196 research outputs found
Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation
The field of protein folding research has been greatly advanced by deep
learning methods, with AlphaFold2 (AF2) demonstrating exceptional performance
and atomic-level precision. As co-evolution is integral to protein structure
prediction, AF2's accuracy is significantly influenced by the depth of multiple
sequence alignment (MSA), which requires extensive exploration of a large
protein database for similar sequences. However, not all protein sequences
possess abundant homologous families, and consequently, AF2's performance can
degrade on such queries, at times failing to produce meaningful results. To
address this, we introduce a novel generative language model, MSA-Augmenter,
which leverages protein-specific attention mechanisms and large-scale MSAs to
generate useful, novel protein sequences not currently found in databases.
These sequences supplement shallow MSAs, enhancing the accuracy of structural
property predictions. Our experiments on CASP14 demonstrate that MSA-Augmenter
can generate de novo sequences that retain co-evolutionary information from
inferior MSAs, thereby improving protein structure prediction quality on top of
strong AF2
Event-based Motion Segmentation with Spatio-Temporal Graph Cuts
Identifying independently moving objects is an essential task for dynamic
scene understanding. However, traditional cameras used in dynamic scenes may
suffer from motion blur or exposure artifacts due to their sampling principle.
By contrast, event-based cameras are novel bio-inspired sensors that offer
advantages to overcome such limitations. They report pixelwise intensity
changes asynchronously, which enables them to acquire visual information at
exactly the same rate as the scene dynamics. We develop a method to identify
independently moving objects acquired with an event-based camera, i.e., to
solve the event-based motion segmentation problem. We cast the problem as an
energy minimization one involving the fitting of multiple motion models. We
jointly solve two subproblems, namely event cluster assignment (labeling) and
motion model fitting, in an iterative manner by exploiting the structure of the
input event data in the form of a spatio-temporal graph. Experiments on
available datasets demonstrate the versatility of the method in scenes with
different motion patterns and number of moving objects. The evaluation shows
state-of-the-art results without having to predetermine the number of expected
moving objects. We release the software and dataset under an open source
licence to foster research in the emerging topic of event-based motion
segmentation
A one-pass clustering based sketch method for network monitoring
Network monitoring solutions need to cope with increasing network traffic volumes, as a result, sketch-based monitoring methods have been extensively studied to trade accuracy for memory scalability and storage reduction. However, sketches are sensitive to skewness in network flow distributions due to hash collisions, and need complicated performance optimization to adapt to line-rate packet streams. We provide Jellyfish, an efficient sketch method that performs one-pass clustering over the network stream. One-pass clustering is realized by adapting the monitoring granularity from the whole network flow to fragments called subflows, which not only reduces the ingestion rate but also provides an efficient intermediate representation for the input to the sketch. Jellyfish provides the network-flow level query interface by reconstructing the network-flow level counters by merging subflow records from the same network flow. We provide probabilistic analysis of the expected accuracy of both existing sketch methods and Jellyfish. Real-world trace-driven experiments show that Jellyfish reduces the average estimation errors by up to six orders of magnitude for per-flow queries, by six orders of magnitude for entropy queries, and up to ten times for heavy-hitter queries.This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61972409; in part by Hong Kong Research Grants Council (RGC) under Grant TRS T41-603/20-R, Grant GRF-16213621, and Grant ITF ACCESS; in part by the Spanish I+D+i project TRAINER-A, funded by MCIN/AEI/10.13039/501100011033, under Grant PID2020-118011GB-C21; and in part by the Catalan Institution
for Research and Advanced Studies (ICREA Academia).Peer ReviewedPostprint (author's final draft
An Analysis of the Cause of Privacy Paradox among SNS Users: take Chinese College Students as an Example
It has been proved that the privacy paradox does exist, yet the cause of the phenomenon remains vague. This article tries to analyze the [Inserted: s]cause of privacy paradox phenomenon on SNS (WeChat) among Chinese college students based on Privacy Calculus Theory and the TPB model and introduces two new factors: the credibility of SNS and the cost of protecting privacy. Through a questionnaire and interview survey,[Inserted: a ] our result shows that there is no significant correlation between usersâ privacy concerns and the intention of privacy disclosure. While the more users trust the SNS platform, the more possibility they tend to disclose their private information[Inserted: te], and the cost of privacy protection can somehow weaken the relationship between the intention and the actual behavior. Therefore, [Inserted: ship]by increasing SNS\u27s credibility, users tend to disclose more personal information to SNS providers, which may improve the competitiveness of SNSs and contribute to their sustainable development
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