Spin-conserving and reversing photoemission from the surface states of Bi2_2Se3_3 and Au (111)

We present a theory based on first-principles calculations explaining (i) why the tunability of spin polarizations of photoelectrons from Bi$_2$Se$_3$ (111) depends on the band index and Bloch wavevector of the surface state and (ii) why such tunability is absent in the case of isosymmetric Au (111). The results provide not only an explanation for the recent, puzzling experimental observations but also a guide toward making highly-tunable spin-polarized electron sources from topological insulators.Comment: 5 pages, 6 figures; Supplemental Material (2 pages) added, typos correcte

Breakdown of the Chiral Anomaly in Weyl Semimetals in a Strong Magnetic Field

The low-energy quasiparticles of Weyl semimetals are a condensed-matter realization of the Weyl fermions introduced in relativistic field theory. Chiral anomaly, the nonconservation of the chiral charge under parallel electric and magnetic fields, is arguably the most important phenomenon of Weyl semimetals and has been explained as an imbalance between the occupancies of the gapless, zeroth Landau levels with opposite chiralities. This widely accepted picture has served as the basis for subsequent studies. Here we report the breakdown of the chiral anomaly in Weyl semimetals in a strong magnetic field based on ab initio calculations. A sizable energy gap that depends sensitively on the direction of the magnetic field may open up due to the mixing of the zeroth Landau levels associated with the opposite-chirality Weyl points that are away from each other in the Brillouin zone. Our study provides a theoretical framework for understanding a wide range of phenomena closely related to the chiral anomaly in topological semimetals, such as magnetotransport, thermoelectric responses, and plasmons, to name a few.Comment: 6+7 pages, 5+6 figures; published versio

C2A: Crowd Consensus Analytics for Virtual Colonoscopy

We present a medical crowdsourcing visual analytics platform called C{$^2$}A to visualize, classify and filter crowdsourced clinical data. More specifically, C$^2$A is used to build consensus on a clinical diagnosis by visualizing crowd responses and filtering out anomalous activity. Crowdsourcing medical applications have recently shown promise where the non-expert users (the crowd) were able to achieve accuracy similar to the medical experts. This has the potential to reduce interpretation/reading time and possibly improve accuracy by building a consensus on the findings beforehand and letting the medical experts make the final diagnosis. In this paper, we focus on a virtual colonoscopy (VC) application with the clinical technicians as our target users, and the radiologists acting as consultants and classifying segments as benign or malignant. In particular, C$^2$A is used to analyze and explore crowd responses on video segments, created from fly-throughs in the virtual colon. C$^2$A provides several interactive visualization components to build crowd consensus on video segments, to detect anomalies in the crowd data and in the VC video segments, and finally, to improve the non-expert user's work quality and performance by A/B testing for the optimal crowdsourcing platform and application-specific parameters. Case studies and domain experts feedback demonstrate the effectiveness of our framework in improving workers' output quality, the potential to reduce the radiologists' interpretation time, and hence, the potential to improve the traditional clinical workflow by marking the majority of the video segments as benign based on the crowd consensus.Comment: IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 21-30, 2016 (10 pages, 11 figures

Crowdsourcing Lung Nodules Detection and Annotation

We present crowdsourcing as an additional modality to aid radiologists in the diagnosis of lung cancer from clinical chest computed tomography (CT) scans. More specifically, a complete workflow is introduced which can help maximize the sensitivity of lung nodule detection by utilizing the collective intelligence of the crowd. We combine the concept of overlapping thin-slab maximum intensity projections (TS-MIPs) and cine viewing to render short videos that can be outsourced as an annotation task to the crowd. These videos are generated by linearly interpolating overlapping TS-MIPs of CT slices through the depth of each quadrant of a patient's lung. The resultant videos are outsourced to an online community of non-expert users who, after a brief tutorial, annotate suspected nodules in these video segments. Using our crowdsourcing workflow, we achieved a lung nodule detection sensitivity of over 90% for 20 patient CT datasets (containing 178 lung nodules with sizes between 1-30mm), and only 47 false positives from a total of 1021 annotations on nodules of all sizes (96% sensitivity for nodules$>$4mm). These results show that crowdsourcing can be a robust and scalable modality to aid radiologists in screening for lung cancer, directly or in combination with computer-aided detection (CAD) algorithms. For CAD algorithms, the presented workflow can provide highly accurate training data to overcome the high false-positive rate (per scan) problem. We also provide, for the first time, analysis on nodule size and position which can help improve CAD algorithms.Comment: 7 pages, SPIE Medical Imaging 201

Momentum-dependent spin selection rule in photoemission with glide symmetry

We present a comprehensive theory on the spin- and angle-resolved photoemission spectroscopy (SARPES) of materials with glide-mirror symmetry, focusing on the role of glide symmetry on the spin selection rule. In the glide-symmetric SARPES configuration, where the surface of a material, the incoming light and the outgoing photoelectrons are invariant under a glide reflection, the spin polarization of photoelectrons is determined by the glide eigenvalue of the initial state, which makes SARPES a powerful tool for studying topological phases protected by glide symmetry. We also show that, due to the nonsymmorphic character of glide symmetry, the spin polarization of a photoelectron whose momentum is in the second surface Brillouin zone is the opposite of the spin polarization of a photoelectron which is ejected from the same initial Bloch state but whose momentum is in the first zone. This momentum dependence of spin selection rule clearly distinguishes glide symmetry from mirror symmetry and is particularly important if the Bloch wavevector of the initial state is close to the first surface Brillouin zone boundary. As a proof of principle, we simulate the SARPES from the surface states of KHgSb (010) and investigate how the spin selection rule imposed by the glide symmetry manifests itself in a real material.Comment: 8 pages, 5 figure

