Starting an Empire

"Fall 2013"Reprinted with permission from the Columbia Missourian."Entrepreneur balances MU classes, other job with running business."Story by Caitlin Shogren and Youngrae Kim ; Photo by Youngrae Ki

Beyond Entropy: Style Transfer Guided Single Image Continual Test-Time Adaptation

Continual test-time adaptation (cTTA) methods are designed to facilitate the continual adaptation of models to dynamically changing real-world environments where computational resources are limited. Due to this inherent limitation, existing approaches fail to simultaneously achieve accuracy and efficiency. In detail, when using a single image, the instability caused by batch normalization layers and entropy loss significantly destabilizes many existing methods in real-world cTTA scenarios. To overcome these challenges, we present BESTTA, a novel single image continual test-time adaptation method guided by style transfer, which enables stable and efficient adaptation to the target environment by transferring the style of the input image to the source style. To implement the proposed method, we devise BeIN, a simple yet powerful normalization method, along with the style-guided losses. We demonstrate that BESTTA effectively adapts to the continually changing target environment, leveraging only a single image on both semantic segmentation and image classification tasks. Remarkably, despite training only two parameters in a BeIN layer consuming the least memory, BESTTA outperforms existing state-of-the-art methods in terms of performance.Comment: 17 pages, 11 figure

MetaWeather: Few-Shot Weather-Degraded Image Restoration via Degradation Pattern Matching

Real-world vision tasks frequently suffer from the appearance of adverse weather conditions including rain, fog, snow, and raindrops in captured images. Recently, several generic methods for restoring weather-degraded images have been proposed, aiming to remove multiple types of adverse weather effects present in the images. However, these methods have considered weather as discrete and mutually exclusive variables, leading to failure in generalizing to unforeseen weather conditions beyond the scope of the training data, such as the co-occurrence of rain, fog, and raindrops. To this end, weather-degraded image restoration models should have flexible adaptability to the current unknown weather condition to ensure reliable and optimal performance. The adaptation method should also be able to cope with data scarcity for real-world adaptation. This paper proposes MetaWeather, a few-shot weather-degraded image restoration method for arbitrary weather conditions. For this, we devise the core piece of MetaWeather, coined Degradation Pattern Matching Module (DPMM), which leverages representations from a few-shot support set by matching features between input and sample images under new weather conditions. In addition, we build meta-knowledge with episodic meta-learning on top of our MetaWeather architecture to provide flexible adaptability. In the meta-testing phase, we adopt a parameter-efficient fine-tuning method to preserve the prebuilt knowledge and avoid the overfitting problem. Experiments on the BID Task II.A dataset show our method achieves the best performance on PSNR and SSIM compared to state-of-the-art image restoration methods. Code is available at (TBA).Comment: 12 pages, 6 figure

Zone-Agnostic Greedy Taxi Dispatch Algorithm Based on Contextual Matching Matrix for Efficient Maximization of Revenue and Profit

This paper addresses the taxi fleet dispatch problem, which is critical for many transport service platforms such as Uber, Lyft, and Didi Chuxing. We focus on maximizing the revenue and profit a taxi platform can generate through the dispatch approaches designed with various criteria. We consider determining the proportion of taxi fleets to different destination zones given the expected rewards from the future states following the distribution decisions learned through reinforcement learning (RL) algorithms. We also take into account more straightforward greedy algorithms that look ahead fewer decision time steps in the future. Our dispatch decision algorithms commonly leverage contextual information and heuristics using a data structure called Contextual Matching Matrix (CMM). The key contribution of our paper is the insight into the trade-off between different design criteria. Primarily, through the evaluation with actual taxi operation data offered by Seoul Metropolitan Government, we challenge the natural expectation that the RL-based approaches yield the best result by showing that a lightweight greedy algorithm can have a competitive advantage. Moreover, we break the norm of dissecting the service area into sub-zones and show that matching passengers beyond arbitrary boundaries generates significantly higher operating income and profit

Post Hotelling's T-square Procedure to Identify Fault Variables

10.1080/00949655.2023.2228958Journal of Statistical Computation and Simulation9411-2

Reactivity of a cobalt(III)-peroxo complex in oxidative nucleophilic reactions

A mononuclear cobalt(III)-peroxo complex bearing a macrocyclic tetradentate N4 ligand, [CoIII(TMC)(O2)]+ (TMC = 1,4,8,11-tetramethyl-1,4,8,11-tetraazacyclotetradecane), was generated in the reaction of [CoII(TMC)]2+ and H2O2 in the presence of triethylamine in CH3CN. The reactivity of the cobalt(III)-peroxo complex was investigated in aldehyde deformylation with various aldehydes and compared with that of iron(III)- and manganese(III)-peroxo complexes, such as [FeIII(TMC)(O2)]+ and [MnIII(TMC)(O2)]+. In this reactivity comparison, the reactivities of metal-peroxo species were found to be in the order of [MnIII(TMC)(O2)]+ > [CoIII(TMC)(O2)]+ > [FeIII(TMC)(O2)]+. A positive Hammett ?? value of 1.8, obtained in the reactions of [CoIII(TMC)(O2)]+ and para-substituted benzaldehydes, demonstrates that the aldehyde deformylation by the cobalt(III)-peroxo species occurs via a nucleophilic reaction