Out-of-Time-Order Correlation at a Quantum Phase Transition

In this paper we numerically calculate the out-of-time-order correlation functions in the one-dimensional Bose-Hubbard model. Our study is motivated by the conjecture that a system with Lyapunov exponent saturating the upper bound $2\pi/\beta$ will have a holographic dual to a black hole at finite temperature. We further conjecture that for a many-body quantum system with a quantum phase transition, the Lyapunov exponent will have a peak in the quantum critical region where there exists an emergent conformal symmetry and is absent of well-defined quasi-particles. With the help of a relation between the R\'enyi entropy and the out-of-time-order correlation function, we argue that the out-of-time-order correlation function of the Bose-Hubbard model will also exhibit an exponential behavior at the scrambling time. By fitting the numerical results with an exponential function, we extract the Lyapunov exponents in the one-dimensional Bose-Hubbard model across the quantum critical regime at finite temperature. Our results on the Bose-Hubbard model support the conjecture. We also compute the butterfly velocity and propose how the echo type measurement of this correlator in the cold atom realizations of the Bose-Hubbard model without inverting the Hamiltonian.Comment: 7 pages, 6 figures, published versio

Out-of-Time-Order Correlation for Many-Body Localization

In this paper we first compute the out-of-time-order correlators (OTOC) for both a phenomenological model and a random-field XXZ model in the many-body localized phase. We show that the OTOC decreases in power law in a many-body localized system at the scrambling time. We also find that the OTOC can also be used to distinguish a many-body localized phase from an Anderson localized phase, while a normal correlator cannot. Furthermore, we prove an exact theorem that relates the growth of the second R\'enyi entropy in the quench dynamics to the decay of the OTOC in equilibrium. This theorem works for a generic quantum system. We discuss various implications of this theorem.Comment: 6 pages, 3 figures, published versio

Structural exploration and inference of the network

This dissertation consists of two parts. In the first part, a learning-based method for classification of online reviews that achieves better classification accuracy is extended. Automatic sentiment classification is becoming a popular and effective way to help online users or companies to process and make sense of customer reviews. The method combines two recent developments. First, valence shifters and individual opinion words are combined as bigrams to use in an ordinal margin classifier. Second, relational information between unigrams expressed in the form of a graph is used to constrain the parameters of the classifier. By combining these two components, it is possible to extract more of the unstructured information present in the data than previous methods, like support vector machine, random forest, hence gaining the potential of better performance. Indeed, the results show a higher classification accuracy on empirical real data with ground truth as well as on simulated data. The second part deals with graphical models. Gaussian graphical models are useful to explore conditional dependence relationships between random variables through estimation of the inverse covariance matrix of a multivariate normal distribution. An estimator for such models appropriate for multiple graphs analysis in two groups is developed. Under this setting, inferring networks separately ignores the common structure, while inferring networks identically would mask the disparity. A generalized method which estimates multiple partial correlation matrices through linear regressions is proposed. The method pursues the sparsity for each matrix, similarities for matrices within each group, and the disparities for matrices between groups. This is achieved by a l1 penalty and a 12 penalty for the pursuit of sparseness and clustering, and a metric that learns the true heterogeneity through optimization procedure. Theoretically, the asymptotic consistency for both constrained l0 method and the proposed method to reconstruct the structures is shown. Its superior performance is illustrated via a number of simulated networks. An application to polychromatic flow cytometry data sets for network inference under different sets of conditions is also included

Development of a semi-empirical ship operational performance model for voyage optimization

