Polarization Effects in Chargino Production at High Energy γγ\gamma\gamma Colliders

We investigate the chargino production process $\gamma\gamma\rightarrow\tilde{W}^{+}\tilde{W}^{-}$ at high energy $\gamma\gamma$ colliders in the framework of the minimal supersymmetric standard model (MSSM). Here the high energy $\gamma$ beams are obtained by the backward Compton scattering of the laser flush by the electron in the basic linear TeV $ee$ colliders. We consider the polarization of the laser photons as well as the electron beams. Appropriate beam polarization could be effective to enhance the cross section and for us to extract the signal from the dominant background $\gamma\gamma\rightarrow{W}^{+}{W}^{-}$.Comment: 7 pages, latex , 3 figures are available upon reques

Fashion-Specific Attributes Interpretation via Dual Gaussian Visual-Semantic Embedding

Several techniques to map various types of components, such as words, attributes, and images, into the embedded space have been studied. Most of them estimate the embedded representation of target entity as a point in the projective space. Some models, such as Word2Gauss, assume a probability distribution behind the embedded representation, which enables the spread or variance of the meaning of embedded target components to be captured and considered in more detail. We examine the method of estimating embedded representations as probability distributions for the interpretation of fashion-specific abstract and difficult-to-understand terms. Terms, such as "casual," "adult-casual,'' "beauty-casual," and "formal," are extremely subjective and abstract and are difficult for both experts and non-experts to understand, which discourages users from trying new fashion. We propose an end-to-end model called dual Gaussian visual-semantic embedding, which maps images and attributes in the same projective space and enables the interpretation of the meaning of these terms by its broad applications. We demonstrate the effectiveness of the proposed method through multifaceted experiments involving image and attribute mapping, image retrieval and re-ordering techniques, and a detailed theoretical/analytical discussion of the distance measure included in the loss function

The Effect of a Definition Frame upon Perception : A Symbolic Interactionist Study of Wrongly Activated Cognitive Frames in a Disaster Situation in Japan

In this paper the author tried to specify the effects of persons\u27 cognitive frames upon their definition of unfamiliar situations. By analyzing a relatively well-organized set of field data of people\u27s evacuation behavior at the time of the 1983 big tidal wave caused by the Mid-Japan Sea Earthquake, how the inhabitants\u27 collective definition of the natural phenomenon affected their definition of the situation and the consequent behavior will be described and explained. It will become obvious that an already established frame of reference shared by the people toward the disasters causes a very strong effect upon their socially constructed perception of environmental change that is itself a purely natural phenomenon, and some educational implications of this fact will be discussed

Adaptive Ranking-based Sample Selection for Weakly Supervised Class-imbalanced Text Classification

To obtain a large amount of training labels inexpensively, researchers have recently adopted the weak supervision (WS) paradigm, which leverages labeling rules to synthesize training labels rather than using individual annotations to achieve competitive results for natural language processing (NLP) tasks. However, data imbalance is often overlooked in applying the WS paradigm, despite being a common issue in a variety of NLP tasks. To address this challenge, we propose Adaptive Ranking-based Sample Selection (ARS2), a model-agnostic framework to alleviate the data imbalance issue in the WS paradigm. Specifically, it calculates a probabilistic margin score based on the output of the current model to measure and rank the cleanliness of each data point. Then, the ranked data are sampled based on both class-wise and rule-aware ranking. In particular, the two sample strategies corresponds to our motivations: (1) to train the model with balanced data batches to reduce the data imbalance issue and (2) to exploit the expertise of each labeling rule for collecting clean samples. Experiments on four text classification datasets with four different imbalance ratios show that ARS2 outperformed the state-of-the-art imbalanced learning and WS methods, leading to a 2%-57.8% improvement on their F1-score

Development and Evaluation of the 1/30U Small-Sized 3 Axis Attitude Control Module, and its Application for the JEM Internal Ball Camera Robot

In this paper, we propose the 1/30U small-sized 3-axis attitude control module. The idea of using an electrical circuit board as a structural component reduces the mass of the mechanical structure and the electrical wiring as well. Adapting the System-On-Chip (SoC) reduced the circuit area while maintaining the complexity of the electric circuit. We managed to develop an attitude control module measuring 31 mm in size and 88 g in weight. This module contains 3-axis reaction wheels, six MEMS-IMU sensors (18-axis acceleration and 18-axis gyro), and an attitude control computer. The module only requires a power supply and external serial communication. The module can also be connected to other navigation sensor. And by adding the extension circuit, this module can drive and control 12 actuators, such as micro thrusters. An on-orbit evaluation was conducted with the JEM Internal Ball Camera Robot as the control system for robot position and attitude. The robot is an autonomous maneuverable ball-shaped camera that is operated by ground operators. Twelve micro fans and the proposed module are integrated inside the robot to realize 6-axes maneuvering, and a navigation camera provides the robot’s relative position and attitude to a target marker. This paper discusses an evaluation of attitude control accuracy to reveal the module’s on-orbit performance