389729
Predicting protein folds with structural repeats using a chain graph model
Protein fold recognition is a key step towards inferring the tertiary structures from amino-acid sequences. Complex folds such as those consisting of interacting structural repeats are prevalent in proteins involved in a wide spectrum of biological functions. However, extant approaches often perform inadequately due to their inability to capture long-range interactions between structural units and to handle low sequence similarities across proteins (under 25% identity). In this paper, we propose a chain graph model built on a causally connected series of segmentation conditional random fields (SCRFs) to address these issues. Specifically, the SCRF model captures long-range interactions within recurring structural units and the Bayesian network backbone decomposes cross-repeat interactions into locally computable modules consisting of repeat-specific SCRFs and a model for sequence motifs. We applied this model to predict β-helices and leucine-rich repeats, and found it significantly outperforms extant methods in predictive accuracy and/or computational efficiency.
3096549
Kernel conditional random fields: representation and clique selection
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models is given which shows how kernel conditional random fields arise from risk minimization procedures defined using Mercer kernels on labeled graphs. A procedure for greedily selecting cliques in the dual representation is then proposed, which allows sparse representations. By incorporating kernels and implicit feature spaces into conditional graphical models, the framework enables semi-supervised learning algorithms for structured data through the use of graph kernels. The framework and clique selection methods are demonstrated in synthetic data experiments, and are also applied to the problem of protein secondary structure prediction.
619505
Semi-Supervised Learning of Attribute-Value Pairs from Product Descriptions
We describe an approach to extract attribute-value pairs from product descriptions. This allows us to represent products as sets of such attribute-value pairs to augment product databases. Such a representation is useful for a variety of tasks where treating a product as a set of attribute-value pairs is more useful than as an atomic entity. Examples of such applications include product recommendations, product comparison, and demand forecasting. We formulate the extraction as a classification problem and use a semi-supervised algorithm (co-EM) along with (Naive Bayes). The extraction system requires very little initial user supervision: using unlabeled data, we automatically extract an initial seed list that serves as training data for the supervised and semi-supervised classification algorithms. Finally, the extracted attributes and values are linked to form pairs using dependency information and co-location scores. We present promising results on product descriptions in two categories of sporting goods.
5651676
Segmentation Conditional Random Fields (SCRFs): A New Approach for Protein Fold Recognition
Protein fold recognition is an important step towards understanding protein three-dimensional structures and their functions. A conditional graphical model, i.e. segmentation conditional random fields (SCRFs), is proposed to solve the problem. In contrast to traditional graphical models such as hidden markov model (HMM), SCRFs follow a discriminative approach. It has the flexibility to include overlapping or long-range interaction features over the whole sequence, as well as global optimally solutions for the parameters. On the other hand, the segmentation setting in SCRFs makes its graphical structures intuitively similar to the protein 3-D structures and more importantly, provides a framework to model the long-range interactions directly. 
 
Our model is applied to predict the parallel β-helix fold, an important fold in bacterial infection of plants and binding of antigens. The cross-family validation shows that SCRFs not only can score all known β-helices higher than non β-helices in Protein Data Bank, but also demonstrate more success in locating each rung in the known β-helix proteins than BetaWrap, a state-of-the-art algorithm for predicting β-helix fold, and HMMER, a general motif detection algorithm based on HMM. Applying our prediction model to Uniprot database, we hypothesize previously unknown β-helices.
12839905
Context sensitive vocabulary and its application in protein secondary structure prediction
Protein secondary structure prediction is an important step towards understanding the relation between protein sequence and structure. However, most current prediction methods use features difficult for biologists to interpret. In this paper, we present a new method that applies information retrieval techniques to solve the problem:we extract a context sensitive biological vocabulary for protein sequences and apply text classification methods to predict protein secondary structure. Experimental results show that our method performs comparably to the state-of-art methods. Furthermore, the context sensitive vocabularies can serve as a useful tool to discover meaningful regular expression patterns for protein structures.
