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A single target is hidden at a location chosen from a predetermined probability distribution. Then, a searcher must find a second probability distribution from which random search points are sampled such that the target is found in the minimum number of trials. Here it will be shown that if the searcher must get very close to the target to find it, then the best search distribution is proportional to the square root of the target distribution. For a Gaussian target distribution, the optimum search distribution is approximately a Gaussian with a standard deviation that varies inversely with how close the searcher must be to the target to find it. For a network, where the searcher randomly samples nodes and looks for the fixed target along edges, the optimum is to either sample a node with probability proportional to the square root of the out degree plus one or not at all.

Let $Z=(Z_t)_{t\ge0}$ be a regular diffusion process started at $0$, let $\ell$ be an independent random variable with a strictly increasing and continuous distribution function $F$, and let $\tau_{\ell}=\inf\{t\ge0\vert Z_t=\ell\}$ be the first entry time of $Z$ at the level $\ell$. We show that the quickest detection problem \[\inf_{\tau}\bigl[\mathsf{P}(\tau 0$. Solving the latter problem we find that the following stopping time is optimal: \[\tau_*=\inf \bigl\{t\ge0\vert f_*(I_t,S_t)\le X_t\le g_*(I_t,S_t)\bigr\},\] where the surfaces $f_*$ and $g_*$ can be characterised as extremal solutions to a couple of first-order nonlinear PDEs expressed in terms of the infinitesimal characteristics of $X$ and $c$. This is done by extending the arguments associated with the maximality principle [Ann. Probab. 26 (1998) 1614-1640] to the three-dimensional setting of the present problem and disclosing the general structure of the solution that is valid in all particular cases. The key arguments developed in the proof should be applicable in similar multi-dimensional settings.

TBN Israel's Yair Pinto reports on the Israel-Hamas War. Pinto reports of an IDF airstrike on a controversial water pumping station inside Lebanon; a crucial piece of infrastructure which supplies water to neighboring communities and effectively turns off the water to Hezbollah.  Israeli forces have also recovered Hamas documents detailing its financial support from Iran, totaling $154 million. In Judea and Samaria, Israeli security forces  continue to arrest wanted terrorism suspects. 

Motivated by anomalies in cosmic ray observations and by attempts to solve questions of the Standad Model of particle physics like the (g-2)_mu discrepancy, U(1) extensions of the Standard Model have been proposed in recent years. Such U(1) extensions allow for the interaction of Dark Matter by exchange of a photon-like massive force carrier \gamma' not included in the Standard Model. In order to search for \gamma' bosons various experimental programs have been started. One approach is the dedicated search at fixed-target experiments at modest energies as performed at MAMI or at the Jefferson Lab. In these experiments the process e(A,Z)->e(A,Z)l^+l^- is investigated and a search for a very narrow resonance in the invariant mass distribution of the l^+l^- pair is performed. In this work we analyze this process in terms of signal and background in order to describe existing data obtained by the A1 experiment at MAMI with the aim to give accurate predictions for exclusion limits in the \gamma' parameter space. We present a detailed theoretical analysis of the cross sections entering in the description of such processes.

We investigate various projection spaces and extract key parameters or features from each space to characterize low-frequency active (LFA) target returns in a low-dimensional space. The projection spaces encompass (1) time embedded phase map, (2) segmented matched filter output, (3) various time frequency distribution functions, such as Reduced Interference Distribution, to capture time-varying echo signatures, and (4) principal component inversion for signal cleaning and characterization. We utilize both dynamic and static features and parameterize them with a hybrid classification methodology consisting of hidden Markov models, classifiers, and data fusion. This clue identification and evaluation process is complemented by concurrent work on target physics to enhance our understanding of the target echo formation process. As a function of target aspect, we can observe (1) back scatter dominated by axial n=O modes propagating back and forth along the length of the shell, (2) direct scatter from shell discontinuities, (3) helical or creeping waves from phase matching between the acoustic waves and membrane waves (both shear and compressional), and (4) the "array response" of the shell, with coherent super- position of elemental scattering sites along the shell leading to a peak response near broadside. As a function of target structures (the empty shell and the ribbed/complex shells), we see considerable complexity brought about by multiple reflections of the membrane waves between the rings. We show the merit of fusing parameters estimated from these projection spaces in characterizing LFA target returns using the MIT/NRL scaled model data. Our hybrid classifiers outperform the matched filter-based recognizer by an average of 5 to 25%.

