Scalability Properties Research Articles

The Bedford Alzheimer nursing-severity scale to assess dementia severity in advanced dementia: a nonparametric item response analysis and a study of its psychometric characteristics.

The Bedford Alzheimer Nursing-Severity Scale (BANS-S) assesses disease severity in patients with advanced Alzheimer's disease. Since Alzheimer is a progressive disease, studying the hierarchy of the items in the scale can be useful to evaluate the progression of the disease. Data from 164 Alzheimer's patients and 186 patients with other dementia were analyzed using the Mokken Scaling Methodology to determine whether respondents can be ordered in the trait dementia severity, and to study whether an ordering between the items exist. The scalability of the scale was evaluated by the H coefficient. Results showed that the BANS-S is a reliable and medium scale (0.4≤H<0.5) for the Alzheimer group. All items with the exception of the item about mobility could be ordered. When later item was eliminated from the scale, the H coefficient decreased indicating that the scalability of the scale in the original form is more accurate than in the shorter version. For the other dementia group, the BANS-S did not fit any of the Mokken Scaling models because the scale was not unidimensional. In this group, a shorter version of the scale without the sleeping cycle item and the mobility item has better reliability and scalability properties than the original scale.

Unstructured deadlock detection technique with scalability and complexity‐efficiency in clouds

SUMMARYTo detect deadlock in distributed systems, the initiator should construct an efficient explicit or implicit global wait‐for graph. In this paper, we present an unstructured deadlock detection algorithm using a gossip protocol in cloud computing environments, where constituting nodes may join and leave at any time. Because of the inherit properties of a gossip protocol, we argue that our proposed deadlock detection algorithm is scalable, fault‐tolerant, and efficient, retaining safety and liveness properties. The correctness proof of the algorithm is also provided. The message complexity of our proposed algorithm is O(n), where n is the number of nodes. Our performance evaluation with scalable settings shows that our approach has a significant advantage over previous deadlock detection algorithms in terms of solving scalability, fault‐tolerance, and complexity–efficiency issues. Copyright © 2013 John Wiley &amp; Sons, Ltd.

Hierarchical spatiotemporal feature extraction using recurrent online clustering

Deep machine learning offers a comprehensive framework for extracting meaningful features from complex observations in an unsupervised manner. The majority of deep learning architectures described in the literature primarily focus on extracting spatial features. However, in real-world settings, capturing temporal dependencies in observations is critical for accurate inference. This paper introduces an enhancement to DeSTIN – a compositional deep learning architecture in which each layer consists of multiple instantiations of a common node – that learns to represent spatiotemporal patterns in data based on a novel recurrent clustering algorithm. Contrary to mainstream deep architectures, such as deep belief networks where layer-by-layer training is assumed, each of the nodes in the proposed architecture is trained independently and in parallel. Moreover, top-down and bottom-up information flows facilitate rich feature formation. A semi-supervised setting is demonstrated achieving state-of-the-art results on the MNIST classification benchmarks. A GPU implementation is discussed further accentuating the scalability properties of the proposed framework.

Scalability properties of multimodular networks with dynamic gating

Brain processes arise from the interaction of a vast number of neurons. While the number of participating elements is enormous, interactions are generally limited by physical and metabolic constraints---a neuron is connected to thousands other neurons, a far lower number than the hundred billion neurons in the brain. Unfortunately, it is the number of connections per neuron, and not the total number of neurons, what often determines the performance of large neural networks (measured, e.g., as memory capacity), a fact that hinders the scalability of these systems. We hypothesize that the scalability problem can be circumvented by using multimodular architectures, in which individual modules composed of local, densely connected recurrent networks interact with one another through sparse connections. We propose a general model of multimodular attractor neural networks in which each module state changes only upon external event and the change depends on the state of a few other modules. To implement this scheme, every module has to disregard the state of any module not involved in a particular interaction. Because a module can potentially interact with several others, ignoring the states of non-relevant modules would require learning of an exponentially large number of conditions. We solve this problem by adding a group of neurons that dynamically gate the interactions between modules. These neurons receive inputs from the modules and event signals through random sparse connections, and respond to combinations of event-states. This information is then sent back to the modules. Because they implement conjunctive representations, the number of necessary gating neurons grows only polynomially with the number of modules. We hypothesize that gating neurons reside in cortical layer 2/3, and that they mediate the interactions between modules in layer 5/6. The laminar organization of the neocortex could thus be a crucial architectural solution to the scalability problem.

