Assessing the Effects of NAFTA ON Canada/US Agricultural Trade

While there seems to be an agreement that Canada-US Free Trade Agreement (CUSTA)/North American Free Trade Agreement (NAFTA) have benefited member countries, some analysts have argued that the agreements had little effect on the bilateral Canada/US agricultural trade as many other factors have contributed to the increased trade flows. Results from this study reveal that the aggregate bilateral agricultural trade flows have generally experienced a steady growth since the implementation of NAFTA with trade flows seemingly favoring Canada more than the US since 1992. At the industry level, the impacts of NAFTA on Canada/US agricultural trade were varied with the sub-sectors analyzed responding differently to the bilateral trade liberalization.CUSTA/NAFTA, agricultural trade, liberalization, integration, trade flows, International Relations/Trade,

Frontier: The Making of the Northern and Eastern Border in Ladakh From 1834 to the Present

Ladakh, despite popular myths of an isolated Himalayan kingdom, has been a land built on trade and regional connections with India to the south, Tibet to the east, and Central Asia to the north. By participating in these social, political, and economic networks, Ladakh was able to amass a rich collection of cultural influences from many far-flung locales. Historically many of these regional interactions have been defined by the physical terrain, which mountain ranges divided Ladakh from other areas, which passes where open and when they were traversable. Even the political boundaries before the partition of India were loosely defined by local governors, mountain-tops and enterprising bandits. What British administrators and later Indian ones did not understand was that Ladakh was not a border state, but a gateway to the rich economies of Tibet and Central Asia and that trade created a flow of ideas that created the contemporary Ladakhi culture of the time. However, since the partition of India and the closing of the border with Chinese Tibet, Ladakh\u27s boundaries have been decided by governments in Delhi, Islamabad and Beijing and often are far divorced from conditions on the ground. This paper will seek to understand how these boundaries developed and how they affect Ladakh today

Fine-Grained Car Detection for Visual Census Estimation

Targeted socioeconomic policies require an accurate understanding of a country's demographic makeup. To that end, the United States spends more than 1 billion dollars a year gathering census data such as race, gender, education, occupation and unemployment rates. Compared to the traditional method of collecting surveys across many years which is costly and labor intensive, data-driven, machine learning driven approaches are cheaper and faster--with the potential ability to detect trends in close to real time. In this work, we leverage the ubiquity of Google Street View images and develop a computer vision pipeline to predict income, per capita carbon emission, crime rates and other city attributes from a single source of publicly available visual data. We first detect cars in 50 million images across 200 of the largest US cities and train a model to predict demographic attributes using the detected cars. To facilitate our work, we have collected the largest and most challenging fine-grained dataset reported to date consisting of over 2600 classes of cars comprised of images from Google Street View and other web sources, classified by car experts to account for even the most subtle of visual differences. We use this data to construct the largest scale fine-grained detection system reported to date. Our prediction results correlate well with ground truth income data (r=0.82), Massachusetts department of vehicle registration, and sources investigating crime rates, income segregation, per capita carbon emission, and other market research. Finally, we learn interesting relationships between cars and neighborhoods allowing us to perform the first large scale sociological analysis of cities using computer vision techniques.Comment: AAAI 201

Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US

The United States spends more than $1B each year on initiatives such as the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed half a decade. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may provide a cheaper and faster alternative. Here, we present a method that determines socioeconomic trends from 50 million images of street scenes, gathered in 200 American cities by Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22M automobiles in total (8% of all automobiles in the US), was used to accurately estimate income, race, education, and voting patterns, with single-precinct resolution. (The average US precinct contains approximately 1000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a 15-minute drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next Presidential election (88% chance); otherwise, it is likely to vote Republican (82%). Our results suggest that automated systems for monitoring demographic trends may effectively complement labor-intensive approaches, with the potential to detect trends with fine spatial resolution, in close to real time.Comment: 41 pages including supplementary material. Under review at PNA

