Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

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

J. Appl. Cryst.の発刊に際して

Photosynthesis provides the primary energy source for almost all life on Earth. One of its remarkable features is the efficiency with which energy is transferred within the light harvesting complexes comprising the photosynthetic apparatus. Suspicions that quantum trickery might be involved in the energy transfer processes at the core of photosynthesis are now confirmed by a new spectroscopic study. The study reveals electronic quantum beats characteristic of wavelike energy motion within the bacteriochlorophyll complex from the green sulphur bacterium Chlorobium tepidum. This wavelike characteristic of the energy transfer process can explain the extreme efficiency of photosynthesis, in that vast areas of phase space can be sampled effectively to find the most efficient path for energy transfer. A spectroscopic study has directly monitored the quantum beating arising from remarkably long-lived electronic quantum coherence in a bacteriochlorophyll complex. This wavelike characteristic of the energy transfer process can explain the extreme efficiency of photosynthesis, in that vast areas of phase space can be sampled effectively to find the most efficient path for energy transfer. Photosynthetic complexes are exquisitely tuned to capture solar light efficiently, and then transmit the excitation energy to reaction centres, where long term energy storage is initiated. The energy transfer mechanism is often described by semiclassical models that invoke ‘hopping’ of excited-state populations along discrete energy levels1,2. Two-dimensional Fourier transform electronic spectroscopy3,4,5 has mapped6 these energy levels and their coupling in the Fenna–Matthews–Olson (FMO) bacteriochlorophyll complex, which is found in green sulphur bacteria and acts as an energy ‘wire’ connecting a large peripheral light-harvesting antenna, the chlorosome, to the reaction centre7,8,9. The spectroscopic data clearly document the dependence of the dominant energy transport pathways on the spatial properties of the excited-state wavefunctions of the whole bacteriochlorophyll complex6,10. But the intricate dynamics of quantum coherence, which has no classical analogue, was largely neglected in the analyses—even though electronic energy transfer involving oscillatory populations of donors and acceptors was first discussed more than 70 years ago11, and electronic quantum beats arising from quantum coherence in photosynthetic complexes have been predicted12,13 and indirectly observed14. Here we extend previous two-dimensional electronic spectroscopy investigations of the FMO bacteriochlorophyll complex, and obtain direct evidence for remarkably long-lived electronic quantum coherence playing an important part in energy transfer processes within this system. The quantum coherence manifests itself in characteristic, directly observable quantum beating signals among the excitons within the Chlorobium tepidum FMO complex at 77 K. This wavelike characteristic of the energy transfer within the photosynthetic complex can explain its extreme efficiency, in that it allows the complexes to sample vast areas of phase space to find the most efficient path.

Evidence for wavelike energy transfer through quantum coherence in photosynthetic systems

We propose an approximation method for high-dimensional 1-periodic functions based on the multivariate ANOVA decomposition. We provide analysis of classical ANOVA decomposition on the torus and pro...

Approximation of High-Dimensional Periodic Functions with Fourier-Based Methods

The classic urban-rural gradient concept assumes a decline in land use intensity from an intensively developed urban core outward to residential suburbs, culminating in lightly developed rural areas The concept provides a common framework directing urban socio-ecological research Given that rural land use includes woodlands and croplands, and croplands appear as ecologically depauperate as urban lands, we investigated land-use patterns along urban-rural gradients for 30 large metropolitan areas in the eastern United States We predicted a bifurcation at the rural end of the gradient between woodland and cropland land use that does not correspond with human population density (expected to be relatively low in rural areas regardless of land use type) Our data indicated that ‘rural’ was a poor substitute for ‘natural’ as the rural end of the gradients bifurcated at the rural end between woodland and cropland – croplands being demonstratively poor ecological habitats Indeed, we found that when defined by habitat quality, the habitat known to be biotically homogenized (urban and cropland) remained steady along the urban-rural gradient Our results do not undermine the utility of the urban-rural gradient framework, but do suggest that the gradient and/or human population density do not necessarily indicate shifts in habitat quality

