3D Model-based Zero-Shot Pose Estimation Pipeline

Most existing learning-based pose estimation methods are typically developed for non-zero-shot scenarios, where they can only estimate the poses of objects present in the training dataset. This setting restricts their applicability to unseen objects in the training phase. In this paper, we introduce a fully zero-shot pose estimation pipeline that leverages the 3D models of objects as clues. Specifically, we design a two-step pipeline consisting of 3D model-based zero-shot instance segmentation and a zero-shot pose estimator. For the first step, there is a novel way to perform zero-shot instance segmentation based on the 3D models instead of text descriptions, which can handle complex properties of unseen objects. For the second step, we utilize a hierarchical geometric structure matching mechanism to perform zero-shot pose estimation which is 10 times faster than the current render-based method. Extensive experimental results on the seven core datasets on the BOP challenge show that the proposed method outperforms the zero-shot state-of-the-art method with higher speed and lower computation cost

The Relationship Between Plasma DPP4 Activity to BDNF Ratio and Mild Cognitive Impairment in Elderly Population With Normal Glucose Tolerance

Objective: Since decreased brain-derived neurotrophic factor (BDNF) and increased dipeptidyl peptidase-4 (DPP4) activity have both been implicated in the pathogenesis of mild cognitive impairment (MCI), the aim of our study was to evaluate the association of MCI with plasma DPP4 activity to BDNF ratio (DBR) in an elderly population with normal glucose tolerance.Methods: We cross-sectionally measured C-reactive protein, interleukin-6, nitrotyrosine, 8-iso-PGF2a, DPP4 activity BDNF and calculated the DBR in a total of 1,066 elderly participants in China. MCI was determined by the Montreal Cognitive Assessment and finally confirmed by neurologists.Results: An inverse correlation was found between DPP4 activity and BDNF (r = -0.456, P < 0.001) and this inverse correlation was partly mediated by nitrotyrosine and 8-iso-PGF2a. Across rising quartiles of DBR, nitrotyrosine, 8-iso-PGF2a, C-reactive protein and interleukin-6 progressively increased, whereas the Montreal Cognitive Assessment score progressively decreased. Subjects in the lowest quartile of BDNF and highest quartiles of DBR and DPP4 activity, had higher MCI risk compared with subjects in the highest quartile of the BDNF and lowest quartiles of DBR and DPP4 activity, respectively (all P < 0.05). The odds ratio for MCI became more pronounced with decreased BDNF and increased DPP4.Conclusion: In conclusion, a negative correlation was found between DPP4 activity and BDNF, and this negative correlation was partly mediated by oxidative stress, not inflammation. The DBR was positively associated with MCI and thus may be used as a novel risk biomarker for MCI in an elderly population with normal glucose tolerance

MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection

Visual anomaly detection plays a crucial role in not only manufacturing inspection to find defects of products during manufacturing processes, but also maintenance inspection to keep equipment in optimum working condition particularly outdoors. Due to the scarcity of the defective samples, unsupervised anomaly detection has attracted great attention in recent years. However, existing datasets for unsupervised anomaly detection are biased towards manufacturing inspection, not considering maintenance inspection which is usually conducted under outdoor uncontrolled environment such as varying camera viewpoints, messy background and degradation of object surface after long-term working. We focus on outdoor maintenance inspection and contribute a comprehensive Maintenance Inspection Anomaly Detection (MIAD) dataset which contains more than 100K high-resolution color images in various outdoor industrial scenarios. This dataset is generated by a 3D graphics software and covers both surface and logical anomalies with pixel-precise ground truth. Extensive evaluations of representative algorithms for unsupervised anomaly detection are conducted, and we expect MIAD and corresponding experimental results can inspire research community in outdoor unsupervised anomaly detection tasks. Worthwhile and related future work can be spawned from our new dataset

Geo6D: Geometric Constraints Learning for 6D Pose Estimation

Numerous 6D pose estimation methods have been proposed that employ end-to-end regression to directly estimate the target pose parameters. Since the visible features of objects are implicitly influenced by their poses, the network allows inferring the pose by analyzing the differences in features in the visible region. However, due to the unpredictable and unrestricted range of pose variations, the implicitly learned visible feature-pose constraints are insufficiently covered by the training samples, making the network vulnerable to unseen object poses. To tackle these challenges, we proposed a novel geometric constraints learning approach called Geo6D for direct regression 6D pose estimation methods. It introduces a pose transformation formula expressed in relative offset representation, which is leveraged as geometric constraints to reconstruct the input and output targets of the network. These reconstructed data enable the network to estimate the pose based on explicit geometric constraints and relative offset representation mitigates the issue of the pose distribution gap. Extensive experimental results show that when equipped with Geo6D, the direct 6D methods achieve state-of-the-art performance on multiple datasets and demonstrate significant effectiveness, even with only 10% amount of data

TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems

Large Language Models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools that require a blend of task planning and the utilization of external tools, such as APIs. However, real-world complex systems present three prevalent challenges concerning task planning and tool usage: (1) The real system usually has a vast array of APIs, so it is impossible to feed the descriptions of all APIs to the prompt of LLMs as the token length is limited; (2) the real system is designed for handling complex tasks, and the base LLMs can hardly plan a correct sub-task order and API-calling order for such tasks; (3) Similar semantics and functionalities among APIs in real systems create challenges for both LLMs and even humans in distinguishing between them. In response, this paper introduces a comprehensive framework aimed at enhancing the Task Planning and Tool Usage (TPTU) abilities of LLM-based agents operating within real-world systems. Our framework comprises three key components designed to address these challenges: (1) the API Retriever selects the most pertinent APIs for the user task among the extensive array available; (2) LLM Finetuner tunes a base LLM so that the finetuned LLM can be more capable for task planning and API calling; (3) the Demo Selector adaptively retrieves different demonstrations related to hard-to-distinguish APIs, which is further used for in-context learning to boost the final performance. We validate our methods using a real-world commercial system as well as an open-sourced academic dataset, and the outcomes clearly showcase the efficacy of each individual component as well as the integrated framework

Genetic Engineering of the Biosynthesis of Glycine Betaine Modulates Phosphate Homeostasis by Regulating Phosphate Acquisition in Tomato

Glycine betaine (GB), as a putative compatible substance, protects plants against the damaging effects of abiotic stresses. Phosphorus deficiency is one type of abiotic stress that is detrimental to plant growth. Maintenance of phosphate (Pi) homeostasis is crucial. This study demonstrates GB-regulated phosphate homeostasis in the tomato (Solanum lycopersicum cv. ‘Moneymaker’) transformed with the choline oxidase gene codA from Arthrobacter globiformis. The codA-transgenic lines displayed more resistance to low-phosphate stress. The data revealed that the wild-type plants were stunted and consistently retained less Pi than transgenic lines, especially when grown under low-phosphate conditions. This difference in Pi retention was attributable to the enhanced Pi uptake ability in the transgenic lines. The transgenic plants translocated more Pi into the plant cell due to the enhanced enzymatic activity of plasma membrane H+-ATPase and increased Pi/H+ co-transport, which improved Pi uptake. The differential expression of ‘PHO regulon’ genes further maintained intracellular Pi homeostasis. Furthermore, GB maintained a higher photosynthesis rate, thus increasing the production and translocation of sucrose via phloem loading to enhance plant response to low-phosphate stress. We conclude that GB mediates Pi uptake and translocation by regulating physiological and biochemical processes that promote adaptation to environmental changes in Pi availability. These processes eventually lead to better growth and development of the codA-transgenic lines. This finding will help to further elucidate the signaling mechanism of how GB perceives and transmits low-phosphate signals to alleviate Pi nutritional stress

Neural oscillations during acupuncture imagery partially parallel that of real needling

IntroductionTasks involving mental practice, relying on the cognitive rehearsal of physical motors or other activities, have been reported to have similar patterns of brain activity to overt execution. In this study, we introduced a novel imagination task called, acupuncture imagery and aimed to investigate the neural oscillations during acupuncture imagery.MethodsHealthy volunteers were guided to watch a video of real needling in the left and right KI3 (Taixi point). The subjects were then asked to perform tasks to keep their thoughts in three 1-min states alternately: resting state, needling imagery left KI3, and needling imagery right KI3. Another group experienced real needling in the right KI3. A 31-channel-electroencephalography was synchronously recorded for each subject. Microstate analyses were performed to depict the brain dynamics during these tasks.ResultsCompared to the resting state, both acupuncture needling imagination and real needling in KI3 could introduce significant changes in neural dynamic oscillations. Moreover, the parameters involving microstate A of needling imagery in the right KI3 showed similar changes as real needling in the right KI3.DiscussionThese results confirm that needling imagination and real needling have similar brain activation patterns. Needling imagery may change brain network activity and play a role in neural regulation. Further studies are needed to explore the effects of acupuncture imagery and the potential application of acupuncture imagery in disease recovery