Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Rapid growth in nanotechnology is increasing the likelihood of engineered nanomaterials coming into contact with humans and the environment. Nanoparticles interacting with proteins, membranes, cells, DNA and organelles establish a series of nanoparticle/biological interfaces that depend on colloidal forces as well as dynamic biophysicochemical interactions. These interactions lead to the formation of protein coronas, particle wrapping, intracellular uptake and biocatalytic processes that could have biocompatible or bioadverse outcomes. For their part, the biomolecules may induce phase transformations, free energy releases, restructuring and dissolution at the nanomaterial surface. Probing these various interfaces allows the development of predictive relationships between structure and activity that are determined by nanomaterial properties such as size, shape, surface chemistry, roughness and surface coatings. This knowledge is important from the perspective of safe use of nanomaterials.

Understanding biophysicochemical interactions at the nano–bio interface

Worldwide commercial interest in carbon nanotubes (CNTs) is reflected in a production capacity that presently exceeds several thousand tons per year. Currently, bulk CNT powders are incorporated in diverse commercial products ranging from rechargeable batteries, automotive parts, and sporting goods to boat hulls and water filters. Advances in CNT synthesis, purification, and chemical modification are enabling integration of CNTs in thin-film electronics and large-area coatings. Although not yet providing compelling mechanical strength or electrical or thermal conductivities for many applications, CNT yarns and sheets already have promising performance for applications including supercapacitors, actuators, and lightweight electromagnetic shields.

/pdf/carbon-nanotubes-present-and-future-commercial-applications-1nhq3xy4hh.pdf

Carbon Nanotubes: Present and Future Commercial Applications

Skyrmions represent topologically stable field configurations with particle-like properties. We used neutron scattering to observe the spontaneous formation of a two-dimensional lattice of skyrmion lines, a type of magnetic vortex, in the chiral itinerant-electron magnet MnSi. The skyrmion lattice stabilizes at the border between paramagnetism and long-range helimagnetic order perpendicular to a small applied magnetic field regardless of the direction of the magnetic field relative to the atomic lattice. Our study experimentally establishes magnetic materials lacking inversion symmetry as an arena for new forms of crystalline order composed of topologically stable spin states.

Skyrmion Lattice in a Chiral Magnet

Magnetic skyrmions are particle-like nanometre-sized spin textures of topological origin found in several magnetic materials, and are characterized by a long lifetime. Skyrmions have been observed both by means of neutron scattering in momentum space and microscopy techniques in real space, and their properties include novel Hall effects, current-driven motion with ultralow current density and multiferroic behaviour. These properties can be understood from a unified viewpoint, namely the emergent electromagnetism associated with the non-coplanar spin structure of skyrmions. From this description, potential applications of skyrmions as information carriers in magnetic information storage and processing devices are envisaged.

Topological properties and dynamics of magnetic skyrmions

The inherent power of the biological brain, with regard to pattern recognition, is unparalleled and cannot even be matched by multi-million dollar supercomputers. Inspired from this, neuromorphic computation, where ideas originating from the complex structure and functionality of the biological brain are utilized for advanced computation has shown great potential. In this regard, we are developing on-chip pattern classification capabilities via inexpensive self-assembly of nanoparticles (NPs). The formation of percolating microstructure of Sn NPs and tunnel junctions leads to a complex atomic-switch network (ASN) poised near criticality. Voltage stimulation is utilized for modulating the synaptic structure of the network, which shows potential for utilization as a ‘reservoir’ in reservoir computing (RC).

Complex network dynamics in self-assembled atomic-switch networks: prospects for neuromorphic computation

Mobile excitons and Fermi edge singularities in an interacting 2D electron gas

On 28 March 2008 a historic process of award modernization, by which Australia's award system will be fundamentally changed, began. Over 4000 existing federal awards and NAPSAs will be replaced by modern awards created primarily along industry lines, and occasionally along occupational lines. Modern awards will operate from January 2010 and, together with the National Employment Standards, will constitute the safety net under Labor's industrial relations system. This article considers some aspects of the award modernization process and the pivotal role of the AIRC in creating modern awards. This article also considers recent case law dealing with the implied duties to act in good faith and of mutual trust and confidence.

Major Tribunal Decisions in 2008

We review research in New Zealand on nano-electronic devices fabricated using atomic clusters as building blocks. Techniques described include those based on percolation, and on self (or directed) assembly in templates. The templates used are V-grooves etched into silicon wafers and lithographically defined patterns in PMMA on silicon. Both methods exploit the tendency, which is demonstrated by both experiments and molecular dynamics simulations, for clusters to bounce when incident with sufficient kinetic energy on a surface. We then review the application of cluster techniques to examples including the filling of high aspect ratio trenches with copper clusters for interconnect applications, fabrication of hydrogen sensors based on palladium clusters, and development of gas sensors (specifically for hydrogen and ammonia) using a process based on the oxidation of tin clusters.

Atomic cluster device research in New Zealand

The invention relates to a method of preparing a pattern of micron sized, and smaller particles on a substrate surface. The pattern can take the form of a conducting pathway of atomic clusters between contacts. The method of the invention can be an alternative to conventional lithographic patterning techniques which does not require a lift-off step.

Simon Brown

Papers

Complex network dynamics in self-assembled atomic-switch networks: prospects for neuromorphic computation

Mobile excitons and Fermi edge singularities in an interacting 2D electron gas

Major Tribunal Decisions in 2008

Atomic cluster device research in New Zealand

Nanoscale and microscale lithography methods and resultant devices