Towards Generalizable Diabetic Retinopathy Grading in Unseen Domains

Diabetic Retinopathy (DR) is a common complication of diabetes and a leading cause of blindness worldwide. Early and accurate grading of its severity is crucial for disease management. Although deep learning has shown great potential for automated DR grading, its real-world deployment is still challenging due to distribution shifts among source and target domains, known as the domain generalization problem. Existing works have mainly attributed the performance degradation to limited domain shifts caused by simple visual discrepancies, which cannot handle complex real-world scenarios. Instead, we present preliminary evidence suggesting the existence of three-fold generalization issues: visual and degradation style shifts, diagnostic pattern diversity, and data imbalance. To tackle these issues, we propose a novel unified framework named Generalizable Diabetic Retinopathy Grading Network (GDRNet). GDRNet consists of three vital components: fundus visual-artifact augmentation (FundusAug), dynamic hybrid-supervised loss (DahLoss), and domain-class-aware re-balancing (DCR). FundusAug generates realistic augmented images via visual transformation and image degradation, while DahLoss jointly leverages pixel-level consistency and image-level semantics to capture the diverse diagnostic patterns and build generalizable feature representations. Moreover, DCR mitigates the data imbalance from a domain-class view and avoids undesired over-emphasis on rare domain-class pairs. Finally, we design a publicly available benchmark for fair evaluations. Extensive comparison experiments against advanced methods and exhaustive ablation studies demonstrate the effectiveness and generalization ability of GDRNet.Comment: Earyly Accepted by MICCAI 2023, the 26th International Conference on Medical Image Computing and Computer Assisted Interventio

A Comprehensive Study on Off-path SmartNIC

SmartNIC has recently emerged as an attractive device to accelerate distributed systems. However, there has been no comprehensive characterization of SmartNIC especially on the network part. This paper presents the first comprehensive study of off-path SmartNIC. Our experimental study uncovers the key performance characteristics of the communication among the client, SmartNIC SoC, and the host. We find without considering SmartNIC hardware architecture, communications with it can cause up to 48% bandwidth degradation due to performance anomalies. We also propose implications to address the anomalies.Comment: This is the short version. Full version will appear at OSDI2

Vertebrobasilar Dolichoectasia and Basilar Artery Dissection Presenting With Trigeminal Neuralgia: A Case Report

Trigeminal neuralgia secondary to vertebrobasilar dolichoectasia and basilar artery dissection is rare. The authors report the case of a 72-year-old man with a 5-year history of right electrical facial pain identical with trigeminal neuralgia. Finally, magnetic resonance imaging and digital subtraction angiography revealed basilar artery dissection and vertebrobasilar dolichoectasia. The patient underwent partial basilar dissecting aneurysm embolization. The facial pain was relieved immediately after the operation and disappeared completely 6 months later. Three years after surgery, the patient had experienced no recurrence of the right facial pain

Seamless Service Provisioning for Mobile Crowdsensing: Towards Integrating Forward and Spot Trading Markets

The challenge of exchanging and processing of big data over Mobile Crowdsensing (MCS) networks calls for the new design of responsive and seamless service provisioning as well as proper incentive mechanisms. Although conventional onsite spot trading of resources based on real-time network conditions and decisions can facilitate the data sharing over MCS networks, it often suffers from prohibitively long service provisioning delays and unavoidable trading failures due to its reliance on timely analysis of complex and dynamic MCS environments. These limitations motivate us to investigate an integrated forward and spot trading mechanism (iFAST), which entails a new hybrid service trading protocol over the MCS network architecture. In iFAST, the sellers (i.e., mobile users with sensing resources) can provide long-term or temporary sensing services to the buyers (i.e., sensing task owners). iFast enables signing long-term contracts in advance of future transactions through a forward trading mode, via analyzing historical statistics of the market, for which the notion of overbooking is introduced and promoted. iFAST further enables the buyers with unsatisfying service quality to recruit temporary sellers through a spot trading mode, upon considering the current market/network conditions. We analyze the fundamental blocks of iFAST, and provide a case study to demonstrate its superior performance as compared to existing methods. Finally, future research directions on reliable service provisioning for next-generation MCS networks are summarized

Eloquent: A More Robust Transmission Scheme for LLM Token Streaming

To render each generated token in real-time for users, the Large Language Model (LLM) server generates tokens one by one and streams each token (or group of a few tokens) through the network to the user right after generation, which we refer to as LLM token streaming. However, under unstable network conditions, the LLM token streaming experience could suffer greatly from stalls since one packet loss could block the rendering of later tokens even if the packets containing them arrive on time. With a measurement study, we show that current applications suffer from increased stalls under unstable networks. For this emerging token streaming problem in LLM Chatbots that differs from previous multimedia and text applications, we propose a novel transmission scheme, called Eloquent, which puts newly generated tokens as well as currently unacknowledged tokens in the next outgoing packet. This ensures that each packet contains some new tokens and, in the meantime, is independently rendered when received, avoiding the aforementioned stalls caused by missing packets. Through simulation under various networks, we show Eloquent reduces stall ratio (proportion of token rendering wait time) by 71.0% compared to the retransmission method commonly used by real chatbot applications and by 31.6% compared to the baseline packet duplication scheme. By tailoring Eloquent to fit the token-by-token generation of LLM, we enable the Chatbots to respond like an eloquent speaker for users to better enjoy pervasive AI.</p