Prosecution of maritime terrorists : the role of national courts in ensuring accountability

Maritime security has emerged as a critical legal and political issue in the contemporary world. Terrorism in the maritime domain is a major maritime security issue. Ten out of the 44 major terrorist groups of the world, as identified in the US Department of State’s Country Reports on Terrorism, have maritime terrorism capabilities. Prosecution of maritime terrorists is a politically and legally difficult issue, which may create conflicts of jurisdiction. Prosecution of alleged maritime terrorists is carried out by national courts. There is no international judicial institution for the prosecution of maritime terrorists. International law has therefore anticipated a vital role for national courts in this respect. The international legal framework for combating maritime terrorism has been elaborately examined in existing literature therefore this paper will only highlight the issues regarding the prosecution of maritime terrorists. This paper argues that despite having comprehensive intentional legal framework for the prosecution of maritime terrorists there is still some scopes for conflicts of jurisdiction particularly where two or more States are interested to prosecute the same offender. This existing legal problem has been further aggravated in the post September 11 era. Due to the political and security implications, States may show reluctance in ensuring the international law safeguards of alleged perpetrators in the arrest, detention and prosecution process. Nevertheless, international law has established a comprehensive system for the prosecution of maritime terrorists where national courts is the main forum of ensuring the international law safeguards of alleged perpetrators as well as ensuring the effective prosecution of maritime terrorists thereby playing an instrumental role in establishing a rule based system for combating maritime terrorism. Using two case studies, this paper shows that the role of national courts has become more important in the present era because there may be some situations where no State is interested to initiate proceedings in international forums for vindicating rights of an alleged offender even if there is a clear evidence of violation of international human rights law in the arrest, detention and prosecution process. This paper presents that despite some bottlenecks national courts are actively playing this critical role. Overall, this paper highlights the instrumental role of national courts in the international legal order.

The evolution and biogeography of the austral horse fly tribe Scionini (Diptera: Tabanidae: Pangoniinae) inferred from multiple mitochondrial and nuclear genes

Phylogenetic relationships within the Tabanidae are largely unknown, despite their considerable medical and ecological importance. The first robust phylogenetic hypothesis for the horse fly tribe Scionini is provided, completing the systematic placement of all tribes in the subfamily Pangoniinae. The Scionini consists of seven mostly southern hemisphere genera distributed in Australia, New Guinea, New Zealand and South America. A 5757. bp alignment of 6 genes, including mitochondrial (COI and COII), ribosomal (28S) and nuclear (AATS and CAD regions 1, 3 and 4) genes, was analysed for 176 taxa using both Bayesian and maximum likelihood approaches. Results indicate the Scionini are strongly monophyletic, with the exclusion of the only northern hemisphere genus Goniops. The South American genera Fidena, Pityocera and Scione were strongly monophyletic, corresponding to current morphology-based classification schemes. The most widespread genus Scaptia was paraphyletic and formed nine strongly supported monophyletic clades, each corresponding to either the current subgenera or several previously synonymised genera that should be formally resurrected. Molecular results also reveal a newly recognised genus endemic to New Zealand, formerly placed within Scaptia. Divergence time estimation was employed to assess the global biogeographical patterns in the Pangoniinae. These analyses demonstrated that the Scionini are a typical Gondwanan group whose diversification was influenced by the fragmentation of that ancient land mass. Furthermore, results indicate that the Scionini most likely originated in Australia and subsequently radiated to New Zealand and South American by both long distance dispersal and vicariance. The phylogenetic framework of the Scionini provided herein will be valuable for taxonomic revisions of the Tabanidae.

Numerical studies and design of hollow flange channel beams subject to combined bending and shear actions

LiteSteel beam (LSB) is a cold-formed steel hollow flange channel section produced using a patented manufacturing process involving simultaneous cold-forming and dual electric resistance welding. It is commonly used as floor joists and bearers in residential, industrial and commercial buildings. Design of the LSB is governed by the Australian cold-formed steel structures code, AS/NZS 4600. Due to the geometry of the LSB, as well as its unique residual stress characteristics and initial geometric imperfections resultant of manufacturing processes, currently available design equations for common cold-formed sections are not directly applicable to the LSB. Many research studies have been carried out to evaluate the behaviour and design of LSBs subject to pure bending actions and predominant shear actions. To date, however, no investigation has been conducted into the strength of LSB sections under combined bending and shear actions. Hence experimental and numerical studies were conducted to assess the combined bending and shear behaviour of LSBs. Finite element models of LSBs were developed to simulate their combined bending and shear behaviour and strength of LSBs. They were then validated by comparing the results with available experimental test results and used in a detailed parametric study. The results from experimental and finite element analyses were compared with current AS/NZS 4600 and AS 4100 design rules. Both experimental and numerical studies show that the AS/NZS 4600 design rule based on circular interaction equation is conservative in predicting the combined bending and shear capacities of LSBs. This paper presents the details of the numerical studies of LSBs and the results. In response to the inadequacies of current approaches to designing LSBs for combined bending and shear, two lower bound design equations are proposed in this paper.