A Fast-Converged Acoustic Modeling for Korean Speech Recognition: A Preliminary Study on Time Delay Neural Network

In this paper, a time delay neural network (TDNN) based acoustic model is proposed to implement a fast-converged acoustic modeling for Korean speech recognition. The TDNN has an advantage in fast-convergence where the amount of training data is limited, due to subsampling which excludes duplicated weights. The TDNN showed an absolute improvement of 2.12% in terms of character error rate compared to feed forward neural network (FFNN) based modelling for Korean speech corpora. The proposed model converged 1.67 times faster than a FFNN-based model did.Comment: 6 pages, 2 figure

Crowd-Assisted Polyp Annotation of Virtual Colonoscopy Videos

Virtual colonoscopy (VC) allows a radiologist to navigate through a 3D colon model reconstructed from a computed tomography scan of the abdomen, looking for polyps, the precursors of colon cancer. Polyps are seen as protrusions on the colon wall and haustral folds, visible in the VC fly-through videos. A complete review of the colon surface requires full navigation from the rectum to the cecum in antegrade and retrograde directions, which is a tedious task that takes an average of 30 minutes. Crowdsourcing is a technique for non-expert users to perform certain tasks, such as image or video annotation. In this work, we use crowdsourcing for the examination of complete VC fly-through videos for polyp annotation by non-experts. The motivation for this is to potentially help the radiologist reach a diagnosis in a shorter period of time, and provide a stronger confirmation of the eventual diagnosis. The crowdsourcing interface includes an interactive tool for the crowd to annotate suspected polyps in the video with an enclosing box. Using our workflow, we achieve an overall polyps-per-patient sensitivity of 87.88% (95.65% for polyps $\geq$5mm and 70% for polyps $<$5mm). We also demonstrate the efficacy and effectiveness of a non-expert user in detecting and annotating polyps and discuss their possibility in aiding radiologists in VC examinations.Comment: 7 pages, SPIE Medical Imaging 201

Crowdsourcing for Identification of Polyp-Free Segments in Virtual Colonoscopy Videos

Virtual colonoscopy (VC) allows a physician to virtually navigate within a reconstructed 3D colon model searching for colorectal polyps. Though VC is widely recognized as a highly sensitive and specific test for identifying polyps, one limitation is the reading time, which can take over 30 minutes per patient. Large amounts of the colon are often devoid of polyps, and a way of identifying these polyp-free segments could be of valuable use in reducing the required reading time for the interrogating radiologist. To this end, we have tested the ability of the collective crowd intelligence of non-expert workers to identify polyp candidates and polyp-free regions. We presented twenty short videos flying through a segment of a virtual colon to each worker, and the crowd was asked to determine whether or not a possible polyp was observed within that video segment. We evaluated our framework on Amazon Mechanical Turk and found that the crowd was able to achieve a sensitivity of 80.0% and specificity of 86.5% in identifying video segments which contained a clinically proven polyp. Since each polyp appeared in multiple consecutive segments, all polyps were in fact identified. Using the crowd results as a first pass, 80% of the video segments could in theory be skipped by the radiologist, equating to a significant time savings and enabling more VC examinations to be performed

Dual Precision Deep Neural Network

On-line Precision scalability of the deep neural networks(DNNs) is a critical feature to support accuracy and complexity trade-off during the DNN inference. In this paper, we propose dual-precision DNN that includes two different precision modes in a single model, thereby supporting an on-line precision switch without re-training. The proposed two-phase training process optimizes both low- and high-precision modes.Comment: 5 pages, 4 figures, 2 table

Recurrent mesectodermal leiomyoma of the ciliary body: a case report.

A 19-yr-old woman with a previous history of a mass of the right ciliary body presented with a decreased visual acuity of right eye. Clinicoradiologic examinations suggested a recurrent mass of the ciliary body. Enucleation of the right eye was performed under the impression of malignant tumor. On microscopic examination, the tumor was a mesectodermal leiomyoma of the ciliary body. On immunohistochemistry, the tumor cells were reactive to smooth muscle actin and vimentin, but not reactive to cytokeratin, S-100 protein, neurofilament, desmin, epithelial membrane antigen, HMB-45, glial fibrillary acidic protein, and synaptophysin. Electron microscopy revealed numerous thin longitudinally placed myofilaments and focal densities in the cytoplasms. In the review of the literature, only 27 cases of mesectodermal leiomyoma of the ciliary body were reported, however, there was no report of recurrent cases. Mesectodermal leiomyoma should be differentiated from other orbital spindle-cell tumors such as amelanotic melanomas and glial tumors. Immunohistochemical and electron microscopic studies may be useful for the correct diagnosis by showing smooth muscle differentiation in the tumor cells