Design implementation and measurement of a collision avoidance multiple broadcast tree network

Packet collisions and their resolution create a performance bottleneck in random access LANs. Collision avoidance switches are a hardware solution to this problem [1, 2]. Collision avoidance switches allow the implementation of random access protocols without the penalty of collisions among packets.In this paper, we describe a design and implementation of a local area network architecture based on collision avoidance, called the Collision Avoidance Multiple Broadcast (CAMB) tree network. Our implementation follows the protocol layering architecture of the IEEE 802 local area networks, and includes CAMB tree switches, station/network interface boards, and support of transport protocols. We also present the performance measurements of our experimental CAMB tree network

New Osmosis Law and Theory: the New Formula that Replaces van't Hoff Osmotic Pressure Equation

This article derived a new abstract concept from the osmotic process and concluded it via "osmotic force" with a new law -- "osmotic law". The "osmotic law" describes that, in an osmotic system, osmolyte moves osmotically from the side with higher "osmotic force" to the side with lower "osmotic force". In addition, it was proved mathematically that the osmotic process could be explained perfectly via "osmotic force" and "osmotic laws", which can prevent the difficulties in using current "osmotic pressure" concept to explain the osmotic process and phenomenon. A theory and equation to describe the curve of osmotic pressure vs. different ideal solution concentrations are also derived, which can overcome the limitedness and incompleteness of van't Hoff osmotic pressure formula (a linear equation) which is applicable to ideal dilute solution only

Collision Avoidance Tree networks

The Collision Avoidance Tree is a new local area network based on a hardware device called collision avoidance switch, which arbitrates random access to a shared communications channel. Collision Avoidance Tree combines the benefits of random access (low delay when traffic is light; simple, distributed, and therefore robust, protocols) with concurrency of transmission, excellent network utilization and suitability for the domain of high-speed, optical networking.The Collision Avoidance Tree is classified in two classes: the Collision Avoidance Single Broadcast (CASB) Tree and the Collision Avoidance Multiple Broadcast (CAMB) Tree. The CASB Tree allows only a single transmission on the network at a given time, while the CAMB Tree is more general and allows concurrent transmissions on the network.This paper describes network architectures (e.g., station and switch protocols) and designs and implementations of the CASB and CAMB Trees. Performance results derived from analyses, simulations, measurements of experimental networks are also presented

Robust Independent Component Analysis via Minimum Divergence Estimation

Independent component analysis (ICA) has been shown to be useful in many applications. However, most ICA methods are sensitive to data contamination and outliers. In this article we introduce a general minimum U-divergence framework for ICA, which covers some standard ICA methods as special cases. Within the U-family we further focus on the gamma-divergence due to its desirable property of super robustness, which gives the proposed method gamma-ICA. Statistical properties and technical conditions for the consistency of gamma-ICA are rigorously studied. In the limiting case, it leads to a necessary and sufficient condition for the consistency of MLE-ICA. This necessary and sufficient condition is weaker than the condition known in the literature. Since the parameter of interest in ICA is an orthogonal matrix, a geometrical algorithm based on gradient flows on special orthogonal group is introduced to implement gamma-ICA. Furthermore, a data-driven selection for the gamma value, which is critical to the achievement of gamma-ICA, is developed. The performance, especially the robustness, of gamma-ICA in comparison with standard ICA methods is demonstrated through experimental studies using simulated data and image data.Comment: 7 figure

A Model of Low-lying States in Strongly Interacting Electroweak Symmetry-Breaking Sector

It is proposed that, in a strongly-interacting electroweak sector, besides the Goldstone bosons, the coexistence of a scalar state ($H$) and vector resonances such as $A_1$ [$I^G(J^P)=1^-(1^+$)], $V$ [$1^+(1^-)$] and $\omega_H^{}$ [$0^-(1^-)$] is required by the proper Regge behavior of the forward scattering amplitudes. This is a consequence of the following well-motivated assumptions: (a). Adler-Weisberger-type sum rules and the superconvergence relations for scattering amplitudes hold in this strongly interacting sector; (b). the sum rules at $t=0$ are saturated by a minimal set of low-lying states with appropriate quantum numbers. It therefore suggests that a complete description should include all these resonances. These states may lead to distinctive experimental signatures at future colliders.Comment: revised version, to appear in Modern Physics Letters A; file also available via anonymous ftp at ftp://ucdhep.ucdavis.edu/han/sews/lowlying.p

R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections

The influence of Deep Learning on image identification and natural language processing has attracted enormous attention globally. The convolution neural network that can learn without prior extraction of features fits well in response to the rapid iteration of Android malware. The traditional solution for detecting Android malware requires continuous learning through pre-extracted features to maintain high performance of identifying the malware. In order to reduce the manpower of feature engineering prior to the condition of not to extract pre-selected features, we have developed a coloR-inspired convolutional neuRal networks (CNN)-based AndroiD malware Detection (R2-D2) system. The system can convert the bytecode of classes.dex from Android archive file to rgb color code and store it as a color image with fixed size. The color image is input to the convolutional neural network for automatic feature extraction and training. The data was collected from Jan. 2017 to Aug 2017. During the period of time, we have collected approximately 2 million of benign and malicious Android apps for our experiments with the help from our research partner Leopard Mobile Inc. Our experiment results demonstrate that the proposed system has accurate security analysis on contracts. Furthermore, we keep our research results and experiment materials on http://R2D2.TWMAN.ORG.Comment: Verison 2018/11/15, IEEE BigData 2018, Seattle, WA, USA, Dec 10-13, 2018. (Accepted