Traditional bridge equations can only handle few variables. To exploit information in the releases of several indicators, the standard approach is to average equations using different regressors. Recently, Giannone, Reichlin and Sala (2004) and Giannone, Reichlin and Small (2008) have proposed to use factors extracted from large monthly datasets to perform bridging which exploit a large number of indicators within the same model (bridging with factors). They propose to use the Kalman filter to estimate the factors and handle missing data. 1 When bridging with factors, however, one can consider alternative estimation methods for the factors than that based on the Kalman filter. Methods that have been used in the Eurosystem include the principal component estimator of the factors (Stock and Watson, 2002b) and the frequency domain-based two-step estimator of Forni et al. (2005). It is therefore natural for this study to consider these estimators in the bridging with factors framework. However, these methods have to be complemented with some tool to handle missing data. We will fill the missing data of each series on the basis of univariate forecasts following common practice with bridge equations. It is important to stress that while there are several studies that apply factor models for forecasting euro area data (Marcellino et al. (2003) for euro area data, Artis et al. (2005) for the United Kingdom, Bruneau et al. (2007) for France, Den Reijer (2007) for the Netherlands, Duarte and Rua (2007) for Portugal, Schumacher (2007) for Germany, and Van Nieuwenhuyze (2005) for Belgium, among others), this paper considers the bridge version of these models which is appropriate for **real**-**time** short-term forecasting and can be meaningfully compared with traditional bridge equations.

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This paper evaluates different models for the short-term forecasting of **real** GDP growth in ten selected European countries and the euro area as a whole. Purely quarterly models are compared with models designed to exploit early releases of monthly indicators for the nowcast and forecast of quarterly GDP growth. Amongst the latter, we consider small bridge equations and forecast equations in which the bridging between monthly and quarterly data is achieved through a regression on factors extracted from large monthly datasets. The forecasting exercise is performed in a simulated **real**-**time** context, which takes account of publication lags in the individual series. In general, we fi nd that models that exploit monthly information outperform models that use purely quarterly data and, amongst the former, factor models perform best.

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Following the result of Buss (2009) that, during a switch of the business cycle phases, the short-term forecasting performance might improve if two, instead of one, regular diﬀerencing is implemented, Tables 3 and 4 show results for twice regularly diﬀerenced data. Table 3 summarizes a comparison of one- period ahead **pseudo** **real**-**time** forecasting performance of single-equation MS and linear VAR models with leading information on twice regularly diﬀerenced series. One can see that the performance of MS models lags behind that of VAR counterparts. Table 4 shows the results of single-equation MS and linear VAR models without leading information on twice regularly diﬀerenced series. Table 4 shows that, comparing the models with the same variables and lag order, single-equation MS models (15)-(18) tend to give smaller RMSFE than the corresponding linear VAR models for all sample spaces, except for model (17) for the ﬁrst half of the sample.

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Afonso et al. [5] proposed hardware filter banks for ECG signal decomposition, where several parameters were independently computed and combined in a decision rule. The authors reported Se = 99.59 % and Sp = 99.56 % for their **real**-**time**, single-channel beat detection algo- rithm tested with the MIT-BIH Arrhythmia Database. Dotsinsky and Stoyanov [6] developed a heuristic, **pseudo**-**real**-**time** algorithm for ventricular beat detection for single-channel ECG, based on steep edges and sharp peaks evaluation criteria. They reported Se = 99.04% and Sp = 99.62%, obtained with two channel recordings from AHA and MIT-BIH Arrhythmia Database

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It‟s nice to know that the metadata and archive files are in sync, and that you have a nice, clean recovery state. Thus, it is common for DR solutions to start at some point in **time**, create the truncated journal, and then “hold off” on replaying the journals until all corresponding archive files have been transferred. This approach favors consistency. Users will not see any “Librarian” errors after recovery.

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a **real** Hilbert space; Marino and Xu [] proved a strong convergence theorem for strict- **pseudo**-contractions in a **real** Hilbert space; Zhou [] extended Marino and Xu’s strong convergence theorem to the more general class of Lipschitz pseudocontractive mappings; Zhou [] generalized and extended the main results of [] to the class of asymptotically pseudocontractive mappings; Zhou and Su [] further extended the main results in [] to a family of uniformly L-Lipschitz continuous and quasi-asymptotically pseudocontractive mappings.

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and deployment of the various services, where the mirror interface, the news feeds all use Web service communication mechanisms. By utilizing sensor, we can reduce the power consumption since the mirror will display information only in the presence of a human. The future prototype is ripe with potential and probably robust in terms of functionality. It utilizes facial recognition software to push up personalized data including health status, a calendar, news feeds, and other information relevant to your morning routine. It uses voice commands to switch between each views, and gestures to interact with content. Rather than confined to a home we can implement the functionality to a glass material. So that it can have a wide range of applications like one can setup this functionality to a glass table, which he used in office. This will help him to know about notifications from many sites at the same **time** in a single screen. Another application is that this functionality can be setup in public places.

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Recommendations: • Global pseudo-data with real world characteristics • GCM or Reanalyses data should be used as source base with real spatial, temporal and climatological characteristic[r]

Motivated by the above, our main purpose of this paper is firstly to introduce a concept of mean-value of uniformly almost periodic functions and give some useful and important properties of it. Then we propose a concept of **pseudo** almost periodic functions which is a new generalization of uniformly almost periodic functions on **time** scales and present some relative results. Finally, we establish some results about the existence and uniqueness of **pseudo** almost periodic solutions to dynamic equations on **time** scales.

