Two decades of numerical modelling to understand long term fluvial archives: Advances and future perspectives

The development and application of numerical models to investigate fluvial sedimentary archives has increased during the last decades resulting in a sustained growth in the number of scientific publications with keywords, 'fluvial models', 'fluvial process models' and 'fluvial numerical models'. In this context we compile and review the current contributions of numerical modelling to the understanding of fluvial archives. In particular, recent advances, current limitations, previous unexpected results and future perspectives are all discussed. Numerical modelling efforts have demonstrated that fluvial systems can display non-linear behaviour with often unexpected dynamics causing significant delay, amplification, attenuation or blurring of externally controlled signals in their simulated record. Numerical simulations have also demonstrated that fluvial records can be generated by intrinsic dynamics without any change in external controls. Many other model applications demonstrate that fluvial archives, specifically of large fluvial systems, can be convincingly simulated as a function of the interplay of (palaeo) landscape properties and extrinsic climate, base level and crustal controls. All discussed models can, after some calibration, produce believable matches with real world systems suggesting that equifinality - where a given end state can be reached through many different pathways starting from different initial conditions and physical assumptions - plays an important role in fluvial records and their modelling. The overall future challenge lies in the development of new methodologies for a more independent validation of system dynamics and research strategies that allow the separation of intrinsic and extrinsic record signals using combined fieldwork and modelling

Reconciling model and information uncertainty in development appraisal

This paper investigates the effect of choices of model structure and scale in development viability appraisal. The paper addresses two questions concerning the application of development appraisal techniques to viability modelling within the UK planning system. The first relates to the extent to which, given intrinsic input uncertainty, the choice of model structure significantly affects model outputs. The second concerns the extent to which, given intrinsic input uncertainty, the level of model complexity significantly affects model outputs. Monte Carlo simulation procedures are applied to a hypothetical development scheme in order to measure the effects of model aggregation and structure on model output variance. It is concluded that, given the particular scheme modelled and unavoidably subjective assumptions of input variance, simple and simplistic models may produce similar outputs to more robust and disaggregated models

Parental involvement in sport:psychometric development and empirical test of a theoretical model

The purposes of the present multistudy were to develop and provide initial construct validity for measures based on the model of parental involvement in sport (Study 1) and examine structural relationships among the constructs of the model (Study 2). In Study 1 (nparents = 342, nathletes = 223), a confirmatory factor analysis was used to verify the psychometric properties of the measures. Content and construct validity were evaluated, as well individual and composite reliability. Multi-group analysis with two independent samples provided evidence of factorial invariance. In Study 2 (nparents = 754, nathletes = 438), structural equation modeling analysis supported the hypothesised model in which athletes’ perceptions of parents’ behaviours mediated the relationship between parents’ reported behaviours and the athletes’ psychological variables conducive to their achievement in sport. The findings provide support for the parental involvement in sport model and demonstrate the role of perceptions of parents’ behaviours on young athletes’ cognitions in sport

III-V Solar Cells

III-V materials show a wide range of gaps making them ideal for the design of high efficiency solar cells. This chapter reviews relevant growth methods and material properties of these materials and discusses methods of combining heterogeneous materials, demonstrating the flexibility of design possible with these materials. The fundamental loss mechanisms of solar cells are analysed and quantified as a prelude to analysing high efficiency cell designs in single, tandem, and triple junction solar cells. The detailed analysis of loss mechanisms is used to obtain understanding of the limiting behaviour of these designs, and show that bulk cells remain dominated by non-radiative losses despite unity ideality factors. To conclude, this is contrasted with the operating regime of nanostructured solar cells which can be shown to operate in a radiatively dominated mode, and which therefore approach ideal solar cell efficiency limits.Comment: Draft of chapter in Materials Challenges: Inorganic Photovoltaic Solar Energy - RSC Energy and Environment Series v. 1

Not All Dialogues are Created Equal: Instance Weighting for Neural Conversational Models

Neural conversational models require substantial amounts of dialogue data for their parameter estimation and are therefore usually learned on large corpora such as chat forums or movie subtitles. These corpora are, however, often challenging to work with, notably due to their frequent lack of turn segmentation and the presence of multiple references external to the dialogue itself. This paper shows that these challenges can be mitigated by adding a weighting model into the architecture. The weighting model, which is itself estimated from dialogue data, associates each training example to a numerical weight that reflects its intrinsic quality for dialogue modelling. At training time, these sample weights are included into the empirical loss to be minimised. Evaluation results on retrieval-based models trained on movie and TV subtitles demonstrate that the inclusion of such a weighting model improves the model performance on unsupervised metrics.Comment: Accepted to SIGDIAL 201

Reconciling Model and Information Uncertainty in Development Appraisal

This paper investigates the effect of choices of model structure and scale in development viability appraisal. The paper addresses two questions concerning the application of development appraisal techniques to viability modelling within the UK planning system. The first relates to the extent to which, given intrinsic input uncertainty, the choice of model structure significantly affects model outputs. The second concerns the extent to which, given intrinsic input uncertainty, the level of model complexity significantly affects model outputs. Monte Carlo simulation procedures are applied to a hypothetical development scheme in order to measure the effects of model aggregation and structure on model output variance. It is concluded that, given the particular scheme modelled and unavoidably subjective assumptions of input variance, simple and simplistic models may produce similar outputs to more robust and disaggregated models.

