RNA properties, such as function and stability, are intricately tied to their two-dimensional conformations. This has spurred the development of computational models for predicting the RNA secondary structures, leveraging dynamic programming or machine learning (ML) techniques. These structures are governed by specific rules; for example, only Watson-Crick and Wobble pairs are allowed, and sequences must not form sharp bends. Recent efforts introduced a systematic approach to post-process the predictions made by ML algorithms, aiming to modify them to respect the constraints. However, we still observe instances violating the requirements, significantly reducing biological relevance. To address this challenge, we present a novel post-processing framework for ML-based predictions on RNA secondary structures, inspired by the assignment problem in integer linear programming. Our algorithm offers a theoretical guarantee, ensuring that the resulting predictions adhere to the fundamental constraints of RNAs. Empirical evidence supports the efficacy of our approach, demonstrating improved predictive performance with no constraint violation, while requiring less running time.

Kalman filter-based tracking-by-detection (KFTBD) trackers are effective methods for solving multi-person tracking tasks. However, in crowd circumstances, noisy detection results (bounding boxes with low-confidence scores) can cause ID switch and tracking failure of trackers since these trackers utilize the detector’s output directly. In this paper, to solve the problem, we suggest a novel tracker called ConfTrack based on a KFTBD tracker. Compared with conventional KFTBD trackers, ConfTrack consists of novel algorithms, including low-confidence object penalization and cascading algorithms for effectively dealing with noisy detector outputs. ConfTrack is tested on diverse domains of datasets such as the MOT17, MOT20, DanceTrack, and HiEve datasets. ConfTrack has proved its robustness in crowd circumstances by achieving the highest score at HOTA and IDF1 metrics in the MOT20 dataset.

In recent years, U-Net has achieved good results in various image processing tasks. However, conventional U-Nets need to be re-trained for individual tasks with enough amount of images with ground-truth. This requirement makes U-Net not applicable to tasks with small amounts of data. In this paper, we propose to use "modular" U-Nets, each of which is pre-trained to perform an existing image processing task, such as dilation, erosion, and histogram equalization. Then, to accomplish a specific image processing task, such as binarization of historical document images, the modular U-Nets are cascaded with inter-module skip connections and fine-tuned to the target task. We verified the proposed model using the Document Image Binarization Competition (DIBCO) 2017 dataset.