Combining multi-spectral satellite data and machine learning has been suggested as a method for monitoring plastic pollutants in the ocean environment. Recent studies have made theoretical progress regarding the identification of marine plastic via machine learning. However, no study has assessed the application of these methods for mapping and monitoring marine-plastic density. As such, this paper comprised of three main components: (1) the development of a machine learning model, (2) the construction of the MAP-Mapper, an automated tool for mapping marine-plastic density, and finally (3) an evaluation of the whole system for out-of-distribution test locations. The findings from this paper leverage the fact that machine learning models need to be high-precision to reduce the impact of false positives on results. The developed MAP-Mapper architectures provide users choices to reach high-precision ($\textit{abbv.}$ -HP) or optimum precision-recall ($\textit{abbv.}$ -Opt) values in terms of the training/test data set. Our MAP-Mapper-HP model greatly increased the precision of plastic detection to 95\%, whilst MAP-Mapper-Opt reaches precision-recall pair of 87\%-88\%. The MAP-Mapper contributes to the literature with the first tool to exploit advanced deep/machine learning and multi-spectral imagery to map marine-plastic density in automated software. The proposed data pipeline has taken a novel approach to map plastic density in ocean regions. As such, this enables an initial assessment of the challenges and opportunities of this method to help guide future work and scientific study.

This paper uses computational experiments to explore the role of exposure in the emergence of construction grammars. While usage-based grammars are hypothesized to depend on a learner's exposure to actual language use, the mechanisms of such exposure have only been studied in a few constructions in isolation. This paper experiments with (i) the growth rate of the constructicon, (ii) the convergence rate of grammars exposed to independent registers, and (iii) the rate at which constructions are forgotten when they have not been recently observed. These experiments show that the lexicon grows more quickly than the grammar and that the growth rate of the grammar is not dependent on the growth rate of the lexicon. At the same time, register-specific grammars converge onto more similar constructions as the amount of exposure increases. This means that the influence of specific registers becomes less important as exposure increases. Finally, the rate at which constructions are forgotten when they have not been recently observed mirrors the growth rate of the constructicon. This paper thus presents a computational model of usage-based grammar that includes both the emergence and the unentrenchment of constructions.

Minerva CQ, a startup based in the San Francisco Bay Area, is making customer service calls quicker and more efficient for both agents and customers, with a focus on those in the energy sector. The NVIDIA Inception member's name is a mashup of the Roman goddess of wisdom and knowledge -- and collaborative intelligence (CQ), or the combination of human and artificial intelligence. The Minerva CQ platform coaches contact-center agents to drive customer conversations -- whether in voice or web-based chat -- toward the most effective resolutions by offering real-time dialogue suggestions, sentiment analysis and optimal journey flows based on the customer's intent. It also surfaces relevant context, articles, forms and more. Powered by the NVIDIA Riva software development kit, Minerva CQ has best-in-class automatic speech recognition (ASR) capabilities in English, Spanish and Italian.

Task-oriented dialogue (TOD) systems have been widely used by mobile phone intelligent assistants to accomplish tasks such as calendar scheduling or hotel reservation. Current TOD systems usually focus on multi-turn text/speech interaction, then they would call back-end APIs designed for TODs to perform the task. However, this API-based architecture greatly limits the information-searching capability of intelligent assistants and may even lead to task failure if TOD-specific APIs are not available or the task is too complicated to be executed by the provided APIs. In this paper, we propose a new TOD architecture: GUI-based task-oriented dialogue system (GUI-TOD). A GUI-TOD system can directly perform GUI operations on real APPs and execute tasks without invoking TOD-specific backend APIs. Furthermore, we release META-GUI, a dataset for training a Multi-modal convErsaTional Agent on mobile GUI. We also propose a multi-model action prediction and response model, which show promising results on META-GUI. The dataset, codes and leaderboard are publicly available.

Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction. While recent methods demonstrate good forecasting abilities, they have three fundamental limitations. (i) Discrete neural architectures: Interlacing individually parameterized spatial and temporal blocks to encode rich underlying patterns leads to discontinuous latent state trajectories and higher forecasting numerical errors. (ii) High complexity: Discrete approaches complicate models with dedicated designs and redundant parameters, leading to higher computational and memory overheads. (iii) Reliance on graph priors: Relying on predefined static graph structures limits their effectiveness and practicability in real-world applications. In this paper, we address all the above limitations by proposing a continuous model to forecast $\textbf{M}$ultivariate $\textbf{T}$ime series with dynamic $\textbf{G}$raph neural $\textbf{O}$rdinary $\textbf{D}$ifferential $\textbf{E}$quations ($\texttt{MTGODE}$). Specifically, we first abstract multivariate time series into dynamic graphs with time-evolving node features and unknown graph structures. Then, we design and solve a neural ODE to complement missing graph topologies and unify both spatial and temporal message passing, allowing deeper graph propagation and fine-grained temporal information aggregation to characterize stable and precise latent spatial-temporal dynamics. Our experiments demonstrate the superiorities of $\texttt{MTGODE}$ from various perspectives on five time series benchmark datasets.

