68 research outputs found
Ordinal proportional cost sharing
We consider cost sharing problems with variable demands of heterogeneous goods. We study the compatibility of two axioms imposed on cost sharing methods: ordinality and average cost pricing for homogeneous (ACPH) goods. We generalize the ordinal proportional method (OPM) for the two-agent case, Sprumont [Journal of Economic Theory 81 (1998) 126162] to arbitrary number of agents
An Efficient Dynamic Programming Algorithm for the Generalized LCS Problem with Multiple Substring Exclusion Constrains
In this paper, we consider a generalized longest common subsequence problem
with multiple substring exclusion constrains. For the two input sequences
and of lengths and , and a set of constrains
of total length , the problem is to find a common subsequence of and
excluding each of constrain string in as a substring and the length of
is maximized. The problem was declared to be NP-hard\cite{1}, but we
finally found that this is not true. A new dynamic programming solution for
this problem is presented in this paper. The correctness of the new algorithm
is proved. The time complexity of our algorithm is .Comment: arXiv admin note: substantial text overlap with arXiv:1301.718
A note on the largest number of red nodes in red-black trees
In this paper, we are interested in the number of red nodes in red-black
trees. We first present an time dynamic programming solution for
computing , the largest number of red internal nodes in a red-black tree
on keys. Then the algorithm is improved to some time recursive
and nonrecursive algorithms. Based on these improved algorithms we finally find
a closed-form solution of
Complete Solutions for a Combinatorial Puzzle in Linear Time
In this paper we study a single player game consisting of black checkers
and white checkers, called shifting the checkers. We have proved that the
minimum number of steps needed to play the game for general and is . We have also presented an optimal algorithm to generate an optimal
move sequence of the game consisting of black checkers and white
checkers, and finally, we present an explicit solution for the general game
Rethinking the Value of Gazetteer in Chinese Named Entity Recognition
Gazetteer is widely used in Chinese named entity recognition (NER) to enhance
span boundary detection and type classification. However, to further understand
the generalizability and effectiveness of gazetteers, the NLP community still
lacks a systematic analysis of the gazetteer-enhanced NER model. In this paper,
we first re-examine the effectiveness several common practices of the
gazetteer-enhanced NER models and carry out a series of detailed analysis to
evaluate the relationship between the model performance and the gazetteer
characteristics, which can guide us to build a more suitable gazetteer. The
findings of this paper are as follows: (1) the gazetteer improves most of the
situations that the traditional NER model datasets are difficult to learn. (2)
the performance of model greatly benefits from the high-quality pre-trained
lexeme embeddings. (3) a good gazetteer should cover more entities that can be
matched in both the training set and testing set.Comment: Accepted by NLPCC 202
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An effective fuel level data cleaning and repairing method for vehicle monitor platform
With energy scarcity and environmental pollution becoming increasingly serious, the accurate estimation of fuel consumption of vehicles has been important in vehicle management and transportation planning towards a sustainable green transition. Fuel consumption is calculated by fuel level data collected from high precision fuel level sensors. However, in the vehicle monitor platform, there are many types of error in the data collection and transmission processes, such as the noise, interference, and collision errors are common in the high speed and dynamic vehicle environment. In this paper, an effective method for cleaning and repairing the fuel level data is proposed, which adopts the threshold to acquire abnormal fuel data, the time quantum to identify abnormal data, and linear interpolation based algorithm to correct data errors. Specifically, a modified Gaussian Mixture Model (GMM) based on the synchronous iteration method is proposed to acquire the thresholds, which uses the Particle Swarm Optimization (PSO) algorithm and the steepest descent algorithm to optimize the parameters of GMM. The experiment results based on the fuel level data of vehicles collected over one month prove the modified GMM is superior to GMM-EM on fuel level data, and the proposed method is effective for cleaning and repairing outliers of fuel level data
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