The Cine MRI of the small intestine is a dynamic magnetic resonance imaging technique used to observe and evaluate small intestine motility. It captures sequential images of the organ in motion over time through rapid imaging. The Transformer architecture is highly effective at capturing long-range dependencies, enabling it to extract more comprehensive temporal information. As the image resolution provided by medical devices continues to improve, images now contain far more intricate details. However, most existing Transformer-based 3D segmentation models, constrained by computational resources, cannot directly handle high-resolution sequential Cine MRI data. In this paper, we propose an efficient joint training architecture for high-resolution 3D small intestine segmentation, consisting of a Local Sequential Encoder (LSE), a Global Multi-Scale Encoder (GMSE), and a Spatial Sequential Hybrid Decoder (SSHD). The LSE partitions high-resolution temporal images into multiple patches and uses a parameter-sharing mechanism to extract local detail features with a low computational cost. To prevent the loss of global features due to patch partitioning, the GMSE complements the LSE through joint training. Both encoders are trained simultaneously, the LSE captures local details, while the GMSE focuses on global, multi-scale features. Their combined outputs enable the model to balance fine-grained and large-scale information, improving segmentation accuracy. Furthermore, to address the periodic nature of small intestine peristalsis, we propose a dynamic consistency loss, which effectively quantifies motion amplitude differences between predicted and ground truth sequences. Experimental results on our dataset demonstrate that the proposed architecture significantly improves prediction accuracy compared to existing advanced 3D segmentation methods, while also delivering superior performance with low computational cost than recent advanced methods.

Contact-implicit trajectory optimization (CITO) is an effective method to plan complex trajectories for various contact-rich systems including manipulation and locomotion. CITO formulates a mathematical program with complementarity constraints (MPCC) that enforces that contact forces must be zero when points are not in contact. However, MPCC solve times increase steeply with the number of allowable points of contact, which limits CITO's applicability to problems in which only a few, simple geometries are allowed us to make contact. This article introduces simultaneous trajectory optimization and contact selection (STOCS), as an extension of CITO that overcomes this limitation. The innovation of STOCS is to identify salient contact points and times inside the iterative trajectory optimization process. This effectively reduces the number of variables and constraints in each MPCC invocation. The STOCS framework, instantiated with key contact identification subroutines, renders the optimization of manipulation trajectories computationally tractable even for high-fidelity geometries consisting of tens of thousands of vertices.

Large language models (LLMs) have shown excellent performance on general machine translation. However, LLMs suffer from high deployment cost and unsatisfying quality on low-resource domains. To this end, we explore to build base translation models with LLM-enhanced data augmentation. For data augmentation, we propose a cross search method to obtain qualified parallel in-domain corpus. This method encompasses two distinct approaches: antagony-cross search and similarity-cross search. Antagony-cross search helps to generate monolingual data that closely aligns with the target domain by employing token-level control. Similarity-cross search keeps the alignment between source and target sentences through a similarity score in back translation, so that the generated target language is closer to the source language semantically. With the proposed method, we generate millions of high-quality parallel in-domain corpus from low-resource monolingual data. Our proposed method achieves improvements of approximately 0.5-4 BLEU scores in these domains.

This paper presents a novel experience-based technique, called EHGO, for sample-efficient adaptive control of nonlinear systems in the presence of dynamical modeling errors. The starting point for EHGO is a database seeded with many trajectories optimized under a reference estimate of real system dynamics. When executed on the real system, these trajectories will be suboptimal due to errors in the reference dynamics. The approach then leverages a hybrid gradient optimization technique, GRILC, which observes executed trajectories and computes gradients from the reference model to refine the control policy without requiring an explicit model of the real system. In past work, GRILC was applied in a restrictive setting in which a robot executes multiple rollouts from identical start states. In this paper, we show how to leverage a database to enable GRILC to operate across a wide envelope of possible start states in different iterations. The database is used to balance between start state proximity and recentness-of-experience via a learned distance metric to generate good initial guesses. Experiments on three dynamical systems (pendulum, car, drone) show that the proposed approach adapts quickly to online experience even when the reference model has significant errors. In these examples EHGO generates near-optimal solutions within hundreds of epochs of real execution, which can be orders of magnitude more sample efficient than reinforcement learning techniques.

