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The practical deployment of Neural Radiance Fields (NeRF) in rendering applications faces several challenges, with the most critical one being low rendering speed on even high-end graphic processing units (GPUs). In this paper, we present ICARUS, a specialized accelerator architecture tailored for NeRF rendering. Unlike GPUs using general purpose computing and memory architectures for NeRF, ICARUS executes the complete NeRF pipeline using dedicated plenoptic cores (PLCore) consisting of a positional encoding unit (PEU), a multi-layer perceptron (MLP) engine, and a volume rendering unit (VRU). A PLCore takes in positions & directions and renders the corresponding pixel colors without any intermediate data going off-chip for temporary storage and exchange, which can be time and power consuming. To implement the most expensive component of NeRF, i.e., the MLP, we transform the fully connected operations to approximated reconfigurable multiple constant multiplications (MCMs), where common subexpressions are shared across different multiplications to improve the computation efficiency. We build a prototype ICARUS using Synopsys HAPS-80 S104, a field programmable gate array (FPGA)-based prototyping system for large-scale integrated circuits and systems design. We evaluate the power-performancearea (PPA) of a PLCore using 40nm LP CMOS technology. Working at 400 MHz, a single PLCore occupies 16.5 mm 2 and consumes 282.8 mW, translating to 0.105 uJ/sample. The results are compared with those of GPU and tensor processing unit (TPU) implementations.

In order to solve the problems of restricted classroom and lack of repeated training in good curriculum, the design and development of a school life adaptation curriculum based on VR is proposed. This study identified six life adaptation themes for design and development (recognizing facial expressions, crossing the road, how to get lost, shopping, taking public transport, and job interview) and set the objectives, design principles, specific content, teaching evaluation, and other aspects of virtual reality life adaptation course. Combined with the characteristics of virtual reality, the implementation suggestions and matters needing attention are put forward. The results showed that, in the recognition of human facial expressions, 51 experts thought it was appropriate, 3 experts thought it needed to be modified, and no expert thought it was inappropriate. On the topic of crossing the road, 46 experts thought it was appropriate, eight thought it needed to be modified, and no expert thought it was inappropriate. On the theme of taking public transportation, 48 experts thought it was appropriate, 6 experts thought it needed to be modified, and no expert thought it was inappropriate. For the three major topics of how to get lost, shopping, and job interview, 2-3 experts put forward different opinions and think that the topic is not appropriate mainly because the grade arrangement is not appropriate. Conclusion. The VR-based curriculum for school life adaptation has been reviewed by experts and revised by researchers. It is scientific, interesting, and operable, solves the teaching problems of life adaptation teachers, and provides a safe, reliable, and effective practice channel for students.

This paper is concerned with a discounted optimal control problem of partially observed forward-backward stochastic systems with jumps on infinite horizon. The control domain is convex and a kind of infinite horizon observation equation is introduced. The uniquely solvability of infinite horizon forward (backward) stochastic differential equation with jumps is obtained and more extended analysis, especially for the backward case, is made. Some new estimates are first given and proved for the critical variational inequality. Then an ergodic maximum principle is obtained by introducing some infinite horizon adjoint equations whose uniquely solvabilities are guaranteed necessarily. Finally, some comparison are made with two kinds of representative infinite horizon stochastic systems and their related optimal controls., 50 pages

IIn this paper, we study a partially observed progressive optimal control problem of forward-backward stochastic differential equations with random jumps, where the control domain is not necessarily convex, and the control variable enter into all the coefficients. In our model, the observation equation is not only driven by a Brownian motion but also a Poisson random measure, which also have correlated noises with the state equation. For preparation, we first derive the existence and uniqueness of the solutions to the fully coupled forward-backward stochastic system with random jumps and its estimation in $L^\beta(\beta\geq2)$-space under some assumptions, and the non-linear filtering equation of partially observed stochastic system with random jumps. Then we derive the partially observed global maximum principle with random jumps. To show its applications, a partially observed linear quadratic progressive optimal control problem with random jumps is investigated, by the maximum principle and stochastic filtering. State estimate feedback representation of the optimal control is given in a more explicit form by introducing some ordinary differential equations., Comment: 97 pages

The paper is concerned with a linear quadratic (LQ) Stackelberg game of backward stochastic differential equations (BSDEs) with partial information, where the information of the leader is a sub-σ-algebra of that of the follower which is a kind of new feature of asymmetric information. By the maximum principle, the state feedback representation for the optimal control of the follower is first given via two Riccati equations. Then two new high-dimensional Riccati equations, a backward stochastic differential filtering equation (BSDFE) and a stochastic differential filtering equation (SDFE) are introduced to represent the state estimate feedback for the optimal control of the leader.

In this paper, 316 stainless steel was treated by laser shock peening (LSP) technology, and the micro-hardness, residual stress and rolling contact fatigue (RCF) performance were investigated. The result suggests that LSP can effectively improve the RCF performance of 316 stainless steel. The main factor is that LSP can induce high amplitude compressive residual stress on the surface layer. With the increase of residual compressive stress, the worn mechanism of RCF surface changes from delamination to micro-plastic deformation. It is also found that the RCF of 316 stainless steel treated by LSP was sensitive to the contact stress.

This paper is concerned with a risk-sensitive stochastic control problem, motivated by an optimal investment problem under correlated noises in the financial market. A new stochastic maximum principle for this kind of problem is obtained first, where the adjoint equations and maximum condition heavily depend on the risk-sensitive parameter and the correlation coefficient. Then the theoretical result is applied to the optimal investment problem with correlated noises, and the optimal investment strategy is obtained in a state feedback form, under a critical condition satisfied by the risk-sensitive parameter and the correlation coefficient. Numerical simulation and figures are given to explicitly illustrate the change and the sensitivity for optimal solution with respect to the risk-sensitive parameter and the correlation coefficient.

This paper is concerned with a Stackelberg game of backward stochastic differential equations (BSDEs), where the coefficients of the backward system and the cost functionals are deterministic, and the control domain is convex. Necessary and sufficient conditions of the optimality for the follower and the leader are first given for the general problem, by the stochastic maximum principles of BSDEs and forward-backward stochastic differential equations (FBSDEs), respectively. Then a linear-quadratic (LQ) Stackelberg game of BSDEs is investigated under standard assumptions. The state feedback representation for the optimal control of the follower is first given via two Riccati equations. Then the leader's problem is formulated as an optimal control problem of FBSDE with the control-independent diffusion term. Two high-dimensional Riccati equations are introduced to represent the state feedback for the optimal control of the leader. The solvability of the four Riccati equations are discussed. Theoretic results are applied to an optimal consumption rate problem of two players in the financial market., 26 pages

This paper is concerned with a Stackelberg game of backward stochastic differential equations (BSDEs) with partial information, where the information of the follower is a sub-$\sigma$-algebra of that of the leader. Necessary and sufficient conditions of the optimality for the follower and the leader are first given for the general problem, by the partial information stochastic maximum principles of BSDEs and forward-backward stochastic differential equations (FBSDEs), respectively. Then a linear-quadratic (LQ) Stackelberg game of BSDEs with partial information is investigated. The state estimate feedback representation for the optimal control of the follower is first given via two Riccati equations. Then the leader's problem is formulated as an optimal control problem of FBSDE. Four high-dimensional Riccati equations are introduced to represent the state estimate feedback for the optimal control of the leader. Theoretic results are applied to a pension fund management problem of two players in the financial market., Comment: 39 pages