arXiv subjects

Shuo Yang

Publications and source records attributed to Shuo Yang.

At least 397 records · Page 22Linked to original sources

arXiv2017

Algorithm-Directed Crash Consistence in Non-Volatile Memory for HPC

Fault tolerance is one of the major design goals for HPC. The emergence of non-volatile memories (NVM) provides a solution to build fault tolerant HPC. Data in NVM-based main memory are not lost when the system crashes because of the non-volatility nature of NVM. However, because of volatile caches, data must be logged and explicitly flushed from caches into NVM to ensure consistence and correctness before crashes, which can cause large runtime overhead. In this paper, we introduce an algorithm-based method to establish crash consistence in NVM for HPC applications. We slightly extend application data structures or sparsely flush cache blocks, which introduce ignorable runtime overhead. Such extension or cache flushing allows us to use algorithm knowledge to \textit{reason} data consistence or correct inconsistent data when the application crashes. We demonstrate the effectiveness of our method for three algorithms, including an iterative solver, dense matrix multiplication, and Monte-Carlo simulation. Based on comprehensive performance evaluation on a variety of test environments, we demonstrate that our approach has very small runtime overhead (at most 8.2\% and less than 3\% in most cases), much smaller than that of traditional checkpoint, while having the same or less recomputation cost.

cs.DC↗

arXiv2017

Residual Attention Network for Image Classification

In this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion. Our Residual Attention Network is built by stacking Attention Modules which generate attention-aware features. The attention-aware features from different modules change adaptively as layers going deeper. Inside each Attention Module, bottom-up top-down feedforward structure is used to unfold the feedforward and feedback attention process into a single feedforward process. Importantly, we propose attention residual learning to train very deep Residual Attention Networks which can be easily scaled up to hundreds of layers. Extensive analyses are conducted on CIFAR-10 and CIFAR-100 datasets to verify the effectiveness of every module mentioned above. Our Residual Attention Network achieves state-of-the-art object recognition performance on three benchmark datasets including CIFAR-10 (3.90% error), CIFAR-100 (20.45% error) and ImageNet (4.8% single model and single crop, top-5 error). Note that, our method achieves 0.6% top-1 accuracy improvement with 46% trunk depth and 69% forward FLOPs comparing to ResNet-200. The experiment also demonstrates that our network is robust against noisy labels.

cs.CV↗

arXiv2017

Synthesis of Borophene Nanoribbons on Ag(110) Surface

We present the successful synthesis of single-atom-thick borophene nanoribbons (BNRs) by self-assembly of boron on Ag(110) surface. The scanning tunneling microscopy (STM) studies reveal high quality BNRs: all the ribbons are along the [-110] direction of Ag(110), and can run across the steps on the surface. The width of ribbons is distributed in a narrow range around 10.3 nm. High resolution STM images revealed four ordered surface structures in BNRs. Combined with DFT calculations, we found that all the four structures of boron nanoribbons consist of the boron chains with different width, separated by hexagonal hole arrays. The successful synthesis of BNRs enriches the low dimensional allotrope of boron and may promote further applications of borophene

cond-mat.mtrl-sci↗

arXiv2017

$h\toμτ$ and muon g-2 in the alignment limit of two-Higgs-doublet model

We examine the $h\to μτ$ and muon g-2 in the exact alignment limit of two-Higgs-doublet model. In this case, the couplings of the SM-like Higgs to the SM particles are the same as the Higgs couplings in the SM at the tree level, and the tree-level lepton-flavor-violating coupling $hμτ$ is absent. We assume the lepton-flavor-violating $μτ$ excess observed by CMS to be respectively from the other neutral Higgses, $H$ and $A$, which almost degenerates with the SM-like Higgs at the 125 GeV. After imposing the relevant theoretical constraints and experimental constraints from the precision electroweak data, $B$-meson decays, $τ$ decays and Higgs searches, we find that the muon g-2 anomaly and $μτ$ excess favor the small lepton Yukawa coupling and top Yukawa coupling of the non-SM-like Higgs around 125 GeV, and the lepton-flavor-violating coupling is sensitive to another heavy neutral Higgs mass. In addition, if the $μτ$ excess is from $H$ around 125 GeV, the experimental data of the heavy Higgs decaying into $μτ$ favor $m_A>230$ GeV for a relatively large $H\bar{t}t$ coupling.

hep-ph↗

arXiv2017

Hand3D: Hand Pose Estimation using 3D Neural Network

We propose a novel 3D neural network architecture for 3D hand pose estimation from a single depth image. Different from previous works that mostly run on 2D depth image domain and require intermediate or post process to bring in the supervision from 3D space, we convert the depth map to a 3D volumetric representation, and feed it into a 3D convolutional neural network(CNN) to directly produce the pose in 3D requiring no further process. Our system does not require the ground truth reference point for initialization, and our network architecture naturally integrates both local feature and global context in 3D space. To increase the coverage of the hand pose space of the training data, we render synthetic depth image by transferring hand pose from existing real image datasets. We evaluation our algorithm on two public benchmarks and achieve the state-of-the-art performance. The synthetic hand pose dataset will be available.

cs.CV↗

arXiv2017

Loop optimization for tensor network renormalization

We introduce a tensor renormalization group scheme for coarse-graining a two-dimensional tensor network that can be successfully applied to both classical and quantum systems on and off criticality. The key innovation in our scheme is to deform a 2D tensor network into small loops and then optimize the tensors on each loop. In this way, we remove short-range entanglement at each iteration step and significantly improve the accuracy and stability of the renormalization flow. We demonstrate our algorithm in the classical Ising model and a frustrated 2D quantum model.

cond-mat.str-el↗

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↗