Peer-to-Peer (P2P) streaming technologies can take advantage of the upload capacity of clients, and hence can scale to large content distribution networks with lower cost. A fundamental question for P2P streaming systems is the maximum streaming rate that all users can sustain. Prior works have studied the optimal streaming rate for a complete network, where every peer is assumed to communicate with all other peers. This is however an impractical assumption in real systems. In this paper, we are interested in the achievable streaming rate when each peer can only connect to a small number of neighbors. We show that even with a random peer selection algorithm and uniform rate allocation, as long as each peer maintains Ω(log N) downstream neighbors, where N is the total number of peers in the system, the system can asymptotically achieve a streaming rate that is close to the optimal streaming rate of a complete network.We then extend our analysis to multi-channel P2P networks, and we study the scenario where "helpers" from channels with excessive upload capacity can help peers in channels with insufficient upload capacity. We show that by letting each peer select Ω(log N) neighbors randomly from either the peers in the same channel or from the helpers, we can achieve a close-to-optimal streaming capacity region. Simulation results are provided to verify our analysis.
The detection of protein complexes from protein-protein interaction network is a fundamental issue in bioinformatics and systems biology. To solve this problem, numerous methods have been proposed from different angles in the past decades. However, the study on detecting statistically significant protein complexes still has not received much attention. Although there are a few methods available in the literature for identifying statistically significant protein complexes, none of these methods can provide a more strict control on the error rate of a protein complex in terms of family-wise error rate (FWER). In this paper, we propose a new detection method SSF that is capable of controlling the FWER of each reported protein complex. More precisely, we first present a p-value calculation method based on Fisher's exact test to quantify the association between each protein and a given candidate protein complex. Consequently, we describe the key modules of the SSF algorithm: a seed expansion procedure for significant protein complexes search and a set cover strategy for redundancy elimination. The experimental results on five benchmark data sets show that: (1) our method can achieve the highest precision; (2) it outperforms three competing methods in terms of normalized mutual information (NMI) and F1 score in most cases.
Generalist vision language models (VLMs) have made significant strides in computer vision, but they fall short in specialized fields like healthcare, where expert knowledge is essential. In traditional computer vision tasks, creative or approximate answers may be acceptable, but in healthcare, precision is paramount.Current large multimodal models like Gemini and GPT-4o are insufficient for medical tasks due to their reliance on memorized internet knowledge rather than the nuanced expertise required in healthcare. VLMs are usually trained in three stages: vision pre-training, vision-language pre-training, and instruction fine-tuning (IFT). IFT has been typically applied using a mixture of generic and healthcare data. In contrast, we propose that for medical VLMs, a fourth stage of specialized IFT is necessary, which focuses on medical data and includes information from domain expert models. Domain expert models developed for medical use are crucial because they are specifically trained for certain clinical tasks, e.g. to detect tumors and classify abnormalities through segmentation and classification, which learn fine-grained features of medical data$-$features that are often too intricate for a VLM to capture effectively especially in radiology. This paper introduces a new framework, VILA-M3, for medical VLMs that utilizes domain knowledge via expert models. Through our experiments, we show an improved state-of-the-art (SOTA) performance with an average improvement of ~9% over the prior SOTA model Med-Gemini and ~6% over models trained on the specific tasks. Our approach emphasizes the importance of domain expertise in creating precise, reliable VLMs for medical applications.
