Activation of Matrix Metalloproteinase (MMP)-9 by Membrane-Type-1 Matrix Metalloproteinase/MMP-2 axis Stimulates Tumor Metastasis

13301甲第4512号博士（医学）金沢大学博士論文本文Full 以下に掲載予定：Cancer Science. Wiley-Blackwell. 共著者：Zichen Li, Takahisa Takino, Yoshio Endo, Hiroshi Sat

Efficient Offline Policy Optimization with a Learned Model

MuZero Unplugged presents a promising approach for offline policy learning from logged data. It conducts Monte-Carlo Tree Search (MCTS) with a learned model and leverages Reanalyze algorithm to learn purely from offline data. For good performance, MCTS requires accurate learned models and a large number of simulations, thus costing huge computing time. This paper investigates a few hypotheses where MuZero Unplugged may not work well under the offline RL settings, including 1) learning with limited data coverage; 2) learning from offline data of stochastic environments; 3) improperly parameterized models given the offline data; 4) with a low compute budget. We propose to use a regularized one-step look-ahead approach to tackle the above issues. Instead of planning with the expensive MCTS, we use the learned model to construct an advantage estimation based on a one-step rollout. Policy improvements are towards the direction that maximizes the estimated advantage with regularization of the dataset. We conduct extensive empirical studies with BSuite environments to verify the hypotheses and then run our algorithm on the RL Unplugged Atari benchmark. Experimental results show that our proposed approach achieves stable performance even with an inaccurate learned model. On the large-scale Atari benchmark, the proposed method outperforms MuZero Unplugged by 43%. Most significantly, it uses only 5.6% wall-clock time (i.e., 1 hour) compared to MuZero Unplugged (i.e., 17.8 hours) to achieve a 150% IQM normalized score with the same hardware and software stacks. Our implementation is open-sourced at https://github.com/sail-sg/rosmo.Comment: ICLR202

Disturbance observer-based neural network control of cooperative multiple manipulators with input saturation

In this paper, the complex problems of internal forces and position control are studied simultaneously and a disturbance observer-based radial basis function neural network (RBFNN) control scheme is proposed to: 1) estimate the unknown parameters accurately; 2) approximate the disturbance experienced by the system due to input saturation; and 3) simultaneously improve the robustness of the system. More specifically, the proposed scheme utilizes disturbance observers, neural network (NN) collaborative control with an adaptive law, and full state feedback. Utilizing Lyapunov stability principles, it is shown that semiglobally uniformly bounded stability is guaranteed for all controlled signals of the closed-loop system. The effectiveness of the proposed controller as predicted by the theoretical analysis is verified by comparative experimental studies

Dynamic Group Signature Scheme on Lattice with Verifier-local Revocation

The verifier-local revocation mechanism (VLR) is an ideal function of group signature. As long as the verifier knows the revocation list, he/she can verify the legitimacy of the signer, prevent the revoked user from impersonating a legitimate user for signature, ensure the timeliness of signature information and save resources. Group signature is often required to realize users\u27 dynamic addition and revocation. Therefore, an efficient lattice signature scheme with a local revocation mechanism and alter the number of users has become an important topic. In this paper, a zero-knowledge proof scheme on the lattice has been proposed. Based on it, a group signature scheme with VLR has been constructed. This scheme can effectively join and revocation without generating the key pair again. The tracking mechanism uses an encryption scheme. As long as given a correct tracking key, the signer index can be opened quickly. And this algorithm has short public key, logarithmic signature length, and efficient implementation of the VLR function