Knowing the Galactic 3D dust distribution is relevant for understanding many processes in the interstellar medium and for correcting many astronomical observations for dust absorption and emission. Here, we aim for a 3D reconstruction of the Galactic dust distribution with an increase in the number of meaningful resolution elements by orders of magnitude with respect to previous reconstructions, while taking advantage of the dust's spatial correlations to inform the dust map. We use iterative grid refinement to define a log-normal process in spherical coordinates. This log-normal process assumes a fixed correlation structure, which was inferred in an earlier reconstruction of Galactic dust. Our map is informed through 111 Million data points, combining data of PANSTARRS, 2MASS, Gaia DR2 and ALLWISE. The log-normal process is discretized to 122 Billion degrees of freedom, a factor of 400 more than our previous map. We derive the most probable posterior map and an uncertainty estimate using natural gradient descent and the Fisher-Laplace approximation. The dust reconstruction covers a quarter of the volume of our Galaxy, with a maximum coordinate distance of $16\,\text{kpc}$, and meaningful information can be found up to at distances of $4\,$kpc, still improving upon our earlier map by a factor of 5 in maximal distance, of $900$ in volume, and of about eighteen in angular grid resolution. Unfortunately, the maximum posterior approach chosen to make the reconstruction computational affordable introduces artifacts and reduces the accuracy of our uncertainty estimate. Despite of the apparent limitations of the presented 3D dust map, a good part of the reconstructed structures are confirmed by independent maser observations. Thus, the map is a step towards reliable 3D Galactic cartography and already can serve for a number of tasks, if used with care.

Abstract--Artificial Intelligence (AI) has found its applications in a variety of environments ranging from data science to cybersecurity. AI helps break through the limitations of traditional algorithms and provides more efficient and flexible methods for solving problems. In this paper, we focus on the applications of artificial intelligence in authentication, which is used in a wide range of scenarios including facial recognition to access buildings, keystroke dynamics to unlock smartphones. With the emerging AI-assisted authentication schemes, our comprehensive survey provides an overall understanding on a high level, which paves the way for future research in this area. In contrast to other relevant surveys, our research is the first of its kind to focus on the roles of AI in authentication. Learning and neural networks are The traditional password-based authentication method has two main mechanisms used in AI. Learning is the process of slowly faded out due to its inadequate ...

Twilio powers real-time business communications and data solutions that help companies and developers worldwide build better applications and customer experiences. Although we're headquartered in San Francisco, we have a presence throughout South America, Europe, Asia, and Australia. We're on a journey to becoming a globally anti-racist, anti-oppressive, anti-bias company that actively opposes racism and all forms of oppression and bias. At Twilio, we support diversity, equity & inclusion wherever we do business. We employ thousands of Twilions worldwide, and we're looking for more builders, creators, and visionaries to help fuel our growth momentum.

A triangle center such as the incenter, barycenter, etc., is specified by a function thrice-and cyclically applied on sidelengths and/or angles. Consider the 1d family of 3-periodics in the elliptic billiard, and the loci of its triangle centers. Some will sweep ellipses, and others higher-degree algebraic curves. We propose two rigorous methods to prove if the locus of a given center is an ellipse: one based on computer algebra, and another based on an algebro-geometric method. We also prove that if the triangle center function is rational on sidelengths, the locus is algebraic.

This work gives introduction to traffic control by connected automated vehicles. The influence of vehicle control on vehicular traffic and traffic control strategies are discussed and compared. It is highlighted that vehicle-to-everything connectivity allows connected automated vehicles to access the state of the traffic behind them such that feedback can be utilized to mitigate evolving congestions. Numerical simulations demonstrate that such connectivity-based traffic control is beneficial for smoothness and energy efficiency of highway traffic. The dynamics and stability of traffic flow, under the proposed controllers, are analyzed in detail to construct stability charts that guide the selection of stabilizing control gains.

