Robot Motion Planning Method Based on Incremental High-Dimensional Mixture Probabilistic Model

The sampling-based motion planner is the mainstream method to solve the motion planning problem in high-dimensional space. In the process of exploring robot configuration space, this type of algorithm needs to perform collision query on a large number of samples, which greatly limits their planning efficiency. Therefore, this paper uses machine learning methods to establish a probabilistic model of the obstacle region in configuration space by learning a large number of labeled samples. Based on this, the high-dimensional samples' rapid collision query is realized. The influence of number of Gaussian components on the fitting accuracy is analyzed in detail, and a self-adaptive model training method based on Greedy expectation-maximization (EM) algorithm is proposed. At the same time, this method has the capability of online updating and can eliminate model fitting errors due to environmental changes. Finally, the model is combined with a variety of sampling-based motion planners and is validated in multiple sets of simulations and real world experiments. The results show that, compared with traditional methods, the proposed method has significantly improved the planning efficiency

mChartQA: A universal benchmark for multimodal Chart Question Answer based on Vision-Language Alignment and Reasoning

In the fields of computer vision and natural language processing, multimodal chart question-answering, especially involving color, structure, and textless charts, poses significant challenges. Traditional methods, which typically involve either direct multimodal processing or a table-to-text conversion followed by language model analysis, have limitations in effectively handling these complex scenarios. This paper introduces a novel multimodal chart question-answering model, specifically designed to address these intricate tasks. Our model integrates visual and linguistic processing, overcoming the constraints of existing methods. We adopt a dual-phase training approach: the initial phase focuses on aligning image and text representations, while the subsequent phase concentrates on optimizing the model's interpretative and analytical abilities in chart-related queries. This approach has demonstrated superior performance on multiple public datasets, particularly in handling color, structure, and textless chart questions, indicating its effectiveness in complex multimodal tasks

Sampling Importance Resampling Algorithm with Nonignorable Missing Response Variable Based on Smoothed Quantile Regression

The presence of nonignorable missing response variables often leads to complex conditional distribution patterns that cannot be effectively captured through mean regression. In contrast, quantile regression offers valuable insights into the conditional distribution. Consequently, this article places emphasis on the quantile regression approach to address nonrandom missing data. Taking inspiration from fractional imputation, this paper proposes a novel smoothed quantile regression estimation equation based on a sampling importance resampling (SIR) algorithm instead of nonparametric kernel regression methods. Additionally, we present an augmented inverse probability weighting (AIPW) smoothed quantile regression estimation equation to reduce the influence of potential misspecification in a working model. The consistency and asymptotic normality of the empirical likelihood estimators corresponding to the above estimating equations are proven under the assumption of a correctly specified parameter working model. Furthermore, we demonstrate that the AIPW estimation equation converges to an IPW estimation equation when a parameter working model is misspecified, thus illustrating the robustness of the AIPW estimation approach. Through numerical simulations, we examine the finite sample properties of the proposed method when the working models are both correctly specified and misspecified. Furthermore, we apply the proposed method to analyze HIV—CD4 data, thereby exploring variations in treatment effects and the influence of other covariates across different quantiles.Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of ChinaPeer Reviewe

Enhancing Human-like Multi-Modal Reasoning: A New Challenging Dataset and Comprehensive Framework

Multimodal reasoning is a critical component in the pursuit of artificial intelligence systems that exhibit human-like intelligence, especially when tackling complex tasks. While the chain-of-thought (CoT) technique has gained considerable attention, the existing ScienceQA dataset, which focuses on multimodal scientific questions and explanations from elementary and high school textbooks, lacks a comprehensive evaluation of diverse approaches. To address this gap, we present COCO Multi-Modal Reasoning Dataset(COCO-MMRD), a novel dataset that encompasses an extensive collection of open-ended questions, rationales, and answers derived from the large object dataset COCO. Unlike previous datasets that rely on multiple-choice questions, our dataset pioneers the use of open-ended questions in the context of multimodal CoT, introducing a more challenging problem that effectively assesses the reasoning capability of CoT models. Through comprehensive evaluations and detailed analyses, we provide valuable insights and propose innovative techniques, including multi-hop cross-modal attention and sentence-level contrastive learning, to enhance the image and text encoders. Extensive experiments demonstrate the efficacy of the proposed dataset and techniques, offering novel perspectives for advancing multimodal reasoning

Virtual blebbistatin: A robust and rapid software approach to motion artifact removal in optical mapping of cardiomyocytes

