Real-Time Recommendation of Streamed Data

This tutorial addressed two trending topics in the field of recommender systems research, namely A/B testing and real-time recommendations of streamed data. Focusing on the news domain, participants learned how to benchmark the performance of stream-based recommendation algorithms in a live recommender system and in a simulated environment

Benchmarking News Recommendations in a Living Lab

Most user-centric studies of information access systems in literature suffer from unrealistic settings or limited numbers of users who participate in the study. In order to address this issue, the idea of a living lab has been promoted. Living labs allow us to evaluate research hypotheses using a large number of users who satisfy their information need in a real context. In this paper, we introduce a living lab on news recommendation in real time. The living lab has first been organized as News Recommendation Challenge at ACM RecSys’13 and then as campaign-style evaluation lab NEWSREEL at CLEF’14. Within this lab, researchers were asked to provide news article recommendations to millions of users in real time. Different from user studies which have been performed in a laboratory, these users are following their own agenda. Consequently, laboratory bias on their behavior can be neglected. We outline the living lab scenario and the experimental setup of the two benchmarking events. We argue that the living lab can serve as reference point for the implementation of living labs for the evaluation of information access systems

Overview of CLEF NEWSREEL 2014: News Recommendations Evaluation Labs

This paper summarises objectives, organisation, and results of the first news recommendation evaluation lab (NEWSREEL 2014). NEWSREEL targeted the evaluation of news recommendation algorithms in the form of a campaignstyle evaluation lab. Participants had the chance to apply two types of evaluation schemes. On the one hand, participants could apply their algorithms onto a data set. We refer to this setting as off-line evaluation. On the other hand, participants could deploy their algorithms on a server to interactively receive recommendation requests. We refer to this setting as on-line evaluation. This setting ought to reveal the actual performance of recommendation methods. The competition strived to illustrate differences between evaluation with historical data and actual users. The on-line evaluation does reflect all requirements which active recommender systems face in practise. These requirements include real-time responses and large-scale data volumes. We present the competition’s results and discuss commonalities regarding participants’ approaches

Optimizing MapReduce for Highly Distributed Environments

MapReduce, the popular programming paradigm for large-scale data processing, has traditionally been deployed over tightly-coupled clusters where the data is already locally available. The assumption that the data and compute resources are available in a single central location, however, no longer holds for many emerging applications in commercial, scientific and social networking domains, where the data is generated in a geographically distributed manner. Further, the computational resources needed for carrying out the data analysis may be distributed across multiple data centers or community resources such as Grids. In this paper, we develop a modeling framework to capture MapReduce execution in a highly distributed environment comprising distributed data sources and distributed computational resources. This framework is flexible enough to capture several design choices and performance optimizations for MapReduce execution. We propose a model-driven optimization that has two key features: (i) it is end-to-end as opposed to myopic optimizations that may only make locally optimal but globally suboptimal decisions, and (ii) it can control multiple MapReduce phases to achieve low runtime, as opposed to single-phase optimizations that may control only individual phases. Our model results show that our optimization can provide nearly 82% and 64% reduction in execution time over myopic and single-phase optimizations, respectively. We have modified Hadoop to implement our model outputs, and using three different MapReduce applications over an 8-node emulated PlanetLab testbed, we show that our optimized Hadoop execution plan achieves 31-41% reduction in runtime over a vanilla Hadoop execution. Our model-driven optimization also provides several insights into the choice of techniques and execution parameters based on application and platform characteristics

The plista dataset

Releasing datasets has fostered research in fields such as information retrieval and recommender systems. Datasets are typically tailored for specific scenarios. In this work, we present the plista dataset. The dataset contains a collection of news articles published on 13 news portals. Additionally, the dataset comprises user interactions with those articles. We inctroduce the dataset’s main characteristics. Further, we illustrate possible applications of the dataset

