State of Utah in the interest of Baby Girl Marie v. Nadine Munoz : Appellant\u27s Reply Brief

Appeal by natural mother from Judgment of the First District Juvenile Court, Weber County, the honorable Charles E. Bradford presiding

Low complexity TOA estimator for multiuser DS-UWB system

International audienceIn this paper, we present a low complexity Time Of Arrival (TOA) estimator for direct-sequence ultra-wideband (DS-UWB) ranging system. With the assumption that TOA is the integer multiples of chip duration, our decoupled multiuser ranging (DEMR) estimator employs integrate-and-dump filter (IDF) in chip sampling rate instead of matched filter (MF) as the front-end to reduce sampling rate and to simplify the structure of estimator. This subsampling estimator is simplified substantially in dense multipath environment furthermore due to the long repetition time of DS-UWB pulse. Simulation results show that compared with other low complexity TOA estimator, DEMR estimator is not only quite near-far resistant, but also can obtain noticeable ranging performance in the fully loaded system

Chaos-based TOA estimator for DS-UWB ranging systems in multiuser environment

International audienceIn this paper, we present a chaos-based decoupled multiuser ranging (DEMR) estimator for multiuser DS-UWB ranging system. In the DEMR estimator, users are decoupled by the knowledge of all the users' limited number of data bits. Then, the ranging performance of each user mainly depends on the non-cyclic autocorrelation property of the spreading code. Based on this property, we improve DEMR estimator by using the selected binary chaotic sequences instead of the Gold sequences in order to increase the system capacity and to improve the ranging accuracy. Simulations in CM1 channel show that the chaos-based DEMR estimator is quite near-far resistant and achieves a noticeable ranging accuracy even in a heavily loaded system. Compared with using Gold sequences, chaos-based DEMR not only works with more users than full load of Gold sequences but also improves the ranging accuracy especially under low SNR condition

Bis[μ-bis­(diphenyl­phosphino)methane-κ2 P:P′]bis­[(4-toluene­sulfonato-κO)silver(I)] monohydrate

The title complex, [Ag2(C7H7O3S)2(C25H22P2)2]·H2O, was obtained by the reaction of silver toluene­sulfonate with diphenyl­phosphinomethane (dppm) in acetonitrile. There are two unique half-mol­ecules of the complex in the asymmetric unit, together with one water mol­ecule, which is disordered over two positions with site occupancy factors of 0.6 and 0.4. In each centrosymmetric neutral dimeric mol­ecule, two Ag atoms are bridged by a pair of dppm ligands to give an eight-membered Ag2P4C2 ring with a distorted AgOP2 trigonal–planar environment. The Ag—Ag distances of 2.9215 (9) and 3.027 (1) Å indicate a direct bonding inter­action

Mott Transition vs Multicritical Phenomenon of Superconductivity and Antiferromagnetism -- Application to κ\kappa-(BEDT-TTF)2_2X --

Interplay between the Mott transition and the multicritical phenomenon of d-wave superconductivity (SC) and antiferromagnetism (AF) is studied theoretically. We describe the Mott transition, which is analogous to a liquid-gas phase transition, in terms of an Ising-type order parameter $\eta$. We reveal possible mean-field phase diagrams produced by this interplay. Renormalization group analysis up to one-loop order gives flows of coupling constants, which in most cases lead to fluctuation-induced first-order phase transitions even when the SO(5) symmetry exists betwen the SC and AF. Behaviors of various physical quantities around the Mott critical point are predicted. Experiments in $\kappa$-(BEDT-TTF)$_2$X are discussed from this viewpoint.Comment: 4 pages, 9 figures, to appear in J. Phys. Soc. Jp

Breakdown of Fermi-liquid theory in a cuprate superconductor

The behaviour of electrons in solids is remarkably well described by Landau's Fermi-liquid theory, which says that even though electrons in a metal interact they can still be treated as well-defined fermions, called ``quasiparticles''. At low temperature, the ability of quasiparticles to transport heat is strictly given by their ability to transport charge, via a universal relation known as the Wiedemann-Franz law, which no material in nature has been known to violate. High-temperature superconductors have long been thought to fall outside the realm of Fermi-liquid theory, as suggested by several anomalous properties, but this has yet to be shown conclusively. Here we report on the first experimental test of the Wiedemann-Franz law in a cuprate superconductor, (Pr,Ce)$_2$CuO$_4$. Our study reveals a clear departure from the universal law and provides compelling evidence for the breakdown of Fermi-liquid theory in high-temperature superconductors.Comment: 7 pages, 3 figure

Predictive Cyber Situational Awareness and Personalized Blacklisting: A Sequential Rule Mining Approach

Cybersecurity adopts data mining for its ability to extract concealed and indistinct patterns in the data, such as for the needs of alert correlation. Inferring common attack patterns and rules from the alerts helps in understanding the threat landscape for the defenders and allows for the realization of cyber situational awareness, including the projection of ongoing attacks. In this paper, we explore the use of data mining, namely sequential rule mining, in the analysis of intrusion detection alerts. We employed a dataset of 12 million alerts from 34 intrusion detection systems in 3 organizations gathered in an alert sharing platform, and processed it using our analytical framework. We execute the mining of sequential rules that we use to predict security events, which we utilize to create a predictive blacklist. Thus, the recipients of the data from the sharing platform will receive only a small number of alerts of events that are likely to occur instead of a large number of alerts of past events. The predictive blacklist has the size of only 3 % of the raw data, and more than 60 % of its entries are shown to be successful in performing accurate predictions in operational, real-world settings

Geometrical Insights for Implicit Generative Modeling

Learning algorithms for implicit generative models can optimize a variety of criteria that measure how the data distribution differs from the implicit model distribution, including the Wasserstein distance, the Energy distance, and the Maximum Mean Discrepancy criterion. A careful look at the geometries induced by these distances on the space of probability measures reveals interesting differences. In particular, we can establish surprising approximate global convergence guarantees for the $1$-Wasserstein distance,even when the parametric generator has a nonconvex parametrization.Comment: this version fixes a typo in a definitio

The Health Impact Fund: How Might It Work for Novel Anticoagulants in Atrial Fibrillation?

Cardiovascular diseases represent the greatest burden of global disease. Spending on cardiovascular diseases is higher than for other diseases, with the majority being spent on drugs. Therefore, these drugs and these diseases are hugely important to health systems, society, and pharmaceutical companies. The Health Impact Fund represents a new mechanism by which pharmaceutical innovators would be rewarded on the basis of the health impact of their new drugs. This review illustrates the concept of the Health Impact Fund using the example of novel anticoagulants for prevention of stroke and thromboembolism in atrial fibrillation. By considering existing data and the current situation for novel anticoagulants, we suggest that epidemiologic data and modeling techniques can be used to predict future trends in disease and the health impact of new drugs. The Health Impact Fund may offer potential benefits to pharmaceutical companies, patients, and governments and warrants proper investigation