Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio

Association between PNPLA8 gene polymorphism and schizophrenia in male patients

Abnormal phospholipid metabolism in the brain plays an important role in neuropsychiatric diseases. Phospholipase A2 is crucial for maintaining normal neuro-physiological function. The aim of this study was to investigate the association between polymorphisms of the membrane-associated calcium-independent phospholipase A2 gamma (PNPLA8) gene and schizophrenia in Han Chinese in north China. The PCR-based ligase detection reaction was applied to detect 3 single nucleotide polymorphisms (SNPs) in the PNPLA8 gene among 201 Chinese pedigrees. The genotypic frequency of the PNPLA8 polymorphisms did not deviate from the Hardy-Weinberg equilibrium both in affected offspring and parental groups. Haploid relative risk (HRR) and transmission disequilibrium tests (TDT) showed that the 3 SNPs were not associated with schizophrenia (p>0.05), but further analysis with TDT showed that the rs40876 polymorphism was associated with schizophrenia in males (χ2=4.667, p=0.031). Our data suggest that rs40876 in PNPLA8 may be associated with schizophrenia in males

Dynamic causal effects evaluation in A/B testing with a reinforcement learning framework

A/B testing, or online experiment is a standard business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries. Major challenges arise in online experiments of two-sided marketplace platforms (e.g., Uber) where there is only one unit that receives a sequence of treatments over time. In those experiments, the treatment at a given time impacts current outcome as well as future outcomes. The aim of this article is to introduce a reinforcement learning framework for carrying A/B testing in these experiments, while characterizing the long-term treatment effects. Our proposed testing procedure allows for sequential monitoring and online updating. It is generally applicable to a variety of treatment designs in different industries. In addition, we systematically investigate the theoretical properties (e.g., size and power) of our testing procedure. Finally, we apply our framework to both simulated data and a real-world data example obtained from a technological company to illustrate its advantage over the current practice. A Python implementation of our test is available at https://github.com/callmespring/CausalRL. Supplementary materials for this article are available online

A Reinforcement Learning Framework for Time-Dependent Causal Effects Evaluation in A/B Testing

A/B testing, or online experiment is a standard business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries. Major challenges arise in online experiments where there is only one unit that receives a sequence of treatments over time. In those experiments, the treatment at a given time impacts current outcome as well as future outcomes. The aim of this paper is to introduce a reinforcement learning framework for carrying A/B testing, while characterizing the long-term treatment effects. Our proposed testing procedure allows for sequential monitoring and online updating, so it is generally applicable to a variety of treatment designs in different industries. In addition, we systematically investigate the theoretical properties (e.g., asymptotic distribution and power) of our testing procedure. Finally, we apply our framework to both synthetic datasets and a real-world data example obtained from a ride-sharing company to illustrate its usefulness

Feature selective temporal prediction of Alzheimer’s disease progression using hippocampus surface morphometry

IntroductionPrediction of Alzheimer’s disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi‐task machine learning method (cFSGL) with a novel MR‐based multivariate morphometric surface map of the hippocampus (mTBM) to predict future cognitive scores of patients.MethodsPrevious work has shown that a multi‐task learning framework that performs prediction of all future time points simultaneously (cFSGL) can be used to encode both sparsity as well as temporal smoothness. The authors showed that this method is able to predict cognitive outcomes of ADNI subjects using FreeSurfer‐based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied a multivariate tensor‐based parametric surface analysis method (mTBM) to extract features from the hippocampal surfaces.ResultsWe combined mTBM features with traditional surface features such as middle axis distance, the Jacobian determinant as well as 2 of the Jacobian principal eigenvalues to yield 7 normalized hippocampal surface maps of 300 points each. By combining these 7 × 300 = 2100 features together with the previous ~350 features, we illustrate how this type of sparsifying method can be applied to an entire surface map of the hippocampus that yields a feature space that is 2 orders of magnitude larger than what was previously attempted.ConclusionsBy combining the power of the cFSGL multi‐task machine learning framework with the addition of AD sensitive mTBM feature maps of the hippocampus surface, we are able to improve the predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.In this work, we present our results of using machine learning to predict temporal behavior changes in Alzheimers Disease using entire topological feature maps of the hippocampus surface (2100 feature points). Our paper demonstrates that it is possible to use an entire topological map instead of just imaging derived volumetric measurements for predicting behavioral changes. We compare these results with previous results using only volumetric MR imaging features (309 features points) and show through repeated cross‐validation rounds that we are able to get better predictive power.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137757/1/brb3733_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137757/2/brb3733.pd

Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias

Node representation learning on attributed graphs -- whose nodes are associated with rich attributes (e.g., texts and protein sequences) -- plays a crucial role in many important downstream tasks. To encode the attributes and graph structures simultaneously, recent studies integrate pre-trained models with graph neural networks (GNNs), where pre-trained models serve as node encoders (NEs) to encode the attributes. As jointly training large NEs and GNNs on large-scale graphs suffers from severe scalability issues, many methods propose to train NEs and GNNs separately. Consequently, they do not take feature convolutions in GNNs into consideration in the training phase of NEs, leading to a significant learning bias from that by the joint training. To address this challenge, we propose an efficient label regularization technique, namely Label Deconvolution (LD), to alleviate the learning bias by a novel and highly scalable approximation to the inverse mapping of GNNs. The inverse mapping leads to an objective function that is equivalent to that by the joint training, while it can effectively incorporate GNNs in the training phase of NEs against the learning bias. More importantly, we show that LD converges to the optimal objective function values by thejoint training under mild assumptions. Experiments demonstrate LD significantly outperforms state-of-the-art methods on Open Graph Benchmark datasets

Mouse Model Resources for Vision Research

The need for mouse models, with their well-developed genetics and similarity to human physiology and anatomy, is clear and their central role in furthering our understanding of human disease is readily apparent in the literature. Mice carrying mutations that alter developmental pathways or cellular function provide model systems for analyzing defects in comparable human disorders and for testing therapeutic strategies. Mutant mice also provide reproducible, experimental systems for elucidating pathways of normal development and function. Two programs, the Eye Mutant Resource and the Translational Vision Research Models, focused on providing such models to the vision research community are described herein. Over 100 mutant lines from the Eye Mutant Resource and 60 mutant lines from the Translational Vision Research Models have been developed. The ocular diseases of the mutant lines include a wide range of phenotypes, including cataracts, retinal dysplasia and degeneration, and abnormal blood vessel formation. The mutations in disease genes have been mapped and in some cases identified by direct sequencing. Here, we report 3 novel alleles of Crxtvrm65, Rp1tvrm64, and Rpe65tvrm148 as successful examples of the TVRM program, that closely resemble previously reported knockout models

Association Analysis of MET

To investigate the association of MET SNPs with gender disparity in thyroid tumors, as well as the metastasis and prognosis of patients, 858 patients with papillary thyroid carcinoma (PTC), 556 patients with nodular goiter, and 896 population-based normal controls were recruited. The genotyping of MET SNPs was carried out using the Sequenom MassARRAY system. The distribution of MET SNPs (rs1621 and rs6566) was different among groups. Gender stratification analysis revealed a significant association between the rs1621 genotype and PTC in female patients (P=0.037), but not in male patients (P>0.05). For female patients, the rs1621 AG genotype was significantly higher in patients with PTC than in normal controls (P=0.01) and revealed an increasing risk of PTC (OR: 1.465, 95% CI: 1.118–1.92). However, association analysis of the rs1621 genotype with metastasis and prognosis revealed no significant correlation in both male and female patients. The findings of our study showed that polymorphism of SNP locus rs1621 in MET gene may be associated with gender disparity in PTC. Higher AG genotypes in rs1621 were correlated with PTC in female patients, but not in male patients

Association of ATM Gene Polymorphism with PTC Metastasis in Female Patients

Ataxia telangiectasia mutated (ATM) gene is critical in the process of recognizing and repairing DNA lesions and is related to invasion and metastasis of malignancy. The incidence rate of papillary thyroid cancer (PTC) has increased for several decades and is higher in females than males. In this study, we want to investigate whether ATM polymorphisms are associated with gender-specific metastasis of PTC. 358 PTC patients in Northern China, including 109 males and 249 females, were included in our study. Four ATM single nucleotide polymorphisms (SNPs) were genotyped using Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF-MS). Association between genotypes and the gender-specific risk of metastasis was assessed by odds ratios (OR) and 95% confidence intervals (CI) under the unconditional logistic regression analysis. Significant associations were observed between rs189037 and metastasis of PTC in females under different models of inheritance (codominant model: OR=0.15, 95% CI 0.04–0.56, P=0.01 for GA versus GG and OR=0.08, 95% CI 0.01–0.74, P=0.03 for AA versus GG, resp.; dominant model: OR=0.49, 95% CI 0.25–0.98, P=0.04; overdominant model: OR=0.47, 95% CI 0.25–0.89, P=0.02). However, no association remained significant after Bonferroni correction. Our findings suggest a possible association between ATM rs189037 polymorphisms and metastasis in female PTCs