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We studied the temperature dependences of the optical spectra for optimally and underdoped Bi$_2$Sr$_2$Ca$_2$Cu$_3$O$_{10+z}$ single crystals. Similarly to the other cuprates' cases, a gap-like conductivity suppression was observed with reducing the temperature from above $T_{\mathrm{c}}$, creating a peak in the conductivity spectrum. The conductivity peak energy was insensitive to the doping level, namely $T_{\mathrm{c}}$, which suggests that this gap is not a superconducting gap but is related to the pseudogap. Comparing the data of various mono-, double-, and triple-layer cuprates, we found a clear correlation between the optimal $T_{\mathrm{c}}$ of each material and the pseudogap-related conductivity peak energy., Comment: 12 pages, 5 figures

In this paper, we propose a solution that won the 10th prize in the KDD Cup 2023 Challenge Task 2 (Next Product Recommendation for Underrepresented Languages/Locales). Our approach involves two steps: (i) Identify candidate item sets based on co-visitation, and (ii) Re-ranking the items using LightGBM with locale-independent features, including session-based features and product similarity. The experiment demonstrated that the locale-independent model performed consistently well across different test locales, and performed even better when incorporating data from other locales into the training.

Urban air pollution is a major environmental problem affecting human health and quality of life. Monitoring stations have been established to continuously obtain air quality information, but they do not cover all areas. Thus, there are numerous methods for spatially fine-grained air quality inference. Since existing methods aim to infer air quality of locations only in monitored cities, they do not assume inferring air quality in unmonitored cities. In this paper, we first study the air quality inference in unmonitored cities. To accurately infer air quality in unmonitored cities, we propose a neural network-based approach AIREX. The novelty of AIREX is employing a mixture-of-experts approach, which is a machine learning technique based on the divide-and-conquer principle, to learn correlations of air quality between multiple cities. To further boost the performance, it employs attention mechanisms to compute impacts of air quality inference from the monitored cities to the locations in the unmonitored city. We show, through experiments on a real-world air quality dataset, that AIREX achieves higher accuracy than state-of-the-art methods.

Urban conditions are monitored by a wide variety of sensors that measure several attributes, such as temperature and traffic volume. The correlations of sensors help to analyze and understand the urban conditions accurately. The correlated attribute pattern (CAP) mining discovers correlations among multiple attributes from the sets of sensors spatially close to each other and temporally correlated in their measurements. In this paper, we develop a visualization system for CAP mining and demonstrate analysis of smart city data. Our visualization system supports an intuitive understanding of mining results via sensor locations on maps and temporal changes of their measurements. In our demonstration scenarios, we provide four smart city datasets collected from China and Santander, Spain. We demonstrate that our system helps interactive analysis of smart city data.

We show that a subspace of exotic Hida-Kubo-Takenaka space is naturally imbedded into the usual Hida-Kubo-Takenaka space under some conditions. We also study on Heat Equations associated with exotic Laplacians, such as L\'evy Laplacian., Comment: 9 pages

In this paper, we investigate the roles of compact sets in the space of tempered distributions $\mathscr{S}^{\prime}$. The key notion is "k-spaces", which constitute a fairly general class of topological spaces. In a k-space, the system of compact sets controls continuous functions and Borel measures. Focusing on the k-space structure of $\mathscr{S}^{\prime}$, we prove some theorems which seem to be fundamental for infinite dimensional harmonic analysis from a new and unified view point. For example, the invariance principle of Donsker for the white noise measure is shown in terms of infinite dimansional characteristic functions., Comment: 9 pages