NASA has pushed back the launch of the Ingenuity helicopter's first flight on the Martian surface. Takeoff of the 4-pound (1.8-kilogram) robotic helicopter, currently attached to NASA's Perseverance rover, is now slated for no earlier than April 11. Deployment of Ingenuity, which has become affectionately known as'Ginny', had been originally planned for April 8. If successful, Ingenuity will be the first powered and controlled flight of an aircraft on any planet other than Earth. Ingenuity carries a small amount of fabric that covered one of the wings of the Wright brothers' aircraft, known as the Flyer, during the first powered, controlled flight on Earth in 1903.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A former Air Force intelligence analyst pleaded guilty Wednesday to leaking classified documents to a reporter about military drone strikes against al-Qaida and other terrorist targets. The guilty plea from Daniel Hale, 33, of Nashville, Tennessee, comes just days before he was slated to go on trial in federal court in Alexandria, Virginia, for violating the World War I-era Espionage Act. Hale admitted leaking roughly a dozen secret and top-secret documents to a reporter in 2014 and 2015, when he was working for a contractor as an analyst at the National Geospatial-Intelligence Agency (NGA).

In December 2017, AlphaZero, a chess-playing, artificial intelligence (AI) developed by Google, defeated Stockfish 8, the reigning world champion program at that time. AlphaZero calculates around 80,000 moves per second, according to The Guardian. Yet, out of 100 matches, AlphaZero won 28 and tied 72. Stockfish's open-source algorithm has been continually tweaked by human input over the years. The New Yorker reports that coders suggest an idea to update the algorithm, and the two versions are then pitted against each other for thousands of matches to see which comes out on top. Google claims that AlphaZero's machine learning algorithm had no human input beyond the programming of the basic rules of chess.

Artificial Intelligence (AI) has recently shown its capabilities for almost every field of life. Machine Learning, which is a subset of AI, is a `HOT' topic for researchers. Machine Learning outperforms other classical forecasting techniques in almost all-natural applications. It is a crucial part of modern research. As per this statement, Modern Machine Learning algorithms are hungry for big data. Due to the small datasets, the researchers may not prefer to use Machine Learning algorithms. To tackle this issue, the main purpose of this survey is to illustrate, demonstrate related studies for significance of a semi-parametric Machine Learning framework called Grey Machine Learning (GML). This kind of framework is capable of handling large datasets as well as small datasets for time series forecasting likely outcomes. This survey presents a comprehensive overview of the existing semi-parametric machine learning techniques for time series forecasting. In this paper, a primer survey on the GML framework is provided for researchers. To allow an in-depth understanding for the readers, a brief description of Machine Learning, as well as various forms of conventional grey forecasting models are discussed. Moreover, a brief description on the importance of GML framework is presented.

While there are more than 7000 languages in the world, most translation research efforts have targeted a few high-resource languages. Commercial translation systems support only one hundred languages or fewer, and do not make these models available for transfer to low resource languages. In this work, we present useful tools for machine translation research: MTData, NLCodec, and RTG. We demonstrate their usefulness by creating a multilingual neural machine translation model capable of translating from 500 source languages to English. We make this multilingual model readily downloadable and usable as a service, or as a parent model for transfer-learning to even lower-resource languages.

