Particle Filtering for Model-Based Anomaly Detection in Sensor Networks

A novel technique has been developed for anomaly detection of rocket engine test stand (RETS) data. The objective was to develop a system that postprocesses a csv file containing the sensor readings and activities (time-series) from a rocket engine test, and detects any anomalies that might have occurred during the test. The output consists of the names of the sensors that show anomalous behavior, and the start and end time of each anomaly. In order to reduce the involvement of domain experts significantly, several data-driven approaches have been proposed where models are automatically acquired from the data, thus bypassing the cost and effort of building system models. Many supervised learning methods can efficiently learn operational and fault models, given large amounts of both nominal and fault data. However, for domains such as RETS data, the amount of anomalous data that is actually available is relatively small, making most supervised learning methods rather ineffective, and in general met with limited success in anomaly detection. The fundamental problem with existing approaches is that they assume that the data are iid, i.e., independent and identically distributed, which is violated in typical RETS data. None of these techniques naturally exploit the temporal information inherent in time series data from the sensor networks. There are correlations among the sensor readings, not only at the same time, but also across time. However, these approaches have not explicitly identified and exploited such correlations. Given these limitations of model-free methods, there has been renewed interest in model-based methods, specifically graphical methods that explicitly reason temporally. The Gaussian Mixture Model (GMM) in a Linear Dynamic System approach assumes that the multi-dimensional test data is a mixture of multi-variate Gaussians, and fits a given number of Gaussian clusters with the help of the wellknown Expectation Maximization (EM) algorithm. The parameters thus learned are used for calculating the joint distribution of the observations. However, this GMM assumption is essentially an approximation and signals the potential viability of non-parametric density estimators. This is the key idea underlying the new approach

Multi-Agent Reinforcement Learning as a Rehearsal for Decentralized Planning

Decentralized partially observable Markov decision processes (Dec-POMDPs) are a powerful tool for modeling multi-agent planning and decision-making under uncertainty. Prevalent Dec-POMDP solution techniques require centralized computation given full knowledge of the underlying model. Multi-agent reinforcement learning (MARL) based approaches have been recently proposed for distributed solution of Dec-POMDPs without full prior knowledge of the model, but these methods assume that conditions during learning and policy execution are identical. In some practical scenarios this may not be the case. We propose a novel MARL approach in which agents are allowed to rehearse with information that will not be available during policy execution. The key is for the agents to learn policies that do not explicitly rely on these rehearsal features. We also establish a weak convergence result for our algorithm, RLaR, demonstrating that RLaR converges in probability when certain conditions are met. We show experimentally that incorporating rehearsal features can enhance the learning rate compared to non-rehearsal-based learners, and demonstrate fast, (near) optimal performance on many existing benchmark Dec-POMDP problems. We also compare RLaR against an existing approximate Dec-POMDP solver which, like RLaR, does not assume a priori knowledge of the model. While RLaR׳s policy representation is not as scalable, we show that RLaR produces higher quality policies for most problems and horizons studied

The Effects Combining Cryocompression Therapy following an Acute Bout of Resistance Exercise on Performance and Recovery

Compression and cold therapy used separately have shown to reduce negative effects of tissue damage. The combining compression and cold therapy (cryocompression) as a single recovery modality has yet to be fully examined. To examine the effects of cryocompression on recovery following a bout of heavy resistance exercise, recreationally resistance trained men (n =16) were recruited, matched, and randomly assigned to either a cryocompression group (CRC) or control group (CON). Testing was performed before and then immediately after exercise, 60 minutes, 24 hours, and 48 hours after a heavy resistance exercise workout (barbell back squats for 4 sets of 6 reps at 80% 1RM, 90 sec rest between sets, stiff legged deadlifts for 4 sets of 8 reps at 1.0 X body mass with 60 sec rest between sets, 4 sets of 10 eccentric Nordic hamstring curls, 45 sec rest between sets). The CRC group used the CRC system for 20-mins of cryocompression treatment immediately after exercise, 24 hours, and 48 hours after exercise. CON sat quietly for 20-mins at the same time points. Muscle damage [creatine kinase], soreness (visual analog scale, 0-100), pain (McGill Pain Q, 0-5), fatigue, sleep quality, and jump power were significantly (p \u3c 0.05) improved for CRC compared to CON at 24 and 48 hours after exercise. Pain was also significantly lower for CRC compared to CON at 60-mins post exercise. These findings show that cryocompression can enhance recovery and performance following a heavy resistance exercise workout