Bayesian optimization (BO) has become an established framework and popular tool for hyperparameter optimization (HPO) of machine learning (ML) algorithms. While known for its sample-efficiency, vanilla BO can not utilize readily available prior beliefs the practitioner has on the potential location of the optimum. To address this issue, we propose πBO, an acquisition function generalization which incorporates prior beliefs about the location of the optimum in the form of a probability distribution, provided by the user. In contrast to previous approaches, πBO is conceptually simple and can easily be integrated with existing libraries and many acquisition functions. We provide regret bounds when πBO is applied to the common Expected Improvement acquisition function and prove convergence at regular rates independently of the prior. Further, our experiments show that πBO outperforms competing approaches across a wide suite of benchmarks and prior characteristics. We also demonstrate that πBO improves on the state-of-theart performance for a popular deep learning task, with a 12.5 time-to-accuracy speedup over prominent BO approaches. The optimization of expensive black-box functions is a prominent task, arising across a wide range of applications. Despite the demonstrated effectiveness of BO for HPO (Bergstra et al., 2011; Turner et al., 2021), its adoption among practitioners remains limited. In a survey covering NeurIPS 2019 and ICLR 2020 (Bouthillier & Varoquaux, 2020), manual search was shown to be the most prevalent tuning method, with BO accounting for less than 7% of all tuning efforts. As the understanding of hyperparameter settings in deep learning (DL) models increase (Smith, 2018), so too does the tuning proficiency of practitioners (Anand et al., 2020). As previously displayed (Smith, 2018; Anand et al., 2020; Souza et al., 2021; Wang et al., 2019), this knowledge manifests in choosing single configurations or regions of hyperparameters that presumably yield good results, demonstrating a belief over the location of the optimum. BO's deficit to properly incorporate said beliefs is a reason why practitioners prefer manual search to BO (Wang et al., 2019), despite its documented shortcomings (Bergstra & Bengio, 2012). To improve the usefulness of automated HPO approaches for ML practictioners, the ability to incorporate such knowledge is pivotal. Well-established BO frameworks (Snoek et al., 2012; Hutter et al., 2011; The GPyOpt authors, 2016; Kandasamy et al., 2020; Balandat et al., 2020) support user input to a limited extent, such as by biasing the initial design, or by narrowing the search space; however, this type of hard prior can lead to poor performance by missing important regions.

As part of the 36th AAAI Conference on Artificial Intelligence (AAAI2022), 39 different workshops were held, covering a wide range of different AI topics. We hear from the organisers of the workshops on AI-Based Design and Manufacturing, and Graphs and more Complex structures for Learning and Reasoning, who provide a summary of their events. The first AI for Design and Manufacturing (ADAM) Workshop, conducted virtually as part of AAAI-22, was organized in order to bring together world experts in core AI, scientific computing, geometric modeling, design, and manufacturing. The primary objectives were to outline the major research challenges in this rapidly growing sub-field of AI; cross-pollinate collaborations between AI researchers and domain experts in engineering design and manufacturing; and sketch open problems of common interest. This one-day workshop consisted of two plenary talks, four keynote talks, and twenty-four lightning talks by authors of accepted papers.

The Atacama Desert, located in South America, is one of the driest regions on Earth. Several types of endemic plants are still present at the site. After collecting several species that grow between 2,400 and 4,500 meters above sea level, scientists from INRAE, Purdue University and the Pontifical Catholic University of Santiago in Chile have been able to identify common molecular markers that allow an understanding of the mechanisms of these plants' resilience in the face of a harsh environment. The researchers used an innovative approach using artificial intelligence. The results of their work are detailed in review The new botany.

Conventional methods for query autocompletion aim to predict which completed query a user will select from a list. A shortcoming of this approach is that users often do not know which query will provide the best retrieval performance on the current information retrieval system, meaning that any query autocompletion methods trained to mimic user behavior can lead to suboptimal query suggestions. To overcome this limitation, we propose a new approach that explicitly optimizes the query suggestions for downstream retrieval performance. We formulate this as a problem of ranking a set of rankings, where each query suggestion is represented by the downstream item ranking it produces. We then present a learning method that ranks query suggestions by the quality of their item rankings. The algorithm is based on a counterfactual learning approach that is able to leverage feedback on the items (e.g., clicks, purchases) to evaluate query suggestions through an unbiased estimator, thus avoiding the assumption that users write or select optimal queries. We establish theoretical support for the proposed approach and provide learning-theoretic guarantees. We also present empirical results on publicly available datasets, and demonstrate real-world applicability using data from an online shopping store.

Parameterized Quantum Circuits (PQC) are drawing increasing research interest thanks to its potential to achieve quantum advantages on near-term Noisy Intermediate Scale Quantum (NISQ) hardware. In order to achieve scalable PQC learning, the training process needs to be offloaded to real quantum machines instead of using exponential-cost classical simulators. One common approach to obtain PQC gradients is parameter shift whose cost scales linearly with the number of qubits. We present QOC, the first experimental demonstration of practical on-chip PQC training with parameter shift. Nevertheless, we find that due to the significant quantum errors (noises) on real machines, gradients obtained from naive parameter shift have low fidelity and thus degrading the training accuracy. To this end, we further propose probabilistic gradient pruning to firstly identify gradients with potentially large errors and then remove them. Specifically, small gradients have larger relative errors than large ones, thus having a higher probability to be pruned. We perform extensive experiments with the Quantum Neural Network (QNN) benchmarks on 5 classification tasks using 5 real quantum machines. The results demonstrate that our on-chip training achieves over 90% and 60% accuracy for 2-class and 4-class image classification tasks. The probabilistic gradient pruning brings up to 7% PQC accuracy improvements over no pruning. Overall, we successfully obtain similar on-chip training accuracy compared with noise-free simulation but have much better training scalability. The QOC code is available in the TorchQuantum library.