Phonon and plasmon excitation in inelastic electron tunneling spectroscopy of graphite

The inelastic electron tunneling spectrum (IETS)of highly oriented pyrolitic graphite (HOPG) has been measured with scanning tunneling spectroscopy (STS) at 6K. The observed spectral features are in very good agreement with the vibrational density of states (vDOS) of graphite calculated from first principles. We discuss the enhancement of certain phonon modes by phonon-assisted tunneling in STS based on the restrictions imposed by the electronic structure of graphite. We also demonstrate for the first time the local excitation of surface-plasmons in IETS which are detected at an energy of 40 meV.Comment: PRB rapid communication, submitte

A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings

The purpose of this study was to evaluate a signal regularity-based automated seizure prediction algorithm for scalp EEG. Signal regularity was quantified using the Pattern Match Regularity Statistic (PMRS), a statistical measure. The primary feature of the prediction algorithm is the degree of convergence in PMRS (“PMRS entrainment”) among the electrode groups determined in the algorithm training process. The hypothesis is that the PMRS entrainment increases during the transition between interictal and ictal states, and therefore may serve as an indicator for prediction of an impending seizure.Запропоновано алгоритм автоматизованого прогнозування епілептичного нападу на основі аналізу регулярності сигналу ЕЕГ головного мозку. Регулярність сигналу розраховується на основі введеної величини регулярної статистики збігу фрагментів (Pattern Match Regularity Statistics — PMRS). Відмінною рисою алгоритму є ступінь збіжності в значеннях PMRS, розрахованих на основі показань із різних груп електродів, визначених у процесі навчання алгоритму. В основі алгоритму лежить гіпотеза про те, що збіжність у значеннях величини PMRS збільшується під час переходу в стан нападу і в такий спосіб може слугувати індикатором для прогнозування нападу

Signal regularity-based automated seizure detection system for scalp EEG monitoring

Розглянуто роботу автоматизованої системи реєстрації ЕЕГ головного мозку для раннього виявлення епілептичних нападів. Розроблено комп’ютерний алгоритм для перетворення складних багатоканальних сигналів ЕЕГ мозку на кілька динамічних показників, супроводжуваних дослідженнями їхніх просторово-часових властивостей. Робота алгоритму аналізується на великому клінічному наборі даних.The purpose of the present study was to build a clinically useful automated seizure detection system for scalp EEG recordings. To achieve this, a computer algorithm was designed to translate complex multi-channel scalp EEG signals into several dynamical descriptors, followed by the investigations of their spatiotemporal properties that relate to the ictal (seizure) EEG patterns as well as to normal physiologic and artifact signals. This paper describes in detail this novel seizure detection algorithm and reports its performance in a large clinical dataset

High-time Resolution Astrophysics and Pulsars

The discovery of pulsars in 1968 heralded an era where the temporal characteristics of detectors had to be reassessed. Up to this point detector integration times would normally be measured in minutes rather seconds and definitely not on sub-second time scales. At the start of the 21st century pulsar observations are still pushing the limits of detector telescope capabilities. Flux variations on times scales less than 1 nsec have been observed during giant radio pulses. Pulsar studies over the next 10 to 20 years will require instruments with time resolutions down to microseconds and below, high-quantum quantum efficiency, reasonable energy resolution and sensitive to circular and linear polarisation of stochastic signals. This chapter is review of temporally resolved optical observations of pulsars. It concludes with estimates of the observability of pulsars with both existing telescopes and into the ELT era.Comment: Review; 21 pages, 5 figures, 86 references. Book chapter to appear in: D.Phelan, O.Ryan & A.Shearer, eds.: High Time Resolution Astrophysics (Astrophysics and Space Science Library, Springer, 2007). The original publication will be available at http://www.springerlink.co

SciPy 1.0: fundamental algorithms for scientific computing in Python.

SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments

Global change effects on plant communities are magnified by time and the number of global change factors imposed

Global change drivers (GCDs) are expected to alter community structure and consequently, the services that ecosystems provide. Yet, few experimental investigations have examined effects of GCDs on plant community structure across multiple ecosystem types, and those that do exist present conflicting patterns. In an unprecedented global synthesis of over 100 experiments that manipulated factors linked to GCDs, we show that herbaceous plant community responses depend on experimental manipulation length and number of factors manipulated. We found that plant communities are fairly resistant to experimentally manipulated GCDs in the short term (<10 y). In contrast, long-term (?10 y) experiments show increasing community divergence of treatments from control conditions. Surprisingly, these community responses occurred with similar frequency across the GCD types manipulated in our database. However, community responses were more common when 3 or more GCDs were simultaneously manipulated, suggesting the emergence of additive or synergistic effects of multiple drivers, particularly over long time periods. In half of the cases, GCD manipulations caused a difference in community composition without a corresponding species richness difference, indicating that species reordering or replacement is an important mechanism of community responses to GCDs and should be given greater consideration when examining consequences of GCDs for the biodiversity.ecosystem function relationship. Human activities are currently driving unparalleled global changes worldwide. Our analyses provide the most comprehensive evidence to date that these human activities may have widespread impacts on plant community composition globally, which will increase in frequency over time and be greater in areas where communities face multiple GCDs simultaneously. - 2019 National Academy of Sciences. All rights reserved.ACKNOWLEDGMENTS. This work was conducted as a part of a Long-Term Ecological Research (LTER) Synthesis Group funded by NSF Grants EF-0553768 and DEB#1545288 through the LTER Network Communications Office and the National Center for Ecological Analysis and Synthesis, University of California, Santa Barbara. M.L.A. was supported by a fellowship from the Socio-Environmental Synthesis Center (SESYNC), which also provided computing support. SESYNC is funded by NSF Grant DBI-1052875. Funding for individual experiments included in this analysis can be found in SI Appendix, section 7.Scopu