Bridging the Gap Between Training and Inference for Spatio-Temporal Forecasting

Spatio-temporal sequence forecasting is one of the fundamental tasks in spatio-temporal data mining. It facilitates many real world applications such as precipitation nowcasting, citywide crowd flow prediction and air pollution forecasting. Recently, a few Seq2Seq based approaches have been proposed, but one of the drawbacks of Seq2Seq models is that, small errors can accumulate quickly along the generated sequence at the inference stage due to the different distributions of training and inference phase. That is because Seq2Seq models minimise single step errors only during training, however the entire sequence has to be generated during the inference phase which generates a discrepancy between training and inference. In this work, we propose a novel curriculum learning based strategy named Temporal Progressive Growing Sampling to effectively bridge the gap between training and inference for spatio-temporal sequence forecasting, by transforming the training process from a fully-supervised manner which utilises all available previous ground-truth values to a less-supervised manner which replaces some of the ground-truth context with generated predictions. To do that we sample the target sequence from midway outputs from intermediate models trained with bigger timescales through a carefully designed decaying strategy. Experimental results demonstrate that our proposed method better models long term dependencies and outperforms baseline approaches on two competitive datasets.Comment: ECAI 2020 Accepted, preprin

Accounting for Global Dispersion of Current Accounts.

We undertake a quantitative analysis of the dispersion of current accounts in an open economy version of incomplete insurance model, incorporating important market frictions in trade and financial flows. Calibrated with conventional parameter values, the stochastic stationary equilibrium of the model with limited borrowing can account for about two-thirds of the global dispersion of current accounts. The easing of financial frictions can explain nearly all changes in the current account dispersion in the past four decades whereas the easing of trade frictions has almost no impact on the current account dispersion.Distribution of Current Account, Incomplete Markets, Frictions.

Increased Risk of Ischemic Stroke during Sleep in Apneic Patients.

BACKGROUND AND PURPOSE:The literature indicates that obstructive sleep apnea (OSA) increases the risk of ischemic stroke. However, the causal relationship between OSA and ischemic stroke is not well established. This study examined whether preexisting OSA symptoms affect the onset of acute ischemic stroke. METHODS:We investigated consecutive patients who were admitted with acute ischemic stroke, using a standardized protocol including the Berlin Questionnaire on symptoms of OSA prior to stroke. The collected stroke data included the time of the stroke onset, risk factors, and etiologic subtypes. The association between preceding OSA symptoms and wake-up stroke (WUS) was assessed using multivariate logistic regression analysis. RESULTS:We identified 260 subjects with acute ischemic strokes with a definite onset time, of which 25.8% were WUS. The presence of preexisting witnessed or self-recognized sleep apnea was the only risk factor for WUS (adjusted odds ratio=2.055, 95% confidence interval=1.035-4.083, p=0.040). CONCLUSIONS:Preexisting symptoms suggestive of OSA were associated with the occurrence of WUS. This suggests that OSA contributes to ischemic stroke not only as a predisposing risk factor but also as a triggering factor. Treating OSA might therefore be beneficial in preventing stroke, particularly that occurring during sleep

Rosen-Zener interferometry with Ultracold Atoms

We propose a time-domain "interferometer" based on ultracold Bose atoms loaded on a double well potential. By the adiabatic Rosen-Zener process, the barrier between two wells is ramped down slowly, held for a while, then ramped back. Starting with a coherent state of double well system, the final occupations on one well show interesting interference fringes in the time-domain. The fringe pattern is sensitive to the initial state, the interatomic interaction, and the external forces such as gravity which can change the shape of the double well. In this sense, this interferometric scheme has the potentials for precision measurements with ultracold atoms. The underlying mechanism is revealed and possible applications are discussed.Comment: 4 pages, 5 figure

Quality Assurance Testing for Screening Defective Aluminum Die-cast Rotors of Squirrel Cage Induction Machines

The recent trend in squirrel cage induction motor manufacturing is to replace fabricated copper rotors with aluminum die-cast rotors to reduce manufacturing cost to stay competitive in the global market. Porosity in aluminum die-cast squirrel cage rotors is inevitably introduced during the die cast process. Porosity can cause degradation in motor performance and can lead to a forced outage causing irreversible damage in extreme cases. Many offline and online quality assurance test methods have been developed and applied for assessment of rotor quality. However, years of experience with the existing test methods revealed that they are not suitable for quality testing or capable of providing a quantitative assessment of rotor porosity with sufficient sensitivity. In this paper, a new offline test method capable of providing sensitive assessment of rotor porosity is proposed. It is shown that rotors with minor and distributed porosity that are difficult to detect with other tests can be screened out during manufacturing. The proposed method is verified through a 3-D finite element analysis and experimental testing on closed and semiopen slot aluminum die cast rotors of 5.5 kW induction motors with porosity

Stability of the warped black string with nontrivial topology in five-dimensional Anti-de Sitter spacetime

We investigate the stability of a new warped black string with nontrivial topologies in five-dimensional Anti-de Sitter spacetime. After studying the linear gravitational perturbation, we find that this black string is unstable when the Kaluza-Klein mass falls in a certain range and the instability exists for all topological spacetimes.Comment: 15 pages and 3 figures, revised version to appear in Phys. Rev.

Unsupervised Representation Learning by Sorting Sequences

We present an unsupervised representation learning approach using videos without semantic labels. We leverage the temporal coherence as a supervisory signal by formulating representation learning as a sequence sorting task. We take temporally shuffled frames (i.e., in non-chronological order) as inputs and train a convolutional neural network to sort the shuffled sequences. Similar to comparison-based sorting algorithms, we propose to extract features from all frame pairs and aggregate them to predict the correct order. As sorting shuffled image sequence requires an understanding of the statistical temporal structure of images, training with such a proxy task allows us to learn rich and generalizable visual representation. We validate the effectiveness of the learned representation using our method as pre-training on high-level recognition problems. The experimental results show that our method compares favorably against state-of-the-art methods on action recognition, image classification and object detection tasks.Comment: ICCV 2017. Project page: http://vllab1.ucmerced.edu/~hylee/OPN