Collective Antenna Effects in the Terahertz and Infrared Response of Highly Aligned Carbon Nanotube Arrays

We study macroscopically-aligned single-wall carbon nanotube arrays with uniform lengths via polarization-dependent terahertz and infrared transmission spectroscopy. Polarization anisotropy is extreme at frequencies less than $\sim$3 THz with no sign of attenuation when the polarization is perpendicular to the alignment direction. The attenuation for both parallel and perpendicular polarizations increases with increasing frequency, exhibiting a pronounced and broad peak around 10 THz in the parallel case. We model the electromagnetic response of the sample by taking into account both radiative scattering and absorption losses. We show that our sample acts as an effective antenna due to the high degree of alignment, exhibiting much larger radiative scattering than absorption in the mid/far-infrared range. Our calculated attenuation spectrum clearly shows a non-Drude peak at $\sim$10 THz in agreement with the experiment.Comment: 5 pages, 5 figure

Peak Alignment of Gas Chromatography-Mass Spectrometry Data with Deep Learning

We present ChromAlignNet, a deep learning model for alignment of peaks in Gas Chromatography-Mass Spectrometry (GC-MS) data. In GC-MS data, a compound's retention time (RT) may not stay fixed across multiple chromatograms. To use GC-MS data for biomarker discovery requires alignment of identical analyte's RT from different samples. Current methods of alignment are all based on a set of formal, mathematical rules. We present a solution to GC-MS alignment using deep learning neural networks, which are more adept at complex, fuzzy data sets. We tested our model on several GC-MS data sets of various complexities and analysed the alignment results quantitatively. We show the model has very good performance (AUC $\sim 1$ for simple data sets and AUC $\sim 0.85$ for very complex data sets). Further, our model easily outperforms existing algorithms on complex data sets. Compared with existing methods, ChromAlignNet is very easy to use as it requires no user input of reference chromatograms and parameters. This method can easily be adapted to other similar data such as those from liquid chromatography. The source code is written in Python and available online

Kondo effect in quantum dots coupled to ferromagnetic leads

We study the Kondo effect in a quantum dot which is coupled to ferromagnetic leads and analyse its properties as a function of the spin polarization of the leads. Based on a scaling approach we predict that for parallel alignment of the magnetizations in the leads the strong-coupling limit of the Kondo effect is reached at a finite value of the magnetic field. Using an equation-of-motion technique we study nonlinear transport through the dot. For parallel alignment the zero-bias anomaly may be split even in the absence of an external magnetic field. For antiparallel spin alignment and symmetric coupling, the peak is split only in the presence of a magnetic field, but shows a characteristic asymmetry in amplitude and position.Comment: 5 pages, 2 figure

Binary matrices of optimal autocorrelations as alignment marks

We define a new class of binary matrices by maximizing the peak-sidelobe distances in the aperiodic autocorrelations. These matrices can be used as robust position marks for in-plane spatial alignment. The optimal square matrices of dimensions up to 7 by 7 and optimal diagonally-symmetric matrices of 8 by 8 and 9 by 9 were found by exhaustive searches.Comment: 8 pages, 6 figures and 1 tabl

Normalized Alignment of Dependency Trees for Detecting Textual Entailment

In this paper, we investigate the usefulness of normalized alignment of dependency trees for entailment prediction. Overall, our approach yields an accuracy of 60% on the RTE2 test set, which is a significant improvement over the baseline. Results vary substantially across the different subsets, with a peak performance on the summarization data. We conclude that normalized alignment is useful for detecting textual entailments, but a robust approach will probably need to include additional sources of information