Frequency decomposition techniques for increased discriminative 3D facial information capture

Eigen-based techniques and other monolithic approaches to face recognition have long been a cornerstone in the face recognition community due to the high dimensionality of face images. Eigen-face techniques provide minimal reconstruction error and limit high-frequency content while linear discriminant-based techniques (fisher-faces) allow the construction of subspaces which preserve discriminatory information. This paper presents a frequency decomposition approach for improved face recognition performance utilising three well-known techniques: Wavelets; Gabor / Log-Gabor; and the Discrete Cosine Transform. Experimentation illustrates that frequency domain partitioning prior to dimensionality reduction increases the information available for classification and greatly increases face recognition performance for both eigen-face and fisher-face approaches.

2-(4-Aminophenyl)-1-phenyldiazenium 2,4,6-trinitrophenolate

In the structure of the title salt, C12H12N3+ C6H2N3O7-, the diazenyl group of the 4-(phenyldiazenyl)aniline molecule is protonated and forms a hydrogen bond with the phenolate O acceptor of the picrate anion. Structure extension occurs through two symmetrical inter-ion three-centre amine N---H...O,O'(nitro) hydrogen-bonding associations [graph set R2/1(4)] giving a convoluted two-dimensional network structure.

Practical application of pseudospectral optimization to robot path planning

To obtain minimum time or minimum energy trajectories for robots it is necessary to employ planning methods which adequately consider the platform’s dynamic properties. A variety of sampling, graph-based or local receding-horizon optimisation methods have previously been proposed. These typically use simplified kino-dynamic models to avoid the significant computational burden of solving this problem in a high dimensional state-space. In this paper we investigate solutions from the class of pseudospectral optimisation methods which have grown in favour amongst the optimal control community in recent years. These methods have high computational efficiency and rapid convergence properties. We present a practical application of such an approach to the robot path planning problem to provide a trajectory considering the robot’s dynamic properties. We extend the existing literature by augmenting the path constraints with sensed obstacles rather than predefined analytical functions to enable real world application.

Overview of the INEX 2010 XML mining track : clustering and classification of XML documents

The XML Document Mining track was launched for exploring two main ideas: (1) identifying key problems and new challenges of the emerging field of mining semi-structured documents, and (2) studying and assessing the potential of Machine Learning (ML) techniques for dealing with generic ML tasks in the structured domain, i.e., classification and clustering of semi-structured documents. This track has run for six editions during INEX 2005, 2006, 2007, 2008, 2009 and 2010. The first five editions have been summarized in previous editions and we focus here on the 2010 edition. INEX 2010 included two tasks in the XML Mining track: (1) unsupervised clustering task and (2) semi-supervised classification task where documents are organized in a graph. The clustering task requires the participants to group the documents into clusters without any knowledge of category labels using an unsupervised learning algorithm. On the other hand, the classification task requires the participants to label the documents in the dataset into known categories using a supervised learning algorithm and a training set. This report gives the details of clustering and classification tasks.

Proton-transfer compounds of isnipecotamide with the aromatic dicarboxylic acids 4-nitrophthalic, 4,5-dichlorophthalic, 5-nitroisophthalic and terephthalic acid

