A Generalized Framework for Video Instance Segmentation

The handling of long videos with complex and occluded sequences has recently emerged as a new challenge in the video instance segmentation (VIS) community. However, existing methods have limitations in addressing this challenge. We argue that the biggest bottleneck in current approaches is the discrepancy between training and inference. To effectively bridge this gap, we propose a Generalized framework for VIS, namely GenVIS, that achieves state-of-the-art performance on challenging benchmarks without designing complicated architectures or requiring extra post-processing. The key contribution of GenVIS is the learning strategy, which includes a query-based training pipeline for sequential learning with a novel target label assignment. Additionally, we introduce a memory that effectively acquires information from previous states. Thanks to the new perspective, which focuses on building relationships between separate frames or clips, GenVIS can be flexibly executed in both online and semi-online manner. We evaluate our approach on popular VIS benchmarks, achieving state-of-the-art results on YouTube-VIS 2019/2021/2022 and Occluded VIS (OVIS). Notably, we greatly outperform the state-of-the-art on the long VIS benchmark (OVIS), improving 5.6 AP with ResNet-50 backbone. Code is available at https://github.com/miranheo/GenVIS.Comment: CVPR 202

Collective Adaptive Responses Through Coping and Sensemaking Under Stress

This study explores adaptive response mechanisms as an appraisal of confronted stress through people’s activities of sending and receiving tweets with other Twitter users to share their thoughts, opinions or information on responses to a man-made disaster. We propose a model with a theoretical integration that represents the varying relations between stress and individuals’ responses over time. Using Twitter data from April 16, 2014, on the Sewol ferry disaster in Korea, our study found that the development of the collective response was characterized by individuals’ recurrent attempts to make sense of the causes and outcomes of an unexpected event in terms of time-dependent flows during the occurrence of the event. Collective sensemaking recurred when people could not make sense of the causes and results of unexpected events to facilitate the coping process. Our model provides insights into when and how a trigger event such as a disaster influences the development of shared collective response patterns

A Relative Value Trading System Based on a Correlation and Rough Set Analysis for the Foreign Exchange Futures Market

This paper describes the conceptual framework of a relative value (RV)-based trading system focused on the data characteristics of the foreign exchange futures market using a correlation and rough set analysis. RV trading is an investment strategy that can generate potential profits based on the RV of two securities, regardless of market direction. We select pairs with a positive correlation, negative correlation, or no correlation based on the correlation coefficients between foreign exchange futures contracts. To implement and experiment with the proposed system, trading rules are generated using a rough set analysis that employs technical indicators derived from the RVs of the pairs. The performance of the proposed trading system is analyzed using the momentum and buy-and-hold trading strategies as benchmarks. The experimental results and analyses demonstrate that the level of the correlation of the pairs must be considered when developing stable and profitable RV trading systems in a foreign exchange futures market

Firewall ruleset visualization analysis tool based on segmentation

Although most companies operate a firewall to protect their information assets, they have difficulties in identifying the control conditions of firewalls. This study proposes an analysis tool to visualize segment-based firewall rules to facilitate verification of the current control conditions. The proposed visualization tool analyzes the current control conditions of packets automatically, thereby eliminating the need for manual inspection as before, and displays the conditions with a visualization model to allow them to be easily verified. This enables managers to perform fast and accurate verification to assess whether packets are allowed or denied. This present study involved implementing the proposed visualization tool, and simulations were conducted to verify that the proposed approach was achievable. The present study also included conducting interviews with firewall experts whose feedback was positive. A video of the proposed visualization tool can be found on the following web site: https://youtu.be/q4HMnBvXbk

Prefetching Method for Low-Latency Web AR in the WMN Edge Server

Recently, low-latency services for large-capacity data have been studied given the development of edge servers and wireless mesh networks. The 3D data provided for augmented reality (AR) services have a larger capacity than general 2D data. In the conventional WebAR method, a variety of data such as HTML, JavaScript, and service data are downloaded when they are first connected. The method employed to fetch all AR data when the client connects for the first time causes initial latency. In this study, we proposed a prefetching method for low-latency AR services. Markov model-based prediction via the partial matching (PPM) algorithm was applied for the proposed method. Prefetched AR data were predicted during AR services. An experiment was conducted at the Nowon Career Center for Youth and Future in Seoul, Republic of Korea from 1 June 2022 to 31 August 2022, and a total of 350 access data points were collected over three months; the prefetching method reduced the average total latency of the client by 81.5% compared to the conventional method

General rules for functional microRNA targeting

The functional rules for microRNA (miRNA) targeting remain controversial despite their biological importance because only a small fraction of distinct interactions, called site types, have been examined among an astronomical number of site types that can occur between miRNAs and their target mRNAs. To systematically discover functional site types and to evaluate the contradicting rules reported previously, we used large-scale transcriptome data and statistically examined whether each of approximately 2 billion site types is enriched in differentially downregulated mRNAs responding to overexpressed miRNAs. Accordingly, we identified seven non-canonical functional site types, most of which are novel, in addition to four canonical site types, while also removing numerous false positives reported by previous studies. Extensive experimental validation and significantly elevated 3′ UTR sequence conservation indicate that these non-canonical site types may have biologically relevant roles. Our expanded catalog of functional site types suggests that the gene regulatory network controlled by miRNAs may be far more complex than currently understood. © 2016 Macmillan Publishers Limited, part of Springer Nature. All rights reserved