Tidal Evolution Related to Changing Sea Level; Worldwide and Regional Surveys, and the Impact to Estuaries and Other Coastal Zones

Global sea level rise understanding is critical for coastal zones, and estuaries are particularly vulnerable to water level changes. Sea level is increasing worldwide due to several climactic factors, and tidal range may also change in estuaries due to sea level rise and anthropogenic harbor improvements that may modify friction and resonance, increasing risks to population centers. Tidal range changes may further complicate the risks of sea level rise, increasing the frequency of nuisance flooding, and may affect tide-sensitive ecosystems. Higher total water levels threaten to increase flood zone areas in estuarine regions, which can impact the infrastructure, industry, and public health of coastal populations, as well as disrupting sensitive biological habitats. Therefore, it is of critical interest to analyze how tidal range changes under sea level changes. This chapter describes the tidal anomaly correlation (TAC) methodology which can quantify the tidal evolution related to sea level changes. A basin-wide survey of Pacific and Atlantic Ocean tide gauges is detailed, showing that tidal changes due to sea level rise is present at most locations surveyed. A focused regional study of Hong Kong is also described as an example of how tidal evolution can impact high population density coastal zones

Circulations in the Pearl River Estuary: Observation and Modeling

This chapter reports a cruise survey on the Pearl River Estuary (PRE) and adjacent costal water in the period between May 3, 2014 and May 11, 2014. The circulation and salinity structure were sampled for different tidal phases. With the cruise data, a “sandwich” structure of the lateral salinity distribution and a two-layer structure of longitudinal circulation were identified, together with high variations influenced by wind and tide. Furthermore, longitudinally orientated convergence or divergence of the lateral velocity close to the channel location for certain tidal conditions was observed. The finite volume community ocean model (FVCOM) is configured and run with high spatial resolution of 100 m in the PRE. An atmospheric model, the Weather Research and Forecasting (WRF) Model, is also run to provide high spatial and temporal resolution of atmospheric forcing for the FVCOM. The FVCOM modeling skill assessment is conducted using the cruise salinity and velocity data, as well as water levels, showing that the model can well simulate the velocity and salinity structures. The numerical model reveals that there is a strong neap-spring cycle for the PRE de-tided circulation with 0.37 m s−1 during the neap tide about 42% stronger than that (0.26 m s−1) during the spring tide in the surface layer

Variations of the Absorption of Chromophoric Dissolved Organic Matter in the Pearl River Estuary

Analysis of in-situ measurements during a spring cruise survey in the Pearl River Estuary (PRE) reveals that, controlled by the two-layer gravitational circulation, chromophoric dissolved organic matter (CDOM) absorption shows a clear horizontal distribution pattern at both water surface and bottom, with higher CDOM absorption and lower spectral slope in the northwestern estuary, and a reversed pattern in the southeastern estuary and near the Hong Kong waters. The surface CDOM has higher absorption and lower spectral slope than the bottom. Horizontal transport is suggested to be the dominant hydrodynamic mechanism affecting CDOM distribution pattern in the PRE. With a regional algorithm tailored for the PRE CDOM absorption retrieval, a time series of CDOM absorption and spectral slope in the PRE and the Hong Kong waters in spring from 2012 to 2018 is produced based on satellite images obtained by four sensors with different spatial and spectral resolutions: the Visible Infrared Imaging Radiometer Suite (VIIRS), the Ocean and Land Colour Instrument (OLCI), the Hyperspectral Imager for the Coastal Ocean (HICO), and the Operational Land Imager (OLI). A correlation is revealed between the multi-temporal CDOM absorption and the monthly averaged river discharge, indicating the capability of CDOM ocean color products in identifying hydrodynamic processes in estuaries and coastal waters

Global Water Level variability observed after the Hunga Tonga-Hunga Ha’apai volcanic tsunami of 2022

