Growing Regression Forests by Classification: Applications to Object Pose Estimation

In this work, we propose a novel node splitting method for regression trees and incorporate it into the regression forest framework. Unlike traditional binary splitting, where the splitting rule is selected from a predefined set of binary splitting rules via trial-and-error, the proposed node splitting method first finds clusters of the training data which at least locally minimize the empirical loss without considering the input space. Then splitting rules which preserve the found clusters as much as possible are determined by casting the problem into a classification problem. Consequently, our new node splitting method enjoys more freedom in choosing the splitting rules, resulting in more efficient tree structures. In addition to the Euclidean target space, we present a variant which can naturally deal with a circular target space by the proper use of circular statistics. We apply the regression forest employing our node splitting to head pose estimation (Euclidean target space) and car direction estimation (circular target space) and demonstrate that the proposed method significantly outperforms state-of-the-art methods (38.5% and 22.5% error reduction respectively).Comment: Paper accepted by ECCV 201

DEEP NEURAL NETWORKS AND REGRESSION MODELS FOR OBJECT DETECTION AND POSE ESTIMATION

Estimating the pose, orientation and the location of objects has been a central problem addressed by the computer vision community for decades. In this dissertation, we propose new approaches for these important problems using deep neural networks as well as tree-based regression models. For the first topic, we look at the human body pose estimation problem and propose a novel regression-based approach. The goal of human body pose estimation is to predict the locations of body joints, given an image of a person. Due to significant variations introduced by pose, clothing and body styles, it is extremely difficult to address this task by a standard application of the regression method. Thus, we address this task by dividing the whole body pose estimation problem into a set of local pose estimation problems by introducing a dependency graph which describes the dependency among different body joints. For each local pose estimation problem, we train a boosted regression tree model and estimate the pose by progressively applying the regression along the paths in a dependency graph starting from the root node. Our next work is on improving the traditional regression tree method and demonstrate its effectiveness for pose/orientation estimation tasks. The main issues of the traditional regression training are, 1) the node splitting is limited to binary splitting, 2) the form of the splitting function is limited to thresholding on a single dimension of the input vector and 3) the best splitting function is found by exhaustive search. We propose a novel node splitting algorithm for regression tree training which does not have the issues mentioned above. The algorithm proceeds by first applying k-means clustering in the output space, conducting multi-class classification by support vector machine (SVM) and determining the constant estimate at each leaf node. We apply the regression forest that includes our regression tree models to head pose estimation, car orientation estimation and pedestrian orientation estimation tasks and demonstrate its superiority over various standard regression methods. Next, we turn our attention to the role of pose information for the object detection task. In particular, we focus on the detection of fashion items a person is wearing or carrying. It is clear that the locations of these items are strongly correlated with the pose of the person. To address this task, we first generate a set of candidate bounding boxes by using an object proposal algorithm. For each candidate bounding box, image features are extracted by a deep convolutional neural network pre-trained on a large image dataset and the detection scores are generated by SVMs. We introduce a pose-dependent prior on the geometry of the bounding boxes and combine it with the SVM scores. We demonstrate that the proposed algorithm achieves significant improvement in the detection performance. Lastly, we address the object detection task by exploring a way to incorporate an attention mechanism into the detection algorithm. Humans have the capability of allocating multiple fixation points, each of which attends to different locations and scales of the scene. However, such a mechanism is missing in the current state-of-the-art object detection methods. Inspired by the human vision system, we propose a novel deep network architecture that imitates this attention mechanism. For detecting objects in an image, the network adaptively places a sequence of glimpses at different locations in the image. Evidences of the presence of an object and its location are extracted from these glimpses, which are then fused for estimating the object class and bounding box coordinates. Due to the lack of ground truth annotations for the visual attention mechanism, we train our network using a reinforcement learning algorithm. Experiment results on standard object detection benchmarks show that the proposed network consistently outperforms the baseline networks that do not employ the attention mechanism

Effect of Winter-Flooding and Organic Farming on Density of Aquatic Oligochaetes in Ricefields: Case Study in Miyagi Prefecture, Northeastern Japan

Original Pape

Clinical Feature of Men Who Benefit from Dose Escalation of Naftopidil for Lower Urinary Tract Symptoms: A Prospective Study

Objectives. To examine the feature of men who benefit from dose escalation of naftopidil for lower urinary tract symptoms (LUTSs). Methods. Based on the IPSS, men reporting LUTS were prospectively studied using 50 mg/day of naftopidil for the first 4 weeks; satisfied patients continued its 50 mg/day (n = 11), and those reporting unsatisfactory improvement received its 75 mg/day (n = 35) for the next 4 weeks. Results. The 75 mg group showed improvement in the total IPSS and QOL score in a dose-dependent manner (at 4 weeks: P < .001, at 4 weeks versus 8 weeks: P < .05). In the 50 mg group, both scores reduced at 4 weeks, thereafter unchanged. The baseline slow stream score alone was higher in the 75 mg group (P = .013). The rate of change in the QOL score during the initial 4 weeks (ΔQOL) and Δnocturia was smaller in the 75 mg group (P < .05). Conclusions. Men with high slow stream score and unsatisfactory improvement in nocturia may benefit from dose escalation of naftopidil

