Imagine a hurricane has just devastated your town – and flattened your home. Before you could even begin to consider rebuilding, you'd wait weeks – or even months – for a property assessor just to visit to take a look at the damage, let alone unlock the funds you need to get back on your feet. But what if you could take pictures of the damage on the day of the hurricane, using your smartphone, and upload them – and when your insurer receives them, they deposit the funds into your bank account on the same day? If this sounds like the future, it's not – it's happening now. Real-world impact In September 2021, we at Tractable launched our estimating technology for the home, in Japan.

I look back at my accomplishments in 2021. I worked on various applied ML projects from climate change, protecting sharks to art & design. Highlights include open-source projects, TensorFlow, computer vision, deep learning, art and design. As always, I have been busy learning and sharing my knowledge with the community! Early 2022 I worked as an ML researcher on a Frontier Development Lab project using ML for climate change (MLCC).

Microsoft continues to make significant investments in deep learning, computer vision, and AI. The Microsoft Maps Team has been leveraging that investment to identify map features at scale and produce high-quality building footprint data sets with the overall goal to add to the OpenStreetMap and MissingMaps humanitarian efforts. As of this post, the following locations are available and Microsoft offers access to this data under the Open Data Commons Open Database License (ODbL). Country/Region Million buildings United States of America 129.6 Nigeria and Kenya 50.5 South America 44.5 Uganda and Tanzania 17.9 Canada 11.8 Australia 11.3 As you might expect, the vintage of the footprints depends on the collection date of the underlying imagery. Bing Maps Imagery is a composite of multiple sources with different capture dates (ranging 2012 to 2021).

Pair-wise loss is an approach to metric learning that learns a semantic embedding by optimizing a loss function that encourages images from the same semantic class to be mapped closer than images from different classes. The literature reports a large and growing set of variations of the pair-wise loss strategies. Here we decompose the gradient of these loss functions into components that relate to how they push the relative feature positions of the anchor-positive and anchor-negative pairs. This decomposition allows the unification of a large collection of current pair-wise loss functions. Additionally, explicitly constructing pair-wise gradient updates to separate out these effects gives insights into which have the biggest impact, and leads to a simple algorithm that beats the state of the art for image retrieval on the CAR, CUB and Stanford Online products datasets.

When explaining AI behavior to humans, how is the communicated information being comprehended by the human explainee, and does it match what the explanation attempted to communicate? When can we say that an explanation is explaining something? We aim to provide an answer by leveraging theory of mind literature about the folk concepts that humans use to understand behavior. We establish a framework of social attribution by the human explainee, which describes the function of explanations: the concrete information that humans comprehend from them. Specifically, effective explanations should be coherent (communicate information which generalizes to other contrast cases), complete (communicating an explicit contrast case, objective causes, and subjective causes), and interactive (surfacing and resolving contradictions to the generalization property through iterations). We demonstrate that many XAI mechanisms can be mapped to folk concepts of behavior. This allows us to uncover their modes of failure that prevent current methods from explaining effectively, and what is necessary to enable coherent explanations.

Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes. Our study is motivated by the fact that, since most of existing NA methods have attempted to discover all node pairs at once, they do not harness information enriched through interim discovery of node correspondences to more accurately find the next correspondences during the node matching. To tackle this challenge, we propose Grad-Align, a new NA method that gradually discovers node pairs by making full use of node pairs exhibiting strong consistency, which are easy to be discovered in the early stage of gradual matching. Specifically, Grad-Align first generates node embeddings of the two networks based on graph neural networks along with our layer-wise reconstruction loss, a loss built upon capturing the first-order and higher-order neighborhood structures. Then, nodes are gradually aligned by computing dual-perception similarity measures including the multi-layer embedding similarity as well as the Tversky similarity, an asymmetric set similarity using the Tversky index applicable to networks with different scales. Additionally, we incorporate an edge augmentation module into Grad-Align to reinforce the structural consistency. Through comprehensive experiments using real-world and synthetic datasets, we empirically demonstrate that Grad-Align consistently outperforms state-of-the-art NA methods.

If we want to broaden participation, we must educate our students based on the early 17th-century origins of the word "computer," a human who performs calculations.1 Computers were exclusively human until the early 19th century when English polymath and inventor Charles Babbage introduced the Difference Engine, the first mechanical computer. The term "human computer" was then used to differentiate a person who computes from a mechanical computer. Human computers were often women who undertook long and tedious calculations to power some of the most significant advances in science, industry, and space technology in the 20th century.

The Sapphire Pulse delivers a whisper-quiet spin on AMD's affordable Radeon RX 6500 XT, with the company's Trixx Boost software giving performance a helping hand. It's a good option for newcomers to PC gaming as long as you operate within limits imposed by the unusual technical configuration of AMD's GPU. AMD's Radeon RX 6500 XT is a humble graphics card built to bring 1080p gaming to the masses at a time when the masses haven't had an affordable GPU option for years. Sapphire's popular "Pulse" brand relentlessly focuses on delivering solid gaming experiences without cost-adding frills you may not want. On paper, it sounds like a peanut butter and jelly-type situation. But does the Sapphire Pulse Radeon RX 6500 XT, which retails for AMD's $199 suggested price, hold up in practice?

Neonatal respiratory distress is a common condition that if left untreated, can lead to short- and long-term complications. This paper investigates the usage of digital stethoscope recorded chest sounds taken within 1min post-delivery, to enable early detection and prediction of neonatal respiratory distress. Fifty-one term newborns were included in this study, 9 of whom developed respiratory distress. For each newborn, 1min anterior and posterior recordings were taken. These recordings were pre-processed to remove noisy segments and obtain high-quality heart and lung sounds. The random undersampling boosting (RUSBoost) classifier was then trained on a variety of features, such as power and vital sign features extracted from the heart and lung sounds. The RUSBoost algorithm produced specificity, sensitivity, and accuracy results of 85.0%, 66.7% and 81.8%, respectively.

The peculiarity of adversarial team games resides in the asymmetric information available to the team members during the play, which makes the equilibrium computation problem hard even with zero-sum payoffs. The algorithms available in the literature work with implicit representations of the strategy space and mainly resort to Linear Programming and column generation techniques to enlarge incrementally the strategy space. Such representations prevent the adoption of standard tools such as abstraction generation, game solving, and subgame solving, which demonstrated to be crucial when solving huge, real-world two-player zero-sum games. Differently from these works, we answer the question of whether there is any suitable game representation enabling the adoption of those tools. In particular, our algorithms convert a sequential team game with adversaries to a classical two-player zero-sum game. In this converted game, the team is transformed into a single coordinator player who only knows information common to the whole team and prescribes to the players an action for any possible private state. Interestingly, we show that our game is more expressive than the original extensive-form game as any state/action abstraction of the extensive-form game can be captured by our representation, while the reverse does not hold. Due to the NP-hard nature of the problem, the resulting Public Team game may be exponentially larger than the original one. To limit this explosion, we provide three algorithms, each returning an information-lossless abstraction that dramatically reduces the size of the tree. These abstractions can be produced without generating the original game tree. Finally, we show the effectiveness of the proposed approach by presenting experimental results on Kuhn and Leduc Poker games, obtained by applying state-of-art algorithms for two-player zero-sum games on the converted games