Local Feature Based Geometric-Resistant Image Information Hiding

Watermarking aims to hide particular information into some carrier but does not change the visual cognition of the carrier itself. Local features are good candidates to address the watermark synchronization error caused by geometric distortions and have attracted great attention for content-based image watermarking. This paper presents a novel feature point-based image watermarking scheme against geometric distortions. Scale invariant feature transform (SIFT) is first adopted to extract feature points and to generate a disk for each feature point that is invariant to translation and scaling. For each disk, orientation alignment is then performed to achieve rotation invariance. Finally, watermark is embedded in middle-frequency discrete Fourier transform (DFT) coefficients of each disk to improve the robustness against common image processing operations. Extensive experimental results and comparisons with some representative image watermarking methods confirm the excellent performance of the proposed method in robustness against various geometric distortions as well as common image processing operations.

Evaluation of bioeffects of a long-term administering ChangLe on rat by proton NMR method and biochemical examination

High resolution H-1 nuclear magnetic resonance ( NMR) spectroscopy has been employed to assess long-term toxicological effects of ChangLe (a kind of rare earth complex applied in agriculture). Male Wistar rats were administrated orally with ChangLe at doses of 0, 0.1, 0.2, 2.0, 10 and 20 mg/kg body weight daily, respectively, for 6 months. Urine was collected at-day 30, 60, go and serum samples were taken after 6 months. Many low-molecular weight metabolites were identified by H-1 NMR spectra of rat urine. A decrease in citrate and an increase in ketone bodies, creatinine, DMA, DMG, TMAO, and taurine in the urine of the rats. receiving high doses were found by H-1 NMR spectra. These may mean that high-dosage of ChangLe impairs the specific region of liver and kidney, such as renal tubule and mitochondria. The decrease in citrate and the increase in succinate and alpha-ketoglutarate were attributed to a combination of the inhibition of certain citric acid enzymes, renal tubular acidosis and the abnormal fatty acid catabolism. The information of the renal capillary necrosis could be derived from the increase in DMIA, DMG and TMAO. The increase in taurine was due to hepatic mitochondria dysfunction. The conclusions were supported by the results of biochemical measure. merits and enzymatic assay.

A double logarithmic method for studying the adsorption effects in an irreversible electrochemical process

The adsorption of an electroinactive product greatly influences an irreversible electrochemical reaction in three ways, including self-block, self-inhibition, and self-acceleration, and changes not only the heterogeneous electron-transfer rate constant but also the modified formal potential and electron-transfer coefficient of the electrochemical reaction. In order to study these adsorption effects, a double logarithmic method was suggested to be used in processing the potential-controlled thin layer spectroelectrochemical data. The result shows three types of double logarithmic plots for three kinds of adsorption effects. These double logarithmic plots can be a diagnostic criterion of the adsorption effects and enable us to determine some thermodynamic and kinetic parameters. The combination of nonlinear regression with double logarithmic method is a convenient way to examine the suggested mechanism and to extract more information from the limited experimental data. Some examples are given to test the theoretical results. (C) 1999 The Electrochemical Society. S0013-4651(98)05-012-5. All rights reserved.

A double logarithmic method in thin-layer spectroelectrochemistry for distinction among mechanisms of an irreversible electrochemical process

A simple double logarithmic method in potential-controlled thin-layer spectroelectrochemistry for an irreversible electrochemical process has been studied by numerical analysis and examined by experimental examples. This simple algorithm has a novel function offering some important information about the mechanism of a complex electrochemical process directly from a limited amount of potential-spectrum data, and can be used to distinguish different reaction mechanisms such as E, EC, EE, as well as to determine the electron-transfer coefficient, a, and the kinetically modified E-0'. Combination of the double logarithmic method with nonlinear regression provides a powerful tool to examine the proposed mechanism and also to estimate other thermodynamic and kinetic parameters. (C) 1999 The Electrochemical Society. S0013-4651(98)06-090-X. All rights reserved.

