The polymeric conformational effect on capacitive deionization performance of graphene oxide/polypyrrole composite electrode

Exploitation of novel faradic materials is an alternative implementation for solving the problem of poor specific electrosorption capacity that conventional carbon materials are encountered in capacitive deionization. Particularly, composite electrode is just a suitable choice because of its potentially high ion-storage ability. Herein, a cyclic voltammetric treatment method with different low limit of potential window was used to manipulate the polymeric conformation and doping level of graphene oxide/polypyrrole (GO/PPy) composite electrode. Based on it, the effect of polymeric structure on the electrosorption performance was systematically studied. When the low limit of potential window is shifted negatively enough, the irreversible polymeric conformational shrinks of GO/PPy are promoted, which not only hinders the insertion process of ions, but also decreases the doping level of polymer due to the intensive interchain-action produced by more entangled polymeric chain. Thus, the number of intercalated ions should decrease, which is expressed by electrochemical impedance spectroscopy (EIS) results and is proportional to the electrosorption capacity of GO/PPy composite electrode in membrane capacitive deionization (MCDI) process. Our work suggests that the less packing density, higher doping level and more charge delocalization on PPy backbone in electrode are beneficial to enhance its capacitive deionization performance

On procedures for measuring deprivation and living standards of societies in a multi-attribute framework

When a society's overall deprivation or living standard is assessed in a multi-attribute framework, the following procedure is often used. First, for each attribute, a summary index is constructed to reflect a society's performance in relation to this attribute. Then, an indicator of the overall performance of the society in terms of all the attributes together is constructed. This paper discusses a difficulty associated with this procedure. We show that the difficulty lies in its inability to reconcile two highly attractive ethical principles - the first reflecting a requirement of treating individuals symmetrically and the second reflecting a requirement for equity-sensitivity. This problem implies that this widely-used procedure must lead to possibly untenable conclusions, and that it is necessary to adopt alternative procedures. The alternative procedure must permit describing a society's overall deprivation or living standard as an aggregate of the comprehensive deprivations or living standards experienced by the individuals in the society. Working Paper 08-0

Quantum Phase Transitions beyond the Landau's Paradigm in Sp(4) Spin System

We propose quantum phase transitions beyond the Landau's paradigm of Sp(4) spin Heisenberg models on the triangular and square lattices, motivated by the exact Sp(4)$\simeq$ SO(5) symmetry of spin-3/2 fermionic cold atomic system with only $s-$wave scattering. On the triangular lattice, we study a phase transition between the $\sqrt{3}\times\sqrt{3}$ spin ordered phase and a $Z_2$ spin liquid phase, this phase transition is described by an O(8) sigma model in terms of fractionalized spinon fields, with significant anomalous scaling dimensions of spin order parameters. On the square lattice, we propose a deconfined critical point between the Neel order and the VBS order, which is described by the CP(3) model, and the monopole effect of the compact U(1) gauge field is expected to be suppressed at the critical point.Comment: 6 pages, 3 figure

TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References

In this paper, we introduce the semantic knowledge of medical images from their diagnostic reports to provide an inspirational network training and an interpretable prediction mechanism with our proposed novel multimodal neural network, namely TandemNet. Inside TandemNet, a language model is used to represent report text, which cooperates with the image model in a tandem scheme. We propose a novel dual-attention model that facilitates high-level interactions between visual and semantic information and effectively distills useful features for prediction. In the testing stage, TandemNet can make accurate image prediction with an optional report text input. It also interprets its prediction by producing attention on the image and text informative feature pieces, and further generating diagnostic report paragraphs. Based on a pathological bladder cancer images and their diagnostic reports (BCIDR) dataset, sufficient experiments demonstrate that our method effectively learns and integrates knowledge from multimodalities and obtains significantly improved performance than comparing baselines.Comment: MICCAI2017 Ora

New model of calculating the energy transfer efficiency for the spherical theta-pinch device

Ion-beam-plasma-interaction plays an important role in the field of Warm Dense Matter (WDM) and Inertial Confinement Fusion (ICF). A spherical theta pinch is proposed to act as a plasma target in various applications including a plasma stripper cell. One key parameter for such applications is the free electron density. A linear dependency of this density to the amount of energy transferred into the plasma from an energy storage was found by C. Teske. Since the amount of stored energy is known, the energy transfer efficiency is a reliable parameter for the design of a spherical theta pinch device. The traditional two models of energy transfer efficiency are based on assumptions which comprise the risk of systematical errors. To obtain precise results, this paper proposes a new model without the necessity of any assumption to calculate the energy transfer efficiency for an inductively coupled plasma device. Further, a comparison of these three different models is given at a fixed operation voltage for the full range of working gas pressures. Due to the inappropriate assumptions included in the traditional models, one owns a tendency to overestimate the energy transfer efficiency whereas the other leads to an underestimation. Applying our new model to a wide spread set of operation voltages and gas pressures, an overall picture of the energy transfer efficiency results

Learning-aided Stochastic Network Optimization with Imperfect State Prediction

We investigate the problem of stochastic network optimization in the presence of imperfect state prediction and non-stationarity. Based on a novel distribution-accuracy curve prediction model, we develop the predictive learning-aided control (PLC) algorithm, which jointly utilizes historic and predicted network state information for decision making. PLC is an online algorithm that requires zero a-prior system statistical information, and consists of three key components, namely sequential distribution estimation and change detection, dual learning, and online queue-based control. Specifically, we show that PLC simultaneously achieves good long-term performance, short-term queue size reduction, accurate change detection, and fast algorithm convergence. In particular, for stationary networks, PLC achieves a near-optimal $[O(\epsilon)$, $O(\log(1/\epsilon)^2)]$ utility-delay tradeoff. For non-stationary networks, \plc{} obtains an $[O(\epsilon), O(\log^2(1/\epsilon)$ $+ \min(\epsilon^{c/2-1}, e_w/\epsilon))]$ utility-backlog tradeoff for distributions that last $\Theta(\frac{\max(\epsilon^{-c}, e_w^{-2})}{\epsilon^{1+a}})$ time, where $e_w$ is the prediction accuracy and $a=\Theta(1)>0$ is a constant (the Backpressue algorithm \cite{neelynowbook} requires an $O(\epsilon^{-2})$ length for the same utility performance with a larger backlog). Moreover, PLC detects distribution change $O(w)$ slots faster with high probability ($w$ is the prediction size) and achieves an $O(\min(\epsilon^{-1+c/2}, e_w/\epsilon)+\log^2(1/\epsilon))$ convergence time. Our results demonstrate that state prediction (even imperfect) can help (i) achieve faster detection and convergence, and (ii) obtain better utility-delay tradeoffs

Improved approximation algorithm for k-level UFL with penalties, a simplistic view on randomizing the scaling parameter

The state of the art in approximation algorithms for facility location problems are complicated combinations of various techniques. In particular, the currently best 1.488-approximation algorithm for the uncapacitated facility location (UFL) problem by Shi Li is presented as a result of a non-trivial randomization of a certain scaling parameter in the LP-rounding algorithm by Chudak and Shmoys combined with a primal-dual algorithm of Jain et al. In this paper we first give a simple interpretation of this randomization process in terms of solving an aux- iliary (factor revealing) LP. Then, armed with this simple view point, Abstract. we exercise the randomization on a more complicated algorithm for the k-level version of the problem with penalties in which the planner has the option to pay a penalty instead of connecting chosen clients, which results in an improved approximation algorithm