Classification of involutions on Enriques surfaces

We present the classification of involutions on Enriques surfaces. We classify those into 18 types with the help of the lattice theory due to Nikulin. We also give all examples of the classification.Comment: 25 pages, 42 figure

食材性オオゴキブリの生態学的研究

京都大学新制・課程博士博士(農学)甲第24656号農博第2539号新制||農||1097(附属図書館)学位論文||R5||N5437(農学部図書室)京都大学大学院農学研究科森林科学専攻(主査)教授 北山 兼弘, 教授 田中 千尋, 教授 松浦 健二学位規則第4条第1項該当Doctor of Agricultural ScienceKyoto UniversityDGA

Twisted intersection colorings, invariants and double coverings of twisted links

Twisted links are a generalization of classical links and correspond to stably equivalence classes of links in thickened surfaces. In this paper we introduce twisted intersection colorings of a diagram and construct two invariants of a twisted link using such colorings. As an application, we show that there exist infinitely many pairs of twisted links such that for each pair the two twisted links are not equivalent but their double coverings are equivalent. We also introduce a method of constructing a pair of twisted links whose double coverings are equivalent

Coupling between pore formation and phase separation in charged lipid membranes

We investigated the effect of charge on the membrane morphology of giant unilamellar vesicles (GUVs) composed of various mixtures containing charged lipids. We observed the membrane morphologies by fluorescent and confocal laser microscopy in lipid mixtures consisting of a neutral unsaturated lipid [dioleoylphosphatidylcholine (DOPC)], a neutral saturated lipid [dipalmitoylphosphatidylcholine (DPPC)], a charged unsaturated lipid [dioleoylphosphatidylglycerol (DOPG$^{\scriptsize{(-)}}$)], a charged saturated lipid [dipalmitoylphosphatidylglycerol (DPPG$^{\scriptsize{(-)}}$)], and cholesterol (Chol). In binary mixtures of neutral DOPC/DPPC and charged DOPC/DPPG$^{\scriptsize{(-)}}$, spherical vesicles were formed. On the other hand, pore formation was often observed with GUVs consisting of DOPG$^{\scriptsize{(-)}}$ and DPPC. In a DPPC/DPPG$^{\scriptsize{(-)}}$/Chol ternary mixture, pore-formed vesicles were also frequently observed. The percentage of pore-formed vesicles increased with the DPPG$^{\scriptsize{(-)}}$ concentration. Moreover, when the head group charges of charged lipids were screened by the addition of salt, pore-formed vesicles were suppressed in both the binary and ternary charged lipid mixtures. We discuss the mechanisms of pore formation in charged lipid mixtures and the relationship between phase separation and the membrane morphology. Finally, we reproduce the results seen in experimental systems by using coarse-grained molecular dynamics simulations.Comment: 34 pages, 10 figure

An On-Device Federated Learning Approach for Cooperative Anomaly Detection

Most edge AI focuses on prediction tasks on resource-limited edge devices while the training is done at server machines. However, retraining or customizing a model is required at edge devices as the model is becoming outdated due to environmental changes over time. To follow such a concept drift, a neural-network based on-device learning approach is recently proposed, so that edge devices train incoming data at runtime to update their model. In this case, since a training is done at distributed edge devices, the issue is that only a limited amount of training data can be used for each edge device. To address this issue, one approach is a cooperative learning or federated learning, where edge devices exchange their trained results and update their model by using those collected from the other devices. In this paper, as an on-device learning algorithm, we focus on OS-ELM (Online Sequential Extreme Learning Machine) to sequentially train a model based on recent samples and combine it with autoencoder for anomaly detection. We extend it for an on-device federated learning so that edge devices can exchange their trained results and update their model by using those collected from the other edge devices. This cooperative model update is one-shot while it can be repeatedly applied to synchronize their model. Our approach is evaluated with anomaly detection tasks generated from a driving dataset of cars, a human activity dataset, and MNIST dataset. The results demonstrate that the proposed on-device federated learning can produce a merged model by integrating trained results from multiple edge devices as accurately as traditional backpropagation based neural networks and a traditional federated learning approach with lower computation or communication cost

4-Chloro-2′,4′,6′-triethyl­benzophenone: a redetermination

The structure of the title compound [systematic name: (4-chloro­phen­yl)(2,4,6-trimethyl­phen­yl)methanone], C19H21ClO, has been redetermined at 100 K. The redetermination is of significantly higher precision than the previous structure determination at 133 K and reveals disorder of the one of the o-ethyl groups [occupancy factors = 0.77 (1) and 0.23 (1)] that was not identified in the previous report [Takahashi & Ito (2010 ▶). CrystEngComm, 12, 1628–1634]. The C—C—C—C torsion angles of the major and minor disorder components of the ethyl group with respect to the attached benzene ring are −103.7 (2) and −172.0 (6)°, respectively. It is of inter­est that the title compound does not display a single-crystal-to-single-crystal polymorphic phase transition on cooling, as was observed for a closely related compound, a fact that can be attributed to the disorder in the ethyl group