arXiv subjects

Jian Zhang

Publications and source records attributed to Jian Zhang.

At least 649 records · Page 36Linked to original sources

arXiv2019

Deep Reinforcement Learning for Image Hashing

Deep hashing methods have received much attention recently, which achieve promising results by taking advantage of the strong representation power of deep networks. However, most existing deep hashing methods learn a whole set of hashing functions independently, while ignore the correlations between different hashing functions that can promote the retrieval accuracy greatly. Inspired by the sequential decision ability of deep reinforcement learning, we propose a new Deep Reinforcement Learning approach for Image Hashing (DRLIH). Our proposed DRLIH approach models the hashing learning problem as a sequential decision process, which learns each hashing function by correcting the errors imposed by previous ones and promotes retrieval accuracy. To the best of our knowledge, this is the first work to address hashing problem from deep reinforcement learning perspective. The main contributions of our proposed DRLIH approach can be summarized as follows: (1) We propose a deep reinforcement learning hashing network. In the proposed network, we utilize recurrent neural network (RNN) as agents to model the hashing functions, which take actions of projecting images into binary codes sequentially, so that the current hashing function learning can take previous hashing functions' error into account. (2) We propose a sequential learning strategy based on proposed DRLIH. We define the state as a tuple of internal features of RNN's hidden layers and image features, which can reflect history decisions made by the agents. We also propose an action group method to enhance the correlation of hash functions in the same group. Experiments on three widely-used datasets demonstrate the effectiveness of our proposed DRLIH approach.

cs.CV↗

arXiv2019

E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction

Predicting the potential relations between nodes in networks, known as link prediction, has long been a challenge in network science. However, most studies just focused on link prediction of static network, while real-world networks always evolve over time with the occurrence and vanishing of nodes and links. Dynamic network link prediction thus has been attracting more and more attention since it can better capture the evolution nature of networks, but still most algorithms fail to achieve satisfied prediction accuracy. Motivated by the excellent performance of Long Short-Term Memory (LSTM) in processing time series, in this paper, we propose a novel Encoder-LSTM-Decoder (E-LSTM-D) deep learning model to predict dynamic links end to end. It could handle long term prediction problems, and suits the networks of different scales with fine-tuned structure. To the best of our knowledge, it is the first time that LSTM, together with an encoder-decoder architecture, is applied to link prediction in dynamic networks. This new model is able to automatically learn structural and temporal features in a unified framework, which can predict the links that never appear in the network before. The extensive experiments show that our E-LSTM-D model significantly outperforms newly proposed dynamic network link prediction methods and obtain the state-of-the-art results.

cs.SI↗

arXiv2019

Achieving sub-1 Ohm-mm Non-Recess S/D Contact Resistance in GaN HEMTs Utilizing Simple CMOS Compatible La/Ti/Al/Ti Metal Contacts

In this paper, we report the use of lanthanum (La) in S/D contacts of GaN HEMTs, achieving 0.97 Ohm-mm contact resistance without S/D recess. The HEMTs show well-behaved electrical characteristics and satisfactory reliability. Our studies show that La, a CMOS compatible metal, is promising to lower GaN HEMT S/D contact resistance. La's low work function (3.5 eV) is beneficial for reducing the barrier between the metals and GaN. The Ohmic contact formation mechanism involved was shown to be different from conventional Ti/Al films. Spherical-shaped high-La regions formed near the surface during annealing. La diffuses into the AlGaN layer, and the overlap of La and Al peaks is significantly increased compared with that before annealing.

physics.app-ph↗

arXiv2019

Renormalizations in unconventional superconducting states of Ce$_{1-x}$Yb$_{x}$CoIn$_{5}$

