Recent advances in neural networks have generated considerable excitement about AI. But AI is not all about neural networks. Other avenues in AI research tackle problems such as building effective models of the world or logical reasoning and are especially useful for dealing with the limitations of neural networks. In this post, we examine one specific problem in AI: planning. One simple definition is that planning is an exploration to decide what actions need to be taken to achieve a given goal. The result of a planning process (i.e., the plan) is a collection of actions that would take us from the current (also known as the initial) state to a goal state. AI planning is about how to teach a machine to plan ahead.

Sandra Aistars is a Clinical Professor at Antonin Scalia Law School, George Mason University, leading the law school's Arts & Entertainment Advocacy Program. Throughout her career she has served in positions that required mastery of intellectual property issues, federal policy process and development, and the ability to understand and manage the implications of intellectual property policies across a portfolio of businesses. In addition, Aistars has a wealth of experience working with policy makers in Washington and internationally. She has served on trade missions and been an industry advisor to the Department of Commerce on intellectual property implications for international trade negotiations; worked on legislative and regulatory matters worldwide; frequently testified before Congress and federal agencies regarding intellectual property matters; chaired cross-industry coalitions and technology standards efforts; and is regularly tapped by government agencies to lecture in U.S. government-sponsored study tours for visiting legislators, judges, prosecutors, and regulators. Aistars has also previously served as Vice President and Associate General Counsel at Time Warner Inc.

Simulation of fluid flow in porous media has many applications, from the micro-scale (cell membranes, filters, rocks) to macro-scale (groundwater, hydrocarbon reservoirs, and geothermal) and beyond. Direct simulation of flow in porous media requires significant computational resources to solve within reasonable timeframes. An integrated method combining predictions of fluid flow (fast, limited accuracy) with direct flow simulation (slow, high accuracy) is outlined. In the tortuous flow paths of porous media, Deep Learning techniques based on Convolutional Neural Networks (CNNs) are shown to give an accurate estimate of the steady state velocity fields (in all axes), and by extension, the macro-scale permeability. This estimate can be used as-is, or as initial conditions in direct simulation to reach a fully accurate result in a fraction of the compute time. A Gated U-Net Convolutional Neural Network is trained on a datasets of 2D and 3D porous media generated by correlated fields, with their steady state velocity fields calculated from direct LBM simulation. Sensitivity analysis indicates that network accuracy is dependent on (1) the tortuosity of the domain, (2) the size of convolution filters, (3) the use of distance maps as input, (4) the use of mass conservation loss functions. Permeability estimation from these predicted fields reaches over 90\% accuracy for 80\% of cases. It is further shown that these velocity fields are error prone when used for solute transport simulation. Using the predicted velocity fields as initial conditions is shown to accelerate direct flow simulation to physically true steady state conditions an order of magnitude less compute time. Using Deep Learning predictions (or potentially any other approximation method) to accelerate flow simulation to steady state in complex pore structures shows promise as a technique push the boundaries fluid flow modelling.

Many convolutional neural network (CNN) accelerators face performance- and energy-efficiency challenges which are crucial for embedded implementations, due to high DRAM access latency and energy. Recently, some DRAM architectures have been proposed to exploit subarray-level parallelism for decreasing the access latency. Towards this, we present a design space exploration methodology to study the latency and energy of different mapping policies on different DRAM architectures, and identify the pareto-optimal design choices. The results show that the energy-efficient DRAM accesses can be achieved by a mapping policy that orderly prioritizes to maximize the row buffer hits, bank- and subarray-level parallelism.

The majority of search-based HTN planning systems can be divided into those searching a space of partial plans (a plan space) and those performing progression search, i.e., that build the solution in a forward manner. So far, all HTN planners that guide the search by using heuristic functions are based on plan space search. Those systems represent the set of search nodes more effectively by maintaining a partial ordering between tasks, but they have only limited information about the current state during search. In this article, we propose the use of progression search as basis for heuristic HTN planning systems. Such systems can calculate their heuristics incorporating the current state, because it is tracked during search. Our contribution is the following: We introduce two novel progression algorithms that avoid unnecessary branching when the problem at hand is partially ordered and show that both are sound and complete. We show that defining systematicity is problematic for search in HTN planning, propose a definition, and show that it is fulfilled by one of our algorithms. Then, we introduce a method to apply arbitrary classical planning heuristics to guide the search in HTN planning. It relaxes the HTN planning model to a classical model that is only used for calculating heuristics. It is updated during search and used to create heuristic values that are used to guide the HTN search. We show that it can be used to create HTN heuristics with interesting theoretical properties like safety, goal-awareness, and admissibility. Our empirical evaluation shows that the resulting system outperforms the state of the art in search-based HTN planning.