Previously held under moratorium from 1st December 2016 until 1st December 2021.Voyage optimization is the endeavour to select the optimum route and optimum speed along the voyage in order to maximise the ship performance in energy efficiency and the reduction of the Green House Gas emission footprint within fixed voyage duration. For achieving these goals, it is essential to develop an easy-to-use and accurate enough ship operational performance prediction model, which is the main aim of this study. A detailed critical review of the literature regarding the prediction of ship’s added resistance in waves and its operational performance modelling has been carried out. The existing research gap has been identified and addressed herein. The empirical added resistance prediction formulae have been improved based on the actual ship operational performance data and developed as a semi-empirical added resistance prediction method, which estimates the speed loss due to added resistance. Together with the calm water resistance model, propulsion efficiency model, main engine Specific Fuel Oil Consumption (SFOC) diagram, correction factor indicating fouling effect on fuel consumption, and actual ship operational performance data, the novel semi-empirical ship operational performance prediction model for oil tanker and container ship have been developed and validated. The easy-to-use and practical semi-empirical model is able to accurately predict main engine fuel consumption rate at varying speeds and wave angle encountered. This has been tested successfully on an oil tanker and a container ship. A GRIDS system has been developed to indicate the combination of potential routes and the corresponding weather forecast along each route between departure port and destination. By integrating the GRIDS system with the proposed semi-empirical ship operational performance prediction model, a weather routing model and a speed optimization model have been developed for voyage optimization. In this study, weather routing is achieved by optimum route selection. Its objectives include minimum passage time and minimum fuel consumption under fixed main engine output. Speed optimization is achieved by evaluating the predicted main engine fuel consumption with different speed combinations along the voyage, while a fixed Estimated Time of Arrival(ETA) is set as the constraint. Finally, the main findings are discussed and conclusions are drawn with some recommendations for future research.Voyage optimization is the endeavour to select the optimum route and optimum speed along the voyage in order to maximise the ship performance in energy efficiency and the reduction of the Green House Gas emission footprint within fixed voyage duration. For achieving these goals, it is essential to develop an easy-to-use and accurate enough ship operational performance prediction model, which is the main aim of this study. A detailed critical review of the literature regarding the prediction of ship’s added resistance in waves and its operational performance modelling has been carried out. The existing research gap has been identified and addressed herein. The empirical added resistance prediction formulae have been improved based on the actual ship operational performance data and developed as a semi-empirical added resistance prediction method, which estimates the speed loss due to added resistance. Together with the calm water resistance model, propulsion efficiency model, main engine Specific Fuel Oil Consumption (SFOC) diagram, correction factor indicating fouling effect on fuel consumption, and actual ship operational performance data, the novel semi-empirical ship operational performance prediction model for oil tanker and container ship have been developed and validated. The easy-to-use and practical semi-empirical model is able to accurately predict main engine fuel consumption rate at varying speeds and wave angle encountered. This has been tested successfully on an oil tanker and a container ship. A GRIDS system has been developed to indicate the combination of potential routes and the corresponding weather forecast along each route between departure port and destination. By integrating the GRIDS system with the proposed semi-empirical ship operational performance prediction model, a weather routing model and a speed optimization model have been developed for voyage optimization. In this study, weather routing is achieved by optimum route selection. Its objectives include minimum passage time and minimum fuel consumption under fixed main engine output. Speed optimization is achieved by evaluating the predicted main engine fuel consumption with different speed combinations along the voyage, while a fixed Estimated Time of Arrival(ETA) is set as the constraint. Finally, the main findings are discussed and conclusions are drawn with some recommendations for future research

Gifted English language learners: the cultural and practical experiences of Chinese students learning English in Scotland and in China

