43 research outputs found
Aberrant resting-state brain activity in Huntington's disease: A voxel-based meta-analysis
IntroductionFunctional neuroimaging could provide abundant information of underling pathophysiological mechanisms of the clinical triad including motor, cognitive and psychiatric impairment in Huntington's Disease (HD).MethodsWe performed a voxel-based meta-analysis using anisotropic effect size-signed differential mapping (AES-SDM) method.Results6 studies (78 symptomatic HD, 102 premanifest HD and 131 healthy controls) were included in total. Altered resting-state brain activity was primarily detected in the bilateral medial part of superior frontal gyrus, bilateral anterior cingulate/paracingulate gyrus, left insula, left striatum, right cortico-spinal projections area, right inferior temporal gyrus area, right thalamus, right cerebellum and right gyrus rectus area. Premanifest and symptomatic HD patients showed different alterative pattern in the subgroup analyses.DiscussionThe robust and consistent abnormalities in the specific brain regions identified in the current study could help to understand the pathophysiology of HD and explore reliable neuroimaging biomarkers for monitoring disease progression, or even predicting the onset of premanifest HD patients
A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Arms
Robotic arms are widely used in automatic industries. However, with wide
applications of deep learning in robotic arms, there are new challenges such as
the allocation of grasping computing power and the growing demand for security.
In this work, we propose a robotic arm grasping approach based on deep learning
and edge-cloud collaboration. This approach realizes the arbitrary grasp
planning of the robot arm and considers the grasp efficiency and information
security. In addition, the encoder and decoder trained by GAN enable the images
to be encrypted while compressing, which ensures the security of privacy. The
model achieves 92% accuracy on the OCID dataset, the image compression ratio
reaches 0.03%, and the structural difference value is higher than 0.91
Graph-guided Architecture Search for Real-time Semantic Segmentation
Designing a lightweight semantic segmentation network often requires
researchers to find a trade-off between performance and speed, which is always
empirical due to the limited interpretability of neural networks. In order to
release researchers from these tedious mechanical trials, we propose a
Graph-guided Architecture Search (GAS) pipeline to automatically search
real-time semantic segmentation networks. Unlike previous works that use a
simplified search space and stack a repeatable cell to form a network, we
introduce a novel search mechanism with new search space where a lightweight
model can be effectively explored through the cell-level diversity and
latencyoriented constraint. Specifically, to produce the cell-level diversity,
the cell-sharing constraint is eliminated through the cell-independent manner.
Then a graph convolution network (GCN) is seamlessly integrated as a
communication mechanism between cells. Finally, a latency-oriented constraint
is endowed into the search process to balance the speed and performance.
Extensive experiments on Cityscapes and CamVid datasets demonstrate that GAS
achieves the new state-of-the-art trade-off between accuracy and speed. In
particular, on Cityscapes dataset, GAS achieves the new best performance of
73.5% mIoU with speed of 108.4 FPS on Titan Xp.Comment: CVPR202
Multiscale Superpixel Structured Difference Graph Convolutional Network for VL Representation
Within the multimodal field, the key to integrating vision and language lies
in establishing a good alignment strategy. Recently, benefiting from the
success of self-supervised learning, significant progress has been made in
multimodal semantic representation based on pre-trained models for vision and
language. However, there is still room for improvement in visual semantic
representation. The lack of spatial semantic coherence and vulnerability to
noise makes it challenging for current pixel or patch-based methods to
accurately extract complex scene boundaries. To this end, this paper develops
superpixel as a comprehensive compact representation of learnable image data,
which effectively reduces the number of visual primitives for subsequent
processing by clustering perceptually similar pixels. To mine more precise
topological relations, we propose a Multiscale Difference Graph Convolutional
Network (MDGCN). It parses the entire image as a fine-to-coarse hierarchical
structure of constituent visual patterns, and captures multiscale features by
progressively merging adjacent superpixels as graph nodes. Moreover, we predict
the differences between adjacent nodes through the graph structure,
facilitating key information aggregation of graph nodes to reason actual
semantic relations. Afterward, we design a multi-level fusion rule in a
bottom-up manner to avoid understanding deviation by learning complementary
spatial information at different regional scales. Our proposed method can be
well applied to multiple downstream task learning. Extensive experiments
demonstrate that our method is competitive with other state-of-the-art methods
in visual reasoning. Our code will be released upon publication
Controllable Multi-Objective Re-ranking with Policy Hypernetworks
Multi-stage ranking pipelines have become widely used strategies in modern
recommender systems, where the final stage aims to return a ranked list of
items that balances a number of requirements such as user preference,
diversity, novelty etc. Linear scalarization is arguably the most widely used
technique to merge multiple requirements into one optimization objective, by
summing up the requirements with certain preference weights. Existing
final-stage ranking methods often adopt a static model where the preference
weights are determined during offline training and kept unchanged during online
serving. Whenever a modification of the preference weights is needed, the model
has to be re-trained, which is time and resources inefficient. Meanwhile, the
most appropriate weights may vary greatly for different groups of targeting
users or at different time periods (e.g., during holiday promotions). In this
paper, we propose a framework called controllable multi-objective re-ranking
(CMR) which incorporates a hypernetwork to generate parameters for a re-ranking
model according to different preference weights. In this way, CMR is enabled to
adapt the preference weights according to the environment changes in an online
manner, without retraining the models. Moreover, we classify practical
business-oriented tasks into four main categories and seamlessly incorporate
them in a new proposed re-ranking model based on an Actor-Evaluator framework,
which serves as a reliable real-world testbed for CMR. Offline experiments
based on the dataset collected from Taobao App showed that CMR improved several
popular re-ranking models by using them as underlying models. Online A/B tests
also demonstrated the effectiveness and trustworthiness of CMR
Evaluating Open-QA Evaluation
This study focuses on the evaluation of the Open Question Answering (Open-QA)
task, which can directly estimate the factuality of large language models
(LLMs). Current automatic evaluation methods have shown limitations, indicating
that human evaluation still remains the most reliable approach. We introduce a
new task, Evaluating QA Evaluation (QA-Eval) and the corresponding dataset
EVOUNA, designed to assess the accuracy of AI-generated answers in relation to
standard answers within Open-QA. Our evaluation of these methods utilizes
human-annotated results to measure their performance. Specifically, the work
investigates methods that show high correlation with human evaluations, deeming
them more reliable. We also discuss the pitfalls of current methods and methods
to improve LLM-based evaluators. We believe this new QA-Eval task and
corresponding dataset EVOUNA will facilitate the development of more effective
automatic evaluation tools and prove valuable for future research in this area.
