Seeking new forms of expression in computer music, a small number of laptop composers are braving the challenges of coding music on the fly. Not content to submit meekly to the rigid interfaces of performance software like Ableton Live or Reason, they work with programming languages, building their own custom software, tweaking or writing the programs themselves as they perform. Often this activity takes place within some established language for computer music like SuperCollider, but there is no reason to stop errant minds pursuing their innovations in general scripting languages like Perl. This paper presents an introduction to the field of live coding, of real-time scripting during laptop music performance, and the improvisatory power and risks involved. We look at two test cases, the command-line music of slub utilising, amongst a grab-bag of technologies, Perl and REALbasic, and Julian Rohrhuber's Just In Time library for SuperCollider. We try to give a flavour of an exciting but hazardous world at the forefront of live laptop performance.

SYNOPTIC ABSTRACTThe results of an extensive Simulated Annealing algorithm for reported. The papers and books are with particular reference to the employed. literature survey of the optimization problems are classified and annotated, type of cooling schedule

* Corresponding author 1 Research applying machine learning to music modeling and generation typically proposes model architectures, training methods and datasets, and gauges system performance using quantitative measures like sequence likelihoods and/or qualitative listening tests. Rarely does such work explicitly question and analyse its usefulness for and impact on real-world practitioners, and then build on those outcomes to inform the development and application of machine learning. This article attempts to do these things for machine learning applied to music creation. Together with practitioners, we develop and use several applications of machine learning for music creation, and present a public concert of the results. We reflect on the entire experience to arrive at several ways of advancing these and similar applications of machine learning to music creation.

General practice will play a key role in both prevention and management of an influenza pandemic. Australian pandemic plans acknowledge a role for general practice, but there are few published data addressing the issues that general practitioners and their practices will face in dealing with such a crisis. The outcome will revolve around preparation in three key areas: ➢Definition of the role of general practice within a broad primary care pandemic response, and adequate preparation within general practices so they can play that role well. Planning exercises and forums must include GPs, and rehearsals must include practical experience for general practices and their staff. Local Divisions of General Practice and GP practices can advocate for this, can define their role, and can prepare by using pandemic preparedness checklists. ➢Definition and enactment of communication strategies to facilitate transfer of useful clinical and administrative data from practices and rapid dissemination of information into the community via general practice. ➢Resource provision, which should be centrally funded but locally distributed, with personal protective equipment, vaccines and antivirals readily available for distribution. Resources must include support for human resource management to ensure appropriate health care professionals reach areas of workforce demand. Administrative, clinical and financial resources must be available to train GPs and practices in pandemic awareness and response.

The UT Austin Villa team, from the University of Texas at Austin, won the RoboCup 3D Simulation League in 2012 having also won the competition the previous year. This paper describes the changes and improvements made to the team between 2011 and 2012 that allowed it to repeat as champions.