Unparticle physics in top pair signals at the LHC and ILC

We study the effects of unparticle physics in the pair productions of top quarks at the LHC and ILC. By considering vector, tensor and scalar unparticle operators, as appropriate, we compute the total cross sections for pair production processes depending on scale dimension d_{\U}. We find that the existence of unparticles would lead to measurable enhancements on the SM predictions at the LHC. In the case of ILC this may become two orders of magnitude larger than that of SM, for smaller values of d_\U, a very striking signal for unparticles.Comment: 19 pages, 9 figures, analysis for ILC has been adde

Prediction of lethal and synthetically lethal knock-outs in regulatory networks

The complex interactions involved in regulation of a cell's function are captured by its interaction graph. More often than not, detailed knowledge about enhancing or suppressive regulatory influences and cooperative effects is lacking and merely the presence or absence of directed interactions is known. Here we investigate to which extent such reduced information allows to forecast the effect of a knock-out or a combination of knock-outs. Specifically we ask in how far the lethality of eliminating nodes may be predicted by their network centrality, such as degree and betweenness, without knowing the function of the system. The function is taken as the ability to reproduce a fixed point under a discrete Boolean dynamics. We investigate two types of stochastically generated networks: fully random networks and structures grown with a mechanism of node duplication and subsequent divergence of interactions. On all networks we find that the out-degree is a good predictor of the lethality of a single node knock-out. For knock-outs of node pairs, the fraction of successors shared between the two knocked-out nodes (out-overlap) is a good predictor of synthetic lethality. Out-degree and out-overlap are locally defined and computationally simple centrality measures that provide a predictive power close to the optimal predictor.Comment: published version, 10 pages, 6 figures, 2 tables; supplement at http://www.bioinf.uni-leipzig.de/publications/supplements/11-01

Why should we care about quantum discord?

Entanglement is a central feature of quantum theory. Mathematical properties and physical applications of pure state entanglement make it a template to study quantum correlations. However, an extension of entanglement measures to mixed states in terms of separability does not always correspond to all the operational aspects. Quantum discord measures allow an alternative way to extend the idea of quantum correlations to mixed states. In many cases these extensions are motivated by physical scenarios and quantum information protocols. In this chapter we discuss several settings involving correlated quantum systems, ranging from distributed gates to detectors testing quantum fields. In each setting we show how entanglement fails to capture the relevant features of the correlated system, and discuss the role of discord as a possible alternative.Comment: Written for "Lectures on general quantum correlations and their applications

VBDFT(s) - a semi-empirical valence bond method: Application to linear polyenes containing oxygen and nitrogen heteroatoms

A semi-empirical valence bond (VB) method, VBDFT(s), is applied to the series of linear polyenes with heteroatoms CM -1HMO, CM - 2HM - 2O2, CM - 1HM + 1N, and CM - 2HMN2 (M = 4-26). The computational results show that the VBDFT(s) method, which was first applied to linear polyenes, is also suitable for treatment of linear polyenes with polar bonds. Properties such as the wavefunction, extent of delocalization, the resonance energy, and the energy additivity are discussed

Institutional Export Barriers on Exporters from Emerging Markets: Evidence from China

The emerging markets have become the increasingly important trading nations in the global economy. Given its significance to practitioners and policymakers, export barriers has been the popular topic in the international business studies. However, research about export barriers caused by the local institutions are under developed, though institutional voids and institutional inefficiency are reported as the major determinants for business development in emerging markets. This paper aims to fill in this gap by exploring the institutional export barriers in emerging markets. Based on existing studies on export barriers and institutional perspective, a conceptual framework is initially developed by separating formal and informal institutional export barriers. Then three specific institutional export barriers are identified, including government policy, weak legal system and informal and personal networks. In the meanwhile, this paper sheds light on how the institutional export barriers are developed and obstruct exporting in emerging markets

Unparticle Physics in Single Top Signals

We study the single production of top quarks in $e^+e^-, ep$ and $pp$ collisions in the context of unparticle physics through the Flavor Violating (FV) unparticle vertices and compute the total cross sections for single top production as functions of scale dimension d_{\U}. We find that among all, LHC is the most promising facility to probe the unparticle physics via single top quark production processes.Comment: 14 pages, 10 figure

Manipulating the Optical Properties of Carbon Dots by Fine-Tuning their Structural Features

Contains fulltext : 207736.pdf (publisher's version ) (Closed access)11 p

Dimensionality and dynamics in the behavior of C. elegans

A major challenge in analyzing animal behavior is to discover some underlying simplicity in complex motor actions. Here we show that the space of shapes adopted by the nematode C. elegans is surprisingly low dimensional, with just four dimensions accounting for 95% of the shape variance, and we partially reconstruct "equations of motion" for the dynamics in this space. These dynamics have multiple attractors, and we find that the worm visits these in a rapid and almost completely deterministic response to weak thermal stimuli. Stimulus-dependent correlations among the different modes suggest that one can generate more reliable behaviors by synchronizing stimuli to the state of the worm in shape space. We confirm this prediction, effectively "steering" the worm in real time.Comment: 9 pages, 6 figures, minor correction

Do female association preferences predict the likelihood of reproduction?

Sexual selection acting on male traits through female mate choice is commonly inferred from female association preferences in dichotomous mate choice experiments. However, there are surprisingly few empirical demonstrations that such association preferences predict the likelihood of females reproducing with a particular male. This information is essential to confirm association preferences as good predictors of mate choice. We used green swordtails (<i>Xiphophorus helleri</i>) to test whether association preferences predict the likelihood of a female reproducing with a male. Females were tested for a preference for long- or short-sworded males in a standard dichotomous choice experiment and then allowed free access to either their preferred or non-preferred male. If females subsequently failed to produce fry, they were provided a second unfamiliar male with similar sword length to the first male. Females were more likely to reproduce with preferred than non-preferred males, but for those that reproduced, neither the status (preferred/non-preferred) nor the sword length (long/short) of the male had an effect on brood size or relative investment in growth by the female. There was no overall preference based on sword length in this study, but male sword length did affect likelihood of reproduction, with females more likely to reproduce with long- than short-sworded males (independent of preference for such males in earlier choice tests). These results suggest that female association preferences are good indicators of female mate choice but that ornament characteristics of the male are also important

Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder.

Quantum autoencoders serve as efficient means for quantum data compression. Here, we propose and demonstrate their use to reduce resource costs for quantum teleportation of subspaces in high-dimensional systems. We use a quantum autoencoder in a compress-teleport-decompress manner and report the first demonstration with qutrits using an integrated photonic platform for future scalability. The key strategy is to compress the dimensionality of input states by erasing redundant information and recover the initial states after chip-to-chip teleportation. Unsupervised machine learning is applied to train the on-chip autoencoder, enabling the compression and teleportation of any state from a high-dimensional subspace. Unknown states are decompressed at a high fidelity (~0.971), obtaining a total teleportation fidelity of ~0.894. Subspace encodings hold great potential as they support enhanced noise robustness and increased coherence. Laying the groundwork for machine learning techniques in quantum systems, our scheme opens previously unidentified paths toward high-dimensional quantum computing and networking