Jumping Frogs on Cyclic Graphs

From the traditional game of Solitaire to modern video games like Candy Crush and Five Nights at Freddy’s, single-player games have captivated audiences for gener- ations. We investigate a lesser-known single-player game, the Jumping Frogs problem, on various classes of simple graphs, a graph with no multiple edges or looped ver- tices. We determine whether frogs can be stacked together on one vertex of a given graph. In a graph with k vertices and one frog on each vertex, the frogs must make legal jumps to form a stack of k frogs. The problem is known to be solvable on classes of trees, but no work has yet been done on cyclic graphs. We examine various classes of cyclic graphs and determine whether these graphs have the property we call partyability

Probing the Physics and Geometry of Active Galactic Nuclei

Super Massive Black Holes (SMBH) inhabit the centre of every major galaxy, some of which power Active Galactic Nuclei (AGN) via mass accretion. In this thesis I explore two of the main approaches taken to study AGN, firstly the use of a wide sample, and secondly detailed observations of a single object. Multi-wavelength emission from AGN can be broken down into physical components which dominate in different wavebands, namely the dusty torus (IR), the accretion disc (optical/UV), the soft X-ray excess (UV/soft X-ray) and hot corona (hard X-rays). I first outline our new sample of around 700 SDSS Optical/UV/X-ray selected AGN, the SOUX sample, where the SDSS emission lines give single epoch black hole mass estimates while the UV/X-rays determine the continuum. I bin and stack the sample in mass and luminosity to look for trends as a function of these two important parameters. I perform detailed continuum fitting on the SOUX sample including the disc, soft X-ray excess and hot corona, and demonstrate clear issues in all current accretion flow models. Simple discs cannot match the UV extent of the most massive AGN irrespective of black hole spin as the high spin fits used in previous studies did not include the strong gravitational redshift which results from general relativistic ray tracing from the inner disc to the observer. I conclude that either there is a systematic over-prediction in black hole masses, or, more likely, the accretion flow in AGN does not take the form of a standard disc but may be fully Comptonised or take the form of something altogether different. I then switch gear to use the variable accretion flow emission to constrain the physical size scale of the dusty torus and Paschen Broad Line Region (BLR) from near-IR reverberation mapping. For Mrk 509, I develop a technique of photometric re-scaling and am able to place loose constraints on the size scales of these regions despite the challenging dataset. This technique is repeated with the much more robust data-set on Mrk 817, observed as part of the wider STORM2 collaboration. Both of these show that the Paschen BLR is co-spatial with the Balmer BLR, which is important as the Paschen BLR is now resolvable with GRAVITY data in a few objects, so can be used to test single epoch mass estimates. I then summarise the presented work and discuss potential avenues of investigation to take this further

Recognizing Seatbelt-Fastening Behavior with Wearable Technology and Machine Learning

In the case of many fatal automobile accidents, the victims were found to have not been wearing a seatbelt. This occurs in spite of the numerous safety sensors and warning indicators embedded within modern vehicles. Indeed, there is yet room for improvement in terms of seatbelt adoption. This work aims to lay the foundation for a novel method of encouraging seatbelt use: the utilization of wearable technology. Wearable technology has enabled considerable advances in health and wellness. Specifically, fitness trackers have achieved widespread popularity for their ability to quantify and analyze patterns of physical activity. Thanks to wearable technology’s ease of use and convenient integration with mobile phones, users are quick to adopt. Of course, the practicality of wearable technology depends on activity recognition—the models and algorithms which are used to identify a pattern of sensor data as a particular physical activity (e.g. running, sitting, sleeping). Activity recognition is the basis of this research. In order to utilize wearable trackers toward the cause of seatbelt usage, there must exist a system for identifying whether a user has buckled their seatbelt. This was our primary goal. To develop such a system, we collected motion data from 20 different users. From this data, we identified trends which inspired the development of novel features. With these features, machine learning was used to train models to identify the motion of fastening a seatbelt in real time. This model serves as the basis for future work in systems which may provide more intelligent feedback as well as methods for interventions in dangerous user behavior

Thermodynamic and experimental SiC-ZrC CVD process development

Please click Additional Files below to see the full abstract

The SOUX AGN sample: SDSS–XMM-Newton optical, ultraviolet, and X-ray selected active galactic nuclei spanning a wide range of parameter space – sample definition

We assemble a sample of 696 type 1 active galactic nuclei (AGN) up to a redshift of z = 2.5, all of which have an SDSS spectrum containing at least one broad emission line (H α, H β, or Mg II) and an XMM-Newton X-ray spectrum containing at least 250 counts in addition to simultaneous optical/ultraviolet photometry from the XMM Optical Monitor. Our sample includes quasars and narrow-line Seyfert 1s: thus our AGN span a wide range in luminosity, black hole mass, and accretion rate. We determine single-epoch black hole mass relations for the three emission lines and find that they provide broadly consistent mass estimates whether the continuum or emission line luminosity is used as the proxy for the broad emission line region radius. We explore variations of the UV/X-ray energy index αox with the UV continuum luminosity and with black hole mass and accretion rate, and make comparisons to the physical quasar spectral energy distribution model QSOSED. The majority of the AGN in our sample lie in a region of parameter space with 0.02 < L/LEdd < 2 as defined by this model, with narrow-line type 1 AGN offset to lower masses and higher accretion rates than typical broad-line quasars. We find differences in the dependence of αox on UV luminosity between both narrow/broad-line and radio-loud/quiet subsets of AGN: αox has a slightly weaker dependence on UV luminosity for broad-line AGN and radio-loud AGN have systematically harder αox

