In this study, we explore the implications of integrating social distancing with emergency evacuation when a hurricane approaches a major city during the COVID-19 pandemic. Specifically, we compare DNN (Deep Neural Network)-based and non-DNN methods for generating evacuation strategies that minimize evacuation time while allowing for social distancing in rescue vehicles. A central question is whether a DNN-based method provides sufficient extra efficiency to accommodate social distancing, in a time-constrained evacuation operation. We describe the problem as a Capacitated Vehicle Routing Problem and solve it using one non-DNN solution (Sweep Algorithm) and one DNN-based solution (Deep Reinforcement Learning). DNN-based solution can provide decision-makers with more efficient routing than non-DNN solution. Although DNN-based solution can save considerable time in evacuation routing, it does not come close to compensating for the extra time required for social distancing and its advantage disappears as the vehicle capacity approaches the number of people per household.

Traditional seismic processing workflows (SPW) are expensive, requiring over a year of human and computational effort. Deep learning (DL) based data-driven seismic workflows (DSPW) hold the potential to reduce these timelines to a few minutes. Raw seismic data (terabytes) and required subsurface prediction (gigabytes) are enormous. This large-scale, spatially irregular time-series data poses seismic data ingestion (SDI) as an unconventional yet fundamental problem in DSPW. Current DL research is limited to small-scale simplified synthetic datasets as they treat seismic data like images and process them with convolution networks. Real seismic data, however, is at least 5D. Applying 5D convolutions to this scale is computationally prohibitive. Moreover, raw seismic data is highly unstructured and hence inherently non-image like. We propose a fundamental shift to move away from convolutions and introduce SESDI: Set Embedding based SDI approach. SESDI first breaks down the mammoth task of large-scale prediction into an efficient compact auxiliary task. SESDI gracefully incorporates irregularities in data with its novel model architecture. We believe SESDI is the first successful demonstration of end-to-end learning on real seismic data. SESDI achieves SSIM of over 0.8 on velocity inversion task on real proprietary data from the Gulf of Mexico and outperforms the state-of-the-art U-Net model on synthetic datasets.

For nearly a year now, the coronavirus pandemic rendered most summer vacations and holiday getaways impossible, rerouting itineraries to our living rooms. But there is a silver lining for those seeking a change of scenery amid our extended staycations. Already these past 12 months we've seen a rise in those spending their idle hours in video games, and certain titles can actually help scratch that itch to travel. Some games fit that need to hit the skies literally, such as "Microsoft Flight Simulator 2020," which lets players fly a plane across a hyper-realistic Earth crafted from satellite imagery. Still, this doesn't replicate exploring a foreign country's culture or taking in the sights and sounds of a bustling city far from home.

Deep learning applications are drastically progressing in seismic processing and interpretation tasks. However, the majority of approaches subsample data volumes and restrict model sizes to minimise computational requirements. Subsampling the data risks losing vital spatio-temporal information which could aid training whilst restricting model sizes can impact model performance, or in some extreme cases, renders more complicated tasks such as segmentation impossible. This paper illustrates how to tackle the two main issues of training of large neural networks: memory limitations and impracticably large training times. Typically, training data is preloaded into memory prior to training, a particular challenge for seismic applications where data is typically four times larger than that used for standard image processing tasks (float32 vs. uint8). Using a microseismic use case, we illustrate how over 750GB of data can be used to train a model by using a data generator approach which only stores in memory the data required for that training batch. Furthermore, efficient training over large models is illustrated through the training of a 7-layer UNet with input data dimensions of 4096 4096 ( 7.8M parameters). Through a batch-splitting distributed training approach, training times are reduced by a factor of four. The combination of data generators and distributed training removes any necessity of data subsampling or restriction of neural network sizes, offering the opportunity of utilisation of larger networks, higher-resolution input data or moving from 2D to 3D problem spaces.

Reduced-order dynamical models play a central role in developing our understanding of predictability of climate irrespective of whether we are dealing with the actual climate system or surrogate climate-models. In this context, the Linear-Inverse-Modeling (LIM) approach, by capturing a few essential interactions between dynamical components of the full system, has proven valuable in providing insights into predictability of the full system. We demonstrate that Reservoir Computing (RC), a form of learning suitable for systems with chaotic dynamics, provides an alternative nonlinear approach that improves on the predictive skill of the LIM approach. We do this in the example setting of predicting sea-surface-temperature in the North Atlantic in the pre-industrial control simulation of a popular earth system model, the Community-Earth-System-Model so that we can compare the performance of the new RC based approach with the traditional LIM approach both when learning data is plentiful and when such data is more limited. The improved predictive skill of the RC approach over a wide range of conditions -- larger number of retained EOF coefficients, extending well into the limited data regime, etc. -- suggests that this machine-learning technique may have a use in climate predictability studies. While the possibility of developing a climate emulator -- the ability to continue the evolution of the system on the attractor long after failing to be able to track the reference trajectory -- is demonstrated in the Lorenz-63 system, it is suggested that further development of the RC approach may permit such uses of the new approach in more realistic predictability studies.

