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Tao Wen,1,* Xuan Liu,2,* Tao Pang,1,* Mingming Li,1 Guangyang Jiao,3 Xiangcheng Fan,1 Jigui Tang,4 Ci’an Zhang,4 Zhipeng Wang,1 Xiaoqiang Yue,4,* Wansheng Chen,1,3,5 Feng Zhang1,5 1Department of Pharmacy, Changzheng Hospital, Naval Medical University, Shanghai, People’s Republic of China; 2Oncology-Department, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai, People’s Republic of China; 3The SATCM Key Laboratory for New Resources & Quality Evaluation of Chinese Medicine, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China; 4Department of Traditional Chinese Medicine, Changzheng Hospital, Naval Medical University, Shanghai, People’s Republic of China; 5Shanghai Key Laboratory for Pharmaceutical Metabolite Research, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Feng Zhang; Wansheng Chen, Changzheng Hospital, Naval Medical University, No. 415, Fengyang Road, Shanghai, 200003, People’s Republic of China, Email fengzhang@smmu.edu.cn; chenwansheng@smmu.edu.cnPurpose: The aim of this study was to verify the effectiveness and explore the mechanism of Chaihu-Guizhi-Ganjiang decoction (CGGD) in the treatment of chronic non-atrophic gastritis (CNAG) with gallbladder heat and spleen cold syndrome (GHSC) by metabolomics based on UHPLC-Q-TOF/MS.Patients and Methods: An observational controlled before-after study was conducted to verify the effectiveness of CGGD in the treatment of CNAG with GHSC from January to June 2023, enrolling 27 patients, who took CGGD for 28 days. 30 healthy volunteers were enrolled as the controls. The efficacy was evaluated by comparing the traditional Chinese medicine (TCM) syndrome and CNAG scores, and clinical parameters before and after treatment. The plasma levels of hormones related to gastrointestinal function were collected by ELISA. The mechanisms of

The ambiguous appearance, tiny scale, and fine-grained classes of objects in remote sensing imagery inevitably lead to the noisy annotations in category labels of detection dataset. However, the effects and treatments of the label noises are underexplored in modern oriented remote sensing object detectors. To address this issue, we propose a robust oriented remote sensing object detection method through dynamic loss decay (DLD) mechanism, inspired by the two phase ``early-learning'' and ``memorization'' learning dynamics of deep neural networks on clean and noisy samples. To be specific, we first observe the end point of early learning phase termed as EL, after which the models begin to memorize the false labels that significantly degrade the detection accuracy. Secondly, under the guidance of the training indicator, the losses of each sample are ranked in descending order, and we adaptively decay the losses of the top K largest ones (bad samples) in the following epochs. Because these large losses are of high confidence to be calculated with wrong labels. Experimental results show that the method achieves excellent noise resistance performance tested on multiple public datasets such as HRSC2016 and DOTA-v1.0/v2.0 with synthetic category label noise. Our solution also has won the 2st place in the "fine-grained object detection based on sub-meter remote sensing imagery" track with noisy labels of 2023 National Big Data and Computing Intelligence Challenge.

Heart murmurs are a common manifestation of cardiovascular diseases and can provide crucial clues to early cardiac abnormalities. While most current research methods primarily focus on the accuracy of models, they often overlook other important aspects such as the interpretability of machine learning algorithms and the uncertainty of predictions. This paper introduces a heart murmur detection method based on a parallel-attentive model, which consists of two branches: One is based on a self-attention module and the other one is based on a convolutional network. Unlike traditional approaches, this structure is better equipped to handle long-term dependencies in sequential data, and thus effectively captures the local and global features of heart murmurs. Additionally, we acknowledge the significance of understanding the uncertainty of model predictions in the medical field for clinical decision-making. Therefore, we have incorporated an effective uncertainty estimation method based on Monte Carlo Dropout into our model. Furthermore, we have employed temperature scaling to calibrate the predictions of our probabilistic model, enhancing its reliability. In experiments conducted on the CirCor Digiscope dataset for heart murmur detection, our proposed method achieves a weighted accuracy of 79.8% and an F1 of 65.1%, representing state-of-the-art results.