Fluorescent reporters of cardiac electrophysiology provide valuable information on heart cell and tissue function. However, motion artifacts caused by cardiac muscle contraction interfere with accurate measurement of fluorescence signals. Although drugs such as blebbistatin can be applied to stop cardiac tissue from contracting by uncoupling calcium-contraction, their usage prevents the study of excitation-contraction coupling and, as we show, impacts cellular structure. We therefore developed a robust method to remove motion computationally from images of contracting cardiac muscle and to map fluorescent reporters of cardiac electrophysiological activity onto images of undeformed tissue. When validated on cardiomyocytes derived from human induced pluripotent stem cells (iPSCs), in both monolayers and engineered tissues, the method enabled efficient and robust reduction of motion artifact. As with pharmacologic approaches using blebbistatin for motion removal, our algorithm improved the accuracy of optical mapping, as demonstrated by spatial maps of calcium transient decay. However, unlike pharmacologic motion removal, our computational approach allowed direct analysis of calcium-contraction coupling. Results revealed calcium-contraction coupling to be more uniform across cells within engineered tissues than across cells in monolayer culture. The algorithm shows promise as a robust and accurate tool for optical mapping studies of excitation-contraction coupling in heart tissue

Gene Expression in the Hippocampus in a Rat Model of Premenstrual Dysphoric Disorder After Treatment With Baixiangdan Capsules

Objective: To explore the targets, signal regulatory networks and mechanisms involved in Baixiangdan (BXD) capsule regulation of premenstrual dysphoric disorder (PMDD) at the gene transcription level, since the etiology and pathogenesis of PMDD are not well understood.Methods: The PMDD rat model was prepared using the resident-intruder paradigm. The rats were tested for aggressive behavior, and those with scores in the lowest 30% were used as controls, while rats with scores in the highest 30% were divided into a PMDD model group, BXD administration group and fluoxetine administration group, which were evaluated with open-field tests and aggressive behavior tests. We also analyzed gene expression profiles in the hippocampus for each group, and verified differential expression of genes by real-time PCR.Results: Before and after BXD or fluoxetine administration, scores in the open-field test exhibited no significant differences. The aggressive behavior of the PMDD model rats was improved to a degree after administration of both substances. Gene chip data indicated that 715 genes were differentially expressed in the control and BXD groups. Other group-to-group comparisons exhibited smaller numbers of differentially expressed genes. The effective targets of both drugs included the Htr2c, Cdh3, Serpinb1a, Ace, Trpv4, Cacna1a, Mapk13, Mapk8, Cyp2c13, and Htr1a genes. The results of real-time PCR tests were in accordance with the gene chip data. Based on the target genes and signaling pathway network analysis, we have elaborated the impact and likely mechanism of BXD in treating PMDD and premenstrual irritability.Conclusion: Our work contributes to the understanding of PMDD pathogenesis and the mechanisms of BXD treatment. We speculate that the differentially expressed genes could participate in neuroactive ligand-receptor interactions, mitogen-activated protein kinase, calcium, and gamma-aminobutyric acid signal transduction

Risk assessment of the Xigou debris flow in the Three Gorges Reservoir Area

On June 18, 2018, under the influence of heavy rainfall, a debris flow disaster broke out in Xigou village of the Three Gorges Reservoir Area in Chongqing, causing some residential houses to be buried along with great economic losses. The on-site investigation found many loose solid material sources in the debris flow gully. Under the conditions of heavy rainfall, debris flows are prone to occur again, which would seriously threaten the lives and property of nearby residents. In this paper, taking the Xigou debris flow as a research case, numerical simulation by rapid mass movements simulation (RAMMS) is used to invert the movement process of the 2018 debris flow event; the dynamic calculation parameters of the Xigou debris flow event are obtained; a quantitative hazard prediction of debris flows with different recurrence intervals (30, 50, and 100 years) is carried out in the study area; and risk assessment is conducted based on the vulnerability characteristics of the disaster-bearing bodies in the study area. The results show that the maximum accumulation thickness of debris flow in the 30-year, 50-year, and 100-year recurrence intervals is 6.54 m, 10.18 m, and 10.00 m, respectively, and the debris flow in the 100-year recurrence interval has the widest influence range and greatest hazard. The low-, medium-, and high-risk areas account for 75%, 23%, and 2%, respectively. The high-risk area mainly includes some buildings near the #1 and #2 gullies. This study provides support for the prevention and control of potential debris flow disasters in Xigou village and a scientific basis for disaster prevention and mitigation in the Three Gorges Reservoir area