arXiv subjects

Jun Zhao

Publications and source records attributed to Jun Zhao.

At least 559 records · Page 31Linked to original sources

arXiv2015

Strong Interplay between Stripe Spin Fluctuations, Nematicity and Superconductivity in FeSe

Elucidating the microscopic origin of nematic order in iron-based superconducting materials is important because the interactions that drive nematic order may also mediate the Cooper pairing. Nematic order breaks fourfold rotational symmetry in the iron plane, which is believed to be driven by either orbital or spin degrees of freedom. However, as the nematic phase often develops at a temperature just above or coincides with a stripe magnetic phase transition, experimentally determining the dominant driving force of nematic order is difficult. Here, we use neutron scattering to study structurally the simplest iron-based superconductor FeSe, which displays a nematic (orthorhombic) phase transition at $T_s=90$ K, but does not order antiferromagnetically. Our data reveal substantial stripe spin fluctuations, which are coupled with orthorhombicity and are enhanced abruptly on cooling to below $T_s$. Moreover, a sharp spin resonance develops in the superconducting state, whose energy (~4 meV) is consistent with an electron boson coupling mode revealed by scanning tunneling spectroscopy, thereby suggesting a spin fluctuation-mediated sign-changing pairing symmetry. By normalizing the dynamic susceptibility into absolute units, we show that the magnetic spectral weight in FeSe is comparable to that of the iron arsenides. Our findings support recent theoretical proposals that both nematicity and superconductivity are driven by spin fluctuations.

cond-mat.supr-con↗

arXiv2015

Crossover of the pairing symmetry from $s$- to $d$-wave in iron pnictide superconductors

One of the major themes in the physics of condensed matter is unconventional superconductivity in correlated electronic systems. The symmetry structure of Cooper pairs is thought to be the key to the understanding of the pairing mechanism of their superconductivity. In iron pnictide superconductors, the precise symmetry of the order parameter as well its evolution with doping remains highly controversial. There is no general consensus on the nature of pairing in iron-based superconductors leaving the perspectives ranging from $s^{++}$ wave, to $s^{\pm}$, and $d$ wave. Here we show that doping induced a sudden change of the pairing symmetry from the $s^{++}$- to $d$-wave in the BaFe$_{2-x}$Ni$_{x}$As$_{2}$ system with an $s^{\pm}$ state near the crossover boundary at $x=0.15$. The presence of the $s^{\pm}$ state is of particular scientific interest because an intermediate phase breaks the pairing symmetry when the $s$- and $d$-wave phases come together to interfere. For $x <$ 0.15, the temperature dependence of the London penetration depth, lower critical field, and specific heat in the superconducting (SC) state provides strong evidence for a two-band $s$-wave order parameter. Upon doping for $x >$ 0.15, we find the temperature and magnetic-field contributions to the specific heat in $T^{2}$ and $\sqrt{H}$, respectively, indicating line nodes in the SC gap. These findings thus clarify the symmetry nature as well as the evolution of the SC order parameter with doping in pnictide superconductors.

cond-mat.supr-con↗

arXiv2015

A curious gap in one-dimensional geometric random graphs between connectivity and the absence of isolated node

One-dimensional geometric random graphs are constructed by distributing $n$ nodes uniformly and independently on a unit interval and then assigning an undirected edge between any two nodes that have a distance at most $r_n$. These graphs have received much interest and been used in various applications including wireless networks. A threshold of $r_n$ for connectivity is known as $r_n^{*} = \frac{\ln n}{n}$ in the literature. In this paper, we prove that a threshold of $r_n$ for the absence of isolated node is $\frac{\ln n}{2 n}$ (i.e., a half of the threshold $r_n^{*}$). Our result shows there is a curious gap between thresholds of connectivity and the absence of isolated node in one-dimensional geometric random graphs; in particular, when $r_n$ equals $\frac{c\ln n}{ n}$ for a constant $c \in( \frac{1}{2}, 1)$, a one-dimensional geometric random graph has no isolated node but is not connected. This curious gap in one-dimensional geometric random graphs is in sharp contrast to the prevalent phenomenon in many other random graphs such as two-dimensional geometric random graphs, Erdős-Rényi graphs, and random intersection graphs, all of which in the asymptotic sense become connected as soon as there is no isolated node.

