New records of genus Aradus Fabricius (Hemiptera: Aradidae) from Korea, with first male description of Aradus gretae Kiritshenko

Three species of genus Aradus Fabricius (A. betulae (Linnaeus), A. betulinus Fallén, and A. gretae Kiritshenko) are newly recognized from the Korean Peninsula. Photographs of habitus and diagnosis are presented for the newly recorded species. The male of Aradus gretae Kiritshenko is described for the first time. A checklist and key to species of the genus Aradus recorded from Korea are provided

Additional record of Tuponia Reuter (Heteroptera, Miridae, Phylinae) from Korea, with a new synonym and discussion on distribution

BackgroundThe genus Tuponia Reuter, 1875 belongs to the subfamily Phylinae and comprises 91 species worldwide. Before this study, only T. koreana Kim & Jung had been recorded from the Korean Peninsula. New information Two species of Tuponia Reuter, 1910 are recognised from the Korean Peninsula including the first record of T. mongolica Drapolyuk, 1980. T. koreana Kim & Jung, 2021 is proposed as a junior synonym of T. chinensis Zheng & Li, 1992. The species is identified, based on the dorsal habitus and male and female genitalic structures. A brief discussion of the distribution of Korean Tuponia species also is presented.Y

Newly recorded genus Pantilius Curtis (Hemiptera: Heteroptera: Miridae) from the Korean Peninsula, with a key to the world Pantilius species

A genus, Pantilius Curtis (Hemiptera: Heteroptera: Miridae: Mirinae), is reported for the first time from the Korean Peninsula, based on finding a species of Pantilius hayashii Miyamoto and Yasunaga, 1989, which was hitherto known only from Honshu, Japan. The morphological information, such as description and diagnosis, is presented with photographs and illustrations of adult habitus and male genitalia

WALDIO: Eliminating the Filesystem Journaling in Resolving the Journaling of Journal Anomaly

This work is dedicated to resolve the Journaling of Journal Anomaly in Android IO stack.We orchestrate SQLite and EXT4 filesystem so that SQLite???s file-backed journaling activity can dispense with the expensive filesystem intervention, the journaling, without compromising the file integrity under unexpected filesystem failure. In storing the logs, we exploit the direct IO to suppress the filesystem interference. This work consists of three key ingredients: (i) Preallocation with Explicit Journaling, (ii) Header Embedding, and (iii) Group Synchronization. Preallocation with Explicit Journaling eliminates the filesystem journaling properly protecting the file metadata against the unexpected system crash. We redesign the SQLite B-tree structure with Header Embedding to make it direct IO compatible and block IO friendly. With Group Synch, we minimize the synchronization overhead of direct IO and make the SQLite operation NAND Flash friendly. Combining the three technical ingredients, we develop a new journal mode in SQLite, the WALDIO. We implement it on the commercially available smartphone. WALDIO mode achieves 5.1x performance (insert/sec) against WAL mode which is the fastest journaling mode in SQLite. It yields 2.7x performance (inserts/ sec) against the LS-MVBT, the fastest SQLite journaling mode known to public. WALDIO mode achieves 7.4x performance (insert/sec) against WAL mode when it is relieved from the overhead of explicitly synchronizing individual log-commit operations. WALDIO mode reduces the IO volume to 1/6 compared against the WAL mode

Taxonomic review of the genus Castanopsides Yasunaga (Hemiptera: Heteroptera: Miridae: Mirinae) from the Korean Peninsula

A genus Castanopsides Yasunaga (Hemiptera: Heteroptera: Miridae: Mirinae) is reviewed from the Korean Peninsula. Castanopsides falkovitshi (Kerzhner 1979) is reported for the first time. Descriptions of male genitalia, diagnoses, and a key to the Korean Castanopsides are provided with illustrations and photographs

Optimal Path Generation with Obstacle Avoidance and Subfield Connection for an Autonomous Tractor

As autonomous tractors become more common crop harvesting applications, the need to optimize the global servicing path becomes crucial for maximizing efficiency and crop yield. In recent years, several methods of path generation have been researched, but very few have studied their applications on complex field shapes. In this study, a method of creating the optimal servicing path for simple and complex field shapes is proposed. The proposed algorithm creates subfields for a target land, optimizes the track direction for several subfields individually, merges subfields that result in overall increased efficiency, and finds the minimum non-operating paths to travel from subfield to subfield while selecting the respective optimal subfield starting locations. Additionally, it is required that this process must be done within 3 seconds to meet performance requirements. Results from 3 separate field shapes show that the field traversal efficiency can range from 68.0% to 94.4%, and the coverage ratio can range from 98.8% to 99.9% for several different conditions. In comparison with previous studies using the same field shape, the proposed methods demonstrate an increase of 5.5% in field traversal efficiency

DESIRE: Distant future prediction in dynamic scenes with interacting agents

We introduce a Deep Stochastic IOC1 RNN Encoderdecoder framework, DESIRE, for the task of future predictions of multiple interacting agents in dynamic scenes. DESIRE effectively predicts future locations of objects in multiple scenes by 1) accounting for the multi-modal nature of the future prediction (i.e., given the same context, future may vary), 2) foreseeing the potential future outcomes and make a strategic prediction based on that, and 3) reasoning not only from the past motion history, but also from the scene context as well as the interactions among the agents. DESIRE achieves these in a single end-to-end trainable neural network model, while being computationally efficient. The model first obtains a diverse set of hypothetical future prediction samples employing a conditional variational autoencoder, which are ranked and refined by the following RNN scoring-regression module. Samples are scored by accounting for accumulated future rewards, which enables better long-term strategic decisions similar to IOC frameworks. An RNN scene context fusion module jointly captures past motion histories, the semantic scene context and interactions among multiple agents. A feedback mechanism iterates over the ranking and refinement to further boost the prediction accuracy. We evaluate our model on two publicly available datasets: KITTI and Stanford Drone Dataset. Our experiments show that the proposed model significantly improves the prediction accuracy compared to other baseline methods. ?? 2017 IEEE