Integration of Bicycle Commuting to Public Transit in New York City

In this thesis, the author is dedicated to exploring the bicycle commuting trend in New York City and discussing the integration of bike-share system to public transit modes. Rather than looking into the whole population of cyclists, the analysis focuses on the group using Citi Bike, the bike-share system in NYC, as a commuting tool. Determinants of Citi Bike usage is examined through bivariate and multivariate correlation analysis. Specifically, the thesis consists of 6 parts. Chapter 1 goes through an overview on the basic development of trend of bicycle commuting. Chapter 2 looked into a bunch of early studies researching on the determinants of cycling level and statistical analysis methods. Extra attention is paid to the discussion about what has been influencing the usage of bike-share system. Chapter 3 overall introduces the data sources and ideas about data preprocessing. Research question is raised and the basic hypothesis described. More importantly, the principle and techniques of the two major analysis in this study is explained in detail. Implementation process and findings of the major analysis, the temporal and spatial analysis as well as the correlation analysis, are discussed separately in Chapter 4 and Chapter 5. Eventually, Chapter 6 concludes on the findings and arguments the author has proposed through the whole study and raises some of the ideas for further studies

Number-conserving interacting fermion models with exact topological superconducting ground states

We present a method to construct number-conserving Hamiltonians whose ground states exactly reproduce an arbitrarily chosen BCS-type mean-field state. Such parent Hamiltonians can be constructed not only for the usual $s$-wave BCS state, but also for more exotic states of this form, including the ground states of Kitaev wires and 2D topological superconductors. This method leads to infinite families of locally-interacting fermion models with exact topological superconducting ground states. After explaining the general technique, we apply this method to construct two specific classes of models. The first one is a one-dimensional double wire lattice model with Majorana-like degenerate ground states. The second one is a two-dimensional $p_x+ip_y$ superconducting model, where we also obtain analytic expressions for topologically degenerate ground states in the presence of vortices. Our models may provide a deeper conceptual understanding of how Majorana zero modes could emerge in condensed matter systems, as well as inspire novel routes to realize them in experiment.Comment: 5 pages, 2 figures; supplement: 4 pages, 1 figur

Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition

Recognizing facial action units (AUs) during spontaneous facial displays is a challenging problem. Most recently, Convolutional Neural Networks (CNNs) have shown promise for facial AU recognition, where predefined and fixed convolution filter sizes are employed. In order to achieve the best performance, the optimal filter size is often empirically found by conducting extensive experimental validation. Such a training process suffers from expensive training cost, especially as the network becomes deeper. This paper proposes a novel Optimized Filter Size CNN (OFS-CNN), where the filter sizes and weights of all convolutional layers are learned simultaneously from the training data along with learning convolution filters. Specifically, the filter size is defined as a continuous variable, which is optimized by minimizing the training loss. Experimental results on two AU-coded spontaneous databases have shown that the proposed OFS-CNN is capable of estimating optimal filter size for varying image resolution and outperforms traditional CNNs with the best filter size obtained by exhaustive search. The OFS-CNN also beats the CNN using multiple filter sizes and more importantly, is much more efficient during testing with the proposed forward-backward propagation algorithm

FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

We present a Few-Shot Relation Classification Dataset (FewRel), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The relation of each sentence is first recognized by distant supervision methods, and then filtered by crowdworkers. We adapt the most recent state-of-the-art few-shot learning methods for relation classification and conduct a thorough evaluation of these methods. Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. We also show that a range of different reasoning skills are needed to solve our task. These results indicate that few-shot relation classification remains an open problem and still requires further research. Our detailed analysis points multiple directions for future research. All details and resources about the dataset and baselines are released on http://zhuhao.me/fewrel.Comment: EMNLP 2018. The first four authors contribute equally. The order is determined by dice rolling. Visit our website http://zhuhao.me/fewre

Distributionally Robust Unsupervised Dense Retrieval Training on Web Graphs

This paper introduces Web-DRO, an unsupervised dense retrieval model, which clusters documents based on web structures and reweights the groups during contrastive training. Specifically, we first leverage web graph links and contrastively train an embedding model for clustering anchor-document pairs. Then we use Group Distributional Robust Optimization to reweight different clusters of anchor-document pairs, which guides the model to assign more weights to the group with higher contrastive loss and pay more attention to the worst case during training. Our experiments on MS MARCO and BEIR show that our model, Web-DRO, significantly improves the retrieval effectiveness in unsupervised scenarios. A comparison of clustering techniques shows that training on the web graph combining URL information reaches optimal performance on clustering. Further analysis confirms that group weights are stable and valid, indicating consistent model preferences as well as effective up-weighting of valuable groups and down-weighting of uninformative ones. The code of this paper can be obtained from https://github.com/OpenMatch/Web-DRO.Comment: 9 pages, 5 figures, 5 table

Dielectric response of soft mode in ferroelectric SrTiO3

We report far-infrared dielectric properties of powder form ferroelectric SrTiO3. Terahertz time-domain spectroscopy (THz-TDS) measurement reveals that the low-frequency dielectric response of SrTiO3 is a consequence of the lowest transverse optical (TO) soft mode TO1 at 2.70 THz (90.0 1/cm), which is directly verified by Raman spectroscopy. This result provides a better understanding of the relation of low-frequency dielectric function with the optical phonon soft mode for ferroelectric materials. Combining THz-TDS with Raman spectra, the overall low-frequency optical phonon response of SrTiO3 is presented in an extended spectral range from 6.7 1/cm to 1000.0 1/cm.Comment: 14 pages; 4 figure