3,032 research outputs found
Spin-conserving and reversing photoemission from the surface states of BiSe and Au (111)
We present a theory based on first-principles calculations explaining (i) why
the tunability of spin polarizations of photoelectrons from BiSe (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{}A
to visualize, classify and filter crowdsourced clinical data. More
specifically, CA 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, CA is used to analyze and explore crowd
responses on video segments, created from fly-throughs in the virtual colon.
CA 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 nodules4mm). 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
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
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
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
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