Staggering effects in nuclear and molecular spectra

It is shown that the recently observed Delta J = 2 staggering effect (i.e. the relative displacement of the levels with angular momenta J, J+4, J+8, ..., relatively to the levels with angular momenta J+2, J+6, J+10, ...) seen in superdeformed nuclear bands is also occurring in certain electronically excited rotational bands of diatomic molecules (YD, CrD, CrH, CoH), in which it is attributed to interband interactions (bandcrossings). In addition, the Delta J = 1 staggering effect (i.e. the relative displacement of the levels with even angular momentum J with respect to the levels of the same band with odd J) is studied in molecular bands free from Delta J = 2 staggering (i.e. free from interband interactions/bandcrossings). Bands of YD offer evidence for the absence of any Delta J = 1 staggering effect due to the disparity of nuclear masses, while bands of sextet electronic states of CrD demonstrate that Delta J = 1 staggering is a sensitive probe of deviations from rotational behaviour, due in this particular case to the spin-rotation and spin-spin interactions.Comment: LaTeX, 16 pages plus 30 figures given in separate .ps files. To appear in the proceedings of the 4th European Workshop on Quantum Systems in Chemistry and Physics (Marly-le-Roi, France, 1999), ed. J. Maruani et al. (Kluwer, Dordrecht

DOCK2 is involved in the host genetics and biology of severe COVID-19

「コロナ制圧タスクフォース」COVID-19疾患感受性遺伝子DOCK2の重症化機序を解明 --アジア最大のバイオレポジトリーでCOVID-19の治療標的を発見--. 京都大学プレスリリース. 2022-08-10.Identifying the host genetic factors underlying severe COVID-19 is an emerging challenge. Here we conducted a genome-wide association study (GWAS) involving 2, 393 cases of COVID-19 in a cohort of Japanese individuals collected during the initial waves of the pandemic, with 3, 289 unaffected controls. We identified a variant on chromosome 5 at 5q35 (rs60200309-A), close to the dedicator of cytokinesis 2 gene (DOCK2), which was associated with severe COVID-19 in patients less than 65 years of age. This risk allele was prevalent in East Asian individuals but rare in Europeans, highlighting the value of genome-wide association studies in non-European populations. RNA-sequencing analysis of 473 bulk peripheral blood samples identified decreased expression of DOCK2 associated with the risk allele in these younger patients. DOCK2 expression was suppressed in patients with severe cases of COVID-19. Single-cell RNA-sequencing analysis (n = 61 individuals) identified cell-type-specific downregulation of DOCK2 and a COVID-19-specific decreasing effect of the risk allele on DOCK2 expression in non-classical monocytes. Immunohistochemistry of lung specimens from patients with severe COVID-19 pneumonia showed suppressed DOCK2 expression. Moreover, inhibition of DOCK2 function with CPYPP increased the severity of pneumonia in a Syrian hamster model of SARS-CoV-2 infection, characterized by weight loss, lung oedema, enhanced viral loads, impaired macrophage recruitment and dysregulated type I interferon responses. We conclude that DOCK2 has an important role in the host immune response to SARS-CoV-2 infection and the development of severe COVID-19, and could be further explored as a potential biomarker and/or therapeutic target

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Redox regulation of hepatitis C in nonalcoholic and alcoholic liver

Hepatitis C virus (HCV) is an RNA virus of the Flaviviridae family that is estimated to have infected 170 million people worldwide. HCV can cause serious liver disease in humans, such as cirrhosis, steatosis, and hepatocellular carcinoma. HCV induces a state of oxidative/nitrosative stress in patients through multiple mechanisms, and this redox perturbation has been recognized as a key player in HCV-induced pathogenesis. Studies have shown that alcohol synergizes with HCV in the pathogenesis of liver disease, and part of these effects may be mediated by reactive species that are generated during hepatic metabolism of alcohol. Furthenriore, reactive species and alcohol may influence HCV replication and the outcome of interferon therapy. Alcohol consumption has also been associated with increased sequence heterogeneity of the HCV RNA sequences, suggesting multiple modes of interaction between alcohol and HCV. This review summarizes the current understanding of oxidative and nitrosative stress during HCV infection and possible combined effects of HCV, alcohol, and reactive species in the pathogenesis of liver disease. (c) 2007 Elsevier Inc. All rights reserved