The structures of the 1:1 proton-transfer compounds of isonipecotamide (4-piperidinecarboxamide) with 4-nitrophthalic acid, 4-carbamoylpiperidinium 2-carboxy-4-nitrobenzoate, C6H13N2O8+ C8H4O6- (I), 4,5-dichlorophthalic acid, 4-carbamoylpiperidinium 2-carboxy-4,5-dichlorobenzoate, C6H13N2O8+ C8H3Cl2O4- (II) and 5-nitroisophthalic acid, 4-carbamoylpiperidinium 3-carboxy-5-nitrobenzoate, C6H13N2O8+ C8H4O6- (III) as well as the 2:1 compound with terephthalic acid, bis(4-carbamoylpiperidinium)benzene-1,2-dicarboxylate dihydrate, 2(C6H13N2O8+) C8H4O42- . 2H2O (IV)have been determined at 200 K. All salts form hydrogen-bonded structures, one-dimensional in (II) and three-dimensional in (I), (III) and (IV). In (I) and (III) the centrosymmetric R2/2(8) cyclic amide-amide association is found while in (IV) several different types of water-bridged cyclic associations are present [graph sets R2/4(8), R3/4(10), R4/4(12), R3/3(18) and R4/6(22)]. The one-dimensional structure of (I), features the common 'planar' hydrogen 4,5-dichlorophthalate anion together with enlarged cyclic R3/3(13) and R3/4(17) associations. With the structures of (I) and (III) the presence of head-to-tail hydrogen phthalate chain substructures is found. In (IV) head-to-tail primary cation-anion associations are extended longitudinally into chains through the water-bridged cation associations and laterally by piperidinium N-H...O(carboxyl) and water O-H...O(carboxyl) hydrogen bonds. The structures reported here further demonstrate the utility of the isonipecotamide cation as a synthon for the generation of stable hydrogen-bonded structures. An additional example of cation--anion association with this cation is also shown in the asymmetric three-centre piperidinium N-H...O,O'(carboxyl) interaction in the first-reported structure of a 2:1 isonipecotamide-carboxylate salt.

rac-2-Aminopyridinium cis-2-carboxycyclohexane-1-carboxylate

In the structure of the title compound, C5H7N2+ C8H11O4-, the cis-anions associate through head-to-tail carboxylic acid carboxyl O-H...O hydrogen-bonds [graph set C(7)], forming chains which extend along c and are inter-linked through the carboxyl groups forming cyclic R2/2(8) associations with the pyridinium and an amine H donor of the cation. Further amine...carboxyl N-H...O interactions form enlarged centrosymmetric rings [graph set R4/4(18)] and extensions down b to give a three-dimensional structure.

2,3-Dimethoxy-10-oxostrychnidinium 2-(2,4,6-trinitroanilino)benzoate monohydrate : a 1:1 proton-transfer salt of brucine with o-picraminobenzoic acid

In the structure of the 1:1 proton-transfer compound of brucine with 2-(2,4,6-trinitroanilino)benzoic acid C23H27N2O4+ . C13H7N4O8- . H~2~O, the brucinium cations form the classic undulating ribbon substructures through overlapping head-to-tail interactions while the anions and the three related partial water molecules of solvation (having occupancies of 0.73, 0.17 and 0.10) occupy the interstitial regions of the structure. The cations are linked to the anions directly through N-H...O(carboxyl) hydrogen bonds and indirectly by the three water molecules which form similar conjoint cyclic bridging units [graph set R2/4(8)] through O-H...O(carbonyl) and O(carboxyl) hydrogen bonds, giving a two-dimensional layered structure. Within the anion, intramolecular N-H...O(carboxyl) and N H...O(nitro) hydrogen bonds result in the benzoate and picrate rings being rotated slightly out of coplanarity inter-ring dihedral angle 32.50(14)\%]. This work provides another example of the molecular selectivity of brucine in forming stable crystal structures and also represents the first reported structure of any form of the guest compound 2-(2,4,6-trinitroanilino)benzoic acid.

rac-cis-Cyclohexaane-1,2-dicarboxylic acid-isoquinoline (1/1)

In the structure of the title molecular adduct C8H12O4 . C9H7N, the two species are interlinked through a carboxylic acid-isoquinoline O-H...N hydrogen bond, these molecular pairs then inter-associate through the second acid group of the cis-cyclohexane-1,2-dicarboxylic acids, forming a classic centrosymmetric cyclic head-to-head carboxylic acid--carboxyl O---H...O hydrogen-bonding association [graph set R^2^~2~(8)], giving a zero-dimensional structure.