OBJECTIVES: We aimed to develop a computer-aided detection (CAD) system to localize and detect the malposition of endotracheal tubes (ETTs) on portable supine chest radiographs (CXRs). DESIGN: This was a retrospective diagnostic study. DeepLabv3+ with ResNeSt50 backbone and DenseNet121 served as the model architecture for segmentation and classification tasks, respectively. SETTING: Multicenter study. PATIENTS: For the training dataset, images meeting the following inclusion criteria were included: 1) patient age greater than or equal to 20 years; 2) portable supine CXR; 3) examination in emergency departments or ICUs; and 4) examination between 2015 and 2019 at National Taiwan University Hospital (NTUH) (NTUH-1519 dataset: 5,767 images). The derived CAD system was tested on images from chronologically (examination during 2020 at NTUH, NTUH-20 dataset: 955 images) or geographically (examination between 2015 and 2020 at NTUH Yunlin Branch [YB], NTUH-YB dataset: 656 images) different datasets. All CXRs were annotated with pixel-level labels of ETT and with image-level labels of ETT presence and malposition. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: For the segmentation model, the Dice coefficients indicated that ETT would be delineated accurately (NTUH-20: 0.854; 95% CI, 0.824–0.881 and NTUH-YB: 0.839; 95% CI, 0.820–0.857). For the classification model, the presence of ETT could be accurately detected with high accuracy (area under the receiver operating characteristic curve [AUC]: NTUH-20, 1.000; 95% CI, 0.999–1.000 and NTUH-YB: 0.994; 95% CI, 0.984–1.000). Furthermore, among those images with ETT, ETT malposition could be detected with high accuracy (AUC: NTUH-20, 0.847; 95% CI, 0.671–0.980 and NTUH-YB, 0.734; 95% CI, 0.630–0.833), especially for endobronchial intubation (AUC: NTUH-20, 0.991; 95% CI, 0.969–1.000 and NTUH-YB, 0.966; 95% CI, 0.933–0.991). CONCLUSIONS: The derived CAD system could localize ETT and detect ETT malposition with excellent performance, especially for endobronchial intubation, and with favorable potential for external generalizability.
In this paper, we propose the Napping Guard attack which can deanonymize hidden services in a stealthy way. The key insight of our method is utilizing a design flaw of hidden service's requests to build a simplex covert channel, which can send message from a malicious guard relay to the collusive Client-OP. With the help of this covert channel, the guard relay delivers the actual IP address of the hidden service to the collusive Client-OP. Considering the Client-OP knows the onion address of hidden service, the adversary is able to deanonymize the hidden service through correlating the actual IP address and onion address on Client-OP. In particular, compared with previous attacks, our covert channel utilizes latency signal instead of traffic signal, and eliminates the dependency of malicious Rend-Point, so as to achieve a better concealment and lower cost. Our experiment shows that the covert channel is reliable that has the precision and recall about 99.35% and 99.19%. In addition, we also propose a mitigation of Napping Guard attack, and report the design flaw to the Tor project.
Abstract Tor exit relays are operated by volunteers and the trustworthiness of Tor exit relays need to be revisited in a long-term manner. In this paper, we monitored the Tor network by developing a fast and distributed exit relay scanner (ExitSniffer) to probe all exit relays over a period of 16 months continuously, seeking to expose the anomalous binding relationship phenomena of exit routers simply by comparing the returnIP and consensusIP. We totally find 1983 malicious exit relays which average contribute 10.12% bandwidth of total Tor exit relays bandwidth monthly, resulting tremendous threaten for Tor user’s anonymity according to the current path-relay selecting algorithm. There exits two types of anomalous binding relationship consists 35 exit relay families, with different size ranging from 2 to 230, which are neither announced in the consensus document or detected by the Tor network.
Peer-to-peer (P2P) streaming technologies can take advantage of the upload capacity of clients, and hence can scale to large content distribution networks with lower cost. A fundamental question for P2P streaming systems is the maximum streaming rate that all users can sustain. Prior works have studied the optimal streaming rate for a complete network, where every peer is assumed to be able to communicate with all other peers. This is, however, an impractical assumption in real systems. In this paper, we are interested in the achievable streaming rate when each peer can only connect to a small number of neighbors. We show that even with a random peer-selection algorithm and uniform rate allocation, as long as each peer maintains Ω(logN) downstream neighbors, where N is the total number of peers in the system, the system can asymptotically achieve a streaming rate that is close to the optimal streaming rate of a complete network. These results reveal a number of important insights into the dynamics of the system, based on which we then design simple improved algorithms that can reduce the constant factor in front of the Ω(logN) term, yet can achieve the same level of performance guarantee. Simulation results are provided to verify our analysis.
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