The info war is spreading into numerous high level struggles. The SAG is back, thanks to Sean. Odd articles are starting to showing up. Those henchmen once paid are now flipping. The public can no longer recognize the truth. It's time to call evil by it's true name. Multi tasking during the demon battle. Doing the right thing means sometimes helping losers. Center yourself and take some prayer time. CAA funding and what they really do. J6 lockups are intimidation. No doubt, their target is the family. Working the system after the dust settles. All MSM is scripted. Those outsourced are also unprotected. So much backup for that one laptop. Revenge is bait. Does whistle blower mean cloud access? From here, it looks like unneeded deal cutting. The Finger Lakes and a zone of evil. The entire fight is about control, of information and of us. It's not like she told us so, it's more like a friendly warning. Learn more about your ad choices. Visit megaphone.fm/adchoices

405 pages ; 22 cm

483 pages ; 20 cm

Fully polarized Xpatch signatures are transformed to two left circularly polarized signals. These two signals are then filtered by a linear FM pulse compression ('chirp') transfer function, corrupted by AWGN, and filtered by a filter matched to the 'chirp' transfer function. The bandwidth of the 'chirp' radar is about 750 MHz. Range profile feature extraction is performed using the TLS Prony Model parameter estimation technique developed at Ohio State University. Using the Prony Model, each scattering center is described by a polarization ellipse, relative energy, frequency response, and range. This representation of the target is vector quantized using a K-means clustering algorithm. Sequences of vector quantized scattering centers as well as sequences of vector quantized range profiles are used to synthesize target specific Hidden Markov Models (HMM's). The identification decision is made by determining which HMM has the highest probability of generating the unknown sequence. The data consist of synthesized Xpatch signatures of two targets which have been difficult to separate with other RTI algorithms. The RTI algorithm developed for this thesis is clearly able to separate these two targets over a 10 by 10 degree (1 degree granularity) aspect angle window off the nose for SNRs as low as 0 dB. The classification rate is 100 % for SNRs of 5 - 20 dB, 95 % for a SNR of 0 dB and it drops rapidly for SNRs lower than 0 dB.

Hidden Target (DE)(Gingerbread Studios, City Interactive S.A.)(2009)

Facebook executives often talk about their mission as “bringing the world closer together.” With more than 2 billion users, it’s true that the platform does connect people. But one thing that Mark Zuckerberg’s two days of congressional testimony highlighted is that simply connecting people is not how Facebook reached a market cap of nearly $500 billion. At its core, Facebook is an advertising company. In 2017, 98 percent of its $40 billion in revenue came from advertising. It’s not your data that Facebook is selling, it’s your attention. Facebook’s ad platform translates your clicks and posts into “user attributes,” and places you in certain categories for advertisers. For example, your activity could suggest an that you recently had a child, that you’re a Bernie Sanders fan, or that you have an “affinity” for certain racial groups. One of the most powerful features is called “Custom Audiences,” which allows an advertiser to target individuals based on offline information such as email address or phone numbers. An advertiser can upload that data to Facebook, which will match them with its user base to find who they belong to. Then there are so-called “dark posts,” a feature Facebook is in the process of changing, which only show up in the news feeds of specific audiences and are completely hidden to everyone else. These tools can be used by anyone who wants to advertise on Facebook, and critics say that’s an open invitation for malicious actors. Watch this video to learn more about how Facebook’s ad targeting works, and why it’s so effective. Subscribe to VICE News here: [URL REMOVED] Check out VICE News for more: [URL REMOVED] Follow VICE News here: Facebook: [URL REMOVED] Twitter: [URL REMOVED] Tumblr: [URL REMOVED] Instagram: [URL REMOVED] More videos from the VICE network: [URL REMOVED]