We have measured the superconducting penetration depth~$Λ(T)$ in the heavy-fermion/intermediate-valent superconducting alloy series~Ce$_{1-x}$Yb$_x$CoIn$_5$ using transverse-field muon spin relaxation, to study the effect of intermediate-valent Yb doping on Fermi-liquid renormalization. From $Λ(T)$ we determine the superfluid density $ρ_s(T)$, and find that it decreases continuously with increasing nominal Yb concentration~$x$, i.e., with increasing intermediate valence. The temperature-dependent renormalization of the "normal" fluid density~$ρ_N(T) = ρ_s(0) - ρ_s(T)$ in both the heavy-fermion and intermediate valence limits is proportional to the temperature-dependent renormalization of the specific heat. This indicates that the temperature-dependent Fermi-liquid Landau parameters of the superconducting quasiparticles entering the two different physical quantities are the same. These results represent an important advance in understanding of both intermediate valence and heavy-fermion phenomena in superconductors.

cond-mat.str-el↗

arXiv2019

Broken time-reversal symmetry in superconducting Pr$_{1-x}$La$_{x}$Pt$_{4}$Ge$_{12}$

The superconducting state of the filled skutterudite alloy series Pr$_{1-x}$La$_{x}$Pt$_{4}$Ge$_{12}$ has been systematically studied by specific heat, zero-field muon spin relaxation ($μ$SR), and superconducting critical field measurements. An additional inhomogeneous local magnetic field, indicative of broken time-reversal symmetry (TRS), is observed in the superconducting states of the alloys. For $x \lesssim 0.5$ the broken-TRS phase sets in below a temperature $T_m$ distinctly lower than the superconducting transition temperature $T_c$. For $x \gtrsim 0.5$ $T_m \approx T_c$. The local field strength decreases as $x \to 1$, where LaPt$_{4}$Ge$_{12}$ is characterized by conventional pairing. The lower critical field $H_{c1}(T)$ of PrPt$_{4}$Ge$_{12}$ shows the onset of a second quadratic temperature region below $T_q \sim T_m$. Upper critical field $H_{c2}(T)$ measurements suggest multiband superconductivity, and point gap nodes are consistent with the specific heat data. In Pr$_{1-x}$La$_{x}$Pt$_{4}$Ge$_{12}$ only a single specific heat discontinuity is observed at $T_c$, in contrast to the second jump seen in PrOs$_{4}$Sb$_{12}$ below $T_c$. These results suggest that superconductivity in PrPt$_{4}$Ge$_{12}$ is characterized by a complex order parameter.

cond-mat.supr-con↗

arXiv2019

Assessment of central serous chorioretinopathy (CSC) depicted on color fundus photographs using deep Learning

To investigate whether and to what extent central serous chorioretinopathy (CSC) depicted on color fundus photographs can be assessed using deep learning technology. We collected a total of 2,504 fundus images acquired on different subjects. We verified the CSC status of these images using their corresponding optical coherence tomography (OCT) images. A total of 1,329 images depicted CSC. These images were preprocessed and normalized. This resulting dataset was randomly split into three parts in the ratio of 8:1:1 respectively for training, validation, and testing purposes. We used the deep learning architecture termed InceptionV3 to train the classifier. We performed nonparametric receiver operating characteristic (ROC) analyses to assess the capability of the developed algorithm to identify CSC. The Kappa coefficient between the two raters was 0.48 (p < 0.001), while the Kappa coefficients between the computer and the two raters were 0.59 (p < 0.001) and 0.33 (p < 0.05).Our experiments showed that the computer algorithm based on deep learning can assess CSC depicted on color fundus photographs in a relatively reliable and consistent way.

cs.CV↗

arXiv2018

Place recognition: An Overview of Vision Perspective

Place recognition is one of the most fundamental topics in computer vision and robotics communities, where the task is to accurately and efficiently recognize the location of a given query image. Despite years of wisdom accumulated in this field, place recognition still remains an open problem due to the various ways in which the appearance of real-world places may differ. This paper presents an overview of the place recognition literature. Since condition invariant and viewpoint invariant features are essential factors to long-term robust visual place recognition system, We start with traditional image description methodology developed in the past, which exploit techniques from image retrieval field. Recently, the rapid advances of related fields such as object detection and image classification have inspired a new technique to improve visual place recognition system, i.e., convolutional neural networks (CNNs). Thus we then introduce recent progress of visual place recognition system based on CNNs to automatically learn better image representations for places. Eventually, we close with discussions and future work of place recognition.