The eruption of the Hunga Tonga-Hunga Ha\u27apai volcano on 15 January 2022 provided a rare opportunity to understand global tsunami impacts of explosive volcanism and to evaluate future hazards, including dangers from “volcanic meteotsunamis” (VMTs) induced by the atmospheric shock waves that followed the eruption. The propagation of the volcanic and marine tsunamis was analyzed using globally distributed 1 min measurements of air pressure and water level (WL) (from both tide gauges and deep-water buoys). The marine tsunami propagated primarily throughout the Pacific, reaching nearly 2 m at some locations, though most Pacific locations recorded maximums lower than 1 m. However, the VMT resulting from the atmospheric shock wave arrived before the marine tsunami and propagated globally, producing water level perturbations in the Indian Ocean, the Mediterranean, and the Caribbean. The resulting water level response of many Pacific Rim gauges was amplified, likely related to wave interaction with bathymetry. The meteotsunami repeatedly boosted tsunami wave energy as it circled the planet several times. In some locations, the VMT was amplified by as much as 35-fold relative to the inverse barometer due to near-Proudman resonance and topographic effects. Thus, a meteotsunami from a larger eruption (such as the Krakatoa eruption of 1883) could yield atmospheric pressure changes of 10 to 30 mb, yielding a 3–10 m near-field tsunami that would occur in advance of (usually) larger marine tsunami waves, posing additional hazards to local populations. Present tsunami warning systems do not consider this threat

Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus

The ability to ask questions is important in both human and machine intelligence. Learning to ask questions helps knowledge acquisition, improves question-answering and machine reading comprehension tasks, and helps a chatbot to keep the conversation flowing with a human. Existing question generation models are ineffective at generating a large amount of high-quality question-answer pairs from unstructured text, since given an answer and an input passage, question generation is inherently a one-to-many mapping. In this paper, we propose Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions. Our system consists of: i) an information extractor, which samples from the text multiple types of assistive information to guide question generation; ii) neural question generators, which generate diverse and controllable questions, leveraging the extracted assistive information; and iii) a neural quality controller, which removes low-quality generated data based on text entailment. We compare our question generation models with existing approaches and resort to voluntary human evaluation to assess the quality of the generated question-answer pairs. The evaluation results suggest that our system dramatically outperforms state-of-the-art neural question generation models in terms of the generation quality, while being scalable in the meantime. With models trained on a relatively smaller amount of data, we can generate 2.8 million quality-assured question-answer pairs from a million sentences found in Wikipedia.Comment: Accepted by The Web Conference 2020 (WWW 2020) as full paper (oral presentation

Widespread perturbation of ETS factor binding sites in cancer.

Although \u3e90% of somatic mutations reside in non-coding regions, few have been reported as cancer drivers. To predict driver non-coding variants (NCVs), we present a transcription factor (TF)-aware burden test based on a model of coherent TF function in promoters. We apply this test to NCVs from the Pan-Cancer Analysis of Whole Genomes cohort and predict 2555 driver NCVs in the promoters of 813 genes across 20 cancer types. These genes are enriched in cancer-related gene ontologies, essential genes, and genes associated with cancer prognosis. We find that 765 candidate driver NCVs alter transcriptional activity, 510 lead to differential binding of TF-cofactor regulatory complexes, and that they primarily impact the binding of ETS factors. Finally, we show that different NCVs within a promoter often affect transcriptional activity through shared mechanisms. Our integrated computational and experimental approach shows that cancer NCVs are widespread and that ETS factors are commonly disrupted

Classifying News Media Coverage for Corruption Risks Management with Deep Learning and Web Intelligence