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The problem of undoing this superposition is called blind source separation (BSS). Typically, one assumes that the mixing is linear and instantaneous, which is a valid approximation in brain signals [6]. One must also make some assumptions on the sources, such as in indepen- dent component analysis (ICA) where the assumption is mutual statistical independence of the sources [7]. ICA has seen multiple applications in EEG and MEG pro- cessing (for recent applications see, e.g., [8,9]). Diﬀerent BSS approaches use criteria other than statistical indepen- dence, such as non-negativity of sources [10,11] or **time**- dependent frequency spectrum criteria [12,13]. In our case, independence of the sources is not a valid assump- tion, because phase-locked sources are highly mutually dependent. Also, phase-locking is not equivalent to fre- quency coherence: in fact, two signals may have a severe overlap between their frequency spectra but still exhibit low or no phase synchrony at all [14]. In this article, we address the problem of how to separate such phase-locked sources using a phase-speciﬁc criterion.

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To characterize the performance of RBED, we com- pare it to the Linux scheduler and to a hierarchical EDF/best-effort scheduler we developed called EDF- Linux. Both Linux and EDF-Linux use the 2.4.20 kernel. EDF-Linux maintains two ready queues, one for peri- odic **real**-**time** tasks, scheduled by EDF, and another for best-effort tasks, scheduled by the default Linux sched- uler whenever the **real**-**time** queue is empty. All experi- ments were performed on a standard PC Desktop equipped with a 1 GHz Pentium III processor, 512MB RAM, and a 40GB hard drive. In developing our prototype we have run the system over long periods of **time**. Our general impres- sion is that the scheduler works well. Best-effort tasks ex- hibit “normal” behavior (with default scheduling quanta of 60 ms, the same as the Linux scheduler) and are never com- pletely starved, **real**-**time** tasks meet their deadlines, and soft **real**-**time** tasks meet their deadlines or run at lower per- formance levels depending upon the amount of resources available. Below we present a series of snapshots that il- lustrate how RBED performs in practice. For simplicity we have drawn the graphs relative to an origin start- ing at (0,0) even though the snapshots were taken from the middle of longer executions.

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Jurdzinski, Marcin, Lazic, Ranko and Schmitz, Sylvain (2015) Fixed-dimensional energy games are in **pseudo**-polynomial **time**. In: Halldórsson , Magnús M. and Iwama , Kazuo and Kobayashi , Naoki and Speckmann , Bettina , (eds.) Automata, Languages, and Programming : 42nd International Colloquium, ICALP 2015, Kyoto, Japan, July 6-10, 2015, Proceedings, Part II. Lecture Notes in Computer Science, Volume 9135. Berlin Heidelberg: Springer Verlag, pp. 260-272.

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Mechanical De-Cap.. Real Time X-ray Examples.. Real Time X-ray Examples.. Real Time X-ray Examples.. Real Time X-ray Examples.. Real Time X-ray Examples.. Real Time X-ray Examples.. Real[r]

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where f ij ∈ R [X 1 , ..., X n ] and ∗ ij is either (<) or (=) for i = 1, ..., s and j = 1, ..., r i . This sets are defined by polynomials and/or inequalities in R . The **real** algebraic sets are semi-algebraic. Boolean operations of semi-algebraic sets are semi-algebraic. Definition 2 (BCR, 1998, P. 28) The function f : R m ⊇ A → B ⊆ R n is semi-

Implementation of vNIC handling • Adding real-time information (DSCP and deadline time). ▫ Implemented on vNIC(ne 2000 _pci) in qemu-kvm[r]

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No one is certain who invented Talk , but it was heavily used between the lines connect- ing MIT, BBN Consulting in Massachusetts, RAND in Santa Monica, and UCLA in the late 1960s. Talk was a simple program that allowed users on networked computers to type messages to one another in **real** **time**. As more computers were connected to ARPANET during the 1970s, Talk became a means of communicating with anyone linked to ARPANET. This valuable tool, however, began to affect the productivity of government and corporate employees. Mandates were issued not to use Talk unless it was truly important. But throughout the 1970s and 1980s, users continued to hold chat sessions. Not only was the exchange of information useful, but also communicating with others in this fashion was enjoyable and personable.

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In summary, given the difficulty of interpreting the DC in the PPMI approach and seekingto correct the non-circularity of the index in an easy way at the same **time**, in this paper, we define a new PPMI which fixes a baseline group. This allows both the PPMI index and its components to be adequately interpreted, thus making it easier for practitioners to apply. Moreover, the new index and its decomposition meet circularity. Finally, assuming a baseline group as reference technology allows the CDMI index to satisfy the circular relation property directly.

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In this paper, we analyze the convergence performance of the **pseudo** multi-hop distributed consensus algorithm with communication **time**-delay. When there is communication **time**-delay among agents, the performance of distributed consensus algorithm degrades. We analyze the convergence performance of the **pseudo** multi-hop distributed consensus algorithm with **time**-delay, and calculate the maximum **time**-delay. By calculating, the maximum **time**-delay in the **pseudo** multi-hop algorithm is equal to the maximum **time**-delay in the multi-hop algorithm.

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[55] Juan López Campos, J. Javier Gutiérrez and M. González Harbour. “CAN-RT-TOP: **Real**- **Time** Task-Oriented Protocol over CAN for Analyzable Distributed Applications”. 3rd International Workshop on **Real**-**Time** Networks (RTN), Catania, Sicily (Italy), July 2004. [56] José María Martínez and Michael González Harbour. “RT-EP: A Fixed-Priority **Real** **Time**

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The location of the output buffer in the first 64K of real memory and the two's complement of the buffer length are transferred by the CPU to the pseudo-memory [r]