Proton strings and rings in atypical nucleation of ferroelectricity in ice

Ordinary ice has a proton-disordered phase which is kinetically metastable, unable to reach, spontaneously, the ferroelectric (FE) ground state at low temperature where a residual Pauling entropy persists. Upon light doping with KOH at low temperature, the transition to FE ice takes place, but its microscopic mechanism still needs clarification. We introduce a lattice model based on dipolar interactions plus a competing, frustrating term that enforces the ice rule (IR). In the absence of IR-breaking defects, standard Monte Carlo (MC) simulation leaves this ice model stuck in a state of disordered proton ring configurations with the correct Pauling entropy. A replica exchange accelerated MC sampling strategy succeeds, without open path moves, interfaces, or off-lattice configurations, in equilibrating this defect-free ice, reaching its low-temperature FE order through a well-defined first-order phase transition. When proton vacancies mimicking the KOH impurities are planted into the IR-conserving lattice, they enable standard MC simulation to work, revealing the kinetics of evolution of ice from proton disorder to partial FE order below the transition temperature. Replacing ordinary nucleation, each impurity opens up a proton ring generating a linear string, an actual FE hydrogen bond wire that expands with time. Reminiscent of those described for spin ice, these impurity-induced strings are proposed to exist in doped water ice too, where IRs are even stronger. The emerging mechanism yields a dependence of the long-time FE order fraction upon dopant concentration, and upon quenching temperature, that compares favorably with that known in real-life KOH doped ice.

Chord language and theory of everything

Is there a unified natural principle or theory of all things behind the natural phenomena that are observed in different disciplines and scattered? If there is such a thing, it should have a wide universality, and the chord language phenomenon seems to have this feature. Chord languages have distinct physical and mathematical forms: discrete spectrum, chords (open, closed, non-linear string), symmetrical, mirrored, etc. In music, painting, meridian (Chinese ancient medical theory) and other disciplines, the generation of time, space, life, spiritual and other semantic expressions, beyond the universality of the discipline, easy to observe, verify, Like the theory of all things. The following is the chord value formula used in music and painting. Is this a quantum formula? S = HV, (S = semitone, H = equal temperament constant, V = frequency), minimum discrete value. I=H^n.V (I=sound, n=sound value), allowing discrete values; C=H^n1, n2, n3, n*.V (C=chord), discrete value spectrum. Music and painting are chord language phenomena, with discrete spectral forms unique to chord language: chords, tones, scales, etc., respectively: time and space, mathematically mutual: reverse-mirror relationship. Chord language is not only a spiritual phenomenon (music, painting), but also a physical phenomenon (discrete spectrum, string), observing chord language events, such as: music, painting, etc. - also observing physical events, it has spiritual-natural isomorphism, This is its philosophical significance: there is a certain natural principle and law between spirit and nature. The chord language is an ancient knowledge system whose mathematical model dates back to Pythagorean temperament in ancient Greece. After the gradual improvement of musicians of the dynasties, the ancient Chinese medical theory - Meridian observation of positive-negative (yin-yang) rules - this is an important attribute of chord language, and spatial (geometric) semantics can be observed in chord paintings: Strings (open, closed, non-line string), these observations complete the recognition of the chord languageknowledge.

Penalized Variational Autoencoder for Molecular Design

Variational autoencoders have emerged as one of the most common approaches for automating molecular generation. We seek to learn a cross-domain latent space capturing chemical and biological information, simultaneously. To do so, we introduce the Penalized Variational Autoencoder which directly operates on SMILES, a linear string representation of molecules, with a weight penalty term in the decoder to address the imbalance in the character distribution of SMILES strings. We find that this greatly improves upon previous variational autoencoder approaches in the quality of the latent space and the generalization ability of the latent space to new chemistry. Next, we organize the latent space according to chemical and biological properties by jointly training the Penalized Variational Autoencoder with linear units. Extensive experiments on a range of tasks, including reconstruction, validity, and transferability demonstrates that the proposed methods here substantially outperform previous SMILES and graph-based methods, as well as introduces a new way to generate molecules from a set of desired properties, without prior knowledge of a chemical structure.

Penalized Variational Autoencoder for Molecular Design

Variational autoencoders have emerged as one of the most common approaches for automating molecular generation. We seek to learn a cross-domain latent space capturing chemical and biological information, simultaneously. To do so, we introduce the Penalized Variational Autoencoder which directly operates on SMILES, a linear string representation of molecules, with a weight penalty term in the decoder to address the imbalance in the character distribution of SMILES strings. We find that this greatly improves upon previous variational autoencoder approaches in the quality of the latent space and the generalization ability of the latent space to new chemistry. Next, we organize the latent space according to chemical and biological properties by jointly training the Penalized Variational Autoencoder with linear units. Extensive experiments on a range of tasks, including reconstruction, validity, and transferability demonstrates that the proposed methods here substantially outperform previous SMILES and graph-based methods, as well as introduces a new way to generate molecules from a set of desired properties, without prior knowledge of a chemical structure.

Penalized Variational Autoencoder for Molecular Design

Variational autoencoders have emerged as one of the most common approaches for automating molecular generation. We seek to learn a cross-domain latent space capturing chemical and biological information, simultaneously. To do so, we introduce the Penalized Variational Autoencoder which directly operates on SMILES, a linear string representation of molecules, with a weight penalty term in the decoder to address the imbalance in the character distribution of SMILES strings. We find that this greatly improves upon previous variational autoencoder approaches in the quality of the latent space and the generalization ability of the latent space to new chemistry. Next, we organize the latent space according to chemical and biological properties by jointly training the Penalized Variational Autoencoder with linear units. Extensive experiments on a range of tasks, including reconstruction, validity, and transferability demonstrates that the proposed methods here substantially outperform previous SMILES and graph-based methods, as well as introduces a new way to generate molecules from a set of desired properties, without prior knowledge of a chemical structure.

Sentence Comprehension

This chapter on sentence comprehension reviews prominent sentence comprehension models addressing how people can turn a linear string of words into an understanding of sentence meaning: who did what to whom. A key method is comparing the factors that make one kind of sentence more difficult to comprehend than another, and the chapter discusses the roles of syntactic ambiguity and syntactic complexity in comprehension, including theoretical accounts emphasizing innate comprehension mechanisms or changes in comprehension patterns with experience. The chapter also discusses probabilistic and information theoretic models, the relationship between comprehension and production processes, and incrementality in sentence comprehension—the degree to which comprehension processes are devoted to interpreting the present input, updating the past, and predicting future input. The chapter also addresses future directions and the degree to which sentence comprehension research can be integrated with work addressing other aspects of language comprehension.

On Boundary Damping for Semi-Infinite Strings and Beams

In this paper, initial boundary value problems for a linear string and beam equation are considered. The main aim is to study the reflection of an incident wave at the boundary and the damping properties for different types of boundary conditions such as a mass-spring-dashpot for semi-infinite strings, and pinned, sliding, clamped and damping boundary conditions for semi-infinite beams. The system of transverse vibrations are divided into model 1 and model 2 which are described as a string problem and beam problem, respectively. In order to construct explicit solutions of the boundary value problem for the first model the D’Alembert method will be used to the one dimensional wave equation on the semi-infinite domain, and for the second model the method of Laplace transforms will be applied to a beam equation on a semi-infinite domain. It will be shown how waves are damped and reflected for different types of boundaries and how much energy is dissipated at the boundary.