Less is More: Fairness in Wide-Area Proof-of-Work Blockchain Networks

Blockchain is rapidly emerging as an important class of network application, with a unique set of trust, security and transparency properties. In a blockchain system, participants record and update the `server-side' state of an application as blocks of a replicated, immutable ledger using a consensus protocol over the Internet. Mining blocks has become lucrative in recent years; e.g., a miner receives over USD 200,000 per mined block in Bitcoin today. A key factor affecting mining rewards, is the latency of broadcasting blocks over the network. In this paper, we consider the problem of topology design for optimizing mining rewards in a wide-area blockchain network that uses a Proof-of-Work protocol for consensus. Contrary to general wisdom that a faster network is always better for miners, we show a counter intuitive result where a slower network is actually beneficial to some miners. This is because competing miners must choose neighbors that not only decrease their own latency to others, but also ensure that the latency between other miners do not decrease because of itself. We formalize this problem, and provide both theoretical analysis and experimental results to support our claim

Research Report on the Effect of Network Teaching Mode of Art Courses under the Concept of Ideological and Political Education

 Since the National Conference on ideological and political work in Colleges and Universities, the Party Committee of Beijing United University has closely focused on the fundamental problem of “what kind of person to train, how to train and for whom to train”, and regards the course of ideological and political thinking as the fundamental measure to carry out the fundamental task of building up people by virtue. In 2020, in the event of the new epidemic situation, the school actively implemented the work plan of “stopping classes and not stopping learning” in Beijing, and opened the historic revolution of the whole school network teaching in education and teaching.In recent years, Beijing Union University in the “curriculum ideological and political” construction is constantly open up. In order to promote teaching practice and teaching research, the school teacher teaching development center set up the first teaching promoters of Beijing United University in 2019. The project team was set up by the school teaching promoters to study the effect of the online teaching mode of art courses under the concept of ideological and political education

Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models

The Segment Anything Model (SAM) exhibits remarkable versatility and zero-shot learning abilities, owing largely to its extensive training data (SA-1B). Recognizing SAM's dependency on manual guidance given its category-agnostic nature, we identified unexplored potential within few-shot semantic segmentation tasks for remote sensing imagery. This research introduces a structured framework designed for the automation of few-shot semantic segmentation. It utilizes the SAM model and facilitates a more efficient generation of semantically discernible segmentation outcomes. Central to our methodology is a novel automatic prompt learning approach, leveraging prior guided masks to produce coarse pixel-wise prompts for SAM. Extensive experiments on the DLRSD datasets underline the superiority of our approach, outperforming other available few-shot methodologies

Severe adult ileosigmoid intussusception prolapsing from the rectum: A case report

Intussusception is a pediatric condition that rarely presents in adults. In this article, we report a case of a 36 year-old man initially presenting with abdominal pain and rectal prolapse, however, surgical reduction of the rectal prolapse did no relief his symptoms. Physical examination, abdominal plain film, barium enema and colonoscopy confirmed the presence of a large intra-abdominal mass, but the origin of the mass was revealed only upon laparotomy. During the surgery, it was noted that the ileum and the sigmoid colon was connected by a 15 cm × 12cm mass, covered by an extremely dilated intestinal tissue. The resected tissue pathology demonstrated a 9 cm × 6 cm × 5 cm submucosal lipoma at the ileocecal junction without evidence of malignancy. The patient's post-surgical course was uneventful. Diagnostic and therapeutic problems related to adult intussusception are reviewed

SegPrompt: Boosting Open-world Segmentation via Category-level Prompt Learning

Current closed-set instance segmentation models rely on pre-defined class labels for each mask during training and evaluation, largely limiting their ability to detect novel objects. Open-world instance segmentation (OWIS) models address this challenge by detecting unknown objects in a class-agnostic manner. However, previous OWIS approaches completely erase category information during training to keep the model's ability to generalize to unknown objects. In this work, we propose a novel training mechanism termed SegPrompt that uses category information to improve the model's class-agnostic segmentation ability for both known and unknown categories. In addition, the previous OWIS training setting exposes the unknown classes to the training set and brings information leakage, which is unreasonable in the real world. Therefore, we provide a new open-world benchmark closer to a real-world scenario by dividing the dataset classes into known-seen-unseen parts. For the first time, we focus on the model's ability to discover objects that never appear in the training set images. Experiments show that SegPrompt can improve the overall and unseen detection performance by 5.6% and 6.1% in AR on our new benchmark without affecting the inference efficiency. We further demonstrate the effectiveness of our method on existing cross-dataset transfer and strongly supervised settings, leading to 5.5% and 12.3% relative improvement.Comment: Accepted to Proc. Int. Conf. Computer Vision (ICCV) 2023. Code is at: https://github.com/aim-uofa/SegPromp