On the coherence/incoherence of electron transport in semiconductor heterostructure optoelectronic devices

This paper compares and contrasts different theoretical approaches based on incoherent electron scattering transport with experimental measurements of optoelectronic devices formed from semiconductor heterostructures. The Monte Carlo method which makes no a priori assumptions about the carrier distribution in momentum or phase space is compared with less computationally demanding energy-balance rate equation models which assume thermalised carrier distributions. It is shown that the two approaches produce qualitatively similar results for hole transport in p-type Si1-xGex/Si superlattices designed for terahertz emission. The good agreement of the predictions of rate equation calculations with experimental measurements of mid- and far-infrared quantum cascade lasers, quantum well infrared photodetectors and quantum dot infrared photodetectors substantiate the assumption of incoherent scattering dominating the transport in these quantum well based devices. However, the paper goes on to consider the possibility of coherent transport through the density matrix method and suggests an experiment that could allow coherent and incoherent transport to be distinguished from each other

Relative Geologic Time By Dynamic Time Warping

This thesis considers an approach to tackle a core problem within seismic interpretation, which is bringing an autonomously generated interpretation of the seismic data, which is now known as a Relative Geologic Time. The proposed method readily utilizes the method of Dynamic Time Warping, which is an established method within signal processing. Using Dynamic Time Warping is thought to replicate similar interpretations an interpreter would conduct when fulfilling an interpretation of the subsurface. Utilizing Dynamic Time Warping to seismic data results in a fully autonomous interpretation of the subsurface, conducted in minutes and seconds. The method is simple and extendable, which can easily be further expanded. The workflow established during the thesis work results in a method that successfully produces an RGT volume. However, problems related to the method must be improved to enhance the outcome further and diminish errors present in the result. Furthermore, even with problems associated with the method, potential solutions are described in detail in the discussion and appendix. Discussion affiliated with previous attempts in solving Relative Geologic Time volumes is emphasized. The research conducted in Dynamic Time Warping is promising and emits potential for further research. LaTeX setup by Gunn and Patel (2017)

ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks.

BACKGROUND:The coordination of genomic functions is a critical and complex process across biological systems such as phenotypes or states (e.g., time, disease, organism, environmental perturbation). Understanding how the complexity of genomic function relates to these states remains a challenge. To address this, we have developed a novel computational method, ManiNetCluster, which simultaneously aligns and clusters gene networks (e.g., co-expression) to systematically reveal the links of genomic function between different conditions. Specifically, ManiNetCluster employs manifold learning to uncover and match local and non-linear structures among networks, and identifies cross-network functional links. RESULTS:We demonstrated that ManiNetCluster better aligns the orthologous genes from their developmental expression profiles across model organisms than state-of-the-art methods (p-value <2.2×10-16). This indicates the potential non-linear interactions of evolutionarily conserved genes across species in development. Furthermore, we applied ManiNetCluster to time series transcriptome data measured in the green alga Chlamydomonas reinhardtii to discover the genomic functions linking various metabolic processes between the light and dark periods of a diurnally cycling culture. We identified a number of genes putatively regulating processes across each lighting regime. CONCLUSIONS:ManiNetCluster provides a novel computational tool to uncover the genes linking various functions from different networks, providing new insight on how gene functions coordinate across different conditions. ManiNetCluster is publicly available as an R package at https://github.com/daifengwanglab/ManiNetCluster

Weakly-Supervised Alignment of Video With Text

Suppose that we are given a set of videos, along with natural language descriptions in the form of multiple sentences (e.g., manual annotations, movie scripts, sport summaries etc.), and that these sentences appear in the same temporal order as their visual counterparts. We propose in this paper a method for aligning the two modalities, i.e., automatically providing a time stamp for every sentence. Given vectorial features for both video and text, we propose to cast this task as a temporal assignment problem, with an implicit linear mapping between the two feature modalities. We formulate this problem as an integer quadratic program, and solve its continuous convex relaxation using an efficient conditional gradient algorithm. Several rounding procedures are proposed to construct the final integer solution. After demonstrating significant improvements over the state of the art on the related task of aligning video with symbolic labels [7], we evaluate our method on a challenging dataset of videos with associated textual descriptions [36], using both bag-of-words and continuous representations for text.Comment: ICCV 2015 - IEEE International Conference on Computer Vision, Dec 2015, Santiago, Chil

Discovering Functional Communities in Dynamical Networks

Many networks are important because they are substrates for dynamical systems, and their pattern of functional connectivity can itself be dynamic -- they can functionally reorganize, even if their underlying anatomical structure remains fixed. However, the recent rapid progress in discovering the community structure of networks has overwhelmingly focused on that constant anatomical connectivity. In this paper, we lay out the problem of discovering_functional communities_, and describe an approach to doing so. This method combines recent work on measuring information sharing across stochastic networks with an existing and successful community-discovery algorithm for weighted networks. We illustrate it with an application to a large biophysical model of the transition from beta to gamma rhythms in the hippocampus.Comment: 18 pages, 4 figures, Springer "Lecture Notes in Computer Science" style. Forthcoming in the proceedings of the workshop "Statistical Network Analysis: Models, Issues and New Directions", at ICML 2006. Version 2: small clarifications, typo corrections, added referenc

Time-Warping Invariant Quantum Recurrent Neural Networks via Quantum-Classical Adaptive Gating

Adaptive gating plays a key role in temporal data processing via classical recurrent neural networks (RNN), as it facilitates retention of past information necessary to predict the future, providing a mechanism that preserves invariance to time warping transformations. This paper builds on quantum recurrent neural networks (QRNNs), a dynamic model with quantum memory, to introduce a novel class of temporal data processing quantum models that preserve invariance to time-warping transformations of the (classical) input-output sequences. The model, referred to as time warping-invariant QRNN (TWI-QRNN), augments a QRNN with a quantum-classical adaptive gating mechanism that chooses whether to apply a parameterized unitary transformation at each time step as a function of the past samples of the input sequence via a classical recurrent model. The TWI-QRNN model class is derived from first principles, and its capacity to successfully implement time-warping transformations is experimentally demonstrated on examples with classical or quantum dynamics.Comment: Submitted for publicatio