Nanotunneling Junction-based Hyperspectal Polarimetric Photodetector and Detection Method

A photodetector, detector array, and method of operation thereof in which nanojunctions are formed by crossing layers of nanowires. The crossing nanowires are separated by a few nm thick electrical barrier layer which allows tunneling. Each nanojunction is coupled to a slot antenna for efficient and frequency-selective coupling to photo signals. The nanojunctions formed at the intersection of the crossing wires defines a vertical tunneling diode that rectifies the AC signal from a coupled antenna and generates a DC signal suitable for reforming a video image. The nanojunction sensor allows multi/hyper spectral imaging of radiation within a spectral band ranging from terahertz to visible light, and including infrared (IR) radiation. This new detection approach also offers unprecedented speed, sensitivity and fidelity at room temperature

Transcriptionally active HERV-H retrotransposons demarcate topologically associating domains in human pluripotent stem cells.

Chromatin architecture has been implicated in cell type-specific gene regulatory programs, yet how chromatin remodels during development remains to be fully elucidated. Here, by interrogating chromatin reorganization during human pluripotent stem cell (hPSC) differentiation, we discover a role for the primate-specific endogenous retrotransposon human endogenous retrovirus subfamily H (HERV-H) in creating topologically associating domains (TADs) in hPSCs. Deleting these HERV-H elements eliminates their corresponding TAD boundaries and reduces the transcription of upstream genes, while de novo insertion of HERV-H elements can introduce new TAD boundaries. The ability of HERV-H to create TAD boundaries depends on high transcription, as transcriptional repression of HERV-H elements prevents the formation of boundaries. This ability is not limited to hPSCs, as these actively transcribed HERV-H elements and their corresponding TAD boundaries also appear in pluripotent stem cells from other hominids but not in more distantly related species lacking HERV-H elements. Overall, our results provide direct evidence for retrotransposons in actively shaping cell type- and species-specific chromatin architecture

Resummation of heavy jet mass and comparison to LEP data

The heavy jet mass distribution in e+e- collisions is computed to next-to-next-to-next-to leading logarithmic (NNNLL) and next-to-next-to leading fixed order accuracy (NNLO). The singular terms predicted from the resummed distribution are confirmed by the fixed order distributions allowing a precise extraction of the unknown soft function coefficients. A number of quantitative and qualitative comparisons of heavy jet mass and the related thrust distribution are made. From fitting to ALEPH data, a value of alpha_s is extracted, alpha_s(m_Z)=0.1220 +/- 0.0031, which is larger than, but not in conflict with, the corresponding value for thrust. A weighted average of the two produces alpha_s(m_Z) = 0.1193 +/- 0.0027, consistent with the world average. A study of the non-perturbative corrections shows that the flat direction observed for thrust between alpha_s and a simple non-perturbative shape parameter is not lifted in combining with heavy jet mass. The Monte Carlo treatment of hadronization gives qualitatively different results for thrust and heavy jet mass, and we conclude that it cannot be trusted to add power corrections to the event shape distributions at this accuracy. Whether a more sophisticated effective field theory approach to power corrections can reconcile the thrust and heavy jet mass distributions remains an open question.Comment: 33 pages, 14 figures. v2 added effect of lower numerical cutoff with improved extraction of the soft function constants; power correction discussion clarified. v3 small typos correcte

Confidence in prediction: an approach for dynamic weighted ensemble.

Combining classifiers in an ensemble is beneficial in achieving better prediction than using a single classifier. Furthermore, each classifier can be associated with a weight in the aggregation to boost the performance of the ensemble system. In this work, we propose a novel dynamic weighted ensemble method. Based on the observation that each classifier provides a different level of confidence in its prediction, we propose to encode the level of confidence of a classifier by associating with each classifier a credibility threshold, computed from the entire training set by minimizing the entropy loss function with the mini-batch gradient descent method. On each test sample, we measure the confidence of each classifier’s output and then compare it to the credibility threshold to determine whether a classifier should be attended in the aggregation. If the condition is satisfied, the confidence level and credibility threshold are used to compute the weight of contribution of the classifier in the aggregation. By this way, we are not only considering the presence but also the contribution of each classifier based on the confidence in its prediction on each test sample. The experiments conducted on a number of datasets show that the proposed method is better than some benchmark algorithms including a non-weighted ensemble method, two dynamic ensemble selection methods, and two Boosting methods

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics

Control and Characterization of Individual Grains and Grain Boundaries in Graphene Grown by Chemical Vapor Deposition

The strong interest in graphene has motivated the scalable production of high quality graphene and graphene devices. Since large-scale graphene films synthesized to date are typically polycrystalline, it is important to characterize and control grain boundaries, generally believed to degrade graphene quality. Here we study single-crystal graphene grains synthesized by ambient CVD on polycrystalline Cu, and show how individual boundaries between coalescing grains affect graphene's electronic properties. The graphene grains show no definite epitaxial relationship with the Cu substrate, and can cross Cu grain boundaries. The edges of these grains are found to be predominantly parallel to zigzag directions. We show that grain boundaries give a significant Raman "D" peak, impede electrical transport, and induce prominent weak localization indicative of intervalley scattering in graphene. Finally, we demonstrate an approach using pre-patterned growth seeds to control graphene nucleation, opening a route towards scalable fabrication of single-crystal graphene devices without grain boundaries.Comment: New version with additional data. Accepted by Nature Material