Scalable properties of metal clusters: A comparative DFT study of ionic-core treatments

To assess various ionic-core approximations we carried out density functional calculations on a series of octahedral palladium and gold model clusters Mn with n=13–147. We compared results for average bond lengths, cohesive energies, vertical ionization potentials, and electron affinities to the corresponding all-electron scalar relativistic results. We used extrapolated bulk values to compare with experiments. The results of the projector-augmented wave method agree best with the all-electron results. When modeling palladium–gold nanoalloy particles, one should keep in mind that current ECP or PAW modeling of palladium moieties appears to be more accurate than modeling of gold species.

Bessel-beam-written nanoslit arrays and characterization of their optical response

Nanoslit arrays are fabricated on thin metal film coated glass slides using femtosecond laser pulses with Bessel beam profiles. The optical properties of the fabricated structures with different periodicities are characterized with transmission spectroscopy. Experimental results reveal the existence of two separate surface plasmon resonance modes occurring at the metal-air and metal-glass interfaces. These two resonance modes cause two minima in the high transmission spectra of the sub-skin depth thick thin films in the visible and near infrared regions. The existence of double surface plasmon resonance modes is verified with additional experiments, theoretical and numerical studies. Due to its relaxed alignment constraints, reduced aberrations, scalability property to shorter wavelengths, and resulting shorter dimensions, nanofabrication with diffraction-free Bessel beams is an easy, cheap, and advantageous alternative to regular lithography techniques to fabricate nanoslit arrays. The shift of the resonance wavelength with a change in the refractive index of the surrounding medium can be exploited for enhanced sensing.

Molecular Architecture of Cobalt Porphyrin Multilayers on Reduced Graphene Oxide Sheets for High‐Performance Oxygen Reduction Reaction

Thanks to their lightweight, highly efficient, modular and scalable properties, polymer electrolyte membrane fuel cells (PEMFCs) have long been thought to be a promising candidate for applications in transportation and in both stationary and portable electronics. [1] Unfortunately, despite the above advantages, until now the fuel-cell technologies have failed to reach mass commercialization, and the main problems include short operational time and high cost of the materials used. [1b–d] For example, platinum-based materials are generally believed to be ideal catalysts for the oxygen reduction reaction (ORR) at the cathodes of PEMFCs; however, their disadvantages, for example, low tolerance to methanol fuel, high price and scarcity, limit the practical application of platinum-based catalysts. [2] Therefore, replacement of platinum-based materials with non-precious-metal catalysts, which are of low cost, high catalytic activity, and robust, has become one of the key issues for the realization of mass applications of PEMFCs. [3a,b]

An Automatic High-Throughput Single Nucleotide Polymorphism Genotyping Approach Based on Universal Tagged Arrays and Magnetic Nanoparticles

Recent developments in highly parallel genome-wide studies are transforming the association of human health and diseases. In these studies, multiple SNP loci from large amount of samples need to be investigated to obtain a result with a high degree of confidence. Herein, we describe a novel, cost-effective and automated method for high-throughput single nucleotide polymorphisms (SNPs) genotyping based on universal tagged array and magnetic separation. By using two kinds of functionalized magnetic nanoparticles, the whole operation procedure including genome DNA extraction and SNP genotyping can be automatically performed by JANUS automated workstation (Perkin Elmer Inc.). Four different SNPs loci from 80 samples were scored using only one pair of universal dual-color probes, the phase of numerous SNPs can be automated assessed simultaneously. The results demonstrated that the expected scores and good discrimination were obtained between the two alleles from these four SNP loci. Due to adequately taking the advantages of high parallel read-out and intrinsically scalable properties of microarray, and the automated magnetic separation handling technology is highly adaptable fro multiplexing sample preparation and automated SNP analysis, also avoid the complex procedure including purification and concentration, the new strategy is high-throughput, simple, flexible, cost-effective, and will be very suitable for large-scale genotyping.