Shifting : one-inclusion mistake bounds and sample compression

We present new expected risk bounds for binary and multiclass prediction, and resolve several recent conjectures on sample compressibility due to Kuzmin and Warmuth. By exploiting the combinatorial structure of concept class F, Haussler et al. achieved a VC(F)/n bound for the natural one-inclusion prediction strategy. The key step in their proof is a d = VC(F) bound on the graph density of a subgraph of the hypercube—oneinclusion graph. The first main result of this paper is a density bound of n [n−1 <=d-1]/[n <=d] < d, which positively resolves a conjecture of Kuzmin and Warmuth relating to their unlabeled Peeling compression scheme and also leads to an improved one-inclusion mistake bound. The proof uses a new form of VC-invariant shifting and a group-theoretic symmetrization. Our second main result is an algebraic topological property of maximum classes of VC-dimension d as being d contractible simplicial complexes, extending the well-known characterization that d = 1 maximum classes are trees. We negatively resolve a minimum degree conjecture of Kuzmin and Warmuth—the second part to a conjectured proof of correctness for Peeling—that every class has one-inclusion minimum degree at most its VCdimension. Our final main result is a k-class analogue of the d/n mistake bound, replacing the VC-dimension by the Pollard pseudo-dimension and the one-inclusion strategy by its natural hypergraph generalization. This result improves on known PAC-based expected risk bounds by a factor of O(logn) and is shown to be optimal up to an O(logk) factor. The combinatorial technique of shifting takes a central role in understanding the one-inclusion (hyper)graph and is a running theme throughout.

Multiview point cloud kernels for semisupervised learning [Lecture Notes]

In semisupervised learning (SSL), a predictive model is learn from a collection of labeled data and a typically much larger collection of unlabeled data. These paper presented a framework called multi-view point cloud regularization (MVPCR), which unifies and generalizes several semisupervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbert spaces (RKHSs). Special cases of MVPCR include coregularized least squares (CoRLS), manifold regularization (MR), and graph-based SSL. An accompanying theorem shows how to reduce any MVPCR problem to standard supervised learning with a new multi-view kernel.

Shifting : one-inclusion mistake bounds and sample compression

We present new expected risk bounds for binary and multiclass prediction, and resolve several recent conjectures on sample compressibility due to Kuzmin and Warmuth. By exploiting the combinatorial structure of concept class F, Haussler et al. achieved a VC(F)/n bound for the natural one-inclusion prediction strategy. The key step in their proof is a d=VC(F) bound on the graph density of a subgraph of the hypercube—one-inclusion graph. The first main result of this report is a density bound of n∙choose(n-1,≤d-1)/choose(n,≤d) < d, which positively resolves a conjecture of Kuzmin and Warmuth relating to their unlabeled Peeling compression scheme and also leads to an improved one-inclusion mistake bound. The proof uses a new form of VC-invariant shifting and a group-theoretic symmetrization. Our second main result is an algebraic topological property of maximum classes of VC-dimension d as being d-contractible simplicial complexes, extending the well-known characterization that d=1 maximum classes are trees. We negatively resolve a minimum degree conjecture of Kuzmin and Warmuth—the second part to a conjectured proof of correctness for Peeling—that every class has one-inclusion minimum degree at most its VC-dimension. Our final main result is a k-class analogue of the d/n mistake bound, replacing the VC-dimension by the Pollard pseudo-dimension and the one-inclusion strategy by its natural hypergraph generalization. This result improves on known PAC-based expected risk bounds by a factor of O(log n) and is shown to be optimal up to a O(log k) factor. The combinatorial technique of shifting takes a central role in understanding the one-inclusion (hyper)graph and is a running theme throughout

Identifying optimal lipid raft characteristics required to promote nanoscale protein-protein interactions on the plasma membrane