Weblogs in Higher Education - Why Do Students (Not) Blog?

Positive impacts on learning through blogging, such as active knowledge construction and reflective writing, have been reported. However, not many students use weblogs in informal contexts, even when appropriate facilities are offered by their universities. While motivations for blogging have been subject to empirical studies, little research has addressed the issue of why students choose not to blog. This paper presents an empirical study undertaken to gain insights into the decision making process of students when deciding whether to keep a blog or not. A better understanding of students' motivations for (not) blogging may help decision makers at universities in the process of selecting, introducing, and maintaining similar services. As informal learning gains increased recognition, results of this study can help to advance appropriate designs of informal learning contexts in Higher Education. The method of ethnographic decision tree modelling was applied in an empirical study conducted at the Vienna University of Technology, Austria. Since 2004, the university has been offering free weblog accounts for all students and staff members upon entering school, not bound to any course or exam. Qualitative, open interviews were held with 3 active bloggers, 3 former bloggers, and 3 non‑ bloggers to elicit their decision criteria. Decision tree models were developed out of the interviews. It turned out that the modelling worked best when splitting the decision process into two parts: one model representing decisions on whether to start a weblog at all, and a second model representing criteria on whether to continue with a weblog once it was set up. The models were tested for their validity through questionnaires developed out of the decision tree models. 30 questionnaires have been distributed to bloggers, former bloggers and non‑ bloggers. Results show that the main reasons for students not to keep a weblog include a preference for direct (online) communication, and concerns about the loss of privacy through blogging. Furthermore, the results indicate that intrinsic motivation factors keep students blogging, whereas stopping a weblog is mostly attributable to external factors

Probabilistic computing with future deep sub-micrometer devices: a modelling approach

An approach is described that investigates the potential of probabilistic "neural" architectures for computation with deep sub-micrometer (DSM) MOSFETs. Initially, noisy MOSFET models are based upon those for a 0.35 /spl mu/m MOS technology with an exaggerated 1/f characteristic. We explore the manifestation of the 1/f characteristic at the output of a 2-quadrant multiplier when the key n-channel MOSFETs are replaced by "noisy" MOSFETs. The stochastic behavior of this noisy multiplier has been mapped on to a software (Matlab) model of a continuous restricted Boltzmann machine (CRBM) - an analogue-input stochastic computing structure. Simulation of this DSM CRBM implementation shows little degradation from that of a "perfect" CRBM. This paper thus introduces a methodology for a form of "technology-downstreaming" and highlights the potential of probabilistic architectures for DSM computation

From Word to Sense Embeddings: A Survey on Vector Representations of Meaning

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence Researc

Non Linear Modelling of Financial Data Using Topologically Evolved Neural Network Committees

Most of artificial neural network modelling methods are difficult to use as maximising or minimising an objective function in a non-linear context involves complex optimisation algorithms. Problems related to the efficiency of these algorithms are often mixed with the difficulty of the a priori estimation of a network's fixed topology for a specific problem making it even harder to appreciate the real power of neural networks. In this thesis, we propose a method that overcomes these issues by using genetic algorithms to optimise a network's weights and topology, simultaneously. The proposed method searches for virtually any kind of network whether it is a simple feed forward, recurrent, or even an adaptive network. When the data is high dimensional, modelling its often sophisticated behaviour is a very complex task that requires the optimisation of thousands of parameters. To enable optimisation techniques to overpass their limitations or failure, practitioners use methods to reduce the dimensionality of the data space. However, some of these methods are forced to make unrealistic assumptions when applied to non-linear data while others are very complex and require a priori knowledge of the intrinsic dimension of the system which is usually unknown and very difficult to estimate. The proposed method is non-linear and reduces the dimensionality of the input space without any information on the system's intrinsic dimension. This is achieved by first searching in a low dimensional space of simple networks, and gradually making them more complex as the search progresses by elaborating on existing solutions. The high dimensional space of the final solution is only encountered at the very end of the search. This increases the system's efficiency by guaranteeing that the network becomes no more complex than necessary. The modelling performance of the system is further improved by searching not only for one network as the ideal solution to a specific problem, but a combination of networks. These committces of networks are formed by combining a diverse selection of network species from a population of networks derived by the proposed method. This approach automatically exploits the strengths and weaknesses of each member of the committee while avoiding having all members giving the same bad judgements at the same time. In this thesis, the proposed method is used in the context of non-linear modelling of high-dimensional financial data. Experimental results are'encouraging as both robustness and complexity are concerned.Imperial Users onl