arXiv subjects

Shuo Yang

Publications and source records attributed to Shuo Yang.

At least 397 records · Page 22Linked to original sources

arXiv2016

Joint Hand Detection and Rotation Estimation by Using CNN

Hand detection is essential for many hand related tasks, e.g. parsing hand pose, understanding gesture, which are extremely useful for robotics and human-computer interaction. However, hand detection in uncontrolled environments is challenging due to the flexibility of wrist joint and cluttered background. We propose a deep learning based approach which detects hands and calibrates in-plane rotation under supervision at the same time. To guarantee the recall, we propose a context aware proposal generation algorithm which significantly outperforms the selective search. We then design a convolutional neural network(CNN) which handles object rotation explicitly to jointly solve the object detection and rotation estimation tasks. Experiments show that our method achieves better results than state-of-the-art detection models on widely-used benchmarks such as Oxford and Egohands database. We further show that rotation estimation and classification can mutually benefit each other.

cs.CV↗

arXiv2016

ANTARES: Progress towards building a `Broker' of time-domain alerts

The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is a joint effort of NOAO and the Department of Computer Science at the University of Arizona to build prototype software to process alerts from time-domain surveys, especially LSST, to identify those alerts that must be followed up immediately. Value is added by annotating incoming alerts with existing information from previous surveys and compilations across the electromagnetic spectrum and from the history of past alerts. Comparison against a knowledge repository of properties and features of known or predicted kinds of variable phenomena is used for categorization. The architecture and algorithms being employed are described.

astro-ph.IM↗

arXiv2016

Search for anomalous couplings via single top quark production in association with a photon at LHC

Considering the experimental constraints given by CMS collaboration at $\sqrt{s} = 8$ TeV on the strength of top quark flavour-changing neutral-current (FCNC) couplings $tqγ$ and $tqg$, we investigate the production of top quark in association with a photon and carry out a full simulation for the signals $\ellνbγ$ and $jjbγ$ at 14 TeV LHC. In our numerical analysis, the contributions of single top production with a photon radiation off the top decay products are also included. The discovery potential for anomalous couplings $tqγ$ and $tqg$ with an integrated luminosity of 100 fb$^{-1}$ are examined in detail.

hep-ph↗

arXiv2016

Application of Statistical Relational Learning to Hybrid Recommendation Systems

Recommendation systems usually involve exploiting the relations among known features and content that describe items (content-based filtering) or the overlap of similar users who interacted with or rated the target item (collaborative filtering). To combine these two filtering approaches, current model-based hybrid recommendation systems typically require extensive feature engineering to construct a user profile. Statistical Relational Learning (SRL) provides a straightforward way to combine the two approaches. However, due to the large scale of the data used in real world recommendation systems, little research exists on applying SRL models to hybrid recommendation systems, and essentially none of that research has been applied on real big-data-scale systems. In this paper, we proposed a way to adapt the state-of-the-art in SRL learning approaches to construct a real hybrid recommendation system. Furthermore, in order to satisfy a common requirement in recommendation systems (i.e. that false positives are more undesirable and therefore penalized more harshly than false negatives), our approach can also allow tuning the trade-off between the precision and recall of the system in a principled way. Our experimental results demonstrate the efficiency of our proposed approach as well as its improved performance on recommendation precision.

cs.AI↗

arXiv2015

WIDER FACE: A Face Detection Benchmark

Face detection is one of the most studied topics in the computer vision community. Much of the progresses have been made by the availability of face detection benchmark datasets. We show that there is a gap between current face detection performance and the real world requirements. To facilitate future face detection research, we introduce the WIDER FACE dataset, which is 10 times larger than existing datasets. The dataset contains rich annotations, including occlusions, poses, event categories, and face bounding boxes. Faces in the proposed dataset are extremely challenging due to large variations in scale, pose and occlusion, as shown in Fig. 1. Furthermore, we show that WIDER FACE dataset is an effective training source for face detection. We benchmark several representative detection systems, providing an overview of state-of-the-art performance and propose a solution to deal with large scale variation. Finally, we discuss common failure cases that worth to be further investigated. Dataset can be downloaded at: mmlab.ie.cuhk.edu.hk/projects/WIDERFace

cs.CV↗

arXiv2015

From Facial Parts Responses to Face Detection: A Deep Learning Approach

In this paper, we propose a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically, our method achieves a high recall rate of 90.99% on the challenging FDDB benchmark, outperforming the state-of-the-art method by a large margin of 2.91%. Importantly, we consider finding faces from a new perspective through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is carefully formulated considering challenging cases where faces are only partially visible. This consideration allows our network to detect faces under severe occlusion and unconstrained pose variation, which are the main difficulty and bottleneck of most existing face detection approaches. We show that despite the use of DCN, our network can achieve practical runtime speed.

