DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer

We have witnessed rapid evolution of deep neural network architecture design in the past years. These latest progresses greatly facilitate the developments in various areas such as computer vision and natural language processing. However, along with the extraordinary performance, these state-of-the-art models also bring in expensive computational cost. Directly deploying these models into applications with real-time requirement is still infeasible. Recently, Hinton etal. have shown that the dark knowledge within a powerful teacher model can significantly help the training of a smaller and faster student network. These knowledge are vastly beneficial to improve the generalization ability of the student model. Inspired by their work, we introduce a new type of knowledge -- cross sample similarities for model compression and acceleration. This knowledge can be naturally derived from deep metric learning model. To transfer them, we bring the "learning to rank" technique into deep metric learning formulation. We test our proposed DarkRank method on various metric learning tasks including pedestrian re-identification, image retrieval and image clustering. The results are quite encouraging. Our method can improve over the baseline method by a large margin. Moreover, it is fully compatible with other existing methods. When combined, the performance can be further boosted

Contemporary issues in static and dynamic prediction:some applications and evaluation in the clinical context

Prediction models that estimate the probabilities of developing a specific disease (diagnostic model) or a specific endpoint of disease (prognostic model) given a set of subject’s characteristics are closely connected to personalized medicine of which the key idea is to base medical decisions on individual patient characteristics rather than on population averages. Depending on decision point, prediction models can be divided into two categories: static prediction models (making one-off decision) and dynamic prediction models (making dynamically updated decisions). While multivariable logistic and Cox regression are commonly used to develop prediction models, they are not the master key to every situation. Various issues such as clustered data, competing risks and time-dependent variable may occur when a simple logistic or Cox model cannot estimate the risk correctly in static and dynamic prediction. Although adapted or more advanced approaches have been developed to address those issues in medical statistics field, they are not appropriately applied in clinical research. To fill this gap, this thesis illustrated how sophisticated statistical models can be appropriately applied to obtain better predictions using a series of clinical case studies

Investigation of Partially Premixed Combustion Instabilities through Experimental, Theoretical, and Computational Methods.

Partially premixed combustion has the merits of lower emission as well as higher efficiency. However, its practical application has been hindered by its inherent instabilities. This work is a study of instabilities in partially premixed combustion, through a combination of numerical simulation, theoretical modeling, and experimental investigation, with the hope of furthering our understanding of the underlying physics. Specifically, a Flamelet/Progress Variable (FPV) combustion model in the context of Large Eddy Simulation (LES) is extended to simulate a piloted (partially) premixed jet burner (PPJB). The ability and shortcomings of this state-of-the-art high fidelity combustion model are assessed. Furthermore, a Modular Reduced-order Model Framework (MRMF) is developed to integrate a range of elementary models to describe the instabilities that may occur in combustors utilizing partially premixed combustion technologies. A multi-chamber Helmholtz analysis is implemented, which is shown to be an improvement over previous single-chamber analyses. The assumptions and predictions of the proposed model are assessed by pressure and simultaneous Particle Image Velocimetry (PIV)–formaldehyde (CH2O) Planar Laser Induced Fluorescence (PLIF) measurements on a Gas Turbine Model Combustor (GTMC) at a sustained rate of 4 kHz. The proposed model is shown to be able to predict the instability frequency at experimental conditions. It also explains the trends of the variation of instability frequency as mass flow rates and burner geometry are changed, as well as the measured phase shift between different chambers of the burner. Finally, under the current framework an explanation of the dependence of the existence of combustion instability on equivalence ratio is provided.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111342/1/yuntaoc_1.pd

Inhibitory effects of tamoxifen and tanshinone, alone or in combination, on the proliferation of breast cancer cells via activation of p38 MAPK signalling pathway

