Offline-to-Online Knowledge Distillation for Video Instance Segmentation

In this paper, we present offline-to-online knowledge distillation (OOKD) for video instance segmentation (VIS), which transfers a wealth of video knowledge from an offline model to an online model for consistent prediction. Unlike previous methods that having adopting either an online or offline model, our single online model takes advantage of both models by distilling offline knowledge. To transfer knowledge correctly, we propose query filtering and association (QFA), which filters irrelevant queries to exact instances. Our KD with QFA increases the robustness of feature matching by encoding object-centric features from a single frame supplemented by long-range global information. We also propose a simple data augmentation scheme for knowledge distillation in the VIS task that fairly transfers the knowledge of all classes into the online model. Extensive experiments show that our method significantly improves the performance in video instance segmentation, especially for challenging datasets including long, dynamic sequences. Our method also achieves state-of-the-art performance on YTVIS-21, YTVIS-22, and OVIS datasets, with mAP scores of 46.1%, 43.6%, and 31.1%, respectively

Electronic Manipulation of Magnon Topology by Chirality Injection from Boundaries

Magnon bands are known to exhibit nontrivial topology in ordered magnets under suitable conditions, engendering topological phases referred to as magnonic topological insulators. Conventional methods to drive a magnonic topological phase transition are bulk magnetic or thermal operations such as changing the direction of an external magnetic field or varying the temperature of the system, which are undesired in device applications of magnon topology. In this work, we lift the limitation of the magnon topology control on the bulk non-electronic manipulation by proposing a scheme to manipulate magnonic topological phases by electronic boundary operations of spin chirality injection. More specifically, we consider a ferromagnetic honeycomb lattice and show that a finite spin chirality injected from the boundary of the system via the spin Hall effects introduces a tunable sublattice-symmetry-breaking mass term to the bosonic counterpart of the Haldane model for the Chern insulators and thereby allows us to electronically manipulate the bulk topology of magnons from the boundary. The "shoulder" in the thermal Hall conductivity profile is proposed as an experimental probe of the chirality-induced topological phase transition. The scheme for the boundary manipulation of the magnon topology is shown to work for a honeycomb antiferromagnet as well. We envisage that the interfacial chirality injection may offer a nonintrusive electronic means to tune the static and the dynamical bulk properties of general magnetic systems.Comment: 5 pages, 3 figure

Smart Tourism of the Korea: A Case Study

The utilization of Information Technology (IT) is spreading in tourism industry with explosive growth of Internet, Social Network Service (SNS) through smart phone applications. Especially, since intensive information has high value on tourism area, IT is becoming a crucial factor in the tourism industry. The smart tourism is explained as an holistic approach that provide tour information, service related to travel, such as destination, food, transportation, reservation, travel guide, conveniently to tourists through IT devices. In our research, we focus on the Korea Tourism Organization’s (KTO’s) smart tourism case. This research concentrates on the necessity and effectiveness of smart tourism which delivers travel information in real-time base. Also, our study overview how KTO’s IT operation manages each channel, website, SNS, applications and finally suggests the smart tourism’s future direction for the successful realization

Advancing Bayesian Optimization via Learning Correlated Latent Space

Bayesian optimization is a powerful method for optimizing black-box functions with limited function evaluations. Recent works have shown that optimization in a latent space through deep generative models such as variational autoencoders leads to effective and efficient Bayesian optimization for structured or discrete data. However, as the optimization does not take place in the input space, it leads to an inherent gap that results in potentially suboptimal solutions. To alleviate the discrepancy, we propose Correlated latent space Bayesian Optimization (CoBO), which focuses on learning correlated latent spaces characterized by a strong correlation between the distances in the latent space and the distances within the objective function. Specifically, our method introduces Lipschitz regularization, loss weighting, and trust region recoordination to minimize the inherent gap around the promising areas. We demonstrate the effectiveness of our approach on several optimization tasks in discrete data, such as molecule design and arithmetic expression fitting, and achieve high performance within a small budget

In vivo fluorescence imaging of conjunctival goblet cells

Conjunctival goblet cells (GCs) are specialized epithelial cells that secrete mucins onto the ocular surface to maintain the wet environment. Assessment of GCs is important because various ocular surface diseases are associated with their loss. Although there are GC assessment methods available, the current methods are either invasive or difficult to use. In this report, we developed a simple and non-invasive GC assessment method based on fluorescence imaging. Moxifloxacin ophthalmic solution was used to label GCs via topical administration, and then various fluorescence microscopies could image GCs in high contrasts. Fluorescence imaging of GCs in the mouse conjunctiva was confirmed by both confocal reflection microscopy and histology with Periodic acid-Schiff (PAS) labeling. Real-time in-vivo conjunctival GC imaging was demonstrated in a rat model by using both confocal fluorescence microscopy and simple wide-field fluorescence microscopy. Different GC densities were observed in the forniceal and bulbar conjunctivas of the rat eye. Moxifloxacin based fluorescence imaging provides high-contrast images of conjunctival GCs non-invasively and could be useful for the study or diagnosis of GC related ocular surface diseases.11Ysciescopu

Learning Whole-body Manipulation for Quadrupedal Robot

We propose a learning-based system for enabling quadrupedal robots to manipulate large, heavy objects using their whole body. Our system is based on a hierarchical control strategy that uses the deep latent variable embedding which captures manipulation-relevant information from interactions, proprioception, and action history, allowing the robot to implicitly understand object properties. We evaluate our framework in both simulation and real-world scenarios. In the simulation, it achieves a success rate of 93.6 % in accurately re-positioning and re-orienting various objects within a tolerance of 0.03 m and 5 {\deg}. Real-world experiments demonstrate the successful manipulation of objects such as a 19.2 kg water-filled drum and a 15.3 kg plastic box filled with heavy objects while the robot weighs 27 kg. Unlike previous works that focus on manipulating small and light objects using prehensile manipulation, our framework illustrates the possibility of using quadrupeds for manipulating large and heavy objects that are ungraspable with the robot's entire body. Our method does not require explicit object modeling and offers significant computational efficiency compared to optimization-based methods. The video can be found at https://youtu.be/fO_PVr27QxU