Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-Identification

In person re-identification (ReID) task, because of its shortage of trainable dataset, it is common to utilize fine-tuning method using a classification network pre-trained on a large dataset. However, it is relatively difficult to sufficiently fine-tune the low-level layers of the network due to the gradient vanishing problem. In this work, we propose a novel fine-tuning strategy that allows low-level layers to be sufficiently trained by rolling back the weights of high-level layers to their initial pre-trained weights. Our strategy alleviates the problem of gradient vanishing in low-level layers and robustly trains the low-level layers to fit the ReID dataset, thereby increasing the performance of ReID tasks. The improved performance of the proposed strategy is validated via several experiments. Furthermore, without any add-ons such as pose estimation or segmentation, our strategy exhibits state-of-the-art performance using only vanilla deep convolutional neural network architecture.Comment: Accepted to AAAI 201

Over-Fit: Noisy-Label Detection based on the Overfitted Model Property

Due to the increasing need to handle the noisy label problem in a massive dataset, learning with noisy labels has received much attention in recent years. As a promising approach, there have been recent studies to select clean training data by finding small-loss instances before a deep neural network overfits the noisy-label data. However, it is challenging to prevent overfitting. In this paper, we propose a novel noisy-label detection algorithm by employing the property of overfitting on individual data points. To this end, we present two novel criteria that statistically measure how much each training sample abnormally affects the model and clean validation data. Using the criteria, our iterative algorithm removes noisy-label samples and retrains the model alternately until no further performance improvement is made. In experiments on multiple benchmark datasets, we demonstrate the validity of our algorithm and show that our algorithm outperforms the state-of-the-art methods when the exact noise rates are not given. Furthermore, we show that our method can not only be expanded to a real-world video dataset but also can be viewed as a regularization method to solve problems caused by overfitting.Comment: 10 pages, 7 figure

A Study on the Effect of Consumer Involvement and Affect Intensity before and after Plagiarism Suspicion on the Purchase Intention of Music Goods

This study aims to examine the effect of consumers' involvement and affect intensity on the purchase intention of music items. In particular  domestically, there is no clear standard for judgment of plagiarism, and thus it is expected that plagiarism suspicion is likely to affect consumers' involvement and affect intensity, and as a result, their purchase intention as well. Accordingly, consumer characteristics (involvement, affect intensity) were chosen as independent variables, and consumers' purchase intention on music items as a subordinate variable, respectively. The first questionnaire-based survey was conducted before the awareness of plagiarism suspicion, followed by the second survey after the awareness of plagiarism suspicion. It turned out that the higher level of involvement and affect intensity, both of which are consumer characteristics, the higher level of purchase intention of music goods. While plagiarism suspicion caused C.R values to decrease in every item, a significant difference was observed only in the relation of ‘involvement - purchase intention’. This study shows that music items which involve plagiarism suspicion result in changes in consumers' purchase intention, which will cause damage to the creators and performers of related music items. Thus, for the development of the music industry and creative activity, tools and standards that can clearly distinguish plagiarism need to be developed

Class-Attentive Diffusion Network for Semi-Supervised Classification

Recently, graph neural networks for semi-supervised classification have been widely studied. However, existing methods only use the information of limited neighbors and do not deal with the inter-class connections in graphs. In this paper, we propose Adaptive aggregation with Class-Attentive Diffusion (AdaCAD), a new aggregation scheme that adaptively aggregates nodes probably of the same class among K-hop neighbors. To this end, we first propose a novel stochastic process, called Class-Attentive Diffusion (CAD), that strengthens attention to intra-class nodes and attenuates attention to inter-class nodes. In contrast to the existing diffusion methods with a transition matrix determined solely by the graph structure, CAD considers both the node features and the graph structure with the design of our class-attentive transition matrix that utilizes a classifier. Then, we further propose an adaptive update scheme that leverages different reflection ratios of the diffusion result for each node depending on the local class-context. As the main advantage, AdaCAD alleviates the problem of undesired mixing of inter-class features caused by discrepancies between node labels and the graph topology. Built on AdaCAD, we construct a simple model called Class-Attentive Diffusion Network (CAD-Net). Extensive experiments on seven benchmark datasets consistently demonstrate the efficacy of the proposed method and our CAD-Net significantly outperforms the state-of-the-art methods. Code is available at https://github.com/ljin0429/CAD-Net.Comment: Accepted to AAAI 202

Effects of Visual, Seat, and Platform Motion During Flight Simulator Air Transport Pilot Training and Evaluation

Access to affordable and effective flight-simulation training devices (FSTDs) is critical to safely train airline crews in aviating, navigating, communicating, making decisions, and managing flightdeck and crew resources. This paper provides an overview of the Federal Aviation Administration- Volpe Center Flight Simulator Human Factors Program examining the requirements for the qualification and use of FSTDs. We will summarize past research investigating the need for a full hexapod-platform motion system, describe regulatory and industry developments, and report on current activities

Discordance between Train-of-Four Response and Clinical Symptoms in a Patient with Amyotrophic Lateral Sclerosis

A 47-year-old woman with amyotrophic lateral sclerosis was scheduled for total thyroidectomy with cervical node dissection. During anesthetic management by total intravenous anesthesia using remifentanil, propofol, and rocuronium, train-of-four (TOF) monitoring findings were not consistent with clinical signs. Sugammadex successfully reversed shallow respiration

Thiazolidinediones Regulate Adipose Lineage Dynamics

SummaryWhite adipose tissue regulates metabolism; the importance of this control is highlighted by the ongoing pandemic of obesity and associated complications such as diabetes, atherosclerosis, and cancer. White adipose tissue maintenance is a dynamic process, yet very little is known about how pharmacologic stimuli affect such plasticity. Combining in vivo lineage marking and BrdU labeling strategies, we found that rosiglitazone, a member of the thiazolidinedione class of glucose-lowering medicines, markedly increases the evolution of adipose progenitors into adipocytes. Notably, chronic rosiglitazone administration disrupts the adipogenic and self-renewal capacities of the stem cell compartment and alters its molecular characteristics. These data unravel unknown aspects of adipose dynamics and provide a basis to manipulate the adipose lineage for therapeutic ends

Loss of Heterozygosities in Five Tumor Suppressor Genes (FHIT Gene, p16, pRb, E-Cadherin and p53) in Thyroid Tumors

ObjectivesTo evaluate the loss of heterozygosities (LOH) of chromosomes 3p14 (FHIT gene), 9p21 (p16), 13q21 (pRb), 6q22 (E-cadherin) and 17p13 (p53) in various thyroid tumors.MethodsEighty thyroid tumor cases (20 follicular adenomas, 10 follicular carcinomas, and 50 papillary carcinomas) have been analyzed for the presence of LOH in chromosomes 3p14, 9p21, 13q21, 6q22, and 17p13 allelic loss, using microsatellite markers and DNA obtained from formalin-fixed paraffin-embedded archival tissues.ResultsLOH on 3p14 was found in 10.5%, 33.3%, and 30.4% of follicular adenomas, follicular carcinomas, and papillary carcinomas, respectively. LOH on 9p21 was detected in 6%, 44.4%, and 47.8%, respectively. LOH on pRb gene was found in 5.3%, 20.0%, and 35.4%, respectively. LOH on E-cadherin gene was found in 5.3%, 22.2%, and 43.8%, respectively. LOH on 17p13 was detected in 0%, 40%, and 45.8%, respectively. LOH in FHIT gene, p16, pRb, E-cadherin, and p53 genes were more frequently identified in follicular carcinoma and papillary carcinoma than in follicular adenoma.ConclusionLOH results of the five tumor suppressor genes (FHIT gene, p16, pRb, E-cadherin, and p53) showed statistical differences between benign tumor and malignant tumor. Among papillary carcinoma, LOH in p16, E-cadherin and p53 genes well correlated with poorly differentiated grade, and LOH of E-cadherin was associated with lymph node metastasis