State of the art. (a) Comcast and the Township acknowledge that the technology of Cable Systems is an evolving field. Comcast’s Cable System in the Township shall be capable of offering Cable Services that are comparable to other Cable Systems owned and managed by Comcast or its Affiliated Entities in the County of Allegheny in the Commonwealth of Pennsylvania (“Comparable Systems”) pursuant to the terms of this section. The Township may send a written notice to Comcast, not to exceed one request every two (2) years, requesting information on Cable Services offered by such Comparable Systems.
State of the art. It is not possible to summarize the huge number of studies and papers on probabilistic seismic hazard assessment (PSHA) all around the world in the last decades, where different approaches to the determination of maximum magnitude were defined and applied. What we can remark from this large bibliography is that two main strategies were followed in the past: from one side, the maximum magnitude was determined by people in charge of the definition of the catalog and/or the seismic source zones model; on the other side, the maximum magnitude was determined by people in charge of the hazard computation (Figure 2.1).
State of the art. This sub-section includes short descriptions of the most common algorithms exploited for crawling, followed by a discussion of web page classification techniques applied to focused crawling.
State of the art. A. Text normalization Text normalization is rather a technical problem. File format detection is done simply by checking file endings (html, txt, doc, pdf, etc.) or by the Linux/Unix file(1) command which identifies format specific character sequences in the files. Text encoding is identified e.g. by the Linux/Unix command enca(1) which identifies encoding specific sequences in the files. The same tool can be used for text encoding conversion.
State of the art. The Technical and Organisational Measures are subject to technical progress and further development. In this respect, it is permissible for the Supplier to implement alternative adequate measures. In so doing, the security level of the defined measures must not be reduced. Substantial changes must be documented.
State of the art. One of the key enabling technologies that can improve the spatiotemporal resolution of brain interfaces are the CMOS read-out integrated circuits (ROICs) specifically conceived for neural mapping. Their research interest is evident given the number of neural ROICs published in literature during the last fifteen years [1-75]. In general, the morphology of neural ROICs can be classified according to the three major applications illustrated in Figure 1(a): high-density arrays for cell culture [1-16], penetrating xxxxxx for intracortical recording [17-42] and conformal arrays for micro- electrocorticography (µECoG) [43-75]. Electrophysiological signals to be read out in each recording site may contain low-frequency (i.e. 1 Hz to 1 kHz) local field potentials (LFPs) as well as high- frequency (i.e. 1 kHz to 10 kHz) single-unit active potential spikes, depending on the particular array density, with practical dynamic range values around 10 bit and full-scale amplitudes in the mV-range [76]. In most cases, recording sites are built from passive microelectrode arrays (MEAs) for either cell culture [3], intracortical recording [24] or µECoG [77]. Active sensors, like the liquid-gate graphene field-effect transistors (GFETs) of this project, are opening the possibility of early multiplexing and they can unlock the infra-slow (i.e. below 0.1 Hz) components of neural signals [78]. The required connectivity from these recording sites of the sensing array to the analog frontend (AFE) circuits of the ROIC can follow dedicated point-to-point [16, 29-42, 46-75] or time-domain multiplexing [1-15, 17-28, 43-45] schemes. In terms of integration, monolithic solutions [1-15, 21-28, 41, 42, 45] build the sensing array in the same chip substrate as the read-out circuits, typically by CMOS post-processing techniques, while hybrid devices [16-20, 29-40, 43, 44, 46-75] combine separated technologies for each part through advanced packaging.
State of the art. We review here the already existing and potential relations between MIR and musicology, digital libraries, education and eHealth, which we identi ed as particularly relevant for our eld of research. Applications in musicology The use of technology in music research has a long history (e.g. see Goebl [19] for a review of measurement techniques in music performance research). Before MIR tools became available, music analysis was often performed with hardware or software created for other purposes, such as audio editors or speech analysis tools. For example, Repp used software to display the time-domain audio signal, and he read the onset times from this display, using audio playback of short segments to resolve uncertainties [27]. This methodology required a large amount of human intervention in order to obtain suf ciently accurate data for the study of performance interpretation, limiting the size and number of studies that could be undertaken. For larger scale and quantitative studies, automatic analysis techniques are necessary. An example application of MIR to music analysis is the beat tracking system BeatRoot [15], which has been used in studies of expressive timing [18, 20, 30]. The SALAMI (Structural Analysis of Large Amounts of Music Information 75) project is another example of facilitation of large-scale computational musicology through MIR-based tools. A general framework for visualisation and annotation of musical recordings is Sonic Visualiser [8], which has an extensible architecture with analysis algorithms supplied by plug-ins. Such audio analysis systems are becoming part of the standard tools employed by empirical musicologists [9, 10, 22], although there are still limitations on the aspects of the music that can be reliably extracted, with details such as tone duration, articulation and the use of the pedals on the piano being considered beyond the scope of current algorithms [24]. Other software such as GRM Acousmographe, IRCAM Audiosculpt [5], Praat [4] and the MIRtoolbox 76, which supports the extraction of high-level descriptors suitable for systematic musicology applications, are also commonly used. For analysing musical scores, the Humdrum toolkit [21] has been used extensively. It is based on the UNIX operating system's model of providing a large set of simple tools which can be combined to produce arbitrarily complex operations. Recently, music21 [11] has provided a more contemporary toolkit, based on the Python programming langua...