ARROW: Azimuth-Range ROuting for large-scale Wireless sensor networks

During the past few years, the development of wireless sensor network technologies has spurred the design of novel protocol paradigms capable of meeting the needs of a wide broad of applications while taking into account the inherent constraints of the underlying network technologies, e.g. limited energy and computational capacities. Geographic routing is one of such paradigms whose principles of operation are based on the geographic location of the network nodes. Even though the large number of works already reported in the literature, there are still many open issues towards the design of robust and scalable geographic routing algorithms. In this study, after an analysis of the most relevant solutions reported in the literature, we introduce Azimuth-Range ROuting for large-scale Wireless (ARROW) sensor networks. ARROW goes a step further on the design of geographic routing protocols by defining a simple and robust routing protocol whose operation principles completely free the network nodes of the burden of keeping routing records. Under ARROW, nodes carry out all routing decisions exclusively using the information imbedded in the data packets while avoiding the risk of routing loops, a major challenge when designing routing protocols for large-scale networks. Moreover, ARROW is supplemented with a simple yet effective forwarder resolution protocol, also introduced in this study, allowing the fast and loop-free selection of the forwarding node in a hop-to-hop basis. Both protocols, ARROW and the proposed forwarder resolution protocol, are validated by extensive computer simulations. Our results show that both protocols exhibit excellent scalability properties by limiting the overhead.

Chaos-based partial encryption of SPIHT coded color images

An embedded partial encryption technique of compressed color images based on chaos is proposed. Modifying the bit values of different types which are obtained by Color-SPIHT (CSPIHT) compression algorithm leads to varying degradation degree of retrieved images. This signifies that the six bit types have various contributions to decoding. As long as the most important bits that have the greatest contribution are encrypted, we can ensure no information leaking out and minimal computation. The piecewise linear chaotic map (PWLCM) is appropriate for generating random number sequence used in proposed cryptosystem for its perfect ergodicity and confusion. Thorough performance and security analysis as regards to diverse evaluation metrics ascertains that the scheme has less computational amount than 0.49%, resists against common cryptanalytic attacks, and preserves the scalability property of the encoder.

Nonlinear Output Regulation by Post-processing Internal Model for Multi-Input Multi-Output Systems

The paper deals with the problem of output regulation for the class of multi-input multi-output square nonlinear systems satisfying a minimum-phase assumption and a “positivity” condition on the high-frequency gain matrix. By following a design paradigm proposed in [12] for single-input single-output nonlinear systems, it is shown how an internal model-based controller can be obtained, whose dimension depends on the number of regulated outputs and on the dimension of the exosystem. Thanks to a scalability property of the regulator structure highlighted in [10], it is shown how a “pre-processing” internal model can be shifted from input to output, yielding in this way a “post-processing” internal model. This makes it possible to run a high-gain asymptotic analysis that bypasses the need of finding a normal form, as it would normally be the case.

An integrated microfluidic platform for evaluating in vivo antimicrobial activity of natural compounds using a whole-animal infection model

The nematode Caenorhabditis elegans is a useful model host for pathogenesis research that can be infected by a large number of human pathogens. Conventionally, nematode-pathogen infection assays are mainly performed on agar medium which are labor-intensive and time-consuming. To overcome these challenges, we develop for the first time an integrated microfluidic device for evaluating in vivo antimicrobial activity of natural compounds, which allows infection and anti-infection assays to be sequentially and automatically carried out in liquid medium. The device consists of a worm dispenser with 32 trap-construction chambers and concentration gradient generators, in which the processes of introduction, dispensation, confinement of worms in the chamber and drug delivery to the chamber can be integrated into a single device. In addition, the operation of the device is simple and does not require any expensive robotic fluid handling systems to dispense samples. To demonstrate the ability of this device, we devise an on-line screening experiment using a Caenorhabditis elegans-Staphylococcus aureus infection model and characterize the survival rate of the infected worms treated with antibiotics. Then, we applied the system to evaluate the antibacterial activity of several components of rhubarb: aloe-emodin, rhein and emodin at various concentrations. The device is able to load uniform worms into each chamber within 10 min and then generate various chemical concentrations automatically and simultaneously. Furthermore, the on-chip method only requires 6 h to establish the infection model and 48 h to perform the subsequent treatments. Based on the excellent advantages and scalable properties of microfluidics, the microfluidic platform holds a great potential in high-throughput screening for antimicrobials.