Facebook executives often talk about their mission as “bringing the world closer together.” With more than 2 billion users, it’s true that the platform does connect people. But one thing that Mark Zuckerberg’s two days of congressional testimony highlighted is that simply connecting people is not how Facebook reached a market cap of nearly $500 billion. At its core, Facebook is an advertising company. In 2017, 98 percent of its $40 billion in revenue came from advertising. It’s not your data that Facebook is selling, it’s your attention. Facebook’s ad platform translates your clicks and posts into “user attributes,” and places you in certain categories for advertisers. For example, your activity could suggest an that you recently had a child, that you’re a Bernie Sanders fan, or that you have an “affinity” for certain racial groups. One of the most powerful features is called “Custom Audiences,” which allows an advertiser to target individuals based on offline information such as email address or phone numbers. An advertiser can upload that data to Facebook, which will match them with its user base to find who they belong to. Then there are so-called “dark posts,” a feature Facebook is in the process of changing, which only show up in the news feeds of specific audiences and are completely hidden to everyone else. These tools can be used by anyone who wants to advertise on Facebook, and critics say that’s an open invitation for malicious actors. Watch this video to learn more about how Facebook’s ad targeting works, and why it’s so effective. Subscribe to VICE News here: [URL REMOVED] Check out VICE News for more: [URL REMOVED] Follow VICE News here: Facebook: [URL REMOVED] Twitter: [URL REMOVED] Tumblr: [URL REMOVED] Instagram: [URL REMOVED] More videos from the VICE network: [URL REMOVED]

Płyta (ISO) z grą Hidden Target (Jonathan Kane: The Protector w anglojęzycznych krajach )  z czasopisma Imperium Gier DVD Wydanie Specjalne nr 1/2009.

Juego de tiros que mezcla primera y tercera persona, también conocido como " Jonathan Kane: The Protector ". Voces en inglés, texto en español. Probado en Windows 10.

New physics can be light if it is hidden, coupling very weakly to the Standard Model. In this work we investigate the discovery prospects of Abelian hidden sectors in lower-energy fixed-target and high-precision experiments. We focus on a minimal supersymmetric realization consisting of an Abelian vector multiplet, coupled to hypercharge by kinetic mixing, and a pair of chiral Higgs multiplets. This simple theory can give rise to a broad range of experimental signals, including both commonly-studied patterns of hidden vector decay as well as new and distinctive hidden sector cascades. We find limits from the production of hidden states other than the vector itself. In particular, we show that if the hidden Abelian symmetry is higgsed, and the corresponding hidden Higgs boson has visible decays, it severely restricts the ability of the hidden sector to explain the anomalous muon magnetic moment.

New physics can be light if it is hidden, coupling very weakly to the Standard Model. In this work we investigate the discovery prospects of Abelian hidden sectors in lower-energy fixed-target and high-precision experiments. We focus on a minimal supersymmetric realization consisting of an Abelian vector multiplet, coupled to hypercharge by kinetic mixing, and a pair of chiral Higgs multiplets. This simple theory can give rise to a broad range of experimental signals, including both commonly-studied patterns of hidden vector decay as well as new and distinctive hidden sector cascades. We find limits from the production of hidden states other than the vector itself. In particular, we show that if the hidden Abelian symmetry is higgsed, and the corresponding hidden Higgs boson has visible decays, it severely restricts the ability of the hidden sector to explain the anomalous muon magnetic moment.

New physics can be light if it is hidden, coupling very weakly to the Standard Model. In this work we investigate the discovery prospects of Abelian hidden sectors in lower-energy fixed-target and high-precision experiments. We focus on a minimal supersymmetric realization consisting of an Abelian vector multiplet, coupled to hypercharge by kinetic mixing, and a pair of chiral Higgs multiplets. This simple theory can give rise to a broad range of experimental signals, including both commonly-studied patterns of hidden vector decay as well as new and distinctive hidden sector cascades. We find limits from the production of hidden states other than the vector itself. In particular, we show that if the hidden Abelian symmetry is higgsed, and the corresponding hidden Higgs boson has visible decays, it severely restricts the ability of the hidden sector to explain the anomalous muon magnetic moment.

