The Good Men of Suan Kulap An Ethnographic Genealogy of an Elite Thai School and the Making of Political Subjects

This thesis is concerned with the institutional culture of Suan Kulap Withayalai, Thailand’s oldest state-run secondary school and the alma mater of seven prime ministers. Using the critical theory of genealogy combined with methodologies common to ethnography, it traces the gradual development of the school’s symbolic and disciplinary infrastructure through time. In addition, it also provides a descriptive analysis of the school’s ritual events and ideological projects during the 2018/9 academic year. Using a range of sources, including historical documents, personal writings, life histories, and participant observation, the thesis explores how the school has been deeply implicated in the maintenance of power in Thailand and how it has sought to produce certain kinds of political subjects favourable to the interest of a shifting centre. It positions Suan Kulap as an overlooked institution in the scholarship of Thailand’s political culture. The thesis also augments and challenges the current literature pertaining to Thai education, which relies heavily on the study of pedagogic materials and has tended to characterise students as submissive recipients of the state’s ideological messaging. Highlighting the writings and words of those who worked and studied at the school, the thesis creates an alternative narrative of crypto-colonial innovation and sporadic conflict that is assiduously suppressed by the school administration. At a time when Thai high school students are protesting en masse against what they see as an authoritarian educational system, this thesis provides a historically informed ethnography of the practices that Thai schools engage in to maintain political stasis. More generally, the thesis is an intimate portrait of an institution deeply implicated, and reflective of, the ideological struggles and psychosocial movements that have shaped recent Thai history

Sparse Representation of 3D Images for Piecewise Dimensionality Reduction with High Quality Reconstruction

Sparse representation of 3D images is considered within the context of data reduction. The goal is to produce high quality approximations of 3D images using fewer elementary components than the number of intensity points in the 3D array. This is achieved by means of a highly redundant dictionary and a dedicated pursuit strategy especially designed for low memory requirements. The benefit of the proposed framework is illustrated in the first instance by demonstrating the gain in dimensionality reduction obtained when approximating true color images as very thin 3D arrays, instead of performing an independent channel by channel approximation. The full power of the approach is further exemplified by producing high quality approximations of hyper-spectral images with a reduction of up to 371 times the number of data points in the representation

Monte Carlo Tree Search for games with Hidden Information and Uncertainty

Monte Carlo Tree Search (MCTS) is an AI technique that has been successfully applied to many deterministic games of perfect information, leading to large advances in a number of domains, such as Go and General Game Playing. Imperfect information games are less well studied in the field of AI despite being popular and of significant commercial interest, for example in the case of computer and mobile adaptations of turn based board and card games. This is largely because hidden information and uncertainty leads to a large increase in complexity compared to perfect information games. In this thesis MCTS is extended to games with hidden information and uncertainty through the introduction of the Information Set MCTS (ISMCTS) family of algorithms. It is demonstrated that ISMCTS can handle hidden information and uncertainty in a variety of complex board and card games. This is achieved whilst preserving the general applicability of MCTS and using computational budgets appropriate for use in a commercial game. The ISMCTS algorithm is shown to outperform the existing approach of Perfect Information Monte Carlo (PIMC) search. Additionally it is shown that ISMCTS can be used to solve two known issues with PIMC search, namely strategy fusion and non-locality. ISMCTS has been integrated into a commercial game, Spades by AI Factory, with over 2.5 million downloads. The Information Capture And ReUSe (ICARUS) framework is also introduced in this thesis. The ICARUS framework generalises MCTS enhancements in terms of information capture (from MCTS simulations) and reuse (to improve MCTS tree and simulation policies). The ICARUS framework is used to express existing enhancements, to provide a tool to design new ones, and to rigorously define how MCTS enhancements can be combined. The ICARUS framework is tested across a wide variety of games

Does venous thromboembolism prophylaxis affect the risk of venous thromboembolism and adverse events following primary hip and knee replacement?:A retrospective cohort study