The advent of large language models (LLMs) has gained tremendous attention over the past year. Previous studies have shown the astonishing performance of LLMs not only in other tasks but also in emotion recognition in terms of accuracy, universality, explanation, robustness, few/zero-shot learning, and others. Leveraging the capability of LLMs inevitably becomes an essential solution for emotion recognition. To this end, we further comprehensively investigate how LLMs perform in linguistic emotion recognition if we concentrate on this specific task. Specifically, we exemplify a publicly available and widely used LLM -- Chat General Language Model, and customise it for our target by using two different modal adaptation techniques, i.e., deep prompt tuning and low-rank adaptation. The experimental results obtained on six widely used datasets present that the adapted LLM can easily outperform other state-of-the-art but specialised deep models. This indicates the strong transferability and feasibility of LLMs in the field of emotion recognition.

After the inception of emotion recognition or affective computing, it has increasingly become an active research topic due to its broad applications. Over the past couple of decades, emotion recognition models have gradually migrated from statistically shallow models to neural network-based deep models, which can significantly boost the performance of emotion recognition models and consistently achieve the best results on different benchmarks. Therefore, in recent years, deep models have always been considered the first option for emotion recognition. However, the debut of large language models (LLMs), such as ChatGPT, has remarkably astonished the world due to their emerged capabilities of zero/few-shot learning, in-context learning, chain-of-thought, and others that are never shown in previous deep models. In the present paper, we comprehensively investigate how the LLMs perform in emotion recognition in terms of diverse aspects, including in-context learning, few-short learning, accuracy, generalisation, and explanation. Moreover, we offer some insights and pose other potential challenges, hoping to ignite broader discussions about enhancing emotion recognition in the new era of advanced and generalised large models.

In order to improve the identification ability of electronic nosetostar anise from different areas, this study uses sensor array optimization, Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) to effectively improve the discrimination and prediction ability of electronic nose. Firstly, the initial sensor arrays are selected according to the aroma components of star anise. On the basis of extracting sensor eigenvalues to form the initial feature matrix, the final sensor arrays are selected by combining variable correlation analysis and coefficient of variation analysis (RSD). Linear Discriminant Analysis (LDA) is applied to the sensor array before and after optimization to increase the group spacing of star anise from different producing areas. Particle swarm Support Vector Machine (PSO-SVM) and Genetic Support Vector Machine (GA-SVM) are used to distinguish the producing areas of star anise samples. The accuracy of PSO-SVM training set is 99.16%; that of test set is 93.33%; that of GA-SVM training set is 99.16% and the accuracy of test set is 90%. The results show that PSO-SVM model has high precision and convergence accuracy and it is more feasible to distinguish the origin of star anises.

The empirical success of Reinforcement Learning (RL) in the setting of contact-rich manipulation leaves much to be understood from a model-based perspective, where the key difficulties are often attributed to (i) the explosion of contact modes, (ii) stiff, non-smooth contact dynamics and the resulting exploding / discontinuous gradients, and (iii) the non-convexity of the planning problem. The stochastic nature of RL addresses (i) and (ii) by effectively sampling and averaging the contact modes. On the other hand, model-based methods have tackled the same challenges by smoothing contact dynamics analytically. Our first contribution is to establish the theoretical equivalence of the two methods for simple systems, and provide qualitative and empirical equivalence on a number of complex examples. In order to further alleviate (ii), our second contribution is a convex, differentiable and quasi-dynamic formulation of contact dynamics, which is amenable to both smoothing schemes, and has proven through experiments to be highly effective for contact-rich planning. Our final contribution resolves (iii), where we show that classical sampling-based motion planning algorithms can be effective in global planning when contact modes are abstracted via smoothing. Applying our method on a collection of challenging contact-rich manipulation tasks, we demonstrate that efficient model-based motion planning can achieve results comparable to RL with dramatically less computation. Video: https://youtu.be/12Ew4xC-VwA, Comment: The first two authors contributed equally to this work

