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Free, publiclyaccessible full text available January 1, 2023

Free, publiclyaccessible full text available January 1, 2023

Free, publiclyaccessible full text available January 1, 2023

Single crystals of BaTiO3 exhibit small switching fields and energies, but thinfilm performance is considerably worse, thus precluding their use in nextgeneration devices. Here, we demonstrate highquality BaTiO3 thin films with nearly bulklike 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 25nmthick films with 5µmdiameter 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 nextgeneration devices.Free, publiclyaccessible full text available May 26, 2023

In multiobjective 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 componentwise sum of the cost vectors of the edges of the former path is 'less than' the componentwise sum of the cost vectors of the edges of the latter path. The Paretooptimal 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 multiobjective search timeconsuming. In this paper, we therefore study how to find an approximate Paretooptimal solution set for a userprovided vector of approximation factors. The size of such a solution set can be significantly smaller than the size of the Paretooptimal solution set, which enables the design of approximate multiobjective 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 stateoftheart approximate biobjective search algorithm (where there are only two cost components) but (1) makes PPA* more efficient for biobjective search and (2) generalizes it to multiobjective search for any numbermore »Free, publiclyaccessible full text available January 1, 2023

The Paretooptimal frontier for a biobjective search problem instance consists of all solutions that are not worse than any other solution in both objectives. The size of the Paretooptimal frontier can be exponential in the size of the input graph, and hence finding it can be hard. Some existing works leverage a userspecified approximation factor ε to compute an approximate Paretooptimal frontier that can be significantly smaller than the Paretooptimal frontier. In this paper, we propose an anytime approximate biobjective search algorithm, called Anytime BiObjective A*ε (ABOA*ε). ABOA*ε is useful when deliberation time is limited. It first finds an approximate Paretooptimal frontier quickly, iteratively improves it while time allows, and eventually finds the Paretooptimal frontier. It efficiently reuses the search effort from previous iterations and makes use of a novel pruning technique. Our experimental results show that ABOA*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 ABOA*ε even slightly outperforms BOA*, a stateoftheart biobjective search algorithm, for finding the Paretooptimal frontier. Moreover, given only a limited amount of deliberation time, ABOA*ε finds solutions that collectively approximate the Paretooptimal frontier muchmore »Free, publiclyaccessible full text available January 1, 2023

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 easytouse visual interface allows users to gain insights into AutoOD’s selftuning process and explore the underlying patterns within their datasets.Free, publiclyaccessible full text available January 1, 2023

Free, publiclyaccessible full text available December 1, 2022

In this paper we consider the training stability of recurrent neural networks (RNNs) and propose a family of RNNs, namely SBORNN, 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 upperlevel optimization for learning hidden states and their hyperparameters, respectively. We prove that under mild conditions there is no vanishing or exploding gradient in training SBORNN. 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 https://zhangvislab.github.io.Free, publiclyaccessible full text available December 1, 2022

There are many settings that extend the basic shortestpath search problem. In BoundedCost Search, we are given a constant bound, and the task is to find a solution within the bound. In BiObjective 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 BoundedCost BiObjective 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, publiclyaccessible full text available January 1, 2023