Flexible Embedding Research Articles

Polarizable Embedding Density Matrix Renormalization Group.

The polarizable embedding (PE) approach is a flexible embedding model where a preselected region out of a larger system is described quantum mechanically, while the interaction with the surrounding environment is modeled through an effective operator. This effective operator represents the environment by atom-centered multipoles and polarizabilities derived from quantum mechanical calculations on (fragments of) the environment. Thereby, the polarization of the environment is explicitly accounted for. Here, we present the coupling of the PE approach with the density matrix renormalization group (DMRG). This PE-DMRG method is particularly suitable for embedded subsystems that feature a dense manifold of frontier orbitals which requires large active spaces. Recovering such static electron-correlation effects in multiconfigurational electronic structure problems, while accounting for both electrostatics and polarization of a surrounding environment, allows us to describe strongly correlated electronic structures in complex molecular environments. We investigate various embedding potentials for the well-studied first excited state of water with active spaces that correspond to a full configuration-interaction treatment. Moreover, we study the environment effect on the first excited state of a retinylidene Schiff base within a channelrhodopsin protein. For this system, we also investigate the effect of dynamical correlation included through short-range density functional theory.

Virtual Network Embedding through Security Risk Awareness and Optimization

Network virtualization promises to play a dominant role in shaping the future Internet by overcoming the Internet ossification problem. However, due to the injecting of additional virtualization layers into the network architecture, several new security risks are introduced by the network virtualization. Although traditional protection mechanisms can help in virtualized environment, they are not guaranteed to be successful and may incur high security overheads. By performing the virtual network (VN) embedding in a security-aware way, the risks exposed to both the virtual and substrate networks can be minimized, and the additional techniques adopted to enhance the security of the networks can be reduced. Unfortunately, existing embedding algorithms largely ignore the widespread security risks, making their applicability in a realistic environment rather doubtful. In this paper, we attempt to address the security risks by integrating the security factors into the VN embedding. We first abstract the security requirements and the protection mechanisms as numerical concept of security demands and security levels, and the corresponding security constraints are introduced into the VN embedding. Based on the abstraction, we develop three security-risky modes to model various levels of risky conditions in the virtualized environment, aiming at enabling a more flexible VN embedding. Then, we present a mixed integer linear programming formulation for the VN embedding problem in different security-risky modes. Moreover, we design three heuristic embedding algorithms to solve this problem, which are all based on the same proposed node-ranking approach to quantify the embedding potential of each substrate node and adopt the k-shortest path algorithm to map virtual links. Simulation results demonstrate the effectiveness and efficiency of our algorithms.

A Reversible Steganography Suitable for Embedding Small Amounts of Data

A reversible information hiding scheme that is suitable for embedding small amounts of data is proposed in this paper. The secret data are transformed to base-k data stream, and each number is embedded into a pixel pair if the pixels' difference is within a certain range. Referring a pre-determined table, the gray values of the pixel pair are modified so as to carry the secret number. In order to distinguish the secret-carry pairs from no-secret-carry pairs, the differences of the pixel pairs without secret data embedded are expanded by modification of the difference histogram. The proposed information hiding scheme has flexible embedding capacity and low overhead. The peak signal-to-noise ratio (PSNR) is greater than those of existing methods that are based on histogram modification when the embedding capacity is low. Experimental results showed that the proposed scheme provides good embedding performances for small amounts of embedded data.

Efficient wet paper embedding for steganography with multilayer construction

This work proposes an efficient data hiding scheme for wet paper channel by using a multilayer construction, in which a number of node-bits in different layers are derived from all cover bits and used to carry the secret data. By applying the wet paper coding method to the node-bits and altering the changeable cover bits, the node-bits are modified into their desired values and the secret data are embedded in a layer-by-layer manner. An equilibration mechanism is also introduced to flip the denser changeable cover bits with higher probability. This way, paper folding method is equivalent to a special case of the proposed scheme, and a family of data hiding methods with more flexible relative payload and higher embedding efficiency can be generated.

Prototyping chips in minutes: Direct Laser Plotting (DLP) of functional microfluidic structures

We report a fast and simple prototyping method to fabricate polymer-based microfluidic chips using Direct Laser Plotting (DLP) technique, by which various functional micro-structures can be realized within minutes, in a mask-free and out-of-cleanroom fashion. A 2D Computer-Aid-Design (CAD) software was employed to layout the required micro-structures and micro-channels, a CO2 laser plotter was then used to construct the microstructures. The desired patterns can be plotted directly on PDMS substrates and bio-compatible polymer films by manipulating the strength and density of laser pulses. With the DLP technique, chip-embedded micro-electrodes, micro-mixers and 3D microfluidic chips with 5 layers, which normally require several days of work in a cleanroom facility, can be fabricated in minutes in common laboratory. This novel method can produce microfluidic channels with average feature size of 100μm, while feature size of 50μm or smaller is achievable by making use of the interference effect from laser impulsion. In this report, we present the optimized parameters for successful fabrication of 3D microchannels, micro-mixers and microfluidic chips for protein concentration measurements (Bovine Serum Albumine (BSA) test), and a novel procedure to pattern flexible embedding electrodes on PDMS-based microfluidic chips. DLP offers a convenient and low cost alternative to conventional microfluidic channel fabrication technique which relies on complicated and hazardous soft lithography process.

