122 research outputs found
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
FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification
Histopathological tissue classification is a fundamental task in
computational pathology. Deep learning-based models have achieved superior
performance but centralized training with data centralization suffers from the
privacy leakage problem. Federated learning (FL) can safeguard privacy by
keeping training samples locally, but existing FL-based frameworks require a
large number of well-annotated training samples and numerous rounds of
communication which hinder their practicability in the real-world clinical
scenario. In this paper, we propose a universal and lightweight federated
learning framework, named Federated Deep-Broad Learning (FedDBL), to achieve
superior classification performance with limited training samples and only
one-round communication. By simply associating a pre-trained deep learning
feature extractor, a fast and lightweight broad learning inference system and a
classical federated aggregation approach, FedDBL can dramatically reduce data
dependency and improve communication efficiency. Five-fold cross-validation
demonstrates that FedDBL greatly outperforms the competitors with only
one-round communication and limited training samples, while it even achieves
comparable performance with the ones under multiple-round communications.
Furthermore, due to the lightweight design and one-round communication, FedDBL
reduces the communication burden from 4.6GB to only 276.5KB per client using
the ResNet-50 backbone at 50-round training. Since no data or deep model
sharing across different clients, the privacy issue is well-solved and the
model security is guaranteed with no model inversion attack risk. Code is
available at https://github.com/tianpeng-deng/FedDBL
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
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