1,656 research outputs found
Resolution limit in community detection
Detecting community structure is fundamental to clarify the link between
structure and function in complex networks and is used for practical
applications in many disciplines. A successful method relies on the
optimization of a quantity called modularity [Newman and Girvan, Phys. Rev. E
69, 026113 (2004)], which is a quality index of a partition of a network into
communities. We find that modularity optimization may fail to identify modules
smaller than a scale which depends on the total number L of links of the
network and on the degree of interconnectedness of the modules, even in cases
where modules are unambiguously defined. The probability that a module conceals
well-defined substructures is the highest if the number of links internal to
the module is of the order of \sqrt{2L} or smaller. We discuss the practical
consequences of this result by analyzing partitions obtained through modularity
optimization in artificial and real networks.Comment: 8 pages, 3 figures. Clarification of definition of community in
Section II + minor revision
Natural clustering: the modularity approach
We show that modularity, a quantity introduced in the study of networked
systems, can be generalized and used in the clustering problem as an indicator
for the quality of the solution. The introduction of this measure arises very
naturally in the case of clustering algorithms that are rooted in Statistical
Mechanics and use the analogy with a physical system.Comment: 11 pages, 5 figure enlarged versio
์ฌ์ธตํ์ต์ ์ด์ฉํ ์ก์ฒด๊ณ์ ์ฑ์ง ์์ธก
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :์์ฐ๊ณผํ๋ํ ํํ๋ถ,2020. 2. ์ ์ฐ์ค.์ต๊ทผ ๊ธฐ๊ณํ์ต ๊ธฐ์ ์ ๊ธ๊ฒฉํ ๋ฐ์ ๊ณผ ์ด์ ํํ ๋ถ์ผ์ ๋ํ ์ ์ฉ์ ๋ค์ํ ํํ์ ์ฑ์ง์ ๋ํ ๊ตฌ์กฐ-์ฑ์ง ์ ๋ ๊ด๊ณ๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ํ ์์ธก ๋ชจํ์ ๊ฐ๋ฐ์ ๊ฐ์ํ๊ณ ์๋ค. ์ฉ๋งคํ ์์ ์๋์ง๋ ๊ทธ๋ฌํ ๊ธฐ๊ณํ์ต์ ์ ์ฉ ์์ค ํ๋์ด๋ฉฐ ๋ค์ํ ์ฉ๋งค ๋ด์ ํํ๋ฐ์์์ ์ค์ํ ์ญํ ์ ํ๋ ๊ทผ๋ณธ์ ์ฑ์ง ์ค ํ๋์ด๋ค. ๋ณธ ์ฐ๊ตฌ์์ ์ฐ๋ฆฌ๋ ๋ชฉํ๋ก ํ๋ ์ฉ๋งคํ ์์ ์๋์ง๋ฅผ ์์๊ฐ์ ์ํธ์์ฉ์ผ๋ก๋ถํฐ ๊ตฌํ ์ ์๋ ์๋ก์ด ์ฌ์ธตํ์ต ๊ธฐ๋ฐ ์ฉ๋งคํ ๋ชจํ์ ์๊ฐํ๋ค. ์ ์๋ ์ฌ์ธตํ์ต ๋ชจํ์ ๊ณ์ฐ ๊ณผ์ ์ ์ฉ๋งค์ ์ฉ์ง ๋ถ์์ ๋ํ ๋ถํธํ ํจ์๊ฐ ๊ฐ ์์์ ๋ถ์๋ค์ ๊ตฌ์กฐ์ ์ฑ์ง์ ๋ํ ๋ฒกํฐ ํํ์ ์ถ์ถํ๋ฉฐ, ์ด๋ฅผ ํ ๋๋ก ์์๊ฐ ์ํธ์์ฉ์ ๋ณต์กํ ํผ์
ํธ๋ก ์ ๊ฒฝ๋ง ๋์ ๋ฒกํฐ๊ฐ์ ๊ฐ๋จํ ๋ด์ ์ผ๋ก ๊ตฌํ ์ ์๋ค. 952๊ฐ์ง์ ์ ๊ธฐ์ฉ์ง๊ณผ 147๊ฐ์ง์ ์ ๊ธฐ์ฉ๋งค๋ฅผ ํฌํจํ๋ 6,493๊ฐ์ง์ ์คํ์น๋ฅผ ํ ๋๋ก ๊ธฐ๊ณํ์ต ๋ชจํ์ ๊ต์ฐจ ๊ฒ์ฆ ์ํ์ ์ค์ํ ๊ฒฐ๊ณผ, ํ๊ท ์ ๋ ์ค์ฐจ ๊ธฐ์ค 0.2 kcal/mol ์์ค์ผ๋ก ๋งค์ฐ ๋์ ์ ํ๋๋ฅผ ๊ฐ์ง๋ค. ์ค์บํด๋-๊ธฐ๋ฐ ๊ต์ฐจ ๊ฒ์ฆ์ ๊ฒฐ๊ณผ ์ญ์ 0.6 kcal/mol ์์ค์ผ๋ก, ์ธ์ฝ์ผ๋ก ๋ถ๋ฅํ ์ ์๋ ๋น๊ต์ ์๋ก์ด ๋ถ์ ๊ตฌ์กฐ์ ๋ํ ์์ธก์ ๋ํด์๋ ์ฐ์ํ ์ ํ๋๋ฅผ ๋ณด์ธ๋ค. ๋ํ, ์ ์๋ ํน์ ๊ธฐ๊ณํ์ต ๋ชจํ์ ๊ทธ ๊ตฌ์กฐ ์ ํน์ ์ฉ๋งค์ ํนํ๋์ง ์์๊ธฐ ๋๋ฌธ์ ๋์ ์๋์ฑ์ ๊ฐ์ง๋ฉฐ ํ์ต์ ์ด์ฉํ ๋ฐ์ดํฐ์ ์๋ฅผ ๋์ด๋ ๋ฐ ์ฉ์ดํ๋ค. ์์๊ฐ ์ํธ์์ฉ์ ๋ํ ๋ถ์์ ํตํด ์ ์๋ ์ฌ์ธตํ์ต ๋ชจํ ์ฉ๋งคํ ์์ ์๋์ง์ ๋ํ ๊ทธ๋ฃน-๊ธฐ์ฌ๋๋ฅผ ์ ์ฌํํ ์ ์์์ ์ ์ ์์ผ๋ฉฐ, ๊ธฐ๊ณํ์ต์ ํตํด ๋จ์ํ ๋ชฉํ๋ก ํ๋ ์ฑ์ง๋ง์ ์์ธกํ๋ ๊ฒ์ ๋์ด ๋์ฑ ์์ธํ ๋ฌผ๋ฆฌํํ์ ์ดํด๋ฅผ ํ๋ ๊ฒ์ด ๊ฐ๋ฅํ ๊ฒ์ด๋ผ ๊ธฐ๋ํ ์ ์๋ค.Recent advances in machine learning technologies and their chemical applications lead to the developments of diverse structure-property relationship based prediction models for various chemical properties; the free