KDDI Research

Serializability is a standard that guarantees the correct execution of database operations. Since cloud databases do not always guarantee serializability, users must check for serializability on their own. However, what operations are executed in the cloud databases is a black box for users, making it difficult for them to make a judgment. This is a combinatorial optimization problem called the “black-box serializability problem, ” which is a satisfiability problem known to be NP-complete. A seminal work called Cobra has proposed an architecture that solves this problem using the SMT solver, a general-purpose solver for the satisfiability problem. Still, as the number of transactions increases, the search space will expand exponentially, so it becomes challenging to determine serializability. On the other hand, quantum annealing is excellent for fast-solving combinatorial optimization problems and can be applied to this satisfiability problem. This paper proposes a fast solver for the black-box serializability problem using quantum annealing extending Cobra. The evaluation results show that the proposed method is 751-890 times faster than Cobra.

Graph-based recommendation models, which utilize a user-item interaction graph whose edges represent users' preferences, are well known because of their effectiveness. However, many existing models are intrinsically transductive, meaning they can make recommendations only for users and items that exist in training data. To solve this challenge, some researchers focus on recommendations in an inductive scenario where new users/items emerge in the inference phase. In this paper, we propose a contrastive learning framework for the inductive scenario, INductive Contrastive Learning (INCL). The situation where new users/items emerge can be interpreted as changes in the interaction graph. Therefore, it is crucial to capture the change in the interaction graph for inductive recommendations. Based on the perspective, INCL creates an augmented graph by adding or removing edges of the original interaction graph and simulates changes in the interaction graph. By performing contrastive learning, INCL fosters each representation generated from the original interaction graph and the augmented graph to be similar. As a result, INCL is trained to generate robust representations that can adapt to changes in the interaction graph. Experimental results on real-world datasets demonstrate that INCL outperforms existing models in the inductive scenario, achieving up to 2.13% improvement in Recall@20.

Background DNA methylation is a covalent bond modification that is observed mainly at cytosine bases in the context of CG pairs. DNA methylation patterns reflect the status of individual tissues, such as cell composition, age, and the local environment, in mammals. Genetic factors also impact DNA methylation, and the genetic diversity among various dog breeds provides a valuable platform for exploring this topic. Compared to those in the human genome, studies on the profiling of methylation in the dog genome have been less comprehensive. Results Our study provides extensive profiling of DNA methylation in the whole blood of three dog breeds using whole-genome bisulfite sequencing. The difference in DNA methylation between breeds was moderate after removing CpGs overlapping with potential genetic variation. However, variance in methylation between individuals was common and often occurred in promoters and CpG islands (CGIs). Moreover, we adopted contextual awareness methodology to characterize DNA primary sequences using natural language processing (NLP). This method could be used to effectively separate unmethylated CGIs from highly methylated CGIs in the sequences that are identified by the conventional criteria. Conclusions This study presents a comprehensive DNA methylation landscape in the dog blood. Our observations reveal the similar methylation patterns across dog breeds, while CGI regions showed high variations in DNA methylation level between individuals. Our study also highlights the potential of NLP approach for analyzing low-complexity DNA sequences, such as CGIs.

Functional maturation of the visual cortex is induced by visual experiences during critical periods. Blind animals and humans exhibit improved auditory abilities after losing their vision. Here we investigated the response of the visual cortex to white noise stimuli during the progression of photoreceptor degeneration in a rat model of blindness (Royal College of Surgeons [RCS] (rdy/rdy) rats). Optical coherence tomography of RCS (+/+) rats with normal visual function revealed normal photoreceptor cells, whereas 3-month-old RCS (rdy/rdy) rats demonstrated photoreceptor cell degeneration. Visual cortex responses (VCRs) to a single flash stimulus were negligible in 3-month-old photoreceptor-degenerated rats. However, VCRs with white noise stimuli were significantly increased in blind versus RCS rats (+/+). Slight changes in the intrinsic optical signals of the control rats were observed on the ventral side of the visual cortex. In contrast, responses were markedly increased throughout the visual cortex of RCS (rdy/rdy) rats. These results indicate that the visual cortex rapidly acquires auditory system function over the first 3 months of life and that the entire visual cortex, rather than just the portion close to the auditory cortex, responds to white noise.

Analog radio-over-fiber (RoF) technologies have been studied as a suitable solution for mobile applications such as mobile fronthaul and accommodation of distributed antennas. On the other hand, multiple reflections are known to cause significant degradation in the transmission signal quality of analog RoF systems. In this paper, the influence of multiple reflections and the effectiveness of its mitigation scheme, high-frequency phase dithering and polarization randomization, are experimentally studied for different types of optical transmitters based on directly modulated laser (DML), electro-absorption modulator laser (EML), and lithium niobate Mach-Zehnder modulator (LN-MZM). The noise induced by multiple reflections and its mitigation effect are quantitatively evaluated in various conditions for the multiple reflections and the mitigation techniques. Additionally, transmission qualities of intermediate frequency over fiber (IFoF) signals under multiple reflections condition are clarified. The operational conditions for achieving an error vector magnitude (EVM) criterion of 64-QAM OFDM signal are also presented.