Developing molecular identification numbers by an all-paths method

A new topological index is devised from an all-paths method. This molecular topological index has highly discriminating power for various kinds of organic compounds such as alkane trees, complex cyclic or polycyclic graphs, and structures containing heteroatoms and thus can be used as a Molecular IDentification number (MID) for chemical documentation. Some published MIDs derived from an all-paths method and their structural selectivity for alkane trees are also reviewed.

A new topological index for the Changchun institute of applied chemistry C-13 NMR information system

A method to assign a single number representation for each atom (node) in a molecular graph, Atomic IDentification (AID) number, is proposed based on the counts of weighted paths terminated on that atom. Then, a new topological index, Molecular IDentification (MID) number is developed from AID. The MID is tested systematically, over half a million of structures are examined, and MID shows high discrimination for various structural isomers. Thus it can be used for documentation in the Changchun Institute of Chemistry C-13 NMR information system.

A parametric method of retrieving ocean wave spectra from synthetic aperture radar images

A parametric method that extracts the ocean wave directional spectra from synthetic aperture radar (SAR) image is presented. The 180 degrees ambiguity of SAR image and the loss of information beyond the azimuthal cutoff can be overcome with this method. The ocean wave spectra can be obtained from SAR image directly by using iteration inversion mapping method with forward nonlinear mapping. Some numerical experiments have been made by using ERS-1 satellite SAR imagette data. The ocean wave direction retrieved from SAR imagette data is in agreement with the wind direction from the scatterometer data.

Pretreatment Method for Nitrogen Isotopic Analysis of Nitrate in Seawater

A suitable method for the pretreutment of dissolved nitrate samples in seawaters for nitrogen isotopic analysis was established. First, the seawater samples were processed by removing nitrite and amonium. Then Devard's alloy was added in sample for conversion of dissolved nitrate to ammonium. The sample was distilled, and then the ammonium condensate was collected with zeolite. after distillation, the collected condensate was filtered and prepared for determining nitropic values. Some tests of the method were conducted. The distillation condition, the influence of salinity on nitrogen isotopic analysis, absorption of ammonium onto zeolite and an improved method on a large volume of seawater were discussed in this study. The results showed that the distillation step had an average recovery of (104.9 +/- 4.2) % (n = 6) when distillating every 300 mL aliquot of the sample under a strong alkaline condition with 0.5 g devard's alloy and a distillation time of 30 min. The nitrogen isotopic fractionation decreased markedly when salinity was increased from 0% to 0.5%; further increase(1% - 3.5%) showed little effect. The adsorption rate of ammonium onto zeolite had a high yield of (95.96 +/- 1.08) % (n = 6) in average. An improved collection method was used to process a large volume of seawater with several distillations, and had good effect on analysis. The method had been applied to analyze water samples collected from Changjiang estuary. The analytical results indicate that the method is suitable for delta N-15 analysis of dissolved nitrate in seawaters. The present method could provide valuable information about the source and cycle mechanism of dissolved nitrogen in estuary waters.

A Unified Statistical and Information Theoretic Framework for Multi-modal Image Registration

We formulate and interpret several multi-modal registration methods in the context of a unified statistical and information theoretic framework. A unified interpretation clarifies the implicit assumptions of each method yielding a better understanding of their relative strengths and weaknesses. Additionally, we discuss a generative statistical model from which we derive a novel analysis tool, the "auto-information function", as a means of assessing and exploiting the common spatial dependencies inherent in multi-modal imagery. We analytically derive useful properties of the "auto-information" as well as verify them empirically on multi-modal imagery. Among the useful aspects of the "auto-information function" is that it can be computed from imaging modalities independently and it allows one to decompose the search space of registration problems.