16964204
Workload characterization and cost-quality tradeoffs in MPEG-4 decoding on resource-constrained devices
There has been a lot of interest in the embedded systems community on architectures and design methods that are targeted towards multimedia applications. This trend is pri marily motivated by the proliferation of resource and power-constrained portable devices (such as mobile phones and PDAs), a major portion of whose workload is made up of multimedia applications. In this paper, we investigate the tradeoffs between video quality and the processor cycle requirements in such resource-constrained devices, in the particular context of MPEG-4 decoding using an open source codec called XviD. The XviD codec implements a number of powerful coding tools from MPEG-4, which are organized as profiles and levels. Given the specification of an architecture on which an XviD decoder is implemented, the work presented here would guide a multimedia applications developer in selecting appropriate profiles and levels for the corresponding encoder application. While the selection of such profiles has so far been primarily influenced by the network bandwidth in the case of video streaming, our work stresses the importance of additionally taking into account the architecture of the device running the decoder application. Although the relevance of this observation is increasingly being realized, sufficient work has not yet been done to provide guidelines on how to systematically make such selections. This work attempts to address this shortcoming.
16602968
Performance prediction of J2EE applications using messaging protocols
Predicting the performance of component-based applications is difficult due to the complexity of the underlying component technology. This problem is exacerbated when a messaging protocol is introduced to create a loosely coupled software architecture. Messaging uses asynchronous communication, and must address quality of service issues such as message persistence and flow control. In this paper, we present an approach to predicting the performance of Java 2 Enterprise Edition (J2EE) applications using messaging services. The prediction is done during application design, without access to the application implementation. This is achieved by modeling the interactions among J2EE and messaging components using queuing network models, calibrating the performance model with architecture attributes associated with these components, and populating the model parameters using a lightweight, application-independent benchmark. Benchmarking avoids the need for prototype testing in order to obtain the value of model parameters, and thus reduces the performance prediction effort. A case study is carried out to predict the performance of a J2EE application with asynchronous communication. Analysis of the resulting predictions shows the error is within 15%.
44693659
Global exponential stability of generalized recurrent neural networks with discrete and distributed delays
This paper is concerned with analysis problem for the global exponential stability of a class of recurrent neural networks (RNNs) with mixed discrete and distributed delays. We first prove the existence and uniqueness of the equilibrium point under mild conditions, assuming neither differentiability nor strict monotonicity for the activation function. Then, by employing a new Lyapunov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the RNNs to be globally exponentially stable. Therefore, the global exponential stability of the delayed RNNs can be easily checked by utilizing the numerically efficient Matlab LMI toolbox, and no tuning of parameters is required. A simulation example is exploited to show the usefulness of the derived LMI-based stability conditions.
6898492
Using Conditional Random Fields for Sentence Boundary Detection in Speech
Sentence boundary detection in speech is important for enriching speech recognition output, making it easier for humans to read and downstream modules to process. In previous work, we have developed hidden Markov model (HMM) and maximum entropy (Maxent) classifiers that integrate textual and prosodic knowledge sources for detecting sentence boundaries. In this paper, we evaluate the use of a conditional random field (CRF) for this task and relate results with this model to our prior work. We evaluate across two corpora (conversational telephone speech and broadcast news speech) on both human transcriptions and speech recognition output. In general, our CRF model yields a lower error rate than the HMM and Maxent models on the NIST sentence boundary detection task in speech, although it is interesting to note that the best results are achieved by three-way voting among the classifiers. This probably occurs because each model has different strengths and weaknesses for modeling the knowledge sources.
206090245
Ensemble learning via negative correlation
This paper presents a learning approach, i.e. negative correlation learning, for neural network ensembles. Unlike previous learning approaches for neural network ensembles, negative correlation learning attempts to train individual networks in an ensemble and combines them in the same learning process. In negative correlation learning, all the individual networks in the ensemble are trained simultaneously and interactively through the correlation penalty terms in their error functions. Rather than producing unbiased individual networks whose errors are uncorrelated, negative correlation learning can create negatively correlated networks to encourage specialisation and cooperation among the individual networks. Empirical studies have been carried out to show why and how negative correlation learning works. The experimental results show that negative correlation learning can produce neural network ensembles with good generalisation ability.