This paper describes a machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple distinct ML techniques and utilizing diverse data sources. Our framework, which we refer to as Hurricast (HURR), is built upon the combination of distinct data processing techniques using gradient-boosted trees and novel encoder-decoder architectures, including CNN, GRU and Transformers components. We propose a deep-feature extractor methodology to mix spatial-temporal data with statistical data efficiently. Our multimodal framework unleashes the potential of making forecasts based on a wide range of data sources, including historical storm data, reanalysis atmospheric images, and operational forecasts. Evaluating our models with current operational forecasts in North Atlantic and Eastern Pacific basins on the last years of available data, results show our models consistently outperform statistical-dynamical models and, albeit less accurate than the best dynamical models, our framework computes forecasts in seconds. Furthermore, the inclusion of Hurricast into an operational forecast consensus model leads to a significant improvement of 5% - 15% over NHC's official forecast, thus highlighting the complementary properties with existing approaches. In summary, our work demonstrates that combining different data sources and distinct machine learning methodologies can lead to superior tropical cyclone forecasting.

Training a multimodal network is challenging and it requires complex architectures to achieve reasonable performance. We show that one reason for this phenomena is the difference between the convergence rate of various modalities. We address this by pre-training modality-specific sub-networks in multimodal architectures independently before end-to-end training of the entire network. Furthermore, we show that the addition of an attention mechanism between sub-networks after pre-training helps identify the most important modality during ambiguous scenarios boosting the performance. We demonstrate that by performing these two tricks a simple network can achieve similar performance to a complicated architecture that is significantly more expensive to train on multiple tasks including sentiment analysis, emotion recognition, and speaker trait recognition.

MediaTek has announced a pair of Chromebook chipsets it says can balance power with improved battery life performance. The company designed the 6nm MT8195 for premium Chromebooks and the 7nm MT8192 for broader use. The four Arm CortexA78 cores handle more resource-intensive apps, while the Arm Cortex-A55 cores take care of background tasks and maximize battery life through power efficiency. A five-core Arm-Mali G57 GPU powers the visuals and the MT8195 includes quad-channel 2133MHz LPDDR4X on the memory side. The chipset can run up to three displays simultaneously, and it supports Dolby Vision and 7.1 surround sound audio.

It's been a wild ride this year, but you can always rely on Assassin's Creed to lighten the mood. Let's see what those zany historians at Ubisoft have cooked up for us in the excitingly named Assassin's Creed Valhalla … Peterborough, is it? I have nothing against our beautiful cathedral cities, rolling plains and park-and-ride services, but after 12 months of Brexit, Covid-19 and forest fires, plus the cancellation of the Eurovision song contest, I was hoping for something a little less Tough Mudder from this giddy, quasi-historical, action-adventure series, which previously had us gallivanting around Atlantis. For the first few hours, you're thrown into the icy political drama of ninth-century Norway, where Viking warrior Eivor runs around snow-blasted islands having stern conversations about the future of her clan. I went with female Eivor.)

This data/information is entered into one of my published books, “A Few Tech Definitions From A to Z…” I thought I might help as many as I can to understand what all those “techie speak” words, abbreviations, and symbols imply. To the student(s) who

Australian water infrastructure is more than a hundred years old, thus has begun to show its age through water main failures. Our work concerns approximately half a million pipelines across major Australian cities that deliver water to houses and businesses, serving over five million customers. Failures on these buried assets cause damage to properties and water supply disruptions. We applied Machine Learning techniques to find a cost-effective solution to the pipe failure problem in these Australian cities, where on average 1500 of water main failures occur each year. To achieve this objective, we construct a detailed picture and understanding of the behaviour of the water pipe network by developing a Machine Learning model to assess and predict the failure likelihood of water main breaking using historical failure records, descriptors of pipes and other environmental factors. Our results indicate that our system incorporating a nonparametric survival analysis technique called "Random Survival Forest" outperforms several popular algorithms and expert heuristics in long-term prediction. In addition, we construct a statistical inference technique to quantify the uncertainty associated with the long-term predictions.