Recent years, typhoon damages has become social problem owing to climate change. Especially, 9 September 2019, Typhoon Faxai passed on the south Chiba prefecture in Japan, whose damages included with electric and water provision stop and house roof break because of strong wind recorded on the maximum 45 meter per second. A large amount of tree fell down, and the neighbor electric poles also fell down at the same time. These disaster features have caused that it took eighteen days for recovery longer than past ones. Initial responses are important for faster recovery. As long as we can, aerial survey for global screening of devastated region would be required for decision support to respond where to recover ahead. This paper proposes a practical method to visualize the damaged areas focused on the typhoon disaster features using aerial photography. This method can classify eight classes which contains land covers without damages and areas with disaster, where an aerial photograph is partitioned into 4,096 grids that is 64 by 64, with each unit image of 48 meter square. Using target feature class probabilities, we can visualize disaster features map to scale the color range from blue to red or yellow. Furthermore, we can realize disaster feature mapping on each unit grid images to compute the convolutional activation map using Grad-CAM based on deep neural network layers for classification. This paper demonstrates case studies applied to aerial photographs recorded at the south Chiba prefecture in Japan after typhoon disaster.

In medical image diagnosis, pathology image analysis using semantic segmentation becomes important for efficient screening as a field of digital pathology. The spatial augmentation is ordinary used for semantic segmentation. Tumor images under malignant are rare and to annotate the labels of nuclei region takes much time-consuming. We require an effective use of dataset to maximize the segmentation accuracy. It is expected that some augmentation to transform generalized images influence the segmentation performance. We propose a "synthetic augmentation" using label-to-image translation, mapping from a semantic label with the edge structure to a real image. Exactly this paper deal with stain slides of nuclei in tumor. Actually, we demonstrate several segmentation algorithms applied to the initial dataset that contains real images and labels using synthetic augmentation in order to add their generalized images. We computes and reports that a proposed synthetic augmentation procedure improve their accuracy.

Sleep has been shown to be an indispensable and important component of patients recovery process. Nonetheless, sleep quality of patients in the Intensive Care Unit (ICU) is often low, due to factors such as noise, pain, and frequent nursing care activities. Frequent sleep disruptions by the medical staff and/or visitors at certain times might lead to disruption of patient sleep-wake cycle and can also impact the severity of pain. Examining the association between sleep quality and frequent visitation has been difficult, due to lack of automated methods for visitation detection. In this study, we recruited 38 patients to automatically assess visitation frequency from captured video frames. We used the DensePose R-CNN (ResNet-101) model to calculate the number of people in the room in a video frame. We examined when patients are interrupted the most, and we examined the association between frequent disruptions and patient outcomes on pain and length of stay.

Existential rules are a positive fragment of first-order logic that generalizes function-free Horn rules by allowing existentially quantified variables in rule heads. This family of languages has recently attracted significant interest in the context of ontology-mediated query answering. Forward chaining, also known as the chase, is a fundamental tool for computing universal models of knowledge bases, which consist of existential rules and facts. Several chase variants have been defined, which differ on the way they handle redundancies. A set of existential rules is bounded if it ensures the existence of a bound on the depth of the chase, independently from any set of facts. Deciding if a set of rules is bounded is an undecidable problem for all chase variants. Nevertheless, when computing universal models, knowing that a set of rules is bounded for some chase variant does not help much in practice if the bound remains unknown or even very large. Hence, we investigate the decidability of the k-boundedness problem, which asks whether the depth of the chase for a given set of rules is bounded by an integer k. We identify a general property which, when satisfied by a chase variant, leads to the decidability of k-boundedness. We then show that the main chase variants satisfy this property, namely the oblivious, semi-oblivious (aka Skolem), and restricted chase, as well as their breadth-first versions. This paper is under consideration for publication in Theory and Practice of Logic Programming.

Stochastic block models (SBMs) are often used to find assortative community structures in networks, such that the probability of connections within communities is higher than in between communities. However, classic SBMs are not limited to assortative structures. In this study, we discuss the implications of this model-inherent indifference towards assortativity or disassortativity, and show that this characteristic can lead to undesirable outcomes for networks which are presupposedy assortative but which contain a reduced amount of information. To circumvent this issue, we introduce a constrained SBM that imposes strong assortativity constraints, along with efficient algorithmic approaches to solve it. These constraints significantly boost community recovery capabilities in regimes that are close to the information-theoretic threshold. They also permit to identify structurally-different communities in networks representing cerebral-cortex activity regions.