cs.CV↗

arXiv2015

DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection

In this paper, we propose deformable deep convolutional neural networks for generic object detection. This new deep learning object detection framework has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty. A new pre-training strategy is proposed to learn feature representations more suitable for the object detection task and with good generalization capability. By changing the net structures, training strategies, adding and removing some key components in the detection pipeline, a set of models with large diversity are obtained, which significantly improves the effectiveness of model averaging. The proposed approach improves the mean averaged precision obtained by RCNN \cite{girshick2014rich}, which was the state-of-the-art, from 31\% to 50.3\% on the ILSVRC2014 detection test set. It also outperforms the winner of ILSVRC2014, GoogLeNet, by 6.1\%. Detailed component-wise analysis is also provided through extensive experimental evaluation, which provide a global view for people to understand the deep learning object detection pipeline.

cs.CV↗

arXiv2015

Observation of Anderson localization in ultrathin films of three-dimensional topological insulators

Anderson localization, the absence of diffusive transport in disordered systems, has been manifested as hopping transport in numerous electronic systems, whereas in recently discovered topological insulators it has not been directly observed. Here we report experimental demonstration of transition from diffusive transport in the weak antilocalization regime to variable range hopping transport in the Anderson localization regime with ultrathin (Bi$_{1-x}$Sb$_x$)$_2$Te$_3$ films. As disorder becomes stronger, negative magnetoconductivity due to the weak antilocalization is gradually suppressed, and eventually positive magnetoconductivity emerges when the electron system becomes strongly localized. This works reveals the critical role of disorder in the quantum transport properties of ultrathin topological insulator films, in which theories have predicted rich physics related to topological phase transitions.

cond-mat.mes-hall↗

arXiv2015

The signatures of the quintuplet leptons at the LHC

We investigate production and detection prospects of the quintuplet heavy leptons at the LHC in the context of a new model which is proposed as a viable and testable solution to the neutrino masses problem. We classify the signals, carry out a full simulation on the signals and the relevant backgrounds at the 14 TeV LHC. After applying suitable kinematic cuts, the background events are substantially suppressed. The signals of the heavy leptons might be detected at the 14 TeV LHC.

hep-ph↗

arXiv2015

Proximity effect between a topological insulator and a magnetic insulator with large perpendicular anisotropy

We report that thin films of a prototype topological insulator, Bi$_{2}$Se$_{3}$, can be epitaxially grown onto the (0001) surface of BaFe$_{12}$O$_{19}$(BaM), a magnetic insulator with high Curie temperature and large perpendicular anisotropy. In the Bi$_2$Se$_3$ thin films grown on non-magnetic substrates, classic weak antilocalization (WAL) is manifested as cusp-shaped positive magnetoresistance (MR) in perpendicular magnetic fields and parabola-shaped positive MR in parallel fields, whereas in Bi$_{2}$Se$_{3}$/BaM heterostructures the low field MR is parabola-shaped, which is positive in perpendicular fields and negative in parallel fields. The magnetic field and temperature dependence of the MR is explained as a consequence of the suppression of WAL due to strong magnetic interactions at the Bi$_{2}$Se$_{3}$/BaM interface.

cond-mat.mes-hall↗

arXiv2015

Criticality in Translation-Invariant Parafermion Chains

In this work we numerically study critical phases in translation-invariant $\mathbb{Z}_N$ parafermion chains with both nearest- and next-nearest-neighbor hopping terms. The model can be mapped to a $\mathbb{Z}_N$ spin model with nearest-neighbor couplings via a generalized Jordan-Wigner transformation and translation invariance ensures that the spin model is always self-dual. We first study the low-energy spectrum of chains with only nearest-neighbor coupling, which are mapped onto standard self-dual $\mathbb{Z}_N$ clock models. For $3\leq N\leq 6$ we match the numerical results to the known conformal field theory(CFT) identification. We then analyze in detail the phase diagram of a $N=3$ chain with both nearest and next-nearest neighbor hopping and six critical phases with central charges being $4/5$, 1 or 2 are found. We find continuous phase transitions between $c=1$ and $c=2$ phases, while the phase transition between $c=4/5$ and $c=1$ is conjectured to be of Kosterlitz-Thouless type.

cond-mat.str-el↗

arXiv2014

Chiral projected entangled-pair state with topological order

We show that projected entangled-pair states (PEPS) can describe chiral topologically ordered phases. For that, we construct a simple PEPS for spin-1/2 particles in a two-dimensional lattice. We reveal a symmetry in the local projector of the PEPS that gives rise to the global topological character. We also extract characteristic quantities of the edge conformal field theory using the bulk-boundary correspondence.

cond-mat.str-el↗

arXiv2014

DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection

In this paper, we propose multi-stage and deformable deep convolutional neural networks for object detection. This new deep learning object detection diagram has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty. With the proposed multi-stage training strategy, multiple classifiers are jointly optimized to process samples at different difficulty levels. A new pre-training strategy is proposed to learn feature representations more suitable for the object detection task and with good generalization capability. By changing the net structures, training strategies, adding and removing some key components in the detection pipeline, a set of models with large diversity are obtained, which significantly improves the effectiveness of modeling averaging. The proposed approach ranked \#2 in ILSVRC 2014. It improves the mean averaged precision obtained by RCNN, which is the state-of-the-art of object detection, from $31\%$ to $45\%$. Detailed component-wise analysis is also provided through extensive experimental evaluation.

