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Modular self-assembling systems typically assume that modules are present to assemble. But in sparsely observed ocean environments, modules of an aquatic modular robotic system may be separated by distances they do not have the energy to cross, and the information needed for optimal path planning is often unavailable. In this work we present a flow-based rendezvous and docking controller that allows aquatic robots in gyre-like environments to rendezvous with and dock to a target by leveraging environmental forces. This approach does not require complete knowledge of the flow, but suffices with imperfect knowledge of the flow's center and shape. We validate the performance of this control approach in both simulations and experiments relative to naive rendezvous and docking strategies, and show that energy efficiency improves as the scale of the gyre increases. 

Abstract Tumor necrosis factor-α-induced protein 8 (TNFAIP8) is a member of the TIPE/TNFAIP8 family which regulates tumor growth and survival. Our goal is to delineate the detailed oncogenic role of TNFAIP8 in skin cancer development and progression. Here we demonstrated that higher expression of TNFAIP8 is associated with basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma development in patient tissues. Induction of TNFAIP8 expression by TNFα or by ectopic expression of TNFAIP8 in SCC or melanoma cell lines resulted in increased cell growth/proliferation. Conversely, silencing of TNFAIP8 decreased cell survival/cell migration in skin cancer cells. We also showed that miR-205-5p targets the 3′UTR of TNFAIP8 and inhibits TNFAIP8 expression. Moreover, miR-205-5p downregulates TNFAIP8 mediated cellular autophagy, increased sensitivity towards the B-RAF V600E mutant kinase inhibitor vemurafenib, and induced cell apoptosis in melanoma cells. Collectively our data indicate that miR-205-5p acts as a tumor suppressor in skin cancer by targeting TNFAIP8. 

Monte Carlo (MC) methods are widely used in many research areas such as physical simulation, statistical analysis, and machine learning. Application of MC methods requires drawing fast mixing samples from a given probability distribution. Among existing sampling methods, the Hamiltonian Monte Carlo (HMC) utilizes gradient information during Hamiltonian simulation and can produce fast mixing samples at the highest efficiency. However, without carefully chosen simulation parameters for a specific problem, HMC generally suffers from simulation locality and computation waste. As a result, the No-U-Turn Sampler (NUTS) has been proposed to automatically tune these parameters during simulation and is the current state-of-the-art sampling algorithm. However, application of NUTS requires frequent gradient calculation of a given distribution and high-volume vector processing, especially for large-scale problems, leading to drawing an expensively large number of samples and a desire of hardware acceleration. While some hardware acceleration works have been proposed for traditional Markov Chain Monte Carlo (MCMC) and HMC methods, there is no existing work targeting hardware acceleration of the NUTS algorithm. In this paper, we present the first NUTS accelerator on FPGA while addressing the high complexity of this state-of-the-art algorithm. Our hardware and algorithm co-optimizations include an incremental resampling technique which leads to a more memory efficient architecture and pipeline optimization for multi-chain sampling to maximize the throughput. We also explore three levels of parallelism in the NUTS accelerator to further boost performance. Compared with optimized C++ NUTS package: RSTAN, our NUTS accelerator can reach a maximum speedup of 50.6X and an energy improvement of 189.7X.