Complete description of polarization effects in emission of a photon by an electron in the field of a strong laser wave

We consider emission of a photon by an electron in the field of a strong laser wave. Polarization effects in this process are important for a number of physical problems. A probability of this process for circularly or linearly polarized laser photons and for arbitrary polarization of all other particles is calculated. We obtain the complete set of functions which describe such a probability in a compact invariant form. Besides, we discuss in some detail the polarization effects in the kinematics relevant to the problem of electron to photon conversion at photon-photon and photon-electron colliders.Comment: 18 pages, minor changes, published versio

Singular Cross Sections in Muon Colliders

We address the problem that the cross section for the collisions of unstable particles diverges, if calculated by standard methods. This problem is considered for beams much smaller than the decay length of the unstable particle, much larger than the decay length and finally also for pancake- shaped beams. We find that in all cases this problem can be solved by taking into account the production/propagation of the unstable particle and/or the width of the incoming wave packets in momentum space.Comment: 12 pages, 3 figures. References corrected. Removed one sentence about a fact that was known. Added explaination why one of our graphs is different as compared to one of the references. Clearified explaination in sec. 3.

The Electroweak Standard Model in the Axial Gauge

We derive the Feynman rules of the standard model in the axial gauge. After this we prove that the fields $\phi_W$ and $\phi_Z$ do not correspond to physical particles. As a consequence, these fields cannot appear as incoming or outgoing lines in Feynman graphs. We then calculate the contribution of these fields in the case of a particular decay mode of the top quark.Comment: 16 pages, no figures. Added derivation of polarization su

Jamming-Resistant Learning in Wireless Networks

We consider capacity maximization in wireless networks under adversarial interference conditions. There are n links, each consisting of a sender and a receiver, which repeatedly try to perform a successful transmission. In each time step, the success of attempted transmissions depends on interference conditions, which are captured by an interference model (e.g. the SINR model). Additionally, an adversarial jammer can render a (1-delta)-fraction of time steps unsuccessful. For this scenario, we analyze a framework for distributed learning algorithms to maximize the number of successful transmissions. Our main result is an algorithm based on no-regret learning converging to an O(1/delta)-approximation. It provides even a constant-factor approximation when the jammer exactly blocks a (1-delta)-fraction of time steps. In addition, we consider a stochastic jammer, for which we obtain a constant-factor approximation after a polynomial number of time steps. We also consider more general settings, in which links arrive and depart dynamically, and where each sender tries to reach multiple receivers. Our algorithms perform favorably in simulations.Comment: 22 pages, 2 figures, typos remove

Expressiveness and Completeness in Abstraction

We study two notions of expressiveness, which have appeared in abstraction theory for model checking, and find them incomparable in general. In particular, we show that according to the most widely used notion, the class of Kripke Modal Transition Systems is strictly less expressive than the class of Generalised Kripke Modal Transition Systems (a generalised variant of Kripke Modal Transition Systems equipped with hypertransitions). Furthermore, we investigate the ability of an abstraction framework to prove a formula with a finite abstract model, a property known as completeness. We address the issue of completeness from a general perspective: the way it depends on certain abstraction parameters, as well as its relationship with expressiveness.Comment: In Proceedings EXPRESS/SOS 2012, arXiv:1208.244

Matrix effect in bio-analysis of illicit drugs with LC-MS/MS: Influence of ionization type, sample preparation, and biofluid

AbstractThe purpose of the present work was to evaluate the synergistic effect of ionization type, sample preparation technique, and bio-fluid on the presence of matrix effect in quantitative liquid chromatography (LC)-MS/MS analysis of illicit drugs by post-column infusion experiments with morphine (10-μg/mL solution). Three bio-fluids (urine, oral fluid, and plasma) were pretreated with four sample preparation procedures [direct injection, dilution, protein precipitation, solid-phase extraction (SPE)] and analyzed by both LC-electrospray ionization (ESI)-MS/MS and LC-atmospheric pressure chemical ionization (APCI)-MS/MS. Our results indicated that both ionization types showed matrix effect, but ESI was more susceptible than APCI. Sample preparation could reduce (clean up) or magnify (pre-concentrate) matrix effect. Residual matrix components were specific to each bio-fluid and interfered at different time points in the chromatogram. We evaluated matrix effect in an early stage of method development and combined optimal ionization type and sample preparation technique for each bio-fluid. Simple dilution of urine was sufficient to allow for the analysis of the analytes of interest by LC-APCI-MS/MS. Acetonitrile protein precipitation provided both sample clean up and concentration for oral fluid analysis, while SPE was necessary for extensive clean up of plasma prior to LC-APCI-MS/MS

An Online Approach to Dynamic Channel Access and Transmission Scheduling

Making judicious channel access and transmission scheduling decisions is essential for improving performance as well as energy and spectral efficiency in multichannel wireless systems. This problem has been a subject of extensive study in the past decade, and the resulting dynamic and opportunistic channel access schemes can bring potentially significant improvement over traditional schemes. However, a common and severe limitation of these dynamic schemes is that they almost always require some form of a priori knowledge of the channel statistics. A natural remedy is a learning framework, which has also been extensively studied in the same context, but a typical learning algorithm in this literature seeks only the best static policy, with performance measured by weak regret, rather than learning a good dynamic channel access policy. There is thus a clear disconnect between what an optimal channel access policy can achieve with known channel statistics that actively exploits temporal, spatial and spectral diversity, and what a typical existing learning algorithm aims for, which is the static use of a single channel devoid of diversity gain. In this paper we bridge this gap by designing learning algorithms that track known optimal or sub-optimal dynamic channel access and transmission scheduling policies, thereby yielding performance measured by a form of strong regret, the accumulated difference between the reward returned by an optimal solution when a priori information is available and that by our online algorithm. We do so in the context of two specific algorithms that appeared in [1] and [2], respectively, the former for a multiuser single-channel setting and the latter for a single-user multichannel setting. In both cases we show that our algorithms achieve sub-linear regret uniform in time and outperforms the standard weak-regret learning algorithms.Comment: 10 pages, to appear in MobiHoc 201