6,343 research outputs found
Spectral Decomposition of Missing Transverse Energy at Hadron Colliders
We propose a spectral decomposition to systematically extract information of
dark matter at hadron colliders. The differential cross section of events with
missing transverse energy (MET) can be expressed by a linear combination of
basis functions. In the case of -channel mediator models for dark matter
particle production, basis functions are identified with the differential cross
sections of sub-processes of virtual mediator and visible particle production
while the coefficients of basis functions correspond to dark matter invariant
mass distribution in the manner of the K\"all\'en-Lehmann spectral
decomposition. For a given MET data set and mediator model, we show that one
can differentiate a certain dark matter-mediator interaction from another
through spectral decomposition.Comment: 6+4 pages, 6 figures, PRL versio
Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting
This paper proposes a weakly- and self-supervised deep convolutional neural
network (WSSDCNN) for content-aware image retargeting. Our network takes a
source image and a target aspect ratio, and then directly outputs a retargeted
image. Retargeting is performed through a shift map, which is a pixel-wise
mapping from the source to the target grid. Our method implicitly learns an
attention map, which leads to a content-aware shift map for image retargeting.
As a result, discriminative parts in an image are preserved, while background
regions are adjusted seamlessly. In the training phase, pairs of an image and
its image-level annotation are used to compute content and structure losses. We
demonstrate the effectiveness of our proposed method for a retargeting
application with insightful analyses.Comment: 10 pages, 11 figures. To appear in ICCV 2017, Spotlight Presentatio
Unsupervised Pre-Training For Data-Efficient Text-to-Speech On Low Resource Languages
Neural text-to-speech (TTS) models can synthesize natural human speech when
trained on large amounts of transcribed speech. However, collecting such
large-scale transcribed data is expensive. This paper proposes an unsupervised
pre-training method for a sequence-to-sequence TTS model by leveraging large
untranscribed speech data. With our pre-training, we can remarkably reduce the
amount of paired transcribed data required to train the model for the target
downstream TTS task. The main idea is to pre-train the model to reconstruct
de-warped mel-spectrograms from warped ones, which may allow the model to learn
proper temporal assignment relation between input and output sequences. In
addition, we propose a data augmentation method that further improves the data
efficiency in fine-tuning. We empirically demonstrate the effectiveness of our
proposed method in low-resource language scenarios, achieving outstanding
performance compared to competing methods. The code and audio samples are
available at: https://github.com/cnaigithub/SpeechDewarpingComment: ICASSP 202
Dynamical mean-field theory of Hubbard-Holstein model at half-filling: Zero temperature metal-insulator and insulator-insulator transitions
We study the Hubbard-Holstein model, which includes both the
electron-electron and electron-phonon interactions characterized by and
, respectively, employing the dynamical mean-field theory combined with
Wilson's numerical renormalization group technique. A zero temperature phase
diagram of metal-insulator and insulator-insulator transitions at half-filling
is mapped out which exhibits the interplay between and . As () is
increased, a metal to Mott-Hubbard insulator (bipolaron insulator) transition
occurs, and the two insulating states are distinct and can not be adiabatically
connected. The nature of and transitions between the three states are
discussed.Comment: 5 pages, 4 figures. Submitted to Physical Review Letter
Schwannoma Mimicking Laryngocele
A schwannoma of the larynx is a rare benign tumor that usually presents as a submucosal mass in the pyriform sinus and the aryepiglottic space, and this type of schwannoma constitutes a diagnostic and therapeutic challenge for otolaryngologists. We present here two cases of supraglottic schwannomas that were misdiagnosed as laryngoceles. Both were excised through a lateral thyrotomy approach without a tracheostomy, and the laryngeal function was successfully maintained. We discuss the clinical and imaging findings and the management of this rare neoplasm with focusing on the differential diagnosis of laryngeal schwannoma and laryngocele. We also review the relevant medical literature
Introduction on atomic layer deposition for high-k dielectric & high mobility oxide semiconductor thin film transistors
Amorphous oxide semiconductors have been widely studied for the potential use in flat panel displays such as active matrix liquid crystal display (LCD) and Organic light emitting diodes (OLEDs). Since reporting amorphous InGaZnO semiconductor thin film transistor (TFT) in 2003 & 2004, many multi-component oxide semiconductors have been intensively investigated and developed by reactive sputtering method. Very recently, the sputtered InGaZnO TFTs are already adopted in mass-production to fabricate AMOLED TVs. However, there remain several problems such as high mobility & stability issues. Also, virtual and argument reality (VR, AR) applications are rapidly emerging in display markets but the main issues are high resolution and low-voltage driving technologies.
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