Bernoulli Particle/Box-Particle Filters for Detection and Tracking in the Presence of Triple Measurement Uncertainty

This work presents sequential Bayesian detection and estimation methods for nonlinear dynamic stochastic systems using measurements affected by three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. Following Mahler’s framework for information fusion, the paper develops the optimal Bayes filter for this problem in the form of the Bernoulli filter for interval measurements. Two numerical implementations of the optimal filter are developed. The first is the Bernoulli particle filter (PF), which turns out to require a large number of particles in order to achieve a satisfactory performance. For the sake of reduction in the number of particles, the paper also develops an implementation based on box particles, referred to as the Bernoulli Box-PF. A box particle is a random sample that occupies a small and controllable rectangular region of non-zero volume in the target state space. Manipulation of boxes utilizes the methods of interval analysis. The two implementations are compared numerically and found to perform remarkably well: the target is reliably detected and the posterior probability density function of the target state is estimated accurately. The Bernoulli Box-PF, however, when designed carefully, is computationally more efficient

A Box Particle Filter for Stochastic and Set-theoretic Measurements with Association Uncertainty

This work develops a novel estimation approach for nonlinear dynamic stochastic systems by combining the sequential Monte Carlo method with interval analysis. Unlike the common pointwise measurements, the proposed solution is for problems with interval measurements with association uncertainty. The optimal theoretical solution can be formulated in the framework of random set theory as the Bernoulli filter for interval measurements. The straightforward particle filter implementation of the Bernoulli filter typically requires a huge number of particles since the posterior probability density function occupies a significant portion of the state space. In order to reduce the number of particles, without necessarily sacrificing estimation accuracy, the paper investigates an implementation based on box particles. A box particle occupies a small and controllable rectangular region of non-zero volume in the target state space. The numerical results demonstrate that the filter performs remarkably well: both target state and target presence are estimated reliably using a very small number of box particles

On Infinite Quon Statistics and "Ambiguous" Statistics

We critically examine a recent suggestion that "ambiguous" statistics is equivalent to infinite quon statistics and that it describes a dilute, nonrelativistics ideal gas of extremal black holes. We show that these two types of statistics are different and that the description of extremal black holes in terms of "ambiguous" statistics cannot be applied.Comment: Latex, 9 pages, no figures, to appear in Mod.Phys.Lett.

The Water Footprint of Data Centers

The internet and associated Information and Communications Technologies (ICT) are diffusing at an astounding pace. As data centers (DCs) proliferate to accommodate this rising demand, their environmental impacts grow too. While the energy efficiency of DCs has been researched extensively, their water footprint (WF) has so far received little to no attention. This article conducts a preliminary WF accounting for cooling and energy consumption in DCs. The WF of DCs is estimated to be between 1047 and 151,061 m3/TJ. Outbound DC data traffic generates a WF of 1–205 liters per gigabyte (roughly equal to the WF of 1 kg of tomatos at the higher end). It is found that, typically, energy consumption constitues by far the greatest share of DC WF, but the level of uncertainty associated with the WF of different energy sources used by DCs makes a comprehensive assessment of DCs’ water use efficiency very challenging. Much better understanding of DC WF is urgently needed if a meaningful evaluation of this rapidly spreading service technology is to be gleaned and response measures are to be put into effect

Measurements on HV-CMOS Active Sensors After Irradiation to HL-LHC fluences

During the long shutdown (LS) 3 beginning 2022 the LHC will be upgraded for higher luminosities pushing the limits especially for the inner tracking detectors of the LHC experiments. In order to cope with the increased particle rate and radiation levels the ATLAS Inner Detector will be completely replaced by a purely silicon based one. Novel sensors based on HV-CMOS processes prove to be good candidates in terms of spatial resolution and radiation hardness. In this paper measurements conducted on prototypes built in the AMS H18 HV-CMOS process and irradiated to fluences of up to $2\cdot10^{16}\,\text{n}_\text{eq}\text{cm}^{-2}$ are presented.Comment: 10 pages, 17 figures, proceedings contribution to the International Workshop on Semiconductor Pixel Detectors for Particles and Imaging (PIXEL2014), submitted to Jinst, revised version as requested by Jinst: added more information to testbeam chapter, replaced hole density by electric field distribution plots, corrected typos and plot label

Properties of latent interface-trap buildup in irradiated metal-oxide-semiconductor transistors determined by switched bias isothermal annealing experiments

Isothermal annealing experiments with switched gate bias have been performed to determine the properties of the latent interface-trap buildup during postirradiation annealing of metal-oxide-semiconductor transistors. It has been found that a bias-independent process occurs until the start of the latent interface-trap buildup. During the buildup itself, oxide-trap charge is not permanently neutralized, but is temporarily compensated. (C) 2000 American Institute of Physics. (DOI: 10.1063/1.1336159