Physical Modeling of Process-Machine-Interactions in Micro Machining

Increasing demands for smaller and smarter devices in a variety of applications requires the investigation of process-machine-interactions in micro manufacturing to ensure process results that guarantee part functionality. One approach is the use of simulation-based physical models. In this contribution, methods for the physical modeling of high-precision air bearing and magnetic bearing spindles are presented in addition to a kinematic model of the micro milling process. Both models are superimposed in order to carry out investigations of the slot bottom surface roughness in micro end milling. The results show that process-machine-interactions in micro manufacturing can be modeled by the superposition of a physical model of the machine tool spindle taking cutting forces into consideration and a purely kinematic model of the machining process, providing the necessary tools for a variety of further investigations into process-machine-interactions in micro manufacturing

Advanced Development of Space Photovoltaic Concentrators Using Robust Lenses, Multi-Junction Cells, and Graphene Radiators

At the past three PVSCs, our team has presented recent advances in our space photovoltaic concentrator technology. In the past year, under multiple NASA-funded research and technology development programs, our team has made much additional progress in the advanced development of space photovoltaic concentrators. New robust Fresnel lenses, new high-efficiency multi-junction cells, and new graphene radiators have been developed. The paper will present the latest advances in this technology

PHASES High Precision Differential Astrometry of delta Equulei

delta Equulei is among the most well-studied nearby binary star systems. Results of its observation have been applied to a wide range of fundamental studies of binary systems and stellar astrophysics. It is widely used to calibrate and constrain theoretical models of the physics of stars. We report 27 high precision differential astrometry measurements of delta Equulei from the Palomar High-precision Astrometric Search for Exoplanet Systems (PHASES). The median size of the minor axes of the uncertainty ellipses for these measurements is 26 micro-arcseconds. These data are combined with previously published radial velocity data and other previously published differential astrometry measurements using other techniques to produce a combined model for the system orbit. The distance to the system is determined to within a twentieth of a parsec and the component masses are determined at the level of a percent. The constraints on masses and distance are limited by the precisions of the radial velocity data; we outline plans improve this deficiency and discuss the outlook for further study of this binary.Comment: Accepted by AJ. Complete versions of tables 2-7 now available at http://stuff.mit.edu/~matthew1/deltaEquTables/ (removed from astroph server

JWST reveals a population of ultra-red, flattened disk galaxies at 2<z<6 previously missed by HST

With just a month of data, JWST is already transforming our view of the Universe, revealing and resolving starlight in unprecedented populations of galaxies. Although ``HST-dark" galaxies have previously been detected at long wavelengths, these observations generally suffer from a lack of spatial resolution which limits our ability to characterize their sizes and morphologies. Here we report on a first view of starlight from a subset of the HST-dark population that are bright with JWST/NIRCam (4.4$\mu$m<24.5mag) and very faint or even invisible with HST ($<$1.6$\mu$m). In this Letter we focus on a dramatic and unanticipated population of physically extended galaxies ($\gtrsim$0.17''). These 12 galaxies have photometric redshifts $2<z<6$, high stellar masses $M_{\star}\gtrsim 10^{10}~M_{\odot}$, and significant dust-attenuated star formation. Surprisingly, the galaxies have elongated projected axis ratios at 4.4$\mu$m, suggesting that the population is disk-dominated or prolate. Most of the galaxies appear red at all radii, suggesting significant dust attenuation throughout. We refer to these red, disky, HST-dark galaxies as Ultra-red Flattened Objects (UFOs). With $r_e$(F444W)$\sim1-2$~kpc, the galaxies are similar in size to compact massive galaxies at $z\sim2$ and the cores of massive galaxies and S0s at $z\sim0$. The stellar masses, sizes, and morphologies of the sample suggest that some could be progenitors of lenticular or fast-rotating galaxies in the local Universe. The existence of this population suggests that our previous censuses of the universe may have missed massive, dusty edge-on disks, in addition to dust-obscured starbursts