Hao Tang

Hao Tang
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Hao Tang
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Computer Science - Computation and Language (6)
 
Quantum Physics (3)
 
Computer Science - Computer Vision and Pattern Recognition (2)
 
Physics - Mesoscopic Systems and Quantum Hall Effect (2)
 
Physics - Optics (2)
 
Computer Science - Neural and Evolutionary Computing (1)
 
Physics - Medical Physics (1)
 
Physics - Materials Science (1)
 
Computer Science - Learning (1)
 
Physics - Disordered Systems and Neural Networks (1)
 
Computer Science - Artificial Intelligence (1)
 
Statistics - Machine Learning (1)

Publications Authored By Hao Tang

Quantum walks, in virtue of the coherent superposition and quantum interference, possess the exponential superiority over its classical counterpart in applications of quantum searching and quantum simulation. A straitforward physical implementation involving merely photonic source, linear evolution network and detection make it very appealing, in light of the stringent requirements of universal quantum computing. The quantum enhanced power is highly related to the state space of quantum walks, which can be expanded by enlarging the dimension of evolution network and/or photon number. Read More

Quantum memory, capable of stopping flying photons and storing their quantum coherence, is essential for scalable quantum technologies. A broadband quantum memory operating at room temperature will enable building large-scale quantum systems for real-life applications, for instance, high-speed quantum repeater for long-distance quantum communication and synchronised multi-photon quantum sources for quantum computing and quantum simulation. Albeit advances of pushing bandwidth from narrowband to broadband and storage media from ultra-cold atomic gas to room-temperature atomic vapour, due to either intrinsic high noises or short lifetime, it is still challenging to find a room-temperature broadband quantum memory beyond conceptional demonstration. Read More

End-to-end training of deep learning-based models allows for implicit learning of intermediate representations based on the final task loss. However, the end-to-end approach ignores the useful domain knowledge encoded in explicit intermediate-level supervision. We hypothesize that using intermediate representations as auxiliary supervision at lower levels of deep networks may be a good way of combining the advantages of end-to-end training and more traditional pipeline approaches. Read More

Recent work on discriminative segmental models has shown that they can achieve competitive speech recognition performance, using features based on deep neural frame classifiers. However, segmental models can be more challenging to train than standard frame-based approaches. While some segmental models have been successfully trained end to end, there is a lack of understanding of their training under different settings and with different losses. Read More

We study the problem of recognizing video sequences of fingerspelled letters in American Sign Language (ASL). Fingerspelling comprises a significant but relatively understudied part of ASL. Recognizing fingerspelling is challenging for a number of reasons: It involves quick, small motions that are often highly coarticulated; it exhibits significant variation between signers; and there has been a dearth of continuous fingerspelling data collected. Read More

Discriminative segmental models offer a way to incorporate flexible feature functions into speech recognition. However, their appeal has been limited by their computational requirements, due to the large number of possible segments to consider. Multi-pass cascades of segmental models introduce features of increasing complexity in different passes, where in each pass a segmental model rescores lattices produced by a previous (simpler) segmental model. Read More

Positional single photon incidence response (P-SPIR) theory is researched in this paper to generate more accurate PSF-contained system matrix simply and quickly. The method has been proved highly effective to improve the spatial resolution by applying to the Eplus-260 primate PET designed by the Institute of High Energy Physics of the Chinese Academy of Sciences(IHEP). Simultaneously, to meet the clinical needs, GPU acceleration is put to use. Read More

Quantum interference and quantum correlation, as two main features of quantum optics, play an essential role in quantum information applications, such as multi-particle quantum walk and boson sampling. While many experimental demonstrations have been done in one-dimensional waveguide arrays, it remains unexplored in higher dimensions due to tight requirement of manipulating and detecting photons in large-scale. Here, we experimentally observe non-classical correlation of two identical photons in a fully coupled two-dimensional structure, i. Read More

We study the problem of recognition of fingerspelled letter sequences in American Sign Language in a signer-independent setting. Fingerspelled sequences are both challenging and important to recognize, as they are used for many content words such as proper nouns and technical terms. Previous work has shown that it is possible to achieve almost 90% accuracies on fingerspelling recognition in a signer-dependent setting. Read More

Discriminative segmental models, such as segmental conditional random fields (SCRFs) and segmental structured support vector machines (SSVMs), have had success in speech recognition via both lattice rescoring and first-pass decoding. However, such models suffer from slow decoding, hampering the use of computationally expensive features, such as segment neural networks or other high-order features. A typical solution is to use approximate decoding, either by beam pruning in a single pass or by beam pruning to generate a lattice followed by a second pass. Read More

We report the study of a novel linear magneto-resistance (MR) under perpendicular magnetic fields in Bi2Se3 nanoribbons. Through angular dependence magneto-transport experiments, we show that this linear MR is purely due to two-dimensional (2D) transport, in agreement with the recently discovered linear MR from 2D topological surface state in bulk Bi2Te3, and the linear MR of other gapless semiconductors and graphene. We further show that the linear MR of Bi2Se3 nanoribbons persists to room temperature, underscoring the potential of exploiting topological insulator nanomaterials for room temperature magneto-electronic applications. Read More

Magneto-resistance (MR) of Bi$_2$Se$_3$ nanoribbons is studied over a broad range of temperature ($T$=300K-2K) and under various magnetic field ($B$) orientations. The MR is strongly anisotropic with the perpendicular MR much larger than the longitudinal and transverse MRs. The perpendicular MR exhibits quadratic $B$-dependence in low fields and becomes linear at high $B$. Read More