This article is from BMC Bioinformatics , volume 13 . Abstract Background: Hidden Markov Models (HMMs) are a powerful tool for protein domain identification. The Pfam database notably provides a large collection of HMMs which are widely used for the annotation of proteins in new sequenced organisms. In Pfam, each domain family is represented by a curated multiple sequence alignment from which a profile HMM is built. In spite of their high specificity, HMMs may lack sensitivity when searching for domains in divergent organisms. This is particularly the case for species with a biased amino-acid composition, such as P. falciparum, the main causal agent of human malaria. In this context, fitting HMMs to the specificities of the target proteome can help identify additional domains. Results: Using P. falciparum as an example, we compare approaches that have been proposed for this problem, and present two alternative methods. Because previous attempts strongly rely on known domain occurrences in the target species or its close relatives, they mainly improve the detection of domains which belong to already identified families. Our methods learn global correction rules that adjust amino-acid distributions associated with the match states of HMMs. These rules are applied to all match states of the whole HMM library, thus enabling the detection of domains from previously absent families. Additionally, we propose a procedure to estimate the proportion of false positives among the newly discovered domains. Starting with the Pfam standard library, we build several new libraries with the different HMM-fitting approaches. These libraries are first used to detect new domain occurrences with low E-values. Second, by applying the Co-Occurrence Domain Discovery (CODD) procedure we have recently proposed, the libraries are further used to identify likely occurrences among potential domains with higher E-values. Conclusion: We show that the new approaches allow identification of several domain families previously absent in the P. falciparum proteome and the Apicomplexa phylum, and identify many domains that are not detected by previous approaches. In terms of the number of new discovered domains, the new approaches outperform the previous ones when no close species are available or when they are used to identify likely occurrences among potential domains with high E-values. All predictions on P. falciparum have been integrated into a dedicated website which pools all known/new annotations of protein domains and functions for this organism. A software implementing the two proposed approaches is available at the same address: http://www.lirmm.fr/∼terrapon/HMMfit/

This article is from BMC Bioinformatics , volume 13 . Abstract Background: Hidden Markov Models (HMMs) are a powerful tool for protein domain identification. The Pfam database notably provides a large collection of HMMs which are widely used for the annotation of proteins in new sequenced organisms. In Pfam, each domain family is represented by a curated multiple sequence alignment from which a profile HMM is built. In spite of their high specificity, HMMs may lack sensitivity when searching for domains in divergent organisms. This is particularly the case for species with a biased amino-acid composition, such as P. falciparum, the main causal agent of human malaria. In this context, fitting HMMs to the specificities of the target proteome can help identify additional domains. Results: Using P. falciparum as an example, we compare approaches that have been proposed for this problem, and present two alternative methods. Because previous attempts strongly rely on known domain occurrences in the target species or its close relatives, they mainly improve the detection of domains which belong to already identified families. Our methods learn global correction rules that adjust amino-acid distributions associated with the match states of HMMs. These rules are applied to all match states of the whole HMM library, thus enabling the detection of domains from previously absent families. Additionally, we propose a procedure to estimate the proportion of false positives among the newly discovered domains. Starting with the Pfam standard library, we build several new libraries with the different HMM-fitting approaches. These libraries are first used to detect new domain occurrences with low E-values. Second, by applying the Co-Occurrence Domain Discovery (CODD) procedure we have recently proposed, the libraries are further used to identify likely occurrences among potential domains with higher E-values. Conclusion: We show that the new approaches allow identification of several domain families previously absent in the P. falciparum proteome and the Apicomplexa phylum, and identify many domains that are not detected by previous approaches. In terms of the number of new discovered domains, the new approaches outperform the previous ones when no close species are available or when they are used to identify likely occurrences among potential domains with high E-values. All predictions on P. falciparum have been integrated into a dedicated website which pools all known/new annotations of protein domains and functions for this organism. A software implementing the two proposed approaches is available at the same address: http://www.lirmm.fr/∼terrapon/HMMfit/