The explosion in the availability of GPS-enabled devices has resulted in an abundance of trajectory data. In reality, however, majority of these trajectories are collected at a low sampling rate and only provide partial observations on their actually traversed routes. Consequently, they are mired with uncertainty. In this paper, we develop a technique called Infer Tra to infer uncertain trajectories from network-constrained partial observations. Rather than predicting the most likely route, the inferred uncertain trajectory takes the form of an edge-weighted graph and summarizes all probable routes in a holistic manner. For trajectory inference, Infer Tra employs Gibbs sampling by learning a Network Mobility Model (NMM) from a database of historical trajectories. Extensive experiments on real trajectory databases show that the graph-based approach of Infer Tra is up to 50 more accurate, 20 times faster, and immensely more versatile than state-of-the-art techniques.
HRIS @cite_16 is the first and the only work to infer network-constrained trajectories from partial observations. In this section, we outline how InferTra is different.
@cite_16
2049626361
The increasing availability of GPS-embedded mobile devices has given rise to a new spectrum of location-based services, which have accumulated a huge collection of location trajectories. In practice, a large portion of these trajectories are of low-sampling-rate. For instance, the time interval between consecutive GPS points of some trajectories can be several minutes or even hours. With such a low sampling rate, most details of their movement are lost, which makes them difficult to process effectively. In this work, we investigate how to reduce the uncertainty in such kind of trajectories. Specifically, given a low-sampling-rate trajectory, we aim to infer its possible routes. The methodology adopted in our work is to take full advantage of the rich information extracted from the historical trajectories. We propose a systematic solution, History based Route Inference System (HRIS), which covers a series of novel algorithms that can derive the travel pattern from historical data and incorporate it into the route inference process. To validate the effectiveness of the system, we apply our solution to the map-matching problem which is an important application scenario of this work, and conduct extensive experiments on a real taxi trajectory dataset. The experiment results demonstrate that HRIS can achieve higher accuracy than the existing map-matching algorithms for low-sampling-rate trajectories. Part 1 Introduction: nonlinear programming local methods global methods Numerica outline. Part 2 A tour of Numerica: getting started generic constraints constants ranges input parameters aggregation operators functions sets unconstrained optimization constrained optimization local constraint solving local unconstrained optimization soft constraints real constraints and uncertain data display accuracy. Part 3 The meaning of Numerica: interval analysis constraint solving unconstrained optimization interpretation of the results. Part 4 Modelling in Numerica: what can go wrong in Numerica improving Numerica statements. Part 5 The syntax of Numerica: overall structure expressions the constant section the input section the set section the variable section the function section the body section the display section the pragma section scoping rules. Part 6 The semantics of Numerica: interval arithmetic semantics of constraint solving semantics of unconstrained minimization semantics of constrained minimization non-canonical boxes. Part 7 An implementation of Numerica: overview of the algorithm domain-specific and monotonic interval extensions constraint solving unconstrained optimization constrained optimization advanced techniques an implementation of box consistency. Part 8 Experimental results: constraint solving unconstrained optimization constrained optimization appendices. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. The shading cue is supposed to be a major factor in monocular stereopsis. However, the hypothesis is hardly corroborated by available data. For instance, the conventional stimulus used in perception research, which involves a circular disk with monotonic luminance gradient on a uniform surround, is theoretically ‘explained’ by any quadric surface, including spherical caps or cups (the conventional response categories), cylindrical ruts or ridges, and saddle surfaces. Whereas cylindrical ruts or ridges are reported when the outline is changed from circular to square, saddle surfaces are never reported. We introduce a method that allows us to differentiate between such possible responses. We report observations on a number of variations of the conventional stimulus, including variations of shape and quality of the boundary, and contexts that allow the observer to infer illumination direction. We find strong and expected influences of outline shape, but, perhaps surprisingly, we fail to find any influence of context, and only partial influence of outline quality. Moreover, we report appreciable differences within the generic population. We trace some of the idiosyncrasies (as compared to shape from shading algorithms) of the human observer to generic properties of the environment, in particular the fact that many objects are limited in size and elliptically convex over most of their boundaries.