756 research outputs found
Research and Implement of an Algorithm for Physical Topology Automatic Discovery in Switched Ethernet
AbstractIn this paper, a novel practical algorithmic solution for automatic discovering the physical topology of switched Ethernet was proposed. Our algorithm collects standard SNMP MIB information that is widely supported in modern IP networks and then builds the physical topology of the active network. We described the relative definitions, system model and proved the correctness of the algorithm. Practically, the algorithm was implemented in our visualization network monitoring system. We also presented the main steps of the algorithm, core codes and running results on the lab network. The experimental results clearly validate our approach, demonstrating that our algorithm is simple and effective which can discover the accurate up-to-date physical network topology
The Lasso with general Gaussian designs with applications to hypothesis testing
The Lasso is a method for high-dimensional regression, which is now commonly
used when the number of covariates is of the same order or larger than the
number of observations . Classical asymptotic normality theory is not
applicable for this model due to two fundamental reasons: The regularized
risk is non-smooth; The distance between the estimator and the true parameters vector cannot be
neglected. As a consequence, standard perturbative arguments that are the
traditional basis for asymptotic normality fail.
On the other hand, the Lasso estimator can be precisely characterized in the
regime in which both and are large, while is of order one. This
characterization was first obtained in the case of standard Gaussian designs,
and subsequently generalized to other high-dimensional estimation procedures.
Here we extend the same characterization to Gaussian correlated designs with
non-singular covariance structure. This characterization is expressed in terms
of a simpler ``fixed design'' model. We establish non-asymptotic bounds on the
distance between distributions of various quantities in the two models, which
hold uniformly over signals in a suitable sparsity class,
and values of the regularization parameter.
As applications, we study the distribution of the debiased Lasso, and show
that a degrees-of-freedom correction is necessary for computing valid
confidence intervals
Query-aware Long Video Localization and Relation Discrimination for Deep Video Understanding
The surge in video and social media content underscores the need for a deeper
understanding of multimedia data. Most of the existing mature video
understanding techniques perform well with short formats and content that
requires only shallow understanding, but do not perform well with long format
videos that require deep understanding and reasoning. Deep Video Understanding
(DVU) Challenge aims to push the boundaries of multimodal extraction, fusion,
and analytics to address the problem of holistically analyzing long videos and
extract useful knowledge to solve different types of queries. This paper
introduces a query-aware method for long video localization and relation
discrimination, leveraging an imagelanguage pretrained model. This model
adeptly selects frames pertinent to queries, obviating the need for a complete
movie-level knowledge graph. Our approach achieved first and fourth positions
for two groups of movie-level queries. Sufficient experiments and final
rankings demonstrate its effectiveness and robustness.Comment: ACM MM 2023 Grand Challeng
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