The assessment of тт-тт selective stationary phases for two-dimensional HPLC analysis of foods : application to the analysis of coffee

Differences between alkyl, dipole–dipole, hydrogen bonding, and π-π selective surfaces represented by non-resonance and resonance π-stationary phases have been assessed for the separation of ‘Ristretto’ café espresso by employing 2DHPLC techniques with C18 phase selectivity detection. Geometric approach to factor analysis (GAFA) was used to measure the detected peaks (N), spreading angle (β), correlation, practical peak capacity (n_p) and percentage usage of the separations space, as an assessment of selectivity differences between regional quadrants of the two-dimensional separation plane. Although all tested systems were correlated to some degree to the C18 dimension, regional measurement of separation divergence revealed that performance of specific systems was better for certain sample components. The results illustrate that because of the complexity of the ‘real’ sample obtaining a truly orthogonal two-dimensional system for complex samples of natural origin may be practically impossible.

Dynamic biometrics fusion at feature level for video-based human recognition

This paper proposes a novel human recognition method in video, which combines human face and gait traits
using a dynamic multi-modal biometrics fusion scheme. The Fisherface approach is adopted to extract face
features, while for gait features, Locality Preserving Projection (LPP) is used to achieve low-dimensional
manifold embedding of the temporal silhouette data derived from image sequences. Face and gait features are
fused dynamically at feature level based on a distance-driven fusion method. Encouraging experimental results
are achieved on the video sequences containing 20 people, which show that dynamically fused features produce
a more discriminating power than any individual biometric as well as integrated features built on common static
fusion schemes.

A VMD plugin for NAMD simulations on Amazon EC2

VMD and NAMD are two major molecular dynamics simulation software packages, which can work together for mining structural information of bio-molecules. Carrying out such molecular dynamics simulations can help researchers to understand the roles and functions of various bio-molecules in life science research. Recently, clouds have provided HPC clusters on demand that allow users to benefit from their flexibility, elasticity, and lower costs. Although cloud computing promises to provide seamless access to HPC clusters through the abstraction of services, which hide the details of the underlying software and hardware infrastructure, users without in depth computing knowledge are still forced to cope with many low level system and programming details. Therefore, we have designed and developed a software plugin of VMD, which can provide an integrated framework for NAMD to be executed on Amazon EC2. The proposed Amazon EC2 Plugin for VMD frees users from performing many tedious computing tasks such as launching, connecting and terminating Amazon EC2 compute instances; configuring a HPC cluster; and installing middleware and software applications before the system is readily available for any scientific investigation. This allows VMD/NAMD users to spend less time getting applications to work on HPC clusters but more time for bio-research.

Document clustering in correlation similarity measure space

This paper presents a new spectral clustering method called correlation preserving indexing (CPI), which is performed in the correlation similarity measure space. In this framework, the documents are projected into a low-dimensional semantic space in which the correlations between the documents in the local patches are maximized while the correlations between the documents outside these patches are minimized simultaneously. Since the intrinsic geometrical structure of the document space is often embedded in the similarities between the documents, correlation as a similarity measure is more suitable for detecting the intrinsic geometrical structure of the document space than euclidean distance. Consequently, the proposed CPI method can effectively discover the intrinsic structures embedded in high-dimensional document space. The effectiveness of the new method is demonstrated by extensive experiments conducted on various data sets and by comparison with existing document clustering methods.

Fast growing self organizing map for text clustering

This paper presents an integration of a novel document vector representation technique and a novel Growing Self Organizing Process. In this new approach, documents are represented as a low dimensional vector, which is composed of the indices and weights derived from the keywords of the document.

An index based similarity calculation method is employed on this low dimensional feature space and the growing self organizing process is modified to comply with the new feature representation model.

The initial experiments show that this novel integration outperforms the state-of-the-art Self Organizing Map based techniques of text clustering in terms of its efficiency while preserving the same accuracy level.

Face recognition with image sets using manifold density divergence

In many automatic face recognition applications, a set of a person's face images is available rather than a single image. In this paper, we describe a novel method for face recognition using image sets. We propose a flexible, semi-parametric model for learning probability densities confined to highly non-linear but intrinsically low-dimensional manifolds. The model leads to a statistical formulation of the recognition problem in terms of minimizing the divergence between densities estimated on these manifolds. The proposed method is evaluated on a large data set, acquired in realistic imaging conditions with severe illumination variation. Our algorithm is shown to match the best and outperform other state-of-the-art algorithms in the literature, achieving 94% recognition rate on average.

