AlignDet: Aligning Pre-training and Fine-tuning in Object Detection

The paradigm of large-scale pre-training followed by downstream fine-tuning has been widely employed in various object detection algorithms. In this paper, we reveal discrepancies in data, model, and task between the pre-training and fine-tuning procedure in existing practices, which implicitly limit the detector's performance, generalization ability, and convergence speed. To this end, we propose AlignDet, a unified pre-training framework that can be adapted to various existing detectors to alleviate the discrepancies. AlignDet decouples the pre-training process into two stages, i.e., image-domain and box-domain pre-training. The image-domain pre-training optimizes the detection backbone to capture holistic visual abstraction, and box-domain pre-training learns instance-level semantics and task-aware concepts to initialize the parts out of the backbone. By incorporating the self-supervised pre-trained backbones, we can pre-train all modules for various detectors in an unsupervised paradigm. As depicted in Figure 1, extensive experiments demonstrate that AlignDet can achieve significant improvements across diverse protocols, such as detection algorithm, model backbone, data setting, and training schedule. For example, AlignDet improves FCOS by 5.3 mAP, RetinaNet by 2.1 mAP, Faster R-CNN by 3.3 mAP, and DETR by 2.3 mAP under fewer epochs.Comment: Accepted by ICCV 2023. Code and Models are publicly available. Project Page: https://liming-ai.github.io/AlignDe

Angiotensin-converting enzyme gene 2350 G/A polymorphism and susceptibility to atrial fibrillation in Han Chinese patients with essential hypertension

OBJECTIVE: The angiotensin-converting enzyme gene is one of the most studied candidate genes related to atrial fibrillation. Among the polymorphisms of the angiotensin-converting enzyme gene, the 2350 G/A polymorphism (rs4343) is known to have the most significant effects on the plasma angiotensin-converting enzyme concentration. The aim of the present study was to investigate the association of the angiotensin-converting enzyme 2350 G/A polymorphism with atrial fibrillation in Han Chinese patients with essential hypertension. METHODS: A total of 169 hypertensive patients were eligible for this study. Patients with atrial fibrillation (n = 75) were allocated to the atrial fibrillation group, and 94 subjects without atrial fibrillation were allocated to the control group. The PCR-based restriction fragment length polymorphism technique was used to assess the genotype frequencies. RESULTS: The distributions of the angiotensin-converting enzyme 2350 G/A genotypes (GG, GA, and AA, respectively) were 40.43%, 41.49%, and 18.08% in the controls and 18.67%, 46.67%, and 34.66% in the atrial fibrillation subjects (p = 0.037). The frequency of the A allele in the atrial fibrillation group was significantly greater than in the control group (58.00% vs. 38.83%, p = 0.0007). Compared with the wild-type GG genotype, the GA and AA genotypes had an increased risk for atrial fibrillation. Additionally, atrial fibrillation patients with the AA genotype had greater left atrial dimensions than the patients with the GG or GA genotypes (

Ecological functions of uncultured microorganisms in the cobalt-rich ferromanganese crust of a seamount in the central Pacific are elucidated by fosmid sequencing

