Quantifying Quantum-Mechanical Processes

The act of describing how a physical process changes a system is the basis for understanding observed phenomena. For quantum-mechanical processes in particular, the affect of processes on quantum states profoundly advances our knowledge of the natural world, from understanding counter-intuitive concepts to the development of wholly quantum-mechanical technology. Here, we show that quantum-mechanical processes can be quantified using a generic classical-process model through which any classical strategies of mimicry can be ruled out. We demonstrate the success of this formalism using fundamental processes postulated in quantum mechanics, the dynamics of open quantum systems, quantum-information processing, the fusion of entangled photon pairs, and the energy transfer in a photosynthetic pigment-protein complex. Since our framework does not depend on any specifics of the states being processed, it reveals a new class of correlations in the hierarchy between entanglement and Einstein-Podolsky-Rosen steering and paves the way for the elaboration of a generic method for quantifying physical processes

A QoS aware services mashup model for cloud computing applications

Purpose: With the popularity of cloud computing, cloud services have become to be application programming platform where users can create new applications mashup(composing) the functionality offered byothers.By composing of distributed, cloud services dynamicallyto provide more complex tasks, services mashup provides an attractive way for building large-scale Internet applications.One of the challenging issues of cloud services mashup is how to find service paths to route the service instances provider through whilemeeting the applications’ resource requirements so that the QoS constraints are satisfied. However, QoS aware service routing problem istypically NP-hard.The purpose of this paper is to propose a QoS Aware Services Mashup(QASM) model to solve this problem more effectively. Design/methodology/approach: In this paper, we focus on the QoS aware services selection problem in cloud services mashup, for example, given the user service composition requirements and their QoS constraint descriptions, how to select the required serviceinstances and route the data flows through these instances so that the QoS requirements are satisfied. We design a heuristic algorithm to find service paths to route the data flows through whilemeeting the applications’ resource requirements and specific QoS constraints. Findings: This study propose a QoS Aware Services Mashup(QASM) model to solve this problem more effectively. Simulations show that QASM can achieve desired QoS assurances as well as load balancing in cloud services environment. Originality/value: This paper present a QASM model for providing high performance distributed applications in the cloud computingPeer Reviewe

A Comparative Study for 2D and 3D Computer-aided Diagnosis Methods for Solitary Pulmonary Nodules

Many computer-aided diagnosis (CAD) methods, including 2D and 3D approaches, have been proposed for solitary pulmonary nodules (SPNs). However, the detection and diagnosis of SPNs remain challenging in many clinical circumstances. One goal of this work is to investigate the relative diagnostic accuracy of 2D and 3D methods. An additional goal is to develop a two-stage approach that combines the simplicity of 2D and the accuracy of 3D methods. The experimental results show statistically significant differences between the diagnostic accuracy of 2D and 3D methods. The results also show that with a very minor drop in diagnostic performance the two-stage approach can significantly reduce the number of nodules needed to be processed by the 3D method, streamlining the computational demand

When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective

Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications. This article investigates the structure of mobile sensing schemes and introduces crowdsourcing methods for mobile sensing. Inspired by social network, one can establish trust among participatory agents to leverage the wisdom of crowds for mobile sensing. A prototype of social network inspired mobile multimedia and sensing application is presented for illustrative purpose. Numerical experiments on real-world datasets show improved performance of mobile sensing via crowdsourcing. Challenges for mobile sensing with respect to Internet layers are discussed.Comment: To appear in Oct. IEEE Communications Magazine, feature topic on "Social Networks Meet Next Generation Mobile Multimedia Internet

Interest Rate Rules, Target Policies, and Endogenous Economic Growth in an Open Economy

This paper sets up an endogenous growth model of an open economy in which the monetary authority implements a gradualist interest-rate rule with targets for inflation and economic growth. We show that, under a passive rule, a monetary equilibrium exists and is unique; moreover, the equilibrium is locally determinate. Under an active rule, the open economy either generates multiple equilibria or does not have any equilibrium. If equilibria exist, the high-growth equilibrium is locally determinate while the low-growth equilibrium is a source. Besides these, the stabilization and growth effects of alternative target policies are also explored in this study.Nominal interest rate rules, gradualism, endogenous economic growth

Noninvasive prediction of Blood Lactate through a machine learning-based approach.

We hypothesized that blood lactate concentration([Lac]blood) is a function of cardiopulmonary variables, exercise intensity and some anthropometric elements during aerobic exercise. This investigation aimed to establish a mathematical model to estimate [Lac]blood noninvasively during constant work rate (CWR) exercise of various intensities. 31 healthy participants were recruited and each underwent 4 cardiopulmonary exercise tests: one incremental and three CWR tests (low: 35% of peak work rate for 15 min, moderate: 60% 10 min and high: 90% 4 min). At the end of each CWR test, venous blood was sampled to determine [Lac]blood. 31 trios of CWR tests were employed to construct the mathematical model, which utilized exponential regression combined with Taylor expansion. Good fitting was achieved when the conditions of low and moderate intensity were put in one model; high-intensity in another. Standard deviation of fitting error in the former condition is 0.52; in the latter is 1.82 mmol/liter. Weighting analysis demonstrated that, besides heart rate, respiratory variables are required in the estimation of [Lac]blood in the model of low/moderate intensity. In conclusion, by measuring noninvasive cardio-respiratory parameters, [Lac]blood during CWR exercise can be determined with good accuracy. This should have application in endurance training and future exercise industry

Supervised Collective Classification for Crowdsourcing

Crowdsourcing utilizes the wisdom of crowds for collective classification via information (e.g., labels of an item) provided by labelers. Current crowdsourcing algorithms are mainly unsupervised methods that are unaware of the quality of crowdsourced data. In this paper, we propose a supervised collective classification algorithm that aims to identify reliable labelers from the training data (e.g., items with known labels). The reliability (i.e., weighting factor) of each labeler is determined via a saddle point algorithm. The results on several crowdsourced data show that supervised methods can achieve better classification accuracy than unsupervised methods, and our proposed method outperforms other algorithms.Comment: to appear in IEEE Global Communications Conference (GLOBECOM) Workshop on Networking and Collaboration Issues for the Internet of Everythin

6-Mercaptopurine attenuates tumor necrosis factor-α production in microglia through Nur77-mediated transrepression and PI3K/Akt/mTOR signaling-mediated translational regulation

Physical interaction between Nur77 and p65. BV-2 cells were pretreated with 6-MP (50 μM) for 16 h followed by exposure to LPS (100 ng/ml) for 60 min. Nuclear extracts were harvested for immunoprecipitation (IP) experiments using anti-Nur77 and anti-p65 antibodies. Immunoblot (IB) analyses of the immunoprecipitates were performed using these antibodies. The immunoblots are representative of three independent experiments. (TIF 280 kb

Realtime object extraction and tracking with an active camera using image mosaics

[[abstract]]Moving object extraction plays a key role in applications such as object-based videoconference, surveillance, and so on. The dimculties of moving object segmentation lie in the fact that physical objects are normally not homogeneous with to low-level features and it's usually tough to segment them accnrately and efficiently. Object segmentation based on prestored background information has proved to be effective and efficient in several applications such as videophone, video conferencing, and surveillance, etc. The previous works, however, were mainly concentrated on object segmentation with a static camera and in a stationary background. In this paper, we propose a robust and fast segmentation algorithm and a reliable tracking strategy without knowing the shape of the object in advance. The proposed system can real-time extract the foreground from the background and track the moving object with an active (pan-tilt) camera such that the moving object always stays around the center of images.[[fileno]]2030144030033[[department]]電機工程學