arXiv subjects

Tad Hogg

Publications and source records attributed to Tad Hogg.

At least 37 records · Page 2Linked to original sources

arXiv2016

Energy Dissipation by Metamorphic Micro-Robots in Viscous Fluids

Microscopic robots could perform tasks with high spatial precision, such as acting on precisely-targeted cells in biological tissues. Some tasks may benefit from robots that change shape, such as elongating to improve chemical gradient sensing or contracting to squeeze through narrow channels. This paper evaluates the energy dissipation for shape-changing (i.e., metamorphic) robots whose size is comparable to bacteria. Unlike larger robots, surface forces dominate the dissipation. Theoretical estimates indicate that the power likely to be available to the robots, as determined by previous studies, is sufficient to change shape fairly rapidly even in highly-viscous biological fluids. Achieving this performance will require significant improvements in manufacturing and material properties compared to current micromachines. Furthermore, optimally varying the speed of shape change only slightly reduces energy use compared to uniform speed, thereby simplifying robot controllers.

cs.RO↗

arXiv2016

The DARPA Twitter Bot Challenge

A number of organizations ranging from terrorist groups such as ISIS to politicians and nation states reportedly conduct explicit campaigns to influence opinion on social media, posing a risk to democratic processes. There is thus a growing need to identify and eliminate "influence bots" - realistic, automated identities that illicitly shape discussion on sites like Twitter and Facebook - before they get too influential. Spurred by such events, DARPA held a 4-week competition in February/March 2015 in which multiple teams supported by the DARPA Social Media in Strategic Communications program competed to identify a set of previously identified "influence bots" serving as ground truth on a specific topic within Twitter. Past work regarding influence bots often has difficulty supporting claims about accuracy, since there is limited ground truth (though some exceptions do exist [3,7]). However, with the exception of [3], no past work has looked specifically at identifying influence bots on a specific topic. This paper describes the DARPA Challenge and describes the methods used by the three top-ranked teams.

cs.SI↗

arXiv2016

Disentangling the Effects of Social Signals

Peer recommendation is a crowdsourcing task that leverages the opinions of many to identify interesting content online, such as news, images, or videos. Peer recommendation applications often use social signals, e.g., the number of prior recommendations, to guide people to the more interesting content. How people react to social signals, in combination with content quality and its presentation order, determines the outcomes of peer recommendation, i.e., item popularity. Using Amazon Mechanical Turk, we experimentally measure the effects of social signals in peer recommendation. Specifically, after controlling for variation due to item content and its position, we find that social signals affect item popularity about half as much as position and content do. These effects are somewhat correlated, so social signals exacerbate the "rich get richer" phenomenon, which results in a wider variance of popularity. Further, social signals change individual preferences, creating a "herding" effect that biases people's judgments about the content. Despite this, we find that social signals improve the efficiency of peer recommendation by reducing the effort devoted to evaluating content while maintaining recommendation quality.

physics.soc-ph↗

arXiv2014

Using Surface-Motions for Locomotion of Microscopic Robots in Viscous Fluids

Microscopic robots could perform tasks with high spatial precision, such as acting in biological tissues on the scale of individual cells, provided they can reach precise locations. This paper evaluates the feasibility of in vivo locomotion for micron-size robots. Two appealing methods rely only on surface motions: steady tangential motion and small amplitude oscillations. These methods contrast with common microorganism propulsion based on flagella or cilia, which are more likely to damage nearby cells if used by robots made of stiff materials. The power potentially available to robots in tissue supports speeds ranging from one to hundreds of microns per second, over the range of viscosities found in biological tissue. We discuss design trade-offs among propulsion method, speed, power, shear forces and robot shape, and relate those choices to robot task requirements. This study shows that realizing such locomotion requires substantial improvements in fabrication capabilities and material properties over current technology.

cs.RO↗

arXiv2013

Stochastic Models Predict User Behavior in Social Media

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 transitioning through a series of states before responding to an advocate's post better predicts response than models that fail to take these states into account. We demonstrate other benefits of stochastic models, such as their ability to identify users who are highly interested in advocate's posts.

cs.CY↗

arXiv2012

Acoustic Communication for Medical Nanorobots

Communication among microscopic robots (nanorobots) can coordinate their activities for biomedical tasks. The feasibility of in vivo ultrasonic 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 10,000 bits/second 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.

cs.RO↗

arXiv2012

Thermodynamic Principles in Social Collaborations

A thermodynamic framework is presented to characterize the evolution of efficiency, order, and quality in social content production systems, and this framework is applied to the analysis of Wikipedia. Contributing editors are characterized by their (creative) energy levels in terms of number of edits. We develop a definition of entropy that can be used to analyze the efficiency of the system as a whole, and relate it to the evolution of power-law distributions and a metric of quality. The concept is applied to the analysis of eight years of Wikipedia editing data and results show that (1) Wikipedia has become more efficient during its evolution and (2) the entropy-based efficiency metric has high correlation with observed readership of Wikipedia pages.

cs.SI↗

arXiv2012

Social Dynamics of Digg

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.

cs.CY↗

arXiv2010

Using Stochastic Models to Describe and Predict Social Dynamics of Web Users

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 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 ever-growing amount of content. Predicting popularity of content in social media, however, is challenging due to the complex interactions between content quality and how the social media site chooses to highlight content. Moreover, most social media sites also 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 \emph{a priori}, we show that stochastic models of user behavior on these sites allows 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 based on simply extrapolating from the early votes. Using data from one such site, the news aggregator Digg, we show how a stochastic model of user behavior 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, identifying 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.

cs.CY↗

arXiv2010

Using a Model of Social Dynamics to Predict Popularity of News

cs.CY↗

arXiv2009

Chemical Power for Microscopic Robots in Capillaries

The power available to microscopic robots (nanorobots) that oxidize bloodstream glucose while aggregated in circumferential rings on capillary walls is evaluated with a numerical model using axial symmetry and time-averaged release of oxygen from passing red blood cells. Robots about one micron in size can produce up to several tens of picowatts, in steady-state, if they fully use oxygen reaching their surface from the blood plasma. Robots with pumps and tanks for onboard oxygen storage could collect oxygen to support burst power demands two to three orders of magnitude larger. We evaluate effects of oxygen depletion and local heating on surrounding tissue. These results give the power constraints when robots rely entirely on ambient available oxygen and identify aspects of the robot design significantly affecting available power. More generally, our numerical model provides an approach to evaluating robot design choices for nanomedicine treatments in and near capillaries.

cs.RO↗

arXiv2009

Stochastic Models of User-Contributory Web Sites

We describe a general stochastic processes-based approach to modeling user-contributory web sites, where users create, rate and share content. These models describe aggregate measures of activity and how they arise from simple models of individual users. This approach provides a tractable method to understand user activity on the web site and how this activity depends on web site design choices, especially the choice of what information about other users' behaviors is shown to each user. We illustrate this modeling approach in the context of user-created content on the news rating site Digg.

cs.CY↗

arXiv2009

Private Database Queries Using Quantum States with Limited Coherence Times

We describe a method for private database queries using exchange of quantum states with bits encoded in mutually incompatible bases. For technology with limited coherence time, the database vendor can announce the encoding after a suitable delay to allow the user to privately learn one of two items in the database without the ability to also definitely infer the second item. This quantum approach also allows the user to choose to learn other functions of the items, such as the exclusive-or of their bits, but not to gain more information than equivalent to learning one item, on average. This method is especially useful for items consisting of a few bits by avoiding the substantial overhead of conventional cryptographic approaches.

quant-ph↗

arXiv2008

Modeling Microscopic Chemical Sensors in Capillaries

Nanotechnology-based microscopic robots could provide accurate in vivo measurement of chemicals in the bloodstream for detailed biological research and as an aid to medical treatment. Quantitative performance estimates of such devices require models of how chemicals in the blood diffuse to the devices. This paper models microscopic robots and red blood cells (erythrocytes) in capillaries using realistic distorted cell shapes. The models evaluate two sensing scenarios: robots moving with the cells past a chemical source on the vessel wall, and robots attached to the wall for longer-term chemical monitoring. Using axial symmetric geometry with realistic flow speeds and diffusion coefficients, we compare detection performance with a simpler model that does not include the cells. The average chemical absorption is quantitatively similar in both models, indicating the simpler model is an adequate design guide to sensor performance in capillaries. However, determining the variation in forces and absorption as cells move requires the full model.

cs.RO↗

arXiv2008

Experiments with Probabilistic Quantum Auctions

We describe human-subject laboratory experiments on probabilistic auctions based on previously proposed auction protocols involving the simulated manipulation and communication of quantum states. These auctions are probabilistic in determining which bidder wins, or having no winner, rather than always having the highest bidder win. Comparing two quantum protocols in the context of first-price sealed bid auctions, we find the one predicted to be superior by game theory also performs better experimentally. We also compare with a conventional first price auction, which gives higher performance. Thus to provide benefits, the quantum protocol requires more complex economic scenarios such as maintaining privacy of bids over a series of related auctions or involving allocative externalities.

quant-ph↗

arXiv2008

Quantum Auctions using Adiabatic Evolution: The Corrupt Auctioneer and Circuit Implementations

We examine a proposed auction using quantum states to represent bids and distributed adiabatic search to find the winner. When the auctioneer follows the protocol, the final measurement giving the outcome of the auction also destroys the bid states, thereby preserving privacy of losing bidders. We describe how a dishonest auctioneer could alter the protocol to violate this privacy guarantee, and present methods by which bidders can counter such attacks. We also suggest possible quantum circuit implementations of the auctions protocol, and quantum circuits to perpetrate and to counter attacks by a dishonest auctioneer.

quant-ph↗

arXiv2008

Diversity of Online Community Activities

Web sites where users create and rate content as well as form networks with other users display long-tailed distributions in many aspects of behavior. Using behavior on one such community site, Essembly, we propose and evaluate plausible mechanisms to explain these behaviors. Unlike purely descriptive models, these mechanisms rely on user behaviors based on information available locally to each user. For Essembly, we find the long-tails arise from large differences among user activity rates and qualities of the rated content, as well as the extensive variability in the time users devote to the site. We show that the models not only explain overall behavior but also allow estimating the quality of content from their early behaviors.

cs.CY↗

arXiv2007

Quantum Auctions

We present a quantum auction protocol using superpositions to represent bids and distributed search to identify the winner(s). Measuring the final quantum state gives the auction outcome while simultaneously destroying the superposition. Thus non-winning bids are never revealed. Participants can use entanglement to arrange for correlations among their bids, with the assurance that this entanglement is not observable by others. The protocol is useful for information hiding applications, such as partnership bidding with allocative externality or concerns about revealing bidding preferences. The protocol applies to a variety of auction types, e.g., first or second price, and to auctions involving either a single item or arbitrary bundles of items (i.e., combinatorial auctions). We analyze the game-theoretical behavior of the quantum protocol for the simple case of a sealed-bid quantum, and show how a suitably designed adiabatic search reduces the possibilities for bidders to game the auction. This design illustrates how incentive rather that computational constraints affect quantum algorithm choices.

quant-ph↗