasping, and horizontal lifting. The performance of this gripper is further characterized and versatile grasping on various objects is conducted, proving the robust performance and potential application of the proposed design method.

Bimanual activities like coffee stirring, which require coordination of dual arms, are common in daily life and intractable to learn by robots. Adopting reinforcement learning to learn these tasks is a promising topic since it enables the robot to explore how dual arms coordinate together to accomplish the same task. However, this field has two main challenges: coordination mechanism and long-horizon task decomposition. Therefore, we propose the Mixline method to learn sub-tasks separately via the online algorithm and then compose them together based on the generated data through the offline algorithm. We constructed a learning environment based on the GPU-accelerated Isaac Gym. In our work, the bimanual robot successfully learned to grasp, hold and lift the spoon and cup, insert them together and stir the coffee. The proposed method has the potential to be extended to other long-horizon bimanual tasks., Comment: 10 pages, conference

This letter describes an approach to achieve well-known Chinese cooking art stir-fry on a bimanual robot system. Stir-fry requires a sequence of highly dynamic coordinated movements, which is usually difficult to learn for a chef, let alone transfer to robots. In this letter, we define a canonical stir-fry movement, and then propose a decoupled framework for learning this deformable object manipulation from human demonstration. First, the dual arms of the robot are decoupled into different roles (a leader and follower) and learned with classical and neural network-based methods separately, then the bimanual task is transformed into a coordination problem. To obtain general bimanual coordination, we secondly propose a Graph and Transformer based model -- Structured-Transformer, to capture the spatio-temporal relationship between dual-arm movements. Finally, by adding visual feedback of content deformation, our framework can adjust the movements automatically to achieve the desired stir-fry effect. We verify the framework by a simulator and deploy it on a real bimanual Panda robot system. The experimental results validate our framework can realize the bimanual robot stir-fry motion and have the potential to extend to other deformable objects with bimanual coordination., Comment: 8 pages, 8 figures, published to RA-L

We present PeerSum, a new MDS dataset using peer reviews of scientific publications. Our dataset differs from the existing MDS datasets in that our summaries (i.e., the meta-reviews) are highly abstractive and they are real summaries of the source documents (i.e., the reviews) and it also features disagreements among source documents. We found that current state-of-the-art MDS models struggle to generate high-quality summaries for PeerSum, offering new research opportunities., Comment: This is because the paper has changed so much and the arxiv paper no longer represents the PeerSum

Mechanism design on social networks is a hot research direction recently, and we have seen many interesting results in auctions and matching. Compared to the traditional settings, the new goal of the network settings is that we need to design incentives to incentivize the participants of the game to invite their neighbors on the network to join the game. This is challenging because they are competing for something (e.g., resources or matches) in the game. In one-sided matching, especially house exchange, the well-known unique truthful, stable and optimal solution called Top Trading Cycle (TTC) cannot achieve the new goal. Existing works have tried to add constraints on TTC to obtain the incentive, but it only works in trees and it does not guarantee any stability. In this paper, we move this forward and propose the first mechanism called Leave and Share (LS) which not only achieves the goal in all networks but also gives the most stable solution in the new settings. In terms of optimality, as it is impossible to achieve it in any network, we conduct simulations to compare it with the extensions of TTC.

We compare 500~pc scale, resolved observations of ionised and molecular gas for the $z\sim0.02$ starbursting disk galaxy IRAS08339+6517, using measurements from KCWI and NOEMA. We explore the relationship of the star formation driven ionised gas outflows with colocated galaxy properties. We find a roughly linear relationship between the outflow mass flux ($\dot{\Sigma}_{\rm out}$) and star formation rate surface density ($\Sigma_{\rm SFR}$), $\dot{\Sigma}_{\rm out}\propto\Sigma_{\rm SFR}^{1.06\pm0.10}$, and a strong correlation between $\dot{\Sigma}_{\rm out}$ and the gas depletion time, such that $\dot{\Sigma}_{\rm out} \propto t_{dep}^{-1.1\pm0.06}$. Moreover, we find these outflows are so-called ``breakout" outflows, according to the relationship between the gas fraction and disk kinematics. Assuming that ionised outflow mass scales with total outflow mass, our observations suggest that the regions of highest $\Sigma_{\rm SFR}$ in IRAS08 are removing more gas via the outflow than through the conversion of gas into stars. Our results are consistent with a picture in which the outflow limits the ability for a region of a disk to maintain short depletion times. Our results underline the need for resolved observations of outflows in more galaxies., Comment: 16 pages, 7 figures, Submitted to ApJ

