Graph Filter Transfer via Probability Density Ratio Weighting

The problem of recovering graph signals is one of the main topics in graph signal processing. A representative approach to this problem is the graph Wiener filter, which utilizes the statistical information of the target signal computed from historical data to construct an effective estimator. However, we often encounter situations where the current graph differs from that of historical data due to topology changes, leading to performance degradation of the estimator. This paper proposes a graph filter transfer method, which learns an effective estimator from historical data under topology changes. The proposed method leverages the probability density ratio of the current and historical observations and constructs an estimator that minimizes the reconstruction error in the current graph domain. The experiment on synthetic data demonstrates that the proposed method outperforms other methods.Comment: 5 pages, submitted to ICASSP 202

セイソクカツキジヘングラフガクシュウ

博士（工学）東京農工大

Preparation of N-2-Nitrophenylsulfenyl Imino Peptides and Their Catalyst-Controlled Diastereoselective Indolylation

N-2-Nitrophenylsulfenyl (Nps) imino dipeptides bearing various functional groups were successfully prepared via MnO2-mediated oxidation and then subjected to diastereoselective indolylation. Each diastereomer of the adduct was selectively obtained from the same substrates using the appropriate chiral phosphoric acid catalysts. These transformations would be useful for synthesizing non-canonical amino acid-containing peptides as novel drug candidates

Graph Signal Restoration Using Nested Deep Algorithm Unrolling

Graph signal processing is a ubiquitous task in many applications such as sensor, social, transportation and brain networks, point cloud processing, and graph neural networks. Graph signals are often corrupted through sensing processes, and need to be restored for the above applications. In this paper, we propose two graph signal restoration methods based on deep algorithm unrolling (DAU). First, we present a graph signal denoiser by unrolling iterations of the alternating direction method of multiplier (ADMM). We then propose a general restoration method for linear degradation by unrolling iterations of Plug-and-Play ADMM (PnP-ADMM). In the second method, the unrolled ADMM-based denoiser is incorporated as a submodule. Therefore, our restoration method has a nested DAU structure. Thanks to DAU, parameters in the proposed denoising/restoration methods are trainable in an end-to-end manner. Since the proposed restoration methods are based on iterations of a (convex) optimization algorithm, the method is interpretable and keeps the number of parameters small because we only need to tune graph-independent regularization parameters. We solve two main problems in existing graph signal restoration methods: 1) limited performance of convex optimization algorithms due to fixed parameters which are often determined manually. 2) large number of parameters of graph neural networks that result in difficulty of training. Several experiments for graph signal denoising and interpolation are performed on synthetic and real-world data. The proposed methods show performance improvements to several existing methods in terms of root mean squared error in both tasks

Novel urinary glycan profiling by lectin array serves as the biomarkers for predicting renal prognosis in patients with IgA nephropathy

In IgA nephropathy (IgAN), IgA1 molecules are characterized by galactose deficiency in O-glycans. Here, we investigated the association between urinary glycosylation profile measured by 45 lectins at baseline and renal prognosis in 142 patients with IgAN. The primary outcome was estimated glomerular filtration rate (eGFR) decline (>4 mL/min/1.73 m(2)/year), or eGFR >= 30% decline from baseline, or initiation of renal replacement therapies within 3 years. During follow-up (3.4 years, median), 26 patients reached the renal outcome (Group P), while 116 patients were with good renal outcome (Group G). Multivariate logistic regression analyses revealed that lectin binding signals of Erythrina cristagalli lectin (ECA) (odds ratio [OR] 2.84, 95% confidence interval [CI] 1.11-7.28) and Narcissus pseudonarcissus lectin (NPA) (OR 2.32, 95% CI 1.11-4.85) adjusted by age, sex, eGFR, and urinary protein were significantly associated with the outcome, and they recognize Gal(beta 1-4)GlcNAc and high-mannose including Man(alpha 1-6)Man, respectively. The addition of two lectin-binding glycan signals to the interstitial fibrosis/tubular atrophy score further improved the model fitness (Akaike's information criterion) and incremental predictive abilities (c-index, net reclassification improvement, and integrated discrimination improvement). Urinary N-glycan profiling by lectin array is useful in the prediction of IgAN prognosis, since ECA and NPA recognize the intermediate glycans during N-glycosylation of various glycoproteins

Rapid Synthesis and Charge?Discharge Properties of LiMnPO4 Nanocrystallite-embedded Porous Carbons

LiMnPO4 nanocrystallite-embedded porous carbons were successfully synthesized within a few minutes by a microwave-heating process. The nanocomposites showed higher charge?discharge capacity and better rate capability than bulk-LiMnPO4 particles synthesized in a similar manner without porous carbons

Dissociative adsorption of supersonic CH₃Cl on Cu oxide Surfaces: Cu₂O(111) and bulk Cu₂O precursor “29”-Structure on Cu(111)

Hayashida K., Tsuda Y., Murase N., et al. Dissociative adsorption of supersonic CH₃Cl on Cu oxide Surfaces: Cu₂O(111) and bulk Cu₂O precursor “29”-Structure on Cu(111). Applied Surface Science 669, 160475 (2024); https://doi.org/10.1016/j.apsusc.2024.160475.To examine the elementary steps of the Rochow-Müller process we placed copper oxides, viz., Cu₂O(111) and the bulk Cu₂O precursor “29”-structure on Cu(111), under supersonic molecular beams (SSMB) of CH₃Cl. The SSMB energies range from 0.5-1.9 eV. We employed X-ray photoemission spectroscopy (XPS) in conjunction with synchrotron radiation (SR) to determine the resulting adsorbed species present. We identified two reaction paths, viz., Reaction I and Reaction II. Reaction I involves dissociative adsorption of CH₃Cl. In Reaction II, CH₃Cl also dissociates, but with Cl as the dominant adsorbed species (higher than that of adsorbed carbonaceous species, as observed for Reaction I). For the incident energies and exposure conditions considered, we found Reaction II as the dominant reaction path for CH₃Cl reaction on both Cu₂O(111) and the “29”-structure on Cu(111)