physics.soc-ph↗

arXiv2015

Random intersection graphs and their applications in security, wireless communication, and social networks

Random intersection graphs have received much interest and been used in diverse applications. They are naturally induced in modeling secure sensor networks under random key predistribution schemes, as well as in modeling the topologies of social networks including common-interest networks, collaboration networks, and actor networks. Simply put, a random intersection graph is constructed by assigning each node a set of items in some random manner and then putting an edge between any two nodes that share a certain number of items. Broadly speaking, our work is about analyzing random intersection graphs, and models generated by composing it with other random graph models including random geometric graphs and Erdős-Rényi graphs. These compositional models are introduced to capture the characteristics of various complex natural or man-made networks more accurately than the existing models in the literature. For random intersection graphs and their compositions with other random graphs, we study properties such as ($k$-)connectivity, ($k$-)robustness, and containment of perfect matchings and Hamilton cycles. Our results are typically given in the form of asymptotically exact probabilities or zero-one laws specifying critical scalings, and provide key insights into the design and analysis of various real-world networks.

cs.DM↗

arXiv2015

Exact Analysis of k-Connectivity in Secure Sensor Networks with Unreliable Links

The Eschenauer--Gligor (EG) random key predistribution scheme has been widely recognized as a typical approach to secure communications in wireless sensor networks (WSNs). However, there is a lack of precise probability analysis on the reliable connectivity of WSNs under the EG scheme. To address this, we rigorously derive the asymptotically exact probability of $k$-connectivity in WSNs employing the EG scheme with unreliable links represented by independent on/off channels, where $k$-connectivity ensures that the network remains connected despite the failure of any $(k-1)$ sensors or links. Our analytical results are confirmed via numerical experiments, and they provide precise guidelines for the design of secure WSNs that exhibit a desired level of reliability against node and link failures.

math.CO↗

arXiv2015

Threshold Functions in Random s-Intersection Graphs

Random $s$-intersection graphs have recently received considerable attention in a wide range of application areas. In such a graph, each vertex is equipped with a set of items in some random manner, and any two vertices establish an undirected edge in between if and only if they have at least $s$ common items. In particular, in a uniform random $s$-intersection graph, each vertex independently selects a fixed number of items uniformly at random from a common item pool, while in a binomial random $s$-intersection graph, each item in some item pool is independently attached to each vertex with the same probability. For binomial/uniform random $s$-intersection graphs, we establish threshold functions for perfect matching containment, Hamilton cycle containment, and $k$-robustness, where $k$-robustness is in the sense of Zhang and Sundaram [IEEE Conf. on Decision & Control '12]. We show that these threshold functions resemble those of classical Erdős-Rényi graphs, where each pair of vertices has an undirected edge independently with the same probability.

physics.soc-ph↗

arXiv2015

k-Connectivity of Random Key Graphs

Random key graphs represent topologies of secure wireless sensor networks that apply the seminal Eschenauer-Gligor random key predistribution scheme to secure communication between sensors. These graphs have received much attention and also been used in diverse application areas beyond secure sensor networks; e.g., cryptanalysis, social networks, and recommender systems. Formally, a random key graph with $n$ nodes is constructed by assigning each node $X_n$ keys selected uniformly at random from a pool of $Y_n$ keys and then putting an undirected edge between any two nodes sharing at least one key. Considerable progress has been made in the literature to analyze connectivity and $k$-connectivity of random key graphs, where $k$-connectivity of a graph ensures connectivity even after the removal of $k$ nodes or $k$ edges. Yet, it still remains an open question for $k$-connectivity in random key graphs under $X_n \geq 2$ and $X_n = o(\sqrt{\ln n})$ (the case of $X_n=1$ is trivial). In this paper, we answer the above problem by providing an exact analysis of $k$-connectivity in random key graphs under $X_n \geq 2$.

physics.soc-ph↗

arXiv2015

Monotone Increasing Properties and Their Phase Transitions in Uniform Random Intersection Graphs