cs.CV↗

arXiv2018

Combinatorial construction of Gelfand-Tsetlin modules for $\mathfrak{gl}_n$

We propose a new effective method of constructing explicitly Gelfand -Tsetlin modules for $\mathfrak{gl}_n$. We obtain a large family of simple modules that have a basis consisting of Gelfand-Tsetlin tableaux, the action of the Lie algebra is given by the Gelfand-Tsetlin formulas and with all Gelfand-Tsetlin multiplicities equal $1$. As an application of our construction we prove necessary and sufficient condition for the Gelfand and Graev's continuation construction to define a module which was conjectured by Lemire and Patera.

math.RT↗

arXiv2018

Quantum Howe duality and invariant polynomials

We construct two examples of q-deformed classical Howe dual pairs (sl(2,C), sl(2,C)) and (sl(2,C), sl(n,C)). Moreover, we obtain a noncommutative version of the first fundamental theorem of classical invariant theory. Our approach to these duality differs from the paper of Lehrer-Zhang-Zhang. Furthermore, we solve the tensor product decomposition problem for Verma modules over U_q(sl(2,C)) provided q is not a root of unity.

math.QA↗

arXiv2018

Scene Text Recognition from Two-Dimensional Perspective

Inspired by speech recognition, recent state-of-the-art algorithms mostly consider scene text recognition as a sequence prediction problem. Though achieving excellent performance, these methods usually neglect an important fact that text in images are actually distributed in two-dimensional space. It is a nature quite different from that of speech, which is essentially a one-dimensional signal. In principle, directly compressing features of text into a one-dimensional form may lose useful information and introduce extra noise. In this paper, we approach scene text recognition from a two-dimensional perspective. A simple yet effective model, called Character Attention Fully Convolutional Network (CA-FCN), is devised for recognizing the text of arbitrary shapes. Scene text recognition is realized with a semantic segmentation network, where an attention mechanism for characters is adopted. Combined with a word formation module, CA-FCN can simultaneously recognize the script and predict the position of each character. Experiments demonstrate that the proposed algorithm outperforms previous methods on both regular and irregular text datasets. Moreover, it is proven to be more robust to imprecise localizations in the text detection phase, which are very common in practice.

cs.CV↗

arXiv2018

Possible gapless spin liquid in a rare-earth kagomé lattice magnet Tm$_{3}$Sb$_{3}$Zn$_{2}$O$_{14}$

We report the thermodynamic and muon spin relaxation ($μ$SR) evidences for a possible gapless spin liquid in Tm$_{3}$Sb$_{3}$Zn$_{2}$O$_{14}$, with the rare-earth ions Tm$^{3+}$ forming a two-dimensional kagomé lattice. We extract the magnetic specific heat of Tm$_{3}$Sb$_{3}$Zn$_{2}$O$_{14}$ by subtracting the phonon contribution of the non-magnetic isostructural material La$_{3}$Sb$_{3}$Zn$_{2}$O$_{14}$. We obtain a clear linear-$T$ temperature dependence of magnetic specific heat at low temperatures, with the heat-capacity coefficient $γ$ = 26.6(1) mJ mol-Tm$^{-1}$ K$^{-2}$ in the zero temperature limit. No long-range magnetic order was observed down to 0.35 K in the heat capacity measurement. A broad hump around 9 K was observed in the zero field magnetic specific heat and is gradually suppressed in the magnetic fields that also create a spin gap in the specific heat. The absence of magnetic order is further confirmed by the $μ$SR measurement down to 20 mK. We find that the spin-lattice relaxation time remains constant down to the lowest temperatures. We point out that the physics in Tm$_{3}$Sb$_{3}$Zn$_{2}$O$_{14}$ is fundamentally different from the Cu-based herbertsmithite and propose spin liquid ground states with non-Kramers doublets on the kagomé lattice to account for the experimental results.

cond-mat.str-el↗

arXiv2018

Performance assessment of the deep learning technologies in grading glaucoma severity