We propose the framework of Series Elastic End Effectors in 6D (SEED), which combines a spatially compliant element with visuotactile sensing to grasp and manipulate tools in the wild. Our framework generalizes the benefits of series elasticity to 6-dof, while providing an abstraction of control using visuotactile sensing. We propose an algorithm for relative pose estimation from visuotactile sensing, and a spatial hybrid force-position controller capable of achieving stable force interaction with the environment. We demonstrate the effectiveness of our framework on tools that require regulation of spatial forces. Video link: https://youtu.be/2-YuIfspDrk, Comment: Submitted to Robosoft 2022

Air Force/Lincoln Laboratory (Award 7000374874), United States. National Aeronautics and Space Administration (Award NNX16AC49A), United States. Army Research Office (Award W911NF-15-1-0166), United States. Defense Advanced Research Projects Agency (Award SC001-0000001002)

We believe that the future of robot motion planning will look very different than how it looks today: instead of complex collision avoidance trajectories with a brittle dependence on sensing and estimation of the environment, motion plans should consist of smooth, simple trajectories and be executed by robots that are not afraid of making contact. Here we present a "contact-aware" controller which continues to execute a given trajectory despite unexpected collisions while keeping the contact force stable and small. We introduce a quadratic programming (QP) formulation, which minimizes a trajectory-tracking error subject to quasistatic dynamics and contact-force constraints. Compared with the classical null-space projection technique, the inequality constraint on contact forces in the proposed QP controller allows for more gentle release when the robot comes out of contact. In the quasistatic dynamics model, control actions consist only of commanded joint positions, allowing the QP controller to run on stiffness-controlled robots which do not have a straightforward torque-control interface nor accurate dynamic models. The effectiveness of the proposed QP controller is demonstrated on a KUKA iiwa arm., Comment: Submitted to ICRA2022

The empirical success of derivative-free methods in reinforcement learning for planning through contact seems at odds with the perceived fragility of classical gradient-based optimization methods in these domains. What is causing this gap, and how might we use the answer to improve gradient-based methods? We believe a stochastic formulation of dynamics is one crucial ingredient. We use tools from randomized smoothing to analyze sampling-based approximations of the gradient, and formalize such approximations through the gradient bundle. We show that using the gradient bundle in lieu of the gradient mitigates fast-changing gradients of non-smooth contact dynamics modeled by the implicit time-stepping, or the penalty method. Finally, we apply the gradient bundle to optimal control using iLQR, introducing a novel algorithm which improves convergence over using exact gradients. Combining our algorithm with a convex implicit time-stepping formulation of contact, we show that we can tractably tackle planning-through-contact problems in manipulation., Comment: The first two authors contributed equally. Accepted to Robotics and Automation Letters (RA-L)

In Japan, inspection of irrigation water canals has been mostly conducted manually. However, the huge demand for more regular inspections as infrastructure ages, coupled with the limited time window available for inspection, has rendered manual inspection increasingly insufficient. With shortened inspection time and reduced labor cost, automated inspection using a combination of unmanned aerial vehicles (UAVs) and ground vehicles (cars) has emerged as an attractive alternative to manual inspection. In this paper, we propose a path planning framework that generates optimal plans for UAVs and cars to inspect water canals in a large agricultural area (tens of square kilometers). In addition to optimality, the paths need to satisfy several constraints, in order to guarantee UAV navigation safety and to abide by local traffic regulations. In the proposed framework, the canal and road networks are first modeled as two graphs, which are then partitioned into smaller subgraphs that can be covered by a given fleet of UAVs within one battery charge. The problem of finding optimal paths for both UAVs and cars on the graphs, subject to the constraints, is formulated as a mixed-integer quadratic program (MIQP). The proposed framework can also quickly generate new plans when a current plan is interrupted. The effectiveness of the proposed framework is validated by simulation results showing the successful generation of plans covering all given canal segments, and the ability to quickly revise the plan when conditions change.