109,593 research outputs found
Analysis of priority queues with session-based arrival streams
In this paper, we analyze a discrete-time priority queue with session-based arrivals. We consider a user population, where each user can start and end sessions. Sessions belong to one of two classes and generate a variable number of fixed-length packets which arrive to the queue at the rate of one packet per slot. The lengths of the sessions are generally distributed. Packets of the first class have transmission priority over the packets of the other class. The model is motivated by a web server handling delay-sensitive and delay-insensitive content. By using probability generating functions, some performance measures of the queue such as the moments of the packet delays of both classes are calculated. The impact of the priority scheduling discipline and of the session nature of the arrival process is shown by some numerical examples
Time-dependent stochastic shortest path(s) algorithms for a scheduled transportation network
Following on from our work concerning travellersâ preferences in public transportation networks (Wu and Hartley, 2004), we introduce the concept of stochasticity to our algorithms. Stochasticity greatly increases the complexity of the route finding problem, so greater algorithmic efficiency becomes imperative. Public transportation networks (buses, trains) have two important features: edges can only be traversed at certain points in time and the weights of these edges change in a day and have an uncertainty associated with them. These features determine that a public transportation network is a stochastic and time-dependent network. Finding multiple shortest paths in a both stochastic and time-dependent network is currently regarded as the most difficult task in the route finding problems (Loui, 1983). This paper discusses the use of k-shortest-paths (KSP) algorithms to find optimal route(s) through a network in which the edge weights are defined by probability distributions. A comprehensive review of shortest path(s) algorithms with probabilistic graphs was conducted
Representation learning for very short texts using weighted word embedding aggregation
Short text messages such as tweets are very noisy and sparse in their use of
vocabulary. Traditional textual representations, such as tf-idf, have
difficulty grasping the semantic meaning of such texts, which is important in
applications such as event detection, opinion mining, news recommendation, etc.
We constructed a method based on semantic word embeddings and frequency
information to arrive at low-dimensional representations for short texts
designed to capture semantic similarity. For this purpose we designed a
weight-based model and a learning procedure based on a novel median-based loss
function. This paper discusses the details of our model and the optimization
methods, together with the experimental results on both Wikipedia and Twitter
data. We find that our method outperforms the baseline approaches in the
experiments, and that it generalizes well on different word embeddings without
retraining. Our method is therefore capable of retaining most of the semantic
information in the text, and is applicable out-of-the-box.Comment: 8 pages, 3 figures, 2 tables, appears in Pattern Recognition Letter
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