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The interaction between Ribonucleic Acids (RNAs) and proteins, also called RNA Protein Interaction (RPI), plays an important role in the life activities of organisms, including in various regulatory processes, such as gene splicing, gene localization, and disease pathogenesis. RPI Prediction (RPIP) predicts the interactions between RNAs and proteins, which includes looking for the existence of interactions and the binding sites of interactions, and adding RNA-protein functional annotations (such as immunity regulation, neuroprotection, etc). Due to the huge amounts of complex biological data, Deep Learning-based RPIP (DL-based RPIP) has been widely investigated, as it can extract high-dimensional features from data and make accurate predictions. Over the last decade, there have been many achievements and contributions in DL-based RPIP. Although some previous studies review DL-based RPIP, to the best of our knowledge, there is still a lack of a comprehensive survey. In this paper, we extensively survey DL-based RPIP in terms of its entire process, including: feature encoding, deep learning modeling, results evaluation, RPIP application domains, and available websites and software. We also identify some open research challenges, and discuss the potential future work for DL-based RPIP.

In this paper, Peetre's conjecture about the real interpolation space of Besov space {\bf is solved completely } by using the classification of vertices of cuboids defined by {\bf wavelet coefficients and wavelet's grid structure}. Littlewood-Paley analysis provides only a decomposition of the function on the ring. We extend Lorentz's rearrangement function and Hunt's Marcinkiewicz interpolation theorem to more general cases. We use the method of calculating the topological quantity of the grid to replace the traditional methods of data classification such as gradient descent method and distributed algorithm. We developed a series of new techniques to solve this longstanding open problem. These skills make up for the deficiency of Lions-Peetre iterative theorem in dealing with strong nonlinearity. Using the properties of wavelet basis, a series of {\bf functional nonlinearities} are studied. Using the lattice property of wavelet, we study the lattice topology. By three kinds of {\bf topology nonlinearities}, we give the specific wavelet expression of K functional., Comment: 41 pages

We propose a two-layer stochastic game model to study reinsurance contracting and competition in a market with one insurer and two competing reinsurers. The insurer negotiates with both reinsurers simultaneously for proportional reinsurance contracts that are priced using the variance premium principle. The reinsurance contracting between the insurer and each reinsurer is modeled as a Stackelberg game. The two reinsurers compete for business from the insurer and optimize the so-called relative performance, instead of their own surplus, and their competition is settled by a noncooperative Nash game. We obtain a sufficient and necessary condition, related to the competition degrees of the two reinsurers, for the existence of an equilibrium. We show that the equilibrium, if exists, is unique, and the equilibrium strategy of each player is constant, fully characterized in semiclosed form. Furthermore, we obtain interesting sensitivity results for the equilibrium strategies through both analytical and numerical studies.

We study an optimal reinsurance problem under a diffusion risk model for an insurer who aims to minimize the probability of lifetime ruin. To rule out moral hazard issues, we only consider moral-hazard-free reinsurance contracts by imposing the incentive compatibility constraint on indemnity functions. The reinsurance premium is calculated under an extended distortion premium principle, in which the distortion function is not necessarily concave. We first show that an optimal reinsurance contract always exists and then derive two sufficient and necessary conditions to characterize it. Due to the presence of the incentive compatibility constraint and the nonconcavity of the distortion, the optimal contract is obtained as a solution to a double obstacle problem. At last, we apply the general result to study three examples and obtain the optimal contract in (semi)closed form.

We consider monotone mean-variance (MMV) portfolio selection problems with a conic convex constraint under diffusion models, and their counterpart problems under mean-variance (MV) preferences. We obtain the precommitted optimal strategies to both problems in closed form and find that they coincide, without and with the presence of the conic constraint. This result generalizes the equivalence between MMV and MV preferences from non-constrained cases to a specific constrained case. A comparison analysis reveals that the orthogonality property under the conic convex set is a key to ensuring the equivalence result.

To break the bottlenecks of mainstream cloud-based machine learning (ML) paradigm, we adopt device-cloud collaborative ML and build the first end-to-end and general-purpose system, called Walle, as the foundation. Walle consists of a deployment platform, distributing ML tasks to billion-scale devices in time; a data pipeline, efficiently preparing task input; and a compute container, providing a cross-platform and high-performance execution environment, while facilitating daily task iteration. Specifically, the compute container is based on Mobile Neural Network (MNN), a tensor compute engine along with the data processing and model execution libraries, which are exposed through a refined Python thread-level virtual machine (VM) to support diverse ML tasks and concurrent task execution. The core of MNN is the novel mechanisms of operator decomposition and semi-auto search, sharply reducing the workload in manually optimizing hundreds of operators for tens of hardware backends and further quickly identifying the best backend with runtime optimization for a computation graph. The data pipeline introduces an on-device stream processing framework to enable processing user behavior data at source. The deployment platform releases ML tasks with an efficient push-then-pull method and supports multi-granularity deployment policies. We evaluate Walle in practical e-commerce application scenarios to demonstrate its effectiveness, efficiency, and scalability. Extensive micro-benchmarks also highlight the superior performance of MNN and the Python thread-level VM. Walle has been in large-scale production use in Alibaba, while MNN has been open source with a broad impact in the community., Comment: Accepted by OSDI 2022