Objective: To validate and compare the performance of eight available deep learning architectures in grading the severity of glaucoma based on color fundus images. Materials and Methods: We retrospectively collected a dataset of 5978 fundus images and their glaucoma severities were annotated by the consensus of two experienced ophthalmologists. We preprocessed the images to generate global and local regions of interest (ROIs), namely the global field-of-view images and the local disc region images. We then divided the generated images into three independent sub-groups for training, validation, and testing purposes. With the datasets, eight convolutional neural networks (CNNs) (i.e., VGG16, VGG19, ResNet, DenseNet, InceptionV3, InceptionResNet, Xception, and NASNetMobile) were trained separately to grade glaucoma severity, and validated quantitatively using the area under the receiver operating characteristic (ROC) curve and the quadratic kappa score. Results: The CNNs, except VGG16 and VGG19, achieved average kappa scores of 80.36% and 78.22% when trained from scratch on global and local ROIs, and 85.29% and 82.72% when fine-tuned using the pre-trained weights, respectively. VGG16 and VGG19 achieved reasonable accuracy when trained from scratch, but they failed when using pre-trained weights for global and local ROIs. Among these CNNs, the DenseNet had the highest classification accuracy (i.e., 75.50%) based on pre-trained weights when using global ROIs, as compared to 65.50% when using local ROIs. Conclusion: The experiments demonstrated the feasibility of the deep learning technology in grading glaucoma severity. In particular, global field-of-view images contain relatively richer information that may be critical for glaucoma assessment, suggesting that we should use the entire field-of-view of a fundus image for training a deep learning network.

cs.CV↗

arXiv2018

Temperature-Controlled Slip of Polymer Melts on Ideal Substrates

The temperature dependence of the hydrodynamic boundary condition between a PDMS melt and two different non-attractive surfaces made of either an OTS (octadecyltrichlorosilane) self-assembled monolayer (SAM) or a grafted layer of short PDMS chains has been characterized. A slip length proportional to the fluid viscosity is observed on both surfaces. The slip temperature dependence is deeply influenced by the surfaces. The viscous stress exerted by the polymer liquid on the surface is observed to follow exactly the same temperature dependences as the friction stress of a cross-linked elastomer sliding on the same surfaces. Far above the glass transition temperature, these observations are rationalized in the framework of a molecular model based on activation energies: increase or decrease of the slip length with increasing temperatures can be observed depending on how the activation energy of the bulk viscosity compares to that of the interfacial Navier's friction coefficient.

cond-mat.soft↗

arXiv2018

Deep Bi-Dense Networks for Image Super-Resolution

This paper proposes Deep Bi-Dense Networks (DBDN) for single image super-resolution. Our approach extends previous intra-block dense connection approaches by including novel inter-block dense connections. In this way, feature information propagates from a single dense block to all subsequent blocks, instead of to a single successor. To build a DBDN, we firstly construct intra-dense blocks, which extract and compress abundant local features via densely connected convolutional layers and compression layers for further feature learning. Then, we use an inter-block dense net to connect intra-dense blocks, which allow each intra-dense block propagates its own local features to all successors. Additionally, our bi-dense construction connects each block to the output, alleviating the vanishing gradient problems in training. The evaluation of our proposed method on five benchmark datasets shows that our DBDN outperforms the state of the art in SISR with a moderate number of network parameters.

cs.CV↗

arXiv2018

Equivalent Model of Transient Gyrotron Cathode Response

The gyrotron is the high power millimeter wave source in the electron cyclotron resonance heating system for the tokamak. The cathode power supply is one of the most important ancillary devices for gyrotron. Some interesting transient phenomena about the cathode voltage and the cathode current was found in the gyrotron operation in the electron cyclotron resonance heating system for the Experimental Advanced Superconducting Tokamak. The cathode voltage drops to about 10% of the original value by about 90 μs in the overcurrent case, which is much longer than the 25 μs in the normal case. In order to explain these phenomena, an equivalent circuit model of the magnetron injection gun was proposed. The equivalent circuit is composed of parallel resistors and capacitors, and it can explain the test results very well. Using the equivalent circuit model to analyze gyrotrons may become an effective means of developing and operating an electron cyclotron heating system.

