Alternative energy is becoming a growing source of power in the United States, including wind, hydroelectric and solar. The Solar Decathlon is a competition run by the US Department of Energy every two years. Washington State University (WSU) is one of twenty teams recently selected to compete in the fall 2017 challenge. A central part to WSU's entry is incorporating new and existing smart home technology from the ground up. The smart home can help to optimize energy loads, battery life and general comfort of the user in the home. This paper discusses the high-level goals of the project, hardware selected, build strategy and anticipated approach.

Currently two barriers exist that sabotage clothing semantic parsing research: existing methods are time-consuming and the lack of large publicly available dataset that enables parsing at multiple scales. To mitigate these two dilemmas, we hereby embrace deep learning method and design a lightweight multi-scale inception neural network which is at both inside and outside multi-scale inception during training. Moreover, atrous convolution block is involved to enlarge the field of view while bringing neither extra computation cost nor parameters. Then the pre-trained model is further pruned and compressed by fine-tuning on a lightweight version of the same network used earlier, in which the inactive feature response and connections below a pre-defined threshold are directly removed. Besides, we construct so far the largest fashion guided clothing semantic parsing dataset (FCP) which contains a total of 5,000 clothing images and each image associates with both pixel-level, object-level and image-level annotations. All clothing in the dataset are recommended by fashion experts or trendsetters and contains as many as 65 common clothing items, accessories. We organize the dataset as Wordnet tree structure so that it enables fashionably parsing hierarchically. Finally, we conduct extensive experiments on three currently available datasets. Both quantitative and qualitative results demonstrate the priority and feasibility of our method, comparing with several other deep learning based methods. Our method achieves 35 FPS in a single Nvidia Titian X GPU with only minimal accuracy loss.

Dense object detection and temporal tracking are needed across applications domains ranging from people-tracking to analysis of satellite imagery over time. The detection and tracking of malignant skin cancers and benign moles poses a particularly challenging problem due to the general uniformity of large skin patches, the fact that skin lesions vary little in their appearance, and the relatively small amount of data available. Here we introduce a novel data synthesis technique that merges images of individual skin lesions with full-body images and heavily augments them to generate significant amounts of data. We build a convolutional neural network (CNN) based system, trained on this synthetic data, and demonstrate superior performance to traditional detection and tracking techniques. Additionally, we compare our system to humans trained with simple criteria. Our system is intended for potential clinical use to augment the capabilities of healthcare providers. While domain-specific, we believe the methods invoked in this work will be useful in applying CNNs across domains that suffer from limited data availability.

We have implemented a simulation of a robot functioning in the domain of eldercare whose behavior is completely determined by an ethical principle. Using a subset of the perceptions and duties that will be required of such a robot, this simulation demonstrates selection of ethically preferable actions in real time using a case-supported principle-based paradigm. We believe that this work could serve as the basis for ensuring that the behavior of all eldercare robots that are created in the future will be ethically justifiable. Further, we believe that the methods used in this project can be employed in other domains as well, to ensure that the robots that humans interact with in these domains will behave ethically.

In this paper, we propose a novel deep manifold clustering (DMC) method for learning effective deep representations and partitioning a dataset into clusters where each cluster contains data points from a single nonlinear manifold. Different from other previous research efforts, we adopt deep neural network to classify and parameterize unlabeled data which lie on multiple manifolds. Firstly, motivated by the observation that nearby points lie on the local of manifold should possess similar representations, a locality preserving objective is defined to iteratively explore data relation and learn structure preserving representations. Secondly, by finding the corresponding cluster centers from the representations, a clustering-oriented objective is then proposed to guide the model to extract both discriminative and cluster-specific representations. Finally, by integrating two objectives into a single model with a unified cost function and optimizing it by using back propagation, we can obtain not only more powerful representations, but also more precise clusters of data. In addition, our model can be intuitively extended to cluster out-of-sample datum. The experimental results and comparisons with existing state-of-the-art methods show that the proposed method consistently achieves the best performance on various benchmark datasets.

