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Cryptocurrencies have emerged as a new form of digital money that has not escaped the eyes of cyber-attackers. Traditionally, they have been maliciously used as a medium of exchange for proceeds of crime in the cyber dark-market by cyber-criminals. However, cyber-criminals have devised an exploitative technique of directly acquiring cryptocurrencies from benign users' CPUs without their knowledge through a process called crypto mining. The presence of crypto mining activities in a network is often an indicator of compromise of illegal usage of network resources for crypto mining purposes. Crypto mining has had a financial toll on victims such as corporate networks and individual home users. This paper addresses the detection of crypto mining attacks in a generic network environment using dynamic network characteristics. It tackles an in-depth overview of crypto mining operational details and proposes a semi-supervised machine learning approach to detection using various crypto mining features derived from complex network characteristics. The results demonstrate that the integration of semi-supervised learning with complex network theory modeling is effective at detecting crypto mining activities in a network environment. Such an approach is helpful during security mitigation by network security administrators and law enforcement agencies.

We show that the higher dimensional Weierstrass and Ehrlich-Aberth methods for finding roots of polynomials have infinite orbits that diverge to infinity. This is possible for the Jacobi update scheme (all coordinates are updated in parallel) as well as Gauss-Seidel (any coordinate update is used for all subsequent coordinates)., Comment: 10 pages

We present a fully Bayesian approach for the inference of radial profiles of impurity transport coefficients and compare its results to neoclassical, gyrofluid and gyrokinetic modeling. Using nested sampling, the Bayesian Impurity Transport InferencE (BITE) framework can handle complex parameter spaces with multiple possible solutions, offering great advantages in interpretative power and reliability with respect to previously demonstrated methods. BITE employs a forward model based on the pySTRAHL package, built on the success of the well-known STRAHL code [Dux, IPP Report, 2004], to simulate impurity transport in magnetically-confined plasmas. In this paper, we focus on calcium (Ca, Z=20) Laser Blow-Off injections into Alcator C-Mod plasmas. Multiple Ca atomic lines are diagnosed via high-resolution X-ray Imaging Crystal Spectroscopy and Vacuum Ultra-Violet measurements. We analyze a sawtoothing I-mode discharge for which neoclassical and turbulent (quasilinear and nonlinear) predictions are also obtained. We find good agreement in diffusion across the entire radial extent, while turbulent convection and density profile peaking are estimated to be larger in experiment than suggested by theory. Efforts and challenges associated with the inference of experimental pedestal impurity transport are discussed., Comment: 38 pages, 19 figures, submitted for publication in Nuclear Fusion

Vertical diamond Schottky diodes with blocking voltages $V_{\text{BD}} > 2.4 \text{ kV}$ and on-resistances $R_{\text{On}} < 400 \text{ m}\Omega \text{cm}^{2}$ were fabricated on homoepitaxially grown diamond layers with different surface morphologies. The morphology (smooth as-grown, hillock-rich, polished) influences the Schottky barrier, the carrier transport properties, and consequently the device performance. The smooth as-grown sample exhibited a low reverse current density $J_{\text{Rev}} < 10^{-4} \text{ A}/\text{cm}^{2}$ for reverse voltages up to $2.2 \text{ kV}$. The hillock-rich sample blocked similar voltages with a slight increase in the reverse current density ($J_{\text{Rev}} < 10^{-3} \text{ A}/\text{cm}^{2}$). The calculated 1D-breakdown field, however, was reduced by $30 \text{ } \%$, indicating a field enhancement induced by the inhomogeneous surface. The polished sample demonstrated a similar breakdown voltage and reverse current density as the smooth as-grown sample, suggesting that a polished surface can be suitable for device fabrication. However, a statistical analysis of several diodes of each sample showed the importance of the substrate quality: A high density of defects both reduces the feasible device area and increases the reverse current density. In forward direction, the hillock-rich sample exhibited a secondary Schottky barrier, which could be fitted with a modified thermionic emission model employing the Lambert W-function. Both polished and smooth sample showed nearly ideal thermionic emission with ideality factors $1.08$ and $1.03$, respectively. Compared with literature, all three diodes exhibit an improved Baliga Figure of Merit for diamond Schottky diodes with $V_{\text{BD}} > 2 \text{ kV}$., Comment: Accepted April 20, 2020

Designing agent that can autonomously discover and learn a diversity of structures and skills in unknown changing environments is key for lifelong machine learning. A central challenge is how to learn incrementally representations in order to progressively build a map of the discovered structures and re-use it to further explore. To address this challenge, we identify and target several key functionalities. First, we aim to build lasting representations and avoid catastrophic forgetting throughout the exploration process. Secondly we aim to learn a diversity of representations allowing to discover a "diversity of diversity" of structures (and associated skills) in complex high-dimensional environments. Thirdly, we target representations that can structure the agent discoveries in a coarse-to-fine manner. Finally, we target the reuse of such representations to drive exploration toward an "interesting" type of diversity, for instance leveraging human guidance. Current approaches in state representation learning rely generally on monolithic architectures which do not enable all these functionalities. Therefore, we present a novel technique to progressively construct a Hierarchy of Observation Latent Models for Exploration Stratification, called HOLMES. This technique couples the use of a dynamic modular model architecture for representation learning with intrinsically-motivated goal exploration processes (IMGEPs). The paper shows results in the domain of automated discovery of diverse self-organized patterns, considering as testbed the experimental framework from Reinke et al. (2019).