Uniform random intersection graphs have received much interest and been used in diverse applications. A uniform random intersection graph with $n$ nodes is constructed as follows: each node selects a set of $K_n$ different items uniformly at random from the same pool of $P_n$ distinct items, and two nodes establish an undirected edge in between if and only if they share at least one item. For such graph denoted by $G(n, K_n, P_n)$, we present the following results in this paper. First, we provide an exact analysis on the probabilities of $G(n, K_n, P_n)$ having a perfect matching and having a Hamilton cycle respectively, under $P_n = ω\big(n (\ln n)^5\big)$ (all asymptotic notation are understood with $n \to \infty$). The analysis reveals that just like ($k$-)connectivity shown in prior work, for both properties of perfect matching containment and Hamilton cycle containment, $G(n, K_n, P_n)$ also exhibits phase transitions: for each property above, as $K_n$ increases, the limit of the probability that $G(n, K_n, P_n)$ has the property increases from $0$ to $1$. Second, we compute the phase transition widths of $G(n, K_n, P_n)$ for $k$-connectivity (KC), perfect matching containment (PMC), and Hamilton cycle containment (HCC), respectively. For a graph property $R$ and a positive constant $a < \frac{1}{2}$, with the phase transition width $d_n(R, a)$ defined as the difference between the minimal $K_n$ ensuring $G(n, K_n, P_n)$ having property $R$ with probability at least $1-a$ or $a$, we show for any positive constants $a<\frac{1}{2}$ and $k$: (i) If $P_n=Ω(n)$ and $P_n=o(n\ln n)$, then $d_n(KC, a)$ is either $0$ or $1$ for each $n$ sufficiently large. (ii) If $P_n=Θ(n\ln n)$, then $d_n(KC, a)=Θ(1)$. (iii) If $P_n=ω(n\ln n)$, then $d_n(KC, a)=ω(1)$. (iv) If $P_n=ω\big(n (\ln n)^5\big)$, $d_n(PMC, a)$ and $d_n(HCC, a)$ are both $ω(1)$.

physics.soc-ph↗

arXiv2015

Nodeless superconductivity in the presence of spin-density wave in pnictide superconductors: The case of BaFe$_{2-x}$Ni$_{x}$As$_{2}$

The characteristics of Fe-based superconductors are manifested in their electronic, magnetic properties, and pairing symmetry of the Cooper pair, but the latter remain to be explored. Usually in these materials, superconductivity coexists and competes with magnetic order, giving unconventional pairing mechanisms. We report on the results of the bulk magnetization measurements in the superconducting state and the low-temperature specific heat down to 0.4 K for BaFe$_{2-x}$Ni$_{x}$As$_{2}$ single crystals. The {electronic} specific heat displays a pronounced anomaly at the superconducting transition temperature and a small residual part {at low temperatures in the superconducting state}. The normal-state Sommerfeld coefficient increases with Ni doping for $x$ = 0.092, 0.096, and 0.10, which illustrates the competition between magnetism and superconductivity. Our analysis of the temperature dependence of the superconducting-state specific heat and the London penetration depth provides strong evidence for a two-band $s$-wave order parameter. Further, the data of the London penetration depth calculated from the lower critical field follow an exponential temperature dependence, characteristic of a fully gapped superconductor. These observations clearly show that the superconducting gap in the nearly optimally doped compounds is nodeless.

cond-mat.supr-con↗

arXiv2014

On $k$-connectivity and minimum vertex degree in random $s$-intersection graphs

Random $s$-intersection graphs have recently received much interest in a wide range of application areas. Broadly speaking, a random $s$-intersection graph is constructed by first assigning each vertex a set of items in some random manner, and then putting an undirected edge between all pairs of vertices that share at least $s$ items (the graph is called a random intersection graph when $s=1$). A special case of particular interest is a uniform random $s$-intersection graph, where each vertex independently selects the same number of items uniformly at random from a common item pool. Another important case is a binomial random $s$-intersection graph, where each item from a pool is independently assigned to each vertex with the same probability. Both models have found numerous applications thus far including cryptanalysis, and the modeling of recommender systems, secure sensor networks, online social networks, trust networks and small-world networks (uniform random $s$-intersection graphs), as well as clustering analysis, classification, and the design of integrated circuits (binomial random $s$-intersection graphs). In this paper, for binomial/uniform random $s$-intersection graphs, we present results related to $k$-connectivity and minimum vertex degree. Specifically, we derive the asymptotically exact probabilities and zero-one laws for the following three properties: (i) $k$-vertex-connectivity, (ii) $k$-edge-connectivity and (iii) the property of minimum vertex degree being at least $k$.