NASA's Orion spacecraft arrived at the moon yesterday after travelling some 230,000 miles (370,000km) in five days. The capsule zoomed over the landing sites of Apollo 11, 12 and 14 as it came within 80 miles (130km) of the lunar surface. But now it is circling the moon, what exactly will it be doing for the next 10 days before it heads home? As you'd expect there are a multitude of science experiments NASA is carrying out, including checking radiation levels, seeing how'space seeds' behave and monitoring a dummy called Commander Moonikin to see how he is coping with the journey. Artemis I will not only fly farther than any spacecraft built for humans - around 40,000 miles (64,000km) beyond the far side of the moon - it will also stay in space the longest without docking to a space station, and return home faster and hotter than ever before.

Nuro, the autonomous vehicle delivery startup backed by SoftBank, Google and Tiger Global Management, is laying off about 300 people, or 20% of its workforce, in an effort to preserve cash amid a stormy economic outlook, according to an email sent to employees this morning. Several Nuro employees also posted on Twitter and LinkedIn this morning that they had been affected by the layoffs. In the email viewed by TechCrunch, co-founders Jiajun Zhu and Dave Ferguson informed employees they would receive an update later this morning letting them know if they are impacted by this layoff and with information on next steps. Each and every one of you have made important contributions to this company, and saying goodbye to talented Nurons is not a decision we have taken lightly. For those of you leaving Nuro, we are very sorry for this outcome -- this is not the experience we wanted to create for you.

Heat waves are projected to increase in frequency and severity with global warming. Improved warning systems would help reduce the associated loss of lives, wildfires, power disruptions, and reduction in crop yields. In this work, we explore the potential for deep learning systems trained on historical data to forecast extreme heat on short, medium and subseasonal timescales. To this purpose, we train a set of neural weather models (NWMs) with convolutional architectures to forecast surface temperature anomalies globally, 1 to 28 days ahead, at $\sim200~\mathrm{km}$ resolution and on the cubed sphere. The NWMs are trained using the ERA5 reanalysis product and a set of candidate loss functions, including the mean squared error and exponential losses targeting extremes. We find that training models to minimize custom losses tailored to emphasize extremes leads to significant skill improvements in the heat wave prediction task, compared to NWMs trained on the mean squared error loss. This improvement is accomplished with almost no skill reduction in the general temperature prediction task, and it can be efficiently realized through transfer learning, by re-training NWMs with the custom losses for a few epochs. In addition, we find that the use of a symmetric exponential loss reduces the smoothing of NWM forecasts with lead time. Our best NWM is able to outperform persistence in a regressive sense for all lead times and temperature anomaly thresholds considered, and shows positive regressive skill compared to the ECMWF subseasonal-to-seasonal control forecast after two weeks.

Avian botulism is a paralytic bacterial disease in birds often leading to high fatality. In-vitro diagnostic techniques such as Mouse Bioassay, ELISA, PCR are usually non-preventive, post-mortem in nature, and require invasive sample collection from affected sites or dead birds. In this study, we build a first-ever multi-spectral, remote-sensing imagery based global Bird-Area Water-bodies Dataset (BAWD) (i.e. fused satellite images of warm-water lakes/marshy-lands or similar water-body sites that are important for avian fauna) backed by on-ground reporting evidence of outbreaks. BAWD consists of 16 topographically diverse global sites monitored over a time-span of 4 years (2016-2021). We propose a first-ever Artificial Intelligence based (AI) model to predict potential outbreak of Avian botulism called AVI-BoT (Aerosol Visible, Infra-red (NIR/SWIR) and Bands of Thermal). We also train and investigate a simpler (5-band) Causative-Factor model (based on prominent physiological factors reported in literature) to predict Avian botulism. AVI-BoT demonstrates a training accuracy of 0.96 and validation accuracy of 0.989 on BAWD, far superior in comparison to our model based on causative factors. We also perform an ablation study and perform a detailed feature-space analysis. We further analyze three test case study locations - Lower Klamath National Wildlife Refuge and Langvlei and Rondevlei lakes where an outbreak had occurred, and Pong Dam where an outbreak had not occurred and confirm predictions with on-ground reportings. The proposed technique presents a scale-able, low-cost, non-invasive methodology for continuous monitoring of bird-habitats against botulism outbreaks with the potential of saving valuable fauna lives.

We address Lagrangian drift simulation in geophysical dynamics and explore deep learning approaches to overcome known limitations of state-of-the-art model-based and Markovian approaches in terms of computational complexity and error propagation. We introduce a novel architecture, referred to as DriftNet, inspired from the Eulerian Fokker-Planck representation of Lagrangian dynamics. Numerical experiments for Lagrangian drift simulation at the sea surface demonstrates the relevance of DriftNet w.r.t. state-of-the-art schemes. Benefiting from the fully-convolutional nature of Drift-Net, we explore through a neural inversion how to diagnose modelderived velocities w.r.t. real drifter trajectories.