Theory for effects of pressure on heavy-fermion alloys

The effects of pressure on heavy-fermion alloys are studied in the framework of Yoshimori-Kasai model under the coherent potential approximation. A unified picture is presented for both the electron-type heavy-fermion systems and the hole-type heavy-fermion systems. The density of states of $f$ electrons is calculated over the whole range of the doping concentration under the applied pressure. The Kondo temperature, the specific-heat coefficient, and the electrical resistivity are obtained, in agreement with the experiments qualitatively. The contrasting pressure-dependent effects for two types of heavy-fermion alloys are discussed to reveal the coherence in the system under pressure.Comment: 8 figure

On Noether approach in the cosmological model with scalar and gauge fields: symmetries and the selection rule

In this paper, based on the works of Capozziello et al., we have studied the Noether symmetry approach in the cosmological model with scalar and gauge fields proposed recently by Soda et al. The correct Noether symmetries and related Lie algebra are given according to the minisuperspace quantum cosmological model. The Wheeler-De Witt (WDW) equation is presented after quantization and the classical trajectories are then obtained in the semi-classical limit. The oscillating features of the wave function in the cosmic evolution recover the so-called Hartle criterion, and the selection rule in minisuperspace quantum cosmology is strengthened. Then we have realized now the proposition that Noether symmetries select classical universes

A Linear Algorithm for Finding the Sink of Unique Sink Orientations on Grids

An orientation of a grid is called unique sink orientation (USO) if each of its nonempty subgrids has a unique sink. Particularly, the original grid itself has a unique global sink. In this work we investigate the problem of how to find the global sink using minimum number of queries to an oracle. There are two different oracle models: the vertex query model where the orientation of all edges incident to the queried vertex are provided, and the edge query model where the orientation of the queried edge is provided. In the 2-dimensional case, we design an optimal linear deterministic algorithm for the vertex query model and an almost linear deterministic algorithm for the edge query model, previously the best known algorithms run in O(N logN) time for the vertex query model and O(N^1.404) time for the edge query model

Your Actions Tell Where You Are: Uncovering Twitter Users in a Metropolitan Area

Twitter is an extremely popular social networking platform. Most Twitter users do not disclose their locations due to privacy concerns. Although inferring the location of an individual Twitter user has been extensively studied, it is still missing to effectively find the majority of the users in a specific geographical area without scanning the whole Twittersphere, and obtaining these users will result in both positive and negative significance. In this paper, we propose LocInfer, a novel and lightweight system to tackle this problem. LocInfer explores the fact that user communications in Twitter exhibit strong geographic locality, which we validate through large-scale datasets. Based on the experiments from four representative metropolitan areas in U.S., LocInfer can discover on average 86.6% of the users with 73.2% accuracy in each area by only checking a small set of candidate users. We also present a countermeasure to the users highly sensitive to location privacy and show its efficacy by simulations.Comment: Accepted by IEEE Conference on Communications and Network Security (CNS) 201

Existence and multiplicity of solutions for nonlocal systems with Kirchhoff type

Firstly, we use Nehari manifold and Mountain Pass Lemma to prove an existence result of positive solutions for a class of nonlocal elliptic system with Kirchhoff type. Then a multiplicity result is established by cohomological index of Fadell and Rabinowitz. We also consider the critical case and prove existence of positive least energy solution when the parameter $\beta$ is sufficiently large.Comment: appears in Acta. Math. Applica. Sinica, 201

TrueTop: A Sybil-Resilient System for User Influence Measurement on Twitter

Influential users have great potential for accelerating information dissemination and acquisition on Twitter. How to measure the influence of Twitter users has attracted significant academic and industrial attention. Existing influential measurement techniques, however, are vulnerable to sybil users that are thriving on Twitter. Although sybil defenses for online social networks have been extensively investigated, they commonly assume unique mappings from human-established trust relationships to online social associations and thus do not apply to Twitter where users can freely follow each other. This paper presents TrueTop, the first sybil-resilient system to measure the influence of Twitter users. TrueTop is firmly rooted in two observations from real Twitter datasets. First, although non-sybil users may incautiously follow strangers, they tend to be more careful and selective in retweeting, replying to, and mentioning other Twitter users. Second, influential users usually get much more retweets, replies, and mentions than non-influential users. Detailed theoretical studies and synthetic simulations show that TrueTop can generate very accurate influence measurement results and also have strong resilience to sybil attacks.Comment: Accepted by IEEE/ACM Transactions on Networking. This is the Final versio

Weighted Data Normalization Based on Eigenvalues for Artificial Neural Network Classification

Artificial neural network (ANN) is a very useful tool in solving learning problems. Boosting the performances of ANN can be mainly concluded from two aspects: optimizing the architecture of ANN and normalizing the raw data for ANN. In this paper, a novel method which improves the effects of ANN by preprocessing the raw data is proposed. It totally leverages the fact that different features should play different roles. The raw data set is firstly preprocessed by principle component analysis (PCA), and then its principle components are weighted by their corresponding eigenvalues. Several aspects of analysis are carried out to analyze its theory and the applicable occasions. Three classification problems are launched by an active learning algorithm to verify the proposed method. From the empirical results, conclusion comes to the fact that the proposed method can significantly improve the performance of ANN

Graph Embedding with Rich Information through Heterogeneous Network

Graph embedding has attracted increasing attention due to its critical application in social network analysis. Most existing algorithms for graph embedding only rely on the typology information and fail to use the copious information in nodes as well as edges. As a result, their performance for many tasks may not be satisfactory. In this paper, we proposed a novel and general framework of representation learning for graph with rich text information through constructing a bipartite heterogeneous network. Specially, we designed a biased random walk to explore the constructed heterogeneous network with the notion of flexible neighborhood. The efficacy of our method is demonstrated by extensive comparison experiments with several baselines on various datasets. It improves the Micro-F1 and Macro-F1 of node classification by 10% and 7% on Cora dataset.Comment: 9 pages, 7 figures, 4 table

Reduce Meaningless Words for Joint Chinese Word Segmentation and Part-of-speech Tagging

Conventional statistics-based methods for joint Chinese word segmentation and part-of-speech tagging (S&T) have generalization ability to recognize new words that do not appear in the training data. An undesirable side effect is that a number of meaningless words will be incorrectly created. We propose an effective and efficient framework for S&T that introduces features to significantly reduce meaningless words generation. A general lexicon, Wikepedia and a large-scale raw corpus of 200 billion characters are used to generate word-based features for the wordhood. The word-lattice based framework consists of a character-based model and a word-based model in order to employ our word-based features. Experiments on Penn Chinese treebank 5 show that this method has a 62.9% reduction of meaningless word generation in comparison with the baseline. As a result, the F1 measure for segmentation is increased to 0.984

The stochastic nature induced by laser noise in narrow transitions

We use a probabilistic method to describe the effect of laser noise on the laser-atom interaction, in the case that the atom is a two level system without spontaneous emission. The stochastic differential equation for the laser-atom interaction is analyzed in the sense of perturbation approach, and we construct a stochastic process corresponding to the time evolution of the atom's wave function, whose extra randomness is induced by the laser noise. It also provides the layout of a theory for the possible experiment of measuring the laser line width by driving a narrow atomic transition