Rural land use bifurcation in the urban-rural gradient

We measured δ 13 C of tree ring holocellulose to assess intra- and interannual variation in integrated leaf gas exchange responses of Fremont cottonwood ( Populus fremontii ) to monsoonal moisture inputs in southeastern Arizona. We predicted that δ 13 C of trees growing along drought-susceptible intermittent reaches of this semiarid river system would be more responsive to monsoonal moisture inputs than trees found along perennial reaches, where groundwater is consistently available. We sampled stem xylem cores from 7 trees, each at an intermittent and perennial reach of the San Pedro River near Tombstone, Arizona. We identified and subdivided individual rings from 1990 to 2000. δ 13 C of holocellulose from these subdivisions was compared with precipitation amount, atmospheric vapor pressure deficit ( D a ), and 90% exceedence flows ( Q 90 ) calculated from seasonal flow duration data. δ 13 C values were higher at the intermittent reach than at the perennial reach. Furthermore, annual ring δ 13 C values at the perennial reach were not correlated with stream flow, precipitation, or D a . δ 13 C values for trees at the intermittent reach were negatively correlated with monsoon stream flow, precipitation, or D a . δ 13 C values for trees at the intermittent reach were negatively correlated with monsoon season (1 July-15 September) Q 90 ( r 2 = 0.50, P = 0.015) and positively correlated with D a ( r 2 = 0.45, P = 0.03). Shifts in δ 13 C between the inner- and outer-third of the annual ring were used as a measure of intra-annual variation. These shifts were correlated with monsoon season D a ( r 2 = 0.57, P = 0.01) and Q 90 ( r 2 = 0.59, P = 0.005) for trees growing along the intermittent reach. Intra- and interannual variation in integrated photosynthetic response exists at the population-scale for these native, riparian forests. Changes in monsoonal precipitation and stream flow may differentially alter photosynthetic gas exchange of P. fremontii and function of these riparian ecosystems.

https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=1886&context=wnan

Response of tree ring holocellulose δ 13 C to moisutre availability in Populus fremontii at perennial and intermittent stream reaches

Urban riparian plant communities exist at the interface between terrestrial and aquatic habitats, and they are rich sources of species biodiversity and ecosystem services. The periodic floods that promote species diversity in riparian plant communities also increase their vulnerability to nonnative plant invasions. Plant invasions are constrained by seed and suitable habitat availability. However, how seed dispersal and establishment limitations interact to shape nonnative plant invasions in riparian communities is poorly understood. We use Stream Visual Assessment Protocol data to evaluate the hydrological and geomorphological parameters that influence the seeding and establishment of six common nonnative species in urban riparian habitats: garlic mustard, purple loosestrife, reed canarygrass, common reed, Japanese knotweed, and multiflora rose. To address this objective, we analyzed stream reach data collected during a basin-wide environmental assessment of the extensively urbanized upper Niagara River watershed. We found limited support for our prediction that propagule limitation constrains the distribution of nonnative riparian species, likely because these species are well established in the study area. Instead, we found that opportune stream reach characteristics better predict the distribution of the common invasive riparian species—most notably open tree canopy. Given that there is widespread investment in urban riparian forest restoration to improve water quality, increase stream-bank stability, enhance wildlife habitat and promote recreation, our data suggest that riparian forests may provide the additional benefit of reducing the abundance of some, but not all, invasive plants. Nomenclature: Garlic mustard, Alliaria petiolata (Bieb.) Cavara & Grande; purple loosestrife, Lythrum salicaria L.; reed canarygrass, Phalaris arundinacea L.; common reed, Phragmites australis (Cav.) Trin. ex Steud.; Japanese

/pdf/stream-structural-limitations-on-invasive-communities-in-49p3phyqvq.pdf

Stream Structural Limitations on Invasive Communities in Urban Riparian Areas

In spherical surface wave tomography, one measures the integrals of a function defined on the sphere along great circle arcs. This forms a generalization of the Funk–Radon transform, which assigns to a function its integrals along full great circles. We show a singular value decomposition (SVD) for the surface wave tomography provided we have full data.

Daniel Potts

Papers

Approximation of High-Dimensional Periodic Functions with Fourier-Based Methods

Rural land use bifurcation in the urban-rural gradient

Response of tree ring holocellulose δ 13 C to moisutre availability in Populus fremontii at perennial and intermittent stream reaches

Stream Structural Limitations on Invasive Communities in Urban Riparian Areas

An SVD in Spherical Surface Wave Tomography