energy of solvation is one of them and plays a dominant role as a fundamental measure of solvation chemistry. Here, we introduce a novel machine learning-based solvation model, which calculates the target solvation free energy from pairwise atomistic interactions. The novelty of our proposed solvation model involves rather simple architecture: two encoding function extracts vector representations of the atomic and the molecular features from the given chemical structure, while the inner product between two atomistic features calculates their interactions, instead of black-boxed perceptron networks. The cross-validation result on 6,493 experimental measurements for 952 organic solutes and 147 organic solvents achieves an outstanding performance, which is 0.2 kcal/mol in MUE. The scaffold-based split method exhibits 0.6 kcal/mol, which shows that the proposed model guarantees reasonable accuracy even for extrapolated cases. Moreover, the proposed model shows an excellent transferability for enlarging training data due to its solvent-non-specific nature. Analysis of the atomistic interaction map shows there is a great potential that our proposed model reproduces group contributions on the solvation energy, which makes us believe that the proposed model not only provides the predicted target property, but also gives us more detailed physicochemical insights.1. Introduction 1
2. Delfos: Deep Learning Model for Prediction of Solvation Free Energies in Generic Organic Solvents 7
2.1. Methods 7
2.1.1. Embedding of Chemical Contexts 7
2.1.2. Encoder-Predictor Network 9
2.2. Results and Discussions 13
2.2.1. Computational Setup and Results 13
2.2.2. Transferability of the Model for New Compounds 17
2.2.3. Visualization of Attention Mechanism 26
3. Group Contribution Method for the Solvation Energy Estimation with Vector Representations of Atom 29
3.1. Model Description 29
3.1.1. Word Embedding 29
3.1.2. Network Architecture 33
3.2. Results and Discussions 39
3.2.1. Computational Details 39
3.2.2. Prediction Accuracy 42
3.2.3. Model Transferability 44
3.2.4. Group Contributions of Solvation Energy 49
4. Empirical Structure-Property Relationship Model for Liquid Transport Properties 55
5. Concluding Remarks 61
A. Analyzing Kinetic Trapping as a First-Order Dynamical Phase Transition in the Ensemble of Stochastic Trajectories 65
A1. Introduction 65
A2. Theory 68
A3. Lattice Gas Model 70
A4. Mathematical Model 73
A5. Dynamical Phase Transitions 75
A6. Conclusion 82
B. Reaction-Path Thermodynamics of the Michaelis-Menten Kinetics 85
B1. Introduction 85
B2. Reaction Path Thermodynamics 88
B3. Fixed Observation Time 94
B4. Conclusions 101Docto
Correlation of creep rate with microstructural changes during high temperature creep