Semisupervised Neural Networks for Efficient Hyperspectral Image Classification

A framework for semisupervised remote sensing image classification based on neural networks is presented. The methodology consists of adding a flexible embedding regularizer to the loss function used for training neural networks. Training is done using stochastic gradient descent with additional balancing constraints to avoid falling into local minima. The method constitutes a generalization of both supervised and unsupervised methods and can handle millions of unlabeled samples. Therefore, the proposed approach gives rise to an operational classifier, as opposed to previously presented transductive or Laplacian support vector machines (TSVM or LapSVM, respectively). The proposed methodology constitutes a general framework for building computationally efficient semisupervised methods. The method is compared with LapSVM and TSVM in semisupervised scenarios, to SVM in supervised settings, and to online and batch <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means for unsupervised learning. Results demonstrate the improved classification accuracy and scalability of this approach on several hyperspectral image classification problems.

EmXJ : 유연한 임베딩을 위한 XML 처리기 구성 프레임워크

유무선 인터넷의 발달로, 휴대폰, PDA, 홈패드, 스마트폰, 핸드헬드 PC 등, 다양한 형태의 자원제약을 가지는 개인용 혹은 상업용 임베디드 시스템들이 속속 등장하고 있으며, 이들로 임베딩되는 소프트웨어도, 과거 이들이 지녔던 주요 특성인 경직성(fixedness)보다도, 이제는 오히려 유연성(flexibility)을 요구하고 있다. 즉, 자원 제약 특성이 서로 다른 장치들로 유연하게 임베딩될 수 있는 특성을 요구하고 있다. 웹 상의 정보 표현을 위한 표준으로자리잡은 확장성 표기 언어인 XML을 위한 처리기는 각종 인터넷 단말에서의 자료 브라우징을 위해 필수적으로 임베딩 되어야할 소프트웨어이다. 본 논문에서는 서로 다른 기능과 자원 제약 특성을 가진 장치들에 따라 유연성 있는 임베딩을 위한 XML 처리기 구성 프레임워크인, EmXJ가 설계, 구현되며, 그 특성이 기존의 XML 처리기와 비교 분석된다. With the rapid development of wired or wireless Internet, various kinds of resource constrained mobile devices, such as cellular phone, PDA, homepad, smart phone, handhold PC, and so on, have been emerging into personal or commercial usages. Most software to be embedded into those devices has been forced to have the characteristic of flexibility rather than the fixedness which was an inherent property of embedded system. It means that recent technologies require the flexible embedding into the variety of resource constrained mobile devices. A document processor for XML which has been positioned as a standard mark-up language for information representation on the Web, is one of the essential software to be embedded into those devices for browsing the information. In this paper, a framework for configurable XML processor called EmXJ is designed and implemented for flexible embedding into various types of resource constrained mobile devices, and its advantages are compared to conventional XML processors.

Developing Multimedia CALL: The Telos Language Partner Approach

The paper looks at the multimedia dimension of CALL from the point of view of using an authoring tool developed for the production and customisation of (online and offline) multimedia language learning materials. The interaction between learners and teachers in Internet-based pedagogic communication environments is not dealt with. The main emphasis is on the production and customisation of multimedia language learning materials and on the kind of software support needed for their successful embedding in an overall learning and teaching context. The paper also considers the kind of software support needed for the successful embedding of such materials in an overall learning and teaching context. Some of the principles and requirements underlying the development of Multimedia CALL are discussed, but the main focus is on the description of the multimedia learning and authoring software Telos Language Partner. This is a multimedia PC-software designed to incorporate the principles and requirements of multimedia language learning and tutoring discussed in the paper. The software was developed and evaluated with support from the European projects ‘Eloquent’ (Lingua) and ‘Telos’ (Telematics Applications Programme, ET 3005). It supports relevant and authentic language learning activities; intuitive editing functions facilitate the low-cost production, customisation and flexible pedagogical embedding of multimedia language learning contents.

A Novel Scheme for Accurate Md Simulations of Large Systems

ABSTRACTWe present a simple and informationally efficient approach to electronic-structure-based simulations of large material science systems. The algorithm is based on a flexible embedding scheme, in which the parameters of a model potential are fitted at run time to some precise information relevant to localised portions of the system. Such information is computed separately on small subsystems by electronic-structure “black box” subprograms, e.g. based on tight-binding and/or ab initio models. The scheme allows to enforce electronic structure precision only when and where needed, and to minimise the computed information within a desired accuracy, which can be systematically controlled. Moreover, it is inherently linear scaling, and highly suitable for modern parallel platforms, including those based on non-uniform processing. The method is demonstrated by performing computations of tight-binding accuracy on solid state systems in the ten thousand atoms size scale.