Acute triangles in triangulations on the plane with minimum degree at least 4

AbstractIn this paper, we show that every maximal plane graph with minimum degree at least 4 and m finite faces other than an octahedron can be drawn in the plane so that at least (m+3)/2 faces are acute triangles. Moreover, this bound is sharp

Attention network for predicting T-cell receptor–peptide binding can associate attention with interpretable protein structural properties

Understanding how a T-cell receptor (TCR) recognizes its specific ligand peptide is crucial for gaining an insight into biological functions and disease mechanisms. Despite its importance, experimentally determining TCR–peptide–major histocompatibility complex (TCR–pMHC) interactions is expensive and time-consuming. To address this challenge, computational methods have been proposed, but they are typically evaluated by internal retrospective validation only, and few researchers have incorporated and tested an attention layer from language models into structural information. Therefore, in this study, we developed a machine learning model based on a modified version of Transformer, a source–target attention neural network, to predict the TCR–pMHC interaction solely from the amino acid sequences of the TCR complementarity-determining region (CDR) 3 and the peptide. This model achieved competitive performance on a benchmark dataset of the TCR–pMHC interaction, as well as on a truly new external dataset. Additionally, by analyzing the results of binding predictions, we associated the neural network weights with protein structural properties. By classifying the residues into large- and small-attention groups, we identified statistically significant properties associated with the largely attended residues such as hydrogen bonds within CDR3. The dataset that we created and the ability of our model to provide an interpretable prediction of TCR–peptide binding should increase our knowledge about molecular recognition and pave the way for designing new therapeutics

Drinfeld comultiplication and vertex operators

For the current realization of the quantum affine algebras, Drinfeld gave a simple comultiplication of the quantum current operators. With this comultiplication, we study the related vertex operators for the case of U_q(\hgtsl_n) and give an explicit bosonization of these new vertex operators. We use these vertex operators to construct the quantum current operators of U_q(\hgtsl_n) and discuss its connection with quantum boson-fermion correspondence.Comment: Amslatex 13 page

On the Evaluation of a Huggable Interface to Mediate Remote Affective Communication

Art and Design Research for the Future: Innovation and Art & Design ; September 26, 2017Conference: Tsukuba Global Science Week 2017Date: September 25-27, 2017Venue: Tsukuba International Congress CenterSponsored: University of Tsukub

Neural Fourier Transform: A General Approach to Equivariant Representation Learning

Symmetry learning has proven to be an effective approach for extracting the hidden structure of data, with the concept of equivariance relation playing the central role. However, most of the current studies are built on architectural theory and corresponding assumptions on the form of data. We propose Neural Fourier Transform (NFT), a general framework of learning the latent linear action of the group without assuming explicit knowledge of how the group acts on data. We present the theoretical foundations of NFT and show that the existence of a linear equivariant feature, which has been assumed ubiquitously in equivariance learning, is equivalent to the existence of a group invariant kernel on the dataspace. We also provide experimental results to demonstrate the application of NFT in typical scenarios with varying levels of knowledge about the acting group

Discovery of X rays from Class 0 protostar candidates in OMC-3

We have observed the Orion Molecular Clouds 2 and 3 (OMC-2 and OMC-3) with the Chandra X-ray Observatory (CXO). The northern part of OMC-3 is found to be particularly rich in new X-ray features; four hard X-ray sources are located in and along the filament of cloud cores. Two sources coincide positionally with the sub$mm$-$mm$ dust condensations of MMS 2 and 3 or an outflow radio source VLA 1, which are in a very early phase of star formation. The X-ray spectra of these sources show an absorption column of (1-3) x 10^23 H cm-2. Assuming a moderate temperature plasma, the X-ray luminosity in the 0.5-10 keV band is estimated to be ~10^30 erg s^-1 at a distance of 450 pc. From the large absorption, positional coincidence and moderate luminosity, we infer that the hard X-rays are coming from very young stellar objects embedded in the molecular cloud cores. We found another hard X-ray source near the edge of the dust filament. The extremely high absorption of 3 x 10^23 H cm^-2 indicates that the source must be surrounded by dense gas, suggesting that it is either a YSO in an early accretion phase or a Type II AGN (e.g. a Seyfert 2), although no counterpart is found at any other wavelength. In contrast to the hard X-ray sources, soft X-ray sources are found spread around the dust filaments, most of which are identified with IR sources in the T Tauri phase.Comment: 9 pages, To be appeared in ApJ v554 n2 Jun 20, 2001 issue, related press release is available at http://science.psu.edu/alert/Tsuboi11-2000.htm, Figure 1 and figure 2 with the best resolution is available at ftp.astro.psu.edu/pub/tsuboi/OMC/010205

東京大学におけるコンテンツ獲得戦略 : 工学系研究科学位論文の登録・公開を中心に

2006年5月16日に開催された、 国立情報学研究所平成17年度CSI委託事業報告交流会において発表した報告資料。東京大学における機関リポジトリへ登録するコンテンツ獲得戦略について、特に学位論文の登録制度化について報告した。国立情報学研究所　平成17年度CSI委託事業報告交流