Author Posting. © The Author(s), 2014. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Acta Oceanologica Sinica 34 (2015):92-113, doi:10.1007/s13131-015-0650-7.Cobalt-rich ferromanganese is an important seafloor mineral and is abundantly present in the seamount crusts. Such crusts form potential hotspots for biogeochemical activity and microbial diversity, yet our understanding of their microbial communities is lacking. In this study, we used a cultivation-independent approach to recover genomic information and derive ecological functions of the microbes in a sediment sample collected from the cobalt-rich ferromanganese crust of a seamount region in the central Pacific. A total of 78 distinct clones were obtained by fosmid library screening with a 16S rRNA based PCR method. Proteobacteria and MGI Thaumarchaeota dominated the bacterial and archaeal 16S rRNA gene sequence results in the microbial community. Nine fosmid clones were sequenced and annotated. Numerous genes encoding proteins involved in metabolic functions and heavy metal resistance were identified, suggesting alternative metabolic pathways and stress responses that are essential for microbial survival in the cobalt-rich ferromanganese crust. In addition, genes that participate in the synthesis of organic acids and exoploymers were discovered. Reconstruction of the metabolic pathways revealed that the nitrogen cycle is an important biogeochemical process in the cobalt-rich ferromanganese crust. In addition, horizontal gene transfer (HGT) events have been observed, and most of them came from bacteria, with some occurring in archaea and plants. Clone W4-93a, belonging to MGI Thaumarchaeota, contained a region of gene synteny. Comparative analyses suggested that a high frequency of HGT events as well as genomic divergence play important roles in the microbial adaption to the deep-sea environment.China Ocean Mineral Resources R & D Association (COMRA) Special Foundation (No. DY125-15-R-03 and DY125-13-E-01); the National Natural Science Foundation of China (No. 41276173); the Zhejiang Provincial Natural Science Foundation of China (No. LQ13D060002) and the Scientific Research Fund of the Second Institute of Oceanography, SOA (No. JT1305).2016-04-1

Increase in neuroexcitability of unmyelinated C-type vagal ganglion neurons during initial postnatal development of visceral afferent reflex functions

BACKGROUND: Baroreflex gain increase up closely to adult level during initial postnatal weeks, and any interruption within this period will increase the risk of cardiovascular problems in later of life span. We hypothesize that this short period after birth might be critical for postnatal development of vagal ganglion neurons (VGNs). METHODS: To evaluate neuroexcitability evidenced by discharge profiles and coordinate changes, ion currents were collected from identified A- and C-type VGNs at different developmental stages using whole-cell patch clamping. RESULTS: C-type VGNs underwent significant age-dependent transition from single action potential (AP) to repetitive discharge. The coordinate changes between TTX-S and TTX-R Na(+) currents were also confirmed and well simulated by computer modeling. Although 4-AP or iberiotoxin age dependently increased firing frequency, AP duration was prolonged in an opposite fashion, which paralleled well with postnatal changes in 4-AP- and iberiotoxin-sensitive K(+) current activity, whereas less developmental changes were verified in A-types. CONCLUSION: These data demonstrate for the first time that the neuroexcitability of C-type VGNs increases significantly compared with A-types within initial postnatal weeks evidenced by AP discharge profiles and coordinate ion channel changes, which explain, at least in part, that initial postnatal weeks may be crucial for ontogenesis in visceral afferent reflex function

UGC: Unified GAN Compression for Efficient Image-to-Image Translation

Recent years have witnessed the prevailing progress of Generative Adversarial Networks (GANs) in image-to-image translation. However, the success of these GAN models hinges on ponderous computational costs and labor-expensive training data. Current efficient GAN learning techniques often fall into two orthogonal aspects: i) model slimming via reduced calculation costs; ii)data/label-efficient learning with fewer training data/labels. To combine the best of both worlds, we propose a new learning paradigm, Unified GAN Compression (UGC), with a unified optimization objective to seamlessly prompt the synergy of model-efficient and label-efficient learning. UGC sets up semi-supervised-driven network architecture search and adaptive online semi-supervised distillation stages sequentially, which formulates a heterogeneous mutual learning scheme to obtain an architecture-flexible, label-efficient, and performance-excellent model

AutoDiffusion: Training-Free Optimization of Time Steps and Architectures for Automated Diffusion Model Acceleration