Uniform sets with few progressions via colorings

Ruzsa asked whether there exist Fourier-uniform subsets of $\mathbb Z/N\mathbb Z$ with density $\alpha$ and 4-term arithmetic progression (4-APs) density at most $\alpha^C$, for arbitrarily large $C$. Gowers constructed Fourier uniform sets with density $\alpha$ and 4-AP density at most $\alpha^{4+c}$ for some small constant $c>0$. We show that an affirmative answer to Ruzsa's question would follow from the existence of an $N^{o(1)}$-coloring of $[N]$ without symmetrically colored 4-APs. For a broad and natural class of constructions of Fourier-uniform subsets of $\mathbb Z/N\mathbb Z$, we show that Ruzsa's question is equivalent to our arithmetic Ramsey question. We prove analogous results for all even-length APs. For each odd $k\geq 5$, we show that there exist $U^{k-2}$-uniform subsets of $\mathbb Z/N\mathbb Z$ with density $\alpha$ and $k$-AP density at most $\alpha^{c_k \log(1/\alpha)}$. We also prove generalizations to arbitrary one-dimensional patterns.Comment: 20 page

Modelling Physical Mechanisms of Nodule Development in Phonotraumatic Vocal Hyperfunction using Computational Vocal Fold Models

Vocal hyperfunction is a prevalent voice disorder with significant impacts on the daily lives of patients, but has poorly understood causes. At its root, vocal hyperfunction is neurological, involving excessive muscular activation due to compensation for some underlying issue. In order to improve understanding of the causes of this disorder and ultimately improve its treatment, this thesis uses computational models to investigate mechanical aspects in the development of vocal fold nodules in phonotraumatic vocal hyperfunction (a specific class of vocal hyperfunction), specifically: whether biomechanical differences in stiffness of the vocal folds can lead to inefficient speech production that predisposes one to developing these nodule, and whether swelling can establish an amplifying feedback loop, a so-called "vicious cycle", wherein swelling leads to compensatory adjustments that incur further swelling and ultimately lead to nodule. To address these questions a two-dimensional finite-element vocal fold model coupled with a simplified one-dimensional flow model was developed with modifications to this basic model made to study the phenomena of interest. Towards modelling swelling, a computationally efficient approach to model the epithelium layer of the vocal folds is also developed and validated. To investigate the first research question, the aforementioned model was adapted to study phonation onset pressure, a measure of effort required to produce speech, as a function of vocal fold stiffness. The results show that onset pressure is primarily dependent on just three stiffness distributions: smooth distributions with body-cover stiffness differences and smooth distributions with inferior-superior stiffness differences minimize onset pressure while a uniform stiffness increase increases onset pressure. Since a uniform stiffness increase increases the natural frequency of the vocal folds, this increase in onset pressure is roughly associated with increases in frequency. This suggests that for a given average stiffness (onset frequency) deviations from an optimal body-cover and inferior-superior-like distribution lead to increases in phonatory effort that could increase susceptibility to vocal hyperfunction. To investigate the second research question, the finite element model was augmented with a model of swelling, as well as an epithelium using a membrane model. Results showed that swelling has negligible impact on loudness of speech but significantly influences frequency, and that furthermore, swelling increases measures of phonotrauma. These results suggest that swelling could incur a vicious cycle. Specifically, a decrease in fundamental frequency initiates compensatory adjustments through increased muscle tension and subglottal pressure, which tends to increase phonotrauma in the folds, and increased swelling with phonotrauma does not tend to limit further swelling. This result demonstrates how swelling can potentially lead to the formation of nodule

Evaluation of High-Speed Videoendoscopy for Bayesian Inference on Reduced Order Vocal Fold Models

