Learning to Identify Ambiguous and Misleading News Headlines

Accuracy is one of the basic principles of journalism. However, it is increasingly hard to manage due to the diversity of news media. Some editors of online news tend to use catchy headlines which trick readers into clicking. These headlines are either ambiguous or misleading, degrading the reading experience of the audience. Thus, identifying inaccurate news headlines is a task worth studying. Previous work names these headlines "clickbaits" and mainly focus on the features extracted from the headlines, which limits the performance since the consistency between headlines and news bodies is underappreciated. In this paper, we clearly redefine the problem and identify ambiguous and misleading headlines separately. We utilize class sequential rules to exploit structure information when detecting ambiguous headlines. For the identification of misleading headlines, we extract features based on the congruence between headlines and bodies. To make use of the large unlabeled data set, we apply a co-training method and gain an increase in performance. The experiment results show the effectiveness of our methods. Then we use our classifiers to detect inaccurate headlines crawled from different sources and conduct a data analysis.Comment: Accepted by IJCAI 201

Determining the luminosity function of Swift long gamma-ray bursts with pseudo-redshifts

The determination of luminosity function (LF) of gamma-ray bursts (GRBs) is of an important role for the cosmological applications of the GRBs, which is however hindered seriously by some selection effects due to redshift measurements. In order to avoid these selection effects, we suggest to calculate pseudo-redshifts for Swift GRBs according to the empirical L-E_p relationship. Here, such a $L-E_p$ relationship is determined by reconciling the distributions of pseudo- and real redshifts of redshift-known GRBs. The values of E_p taken from Butler's GRB catalog are estimated with Bayesian statistics rather than observed. Using the GRB sample with pseudo-redshifts of a relatively large number, we fit the redshift-resolved luminosity distributions of the GRBs with a broken-power-law LF. The fitting results suggest that the LF could evolve with redshift by a redshift-dependent break luminosity, e.g., L_b=1.2\times10^{51}(1+z)^2\rm erg s^{-1}. The low- and high-luminosity indices are constrained to 0.8 and 2.0, respectively. It is found that the proportional coefficient between GRB event rate and star formation rate should correspondingly decrease with increasing redshifts.Comment: 5 pages, 5 figures, accepted for publication in ApJ

Quantum nondemolition measurements of a flux qubit coupled to a noisy detector

We theoretically study the measurement-induced dephasing caused by back action noise in quantum nondemolition measurements of a superconducting flux qubit which is coupled to a superconducting quantum interference device (SQUID). Our analytical results indicate that information on qubit flows from qubit to detector, while quantum fluctuations which may cause dephasing of the qubit also inject to qubit. Furthermore, the measurement probability is frequency dependent in a short time scale and has a close relationship with the measurement-induced dephasing. When the detuning between driven and bare resonator equals coupling strength, we will access the state of qubit more easily. In other words, we obtain the maximum measurement rate. Finally, we analyzed mixed effect caused by coupling between non-diagonal term and external variable. We found that the initial information of qubit is destroyed due to quantum tunneling between the qubit states.Comment: 6 pages, 3 figure

Protein folding tames chaos

Protein folding produces characteristic and functional three-dimensional structures from unfolded polypeptides or disordered coils. The emergence of extraordinary complexity in the protein folding process poses astonishing challenges to theoretical modeling and computer simulations. The present work introduces molecular nonlinear dynamics (MND), or molecular chaotic dynamics, as a theoretical framework for describing and analyzing protein folding. We unveil the existence of intrinsically low dimensional manifolds (ILDMs) in the chaotic dynamics of folded proteins. Additionally, we reveal that the transition from disordered to ordered conformations in protein folding increases the transverse stability of the ILDM. Stated differently, protein folding reduces the chaoticity of the nonlinear dynamical system, and a folded protein has the best ability to tame chaos. Additionally, we bring to light the connection between the ILDM stability and the thermodynamic stability, which enables us to quantify the disorderliness and relative energies of folded, misfolded and unfolded protein states. Finally, we exploit chaos for protein flexibility analysis and develop a robust chaotic algorithm for the prediction of Debye-Waller factors, or temperature factors, of protein structures