Better text compression from fewer lexical n-grams

Word-based context models for text compression have the capacity to outperform more simple character-based models, but are generally unattractive because of inherent problems with exponential model growth and corresponding data sparseness. These ill-effects can be mitigated in an adaptive lossless compression scheme by modelling syntactic and semantic lexical dependencies independently

Automatically linking MEDLINE abstracts to the Gene Ontology

Much has been written recently about the need for effective tools and methods for mining the wealth of information present in biomedical literature (Mack and Hehenberger, 2002; Blagosklonny and Pardee, 2001; Rindflesch et al., 2002)—the activity of conceptual biology. Keyword search engines operating over large electronic document stores (such as PubMed and the PNAS) offer some help, but there are fundamental obstacles that limit their effectiveness. In the first instance, there is no general consensus among scientists about the vernacular to be used when describing research about genes, proteins, drugs, diseases, tissues and therapies, making it very difficult to formulate a search query that retrieves the right documents. Secondly, finding relevant articles is just one aspect of the investigative process. A more fundamental goal is to establish links and relationships between facts existing in published literature in order to “validate current hypotheses or to generate new ones” (Barnes and Robertson, 2002)—something keyword search engines do little to support

Multi-argument classification for semantic role labeling

This paper describes a Multi-Argument Classification (MAC) approach to Semantic Role Labeling. The goal is to exploit dependencies between semantic roles by simultaneously classifying all arguments as a pattern. Argument identification, as a pre-processing stage, is carried at using the improved Predicate-Argument Recognition Algorithm (PARA) developed by Lin and Smith (2006). Results using standard evaluation metrics show that multi-argument classification, archieving 76.60 in F₁ measurement on WSJ 23, outperforms existing systems that use a single parse tree for the CoNLL 2005 shared task data. This paper also describes ways to significantly increase the speed of multi-argument classification, making it suitable for real-time language processing tasks that require semantic role labelling

Investigation of the aerodynamic characteristics of a lifting body in ground proximity

The use of cambered hull shapes in the next generation of lighter-than-air vehicles to enhance aerodynamic performance, together with optimized take-off manoeuvre profiles, will require a more detailed understanding of ground proximity effects for such aircraft. A series of sub-scale wind tunnel tests at Re = 1.4 x 106 on a 6:1 prolate spheroid are used to identify potential changes in aerodynamic lift, drag and pitching moment coefficients that are likely to be experienced on the vehicle hull in isolation when in close ground proximity. The experimental data is supported by a preliminary assessment of surface pressure changes using a high order panel method (PANAIR) and RANS CFD simulations to assess the flow structure. The effect of ground proximity, most evident when non-dimensional ground clearance (h/c) < 0.3, is to reduce lift coefficient, increase drag coefficient and increase the body pitching moment coefficient

Using the online cross-entropy method to learn relational policies for playing different games

By defining a video-game environment as a collection of objects, relations, actions and rewards, the relational reinforcement learning algorithm presented in this paper generates and optimises a set of concise, human-readable relational rules for achieving maximal reward. Rule learning is achieved using a combination of incremental specialisation of rules and a modified online cross-entropy method, which dynamically adjusts the rate of learning as the agent progresses. The algorithm is tested on the Ms. Pac-Man and Mario environments, with results indicating the agent learns an effective policy for acting within each environment

Continuous and Reinforcement Learning Methods for First-Person Shooter Games

Machine learning is now widely studied as thebasis for artificial intelligence systems within computer games.Most existing work focuses on methods for learning staticexpert systems, typically emphasizing candidate selection. Thispaper extends this work by exploring the use of continuous andreinforcement learning techniques to develop fully-adaptivegame AI for first-person shooter bots. We begin by outlining aframework for learning static control models for tanks withinthe game BZFlag, then extend that framework using continuouslearning techniques that allow computer controlled tanks to adaptto the game style of other players, extending overall playability bythwarting attempts to infer the underlying AI. We further showhow reinforcement learning can be used to create bots that learnhow to play based solely through trial and error, providing gameengineers with a practical means to produce large numbers ofbots, each with individual intelligences and unique behaviours;all from a single initial AI model

Detection and Characterization of the Tin Dihydride (SnH\u3csub\u3e2\u3c/sub\u3e and SnD\u3csub\u3e2\u3c/sub\u3e) Molecule in the Gas Phase

The SnH2 and SnD2 molecules have been detected for the first time in the gas phase by laser-induced fluorescence (LIF) and emission spectroscopic techniques through the Ã1B1–X̃1A1 electronic transition. These reactive species were prepared in a pulsed electric discharge jet using (CH3)4Sn or SnH4/SnD4 precursors diluted in high pressure argon. Transitions to the electronic excited state of the jet-cooled molecules were probed with LIF, and the ground state energy levels were measured from single rovibronic level emission spectra. The LIF spectrum of SnD2 afforded sufficient rotational structure to determine the ground and excited state geometries: r″0 = 1.768 Å, θ″0 = 91.0°, r′0 = 1.729 Å, θ′0 = 122.9°. All of the observed LIF bands show evidence of a rotational-level-dependent predissociation process which rapidly decreases the fluorescence yield and lifetime with increasing rotational angular momentum in each excited vibronic level. This behavior is analogous to that observed in SiH2 and GeH2 and is suggested to lead to the formation of ground state tin atoms and hydrogen molecules

The impact of study support : a report of a longitudinal study into the impact of participation in out-of-school-hours learning on the academic attainment, attitudes and school attendance of secondary school students

Study support makes a difference. It has an impact on three key aspects of students’ school careers: • attainment at GCSE and KS3 SATs; • attitudes to school; • attendance at school. These findings were consistent for all groups of students in all schools in the study. - Study support can help to improve schools and can influence the attitudes to learning of teachers and parents as well as students

Laser-Induced Fluorescence Detection of the Elusive SiCF Free Radical

The SiCF free radical has been spectroscopically identified for the first time. The radical was produced in an electric discharge jet using CF3Si(CH3)3 or CF3SiH3 vapor in high pressure argon as the precursor. The laser-induced fluorescence spectrum of the à 2Σ+ − X̃ 2Π band system in the 610 − 550 nm region was recorded and the 2Π3/2 spin component of the 0—0 band was studied at high resolution. Rotational analysis gave the B values for the combining states, and by fixing the CF bond lengths at ab initio values we obtained r″(Si–C) = 1.692(1)Å and r′(Si–C) = 1.594(1)Å. The bond lengths correspond to a silicon-carbon double bond in the ground state and an unusual Si−C triple bond in the excited state. Single vibronic level emission spectra yielded the ground state bending and stretching energy levels. These were fitted to a Renner-Teller model that included spin-orbit and limited vibrational anharmonicity effects