Creep tests were conducted on Haynes 188 cobalt-base alloy and alpha titanium. The tests on Haynes 188 were conducted at 1600 F and 1800 F for stresses from 3 to 20 ksi, and the as-received, mill-annealed results were compared to specimens given 5%, 10%, and 15% room temperature prestrains and then annealed one hour at 1800 F. The tests on alpha titanium were performed at 7,250 and 10,000 psi at 500 C. One creep test was done at 527 C and 10,000 psi to provide information on kinetics. Results for annealed titanium were compared to specimens given 10% and 20% room temperature prestrains followed by 100 hours recovery at 550 C. Electron microscopy was used to relate dislocation and precipitate structure to the creep behavior of the two materials. The results on Haynes 188 alloy reveal that the time to reach 0.5% creep strain at 1600 F increases with increasing prestrain for exposure times less than 1,000 hours, the increase at 15% prestrain being more than a factor of ten
Simple models of protein folding and of non--conventional drug design
While all the information required for the folding of a protein is contained
in its amino acid sequence, one has not yet learned how to extract this
information to predict the three--dimensional, biologically active, native
conformation of a protein whose sequence is known. Using insight obtained from
simple model simulations of the folding of proteins, in particular of the fact
that this phenomenon is essentially controlled by conserved (native) contacts
among (few) strongly interacting ("hot"), as a rule hydrophobic, amino acids,
which also stabilize local elementary structures (LES, hidden, incipient
secondary structures like --helices and --sheets) formed early
in the folding process and leading to the postcritical folding nucleus (i.e.,
the minimum set of native contacts which bring the system pass beyond the
highest free--energy barrier found in the whole folding process) it is possible
to work out a succesful strategy for reading the native structure of designed
proteins from the knowledge of only their amino acid sequence and of the
contact energies among the amino acids. Because LES have undergone millions of
years of evolution to selectively dock to their complementary structures, small
peptides made out of the same amino acids as the LES are expected to
selectively attach to the newly expressed (unfolded) protein and inhibit its
folding, or to the native (fluctuating) native conformation and denaturate it.
These peptides, or their mimetic molecules, can thus be used as effective
non--conventional drugs to those already existing (and directed at neutralizing
the active site of enzymes), displaying the advantage of not suffering from the
uprise of resistance
Ordinal Motifs in Lattices
Lattices are a commonly used structure for the representation and analysis of
relational and ontological knowledge. In particular, the analysis of these
requires a decomposition of a large and high-dimensional lattice into a set of
understandably large parts. With the present work we propose /ordinal motifs/
as analytical units of meaning. We study these ordinal substructures (or
standard scales) through (full) scale-measures of formal contexts from the
field of formal concept analysis. We show that the underlying decision problems
are NP-complete and provide results on how one can incrementally identify
ordinal motifs to save computational effort. Accompanying our theoretical
results, we demonstrate how ordinal motifs can be leveraged to retrieve basic
meaning from a medium sized ordinal data set
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