A FAMILY OF ULTRA-THIN, POLARIZATION-INSENSITIVE, MULTI-BAND, HIGHLY ABSORBING METAMATERIAL STRUCTURES

The systematic design of size-conflned, polarization- independent metamaterial absorbers that operate in the microwave regime is presented in this paper. The novel unit cell is additionally implemented to create e-cient multi-band and broadband structures by exploiting the scalability property of metamaterials. Numerical simulations along with experimental results from fabricated prototypes verify the highly absorptive performance of the devices, so developed. Moreover, a detailed qualitative and quantitative analysis is provided in order to attain a more intuitive and sound physical interpretation of the underlying absorption mechanism. The assets of the proposed concept, applied to the design of difierent patterns, appear to be potentially instructive for various EMI/EMC conflgurations.

Attacks and improvements on the RFID authentication protocols based on matrix

Most of the Radio Frequency IDentification (RFID) authentication protocols, proposed to preserve security and privacy, are analysed to show that they can not provide security against some passive or active attacks. In this paper, the security of two matrix-based protocols, proposed by Karthikeyan and Nesterenko (KN protocol) and Ramachandra et al. (RRS protocol) that conform to Electronic Product Code Class-1 Generation-2 (EPC Class-1 Gen-2) standard, are investigated. Using the linear relationship of multiplication of matrix and vector, we point out that both protocols can not provide scalability, and they are vulnerable to passive impersonation attack. In addition, both protocols are totally insecure if the adversary can compromise one tag to extract the secrets. A modified lightweight matrix-based authentication protocol is presented, which can resist mainly common attacks on an RFID authentication system including eavesdropping, relay attack, desynchronization attack, impersonation attack and tag tracking attack. The new protocol also has the desirable scalability property and can keep secure under compromising attack.

A polarization-/angle-insensitive, bandwidth-optimized, metamaterial absorber in the microwave regime

An enhanced metamaterial absorber based on the circumscribed-cross resonator is introduced in this paper. The new structure is polarization-independent, due to the symmetry of its unit cell, and is proven efficient for the attenuation of obliquely incident waves. The absorption mechanism is thoroughly investigated and is found to be mainly related to the losses of the dielectric substrate. Furthermore, by exploiting the scalability property of metamaterials, the operational bandwidth of our design can be drastically improved by placing unit cells with properly scaled resonators adjacent to each other. In this context, various combinations of three, four, and nine unit cells that can increase the full width at half maximum up to as much as 11.18 %, are developed. The overall performance of the proposed configurations is deemed promising for the realization of microwave metamaterial absorbers for several practical applications.

A micromechanical model of distributed damage due to void growth in general materials and under general deformation histories

SUMMARYWe develop a multiscale model of ductile damage by void growth in general materials undergoing arbitrary deformations. The model is formulated in the spirit of multiscale finite element methods (FE 2), that is, the macroscopic behavior of the material is obtained by a simultaneous numerical evaluation of the response of a representative volume element. The representative microscopic model considered in this work consists of a space‐filling assemblage of hollow spheres. Accordingly, we refer to the present model as the packed hollow sphere (PHS) model. A Ritz–Galerkin method based on spherical harmonics, specialized quadrature rules, and exact boundary conditions is employed to discretize individual voids at the microscale. This discretization results in material frame indifference, and it exactly preserves all material symmetries. The effective macroscopic behavior is then obtained by recourse to Hill's averaging theorems. The deformation and stress fields of the hollow spheres are globally kinematically and statically admissible regardless of material constitution and deformation history, which leads to exact solutions over the entire representative volume under static conditions. Excellent convergence and scalability properties of the PHS model are demonstrated through convergence analyses and examples of application. We also illustrate the broad range of material behaviors that are captured by the PHS model, including elastic and plastic cavitation and the formation of a vertex in the yield stress of porous metals at low triaxiality. This vertex allows ductile damage to occur under shear‐dominated conditions, thus overcoming a well‐known deficiency of Gurson's model. Copyright © 2012 John Wiley &amp; Sons, Ltd.

