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  4. Single crystals of BaTiO3 exhibit small switching fields and energies, but thin-film performance is considerably worse, thus precluding their use in next-generation devices. Here, we demonstrate high-quality BaTiO3 thin films with nearly bulk-like properties. Thickness scaling provides access to the coercive voltages (<100 mV) and fields (<10 kV cm−1) required for future applications and results in a switching energy of <2 J cm−3 (corresponding to <2 aJ per bit in a 10 × 10 × 10 nm3 device). While reduction in film thickness reduces coercive voltage, it does so at the expense of remanent polarization. Depolarization fields impact polar state stability in thicker films but fortunately suppress the coercive field, thus driving a deviation from Janovec–Kay–Dunn scaling and enabling a constant coercive field for films <150 nm in thickness. Switching studies reveal fast speeds (switching times of ~2 ns for 25-nm-thick films with 5-µm-diameter capacitors) and a pathway to subnanosecond switching. Finally, integration of BaTiO3 thin films onto silicon substrates is shown. We also discuss what remains to be demonstrated to enable the use of these materials for next-generation devices.
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  5. In multi-objective search, edges are annotated with cost vectors consisting of multiple cost components. A path dominates another path with the same start and goal vertices iff the component-wise sum of the cost vectors of the edges of the former path is 'less than' the component-wise sum of the cost vectors of the edges of the latter path. The Pareto-optimal solution set is the set of all undominated paths from a given start vertex to a given goal vertex. Its size can be exponential in the size of the graph being searched, which makes multi-objective search time-consuming. In this paper, we therefore study how to find an approximate Pareto-optimal solution set for a user-provided vector of approximation factors. The size of such a solution set can be significantly smaller than the size of the Pareto-optimal solution set, which enables the design of approximate multi-objective search algorithms that are efficient and produce small solution sets. We present such an algorithm in this paper, called A*pex. A*pex builds on PPA*, a state-of-the-art approximate bi-objective search algorithm (where there are only two cost components) but (1) makes PPA* more efficient for bi-objective search and (2) generalizes it to multi-objective search for any numbermore »of cost components. We first analyze the correctness of A*pex and then experimentally demonstrate its efficiency advantage over existing approximate algorithms for bi- and tri-objective search.« less
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  6. The Pareto-optimal frontier for a bi-objective search problem instance consists of all solutions that are not worse than any other solution in both objectives. The size of the Pareto-optimal frontier can be exponential in the size of the input graph, and hence finding it can be hard. Some existing works leverage a user-specified approximation factor ε to compute an approximate Pareto-optimal frontier that can be significantly smaller than the Pareto-optimal frontier. In this paper, we propose an anytime approximate bi-objective search algorithm, called Anytime Bi-Objective A*-ε (A-BOA*ε). A-BOA*ε is useful when deliberation time is limited. It first finds an approximate Pareto-optimal frontier quickly, iteratively improves it while time allows, and eventually finds the Pareto-optimal frontier. It efficiently reuses the search effort from previous iterations and makes use of a novel pruning technique. Our experimental results show that A-BOA*ε substantially outperforms baseline algorithms that do not reuse previous search effort, both in terms of runtime and number of node expansions. In fact, the most advanced variant of A-BOA*ε even slightly outperforms BOA*, a state-of-the-art bi-objective search algorithm, for finding the Pareto-optimal frontier. Moreover, given only a limited amount of deliberation time, A-BOA*ε finds solutions that collectively approximate the Pareto-optimal frontier muchmore »better than the solutions found by BOA*.« less
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  7. Anomaly detection is a critical task in applications like preventing financial fraud, system malfunctions, and cybersecurity attacks. While previous research has offered a plethora of anomaly detection algorithms, effective anomaly detection remains challenging for users due to the tedious manual tuning process. Currently, model developers must determine which of these numerous algorithms is best suited for their particular domain and then must tune many parameters by hand to make the chosen algorithm perform well. This demonstration showcases AutoOD, the first unsupervised selftuning anomaly detection system which frees users from this tedious manual tuning process. AutoOD outperforms the best unsupervised anomaly detection methods it deploys, with its performance similar to those of supervised anomaly classification models, yet without requiring ground truth labels. Our easy-to-use visual interface allows users to gain insights into AutoOD’s self-tuning process and explore the underlying patterns within their datasets.
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  9. In this paper we consider the training stability of recurrent neural networks (RNNs) and propose a family of RNNs, namely SBO-RNN, that can be formulated using stochastic bilevel optimization (SBO). With the help of stochastic gradient descent (SGD), we manage to convert the SBO problem into an RNN where the feedforward and backpropagation solve the lower and upper-level optimization for learning hidden states and their hyperparameters, respectively. We prove that under mild conditions there is no vanishing or exploding gradient in training SBO-RNN. Empirically we demonstrate our approach with superior performance on several benchmark datasets, with fewer parameters, less training data, and much faster convergence. Code is available at
    Free, publicly-accessible full text available December 1, 2022
  10. There are many settings that extend the basic shortest-path search problem. In Bounded-Cost Search, we are given a constant bound, and the task is to find a solution within the bound. In Bi-Objective Search, each edge is associated with two costs (objectives), and the task is to minimize both objectives. In this paper, we combine both settings into a new setting of Bounded-Cost Bi-Objective Search. We are given two bounds, one for each objective, and the task is to find a solution within these bounds. We provide a scheme for normalizing the two objectives, introduce several algorithms for this new setting and compare them experimentally.
    Free, publicly-accessible full text available January 1, 2023