The dynamic lateral segregation of signaling proteins into microdomains is proposed to facilitate signal transduction, but the constraints on microdomain size, mobility, and diffusion that might realize this function are undefined. Here we interrogate a stochastic spatial model of the plasma membrane to determine how microdomains affect protein dynamics. Taking lipid rafts as representative microdomains, we show that reduced protein mobility in rafts segregates dynamically partitioning proteins, but the equilibrium concentration is largely independent of raft size and mobility. Rafts weakly impede small-scale protein diffusion but more strongly impede long-range protein mobility. The long-range mobility of raft-partitioning and raft-excluded proteins, however, is reduced to a similar extent. Dynamic partitioning into rafts increases specific interprotein collision rates, but to maximize this critical, biologically relevant function, rafts must be small (diameter, 6 to 14 nm) and mobile. Intermolecular collisions can also be favored by the selective capture and exclusion of proteins by rafts, although this mechanism is generally less efficient than simple dynamic partitioning. Generalizing these results, we conclude that microdomains can readily operate as protein concentrators or isolators but there appear to be significant constraints on size and mobility if microdomains are also required to function as reaction chambers that facilitate nanoscale protein-protein interactions. These results may have significant implications for the many signaling cascades that are scaffolded or assembled in plasma membrane microdomains.

4-Aminopyridinium cis-2-carboxycyclohexane-1-carboxylate

In the structure of the title compound, C5H7N2+ C8H11O4-, the cis-monoanions associate through short carboxylic acid-carboxyl O-H...O hydrogen bonds [graph set C(7)], forming zigzag chains which extend along c and are inter-linked through pyridinium and amine N-H...O(carboxyl) hydrogen bonds giving a three-dimensional network structure.

Hydrogen-bonding in the structures of the hydrated proton-transfer compounds of isonipecotamide with the isomeric indole-2 and indole-3-carboxylic acids

The structures of two hydrated proton-transfer compounds of 4-piperidinecarboxamide (isonipecotamide) with the isomeric heteroaromatic carboxylic acids indole-2-carboxylic acid and indole-3-carboxylic acid, namely 4-carbamoylpiperidinium indole-2-carboxylate dihydrate (1) and 4-carbamoylpiperidinium indole-3-carboxylate hemihydrate (2) have been determined at 200 K. Crystals of both 1 and 2 are monoclinic, space groups P21/c and P2/c respectively with Z = 4 in cells having dimensions a = 10.6811(4), b = 12.2017(4), c = 12.5456(5) Å, β = 96.000(4)o (1) and a = 15.5140(4), b = 10.2908(3), c = 9.7047(3) Å, β = 97.060(3)o (2). Hydrogen-bonding in 1 involves a primary cyclic interaction involving complementary cation amide N-H…O(carboxyl) anion and anion hetero N-H…O(amide) cation hydrogen bonds [graph set R22(9)]. Secondary associations involving also the water molecules of solvation give a two-dimensional network structure which includes weak water O-H…π interactions. In the three-dimensional hydrogen-bonded structure of 2, there are classic centrosymmetric cyclic head-to-head hydrogen-bonded amide-amide interactions [graph set R22(8)] as well as lateral cyclic amide-O linked amide-amide extensions [graph set R24(8)]. The anions and the water molecule, which lies on a twofold rotation axis, are involved in secondary extensions.

Modern construction economics [Book Review]

The editor, Gerard de Valence, points out in the preface, this book is neither a textbook nor a guide to what is done by construction managers and construction economists – read quantity surveyors and the like. Rather, de Valence notes it comprises a collection of chapters each of which focus on matters at the industry level and, in doing so, illustrates that a substantially improved understanding of the building and construction industry can be gained beyond the economics of delivering projects. Before giving some thought to how far each of the chapters achieve this, it’s worth reflecting on the virtues of developing construction economics as its own discipline or sub-discipline in general economics and the bold manner by which de Valence is proposing we do this. That is, de Valence proposes partitioning industry and project economics - as explained in the preface and in Chapter 1. de Valence’s view that “the time seems right” for these developments is also worthy of some consideration.