Algorithms for the minimum sum coloring problem: a review

The Minimum Sum Coloring Problem (MSCP) is a variant of the well-known vertex coloring problem which has a number of AI related applications. Due to its theoretical and practical relevance, MSCP attracts increasing attention. The only existing review on the problem dates back to 2004 and mainly covers the history of MSCP and theoretical developments on specific graphs. In recent years, the field has witnessed significant progresses on approximation algorithms and practical solution algorithms. The purpose of this review is to provide a comprehensive inspection of the most recent and representative MSCP algorithms. To be informative, we identify the general framework followed by practical solution algorithms and the key ingredients that make them successful. By classifying the main search strategies and putting forward the critical elements of the reviewed methods, we wish to encourage future development of more powerful methods and motivate new applications

A memetic algorithm for the minimum sum coloring problem

Given an undirected graph $G$, the Minimum Sum Coloring problem (MSCP) is to find a legal assignment of colors (represented by natural numbers) to each vertex of $G$ such that the total sum of the colors assigned to the vertices is minimized. This paper presents a memetic algorithm for MSCP based on a tabu search procedure with two neighborhoods and a multi-parent crossover operator. Experiments on a set of 77 well-known DIMACS and COLOR 2002-2004 benchmark instances show that the proposed algorithm achieves highly competitive results in comparison with five state-of-the-art algorithms. In particular, the proposed algorithm can improve the best known results for 17 instances. We also provide upper bounds for 18 additional instances for the first time.Comment: Submitted manuscrip

Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path

Relation classification is an important research arena in the field of natural language processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the relation of two entities in a sentence. Our neural architecture leverages the shortest dependency path (SDP) between two entities; multichannel recurrent neural networks, with long short term memory (LSTM) units, pick up heterogeneous information along the SDP. Our proposed model has several distinct features: (1) The shortest dependency paths retain most relevant information (to relation classification), while eliminating irrelevant words in the sentence. (2) The multichannel LSTM networks allow effective information integration from heterogeneous sources over the dependency paths. (3) A customized dropout strategy regularizes the neural network to alleviate overfitting. We test our model on the SemEval 2010 relation classification task, and achieve an $F_1$-score of 83.7\%, higher than competing methods in the literature.Comment: EMNLP '1

Building Program Vector Representations for Deep Learning

Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly makes deep learning a reality in this new field. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than "shallow" methods, such as logistic regression and the support vector machine. This result confirms the feasibility of deep learning to analyze programs. It also gives primary evidence of its success in this new field. We believe deep learning will become an outstanding technique for program analysis in the near future.Comment: This paper was submitted to ICSE'1

Engineering properties of vertical cutoff walls consisting of reactive magnesia-activated slag and bentonite: workability, strength and hydraulic conductivity

Soil–cement–bentonite (SCB) vertical cutoff walls are commonly used to control the flow of contaminated groundwater at polluted sites. However, conventional backfill consisting of ordinary portland cement (OPC) is associated with a relatively high CO2 footprint. Potential chemical interactions between OPC and bentonite could also undermine the long-term durability of SCB materials. This paper proposes an innovative backfill material for cutoff walls that is composed of MgO-activated ground granulated blast furnace slag (GGBS), bentonite, and soil. The OPC–soil, OPC–bentonite–soil, and OPC–GGBS–bentonite–soil backfill materials are also tested for comparison purposes. The workability of fresh backfills and unconfined compressive strength of aged backfills are investigated. The hydraulic conductivities of aged backfills permeated with tap water, Na2SO4, and Pb–Zn solutions are assessed. The unconfined compressive strength and hydraulic conductivity of the proposed backfill permeated with tap water are in the range of 230–520 kPa and 1.1×10−10  to  6.3×10−10  m/s after 90 days of curing, respectively, depending on the mix composition. The hydraulic conductivity of the proposed MgO–GGBS–bentonite–soil backfill permeated with sodium sulfate (Na2SO4) or lead–zinc (Pb–Zn) solution is well below the commonly used limit, while the OPC–bentonite–soil backfill shows a significant loss in impermeability. Environmental and economic analyses indicate that, compared with conventional backfill made from OPC–bentonite–soil mixtures, the proposed backfill reduces CO2 emissions by approximately 84.7%–85.1% and costs by 15.3%–16.9%. The environmental and economic advantages will promote the use of MgO-activated GGBS–bentonite mixtures in cutoff walls and lead to their increased application in land remediation projects