Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences Review articles
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Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences RSS feed -- recent Review articles articles1471-2946Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences1364-5021<![CDATA[Globular cluster formation and evolution in the context of cosmological galaxy assembly: open questions]]>
http://rspa.royalsocietypublishing.org/cgi/content/short/474/2210/20170616?rss=1
We discuss some of the key open questions regarding the formation and evolution of globular clusters (GCs) during galaxy formation and assembly within a cosmological framework. The current state of the art for both observations and simulations is described, and we briefly mention directions for future research. The oldest GCs have ages greater than or equal to 12.5 Gyr and formed around the time of reionization. Resolved colour-magnitude diagrams of Milky Way GCs and direct imaging of lensed proto-GCs at z~6 with the James Webb Space Telescope (JWST) promise further insight. GCs are known to host multiple populations of stars with variations in their chemical abundances. Recently, such multiple populations have been detected in ~2 Gyr old compact, massive star clusters. This suggests a common, single pathway for the formation of GCs at high and low redshift. The shape of the initial mass function for GCs remains unknown; however, for massive galaxies a power-law mass function is favoured. Significant progress has been made recently modelling GC formation in the context of galaxy formation, with success in reproducing many of the observed GC-galaxy scaling relations.
]]>2018-02-14T00:05:27-08:00info:doi/10.1098/rspa.2017.0616hwp:master-id:royprsa;rspa.2017.06162018-02-14Review articles47422102017061620170616<![CDATA[Quantum machine learning: a classical perspective]]>
http://rspa.royalsocietypublishing.org/cgi/content/short/474/2209/20170551?rss=1
Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.
]]>2018-01-17T00:05:29-08:00info:doi/10.1098/rspa.2017.0551hwp:master-id:royprsa;rspa.2017.05512018-01-17Review articles47422092017055120170551<![CDATA[How to characterize a nonlinear elastic material? A review on nonlinear constitutive parameters in isotropic finite elasticity]]>
http://rspa.royalsocietypublishing.org/cgi/content/short/473/2207/20170607?rss=1
The mechanical response of a homogeneous isotropic linearly elastic material can be fully characterized by two physical constants, the Young’s modulus and the Poisson’s ratio, which can be derived by simple tensile experiments. Any other linear elastic parameter can be obtained from these two constants. By contrast, the physical responses of nonlinear elastic materials are generally described by parameters which are scalar functions of the deformation, and their particular choice is not always clear. Here, we review in a unified theoretical framework several nonlinear constitutive parameters, including the stretch modulus, the shear modulus and the Poisson function, that are defined for homogeneous isotropic hyperelastic materials and are measurable under axial or shear experimental tests. These parameters represent changes in the material properties as the deformation progresses, and can be identified with their linear equivalent when the deformations are small. Universal relations between certain of these parameters are further established, and then used to quantify nonlinear elastic responses in several hyperelastic models for rubber, soft tissue and foams. The general parameters identified here can also be viewed as a flexible basis for coupling elastic responses in multi-scale processes, where an open challenge is the transfer of meaningful information between scales.
]]>2017-11-29T01:11:16-08:00info:doi/10.1098/rspa.2017.0607hwp:master-id:royprsa;rspa.2017.06072017-11-29Review articles47322072017060720170607<![CDATA[Analysing causal structures with entropy]]>
http://rspa.royalsocietypublishing.org/cgi/content/short/473/2207/20170483?rss=1
A central question for causal inference is to decide whether a set of correlations fits a given causal structure. In general, this decision problem is computationally infeasible and hence several approaches have emerged that look for certificates of compatibility. Here, we review several such approaches based on entropy. We bring together the key aspects of these entropic techniques with unified terminology, filling several gaps and establishing new connections, all illustrated with examples. We consider cases where unobserved causes are classical, quantum and post-quantum, and discuss what entropic analyses tell us about the difference. This difference has applications to quantum cryptography, where it can be crucial to eliminate the possibility of classical causes. We discuss the achievements and limitations of the entropic approach in comparison to other techniques and point out the main open problems.
]]>2017-11-01T03:11:33-07:00info:doi/10.1098/rspa.2017.0483hwp:master-id:royprsa;rspa.2017.04832017-11-01Review articles47322072017048320170483