math.CO↗

arXiv2014

On the strengths of connectivity and robustness in general random intersection graphs

Random intersection graphs have received much attention for nearly two decades, and currently have a wide range of applications ranging from key predistribution in wireless sensor networks to modeling social networks. In this paper, we investigate the strengths of connectivity and robustness in a general random intersection graph model. Specifically, we establish sharp asymptotic zero-one laws for $k$-connectivity and $k$-robustness, as well as the asymptotically exact probability of $k$-connectivity, for any positive integer $k$. The $k$-connectivity property quantifies how resilient is the connectivity of a graph against node or edge failures. On the other hand, $k$-robustness measures the effectiveness of local diffusion strategies (that do not use global graph topology information) in spreading information over the graph in the presence of misbehaving nodes. In addition to presenting the results under the general random intersection graph model, we consider two special cases of the general model, a binomial random intersection graph and a uniform random intersection graph, which both have numerous applications as well. For these two specialized graphs, our results on asymptotically exact probabilities of $k$-connectivity and asymptotic zero-one laws for $k$-robustness are also novel in the literature.

cs.DM↗

arXiv2014

Structuring research methods and data with the Research Object model: genomics workflows as a case study

One of the main challenges for biomedical research lies in the computer-assisted integrative study of large and increasingly complex combinations of data in order to understand molecular mechanisms. The preservation of the materials and methods of such computational experiments with clear annotations is essential for understanding an experiment, and this is increasingly recognized in the bioinformatics community. Our assumption is that offering means of digital, structured aggregation and annotation of the objects of an experiment will provide necessary meta-data for a scientist to understand and recreate the results of an experiment. To support this we explored a model for the semantic description of a workflow-centric Research Object (RO), where an RO is defined as a resource that aggregates other resources, e.g., datasets, software, spreadsheets, text, etc. We applied this model to a case study where we analysed human metabolite variation by workflows.

q-bio.GN↗

arXiv2014

On Topological Properties of Wireless Sensor Networks under the q-Composite Key Predistribution Scheme with On/Off Channels

The q-composite key predistribution scheme [1] is used prevalently for secure communications in large-scale wireless sensor networks (WSNs). Prior work [2]-[4] explores topological properties of WSNs employing the q-composite scheme for q = 1 with unreliable communication links modeled as independent on/off channels. In this paper, we investigate topological properties related to the node degree in WSNs operating under the q-composite scheme and the on/off channel model. Our results apply to general q and are stronger than those reported for the node degree in prior work even for the case of q being 1. Specifically, we show that the number of nodes with certain degree asymptotically converges in distribution to a Poisson random variable, present the asymptotic probability distribution for the minimum degree of the network, and establish the asymptotically exact probability for the property that the minimum degree is at least an arbitrary value. Numerical experiments confirm the validity of our analytical findings.

cs.CR↗

arXiv2014

Neutron Scattering Measurements of Spatially Anisotropic Magnetic Exchange Interactions in Semiconducting K0.85Fe1.54Se2 (TN=280 K)

We use neutron scattering to study the spin excitations associated with the stripe antiferromagnetic (AFM) order in semiconducting K$_{0.85}$Fe$_{1.54}$Se$_2$ ($T_N$=$280$ K). We show that the spin wave spectra can be accurately described by an effective Heisenberg Hamiltonian with highly anisotropic in-plane couplings at $T$= $5$ K. At high temperature ($T$= $300$ K) above $T_N$, short range magnetic correlation with anisotropic correlation lengths are observed. Our results suggest that, despite the dramatic difference in the Fermi surface topology, the in-plane anisotropic magnetic couplings are a fundamental property of the iron based compounds; this implies that their antiferromagnetism may originate from local strong correlation effects rather than weak coupling Fermi surface nesting.

