2,112 research outputs found
Undersampling GA-SVM for network intrusion detection
Network intrusion detection is one of the hottest issues in the world. An increasing number of researchers and engineers deal with this problem by using machine learning methods. However, how to improve the identification accuracy of all the attack classes remains unsolved since the dataset is an imbalanced one with high imbalance ratio. This thesis work intends to build a classifier to achieve high classification accuracy. It proposes an undersampling Genetic Algorithm-Support Vector Machine (GA-SVM) method to handle this problem. It applies an undersampling method in GA-SVM. To solve the multiclassification problem with a binary classifier, this work proposes to utilize the undersampling GA-SVM with several classic structures. After adjusting the parameter in genetic algorithm and undersampling ratio in each support vector machine, this work concludes that the proposed undersampling GA-SVM improves the performance of an intrusion detection system. Among its variants, the decision tree-based undersampling GA-SVM offers the best performance
Simulating gravitational waves passing through the spacetime of a black hole
We investigate how GWs pass through the spacetime of a Schwarzschild black
hole using time-domain numerical simulations. Our work is based on the
perturbed 3+1 Einstein's equations up to the linear order. We show explicitly
that our perturbation equations are covariant under infinitesimal coordinate
transformations. Then we solve a symmetric second-order hyperbolic wave
equation with a spatially varying wave speed. As the wave speed in our wave
equation vanishes at the horizon, our formalism can naturally avoid boundary
conditions at the horizon. Our formalism also does not contain coordinate
singularities and, therefore, does not need regularity conditions. Then, based
on our code, we simulate both finite and continuous initially plane-fronted
wave trains passing through the Schwarzschild black hole. We find that for the
finite wave train, the wave zone of GWs is wildly twisted by the black hole.
While for the continuous wave train, unlike geometric optics, GWs can not be
sheltered by the back hole. A strong beam and an interference pattern appear
behind the black hole along the optical axis. Moreover, we find that the
back-scattering due to the interaction between GWs and the background curvature
is strongly dependent on the direction of the propagation of the trailing
wavefront relative to the black hole.Comment: 24 pages, 9 figure
REST: Retrieval-Based Speculative Decoding
We introduce Retrieval-Based Speculative Decoding (REST), a novel algorithm
designed to speed up language model generation. The key insight driving the
development of REST is the observation that the process of text generation
often includes certain common phases and patterns. Unlike previous methods that
rely on a draft language model for speculative decoding, REST harnesses the
power of retrieval to generate draft tokens. This method draws from the
reservoir of existing knowledge, retrieving and employing relevant tokens based
on the current context. Its plug-and-play nature allows for seamless
integration and acceleration of any language models, all without necessitating
additional training. When benchmarked on 7B and 13B language models in a
single-batch setting, REST achieves a significant speedup of 1.62X to 2.36X on
code or text generation. The code of REST is available at
https://github.com/FasterDecoding/REST
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