Jung Ho Park

Jung Ho Park, Changyeob Lee, Shangqing Cao et al.

We examine how aircraft seat configuration interacts with daily operation in Regional Air Mobility by applying a joint supply-demand optimization framework that simultaneously determines market share, fare, and flight schedule. The framework integrates a binary logit discrete choice model into a task assignment formulation, capturing passengers' mode choice between Regional Air Mobility and driving across spatiotemporal origin-destination pairs. We evaluate three U.S. college town corridors under 4-, 6-, and 8-seat configurations across cost scales from 0.4 to 1.0 and fleet sizes from 12 to 30 aircraft. Profitability and throughput serve as primary performance metrics, and we analyze pricing power, operating cost, and revenue to explain performance variation across markets. We find that larger aircraft configurations and fleet sizes do not improve profitability universally. Larger aircraft are preferred where economies of scale are favorable and demand is sufficient and directionally balanced. The best configuration in these case studies is the 4-seat in imbalanced markets and the 6-seat in balanced or dense markets.

Jung Ho Park, Won-Kwang Park

In inverse scattering problem, it is well-known that subspace migration yields very accurate locations of small perfectly conducting cracks when applied frequency is known. In contrast, when applied frequency is unknown, inaccurate locations are identified via subspace migration with wrong frequency data. However, this fact has been examined through the experimental results so, the reason of such phenomenon has not been theoretically investigated. In this paper, we analyze mathematical structure of subspace migration with unknown frequency by establishing a relationship with Bessel function of order zero of the first kind. Identified structure of subspace migration and corresponding results of numerical simulation answer that why subspace migration with unknown frequency yields inaccurate location of cracks and gives an idea of improvement.

Jung Ho Park, Jordan Kam, Vishwanath Bulusu et al.

This article presents dynamic directional lane allocation in urban air mobility (UAM) corridors as a discrete-time mixed-integer linear program (MILP). This formulation activates, deactivates, and reverses lane direction as bi-directional airspace demand evolves. We model demand from disaggregate ground travel data by decomposing each trip into a multi-modal sequence with first-, middle-, and last-mile legs and routing the UAM-served middle-mile segment through a vertiport-side dispatch model. We use the San Francisco Bay Area as a case study by placing a multi-region spanning corridor between Contra Costa county and Silicon Valley. We find that the dynamic policy cuts unused airspace capacity by 5x, increases mean lane utilization from 36-48% to 67% at the same service level relative to baselines, and reduces commuting-population mean travel time by up to 21.6%. These results show that dynamic configuration of airspace capacity alleviates a significant percentage of the under-utilization issue of lane-based UAM airspace design and UAM concept of operations. This dynamic allocation also provides a safe, structural way to increase throughput, making UAM a more viable complement to multimodal door-to-door mobility systems.

Taejin Paik, Jaemin Park, Jung Ho Park

In this paper, we focus on the intersections of a manifold's local structures to analyze the global structure of a manifold. We obtain local regions on data manifolds such as the latent space of StyleGAN2, using Mapper, a tool from topological data analysis. We impose gluing compatibility conditions on overlapping local regions, which guarantee that the local structures can be glued together to the global structure of a manifold. We propose a novel generative flow model called Atlas flow that uses compatibility to reattach the local regions. Our model shows that the generating processes perform well on synthetic dataset samples of well-known manifolds with noise. Furthermore, we investigate the style vector manifold of StyleGAN2 using our model.

Yuhan Tang, Kangxin Cui, Jung Ho Park et al.

Ride-hailing platforms face the challenge of balancing passenger waiting times with overall system efficiency under highly uncertain supply-demand conditions. Adaptive delayed matching creates a trade-off between matching and pickup delays by deciding whether to assign drivers immediately or batch requests. Since outcomes accumulate over long horizons with stochastic dynamics, reinforcement learning (RL) is a suitable framework. However, existing approaches often oversimplify traffic dynamics or use shallow encoders that miss complex spatiotemporal patterns. We introduce the Regime-Aware Spatio-Temporal Mixture-of-Experts (RAST-MoE), which formalizes adaptive delayed matching as a regime-aware MDP equipped with a self-attention MoE encoder. Unlike monolithic networks, our experts specialize automatically, improving representation capacity while maintaining computational efficiency. A physics-informed congestion surrogate preserves realistic density-speed feedback, enabling millions of efficient rollouts, while an adaptive reward scheme guards against pathological strategies. With only 12M parameters, our framework outperforms strong baselines. On real-world Uber trajectory data (San Francisco), it improves total reward by over 13%, reducing average matching and pickup delays by 10% and 15% respectively. It demonstrates robustness across unseen demand regimes and stable training. These findings highlight the potential of MoE-enhanced RL for large-scale decision-making with complex spatiotemporal dynamics.

Sua Lee, Kyubum Shin, Jung Ho Park

Recent advances in pre-trained Vision Language Models (VLM) have shown promising potential for effectively adapting to downstream tasks through prompt learning, without the need for additional annotated paired datasets. To supplement the text information in VLM trained on correlations with vision data, new approaches leveraging Large Language Models (LLM) in prompts have been proposed, enhancing robustness to unseen and diverse data. Existing methods typically extract text-based responses (i.e., descriptions) from LLM to incorporate into prompts; however, this approach suffers from high variability and low reliability. In this work, we propose Description-free Multi-prompt Learning(DeMul), a novel method that eliminates the process of extracting descriptions and instead directly distills knowledge from LLM into prompts. By adopting a description-free approach, prompts can encapsulate richer semantics while still being represented as continuous vectors for optimization, thereby eliminating the need for discrete pre-defined templates. Additionally, in a multi-prompt setting, we empirically demonstrate the potential of prompt weighting in reflecting the importance of different prompts during training. Experimental results show that our approach achieves superior performance across 11 recognition datasets.

Hoin Jung, Hyunsoo Cho, Myungje Choi et al.

When it comes to a personalized item recommendation system, It is essential to extract users' preferences and purchasing patterns. Assuming that users in the real world form a cluster and there is common favoritism in each cluster, in this work, we introduce Co-Clustering Wrapper (CCW). We compute co-clusters of users and items with co-clustering algorithms and add CF subnetworks for each cluster to extract the in-group favoritism. Combining the features from the networks, we obtain rich and unified information about users. We experimented real world datasets considering two aspects: Finding the number of groups divided according to in-group preference, and measuring the quantity of improvement of the performance.

Jaewoong Choi, Junho Lee, Changyeon Yoon et al.

The discovery of the disentanglement properties of the latent space in GANs motivated a lot of research to find the semantically meaningful directions on it. In this paper, we suggest that the disentanglement property is closely related to the geometry of the latent space. In this regard, we propose an unsupervised method for finding the semantic-factorizing directions on the intermediate latent space of GANs based on the local geometry. Intuitively, our proposed method, called Local Basis, finds the principal variation of the latent space in the neighborhood of the base latent variable. Experimental results show that the local principal variation corresponds to the semantic factorization and traversing along it provides strong robustness to image traversal. Moreover, we suggest an explanation for the limited success in finding the global traversal directions in the latent space, especially W-space of StyleGAN2. We show that W-space is warped globally by comparing the local geometry, discovered from Local Basis, through the metric on Grassmannian Manifold. The global warpage implies that the latent space is not well-aligned globally and therefore the global traversal directions are bound to show limited success on it.