State of the art. In [87], the authors first recall the security and data privacy loss risks exposed by multi-party learning models likely to take place in 5G network management (e.g., operators may not share their network operating metadata) as well as the merits of Intel SGX to mitigate these risks. Because of the expected performance losses incumbent to SGX, the authors produce some optimizations for customized binary integration of learning algorithms (K-means, CNN, SVM, Matrix factorization) and stress the requirements for data obliviousness which preserve privacy for the training and sample data, collected and generated outside SGX. In doing so, the authors map the security and privacy issues holistically, all way through the complete AI data pipeline. The incurred overhead when running the model inside SGX varies from a more than satisfactory 1% to a more impacting 91% according to the algorithm type (respectively CNN and K-Means). In [88], the authors deliver efficient deep learning on multi-source private data, leveraging differential privacy on commercial TEEs. Their technology dubbed MYELIN shows similar performance (or negligible slow down) when applying DP-protected ML. To do so, their implementation goes through the compilation of a static library embedding the core minimal routines. The static library is then fully run in the TEE, which removes any costly context switch from the TEE mode to the normal execution mode. Specialized hardware accelerators (TPUs) are also viewed as the necessary step to take for highly demanding (fast) decision taking. That is a gray area, with no existing TEE embodiment for specialized hardware to the best of our knowledge. In addition, leveraging TEE data sealing capability looks like another path to consider for further improvements. In [89], the authors deliver a fast, verifiable and private execution of neural networks in trusted hardware, leveraging a commercial TEE. SLALOM splits the execution between a GPU and the TEE while delivering security assurance on the GPU operation correctness using Xxxxxxxxx’s algorithm. Outsourcing linear process from the TEE to the GPU is aimed at boosting performance, in a scheme that can be applied to any faster co-processor. Full TEE-embedded inference was the bottom line of this research, deemed as not satisfactory on the performance aspect. In [90] , the authors recall the need for ever-growing and security-privacy sensitive training data set which calls for cloud operation but this comes...
State of the art. Challenges Climate change raises two fundamental challenges for the ENES scientific community: • To improve our understanding and prediction of climate variability and change, including anthropogenic climate change, requires the analysis of the full complexity of the Earth system, i.e., the physical, biological and chemical dimensions coupled together. • To improve our understanding and prediction of the impacts of climate change in all their socio- economic dimensions requires more accurate predictions on decadal timescales with improved regional detail and enhanced interactions with the climate change impact community. This will be particularly required to prepare for adaptation to climate change. In order to ensure a leading position for Europe, there is also a need to: • Perform the most up-to-date and accurate climate simulations. This requires sophisticated models, world-class high-performance computers and archiving systems, and state-of-the-art software infrastructure to make efficient use of the models, the data and the hardware. • Better integrate the European climate modeling community in order to speed-up the development of models and the use of high-performance computers, improve the efficiency of the modelling community and improve the dissemination of model results to a large user base, including climate services. The challenges have increased over the last years with the increasing need to prepare for adaptation, the need to develop reliable regional decadal prediction, the emergence of climate services, and the technical challenge of exascale future computer architectures. IS-ENES has already taken key steps towards achieving these challenges but further achievements are still needed.
State of the art. This requires that the state of the art be taken into account. It does not however refer to methods which have just recently been newly developed, but to such measures which have already been proven to be appropriate and effective in practical use and which assure a sufficient level of security. The term "state of the art" implies that a present-day assessment is involved and that the state of the art must be regularly checked as to whether it is