Image-based tracking of medical instruments is an integral part of surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracking medical instruments based on laparoscopic video data. However, the proposed methods still tend to fail when applied to challenging images and do not generalize well to data they have not been trained on. This paper introduces the Heidelberg Colorectal (HeiCo) data set - the first publicly available data set enabling comprehensive benchmarking of medical instrument detection and segmentation algorithms with a specific emphasis on method robustness and generalization capabilities. Our data set comprises 30 laparoscopic videos and corresponding sensor data from medical devices in the operating room for three different types of laparoscopic surgery. Annotations include surgical phase labels for all video frames as well as information on instrument presence and corresponding instance-wise segmentation masks for surgical instruments (if any) in more than 10,000 individual frames. The data has successfully been used to organize international competitions within the Endoscopic Vision Challenges 2017 and 2019., Comment: Submitted to Nature Scientific Data

This paper introduces iterated monodromy groups for transcendental functions and discusses them in the simplest setting, for post-singularly finite exponential functions. These groups are self-similar groups in a natural way, based on an explicit construction in terms of kneading sequences. We investigate the group theoretic properties of these groups, and show in particular that they are amenable, but they are not elementary subexponentially amenable., Comment: 16 Pages, Comments welcome!

Reinforcement learning (RL) allows to solve complex tasks such as Go often with a stronger performance than humans. However, the learned behaviors are usually fixed to specific tasks and unable to adapt to different contexts. Here we consider the case of adapting RL agents to different time restrictions, such as finishing a task with a given time limit that might change from one task execution to the next. We define such problems as Time Adaptive Markov Decision Processes and introduce two model-free, value-based algorithms: the Independent Gamma-Ensemble and the n-Step Ensemble. In difference to classical approaches, they allow a zero-shot adaptation between different time restrictions. The proposed approaches represent general mechanisms to handle time adaptive tasks making them compatible with many existing RL methods, algorithms, and scenarios., Comment: ICLR 2020 Workshop: Beyond Tabula Rasa in Reinforcement Learning

We study the action of groups generated by bounded activity automata with infinite alphabets on their orbital Schreier graphs. We introduce an amenability criterion for such groups based on the recurrence of the first level action. This criterion is a natural extension of the result that all groups generated by bounded activity automata with finite alphabets are amenable. Our motivation comes from the investigation of iterated monodromy groups of entire functions., Comment: 9 pages

Finding roots of univariate polynomials is one of the fundamental tasks of numerics, and there is still a wide gap between root finders that are well understood in theory and those that perform well in practice. We investigate the root finding method of Weierstrass, a root finder that tries to approximate all roots of a given polynomial in parallel (in the Jacobi version, i.e., with parallel updates). This method has a good reputation for finding all roots in practice except in obvious cases of symmetry, but very little is known about its global dynamics and convergence properties. We show that the Weierstrass method, like the well known Newton method, is not generally convergent: there are open sets of polynomials $p$ of every degree $d \ge 3$ such that the dynamics of the Weierstrass method applied to $p$ exhibits attracting periodic orbits. Specifically, all polynomials sufficiently close to $Z^3 + Z + 180$ have attracting cycles of period $4$. Here, period $4$ is minimal: we show that for cubic polynomials, there are no periodic orbits of length $2$ or $3$ that attract open sets of starting points. We also establish another convergence problem for the Weierstrass method: for almost every polynomial of degree $d\ge 3$ there are orbits that are defined for all iterates but converge to $\infty$; this is a problem that does not occur for Newton's method. Our results are obtained by first interpreting the original problem coming from numerical mathematics in terms of higher-dimensional complex dynamics, then phrasing the question in algebraic terms in such a way that we could finally answer it by applying methods from computer algebra., Comment: 26 pages, 4 figures

Hybrid oxide molecular beam epitaxy (hMBE), a thin-film deposition technique in which transition metal cations are delivered using a metal-organic precursor, has emerged as the state-of-the-art approach to the synthesis of electronic-grade complex oxide films with a stoichiometric growth window. However, numerous questions remain regarding the chemical mechanisms of the growth process and the surface properties of the resulting films. To examine these properties, thin film SrTiO$_{3}$ (STO) was prepared by hMBE using a titanium tetraisopropoxide (TTIP) precursor for Ti delivery and an elemental Sr source on annealed STO and Nb-doped STO substrates with varying TTIP:Sr flux ratios to examine the conditions for the reported stoichiometric growth window. The films were transferred in vacuo to an x-ray photoelectron spectroscopy system to study the surface elemental composition. Samples were examined using x-ray diffraction to compare our surface sensitive results with previously reported measurements of the bulk of the films in the literature. Ex situ studies by atomic force microscopy, scanning tunneling microscopy and low energy electron microscopy confirmed the presence of surface reconstructions and an Ehrlich-Schwoebel barrier consistent with an A-site SrO termination. We find that a surface exhibiting a mixture of SrO and TiO$_{2}$ termination, or a full SrO termination is necessary to obtain stoichiometric adsorption-controlled growth. These results indicate that surface Sr is necessary to maintain chemical equilibrium for stoichiometric growth during the hMBE process, which is important for the design of future interfacial systems using this technique., Comment: 37 pages, 6 figures, 10 supplemental figures

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts)., Comment: A pre-print