Localization Recall Precision (LRP): A New Performance Metric for Object Detection

Average precision (AP), the area under the recall-precision (RP) curve, is the standard performance measure for object detection. Despite its wide acceptance, it has a number of shortcomings, the most important of which are (i) the inability to distinguish very different RP curves, and (ii) the lack of directly measuring bounding box localization accuracy. In this paper, we propose 'Localization Recall Precision (LRP) Error', a new metric which we specifically designed for object detection. LRP Error is composed of three components related to localization, false negative (FN) rate and false positive (FP) rate. Based on LRP, we introduce the 'Optimal LRP', the minimum achievable LRP error representing the best achievable configuration of the detector in terms of recall-precision and the tightness of the boxes. In contrast to AP, which considers precisions over the entire recall domain, Optimal LRP determines the 'best' confidence score threshold for a class, which balances the trade-off between localization and recall-precision. In our experiments, we show that, for state-of-the-art object (SOTA) detectors, Optimal LRP provides richer and more discriminative information than AP. We also demonstrate that the best confidence score thresholds vary significantly among classes and detectors. Moreover, we present LRP results of a simple online video object detector which uses a SOTA still image object detector and show that the class-specific optimized thresholds increase the accuracy against the common approach of using a general threshold for all classes. At https://github.com/cancam/LRP we provide the source code that can compute LRP for the PASCAL VOC and MSCOCO datasets. Our source code can easily be adapted to other datasets as well.Comment: to appear in ECCV 201

What we observe is biased by what other people tell us: beliefs about the reliability of gaze behavior modulate attentional orienting to gaze cues

For effective social interactions with other people, information about the physical environment must be integrated with information about the interaction partner. In order to achieve this, processing of social information is guided by two components: a bottom-up mechanism reflexively triggered by stimulus-related information in the social scene and a top-down mechanism activated by task-related context information. In the present study, we investigated whether these components interact during attentional orienting to gaze direction. In particular, we examined whether the spatial specificity of gaze cueing is modulated by expectations about the reliability of gaze behavior. Expectations were either induced by instruction or could be derived from experience with displayed gaze behavior. Spatially specific cueing effects were observed with highly predictive gaze cues, but also when participants merely believed that actually non-predictive cues were highly predictive. Conversely, cueing effects for the whole gazed-at hemifield were observed with non-predictive gaze cues, and spatially specific cueing effects were attenuated when actually predictive gaze cues were believed to be non-predictive. This pattern indicates that (i) information about cue predictivity gained from sampling gaze behavior across social episodes can be incorporated in the attentional orienting to social cues, and that (ii) beliefs about gaze behavior modulate attentional orienting to gaze direction even when they contradict information available from social episodes

Three Dimensional Viscous Flow Field in an Axial Flow Turbine Nozzle Passage

The objective of this investigation is experimental and computational study of three dimensional viscous flow field in the nozzle passage of an axial flow turbine stage. The nozzle passage flow field has been measured using a two sensor hot-wire probe at various axial and radial stations. In addition, two component LDV measurements at one axial station (x/c(sum m) = 0.56) were performed to measure the velocity field. Static pressure measurements and flow visualization, using a fluorescent oil technique, were also performed to obtain the location of transition and the endwall limiting streamlines. A three dimensional boundary layer code, with a simple intermittency transition model, was used to predict the viscous layers along the blade and endwall surfaces. The boundary layers on the blade surface were found to be very thin and mostly laminar, except on the suction surface downstream of 70% axial chord. Strong radial pressure gradient, especially close to the suction surface, induces strong cross flow components in the trailing edge regions of the blade. On the end-walls the boundary layers were much thicker, especially near the suction corner of the casing surface, caused by secondary flow. The secondary flow region near the suction-casing surface corner indicates the presence of the passage vortex detached from the blade surface. The corner vortex is found to be very weak. The presence of a closely spaced rotor downstream (20% of the nozzle vane chord) introduces unsteadiness in the blade passage. The measured instantaneous velocity signal was filtered using FFT square window to remove the periodic unsteadiness introduced by the downstream rotor and fans. The filtering decreased the free stream turbulence level from 2.1% to 0.9% but had no influence on the computed turbulence length scale. The computation of the three dimensional boundary layers is found to be accurate on the nozzle passage blade surfaces, away from the end-walls and the secondary flow region. On the nozzle passage endwall surfaces the presence of strong pressure gradients and secondary flow limit the validity of the boundary layer code