The Hunting of the Bump: On Maximizing Statistical Discrepancy

Anomaly detection has important applications in biosurveilance and environmental monitoring. When comparing measured data to data drawn from a baseline distribution, merely, finding clusters in the measured data may not actually represent true anomalies. These clusters may likely be the clusters of the baseline distribution. Hence, a discrepancy function is often used to examine how different measured data is to baseline data within a region. An anomalous region is thus defined to be one with high discrepancy. In this paper, we present algorithms for maximizing statistical discrepancy functions over the space of axis-parallel rectangles. We give provable approximation guarantees, both additive and relative, and our methods apply to any convex discrepancy function. Our algorithms work by connecting statistical discrepancy to combinatorial discrepancy; roughly speaking, we show that in order to maximize a convex discrepancy function over a class of shapes, one needs only maximize a linear discrepancy function over the same set of shapes. We derive general discrepancy functions for data generated from a one- parameter exponential family. This generalizes the widely-used Kulldorff scan statistic for data from a Poisson distribution. We present an algorithm running in $O(\smash[tb]{\frac{1}{\epsilon} n^2 \log^2 n})$ that computes the maximum discrepancy rectangle to within additive error $\epsilon$, for the Kulldorff scan statistic. Similar results hold for relative error and for discrepancy functions for data coming from Gaussian, Bernoulli, and gamma distributions. Prior to our work, the best known algorithms were exact and ran in time $\smash[t]{O(n^4)}$.Comment: 11 pages. A short version of this paper will appear in SODA06. This full version contains an additional short appendi

Sensor network localization for moving sensors

pre-printSensor network localization (SNL) is the problem of determining the locations of the sensors given sparse and usually noisy inter-communication distances among them. In this work we propose an iterative algorithm named PLACEMENT to solve the SNL problem. This iterative algorithm requires an initial estimation of the locations and in each iteration, is guaranteed to reduce the cost function. The proposed algorithm is able to take advantage of the good initial estimation of sensor locations making it suitable for localizing moving sensors, and also suitable for the refinement of the results produced by other algorithms. Our algorithm is very scalable. We have experimented with a variety of sensor networks and have shown that the proposed algorithm outperforms existing algorithms both in terms of speed and accuracy in almost all experiments. Our algorithm can embed 120,000 sensors in less than 20 minutes

Jointly setting upper limits on multiple components of an anisotropic stochastic gravitational-wave background

With the increasing sensitivities of the gravitational wave (GW) detectors and more detectors joining the international network, the chances of detection of a stochastic GW background (SGWB) are progressively increasing. Different astrophysical and cosmological processes are likely to give rise to backgrounds with distinct spectral signatures and distributions on the sky. The observed SGWB will therefore be a superposition of these components. Hence, one of the first questions that will come up after the first detection of a SGWB will likely be about identifying the dominant components and their distributions on the sky. Both these questions were addressed separately in the literature, namely, how to separate components of isotropic backgrounds and how to probe the anisotropy of a single component. Here, we address the question of how to separate distinct anisotropic backgrounds with (sufficiently) different spectral shapes. We first obtain the combined Fisher information matrix from folded data using an efficient analysis pipeline PyStoch, which incorporates covariances between pixels and spectral indices. This is necessary for estimating the detection statistic and setting upper limits. However, based on a recent study, we ignore the pixel-to-pixel noise covariance that does not have a significant effect on the results at the present sensitivity levels of the detectors. We show that the joint analysis accurately separates and estimates backgrounds with different spectral shapes and different sky distributions with no major bias. This does come at the cost of increased variance. Thus making the joint upper limits safer, though less strict than the individual analysis. We finally set joint upper limits on the multicomponent anisotropic background using Advanced LIGO data taken up to the first half of the third observing run.Comment: 14 pages, 10 figures, 2 table

Effect of Admixture on the Compressive Strength of Composite Cement Mortar

The effect of superplasticizer on the development of composite cement based on flyash/limestone powder as per EN-197-2000 has been studied. Various mixes of fly ash and limestone up to 40% has been blended. The results have been compared with clinker of 43 grade ordinary portland cement used in the present study. 1 day strength of mixes with 5% and 10% limestone powder has been found to be is comparable to control. Further, it has been found that 28 days strength of mix with 15% lime stone powder and 25% fly ash gives more than 32.5 R required for composite cement. With the use of superplasticizer, strength has been found comparable or more in all the mixes at 1day to 43 grade OPC. X-ray diffraction (XRD) analysis of various mixes at different hydration times has also been evaluated.

