Learning Contact Dynamics using Physically Structured Neural Networks

Learning physically structured representations of dynamical systems that include contact between different objects is an important problem for learning-based approaches in robotics. Black-box neural networks can learn to approximately represent discontinuous dynamics, but they typically require large quantities of data and often suffer from pathological behaviour when forecasting for longer time horizons. In this work, we use connections between deep neural networks and differential equations to design a family of deep network architectures for representing contact dynamics between objects. We show that these networks can learn discontinuous contact events in a data-efficient manner from noisy observations in settings that are traditionally difficult for black-box approaches and recent physics inspired neural networks. Our results indicate that an idealised form of touch feedback—which is heavily relied upon by biological systems—is a key component of making this learning problem tractable. Together with the inductive biases introduced through the network architectures, our techniques enable accurate learning of contact dynamics from observations

Clear writing as a problem of Russian learners of English

The article reports the outcome of the research conducted in several groups of Russian learners of English and aimed at improving the rhetoric of their writing. It highlights some issues of flawed style with the focus on unclear expression. Treating unclearness as a typical drawback of students' compositions, the author singles out the main kinds of unclear sentences and makes an attempt to find their causes. As follows from the observation unclear writing shows itself in three major manifestations: excessive writing, implicit writing and ambiguous writing. All these types of unclear sentences are caused by several reasons, which include: mother tongue interference; interference of style, genre or register; conventional nature of classroom communication; factors reducing students' capacity to work. The obtained data and the students' feedback enable the author to speak about certain improvement in the students' writing at the end of each round of the research. This fact is also confirmed by comparative study of students' papers at the end of each year: the participants in the research showed better performance than the students who were out of the experiments. The article is completed with the author's views on how to improve the English writing of Russian students © IDOSI Publications, 2013

Variational Integrator Networks for Physically Meaningful Embeddings

Learning workable representations of dynamical systems is becoming an increasingly important problem in a number of application areas. By leveraging recent work connecting deep neural networks to systems of differential equations, we propose variational integrator networks, a class of neural network architectures designed to preserve the geometric structure of physical systems. This class of network architectures facilitates accurate long-term prediction, interpretability, and data-efficient learning, while still remaining highly flexible and capable of modeling complex behavior. We demonstrate that they can accurately learn dynamical systems from both noisy observations in phase space and from image pixels within which the unknown dynamics are embedded

Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Equivariant Projected Kernels

Gaussian processes are machine learning models capable of learning unknown functions in a way that represents uncertainty, thereby facilitating construction of optimal decision-making systems. Motivated by a desire to deploy Gaussian processes in novel areas of science, a rapidly-growing line of research has focused on constructively extending these models to handle non-Euclidean domains, including Riemannian manifolds, such as spheres and tori. We propose techniques that generalize this class to model vector fields on Riemannian manifolds, which are important in a number of application areas in the physical sciences. To do so, we present a general recipe for constructing gauge equivariant kernels, which induce Gaussian vector fields, i.e. vector-valued Gaussian processes coherent with geometry, from scalar-valued Riemannian kernels. We extend standard Gaussian process training methods, such as variational inference, to this setting. This enables vector-valued Gaussian processes on Riemannian manifolds to be trained using standard methods and makes them accessible to machine learning practitioners

Matérn Gaussian Processes on Graphs.

Gaussian processes are a versatile framework for learning unknown functions in a manner that permits one to utilize prior information about their properties. Although many different Gaussian process models are readily available when the input space is Euclidean, the choice is much more limited for Gaussian processes whose input space is an undirected graph. In this work, we leverage the stochastic partial differential equation characterization of Mat´ern Gaussian processes—a widelyused model class in the Euclidean setting—to study their analog for undirected graphs. We show that the resulting Gaussian processes inherit various attractive properties of their Euclidean and Riemannian analogs and provide techniques that allow them to be trained using standard methods, such as inducing points. This enables graph Mat´ern Gaussian processes to be employed in mini-batch and non-conjugate settings, thereby making them more accessible to practitioners and easier to deploy within larger learning frameworks

Inversionless light amplification and optical switching controlled by state-dependent alignment of molecules

We propose a method to achieve amplification without population inversion by anisotropic molecules whose orientation by an external electric field is state-dependent. It is based on decoupling of the lower-state molecules from the resonant light while the excited ones remain emitting. The suitable class of molecules is discussed, the equation for the gain factor is derived, and the magnitude of the inversionless amplification is estimated for the typical experimental conditions. Such switching of the sample from absorbing to amplifying via transparent state is shown to be possible both with the aid of dc and ac control electric fields.Comment: AMS-LaTeX v1.2, 4 pages with 4 figure

HCV IRES manipulates the ribosome to promote the switch from translation initiation to elongation.

The internal ribosome entry site (IRES) of the hepatitis C virus (HCV) drives noncanonical initiation of protein synthesis necessary for viral replication. Functional studies of the HCV IRES have focused on 80S ribosome formation but have not explored its role after the 80S ribosome is poised at the start codon. Here, we report that mutations of an IRES domain that docks in the 40S subunit's decoding groove cause only a local perturbation in IRES structure and result in conformational changes in the IRES-rabbit 40S subunit complex. Functionally, the mutations decrease IRES activity by inhibiting the first ribosomal translocation event, and modeling results suggest that this effect occurs through an interaction with a single ribosomal protein. The ability of the HCV IRES to manipulate the ribosome provides insight into how the ribosome's structure and function can be altered by bound RNAs, including those derived from cellular invaders

Accelerating MCMC Algorithms

Markov chain Monte Carlo algorithms are used to simulate from complex statistical distributions by way of a local exploration of these distributions. This local feature avoids heavy requests on understanding the nature of the target, but it also potentially induces a lengthy exploration of this target, with a requirement on the number of simulations that grows with the dimension of the problem and with the complexity of the data behind it. Several techniques are available towards accelerating the convergence of these Monte Carlo algorithms, either at the exploration level (as in tempering, Hamiltonian Monte Carlo and partly deterministic methods) or at the exploitation level (with Rao-Blackwellisation and scalable methods).Comment: This is a survey paper, submitted WIREs Computational Statistics, to with 6 figure

Quantitative analysis of ribosome–mRNA complexes at different translation stages

Inhibition of primer extension by ribosome–mRNA complexes (toeprinting) is a proven and powerful technique for studying mechanisms of mRNA translation. Here we have assayed an advanced toeprinting approach that employs fluorescently labeled DNA primers, followed by capillary electrophoresis utilizing standard instruments for sequencing and fragment analysis. We demonstrate that this improved technique is not merely fast and cost-effective, but also brings the primer extension inhibition method up to the next level. The electrophoretic pattern of the primer extension reaction can be characterized with a precision unattainable by the common toeprint analysis utilizing radioactive isotopes. This method allows us to detect and quantify stable ribosomal complexes at all stages of translation, including initiation, elongation and termination, generated during the complete translation process in both the in vitro reconstituted translation system and the cell lysate. We also point out the unique advantages of this new methodology, including the ability to assay sites of the ribosomal complex assembly on several mRNA species in the same reaction mixture