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Daniel Potts

Bio: Daniel Potts is an academic researcher from Chemnitz University of Technology. The author has contributed to research in topics: Fast Fourier transform & Fourier transform. The author has an hindex of 37, co-authored 158 publications receiving 5305 citations. Previous affiliations of Daniel Potts include University of California, Irvine & University of Lübeck.


Papers
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Journal ArticleDOI
TL;DR: It is shown that pulse size regulates C balance by determining the temporal duration of activity for different components of the biota, and a greater understanding of the complexities of these eco-hydrologic systems may enhance the ability to describe the ecology of desert ecosystems and their sensitivity to global change.
Abstract: In the arid and semiarid regions of North America, discrete precipitation pulses are important triggers for biological activity. The timing and magnitude of these pulses may differentially affect the activity of plants and microbes, combining to influence the C balance of desert ecosystems. Here, we evaluate how a “pulse” of water influences physiological activity in plants, soils and ecosystems, and how characteristics, such as precipitation pulse size and frequency are important controllers of biological and physical processes in arid land ecosystems. We show that pulse size regulates C balance by determining the temporal duration of activity for different components of the biota. Microbial respiration responds to very small events, but the relationship between pulse size and duration of activity likely saturates at moderate event sizes. Photosynthetic activity of vascular plants generally increases following relatively larger pulses or a series of small pulses. In this case, the duration of physiological activity is an increasing function of pulse size up to events that are infrequent in these hydroclimatological regions. This differential responsiveness of photosynthesis and respiration results in arid ecosystems acting as immediate C sources to the atmosphere following rainfall, with subsequent periods of C accumulation should pulse size be sufficient to initiate vascular plant activity. Using the average pulse size distributions in the North American deserts, a simple modeling exercise shows that net ecosystem exchange of CO2 is sensitive to changes in the event size distribution representative of wet and dry years. An important regulator of the pulse response is initial soil and canopy conditions and the physical structuring of bare soil and beneath canopy patches on the landscape. Initial condition influences responses to pulses of varying magnitude, while bare soil/beneath canopy patches interact to introduce nonlinearity in the relationship between pulse size and soil water response. Building on this conceptual framework and developing a greater understanding of the complexities of these eco-hydrologic systems may enhance our ability to describe the ecology of desert ecosystems and their sensitivity to global change.

966 citations

Journal ArticleDOI
TL;DR: This article provides a survey on the mathematical concepts behind the NFFT and its variants, as well as a general guideline for using the library.
Abstract: NFFT 3 is a software library that implements the nonequispaced fast Fourier transform (NFFT) and a number of related algorithms, for example, nonequispaced fast Fourier transforms on the sphere and iterative schemes for inversion. This article provides a survey on the mathematical concepts behind the NFFT and its variants, as well as a general guideline for using the library. Numerical examples for a number of applications are given.

376 citations

Book ChapterDOI
01 Jan 2001
TL;DR: The robustness of NDFT algorithms with respect to roundoff errors is discussed, and approximative methods for the fast computation of multivariate discrete Fourier transforms for nonequispaced data are considered.
Abstract: In this chapter we consider approximativemethods for the fast computation of multivariate discrete Fourier transforms for nonequispaced data (NDFT) in the time domain and in the frequency domain. In particularwe are interested in the approximation error as function of the arithmetic complexity of the algorithm. We discuss the robustness of NDFTiaalgorithms with respect to roundoff errors and applyNDFTalgorithms for the fast computation of Besseltransforms.

321 citations

Journal ArticleDOI
TL;DR: In this paper, the authors show that grassland ecosystem response to N deposition will be strongly dependent on future precipitation patterns and that water and N addition in combination in combination led to increased dominance of the two most abundant grass species, while N addition regardless of water availability led to decreased species diversity.
Abstract: The world's ecosystems are experiencing simultaneous changes in the supply of multiple limiting resources. Two of these, water and nitrogen (N) can strongly limit grassland production and can affect community composition and biogeochemical cycles in different ways. Grassland ecosystems in California may be particularly vulnerable to current and predicted changes in precipitation and N deposition, and ecosystem responses to potential interactive effects of water and N are not well understood. Here, we show strong colimitation of plant production resulting from factorial addition of water and N. In addition, water and N addition in combination led to increased dominance of the two most abundant grass species, while N addition regardless of water availability led to decreased species diversity. Late season carbon (C) flux response to water addition depended on N. Only plots that received additional water, but not N, still showed net ecosystem C uptake at the end of the experiment. Our results suggest that grassland ecosystem response to N deposition will be strongly dependent on future precipitation patterns.

309 citations

Journal ArticleDOI
TL;DR: In this article, the authors used energy and carbon dioxide fluxes from eddy covariance along with standard meteorological and soil moisture measurements at a semiarid savanna in southern Arizona, United States, to better understand the consequences of warm or cool season drought on ecosystem CO2 exchange in these bimodally forced water-limited regions.
Abstract: [1] Annual precipitation in the central and southern warm-desert region of North America is distributed climatologically between summer and winter periods with two prominent dry periods between them. We used energy and carbon dioxide (CO2) fluxes from eddy covariance along with standard meteorological and soil moisture measurements at a semiarid savanna in southern Arizona, United States, to better understand the consequences of warm or cool season drought on ecosystem CO2 exchange in these bimodally forced water-limited regions. Over the last 100 years, this historic grassland has converted to a savanna by the encroachment of the native mesquite tree (Prosopis velutina Woot.). During each of the 4 years of observation (2004–2007), annual precipitation (P) was below average, but monsoon (July–September) P was both above and below average while cool-season (December–March) P was always less than average by varying degrees. The ecosystem was a net source of CO2 to the atmosphere, ranging from 14 to 95 g C m � 2 yr � 1 with the strength of the source increasing with decreasing precipitation. When the rainfall was closest to the long-term average in its distribution and amount, the ecosystem was essentially carbon neutral. Summer drought resulted in increased carbon losses due mainly to a shortening of the growing season and the length of time later in the season when photosynthetic gain exceeds respiration loss. Severe cool season drought led to decreased spring carbon uptake and seemingly enhanced summer respiration, resulting in conditions that led to the greatest annual net carbon loss.

218 citations


Cited by
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01 May 1993
TL;DR: 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.
Abstract: 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.

29,323 citations

Journal ArticleDOI
TL;DR: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols used xiii 1.
Abstract: 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

14,171 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: 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.).

13,246 citations

Journal ArticleDOI
10 Mar 1970

8,159 citations

Journal ArticleDOI
12 Apr 2007-Nature
TL;DR: Previous two-dimensional electronic spectroscopy investigations of the FMO bacteriochlorophyll complex are extended, and direct evidence is obtained for remarkably long-lived electronic quantum coherence playing an important part in energy transfer processes within this system is obtained.
Abstract: 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.

2,981 citations