From first-order magneto-elastic to magneto-structural transition in (Mn,Fe)1.95P0.50Si0.50 compounds

We report on structural, magnetic and magnetocaloric properties of MnxFe1.95-xP0.50Si0.50 (x > 1.10) compounds. With increasing the Mn:Fe ratio, a first-order magneto-elastic transition gradually changes into a first-order magneto-structural transition via a second-order magnetic transition. The study also shows that thermal hysteresis can be tuned by varying the Mn:Fe ratio. Small thermal hysteresis (less than 1 K) can be obtained while maintaining a giant magnetocaloric effect. This achievement paves the way for real refrigeration applications using magnetic refrigerants.Comment: 4 pages, 3 figures, Supplemental Materia

Overlapping Coalition Formation for Efficient Data Fusion in Multi-Sensor Networks

This paper develops new algorithms for coalition formation within multi-sensor networks tasked with performing wide-area surveillance. Specifically, we cast this application as an instance of coalition formation, with overlapping coalitions. We show that within this application area sub-additive coalition valuations are typical, and we thus use this structural property of the problem to we derive two novel algorithms (an approximate greedy one that operates in polynomial time and has a calculated bound to the optimum, and an optimal branch-and-bound one) to find the optimal coalition structure in this instance. We empirically evaluate the performance of these algorithms within a generic model of a multi-sensor network performing wide area surveillance. These results show that the polynomial algorithm typically generated solutions much closer the optimal than the theoretical bound, and prove the effectiveness of our pruning procedure

A Knowledge Representation Model Based on Select and Test Algorithm for Diagnosing Breast Cancer

There exist several terminal diseases whose fatality rate escalates with time of which breast cancer is a frontline disease among such. Computer aided systems have also been well researched through the use intelligent algorithms capable of detecting, diagnosing, and proffering treatment for breast cancer.  While good research breakthrough has been attained in terms of algorithmic solution towards diagnosis of breast cancer, however, not much has been done to sufficiently model knowledge frameworks for diagnostic algorithms that are knowledge-based. While Select and Test (ST) algorithm have proven relevant for implementing diagnostic systems, through support for reasoning, however the knowledge representation pattern that enables inference of missing or ambiguous data still limits the effectiveness of ST algorithm. This paper therefore proposes a knowledge representation model to systematically model knowledge to aid the performance of ST algorithm. Our proposal is specifically targeted at developing systematic knowledge representation for breast cancer. The approach uses the ontology web language (OWL) to implement the design of the knowledge model proposed.   This study aims at carefully crafting a knowledge model whose implementation seamlessly work with ST algorithm. Furthermore, this study adapted the proposed model into an implementation of ST algorithm an obtained an improved performance compared to the simple knowledge model proposed by the author of ST algorithm. Our knowledge mode resulted in an accuracy gain of 23.5% and obtained and AUC of (0.49, 1.0). This proposed model has therefore shown that combining an inference-oriented knowledge model with an inference-oriented reasoning algorithm improves the performance of computer aided diagnostic (CADx) systems. In future, we intend to enhance the proposed model to support rules. Keywords— Semantic web, ontology, OWL, breast cancer, Select and Test (ST) algorithm, knowledge representatio

Spectral Theory for Non-linear Superconducting Microwave Systems: Extracting Relaxation Rates and Mode Hybridization

The accurate modeling of mode hybridization and calculation of radiative relaxation rates have been crucial to the design and optimization of superconducting quantum devices. In this work, we introduce a spectral theory for the electrohydrodynamics of superconductors that enables the extraction of the relaxation rates of excitations in a general three-dimensional distribution of superconducting bodies. Our approach addresses the long-standing problem of formulating a modal description of open systems that is both efficient and allows for second quantization of the radiative hybridized fields. This is achieved through the implementation of finite but transparent boundaries through which radiation can propagate into and out of the computational domain. The resulting spectral problem is defined within a coarse-grained formulation of the electrohydrodynamical equations that is suitable for the analysis of the non-equilibrium dynamics of multiscale superconducting quantum systems.Comment: 21 pages, 12 figures, journal pape

Unsupervised Representative Selection and Signal Unmixing

This thesis presents unsupervised machine learning algorithms to tackle two related problems: selecting representatives in a dataset and identifying constituent components in mixture data. In both problems, we aim to reveal a few key hidden features that sufficiently explain the data. The main intuition behind our algorithms is that, in an appropriately constructed dictionary, a sparse representation of the data corresponds to selecting these unknown features. Our goal is to efficiently seek such sparse representations under suitable conditions. In the representative selection problem, our objective is to pick a few representative data points that capture distinguished characteristics of a dataset. This corresponds to identifying the vertices of the polytope generated by the data. To do so, we start by modeling each data point as a convex combination of the polytope vertices. Then, in the dictionary formed by the dataset itself, we look for sparse representations of the data which subsequently imply the vertices. To seek such sparse representations, we proposed a greedy pursuit algorithm and a non-convex entropy minimization algorithm. We theoretically justify our proposed algorithms and demonstrate their vertex recovery performance on both synthetic and real data. In the unmixing problem, we assume that each data point is a mixture of a few unknown components, and we wish to decompose data into these underlying constituents. We consider a highly under-sampled regime in which the number of measurements is far less than the data dimension. Furthermore, we solve an even more challenging unmixing problem in which the under-sampled mixture are indirectly observed via a nonlinear operator such as Sigmoid and Relu. To find the unknown constituents, we form a dictionaries with atoms resembling the constituents and seek the sparse representations corresponding to them. We proposed a fast and robust greedy algorithm, called UnmixMP, to find such sparse representations. We prove its robust unmixing performance and support our theoretical analysis by various experiments on both synthetic and real image data. Our algorithms are fast and robust, and supported by rigorous theoretical analysis. Our experimental results shows that the proposed are significantly more robust than state-of-the-art representative selection and unmixing algorithms in the aforementioned settings

Do Pictures in High School Textbooks Perpetuate Stereotypes?

In today's age of political correctness, stereotypes may enter young impressionable minds from the least likely sources. This study analyzed photographs from high school textbooks and categorized them according to the model's ethnicity, gender and type of activity performed. Results indicated that although the numbers of the different types of models gave a true reflection of the U.S. population, there were still some subtle biases as to how different demographics were depicted