Entanglement sudden death in qubit-qutrit systems

We demonstrate the existence of entanglement sudden death (ESD), the complete loss of entanglement in finite time, in qubit-qutrit systems. In particular, ESD is shown to occur in such systems initially prepared in a one-parameter class of entangled mixed states and then subjected to local dephasing noise. Together with previous results, this proves the existence of ESD for some states in all quantum systems for which rigorously defined mixed-state entanglement measures have been identified. We conjecture that ESD exists in all quantum systems prepared in appropriate bipartite states.Comment: 10 pages. To appear in Physics Letters

Drive Cycle Optimisation for Pollution Reduction

Green house gas emissions have abraded environmental quality for human existence. Automobile exhaust is a significant contributor globally to green house gases, among other contributors. This research investigates how vehicle fuel consumption can be tabulated from laboratory tests and road tests. The laboratory tests are used to establish mathematical relationships to predict fuel consumption as a function of such drive-cycle parameters as vehicle speed,acceleration and throttle position. Then, these relationships are applied to calculate fuel consumption during real-life road tests. In the future, the drive-cycle parameters contributing to vehicle fuel consumption could be optimized to lower automobile exhaust’s impact on environmental degradation

Childhood, children and family lives in China

In this chapter we bring into focus those aspects of family lives in China that are concerned with children’s family relationships, and the ways in which such issues are part and parcel of the broader institutionalisation of childhood. We draw on theoretical frameworks in the sociology of childhood and childhood studies (e.g., Prout, 2004; Qvortrup, 2000; Smith and Greene, 2014). Since these theoretical perspectives have developed predominantly in Anglophone literature, some researchers have considered their relevance to, and utility for, China and Chinese childhoods (Goh, 2011; Miao, 2013; Wang YY, 2011, 2014a, 2014b; Zheng, 2012a, 2012b; Ribbens McCarthy et al., 2017). In engaging with existing theories, and applying them to, Chinese children’s family lives, we seek to go beyond any tendency to just ‘add in the missing children’ to existing discussions (Kesby et al., 2006: 186), and give consideration to a variety of cultural and local contexts that characterise China and illuminate why it is necessary to decentre universalist thinking (Jullien, 2008/2014

Nitrogen Abundances and the Distance Moduli of the Pleiades and Hyades

Recent reanalyses of HIPPARCOS parallax data confirm a previously noted discrepancy with the Pleiades distance modulus estimated from main-sequence fitting in the color-magnitude diagram. One proposed explanation of this distance modulus discrepancy is a Pleiades He abundance that is significantly larger than the Hyades value. We suggest that, based on our theoretical and observational understanding of Galactic chemical evolution, nitrogen abundances may serve as a proxy for helium abundances of disk stars. Utilizing high-resolution near-UV Keck/HIRES spectroscopy, we determine N abundances in the Pleiades and Hyades dwarfs from NH features in the 3330 Ang region. While our Hyades N abundances show a modest 0.2 dex trend over a 800 K Teff range, we find the Pleiades N abundance (by number) is 0.13+/-0.05 dex lower than in the Hyades for stars in a smaller overlapping Teff range around 6000 K; possible systematic errors in the lower Pleiades N abundance result are estimated to be at the <0.10 dex level. Our results indicate [N/Fe]=0 for both the Pleiades and Hyades, consistent with the ratios exhibited by local Galactic disk field stars in other studies. If N production is a reliable tracer of He production in the disk, then our results suggest the Pleiades He abundance is no larger than that in the Hyades. This finding is supported by the relative Pleiades-Hyades C, O, and Fe abundances interpreted in the current context of Galactic chemical evolution, and is resistant to the effects on our derived N abundances of a He abundance difference like that needed to explain the Pleiades distance modulus discrepancy. A physical explanation of the Pleiades distance modulus discrepancy does not appear to be related to He abundance.Comment: Accepted for publication in the Publications of the Astronomical Society of the Pacifi

Using social media data to understand mobile customer experience and behavior

Understanding mobile customer experience and behavior is an important task for cellular service providers to improve the satisfaction of their customers. To that end, cellular service providers regularly measure the properties of their mobile network, such as signal strength, dropped calls, call blockage, and radio interface failures (RIFs). In addition to these passive measurements collected within the network, understanding customer sentiment from direct customer feedback is also an important means of evaluating user experience. Customers have varied perceptions of mobile network quality, and also react differently to advertising, news articles, and the introduction of new equipment and services. Traditional methods used to assess customer sentiment include direct surveys and mining the transcripts of calls made to customer care centers. Along with this feedback provided directly to the service providers, the rise in social media potentially presents new opportunities to gain further insight into customers by mining public social media data as well. According to a note from one of the largest online social network (OSN) sites in the US [7], as of September 2010 there are 175 million registered users, and 95 million text messages communicated among users per day. Additionally, many OSNs provide APIs to retrieve publically available message data, which can be used to collect this data for analysis and interpretation. Our plan is to correlate different sources of measurements and user feedback to understand the social media usage patterns from mobile data users in a large nationwide cellular network. In particular, we are interested in quantifying the traffic volume, the growing trend of social media usage and how it interacts with traditional communication channels, such as voice calls, text messaging, etc. In addition, we are interested in detecting interesting network events from users' communication on OSN sites and studying the temporal aspects - how the various types of user feedback behave with respect to timing. We develop a novel approach which combines burst detection and text mining to detect emerging issues from online messages on a large OSN network. Through a case study, our method shows promising results in identifying a burst of activities using the OSN feedback, whereas customer care notes exhibit noticeable delays in detecting such an event which may lead to unnecessary operational expenses. --Mobile customer experience,social media,text data mining,customer feedback

