Thought, the LLM can verbalize that the unknown city is Paris and use this fact to answer downstream questions.

We formalize coherence --a requirement that erroneous predictions, within a slice, should be wrong for the same reason --as a key property that any slice discovery method should satisfy.

Thought, the LLM can verbalize that the unknown city is Paris and use this fact to answer downstream questions.

We formalize coherence --a requirement that erroneous predictions, within a slice, should be wrong for the same reason --as a key property that any slice discovery method should satisfy.

Identifying vessels that do not show up in conventional monitoring systems--known as "dark vessels"--is key to managing and securing the health

Mandela Fadahunsi, who works at a technical training school in Ikeja in Nigeria's Lagos, never believed he could fall victim to a Ponzi scheme. On April 6, the 26-year-old was starting his day when a WhatsApp notification lit up his phone screen. Someone on the group chat for investors of the cryptocurrency investment platform, Crypto Bridge Exchange (CBEX), had tried and failed to withdraw some funds, so they wanted to confirm if it was a general issue. Fadahunsi quickly logged on to his digital wallet and tried to withdraw 500 USDT, a cryptocurrency that stands for United States Dollar Tether, or simply Tether. But 24 hours later, a process that should have taken just 10 minutes was yet to complete.

Selçuk University, Computer Engineering Department, Konya, Turkey, stasdemir@selcuk .edu.tr, https://orcid.org/0000 - 0002 - 2433 - 246X Abstract: Potholes cause vehicle damage and traffic accidents, creating serious safety and economic problems. Therefore, early and accurate detection of potholes is crucial. Existing detection methods are usually only based on 2D RGB images and cannot accurately analyze the physical characteristics of potholes. In this paper, a publicly available dataset of RGB - D images (PothRGBD) is created and an impr oved YOLOv8 - based model is proposed for both pothole detection and pothole physical features analysis. The Intel RealSense D415 depth camera was used to collect RGB and depth data from the road surfaces, resulting in a PothRGBD dataset of 1000 images. The data was labeled in YOLO format suitable for segmentation. A novel YOLO model is proposed based on the YOLOv8n - seg architecture, which is structurally improved with Dynamic Snake Convolution (DSConv), Simple Attention Module (SimAM) and Gaussian Error Lin ear Unit (GELU). The proposed model segmented potholes with irregular edge structure more accurately, and performed perimeter and depth measurements on depth maps with high accuracy. With the proposed model, the values increased to 93.7%, 90.4% and 93.8% respectively. Thus, an improvement of 1.96% in precision, 6.13% in recall and 2.07% in mAP was achieved. The proposed model performs pothole detection as well as perimet er and depth measurement with high accuracy and is suitable for real - time applications due to its low model complexity. In this way, a lightweight and effective model that can be used in deep learning - based intelligent transportation solutions has been acq uired. Pothole Detection, YOLOv8 Segmentation, Depth Estimation, Intelligent Transportation Systems, RGB - D Imaging, Deep Learning 1. Introduction Potholes are one of the most common and dangerous types of road surface deterioration. It usually oc curs when water seeps into the asphalt or concrete surface and weakens the sub - layers, then the traffic load erodes the weakened area [1, 2] . Over time, small cracks widen into deep potholes.