Interstellar (ISM) and circumgalactic mediums (CGM) around galaxies are linked to several physical processes that drive galaxy evolution. For example, the X-ray emission from the CGM gas around ellipticals has been linked to the AGN feedback occurring in the host. Upcoming telescopes, such as HUBS with ~2 eV resolution, can provide us with deep insights about the hot gas properties of such galaxies thus constrain these processes. In this project, we discuss X-ray emission of the ISM and CGM of elliptical galaxies simulated using MACER code. We generate X-ray emission data from the MACER simulations with various feedback models and produce mock observations for an instrument with high spectral resolution, which is a necessary step of selecting sources for the future observations with planned mission such as HUBS. More importantly, we establish connections between the physics of AGN and stellar feedback with the emission spectra from the ISM and CGM to investigate the possibility of using observations to constrain feedback models. We fit the X-ray spectra from these simulations with standard fitting procedures and compare the retrieved physical properties with their counterparts from the simulations to understand whether the future high-resolution observations can reliably reveal the properties of the gas in the galaxies., Comment: 10 pages, 13 Figures, accepted in MNRAS

Data-driven approaches have become a dominant paradigm for robotic grasp planning. However, the performance of these approaches is enormously influenced by the quality of the available training data. In this paper, we propose a framework to generate object shapes to improve the grasping dataset quality, thus enhancing the grasping ability of a pre-designed learning-based grasp planning network. In this framework, the object shapes are embedded into a low-dimensional feature space using an AutoEncoder (encoder-decoder) based structure network. The rarity and graspness scores are defined for each object shape using outlier detection and grasp-quality criteria. Subsequently, new object shapes are generated in feature space that leverages the original high rarity and graspness score objects' features, which can be employed to augment the grasping dataset. Finally, the results obtained from the simulation and real-world experiments demonstrate that the grasping ability of the learning-based grasp planning network can be effectively improved with the generated object shapes.

The Plk1-interacting checkpoint helicase (PICH) protein localizes to ultrafine anaphase DNA bridges in mitosis along with a complex of DNA repair proteins. Previous studies show PICH deficiency-induced embryonic lethality in mice. However, the function of PICH that is required to suppress embryonic lethality in PICH-deficient mammals remains to be determined. Previous clinical studies suggest a link between PICH deficiency and the onset of acquired aplastic anemia. Here, using Pich knock-out (KO) mouse models, the authors provide evidence for a mechanistic link between PICH deficiency and defective hematopoiesis. Fetal livers from Pich-KO embryos exhibit a significantly elevated number of hematopoietic stem cells (HSCs); however, these HSCs display a higher level of apoptosis and a much-reduced ability to reconstitute a functional hematopoietic system when transplanted into lethally irradiated recipients. Moreover, these HSCs show an elevated cytoplasmic dsDNA expression and an activation of the cGAS-STING pathway, resulting in excessive production of type I interferons (IFN). Importantly, deletion of Ifnar1 or cGAS reverses the defective hematopoiesis. The authors conclude that loss of PICH results in defective hematopoiesis via cGAS-STING-mediated type I IFN production.

A base-promoted sequential cyclization and carboxylation of o-alkynylamides or 2-en-4-ynamides with CO2 has been achieved with high efficiency, stereoselectivity, and regioselectivity. This approach begins with 5-exo-dig cyclization followed by trapping the resulting vinyl anion with CO2 and MeI, which provides a convenient access to diverse cyclic and fully substituted acrylates with CO2 as the carboxylic source. © 2022 American Chemical Society.

A visible-light photoredox-catalyzed reductive α-aminomethyl carboxylation of styrenes with sodium glycinates and CO2 has been developed to synthesize a series of α,α-disubstituted γ-amino acids and γ-lactams with high efficiency and regioselectivity. Notably, CO2 released from the decarboxylation step can be reused for the subsequent carboxylation. Distinct from the previous reactions with the same type of substrates leading to simple decarboxylation and olefin hydroalkylation, this process involves additional CO2 sequestration, thus leading to olefin α-aminomethyl carboxylation. These findings not only provide new access to α,α-disubstituted γ-amino acids and γ-lactams but also serve as a proof of concept for CO2 reutilization in decarboxylation reactions.

