Work Stealing Load Balancing Algorithm
A methodology for efficient load balancing of computational problems that can be easily decomposed into multiple tasks, but where it is hard to predict the computation cost of each task, and where new tasks are created dynamically during runtime. We present this methodology and its exploitation and feasibility in the context of graphics processors. Work-stealing allows an idle core to acquire tasks from a core that is overloaded, causing the total work to be distributed evenly among cores, while minimizing the communication costs, as tasks are only redistributed when required. This will often lead to higher throughput than using static partitioning.
Work Stealing with latency …

Nonlinear expectation
In probability theory, a nonlinear expectation is a nonlinear generalization of the expectation. Nonlinear expectations are useful in utility theory as they more closely match human behavior than traditional expectations. …

Turbo Smoothing
Recently, a novel method for developing filtering algorithms, based on the parallel concatenation of Bayesian filters and called turbo filtering, has been proposed. In this manuscript we show how the same conceptual approach can be exploited to devise a new smoothing method, called turbo smoothing. A turbo smoother combines a turbo filter, employed in its forward pass, with the parallel concatenation of two backward information filters used in its backward pass. As a specific application of our general theory, a detailed derivation of two turbo smoothing algorithms for conditionally linear Gaussian systems is illustrated. Numerical results for a specific dynamic system evidence that these algorithms can achieve a better complexity-accuracy tradeoff than other smoothing techniques recently appeared in the literature. …

Proof-of-Deep-Learning (PoDL)
An enormous amount of energy is wasted in Proofof-Work (PoW) mechanisms adopted by popular blockchain applications (e.g., PoW-based cryptocurrencies), because miners must conduct a large amount of computation. Owing to this, one serious rising concern is that the energy waste not only dilutes the value of the blockchain but also hinders its further application. In this paper, we propose a novel blockchain design that fully recycles the energy required for facilitating and maintaining it, which is re-invested to the computation of deep learning. We realize this by proposing Proof-of-Deep-Learning (PoDL) such that a valid proof for a new block can be generated if and only if a proper deep learning model is produced. We present a proof-of-concept design of PoDL that is compatible with the majority of the cryptocurrencies that are based on hash-based PoW mechanisms. Our benchmark and simulation results show that the proposed design is feasible for various popular cryptocurrencies such as Bitcoin, Bitcoin Cash, and Litecoin. …