The SOUX AGN sample: optical/UV/X-ray SEDs and the nature of the disc

We use the SOUX sample of ∼700 active galactic nucleus (AGN) to form average optical-ultraviolet (UV)-X-rays spectral energy distributions (SEDs) on a two-dimensional (2D) grid of MBH and L2500. We compare these with the predictions of a new AGN SED model, QSOSED, which includes prescriptions for both hot and warm Comptonization regions as well as an outer standard disc. This predicts the overall SED fairly well for 7.5 < log(MBH/M⊙) < 9.0 over a wide range in L/LEdd, but at higher masses the outer disc spectra in the model are far too cool to match the data. We create optical-UV composites from the entire Sloan Digital Sky Survey sample and use these to show that the mismatch is due to there being no significant change in spectral shape of the optical-UV continuum across several decades of MBH at constant luminosity. We show for the first time that this cannot be matched by standard disc models with high black hole spin. These apparently fit, but are not self-consistent as they do not include the General Relativistic effects for the emission to reach the observer. At high spin, increased gravitational redshift compensates for almost all of the higher temperature emission from the smaller inner disc radii. The data do not match the predictions made by any current accretion flow model. Either the disc is completely covered by a warm Comptonization layer whose properties change systematically with L/LEdd, or the accretion flow structure is fundamentally different to that of the standard disc models

Vaporous marketing: Uncovering pervasive electronic cigarette advertisements on twitter

Background Twitter has become the wild-west of marketing and promotional strategies for advertisement agencies. Electronic cigarettes have been heavily marketed across Twitter feeds, offering discounts, kid-friendly flavors, algorithmically generated false testimonials, and free samples. Methods All electronic cigarette keyword related tweets from a 10% sample of Twitter spanning January 2012 through December 2014 (approximately 850,000 total tweets) were identified and categorized as Automated or Organic by combining a keyword classification and a machine trained Human Detection algorithm. A sentiment analysis using Hedonometrics was performed on Organic tweets to quantify the change in consumer sentiments over time. Commercialized tweets were topically categorized with key phrasal pattern matching. Results The overwhelming majority (80%) of tweets were classified as automated or promotional in nature. The majority of these tweets were coded as commercialized (83.65% in 2013), up to 33% of which offered discounts or free samples and appeared on over a billion twitter feeds as impressions. The positivity of Organic (human) classified tweets has decreased over time (5.84 in 2013 to 5.77 in 2014) due to a relative increase in the negative words \u27ban\u27, \u27tobacco\u27, \u27doesn\u27t\u27, \u27drug\u27, \u27against\u27, \u27poison\u27, \u27tax\u27 and a relative decrease in the positive words like \u27haha\u27, \u27good\u27, \u27cool\u27. Automated tweets are more positive than organic (6.17 versus 5.84) due to a relative increase in the marketing words like \u27best\u27, \u27win\u27, \u27buy\u27, \u27sale\u27, \u27health\u27, \u27discount\u27 and a relative decrease in negative words like \u27bad\u27, \u27hate\u27, \u27stupid\u27, \u27don\u27t\u27. Conclusions Due to the youth presence on Twitter and the clinical uncertainty of the long term health complications of electronic cigarette consumption, the protection of public health warrants scrutiny and potential regulation of social media marketing

Simon\u27s fundamental rich-get-richer model entails a dominant first-mover advantage

Herbert Simon\u27s classic rich-get-richer model is one of the simplest empirically supported mechanisms capable of generating heavy-tail size distributions for complex systems. Simon argued analytically that a population of flavored elements growing by either adding a novel element or randomly replicating an existing one would afford a distribution of group sizes with a power-law tail. Here, we show that, in fact, Simon\u27s model does not produce a simple power-law size distribution as the initial element has a dominant first-mover advantage, and will be overrepresented by a factor proportional to the inverse of the innovation probability. The first group\u27s size discrepancy cannot be explained away as a transient of the model, and may therefore be many orders of magnitude greater than expected. We demonstrate how Simon\u27s analysis was correct but incomplete, and expand our alternate analysis to quantify the variability of long term rankings for all groups. We find that the expected time for a first replication is infinite, and show how an incipient group must break the mechanism to improve their odds of success. We present an example of citation counts for a specific field that demonstrates a first-mover advantage consistent with our revised view of the rich-get-richer mechanism. Our findings call for a reexamination of preceding work invoking Simon\u27s model and provide an expanded understanding going forward