Purpose: To investigate the effects of tamoxifen and tanshinone administered individually or in combination, on the proliferation of breast cancer (BC) cells, and the underlying mechanism(s) of action. Methods: Human breast cancer cell lines (SNU-306, SNU-334 and SNU-1528), and normal primary mammary epithelial cell line (HMEC) were cultured at 37 °C in Dulbecco's modified Eagle's medium (DMEM) supplemented with 5 % fetal bovine serum (FBS), l glutamine (2 mM), penicillin (100 U/ml) and streptomycin (100 μg/ml) in a humidified incubator containing 5 % CO2. Cell proliferation was determined using MTT assay, while real-time quantitative polymerase chain reaction (qRT-PCR) was used to determine the expressions of apoptosis-related genes. The expressions of p38 mitogenactivated protein kinases (p38 MAPK) were determined by Western blotting. Results: There were only few viable cells in tamoxifen- and tanshinone-treated wells, and cell viability was concentration-dependently reduced. Treatment of SNU-306 cells with tamoxifen (30 µM) or tanshinone (20 µM) alone significantly reduced the expression of Wip1 after 72 h of incubation, and the level of expression was significantly reduced in SNU-306 cells treated with combination of tamoxifen and tanshinones, relative to those treated with tamoxifen or tanshinone alone (p < 0.05). The extent of apoptosis was significantly higher in SNU-306 cells treated with tamoxifen or tanshinone alone or in combination than in control cells (p < 0.05). Expressions of Bax, caspase 3 and p53 were significantly higher in SNU-306 cells than in control cells, and were significantly higher in SNU-306 cells treated with combination of tamoxifen and tanshinone than in those treated with tamoxifen or tanshinone alone (p < 0.05). The level of expression of MAPK was significantly higher in SNU-306 cells treated with tamoxifen or tanshinone alone, and in combination treatment, than in control cells (p < 0.05). Conclusion: Tamoxifen and tanshinone administered alone or in combination promote apoptosis in BC cells via mechanisms involving the up-regulation and phosphorylation of MAPK

4D Unsupervised Object Discovery

Object discovery is a core task in computer vision. While fast progresses have been made in supervised object detection, its unsupervised counterpart remains largely unexplored. With the growth of data volume, the expensive cost of annotations is the major limitation hindering further study. Therefore, discovering objects without annotations has great significance. However, this task seems impractical on still-image or point cloud alone due to the lack of discriminative information. Previous studies underlook the crucial temporal information and constraints naturally behind multi-modal inputs. In this paper, we propose 4D unsupervised object discovery, jointly discovering objects from 4D data -- 3D point clouds and 2D RGB images with temporal information. We present the first practical approach for this task by proposing a ClusterNet on 3D point clouds, which is jointly iteratively optimized with a 2D localization network. Extensive experiments on the large-scale Waymo Open Dataset suggest that the localization network and ClusterNet achieve competitive performance on both class-agnostic 2D object detection and 3D instance segmentation, bridging the gap between unsupervised methods and full supervised ones. Codes and models will be made available at https://github.com/Robertwyq/LSMOL.Comment: Accepted by NeurIPS 2022. 17 pages, 6 figure

Semantic Guided Level-Category Hybrid Prediction Network for Hierarchical Image Classification

Hierarchical classification (HC) assigns each object with multiple labels organized into a hierarchical structure. The existing deep learning based HC methods usually predict an instance starting from the root node until a leaf node is reached. However, in the real world, images interfered by noise, occlusion, blur, or low resolution may not provide sufficient information for the classification at subordinate levels. To address this issue, we propose a novel semantic guided level-category hybrid prediction network (SGLCHPN) that can jointly perform the level and category prediction in an end-to-end manner. SGLCHPN comprises two modules: a visual transformer that extracts feature vectors from the input images, and a semantic guided cross-attention module that uses categories word embeddings as queries to guide learning category-specific representations. In order to evaluate the proposed method, we construct two new datasets in which images are at a broad range of quality and thus are labeled to different levels (depths) in the hierarchy according to their individual quality. Experimental results demonstrate the effectiveness of our proposed HC method.Comment: 3 figure

FrustumFormer: Adaptive Instance-aware Resampling for Multi-view 3D Detection

The transformation of features from 2D perspective space to 3D space is essential to multi-view 3D object detection. Recent approaches mainly focus on the design of view transformation, either pixel-wisely lifting perspective view features into 3D space with estimated depth or grid-wisely constructing BEV features via 3D projection, treating all pixels or grids equally. However, choosing what to transform is also important but has rarely been discussed before. The pixels of a moving car are more informative than the pixels of the sky. To fully utilize the information contained in images, the view transformation should be able to adapt to different image regions according to their contents. In this paper, we propose a novel framework named FrustumFormer, which pays more attention to the features in instance regions via adaptive instance-aware resampling. Specifically, the model obtains instance frustums on the bird's eye view by leveraging image view object proposals. An adaptive occupancy mask within the instance frustum is learned to refine the instance location. Moreover, the temporal frustum intersection could further reduce the localization uncertainty of objects. Comprehensive experiments on the nuScenes dataset demonstrate the effectiveness of FrustumFormer, and we achieve a new state-of-the-art performance on the benchmark. Codes and models will be made available at https://github.com/Robertwyq/Frustum.Comment: Accepted to CVPR 202