Strangeness magnetic form factor of the proton in the extended chiral quark model

Background: Unravelling the role played by nonvalence flavors in baryons is crucial in deepening our comprehension of QCD. Strange quark, a component of the higher Fock states in baryons, is an appropriate tool to investigate nonperturbative mechanisms generated by the pure sea quark. Purpose: Study the magnitude and the sign of the strangeness magnetic moment $\mu_s$ and the magnetic form factor ($G_M^s$) of the proton. Methods: Within an extended chiral constituent quark model, we investigate contributions from all possible five-quark components to $\mu_s$ and $G_M^s (Q^2)$ in the four-vector momentum range $Q^2 \leq 1$ (GeV/c)$^2$. Probability of the strangeness component in the proton wave function is calculated employing the $^3 P_0$ model. Results: Predictions are obtained without any adjustable parameters. Observables $\mu_s$ and $G_M^s (Q^2)$ are found to be small and negative, consistent with the lattice-QCD findings as well as with the latest data released by the PVA4 and HAPPEX Collaborations. Conclusions: Due to sizeable cancelations among different configurations contributing to the strangeness magnetic moment of the proton, it is indispensable to (i) take into account all relevant five-quark components and include both diagonal and non-diagonal terms, (ii) handle with care the oscillator harmonic parameter $\omega_5$ and the ${s \bar s}$ component probability.Comment: References added, typos corrected, accepted for publication by Phys. Rev.

Efficient smile detection by Extreme Learning Machine

Smile detection is a specialized task in facial expression analysis with applications such as photo selection, user experience analysis, and patient monitoring. As one of the most important and informative expressions, smile conveys the underlying emotion status such as joy, happiness, and satisfaction. In this paper, an efficient smile detection approach is proposed based on Extreme Learning Machine (ELM). The faces are first detected and a holistic flow-based face registration is applied which does not need any manual labeling or key point detection. Then ELM is used to train the classifier. The proposed smile detector is tested with different feature descriptors on publicly available databases including real-world face images. The comparisons against benchmark classifiers including Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) suggest that the proposed ELM based smile detector in general performs better and is very efficient. Compared to state-of-the-art smile detector, the proposed method achieves competitive results without preprocessing and manual registration

Reference face graph for face recognition

Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFG). An RFG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFG. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFG-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The RFG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation

Strong CMB Constraint On P-Wave Annihilating Dark Matter

We consider a dark sector consisting of dark matter that is a Dirac fermion and a scalar mediator. This model has been extensively studied in the past. If the scalar couples to the dark matter in a parity conserving manner then dark matter annihilation to two mediators is dominated by the P-wave channel and hence is suppressed at very low momentum. The indirect detection constraint from the anisotropy of the Cosmic Microwave Background is usually thought to be absent in the model because of this suppression. In this letter we show that dark matter annihilation to bound states occurs through the S-wave and hence there is a constraint on the parameter space of the model from the Cosmic Microwave Background.Comment: 5 pages, 3 figure

Improving Christofides' Algorithm for the s-t Path TSP

We present a deterministic (1+sqrt(5))/2-approximation algorithm for the s-t path TSP for an arbitrary metric. Given a symmetric metric cost on n vertices including two prespecified endpoints, the problem is to find a shortest Hamiltonian path between the two endpoints; Hoogeveen showed that the natural variant of Christofides' algorithm is a 5/3-approximation algorithm for this problem, and this asymptotically tight bound in fact has been the best approximation ratio known until now. We modify this algorithm so that it chooses the initial spanning tree based on an optimal solution to the Held-Karp relaxation rather than a minimum spanning tree; we prove this simple but crucial modification leads to an improved approximation ratio, surpassing the 20-year-old barrier set by the natural Christofides' algorithm variant. Our algorithm also proves an upper bound of (1+sqrt(5))/2 on the integrality gap of the path-variant Held-Karp relaxation. The techniques devised in this paper can be applied to other optimization problems as well: these applications include improved approximation algorithms and improved LP integrality gap upper bounds for the prize-collecting s-t path problem and the unit-weight graphical metric s-t path TSP.Comment: 31 pages, 5 figure

Face image super-resolution using 2D CCA

In this paper a face super-resolution method using two-dimensional canonical correlation analysis (2D CCA) is presented. A detail compensation step is followed to add high-frequency components to the reconstructed high-resolution face. Unlike most of the previous researches on face super-resolution algorithms that first transform the images into vectors, in our approach the relationship between the high-resolution and the low-resolution face image are maintained in their original 2D representation. In addition, rather than approximating the entire face, different parts of a face image are super-resolved separately to better preserve the local structure. The proposed method is compared with various state-of-the-art super-resolution algorithms using multiple evaluation criteria including face recognition performance. Results on publicly available datasets show that the proposed method super-resolves high quality face images which are very close to the ground-truth and performance gain is not dataset dependent. The method is very efficient in both the training and testing phases compared to the other approaches. © 2013 Elsevier B.V

Chiral expansion of the π0→γγ\pi^0\rightarrow\gamma\gamma decay width

A chiral field theory of mesons has been applied to study the contribution of the current quark masses to the $\pi^0\rightarrow\gamma\gamma$ decay width at the next leading order. $2\%$ enhancement has been predicted and there is no new parameter.Comment: 9 page

Monte Carlo simulations to understand 'breathing' phenomenon of metal organic frameworks

Metal Organic Frameworks (MOFs) are a new class of porous materials synthesized from metal clusters connected by organic linkers. One of the promising applications of MOFs is carbon capture from fuel gasses, where CO2 is adsorbed in the pores of the material. In this presentation, we explore framework flexibility as a possible mechanism for selective and reversible CO2 adsorption by means of Monte Carlo simulations. Most MOFs are fairly rigid structures, in the sense that they undergo small changes in volume when external stress is applied. Typical volume changes are of the order of a few percent only. Nevertheless, some MOF materials have an unexpectedly high flexibility and impressively shrink or swell under pressure, temperature or adsorption changes. A well-known example is MIL-53, a structure that shows volume changes of over 40%. In an adsorption experiment, the gas pressure is gradually increased while the amount of adsorbed material in the pores is measured. For MIL-53, the measured adsorption isotherm shows interesting features: when MIL-53 is brought into contact with a gas at increasing pressure, the framework's pores constrict, while at even higher pressures, the pores return to their original geometry. The process, referred to as "breathing", is reversible and shows hysteresis. Based on Monte Carlo runs, we have constructed a mean-field model to gain insight in the thermodynamics of the breathing. The model shows that the behavior is the result of the different factors at play in a (Nmof,μ,P,T) ensemble (constant amount of MOF material, constant gas chemical potential, constant gas pressure, constant temperature), i.e. the entropy, the pressure and the resistance given by the adsorbed particles. We further investigate how the MOFs' flexibility could be exploited to design an efficient pressure swing setup