An information-theoretic approach to face recognition from face motion manifolds

In this work, we consider face recognition from face motion manifolds (FMMs). The use of the resistor-average distance (RAD) as a dissimilarity measure between densities confined to FMMs is motivated in the proposed information-theoretic approach to modelling face appearance. We introduce a kernel-based algorithm that makes use of the simplicity of the closed-form expression for RAD between two Gaussian densities, while allowing for modelling of complex and nonlinear, but intrinsically low-dimensional manifolds. Additionally, it is shown how geodesically local FMM structure can be modelled, naturally leading to a stochastic algorithm for generalizing to unseen modes of data variation. Recognition performance of our method is demonstrated experimentally and is shown to exceed that of state-of-the-art algorithms. Recognition rate of 98% was achieved on a database of 100 people under varying illumination

Modelling multilevel data in multimedia : A hierarchical factor analysis approach

Multimedia content understanding research requires rigorous approach to deal with the complexity of the data. At the crux of this problem is the method to deal with multilevel data whose structure exists at multiple scales and across data sources. A common example is modeling tags jointly with images to improve retrieval, classification and tag recommendation. Associated contextual observation, such as metadata, is rich that can be exploited for content analysis. A major challenge is the need for a principal approach to systematically incorporate associated media with the primary data source of interest. Taking a factor modeling approach, we propose a framework that can discover low-dimensional structures for a primary data source together with other associated information. We cast this task as a subspace learning problem under the framework of Bayesian nonparametrics and thus the subspace dimensionality and the number of clusters are automatically learnt from data instead of setting these parameters a priori. Using Beta processes as the building block, we construct random measures in a hierarchical structure to generate multiple data sources and capture their shared statistical at the same time. The model parameters are inferred efficiently using a novel combination of Gibbs and slice sampling. We demonstrate the applicability of the proposed model in three applications: image retrieval, automatic tag recommendation and image classification. Experiments using two real-world datasets show that our approach outperforms various state-of-the-art related methods.

Bayesian nonparametric multilevel clustering with group-level contexts

We present a Bayesian nonparametric framework for multilevel clustering which utilizes group- level context information to simultaneously discover low-dimensional structures of the group contents and partitions groups into clusters. Using the Dirichlet process as the building block, our model constructs a product base-measure with a nested structure to accommodate content and context observations at multiple levels. The proposed model possesses properties that link the nested Dinchiet processes (nDP) and the Dirichlet process mixture models (DPM) in an interesting way: integrating out all contents results in the DPM over contexts, whereas integrating out group-specific contexts results in the nDP mixture over content variables. We provide a Polyaurn view of the model and an efficient collapsed Gibbs inference procedure. Extensive experiments on real-world datasets demonstrate the advantage of utilizing context information via our model in both text and image domains.

Mobile phone-based electrochemiluminescence sensing exploiting the 'USB On-The-Go' protocol

A low-cost system to generate, control and detect electrochemiluminescence using a mobile smartphone is described. A simple tone-detection integrated circuit is used to switch power sourced from the phone's Universal Serial Bus (USB) 'On-The-Go' (OTG) port, using audible tone pulses played over the device's audio jack. We have successfully applied this approach to smartphones from different manufacturers and with different operating system versions. ECL calibrations of a common luminophore, tris(2,2′-bipyridine)ruthenium(II) ([Ru(bpy)3]²⁺), with 2-(dibutylamino)ethanol (DBAE) as a co-reactant, showed no significant difference in light intensities when an electrochemical cell was controlled by a mobile phone in this manner, compared to the same calibration generated using a conventional potentiostat. Combining this novel approach to control the applied potential with the measurement of the emitted light through the smart phone camera (using an in-house built Android app), we explored the ECL properties of a water-soluble iridium(III) complex that emits in the blue region of the spectrum. The iridium(III) complex exhibited superior co-reactant ECL intensities and limits of detection to that of the conventional [Ru(bpy)3]²⁺ luminophore.

