Abstract--Interstellar objects (ISOs) are fascinating and underexplored be best implemented using small spacecraft. The unification of celestial objects, providing physical laboratories to ISO detection, orbit characterization, and cruise trajectory with understand the formation of our solar system and probe the learning-based G&C algorithms for accurate low-V flybys composition and properties of material formed in exoplanetary represents a nearly end-to-end simulation and assessment of a systems. The recent Planetary Science and Astrobiology mission to visit an ISO. This process is simulated using JPL's Decadal Survey emphasized that a dedicated mission to an interstellar SmallSat Development Testbed, which determines the feasibility object would have high scientific value. A dedicated ISOs with varying characteristics, including a discussion of state spacecraft could resolve the shape, rotation properties, surface covariance estimation over the course of a cruise, handoffs from morphology, and composition of an asteroid-like ISO. Mass traditional navigation approaches to novel autonomous navigation spectroscopy techniques can probe the gas composition of a for fast flyby regimes, and overall recommendations about comet-like ISO.

Adversarial vulnerability remains a major obstacle to constructing reliable NLP systems. When imperceptible perturbations are added to raw input text, the performance of a deep learning model may drop dramatically under attacks. Recent work argues the adversarial vulnerability of the model is caused by the non-robust features in supervised training. Thus in this paper, we tackle the adversarial robustness challenge from the view of disentangled representation learning, which is able to explicitly disentangle robust and non-robust features in text. Specifically, inspired by the variation of information (VI) in information theory, we derive a disentangled learning objective composed of mutual information to represent both the semantic representativeness of latent embeddings and differentiation of robust and non-robust features. On the basis of this, we design a disentangled learning network to estimate these mutual information. Experiments on text classification and entailment tasks show that our method significantly outperforms the representative methods under adversarial attacks, indicating that discarding non-robust features is critical for improving adversarial robustness.

Natural Language Generation (NLG) for non-English languages is hampered by the scarcity of datasets in these languages. In this paper, we present the IndicNLG Benchmark, a collection of datasets for benchmarking NLG for 11 Indic languages. We focus on five diverse tasks, namely, biography generation using Wikipedia infoboxes, news headline generation, sentence summarization, paraphrase generation and, question generation. We describe the created datasets and use them to benchmark the performance of several monolingual and multilingual baselines that leverage pre-trained sequence-to-sequence models. Our results exhibit the strong performance of multilingual language-specific pre-trained models, and the utility of models trained on our dataset for other related NLG tasks. Our dataset creation methods can be easily applied to modest-resource languages as they involve simple steps such as scraping news articles and Wikipedia infoboxes, light cleaning, and pivoting through machine translation data. To the best of our knowledge, the IndicNLG Benchmark is the first NLG benchmark for Indic languages and the most diverse multilingual NLG dataset, with approximately 8M examples across 5 tasks and 11 languages. The datasets and models are publicly available at https://ai4bharat.iitm.ac.in/indicnlg-suite.

Transportation is the backbone of the economy and urban development. Improving the efficiency, sustainability, resilience, and intelligence of transportation systems is critical and also challenging. The constantly changing traffic conditions, the uncertain influence of external factors (e.g., weather, accidents), and the interactions among multiple travel modes and multi-type flows result in the dynamic and stochastic natures of transportation systems. The planning, operation, and control of transportation systems require flexible and adaptable strategies in order to deal with uncertainty, non-linearity, variability, and high complexity. In this context, Reinforcement Learning (RL) that enables autonomous decision-makers to interact with the complex environment, learn from the experiences, and select optimal actions has been rapidly emerging as one of the most useful approaches for smart transportation. This paper conducts a bibliometric analysis to identify the development of RL-based methods for transportation applications, typical journals/conferences, and leading topics in the field of intelligent transportation in recent ten years. Then, this paper presents a comprehensive literature review on applications of RL in transportation by categorizing different methods with respect to the specific application domains. The potential future research directions of RL applications and developments are also discussed.

A Knowledge Graph (KG) is a type of knowledge base that stores information in the form of semantic triples formed by a subject, a predicate, and an object. KGs represent both real and abstract entities internally as labelled and uniquely identifiable entities, such as The Moon or Happiness, and can amass information from a multitude of domains and sources by connecting such entities amongst themselves or to literals through relationships, coded via uniquely identified predicates. KGs serve as sources of both human and machine-readable semantically structured data for various crucial applications in the modern web landscape, such as Wikipedia infoboxes, search engines results, voice-activated assistants, and information gathering projects [30]. Developed and maintained by ontology experts, data curators, and even anonymous volunteers, KGs have massively grown in size and adoption in the last decade, mainly as secondary sources of information. This means not storing new information, but taking it from authoritative and reliable sources which are explicitly referenced. As such, KGs depend on well-documented and verifiable provenance to ensure they are regarded as trustworthy and usable [56]. Processes to assess and assure the quality of information provenance are thus crucial to KGs, especially measuring and maintaining verifiability, i.e. the degree to which consumers of KG triples can attest these are truly supported by their sources [56]. However, such processes are currently performed mostly manually, which does not scale with size. Manually ensuring high verifiability on vital KGs such as Wikidata and DBpedia is prohibitive due to their sheer size.

Modern large-scale Pre-trained Language Models (PLMs) have achieved tremendous success on a wide range of downstream tasks. However, most of the LM pre-training objectives only focus on text reconstruction, but have not sought to learn latent-level interpretable representations of sentences. In this paper, we manage to push the language models to obtain a deeper understanding of sentences by proposing a new pre-training objective, Sparse Latent Typing, which enables the model to sparsely extract sentence-level keywords with diverse latent types. Experimental results show that our model is able to learn interpretable latent type categories in a self-supervised manner without using any external knowledge. Besides, the language model pre-trained with such an objective also significantly improves Information Extraction related downstream tasks in both supervised and few-shot settings. Our code is publicly available at: https://github.com/renll/SparseLT.

Born and raised in Shanghai, Tan came to MIT to study high-energy astrophysics and wrote his dissertation on computational modeling of neutron stars. "Coming from China at that time, I had very little experience with computers," he says. "I was fortunate to find many helpful students during my time there." Tan also met his wife, Hong (Zhang) Tan, SM '88, PhD '96, at MIT. The pair were married in the MIT Chapel and today have two sons.

Artificial intelligence (AI) technologies, like machine learning or natural language processing, curate threat intelligence from various historical …

Red Hat Device Edge Artificial Intelligence (AI) technology enhanced the flight characteristics and data processing of Lockheed Martin's Stalker.

Lockheed Martin and Red Hat, Inc. announced their collaboration to advance artificial intelligence (AI) innovation on Lockheed Martin's unmanned military platforms. The adoption of newly developed Red Hat Device Edge technology will enable Lockheed Martin's unmanned systems to operate safely in geographically constrained environments and improves the processing of sensor-derived information. In a recent demonstration, Lockheed Martin used Red Hat Device Edge on a Stalker UAS to show how AI-enhanced sensing can advance joint operations across domains. The Stalker used onboard sensors and AI to adapt in real time to a threat environment. As reported by the company, the Stalker was flying an intelligence, surveillance and reconnaissance (ISR) mission to detect a simulated military target.