Pion-Muon Asymmetry Revisited

Long ago an unexpected and unexplainable phenomena was observed. The distribution of muons from positive pion decay at rest was anisotropic with an excess in the backward direction relative to the direction of the proton beam from which the pions were created. Although this effect was observed by several different groups with pions produced by different means, the result was not accepted by the physics community, because it is in direct conflict with a large set of other experiments indicating that the pion is a pseudoscalar particle. It is possible to satisfy both sets of experiments if helicity-zero vector particles exist and the pion is such a particle. Helicity-zero vector particles have direction but no net spin. For the neutral pion to be a vector particle requires an additional modification to conventional theory as discussed herein. An experiment is proposed which can prove that the asymmetry in the distribution of muons from pion decay is a genuine physical effect because the asymmetry can be modified in a controllable manner. A positive result will also prove that the pion is NOT a pseudoscalar particle.Comment: 9 pages, 3 figure

Antiproton Production in p+Ap+A Collisions at AGS Energies

Inclusive and semi-inclusive measurements are presented for antiproton ($\bar{p}$) production in proton-nucleus collisions at the AGS. The inclusive yields per event increase strongly with increasing beam energy and decrease slightly with increasing target mass. The $\bar{p}$ yield in 17.5 GeV/c p+Au collisions decreases with grey track multiplicity, $N_g$, for $N_g>0$, consistent with annihilation within the target nucleus. The relationship between $N_g$ and the number of scatterings of the proton in the nucleus is used to estimate the $\bar{p}$ annihilation cross section in the nuclear medium. The resulting cross section is at least a factor of five smaller than the free $\bar{p}-p$ annihilation cross section when assuming a small or negligible formation time. Only with a long formation time can the data be described with the free $\bar{p}-p$ annihilation cross section.Comment: 8 pages, 6 figure

Video entity resolution: Applying er techniques for smart video surveillance

situational awareness by allowing domain analysts to focus on the events of higher priority. This in turn leads to improved decision making, allows for better resource management, and helps to reduce information overload. SVS approaches operate by trying to extract and interpret higher “semantic ” level events that occur in video. On of the key challenges of Smart Video Surveillance is that of person identification where the task is for each subject that occur in a video shot to identify the person it corresponds to. The problem of person identification is very complex in the resource constrained environments where transmission delay, bandwidth restriction, and packet loss may prevent the capture of high quality data. In this paper we connect the problem of person identification in video data with the problem of entity resolution that is common in textual data. Specifically, we show how the PI problem can be successfully resolved using a graph-based entity resolution framework called RelDC that leverages relationships among various entities for disambiguation. We apply the proposed solution to a dataset consisting of several weeks of surveillance videos. The results demonstrate the effectiveness and efficiency of our approach even with low quality video data

Exploiting Semantics for Sensor Recalibration in Event Detection System

Event detection from a video stream is becoming an important and challenging task in surveillance and sentient systems. While computer vision has been extensively studied to solve different kinds of detection problems over time, it is still a hard problem and even in a controlled environment only simple events can be detected with a high degree of accuracy. Instead of struggling to improve event detection using image processing only, we bring in semantics to direct traditional image processing. Semantics are the underlying facts that hide beneath video frames, which can not be “seen ” directly by image processing. In this work we demonstrate that time sequence semantics can be exploited to guide unsupervised re-calibration of the event detection system. We present an instantiation of our ideas by using an appliance as an example- Coffee Pot level detection based on video data- to show that semantics can guide the re-calibration of the detection model. This work exploits time sequence semantics to detect when re-calibration is required to automatically relearn a new detection model for the newly-evolved system state and to resume monitoring with a higher rate of accuracy

Masses, decay modes and lifetimes of charmed particles

Translated from Russian. Preprint ITEP--5, 1982SIGLEDEGerman