Intermediate coherent-incoherent charge transport: DNA as a case study

We study an intermediate quantum coherent-incoherent charge transport mechanism in metal-molecule-metal junctions using B\"uttiker's probe technique. This tool allows us to include incoherent effects in a controlled manner, and thus to study situations in which partial decoherence affects charge transfer dynamics. Motivated by recent experiments on intermediate coherent-incoherent charge conduction in DNA molecules [L. Xiang {\it et al.}, Nature Chem. 7, 221-226 (2015)], we focus on two representative structures: alternating (GC)$_n$ and stacked G$_n$C$_n$ sequences; the latter structure is argued to support charge delocalization within G segments, and thus an intermediate coherent-incoherent conduction. We begin our analysis with a highly simplified 1-dimensional tight-binding model, while introducing environmental effects through B\"uttiker's probes. This minimal model allows us to gain fundamental understanding of transport mechanisms and derive analytic results for molecular resistance in different limits. We then use a more detailed ladder-model Hamiltonian to represent double-stranded DNA structures---with environmental effects captured by B\"uttiker's probes. We find that hopping conduction dominates in alternating sequences, while in stacked sequences charge delocalization (visualized directly through the electronic density matrix) supports significant resonant-ballistic charge dynamics reflected by an even-odd effect and a weak distance dependence for resistance. Our analysis illustrates that lessons learned from minimal models are helpful for interpreting charge dynamics in DNA.Comment: 16 pages, 14 figure

Forming Stable Coalitions: The Process Matters

Players are assumed to rank each other as coalition partners. Two processes of coalition formation are defined and illustrated: i) Fallback (FB): Players seek coalition partners by descending lower and lower in their preference rankings until some majority coalition, all of whose members consider each other mutually acceptable, forms. ii) Build-up (BU):Same descent as FB, except only majorities whose members rank each other highest form coalitions. BU coalitions are stable in the sense that no member would prefer to be in another coalition, whereas FB coalitions, whose members need not rank each other highest, may not be stable. BU coalitions are bimodally distributed in a random society, with peaks around simple majority and unanimity the distributions of majorities in the US Supreme Count and in the US House of Representatives follow this pattern. The dynamics of real-life coalition-formation processes are illustrated by two Supreme Court cases.Coalition dynamics, Fallback bargaining, Manipulability, Legislatures, US Supreme Court

Geometric Deep Learning for Molecular Crystal Structure Prediction

We develop and test new machine learning strategies for accelerating molecular crystal structure ranking and crystal property prediction using tools from geometric deep learning on molecular graphs. Leveraging developments in graph-based learning and the availability of large molecular crystal data sets, we train models for density prediction and stability ranking which are accurate, fast to evaluate, and applicable to molecules of widely varying size and composition. Our density prediction model, MolXtalNet-D, achieves state-of-the-art performance, with lower than 2% mean absolute error on a large and diverse test data set. Our crystal ranking tool, MolXtalNet-S, correctly discriminates experimental samples from synthetically generated fakes and is further validated through analysis of the submissions to the Cambridge Structural Database Blind Tests 5 and 6. Our new tools are computationally cheap and flexible enough to be deployed within an existing crystal structure prediction pipeline both to reduce the search space and score/filter crystal structure candidates

Simulations of Disordered Matter in 3D with the Morphological Autoregressive Protocol (MAP) and Convolutional Neural Networks

Disordered molecular systems such as amorphous catalysts, organic thin films, electrolyte solutions, and water are at the cutting edge of computational exploration today. Traditional simulations of such systems at length-scales relevant to experiments in practice require a compromise between model accuracy and quality of sampling. To remedy the situation, we have developed an approach based on generative machine learning called the Morphological Autoregressive Protocol (MAP) which provides computational access to mesoscale disordered molecular configurations at linear cost at generation for materials in which structural correlations decay sufficiently rapidly. The algorithm is implemented using an augmented PixelCNN deep learning architecture that we previously demonstrated produces excellent results in 2 dimensions (2D) for mono-elemental molecular systems. Here, we extend our implementation to multielemental 3D and demonstrate performance using water as our test system in two scenarios: 1. liquid water, and 2. a sample conditioned on the presence of a rare motif. We trained the model on small-scale samples of liquid water produced using path-integral molecular dynamics simulation including nuclear quantum effects under ambient conditions. MAP-generated water configurations are shown to accurately reproduce the properties of the training set and to produce stable trajectories when used as initial conditions in classical and quantum dynamical simulations. We expect our approach to perform equally well on other disordered molecular systems while offering unique advantages in situations when the disorder is quenched rather than equilibrated

The 2014 KIT IWSLT Speech-to-Text Systems for English, German and Italian

This paper describes our German, Italian and English Speech-to-Text (STT) systems for the 2014 IWSLT TED ASR track. Our setup uses ROVER and confusion network combination from various subsystems to achieve a good overall performance. The individual subsystems are built by using different front-ends, (e.g., MVDR-MFCC or lMel), acoustic models (GMM or modular DNN) and phone sets and by training on various subsets of the training data. Decoding is performed in two stages, where the GMM systems are adapted in an unsupervised manner on the combination of the first stage outputs using VTLN, MLLR, and cMLLR. The combination setup produces a final hypothesis that has a significantly lower WER than any of the individual subsystems

Parental perceptions and understanding of information provision, management options and factors influencing the decision-making process in the treatment of children with glue ear

Objectives Otitis media with effusion (OME) is a common cause of hearing loss and possible developmental delay in children, and there are a range of ‘preference sensitive’ treatment options. We aimed to evaluate the attitudes and beliefs of parents of affected children to treatment options including watchful-waiting, hearing aids, grommets, and, oral steroids with the intention of developing our understanding of decision-making and the factors influencing it, sources of parental information, and satisfaction with information provision. Design We recruited a convenience sample of twelve parents of eleven children with OME at a single ENT department of a teaching hospital into a qualitative research study. The children of the parents interviewed had already been recruited into the Oral Steroids for the Resolution of Otitis Media with effusion In Children (OSTRICH) study. Semi structured interviews were audio recorded, transcribed and then coded using an inductive, thematic approach. Results Parents were satisfied with the verbal provision of information during the treatment consultation, although many were keen to receive supplementary printed information. Discussion with family and friends helped the decision-making process, whereas insufficient information and a paternalistic approach were viewed as obstacles. Parents were particularly influenced by the following: the immediacy of the treatment option effect, perceived efficacy, perceived risks and adverse effects, social implications (especially with hearing aids) and past personal and informant experience. Conclusions Parents appreciate clinicians tailoring information provision to parents' information needs and preferred format. Clinicians should also elicit parental attitudes towards the different management options for OME and the factors influencing their decisions, in order to optimise shared-decision making and ultimately provide a better standard of clinical care