830 research outputs found

    Improving the performance of object detection by preserving label distribution

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    Object detection is a task that performs position identification and label classification of objects in images or videos. The information obtained through this process plays an essential role in various tasks in the field of computer vision. In object detection, the data utilized for training and validation typically originate from public datasets that are well-balanced in terms of the number of objects ascribed to each class in an image. However, in real-world scenarios, handling datasets with much greater class imbalance, i.e., very different numbers of objects for each class , is much more common, and this imbalance may reduce the performance of object detection when predicting unseen test images. In our study, thus, we propose a method that evenly distributes the classes in an image for training and validation, solving the class imbalance problem in object detection. Our proposed method aims to maintain a uniform class distribution through multi-label stratification. We tested our proposed method not only on public datasets that typically exhibit balanced class distribution but also on custom datasets that may have imbalanced class distribution. We found that our proposed method was more effective on datasets containing severe imbalance and less data. Our findings indicate that the proposed method can be effectively used on datasets with substantially imbalanced class distribution.Comment: Code is available at https://github.com/leeheewon-01/YOLOstratifiedKFold/tree/mai

    Enhanced Deep Residual Networks for Single Image Super-Resolution

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    Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance. In this paper, we develop an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods. The significant performance improvement of our model is due to optimization by removing unnecessary modules in conventional residual networks. The performance is further improved by expanding the model size while we stabilize the training procedure. We also propose a new multi-scale deep super-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model. The proposed methods show superior performance over the state-of-the-art methods on benchmark datasets and prove its excellence by winning the NTIRE2017 Super-Resolution Challenge.Comment: To appear in CVPR 2017 workshop. Best paper award of the NTIRE2017 workshop, and the winners of the NTIRE2017 Challenge on Single Image Super-Resolutio

    The computation of design vocabulary : prototyping, variation, and composition

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    Thesis (M. Arch.)--Massachusetts Institute of Technology, Dept. of Architecture, 1997.Includes bibliographical references (leaves 66-69).by Lee, Hee Won.M.Arch

    Korean Adoptees as Parents: Intergenerationality of Ethnic, Racial, and Adoption Socialization

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    Objective: Using a socialization framework, this study aimed to understand the intergenerational patterns of ethnic, racial, and adoption socialization practices. Background: Understanding the impact of ethnicity, race, and adoption is a lifelong process for transracially, transnationally adopted individuals. Few studies, however, have explored how adult adoptees socialize their children on ethnicity, race, and adoption and to what extent this socialization is informed by their own transracial, transnational adoption experiences. Method: On the basis of 51 interviews, we investigated adopted Korean Americans’ reappraisal of their ethnic, racial, and adoption socialization experiences growing up transracially and transnationally, as well as their current ethnic, racial, and adoption socialization practices with their children. Results: Despite the generally limited ethnic, racial, and adoption socialization from White adoptive parents, we found via thematic analysis that Korean adoptee parents used strategies such as reculturation with their children, birth family involvement, and emphasis in multiculturalism in response to the need for ethnic, racial, and adoption socialization in the next generation. Conclusion: These themes reflect the unique intergenerational transmission of ethnic heritage, racial experiences, and adoption history based on having grown up in transracial and transnational families of their own. Implications: Findings can inform evidence-based practice in working with adopted individuals and their families, particularly in addressing ethnic, racial, and adoption socialization practices

    The Role of Status Differentials and Homophily in the Formation of Social Support Networks of a Voluntary Organization

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    Given the important implications of social support on managing volunteers and their organizational commitment, we investigated how members of a Korean immigrant church (N = 178) exchanged two distinctive kinds of social support (i.e., informational and tangible). We used theories of centrality and homophily to hypothesize patterns of social connections among organizational members. Employing exponential random graph modeling (ERGM), the current study estimated the likelihood of age and gender homophily/heterophily in forming supportive ties while considering structural parameters. The results of analysis of variance showed that members with higher socioeconomic status and in official staff positions in the church were more central in the informational support exchange. However, ERGM for both types of support networks did not show hypothesized gender and age homophily/heterophily of Korean immigrants’ support exchange, suggesting the importance of other potential organizational and cultural influences. The findings shed light on the internal structuring of organizational support networks and suggest practical implications for managing organizational volunteers

    Zynq-Based Reconfigurable System for Real-Time Edge Detection of Noisy Video Sequences

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    We implement Zynq-based self-reconfigurable system to perform real-time edge detection of 1080p video sequences. While object edge detection is a fundamental tool in computer vision, noises in the video frames negatively affect edge detection results significantly. Moreover, due to the high computational complexity of 1080p video filtering operations, hardware implementation on reconfigurable hardware fabric is necessary. Here, the proposed embedded system utilizes dynamic reconfiguration capability of Zynq SoC so that partial reconfiguration of different filter bitstreams is performed during run-time according to the detected noise density level in the incoming video frames. Pratt’s Figure of Merit (PFOM) to evaluate the accuracy of edge detection is analyzed for various noise density levels, and we demonstrate that adaptive run-time reconfiguration of the proposed filter bitstreams significantly increases the accuracy of edge detection results while efficiently providing computing power to support real-time processing of 1080p video frames. Performance results on configuration time, CPU usage, and hardware resource utilization are also compared

    iCSDB: an integrated database of CRISPR screens.