Decoherence induced spontaneous symmetry breaking

We study time dependence of exchange symmetry properties of Bell states when two qubits interact with local baths having identical parameters. In case of classical noise, we consider a decoherence Hamiltonian which is invariant under swapping the first and second qubits. We find that as the system evolves in time, two of the three symmetric Bell states preserve their qubit exchange symmetry with unit probability, whereas the symmetry of the remaining state survives with a maximum probability of 0.5 at the asymptotic limit. Next, we examine the exchange symmetry properties of the same states under local, quantum mechanical noise which is modeled by two identical spin baths. Results turn out to be very similar to the classical case. We identify decoherence as the main mechanism leading to breaking of qubit exchange symmetry.Comment: 12 page

Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification

Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the graph representation alone. However, in many real-world applications, the side information is available along with the graph data. For example, for neurological disorder identification, in addition to the brain networks derived from neuroimaging data, hundreds of clinical, immunologic, serologic and cognitive measures may also be documented for each subject. These measures compose multiple side views encoding a tremendous amount of supplemental information for diagnostic purposes, yet are often ignored. In this paper, we study the problem of discriminative subgraph selection using multiple side views and propose a novel solution to find an optimal set of subgraph features for graph classification by exploring a plurality of side views. We derive a feature evaluation criterion, named gSide, to estimate the usefulness of subgraph patterns based upon side views. Then we develop a branch-and-bound algorithm, called gMSV, to efficiently search for optimal subgraph features by integrating the subgraph mining process and the procedure of discriminative feature selection. Empirical studies on graph classification tasks for neurological disorders using brain networks demonstrate that subgraph patterns selected by the multi-side-view guided subgraph selection approach can effectively boost graph classification performances and are relevant to disease diagnosis.Comment: in Proceedings of IEEE International Conference on Data Mining (ICDM) 201

Managing Triads in a Military Avionics Service Maintenance Network in Taiwan

Purpose – The purpose of this paper is to investigate how different types of triad structures, and the management mechanisms adopted by the focal company, affect cooperative performance. Design/methodology/approach – This paper uses a social network perspective to examine the triad management phenomenon in the military avionics maintenance context, which is closely associated with the field of operations management. Findings – This paper demonstrates that different triad structures and management mechanisms influence perceived cooperative performance. Four main findings emerged: in a triad, a firm playing a bridging role perceives higher cooperative performance than when playing a peripheral role in the triad or being located in a fully connected triad. When a firm plays the bridging role in a triad, and has a high level of trust, this leads to higher perceived cooperative performance. When a firm plays a peripheral role in a triad, high levels of coordination mechanism combined with high levels of trust result in higher levels of perceived cooperative performance. In a fully linked triad, when the coordination mechanism is well developed, the level of trust is high, so that the resulting level of perceived cooperation is high. Originality/value – This paper extends the knowledge of triad management by providing an in-depth study of a well-defined network setting with exceptionally high-level access to the most senior executives. In practice, this paper shows how to manage differen

Multi-view Graph Embedding with Hub Detection for Brain Network Analysis

Multi-view graph embedding has become a widely studied problem in the area of graph learning. Most of the existing works on multi-view graph embedding aim to find a shared common node embedding across all the views of the graph by combining the different views in a specific way. Hub detection, as another essential topic in graph mining has also drawn extensive attentions in recent years, especially in the context of brain network analysis. Both the graph embedding and hub detection relate to the node clustering structure of graphs. The multi-view graph embedding usually implies the node clustering structure of the graph based on the multiple views, while the hubs are the boundary-spanning nodes across different node clusters in the graph and thus may potentially influence the clustering structure of the graph. However, none of the existing works in multi-view graph embedding considered the hubs when learning the multi-view embeddings. In this paper, we propose to incorporate the hub detection task into the multi-view graph embedding framework so that the two tasks could benefit each other. Specifically, we propose an auto-weighted framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain network analysis. The MVGE-HD framework learns a unified graph embedding across all the views while reducing the potential influence of the hubs on blurring the boundaries between node clusters in the graph, thus leading to a clear and discriminative node clustering structure for the graph. We apply MVGE-HD on two real multi-view brain network datasets (i.e., HIV and Bipolar). The experimental results demonstrate the superior performance of the proposed framework in brain network analysis for clinical investigation and application