Revisiting lepton flavor violation in supersymmetric type II seesaw

In view of the recent measurement of reactor mixing angle $\theta_{13}$ and updated limit on $BR(\mu \to e \gamma)$ by the MEG experiment, we re-examine the charged lepton flavor violations in a framework of supersymmetric type II seesaw mechanism. Supersymmetric type II seesaw predicts strong correlation between $BR(\mu \to e \gamma)$ and $BR(\tau \to \mu \gamma)$ mainly in terms of the neutrino mixing angles. We show that such a correlation can be determined accurately after the measurement of $\theta_{13}$. We compute different factors which can affect this correlation and show that the mSUGRA-like scenarios, in which slepton masses are taken to be universal at the high scale, predicts $3.5 \lesssim BR(\tau \to \mu \gamma)/BR(\mu \to e \gamma) \lesssim 30$ for normal hierarchical neutrino masses. Any experimental indication of deviation from this prediction would rule out the minimal models of supersymmetric type II seesaw. We show that the current MEG limit puts severe constraints on the light sparticle spectrum in mSUGRA model if the seesaw scale lies within $10^{13}$-$10^{15}$ GeV. It is shown that these constraints can be relaxed and relatively light sparticle spectrum can be obtained in a class of models in which the soft mass of triplet scalar is taken to be non-universal at the high scale.Comment: Minor changes in text; accepted for publication in Phys. Rev.

Changes in forward light scatter parameters as a function of refractive error in young adults

Background/aims Some aspects of visual performance worsen with increasing myopia. Whilst the underlying causes are not always clear, reduction in retinal image quality is often attributed to structural changes in the posterior myopic eye. Forward light scatter, originating principally from the cornea and lens, is known to produce veiling glare which subsequently reduces retinal image contrast. It is therefore of interest to investigate whether forward light scatter varies with refractive error. Methods Thirteen young-adult subjects (18–25 years), with mean spherical errors (MSE ± sd, D) RE, − 1.69 ± 2.02 (range 0.38 to − 4.75); LE, − 1.91 ± 1.94 (range 0.50 to − 4.63) underwent binocular assessment of forward light scatter using the AVOT light scatter test. Five glare annuli, with effective eccentricities ranging from 2 to 10°, were used to estimate parameters, k and n, which define the light scatter function of the eye. These were then used to calculate the area under the light scatter function (k′) and the total volume of light scatter (k″). Results Significant correlation was found between increasing myopia and k′ values (RE, p  0.05 for both eyes). Axial length was also not correlated with any of the light scatter parameters measured. Conclusion The preliminary data from this study provide evidence that some light scatter parameters may be correlated with refractive error. Further studies are needed to characterize how changes in the anterior media of the eye, and inclusion of a wider range of refractive errors, may affect forward light scatter

Web-based information systems development and dynamic organisational change: the need for emergent development tools

This paper considers contextual issues relating to the problem of developing web-based information systems in and for emergent organisations. It postulates that the methods available suffer because of sudden and unexpected changing characteristics within the organisation. The Theory of Deferred Action is used as the basis for the development of an emergent development tool. Many tools for managing change in a continuously changing organisation are susceptible to inadequacy. The insights proposed are believed to assist designers in developing functional and relevant approaches within dynamic organisational contexts

Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier

Active authentication refers to the process in which users are unobtrusively monitored and authenticated continuously throughout their interactions with mobile devices. Generally, an active authentication problem is modelled as a one class classification problem due to the unavailability of data from the impostor users. Normally, the enrolled user is considered as the target class (genuine) and the unauthorized users are considered as unknown classes (impostor). We propose a convolutional neural network (CNN) based approach for one class classification in which a zero centered Gaussian noise and an autoencoder are used to model the pseudo-negative class and to regularize the network to learn meaningful feature representations for one class data, respectively. The overall network is trained using a combination of the cross-entropy and the reconstruction error losses. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one class classification. Effectiveness of the proposed framework is demonstrated using three publically available face-based active authentication datasets and it is shown that the proposed method achieves superior performance compared to the traditional one class classification methods. The source code is available at: github.com/otkupjnoz/oc-acnn.Comment: Accepted and to appear at AFGR 201

Forward-backward asymmetry in top quark production from light colored scalars in SO(10) model

The forward-backward asymmetry in top pair production at Tevatron has been reconfirmed by the CDF collaboration with 5.3 fb^{-1} of accumulated data. These measurements also report that the asymmetry is the largest in regions of high invariant mass M_{t\bar{t}} and rapidity difference |\Delta Y|. We consider light colored sextet scalars appearing in a particular non-supersymmetric \10 grand unification model within the \bar{126} scalar representation. These scalar states have masses in the range of 300 \text{GeV}-2 \text{TeV} consistent with the requirements of gauge coupling unification and bounds on the proton lifetime. The cross section and the total asymmetry can be simultaneously explained with the contributions of these scalars within 1$\sigma$. We find that the simultaneous fitting of the cross section, the total asymmetry and the asymmetries in different rapidity and M_{t\bar{t}} bins gives only a marginal improvement over the SM contribution. We also study various production mechanisms of these colored sextet scalars at the LHC.Comment: 22 pages, 11 Figures, References added, Section III and V modified. Version accepted for publication in JHE

C2AE: Class Conditioned Auto-Encoder for Open-set Recognition

Models trained for classification often assume that all testing classes are known while training. As a result, when presented with an unknown class during testing, such closed-set assumption forces the model to classify it as one of the known classes. However, in a real world scenario, classification models are likely to encounter such examples. Hence, identifying those examples as unknown becomes critical to model performance. A potential solution to overcome this problem lies in a class of learning problems known as open-set recognition. It refers to the problem of identifying the unknown classes during testing, while maintaining performance on the known classes. In this paper, we propose an open-set recognition algorithm using class conditioned auto-encoders with novel training and testing methodology. In contrast to previous methods, training procedure is divided in two sub-tasks, 1. closed-set classification and, 2. open-set identification (i.e. identifying a class as known or unknown). Encoder learns the first task following the closed-set classification training pipeline, whereas decoder learns the second task by reconstructing conditioned on class identity. Furthermore, we model reconstruction errors using the Extreme Value Theory of statistical modeling to find the threshold for identifying known/unknown class samples. Experiments performed on multiple image classification datasets show proposed method performs significantly better than state of the art.Comment: CVPR2019 (Oral

Sparse Representation-based Open Set Recognition

We propose a generalized Sparse Representation- based Classification (SRC) algorithm for open set recognition where not all classes presented during testing are known during training. The SRC algorithm uses class reconstruction errors for classification. As most of the discriminative information for open set recognition is hidden in the tail part of the matched and sum of non-matched reconstruction error distributions, we model the tail of those two error distributions using the statistical Extreme Value Theory (EVT). Then we simplify the open set recognition problem into a set of hypothesis testing problems. The confidence scores corresponding to the tail distributions of a novel test sample are then fused to determine its identity. The effectiveness of the proposed method is demonstrated using four publicly available image and object classification datasets and it is shown that this method can perform significantly better than many competitive open set recognition algorithms. Code is public available: https://github.com/hezhangsprinter/SROS