arXiv subjects

Jun Zhu

Publications and source records attributed to Jun Zhu.

548 records · Page 31Linked to original sources

arXiv2011

Jordan higher all-derivable points in triangular algebras

Let ${\mathcal{T}}$ be a triangular algebra. We say that $D=\{D_{n}: n\in N\}\subseteq L({\mathcal{T}})$ is a Jordan higher derivable mapping at $G$ if $D_{n}(ST+TS)=\sum_{i+j=n}(D_{i}(S)D_{j}(T)+D_{i}(T)D_{j}(S))$ for any $S,T\in {\mathcal{T}}$ with $ST=G$. An element $G\in {\mathcal{T}}$ is called a Jordan higher all-derivable point of ${\mathcal{T}}$ if every Jordan higher derivable linear mapping $D=\{D_{n}\}_{n\in N}$ at $G$ is a higher derivation. In this paper, under some mild conditions on ${\mathcal{T}}$, we prove that some elements of ${\mathcal{T}}$ are Jordan higher all-derivable points. This extends some results in [6] to the case of Jordan higher derivations.

math.OA↗

arXiv2011

Characterizations of all-derivable points in nest algebras

Let $\mathcal{A}$ be an operator algebra on a Hilbert space. We say that an element $G\in {\mathcal{A}}$ is an all-derivable point of ${\mathcal{A}}$ if every derivable linear mapping $ϕ$ at $G$ (i.e. $ϕ(ST)=ϕ(S)T+Sϕ(T)$ for any $S,T\in alg{\mathcal{N}}$ with $ST=G$) is a derivation. Suppose that $\mathcal{N}$ is a nontrivial complete nest on a Hilbert space $H$. We show in this paper that $G\in {alg\mathcal{N}}$ is an all-derivable point if and only if $G\neq0$.

math.OA↗

arXiv2010

Photoluminescence from nanocrystalline graphite monofluoride

We synthesize and study the structural and optical properties of nanocrystalline graphene monofluoride and graphite monofluoride, which are carbon-based wide bandgap materials. Using laser excitations 2.41 - 5.08 eV, we identify six emission modes of graphite monofluoride, spanning the visible spectrum from red to violet. The energy and linewidth of the modes point to defect-induced midgap states as the source of the photoemission. We discuss possible candidates. Our findings open the window to electro-optical applications of graphene fluoride.

cond-mat.mtrl-sci↗

arXiv2009

MedLDA: A General Framework of Maximum Margin Supervised Topic Models

Supervised topic models utilize document's side information for discovering predictive low dimensional representations of documents. Existing models apply the likelihood-based estimation. In this paper, we present a general framework of max-margin supervised topic models for both continuous and categorical response variables. Our approach, the maximum entropy discrimination latent Dirichlet allocation (MedLDA), utilizes the max-margin principle to train supervised topic models and estimate predictive topic representations that are arguably more suitable for prediction tasks. The general principle of MedLDA can be applied to perform joint max-margin learning and maximum likelihood estimation for arbitrary topic models, directed or undirected, and supervised or unsupervised, when the supervised side information is available. We develop efficient variational methods for posterior inference and parameter estimation, and demonstrate qualitatively and quantitatively the advantages of MedLDA over likelihood-based topic models on movie review and 20 Newsgroups data sets.

stat.ML↗

arXiv2009

The Deposition of High-Quality HfO2 on Graphene and the Effect of Remote Oxide Phonon Scattering

We demonstrate the atomic layer deposition of high-quality HfO2 film on graphene and report the magnitude of remote oxide phonon (ROP) scattering in dual-oxide graphene transistors. Top gates with 30 nm HfO2 oxide layer exhibit excellent doping capacity of greater than 1.5x10^(13)/cm^(2). The carrier mobility in HfO2-covered graphene reaches 20,000 cm^(2)/Vs at low temperature, which is the highest among oxide-covered graphene and compares to that of pristine samples. The temperature-dependent resistivity exhibits the effect of ROP scattering from both the SiO2 substrate and the HfO2 over-layer. At room temperature, surface phonon modes of the HfO2 film centered at 54 meV dominate and limit the carrier mobility to ~20,000 cm^(2)/Vs. Our results highlight the important choice of oxide in graphene devices.

cond-mat.mtrl-sci↗

arXiv2009

Maximum Entropy Discrimination Markov Networks

In this paper, we present a novel and general framework called {\it Maximum Entropy Discrimination Markov Networks} (MaxEnDNet), which integrates the max-margin structured learning and Bayesian-style estimation and combines and extends their merits. Major innovations of this model include: 1) It generalizes the extant Markov network prediction rule based on a point estimator of weights to a Bayesian-style estimator that integrates over a learned distribution of the weights. 2) It extends the conventional max-entropy discrimination learning of classification rule to a new structural max-entropy discrimination paradigm of learning the distribution of Markov networks. 3) It subsumes the well-known and powerful Maximum Margin Markov network (M$^3$N) as a special case, and leads to a model similar to an $L_1$-regularized M$^3$N that is simultaneously primal and dual sparse, or other types of Markov network by plugging in different prior distributions of the weights. 4) It offers a simple inference algorithm that combines existing variational inference and convex-optimization based M$^3$N solvers as subroutines. 5) It offers a PAC-Bayesian style generalization bound. This work represents the first successful attempt to combine Bayesian-style learning (based on generative models) with structured maximum margin learning (based on a discriminative model), and outperforms a wide array of competing methods for structured input/output learning on both synthetic and real data sets.

stat.ML↗

arXiv2006

Deterministic Quantum Key Distribution Using Gaussian-Modulated Squeezed States

A continuous variable ping-pong scheme, which is utilized to generate deterministically private key, is proposed. The proposed scheme is implemented physically by using Gaussian-modulated squeezed states. The deterministic way, i.e., no basis reconciliation between two parties, leads a two-times efficiency comparing to the standard quantum key distribution schemes. Especially, the separate control mode does not need in the proposed scheme so that it is simpler and more available than previous ping-pong schemes. The attacker may be detected easily through the fidelity of the transmitted signal, and may not be successful in the beam splitter attack strategy.

quant-ph↗

arXiv2006

Resampling methods for spatial regression models under a class of stochastic designs

In this paper we consider the problem of bootstrapping a class of spatial regression models when the sampling sites are generated by a (possibly nonuniform) stochastic design and are irregularly spaced. It is shown that the natural extension of the existing block bootstrap methods for grid spatial data does not work for irregularly spaced spatial data under nonuniform stochastic designs. A variant of the blocking mechanism is proposed. It is shown that the proposed block bootstrap method provides a valid approximation to the distribution of a class of M-estimators of the spatial regression parameters. Finite sample properties of the method are investigated through a moderately large simulation study and a real data example is given to illustrate the methodology.

math.ST↗