physics.ins-det↗

arXiv2018

Multi-pseudo Regularized Label for Generated Data in Person Re-Identification

Sufficient training data normally is required to train deeply learned models. However, due to the expensive manual process for labelling large number of images, the amount of available training data is always limited. To produce more data for training a deep network, Generative Adversarial Network (GAN) can be used to generate artificial sample data. However, the generated data usually does not have annotation labels. To solve this problem, in this paper, we propose a virtual label called Multi-pseudo Regularized Label (MpRL) and assign it to the generated data. With MpRL, the generated data will be used as the supplementary of real training data to train a deep neural network in a semi-supervised learning fashion. To build the corresponding relationship between the real data and generated data, MpRL assigns each generated data a proper virtual label which reflects the likelihood of the affiliation of the generated data to pre-defined training classes in the real data domain. Unlike the traditional label which usually is a single integral number, the virtual label proposed in this work is a set of weight-based values each individual of which is a number in (0,1] called multi-pseudo label and reflects the degree of relation between each generated data to every pre-defined class of real data. A comprehensive evaluation is carried out by adopting two state-of-the-art convolutional neural networks (CNNs) in our experiments to verify the effectiveness of MpRL. Experiments demonstrate that by assigning MpRL to generated data, we can further improve the person re-ID performance on five re-ID datasets, i.e., Market-1501, DukeMTMC-reID, CUHK03, VIPeR, and CUHK01. The proposed method obtains +6.29%, +6.30%, +5.58%, +5.84%, and +3.48% improvements in rank-1 accuracy over a strong CNN baseline on the five datasets respectively, and outperforms state-of-the-art methods.

cs.CV↗

arXiv2018

Determinations of form factors for semileptonic $D\rightarrow K$ decays and leptoquark constraints

By analyzing all existing measurements for $ D\rightarrow K \ell^+ ν_{\ell} $ ( $\ell=e,\ μ$ ) decays, we find that the determinations of both the vector form factor $f_+^K(q^2)$ and scalar form factor $f_0^K(q^2)$ for semileptonic $D\rightarrow K$ decays from these measurements are feasible. By taking the parameterization of the one order series expansion of the $f_+^K(q^2)$ and $f_0^K(q^2)$, $f_+^K(0)|V_{cs}|$ is determined to be $0.7182\pm0.0029$, and the shape parameters of $f_+^K(q^2)$ and $f_0^K(q^2)$ are $r_{+1}=-2.16\pm0.007$ and $r_{01}=0.89\pm3.27$, respectively. Combining with the average $f_+^K(0)$ of $N_f=2+1$ and $N_f=2+1+1$ lattice calculaltion, the $|V_{cs}|$ is extracted to be $0.964\pm0.004\pm0.019$ where the first error is experimental and the second theoretical. Alternatively, the $f_+^K(0)$ is extracted to be $0.7377\pm0.003\pm0.000$ by taking the $|V_{cs}|$ as the value from the global fit with the unitarity constraint of the CKM matrix. Moreover, using the obtained form factors by $N_f=2+1+1$ lattice QCD, we re-analyze these measurements in the context of new physics. Constraints on scalar leptoquarks are obtained for different final states of semileptonic $D \rightarrow K$ decays.

hep-ph↗

arXiv2018

Ultrahigh Magnetic Fields Produced by Shearing Carbon Nanotubes

In laboratories, ultrahigh magnetic fields are usually produced with very large currents through superconducting, resistive or hybrid magnets, which require extreme conditions, such as low temperature, huge cooling water or tens of megawatts of power. In this work we report that when single walled carbon nanotubes (SWNTs) are cut, there are magnetic moments at the shearing end of SWNTs. The average magnetic moment is found to be 41.5+-9.8uB per carbon atom in the end states with a width of 1 nm at temperature of 300.0K, suggesting ultrahigh magnetic fields can be produced. The dangling sigma and pi bonds of the carbon atoms at the shearing ends play important roles for this unexpectedly high magnetic moments because the oxidation temperature of cut SWNTs is found to be as low as 312 in dry air. Producing ultrahigh magnetic field with SWNTs has the advantage of working at higher working temperature and with low energy consumption, suggesting great potentials of applications.

cond-mat.mtrl-sci↗