1,467 research outputs found
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)], a charged saturated
lipid [dipalmitoylphosphatidylglycerol (DPPG)], and
cholesterol (Chol). In binary mixtures of neutral DOPC/DPPC and charged
DOPC/DPPG, spherical vesicles were formed. On the other
hand, pore formation was often observed with GUVs consisting of
DOPG and DPPC. In a DPPC/DPPG/Chol
ternary mixture, pore-formed vesicles were also frequently observed. The
percentage of pore-formed vesicles increased with the DPPG
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
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