Shape from Shading: A Method for Obtaining the Shape of a Smooth Opaque Object from One View

A method will be described for finding the shape of a smooth apaque object form a monocular image, given a knowledge of the surface photometry, the position of the lightsource and certain auxiliary information to resolve ambiguities. This method is complementary to the use of stereoscopy which relies on matching up sharp detail and will fail on smooth objects. Until now the image processing of single views has been restricted to objects which can meaningfully be considered two-dimensional or bounded by plane surfaces. It is possible to derive a first-order non-linear partial differential equation in two unknowns relating the intensity at the image points to the shape of the objects. This equation can be solved by means of an equivalent set of five ordinary differential equations. A curve traced out by solving this set of equations for one set of starting values is called a characteristic strip. Starting one of these strips from each point on some initial curve will produce the whole solution surface. The initial curves can usually be constructed around so-called singular points. A number of applications of this metod will be discussed including one to lunar topography and one to the scanning electron microscope. In both of these cases great simplifications occur in the equations. A note on polyhedra follows and a quantitative theory of facial make-up is touched upon. An implementation of some of these ideas on the PDP-6 computer with its attached image-dissector camera at the Artificial intelligence Laboratory will be described, and also a nose-recognition program.

Understanding Iconography: a method to allow rich picture interpretation to improve.

Information Systems for complex situations often fail to adequately deliver quality and suitability. One reason for this failure is an inability to identify comprehensive user requirements. Seldom do all stakeholders, especially those "invisible‟ or "back room‟ system users, have a voice when systems are designed. If this is a global problem then it may impact on both the public and private sectors in terms of their ability to perform, produce and stay competitive. To improve upon this, system designers use rich pictures as a diagrammatic means of identifying differing world views with the aim of creating shared understanding of the organisation. Rich pictures have predominantly been used as freeform, unstructured tools with no commonly agreed syntax. This research has collated, analysed and documented a substantial collection of rich pictures into a single dataset. Attention has been focussed on three main research areas; how the rich picture is facilitated, how the rich picture is constructed and how to interpret the resultant pictures. This research highlights the importance of the rich picture tool and argues the value of adding levels of structure, in certain cases. It is shown that there are considerable benefits for both the interpreter and the creator by providing a pre-drawing session, a common key of symbols and a framework for icon understanding. In conclusion, it is suggested that there is some evidence that a framework which aims to support the process of the rich picture and aid interpretation is valuable.

Understanding student information behavior in relation to electronic information services: lessons from longitudinal monitoring and evaluation Part 1

Rowley, J.& Urquhart, C. (2007). Understanding student information behavior in relation to electronic information services: lessons from longitudinal monitoring and evaluation Part 1. Journal of the American Society for Information Science and Technology, 58(8), 1162-1174. Sponsorship: JISC

Integration of relational and hierarchical network information for protein function prediction

BACKGROUND:In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.RESULTS:We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing.CONCLUSION:A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor) and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased positive predictive value), and that this increase is consistent uniformly with GO-term depth. Additional in silico validation on a collection of new annotations recently added to GO confirms the advantages suggested by the cross-validation study. Taken as a whole, our results show that a hierarchical approach to network-based protein function prediction, that exploits the ontological structure of protein annotation databases in a principled manner, can offer substantial advantages over the successive application of 'flat' network-based methods.

Improving supply chain performance: Real-time demand information and flexible deliveries

In some supply chains, materials are ordered periodically according to local information. This paper investigates how to improve the performance of such a supply chain. Specifically, we consider a serial inventory system in which each stage implements a local reorder interval policy; i.e., each stage orders up to a local basestock level according to a fixed-interval schedule. A fixed cost is incurred for placing an order. Two improvement strategies are considered: (1) expanding the information flow by acquiring real-time demand information and (2) accelerating the material flow via flexible deliveries. The first strategy leads to a reorder interval policy with full information; the second strategy leads to a reorder point policy with local information. Both policies have been studied in the literature. Thus, to assess the benefit of these strategies, we analyze the local reorder interval policy. We develop a bottom-up recursion to evaluate the system cost and provide a method to obtain the optimal policy. A numerical study shows the following: Increasing the flexibility of deliveries lowers costs more than does expanding information flow; the fixed order costs and the system lead times are key drivers that determine the effectiveness of these improvement strategies. In addition, we find that using optimal batch sizes in the reorder point policy and demand rate to infer reorder intervals may lead to significant cost inefficiency. © 2010 INFORMS.

Computational inference of neural information flow networks.

Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks.