Institute for Molecular Manufacturing

Palo Alto, CA, United States

Institute for Molecular Manufacturing

Palo Alto, CA, United States

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Lerman K.,University of Southern California | Hogg T.,Institute for Molecular Manufacturing
AAAI Workshop - Technical Report | Year: 2013

Online crowdsourcing provides new opportunities for ordinary people to create original content. This has led to a rapidly growing volume of user-generated content, and consequently a challenge to readily identify high quality items. Due to people's limited attention, the presentation of content strongly affects how people allocate effort to the available content. We evaluate this effect experimentally using Amazon Mechanical Turk and show that it is possible to manipulate attention to accomplish desired goals. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.


Lerman K.,University of Southern California | Hogg T.,Institute for Molecular Manufacturing
ACM Transactions on Intelligent Systems and Technology | Year: 2012

The popularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both the hosts of social media content and its consumers. Accurate and timely prediction would enable hosts to maximize revenue through differential pricing for access to content or ad placement. Prediction would also give consumers an important tool for filtering the content. Predicting the popularity of content in social media is challenging due to the complex interactions between content quality and how the social media site highlights its content. Moreover, most social media sites selectively present content that has been highly rated by similar users, whose similarity is indicated implicitly by their behavior or explicitly by links in a social network. While these factors make it difficult to predict popularity a priori, stochastic models of user behavior on these sites can allow predicting popularity based on early user reactions to new content. By incorporating the various mechanisms through which web sites display content, such models improve on predictions that are based on simply extrapolating from the early votes. Specifically, for one such site, the news aggregator Digg, we show how a stochastic model distinguishes the effect of the increased visibility due to the network from how interested users are in the content. We find a wide range of interest, distinguishing stories primarily of interest to users in the network ("niche interests") from those of more general interest to the user community. This distinction is useful for predicting a story's eventual popularity from users' early reactions to the story. © 2012 ACM.


Hogg T.,Institute for Molecular Manufacturing | Lerman K.,University of Southern California
EPJ Data Science | Year: 2012

Online social media provide multiple ways to find interesting content. One important method is highlighting content recommended by user’s friends. We examine this process on one such site, the news aggregator Digg. With a stochastic model of user behavior, we distinguish the effects of the content visibility and interestingness to users. We find a wide range of interest and distinguish stories primarily of interest to a users’ friends from those of interest to the entire user community. We show how this model predicts a story’s eventual popularity from users’ early reactions to it, and estimate the prediction reliability. This modeling framework can help evaluate alternative design choices for displaying content on the site. © 2012 Hogg and Lerman; licensee Springer.


Lerman K.,University of Southern California | Hogg T.,Institute for Molecular Manufacturing
PLoS ONE | Year: 2014

With the advent of social media and peer production, the amount of new online content has grown dramatically. To identify interesting items in the vast stream of new content, providers must rely on peer recommendation to aggregate opinions of their many users. Due to human cognitive biases, the presentation order strongly affects how people allocate attention to the available content. Moreover, we can manipulate attention through the presentation order of items to change the way peer recommendation works. We experimentally evaluate this effect using Amazon Mechanical Turk. We find that different policies for ordering content can steer user attention so as to improve the outcomes of peer recommendation. © 2014 Lerman, Hogg.


PubMed | Institute for Molecular Manufacturing and University of Southern California
Type: Journal Article | Journal: PloS one | Year: 2014

With the advent of social media and peer production, the amount of new online content has grown dramatically. To identify interesting items in the vast stream of new content, providers must rely on peer recommendation to aggregate opinions of their many users. Due to human cognitive biases, the presentation order strongly affects how people allocate attention to the available content. Moreover, we can manipulate attention through the presentation order of items to change the way peer recommendation works. We experimentally evaluate this effect using Amazon Mechanical Turk. We find that different policies for ordering content can steer user attention so as to improve the outcomes of peer recommendation.


Tarasov D.,Russian Academy of Sciences | Izotova E.,Kazan Federal University | Alisheva D.,Kazan Federal University | Akberova N.,Kazan Federal University | Freitas Jr. R.A.,Institute for Molecular Manufacturing
Journal of Computational and Theoretical Nanoscience | Year: 2011