All resources are available at \url{https://github.com/wangcunxiang/QA-Eval}
and it is under the Apache-2.0 License
Lumen contour segmentation in ivoct based on n-type cnn
Automatic segmentation of lumen contour plays an important role in medical imaging and diagnosis, which is the first step towards the evaluation of morphology of vessels under analysis and the identification of possible atherosclerotic lesions. Meanwhile, quantitative information can only be obtained with segmentation, contributing to the appearance of novel methods which can be successfully applied to intravascular optical coherence tomography (IVOCT) images. This paper proposed a new end-to-end neural network (N-Net) for the automatic lumen segmentation, using multi-scale features based deep neural network, for IVOCT images. The architecture of the N-Net contains a multi-scale input layer, a N-type convolution network layer and a cross-entropy loss function. The multi-scale input layer in the proposed N-Net is designed to avoid the loss of information caused by pooling in traditional U-Net and also enriches the detailed information in each layer. The N-type convolutional network is proposed as the framework in the whole deep architecture. Finally, the loss function guarantees the degree of fidelity between the output of proposed method and the manually labeled output. In order to enlarge the training set, data augmentation is also introduced. We evaluated our method against loss, accuracy, recall, dice similarity coefficient, jaccard similarity coefficient and specificity. The experimental results presented in this paper demonstrate the superior performance of the proposed N-Net architecture, comparing to some existing networks, for enhancing the precision of automatic lumen segmentation and increasing the detailed information of edges of the vascular lumen
MetaGPT: Meta Programming for Multi-Agent Collaborative Framework
Recently, remarkable progress has been made in automated task-solving through
the use of multi-agents driven by large language models (LLMs). However,
existing works primarily focuses on simple tasks lacking exploration and
investigation in complicated tasks mainly due to the hallucination problem.
This kind of hallucination gets amplified infinitely as multiple intelligent
agents interact with each other, resulting in failures when tackling
complicated problems.Therefore, we introduce MetaGPT, an innovative framework
that infuses effective human workflows as a meta programming approach into
LLM-driven multi-agent collaboration. In particular, MetaGPT first encodes
Standardized Operating Procedures (SOPs) into prompts, fostering structured
coordination. And then, it further mandates modular outputs, bestowing agents
with domain expertise paralleling human professionals to validate outputs and
reduce compounded errors. In this way, MetaGPT leverages the assembly line work
model to assign diverse roles to various agents, thus establishing a framework
that can effectively and cohesively deconstruct complex multi-agent
collaborative problems. Our experiments conducted on collaborative software
engineering tasks illustrate MetaGPT's capability in producing comprehensive
solutions with higher coherence relative to existing conversational and
chat-based multi-agent systems. This underscores the potential of incorporating
human domain knowledge into multi-agents, thus opening up novel avenues for
grappling with intricate real-world challenges. The GitHub repository of this
project is made publicly available on: https://github.com/geekan/MetaGP
Factors influencing cognitive function in patients with Huntington's disease from China: A cross-sectional clinical study.
BACKGROUND AND AIM
Huntington's disease (HD) is an autosomal dominant inherited neurodegenerative disorder caused by CAG repeats expansion. Cognitive decline contributes to the loss of daily activity in manifest HD. We aimed to examine the cognition status in a Chinese HD cohort and explore factors influencing the diverse cognitive domains.
METHODS
A total of 205 participants were recruited in the study with the assessment by neuropsychological batteries, including the mini-mental state examination (MMSE), Stroop test, symbol digit modalities test (SDMT), trail making test (TMT), verbal fluency test (VFT), and Hopkins verbal learning test-revised, as well as motor and psychiatric assessment. Pearson correlation and multiple linear regression models were applied to investigate the correlation.
RESULTS
Only 41.46% of patients had normal global function first come to our center. There was a significantly difference in MMSE, Stroop test, SDMT, TMT, and VFT across each stage of HD patients (p < .05). Apathy of PBA-s was correlated to MMSE, animal VFT and Stroop-interference tests performance. Severity of motor symptoms, functional capacity, age, and age of motor symptom onset were correlated to all neuropsychological scores, whereas education attainment and diagnostic delay were correlated to most neuropsychological scores except TMT. Severity of motor symptoms, functional capacity, and education attainment showed independent predicting effect (p < .05) in diverse cognitive domains.
CONCLUSION
Cognitive impairment was very common in Chinese HD patients at the first visit and worse in the patients in advanced phase. The severity of motor symptoms and functional capacity were correlated to the diverse cognitive domains