Human behavior refers to the way humans act and interact in response to a stimulus, internal or external. Understanding human behavior through observational study is one of the core methodologies in fields such as psychology and sociology (Margolin, Oliver, Gordis, O'hearn, Medina, Ghosh and Morland, 1998). Human behaviors encompass rich information: from emotional expression, processing, and regulation to the intricate dynamics of interactions, including the context and knowledge of interlocutors and their thinking and problem-solving intent (Li, Baucom and Georgiou, 2020). Furthermore, the behavioral constructs of interest are often dependent on the domain of interaction (Narayanan and Georgiou, 2013). Hence characterization of human behavior usually requires domain-specific knowledge and adequate windows of observation. Notably, across psychological health science and practice (Bone, Lee, Chaspari, Gibson and Narayanan, 2017) such as couple therapy (Christensen, Atkins, Berns, Wheeler, Baucom and Simpson, 2004), suicide cognition evaluation (Bryan, Rudd, Wertenberger, Etienne, Ray-Sannerud, Morrow, Peterson and Young-McCaughon, 2014) and addiction counseling (Xiao, Imel, Georgiou, Atkins and Narayanan, 2015), this is exemplified in the definition and derivation of a variety of domain-specific behavior constructs (e.g., blame and affect patterns exhibited by partners, suicidal ideation of an individual at risk, and empathy expressed by a therapist in the respective aforementioned domains) to support specific subsequent plan of action. Human speech offers rich information about the mental state and traits of the talkers. Vocal cues, including speech and spoken language as well as nonverbal vocalizations and disfluency patterns, have been shown to be informationally relevant in the context of human behavior (e.g., in marital interaction (Baucom, Atkins, Simpson and Christensen, 2009), in motivational interviewing (Amrhein, Miller, Yahne, Palmer and Fulcher, 2003; Imel, Barco, Brown, Baucom, Baer, Kircher and Atkins, 2014; Miller, Benefield and Tonigan, 1993)). Many automatic computational approaches that support measurement, analysis, and modeling of human behaviors from speech have been investigated in affective computing (Lee and Narayanan, 2005), social signal processing (Vinciarelli, Pantic and Bourlard, 2009) and behavioral signal processing (BSP) (Narayanan and Georgiou, 2013).

The damage to cellular towers during natural and man-made disasters can disturb the communication services for cellular users. One solution to the problem is using unmanned aerial vehicles to augment the desired communication network. The paper demonstrates the design of a UAV-Assisted Imitation Learning (UnVAIL) communication system that relays the cellular users' information to a neighbor base station. Since the user equipment (UEs) are equipped with buffers with limited capacity to hold packets, UnVAIL alternates between different UEs to reduce the chance of buffer overflow, positions itself optimally close to the selected UE to reduce service time, and uncovers a network pathway by acting as a relay node. UnVAIL utilizes Imitation Learning (IL) as a data-driven behavioral cloning approach to accomplish an optimal scheduling solution. Results demonstrate that UnVAIL performs similar to a human expert knowledge-based planning in communication timeliness, position accuracy, and energy consumption with an accuracy of 97.52% when evaluated on a developed simulator to train the UAV.

Thinking in terms of causality helps us structure how different parts of a system depend on each other, and how interventions on one part of a system may result in changes to other parts. Therefore, formal models of causality are an attractive tool for reasoning about security, which concerns itself with safeguarding properties of a system against interventions that may be malicious. As we show, many security properties are naturally expressed as nested causal statements: not only do we consider what caused a particular undesirable effect, but we also consider what caused this causal relationship itself to hold. We present a natural way to extend the Halpern-Pearl (HP) framework for causality to capture such nested causal statements. This extension adds expressivity, enabling the HP framework to distinguish between causal scenarios that it could not previously naturally tell apart. We moreover revisit some design decisions of the HP framework that were made with non-nested causal statements in mind, such as the choice to treat specific values of causal variables as opposed to the variables themselves as causes, and may no longer be appropriate for nested ones.

We present AlphaChute: a state-of-the-art algorithm that achieves superhuman performance in the ancient game of Chutes and Ladders. We prove that our algorithm converges to the Nash equilibrium in constant time, and therefore is -- to the best of our knowledge -- the first such formal solution to this game. Surprisingly, despite all this, our implementation of AlphaChute remains relatively straightforward due to domain-specific adaptations. We provide the source code for AlphaChute here in our Appendix.

Claim verification is the task of predicting the veracity of written statements against evidence. Previous large-scale datasets model the task as classification, ignoring the need to retrieve evidence, or are constructed for research purposes, and may not be representative of real-world needs. In this paper, we introduce a novel claim verification dataset with instances derived from search-engine queries, yielding 10,987 claims annotated with evidence that represent real-world information needs. For each claim, we annotate evidence from full Wikipedia articles with both section and sentence-level granularity. Our annotation allows comparison between two complementary approaches to verification: stance classification, and evidence extraction followed by entailment recognition. In our comprehensive evaluation, we find no significant difference in accuracy between these two approaches. This enables systems to use evidence extraction to summarize a rationale for an end-user while maintaining the accuracy when predicting a claim's veracity. With challenging claims and evidence documents containing hundreds of sentences, our dataset presents interesting challenges that are not captured in previous work -- evidenced through transfer learning experiments. We release code and data to support further research on this task.