The ability to use our voice occurs through a complex bio-mechanical process known as phonation. The study of this process is interesting, not only because of the complex physical phenomena involved, but also because of the presence of phonation disorders that can make the everyday task of using ones voice difficult. Clinical studies of phonation aim to help diagnose such disorders using various measurement techniques, such as microphone recordings, video of the vocal folds, and perceptual sound quality measures. In contrast, scientific investigations of phonation have focused on understanding the physical phenomena behind phonation using simplified physical and numerical models constructed using representative population based parameters. A particularly useful type of model, reduced-order numerical models, are simplified representations of the vocal folds with low computational complexity that allow broad parameter changes to be investigated. To bring the physical understanding of phonation from these models into clinical usage, it is necessary to have patient specific parameters. Due to the difficulty of measuring vocal fold parameters and other structures in phonation directly, inverse analysis techniques must be employed. These techniques estimate the parameters of a model, by finding model parameters that lead to outputs of the model which compare well with measured outputs. With the measured outputs being patient specific measurements, these techniques can produce patient specific model parameters. However, this is complicated by the fact that measurements are uncertain, which leads to uncertainty in inferred parameters. The uncertainty in the parameters provides a way to judge how confident clinicians should be in using them. Large measurements errors could result in high uncertainties (and vice versa), which should guide clinicians on whether or not to believe the estimated parameters. Bayesian inference is an inverse analysis technique, that can take into account the inherent uncertainty in measurements in a probabilistic framework. Applying Bayesian inference to reduced-order models and clinical measurements allows patient specific model parameters with associated uncertainties to be inferred. A promising clinical measurement for use in Bayesian inference is high-speed videoendoscopy, in which high-speed video is taken of the vocal folds in motion. This captures the time varying motion of the vocal folds, which allows many quantitative measurements to be derived from the resulting video, for example the glottal width (distance between the vocal folds) or glottal area (area between the vocal folds). High-speed videoendoscopy is subject to variable imaging parameters, in particular the frame rate, spatial resolution, and tilted views of the camera can all modify the resulting video of the vocal folds, changing the uncertainty in the derived measurements. To investigate the effect of these three imaging parameters on Bayesian inference applied to high-speed video endoscopy, a simulated high-speed videoendoscopy experiment was conducted. Using a reduced order model, with known parameters, a set of enlarged, artificial vocal folds were driven in slow motion. These were imaged by a consumer DSLR camera, where the slow motion increased the effective frame rate, and the enlarged vocal folds increased the effective spatial resolution, to a fidelity much greater than typical high-speed videos of the vocal folds. This allowed investigation of the three parameters; titled views of the camera were investigated by physically tilting the camera, while variable frame rates and spatial resolutions were investigated by numerical downsampling of the original recording. Bayesian inference was conducted on these simulated high-speed videos, by measuring the distance between the vocal folds (the glottal width), in order to determine the parameters of the same reduced-order model driving the artificial vocal folds. This provided a reference to compare the estimated parameters with. The changes in estimated parameters from Bayesian inference were then investigated as the angle of view, frame rate, and spatial resolution were modified. From the experiment, the effects of frame rate, spatial resolution, and angle of view in high-speed videoendoscopy were found relative to changes from a reference video. Specifically, uncertainty in estimates increased linearly with respect to downsampling factor of frame rate. A frame rate that is half that of the reference video will have an uncertainty on estimated parameters that is twice as large. Spatial resolution affects the level of uncertainty based on the edge detection techniques that are used to extract quantitative data (i.e., the glottal width in this study). As the spatial resolution was downsampled, the level of error from the edge detection algorithm increased linearly with respect to the downsampling factor, which subsequently led to the same linear increase in the level of uncertainty in the estimate. However, different edge detection algorithms will likely have different accuracies as the resolution of the image decreases. While in this study it is preferable to decrease spatial resolution instead of frame rate, more general conclusions would be dependent on the specific edge detection technique used. The angle of view was found to bias estimates as a result of projecting the vocal folds (glottis) onto an offset image plane (like viewing a coin from an angle, results in increasingly narrow ellipses until a single line is formed, rather than a circle). This decreased the glottal width measured, which biased the estimated parameters. To account for this bias, it is suggested that the angle of view can be treated as an uncertain parameter, which leads to increased uncertainty in the quantitative measures from high-speed video. Alternatively, the angle of view can be estimated as an additional parameter

Evidence for quasi-one-dimensional charge density wave in CuTe by angle-resolved photoemission spectroscopy

We report the electronic structure of CuTe with a high charge density wave (CDW) transition temperature Tc = 335 K by angle-resolved photoemission spectroscopy (ARPES). An anisotropic charge density wave gap with a maximum value of 190 meV is observed in the quasi-one-dimensional band formed by Te px orbitals. The CDW gap can be filled by increasing temperature or electron doping through in situ potassium deposition. Combining the experimental results with calculated electron scattering susceptibility and phonon dispersion, we suggest that both Fermi surface nesting and electron-phonon coupling play important roles in the emergence of the CDW