371 research outputs found
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
This paper provides a unified account of two schools of thinking in
information retrieval modelling: the generative retrieval focusing on
predicting relevant documents given a query, and the discriminative retrieval
focusing on predicting relevancy given a query-document pair. We propose a game
theoretical minimax game to iteratively optimise both models. On one hand, the
discriminative model, aiming to mine signals from labelled and unlabelled data,
provides guidance to train the generative model towards fitting the underlying
relevance distribution over documents given the query. On the other hand, the
generative model, acting as an attacker to the current discriminative model,
generates difficult examples for the discriminative model in an adversarial way
by minimising its discrimination objective. With the competition between these
two models, we show that the unified framework takes advantage of both schools
of thinking: (i) the generative model learns to fit the relevance distribution
over documents via the signals from the discriminative model, and (ii) the
discriminative model is able to exploit the unlabelled data selected by the
generative model to achieve a better estimation for document ranking. Our
experimental results have demonstrated significant performance gains as much as
23.96% on Precision@5 and 15.50% on MAP over strong baselines in a variety of
applications including web search, item recommendation, and question answering.Comment: 12 pages; appendix adde
Emerging Techniques for Thalassemia Gene Detection
Isothermal nucleic acid amplification is a simple process that rapidly and efficiently accumulates nucleic acid sequences at constant temperature such as 37 and 42°C. Isothermal nucleic acid amplification approach offers several advantages over temperature circle methods (such as PCR) including rapid assay results, cost-effectiveness, and portability. Two detection approaches based on circular strand-displacement polymerization reaction (CSDPR) were presented in this chapter for sensitive and specific thalassemia gene detection. One is a lateral flow strip biosensor based on CSDPR for semi-quantitative detection of thalassemia DNA. The other is a spectrophotometric DNA detection approach based on CSDPR for quantitative detection of thalassemia DNA
The Discrete-Time Bulk-Service Geo/Geo/1
This paper deals with a discrete-time bulk-service Geo/Geo/1
queueing system with infinite buffer space and multiple working vacations. Considering an early arrival system, as soon as the server empties the system in a regular busy period, he leaves the system and takes a working vacation for a random duration at time n. The service times both in a working vacation and in a busy period and the vacation times are assumed to be geometrically distributed. By using embedded Markov chain approach and difference operator method, queue length of the whole system at random slots and the waiting time for an arriving customer are obtained. The queue length distributions of the outside observer’s observation epoch are investigated. Numerical experiment is performed to validate the analytical results
Integrated Sensing, Computation, and Communication: System Framework and Performance Optimization
Integrated sensing, computation, and communication (ISCC) has been recently
considered as a promising technique for beyond 5G systems. In ISCC systems, the
competition for communication and computation resources between sensing tasks
for ambient intelligence and computation tasks from mobile devices becomes an
increasingly challenging issue. To address it, we first propose an efficient
sensing framework with a novel action detection module. It can reduce the
overhead of computation resource by detecting whether the sensing target is
static. Subsequently, we analyze the sensing performance of the proposed
framework and theoretically prove its effectiveness with the help of the
sampling theorem. Then, we formulate a sensing accuracy maximization problem
while guaranteeing the quality-of-service (QoS) requirements of tasks. To solve
it, we propose an optimal resource allocation strategy, in which the minimal
resource is allocated to computation tasks, and the rest is devoted to sensing
tasks. Besides, a threshold selection policy is derived. Compared with the
conventional schemes, the results further demonstrate the necessity of the
proposed sensing framework. Finally, a real-world test of action recognition
tasks based on USRP B210 is conducted to verify the sensing performance
analysis, and extensive experiments demonstrate the performance improvement of
our proposal by comparing it with some benchmark schemes
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