Scalable properties of metal clusters: A comparative study of modern exchange-correlation functionals

The performance of eight generalized gradient approximation exchange-correlation (xc) functionals is assessed by a series of scalar relativistic all-electron calculations on octahedral palladium model clusters Pd(n) with n = 13, 19, 38, 55, 79, 147 and the analogous clusters Au(n) (for n up through 79). For these model systems, we determined the cohesive energies and average bond lengths of the optimized octahedral structures. We extrapolate these values to the bulk limits and compare with the corresponding experimental values. While the well-established functionals BP, PBE, and PW91 are the most accurate at predicting energies, the more recent forms PBEsol, VMTsol, and VT{84}sol significantly improve the accuracy of geometries. The observed trends are largely similar for both Pd and Au. In the same spirit, we also studied the scalability of the ionization potentials and electron affinities of the Pd clusters, and extrapolated those quantities to estimates of the work function. Overall, the xc functionals can be classified into four distinct groups according to the accuracy of the computed parameters. These results allow a judicious selection of xc approximations for treating transition metal clusters.

A scalable and format-compliant encryption scheme for H.264/SVC bitstreams

SVC (Scalable Video Coding) is designed to adapt to heterogeneous networks and various terminal devices. This paper presents an encryption scheme for SVC bitstreams which retains the valuable scalability properties of SVC. To this end, we explore PACSI (Payload Content Scalability Information) and RTP (Real-time Transport Protocol) payload format such that encrypted bitstreams are SVC format-compliant. Specifically, the proposed scheme processes the base layer and enhancement layers in different ways. For the base layer, the scheme encrypts VCL (video coding layer) NALU (Network Abstract Layer Unit) into either SEI (Supplement Enhancement Information) NALU or PACSI NALU. For an enhancement layer, the scheme replaces a coded slice in scalable extension NALU with an encryption of PACSI NALU. Thus, the proposed encryption scheme preserves SVC scalability and format-compliance. It produces encrypted bitstreams which have the original SVC structure without emulation markers or illegal codewords for any standard decoder. The analysis and experiments indicate that our algorithm is cost-effective and secure against chosen plaintext attack.

Nephele: Scalable Access Control for Federated File Services

The integration of storage resources across different administrative domains can serve as building block for the development of efficient collaboration environments. In order to improve application portability across such environments, we target data sharing facilities that securely span multiple domains at the filesystem rather than the application level. We introduce the hypergroup as an heterogeneous two-layer construct, where the upper layer consists of administrative domains and the lower layer of users from each participating domain. We use public keys to uniquely identify users and domains, but rely on credentials to securely bind users and domains with hypergroups. Each domain is responsible for authenticating its local users across the federation, and employs access control lists to specify the rights of individual users and hypergroups over local storage resources. In comparison to existing systems, we show both analytically and experimentally reduced transfer cost of remote authorizations and improved scalability properties.

Scalable Compression of 3-D Medical Image Data Using EBCOT with Volume of Interest Coding

Volumetric medical image data usually require a vast amount of resources for storage and transmission. With the wide pervasiveness of medical imaging applications in healthcare settings and the increased interest in telemedicine technologies, it has become essential to reduce both storage and transmission bandwidth requirements needed for archival and communication of related data, preferably by employing lossless compression methods to avoid any negative effects of lossy compression on image quality and diagnostic capabilities. The main objective is to present a 3-D medical image compression method with scalability properties, by quality and resolution up to lossless reconstruction and optimized Volume of Interest (VOI) coding at any bit-rate. The proposed method named as “EBCOT with VOI coding” employs a 3-D integer wavelet transform (3D-IWT) and a modified EBCOT with 3-D contexts to compress the 3-D medical imaging data into a layered bit-stream that is scalable by quality and resolution. VOI coding capabilities are attained after compression by employing a bit-stream reordering procedure, which is based on a weight assignment model that incorporates the position of the VOI and the mean energy of the wavelet coefficients. Optimized VOI coding at any bit-rate is attained by an optimization technique that maximizes the reconstruction quality of the VOI, while allowing for the decoding of background information with peripherally increasing quality around the VOI. Performance evaluations based on real 3-D medical imaging data showed that the proposed method achieves a higher reconstruction quality, in terms of the peak signal-to-noise ratio, than that achieved by 3D-JPEG2000 with VOI coding, when using the MAXSHIFT and general scaling-based methods.