To address this issue, we propose to evict tensors within a sliding window to ensure all evictions are contiguous and are immediately used.

Clustering is an important technique for identifying structural information in large-scale data analysis, where the underlying dataset may be too large to store.

To address this issue, we propose to evict tensors within a sliding window to ensure all evictions are contiguous and are immediately used.

Clustering is an important technique for identifying structural information in large-scale data analysis, where the underlying dataset may be too large to store.

We show how to utilize machine learning approaches to improve sliding window algorithms for approximate frequency estimation problems, under the ``algorithms with predictions'' framework. In this dynamic environment, previous learning-augmented algorithms are less effective, since properties in sliding window resolution can differ significantly from the properties of the entire stream. Our focus is on the benefits of predicting and filtering out items with large next arrival times -- that is, there is a large gap until their next appearance -- from the stream, which we show improves the memory-accuracy tradeoffs significantly. We provide theorems that provide insight into how and by how much our technique can improve the sliding window algorithm, as well as experimental results using real-world data sets. Our work demonstrates that predictors can be useful in the challenging sliding window setting.

Co-speech gesture generation on artificial agents has gained attention recently, mainly when it is based on data-driven models. However, end-to-end methods often fail to generate co-speech gestures related to semantics with specific forms, i.e., Symbolic and Deictic gestures. In this work, we identify which words in a sentence are contextually related to Symbolic and Deictic gestures. Firstly, we appropriately chose 12 gestures recognized by people from the Italian culture, which different humanoid robots can reproduce. Then, we implemented two rule-based algorithms to label sentences with Symbolic and Deictic gestures. The rules depend on the semantic similarity scores computed with the RoBerta model between sentences that heuristically represent gestures and sub-sentences inside an objective sentence that artificial agents have to pronounce. We also implemented a baseline algorithm that assigns gestures without computing similarity scores. Finally, to validate the results, we asked 30 persons to label a set of sentences with Deictic and Symbolic gestures through a Graphical User Interface (GUI), and we compared the labels with the ones produced by our algorithms. For this scope, we computed Average Precision (AP) and Intersection Over Union (IOU) scores, and we evaluated the Average Computational Time (ACT). Our results show that semantic similarity scores are useful for finding Symbolic and Deictic gestures in utterances.

Object detection algorithms for computer vision tasks are some of the most powerful tools in all of machine learning and artificial intelligence. These are decision algorithms that enable computer systems to make inferences about the real world around them, as filtered through a camera. Without object recognition, robots that manipulate objects, autonomous vehicles, and image classification software would be almost impossible to create. Understanding computer vision and object detection will allow you to consider more use cases for these tools, allowing you to apply them to innovative and useful tasks, apps, and systems. As object detection is a batch of algorithms and techniques using computer vision, let's first start by defining computer vision and setting the context for our exploration of object detection.