714 research outputs found
Assessing prioritization measures for a private land conservation program in the U.S. Prairie Pothole Region
Private land conservation has become an important tool for protecting biodiversity and habitat, but methods for prioritizing and scheduling conservation on private land are still being developed. While return on investment methods have been suggested as a potential path forward, the different processes linking private landscapes to the socioeconomic systems in which they are embedded create unique challenges for scheduling conservation with this approach. We investigated a range of scheduling approaches within a return on investment framework for breeding waterfowl and broods in the Prairie Pothole Region of North Dakota, South Dakota, and Montana. Current conservation targeting for waterfowl in the region focuses mostly on the distribution and abundance of breeding waterfowl. We tested whether MaxGain approaches for waterfowl conservation differed from MinLoss approaches in terms of return on investment and which approach performed best in avoiding loss of waterfowl and broods separately. We also examined variation in results based upon the temporal scale of the abundance layers used for input and compared the region's current scheduling approach with results from our simulations. Our results suggested that MinLoss was the most efficient scheduling approach for both breeding waterfowl and broods and that using just breeding waterfowl to target areas for conservation programs might cause organizations to overlook important areas for broods, particularly over shorter timespans. The higher efficiency of MinLoss approaches in our simulations also indicated that incorporating probability of wetland drainage into decision-making improved the overall return on investment. We recommend that future conservation scheduling for easements in the region and for private land conservation in general include some form of return on investment or cost-effective analysis to make conservation more transparent
Astrophysics with the Laser Interferometer Space Antenna
The Laser Interferometer Space Antenna (LISA) will be a transformative experiment for gravitational wave astronomy, and, as such, it will offer unique opportunities to address many key astrophysical questions in a completely novel way. The synergy with ground-based and space-born instruments in the electromagnetic domain, by enabling multi-messenger observations, will add further to the discovery potential of LISA. The next decade is crucial to prepare the astrophysical community for LISA's first observations. This review outlines the extensive landscape of astrophysical theory, numerical simulations, and astronomical observations that are instrumental for modeling and interpreting the upcoming LISA datastream. To this aim, the current knowledge in three main source classes for LISA is reviewed; ultra-compact stellar-mass binaries, massive black hole binaries, and extreme or interme-diate mass ratio inspirals. The relevant astrophysical processes and the established modeling techniques are summarized. Likewise, open issues and gaps in our understanding of these sources are highlighted, along with an indication of how LISA could help making progress in the different areas. New research avenues that LISA itself, or its joint exploitation with upcoming studies in the electromagnetic domain, will enable, are also illustrated. Improvements in modeling and analysis approaches, such as the combination of numerical simulations and modern data science techniques, are discussed. This review is intended to be a starting point for using LISA as a new discovery tool for understanding our Universe
Stochastic Modeling of Intraday Electricity Markets
Limit-Orderbücher sind das Standardinstrument der Preisbildung in modernen Finanzmärkten. Während Strom traditionell in Auktionen gehandelt wird, gibt es Intraday Strommärkte wie beispielsweise den SIDC-Markt, in welchem Käufer und Verkäufer über Limit-Orderbücher zusammentreffen. In dieser Arbeit werden wir stochastische Modelle von Limit-Orderbüchern auf der Grundlage der zugrundeliegenden Marktmikrostruktur entwickeln. Einen besonderen Schwerpunkt legen wir dabei auf die Berücksichtigung besonderer Merkmale der Intraday-Strommärkte, die sich zum Teil deutlich von denen der Finanzmärkte unterscheiden. Die in dieser Arbeit entwickelten Modelle beginnen mit einer realistischen und mikroskopischen Beschreibung der Marktdynamik. Große Preisänderungen über kurze Zeiträume werden ebenso berücksichtigt wie begrenzte grenzüberschreitende Aktivitäten. Diese mikroskopischen Modelle sind im Allgemeinen zu rechenintensiv für praktische Anwendungen. Das Hauptziel dieser Arbeit ist es daher, geeignete Approximationen dieser mikroskopischen Modelle durch sogenannte Skalierungsgrenzprozesse herzuleiten. Zu diesem Zweck werden sorgfältig Skalierungsannahmen formuliert und in die mikroskopischen Modelle eingebaut. Diese Annahmen ermöglichen es uns, ihr Hochfrequenzverhalten zu untersuchen, vorausgesetzt, dass die Größe eines einzelnen Auftrags gegen Null konvergiert, während die Auftragseingangsrate gegen unendlich tendiert. Die Kalibrierung mathematischer Modelle ist aus Anwendersicht eines der Hauptanliegen. Dabei ist bekannt, dass Änderungspunkte (abrupte Schwankungen) in hochfrequenten Finanzdaten vorhanden sind. Falls sie durch endogene Effekte verursacht wurden, muss bei der Schätzung solcher Änderungspunkte die Abhängigkeit von den zugrundeliegenden Daten berücksichtigt werden. Daher erweitern wir im letzten Teil dieser Arbeit die bestehende Literatur zur Erkennung von Änderungspunkten, so dass auch zufällige, von den Daten abhängige Änderungspunkte gehandhabt werden können.Limit order books are the standard instrument for price formation in modern financial markets. While electricity has traditionally been traded through auctions, there are intraday electricity markets, such as the SIDC market, in which buyers and sellers meet via limit order books. In this thesis, stochastic models of limit order books are developed based on the underlying market microstructure. A particular focus is set on incorporating unique characteristics of intraday electricity markets, some of which are quite different from those of financial markets. The developed models in this thesis start with a realistic and microscopic description of the market dynamics. Large price changes over short time periods are considered, as well as limited cross-border activities. These microscopic models are generally computationally too intensive for practical applications. The main goal of this thesis is therefore to derive suitable approximations of these microscopic models by so-called scaling limits. For this purpose, appropriate scaling assumptions are carefully formulated and incorporated into the microscopic models which allow us to study their high-frequency behavior when the size of an individual order converges to zero while the order arrival rate tends to infinity. Calibration of mathematical models is one of the main concerns from a practitioner’s point of view. It is well known that change points (abrupt variations) are present in high-frequency financial data. If they are caused by endogenous effects, the dependence on the underlying data must be considered when estimating such change points. In the final part of this thesis, we extend the existing literature on change point detection so that random change points depending on the data can also be handled
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Modeling Events and Interactions through Temporal Processes -- A Survey
In real-world scenario, many phenomena produce a collection of events that
occur in continuous time. Point Processes provide a natural mathematical
framework for modeling these sequences of events. In this survey, we
investigate probabilistic models for modeling event sequences through temporal
processes. We revise the notion of event modeling and provide the mathematical
foundations that characterize the literature on the topic. We define an
ontology to categorize the existing approaches in terms of three families:
simple, marked, and spatio-temporal point processes. For each family, we
systematically review the existing approaches based based on deep learning.
Finally, we analyze the scenarios where the proposed techniques can be used for
addressing prediction and modeling aspects.Comment: Image replacement
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