Special Report, US, Americas

[banelasfrancesy159]

The High Altitude Water Cherenkov (HAWC) observatory is a very high energy gamma-ray detector located in Mexico. In late 2018, the HAWC collaboration completed a major upgrade consisting of the addition of a sparse outrigger array of 345 small water Cherenkov detectors (WCDs) surrounding the 300 WCDs of the main array and extending the instrumented area by a factor of 4. It provides an improved reconstruction of the showers whose core and footprint are not well contained in the array and increases the effective area in the range of a few TeV to beyond 100 TeV. This improvement in sensitivity will help to have a better understanding of the Galactic sources that accelerate particles up to the knee of the cosmic ray spectrum. In this contribution, we will show the current status, the performance, and the first results from the HAWC outrigger array. The High Altitude Water Cherenkov (HAWC) is a very-high-energy gamma-ray observatory located at 4100 m altitude on the volcano Sierra Negra in the state of Puebla in central Mexico.|Systems that can efficiently search collections of sign language videos have been highlighted as a useful application of sign language technology. However, the problem of searching videos beyond individual keywords has received limited attention in the literature. To address this gap, in this work we introduce the task of sign language retrieval with free-form 111 The terminology “natural language query” is commonly used to describe unconstrained textual queries in spoken languages. However, since sign languages are also natural languages, we adopt for the term “free-form textual query” instead. We propose to tackle this task by learning cross-modal embeddings on the recently introduced large-scale How2Sign dataset of American Sign Language (ASL). We identify that a key bottleneck in the performance of the system is the quality of the sign video embedding which suffers from a scarcity of labelled training data. We, therefore, propose Spot-Align, a framework for interleaving iterative rounds of sign spotting and feature alignment to expand the scope and scale of available training data.




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