In Japan, the compact city policy, in which urban functions are concentrated in a certain area, is being promoted. In order to promote this policy, Location Normalization Plans are formulated, and static data such as population density and land price fluctuation rates are mainly used to evaluate the policy. However, such static data are not updated frequently, and it is difficult to evaluate the actual situation of a region appropriately. In this study, we proposed an evaluation index for compact cities that reflects the viewpoint of living behavior, using mobile phone GPS data. As a result, it was possible to clarify new aspects of urban compactness based on actual living behavior. This will be useful for improving the effectiveness of policies and revising plans according to actual conditions.

Silicon loop-type multimode waveguide structure with fan-out output was proposed as an efficient configuration for photonic reservoir computing (RC). The device aimed to enhance node interaction between spatial and temporal-nodes through its loop-like configuration and the use of multimode waveguides. The structural design, including novel triangular configurations as input and output coupling regions, prioritized low loss and adjustable coupling coefficient, respectively. Fundamental characteristics necessary for RC were evaluated such as spatial effects through field profile imaging and temporal effects via output pulse observations. Experimental validation included speckle observation with near-field imaging, mode mixing with transmission spectra, and impulse response measurements. In our specific case, the final device employed a multimode waveguide with a width of 25 μm and a loop length of 15 mm, featuring 65 spatial nodes and 13 temporal nodes. Fan-out output was utilized to measure higher-order modes without significant losses. Our experimental findings showcased that a single pulse exhibited a spreading factor of 2.35 and could circulate up to four times. Additionally, the longest anticipated memory duration was approximately 800 ps. Also, the RC performances were evaluated in terms of memory capacity (MC) of about 11 and NARMA3 task with its NMSE of about 4 × 10 ⁻³ , which seem superior to the previous result. Furthermore, the used parameters were also confirmed to be scalable, suggesting the potential for achieving expanding node counts in future work for higher RC performances.

We propose a method for adaptively selecting merge candidates for the geometric partitioning mode (GPM) in versatile video coding (VVC). The conventional GPM contributes to improved coding efficiency and subjective quality by partitioning the block into two nonrectangular partitions with motion vectors. The motion vector of the GPM is encoded as an index of the merge candidate list, but it does not consider that the GPM partitions are nonrectangular. In this paper, the distribution of merge candidates was evaluated for each GPM mode and partition, and a characteristic bias was revealed. To improve the coding efficiency of VVC, the proposed method allows GPM to select merge candidates that are specific to the partition. This method also introduces adaptive reference frame selection using template matching of adjacent samples. Following common test conditions in the Joint Video Experts Team (JVET), the experimental results showed an improvement in coding efficiency, with a bitrate savings of 0.16%, compared to the reference software for exploration experiments on enhanced compression beyond VVC capability in the JVET.

The finite difference time domain (FDTD) method has been proposed and used for sound field simulation. To reproduce actual sound wave propagation in sound field simulations, it is necessary to apply the radiation characteristics. With the FDTD method, radiation characteristics can be applied by setting sound pressure in a dense grid arrangement. However, conventional techniques for capturing radiation characteristics use a sparse array of microphones and are considered insufficient for the FDTD simulation. Furthermore, the technique required to apply captured acoustic signals in a dense grid arrangement with the FDTD method has not been considered. In this paper, we propose a novel hardware and software system that captures the radiation characteristics for a dense grid arrangement and applies them to the FDTD method, while controlling the sound wave propagation with the non-propagation region. The proposed system produces the average differences from measured values of sound pressure, propagation time, center frequency, and log-spectral distortion of 1.8 dB, 0.04 ms, 700 Hz, and 3.5 dB, respectively, which is more accurate than the conventional techniques. The result shows that this system is useful for improving the accuracy of sound wave propagation reproduction with the sound field simulation.

In recent years, deep learning-based image compression , particularly through generative models, has emerged as a pivotal area of research. Despite significant advancements, challenges such as diminished sharpness and quality in reconstructed images, learning inefficiencies due to mode collapse, and data loss during transmission persist. To address these issues, we propose a novel compression model that incorporates a denoising step with diffusion models, significantly enhancing image reconstruction fidelity by sub-information(e.g., edge and depth) from leveraging latent space. Empirical experiments demonstrate that our model achieves superior or comparable results in terms of image quality and compression efficiency when measured against the existing models. Notably, our model excels in scenarios of partial image loss or excessive noise by introducing an edge estimation network to preserve the integrity of reconstructed images, offering a robust solution to the current limitations of image compression.