The role of field redefinition on renormalisability of a general N=12N=\frac{1}{2} supersymmetric gauge theories

We investigate some issues on renormalisability of non-anticommutative supersymmetric gauge theory related to field redefinitions. We study one loop corrections to $N=\frac{1}{2}$ supersymmetric $SU(N)\times U(1)$ gauge theory coupled to chiral matter in component formalism, and show the procedure which has been introduced for renormalisation is problematic because some terms which are needed for the renormalisability of theory are missed from the Lagrangian. In order to prove the theory is renormalisable, we redefine the gaugino and the auxiliary fields($\lambda, \bar F$), which result in a modified form of the Lagrangian in the component formalism. Then, we show the modified Lagrangian has extra terms which are necessary for renormalisability of non-anticommutative supersymmetric gauge field theories. Finally we prove $N = \frac{1}{2}$ supersymmetric gauge theory is renormalisable up to one loop corrections using standard method of renormalisation; besides, it is shown the effective action is gauge invariant.Comment: arXiv admin note: text overlap with arXiv:hep-th/0505248 by other author

Development of a Reference Wafer for On-Wafer Testing of Extreme Impedance Devices

This paper describes the design, fabrication, and testing of an on-wafer substrate that has been developed specifically for measuring extreme impedance devices using an on-wafer probe station. Such devices include carbon nano-tubes (CNTs) and structures based on graphene which possess impedances in the κ Ω range and are generally realised on the nano-scale rather than the micro-scale that is used for conventional on-wafer measurement. These impedances are far removed from the conventional 50- reference impedance of the test equipment. The on-wafer substrate includes methods for transforming from the micro-scale towards the nano-scale and reference standards to enable calibrations for extreme impedance devices. The paper includes typical results obtained from the designed wafer

The category {\bf Rel}({\bf Nom})

The category ${\rm Rel}(\mathcal{C})$ may be formed for any category $\mathcal{C}$ with finite limits using the same objects as $\mathcal{C}$ but whose morphisms from $X$ to $Y$ are binary relations in $\mathcal{C}$, that is, subobjects of $X\times Y$. In this paper, concerning the topos ${\bf Nom}$, we study the category ${\bf Rel}({\bf Nom})$. In this category, we define and investigate certain morphisms, such as deterministic morphisms. Then, stochastic mappings between nominal sets are defined by exploiting the underlying relation of functions between nominal sets. This allows one to reinterpret concepts and earlier results in terms of morphisms

“Dicing and splicing” sphingosine kinase and relevance to cancer

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Sphingosine kinase (SphK) is a lipid enzyme that maintains cellular lipid homeostasis. Two SphK isozymes, SphK1 and SphK2, are expressed from different chromosomes and several variant isoforms are expressed from each of the isozymes, allowing for the multi-faceted biological diversity of SphK activity. Historically, SphK1 is mainly associated with oncogenicity, however in reality, both SphK1 and SphK2 isozymes possess oncogenic properties and are recognized therapeutic targets. The absence of mutations of SphK in various cancer types has led to the theory that cancer cells develop a dependency on SphK signaling (hyper-SphK signaling) or “non-oncogenic addiction”. Here we discuss additional theories of SphK cellular mislocation and aberrant “dicing and splicing” as contributors to cancer cell biology and as key determinants of the success or failure of SphK/S1P (sphingosine 1 phosphate) based therapeutics

Mammalian sphingosine kinase (SphK) isoenzymes and isoform expression: Challenges for SphK as an oncotarget

Copyright: © Hatoum et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The various sphingosine kinase (SphK) isoenzymes (isozymes) and isoforms, key players in normal cellular physiology, are strongly implicated in cancer and other diseases. Mutations in SphKs, that may justify abnormal physiological function, have not been recorded. Nonetheless, there is a large and growing body of evidence demonstrating the contribution of gain or loss of function and the imbalance in the SphK/S1P rheostat to a plethora of pathological conditions including cancer, diabetes and inflammatory diseases. SphK is expressed as two isozymes SphK1 and SphK2, transcribed from genes located on different chromosomes and both isozymes catalyze the phosphorylation of sphingosine to S1P. Expression of each SphK isozyme produces alternately spliced isoforms. In recent years the importance of the contribution of SpK1 expression to treatment resistance in cancer has been highlighted and, additionally, differences in treatment outcome appear to also be dependent upon SphK isoform expression. This review focuses on an exciting emerging area of research involving SphKs functions, expression and subcellular localization, highlighting the complexity of targeting SphK in cancer and also comorbid diseases. This review also covers the SphK isoenzymes and isoforms from a historical perspective, from their first discovery in murine species and then in humans, their role(s) in normal cellular function and in disease processes, to advancement of SphK as an oncotarget

Mammalian sphingosine kinase (SphK) isoenzymes and isoform expression: challenges for SphK as an oncotarget.

The various sphingosine kinase (SphK) isoenzymes (isozymes) and isoforms, key players in normal cellular physiology, are strongly implicated in cancer and other diseases. Mutations in SphKs, that may justify abnormal physiological function, have not been recorded. Nonetheless, there is a large and growing body of evidence demonstrating the contribution of gain or loss of function and the imbalance in the SphK/S1P rheostat to a plethora of pathological conditions including cancer, diabetes and inflammatory diseases. SphK is expressed as two isozymes SphK1 and SphK2, transcribed from genes located on different chromosomes and both isozymes catalyze the phosphorylation of sphingosine to S1P. Expression of each SphK isozyme produces alternately spliced isoforms. In recent years the importance of the contribution of SpK1 expression to treatment resistance in cancer has been highlighted and, additionally, differences in treatment outcome appear to also be dependent upon SphK isoform expression. This review focuses on an exciting emerging area of research involving SphKs functions, expression and subcellular localization, highlighting the complexity of targeting SphK in cancer and also comorbid diseases. This review also covers the SphK isoenzymes and isoforms from a historical perspective, from their first discovery in murine species and then in humans, their role(s) in normal cellular function and in disease processes, to advancement of SphK as an oncotarget

PPFL: privacy-preserving federated learning with trusted execution environments

We propose and implement a Privacy-preserving Federated Learning (PPFL) framework for mobile systems to limit privacy leakages in federated learning. Leveraging the widespread presence of Trusted Execution Environments (TEEs) in high-end and mobile devices, we utilize TEEs on clients for local training, and on servers for secure aggregation, so that model/gradient updates are hidden from adversaries. Challenged by the limited memory size of current TEEs, we leverage greedy layer-wise training to train each model's layer inside the trusted area until its convergence. The performance evaluation of our implementation shows that PPFL can significantly improve privacy while incurring small system overheads at the client-side. In particular, PPFL can successfully defend the trained model against data reconstruction, property inference, and membership inference attacks. Furthermore, it can achieve comparable model utility with fewer communication rounds (0.54x) and a similar amount of network traffic (1.002x) compared to the standard federated learning of a complete model. This is achieved while only introducing up to ~15% CPU time, ~18% memory usage, and ~21% energy consumption overhead in PPFL's client-side