130 research outputs found
Attention-based Pyramid Aggregation Network for Visual Place Recognition
Visual place recognition is challenging in the urban environment and is
usually viewed as a large scale image retrieval task. The intrinsic challenges
in place recognition exist that the confusing objects such as cars and trees
frequently occur in the complex urban scene, and buildings with repetitive
structures may cause over-counting and the burstiness problem degrading the
image representations. To address these problems, we present an Attention-based
Pyramid Aggregation Network (APANet), which is trained in an end-to-end manner
for place recognition. One main component of APANet, the spatial pyramid
pooling, can effectively encode the multi-size buildings containing
geo-information. The other one, the attention block, is adopted as a region
evaluator for suppressing the confusing regional features while highlighting
the discriminative ones. When testing, we further propose a simple yet
effective PCA power whitening strategy, which significantly improves the widely
used PCA whitening by reasonably limiting the impact of over-counting.
Experimental evaluations demonstrate that the proposed APANet outperforms the
state-of-the-art methods on two place recognition benchmarks, and generalizes
well on standard image retrieval datasets.Comment: Accepted to ACM Multimedia 201
Cross-Domain Depth Estimation Network for 3D Vessel Reconstruction in OCT Angiography
Optical Coherence Tomography Angiography (OCTA) has been widely used by ophthalmologists for decision-making due to its superiority in providing caplillary details. Many of the OCTA imaging devices used in clinic provide high-quality 2D en face representations, while their 3D data quality are largely limited by low signal-to-noise ratio and strong projection artifacts, which restrict the performance of depth-resolved 3D analysis. In this paper, we propose a novel 2D-to-3D vessel reconstruction framework based on the 2D en face OCTA images. This framework takes advantage of the detailed 2D OCTA depth map for prediction and thus does not rely on any 3D volumetric data. Based on the data with available vessel depth labels, we first introduce a network with structure constraint blocks to estimate the depth map of blood vessels in other cross-domain en face OCTA data with unavailable labels. Afterwards, a depth adversarial adaptation module is proposed for better unsupervised cross-domain training, since images captured using different devices may suffer from varying image contrast and noise levels. Finally, vessels are reconstructed in 3D space by utilizing the estimated depth map and 2D vascular information. Experimental results demonstrate the effectiveness of our method and its potential to guide subsequent vascular analysis in 3D domain
The Internet of Responsibilities-Connecting Human Responsibilities using Big Data and Blockchain
Accountability in the workplace is critically important and remains a
challenging problem, especially with respect to workplace safety management. In
this paper, we introduce a novel notion, the Internet of Responsibilities, for
accountability management. Our method sorts through the list of
responsibilities with respect to hazardous positions. The positions are
interconnected using directed acyclic graphs (DAGs) indicating the hierarchy of
responsibilities in the organization. In addition, the system detects and
collects responsibilities, and represents risk areas in terms of the positions
of the responsibility nodes. Finally, an automatic reminder and assignment
system is used to enforce a strict responsibility control without human
intervention. Using blockchain technology, we further extend our system with
the capability to store, recover and encrypt responsibility data. We show that
through the application of the Internet of Responsibility network model driven
by Big Data, enterprise and government agencies can attain a highly secured and
safe workplace. Therefore, our model offers a combination of interconnected
responsibilities, accountability, monitoring, and safety which is crucial for
the protection of employees and the success of organizations
Improving Text Matching in E-Commerce Search with A Rationalizable, Intervenable and Fast Entity-Based Relevance Model
Discovering the intended items of user queries from a massive repository of
items is one of the main goals of an e-commerce search system. Relevance
prediction is essential to the search system since it helps improve
performance. When online serving a relevance model, the model is required to
perform fast and accurate inference. Currently, the widely used models such as
Bi-encoder and Cross-encoder have their limitations in accuracy or inference
speed respectively. In this work, we propose a novel model called the
Entity-Based Relevance Model (EBRM). We identify the entities contained in an
item and decompose the QI (query-item) relevance problem into multiple QE
(query-entity) relevance problems; we then aggregate their results to form the
QI prediction using a soft logic formulation. The decomposition allows us to
use a Cross-encoder QE relevance module for high accuracy as well as cache QE
predictions for fast online inference. Utilizing soft logic makes the
prediction procedure interpretable and intervenable. We also show that
pretraining the QE module with auto-generated QE data from user logs can
further improve the overall performance. The proposed method is evaluated on
labeled data from e-commerce websites. Empirical results show that it achieves
promising improvements with computation efficiency
SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding
Large language models (LLMs) have shown impressive ability for open-domain
NLP tasks. However, LLMs are sometimes too footloose for natural language
understanding (NLU) tasks which always have restricted output and input format.
Their performances on NLU tasks are highly related to prompts or demonstrations
and are shown to be poor at performing several representative NLU tasks, such
as event extraction and entity typing. To this end, we present SeqGPT, a
bilingual (i.e., English and Chinese) open-source autoregressive model
specially enhanced for open-domain natural language understanding. We express
all NLU tasks with two atomic tasks, which define fixed instructions to
restrict the input and output format but still ``open'' for arbitrarily varied
label sets. The model is first instruction-tuned with extremely fine-grained
labeled data synthesized by ChatGPT and then further fine-tuned by 233
different atomic tasks from 152 datasets across various domains. The
experimental results show that SeqGPT has decent classification and extraction
ability, and is capable of performing language understanding tasks on unseen
domains. We also conduct empirical studies on the scaling of data and model
size as well as on the transfer across tasks. Our model is accessible at
https://github.com/Alibaba-NLP/SeqGPT.Comment: Initial version of SeqGP
3D VESSEL RECONSTRUCTION IN OCT-ANGIOGRAPHY VIA DEPTH MAP ESTIMATION
Optical Coherence Tomography Angiography (OCTA) has been increasingly used in
the management of eye and systemic diseases in recent years. Manual or
automatic analysis of blood vessel in 2D OCTA images (en face angiograms) is
commonly used in clinical practice, however it may lose rich 3D spatial
distribution information of blood vessels or capillaries that are useful for
clinical decision-making. In this paper, we introduce a novel 3D vessel
reconstruction framework based on the estimation of vessel depth maps from OCTA
images. First, we design a network with structural constraints to predict the
depth of blood vessels in OCTA images. In order to promote the accuracy of the
predicted depth map at both the overall structure- and pixel- level, we combine
MSE and SSIM loss as the training loss function. Finally, the 3D vessel
reconstruction is achieved by utilizing the estimated depth map and 2D vessel
segmentation results. Experimental results demonstrate that our method is
effective in the depth prediction and 3D vessel reconstruction for OCTA
images.% results may be used to guide subsequent vascular analysi
Deep Segmentation of OCTA for Evaluation and Association of Changes of Retinal Microvasculature with Alzheimer’s Disease and Mild Cognitive Impairment
BackgroundOptical coherence tomography angiography (OCTA) enables fast and non-invasive high-resolution imaging of retinal microvasculature and is suggested as a potential tool in the early detection of retinal microvascular changes in Alzheimer's Disease (AD). We developed a standardised OCTA analysis framework and compared their extracted parameters among controls and AD/mild cognitive impairment (MCI) in a cross-section study.MethodsWe defined and extracted geometrical parameters of retinal microvasculature at different retinal layers and in the foveal avascular zone (FAZ) from segmented OCTA images obtained using well-validated state-of-the-art deep learning models. We studied these parameters in 158 subjects (62 healthy control, 55 AD and 41 MCI) using logistic regression to determine their potential in predicting the status of our subjects.ResultsIn the AD group, there was a significant decrease in vessel area and length densities in the inner vascular complexes (IVC) compared with controls. The number of vascular bifurcations in AD is also significantly lower than that of healthy people. The MCI group demonstrated a decrease in vascular area, length densities, vascular fractal dimension and the number of bifurcations in both the superficial vascular complexes (SVC) and the IVC compared with controls. A larger vascular tortuosity in the IVC, and a larger roundness of FAZ in the SVC, can also be observed in MCI compared with controls.ConclusionOur study demonstrates the applicability of OCTA for the diagnosis of AD and MCI, and provides a standard tool for future clinical service and research. Biomarkers from retinal OCTA images can provide useful information for clinical decision-making and diagnosis of AD and MCI
ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model
Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique that has been increasingly used to image the retinal vasculature at capillary level resolution. However, automated segmentation of retinal vessels in OCTA has been under-studied due to various challenges such as low capillary visibility and high vessel complexity, despite its significance in understanding many vision-related diseases. In addition, there is no publicly available OCTA dataset with manually graded vessels for training and validation of segmentation algorithms. To address these issues, for the first time in the field of retinal image analysis we construct a dedicated Retinal OCTA SEgmentation dataset (ROSE), which consists of 229 OCTA images with vessel annotations at either centerline-level or pixel level. This dataset with the source code has been released for public access to assist researchers in the community in undertaking research in related topics. Secondly, we introduce a novel split-based coarse-to-fine vessel segmentation network for OCTA images (OCTA-Net), with the ability to detect thick and thin vessels separately. In the OCTA-Net, a split-based coarse segmentation module is first utilized to produce a preliminary confidence map of vessels, and a split-based refined segmentation module is then used to optimize the shape/contour of the retinal microvasculature. We perform a thorough evaluation of the state-of-the-art vessel segmentation models and our OCTA-Net on the constructed ROSE dataset. The experimental results demonstrate that our OCTA-Net yields better vessel segmentation performance in OCTA than both traditional and other deep learning methods. In addition, we provide a fractal dimension analysis on the segmented microvasculature, and the statistical analysis demonstrates significant differences between the healthy control and Alzheimer's Disease group. This consolidates that the analysis of retinal microvasculature may offer a new scheme to study various neurodegenerative diseases
Identification of Glycine Receptor α3 as a Colchicine-Binding Protein
Colchicine (Col) is considered a kind of highly effective alkaloid for preventing and treating acute gout attacks (flares). However, little is known about the underlying mechanism of Col in pain treatment. We have previously developed a customized virtual target identification method, termed IFPTarget, for small-molecule target identification. In this study, by using IFPTarget and ligand similarity ensemble approach (SEA), we show that the glycine receptor alpha 3 (GlyRα3), which play a key role in the processing of inflammatory pain, is a potential target of Col. Moreover, Col binds directly to the GlyRα3 as determined by the immunoprecipitation and bio-layer interferometry assays using the synthesized Col-biotin conjugate (linked Col and biotin with polyethylene glycol). These results suggest that GlyRα3 may mediate Col-induced suppression of inflammatory pain. However, whether GlyRα3 is the functional target of Col and serves as potential therapeutic target in gouty arthritis requires further investigations
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