Detecting Outliers in Data with Correlated Measures

Advances in sensor technology have enabled the collection of large-scale datasets. Such datasets can be extremely noisy and often contain a significant amount of outliers that result from sensor malfunction or human operation faults. In order to utilize such data for real-world applications, it is critical to detect outliers so that models built from these datasets will not be skewed by outliers. In this paper, we propose a new outlier detection method that utilizes the correlations in the data (e.g., taxi trip distance vs. trip time). Different from existing outlier detection methods, we build a robust regression model that explicitly models the outliers and detects outliers simultaneously with the model fitting. We validate our approach on real-world datasets against methods specifically designed for each dataset as well as the state of the art outlier detectors. Our outlier detection method achieves better performances, demonstrating the robustness and generality of our method. Last, we report interesting case studies on some outliers that result from atypical events.Comment: 10 page

A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data

The increased availability of large-scale trajectory data around the world provides rich information for the study of urban dynamics. For example, New York City Taxi Limousine Commission regularly releases source-destination information about trips in the taxis they regulate. Taxi data provide information about traffic patterns, and thus enable the study of urban flow -- what will traffic between two locations look like at a certain date and time in the future? Existing big data methods try to outdo each other in terms of complexity and algorithmic sophistication. In the spirit of "big data beats algorithms", we present a very simple baseline which outperforms state-of-the-art approaches, including Bing Maps and Baidu Maps (whose APIs permit large scale experimentation). Such a travel time estimation baseline has several important uses, such as navigation (fast travel time estimates can serve as approximate heuristics for A search variants for path finding) and trip planning (which uses operating hours for popular destinations along with travel time estimates to create an itinerary).Comment: 12 page

Metallic foreign body deep in the prevertebral space after an endomyocardial biopsy: a case report

INTRODUCTION: Although inspirated or ingested foreign bodies constitute a common otolaryngologic emergency, the removal of a solitary retained foreign body from the neck has seldom been described in the literature. The ingestion of foreign bodies commonly results in perforated viscose or extraluminal migration to adjacent structures quite a long period of time after the fact. To the best of our knowledge, this is the first English language description of an endomyocardial biopsy complicated by a retained foreign body deep in the prevertebral space of the patient’s neck. We report such a case and share our experience in treating it. CASE PRESENTATION: A 68-year-old Asian man suffering right-sided heart failure underwent an endomyocardial biopsy via his right internal jugular vein. After undergoing the procedure, he was found to have retained a metallic cup tip which had become lodged in his neck. A surgeon then performed neck exploration and the foreign body was removed without adverse effect. CONCLUSIONS: Decision making as to whether to remove the foreign body or not remains controversial. However, the later incidence of adhesive fibrosis or, even worse, of a catastrophic abscess or adjacent vascular injury might occur if the foreign body was not removed. Early exploration is suggested, if the patient’s condition makes this feasible

Analysis of Trade Patterns and Duration: Evidence from Food Industry in the OECD countries

This study conducts Kaplan-Meier survival estimates on food trade patterns among the OECD countries and explores whether different survival conditions exist in different t rade patterns of food sub-industries. By applying the extended Cox proportional hazard model, this study examines the effects of consumption on the survival rate for horizontal intra-industry trade pattern at the SITC four-digits level. Our findings show an important policy implication that the stability of horizontal intra-industry trade on the survival duration is driven by consumption in food industry among the OECD countries. Therefore, we suggest that policies should encourage the support of the horizontal intra-industry trade within the food industry to avoid unstable durations of trade in the global supply chain caused by the severe employment adjustments associated with traditional comparative advantage

An Extended Analytic Solution of Combined Refraction and Diffraction of Long Waves Propagating over Circular Island

An analytic solution of long waves scattering by a cylindrical island mounted on a permeable circular shoal was obtained by solving the linear long wave equation (LWE). The solution is in terms of the Bessel function expressed by complex variables. The present solution is suitable for arbitrary bottom configurations described by a power function with two independent parameters. For the case of the paraboloidal shoal, there exists a singular point (α=2) which can be removed using Frobenius series, where α is a real constant. The present solution is reduced to Yu and Zhang’s (2003) solution for impermeable circular shoal. The numerical results show some special features of the combined effect of wave refraction and diffraction caused by a porous circular island. The effect of key parameters of the island dimension, the shoal slope, and permeability on wave scattering was discussed based on the analytic solution

Interpretations of Domain Adaptations via Layer Variational Analysis

Transfer learning is known to perform efficiently in many applications empirically, yet limited literature reports the mechanism behind the scene. This study establishes both formal derivations and heuristic analysis to formulate the theory of transfer learning in deep learning. Our framework utilizing layer variational analysis proves that the success of transfer learning can be guaranteed with corresponding data conditions. Moreover, our theoretical calculation yields intuitive interpretations towards the knowledge transfer process. Subsequently, an alternative method for network-based transfer learning is derived. The method shows an increase in efficiency and accuracy for domain adaptation. It is particularly advantageous when new domain data is sufficiently sparse during adaptation. Numerical experiments over diverse tasks validated our theory and verified that our analytic expression achieved better performance in domain adaptation than the gradient descent method.Comment: Published at ICLR 202