Insurance fraud not only increases the burden on policyholders but may even affect the normal operation of insurance companies. Based on the assumption of bounded rationality, this article establishes the tripartite evolutionary game matrix for policyholders, insurance companies, and government departments. In this article, Gaussian white noise is introduced to simulate the random interference encountered by each subject in the evolutionary game, and the evolutionary stability strategy of the tripartite stochastic evolutionary game under different conditions is discussed. Considering the importance of fraud recognition rate, the recognition accuracy rate is introduced into the stochastic evolutionary game, and the necessity of introducing fraud recognition rate into the game is proved by simulation. In the context of society, we have joined the government supervision department into the game and realized the supervision mode of social co-governance by using the social public opinion to reward and punish the government reputation.

This paper presents a novel iterative closest points (ICP) variant, non-penetration iterative closest points (NPICP), which prevents interpenetration in 6DOF pose optimization and/or joint optimization of multiple object poses. This capability is particularly advantageous in cluttered scenarios, where there are many interactions between objects that constrain the space of valid poses. We use a semi-infinite programming approach to handle non-penetration constraints between complex, non-convex 3D geometries. NPICP is applied to a common use case for ICP as a post-processing method to improve the pose estimation accuracy of a rough guess. The results show that NPICP outperforms ICP, assists in outlier detection, and also outperforms the best result on the IC-BIN dataset in the Benchmark for 6D Object Pose Estimation.

In natural language processing (NLP), stateof-the-art (SOTA) semi-supervised learning (SSL) frameworks have shown great performance on deep pre-trained language models such as BERT, and are expected to significantly reduce the demand for manual labeling.However, our empirical studies indicate that these frameworks are not suitable for lightweight models such as TextCNN, LSTM and etc.In this work, we develop a new SSL framework called FLiText, which stands for Faster and Lighter semi-supervised Text classification.FLiText introduces an inspirer network together with the consistency regularization framework, which leverages a generalized regular constraint on the lightweight models for efficient SSL.As a result, FLiText obtains new SOTA performance for lightweight models across multiple SSL benchmarks on text classification.Compared with existing SOTA SSL methods on TextCNN, FLiText improves the accuracy of lightweight model TextCNN from 51.00% to 90.49% on IMDb, 39.8% to 58.06% on Yelp-5, and from 55.3% to 65.08% on Yahoo.In addition, compared with the fully supervised method on the full dataset, FLi-Text just uses less than 1% of labeled data to improve the accuracy by 6.59%, 3.94%, and 3.22% on the datasets of IMDb, Yelp-5, and Yahoo respectively.

This paper proposes a hybrid planning framework that generates complex dynamic motion plans for jumping legged robots to traverse challenging terrains. By employing a motion primitive, the original problem is decoupled as path planning followed by a trajectory optimization (TO) module that handles dynamics. A variant of a kinodynamic Rapidly-exploring Random Trees (RRT) planner finds a path as a parabola sequence between stance phases. To make this fast, a reachability informed control sampling scheme leverages a precomputed velocity reachability map. The path is post-processed to eliminate redundant jumps and passed to the TO module to find a dynamically feasible trajectory. Simulation results are presented where the proposed hybrid planner solves challenging terrains by executing multiple consecutive jumps, producing novel strategies to leap over large gaps by leveraging dynamics. In a physical experiment, the hybrid planner is tested on a real robot successfully traversing a challenging terrain.

This paper presents a novel computational model to address the problem that contact is an infinite phenomena involving continuous regions of interaction. The problem is cast as a semi-infinite program with complementarity constraints (SIPCC). Rather than pre-discretize contacting surfaces into a finite number of contact points, we use semi-infinite programming (SIP) techniques that operate on the underlying continuous geometry, but dynamically determine a finite number of constraints that are most relevant to solving the problem. Then we solve the series of problems whose solutions converge toward one that contains a true optimum of the original SIPCC. We apply the model to a grasping pose optimization problem for a gripper and a humanoid robot, and our model enables the robots to find a feasible pose to hold (non-)convex objects while ensuring force and torque balance.

Material flow detection on conveyor based on machine vision is the research topic of this paper. A belt conveyor system equipped with a camera and micro-controller is used as the test apparatus. The purpose of this experiment is to obtain the quantity of material on the conveyor belt using machine vision and then develop an intelligent speed adjustment system for belt conveyor according to the quantity, so as to avoid waste of energy and reduce the wear of the conveyor. Three image processing algorithms that developed, applied and compared were: 1) Background Subtraction; 2) Canny edge detection and morphological operations; 3) Particle analysis using. It is observed that all three methods perform well for material detection on the conveyor belt. However, the particle analysis method resulted in higher reliability and accuracy with faster processing speed. The research provides new developmental ideas for intelligent conveyor systems.