Tree-based iterated local search for Markov random fields with applications in image analysis

The maximum a posteriori assignment for general structure Markov random fields is computationally intractable. In this paper, we exploit tree-based methods to efficiently address this problem. Our novel method, named Tree-based Iterated Local Search (T-ILS), takes advantage of the tractability of tree-structures embedded within MRFs to derive strong local search in an ILS framework. The method efficiently explores exponentially large neighborhoods using a limited memory without any requirement on the cost functions. We evaluate the T-ILS on a simulated Ising model and two real-world vision problems: stereo matching and image denoising. Experimental results demonstrate that our methods are competitive against state-of-the-art rivals with significant computational gain.

Ordinal random fields for recommender systems

Recommender Systems heavily rely on numerical preferences, whereas the importance of ordinal preferences has only been recognised in recent works of Ordinal Matrix Factorisation (OMF). Although the OMF can effectively exploit ordinal properties, it captures only the higher-order interactions among users and items, without considering the localised interactions properly. This paper employs Markov Random Fields (MRF) to investigate the localised interactions, and proposes a unified model called Ordinal Random Fields (ORF) to take advantages of both the representational power of the MRF and the ease of modelling ordinal preferences by the OMF. Experimental result on public datasets demonstrates that the proposed ORF model can capture both types of interactions, resulting in improved recommendation accuracy.

Preference relation-based markov random fields

A preference relation-based Top-N recommendation approach, PrefMRF, is proposed to capture both the second-order and the higher-order interactions among users and items. Traditionally Top-N recommendation was achieved by predicting the item ratings fi rst, and then inferring the item rankings, based on the assumption of availability of explicit feed-backs such as ratings, and the assumption that optimizing the ratings is equivalent to optimizing the item rankings. Nevertheless, both assumptions are not always true in real world applications. The proposed PrefMRF approach drops these assumptions by explicitly exploiting the preference relations, a more practical user feedback. Comparing to related work, the proposed PrefMRF approach has the unique property of modeling both the second-order and the higher-order interactions among users and items. To the best of our knowledge, this is the first time both types of interactions have been captured in preference relation-based method. Experiment results on public datasets demonstrate that both types of interactions have been properly captured, and signifi cantly improved Top-N recommendation performance has been achieved.

Epoxy nanocomposites with aligned carbon nanofillers by external electric fields

This paper presents systematic studies on aligning carbon nanofillers in epoxy by external fields, either electric fields or magnetic fields, to create nanocomposites with greatly improved mechanical and electrical properties. Carbon nanofibers (CNFs) and graphene nanoplatelets (GnPs) were observed to align along the field direction in the epoxy resin. Compared to the unmodifed epoxy and those with randomly-oriented carbon nanofillers, the nanocomposites with aligned carbon nanofillers showed significantly higher fracture toughness and electrical conductivity along the direction of the external field. Compared with randomly-oriented nanofillers, aligned GnPs and CNFs produced 40% and 27% improvement in fracture energy at 1.0 wt%, bringing the total increase in fracture energy over the neat polymer to more than 10 times. Several key toughening mechanisms were identified through fractographic analysis, which was used to develop predictive models to quantify the increases in the value of GIc as a result of 1-D and 2D carbon nanofillers. The present findings suggest that aligning carbon nanofillers presents a very promising technique to create multi-scale reinforcement with greatly increased electric conductivity and fracture toughness.