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 $s$-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

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 $U$ and $g$, 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 $U$ and $g$. As $U$ ($g$) 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

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

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. Please click Additional Files below to see the full abstract