The Law of the Sea: International Law Implications of the U.S. Refusal to Sign the Treaty

This study is undertaken to show the impact of the international law aspects of the Law of the Sea Treaty as they relate to the U.S. failure to sign the Treaty. The U.S. has embarked on a course of action, by its refusal to sign the Treaty, that can have major impact on day-to-day national policy issues as they relate to international law governing a state\u27s behavior. The U.S., in refusing to sign the Treaty because of the deep seabed provisions, and yet claiming other provisions as reflecting customary law, is probably correct as viewed in today\u27s realities

Characterizing Safety: Minimal Control Barrier Functions from Scalar Comparison Systems

Verifying set invariance has classical solutions stemming from the seminal work by Nagumo, and defining sets via a smooth barrier function constraint inequality results in computable flow conditions for guaranteeing set invariance. While a majority of these historic results on set invariance consider flow conditions on the boundary, this letter fully characterizes set invariance through minimal barrier functions by directly appealing to a comparison result to define a flow condition over the entire domain of the system. A considerable benefit of this approach is the removal of regularity assumptions of the barrier function. This letter also outlines necessary and sufficient conditions for a valid differential inequality condition, giving the minimum conditions for this type of approach. We also show when minimal barrier functions are necessary and sufficient for set invariance

q-series and L-functions related to half-derivatives of the Andrews--Gordon identity

Studied is a generalization of Zagier's q-series identity. We introduce a generating function of L-functions at non-positive integers, which is regarded as a half-differential of the Andrews--Gordon q-series. When q is a root of unity, the generating function coincides with the quantum invariant for the torus knot.Comment: 21 pages, related papers can be found from http://gogh.phys.s.u-tokyo.ac.jp/~hikami

Distinguishing environmental effects on binary black hole gravitational waveforms

Future gravitational wave interferometers such as the Laser Interferometer Space Antenna, Taiji, DECi-hertz Interferometer Gravitational wave Observatory and TianQin will enable precision studies of the environment surrounding black holes. These detectors will probe the millihertz frequency range, as yet unexplored by current gravitational wave detectors. Furthermore, sources will remain in band for durations of up to years, meaning that the inspiral phase of the gravitational wave signal, which can be affected by the environment, will be observable. In this paper, we study intermediate and extreme mass ratio binary black hole inspirals, and consider three possible environments surrounding the primary black hole: accretion disks, dark matter spikes and clouds of ultra-light scalar fields, also known as gravitational atoms. We present a Bayesian analysis of the detectability and measurability of these three environments. Focusing for concreteness on the case of a detection with LISA, we show that the characteristic imprint they leave on the gravitational waveform would allow us to identify the environment that generated the signal and to accurately reconstruct its model parameters.</p

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table