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Using the South Pole Acoustic Test Setup (SPATS) and a retrievable transmitter deployed in holes drilled for the IceCube experiment, we have measured the attenuation of acoustic signals by South Pole ice at depths between 190 m and 500 m. Three data sets, using different acoustic sources, have been analyzed and give consistent results. The method with the smallest systematic uncertainties yields an amplitude attenuation coefficient α = 3.20 ± 0.57 km−1 between 10 and 30 kHz, considerably larger than previous theoretical estimates. Expressed as an attenuation length, the analyses give a consistent result for λ ≡ 1/α of ∼300 m with 20% uncertainty. No significant depth or frequency dependence has been found

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In the last few years, there has been a surge of interest in learning representations of entities and relations in knowledge graph (KG). However, the recent availability of temporal knowledge graphs (TKGs) that contain time information for each fact created the need for reasoning over time in such TKGs. In this regard, we present a new approach of TKG embedding, TeRo, which defines the temporal evolution of entity embedding as a rotation from the initial time to the current time in the complex vector space. Specially, for facts involving time intervals, each relation is represented as a pair of dual complex embeddings to handle the beginning and the end of the relation, respectively. We show our proposed model overcomes the limitations of the existing KG embedding models and TKG embedding models and has the ability of learning and inferring various relation patterns over time. Experimental results on four different TKGs show that TeRo significantly outperforms existing state-of-the-art models for link prediction. In addition, we analyze the effect of time granularity on link prediction over TKGs, which as far as we know has not been investigated in previous literature.

Entity alignment aims to identify equivalent entity pairs between different knowledge graphs (KGs). Recently, the availability of temporal KGs (TKGs) that contain time information created the need for reasoning over time in such TKGs. Existing embeddingbased entity alignment approaches disregard time information that commonly exists in many large-scale KGs, leaving much room for improvement. In this paper, we focus on the task of aligning entity pairs between TKGs and propose a novel Time-aware Entity Alignment approach based on Graph Neural Networks (TEA-GNN). We embed entities, relations and timestamps of different KGs into a vector space and use GNNs to learn entity representations. To incorporate both relation and time information into the GNN structure of our model, we use a time-aware attention mechanism which assigns different weights to different nodes with orthogonal transformation matrices computed from embeddings of the relevant relations and timestamps in a neighborhood. Experimental results on multiple real-world TKG datasets show that our method significantly outperforms the state-ofthe-art methods due to the inclusion of time information. Our datasets and source code are available at https://github.com/ soledad921/TEA-GNN