cond-mat.supr-con↗

arXiv2014

Representations and $T$*-extensions of hom-Jordan-Lie algebras

The purpose of this paper is to study representations and $T$*-extensions of hom-Jordan-Lie algebras. In particular, adjoint representations, trivial representations, deformations and many properties of $T$*-extensions of hom-Jordan-Lie algebras are studied in detail. Derivations and central extensions of hom-Jordan-Lie algebras are also discussed as an application.

math.RA↗

arXiv2014

Towards $k$-connectivity of the random graph induced by a pairwise key predistribution scheme with unreliable links

We study the secure and reliable connectivity of wireless sensor networks. Security is assumed to be ensured by the random pairwise key predistribution scheme of Chan, Perrig, and Song, and unreliable wireless links are represented by independent on/off channels. Modeling the network by an intersection of a random $K$-out graph and an Erdős-Rényi graph, we present scaling conditions (on the number of nodes, the scheme parameter $K$, and the probability of a wireless channel being on) such that the resulting graph contains no nodes with degree less than $k$ with high probability, when the number of nodes gets large. Results are given in the form of zero-one laws and are shown to improve the previous results by Yağan and Makowski on the absence of isolated nodes (i.e., absence of nodes with degree zero). Via simulations, the established zero-one laws are shown to hold also for the property of $k$-connectivity; i.e., the property that graph remains connected despite the deletion of any $k-1$ nodes or edges.

cs.DM↗

arXiv2014

The Research Object Suite of Ontologies: Sharing and Exchanging Research Data and Methods on the Open Web

Research in life sciences is increasingly being conducted in a digital and online environment. In particular, life scientists have been pioneers in embracing new computational tools to conduct their investigations. To support the sharing of digital objects produced during such research investigations, we have witnessed in the last few years the emergence of specialized repositories, e.g., DataVerse and FigShare. Such repositories provide users with the means to share and publish datasets that were used or generated in research investigations. While these repositories have proven their usefulness, interpreting and reusing evidence for most research results is a challenging task. Additional contextual descriptions are needed to understand how those results were generated and/or the circumstances under which they were concluded. Because of this, scientists are calling for models that go beyond the publication of datasets to systematically capture the life cycle of scientific investigations and provide a single entry point to access the information about the hypothesis investigated, the datasets used, the experiments carried out, the results of the experiments, the people involved in the research, etc. In this paper we present the Research Object (RO) suite of ontologies, which provide a structured container to encapsulate research data and methods along with essential metadata descriptions. Research Objects are portable units that enable the sharing, preservation, interpretation and reuse of research investigation results. The ontologies we present have been designed in the light of requirements that we gathered from life scientists. They have been built upon existing popular vocabularies to facilitate interoperability. Furthermore, we have developed tools to support the creation and sharing of Research Objects, thereby promoting and facilitating their adoption.

cs.DL↗

arXiv2013

Neutron-Diffraction Measurements of an Antiferromagnetic Semiconducting Phase in the Vicinity of the High-Temperature Superconducting State of K$_x$Fe$_{2-y}$Se$_2$

The recently discovered K-Fe-Se high temperature superconductor has caused heated debate regarding the nature of its parent compound. Transport, angle-resolved photoemission spectroscopy, and STM measurements have suggested that its parent compound could be insulating, semiconducting or even metallic [M. H. Fang, H.-D. Wang, C.-H. Dong, Z.-J. Li, C.-M. Feng, J. Chen, and H. Q. Yuan, Europhys. Lett. 94, 27009 (2011); F. Chen et al. Phys. Rev. X 1, 021020 (2011); and W. Li et al.,Phys. Rev. Lett. 109, 057003 (2012)]. Because the magnetic ground states associated with these different phases have not yet been identified and the relationship between magnetism and superconductivity is not fully understood, the real parent compound of this system remains elusive. Here, we report neutron-diffraction experiments that reveal a semiconducting antiferromagnetic (AFM) phase with rhombus iron vacancy order. The magnetic order of the semiconducting phase is the same as the stripe AFM order of the iron pnictide parent compounds. Moreover, while the root5*root5 block AFM phase coexists with superconductivity, the stripe AFM order is suppressed by it. This leads us to conjecture that the new semiconducting magnetic ordered phase is the true parent phase of this superconductor.

cond-mat.supr-con↗