New research from the Georgia Institute of Technology uses machine learning models to better understand water's phase changes, opening more …

Like human intelligence, artificial intelligence (AI) can recognize … this external technology is a deep-structured, machine-learning method …

You know, machine learning comes with a constant cadence of … being adaptable." Because of the distributed nature of DOD, data is always on the move, …

And given rising interest rates and the deep decline in the public markets, … are building artificial intelligence and machine-learning-powered …

Sunquakes are seismic emissions visible on the solar surface, sometimes associated with solar flares. Despite the availability of several manual detection guidelines, to our knowledge, the astrophysical data produced for sunquakes is new to the field ofMachineLearning. We introduce an acoustic holography processed dataset constructed from egression-power maps of solar active regions for Solar Cycles 23 and 24. We then present a pedagogical approach to the application of machine learning representation methods for sunquake detection using AutoEncoders, Contrastive Learning, Object Detection and recurrent techniques, which we enhance by introducing several custom domain-specific data augmentation transformations. With our trained models, we find temporal and spatial locations of peculiar acoustic emission and associate them to eruptive and high energy emission in two C1 class flares.

We propose a method for generating simulated contextual bandit environments for personalization tasks from recommendation datasets like MovieLens, Netflix, Last.fm, Million Song, etc. This allows for personalization environments to be developed based on real-life data to reflect the nuanced nature of real-world user interactions. The obtained environments can be used to develop methods for solving personalization tasks, algorithm benchmarking, model simulation, and more. We demonstrate our approach with numerical examples on MovieLens and IMDb datasets.

The coverage of different stakeholders mentioned in the news articles significantly impacts the slant or polarity detection of the concerned news publishers. For instance, the pro-government media outlets would give more coverage to the government stakeholders to increase their accessibility to the news audiences. In contrast, the anti-government news agencies would focus more on the views of the opponent stakeholders to inform the readers about the shortcomings of government policies. In this paper, we address the problem of stakeholder extraction from news articles and thereby determine the inherent bias present in news reporting. Identifying potential stakeholders in multi-topic news scenarios is challenging because each news topic has different stakeholders. The research presented in this paper utilizes both contextual information and external knowledge to identify the topic-specific stakeholders from news articles. We also apply a sequential incremental clustering algorithm to group the entities with similar stakeholder types. We carried out all our experiments on news articles on four Indian government policies published by numerous national and international news agencies. We also further generalize our system, and the experimental results show that the proposed model can be extended to other news topics.

Artificial Intelligence is a game-changing critical enabler that has the potential to speed up humanity's race against climate change and various …

Realbook is a Python library for easier training of audio deep learning models with Tensorflow made by Spotify's Spotify's Audio Intelligence Lab. Realbook provides callbacks (e.g., spectrogram visualization) and well-tested Keras layers (e.g., STFT, ISTFT, magnitude spectrogram) that we often use when training. These functions have helped standardized consistency across all of our models we and hope realbook will do the same for the open source community. Below are a few highlights of what we have written so far. Let's use realbook to train a binary classifier that takes in audio, converts the audio to a spectrogram and then runs the spectorgram output through two trainable Dense layers.

… studying the Higgs Boson at CERN, to using similar machine learning techniques to gauge perceptions of crime in the Colombian capital of Bogota.