Visual object clustering via mixed-norm regularization

Many vision problems deal with high-dimensional data, such as motion segmentation and face clustering. However, these high-dimensional data usually lie in a low-dimensional structure. Sparse representation is a powerful principle for solving a number of clustering problems with high-dimensional data. This principle is motivated from an ideal modeling of data points according to linear algebra theory. However, real data in computer vision are unlikely to follow the ideal model perfectly. In this paper, we exploit the mixed norm regularization for sparse subspace clustering. This regularization term is a convex combination of the l1norm, which promotes sparsity at the individual level and the block norm l2/1 which promotes group sparsity. Combining these powerful regularization terms will provide a more accurate modeling, subsequently leading to a better solution for the affinity matrix used in sparse subspace clustering. This could help us achieve better performance on motion segmentation and face clustering problems. This formulation also caters for different types of data corruptions. We derive a provably convergent algorithm based on the alternating direction method of multipliers (ADMM) framework, which is computationally efficient, to solve the formulation. We demonstrate that this formulation outperforms other state-of-arts on both motion segmentation and face clustering.

Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)

Electronic medical record (EMR) offers promises for novel analytics. However, manual feature engineering from EMR is labor intensive because EMR is complex - it contains temporal, mixed-type and multimodal data packed in irregular episodes. We present a computational framework to harness EMR with minimal human supervision via restricted Boltzmann machine (RBM). The framework derives a new representation of medical objects by embedding them in a low-dimensional vector space. This new representation facilitates algebraic and statistical manipulations such as projection onto 2D plane (thereby offering intuitive visualization), object grouping (hence enabling automated phenotyping), and risk stratification. To enhance model interpretability, we introduced two constraints into model parameters: (a) nonnegative coefficients, and (b) structural smoothness. These result in a novel model called eNRBM (EMR-driven nonnegative RBM). We demonstrate the capability of the eNRBM on a cohort of 7578 mental health patients under suicide risk assessment. The derived representation not only shows clinically meaningful feature grouping but also facilitates short-term risk stratification. The F-scores, 0.21 for moderate-risk and 0.36 for high-risk, are significantly higher than those obtained by clinicians and competitive with the results obtained by support vector machines.

What shall i share and with whom? A multi-task learning formulation using multi-faceted task relationships

Multi-task learning is a learning paradigm that improves the performance of "related" tasks through their joint learning. To do this each task answers the question "Which other task should I share with"? This task relatedness can be complex - a task may be related to one set of tasks based on one subset of features and to other tasks based on other subsets. Existing multi-task learning methods do not explicitly model this reality, learning a single-faceted task relationship over all the features. This degrades performance by forcing a task to become similar to other tasks even on their unrelated features. Addressing this gap, we propose a novel multi-task learning model that leams multi-faceted task relationship, allowing tasks to collaborate differentially on different feature subsets. This is achieved by simultaneously learning a low dimensional sub-space for task parameters and inducing task groups over each latent subspace basis using a novel combination of L1 and pairwise L∞ norms. Further, our model can induce grouping across both positively and negatively related tasks, which helps towards exploiting knowledge from all types of related tasks. We validate our model on two synthetic and five real datasets, and show significant performance improvements over several state-of-the-art multi-task learning techniques. Thus our model effectively answers for each task: What shall I share and with whom?

A low-cost 3-D laser imaging system

This paper presents the design and development of a low cost three-dimensional laser imaging system for scanning suitable surfaces. A generic, low cost, off-the-shelf laser range finder is used to obtain the primary one dimensional distance measurement. The range finder’s laser beam is reflected by a twin-axis mirror assembly driven by stepper motors providing the system with two angular degrees of freedom, allowing 3-D measurements to be determined. A camera and image processing techniques are used to determine the measured 1-D range value from the generic range-finding device. A computer program then uses the obtained data to create a 3-D point cloud. An algorithm is then used to construct a 3-D wire frame mesh representing the scanned surface. The system has an angular resolution of 1.8° and the results obtained demonstrate the system to have an accuracy of approximately ± 2cm at a scanning distance of 1.0m.

Development of low cost motion-sensing system

Micro-electro-mechanical system (MEMS) technology offers sensors with lower cost, smaller size, lower power consumption. In this paper, a kind of low cost motion-sensing system based MEMS sensors is developed. The objective of the design is low cost, small volume and light weight in order to be used in many fields. The constituting principle of the system is described. Algorithms and hardware of the system are researched. And the definition of coordinate, calculation of pose angle, transform of acceleration and calculation of the velocities and displacement of the moving object are presented with corresponding mathematics model and algorithms. The experiments are carried out in principle and results are given. It is proved that the low cost motion-sensing system is effective and correct.