The computational design of messenger RNA (mRNA) sequences is a critical technology for both scientific research and industrial applications. Recent advances in prediction and optimization models have enabled the automatic scoring and optimization of $5^\prime $ UTR sequences, key upstream elements of mRNA. However, fully automated design of $5^\prime $ UTR sequences with more than two objective scores has not yet been explored. In this study, we present a computational pipeline that optimizes human $5^\prime $ UTR sequences in a multi-objective framework, addressing up to four distinct and conflicting objectives. Our work represents an important advancement in the multi-objective computational design of mRNA sequences, paving the way for more sophisticated mRNA engineering.

Network simulations play an important role in the evolution of V2X (Vehicle-to-Everything) communication services. Large-scale simulations involving a huge number of connected vehicles and/or heavy network traffic, however, typically take a long period of time to complete. In this poster, we explore a mechanism to accelerate network simulations (in terms of simulation time processed per unit wall-clock time) by the means of parallel and distributed simulations (PADS). Multiple instances of network simulators are run in parallel and loosely synchronized to process simulation events in a distributed manner. We develop a proof-of-concept implementation of the framework to showcase its feasibility. The results indicate that the proposed framework achieves up to 10 times faster simulation speed with eight parallel network simulation instances.

Edge computing for connected vehicles is expected to become a reality. Compared to public clouds, the computing resources available on individual edge servers are limited: therefore, dynamically allocating computing resources as needed based on load is effective for the efficient use of edge server resources, and the auto-scale functionality using containers and Kubernetes is widely used. However, if the edge servers are overloaded when requests exceed their maximum processing capacity, long response delays and service outages may occur. Cooperative load balancing among neighboring edge servers is known as a method to prevent edge servers from overload, but standard Kubernetes does not have a control function based on server location or distance between servers, making it difficult to implement this. This paper proposes a service mesh controller to achieve cooperative load balancing among neighboring edge servers in an environment where container execution infrastructure controlled by Kubernetes runs on edge servers. Experimental results show that the proposed method with the service mesh controller reduces the median of response time by 96.3% compared with a conventional method without load balancing while also preventing overloads.

Compression techniques for deep neural network models are becoming very important for the efficient execution of high-performance deep learning systems on edge-computing devices. The concept of model compression is also important for analyzing the generalization error of deep learning, known as the compression-based error bound. However, there is still huge gap between a practically effective compression method and its rigorous background of statistical learning theory. To resolve this issue, we develop a new theoretical framework for model compression and propose a new pruning method called {\it spectral pruning} based on this framework. We define the ``degrees of freedom'' to quantify the intrinsic dimensionality of a model by using the eigenvalue distribution of the covariance matrix across the internal nodes and show that the compression ability is essentially controlled by this quantity. Moreover, we present a sharp generalization error bound of the compressed model and characterize the bias--variance tradeoff induced by the compression procedure. We apply our method to several datasets to justify our theoretical analyses and show the superiority of the the proposed method.

Monitoring service providers have arisen to meet increasing demands from system administrators. Providing a monitoring service requires high-resolution time series, long-term retention, and high write scalability to a time series database (TSDB). Nevertheless, no research to date has examined proposed TSDBs with consideration of the extensibility of data structures for function additions of a monitoring service. As described herein, we introduce a TSDB architecture for automatically tiering on heterogeneous key-value stores (KVS) that combines with an in-memory KVS and an on-disk KVS. Our proposed architecture, by unbundling the data structure on memory and disk as different KVSs, enables the TSDB extensibility to duplicate writes to new data structures optimized for each use case without reducing writing efficiency or data storage efficiency.

Fukushima et al. proved that spatially-coupled codes, without pilot symbols and any optimization for the channels, universally achieve the symmetric information rate (SIR) of generalized erasure channels with memory. We expect that the universality is also valid for fading channels. The receiver performs joint iterative channel estimation and decoding where factor-graphs-based BCJR channel estimator for finite-state Markov channels and the LDPC decoder are considered. We demonstrate that the reliable transmission is possible at a rate close to the SIR.

We propose a new algorithm for overdetermined blind source separation based on multistage independent component analysis (MSICA). To improve the separation performance, we have proposed MSICA in which frequency-domain ICA and time-domain ICA are cascaded. In the original MSICA, the specific mixing model, where the number of microphones is equal to that of sources, was assumed. However, additional microphones are required to achieve an improved separation performance under reverberant environments. This leads to alternative problems, e.g., a complication of the permutation problem. In order to solve them, we propose a new extended MSICA using subarray processing, where the number of microphones and that of sources are set to be the same in every subarray. The experimental results obtained under the real environment reveal that the separation performance of the proposed MSICA is improved as the number of microphones is increased.