BACKGROUND: The optimum chemical venous thromboembolism (VTE) prophylactic agents following total hip and knee replacement (THR and TKR) remain unknown. NICE recommends multiple agents, including direct oral anticoagulants (DOACs), low-molecular weight heparin (LMWH), and aspirin. We assessed whether VTE prophylaxis affected the risk of VTE and adverse events following primary THR and TKR. MATERIALS AND METHODS: We reviewed 982 elective primary THRs (59%) and TKRs (41%) at a large tertiary centre during 2018. The primary outcome was any VTE (DVT and/or PE) within 90-days. Secondary outcomes were adverse events within 90-days (major bleeding and wound complications). The association between VTE prophylaxis and outcomes was assessed. RESULTS: The overall prevalence of VTE and adverse events were 2.7% (n = 27) and 15.2% (n = 136) respectively. The most common agents used were DOAC ± LMWH (50.7%, n = 498), followed by aspirin ± LMWH (35.5%, n = 349) and LMWH alone (4.7%, n = 46). The risk of VTE (aspirin ± LMWH = 3.7%, DOAC = 2.0%, LMWH = 2.2%) was not significantly different between agents (p = 0.294). The risk of any adverse event was significantly higher (p < 0.001) with aspirin ± LMWH (16.1%; n = 56) and LMWH (28.3%; n = 13) compared with DOACs ± LMWH (7.0%; n = 35) in TKRs only, there was no differences between agents for adverse events in THRs (p = 0.644). CONCLUSIONS: Choice of thromboprophylaxis did not influence the risk of VTE following primary THR and TKR. DOACs (+/− LMWH) were associated with the lowest risk of adverse events. Large multicentre trials are still needed to assess the efficacy and safety of these agents following THR and TKR

Dusty gas with SPH - II. Implicit timestepping and astrophysical drag regimes

In a companion paper (Laibe & Price 2011b), we have presented an algorithm for simulating two-fluid gas and dust mixtures in Smoothed Particle Hydrodynamics (SPH). In this paper, we develop an implicit timestepping method that preserves the exact conservation of the both linear and angular momentum in the underlying SPH algorithm, but unlike previous schemes, allows the iterations to converge to arbitrary accuracy and is suited to the treatment of non- linear drag regimes. The algorithm presented in Paper I is also extended to deal with realistic astrophysical drag regimes, including both linear and non-linear Epstein and Stokes drag. The scheme is benchmarked against the test suite presented in Paper I, including i) the analytic solutions of the dustybox problem and ii) solutions of the dustywave, dustyshock, dustysedov and dustydisc obtained with explicit timestepping. We find that the implicit method is 1- 10 times faster than the explicit temporal integration when the ratio r between the the timestep and the drag stopping time is 1 < r < 1000.Comment: Accepted for publication in MNRA

A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

Seshat: The Global History Databank

The vast amount of knowledge about past human societies has not been systematically organized and, therefore, remains inaccessible for empirically testing theories about cultural evolution and historical dynamics. For example, what evolutionary mechanisms were involved in the transition from the small-scale, uncentralized societies, in which humans lived 10,000 years ago, to the large-scale societies with an extensive division of labor, great differentials in wealth and power, and elaborate governance structures of today? Why do modern states sometimes fail to meet the basic needs of their populations? Why do economies decline, or fail to grow? In this article, we describe the structure and uses of a massive databank of historical and archaeological information, Seshat: The Global History Databank. The data that we are currently entering in Seshat will allow us and others to test theories explaining how modern societies evolved from ancestral ones, and why modern societies vary so much in their capacity to satisfy their members’ basic human needsPeer reviewedFinal Published versio

The 2013 Multi-objective Physical Travelling Salesman Problem Competition

This paper presents the game, framework, rules and results of the Multi-objective Physical Travelling Salesman Problem (MO-PTSP) Competition, that was held at the 2013 IEEE Conference on Computational Intelligence in Games (CIG). The MO-PTSP is a real-time game that can be seen as a modification of the Travelling Salesman Problem, where the player controls a ship that must visit a series of waypoints in a maze while minimizing three opposing goals: Time spent, fuel consumed and damage taken. The rankings of the competition are computed using multi-objective concepts, a novel approach in the field of game artificial intelligence competitions. The winning entry of the contest is also explained in detail. This controller is based on the Monte Carlo Tree Search algorithm, and employed Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for parameter tuning