Abstract In recent years, 3D data has been widely used in archaeology and in the field of conservation and restoration of cultural properties. Virtual restoration, which reconstructs the original state in virtual space, is one of the promising applications utilizing 3D scanning data. Though many studies of virtual restoration have been conducted, it is still challenging to restore the cultural properties that consist of multiple deformable components because it is not feasible to identify the original shape uniquely. As a solution to such a problem, we proposed a non-rigid 3D shape assembly method for virtually restoring wooden ships that are composed of multiple timbers. The deformed timber can be well represented by ruled surface. We proposed a free-form deformation method with a ruled surface and an assembly method to align the deformable components mutually. The method employs a bottom-up approach that does not require reference data for target objects. The proposed framework narrows down the searching space for optimization using the physical constraints of wooden materials, and it can also obtain optimal solutions. We also showed an experimental result, where we virtually restored King Khufu’s first solar boat. The boat was originally constructed by assembling several timbers. The boat was reconstructed as a real object and is currently exhibited at a museum. However, unfortunately, the entire shape of the boat is slightly distorted. We applied the proposed method using archaeological knowledge and then showed the virtual restoration results using the acquired 3D data of the boat’s components.

Rivest and Shamir introduced a write-once memory (WOM), a model of storage devices whose storage elements have restrictions on state transitions, and they presented some coding methods to reuse a WOM. An interesting question about a WOM is how efficiently we can reuse it with the best coding method, and as an answer to the question, Fu and Han Vinck determined the capacity of Fiat and Shamir's generalized WOMs. In this paper, we extend their results, introducing write-constrained memories (WCMs) that consider state transition costs, and determining the capacity of WCMs under a certain type of cost constraints.

To formulate rewriting operations on flash memory, we extend Write-Once Memory (WOM) and introduce Erasable WOM (EWOM) which allows block erasures, and then we define codes to rewrite on them. To measure performances of EWOM codes, we introduce the rate tradeoff pair, which is derived from the sum rate. We give an outer bound of the region of the possible tradeoff pairs for a certain class of EWOM's. We also propose fixed-rate EWOM codes calledWOM2E codes that utilize existing WOM codes. It reveals that WOM2E scheme is optimal in terms of rate tradeoff pair when the block size of EWOM is “infinity.”

Rivest and Shamir introduced Write-Once Memory (WOM), a model of storage devices whose storage elements have restrictions on state transitions, and they presented some coding methods to reuse WOM. An interesting question about WOM is how efficiently we can reuse it with the best coding method, and as an answer to the question, Fu and Han Vinck determined the capacity of Fiat and Shamir's generalized WOM. In this paper, we extend their results, introducing Write-Constrained Memory (WCM) that considers state transition cost, and determining the capacity of WCM under a certain type of cost constraints.

The emergence of apparently deterministic and reproducible behaviors from highly fluctuating components in genetic networks has been attracting great attentions. However, only little has been known on the mechanisms of such emergence because of the complexity and the digital nature in genetic networks, which make it hard and untractable to analyze them by usual dynamical and stochastic methods. We propose a new method that employs a numerical evaluation of cumulants and a graphical representation. This method visually describes propagations of fluctuations in genetic networks and facilitates intuitive understanding of stochastic properties of the networks. In addition, this method works well even if the networks consist of many components and reactions and the copy numbers of some components are low.