What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision

We present a novel method for aligning a sequence of instructions to a video of someone carrying out a task. In particular, we focus on the cooking domain, where the instructions correspond to the recipe. Our technique relies on an HMM to align the recipe steps to the (automatically generated) speech transcript. We then refine this alignment using a state-of-the-art visual food detector, based on a deep convolutional neural network. We show that our technique outperforms simpler techniques based on keyword spotting. It also enables interesting applications, such as automatically illustrating recipes with keyframes, and searching within a video for events of interest.Comment: To appear in NAACL 201

Reclaiming the Left

The Left has been charged with a lack of self-reflection and self-criticism. This article aims to address this concern through a demarcation of liberal-left values from illiberal pursuits in an attempt to reclaim (or reform) the Left to provide a genuine political opposition to the Right. Drawing together diverse perspectives, and extrapolating from direct quotations and research, four markers of extremism are identified. These markers relate to ideas of equity, culture, free speech and identity. It is hoped readers see this critique as a useful contribution in a crucial conversation on the values we want to preference in our society, a conversation we need to continue

Numerical Prediction of the Haemodynamic Impact of Coronary Stent Implantation

Arterial restenosis limits the effectiveness of coronary stenting. Restenosis is caused by excessive tissue growth which is stimulated by arterial injury and alterations to the arterial WSS. The altered WSS results from stent-induced disturbances to the natural haelnodynamics of the artery. Recent numerical studies have predcted only minor digerences in altered WSS between different stent designs using a commonly employed threshold assessment technique. While it is possible that there are only minor differences, it is more likely that the assessment technique is incapable of fully elucidating the altered WSS created by stent implantation. This thesis proposes a methodology that involves a more complete level of investigation into the stentinduced alterations to the WSS by incorporating the full suite of WSS-based variables: WSS, WSS gradient (WSSG), WSS angle gradient (WSSAG) and oscillatory shear index (OSI). Each of these variables highlights a different type of alteration to the arterial WSS that could lead to excessive tissue growth. The four variables are analysed quantitatively and qualitatively using statistical methods to assess the effect of the stent implantation. The methodology is applied to three stents with contrasting designs: the Palinaz-Schatz (PS), the Gianturco-Roubin II (GR-11) and the Bx-Velocity (Bx) stents. From the results, the sients are ranked (best to worst) for WSS: GR-11, PS, Bx (Cohen\u27s d: -0.01, -0.6131, for WSSG: PS, Bx, GR-I1 (d: 0.159,0.764), for WSSAG: PS GR-I1 Bx (d: 0.213, 0.082), and for OSI: PS, GR- 11, Bx (d: 0.3 15, 0.380). The proposed method of analysis is shown to elucidate the alterations to the WSS created by the stents to a far greater level than with the previously used threshold technique. This method of stent assessment could be utilised to minimise WSS alterations at the design stage of future bare metal, as well as permanent and bioabsorbable drug-eluting coronary stents

Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification

Despite the steady progress in video analysis led by the adoption of convolutional neural networks (CNNs), the relative improvement has been less drastic as that in 2D static image classification. Three main challenges exist including spatial (image) feature representation, temporal information representation, and model/computation complexity. It was recently shown by Carreira and Zisserman that 3D CNNs, inflated from 2D networks and pretrained on ImageNet, could be a promising way for spatial and temporal representation learning. However, as for model/computation complexity, 3D CNNs are much more expensive than 2D CNNs and prone to overfit. We seek a balance between speed and accuracy by building an effective and efficient video classification system through systematic exploration of critical network design choices. In particular, we show that it is possible to replace many of the 3D convolutions by low-cost 2D convolutions. Rather surprisingly, best result (in both speed and accuracy) is achieved when replacing the 3D convolutions at the bottom of the network, suggesting that temporal representation learning on high-level semantic features is more useful. Our conclusion generalizes to datasets with very different properties. When combined with several other cost-effective designs including separable spatial/temporal convolution and feature gating, our system results in an effective video classification system that that produces very competitive results on several action classification benchmarks (Kinetics, Something-something, UCF101 and HMDB), as well as two action detection (localization) benchmarks (JHMDB and UCF101-24).Comment: ECCV 2018 camera read

A robust multi-objective statistical improvement approach to electric power portfolio selection

Motivated by an electric power portfolio selection problem, a sampling method is developed for simulation-based robust design that builds on existing multi-objective statistical improvement methods. It uses a Bayesian surrogate model regressed on both design and noise variables, and makes use of methods for estimating epistemic model uncertainty in environmental uncertainty metrics. Regions of the design space are sequentially sampled in a manner that balances exploration of unknown designs and exploitation of designs thought to be Pareto optimal, while regions of the noise space are sampled to improve knowledge of the environmental uncertainty. A scalable test problem is used to compare the method with design of experiments (DoE) and crossed array methods, and the method is found to be more efficient for restrictive sample budgets. Experiments with the same test problem are used to study the sensitivity of the methods to numbers of design and noise variables. Lastly, the method is demonstrated on an electric power portfolio simulation code.PhDCommittee Chair: Mavris, Dimitri; Committee Member: Duncan, Scott; Committee Member: Ender, Tommer; Committee Member: German, Brian; Committee Member: Paredis, Chri

SnoScan: An iterative functionality service scanner for large scale networks

Every day we rely on various integral parts of infrastructure to function as a society. These infrastructures, such as power, water, and roads, are all abstract representations of networks. Each of these networks is monitored so that their usability and functionality remain intact. SnoScan is a service scanner for computer networks to allow the constant monitoring and notification of their status with regards to their functionality. While there are many open source service scanners available, they will simply tell you if the service is present or not, but can not recognize an error in functionality. By testing for functionality, an in-depth picture of the status of the network can be revealed and present errors to administrators that would otherwise go unnoticed. A functionality service scanner is only useful when the relevance and importance of knowing the functionality is significant to the service in question. If it is enough to say that if the service is present, the service is intrinsic and requires no further testing. However, if knowing the service is present does not give sufficient evidence that the service is working as intended, further testing of the system is required. SnoScan is intended to monitor computer networks by emulating user behavior and initiating status checks to various services typically hosted in a home or business setting. Knowing that the service is functionally sound will provide more relevant information than a standard service scanner and ultimately a more stable network

American Denominations and Christian Service: The Relationship Between Theology and Service

Although a primary tenet of Christianity is service to others, the level to which denominations extend such assistance greatly varies. Recent research attributes this variance to differences in church theology. Evangelical theology stresses anti-structuralism and de-emphasizes the ethical teachings of Christianity while the opposite is true of non-evangelical theology. These differences are thought to limit assistance to others in evangelical churches and to promote such assistance in non-evangelical churches. Using data from the U.S. Congregational Life Survey, I test these ideas by examining the relationship between type of denomination (evangelical vs. non-evangelical) and whether or not churches have programs such as housing for those in need, prison or jail ministry, substance abuse recovery, etc. Surprisingly, the findings offer virtually no support for the predicted outcomes. I will explain the evidence found in this study, and discuss the ramifications regarding religious research