University of California, Santa Barbara

About: University of California, Santa Barbara is a education organization based out in Santa Barbara, California, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 30281 authors who have published 80852 publications receiving 4626827 citations. The organization is also known as: UC Santa Barbara & UCSB.

An optical-frequency synthesizer using integrated photonics

Abstract: Optical-frequency synthesizers, which generate frequency-stable light from a single microwave-frequency reference, are revolutionizing ultrafast science and metrology, but their size, power requirement and cost need to be reduced if they are to be more widely used. Integrated-photonics microchips can be used in high-coherence applications, such as data transmission1, highly optimized physical sensors2 and harnessing quantum states3, to lower cost and increase efficiency and portability. Here we describe a method for synthesizing the absolute frequency of a lightwave signal, using integrated photonics to create a phase-coherent microwave-to-optical link. We use a heterogeneously integrated III–V/silicon tunable laser, which is guided by nonlinear frequency combs fabricated on separate silicon chips and pumped by off-chip lasers. The laser frequency output of our optical-frequency synthesizer can be programmed by a microwave clock across 4 terahertz near 1,550 nanometres (the telecommunications C-band) with 1 hertz resolution. Our measurements verify that the output of the synthesizer is exceptionally stable across this region (synthesis error of 7.7 × 10−15 or below). Any application of an optical-frequency source could benefit from the high-precision optical synthesis presented here. Leveraging high-volume semiconductor processing built around advanced materials could allow such low-cost, low-power and compact integrated-photonics devices to be widely used. An optical-frequency synthesizer based on stabilized frequency combs has been developed utilizing chip-scale devices as key components, in a move towards using integrated photonics technology for ultrafast science and metrology.

Geographic variation in female preferences for male traits in poecilia reticulata.

Abstract: We examined the preferences of female guppies (Poecilia reticulata) from 11 localities in Trinidad with respect to male color-pattern elements, body shape and size, and overall color and brightness contrast. Females are on average more attracted to males from their own population than from alien populations, and populations appear to vary in the criteria used in female choice. Multiple-regression analysis suggests that mate-preference criteria vary among localities in intensity, sign, and the number of traits used. Although preference estimators and color-pattern parameters are unique to each population, only orange, black, and color contrast showed a correlation between degree of male trait and degree of preference for that trait. There is a clear effect of water color and a possible effect of predation intensity. The results are discussed in light of various models of sexual selection and the early stages of speciation.

Detection and analysis of drive-by-download attacks and malicious JavaScript code

Abstract: JavaScript is a browser scripting language that allows developers to create sophisticated client-side interfaces for web applications. However, JavaScript code is also used to carry out attacks against the user's browser and its extensions. These attacks usually result in the download of additional malware that takes complete control of the victim's platform, and are, therefore, called "drive-by downloads." Unfortunately, the dynamic nature of the JavaScript language and its tight integration with the browser make it difficult to detect and block malicious JavaScript code.This paper presents a novel approach to the detection and analysis of malicious JavaScript code. Our approach combines anomaly detection with emulation to automatically identify malicious JavaScript code and to support its analysis. We developed a system that uses a number of features and machine-learning techniques to establish the characteristics of normal JavaScript code. Then, during detection, the system is able to identify anomalous JavaScript code by emulating its behavior and comparing it to the established profiles. In addition to identifying malicious code, the system is able to support the analysis of obfuscated code and to generate detection signatures for signature-based systems. The system has been made publicly available and has been used by thousands of analysts.