Off-Shell NN Potential and Triton Binding Energy

The NONLOCAL Bonn-B potential predicts 8.0 MeV binding energy for the triton (in a charge-dependent 34-channel Faddeev calculation) which is about 0.4 MeV more than the predictions by LOCAL NN potentials. We pin down origin and size of the nonlocality in the Bonn potential, in analytic and numeric form. The nonlocality is due to the use of the correct off-shell Feynman amplitude of one-boson-exchange avoiding the commonly used on-shell approximations which yield the local potentials. We also illustrate how this off-shell behavior leads to more binding energy. We emphasize that the increased binding energy is not due to on-shell differences (differences in the fit of the NN data or phase shifts). In particular, the Bonn-B potential reproduces accurately the $\epsilon_1$ mixing parameter up to 350 MeV as determined in the recent Nijmegen multi-energy NN phase-shift analysis. Adding the relativistic effect from the relativistic nucleon propagators in the Faddeev equations, brings the Bonn-B result up to 8.2 MeV triton binding. This leaves a difference of only 0.3 MeV to experiment, which may possibly be explained by refinements in the treatment of relativity and the inclusion of other nonlocalities (e.~g., quark-gluon exchange at short range). Thus, it is conceivable that a realistic NN potential which describes the NN data up to 300 MeV correctly may explain the triton binding energy without recourse to 3-N forces; relativity would play a major role for this result.Comment: 5 pages LaTeX and 2 figures (hardcopies, available upon reqest), UI-NTH-940

An intelligent genetic algorithm for PAPR reduction in a multi-carrier CDMA wireless system

Abstract— A novel intelligent genetic algorithm (GA), called Minimum Distance guided GA (MDGA) is proposed for peak-average-power ratio (PAPR) reduction based on partial transmit sequence (PTS) scheme in a synchronous Multi-Carrier Code Division Multiple Access (MC-CDMA) system. In contrast to traditional GA, our MDGA starts with a balanced ratio of exploration and exploitation which is maintained throughout the process. It introduces a novel replacement strategy which increases significantly the convergence rate and reduce dramatically computational complexity as compared to the conventional GA. The simulation results demonstrate that, if compared to the PAPR reduction schemes using exhaustive search and traditional GA, our scheme achieves 99.52% and 50+% reduction in computational complexity respectively

Inconsistences in Interacting Agegraphic Dark Energy Models

It is found that the origin agegraphic dark energy tracks the matter in the matter-dominated epoch and then the subsequent dark-energy-dominated epoch becomes impossible. It is argued that the difficulty can be removed when the interaction between the agegraphic dark energy and dark matter is considered. In the note, by discussing three different interacting models, we find that the difficulty still stands even in the interacting models. Furthermore, we find that in the interacting models, there exists the other serious inconsistence that the existence of the radiation/matter-dominated epoch contradicts the ability of agegraphic dark energy in driving the accelerated expansion. The contradiction can be avoided in one of the three models if some constraints on the parameters hold.Comment: 12 pages, no figure; analysis is added; conclusion is unchange

Access and benefit sharing in participatory plant breeding in Southwest China

This article discusses access and benefit sharing within the context of participatory plant breeding. It presents how Chinese farmers and breeders collaborate in relation to crop improvement and on-farm maintenance of plant genetic resources. Based on more than a decade of action-research, a number of institutional changes were accomplished as a result of the interactions between national and provincial breeding institutes, rural development researchers and local maize farmers. Although the respective legislation in China is not yet adequately formulated, access and benefit sharing can still be addressed in contracts and by labelling products of a particular geographic origin

Remembering ...

published_or_final_versio

Revisiting Affordances for Learning in Mobile Technology Based Environments

The term “affordance” has been used in Information Communications Technology (ICT) based environment to explore the opportunities the educational technologies provide for students. Gibson (1979/1986) defines affordances as the possibilities that the environment offers for the perceiver. The perceiver and the environment make an inseparable pair. In the case of learning, affordances are those relationships that provide a “match” between something in the environment and the learner (Van Lier, 2004) as a whole. The learning environment is a complex system consists of different components and layers. By becoming integrated components of the environment, different learning entities, whether social or individual, constitute affordances for each other. Through interactions, these affordances are integrated, and transform into effectivities (Visser, 2001). Besides, according to Gibson, affordances are invariant. However, they can be increased when the intensity of stimulation changes. They can also form higher order affordances when primary affordances are always found in particular combinations. Finally, Gibson (1979/1986) argues that affordances are not neutral. A focus on affordances for learning in mobile technology-based environment helps us identify how the environment as an integrated whole provides support for students’ learning, what and how components of the environment interact to provide various affordances.published_or_final_versionCentre of Information Technology in Education, University of Hong Kong and Education and Manpower Bureau, the Government of the Hong Kong SA

Learning from heterogeneous data by Bayesian networks

University of Technology, Sydney. Faculty of Engineering and Information Technology.Non-i.i.d. data breaks the traditional assumption that all data points are independent and identically distributed. It is commonly seen in a wide range of application domains, such as transactional data, pattern recognition data, multimedia data, biomedical data and social media data. Two challenges of learning with such data are the existence of strong coupling relationships and mixed structures (heterogeneity) in the data. This thesis mainly focuses on learning from heterogeneous data, which refers to the non-i.i.d. data with mixed structures. To cater for the learning from such heterogeneous data, this thesis presents a number of algorithms based on Bayesian networks (BNs) that provide an effective and efficient method for representation of heterogeneous structures. A wide spectrum of non-i.i.d. data with different heterogeneity is studied. The heterogeneous data investigated in this thesis includes sequential data of unequal lengths, biomedical data mixed with time series and multivariate attributes, and social media data with both user/user friendship networks and user/item preference matrix. Specifically, for modeling a database of sequential behaviors with different lengths, latent Dirichlet hidden Markov models (LDHMMs), are designed to capture the dependent relationships in two levels (i.e., sequence-level and database-level). To learn the parameters of the model, we propose a variational EM-based algorithm. The learned model achieves substantial or comparable improvement over the-state-of-the-art models on predictive tasks, such as predicting unseen sequences and sequence classification. For learning miscellaneous data in clinical gait analysis, whose data consists of both sequential data and multivariate data, a correlated static-dynamic model (CSDM) is constructed. An EM-based framework is applied to estimate the model parameters and some intuitive knowledge can be extracted from the model as by-products. Then, for learning more complicated social media data that records both the user/user friendship networks and user/item preference (rating) matrix in social media, we propose a joint interest-social model (JISM). We approximate the lower bound of the likelihood of the observed user/user and user/item interaction data and propose an iterative approach to learn the model parameters under the variational EM framework. The learned model is then used to predict unknown ratings and generally outperforms other comparison methods. Besides the above pure BNs-based models, we also propose a hybrid approach in the context of the sequence anomaly detection problem. This is because the estimation of the parameters of pure BNs-based model usually falls into local minimums, which may further generate inaccurate results for the sequence anomaly detection. Thus, we propose a model-based feature extractor combined with a discriminative classifier (i.e., SVM) to overcome the above issue, which is theoretically proved to have better performance in terms of Bayes error. The empirical results also support our theoretical proof. To sum up, this dissertation provides a novel perspective from Bayesian networks to harness the heterogeneity of non-i.i.d. data and offers effective and efficient solutions to learning such heterogeneous data