The Credit Act Advisory System (CAAS) : conversion from an expert system prototype to a C++ commercial system

CAAS is a rule-based expert system, which provides advice on the Victorial Credit Act 1984. It is currently in commercial use, and has been developed in conjunction with a law firm. It uses an object-oriented hybrid reasoning approach. The system was initially prototyped using the expert system shell NExpert Object, and was then converted into the C++ language. In this paper we describe the advantages that this methodology has, for both commercial and research development.

Designing intelligent litigation support tools : The IKBALS perspective

In the legal domain, it is rare to find solutions to problems by simply applying algorithms or invoking deductive rules in some knowledge‐based program. Instead, expert practitioners often supplement domain‐specific knowledge with field experience. This type of expertise is often applied in the form of an analogy. This research proposes to combine both reasoning with precedents and reasoning with statutes and regulations in a way that will enhance the statutory interpretation task. This is being attempted through the integration of database and expert system technologies. Case‐based reasoning is being used to model legal precedents while rule‐based reasoning modules are being used to model the legislation and other types of causal knowledge. It is hoped to generalise these findings and to develop a formal methodology for integrating case‐based databases with rule‐based expert systems in the legal domain.

Creating flexible mappings between building information models and cost information

During the early design stages of construction projects, accurate and timely cost feedback is critical to design decision making. This is particularly challenging for cost estimators, as they must quickly and accurately estimate the cost of the building when the design is still incomplete and evolving. State-of-the-art software tools typically use a rule-based approach to generate detailed quantities from the design details present in a building model and relate them to the cost items in a cost estimating database. In this paper, we propose a generic approach for creating and maintaining a cost estimate using flexible mappings between a building model and a cost estimate. The approach uses queries on the building design that are used to populate views, and each view is then associated with one or more cost items. The benefit of this approach is that the flexibility of modern query languages allows the estimator to encode a broad variety of relationships between the design and estimate. It also avoids the use of a common standard to which both designers and estimators must conform, allowing the estimator added flexibility and functionality to their work.

The desirability of depoliticization: Compliance in the international climate regime

The Kyoto Protocol is remarkable among global multilateral environmental agreements for its efforts to depoliticize compliance. However, attempts to create autonomous, arm’s length and rule-based compliance processes with extensive reliance on putatively neutral experts were only partially realized in practice in the first commitment period from 2008 to 2012. In particular, the procedurally constrained facilitative powers vested in the Facilitative Branch were circumvented, and expert review teams (ERTs) assumed pivotal roles in compliance facilitation. The ad hoc diplomatic and facilitative practices engaged in by these small teams of technical experts raise questions about the reliability and consistency of the compliance process. For the future operation of the Kyoto compliance system, it is suggested that ERTs should be confined to more technical and procedural roles, in line with their expertise. There would then be greater scope for the Facilitative Branch to assume a more comprehensive facilitative role, safeguarded by due process guarantees, in accordance with its mandate. However, if – as appears likely – the future compliance trajectories under the United Nations Framework Convention on Climate Change will include a significant role for ERTs without oversight by the Compliance Committee, it is important to develop appropriate procedural safeguards that reflect and shape the various technical and political roles these teams currently play.

Supervised latent dirichlet allocation models for efficient activity representation

Local spatio-temporal features with a Bag-of-visual words model is a popular approach used in human action recognition. Bag-of-features methods suffer from several challenges such as extracting appropriate appearance and motion features from videos, converting extracted features appropriate for classification and designing a suitable classification framework. In this paper we address the problem of efficiently representing the extracted features for classification to improve the overall performance. We introduce two generative supervised topic models, maximum entropy discrimination LDA (MedLDA) and class- specific simplex LDA (css-LDA), to encode the raw features suitable for discriminative SVM based classification. Unsupervised LDA models disconnect topic discovery from the classification task, hence yield poor results compared to the baseline Bag-of-words framework. On the other hand supervised LDA techniques learn the topic structure by considering the class labels and improve the recognition accuracy significantly. MedLDA maximizes likelihood and within class margins using max-margin techniques and yields a sparse highly discriminative topic structure; while in css-LDA separate class specific topics are learned instead of common set of topics across the entire dataset. In our representation first topics are learned and then each video is represented as a topic proportion vector, i.e. it can be comparable to a histogram of topics. Finally SVM classification is done on the learned topic proportion vector. We demonstrate the efficiency of the above two representation techniques through the experiments carried out in two popular datasets. Experimental results demonstrate significantly improved performance compared to the baseline Bag-of-features framework which uses kmeans to construct histogram of words from the feature vectors.

