A scheme for demonstration of fractional statistics of anyons in an exactly solvable model

We propose a scheme to demonstrate fractional statistics of anyons in an exactly solvable lattice model proposed by Kitaev that involves four-body interactions. The required many-body ground state, as well as the anyon excitations and their braiding operations, can be conveniently realized through \textit{dynamic}laser manipulation of cold atoms in an optical lattice. Due to the perfect localization of anyons in this model, we show that a quantum circuit with only six qubits is enough for demonstration of the basic braiding statistics of anyons. This opens up the immediate possibility of proof-of-principle experiments with trapped ions, photons, or nuclear magnetic resonance systems.Comment: 4 pages, 3 figure

Artificial Vascular Bifurcations – Design and Modelling

Sharp corners in additive manufactured (AM) bifurcated vascular networks cause mechanical aggregation and unrealistic numerical results. A novel parametric bifurcation model was adapted in this paper to generate 3D rounded bifurcations that would solve this problem. Parameters and control variables in the parametric model were selected according to the design rules and the extended parametric map. Preliminary computational fluid dynamic studies were carried out in order to link hydrodynamic risks with local geometry designs. Both positive and negative aspects were found in such designs

KDM2B/FBXL10 targets c-Fos for ubiquitylation and degradation in response to mitogenic stimulation.

KDM2B (also known as FBXL10) controls stem cell self-renewal, somatic cell reprogramming and senescence, and tumorigenesis. KDM2B contains multiple functional domains, including a JmjC domain that catalyzes H3K36 demethylation and a CxxC zinc-finger that recognizes CpG islands and recruits the polycomb repressive complex 1. Here, we report that KDM2B, via its F-box domain, functions as a subunit of the CUL1-RING ubiquitin ligase (CRL1/SCF(KDM2B)) complex. KDM2B targets c-Fos for polyubiquitylation and regulates c-Fos protein levels. Unlike the phosphorylation of other SCF (SKP1-CUL1-F-box)/CRL1 substrates that promotes substrates binding to F-box, epidermal growth factor (EGF)-induced c-Fos S374 phosphorylation dissociates c-Fos from KDM2B and stabilizes c-Fos protein. Non-phosphorylatable and phosphomimetic mutations at S374 result in c-Fos protein which cannot be induced by EGF or accumulates constitutively and lead to decreased or increased cell proliferation, respectively. Multiple tumor-derived KDM2B mutations impaired the function of KDM2B to target c-Fos degradation and to suppress cell proliferation. These results reveal a novel function of KDM2B in the negative regulation of cell proliferation by assembling an E3 ligase to targeting c-Fos protein degradation that is antagonized by mitogenic stimulations

Magnetic fields of the W4 superbubble

Superbubbles and supershells are the channels for transferring mass and energy from the Galactic disk to the halo. Magnetic fields are believed to play a vital role in their evolution. We study the radio continuum and polarized emission properties of the W4 superbubble to determine its magnetic field strength. New sensitive radio continuum observations were made at 6 cm, 11 cm, and 21 cm. The total intensity measurements were used to derive the radio spectrum of the W4 superbubble. The linear polarization data were analysed to determine the magnetic field properties within the bubble shells. The observations show a multi-shell structure of the W4 superbubble. A flat radio continuum spectrum that stems from optically thin thermal emission is derived from 1.4 GHz to 4.8 GHz. By fitting a passive Faraday screen model and considering the filling factor fne , we obtain the thermal electron density ne = 1.0/\sqrt{fne} (\pm5%) cm^-3 and the strength of the line-of-sight component of the magnetic field B// = -5.0/\sqrt{fne} (\pm10%) {\mu}G (i.e. pointing away from us) within the western shell of the W4 superbubble. When the known tilted geometry of the W4 superbubble is considered, the total magnetic field Btot in its western shell is greater than 12 {\mu}G. The electron density and the magnetic field are lower and weaker in the high-latitude parts of the superbubble. The rotation measure is found to be positive in the eastern shell but negative in the western shell of the W4 superbubble, which is consistent with the case that the magnetic field in the Perseus arm is lifted up from the plane towards high latitudes. The magnetic field strength and the electron density we derived for the W4 superbubble are important parameters for evolution models of superbubbles breaking out of the Galactic plane.Comment: 13 pages, 8 figures, accepted for publication in Astronomy & Astrophysic

From cyber-security deception to manipulation and gratification through gamification

Over the last two decades the field of cyber-security has experienced numerous changes associated with the evolution of other fields, such as networking, mobile communications, and recently the Internet of Things (IoT) [3]. Changes in mindsets have also been witnessed, a couple of years ago the cyber-security industry only blamed users for their mistakes often depicted as the number one reason behind security breaches. Nowadays, companies are empowering users, modifying their perception of being the weak link, into being the center-piece of the network design [4]. Users are by definition "in control" and therefore a cyber-security asset. Researchers have focused on the gamification of cyber- security elements, helping users to learn and understand the concepts of attacks and threats, allowing them to become the first line of defense to report anoma- lies [5]. However, over the past years numerous infrastructures have suffered from malicious intent, data breaches, and crypto-ransomeware, clearly showing the technical "know-how" of hackers and their ability to bypass any security in place, demonstrating that no infrastructure, software or device can be consid- ered secure. Researchers concentrated on the gamification, learning and teaching theory of cyber-security to end-users in numerous fields through various techniques and scenarios to raise cyber-situational awareness [2][1]. However, they overlooked the users’ ability to gather information on these attacks. In this paper, we argue that there is an endemic issue in the the understanding of hacking practices leading to vulnerable devices, software and architectures. We therefore propose a transparent gamification platform for hackers. The platform is designed with hacker user-interaction and deception in mind enabling researchers to gather data on the techniques and practices of hackers. To this end, we developed a fully extendable gamification architecture allowing researchers to deploy virtualised hosts on the internet. Each virtualised hosts contains a specific vulnerability (i.e. web application, software, etc). Each vulnerability is connected to a game engine, an interaction engine and a scoring engine

An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification

While deep learning methods are increasingly being applied to tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by target users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level radiologist semantic features, and 2) a high-level malignancy prediction score. The low-level semantic outputs quantify the diagnostic features used by radiologists and serve to explain how the model interprets the images in an expert-driven manner. The information from these low-level tasks, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level task of predicting nodule malignancy. This unified architecture is trained by optimizing a global loss function including both low- and high-level tasks, thereby learning all the parameters within a joint framework. Our experimental results using the Lung Image Database Consortium (LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves significantly better results compared to common 3D CNN approaches