Our work represents another step into the detection and prevention of these ever-more present political manipulation efforts. We, therefore, start by focusing on understanding what the state-of-the-art approaches lack -- since the problem remains, this is a fair assumption. We find concerning issues within the current literature and follow a diverging path. Notably, by placing emphasis on using data features that are less susceptible to malicious manipulation and also on looking for high-level approaches that avoid a granularity level that is biased towards easy-to-spot and low impact cases. We designed and implemented a framework -- Twitter Watch -- that performs structured Twitter data collection, applying it to the Portuguese Twittersphere. We investigate a data snapshot taken on May 2020, with around 5 million accounts and over 120 million tweets (this value has since increased to over 175 million). The analyzed time period stretches from August 2019 to May 2020, with a focus on the Portuguese elections of October 6th, 2019. However, the Covid-19 pandemic showed itself in our data, and we also delve into how it affected typical Twitter behavior. We performed three main approaches: content-oriented, metadata-oriented, and network interaction-oriented. We learn that Twitter's suspension patterns are not adequate to the type of political trolling found in the Portuguese Twittersphere -- identified by this work and by an independent peer - nor to fake news posting accounts. We also surmised that the different types of malicious accounts we independently gathered are very similar both in terms of content and interaction, through two distinct analysis, and are simultaneously very distinct from regular accounts.

We investigate the use of spatial interpolation methods for reconstructing the horizontal near-surface wind field given a sparse set of measurements. In particular, random Fourier features is compared to a set of benchmark methods including Kriging and Inverse distance weighting. Random Fourier features is a linear model $\beta(\pmb x) = \sum_{k=1}^K \beta_k e^{i\omega_k \pmb x}$ approximating the velocity field, with frequencies $\omega_k$ randomly sampled and amplitudes $\beta_k$ trained to minimize a loss function. We include a physically motivated divergence penalty term $|\nabla \cdot \beta(\pmb x)|^2$, as well as a penalty on the Sobolev norm. We derive a bound on the generalization error and derive a sampling density that minimizes the bound. Following (arXiv:2007.10683 [math.NA]), we devise an adaptive Metropolis-Hastings algorithm for sampling the frequencies of the optimal distribution. In our experiments, our random Fourier features model outperforms the benchmark models.

We introduce the task of historical text summarisation, where documents in historical forms of a language are summarised in the corresponding modern language. This is a fundamentally important routine to historians and digital humanities researchers but has never been automated. We compile a high-quality gold-standard text summarisation dataset, which consists of historical German and Chinese news from hundreds of years ago summarised in modern German or Chinese. Based on cross-lingual transfer learning techniques, we propose a summarisation model that can be trained even with no cross-lingual (historical to modern) parallel data, and further benchmark it against state-of-the-art algorithms. We report automatic and human evaluations that distinguish the historic to modern language summarisation task from standard cross-lingual summarisation (i.e., modern to modern language), highlight the distinctness and value of our dataset, and demonstrate that our transfer learning approach outperforms standard cross-lingual benchmarks on this task.