Bayesian computational methods for spatial analysis of images

This thesis introduces a new way of using prior information in a spatial model and develops scalable algorithms for fitting this model to large imaging datasets. These methods are employed for image-guided radiation therapy and satellite based classification of land use and water quality. This study has utilized a pre-computation step to achieve a hundredfold improvement in the elapsed runtime for model fitting. This makes it much more feasible to apply these models to real-world problems, and enables full Bayesian inference for images with a million or more pixels.

Migraine with aura and migraine without aura are not distinct entities: further evidence from a large Dutch population study

It is often debated whether migraine with aura (MA) and migraine without aura (MO) are etiologically distinct disorders. A previous study using latent class analysis (LCA) in Australian twins showed no evidence for separate subtypes of MO and MA. The aim of the present study was to replicate these results in a population of Dutch twins and their parents, siblings and partners (N = 10,144). Latent class analysis of International Headache Society (IHS)-based migraine symptoms resulted in the identification of 4 classes: a class of unaffected subjects (class 0), a mild form of nonmigrainous headache (class 1), a moderately severe type of migraine (class 2), typically without neurological symptoms or aura (8% reporting aura symptoms), and a severe type of migraine (class 3), typically with neurological symptoms, and aura symptoms in approximately half of the cases. Given the overlap of neurological symptoms and nonmutual exclusivity of aura symptoms, these results do not support the MO and MA subtypes as being etiologically distinct. The heritability in female twins of migraine based on LCA classification was estimated at .50 (95% confidence intervals [CI] .27 - .59), similar to IHS-based migraine diagnosis (h2 = .49, 95% CI .19-.57). However, using a dichotomous classification (affected-unaffected) decreased heritability for the IHS-based classification (h2 = .33, 95% CI .00-.60), but not the LCA-based classification (h2 = .51, 95% CI .23-.61). Importantly, use of the LCA-based classification increased the number of subjects classified as affected. The heritability of the screening question was similar to more detailed LCA and IHS classifications, suggesting that the screening procedure is an important determining factor in genetic studies of migraine.

Class-specific sparse codes for representing activities

In this paper we investigate the effectiveness of class specific sparse codes in the context of discriminative action classification. The bag-of-words representation is widely used in activity recognition to encode features, and although it yields state-of-the art performance with several feature descriptors it still suffers from large quantization errors and reduces the overall performance. Recently proposed sparse representation methods have been shown to effectively represent features as a linear combination of an over complete dictionary by minimizing the reconstruction error. In contrast to most of the sparse representation methods which focus on Sparse-Reconstruction based Classification (SRC), this paper focuses on a discriminative classification using a SVM by constructing class-specific sparse codes for motion and appearance separately. Experimental results demonstrates that separate motion and appearance specific sparse coefficients provide the most effective and discriminative representation for each class compared to a single class-specific sparse coefficients.

8-13 Hz fluctuations in rectal pressure are an objective marker of clitorally-induced orgasm in women

Orgasm is a subjective experience accompanied by involuntary muscle contractions. We hypothesized that orgasm in women would be distinguishable by frequency analysis of a perineal muscle-derived signal. Rectal pressure, an index of perineal muscle activity, was measured continuously in 23 healthy women during different sexual tasks: receiving clitoral stimulation, imitation of orgasm, and attempt to reach orgasm, in which case the women were asked to report whether orgasm had been reached ("orgasm") or not ("failed orgasm attempt"). We performed spectral analysis on the rectal pressure data and calculated the spectral power in the frequency bands delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-25 Hz). The most significant and most important difference in spectral power between orgasm and both control motor tasks (imitation of orgasm and failed orgasm attempt) was found in the alpha band. An objective rule based on spectral power in the alpha band recognized 94% (29/31) of orgasms and correctly labeled 69% (44/64) of all orgasm attempts as either successful or failed. Because outbursts of alpha fluctuations in rectal pressure only occurred during orgasm and not during voluntary imitation of orgasm or failed attempts, we propose that they represent involuntary contractions of muscles in the rectal vicinity. This is the first objective and quantitative measure that has a strong correspondence with the subjective experience of orgasm.

Human action recognition from video sequences

This PhD research has proposed new machine learning techniques to improve human action recognition based on local features. Several novel video representation and classification techniques have been proposed to increase the performance with lower computational complexity. The major contributions are the construction of new feature representation techniques, based on advanced machine learning techniques such as multiple instance dictionary learning, Latent Dirichlet Allocation (LDA) and Sparse coding. A Binary-tree based classification technique was also proposed to deal with large amounts of action categories. These techniques are not only improving the classification accuracy with constrained computational resources but are also robust to challenging environmental conditions. These developed techniques can be easily extended to a wide range of video applications to provide near real-time performance.