Depth-discriminative Metric Learning for Monocular 3D Object Detection

Monocular 3D object detection poses a significant challenge due to the lack of depth information in RGB images. Many existing methods strive to enhance the object depth estimation performance by allocating additional parameters for object depth estimation, utilizing extra modules or data. In contrast, we introduce a novel metric learning scheme that encourages the model to extract depth-discriminative features regardless of the visual attributes without increasing inference time and model size. Our method employs the distance-preserving function to organize the feature space manifold in relation to ground-truth object depth. The proposed (K, B, eps)-quasi-isometric loss leverages predetermined pairwise distance restriction as guidance for adjusting the distance among object descriptors without disrupting the non-linearity of the natural feature manifold. Moreover, we introduce an auxiliary head for object-wise depth estimation, which enhances depth quality while maintaining the inference time. The broad applicability of our method is demonstrated through experiments that show improvements in overall performance when integrated into various baselines. The results show that our method consistently improves the performance of various baselines by 23.51% and 5.78% on average across KITTI and Waymo, respectively.Comment: Accepted at NeurIPS 202

Co-Existence Test of Primordial Black Holes and Particle Dark Matter

If dark matter (DM) consists of primordial black holes (PBHs) and particles simultaneously, PBHs are generically embedded within particle DM halos. Such "dressed PBHs" (dPBHs) are not subject to typical PBH constraints and can explain the DM abundance in the mass range $10^{-1} \sim 10^2 M_\odot$. We show that diffractive lensing of chirping gravitational waves (GWs) from binary mergers can not only discover, but can also identify dPBH lenses and discriminate them from bare PBHs on the event-by-event basis, with potential to uniquely establish the co-existence of subdominant PBHs and particle DM.Comment: 13 pages, 6 figure

Multi-task Learning for Real-time Autonomous Driving Leveraging Task-adaptive Attention Generator

Real-time processing is crucial in autonomous driving systems due to the imperative of instantaneous decision-making and rapid response. In real-world scenarios, autonomous vehicles are continuously tasked with interpreting their surroundings, analyzing intricate sensor data, and making decisions within split seconds to ensure safety through numerous computer vision tasks. In this paper, we present a new real-time multi-task network adept at three vital autonomous driving tasks: monocular 3D object detection, semantic segmentation, and dense depth estimation. To counter the challenge of negative transfer, which is the prevalent issue in multi-task learning, we introduce a task-adaptive attention generator. This generator is designed to automatically discern interrelations across the three tasks and arrange the task-sharing pattern, all while leveraging the efficiency of the hard-parameter sharing approach. To the best of our knowledge, the proposed model is pioneering in its capability to concurrently handle multiple tasks, notably 3D object detection, while maintaining real-time processing speeds. Our rigorously optimized network, when tested on the Cityscapes-3D datasets, consistently outperforms various baseline models. Moreover, an in-depth ablation study substantiates the efficacy of the methodologies integrated into our framework.Comment: Accepted at ICRA 202

A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process

The authors of this work propose a deep learning-based fault detection model that can be implemented in the field of plastic injection molding. Compared to conventional approaches to fault detection in this domain, recent deep learning approaches prove useful for on-site problems involving complex underlying dynamics with a large number of variables. In addition, the advent of advanced sensors that generate data types in multiple modalities prompts the need for multimodal learning with deep neural networks to detect faults. This process is able to facilitate information from various modalities in an end-to-end learning fashion. The proposed deep learning-based approach opts for an early fusion scheme, in which the low-level feature representations of modalities are combined. A case study involving real-world data, obtained from a car parts company and related to a car window side molding process, validates that the proposed model outperforms late fusion methods and conventional models in solving the problem

Acute Chylous Peritonitis Mimicking Ovarian Torsion in a Patient with Advanced Gastric Carcinoma

The extravasation of chyle into the peritoneal space usually does not accompany an abrupt onset of abdominal pain with symptoms and signs of peritonitis. The rarity of this condition fails to reach preoperative diagnosis prior to laparotomy. Here, we introduce a case of chylous ascites that presented with acute abdominal pain mimicking peritonitis caused by ovarian torsion in a 41-yr-old female patient with advanced gastric carcinoma. An emergency exploratory laparotomy was performed but revealed no evidence of ovarian torsion. Only chylous ascites was discovered in the operative field. She underwent a complete abdominal hysterectomy and salphingo-oophorectomy. Only saline irrigation and suction-up were performed for the chylous ascites. The postoperative course was uneventful. Her bowel movement was restored within 1 week. She was allowed only a fat-free diet, and no evidence of re-occurrence of ascites was noted on clinical observation. She now remains under consideration for additional chemotherapy

Evaluation of Penalized and Nonpenalized Methods for Disease Prediction with Large-Scale Genetic Data

Owing to recent improvement of genotyping technology, large-scale genetic data can be utilized to identify disease susceptibility loci and this successful finding has substantially improved our understanding of complex diseases. However, in spite of these successes, most of the genetic effects for many complex diseases were found to be very small, which have been a big hurdle to build disease prediction model. Recently, many statistical methods based on penalized regressions have been proposed to tackle the so-called "large P and small N" problem. Penalized regressions including least absolute selection and shrinkage operator (LASSO) and ridge regression limit the space of parameters, and this constraint enables the estimation of effects for very large number of SNPs. Various extensions have been suggested, and, in this report, we compare their accuracy by applying them to several complex diseases. Our results show that penalized regressions are usually robust and provide better accuracy than the existing methods for at least diseases under consideration