CONTROLLING IP SPOOFING THROUGH INTER DOMAIN PACKET FILTERS

IP Spoofing is a serious threat to the legitimate use of the Internet. By employing IP spoofing, attackers can overload the destination network thus preventing it from providing service to legitimate user. In this paper, we propose an inter domain packet filter (IDPF) architecture that can minimize the level of IP spoofing on the Internet. A key feature of our scheme is that it does not require global routing information.  IDPFs are constructed from the information implicit in Border Gateway Protocol (BGP) route updates and are deployed in network border routers.  We establish the conditions under which the IDPF framework correctly works in that it does not discard packets with valid source addresses. We show that, even with partial deployment on the Internet, IDPFs can proactively limit the spoofing capability of attackers. In addition, they can help localize the origin of an attack packet to a small number of candidate networks

The anti-inflammatory effects of Curcuma longa and Berberis aristata in endotoxin-induced uveitis in rabbits

Purpose. To investigate the anti-inflammatory effect of topical application of Curcuma longa (C. longa) and Berberis aristata (B. aristata) aqueous extracts on experimental uveitis in the rabbit. Methods. Anterior uveitis was induced in rabbits by intravitreal injection of lipopolysaccharide from Escherichia coli after pretreatment with C. longa and B. aristata aqueous extracts. Subsequently, the anti-inflammatory activity of C. longa and B. aristata was evaluated by grading the clinical signs and histopathologic changes and estimating the inflammatory cell count, protein, and TNF-α levels in the aqueous humor. Results. The anterior segment inflammation in the control group was significantly higher than in both the extract-treated groups, as observed by clinical and histopathologic grading. The inflammatory cell count in the control group was 30.75 ± 7.33 × 105 cells/mL, whereas it was 2.39 ± 0.59 × 105 (P < 0.001 vs. control) and 11.56 ± 2.44 × 105 (P = 0.001 vs. control) cells/mL in the C. longa– and B. aristata–treated groups, respectively. The protein content of the aqueous humor was 18.14 ± 4.98, 3.16 ± 0.55 (P < 0.001 vs. control), and 8.24 ± 1.42 (P < 0.01 vs. control) mg/mL in the control, C. longa–, and B. aristata–treated groups, respectively. The aqueous TNF-α level in the control group was 976.29 ± 66.38 pg/mL and was 311.96 ± 28.50 (P < 0.0001 vs. control) and 654.09 ± 47.66 (P < 0.001vs. control) pg/mL in the C. longa– and B. aristata–treated groups, respectively. Conclusions. Topical instillation of aqueous extracts of C. longa and B. aristata showed potent anti-inflammatory activity against endotoxin-induced uveitis in rabbits

Localised melorheostosis

Melorheostosis is a sclerosing bone dysplasia of unknown aetiology. Diagnosis is mainly based on a combination of clinical and imaging studies. The classical dripping candle wax picture on plain radiograph is diagnostic. We report a case of localized melorheostosis of a 14 year old boy on daily analgesics for two years for pain relief. Radiographs showed endosteal bone formation like dripping candle wax. Biopsy and decompression of the hyperostosis was done. However biopsy did not relieve his symptoms. Based on literature survey he was given a single infusion of zoledronic acid. This gave dramatic relief in pain

Targeted search for the stochastic gravitational-wave background from the galactic millisecond pulsar population

The millisecond pulsars, old-recycled objects spinning with high frequency $\mathcal{O}$(kHz) sustaining the deformation from their spherical shape, may emit gravitational-waves (GW). These are one of the potential candidates contributing to the anisotropic stochastic gravitational-wave background (SGWB) observable in the ground-based GW detectors. Here, we present the results from a likelihood-based targeted search for the SGWB due to millisecond pulsars in the Milky Way, by analyzing the data from the first three observing runs of Advanced LIGO and Advanced Virgo detector. We assume that the shape of SGWB power spectra and the sky distribution is known a priori from the population synthesis model. The information of the ensemble source properties, i.e., the in-band number of pulsars, $N_{obs}$ and the averaged ellipticity, $\mu_\epsilon$ is encoded in the maximum likelihood statistic. We do not find significant evidence for the SGWB signal from the considered source population. The best Bayesian upper limit with $95\%$ confidence for the parameters are $N_{obs}\leq8.8\times10^{4}$ and $\mu_\epsilon\leq1.1\times10^{-7}$, which is comparable to the bounds on mean ellipticity with the GW observations of the individual pulsars. Finally, we show that for the plausible case of $N_{obs}=40,000$, with the one year of observations, the one-sigma sensitivity on $\mu_\epsilon$ might reach $10^{-8}$ and $2.7\times10^{-9}$ for the second-generation detector network having A+ sensitivity and third-generation detector network respectively