Housebound by a pandemic, humanity slowed its emissions of greenhouse gases in 2020. But Earth paid little heed: Temperatures last year tied the modern record, climate scientists reported last week. Overall, the planet was about 1.25°C warmer than in preindustrial times, a trend that puts climate targets in jeopardy, according to jointly reported assessments from NASA, Berkeley Earth, the U.K. Met Office, and the National Oceanic and Atmospheric Administration. The annual update of global surface temperatures—an average of readings from thousands of weather stations and ocean probes—shows 2020 essentially tied records set in 2016. But the years were nothing alike. Temperatures in 2016 were boosted by a strong El Niño, a weather pattern that warms the globe by blocking the rise of cold deep waters in the eastern Pacific Ocean. Last year, however, the Pacific entered La Niña, which has a cooling effect. That La Niña didn't provide more relief is an unwelcome surprise, says Nerilie Abram, a climate scientist at Australian National University. “It makes me worried about how quickly the global warming trend is growing.” The past 6 years are the six warmest on record, but the warming of the atmosphere is unsteady because of its chaotic nature. The ocean, which absorbs more than 90% of the heat from global warming, displays a steadier trend, and here, too, 2020 was a record year. The upper levels of the ocean contained 20 zettajoules (1021 joules) more heat than in 2019, and the rise was double the typical annual increase, scientists reported last week in Advances in Atmospheric Sciences . The subtropical Atlantic Ocean was particularly hot, fueling a record outbreak of hurricanes, says Lijing Cheng, a climate scientist at the Chinese Academy of Sciences's Institute of Atmospheric Physics who led the work. This heat, monitored down to 2000 meters by a fleet of 4000 robotic probes, is spreading deeper into the ocean while also migrating toward the poles. An extreme heat wave struck the northern Pacific, killing marine life. For the first time, warm Atlantic waters were seen penetrating into the Arctic Ocean, melting sea ice from below and reducing its extent nearly to a record low ( Science , 28 August 2020, p. [1043][1]). The warming ocean and melting ice sheets are raising sea levels by 4.8 millimeters per year, and the rate is accelerating ( Science , 20 November 2020, p. [901][2]). On land, 2020 was even more relentless, with temperatures rising 1.96°C above preindustrial levels, a clear record, Berkeley Earth reported. It was the warmest year ever in Asia and Europe and tied for the warmest in South America. Russia was particularly hot, breaking its previous record by 1.2°C, while swaths of Siberia were 7°C warmer than in preindustrial times, leading to large-scale fires and thawing permafrost that caused buildings to founder and set off oil spills ( Science , 7 August 2020, p. [612][3]). “Siberia was crazy,” says Zeke Hausfather, a climate scientist at the Breakthrough Institute and co-author of the Berkeley Earth analysis. “That heat would effectively be impossible without the warming we've seen.” In Australia, record-setting heat and drought fueled catastrophic bushfires at the start of 2020. Fires torched nearly one-quarter of southeastern Australia's forests and destroyed 3000 homes. Climate change was to blame for the country's “Black Summer,” Abram and co-authors concluded in a study published this month in Communications Earth & Environment . Meanwhile, in the United States, unprecedented heat came to the desert Southwest, which is already warming faster than the rest of the country. Phoenix wilted under its hottest summer ever, averaging 36°C. Arizona's Maricopa county, home to Phoenix, is a leader in addressing heat exposure, yet its heat deaths have hit a new record each year since 2016. In 2020, the number approached 300, a jump of some 50% over the previous year, says David Hondula, a climatologist who studies heat mortality at Arizona State University, Tempe. “It was just off the charts in terms of heat.” ![Figure][4] Turning up the heatCREDITS: (GRAPHIC) N. DESAI/ SCIENCE ; (DATA) MET OFFICE; NASA; BERKELEY EARTH; NOAA Although the global economic slowdown of the COVID-19 pandemic cut carbon dioxide (CO2) emissions by some 7%, atmospheric CO2 is long-lived, and warming from previous emissions is preordained. In any case, the drop in emissions is unlikely to last. Later this year, in May, before photosynthesis in the Northern Hemisphere draws down CO2, the U.K. Met Office predicts that levels of atmospheric CO2 will pass 417 parts per million for several weeks, 50% higher than preindustrial levels. Only dramatic action by the world's countries, far beyond existing efforts, can begin to halt this build up, Cheng says. Should the current rate of warming continue, the world will breach the targets set in the Paris climate agreement—limiting warming to 1.5°C or 2°C—by 2035 and 2065, respectively. But Hausfather says it's quite possible that warming, which has largely held steady for the past few decades at 0.19°C per decade, will actually speed up. The rate of warming over the past 14 years is well above the long-term trend. The debate now, he says, is whether that is an omen of an even darker future. [1]: https://www.sciencemag.org/content/369/6507/1043.full [2]: https://www.sciencemag.org/content/370/6519/901.full [3]: https://www.sciencemag.org/content/369/6504/612.full [4]: pending:yes

Deep Learning (DL) is a two-step classification model that consists feature learning, generating feature representations using unsupervised ways and the supervised learning stage at the last step of model using at least two hidden layers on the proposed structures by fully connected layers depending on of the artificial neural networks. The optimization of the predefined classification parameters for the supervised models eases reaching the global optimality with exact zero training error. The autoencoder (AE) models are the highly generalized ways of the unsupervised stages for the DL to define the output weights of the hidden neurons with various representations. As alternatively to the conventional Extreme Learning Machines (ELM) AE, Hessenberg decomposition-based ELM autoencoder (HessELM-AE) is a novel kernel to generate different presentations of the input data within the intended sizes of the models. The aim of the study is analyzing the performance of the novel Deep AE kernel for clinical availability on electroencephalogram (EEG) with stroke patients. The slow cortical potentials (SCP) training in stroke patients during eight neurofeedback sessions were analyzed using Hilbert-Huang Transform. The statistical features of different frequency modulations were fed into the Deep ELM model for generative AE kernels. The novel Deep ELM-AE kernels have discriminated the brain activity with high classification performances for positivity and negativity tasks in stroke patients.