Anthropomorphization can be used as a tool by humans to reason about complex nonhuman phenomena, by ascribing agents with intelligence and goals that are similar to their own. Deep neural networks are complex structures, and we do not understand well how to optimize them. One way to deal with such complexity is to give complex parts of the network (such as the activation functions) goals and actions, even if these parts are unchanging in their behavior. This allows us to transform the problem of finding the parameters of deep neural networks into a game, and use the same approaches that we use for games to generate good parameters for a deep neural networks. This paper presents the results of (Schuurmans and Zinkevich 2016) to the game theory community.

This paper deals with the constrained DAG shortest path problem (CDSP), which finds the shortest path on a given directed acyclic graph (DAG) under any logical constraints posed on taken edges. There exists a previous work that uses binary decision diagrams (BDDs) to represent the logical constraints, and traverses the input DAG and the BDD simultaneously. The time complexity of this BDD-based method is derived from BDD size, and tends to be fast only when BDDs are small. However, since it does not prioritize the search order, there is considerable room for improvement, particularly for large BDDs. We combine the well-known A* search with the BDD-based method synergistically, and implement several novel heuristic functions. The key insight here is that the ‘shortest path’ in the BDD is a solution of a relaxed problem, just as the shortest path in the DAG is. Experiments, particularly practical machine learning applications, show that the proposed method deceases search time by up to 2 orders of magnitude, with the specific result that it is 2,000 times faster than a commercial solver.

We propose a new linear algebraic approach to the computation of Tarskian semantics in logic. We embed a finite model M in first-order logic with N entities in N-dimensional Euclidean space R^N by mapping entities of M to N dimensional one-hot vectors and k-ary relations to order-k adjacency tensors (multi-way arrays). Second given a logical formula F in prenex normal form, we compile F into a set Sigma_F of algebraic formulas in multi-linear algebra with a nonlinear operation. In this compilation, existential quantifiers are compiled into a specific type of tensors, e.g., identity matrices in the case of quantifying two occurrences of a variable. It is shown that a systematic evaluation of Sigma_F in R N gives the truth value, 1(true) or 0(false), of F in M. Based on this framework, we also propose an unprecedented way of computing the least models defined by Datalog programs in linear spaces via matrix equations and empirically show its effectiveness compared to state-of-the-art approaches.

It is often critical in real-world applications to: (i) perform inverse learning of the cost parameters of a multi-objective reward based on observed agent behavior; (ii) perform sensitivity analyses of policies to various parameter settings; and (iii) analyze and optimize policy performance as a function of policy parameters. When such problems have mixed discrete and continuous state and/or action spaces, this leads to parameterized hybrid MDPs (PHMDPs) that are often approximately solved via discretization, sampling, and/or local gradient methods (when optimization is involved). In this paper we combine two recent advances that allow for the first exact solution and optimization of PHMDPs. We first show how each of the aforementioned use cases can be formalized as PHMDPs, which can then be solved via an extension of symbolic dynamic programming (SDP) even when the solution is piecewise nonlinear. Secondly, we leverage recent advances in non-convex solvers such as dReal and dOp (that offer δ-optimality guarantees for nonlinear problems given a symbolic function) for non-convex global optimization in (i), (ii), and (iii) using SDP to derive symbolic solutions to each PHMDP formalization. We demonstrate the efficacy and scalability of our framework by calculating the first known exact solutions to complex nonlinear examples of each of the aforementioned use cases.

We present the Utility Maximizing Design (UMD) model for optimally redesigning stochastic environments to achieve maximized performance. This model suits well contemporary applications that involve the design of environments where robots and humans co-exist an co-operate, e.g., vacuum cleaning robot. We discuss two special cases of the UMD model. The first is the equi-reward UMD (ER-UMD) in which the agents and the system share a utility function, such as for the vacuum cleaning robot. The second is the goal recognition design (GRD) setting, discussed in the literature, in which system and agent utilities are independent. To find the set of optimal modifications to apply to a UMD model, we present a generic method, based on heuristic search. After specifying the conditions for optimality in the general case, we present an admissible heuristic for the ER-UMD case. We also present a novel compilation that embeds the redesign process into a planning problem, allowing use of any off-the-shelf solver to find the best way to modify an environment when a design budget is specified. Our evaluation shows the feasibility of the approach using standard benchmarks from the probabilistic planning competition.