1,284 research outputs found
The M/M/ N
This paper considers the M/M/N repairable queuing system. The customers' arrival
is a Poisson process. The servers are subject to breakdown according to Poisson processes with
different rates in idle time and busy time, respectively. The breakdown servers are repaired by repairmen,
and the repair time is an exponential distribution. Using probability generating function
and transform method, we obtain the steady-state probabilities of the system states, the steady-state
availability of the servers, and the mean queueing length of the model
Certifying the Fairness of KNN in the Presence of Dataset Bias
We propose a method for certifying the fairness of the classification result
of a widely used supervised learning algorithm, the k-nearest neighbors (KNN),
under the assumption that the training data may have historical bias caused by
systematic mislabeling of samples from a protected minority group. To the best
of our knowledge, this is the first certification method for KNN based on three
variants of the fairness definition: individual fairness, -fairness,
and label-flipping fairness. We first define the fairness certification problem
for KNN and then propose sound approximations of the complex arithmetic
computations used in the state-of-the-art KNN algorithm. This is meant to lift
the computation results from the concrete domain to an abstract domain, to
reduce the computational cost. We show effectiveness of this abstract
interpretation based technique through experimental evaluation on six datasets
widely used in the fairness research literature. We also show that the method
is accurate enough to obtain fairness certifications for a large number of test
inputs, despite the presence of historical bias in the datasets
Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting
Open-world Relation Extraction (OpenRE) has recently garnered significant
attention. However, existing approaches tend to oversimplify the problem by
assuming that all unlabeled texts belong to novel classes, thereby limiting the
practicality of these methods. We argue that the OpenRE setting should be more
aligned with the characteristics of real-world data. Specifically, we propose
two key improvements: (a) unlabeled data should encompass known and novel
classes, including hard-negative instances; and (b) the set of novel classes
should represent long-tail relation types. Furthermore, we observe that popular
relations such as titles and locations can often be implicitly inferred through
specific patterns, while long-tail relations tend to be explicitly expressed in
sentences. Motivated by these insights, we present a novel method called KNoRD
(Known and Novel Relation Discovery), which effectively classifies explicitly
and implicitly expressed relations from known and novel classes within
unlabeled data. Experimental evaluations on several Open-world RE benchmarks
demonstrate that KNoRD consistently outperforms other existing methods,
achieving significant performance gains.Comment: 10 pages, 6 figure
A BP Neural Network Model to Predict Reservior Parameters
This paper proposes an artificial neural network (ANN) method to calculate reservoir parameters. By improving the algorithm of BP neural network, convergence speed is enhanced and better result can be achieved. Practical applications prove that neural network technique is of significant importance for reservoir description
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