Attention-based scale sequence network for small object detection.

Recently, with the remarkable development of deep learning technology, achievements are being updated in various computer vision fields. In particular, the object recognition field is receiving the most attention. Nevertheless, recognition performance for small objects is still challenging. Its performance is of utmost importance in realistic applications such as searching for missing persons through aerial photography. The core structure of the object recognition neural network is the feature pyramid network (FPN). You Only Look Once (YOLO) is the most widely used representative model following this structure. In this study, we proposed an attention-based scale sequence network (ASSN) that improves the scale sequence feature pyramid network (ssFPN), enhancing the performance of the FPN-based detector for small objects. ASSN is a lightweight attention module optimized for FPN-based detectors and has the versatility to be applied to any model with a corresponding structure. The proposed ASSN demonstrated performance improvements compared to the baselines (YOLOv7 and YOLOv8) in average precision (AP) of up to 0.6%. Additionally, the AP for small objects ( A P S ) showed also improvements of up to 1.9%. Furthermore, ASSN exhibits higher performance than ssFPN while achieving lightweightness and optimization, thereby improving computational complexity and processing speed. ASSN is open-source based on YOLO version 7 and 8. This can be found in our public repository: https://github.com/smu-ivpl/ASSN.git.

Attention-Based Bi-Prediction Network for Versatile Video Coding (VVC) over 5G Network.

As the demands of various network-dependent services such as Internet of things (IoT) applications, autonomous driving, and augmented and virtual reality (AR/VR) increase, the fifthgeneration (5G) network is expected to become a key communication technology. The latest video coding standard, versatile video coding (VVC), can contribute to providing high-quality services by achieving superior compression performance. In video coding, inter bi-prediction serves to improve the coding efficiency significantly by producing a precise fused prediction block. Although block-wise methods, such as bi-prediction with CU-level weight (BCW), are applied in VVC, it is still difficult for the linear fusion-based strategy to represent diverse pixel variations inside a block. In addition, a pixel-wise method called bi-directional optical flow (BDOF) has been proposed to refine bi-prediction block. However, the non-linear optical flow equation in BDOF mode is applied under assumptions, so this method is still unable to accurately compensate various kinds of bi-prediction blocks. In this paper, we propose an attention-based bi-prediction network (ABPN) to substitute for the whole existing bi-prediction methods. The proposed ABPN is designed to learn efficient representations of the fused features by utilizing an attention mechanism. Furthermore, the knowledge distillation (KD)- based approach is employed to compress the size of the proposed network while keeping comparable output as the large model. The proposed ABPN is integrated into the VTM-11.0 NNVC-1.0 standard reference software. When compared with VTM anchor, it is verified that the BD-rate reduction of the lightweighted ABPN can be up to 5.89% and 4.91% on Y component under random access (RA) and low delay B (LDB), respectively.

Surgical Strategy for Ethmoidal Dural Arteriovenous Fistula.

OBJECTIVE: Ethmoidal dural arteriovenous fistula (DAVF) is a rare type of intracranial DAVF. The aim of this study was to report our experience with a unilateral approach and discuss its effectiveness for ethmoidal DAVF treatment. METHODS: The study included 19 patients who underwent surgical treatment for ethmoidal DAVF between January 1999 and May 2021. RESULTS: Median age of patients was 59.7 years; 16 (84%) patients were male. Three patients had a ruptured ethmoidal DAVF. Preoperative digital subtraction angiography showed that all ethmoidal DAVFs were supplied by the bilateral external carotid artery branches. In 18 (95%) patients, cortical draining veins were located on the unilateral side. Bilateral lesions were identified in only 1 (5%) patient. The frontobasal approach was performed in 5 patients (26%), the pterional approach was performed in 5 (26%) patients, and the lateral supraorbital approach was performed in 9 (47%) patients; median procedural times were 198 minutes, 172 minutes, and 111 minutes, respectively. Cortical draining vein was successfully disconnected in all 19 patients with 20 ethmoidal DAVFs. Complete obliteration of ethmoidal DAVF was confirmed in all patients, with no postoperative complications. No recurrence or related clinical events were reported in 13 (68%) patients over 12 months of clinical and radiological follow-up. CONCLUSIONS: We reconfirmed excellent outcomes of surgical treatment for ethmoidal DAVFs. Three different surgical strategies were attempted, and each had pros and cons. The lateral supraorbital approach is an efficient surgical option for unilateral ethmoidal DAVFs. Careful preoperative examination for the presence of bilateral drainage is essential.

Residual-Based Graph Convolutional Network for Emotion Recognition in Conversation for Smart Internet of Things.

Recently, emotion recognition in conversation (ERC) has become more crucial in the development of diverse Internet of Things devices, especially closely connected with users. The majority of deep learning-based methods for ERC combine the multilayer, bidirectional, recurrent feature extractor and the attention module to extract sequential features. In addition to this, the latest model utilizes speaker information and the relationship between utterances through the graph network. However, before the input is fed into the bidirectional recurrent module, detailed intrautterance features should be obtained without variation of characteristics. In this article, we propose a residual-based graph convolution network (RGCN) and a new loss function. Our RGCN contains the residual network (ResNet)-based, intrautterance feature extractor and the GCN-based, interutterance feature extractor to fully exploit the intra-inter informative features. In the intrautterance feature extractor based on ResNet, the elaborate context feature for each independent utterance can be produced. Then, the condensed feature can be obtained through an additional GCN-based, interutterance feature extractor with the neighboring associated features for a conversation. The proposed loss function reflects the edge weight to improve effectiveness. Experimental results demonstrate that the proposed method achieves superior performance compared with state-of-the-art methods.

Pure Subdural Hemorrhage Caused by Internal Carotid Artery Dorsal Wall Aneurysm Rupture.

A 37-year-old woman was admitted to our hospital with altered mentality. The patient was diagnosed an internal carotid artery (ICA) dorsal wall aneurysm leading to acute subdural hemorrhage (SDH) without occurring subarachnoid hemorrhage and/or internal parenchymal hemorrhage. An aneurysmal neck clipping and hematoma evacuation were performed at once. A pure SDH by ruptured aneurysm is unusual, but it is important to consider it if a SDH patient has no other medical history.