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Recent years have witnessed the emergence of mobile edge computing (MEC), on the premise of a costeffective enhancement in the computational ability of hardwareconstrained wireless devices (WDs) comprising the Internet of Things (IoT). In a general multi-server multi-user MEC system, each WD has a computational task to execute and has to select binary (off)loading decisions, along with the analog-amplitude resource allocation variables in an online manner, with the goal of minimizing the overall energy-delay cost (EDC) with dynamic system states. While past works typically rely on the explicit expression of the EDC function, the present contribution considers a practical setting, where in lieu of system state information, the EDC function is not available in analytical form, and instead only the function values at queried points are revealed. Towards tackling such a challenging online combinatorial problem with only bandit information, novel Bayesian optimization (BO) based approaches are put forth by leveraging the multi-armed bandit (MAB) framework. Per time slot, the discrete offloading decisions are first obtained via the MAB method, and the analog resource allocation variables are subsequently optimized using the BO selection rule. By exploiting both temporal and contextual information, two novel BO approaches, termed time-varying BO and contextual time-varying BO, are developed. Numerical tests validate the merits of the proposed BO approaches compared with contemporary benchmarks under different MEC network sizes. 

Chloroflexus sp. MS-CIW-1 was isolated from a phototrophic mat in Mushroom Spring, an alkaline hot spring in Yellowstone National Park, WY, USA. We report the draft genome of 4.8 Mb consisting of 6 contigs with 3755 protein-coding genes and a GC content of 54.45%. 

Over-the-air federated learning (OTA-FL) is a communication-effective approach for achieving distributed learning tasks. In this paper, we aim to enhance OTA-FL by seamlessly combining sensing into the communication-computation integrated system. Our research reveals that the wireless waveform used to convey OTA-FL parameters possesses inherent properties that make it well-suited for sensing, thanks to its remarkable auto-correlation characteristics. By leveraging the OTA-FL learning statistics, i.e., means and variances of local gradients in each training round, the sensing results can be embedded therein without the need for additional time or frequency resources. Finally, by considering the imperfections of learning statistics that are neglected in the prior works, we end up with an optimized the transceiver design to maximize the OTA-FL performance. Simulations validate that the proposed method not only achieves outstanding sensing performance but also significantly lowers the learning error bound. 

Recent years have witnessed the emergence of mobile edge computing (MEC), on the premise of a costeffective enhancement in the computational ability of hardware-constrained wireless devices (WDs) comprising the Internet of Things (IoT). In a general multi-server multi-user MEC system, each WD has a computational task to execute and has to select binary (off)loading decisions, along with the analog-amplitude resource allocation variables in an online manner, with the goal of minimizing the overall energy-delay cost (EDC) with dynamic system states. While past works typically rely on the explicit expression of the EDC function, the present contribution considers a practical setting, where in lieu of system state information, the EDC function is not available in analytical form, and instead only the function values at queried points are revealed. Towards tackling such a challenging online combinatorial problem with only bandit information, novel Bayesian optimization (BO) based approach is put forth by leveraging the multi-armed bandit (MAB) framework. Per time slot, by exploiting temporal information, the discrete offloading decisions are first obtained via the MAB method, and the analog resource allocation variables are subsequently optimized using the BO selection rule. Numerical tests validate the effectiveness of the proposed BO approach. 

Abstract BackgroundThere are a wide range of developmental strategies in animal phyla, but most insights into adult body plan formation come from direct-developing species. For indirect-developing species, there are distinct larval and adult body plans that are linked together by metamorphosis. Some outstanding questions in the development of indirect-developing organisms include the extent to which larval tissue undergoes cell death during the process of metamorphosis and when and where the tissue that will give rise to the adult originates. How do the processes of cell division and cell death redesign the body plans of indirect developers? In this study, we present patterns of cell proliferation and cell death during larval body plan development, metamorphosis, and adult body plan formation, in the hemichordateSchizocardium californium(Cameron and Perez in Zootaxa 3569:79–88, 2012) to answer these questions. ResultsWe identified distinct patterns of cell proliferation between larval and adult body plan formation ofS. californicum. We found that some adult tissues proliferate during the late larval phase prior to the start of overt metamorphosis. In addition, using an irradiation and transcriptomic approach, we describe a genetic signature of proliferative cells that is shared across the life history states, as well as markers that are unique to larval or juvenile states. Finally, we observed that cell death is minimal in larval stages but begins with the onset of metamorphosis. ConclusionsCell proliferation during the development ofS. californicumhas distinct patterns in the formation of larval and adult body plans. However, cell death is very limited in larvae and begins during the onset of metamorphosis and into early juvenile development in specific domains. The populations of cells that proliferated and gave rise to the larvae and juveniles have a genetic signature that suggested a heterogeneous pool of proliferative progenitors, rather than a set-aside population of pluripotent cells. Taken together, we propose that the gradual morphological transformation ofS. californicumis mirrored at the cellular level and may be more representative of the development strategies that characterize metamorphosis in many metazoan animals.