241 resultados para graph entropy
Resumo:
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.
Resumo:
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.
Resumo:
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.
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We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate is governed by the quality of the error estimate. We consider several penalty functions, involving error estimates on independent test data, empirical VC dimension, empirical VC entropy, and margin-based quantities. We also consider the maximal difference between the error on the first half of the training data and the second half, and the expected maximal discrepancy, a closely related capacity estimate that can be calculated by Monte Carlo integration. Maximal discrepancy penalty functions are appealing for pattern classification problems, since their computation is equivalent to empirical risk minimization over the training data with some labels flipped.
Resumo:
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.
Resumo:
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.
Resumo:
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
Resumo:
Epilepsy is characterized by the spontaneous and seemingly unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into this phenomenon. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, we made a comparative study of the performance of Gaussian mixture model (GMM) and Support Vector Machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Results show that the selected HOS based features achieve 93.11% classification accuracy compared to 88.78% with features derived from the power spectrum for a GMM classifier. The SVM classifier achieves an improvement from 86.89% with features based on the power spectrum to 92.56% with features based on the bispectrum.
Resumo:
Phospholipid (PL) molecules form the main structure of the membrane that prevents the direct contact of opposing articular cartilage layers. In this paper we conceptualise articular cartilage as a giant reverse micelle (GRM) in which the highly hydrated three-dimensional network of phospholipids is electrically charged and able to resist compressive forces during joint movement, and hence loading. Using this hypothetical base, we describe a hydrophilic-hydrophilic (HL-HL) biopair model of joint lubrication by contacting cartilages, whose mechanism is reliant on lamellar cushioning. To demonstrate the viability of our concept, the electrokinetic properties of the membranous layer on the articular surface were determined by measuring via microelectrophoresis, the adsorption of ions H, OH, Na and Cl on phospholipid membrane of liposomes, leading to the calculation of the effective surface charge density. The surface charge density was found to be -0.08 ± 0.002 cm-2 (mean ± S.D.) for phospholipid membranes, in 0.155 M NaCl solution and physiological pH. This value was approximately five times less than that measured in 0.01 M NaCl. The addition of synovial fluid (SF) to the 0.155 M NaCl solution reduced the surface charge density by 30% which was attributed to the binding of synovial fluid macromolecules to the phospholipid membrane. Our experiments show that particles charge and interact strongly with the polar core of RM. We demonstrate that particles can have strong electrostatic interactions when ions and macromolecules are solubilized by reverse micelle (RM). Since ions are solubilized by reverse micelle, the surface entropy influences the change in the charge density of the phospholipid membrane on cartilage surfaces. Reverse micelles stabilize ions maintaining equilibrium, their surface charges contribute to the stability of particles, while providing additional screening for electrostatic processes. © 2008 Elsevier Ireland Ltd. All rights reserved.
Resumo:
Usability in HCI (Human-Computer Interaction) is normally understood as the simplicity and clarity with which the interaction with a computer program or a web site is designed. Identity management systems need to provide adequate usability and should have a simple and intuitive interface. The system should not only be designed to satisfy service provider requirements but it has to consider user requirements, otherwise it will lead to inconvenience and poor usability for users when managing their identities. With poor usability and a poor user interface with regard to security, it is highly likely that the system will have poor security. The rapid growth in the number of online services leads to an increasing number of different digital identities each user needs to manage. As a result, many people feel overloaded with credentials, which in turn negatively impacts their ability to manage them securely. Passwords are perhaps the most common type of credential used today. To avoid the tedious task of remembering difficult passwords, users often behave less securely by using low entropy and weak passwords. Weak passwords and bad password habits represent security threats to online services. Some solutions have been developed to eliminate the need for users to create and manage passwords. A typical solution is based on generating one-time passwords, i.e. passwords for single session or transaction usage. Unfortunately, most of these solutions do not satisfy scalability and/or usability requirements, or they are simply insecure. In this thesis, the security and usability aspects of contemporary methods for authentication based on one-time passwords (OTP) are examined and analyzed. In addition, more scalable solutions that provide a good user experience while at the same time preserving strong security are proposed.
Resumo:
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.
Resumo:
Web service technology is increasingly being used to build various e-Applications, in domains such as e-Business and e-Science. Characteristic benefits of web service technology are its inter-operability, decoupling and just-in-time integration. Using web service technology, an e-Application can be implemented by web service composition — by composing existing individual web services in accordance with the business process of the application. This means the application is provided to customers in the form of a value-added composite web service. An important and challenging issue of web service composition, is how to meet Quality-of-Service (QoS) requirements. This includes customer focused elements such as response time, price, throughput and reliability as well as how to best provide QoS results for the composites. This in turn best fulfils customers’ expectations and achieves their satisfaction. Fulfilling these QoS requirements or addressing the QoS-aware web service composition problem is the focus of this project. From a computational point of view, QoS-aware web service composition can be transformed into diverse optimisation problems. These problems are characterised as complex, large-scale, highly constrained and multi-objective problems. We therefore use genetic algorithms (GAs) to address QoS-based service composition problems. More precisely, this study addresses three important subproblems of QoS-aware web service composition; QoS-based web service selection for a composite web service accommodating constraints on inter-service dependence and conflict, QoS-based resource allocation and scheduling for multiple composite services on hybrid clouds, and performance-driven composite service partitioning for decentralised execution. Based on operations research theory, we model the three problems as a constrained optimisation problem, a resource allocation and scheduling problem, and a graph partitioning problem, respectively. Then, we present novel GAs to address these problems. We also conduct experiments to evaluate the performance of the new GAs. Finally, verification experiments are performed to show the correctness of the GAs. The major outcomes from the first problem are three novel GAs: a penaltybased GA, a min-conflict hill-climbing repairing GA, and a hybrid GA. These GAs adopt different constraint handling strategies to handle constraints on interservice dependence and conflict. This is an important factor that has been largely ignored by existing algorithms that might lead to the generation of infeasible composite services. Experimental results demonstrate the effectiveness of our GAs for handling the QoS-based web service selection problem with constraints on inter-service dependence and conflict, as well as their better scalability than the existing integer programming-based method for large scale web service selection problems. The major outcomes from the second problem has resulted in two GAs; a random-key GA and a cooperative coevolutionary GA (CCGA). Experiments demonstrate the good scalability of the two algorithms. In particular, the CCGA scales well as the number of composite services involved in a problem increases, while no other algorithms demonstrate this ability. The findings from the third problem result in a novel GA for composite service partitioning for decentralised execution. Compared with existing heuristic algorithms, the new GA is more suitable for a large-scale composite web service program partitioning problems. In addition, the GA outperforms existing heuristic algorithms, generating a better deployment topology for a composite web service for decentralised execution. These effective and scalable GAs can be integrated into QoS-based management tools to facilitate the delivery of feasible, reliable and high quality composite web services.
Resumo:
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.
Resumo:
This paper proposes an innovative instance similarity based evaluation metric that reduces the search map for clustering to be performed. An aggregate global score is calculated for each instance using the novel idea of Fibonacci series. The use of Fibonacci numbers is able to separate the instances effectively and, in hence, the intra-cluster similarity is increased and the inter-cluster similarity is decreased during clustering. The proposed FIBCLUS algorithm is able to handle datasets with numerical, categorical and a mix of both types of attributes. Results obtained with FIBCLUS are compared with the results of existing algorithms such as k-means, x-means expected maximization and hierarchical algorithms that are widely used to cluster numeric, categorical and mix data types. Empirical analysis shows that FIBCLUS is able to produce better clustering solutions in terms of entropy, purity and F-score in comparison to the above described existing algorithms.