145 research outputs found
A Conjugate Gradient Method with Global Convergence for Large-Scale Unconstrained Optimization Problems
The conjugate gradient (CG) method has played a special role in solving large-scale nonlinear
optimization problems due to the simplicity of their very low memory requirements. This paper
proposes a conjugate gradient method which is similar to Dai-Liao conjugate gradient method (Dai and Liao, 2001)
but has stronger convergence properties. The given method possesses the sufficient descent condition,
and is globally convergent under strong Wolfe-Powell (SWP) line search for general function. Our
numerical results show that the proposed method is very efficient for the test problems
Progressively Dual Prior Guided Few-shot Semantic Segmentation
Few-shot semantic segmentation task aims at performing segmentation in query
images with a few annotated support samples. Currently, few-shot segmentation
methods mainly focus on leveraging foreground information without fully
utilizing the rich background information, which could result in wrong
activation of foreground-like background regions with the inadaptability to
dramatic scene changes of support-query image pairs. Meanwhile, the lack of
detail mining mechanism could cause coarse parsing results without some
semantic components or edge areas since prototypes have limited ability to cope
with large object appearance variance. To tackle these problems, we propose a
progressively dual prior guided few-shot semantic segmentation network.
Specifically, a dual prior mask generation (DPMG) module is firstly designed to
suppress the wrong activation in foreground-background comparison manner by
regarding background as assisted refinement information. With dual prior masks
refining the location of foreground area, we further propose a progressive
semantic detail enrichment (PSDE) module which forces the parsing model to
capture the hidden semantic details by iteratively erasing the high-confidence
foreground region and activating details in the rest region with a hierarchical
structure. The collaboration of DPMG and PSDE formulates a novel few-shot
segmentation network that can be learned in an end-to-end manner. Comprehensive
experiments on PASCAL-5i and MS COCO powerfully demonstrate that our proposed
algorithm achieves the great performance
A Modified Conjugacy Condition and Related Nonlinear Conjugate Gradient Method
The conjugate gradient (CG) method has played a special role in solving large-scale nonlinear optimization problems due to the simplicity of their very low memory requirements. In this paper, we propose a new conjugacy condition which is similar to Dai-Liao (2001). Based on this condition, the related nonlinear conjugate gradient method is given. With some mild conditions, the given method is globally convergent under the strong Wolfe-Powell line search for general functions. The numerical experiments show that the proposed method is very robust and efficient
Towards Large-Scale Small Object Detection: Survey and Benchmarks
With the rise of deep convolutional neural networks, object detection has
achieved prominent advances in past years. However, such prosperity could not
camouflage the unsatisfactory situation of Small Object Detection (SOD), one of
the notoriously challenging tasks in computer vision, owing to the poor visual
appearance and noisy representation caused by the intrinsic structure of small
targets. In addition, large-scale dataset for benchmarking small object
detection methods remains a bottleneck. In this paper, we first conduct a
thorough review of small object detection. Then, to catalyze the development of
SOD, we construct two large-scale Small Object Detection dAtasets (SODA),
SODA-D and SODA-A, which focus on the Driving and Aerial scenarios
respectively. SODA-D includes 24828 high-quality traffic images and 278433
instances of nine categories. For SODA-A, we harvest 2513 high resolution
aerial images and annotate 872069 instances over nine classes. The proposed
datasets, as we know, are the first-ever attempt to large-scale benchmarks with
a vast collection of exhaustively annotated instances tailored for
multi-category SOD. Finally, we evaluate the performance of mainstream methods
on SODA. We expect the released benchmarks could facilitate the development of
SOD and spawn more breakthroughs in this field. Datasets and codes are
available at: \url{https://shaunyuan22.github.io/SODA}
Multi-grained Temporal Prototype Learning for Few-shot Video Object Segmentation
Few-Shot Video Object Segmentation (FSVOS) aims to segment objects in a query
video with the same category defined by a few annotated support images.
However, this task was seldom explored. In this work, based on IPMT, a
state-of-the-art few-shot image segmentation method that combines external
support guidance information with adaptive query guidance cues, we propose to
leverage multi-grained temporal guidance information for handling the temporal
correlation nature of video data. We decompose the query video information into
a clip prototype and a memory prototype for capturing local and long-term
internal temporal guidance, respectively. Frame prototypes are further used for
each frame independently to handle fine-grained adaptive guidance and enable
bidirectional clip-frame prototype communication. To reduce the influence of
noisy memory, we propose to leverage the structural similarity relation among
different predicted regions and the support for selecting reliable memory
frames. Furthermore, a new segmentation loss is also proposed to enhance the
category discriminability of the learned prototypes. Experimental results
demonstrate that our proposed video IPMT model significantly outperforms
previous models on two benchmark datasets. Code is available at
https://github.com/nankepan/VIPMT.Comment: ICCV 202
HPV E6 induces eIF4E transcription to promote the proliferation and migration of cervical cancer
AbstractIncreasing evidence has placed eukaryotic translation initiation factor 4E (eIF4E) at the hub of tumor development and progression. Several studies have reported that eIF4E is over-expressed in cervical cancer; however, the mechanism remains elusive. The results of this study further confirm over-expression of eIF4E in cervical cancer tumors and cell lines, and we have discovered that the transcription of eIF4E is induced by protein E6 of the human papillomavirus (HPV). Moreover, regulation of eIF4E by E6 significantly influences cell proliferation, the cell cycle, migration, and apoptosis. Therefore, eIF4E emerges as a key player in tumor development and progression and a potential target for CC treatment and prevention
Machine Learning for Prediction of Sudden Cardiac Death in Heart Failure Patients With Low Left Ventricular Ejection Fraction: Study Protocol for a Retrospective Multicentre Registry in China
Introduction: Left ventricular ejection fraction (LVEF) ≤35%, as current significant implantable cardioverter-defibrillator (ICD) indication for primary prevention of sudden cardiac death (SCD) in heart failure (HF) patients, has been widely recognised to be inefficient. Improvement of patient selection for low LVEF (≤35%) is needed to optimise deployment of ICD. Most of the existing prediction models are not appropriate to identify ICD candidates at high risk of SCD in HF patients with low LVEF. Compared with traditional statistical analysis, machine learning (ML) can employ computer algorithms to identify patterns in large datasets, analyse rules automatically and build both linear and non-linear models in order to make data-driven predictions. This study is aimed to develop and validate new models using ML to improve the prediction of SCD in HF patients with low LVEF. Methods and analysis: We will conduct a retroprospective, multicentre, observational registry of Chinese HF patients with low LVEF. The HF patients with LVEF ≤35% after optimised medication at least 3 months will be enrolled in this study. The primary endpoints are all-cause death and SCD. The secondary endpoints are malignant arrhythmia, sudden cardiac arrest, cardiopulmonary resuscitation and rehospitalisation due to HF. The baseline demographic, clinical, biological, electrophysiological, social and psychological variables will be collected. Both ML and traditional multivariable Cox proportional hazards regression models will be developed and compared in the prediction of SCD. Moreover, the ML model will be validated in a prospective study. Ethics and dissemination: The study protocol has been approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (2017-SR-06). All results of this study will be published in international peer-reviewed journals and presented at relevant conferences
Validation of the ITS2 Region as a Novel DNA Barcode for Identifying Medicinal Plant Species
BACKGROUND: The plant working group of the Consortium for the Barcode of Life recommended the two-locus combination of rbcL+matK as the plant barcode, yet the combination was shown to successfully discriminate among 907 samples from 550 species at the species level with a probability of 72%. The group admits that the two-locus barcode is far from perfect due to the low identification rate, and the search is not over. METHODOLOGY/PRINCIPAL FINDINGS: Here, we compared seven candidate DNA barcodes (psbA-trnH, matK, rbcL, rpoC1, ycf5, ITS2, and ITS) from medicinal plant species. Our ranking criteria included PCR amplification efficiency, differential intra- and inter-specific divergences, and the DNA barcoding gap. Our data suggest that the second internal transcribed spacer (ITS2) of nuclear ribosomal DNA represents the most suitable region for DNA barcoding applications. Furthermore, we tested the discrimination ability of ITS2 in more than 6600 plant samples belonging to 4800 species from 753 distinct genera and found that the rate of successful identification with the ITS2 was 92.7% at the species level. CONCLUSIONS: The ITS2 region can be potentially used as a standard DNA barcode to identify medicinal plants and their closely related species. We also propose that ITS2 can serve as a novel universal barcode for the identification of a broader range of plant taxa
Potential of Core-Collapse Supernova Neutrino Detection at JUNO
JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve
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