5,701 research outputs found
Tagged Hierarchical Navigation For Files and Directories
Filesystems for computer storage enable users to organize files in a hierarchy of directories and to label files with descriptive tags separate from the filename. Search and navigation of the filesystem via the hierarchical organization or the tags can be useful to find a collection of related files. However, in current systems, users can use only one of the two approaches at a time which makes it difficult to perform tasks that require a combination of the two approaches. This disclosure describes an augmented filesystem that enables users to perform operations using a combination of depth based drilldown of the filesystem hierarchy and cross-cutting breadth-based approach using the tags. Tags associated with child directories/ files are added to those for the parent directory. Files and directories are displayed with their associated tags and full paths to allow for easy disambiguation
Improving Domain Generalization by Learning without Forgetting: Application in Retail Checkout
Designing an automatic checkout system for retail stores at the human level
accuracy is challenging due to similar appearance products and their various
poses. This paper addresses the problem by proposing a method with a two-stage
pipeline. The first stage detects class-agnostic items, and the second one is
dedicated to classify product categories. We also track the objects across
video frames to avoid duplicated counting. One major challenge is the domain
gap because the models are trained on synthetic data but tested on the real
images. To reduce the error gap, we adopt domain generalization methods for the
first-stage detector. In addition, model ensemble is used to enhance the
robustness of the 2nd-stage classifier. The method is evaluated on the AI City
challenge 2022 -- Track 4 and gets the F1 score on the test A set. Code
is released at the link https://github.com/cybercore-co-ltd/aicity22-track4
Semiclassical Moser--Trudinger inequalities
We extend the Moser--Trudinger inequality of one function to systems of
orthogonal functions. Our results are asymptotically sharp when applied to the
collective behavior of eigenfunctions of Schr\"odinger operators on bounded
domains.Comment: 18 page
Policy Uncertainty and Firm Cash Holdings
This research examines the relation between government economic policy uncertainty and firm cash holdings. We find evidence that policy uncertainty is positively related to firm cash holdings due to firms’ precautionary motives and, to a lesser extent, investment delays. The relation between policy uncertainty and cash holdings is more pronounced for firms dependent on government spending and extends beyond business cyclicality. Further analysis indicates that the effects of policy uncertainty on corporate cash holdings are distinct from those of political, market, or other macroeconomic uncertainty
Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model
Directly modeling the inherent hierarchy and shared structures of human behaviors, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both the natural hierarchical decomposition and shared semantics embedded in the movement trajectories. To this end, we propose the use of the HHMM, a rich stochastic model that has been recently extended to handle shared structures, for representing and recognizing a set of complex indoor activities. Furthermore, in the need of real-time recognition, we propose a Rao-Blackwellised particle filter (RBPF) that efficiently computes the filtering distribution at a constant time complexity for each new observation arrival. The main contributions of this paper lie in the application of the shared-structure HHMM, the estimation of the model\u27s parameters at all levels simultaneously, and a construction of an RBPF approximate inference scheme. The experimental results in a real-world environment have confirmed our belief that directly modeling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM.<br /
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