Diffusion models are emerging expressive generative models, in which a large number of time steps (inference steps) are required for a single image generation. To accelerate such tedious process, reducing steps uniformly is considered as an undisputed principle of diffusion models. We consider that such a uniform assumption is not the optimal solution in practice; i.e., we can find different optimal time steps for different models. Therefore, we propose to search the optimal time steps sequence and compressed model architecture in a unified framework to achieve effective image generation for diffusion models without any further training. Specifically, we first design a unified search space that consists of all possible time steps and various architectures. Then, a two stage evolutionary algorithm is introduced to find the optimal solution in the designed search space. To further accelerate the search process, we employ FID score between generated and real samples to estimate the performance of the sampled examples. As a result, the proposed method is (i).training-free, obtaining the optimal time steps and model architecture without any training process; (ii). orthogonal to most advanced diffusion samplers and can be integrated to gain better sample quality. (iii). generalized, where the searched time steps and architectures can be directly applied on different diffusion models with the same guidance scale. Experimental results show that our method achieves excellent performance by using only a few time steps, e.g. 17.86 FID score on ImageNet 64 $\times$ 64 with only four steps, compared to 138.66 with DDIM. The code is available at https://github.com/lilijiangg/AutoDiffusion

Noninvasive Fractional Flow Reserve Derived From Coronary Computed Tomography Angiography For Identification Of Ischemic Lesions: A Systematic Review and Meta-Analysis

Detection of coronary ischemic lesions by fractional flow reserve (FFR) has been established as the gold standard. In recent years, novel computer based methods have emerged and they can provide simulation of FFR using coronary artery images acquired from coronary computed tomography angiography (FFRCT). This meta-analysis aimed to evaluate diagnostic performance of FFRCT using FFR as the reference standard. Databases of PubMed, Cochrane Library, EMBASE, Medion and Web of Science were searched. Seven studies met the inclusion criteria, including 833 stable patients (1377 vessels or lesions) with suspected or known coronary artery disease (CAD). The patient-based analysis showed pooled estimates of sensitivity, specificity and diagnostic odds ratio (DOR) for detection of ischemic lesions were 0.89 [95%confidence interval (CI), 0.85–0.93], 0.76 (95%CI, 0.64–0.84) and 26.21 (95%CI, 13.14–52.28). At a per-vessel or per-lesion level, the pooled estimates were as follows: sensitivity 0.84 (95%CI, 0.80–0.87), specificity 0.76 (95%CI, 0.67–0.83) and DOR 16.87 (95%CI, 9.41–30.25). Area under summary receiver operating curves was 0.90 (95%CI, 0.87–0.92) and 0.86 (95%CI, 0.83–0.89) at the two analysis levels, respectively. In conclusion, FFRCT technology achieves a moderate diagnostic performance for noninvasive identification of ischemic lesions in stable patients with suspected or known CAD in comparison to invasive FFR measurement

Noninvasive Fractional Flow Reserve Derived From Coronary Computed Tomography Angiography For Identification Of Ischemic Lesions: A Systematic Review and Meta-Analysis

Detection of coronary ischemic lesions by fractional flow reserve (FFR) has been established as the gold standard. In recent years, novel computer based methods have emerged and they can provide simulation of FFR using coronary artery images acquired from coronary computed tomography angiography (FFRCT). This meta-analysis aimed to evaluate diagnostic performance of FFRCT using FFR as the reference standard. Databases of PubMed, Cochrane Library, EMBASE, Medion and Web of Science were searched. Seven studies met the inclusion criteria, including 833 stable patients (1377 vessels or lesions) with suspected or known coronary artery disease (CAD). The patient-based analysis showed pooled estimates of sensitivity, specificity and diagnostic odds ratio (DOR) for detection of ischemic lesions were 0.89 [95%confidence interval (CI), 0.85–0.93], 0.76 (95%CI, 0.64–0.84) and 26.21 (95%CI, 13.14–52.28). At a per-vessel or per-lesion level, the pooled estimates were as follows: sensitivity 0.84 (95%CI, 0.80–0.87), specificity 0.76 (95%CI, 0.67–0.83) and DOR 16.87 (95%CI, 9.41–30.25). Area under summary receiver operating curves was 0.90 (95%CI, 0.87–0.92) and 0.86 (95%CI, 0.83–0.89) at the two analysis levels, respectively. In conclusion, FFRCT technology achieves a moderate diagnostic performance for noninvasive identification of ischemic lesions in stable patients with suspected or known CAD in comparison to invasive FFR measurement