Measurement of the charm and beauty structure functions using the H1 vertex detector at HERA

Inclusive charm and beauty cross sections are measured in e − p and e + p neutral current collisions at HERA in the kinematic region of photon virtuality 5≤Q 2≤2000 GeV2 and Bjorken scaling variable 0.0002≤x≤0.05. The data were collected with the H1 detector in the years 2006 and 2007 corresponding to an integrated luminosity of 189 pb−1. The numbers of charm and beauty events are determined using variables reconstructed by the H1 vertex detector including the impact parameter of tracks to the primary vertex and the position of the secondary vertex. The measurements are combined with previous data and compared to QCD predictions

Study of Charm Fragmentation into D^{*\pm} Mesons in Deep-Inelastic Scattering at HERA

The process of charm quark fragmentation is studied using $D^{*\pm}$ meson production in deep-inelastic scattering as measured by the H1 detector at HERA. Two different regions of phase space are investigated defined by the presence or absence of a jet containing the $D^{*\pm}$ meson in the event. The parameters of fragmentation functions are extracted for QCD models based on leading order matrix elements and DGLAP or CCFM evolution of partons together with string fragmentation and particle decays. Additionally, they are determined for a next-to-leading order QCD calculation in the fixed flavour number scheme using the independent fragmentation of charm quarks to $D^{*\pm}$ mesons.Comment: 33 pages, submitted to EPJ

An approach to developing a prediction model of fertility intent among HIV-positive women and men in Cape Town, South Africa: a case study

As a ‘case-study’ to demonstrate an approach to establishing a fertility-intent prediction model, we used data collected from recently diagnosed HIV-positive women (N = 69) and men (N = 55) who reported inconsistent condom use and were enrolled in a sexual and reproductive health intervention in public sector HIV care clinics in Cape Town, South Africa. Three theoretically-driven prediction models showed reasonable sensitivity (0.70–1.00), specificity (0.66–0.94), and area under the receiver operating characteristic curve (0.79–0.89) for predicting fertility intent at the 6-month visit. A k-fold cross-validation approach was employed to reduce bias due to over-fitting of data in estimating sensitivity, specificity, and area under the curve. We discuss how the methods presented might be used in future studies to develop a clinical screening tool to identify HIV-positive individuals likely to have future fertility intent and who could therefore benefit from sexual and reproductive health counselling around fertility options

Measurement of charged particle spectra in deep-inelastic ep scattering at HERA

Charged particle production in deep-inelastic ep scattering is measured with the H1 detector at HERA. The kinematic range of the analysis covers low photon virtualities, 5 LT Q(2) LT 100 GeV2, and small values of Bjorken-x, 10(-4) LT x LT 10(-2). The analysis is performed in the hadronic centre-of-mass system. The charged particle densities are measured as a function of pseudorapidity (n(*)) and transverse momentum (p(T)(*)) in the range 0 LT n(*) LT 5 and 0 LT p(T)(*) LT 10 GeV in bins of x and Q(2). The data are compared to predictions from different Monte Carlo generators implementing various options for hadronisation and parton evolutions

Jet production in ep collisions at high Q(2) and determination of alpha(s)

The production of jets is studied in deep-inelastic e(+/-) p scattering at large negative four momentum transfer squared 150 LT Q(2) LT 15000 GeV2 using HERA data taken in 1999-2007, corresponding to an integrated luminosity of 395 pb(-1). Inclusive jet, 2-jet and 3-jet cross sections, normalised to the neutral current deep-inelastic scattering cross sections, are measured as functions of Q(2), jet transverse momentum and proton momentum fraction. The measurements are well described by perturbative QCD calculations at next-to-leading order corrected for hadronisation effects. The strong coupling as determined from these measurement

Reinforcement Learning For Decentralized Planning Under Uncertainty

Decentralized partially-observable Markov decision processes (Dec-POMDPs) are a powerful tool for modeling multi-agent planning and decision-making under uncertainty. Prevalent Dec-POMDP solution techniques require centralized computation given full knowledge of the underlying model. But in real world scenarios, model parameters may not be known a priori, or may be difficult to specify. We propose to address these limitations with distributed reinforcement learning (RL). Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved