Reconstruction from incomplete data in cone-beam tomography

A method for reconstruction of an object f(x) x=(x,y,z) from a limited set of cone-beam projection data has been developed. This method uses a modified form of convolution back-projection and projection onto convex sets (POCS) for handling the limited (or incomplete) data problem. In cone-beam tomography, one needs to have a complete geometry to completely reconstruct the original three-dimensional object. While complete geometries do exist, they are of little use in practical implementations. The most common trajectory used in practical scanners is circular, which is incomplete. It is, however, possible to recover some of the information of the original signal f(x) based on a priori knowledge of the nature of f(x). If this knowledge can be posed in a convex set framework, then POCS can be utilized. In this report, we utilize this a priori knowledge as convex set constraints to reconstruct f(x) using POCS. While we demonstrate the effectiveness of our algorithm for circular trajectories, it is essentially geometry independent and will be useful in any limited-view cone-beam reconstruction.

Synthesis and characterization of a rare hemiacetalate bridged incomplete View the MathML source dicubane cluster

Reaction of 2-pyridinecarboxaldehyde [(Py)CHO] with Cu(NO3)2·2.5H2O in the presence of 4-aminopyridine and NaN3 in MeOH lead to an incomplete double-cubane [Cu4{PyCH(O)(OMe)}4(N3)4] (1) in 87% isolated yield, representing a rare type of metal cluster containing bridging hemiacetalate ligand [pyCH(O)(OMe)]−1 which was characterized by single crystal structure analysis and variable temperature magnetic behavior.

Incomplete decomposition of alkali metal azides

The thermal decomposition of sodium azide has been studied in the temperature range 240–360°C in vacuum and under pressure of an inert gas, argon. The results show that the decomposition is partial 360°C. From the observations made in the present work, namely: (i) the decomposition is incomplete both under vacuum and inert gas; (ii) mass spectrometric studies do not reveal any decrease in the intensity of the background species, CO+2, CO+, H2O+, and (iii) sodium metal remains in the ‘free state’ as seen by the formation of a metallic mirror at temperatures above 300°C, it has been argued that the partial nature of decompostion is due to the confinement of the decomposition to intermosaic regions within the lattice.

Learning dynamic prices in multiseller electronic retail markets with price sensitive customers, stochastic demands, and inventory replenishments

In this paper, we use reinforcement learning (RL) as a tool to study price dynamics in an electronic retail market consisting of two competing sellers, and price sensitive and lead time sensitive customers. Sellers, offering identical products, compete on price to satisfy stochastically arriving demands (customers), and follow standard inventory control and replenishment policies to manage their inventories. In such a generalized setting, RL techniques have not previously been applied. We consider two representative cases: 1) no information case, were none of the sellers has any information about customer queue levels, inventory levels, or prices at the competitors; and 2) partial information case, where every seller has information about the customer queue levels and inventory levels of the competitors. Sellers employ automated pricing agents, or pricebots, which use RL-based pricing algorithms to reset the prices at random intervals based on factors such as number of back orders, inventory levels, and replenishment lead times, with the objective of maximizing discounted cumulative profit. In the no information case, we show that a seller who uses Q-learning outperforms a seller who uses derivative following (DF). In the partial information case, we model the problem as a Markovian game and use actor-critic based RL to learn dynamic prices. We believe our approach to solving these problems is a new and promising way of setting dynamic prices in multiseller environments with stochastic demands, price sensitive customers, and inventory replenishments.

Chemistry of the incomplete decomposition of ammonium perchlorate (AP)

Results are presented to show that as the thermal decomposition of orthorhombic ammonium perchlorate proceeds there is an accumulation, in the solid, of hydrochloric and nitric acids, the concentrations of which increase up to 15% decomposition after which they decrease until they reach the original values.

Ruthenium-Oxygen Coordination-Driven Self-Assembly of a Ru-8(II) Incomplete Prism: Synthesis, Structure, and Shape-Selective Molecular Recognition Study

Coordination-driven self-assembly of 1,3,5-benzenetricarboxylate (tma; 1) and oxalato-bridged p-cymeneruthenium(II) building block Ru-2(mu-eta(4)-C2O4)(MeOH)(2)(eta(6)-p-cymene)(2)](O3SCF3)(2) (2) affords an unusual octanuclear incomplete prism Ru-8(eta(6)-p-cymene)(8)(tma)(2)(mu-eta(4)-C2O4)(2)(OMe)(4)](O3SCF3)( 2) (3), which exhibits a remarkable shape-selective binding affinity for neutral phenolic compounds via hydrogen-bonding interactions (p-cymene = p-(PrC6H4Me)-Pr-i). Such a binding was confirmed by single-crystal X-ray diffraction analysis using 1,3,5-trihydroxybenzene as an analyte.