A substantial number of international corporations have been affected by corruption. The research presented in this paper introduces the Integrity Risks Monitor, an analytics dashboard that applies Web Intelligence and Deep Learning to english and german-speaking documents for the task of (i) tracking and visualizing past corruption management gaps and their respective impacts, (ii) understanding present and past integrity issues, (iii) supporting companies in analyzing news media for identifying and mitigating integrity risks. Afterwards, we discuss the design, implementation, training and evaluation of classification components capable of identifying English documents covering the integrity topic of corruption. Domain experts created a gold standard dataset compiled from Anglo-American media coverage on corruption cases that has been used for training and evaluating the classifier. The experiments performed to evaluate the classifiers draw upon popular algorithms used for text classification such as Naïve Bayes, Support Vector Machines (SVM) and Deep Learning architectures (LSTM, BiLSTM, CNN) that draw upon different word embeddings and document representations. They also demonstrate that although classical machine learning approaches such as Naïve Bayes struggle with the diversity of the media coverage on corruption, state-of-the art Deep Learning models perform sufficiently well in the project's context

The Atacama Cosmology Telescope: The LABOCA/ACT Survey of Clusters at All Redshifts

We present a multi-wavelength analysis of eleven Sunyaev Zel'dovich effect (SZE)-selected galaxy clusters (ten with new data) from the Atacama Cosmology Telescope (ACT) southern survey. We have obtained new imaging from the Large APEX Bolometer Camera (345GHz; LABOCA) on the Atacama Pathfinder EXperiment (APEX) telescope, the Australia Telescope Compact Array (2.1GHz; ATCA), and the Spectral and Photometric Imaging Receiver (250, 350, and $500\,\rm\mu m$; SPIRE) on the Herschel Space Observatory. Spatially-resolved 345GHz SZE increments with integrated S/N > 5 are found in six clusters. We compute 2.1GHz number counts as a function of cluster-centric radius and find significant enhancements in the counts of bright sources at projected radii $\theta < \theta_{2500}$. By extrapolating in frequency, we predict that the combined signals from 2.1GHz-selected radio sources and 345GHz-selected SMGs contaminate the 148GHz SZE decrement signal by ~5% and the 345GHz SZE increment by ~18%. After removing radio source and SMG emission from the SZE signals, we use ACT, LABOCA, and (in some cases) new Herschel SPIRE imaging to place constraints on the clusters' peculiar velocities. The sample's average peculiar velocity relative to the cosmic microwave background is $153\pm 383\,\rm km\,s^{-1}$.Comment: 19 pages, 11 figures, Accepted for Publication in The Astrophysical Journa

Evidence for dark energy from the cosmic microwave background alone using the Atacama Cosmology Telescope lensing measurements

For the first time, measurements of the cosmic microwave background radiation (CMB) alone favor cosmologies with $w=-1$ dark energy over models without dark energy at a 3.2-sigma level. We demonstrate this by combining the CMB lensing deflection power spectrum from the Atacama Cosmology Telescope with temperature and polarization power spectra from the Wilkinson Microwave Anisotropy Probe. The lensing data break the geometric degeneracy of different cosmological models with similar CMB temperature power spectra. Our CMB-only measurement of the dark energy density $\Omega_\Lambda$ confirms other measurements from supernovae, galaxy clusters and baryon acoustic oscillations, and demonstrates the power of CMB lensing as a new cosmological tool.Comment: 4 pages, 3 figures; replaced with version accepted by Physical Review Letters, added sentence on models with non-standard primordial power spectr

Detection of the Power Spectrum of Cosmic Microwave Background Lensing by the Atacama Cosmology Telescope

We report the first detection of the gravitational lensing of the cosmic microwave background through a measurement of the four-point correlation function in the temperature maps made by the Atacama Cosmology Telescope. We verify our detection by calculating the levels of potential contaminants and performing a number of null tests. The resulting convergence power spectrum at 2-degree angular scales measures the amplitude of matter density fluctuations on comoving length scales of around 100 Mpc at redshifts around 0.5 to 3. The measured amplitude of the signal agrees with Lambda Cold Dark Matter cosmology predictions. Since the amplitude of the convergence power spectrum scales as the square of the amplitude of the density fluctuations, the 4-sigma detection of the lensing signal measures the amplitude of density fluctuations to 12%.Comment: 4 pages, 4 figures, replaced title and author list with version accepted by Physical Review Letters. Likelihood code can be downloaded from http://bccp.lbl.gov/~sudeep/ACTLensLike.htm