A Rare Giant Pleural Thymoma in Posterior Mediastinum.

Thymoma is an epithelial neoplasm of the thymus, which commonly lies in the anterior mediastinum. Unusually, thymomas can also be found in other locations. Surgical excision, when feasible, appears to provide good results. We encountered a rare case of a thymoma that developed in the right thoracic cavity, and originating from the pleura in posterior mediastinum. We describe the clinical scenario, investigations, and our management of the patient

Victim-Perpetrator Relationship: Reconnoitering Typology of Victimization in Anita Nair’s Eating Wasps

Violence against women is not new from feticide to cyberbullying. It remains unchanged and experienced by all ages of women from perpetrators in the patriarchal hegemony. Every form of violence affects millions of women’s lives irrespective of age, class, culture, language, religion, and education, and it cannot be stopped by anything due to deep-rooted patriarchy and gender stereotypes. Men (gender stereotypes) become the primary reason for the violence against women and women become vulnerable victims as they are considered ‘Other’, the weaker sex. Victimization has a physical and psychological impact on victims’ rest of their lives. Anita Nair, an Indian author vociferates the different forms of victimization in the modern era in her latest literary oeuvre Eating Wasps (2018). Megha, a six-year-old child, Liliana, an Italian spinster and Najma, a Muslim girl do not share any contribution to victimization but being in the hapless situation entitles them to be victimized. Yet, they are denied to accept the tag ‘victim’ and become survivors by fighting against the odds in their society. This study traverses the relationship between victims and perpetrators through the lens of Benjamin Mendelsohn’s typology of victimization and the challenges encountered by victims in the patriarchal environment.

Density of states of colloidal glasses

Glasses are structurally liquid-like, but mechanically solid-like. Most attempts to understand glasses start from liquid state theory. Here we take the opposite point of view, and use concepts from solid state physics. We determine the vibrational modes of a colloidal glass experimentally, and find soft low-frequency modes that are very different in nature from the usual acoustic vibrations of ordinary solids. These modes extend over surprisingly large length scales

On Improving Reliability of SRAM-Based Physically Unclonable Functions

Physically unclonable functions (PUFs) have been touted for their inherent resistance to invasive attacks and low cost in providing a hardware root of trust for various security applications. SRAM PUFs in particular are popular in industry for key/ID generation. Due to intrinsic process variations, SRAM cells, ideally, tend to have the same start-up behavior. SRAM PUFs exploit this start-up behavior. Unfortunately, not all SRAM cells exhibit reliable start-up behavior due to noise susceptibility. Hence, design enhancements are needed for improving reliability. Some of the proposed enhancements in literature include fuzzy extraction, error-correcting codes and voting mechanisms. All enhancements involve a trade-off between area/power/performance overhead and PUF reliability. This paper presents a design enhancement technique for reliability that improves upon previous solutions. We present simulation results to quantify improvement in SRAM PUF reliability and efficiency. The proposed technique is shown to generate a 128-bit key in ≤0.2 μ\u27\u3eμμ s at an area estimate of 4538 μ\u27\u3eμμ m 2\u27\u3e22 with error rate as low as 10−6\u27\u3e10−610−6 for intrinsic error probability of 15%

Incremental online learning in high dimensions

this article, however, is problematic, as it requires a careful selection of initial ridge regression parameters to stabilize the highly rank-deficient full covariance matrix of the input data, and it is easy to create too much bias or too little numerical stabilization initially, which can trap the local distance metric adaptation in local minima.While the LWPR algorithm just computes about a factor 10 times longer for the 20D experiment in comparison to the 2D experiment, RFWR requires a 1000-fold increase of computation time, thus rendering this algorithm unsuitable for high-dimensional regression. In order to compare LWPR's results to other popular regression methods, we evaluated the 2D, 10D, and 20D cross data sets with gaussian process regression (GP) and support vector (SVM) regression in addition to our LWPR method. It should be noted that neither SVM nor GP methods is an incremental method, although they can be considered state-of-the-art for batch regression under relatively small numbers of training data and reasonable input dimensionality. The computational complexity of these methods is prohibitively high for real-time applications. The GP algorithm (Gibbs & MacKay, 1997) used a generic covariance function and optimized over the hyperparameters. The SVM regression was performed using a standard available package (Saunders et al., 1998) and optimized for kernel choices. Figure 6 compares the performance of LWPR and gaussian processes for the above-mentioned data sets using 100, 300, and 500 training data point

Deferring the learning for better generalization in radial basis neural networks

Proceeding of: International Conference Artificial Neural Networks — ICANN 2001. Vienna, Austria, August 21–25, 2001The level of generalization of neural networks is heavily dependent on the quality of the training data. That is, some of the training patterns can be redundant or irrelevant. It has been shown that with careful dynamic selection of training patterns, better generalization performance may be obtained. Nevertheless, generalization is carried out independently of the novel patterns to be approximated. In this paper, we present a learning method that automatically selects the most appropriate training patterns to the new sample to be predicted. The proposed method has been applied to Radial Basis Neural Networks, whose generalization capability is usually very poor. The learning strategy slows down the response of the network in the generalisation phase. However, this does not introduces a significance limitation in the application of the method because of the fast training of Radial Basis Neural Networks

An efficient approach based on trust and reputation for secured selection of grid resources

Security is a principal concern in offering an infrastructure for the formation of general-purpose computational grids. A number of grid implementations have been devised to deal with the security concerns by authenticating the users, hosts and their interactions in an appropriate fashion. Resource management systems that are sophisticated and secured are inevitable for the efficient and beneficial deployment of grid computing services. The chief factors that can be problematic in the secured selection of grid resources are the wide range of selection and the high degree of strangeness. Moreover, the lack of a higher degree of confidence relationship is likely to prevent efficient resource allocation and utilisation. In this paper, we present an efficient approach for the secured selection of grid resources, so as to achieve secure execution of the jobs. This approach utilises trust and reputation for securely selecting the grid resources. To start with, the self-protection capability and reputation weightage of all the entities are computed, and based on those values, the trust factor (TF) of all the entities are determined. The reputation weightage of an entity is the measure of both the user’s feedback and other entities’ feedback. Those entities with higher TF values are selected for the secured execution of jobs. To make the proposed approach more comprehensive, a novel method is employed for evaluating the user’s feedback on the basis of the existing feedbacks available regarding the entities. This approach is proved to be scalable for an increased number of user jobs and grid entities. The experimentation portrays that this approach offers desirable efficiency in the secured selection of grid resources