The use of precisely applied mechanical forces to induce site-specific chemical transformations is called positional mechanosynthesis, and diamond is an important early target for achieving mechanosynthesis experimentally. The next major experimental milestone may be the mechanosynthetic fabrication of atomically precise 3D structures, creating readily accessible diamond-based nanomechanical components engineered to form desired architectures possessing superlative mechanical strength, stiffness, and strength-to-weight ratio. To help motivate this future experimental work, the present paper addresses the basic stability of the simplest nanoscale diamond structures-cubes and octahedra-possessing clean, hydrogenated, or partially hydrogenated surfaces. Computational studies using Density Functional Theory (DFT) with the Car-Parrinello Molecular Dynamics (CPMD) code, consuming ~1,466,852.53 CPU-hours of runtime on the IBM Blue Gene/P supercomputer (23 TFlops), confirmed that fully hydrogenated nanodiamonds up to 2 nm (~900-1800 atoms) in size having only C(111) faces (octahedrons) or only C(110) and C(100) faces (cuboids) maintain stable sp 3 hybridization. Fully dehydrogenated cuboid nanodiamonds above 1 nm retain the diamond lattice pattern, but smaller dehydrogenated cuboids and dehydrogenated octahedron nanodiamonds up to 2 nm reconstruct to bucky-diamond or onion-like carbon (OLC). At least three adjacent passivating H atoms may be removed, even from the most graphitization-prone C(111) face, without reconstruction of the underlying diamond lattice. Copyright © 2011 American Scientific Publishers.


Tarasov D.,Russian Academy of Sciences | Akberova N.,Kazan Federal University | Izotova E.,Kazan Federal University | Alisheva D.,Kazan Federal University | And 2 more authors.
Journal of Computational and Theoretical Nanoscience | Year: 2010

The use of precisely applied mechanical forces to induce site-specific chemical transformations is called positional mechanosynthesis, and diamond is an important early target for achieving mechanosynthesis experimentally. A key step in diamond mechanosynthesis (DMS) employs an ethynyl-based hydrogen abstraction tool (HAbst) for the site-specific mechanical dehydrogenation of H-passivated diamond surfaces, creating an isolated radical site that can accept adatoms via radical-radical coupling in a subsequent positionally controlled reaction step. The abstraction tool, once used (HAbstH), must be recharged by removing the abstracted hydrogen atom from the tooltip, before the tool can be used again. This paper presents the first theoretical study of DMS tool-workpiece operating envelopes and optimal tooltip trajectories for any positionally controlled reaction sequence-and more specifically, one that may be used to recharge a spent hydrogen abstraction tool-during scanning-probe based ultrahigh-vacuum diamond mechanosynthesis. Trajectories were analyzed using Density Functional Theory (DFT) in PC-GAMESS at the B3LYP/6- 311G(d, p)//B3LYP/3-21G(2d, p) level of theory. The results of this study help to define equipment and tooltip motion requirements that may be needed to execute the proposed reaction sequence experimentally and provide support for early developmental targets as part of a comprehensive near-term DMS implementation program. Copyright © 2010 American Scientific Publishers All rights reserved.


Hogg T.,Institute for Molecular Manufacturing | Lerman K.,Information science Institute | Smith L.M.,Information science Institute
Proceedings - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013 | Year: 2013

User response to contributed content in online social media depends on many factors. These include how the site lays out new content, how frequently the user visits the site, how many friends the user follows, how active these friends are, as well as how interesting or useful the content is to the user. We present a stochastic modeling framework that relates a user's behavior to details of the site's user interface and user activity and describe a procedure for estimating model parameters from available data. We apply the model to study discussions of controversial topics on Twitter, specifically, to predict how followers of an advocate for a topic respond to the advocate's posts. We show that a model of user behavior that explicitly accounts for a user discovering the advocate's post by scanning through a list of newer posts, better predicts response than models that do not. © 2013 IEEE.


Hogg T.,Institute for Molecular Manufacturing | Freitas Jr. R.A.,Institute for Molecular Manufacturing
Nano Communication Networks | Year: 2012

Communication among microscopic robots (nanorobots) can coordinate their activities for biomedical tasks. The feasibility of in vivoultrasonic communication is evaluated for micron-size robots broadcasting into various types of tissues. Frequencies between 10MHz and 300MHz give the best tradeoff between efficient acoustic generation and attenuation for communication over distances of about 100 microns. Based on these results, we find power available from ambient oxygen and glucose in the bloodstream can readily support communication rates of about 10 4bits/s between micron-sized robots. We discuss techniques, such as directional acoustic beams, that can increase this rate. The acoustic pressure fields enabling this communication are unlikely to damage nearby tissue, and short bursts at considerably higher power could be of therapeutic use. © 2012 Elsevier Ltd.


Hall J.S.,Institute for Molecular Manufacturing
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Solomonoff [9] explored the possibilities of the future course of AI development, including social effects of the development of intelligent machines which can be produced with exponentially decreasing costs. He introduced arguably the first formal mathematical model of what has since come to be known as the technological Singularity. Since that time a veritable plethora of such models has appeared [8]. We examine the milestones and model in light of 25 years more experience, and offer a revised version. © 2013 Springer-Verlag Berlin Heidelberg.

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