cs.CV↗

arXiv2014

Hadronic $b^\prime$ search at the LHC with top and W taggers

We study the sensitivity of a down type quark $b^{\prime}$ via process $pp\rightarrow b^{\prime}\bar{b^{\prime}} \rightarrow tW^-\bar{t}W^+$ using jet substructure methods at the LHC with the collision energy $\sqrt{s}=14$ TeV. We consider the case that the $b^\prime$ is heavy (say from 800 GeV to 1500 GeV) and concentrate on the feasibility of the full hadronic mode. Both top tagger (the HEP top tagger) and W tagger (the CMS W-tagging) are used to reconstruct all objects in the final states. In order to suppress huge SM background events and take into account various cases with different number of boosted objects, we propose a comprehensive reconstruction procedure so as to extract the most crucial observables of the signal events. When $b^\prime$ mass is 1 TeV, it is found that with a 200 fb$^{-1}$ dataset, the LHC may be able to detect the $b^\prime$ with a significance up to $10$ or better. With a 3000 $fb^{-1}$ dataset, the LHC may be able to probe the $b^\prime$ with a mass around up to 2 TeV, only by using the hadronic mode.

hep-ph↗

arXiv2014

Topology and Criticality in Resonating Affleck-Kennedy-Lieb-Tasaki loop Spin Liquid States

We exploit a natural Projected Entangled-Pair State (PEPS) representation for the resonating Affleck-Kennedy-Lieb-Tasaki loop (RAL) state. By taking advantage of PEPS-based analytical and numerical methods, we characterize the RAL states on various two-dimensional lattices. On square and honeycomb lattices, these states are critical since the dimer-dimer correlations decay as a power law. On kagome lattice, the RAL state has exponentially decaying correlation functions, supporting the scenario of a gapped spin liquid. We provide further evidence that the RAL state on the kagome lattice is a $\mathbb{Z}_2$ spin liquid, by identifying the four topological sectors and computing the topological entropy. Furthermore, we construct a one-parameter family of PEPS states interpolating between the RAL state and a short-range Resonating Valence Bond state and find a critical point, consistent with the fact that the two states belong to two different phases. We also perform a variational study of the spin-1 kagome Heisenberg model using this one-parameter PEPS.

cond-mat.str-el↗

arXiv2014

Determination of charged Higgs couplings at the LHC

We review the study of the charged Higgs and top quark associated production at the LHC with the presence of an additional scalar doublet. Top quark spin effects are related to the Higgs fermion couplings through this process. The angular distributions with respect to top quark spin turn out to be distinctive observables to study the $Htb$ interaction in different models.

hep-ph↗

arXiv2014

Associated production of the heavy charged gauge boson ${W_{H}}$ and a top quark at LHC

In the context of topflavor seesaw model, we study the production of the heavy charged gauge boson ${W_{H}}$ associated with a top quark at the LHC. Focusing on the searching channel $pp\rightarrow tW_H\rightarrow t\bar{t}b \rightarrow lνjjbbb$, we carry out a full simulation of the signal and the relevant standard model backgrounds. The kinematical distributions of final states are presented. It is found that the backgrounds can be significantly suppressed by sets of kinematic cuts, and the signal of the heavy charged boson might be detected at the LHC with $\sqrt{s}=14$ TeV. With a integrated luminosity of $\LL= $ 100 $fb^{-1}$, a $8.3 σ$ signal significance can be achieved for $m_{W_H}=1.6$ TeV.

hep-ph↗

arXiv2013

Charged Higgs Production in Association With W^{\pm} at Large Hadron Colliders

Many new physics models beyond the standard model, such as the littlest higgs models and the left right twin higgs models, predict the existence of the large charged higgs couplings H^-q\bar b and H^+b\bar q, where q=t or the new vector-like heavy quark T; On the other hand, some new physics models like the littlest higgs also predict the gauge-higgs couplings. Such couplings may have rich collider phenomenology. We focus our attention on these couplings induced by the littlest higgs models and the left right twin higgs models models and consider their contributions to the production cross section for W^\pm H^\mp production at the large hadron colliders. We find that the cross sections, in the littlest higgs models, on the parton level gg \to W^\pm H^\mp and q\bar q \to W^\pm H^\mp (q=u,d,s,c,b) may reach tens of several dozen femtobarns in reasonable parameters space at the collision energy of 14 TeV and that the total cross section can even reach a few hundred femtobarns in certain favored space. While in the left right twin higgs models, the production rates are basically one order lower than these in littlest higgs. Therefore, due to the large cross sections of that in the littlest higgs, it may be possible to probe the charged higgs via this process in a large parameter space.

hep-ph↗