Image reconstruction from incomplete projections data: A multiresolution modeling approach

Computerized tomography is an imaging technique which produces cross sectional map of an object from its line integrals. Image reconstruction algorithms require collection of line integrals covering the whole measurement range. However, in many practical situations part of projection data is inaccurately measured or not measured at all. In such incomplete projection data situations, conventional image reconstruction algorithms like the convolution back projection algorithm (CBP) and the Fourier reconstruction algorithm, assuming the projection data to be complete, produce degraded images. In this paper, a multiresolution multiscale modeling using the wavelet transform coefficients of projections is proposed for projection completion. The missing coefficients are then predicted based on these models at each scale followed by inverse wavelet transform to obtain the estimated projection data.

Reinforcement learning applications in dynamic pricing of retail markets

In this paper, we investigate the use of reinforcement learning (RL) techniques to the problem of determining dynamic prices in an electronic retail market. As representative models, we consider a single seller market and a two seller market, and formulate the dynamic pricing problem in a setting that easily generalizes to markets with more than two sellers. We first formulate the single seller dynamic pricing problem in the RL framework and solve the problem using the Q-learning algorithm through simulation. Next we model the two seller dynamic pricing problem as a Markovian game and formulate the problem in the RL framework. We solve this problem using actor-critic algorithms through simulation. We believe our approach to solving these problems is a promising way of setting dynamic prices in multi-agent environments. We illustrate the methodology with two illustrative examples of typical retail markets.

Electrochemical capacitors: Technical challenges and prognosis for future markets

Electrochemical capacitors are potential devices that could help bringing about major advances in future energy storage. They are lightweight and their manufacture and disposal has no detrimental effects on the environment. A comprehensive description of fundamental science of electrochemical capacitors is presented. Similarities and differences between electrochemical capacitors and secondary batteries for electrical energy storage are highlighted and various types of electrochemical capacitors are discussed with special reference to lead-carbon hybrid ultracapacitors. Some envisaged applications of electrochemical capacitors are described along with the technical challenges and prognosis for future markets. (C) 2012 Published by Elsevier Ltd.

Dynamic mechanism design for markets with strategic resources

The assignment of tasks to multiple resources becomes an interesting game theoretic problem, when both the task owner and the resources are strategic. In the classical, nonstrategic setting, where the states of the tasks and resources are observable by the controller, this problem is that of finding an optimal policy for a Markov decision process (MDP). When the states are held by strategic agents, the problem of an efficient task allocation extends beyond that of solving an MDP and becomes that of designing a mechanism. Motivated by this fact, we propose a general mechanism which decides on an allocation rule for the tasks and resources and a payment rule to incentivize agents' participation and truthful reports. In contrast to related dynamic strategic control problems studied in recent literature, the problem studied here has interdependent values: the benefit of an allocation to the task owner is not simply a function of the characteristics of the task itself and the allocation, but also of the state of the resources. We introduce a dynamic extension of Mezzetti's two phase mechanism for interdependent valuations. In this changed setting, the proposed dynamic mechanism is efficient, within period ex-post incentive compatible, and within period ex-post individually rational.

Enhanced photoabsorption efficiency of incomplete nanoshells

The rather low scattering or extinction efficiency of small nanoparticles, metallic and otherwise, is significantly enhanced when they are adsorbed on a larger core particle. But the photoabsorption by particles with varying surface area fractions on a larger core particle is found to be limited by saturation. It is found that the core-shell particle can have a lower absorption efficiency than a dielectric core with its surface partially nucleated with absorbing particles-an ``incomplete nanoshell'' particle. We have both numerically and experimentally studied the optical efficiencies of titania (TiO2) nucleated in various degrees on silica (SiO2) nanospheres. We show that optimal surface nucleation over cores of appropriate sizes and optical properties will have a direct impact on the applications exploiting the absorption and scattering properties of such composite particles.

Average Overlap for Clustering Incomplete Data using Symmetric Non-Negative Matrix Factorization

Clustering techniques which can handle incomplete data have become increasingly important due to varied applications in marketing research, medical diagnosis and survey data analysis. Existing techniques cope up with missing values either by using data modification/imputation or by partial distance computation, often unreliable depending on the number of features available. In this paper, we propose a novel approach for clustering data with missing values, which performs the task by Symmetric Non-Negative Matrix Factorization (SNMF) of a complete pair-wise similarity matrix, computed from the given incomplete data. To accomplish this, we define a novel similarity measure based on Average Overlap similarity metric which can effectively handle missing values without modification of data. Further, the similarity measure is more reliable than partial distances and inherently possesses the properties required to perform SNMF. The experimental evaluation on real world datasets demonstrates that the proposed approach is efficient, scalable and shows significantly better performance compared to the existing techniques.