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    High-throughput screening based on CRISPR-Cas9 libraries has become an attractive and powerful technique to identify target genes for functional studies. However, accessibility of public data is limited due to the lack of user-friendly utilities and up-to-date resources covering experiments from third parties. Here, we describe iCSDB, an integrated database of CRISPR screening experiments using human cell lines. We compiled two major sources of CRISPR-Cas9 screening: the DepMap portal and BioGRID ORCS. DepMap portal itself is an integrated database that includes three large-scale projects of CRISPR screening. We additionally aggregated CRISPR screens from BioGRID ORCS that is a collection of screening results from PubMed articles. Currently, iCSDB contains 1375 genome-wide screens across 976 human cell lines, covering 28 tissues and 70 cancer types. Importantly, the batch effects from different CRISPR libraries were removed and the screening scores were converted into a single metric to estimate the knockout efficiency. Clinical and molecular information were also integrated to help users to select cell lines of interest readily. Furthermore, we have implemented various interactive tools and viewers to facilitate users to choose, examine and compare the screen results both at the gene and guide RNA levels. iCSDB is available at https://www.kobic.re.kr/icsdb/

    Ghostshell: Secure Biometric Authentication using Integrity-based Homomorphic Evaluations

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    Biometric authentication methods are gaining popularity due to their convenience. For an authentication without relying on trusted hardwares, biometrics or their hashed values should be stored in the server. Storing biometrics in the clear or in an encrypted form, however, raises a grave concern about biometric theft through hacking or man-in-the middle attack. Unlike ID and password, once lost biometrics cannot practically be replaced. Encryption can be a tool for protecting them from theft, but encrypted biometrics should be recovered for comparison. In this work, we propose a secure biometric authentication scheme, named Ghostshell, in which an encrypted template is stored in the server and then compared with an encrypted attempt \emph{without} decryption. The decryption key is stored only in a user\u27s device and so biometrics can be kept secret even against a compromised server. Our solution relies on a somewhat homomorphic encryption (SHE) and a message authentication code (MAC). Because known techniques for SHE is computationally expensive, we develop a more practical scheme by devising a significantly efficient matching function exploiting SIMD operations and a one-time MAC chosen for efficient homomorphic evaluations (of multiplication depth 2). When applied to Hamming distance matching on 2400-bit irises, our implementation shows that the computation time is approximately 0.47 and 0.1 seconds for the server and the user, respectively

    Association of plasma amyloid-β oligomerization with theta/beta ratio in older adults

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    BackgroundOligomeric Aβ (OAβ) is a promising candidate marker for Alzheimer’s disease (AD) diagnosis. Electroencephalography (EEG) is a potential tool for early detection of AD. Still, whether EEG power ratios, particularly the theta/alpha ratio (TAR) and theta/beta ratio (TBR), reflect Aβ burden—a primary mechanism underlying cognitive impairment and AD. This study investigated the association of TAR and TBR with amyloid burden in older adults based on MDS-OAβ levels.Methods529 individuals (aged ≥60 years) were recruited. All participants underwent EEG (MINDD SCAN, Ybrain Inc., South Korea) and AlzOn™ test (PeopleBio Inc., Gyeonggi-do, Republic of Korea) for quantifying MDS-OAβ values in the plasma. EEG variables were log-transformed to normalize the data distribution. Using the MDS-OAβ cutoff value (0.78 ng/mL), all participants were classified into two groups: high MDS-OAβ and low MDS-OAβ.ResultsParticipants with high MDS-OAβ levels had significantly higher TARs and TBRs than those with low MDS-OAβ levels. The log-transformed TBRs in the central lobe (β = 0.161, p = 0.0026), frontal lobe (β = 0.145, p = 0.0044), parietal lobe (β = 0.166, p = 0.0028), occipital lobe (β = 0.158, p = 0.0058), and temporal lobe (beta = 0.162, p = 0.0042) were significantly and positively associated with increases in MDS-OAβ levels. After adjusting for the Bonferroni correction, the TBRs in all lobe regions, except the occipital lobe, were significantly associated with increased MDS-OAβ levels.ConclusionWe found a significant association of MDS-OAβ with TBR in older adults. This finding indicates that an increase in amyloid burden may be associated with an increase in the low-frequency band and a decrease in the relatively high-frequency band
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