A Study on Verification of the Dynamic Modeling for a Submerged Body Based on Numerical Simulation

This study proposed a procedure to identify maneuvering coefficients that brought about abnormal motions in the simulation of a submerged body. The first step in responding to abnormal motions was conducting stability analysis to determine whether the submerged body could be simulated. If doing so was feasible, sensitivity analysis was then performed to determine maneuvering coefficients that caused the abnormal motion in the simulation. Finally, we analyzed the order of maneuvering coefficients identified by the sensitivity analysis. We also compared it with empirical formulas and other results obtained from model tests. The dynamics model targeting a high-speed submerged body was indirectly verified by the above procedure. In this study, the effectiveness of the dynamic model was verified, and parameters causing the abnormal motion were identified in accordance with the developed procedure

Price Fairness and Brand Credibility by Effective Disclosure of Cost Information

Building on the prospect theory, this experimental study was aimed at exploring effective ways of disclosing cost and understanding consumers\u27 responses with the following research questions: (1) how varying cost information results in positive consumer perceptions (price fairness and brand credibility), (2) how gain and loss mediate between cost information and positive perceptions, and (3) how loss and gain framed cost information affect perceptions. With a between-subjects experimental design (Table 1), Study 1 examined the effect of cost information by comparing C, S1 and S2 conditions. Study 2 compared S2 and S3 to find the effect of differently framed cost information by adding a different cost structure from traditional retail: S2 (loss framed) emphasized loss derived from non-purchase of focal brand due to traditional retail\u27s higher markup rate than the brand\u27s rate, and S3 (gain framed) emphasized the benefit of purchasing the focal brand because despite of the same retail price, traditional retail provided low true cost and high markup

Self-Supervised Visual Learning by Variable Playback Speeds Prediction of a Video

We propose a self-supervised visual learning method by predicting the variable playback speeds of a video. Without semantic labels, we learn the spatio-temporal visual representation of the video by leveraging the variations in the visual appearance according to different playback speeds under the assumption of temporal coherence. To learn the spatio-temporal visual variations in the entire video, we have not only predicted a single playback speed but also generated clips of various playback speeds and directions with randomized starting points. Hence the visual representation can be successfully learned from the meta information (playback speeds and directions) of the video. We also propose a new layer dependable temporal group normalization method that can be applied to 3D convolutional networks to improve the representation learning performance where we divide the temporal features into several groups and normalize each one using the different corresponding parameters. We validate the effectiveness of our method by fine-tuning it to the action recognition and video retrieval tasks on UCF-101 and HMDB-51.Comment: Accepted by IEEE Access on May 19, 202

Automating Reinforcement Learning with Example-based Resets

Deep reinforcement learning has enabled robots to learn motor skills from environmental interactions with minimal to no prior knowledge. However, existing reinforcement learning algorithms assume an episodic setting, in which the agent resets to a fixed initial state distribution at the end of each episode, to successfully train the agents from repeated trials. Such reset mechanism, while trivial for simulated tasks, can be challenging to provide for real-world robotics tasks. Resets in robotic systems often require extensive human supervision and task-specific workarounds, which contradicts the goal of autonomous robot learning. In this paper, we propose an extension to conventional reinforcement learning towards greater autonomy by introducing an additional agent that learns to reset in a self-supervised manner. The reset agent preemptively triggers a reset to prevent manual resets and implicitly imposes a curriculum for the forward agent. We apply our method to learn from scratch on a suite of simulated and real-world continuous control tasks and demonstrate that the reset agent successfully learns to reduce manual resets whilst also allowing the forward policy to improve gradually over time.Comment: 8 pages, 6 figures; accepted for publication in the IEEE Robotics and Automation Letters (RA-L); source code available at https://github.com/jigangkim/autoreset_rl ; supplementary video available at https://youtu.be/himd0Z5b64

A case of inflammatory myofibroblastic tumor originated from the greater omentum in young adult

Inflammatory myofibroblastic (IMF) tumor is a rare solid tumor that often affects children. IMF tumors occur primarily in the lung, but the tumor may affect any organ system with protean manifestations. A 22-year-old woman was evaluated for palpable low abdominal mass that had been increasing in size since two months prior. Abdominal computed tomography showed a lobulated, heterogeneous contrast enhancing soft tissue mass, 6.5 × 5.7 cm in size in the ileal mesentery. At surgery, the mass originated from the greater omentum laying in the pelvic cavity and was completely excised without tumor spillage. Histologically, the mass was a spindle cell lesion with severe atypism and some mitosis. Immunohistochemistry for anaplastic lymphoma kinase-1 revealed that the lesion was an IMF tumor. Because of its local invasiveness and its tendency to recur, this tumor can be confused with a soft tissue sarcoma. Increasing physician awareness of this entity should facilitate recognition of its clinical characteristics and laboratory findings

Junction resolving enzymes use multivalency to keep the Holliday junction dynamic

Holliday junction (HJ) resolution by resolving enzymes is essential for chromosome segregation and recombination-mediated DNA repair. HJs undergo two types of structural dynamics that determine the outcome of recombination: conformer exchange between two isoforms and branch migration. However, it is unknown how the preferred branch point and conformer are achieved between enzyme binding and HJ resolution given the extensive binding interactions seen in static crystal structures. Single-molecule fluorescence resonance energy transfer analysis of resolving enzymes from bacteriophages (T7 endonuclease I), bacteria (RuvC), fungi (GEN1) and humans (hMus81-Eme1) showed that both types of HJ dynamics still occur after enzyme binding. These dimeric enzymes use their multivalent interactions to achieve this, going through a partially dissociated intermediate in which the HJ undergoes nearly unencumbered dynamics. This evolutionarily conserved property of HJ resolving enzymes provides previously unappreciated insight on how junction resolution, conformer exchange and branch migration may be coordinated.11Nsciescopu