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The almost simultaneous emergence of major animal phyla during the early Cambrian shaped modern animal biodiversity. Reconstructing evolutionary relationships among such closely spaced branches in the animal tree of life has proven to be a major challenge, hindering understanding of early animal evolution and the fossil record. This is particularly true in the species-rich and highly varied Mollusca where dramatic inconsistency among paleontological, morphological, and molecular evidence has led to a long-standing debate about the group’s phylogeny and the nature of dozens of enigmatic fossil taxa. A critical step needed to overcome this issue is to supplement available genomic data, which is plentiful for well-studied lineages, with genomes from rare but key lineages, such as Scaphopoda. Here, by presenting chromosome-level genomes from both extant scaphopod orders and leveraging complete genomes spanning Mollusca, we provide strong support for Scaphopoda as the sister taxon of Bivalvia, revitalizing the morphology-based Diasoma hypothesis originally proposed 50 years ago. Our molecular clock analysis confidently dates the split between Bivalvia and Scaphopoda at ~520 Ma, prompting a reinterpretation of controversial laterally compressed Early Cambrian fossils, includingAnabarella,Watsonella,andMellopegma,as stem diasomes. Moreover, we show that incongruence in the phylogenetic placement of Scaphopoda in previous phylogenomic studies was due to ancient incomplete lineage sorting (ILS) that occurred during the rapid radiation of Conchifera. Our findings highlight the need to consider ILS as a potential source of error in deep phylogeny reconstruction, especially in the context of the unique nature of the Cambrian Explosion. 

Routing protocol design is one of the major challenges for swarm UAV networks. Due to the characteristics of a dynamic network topology, the low-complexity and the large volume of UAV devices, existing routing protocols based on network topology information, and routing table updates are not applicable in swarm UAV networks. In this paper, a Random Network Coding (RNC) enabled routing protocol is proposed to support an efficient routing process, which does not require network topology information or pre-determined routing tables. With the proposed routing protocol, the routing process could be significantly expedited, since each forwarding UAV may have already overheard some encoded packets in previous hops. As a result, some hops may be required to deliver a few encoded packets, and less hops may need to be completed in the whole routing process. The corresponding simulation study is conducted, demonstrating that our proposed routing protocol is able to facilitate a more efficient routing process. 

Dynamic spectrum access (DSA) is regarded as one of the key enabling technologies for future communication networks. In this paper, we introduce a power allocation strategy for distributed DSA networks using a powerful machine learning tool, namely deep reinforcement learning. The introduced power allocation strategy enables DSA users to conduct power allocation in a distributed fashion without relying on channel state information and cooperations among DSA users. Furthermore, to capture the temporal correlation of the underlying DSA network environments, the reservoir computing, a special class of recurrent neural network, is employed to realize the introduced deep reinforcement learning scheme. The combination of reservoir computing and deep reinforcement learning significantly improves the efficiency of the introduced resource allocation scheme. Simulation evaluations are conducted to demonstrate the effectiveness of the introduced power allocation strategy. 

This paper summarizes the main results and contributions of the MagNet Challenge 2023, an open-source research initiative for data-driven modeling of power magnetic materials. The MagNet Challenge has (1) advanced the stateof-the-art in power magnetics modeling; (2) set up examples for fostering an open-source and transparent research community; (3) developed useful guidelines and practical rules for conducting data-driven research in power electronics; and (4) provided a fair performance benchmark leading to insights on the most promising future research directions. The competition yielded a collection of publicly disclosed software algorithms and tools designed to capture the distinct loss characteristics of power magnetic materials, which are mostly open-sourced. We have attempted to bridge power electronics domain knowledge with state-of-the-art advancements in artificial intelligence, machine learning, pattern recognition, and signal processing. The MagNet Challenge has greatly improved the accuracy and reduced the size of data-driven power magnetic material models. The models and tools created for various materials were meticulously documented and shared within the broader power electronics community.