An iridium-catalyzed redox-neutral C-2 and C-3 dual C-H functionalization of indoles with nitrones has been developed, furnishing a range of 7H-indolo[2,3-c]quinolines with high efficiency and regioselectivity under mild reaction conditions. Notably, sequential multiple C-H bond cleavage and C-C bond formation constitute the key events of this process, in which nitrone serves as a building block and an oxidant. Distinct from the previous methods toward 7H-indolo[2,3-c]quinolines, this newly developed reaction features readily available substrates, operational simplicity, broad scope, good to high efficiency, and excellent regioselectivity. © 2021 American Chemical Society.

With the implementation of China’s “going-out” cultural strategy, the English translation of Chinese voice and relevant methods have attracted increasing attention. The omission is one of the common techniques of C-E translation, which is applied to achieve the conciseness and fluency of the translation. However, omission leads to different understandings and practices in C-E translation. To explore whether omission refers to the deletion of both forms of the original text and its meaning, this paper carries out a study based on the principle of condensation in C-E translation brought forward by Wang Jianguo. Through the case analysis of omission, this paper finds that in some cases, the meaning of the original text is thought to be deleted in the translation, the content is actually condensed and thus implied in the translation. And by analyzing cases from the perspective of the principle of condensation in C-E translation, this paper elaborates on the discrepancy of the consciousness of boundary between English and Chinese, which also serve as the foundation of the principle of condensation. Therefore, compared with omission, the principle of condensation possesses specific methods that can be applied on different levels of word, sentence, and text, and thus can guide the C-E translation practice more sufficiently. Moreover, through the exploratory study of the differences between English and Chinese, this principle of condensation can help C-E translation be well-received by target readers.

Recently, the robotic ultrasound system has become an emerging topic owing to the widespread use of medical ultrasound. However, it is still a challenging task to model and to transfer the ultrasound skill from an ultrasound physician. In this paper, we propose a learning-based framework to acquire ultrasound scanning skills from human demonstrations. First, the ultrasound scanning skills are encapsulated into a high-dimensional multi-modal model in terms of interactions among ultrasound images, the probe pose and the contact force. The parameters of the model are learned using the data collected from skilled sonographers' demonstrations. Second, a sampling-based strategy is proposed with the learned model to adjust the extracorporeal ultrasound scanning process to guide a newbie sonographer or a robot arm. Finally, the robustness of the proposed framework is validated with the experiments on real data from sonographers.

Medical ultrasound has become a routine examination approach nowadays and is widely adopted for different medical applications, so it is desired to have a robotic ultrasound system to perform the ultrasound scanning autonomously. However, the ultrasound scanning skill is considerably complex, which highly depends on the experience of the ultrasound physician. In this paper, we propose a learning-based approach to learn the robotic ultrasound scanning skills from human demonstrations. First, the robotic ultrasound scanning skill is encapsulated into a high-dimensional multi-modal model, which takes the ultrasound images, the pose/position of the probe and the contact force into account. Second, we leverage the power of imitation learning to train the multi-modal model with the training data collected from the demonstrations of experienced ultrasound physicians. Finally, a post-optimization procedure with guided explorations is proposed to further improve the performance of the learned model. Robotic experiments are conducted to validate the advantages of our proposed framework and the learned models.

The hot component of the circum-galactic medium (CGM) around star forming galaxies is detected as diffuse X-ray emission. The X-ray spectra from the CGM depend on the temperature and metallicity of the emitting plasma, providing important information about the feeding and feedback of the galaxy. The observed spectra are commonly fitted using simple 1-Temperature (1-T) or 2-T models. However, the actual temperature distribution of the gas can be complex because of the interaction between galactic outflows and halo gas. Here we demonstrate this by analysing 3-D hydrodynamical simulations of the CGM with a realistic outflow model. We investigate the physical properties of the simulated hot CGM, which shows a broad distribution in density, temperature, and metallicity. By constructing and fitting the simulated spectra, we show that, while the 1-T and 2-T models are able to fit the synthesized spectra reasonably well, the inferred temperature(s) does not bear much physical meaning. Instead, we propose a log-normal distribution as a more physical model. The log-normal model better fits the simulated spectra while reproducing the gas temperature distribution. We also show that when the star formation rate is high, the spectra inside the bi-conical outflows are distinct from that outside, as outflows are generally hotter and more metal-enriched. Finally, we produce mock spectra for future missions with the eV-level spectral resolution, such as Athena, Lynx, HUBS and XRISM., Comment: 15 pages, 17 figures. Accepted for publication in MNRAS