In an effort to improve generalization in deep learning, we propose SALR: a sharpness-aware learning rate update technique designed to recover flat minimizers. Our method dynamically updates the learning rate of gradient-based optimizers based on the local sharpness of the loss function. This allows optimizers to automatically increase learning rates at sharp valleys to increase the chance of escaping them. We demonstrate the effectiveness of SALR when adopted by various algorithms over a broad range of networks. Our experiments indicate that SALR improves generalization, converges faster, and drives solutions to significantly flatter regions.

The weaponization of digital communications and social media to conduct disinformation campaigns at immense scale, speed, and reach presents new challenges to identify and counter hostile influence operations (IO). This paper presents an end-to-end framework to automate detection of disinformation narratives, networks, and influential actors. The framework integrates natural language processing, machine learning, graph analytics, and a novel network causal inference approach to quantify the impact of individual actors in spreading IO narratives. We demonstrate its capability on real-world hostile IO campaigns with Twitter datasets collected during the 2017 French presidential elections, and known IO accounts disclosed by Twitter over a broad range of IO campaigns (May 2007-February 2020), over 50 thousand accounts, 17 countries, and different account types including both trolls and bots. Our system detects IO accounts with 96% precision, 79% recall, and 96% area-under-the-PR-curve, maps out salient network communities, and discovers high-impact accounts that escape the lens of traditional impact statistics based on activity counts and network centrality. Results are corroborated with independent sources of known IO accounts from U.S. Congressional reports, investigative journalism, and IO datasets provided by Twitter.

We propose a novel theoretical framework to understand self-supervised learning methods that employ dual pairs of deep ReLU networks (e.g., SimCLR, BYOL). First, we prove that in each SGD update of SimCLR with various loss functions (simple contrastive loss, soft Triplet loss and InfoNCE loss), the weights at each layer are updated by a covariance operator that specifically amplifies initial random selectivities that vary across data samples but survive averages over data augmentations. We show this leads to the emergence of hierarchical features, if the input data are generated from a hierarchical latent tree model. With the same framework, we also show analytically that in BYOL, the combination of Batch-Norm and a predictor network creates an implicit contrastive term, acting as an approximate covariance operator. Additionally, for linear architectures we derive exact solutions for BYOL that provide conceptual insights into how BYOL can learn useful non-collapsed representations without any contrastive terms that separate negative pairs. Extensive ablation studies justify our theoretical findings. Unlike supervised learning (SL) that deals with labeled data, SSL learns meaningful structures from randomly initialized networks without human-provided labels. In this paper, we propose a systematic theoretical analysis of SSL with deep ReLU networks. Our analysis imposes no parametric assumptions on the input data distribution and is applicable to stateof-the-art SSL methods that typically involve two parallel (or dual) deep ReLU networks during training (e.g., SimCLR (Chen et al., 2020a), BYOL (Grill et al., 2020), etc). We do so by developing an analogy between SSL and a theoretical framework for analyzing supervised learning, namely the student-teacher setting (Tian, 2020; Allen-Zhu and Li, 2020; Lampinen and Ganguli, 2018; Saad and Solla, 1996), which also employs a pair of dual networks.

The pandemic has dealt a body blow to many of the world's cities. As they seek to recover from the economic and social fall-out from COVID-19, municipalities are stepping up efforts to deploy big data and artificial intelligence (AI) to improve urban life. Equipped with a real-time view of what is happening across a city, municipalities hope to be able to make timely interventions, while spurring the development of innovative services. "We are using AI to become the eyes of the city," Maarten Sukel, AI lead at the City of Amsterdam, told a recent Science Business webinar entitled: How will real-time data reshape our cities? Although municipalities generally lack the granular behavioural data available to the major Internet platforms, advances in AI are making it easier to analyse the growing volume of data being captured by street level cameras and other sensors.