Temporal point process (TPP) models combined with recurrent neural networks provide a powerful framework for modeling continuous-time event data. While such models are flexible, they are inherently sequential and therefore cannot benefit from the parallelism of modern hardware. By exploiting the recent developments in the field of normalizing flows, we design TriTPP -- a new class of non-recurrent TPP models, where both sampling and likelihood computation can be done in parallel. TriTPP matches the flexibility of RNN-based methods but permits orders of magnitude faster sampling. This enables us to use the new model for variational inference in continuous-time discrete-state systems. We demonstrate the advantages of the proposed framework on synthetic and real-world datasets.

Active learning - the field of machine learning (ML) dedicated to optimal experiment design, has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics [1]. In this work we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate autonomous research methodology (i.e. autonomous hypothesis definition and evaluation) that can place complex, advanced materials in reach, allowing scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. Additionally, this robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. We used the real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) at the synchrotron beamline to accelerate the fundamentally interconnected tasks of rapid phase mapping and property optimization, with each cycle taking seconds to minutes, resulting in the discovery of a novel epitaxial nanocomposite phase-change memory material.

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.

Social media have been deliberately used for malicious purposes, including political manipulation and disinformation. Most research focuses on high-resource languages. However, malicious actors share content across countries and languages, including low-resource ones. Here, we investigate whether and to what extent malicious actors can be detected in low-resource language settings. We discovered that a high number of accounts posting in Tagalog were suspended as part of Twitter's crackdown on interference operations after the 2016 US Presidential election. By combining text embedding and transfer learning, our framework can detect, with promising accuracy, malicious users posting in Tagalog without any prior knowledge or training on malicious content in that language. We first learn an embedding model for each language, namely a high-resource language (English) and a low-resource one (Tagalog), independently. Then, we learn a mapping between the two latent spaces to transfer the detection model. We demonstrate that the proposed approach significantly outperforms state-of-the-art models, including BERT, and yields marked advantages in settings with very limited training data-the norm when dealing with detecting malicious activity in online platforms.

In astronomy, neural networks are often trained on simulated data with the prospect of being applied to real observations. Unfortunately, simply training a deep neural network on images from one domain does not guarantee satisfactory performance on new images from a different domain. The ability to share cross-domain knowledge is the main advantage of modern deep domain adaptation techniques. Here we demonstrate the use of two techniques -- Maximum Mean Discrepancy (MMD) and adversarial training with Domain Adversarial Neural Networks (DANN) -- for the classification of distant galaxy mergers from the Illustris-1 simulation, where the two domains presented differ only due to inclusion of observational noise. We show how the addition of either MMD or adversarial training greatly improves the performance of the classifier on the target domain when compared to conventional machine learning algorithms, thereby demonstrating great promise for their use in astronomy.

Media manipulation through images and videos has been around for decades. For example, in WWII Mousollini released a propaganda image of himself on a horse with his horse handler edited out. The goal was to make himself seem more impressive and powerful [1]. These types of tricks can have significant impacts given the scale of people that see images like these, especially in the internet era. DARPA has an entire program constructed just to develop methods for detecting manipulated media through their media forensics (MEDIFOR) [2].

In August, the Trump administration said it was on track to meet its commitment of roughly doubling nondefense research and development spending on AI and quantum information sciences between 2020 and 2022. The White House in February outlined a plan for annual spending on AI to rise to more than $2 billion between 2020 and 2022, and funding for quantum information science to increase to $860 million over that period. Quantum information science is an area of study that includes quantum-based cryptography, communication and quantum computing. "Both parties realize we need to be competitive in these areas," said Ray Wang, an analyst with Constellation Research Inc., a research and advisory firm. The Biden administration is expected to invest more money in AI and quantum information science, in part because overall spending on research and development is expected to be higher, said Robert D. Atkinson, president of the ITIF.

An app designed for Apple Watch has received approval from the Food and Drug Administration (FDA) for an effective treatment for nightmares caused by post-traumatic stress disorder (PTSD). Called NightWare, the application is now marketed as an aid for the'temporary reduction of sleep disturbances related to nightmares in adults.' The app uses Apple Watch sensors to monitor body movement and sleep and when it detects the user is experiencing a nightmare, the device will vibrate to disturb their sleep. NightWare is currently only available with a prescription and the company stresses it is not a standalone treatment for PTSD. Approximately eight million Americans suffer from PTSD and up to 96 percent of them have nightmares as a result.

Drone Defence Services and the University of Nottingham will develop sensor technology to track aircraft. By monitoring all aircraft, Drone Defence aims to prevent drone misuse and enable drones to safely share the sky with other aircraft.

As America staggered through the final stretch of a bitter and divisive US presidential election last month, China was putting the finishing touches on carefully drawn plans for economic recovery, an enhanced military, and crucially, increased technological self-reliance. The proposals, outlined in the Chinese Communist Party's latest Five Year Plan, highlight a key challenge for president-elect Joe Biden at the outset of his four-year term. President Trump's efforts to kneecap Chinese technology have only partially succeeded. Ironically, they may ultimately accelerate China's development in key cutting-edge technologies such as artificial intelligence, chipmaking, 5G, and biotechnology. Foreign policy experts say the US needs to confront China on issues such as market access, forced technology transfers, and human rights.