This research investigates the potential influences of cultural aspects and contextual practical experiences in relation to the achievement of Chinese gifted English language learners (GELL) studying in China and Scotland. The proportion of English language learners (ELL) in state schools in China has experienced a rapid increase. In sharp contrast to this context, Chinese ELLs are often overlooked for their potential gifted ability in English language learning and are underrepresented within the field of gifted and talented education. However, it is plausible that a Chinese ELL could achieve excellence in English language learning, depending on the effectiveness of the learning, training and support provided. From a review of the literature, it is evident that the identification of, and support for, gifted learners has been subject to extensive investigation. However, there is a dearth of studies looking specifically at GELLs and taking into account the practical and cultural aspects which may influence their identification, learning experiences and achievement. Chinese ELL students studying abroad in English-speaking countries are likely to encounter a diversity of educational methods, cultures and learning environments. In this regard, the comparison of two groups of English-speaking Chinese students – one in Scotland and one in China – offers insights into the distinctions between teaching and learning methodologies experienced by gifted English-speaking Chinese students across these different contexts. This qualitative research used semi-structured in-depth interviews with twelve English language teachers, thirteen Chinese GELLs and nine parents in total in Scotland and China. A snowball-sampling method was implemented to recruit participants. The interviews were digitally-recorded and transcribed, and the transcripts analysed in accordance with thematic analysis under the technique of Interpretative Phenomenological Analysis (IPA). There is no agreed definition within the literature as to what constitutes a GELL. Current literature from the field of gifted education, alongside EEL literature relating to proficiency in English Language learning was used to examine the participants’ understandings of the characteristics of a GELL. Common characteristics were identified and ELL practices within the language learning classroom were explored to better understand how GELL interact with learning opportunities. The study highlights the complex interactions that take place between the selected method of teaching for ELL, the psychological aspects to language learning, such as motivation, attitude, self-concept (cognitive and affective) and cognition along with ii the sociocultural aspects of learning. The students’ demonstrated self-regulated learning strategies which in turn impacted on how the learning strategies within the class were used. How the teacher organises learning to allow these processes to interact is crucial. Findings from the study suggest that a communicative approach can offer optimal learning experiences for GELL. An important finding from this study is that while teachers in both China and Scotland could describe the characteristics of a GELL, and on the basis of this were willing to nominate GELL to participate in the study, they were not always sure how to best support them in the classroom, even when a communicative approach to language learning had been adopted. The primary implication of the findings of this study for professional practice is that English language instruction using the communicative approach can greatly enhance the learning experience for GELLs, but teachers need to actively think about GELL when planning learning opportunities. Where language learning instruction focuses on those who require support, GELLs risk being overlooked and their skills and abilities will remain unchallenged and undeveloped. Teachers’ understanding of GELL and how ELL practices can be harnessed to provide for, and identify, GELL is key to supporting this often over-looked group of learners

Languages put restrictions on large sonority distances

An underlying assumption in terms of sonority distances is that clusters with large sonority distances are more common than those with small distances, as captured in the unmarked status of large sonority distances and formalized in terms of sonority constraints on consonant clusters. A cross-linguistic survey of attested sonority distances in 357 languages reveals that large sonority distances are not most commonly attested. Rather, there is a point of sonority distance at which the largest number of languages is attested. When the sonority distance exceeds a particular value, the number of languages starts to decrease, regardless of the sonority scales tested. The finding puts the unmarked status of large sonority distances to the test, suggesting a potential constraint that prevents large distances from surfacing

Seafarers’ current awareness, knowledge, motivation and ideas towards Low Carbon – Energy Efficient operations

Worldwide there is increasing concern regarding green house gases, in particular carbon emissions and their detrimental effects to our earth’s atmosphere; resulting in climate change. International and National pressure requires the shipping industry to play its’ role in reducing the 3.3% of total global carbon emission that it currently emits into the atmosphere. On the 1st January 2013 the IMO are expected to enforce mandatory measures to reduce shipping carbon emissions and these measures will directly and indirectly affect the daily operations of seafarers, onshore performance staff, and managerial personnel with influence over operational procedures. It is therefore imperative that these personnel have the awareness, knowledge, skills, and motivation necessary to successfully implement the operational changes that are needed. A questionnaire has been distributed to investigate seafarers’ and onshore personnels current levels of awareness, knowledge and motivation towards carbon emissions in general and towards shipping carbon emissions. The questionnaire also asked participants to contribute which level of personnel have the most influence over carbon changes and what are the most important operational improvements that can be made. 317 questionnaire responses were collected in total and the analysis of the results is discussed within this paper. The primary benefit of this study has been to support the development of a specific Low Carbon – Energy Efficiency maritime education and training program, by identifying target group needs and attitudes, and key areas for focus