Limiting empirical spectral distribution for the non-backtracking matrix of an Erd\H{o}s-R\'enyi random graph

In this note, we give a precise description of the limiting empirical spectral distribution (ESD) for the non-backtracking matrices for an Erd\H{o}s-R\'{e}nyi graph assuming $np/\log n$ tends to infinity. We show that derandomizing part of the non-backtracking random matrix simplifies the spectrum considerably, and then we use Tao and Vu's replacement principle and the Bauer-Fike theorem to show that the partly derandomized spectrum is, in fact, very close to the original spectrum.Comment: 19 pages, 1 figure. Adjusted the figure in the new versio

Linking Smartphone GPS Data with Transport Planning: A Framework of Data Aggregation and Anonymization for a Journey Planning App

With the proliferation of GPS tracking data provided by smartphone apps, it is desirable to develop a data processing and anonymizing framework to transform raw GPS data into a suitable format for transport planning models. The paper aims to describe the effort to address such issues by map matching and aggregating the GPS information derived from a journey planning app. The effectiveness and flexibility of such a framework is demonstrated by an analysis of speeding and waiting time patterns in England and Wales by tracking 120 users for a year

Forecasting Corn Futures Volatility in the Presence of Long Memory, Seasonality and Structural Change

Price volatility in the corn market has changed considerably globalization and stronger linkages to the energy complex. Using data from January 1989 through December 2009, we estimate and forecast the volatility in the corn market using futures daily prices. Estimates in a Fractional Integrated GARCH framework identify the importance of long memory, seasonality, and structural change. Recursively generated forecasts for up to 40-day horizons starting in January 2005 highlight the importance of seasonality, and long memory specifications which perform well at more distant horizons particularly with rising volatility. The forecast benefits of allowing for structural change in an adaptive framework are more difficult to identify except at more distant horizons after a large downturn in volatility.corn price volatility, long memory, seasonality, structural change, forecasting, Agricultural Finance, Risk and Uncertainty,

All-optical differential current detection technique for unit protection applications

In this paper we demonstrate a novel, all-optical differential current protection scheme. By monitoring the optical power reflected from two matched hybrid fiber Bragg grating current sensors and using a simple optoelectronic threshold detector, an immediate response to an increase in differential current is achieved. A preliminary laboratory embodiment is constructed in order to characterize the performance of the scheme. The proposed technique does not require a complex sensor interrogation scheme, usually characterized by a limited sampling frequency, and thus will be capable of facilitating inexpensive and fast-acting differential protection over long distances

Transportation mode recognition fusing wearable motion, sound and vision sensors

We present the first work that investigates the potential of improving the performance of transportation mode recognition through fusing multimodal data from wearable sensors: motion, sound and vision. We first train three independent deep neural network (DNN) classifiers, which work with the three types of sensors, respectively. We then propose two schemes that fuse the classification results from the three mono-modal classifiers. The first scheme makes an ensemble decision with fixed rules including Sum, Product, Majority Voting, and Borda Count. The second scheme is an adaptive fuser built as another classifier (including Naive Bayes, Decision Tree, Random Forest and Neural Network) that learns enhanced predictions by combining the outputs from the three mono-modal classifiers. We verify the advantage of the proposed method with the state-of-the-art Sussex-Huawei Locomotion and Transportation (SHL) dataset recognizing the eight transportation activities: Still, Walk, Run, Bike, Bus, Car, Train and Subway. We achieve F1 scores of 79.4%, 82.1% and 72.8% with the mono-modal motion, sound and vision classifiers, respectively. The F1 score is remarkably improved to 94.5% and 95.5% by the two data fusion schemes, respectively. The recognition performance can be further improved with a post-processing scheme that exploits the temporal continuity of transportation. When assessing generalization of the model to unseen data, we show that while performance is reduced - as expected - for each individual classifier, the benefits of fusion are retained with performance improved by 15 percentage points. Besides the actual performance increase, this work, most importantly, opens up the possibility for dynamically fusing modalities to achieve distinct power-performance trade-off at run time

When and Where: Predicting Human Movements Based on Social Spatial-Temporal Events

Predicting both the time and the location of human movements is valuable but challenging for a variety of applications. To address this problem, we propose an approach considering both the periodicity and the sociality of human movements. We first define a new concept, Social Spatial-Temporal Event (SSTE), to represent social interactions among people. For the time prediction, we characterise the temporal dynamics of SSTEs with an ARMA (AutoRegressive Moving Average) model. To dynamically capture the SSTE kinetics, we propose a Kalman Filter based learning algorithm to learn and incrementally update the ARMA model as a new observation becomes available. For the location prediction, we propose a ranking model where the periodicity and the sociality of human movements are simultaneously taken into consideration for improving the prediction accuracy. Extensive experiments conducted on real data sets validate our proposed approach