adPerf: Characterizing the Performance of Third-party Ads

Monetizing websites and web apps through online advertising is widespread in the web ecosystem. The online advertising ecosystem nowadays forces publishers to integrate ads from these third-party domains. On the one hand, this raises several privacy and security concerns that are actively studied in recent years. On the other hand, given the ability of today's browsers to load dynamic web pages with complex animations and Javascript, online advertising has also transformed and can have a significant impact on webpage performance. The performance cost of online ads is critical since it eventually impacts user satisfaction as well as their Internet bill and device energy consumption. In this paper, we apply an in-depth and first-of-a-kind performance evaluation of web ads. Unlike prior efforts that rely primarily on adblockers, we perform a fine-grained analysis on the web browser's page loading process to demystify the performance cost of web ads. We aim to characterize the cost by every component of an ad, so the publisher, ad syndicate, and advertiser can improve the ad's performance with detailed guidance. For this purpose, we develop an infrastructure, adPerf, for the Chrome browser that classifies page loading workloads into ad-related and main-content at the granularity of browser activities (such as Javascript and Layout). Our evaluations show that online advertising entails more than 15% of browser page loading workload and approximately 88% of that is spent on JavaScript. We also track the sources and delivery chain of web ads and analyze performance considering the origin of the ad contents. We observe that 2 of the well-known third-party ad domains contribute to 35% of the ads performance cost and surprisingly, top news websites implicitly include unknown third-party ads which in some cases build up to more than 37% of the ads performance cost

Characterizing the performance of continuous-variable Gaussian quantum gates

The required set of operations for universal continuous-variable quantum computation can be divided into two primary categories: Gaussian and non-Gaussian operations. Furthermore, any Gaussian operation can be decomposed as a sequence of phase-space displacements and symplectic transformations. Although Gaussian operations are ubiquitous in quantum optics, their experimental realizations generally are approximations of the ideal Gaussian unitaries. In this work, we study different performance criteria to analyze how well these experimental approximations simulate the ideal Gaussian unitaries. In particular, we find that none of these experimental approximations converge uniformly to the ideal Gaussian unitaries. However, convergence occurs in the strong sense, or if the discrimination strategy is energy bounded, then the convergence is uniform in the Shirokov-Winter energy-constrained diamond norm and we give explicit bounds in this latter case. We indicate how these energy-constrained bounds can be used for experimental implementations of these Gaussian unitaries in order to achieve any desired accuracy.Comment: v3: 26 pages, 10 figures, final version accepted for publication in Physical Review Researc

Characterizing the impact of geometric properties of word embeddings on task performance

Analysis of word embedding properties to inform their use in downstream NLP tasks has largely been studied by assessing nearest neighbors. However, geometric properties of the continuous feature space contribute directly to the use of embedding features in downstream models, and are largely unexplored. We consider four properties of word embedding geometry, namely: position relative to the origin, distribution of features in the vector space, global pairwise distances, and local pairwise distances. We define a sequence of transformations to generate new embeddings that expose subsets of these properties to downstream models and evaluate change in task performance to understand the contribution of each property to NLP models. We transform publicly available pretrained embeddings from three popular toolkits (word2vec, GloVe, and FastText) and evaluate on a variety of intrinsic tasks, which model linguistic information in the vector space, and extrinsic tasks, which use vectors as input to machine learning models. We find that intrinsic evaluations are highly sensitive to absolute position, while extrinsic tasks rely primarily on local similarity. Our findings suggest that future embedding models and post-processing techniques should focus primarily on similarity to nearby points in vector space.Comment: Appearing in the Third Workshop on Evaluating Vector Space Representations for NLP (RepEval 2019). 7 pages + reference

On the Uplink Performance of Asynchronous LAS-CDMA

In this paper closed-form formulae are derived for characterizing the BER performance of Large Area Synchronous CDMA (LAS-CDMA) as a function of both the number of resolvable paths Lp and the maximum delay difference τmax, as well as the number of users K, when communicating over a Nakagami-m fading channel. Moreover, we comparatively studies the performance of LAS-CDMA and the traditional random code based DS-CDMA

Embedded Network Test-Bed for Validating Real-Time Control Algorithms to Ensure Optimal Time Domain Performance

The paper presents a Stateflow based network test-bed to validate real-time optimal control algorithms. Genetic Algorithm (GA) based time domain performance index minimization is attempted for tuning of PI controller to handle a balanced lag and delay type First Order Plus Time Delay (FOPTD) process over network. The tuning performance is validated on a real-time communication network with artificially simulated stochastic delay, packet loss and out-of order packets characterizing the network.Comment: 6 pages, 12 figure

Solar Total Energy Project (STEP) Performance Analysis of High Temperature Energy Storage Subsystem

The 1982 milestones and lessons learned; performance in 1983; a typical day's operation; collector field performance and thermal losses; and formal testing are highlighted. An initial test that involves characterizing the high temperature storage (hts) subsystem is emphasized. The primary element is on 11,000 gallon storage tank that provides energy to the steam generator during transient solar conditions or extends operating time. Overnight, thermal losses were analyzed. The length of time the system is operated at various levels of cogeneration using stored energy is reviewed